From 594f82fb5ba8e059a7cc46a4b55d1f12b892ea68 Mon Sep 17 00:00:00 2001 From: parsash2 <60193914+parsash2@users.noreply.github.com> Date: Mon, 24 Jun 2024 11:58:52 -0400 Subject: [PATCH 01/13] tutorials-after-initial-feedback Added descriptive text to make the notebooks stand on their own. --- ...tutorial-ux360-draft-processing-file(1).py | 32 + ...a-tutorial-ux360-jwos-first-revision.ipynb | 2832 +++++++++++++++++ ...l-ux360-jwos-feedback-first-revision.ipynb | 800 +++++ 3 files changed, 3664 insertions(+) create mode 100644 use-cases/athena-tutorial-ux360-draft-processing-file(1).py create mode 100644 use-cases/athena-tutorial-ux360-jwos-first-revision.ipynb create mode 100644 use-cases/pyspark-tutorial-ux360-jwos-feedback-first-revision.ipynb diff --git a/use-cases/athena-tutorial-ux360-draft-processing-file(1).py b/use-cases/athena-tutorial-ux360-draft-processing-file(1).py new file mode 100644 index 0000000000..fb8472d011 --- /dev/null +++ b/use-cases/athena-tutorial-ux360-draft-processing-file(1).py @@ -0,0 +1,32 @@ +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +import os + +# Define the input and output paths +input_path = '/opt/ml/processing/input/feature-selection-query-id.csv' +train_output_path = '/opt/ml/processing/output/train/train.csv' +val_output_path = '/opt/ml/processing/output/validation/val.csv' +test_output_path = '/opt/ml/processing/output/test/test.csv' + +# Read the input data +df = pd.read_csv(input_path, header=None) + +# Split the data into training, validation, and test sets +train, temp = train_test_split(df, test_size=0.3, random_state=42) +val, test = train_test_split(temp, test_size=0.5, random_state=42) + +# Save the splits to the output paths +os.makedirs(os.path.dirname(train_output_path), exist_ok=True) +train.to_csv(train_output_path, index=False) + +os.makedirs(os.path.dirname(val_output_path), exist_ok=True) +val.to_csv(val_output_path, index=False) + +os.makedirs(os.path.dirname(test_output_path), exist_ok=True) +test.to_csv(test_output_path, index=False) + +# Print the sizes of the splits +print(f"Training set: {len(train)} samples") +print(f"Validation set: {len(val)} samples") +print(f"Test set: {len(test)} samples") diff --git a/use-cases/athena-tutorial-ux360-jwos-first-revision.ipynb b/use-cases/athena-tutorial-ux360-jwos-first-revision.ipynb new file mode 100644 index 0000000000..aa7adcb5fe --- /dev/null +++ b/use-cases/athena-tutorial-ux360-jwos-first-revision.ipynb @@ -0,0 +1,2832 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ece13bd7-19b2-47b3-976d-cf636fa68003", + "metadata": {}, + "source": [ + "# Create an end to end machine learning workflow using Amazon Athena\n", + "Importing and transforming data can be one of the most challenging tasks in a machine learning workflow. We provide you with a Jupyter notebook that demonstrates a cost-effective strategy for an extract, transform, and load (ETL) workflow. Using Amazon Simple Storage Service (Amazon S3) and Amazon Athena, you learn how to query and transform data from a Jupyter notebook. Amazon S3 is an object storage service that allows you to store data and machine learning artifacts. Amazon Athena enables you to interactively query the data stored in those buckets, saving each query as a CSV file in an Amazon S3 location.\n", + "\n", + "The tutorial imports 29 CSV files for the 2019 NYC taxi dataset from multiple Amazon S3 locations. The goal is to predict the fare amount for each ride. From those 29 files, the notebook creates a single ride fare dataset and a single ride info dataset with deduplicated values. We join the deduplicated datasets into a single dataset.\n", + "\n", + "Amazon Athena stores the query results as a CSV file in the specified location. This CSV file is provided to a SageMaker Processing Job to split the data into training, validation, and test sets. While data can be split using queries, a processing job ensures that the data is in a format that's parseable by the XGBoost algorithm.\n", + "\n", + "__Important__\n", + "\n", + "The notebook must be run in the us-east-1 AWS Region. You also need your own Amazon S3 bucket and a database within Amazon Athena. You won't be able to access the data used in the tutorial otherwise.\n", + "\n", + "For information about creating a bucket, see [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). For information about creating a database, see [Create a database](https://docs.aws.amazon.com/athena/latest/ug/getting-started.html#step-1-create-a-database)." + ] + }, + { + "cell_type": "markdown", + "id": "0b11693f-7c35-41cf-8e4b-4f86eea8f3b0", + "metadata": {}, + "source": [ + "## Code overview\n", + "\n", + "The following code uses the boto3 client to set up an Athena client within the us-east-1 AWS Region. It defines the run_athena_query function that runs queries and checks their status. Afterwards, it uses the function to create the ride fare and ride info table in Amazon Athena using all the CSV files from the year 2019.\n", + "\n", + "The code creates a separate ride fare and ride info table with all of the duplicate values removed. Amazon Athena saves the query results of the select statements as a CSV string. The get_query_results functions saves the CSV string as a CSV file. We use the function to read the results of our test queries into the notebook as pandas dataframes and verify that we're able to get our data successfully. \n", + "\n", + "We join the deduplicated tables into a single dataset that we use for our exploratory data analysis. We perform our exploratory data analysis and run a query to select the final set of features we're using for the analysis. We run the SageMaker processing job using the processing-file.py file. Afterwards, we define our model, train our model, and evaluate it on a test set of 20 samples." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8ab1ff0e-fcde-4976-a1cd-51e75c18deb2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: cb779f49-17e5-49fd-91f9-0fbbf62cb9bb\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'cb779f49-17e5-49fd-91f9-0fbbf62cb9bb'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import required libraries\n", + "import time\n", + "import boto3\n", + "\n", + "def run_athena_query(query_string, database_name, output_location):\n", + " \"\"\"\n", + " Function to execute an Athena query and wait for its completion.\n", + "\n", + " Args:\n", + " query_string (str): The SQL query to be executed.\n", + " database_name (str): The name of the Athena database.\n", + " output_location (str): The S3 location where the query results will be stored.\n", + "\n", + " Returns:\n", + " str: The query execution ID.\n", + " \"\"\"\n", + " # Create an Athena client\n", + " athena_client = boto3.client('athena', region_name='us-east-1')\n", + "\n", + " # Start the query execution\n", + " response = athena_client.start_query_execution(\n", + " QueryString=query_string,\n", + " QueryExecutionContext={'Database': database_name},\n", + " ResultConfiguration={'OutputLocation': output_location}\n", + " )\n", + "\n", + " query_execution_id = response['QueryExecutionId']\n", + " print(f\"Query execution ID: {query_execution_id}\")\n", + "\n", + " while True:\n", + " # Check the query execution status\n", + " query_status = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " state = query_status['QueryExecution']['Status']['State']\n", + "\n", + " if state == 'SUCCEEDED':\n", + " print(\"Query executed successfully.\")\n", + " break\n", + " elif state == 'FAILED':\n", + " print(f\"Query failed with error: {query_status['QueryExecution']['Status']['StateChangeReason']}\")\n", + " break\n", + " else:\n", + " print(f\"Query is currently in {state} state. Waiting for completion...\")\n", + " time.sleep(5) # Wait for 5 seconds before checking again\n", + "\n", + " return query_execution_id\n", + "\n", + "# SQL query to create the 'ride_fare' table\n", + "create_ride_fare_table = \"\"\"\n", + "CREATE EXTERNAL TABLE `ride_fare` (\n", + " `ride_id` bigint, \n", + " `payment_type` smallint, \n", + " `fare_amount` float, \n", + " `extra` float, \n", + " `mta_tax` float, \n", + " `tip_amount` float, \n", + " `tolls_amount` float, \n", + " `total_amount` float\n", + ")\n", + "ROW FORMAT DELIMITED \n", + " FIELDS TERMINATED BY ',' \n", + " LINES TERMINATED BY '\\n' \n", + "STORED AS INPUTFORMAT \n", + " 'org.apache.hadoop.mapred.TextInputFormat' \n", + "OUTPUTFORMAT \n", + " 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'\n", + "LOCATION\n", + " 's3://dsoaws/nyc-taxi-orig-cleaned-split-csv-with-header-per-year-multiple-files/ride-fare/year=2019'\n", + "TBLPROPERTIES (\n", + " 'skip.header.line.count'='1', \n", + " 'transient_lastDdlTime'='1716908234'\n", + ");\n", + "\"\"\"\n", + "\n", + "# Athena database name\n", + "database = 'database_name'\n", + "\n", + "# S3 location for query results\n", + "s3_output_location = 's3://example-s3-bucket/example-s3-prefix'\n", + "\n", + "# Execute the query to create the 'ride_fare' table\n", + "run_athena_query(create_ride_fare_table, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3d249cc5-2d53-4274-8f5e-6ab09ccd3ea6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: e07f4538-b44b-4dc8-923d-6758a4e99913\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'e07f4538-b44b-4dc8-923d-6758a4e99913'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to create a new table with duplicates removed\n", + "remove_duplicates_from_ride_fare = \"\"\"\n", + "CREATE TABLE ride_fare_deduped\n", + "AS\n", + "SELECT DISTINCT *\n", + "FROM ride_fare\n", + "\"\"\"\n", + "\n", + "# Run the preceding query\n", + "run_athena_query(remove_duplicates_from_ride_fare, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2f9a68b9-bd11-49e9-ad72-b44b43d32e47", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: d8128d6d-c3d7-4c44-99ed-b533f69c3cfa\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'d8128d6d-c3d7-4c44-99ed-b533f69c3cfa'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to create the ride_info table\n", + "create_ride_info_table_query = \"\"\"\n", + "CREATE EXTERNAL TABLE `ride_info` (\n", + " `ride_id` bigint, \n", + " `vendor_id` smallint, \n", + " `passenger_count` smallint, \n", + " `pickup_at` string, \n", + " `dropoff_at` string, \n", + " `trip_distance` float, \n", + " `rate_code_id` int, \n", + " `store_and_fwd_flag` string\n", + ")\n", + "ROW FORMAT DELIMITED \n", + " FIELDS TERMINATED BY ',' \n", + " LINES TERMINATED BY '\\n' \n", + "STORED AS INPUTFORMAT \n", + " 'org.apache.hadoop.mapred.TextInputFormat' \n", + "OUTPUTFORMAT \n", + " 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'\n", + "LOCATION\n", + " 's3://dsoaws/nyc-taxi-orig-cleaned-split-csv-with-header-per-year-multiple-files/ride-info/year=2019'\n", + "TBLPROPERTIES (\n", + " 'skip.header.line.count'='1', \n", + " 'transient_lastDdlTime'='1716907328'\n", + ");\n", + "\"\"\"\n", + "\n", + "# Run the query to create the ride_info table\n", + "run_athena_query(create_ride_info_table_query, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "263d883c-f189-43c0-9fbd-1a45093984e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 9f4f8ff3-3c76-4ff4-a848-3d834e848cf7\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'9f4f8ff3-3c76-4ff4-a848-3d834e848cf7'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to create table with duplicates removed\n", + "remove_duplicates_from_ride_info = \"\"\"\n", + "CREATE TABLE ride_info_deduped\n", + "AS\n", + "SELECT DISTINCT *\n", + "FROM ride_info\n", + "\"\"\"\n", + "\n", + "# Run the query to create the table with the duplicates removed\n", + "run_athena_query(remove_duplicates_from_ride_info, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6db6bb67-44a9-4ff4-b662-ad969a84d3d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 0f66a43e-cde0-4361-a050-09a2616ffefa\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'0f66a43e-cde0-4361-a050-09a2616ffefa'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_ride_info_query = '''\n", + "SELECT * FROM ride_info_deduped limit 10\n", + "'''\n", + "\n", + "run_athena_query(test_ride_info_query, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "92d8be21-3f20-453d-8b84-516571d9854d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 1d0f08ba-c579-4ff1-8188-4a7b87043d07\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'1d0f08ba-c579-4ff1-8188-4a7b87043d07'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_ride_fare_query = '''\n", + "SELECT * FROM ride_fare_deduped limit 10\n", + "'''\n", + "\n", + "run_athena_query(test_ride_fare_query, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "50e87ba6-42e9-4d99-862e-7eae16ad810e", + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "def get_query_results(query_execution_id):\n", + " \"\"\"\n", + "\n", + " Function to retrieve the results of an Athena query execution.\n", + "\n", + "\n", + " Args:\n", + "\n", + " query_execution_id (str): The ID of the query execution.\n", + "\n", + "\n", + " Returns:\n", + "\n", + " io.StringIO: A file-like object containing the query results in CSV format.\n", + "\n", + " \"\"\"\n", + " athena_client = boto3.client('athena', region_name='us-east-1')\n", + " s3 = boto3.client('s3')\n", + "\n", + " # Get the query execution details\n", + " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " s3_location = query_execution['QueryExecution']['ResultConfiguration']['OutputLocation']\n", + "\n", + " # Extract bucket and key from S3 output location\n", + " bucket_name, key = s3_location.split('/', 2)[2].split('/', 1)\n", + "\n", + " # Get the CSV file location\n", + " obj = s3.get_object(Bucket=bucket_name, Key=key)\n", + " csv_data = obj['Body'].read().decode('utf-8')\n", + " csv_buffer = io.StringIO(csv_data)\n", + "\n", + " return csv_buffer" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b04abae5-936b-4d96-98e8-d2e2b6a17b9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ride_idvendor_idpassenger_countpickup_atdropoff_attrip_distancerate_code_idstore_and_fwd_flag
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" + ], + "text/plain": [ + " ride_id vendor_id passenger_count pickup_at \\\n", + "0 1005024574809 1 1 2019-05-15T12:11:17.000Z \n", + "1 944895157463 2 2 2019-06-18T22:11:43.000Z \n", + "2 944895157471 1 2 2019-06-18T22:29:47.000Z \n", + "3 1005024574929 2 1 2019-05-15T12:26:17.000Z \n", + "4 1005024574951 2 2 2019-05-15T12:51:35.000Z \n", + "\n", + " dropoff_at trip_distance rate_code_id store_and_fwd_flag \n", + "0 2019-05-15T12:48:59.000Z 3.40 1 N \n", + "1 2019-06-18T22:29:46.000Z 1.92 1 N \n", + "2 2019-06-18T22:37:08.000Z 1.00 1 N \n", + "3 2019-05-15T12:33:01.000Z 0.95 1 N \n", + "4 2019-05-15T13:30:12.000Z 2.65 1 N " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "# Provide the query execution id of the test_ride_info query to get the query results\n", + "ride_info_sample_1 = get_query_results('0f66a43e-cde0-4361-a050-09a2616ffefa')\n", + "\n", + "df_ride_info_sample_1 = pd.read_csv(ride_info_sample_1)\n", + "\n", + "df_ride_info_sample_1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "be89957f-31b1-4710-bfc2-178d6db18592", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amount
06013030473315.02.50.52.050.0010.35
11391571259067116.51.00.54.160.0024.96
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" + ], + "text/plain": [ + " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", + "0 60130304733 1 5.0 2.5 0.5 2.05 \n", + "1 1391571259067 1 16.5 1.0 0.5 4.16 \n", + "2 1391571259101 2 8.0 1.0 0.5 0.00 \n", + "3 60130304799 1 6.5 0.0 0.5 1.96 \n", + "4 60130304800 1 39.5 3.5 0.5 9.90 \n", + "\n", + " tolls_amount total_amount \n", + "0 0.00 10.35 \n", + "1 0.00 24.96 \n", + "2 0.00 12.30 \n", + "3 0.00 11.76 \n", + "4 5.76 59.46 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Provide the query execution id of the test_ride_fare query to get the query results\n", + "\n", + "ride_fare_sample_1 = get_query_results('1d0f08ba-c579-4ff1-8188-4a7b87043d07')\n", + "\n", + "df_ride_fare_sample_1 = pd.read_csv(ride_fare_sample_1)\n", + "\n", + "df_ride_fare_sample_1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b8a76635-3c09-4cbc-b1b4-9318dc611250", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 2ba0fd2b-030f-4e32-8acb-ec0d802b994f\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'2ba0fd2b-030f-4e32-8acb-ec0d802b994f'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to join the tables into a single table containing all the data.\n", + "create_ride_joined_deduped = \"\"\"\n", + "CREATE TABLE combined_ride_data_deduped AS\n", + "SELECT \n", + " rfs.ride_id, \n", + " rfs.payment_type, \n", + " rfs.fare_amount, \n", + " rfs.extra, \n", + " rfs.mta_tax, \n", + " rfs.tip_amount, \n", + " rfs.tolls_amount, \n", + " rfs.total_amount,\n", + " ris.vendor_id, \n", + " ris.passenger_count, \n", + " ris.pickup_at, \n", + " ris.dropoff_at, \n", + " ris.trip_distance, \n", + " ris.rate_code_id, \n", + " ris.store_and_fwd_flag\n", + "FROM \n", + " ride_fare_deduped rfs\n", + "JOIN \n", + " ride_info_deduped ris\n", + "ON \n", + " rfs.ride_id = ris.ride_id;\n", + ";\n", + "\"\"\"\n", + "\n", + "# Run the query to create the ride_data_deduped table\n", + "run_athena_query(create_ride_joined_deduped, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b0791e57-4351-4f27-a8f9-ad741441d214", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 08185c50-d51f-4a5f-b82f-e9de593c6b9b\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'08185c50-d51f-4a5f-b82f-e9de593c6b9b'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to select all values from the table and create the dataset that we're using for our analysis\n", + "ride_combined_full_table_query = \"\"\"\n", + "SELECT * FROM combined_ride_data_deduped\n", + "\"\"\"\n", + "\n", + "# Run the query to select all values from the combined_ride_data_deduped table\n", + "run_athena_query(ride_combined_full_table_query, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "97373c52-882b-4e44-8d75-a80d8d8c58df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s3://parsa-ux360-burner-account-bucket/08185c50-d51f-4a5f-b82f-e9de593c6b9b.csv'" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Function to get the Amazon S3 URI location of Amazon Athena select statements\n", + "def get_csv_file_location(query_execution_id):\n", + " athena_client = boto3.client('athena', region_name='us-east-1')\n", + " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " s3_location = query_execution['QueryExecution']['ResultConfiguration']['OutputLocation']\n", + "\n", + " return s3_location\n", + "\n", + "# Provide the 36 character string at the end of the output of the preceding cell as the query.\n", + "get_csv_file_location('query-id-from-preceding-cell')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "954022d5-bdf9-4dbd-be2e-66d0009ce522", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "download: s3://parsa-ux360-burner-account-bucket/08185c50-d51f-4a5f-b82f-e9de593c6b9b.csv to ./08185c50-d51f-4a5f-b82f-e9de593c6b9b.csv\n" + ] + } + ], + "source": [ + "# Use the S3 URI location returned from the preceding cell to download the dataset and rename it.\n", + "!aws s3 cp s3://example-s3-bucket/query-id.csv .\n", + "!mv query-id.csv nyc-taxi-whole-dataset.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "79d2f2a5-5111-4fb8-90f3-67474f1072c1", + "metadata": {}, + "outputs": [], + "source": [ + "sample_nyc_taxi_combined = pd.read_csv('nyc-taxi-whole-dataset.csv', nrows=20000)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f9dececa-272d-458c-9f64-baa13eca0832", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset shape: (20000, 15)\n" + ] + } + ], + "source": [ + "print(\"Dataset shape: \", sample_nyc_taxi_combined.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "1c117a0f-429e-4913-aded-c839675f9e17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amountvendor_idpassenger_countpickup_atdropoff_attrip_distancerate_code_idstore_and_fwd_flag
03839702413301129.52.50.57.756.1246.67112019-04-19T13:23:59.000Z2019-04-19T13:45:15.000Z10.101N
15154136598824.01.00.50.000.008.30222019-02-25T17:01:30.000Z2019-02-25T17:03:53.000Z0.491N
2377098374310127.00.50.50.000.008.30212019-03-30T20:43:40.000Z2019-03-30T20:52:18.000Z1.151N
3377098374314826.00.50.50.000.007.30212019-03-30T20:15:08.000Z2019-03-30T20:19:32.000Z1.121N
43839702413585114.52.50.53.550.0021.35112019-04-19T13:10:55.000Z2019-04-19T13:32:34.000Z1.901N
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" + ], + "text/plain": [ + " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", + "0 3839702413301 1 29.5 2.5 0.5 7.75 \n", + "1 51541365988 2 4.0 1.0 0.5 0.00 \n", + "2 3770983743101 2 7.0 0.5 0.5 0.00 \n", + "3 3770983743148 2 6.0 0.5 0.5 0.00 \n", + "4 3839702413585 1 14.5 2.5 0.5 3.55 \n", + "\n", + " tolls_amount total_amount vendor_id passenger_count \\\n", + "0 6.12 46.67 1 1 \n", + "1 0.00 8.30 2 2 \n", + "2 0.00 8.30 2 1 \n", + "3 0.00 7.30 2 1 \n", + "4 0.00 21.35 1 1 \n", + "\n", + " pickup_at dropoff_at trip_distance \\\n", + "0 2019-04-19T13:23:59.000Z 2019-04-19T13:45:15.000Z 10.10 \n", + "1 2019-02-25T17:01:30.000Z 2019-02-25T17:03:53.000Z 0.49 \n", + "2 2019-03-30T20:43:40.000Z 2019-03-30T20:52:18.000Z 1.15 \n", + "3 2019-03-30T20:15:08.000Z 2019-03-30T20:19:32.000Z 1.12 \n", + "4 2019-04-19T13:10:55.000Z 2019-04-19T13:32:34.000Z 1.90 \n", + "\n", + " rate_code_id store_and_fwd_flag \n", + "0 1 N \n", + "1 1 N \n", + "2 1 N \n", + "3 1 N \n", + "4 1 N " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = sample_nyc_taxi_combined\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d3c56da9-0a1c-4c58-93e3-77260dfff40b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20000 entries, 0 to 19999\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ride_id 20000 non-null int64 \n", + " 1 payment_type 20000 non-null int64 \n", + " 2 fare_amount 20000 non-null float64\n", + " 3 extra 20000 non-null float64\n", + " 4 mta_tax 20000 non-null float64\n", + " 5 tip_amount 20000 non-null float64\n", + " 6 tolls_amount 20000 non-null float64\n", + " 7 total_amount 20000 non-null float64\n", + " 8 vendor_id 20000 non-null int64 \n", + " 9 passenger_count 20000 non-null int64 \n", + " 10 pickup_at 20000 non-null object \n", + " 11 dropoff_at 20000 non-null object \n", + " 12 trip_distance 20000 non-null float64\n", + " 13 rate_code_id 20000 non-null int64 \n", + " 14 store_and_fwd_flag 20000 non-null object \n", + "dtypes: float64(7), int64(5), object(3)\n", + "memory usage: 2.3+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "dc25bcd9-a4b1-4491-867f-7534336d1ecd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amountvendor_idpassenger_counttrip_distancerate_code_id
count2.000000e+0420000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.0000020000.00000020000.000000
mean1.519688e+121.28660012.9632541.0805860.4958472.1463890.36238118.5214901.6229001.565802.9457991.055550
std1.068094e+120.47431212.0066461.2405460.0534052.6801821.58531514.7065710.4846721.218463.7978480.369014
min5.153977e+101.000000-52.000000-4.500000-0.5000000.0000000.000000-57.3000001.0000000.000000.0000001.000000
25%9.534837e+111.0000006.5000000.0000000.5000000.0000000.00000010.8000001.0000001.000000.9800001.000000
50%1.322852e+121.0000009.5000000.5000000.5000001.8350000.00000014.1600002.0000001.000001.6100001.000000
75%1.417341e+122.00000014.5000002.5000000.5000002.8600000.00000020.1600002.0000002.000003.0500001.000000
max3.839703e+124.000000412.2300007.0000001.44000061.50000026.000000412.5300002.0000008.0000044.5000005.000000
\n", + "
" + ], + "text/plain": [ + " ride_id payment_type fare_amount extra mta_tax \\\n", + "count 2.000000e+04 20000.000000 20000.000000 20000.000000 20000.000000 \n", + "mean 1.519688e+12 1.286600 12.963254 1.080586 0.495847 \n", + "std 1.068094e+12 0.474312 12.006646 1.240546 0.053405 \n", + "min 5.153977e+10 1.000000 -52.000000 -4.500000 -0.500000 \n", + "25% 9.534837e+11 1.000000 6.500000 0.000000 0.500000 \n", + "50% 1.322852e+12 1.000000 9.500000 0.500000 0.500000 \n", + "75% 1.417341e+12 2.000000 14.500000 2.500000 0.500000 \n", + "max 3.839703e+12 4.000000 412.230000 7.000000 1.440000 \n", + "\n", + " tip_amount tolls_amount total_amount vendor_id \\\n", + "count 20000.000000 20000.000000 20000.000000 20000.000000 \n", + "mean 2.146389 0.362381 18.521490 1.622900 \n", + "std 2.680182 1.585315 14.706571 0.484672 \n", + "min 0.000000 0.000000 -57.300000 1.000000 \n", + "25% 0.000000 0.000000 10.800000 1.000000 \n", + "50% 1.835000 0.000000 14.160000 2.000000 \n", + "75% 2.860000 0.000000 20.160000 2.000000 \n", + "max 61.500000 26.000000 412.530000 2.000000 \n", + "\n", + " passenger_count trip_distance rate_code_id \n", + "count 20000.00000 20000.000000 20000.000000 \n", + "mean 1.56580 2.945799 1.055550 \n", + "std 1.21846 3.797848 0.369014 \n", + "min 0.00000 0.000000 1.000000 \n", + "25% 1.00000 0.980000 1.000000 \n", + "50% 1.00000 1.610000 1.000000 \n", + "75% 2.00000 3.050000 1.000000 \n", + "max 8.00000 44.500000 5.000000 " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "18bd92b1-962a-40f2-b15f-7351d869f390", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "vendor_id\n", + "2 12458\n", + "1 7542\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['vendor_id'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e4c4997f-85d8-4f57-a60c-51e3568cfe2e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "passenger_count\n", + "1 14091\n", + "2 2931\n", + "3 851\n", + "5 832\n", + "6 485\n", + "4 433\n", + "0 376\n", + "8 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['passenger_count'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "641c278d-8fed-42b8-98d1-becba90d6259", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot to find the distribution of ride fare values\n", + "import matplotlib.pyplot as plt\n", + "plt.hist(df['fare_amount'], edgecolor='black', bins=30, range=(0,100))\n", + "plt.xlabel('Fare Amount')\n", + "plt.ylabel('Count')\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "9d484f57-f150-45b5-9cc5-cc10a6e8e9f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20000" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['ride_id'].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f627790e-8aed-48e3-9c5d-52775bbb124d", + "metadata": {}, + "outputs": [], + "source": [ + "df.drop('store_and_fwd_flag', axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "c359f4db-b503-4d80-bb4c-55dc411f9b5e", + "metadata": {}, + "outputs": [], + "source": [ + "# We're dropping the time series columns to streamline the analysis.\n", + "time_series_columns_to_drop = ['pickup_at','dropoff_at']\n", + "df.drop(columns=time_series_columns_to_drop, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05abe8af-bf44-471b-b130-19cee0dd822f", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b6a10b9b-e916-48a9-88f5-ae94db2f6576", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting seaborn\n", + " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.10/site-packages (from seaborn) (1.26.4)\n", + "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.10/site-packages (from seaborn) (2.1.4)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.10/site-packages (from seaborn) (3.8.4)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n", + "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.3)\n", + "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", + "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: seaborn\n", + "Successfully installed seaborn-0.13.2\n" + ] + }, + { + "data": { + "image/png": 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mip91Rfn1mZgiU/3Opam8+tPN7f1Axof5O2/qXK/01ddtzLS5rptTPWnTJjeX+ilIm95c6sgccbxU/vK6V2FuoqOjkZycjClTphg6FIPL7mv76quvULZsWfj4+GDs2LF4/vy5oUMzmAYNGuDmzZvYtm0bhBD4559/8Mcff6Bt27aGDk0vzGFsuKljG6xwyGVcoybj6A1FTuMn5dAvL9dxmhkZGVixYgX69esHCwsLQ4eTS6NGjbB3715cvnwZAHD69GkcPHgQbdq0MXBkpArzYsGY6/0WTfE+lWZMecyRrpnyGCYyLnJs5xt6HrCc+k/kMkdeLnPd5TJfXU7zzrXpFzBE3WrSJ6DvOrUu8BFMzN27d5GZmYkyZcooLC9Tpgxu376tdJ/bt28r3f7169e4e/cu3N3d9RZvNm3idnd3x08//YTatWvj5cuXWL58OZo1a4b9+/ejcePGeo9ZU8ZQz9owpnoWQmD06NFo1KgRqlevnud2xlTX6sZsLPV89uxZBAcH48WLF3BycsKGDRvg5+endFtjqWdNYjaWel69ejVOnTqFEydOqLW9sdS1uTKH+jeWz4a25JgfNCW3fKIJOeYeTckxV2mKuY1UEUIgIiICgwYNQlBQEFJTUw0dUqFJTk7GggULEBUVZehQCkybtrupUDcXy5mm13I5u3r1KhYuXIjRo0fj008/xfHjxzF8+HDY2dkhPDzc0OGRAd2+fRulS5fOtbx06dL5Xudat26NLl26wNPTEykpKZg8eTKaNm2KuLg42NnZ4fbt27C1tUXx4sUV9ivo9VPbeN/24sULTJgwAT179kTRokWl5WFhYahYsSLc3Nxw7tw5TJw4EadPn8bu3bvVOq6++qnz2qageUgXOS4qKgrp6eno2rWrtKxq1apYunQp/P398fjxY3z77bdo2LAhTp8+jSpVqhRqvOq0NYy1ftPS0rB9+3b89ttvCsv1Vb/aMOT7VxcK8/2rLUO+h+k/5tQmyC6PsrJeu3bNECEViLI2jdzLmFefW/aAQbmWK1t+bTS5/9vVq1cPv/76K3x8fPDPP//giy++QIMGDXD+/HnZl01Ve1Pu5SNF5pQX85PfZ7pkyZKGDs8g+Fn/j6o+E1Njit+5tJFXf7q5vR/IODF/502d65W++rqNlbbXdXOop4K0yc2hfgrapjeHOjJXHC+VP2X3KsxNUlISJkyYgNjYWFhbc6rU1atXcfDgQdjb22PDhg24e/cuBg8ejPv372PJkiWGDs8gGjRogJUrV6Jbt2548eIFXr9+jQ4dOmDBggWGDk3nzGFsuDlgG0z/5DCuUZNx9IYkp/GTcumXl+s4zY0bN+Lhw4eIiIgwdChKffLJJ3j06BGqVq0KKysrZGZmYsaMGejRo4ehQyMVmBcLxhzvt2iK96nUZ+pjjnTNlMcwkfGRUzvfGOYBy6n/RA5z5OUy111O89XlNO9c01gNVbea9gnou055JyMPOZ8iL4TI98nyyrZXtlzfNInb19cXvr6+0uvg4GDcuHEDc+bMMdoHYhhLPWvCmOp56NChOHPmDA4ePKhyW2Opa3VjNpZ69vX1RUJCAh4+fIh169ahT58+iImJyTPRG0M9axKzMdTzjRs3MGLECOzatQv29vZq72cMdW3OTL3+jeGzURByzA+akls+0YQcc4+m5JarNMXcZt4iIyMxderUfLc5ceIEDh8+jMePH2PixImFFJnuqVvWoKAg6fXff/+NVq1aoUuXLvjwww/1HWKh0bTPwRRo8n1DjrS9lstVVlYWgoKC8OWXXwJ486sa58+fx8KFC416MBFpT91rOKD8u4iq61y3bt2k/69evTqCgoLg6emJrVu3onPnznnul9dx9R1vtlevXqF79+7IysrCDz/8oLBuwIAB0v9Xr14dVapUQVBQEE6dOoXAwECVx86mj35qfeYhbY+9atUqREZG4s8//1R4EF/9+vVRv3596XXDhg0RGBiIBQsWYP78+YUar7ptDWOs36VLl6JYsWLo1KmTwnJ916+mDP3+1Zah3r+aMob3MP3HnOrZVMqaX5tGrmXMq88tm1zLBajfRpNrGVu3bi39v7+/P4KDg1G5cmUsW7ZMyj1yLZu67U25lo+UM/d/z/w+06NHjzZgZIZn7u8NQPs+E7kyxe9c2sirHszt/UDGzZw+k5rSpm5Mtf50fV03pXrSR5vcVOpHn216U6kjc8fxUsrlda/CnGRmZqJnz56YOnUqfHx8DB2OUcjKyoKFhQVWrlwJFxcXAMA333yD//3vf/j+++/h4OBg4AgL34ULFzB8+HB8/vnnCA0NRVpaGsaNG4dBgwZh8eLFhg5Pp8xhbLg5YRtMf+QwrlHTuQKGILfxk3Lpl5frOM3FixejdevW8PDwMHQoSq1ZswYrVqzAb7/9hnfeeQcJCQkYOXIkPDw80KdPH0OHR2pgXiwY1l/eeJ9KfaY85kjXTH0MExknubTzjWEesJz6T+QwR14uc93lMl9dTvPOtYnVUHWrTZ+APuvUssBHMDGlSpWClZVVrid737lzJ9fT97K5ubkp3d7a2rrQnu6vTdzK1K9fH0lJSboOTyeMoZ51xRD1PGzYMGzatAn79u1DuXLl8t3WWOpak5iVMUQ929rawtvbG0FBQZg5cyZq1qyJb7/9Vum2xlLPmsSsTGHXc1xcHO7cuYPatWvD2toa1tbWiImJwfz582FtbY3MzMxc+xhLXZsrc61/Y86pb5NjftCUHPOJJuSYezQlt1ylKeY28zZ06FAkJibm+1e9enX89ddfOHr0KOzs7GBtbQ1vb28AQFBQkGxuMKtb1mx///033nvvPQQHB+Onn34yYOS6o6u2u9wUNBfLgTbXcjlzd3fP1ZldrVo1XL9+3UARkb6pew13c3PDP//8k2v/f//9V6PrnLu7Ozw9PaXvcG5ubsjIyMCDBw8Utsvr+lkY8b569Qpdu3ZFSkoKdu/ejaJFi+a7fWBgIGxsbNT+Xqqvfuq8tiloHipIjluzZg369++PtWvXonnz5vlua2lpiTp16hT4+72++tONsX6FEFiyZAl69+4NW1vbfLfVVf1qw5Dv34IwxPtXlwrrPUz/Mac2gZubGwCYRFnzatPIvYx59bnJvVyA6jZadjnkXMa3FSlSBP7+/khKSpL9v5+q9qbcy0eKzCkvauLtz7S54mc9bzn7TEyJqX7n0pQm/emm/H4g48X8nTd1rlea9nXLWUGu6+ZQTwVpk5t6/eiiTW/qdWTOOF5KOU3uVZiyJ0+e4OTJkxg6dKh0/Zg2bRpOnz4Na2tr/PXXX4YOsdC5u7ujbNmy0gPfgDd9bUII3Lx504CRGc7MmTPRsGFDjBs3DjVq1EBoaCh++OEHLFmyBGlpaYYOT2fMYWy4uWAbTL/kMq6xoOPoC4Pcx08aa7+8HMdpXrt2DXv27DHqH/oeN24cJkyYgO7du8Pf3x+9e/fGqFGjMHPmTEOHRiowLxaMud1v0RTvU2nGlMcc6Zq5jWEiw5N7O78w5wHLqf9ELnPk5TLXXS7z1eU071xX/QKGeBaAqj4BfdcpH/qWg62tLWrXro3du3crLN+9ezcaNGigdJ/g4OBc2+/atQtBQUGwsbHRW6xv0yZuZeLj4+Hu7q7r8HTCGOpZVwqznoUQGDp0KNavX4+//voLFStWVLmPoetam5iVMYb3sxACL1++VLrO0PWcl/xiVqaw67lZs2Y4e/YsEhISpL+goCCEhYUhISEBVlZWufYx1ro2F+Za/8ZwDcqPHPODpkwpn2hCjrlHU8aeqzTF3GbeSpUqhapVq+b7Z29vj/nz5+P06dPSe2Tbtm0A3gzcnDFjhoFLoR51ywoAt27dQpMmTRAYGIjo6GhYWppG94Wu2u5yoatcLAfaXMvlrGHDhrh06ZLCssuXL8PT09NAEZG+qXsNDw4OxqNHj3D8+HFp32PHjuHRo0caXefu3buHGzduSN/hateuDRsbG4XrZ1paGs6dO6f0uPqON/uBb0lJSdizZ49aHfXnz5/Hq1ev1P5eqq9+6ry2KWge0jbHrVq1ChEREfjtt9/Qtm1blecRQiAhIaHA3+/11Z9ubPULADExMbhy5Qr69++v8jy6ql9tGPL9qy1DvX91qbDew/Qfc2oTVKxYEW5ubgplzcjIQExMjGzKqqpNYwplfFt2n5splEtVG61SpUqyL+PbXr58icTERLi7u8v+309Ve1Pu5SNF5pQXNfH2Z9pc8bOet5x9JqbA3L5z5UWb/nRTfD+Q8WP+zps61ytN+7rlSBfXdXOop5w0aZObev3ook1v6nVkzjheKjdN71WYsqJFi+a6fgwaNAi+vr5ISEhAvXr1DB1ioWvYsCH+/vtvPH36VFp2+fJlWFpaGvXDjfTp2bNnucahZY/pEUIYIiSdMoex4eaGbTD9kPu4Rk3H0RcGuY+fNNZ+eTmO04yOjkbp0qWN+rtpXt8HsrKyDBQRqYt5sWDM5X6LpnifSjdMacyRrpnbGCYyPLm38wtjHrCc+k/kPkdeLnPdjXW+upzmneuqX8AQ71VVfQJ6r1NBuaxevVrY2NiIxYsXiwsXLoiRI0eKIkWKiNTUVCGEEBMmTBC9e/eWtr969apwdHQUo0aNEhcuXBCLFy8WNjY24o8//jDquOfOnSs2bNggLl++LM6dOycmTJggAIh169YVSrxPnjwR8fHxIj4+XgAQ33zzjYiPjxfXrl1TGq+x1LOmcRu6nj/++GPh4uIi9u/fL9LS0qS/Z8+eSdsYW11rE7Oh61kIISZOnCgOHDggUlJSxJkzZ8Snn34qLC0txa5du5TGbOh61iZmY6hnZUJCQsSIESOk18ZY16ZErtdvTcjtWq8NOeYHTck1n2hCjrlHU6aSqzTF3EaqpKSkCAAiPj7e0KHo3K1bt4S3t7do2rSpuHnzpsI13BSoarubEnVysSnLeS03JcePHxfW1tZixowZIikpSaxcuVI4OjqKFStWGDo0MgKtWrUSNWrUEEeOHBFHjhwR/v7+ol27dgrb+Pr6ivXr1wsh3rS/xowZIw4fPixSUlLEvn37RHBwsChbtqx4/PixtM+gQYNEuXLlxJ49e8SpU6dE06ZNRc2aNcXr168LNd5Xr16JDh06iHLlyomEhASF69vLly+FEEJcuXJFTJ06VZw4cUKkpKSIrVu3iqpVq4qAgACN4tVHP/WhQ4eElZWVmDVrlkhMTBSzZs0S1tbW4ujRo1rXobbx/vbbb8La2lp8//33CvX48OFDaZvIyEixY8cOkZycLOLj40Xfvn2FtbW1OHbsWKHHq05bw5jqN1uvXr1EvXr1lB5Tn/Wrj/4jfdavpvEa+v2rTcyGfg/Tf0ypTaDqfThr1izh4uIi1q9fL86ePSt69Ogh3N3dFXK8MVOnTSPXMqrqc5NrufKTs40m5zKOGTNG7N+/X1y9elUcPXpUtGvXTjg7O0vXETmXTZ32ppzLR7mZUl7UlqrPtKky9e8R6sqvHtTtM5E7U/7OpQlV9WAu7weSB3PO37rIX/rq6zYWurqum3I96aJNbsr1o4w2bXpzqyO5MoexqJrQx70KudO0TnKaMmWKqFmzZiFFq3+a1seTJ09EuXLlxP/+9z9x/vx5ERMTI6pUqSI+/PBDQxVB5zStk+joaGFtbS1++OEHkZycLA4ePCiCgoJE3bp1DVUEnTKHseHmSE5tMFWfSWMhp3GNqtoPxsyYx0/KpV9ebuM0MzMzRYUKFcQnn3xi6FDy1adPH1G2bFmxZcsWkZKSItavXy9KlSolxo8fb+jQSA1yyouGwPtvmuN9Ks2Z45gjXTOlMUykf3LqQ5XLvHw59Z/IaY68XOa6y32+upzmnauK1VB1q6pPoLDrlA99y8P3338vPD09ha2trQgMDBQxMTHSuj59+oiQkBCF7ffv3y8CAgKEra2t8PLyEgsXLizkiN/QJO7Zs2eLypUrC3t7e1G8eHHRqFEjsXXr1kKLdd++fQJArr8+ffoojVcI46hnTeM2dD0rixWAiI6OlrYxtrrWJmZD17MQQvTr10/6/Lm6uopmzZopdKQbWz1rE7Mx1LMyOZO+Mda1KZHr9VsTcrvWa0OO+UFTcs0nmpBj7tGUqeQqTTG3kSqm/NC36OjoPK/hpiK/trspUScXmzJjHrSkC5s3bxbVq1cXdnZ2omrVquKnn34ydEhkJO7duyfCwsKEs7OzcHZ2FmFhYeLBgwcK27x9LXj27Jlo2bKlcHV1FTY2NqJChQqiT58+4vr16wr7PH/+XAwdOlSUKFFCODg4iHbt2uXapjDizc7Byv727dsnhBDi+vXronHjxqJEiRLC1tZWVK5cWQwfPlzcu3dP4/j00U/9+++/C19fX2FjYyOqVq2q05simsQbEhKSb9tbCCFGjhwpKlSoILUHWrZsKQ4fPmyQeNVtaxhL/QohxMOHD4WDg0Oe12h91q+++o/0Vb+axmsM71999V/p8z1M/zGVNoGq92FWVpaYMmWKcHNzE3Z2dqJx48bi7Nmzhg1aA+q0aeRaRlV9bnItV35yttHkXMZu3boJd3d3YWNjIzw8PETnzp3F+fPnpfVyLpsQqtubci8f5WYqeVFbqj7TpsrUv0eoK796ULfPRO5M+TuXJlTVg7m8H0g+zDV/6yJ/6auv21jo6rpuyvWkiza5KdePMtq06c2tjuTKHMaiakIf9yrkTpv3yNtM7aFv2tRHYmKiaN68uXBwcBDlypUTo0ePNsqHGmlLmzqZP3++8PPzEw4ODsLd3V2EhYWJmzdvFn7weqDNuGkhTPvaairk0gZT9Zk0FnIa16iq/WDMjHn8pJz65eU0TnPnzp0CgLh06ZKhQ8nX48ePxYgRI0SFChWEvb29qFSpkvjss8+kH3wl4yeXvGgIvP+mOd6n0pw5jjnSNVMaw0T6J6c+VLnMy5dT/4k2sRqqXuUy113u89XlNO9cVayGqltVfQKFXacWQggBIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLSC0tDB0BEREREREREREREREREREREREREREREREREREREREREREREREREZMr40DciIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIj3iQ9+IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiPSID30jIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItIjPvSNiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEiP+NA3IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJIX7cNAAAtzElEQVQiIiIiIiI94kPfiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIj0iA99IyIivTlw4ADat28PDw8PWFhYYOPGjRrt/+LFC0RERMDf3x/W1tbo1KlTrm3Wr1+PFi1awNXVFUWLFkVwcDB27typmwIQEREREREREREREREREREREREREREREREREREREREREREREekAH/pGJDOqHpyVmpoKCwsLJCQk6OR8TZo0wciRI/PdxsvLC/PmzdPJ+ci0pKeno2bNmvjuu++02j8zMxMODg4YPnw4mjdvrnSbAwcOoEWLFti2bRvi4uLw3nvvoX379oiPjy9I6ERERERERERERERERAa1f/9+WFhY4OHDh4YOhYiIyOjkHM/CsStERETqi4iIUPoDrERERKS+wui/VWduCPuRiYiIiIiIcouMjEStWrX0eg7eryQiItIvXfd9qpOrVT3Ph0iX+NA3IplJS0tD69atC+1869evx/Tp0wvtfGRaWrdujS+++AKdO3dWuj4jIwPjx49H2bJlUaRIEdSrVw/79++X1hcpUgQLFy7EgAED4ObmpvQY8+bNw/jx41GnTh1UqVIFX375JapUqYLNmzfro0hEZEa0aQyq87BUIiIiY8QBoEREZIr0kd90/aMbREREb8vZv9igQQOkpaXBxcXFcEEZkaVLl6JYsWKGDoOIiPSA99h0h3VJRCRf2lzDed1XjQ+4IyIiOStfvjzS0tJQvXp1Q4dCRERk9jjxn4jIOKjbJzp27Fjs3btX/wG95cSJExg4cKBa2/IBcUREZEjmOhZTk1xNVBisDR0AEakvIyMjzwdf6UuJEiUK9XxkXvr27YvU1FSsXr0aHh4e2LBhA1q1aoWzZ8+iSpUqWh0zKysLT5484XuXiIiIiEgPMjIyYGtra+gwiIiIiIiIzIKtrW2h3xskIiIiIiIiIiIiosKhahyOlZUV+4iJiMikZWZmwsLCApaWloYOhYiITIQQApmZmXBycoKTk1OhntvV1bVQz0dERKQM5/7ljbmajA17Q4iMWJMmTTB06FCMHj0apUqVQosWLXL9KsTx48cREBAAe3t7BAUFIT4+PtdxLly4gDZt2sDJyQllypRB7969cffuXbVjePup53fu3EH79u3h4OCAihUrYuXKlQUtJpmp5ORkrFq1Cr///jveffddVK5cGWPHjkWjRo0QHR2t9XGjoqKQnp6Orl276jBaIspLdq4aOnQoihUrhpIlS2LSpEkQQgAAVqxYgaCgIDg7O8PNzQ09e/bEnTt3ALzpRPX29sacOXMUjnnu3DlYWloiOTkZwJtfRFq0aBHatWsHR0dHVKtWDUeOHMGVK1fQpEkTFClSBMHBwdL22TZv3ozatWvD3t4elSpVwtSpU/H69WtpvYWFBX755Re8//77cHR0RJUqVbBp0yYAQGpqKt577z0AQPHixWFhYYGIiIh86yIiIgIxMTH49ttvYWFhAQsLC6SkpKhdxoULF6J169ZSjv39998V9rl16xa6deuG4sWLo2TJkujYsSNSU1NV/RMREZEZEULgq6++QqVKleDg4ICaNWvijz/+gBACzZs3R6tWraQc/fDhQ1SoUAGfffZZvnlPWbsUAL755hv4+/ujSJEiKF++PAYPHoynT58apNxERGQ+mjRpgmHDhmHkyJEoXrw4ypQpg59++gnp6eno27cvnJ2dUblyZWzfvj3f/LZjxw40atRIase2a9cuV5syLxUrVgQABAQEwMLCAk2aNAHw5lefWrRogVKlSsHFxQUhISE4deqUtN/+/ftha2uL2NhYaVlUVBRKlSqFtLQ0HdQOERHJnbL+xaVLl8LCwgIPHz4E8N+vK27cuBE+Pj6wt7dHixYtcOPGDbXOkZycjI4dO6JMmTJwcnJCnTp1sGfPHoVtvLy88MUXXyA8PBxOTk7w9PTEn3/+iX///RcdO3aEk5MT/P39cfLkSYX91q1bh3feeQd2dnbw8vJCVFSUwvqc9zgBoFixYli6dCmAN32yFhYWWL9+Pd577z04OjqiZs2aOHLkCIA3ubRv37549OiRVD+RkZHqVS4RERk1ZTkwNTUVMTExqFu3Luzs7ODu7o4JEyYo3OtTJTIyEhUqVICdnR08PDwwfPhwtfbL7/4m8CYnWVhYYOfOnQgICICDgwOaNm2KO3fuYPv27ahWrRqKFi2KHj164NmzZ9J+L1++xPDhw1G6dGnY29ujUaNGOHHihLRe2a8ob9y4ERYWFgplqlWrFpYvXw4vLy+4uLige/fuePLkSb51SURExk+bfJjXPpmZmejfvz8qVqwIBwcH+Pr64ttvv9U6NlX9qdntubVr1+Ldd9+Fg4MD6tSpg8uXL+PEiRMICgqCk5MTWrVqhX///VfaLysrC9OmTUO5cuVgZ2eHWrVqYceOHdL67Jyb3SYGgISEBIX8lp0/d+7ciWrVqknnye5zjYyMxLJly/Dnn39KdbR//36t64KIiIzTokWLULZsWWRlZSks79ChA/r06QOgYONJs23btg0+Pj5wcHDAe++9p7S9paqfNLv/NSIiAi4uLhgwYEC+ZcvOswkJCRrFQURExqkgcy8A4MGDBwgLC4OrqyscHBxQpUoVaf5VRkYGhg4dCnd3d9jb28PLywszZ86U9n306BEGDhyI0qVLo2jRomjatClOnz4trVfV9wgAT548QVhYGIoUKQJ3d3fMnTs317zDjIwMjB8/HmXLlkWRIkVQr149hXZYdjtuy5Yt8PPzg52dHa5du6ay7pYsWSLlWHd3dwwdOlRad/36dek+ZtGiRdG1a1f8888/0vqIiAh06tRJ4XgjR46Uxvxk/9sMHz4c48ePR4kSJeDm5qZwL9LLywsA8P7778PCwkJ6TUREhSu/sTU7d+5EUFAQ7OzsEBsbK+W2t/ft1KkTpk6dKuXDjz76CBkZGWqdOz09XRpL4+7unqu9B7zJF/PmzZNe53W/skmTJrh27RpGjRollQMA7t27hx49eqBcuXJwdHSEv78/Vq1apXAOVTkLeDNXZODAgShTpgzs7e1RvXp1bNmyRVp/+PBhNG7cGA4ODihfvjyGDx+O9PR0teqBiIiMj7K5f/nN+8tvLKaqNp0qhw4dQkhICBwdHVG8eHGEhobiwYMHAFSPmwHU6/ssSB7LmauTkpLQuHFj2Nvbw8/PD7t371a7rES6wIe+ERm5ZcuWwdraGocOHcKiRYsU1qWnp6Ndu3bw9fVFXFwcIiMjMXbsWIVt0tLSEBISglq1auHkyZPYsWMH/vnnH60fiBUREYHU1FT89ddf+OOPP/DDDz8odCATqevUqVMQQsDHx0d6ar6TkxNiYmLUnmSb06pVqxAZGYk1a9agdOnSOo6YiPKSnauOHTuG+fPnY+7cufjll18AvGngTZ8+HadPn8bGjRuRkpIiTbK3sLBAv379cj3occmSJdLDILNNnz4d4eHhSEhIQNWqVdGzZ0989NFHmDhxojS58O0bdzt37kSvXr0wfPhwXLhwAYsWLcLSpUsxY8YMhXNNnToVXbt2xZkzZ9CmTRuEhYXh/v37KF++PNatWwcAuHTpEtLS0lQOgP32228RHByMAQMGIC0tDWlpaahQoYLaZZw8eTI++OADnD59Gr169UKPHj2QmJgIAHj27Bnee+89ODk54cCBAzh48KA0UFXdzmUiIjJ9kyZNQnR0NBYuXIjz589j1KhR6NWrFw4cOIBly5bh+PHjmD9/PgBg0KBBKFOmDCIjI1XmPWXtUktLS8yfPx/nzp3DsmXL8Ndff2H8+PGFX2giIjI7y5YtQ6lSpXD8+HEMGzYMH3/8Mbp06YIGDRrg1KlTCA0NRe/eveHq6ppnfktPT8fo0aNx4sQJ7N27F5aWlnj//fdzTQRR5vjx4wCAPXv2IC0tDevXrwfwZmBrnz59EBsbi6NHj6JKlSpo06aNNPg1e5Br79698ejRI5w+fRqfffYZfv75Z7i7u+ujqoiISGaU9S+WL18+13bPnj3DjBkzsGzZMhw6dAiPHz9G9+7d1TrH06dP0aZNG+zZswfx8fEIDQ1F+/btcf36dYXt5s6di4YNGyI+Ph5t27ZF7969ER4ejl69euHUqVPw9vZGeHi4NAElLi4OXbt2Rffu3XH27FlERkZi8uTJ0gPdNPHZZ59h7NixSEhIgI+PD3r06IHXr1+jQYMGmDdvHooWLSrVT877okREJE/KcqCNjQ3atGmDOnXq4PTp01i4cCEWL16ML774Qq1j/vHHH5g7dy4WLVqEpKQkbNy4Ef7+/mrtm9/9zbdFRkbiu+++w+HDh3Hjxg107doV8+bNw2+//YatW7di9+7dWLBggbT9+PHjsW7dOixbtkzKp6Ghobh//75acWVLTk7Gxo0bsWXLFmzZsgUxMTGYNWsWAPW/TxARkfHRJh/mdd3PyspCuXLlsHbtWly4cAGff/45Pv30U6xdu1ar2NTtT50yZQomTZqEU6dOwdraGj169MD48ePx7bffIjY2FsnJyfj8888VyhwVFYU5c+bgzJkzCA0NRYcOHZCUlKRRfM+ePcOcOXOwfPlyHDhwANevX5fai2PHjkXXrl2lB8GlpaWhQYMGWtUDEREZry5duuDu3bvYt2+ftOzBgwfYuXMnwsLCCjyeFABu3LiBzp07o02bNkhISMCHH36ICRMmKOyvbj/p119/jerVqyMuLg6TJ0/WqKzqxEFERMZN27kXwJv5BhcuXMD27duRmJiIhQsXolSpUgCA+fPnY9OmTVi7di0uXbqEFStWSA8mE0Kgbdu2uH37NrZt24a4uDgEBgaiWbNmCv2T+fU9AsDo0aNx6NAhbNq0Cbt370ZsbKzCDyICQN++fXHo0CGsXr0aZ86cQZcuXdCqVSuFtt6zZ88wc+ZM/PLLLzh//rzK+V8LFy7EkCFDMHDgQJw9exabNm2Ct7e3VLZOnTrh/v37iImJwe7du5GcnIxu3bpp9W9TpEgRHDt2DF999RWmTZsmTfjPfhBBdHQ00tLScj2YgIiICkd+98LGjx+PmTNnIjExETVq1FC6/969e5GYmIh9+/Zh1apV2LBhA6ZOnarWuceNG4d9+/Zhw4YN2LVrF/bv34+4uLg8t8/vfuX69etRrlw5TJs2TSoHALx48QK1a9fGli1bcO7cOQwcOBC9e/fGsWPHFI6dX87KyspC69atcfjwYaxYsQIXLlzArFmzYGVlBQA4e/YsQkND0blzZ5w5cwZr1qzBwYMHFeZlEhGR/OSc+5ffvL/8xmKq06bLS0JCApo1a4Z33nkHR44cwcGDB9G+fXtkZmYCUD1uRp2+T13msaysLHTu3BlWVlY4evQofvzxR3zyyScaH4eoQAQRGa2QkBBRq1YthWUAxIYNG4QQQixatEiUKFFCpKenS+sXLlwoAIj4+HghhBCTJ08WLVu2VDjGjRs3BABx6dIltWIYMWKEEEKIS5cuCQDi6NGj0vrExEQBQMydO1fzApJZefu9K4QQq1evFlZWVuLixYsiKSlJ4S8tLS3X/n369BEdO3bM8/irV68WDg4OYsuWLXqInojyEhISIqpVqyaysrKkZZ988omoVq2a0u2PHz8uAIgnT54IIYT4+++/hZWVlTh27JgQQoiMjAzh6uoqli5dKu0DQEyaNEl6feTIEQFALF68WFq2atUqYW9vL71+9913xZdffqlw7uXLlwt3d/c8j/v06VNhYWEhtm/fLoQQYt++fQKAePDggUb1kZ03s6lbxkGDBinsV69ePfHxxx8LIYRYvHix8PX1Vajnly9fCgcHB7Fz50614yMiItP19OlTYW9vLw4fPqywvH///qJHjx5CCCHWrl0r7OzsxMSJE4Wjo6NCmzCvvKesXarM2rVrRcmSJQteECIionyEhISIRo0aSa9fv34tihQpInr37i0tS0tLEwDEkSNH1G7X3blzRwAQZ8+eVRlDSkqKQv9rXl6/fi2cnZ3F5s2bpWUvX74UAQEBomvXruKdd94RH374ocrzERGRecnZv5gzl0VHR+d5ry67/1FTfn5+YsGCBdJrT09P0atXL+l1dm6dPHmytCy7jzb7fk7Pnj1FixYtFI47btw44efnJ73OeZ9ICCFcXFxEdHS0EOK/HPvLL79I68+fPy8AiMTERKn8Li4uWpWTiIiMW84c+Omnn+a6N/b9998LJycnkZmZqXQfT09PaexKVFSU8PHxERkZGQWOLef9zez8vGfPHmmbmTNnCgAiOTlZWvbRRx+J0NBQIcSb/lsbGxuxcuVKaX1GRobw8PAQX331lRBCeZ7bsGGDeHt435QpU4Sjo6N4/PixtGzcuHGiXr160mtl9yuJiEgedJEP8zJ48GDxwQcfSK9VjcXLT87+VGXtuVWrVgkAYu/evdKymTNnCl9fX+m1h4eHmDFjhsKx69SpIwYPHiyEUH7/Mj4+XgAQKSkpQoj/2slXrlyRtvn+++9FmTJldFJWIiKSjw4dOoh+/fpJrxctWiTc3NzE69evdTKedOLEiUrHyr6dq9TpJ/X09BSdOnVSu1w5702qEwcRERmvgs69aN++vejbt6/SbYcNGyaaNm2qcOxse/fuFUWLFhUvXrxQWF65cmWxaNEiIYTqvsfHjx8LGxsb8fvvv0vrHz58KBwdHaV26ZUrV4SFhYW4deuWwnmaNWsmJk6cKIT4rx2XkJCgtBzKeHh4iM8++0zpul27dgkrKytx/fp1aVn2Pcbjx48LIZS3C0eMGCFCQkKk1znHJAnxpo36ySefSK+V3e8kIqLCl9fYmo0bNypsN2XKFFGzZk3pdZ8+fZTOx3+7vzUvT548Eba2tmL16tXSsnv37gkHBwet71e+vW1+2rRpI8aMGSO9VpWzdu7cKSwtLfN8fkDv3r3FwIEDFZbFxsYKS0tL8fz5c5XxEBGR8VFn7l/OeX/Kxqio06bLT48ePUTDhg2VrlNn3Iw6fZ8FzWNv59+dO3cKKysrcePGDWn99u3b2fajQmWpjwfJEZHuBAUF5bkuMTERNWvWhKOjo7QsODhYYZu4uDjs27cPTk5O0l/VqlUBvPkFDk0kJibC2tpaIaaqVauiWLFiGh2HCAACAgKQmZmJO3fuwNvbW+HPzc1No2OtWrUKERER+O2339C2bVs9RUxEealfvz4sLCyk18HBwUhKSkJmZibi4+PRsWNHeHp6wtnZGU2aNAEAXL9+HQDg7u6Otm3bYsmSJQCALVu24MWLF+jSpYvCOd7+hY0yZcoAgPTrFtnLXrx4gcePHwN4k/+mTZumkP+yf8Xj2bNnSo9bpEgRODs7486dO7qoFom6ZcyZw4ODg5GYmCiV58qVK3B2dpbKU6JECbx48ULjfE5ERKbpwoULePHiBVq0aKGQ/3799VcpV3Tp0gWdO3fGzJkzERUVBR8fH7WOraxdum/fPrRo0QJly5aFs7MzwsPDce/ePaSnp+u0XERERDm93Y6zsrJCyZIlc7UPAeTbtktOTkbPnj1RqVIlFC1aFBUrVgTwX1tVG3fu3MGgQYPg4+MDFxcXuLi44OnTpwrHtLW1xYoVK7Bu3To8f/4c8+bN0/p8RERkvvK6V5fdl5if9PR0jB8/Hn5+fihWrBicnJxw8eLFXDlQnf5Y4L98m5iYiIYNGyoco2HDhlI/sSbePre7u7vCeYiIyHwkJiYiODhY4R5kw4YN8fTpU9y8eVPl/l26dMHz589RqVIlDBgwABs2bMDr16/VOreq+5vZcuZLR0dHVKpUSWFZdg5LTk7Gq1evFPKljY0N6tatq1YOf5uXlxecnZ2l1+7u7syVREQmqiD58Mcff0RQUBBcXV3h5OSEn3/+Wev+T3X7U9VpS2bnrMePH+Pvv/9W2pbUNDc6OjqicuXK0mvmRiIi8xQWFoZ169bh5cuXAICVK1eie/fusLKy0sl40sTERKVjZd+mbj9pfvNDVFEnDiIiMm4FmXvx8ccfY/Xq1ahVqxbGjx+Pw4cPS8eJiIhAQkICfH19MXz4cOzatUtaFxcXh6dPn6JkyZIK+TAlJUVhLkJ+fY9Xr17Fq1evULduXWm9i4sLfH19pdenTp2CEAI+Pj4K54mJiVE4j62trULezc+dO3fw999/o1mzZkrXJyYmonz58ihfvry0LPteqKbty5wxsX1JRCQv6rS1lM3Hf/r0KW7cuJHvfsnJycjIyFBof5UoUUIhD+akzf3KzMxMzJgxAzVq1JDy9q5du/LtiwUUc1ZCQgLKlSuX51yRuLg4LF26VCFXh4aGIisrCykpKfnGR0RExitnHtRm3p+6bbq8JCQk5Nl2U2fcjDp9n7rMY4mJiahQoQLKlSuX5/mI9M3a0AEQUf6KFCmS5zohhMr9s7Ky0L59e8yePTvXuuyJEurKPt/biZIoP0+fPsWVK1ek1ykpKUhISECJEiXg4+ODsLAwhIeHIyoqCgEBAbh79y7++usv+Pv7o02bNgDePLwiIyMD9+/fx5MnT5CQkAAAqFWrFoA3D3wLDw/Ht99+i/r16+P27dsAAAcHB7i4uBRqeYlI0YsXL9CyZUu0bNkSK1asgKurK65fv47Q0FBkZGRI23344Yfo3bs35s6di+joaHTr1k2hAxV403jLlp2HlC3LysqS/jt16lR07tw5V1z29vZKj5t9nOxj6JI6ZVTm7XLVrl0bK1euzLWNq6urzuMlIiL5yc5fW7duRdmyZRXW2dnZAQCePXuGuLg4WFlZISkpSe1j52yXXrt2DW3atMGgQYMwffp0lChRAgcPHkT//v3x6tWrApaEiIgof8racfm1D5Vp3749ypcvj59//hkeHh7IyspC9erVFdqqmoqIiMC///6LefPmwdPTE3Z2dggODs51zOwBt/fv38f9+/fz7f8lIiLKi7J7dercvxs3bhx27tyJOXPmwNvbGw4ODvjf//6XK19p2h8rhMh1/pz3MS0sLHItU9aG1DSvExGRacovt6iT88qXL49Lly5h9+7d2LNnDwYPHoyvv/4aMTExudqVb0tPT1fr/iaQO2fld98xr9jfLqelpaXGuTLneYiIyLRomw/Xrl2LUaNGISoqCsHBwXB2dsbXX3+NY8eOaRWHuv2p6rQlc+YsVbkxe1k2dXOjOmNriYjItLRv3x5ZWVnYunUr6tSpg9jYWHzzzTcAdDOeVJ3cok4/KZD//BB1zkFERKZJnbkXrVu3xrVr17B161bs2bMHzZo1w5AhQzBnzhwEBgYiJSUF27dvx549e9C1a1c0b94cf/zxB7KysuDu7o79+/fnOm+xYsWk/9e2jzNbVlaW9MBVKysrhe2cnJyk/3dwcFB7bqKDg0O+65Xl35zL2fdKRGQeCtLWUpWXtGmLaXO/MioqCnPnzsW8efPg7++PIkWKYOTIkfn2xWbHn52zVOXOrKwsfPTRRxg+fHiudRUqVNCkiEREZETezoPazvtTt02Xl/xykDrjZtR9do6u8piy8/E5OlTY+NA3Ihnz8/PD8uXL8fz5cykJHj16VGGbwMBArFu3Dl5eXrC2LthHvlq1anj9+jVOnjwp/TLHpUuX8PDhwwIdl0zXyZMn8d5770mvR48eDQDo06cPli5diujoaHzxxRcYM2YMbt26hZIlSyI4OFh64BsAtGnTBteuXZNeBwQEAPjvi9SiRYvw+vVrDBkyBEOGDJG2yz4HEelfztxz9OhRVKlSBRcvXsTdu3cxa9Ys6ZeTTp48mWv/Nm3aoEiRIli4cCG2b9+OAwcOFDimwMBAXLp0Cd7e3lofw9bWFgAUfmFRnX2Uba9OGY8ePYrw8HCF19nXvMDAQKxZswalS5dG0aJFNS0KERGZAT8/P9jZ2eH69esICQlRus2YMWNgaWmJ7du3o02bNmjbti2aNm0KQLO8d/LkSbx+/RpRUVHSZIu1a9fqqCRERES6oyy/3bt3D4mJiVi0aBHeffddAMDBgwcLdEwAiI2NxQ8//CD1a924cQN3795V2CY5ORmjRo3Czz//jLVr1yI8PBx79+6V8ikREVFe/Ytvy+teXdWqVVUePzY2FhEREXj//fcBvPnxntTU1ALH7efnlyufHj58GD4+PtLgH1dXV6SlpUnrk5KS8OzZM43Oo079EBGRPOW8xvv5+WHdunUKgzsPHz4MZ2fnXD96kRcHBwd06NABHTp0wJAhQ1C1alWcPXsWgYGBee6j7v1NTXl7e8PW1hYHDx5Ez549AbyZVHjy5EmMHDkSwJtc+eTJE6Snp0uDcbN/FE8TzJdERPKlTT5Udt2PjY1FgwYNMHjwYGlZcnKyVjEVtD81L0WLFoWHhwcOHjyIxo0bS8sPHz4stXezfwQxLS0NxYsXB8DcSEREeXNwcEDnzp2xcuVKXLlyBT4+PqhduzYA3Ywn9fPzw8aNGxWW5Rw7q04/aUGpEwcRERm3gs69cHV1RUREBCIiIvDuu+9i3LhxmDNnDoA3ba1u3bqhW7du+N///odWrVrh/v37CAwMxO3bt2FtbQ0vLy+t4q5cuTJsbGxw/PhxKb7Hjx8jKSlJGrMaEBCAzMxM3LlzR2pDFpSzszO8vLywd+9ehflp2fz8/HD9+nXcuHFDiuvChQt49OgRqlWrBuBNnZ07d05hv4SEhHx/IEQZGxsbti+JiIxAQfr7Tp8+nWs+vpOTE8qVK5fvft7e3rCxscHRo0elB8o8ePAAly9fznPuBpD//cq8+nY7duyIXr16AXjzYJukpCQpp6mjRo0auHnzJi5fvgwfH59c6wMDA3H+/PkCtZGJiMi4qTPvT1keKmibrkaNGti7dy+mTp2aa50642bU6fvUZR7Lbk/+/fff8PDwAAAcOXKkwMcl0gRnEhHJWM+ePWFpaYn+/fvjwoUL2LZtm9RRm23IkCG4f/8+evTogePHj+Pq1avYtWsX+vXrp3HD1tfXF61atcKAAQNw7NgxxMXF4cMPP1T55G8yX02aNIEQItdf9sPYbGxsMHXqVKSkpCAjIwNpaWlYv349/P39pWOkpqYqPUa2/fv353sOItK/GzduYPTo0bh06RJWrVqFBQsWYMSIEahQoQJsbW2xYMECXL16FZs2bcL06dNz7W9lZYWIiAhMnDgR3t7eCA4OLnBMn3/+OX799VdERkbi/PnzSExMxJo1azBp0iS1j+Hp6QkLCwts2bIF//77L54+fapyHy8vLxw7dgypqam4e/eu9CsZ6pTx999/x5IlS3D58mVMmTIFx48fx9ChQwEAYWFhKFWqFDp27IjY2FikpKQgJiYGI0aMwM2bN9UuExERmS5nZ2eMHTsWo0aNwrJly5CcnIz4+Hh8//33WLZsGbZu3YolS5Zg5cqVaNGiBSZMmIA+ffrgwYMHADTLe5UrV8br16+lHL98+XL8+OOPhVVUIiIitSnLb8WLF0fJkiXx008/4cqVK/jrr7+kHypQR+nSpeHg4IAdO3bgn3/+waNHjwC8uRG5fPlyJCYm4tixYwgLC1PoN83MzETv3r3RsmVL9O3bF9HR0Th37hyioqJ0Xm4iIpKvvPoX32ZjY4Nhw4bh2LFjOHXqFPr27Yv69etLk+Lz4+3tjfXr1yMhIQGnT59Gz549dfLr9GPGjMHevXsxffp0XL58GcuWLcN3332HsWPHSts0bdoU3333HU6dOoWTJ09i0KBBGk+k8PLywtOnT7F3717cvXtX44fGERGR8cqZAwcPHowbN25g2LBhuHjxIv78809MmTIFo0ePVuvB2UuXLsXixYtx7tw5qQ/TwcEBnp6e+e6n7v1NTRUpUgQff/wxxo0bhx07duDChQsYMGAAnj17hv79+wMA6tWrB0dHR3z66ae4cuUKfvvtN63GPajzfYKIiIyTNvlQ2XXf29sbJ0+exM6dO3H58mVMnjwZJ06c0Cqmgvan5mfcuHGYPXs21qxZg0uXLmHChAlISEjAiBEjALxpw5YvXx6RkZG4fPkytm7dqlV/qpeXF86cOYNLly7h7t27ePXqlU7iJyIi4xMWFiaNj8meIA/oZjzpoEGDkJycLI2VVdZmU6eftKDUiYOIiIxbQeZefP755/jzzz9x5coVnD9/Hlu2bJEeAjN37lysXr0aFy9exOXLl/H777/Dzc0NxYoVQ/PmzREcHIxOnTph586dSE1NxeHDhzFp0iS1f/TC2dkZffr0wbhx47Bv3z6cP38e/fr1g6WlpfSgch8fH4SFhSE8PBzr169HSkoKTpw4gdmzZ2Pbtm1a11lkZCSioqIwf/58JCUl4dSpU1iwYAEAoHnz5qhRowbCwsJw6tQpHD9+HOHh4QgJCUFQUBCAN/cpT548iV9//RVJSUmYMmVKrofAqSP74XO3b9+Wxt4SEVHhK8i9sIyMDGk+/vbt2zFlyhQMHTpU5f1HJycn9O/fH+PGjcPevXtx7tw5RERE5LufqvuVXl5eOHDgAG7duiX9wLC3tzd2796Nw4cPIzExER999BFu376tdvkAICQkBI0bN8YHH3yA3bt3IyUlBdu3b8eOHTsAAJ988gmOHDmCIUOGICEhAUlJSdi0aROGDRum0XmIiMh4qTPvT9lYzIK26SZOnIgTJ05g8ODBOHPmDC5evIiFCxfi7t27ao2bUafvU5d5rHnz5vD19UV4eDhOnz6N2NhYfPbZZxofh6gg+NA3IhlzcnLC5s2bceHCBQQEBOCzzz7D7NmzFbbx8PDAoUOHkJmZidDQUFSvXh0jRoyAi4uLWgNhc4qOjkb58uUREhKCzp07Y+DAgShdurSuikRERDIUHh6O58+fo27duhgyZAiGDRuGgQMHwtXVFUuXLsXvv/8OPz8/zJo1K9fDSbP1798fGRkZ6Nevn05iCg0NxZYtW7B7927UqVMH9evXxzfffKNyIsfbypYti6lTp2LChAkoU6aM9AC2/IwdOxZWVlbw8/ODq6srrl+/Lq1TVcapU6di9erVqFGjBpYtW4aVK1fCz88PAODo6IgDBw6gQoUK6Ny5M6pVq4Z+/frh+fPnKFq0qNplIiIi0zZ9+nR8/vnnmDlzJqpVq4bQ0FBs3rwZXl5e6N+/PyIjIxEYGAgAmDJlCjw8PDBo0CAAmuW9WrVq4ZtvvsHs2bNRvXp1rFy5EjNnziyUMhIREWlCWX6ztLTE6tWrERcXh+rVq2PUqFH4+uuv1T6mtbU15s+fj0WLFsHDwwMdO3YEACxZsgQPHjxAQEAAevfujeHDhyv0m86YMQOpqan46aefAABubm745ZdfMGnSJCQkJOi03EREJF/59S9mc3R0xCeffIKePXsiODgYDg4OWL16tVrHnzt3LooXL44GDRqgffv2CA0NldqJBREYGIi1a9di9erVqF69Oj7//HNMmzYNERER0jZRUVEoX748GjdujJ49e2Ls2LFwdHTU6DwNGjTAoEGD0K1bN7i6uuKrr74qcOxERGQccubAV69eYdu2bTh+/Dhq1qyJQYMGoX///mpPyC9WrBh+/vlnNGzYUPol4c2bN6NkyZL57qfJ/U1NzZo1Cx988AF69+6NwMBAXLlyBTt37kTx4sUBACVKlMCKFSuwbds2+Pv7Y9WqVYiMjNT4POp8nyAiIuOkTT5Udt0fNGgQOnfujG7duqFevXq4d+8eBg8erFVMBe1Pzc/w4cMxZswYjBkzBv7+/tixYwc2bdqEKlWqAHjz0PNVq1bh4sWLqFmzJmbPno0vvvhC4/MMGDAAvr6+CAoKgqurKw4dOqST+ImIyPg0bdoUJUqUwKVLl9CzZ09puS7Gk1aoUAHr1q3D5s2bUbNmTfz444/48ssvFbZRp5+0oNSJg4iIjFtB5l7Y2tpi4sSJqFGjBho3bgwrKyvpPqGTkxNmz56NoKAg1KlTB6mpqdi2bZv0ULZt27ahcePG6NevH3x8fNC9e3ekpqaiTJkyasf+zTffIDg4GO3atUPz5s3RsGFDVKtWDfb29tI20dHRCA8Px5gxY+Dr64sOHTrg2LFjKF++vNZ11qdPH8ybNw8//PAD3nnnHbRr1w5JSUkAAAsLC2zcuBHFixdH48aN0bx5c1SqVAlr1qyR9g8NDcXkyZMxfvx41KlTB0+ePEF4eLjGcURFRWH37t0oX748AgICtC4PEREVTEHuhTVr1gxVqlRB48aN0bVrV7Rv317t+3Fff/01GjdujA4dOqB58+Zo1KgRateunef2qu5XTps2DampqahcuTJcXV0BAJMnT0ZgYCBCQ0PRpEkTuLm5oVOnTmqXL9u6detQp04d9OjRA35+fhg/fjwyMzMBADVq1EBMTAySkpLw7rvvIiAgAJMnT4a7u7vG5yEiIuOkzry/vMZiFqRN5+Pjg127duH06dOoW7cugoOD8eeff8La2hqA6nEz6vR96jKPWVpaYsOGDXj58iXq1q2LDz/8EDNmzND4OEQFYSGEEIYOgoiIiIjkqUmTJqhVqxbmzZtXoOMcOnQITZo0wc2bNzW6cSgn+ZXRwsICGzZs0KojloiIiIiIiIiIiMzD0qVLMXLkSDx8+NDQoRARERERERERERERERGRBnQ198JYpKeno2zZsoiKikL//v0NHQ4REVG+IiIi8PDhQ2zcuNHQoRARERERAQCsDR0AEREREZmvly9f4saNG5g8eTK6du1qkg98M4cyEhERERERERERERERERERERERERERERGRaYqPj8fFixdRt25dPHr0CNOmTQMAdOzY0cCRERERERERERHJj6WhAyAiw7l+/TqcnJzy/Lt+/bqhQyQiIhO3atUq+Pr64tGjR/jqq68MHU6+tM2bciojERERERGRufvyyy/zbPe1bt3a0OERERHl65133skzj61cudLQ4RERERlcbGxsvvf7iIiIzA3HkBIRERkW700SEZHczJkzBzVr1kTz5s2Rnp6O2NhYlCpVqkDHzK9dGhsbq6PIiYiI8sZ+UiIiItVat26dZ6788ssvDR0exwSRLFkIIYShgyAiw3j9+jVSU1PzXO/l5QVra+vCC4iIiMiIMW8SERERERGZvvv37+P+/ftK1zk4OKBs2bKFHBEREZH6rl27hlevXildV6ZMGTg7OxdyRERERMbl+fPnuHXrVp7rvb29CzEaIiIiw+NYGCIiIsPivUkiIiLgypUrea4rW7YsHBwcCjEaIiIyR+wnJSIiUu3WrVt4/vy50nUlSpRAiRIlCjkiRRwTRHLEh74REREREREREREREREREREREREREREREREREREREREREREREREREemRpaEDICIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyZXzoGxERERERERERERERERERERERERERERERERERERERERERERERERGRHvGhb0REREREREREREREREREREREREREREREREREREREREREREREREREesSHvhERERERERERERERERERERERERERERERERERERERERERERERERER6REf+kZEREREREREREREREREREREREREREREREREREREREREREREREREpEd86BsRERERERERERERERERERERERERERERERERERERERERERERERERkR7xoW9ERERERERERERERERERERERERERERERERERERERERERERERERERHr0fwF/sgQB3XC9AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create visualizations showing correlations between variables.\n", + "import seaborn as sns\n", + "target = 'fare_amount'\n", + "features = [col for col in df.columns if col != target]\n", + "\n", + "# Create a figure with subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=len(features), figsize=(50, 10))\n", + "\n", + "# Create scatter plots\n", + "for i, feature in enumerate(features):\n", + " sns.scatterplot(x=df[feature], y=df[target], ax=axes[i])\n", + " axes[i].set_title(f'{feature} vs {target}')\n", + " axes[i].set_xlabel(feature)\n", + " axes[i].set_ylabel(target)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "d8dff114-adb5-4b34-a788-b93e42a2fee4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tip_amount 0.5490632216119445\n", + "tolls_amount 0.6102905023696504\n", + "extra -0.014430938533029767\n", + "mta_tax -0.15051466243094883\n", + "total_amount 0.9774147114602906\n", + "trip_distance 0.8802845818094683\n" + ] + } + ], + "source": [ + "# extra and mta_tax seem weakly correlated\n", + "# total_amount is almost perfectly correlated, indicating target leakage.\n", + "continuous_features = ['tip_amount', 'tolls_amount', 'extra', 'mta_tax', 'total_amount', 'trip_distance']\n", + "\n", + "for i in continuous_features:\n", + " correlation = df['fare_amount'].corr(df[i])\n", + " print(i, correlation)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "3e083025-3312-4fd9-8cd2-4c8e37db5859", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature: payment_type, F-statistic: 14.30, p-value: 0.00000\n", + "Feature: extra, F-statistic: 105.47, p-value: 0.00000\n", + "Feature: mta_tax, F-statistic: 630.56, p-value: 0.00000\n", + "Feature: vendor_id, F-statistic: 8.74, p-value: 0.00312\n", + "Feature: passenger_count, F-statistic: 5.69, p-value: 0.00000\n" + ] + } + ], + "source": [ + "# The mta tax and extra have the most variance between the groups\n", + "from scipy.stats import f_oneway\n", + "# Separate features and target variable\n", + "X = df[['payment_type', 'extra', 'mta_tax', 'vendor_id', 'passenger_count']]\n", + "y = df['fare_amount']\n", + "\n", + "# Perform one-way ANOVA for each feature\n", + "for feature in X.columns:\n", + " groups = [y[X[feature] == group] for group in X[feature].unique()]\n", + " if len(groups) > 1:\n", + " f_statistic, p_value = f_oneway(*groups)\n", + " print(f'Feature: {feature}, F-statistic: {f_statistic:.2f}, p-value: {p_value:.5f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "0dbcf599-076c-468e-9e9b-2e0bd53c3fa7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: e7fbec48-e870-4d00-bb8e-ef1b64851e27\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'e7fbec48-e870-4d00-bb8e-ef1b64851e27'" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Dropping passenger count and total_amount from dataset\n", + "# Final select statement has tip_amount, tolls_amount, extra, mta_tax, trip_distance\n", + "ride_combined_notebook_relevant_features_query = \"\"\"\n", + "SELECT fare_amount, tip_amount, tolls_amount, extra, mta_tax, trip_distance FROM combined_ride_data_deduped\n", + "\"\"\"\n", + "\n", + "# Run the query to create the dataset that we're using to train our model\n", + "run_athena_query(ride_combined_notebook_relevant_features_query, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "624a7833-c815-480e-b1da-c29da3d02c76", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s3://parsa-ux360-burner-account-bucket/e7fbec48-e870-4d00-bb8e-ef1b64851e27.csv'" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For the athena-tutorial-ux360-draft processing script, you're specifying /opt/ml/processing/input/query-id-from-preceding-cell.csv\n", + "get_csv_file_location('e7fbec48-e870-4d00-bb8e-ef1b64851e27')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "788cae3c-a34b-4ee0-899e-0a461e21b210", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating processing-job with name sagemaker-scikit-learn-2024-06-17-14-01-30-730\n" + ] + }, + { + "ename": "ResourceLimitExceeded", + "evalue": "An error occurred (ResourceLimitExceeded) when calling the CreateProcessingJob operation: The account-level service limit 'ml.m5.4xlarge for processing job usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances. Please use AWS Service Quotas to request an increase for this quota. If AWS Service Quotas is not available, contact AWS support to request an increase for this quota.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mResourceLimitExceeded\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[49], line 17\u001b[0m\n\u001b[1;32m 11\u001b[0m sklearn_processor \u001b[38;5;241m=\u001b[39m SKLearnProcessor(framework_version\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0.20.0\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 12\u001b[0m role\u001b[38;5;241m=\u001b[39mrole,\n\u001b[1;32m 13\u001b[0m instance_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mml.m5.4xlarge\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 14\u001b[0m instance_count\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Run the processing job\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[43msklearn_processor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mathena-tutorial-ux360-draft-processing-file.py\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Ensure this path is correct\u001b[39;49;00m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mProcessingInput\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m 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\u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/processing.py:680\u001b[0m, in \u001b[0;36mScriptProcessor.run\u001b[0;34m(self, code, inputs, outputs, arguments, wait, logs, job_name, experiment_config, kms_key)\u001b[0m\n\u001b[1;32m 670\u001b[0m normalized_inputs, normalized_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_normalize_args(\n\u001b[1;32m 671\u001b[0m job_name\u001b[38;5;241m=\u001b[39mjob_name,\n\u001b[1;32m 672\u001b[0m arguments\u001b[38;5;241m=\u001b[39marguments,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 676\u001b[0m kms_key\u001b[38;5;241m=\u001b[39mkms_key,\n\u001b[1;32m 677\u001b[0m )\n\u001b[1;32m 679\u001b[0m experiment_config \u001b[38;5;241m=\u001b[39m check_and_get_run_experiment_config(experiment_config)\n\u001b[0;32m--> 680\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlatest_job \u001b[38;5;241m=\u001b[39m \u001b[43mProcessingJob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_new\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 681\u001b[0m \u001b[43m \u001b[49m\u001b[43mprocessor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 682\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalized_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 683\u001b[0m \u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalized_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 684\u001b[0m \u001b[43m \u001b[49m\u001b[43mexperiment_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexperiment_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 685\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 686\u001b[0m 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experiment_config)\u001b[0m\n\u001b[1;32m 1494\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprocess request: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, json\u001b[38;5;241m.\u001b[39mdumps(request, indent\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_client\u001b[38;5;241m.\u001b[39mcreate_processing_job(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrequest)\n\u001b[0;32m-> 1497\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_intercept_create_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_request\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/session.py:6458\u001b[0m, in \u001b[0;36mSession._intercept_create_request\u001b[0;34m(self, request, create, func_name)\u001b[0m\n\u001b[1;32m 6441\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_intercept_create_request\u001b[39m(\n\u001b[1;32m 6442\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 6443\u001b[0m request: typing\u001b[38;5;241m.\u001b[39mDict,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6446\u001b[0m \u001b[38;5;66;03m# pylint: disable=unused-argument\u001b[39;00m\n\u001b[1;32m 6447\u001b[0m ):\n\u001b[1;32m 6448\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This function intercepts the create job request.\u001b[39;00m\n\u001b[1;32m 6449\u001b[0m \n\u001b[1;32m 6450\u001b[0m \u001b[38;5;124;03m PipelineSession inherits this Session class and will override\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6456\u001b[0m \u001b[38;5;124;03m func_name (str): the name of the function needed intercepting\u001b[39;00m\n\u001b[1;32m 6457\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 6458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/session.py:1495\u001b[0m, in \u001b[0;36mSession.process..submit\u001b[0;34m(request)\u001b[0m\n\u001b[1;32m 1493\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating processing-job with name \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, job_name)\n\u001b[1;32m 1494\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprocess request: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, json\u001b[38;5;241m.\u001b[39mdumps(request, indent\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m))\n\u001b[0;32m-> 1495\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_processing_job\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 550\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 551\u001b[0m )\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1005\u001b[0m error_code \u001b[38;5;241m=\u001b[39m error_info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQueryErrorCode\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m error_info\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 1006\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1007\u001b[0m )\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1011\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n", + "\u001b[0;31mResourceLimitExceeded\u001b[0m: An error occurred (ResourceLimitExceeded) when calling the CreateProcessingJob operation: The account-level service limit 'ml.m5.4xlarge for processing job usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances. Please use AWS Service Quotas to request an increase for this quota. If AWS Service Quotas is not available, contact AWS support to request an increase for this quota." + ] + } + ], + "source": [ + "# Run the processing job to create separate datasets from different files\n", + "import sagemaker\n", + "from sagemaker.sklearn.processing import SKLearnProcessor\n", + "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", + "\n", + "\n", + "\n", + "# Define the SageMaker execution role\n", + "role = sagemaker.get_execution_role()\n", + "\n", + "# Define the SKLearnProcessor\n", + "sklearn_processor = SKLearnProcessor(framework_version='0.20.0',\n", + " role=role,\n", + " instance_type='ml.m5.4xlarge',\n", + " instance_count=2)\n", + "\n", + "# Run the processing job\n", + "sklearn_processor.run(\n", + " code='athena-tutorial-ux360-draft-processing-file.py', # Ensure this path is correct\n", + " inputs=[ProcessingInput(\n", + " source='s3://example-s3-bucket/query-id.csv', # use the output of the preceding cell as the source\n", + " destination='/opt/ml/processing/input'\n", + " )],\n", + " outputs=[\n", + " ProcessingOutput(\n", + " source='/opt/ml/processing/output/train',\n", + " destination='s3://example-s3-bucket/output/train'\n", + " ),\n", + " ProcessingOutput(\n", + " source='/opt/ml/processing/output/validation',\n", + " destination='s3://example-s3-bucket/output/validation'\n", + " ),\n", + " ProcessingOutput(\n", + " source='/opt/ml/processing/output/test',\n", + " destination='s3://example-s3-bucket/output/test'\n", + " )\n", + " ]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "41cb0fb0-079d-421d-a4b8-005ee38fc472", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-06-13 20:40:41 794188811 fourth-train.csv\n", + "2024-06-12 00:14:24 794186734 train.csv\n" + ] + } + ], + "source": [ + "#Verify that train.csv is in the location that you've specified\n", + "!aws s3 ls s3://example-s3-bucket/output/train/" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ee3f29f1-a135-4bf6-bba5-595fb80c471d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-06-13 20:40:41 170181422 fourth-val.csv\n", + "2024-06-12 00:14:24 170183095 val.csv\n" + ] + } + ], + "source": [ + "#Verify that val.csv is in the location that you've specified\n", + "!aws s3 ls s3://example-s3-bucket/output/validation/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1e4e4113-b76c-49d5-a3b0-2327eb174fdf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml\n" + ] + } + ], + "source": [ + "# Specify the input data sources for a training\n", + "from sagemaker.session import TrainingInput\n", + "\n", + "bucket = 'example-s3-bucket'\n", + "\n", + "train_input = TrainingInput(\n", + " f\"s3://{bucket}/output/train/train.csv\", content_type=\"csv\"\n", + ")\n", + "validation_input = TrainingInput(\n", + " f\"s3://{bucket}/output/validation/val.csv\", content_type=\"csv\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d5b6a9b2-54e5-4dfd-9a5e-3c7442f6d5af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.2-1\n" + ] + } + ], + "source": [ + "# Getting the XGBoost container that's in us-east-1\n", + "prefix = \"training-output-data\"\n", + "region = \"us-east-1\"\n", + "\n", + "from sagemaker.debugger import Rule, ProfilerRule, rule_configs\n", + "from sagemaker.session import TrainingInput\n", + "\n", + "# S3 location to store the trained model artifact, so that it can be accessed later\n", + "s3_output_location = f's3://{bucket}/{prefix}/xgboost_model'\n", + "\n", + "container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.2-1\")\n", + "print(container)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "44efb3a1-acf0-4193-987f-85025c7c3894", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the model\n", + "xgb_model = sagemaker.estimator.Estimator(\n", + " image_uri = container,\n", + " role = role,\n", + " instance_count = 2,\n", + " region = region,\n", + " instance_type = 'ml.m5.4xlarge',\n", + " volume_size = 5, \n", + " output_path = s3_output_location,\n", + " sagemaker_session = sagemaker.Session(),\n", + " rules = [\n", + " Rule.sagemaker(rule_configs.create_xgboost_report()),\n", + " ProfilerRule.sagemaker(rule_configs.ProfilerReport())\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e28512bf-d246-4a46-a0c8-24d1a8ad65a8", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the hyperparameters for the model\n", + "xgb_model.set_hyperparameters(\n", + " max_depth = 5,\n", + " eta = 0.2,\n", + " gamma = 4,\n", + " min_child_weight = 6,\n", + " subsample = 0.7,\n", + " objective = \"reg:squarederror\",\n", + " num_round = 10\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "58b77fc0-407d-4743-ae35-7bc7b04478e6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2024-06-13-21-09-20-115\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-06-13 21:09:20 Starting - Starting the training job...\n", + "2024-06-13 21:09:44 Starting - Preparing the instances for trainingCreateXgboostReport: InProgress\n", + "ProfilerReport: InProgress\n", + "...\n", + "2024-06-13 21:10:08 Downloading - Downloading input data......\n", + "2024-06-13 21:11:13 Training - Training image download completed. Training in progress..\u001b[35m[2024-06-13 21:11:20.271 ip-10-2-118-110.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", + "\u001b[35mINFO:sagemaker-containers:Imported framework sagemaker_xgboost_container.training\u001b[0m\n", + "\u001b[35mINFO:sagemaker-containers:Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", + "\u001b[35mReturning the value itself\u001b[0m\n", + "\u001b[35mINFO:sagemaker-containers:No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[35mINFO:sagemaker_xgboost_container.training:Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", + "\u001b[35mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-13 21:11:21.431 ip-10-2-116-62.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", + "\u001b[34mINFO:sagemaker-containers:Imported framework sagemaker_xgboost_container.training\u001b[0m\n", + "\u001b[34mINFO:sagemaker-containers:Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", + "\u001b[34mReturning the value itself\u001b[0m\n", + "\u001b[34mINFO:sagemaker-containers:No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[34mINFO:sagemaker_xgboost_container.training:Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", + "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35mINFO:root:Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", + "\u001b[35mINFO:RabitContextManager:Failed to connect to RabitTracker on attempt 0\u001b[0m\n", + "\u001b[35mINFO:RabitContextManager:Sleeping for 3 sec before retrying\u001b[0m\n", + "\u001b[34mINFO:root:Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:start listen on algo-1:9099\u001b[0m\n", + "\u001b[34mINFO:RabitContextManager:Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9099}\u001b[0m\n", + "\u001b[34mINFO:RabitContextManager:Connected to RabitTracker.\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:No data received from connection ('10.2.116.62', 50370). Closing.\u001b[0m\n", + "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", + "\u001b[35mINFO:RabitContextManager:Connected to RabitTracker.\u001b[0m\n", + "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:No data received from connection ('10.2.118.110', 60326). Closing.\u001b[0m\n", + "\u001b[34mtask NULL got new rank 0\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:Recieve start signal from 10.2.116.62; assign rank 0\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:Recieve start signal from 10.2.118.110; assign rank 1\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:@tracker All of 2 nodes getting started\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:@tracker All nodes finishes job\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:@tracker 0.1758744716644287 secs between node start and job finish\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:start listen on algo-1:9100\u001b[0m\n", + "\u001b[34mINFO:RabitContextManager:Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9100}\u001b[0m\n", + "\u001b[34mINFO:RabitContextManager:Connected to RabitTracker.\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:No data received from connection ('10.2.116.62', 54686). Closing.\u001b[0m\n", + "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", + "\u001b[35mtask NULL got new rank 1\u001b[0m\n", + "\u001b[35mINFO:RabitContextManager:Failed to connect to RabitTracker on attempt 0\u001b[0m\n", + "\u001b[35mINFO:RabitContextManager:Sleeping for 3 sec before retrying\u001b[0m\n", + "\u001b[35mINFO:RabitContextManager:Connected to RabitTracker.\u001b[0m\n", + "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", + "\u001b[35mtask NULL got new rank 1\u001b[0m\n", + "\u001b[35m[2024-06-13 21:11:37.262 ip-10-2-118-110.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", + "\u001b[35m[2024-06-13 21:11:37.263 ip-10-2-118-110.ec2.internal:7 INFO hook.py:199] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", + "\u001b[35m[2024-06-13 21:11:37.263 ip-10-2-118-110.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", + "\u001b[35m[2024-06-13 21:11:37.264 ip-10-2-118-110.ec2.internal:7 INFO hook.py:253] Saving to /opt/ml/output/tensors\u001b[0m\n", + "\u001b[35m[2024-06-13 21:11:37.264 ip-10-2-118-110.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", + "\u001b[35mINFO:root:Debug hook created from config\u001b[0m\n", + "\u001b[35mINFO:root:Train matrix has 30940497 rows and 5 columns\u001b[0m\n", + "\u001b[35mINFO:root:Validation matrix has 6630107 rows\u001b[0m\n", + "\u001b[35m[21:11:37] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:No data received from connection ('10.2.118.110', 59212). Closing.\u001b[0m\n", + "\u001b[34mtask NULL got new rank 0\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:Recieve start signal from 10.2.116.62; assign rank 0\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:Recieve start signal from 10.2.118.110; assign rank 1\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:@tracker All of 2 nodes getting started\u001b[0m\n", + "\u001b[34m[2024-06-13 21:11:37.262 ip-10-2-116-62.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", + "\u001b[34m[2024-06-13 21:11:37.263 ip-10-2-116-62.ec2.internal:7 INFO hook.py:199] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", + "\u001b[34m[2024-06-13 21:11:37.263 ip-10-2-116-62.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", + "\u001b[34m[2024-06-13 21:11:37.263 ip-10-2-116-62.ec2.internal:7 INFO hook.py:253] Saving to /opt/ml/output/tensors\u001b[0m\n", + "\u001b[34m[2024-06-13 21:11:37.264 ip-10-2-116-62.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", + "\u001b[34mINFO:root:Debug hook created from config\u001b[0m\n", + "\u001b[34mINFO:root:Train matrix has 30940497 rows and 5 columns\u001b[0m\n", + "\u001b[34mINFO:root:Validation matrix has 6630107 rows\u001b[0m\n", + "\u001b[34m[21:11:37] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", + "\u001b[35m[2024-06-13 21:11:52.675 ip-10-2-118-110.ec2.internal:7 INFO hook.py:413] Monitoring the collections: predictions, feature_importance, labels, hyperparameters, metrics\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[0]#011train-rmse:255.88295#011validation-rmse:68.20912\u001b[0m\n", + "\u001b[34m[2024-06-13 21:11:52.675 ip-10-2-116-62.ec2.internal:7 INFO hook.py:413] Monitoring the collections: labels, hyperparameters, predictions, metrics, feature_importance\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[1]#011train-rmse:250.89801#011validation-rmse:71.52632\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[2]#011train-rmse:250.13692#011validation-rmse:71.59752\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[3]#011train-rmse:247.76843#011validation-rmse:79.49778\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[4]#011train-rmse:245.87282#011validation-rmse:84.14578\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[5]#011train-rmse:245.98055#011validation-rmse:84.03645\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[6]#011train-rmse:245.69582#011validation-rmse:84.06477\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[7]#011train-rmse:243.92581#011validation-rmse:84.02535\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[8]#011train-rmse:243.96504#011validation-rmse:83.95972\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:[9]#011train-rmse:241.88516#011validation-rmse:77.56747\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:@tracker All nodes finishes job\u001b[0m\n", + "\u001b[34mINFO:RabitTracker:@tracker 112.72817921638489 secs between node start and job finish\u001b[0m\n", + "\n", + "2024-06-13 21:13:47 Uploading - Uploading generated training model\n", + "2024-06-13 21:13:47 Completed - Training job completed\n", + "Training seconds: 440\n", + "Billable seconds: 440\n" + ] + } + ], + "source": [ + "# Train the model on new data\n", + "xgb_model.fit({\"train\": train_input, \"validation\": validation_input}, wait=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c1aa7bc3-feee-4602-a64c-8c1e08526d03", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating model with name: sagemaker-xgboost-2024-06-13-21-14-15-341\n", + "INFO:sagemaker:Creating endpoint-config with name sagemaker-xgboost-2024-06-13-21-14-15-341\n", + "INFO:sagemaker:Creating endpoint with name sagemaker-xgboost-2024-06-13-21-14-15-341\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------!" + ] + } + ], + "source": [ + "# Deploy the model so that we can get predictions from it\n", + "xgb_predictor = xgb_model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a9cc4eea-a6d0-418f-ab35-db437ce2a99d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "download: s3://ux360-nyc-taxi-dogfooding/output/test/fourth-test.csv to ./fourth-test.csv\n" + ] + } + ], + "source": [ + "# Download the test csv file\n", + "!aws s3 cp s3://example-s3-bucket/output/test/test.csv ." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "953f9d9b-04d0-4398-8620-8f9ab4eb407b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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predicted_valuesactual_values
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\n", + "
" + ], + "text/plain": [ + " predicted_values actual_values\n", + "0 6.515090 10.5\n", + "1 6.515090 5.0\n", + "2 38.167862 52.0\n", + "3 7.602119 10.0\n", + "4 13.685721 14.0\n", + "5 9.086472 10.0\n", + "6 9.086472 10.5\n", + "7 6.515090 4.0\n", + "8 7.602119 7.5\n", + "9 6.515090 6.5\n", + "10 10.813796 13.0\n", + "11 6.962557 7.5\n", + "12 7.602119 8.0\n", + "13 9.086472 9.5\n", + "14 7.602119 9.0\n", + "15 7.602119 7.0\n", + "16 27.497194 33.0\n", + "17 22.183336 21.5\n", + "18 6.962557 7.0\n", + "19 8.302290 9.0" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comparison_df" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "48f6f988-0de8-4c44-8c10-9845ef4d476d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "predicted_values float64\n", + "actual_values float64\n", + "dtype: object" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Verify that the datatypes of both columns are floats\n", + "comparison_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "781fe125-4a2e-4527-8c45-fcd20558f4bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.6295376259632905\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# Calculate the squared differences between the predicted and actual values\n", + "comparison_df['squared_diff'] = (comparison_df['actual_values'] - comparison_df['predicted_values']) ** 2\n", + "\n", + "# Calculate the mean of the squared differences\n", + "mean_squared_diff = comparison_df['squared_diff'].mean()\n", + "\n", + "# Take the square root of the mean to get the RMSE\n", + "rmse = np.sqrt(mean_squared_diff)\n", + "\n", + "print(f\"RMSE: {rmse}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "d90f9ba9-0a80-4f0c-8b47-94fb7bed01f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 9ecad177-c46b-4ec8-b387-20d099fb30de\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'9ecad177-c46b-4ec8-b387-20d099fb30de'" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Delete the database\n", + "delete_database = \"\"\"\n", + "DROP DATABASE mydatabase\n", + "\"\"\"\n", + "\n", + "run_athena_query(delete_database, database, s3_output_location)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a6e651d-3e68-4c1b-8a28-3e15604b5ec1", + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the S3 bucket\n", + "!aws s3 rb s3://example-s3-bucket --force " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c883864-e707-46d2-a183-76e5f2090368", + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the endpoint\n", + "xgb_predictor.delete_endpoint()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/use-cases/pyspark-tutorial-ux360-jwos-feedback-first-revision.ipynb b/use-cases/pyspark-tutorial-ux360-jwos-feedback-first-revision.ipynb new file mode 100644 index 0000000000..2376903772 --- /dev/null +++ b/use-cases/pyspark-tutorial-ux360-jwos-feedback-first-revision.ipynb @@ -0,0 +1,800 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0a1828f9-efdc-4d12-a676-a2f3432e9ab0", + "metadata": {}, + "source": [ + "# Perform ETL and train a model using PySpark\n", + "\n", + "To perform extract transform load (ETL) operations on multiple files, we recommend opening a Jupyter notebook within Amazon SageMaker Studio and using the PySpark Kernel. The PySpark kernel is connected to an AWS Glue Interactive Session. The session connects your notebook to a cluster that automatically scales up the storage and compute to meet your data processing needs. When you shut down the kernel, the session stops and you're no longer charged for the compute on the cluster.\n", + "\n", + "Within the notebook you can use Spark commands to join and transform your data. Writing Spark commands is both faster and easier than writing SQL queries. For example, you can use the join command to join two tables. Instead of writing a query that can sometimes take minutes to complete, you can join a table within seconds.\n", + "\n", + "To show the utility of using the PySpark kernel for your ETL and model training worklows, you can use the NYC taxi fare prediction notebook (link to notebook). The notebook uses the NYC taxi dataset to predict the fare amount. It imports data from multiple files across different Amazon Simple Storage Service (Amazon S3) locations. Amazon S3 is an object storage service that you can use to save and access data and machine learning artifacts for your models. For more information about Amazon S3, see What is Amazon S3?\n", + "\n", + "__Important:__\n", + "\n", + "This tutorial assumes that you've in the us-east-1 AWS Region. It also assumes that you've provided the IAM role you're using to run the notebook with permissions to use Glue. For more information, see [Setting up](docs.aws.amazon.com/sagemaker/latest/dg/create-end-to-end-ml-workflow-athena.html#setting-up)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "94172c75-f8a9-4590-a443-c872fb5c5d6e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "You are already connected to a glueetl session ec5c76e1-bd30-493a-9370-5a33b8bb3474.\n", + "\n", + "No change will be made to the current session that is set as glueetl. The session configuration change will apply to newly created sessions.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Additional python modules to be included:\n", + "sagemaker\n" + ] + } + ], + "source": [ + "# Load the SageMaker Python SDK into the kernel\n", + "%additional_python_modules sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2ea1c3a4-8881-48b0-8888-9319812750e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Set up the utilities needed to work with AWS Glue.\n", + "import sys\n", + "from awsglue.transforms import Join\n", + "from awsglue.utils import getResolvedOptions\n", + "from pyspark.context import SparkContext\n", + "from awsglue.context import GlueContext\n", + "from awsglue.job import Job\n", + "\n", + "glueContext = GlueContext(SparkContext.getOrCreate())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ba577de7-9ffe-4bae-b4c0-b225181306d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Import all ride info parquet files for 2019.\n", + "df_ride_info = glueContext.create_dynamic_frame_from_options(\n", + " connection_type=\"s3\", format=\"parquet\",\n", + " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-info/year=2019/\"], \"recurse\": True}).toDF()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6efc3d4a-81d7-40f5-bb62-cd206924a0c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Import all ride fare parquet files for the year 2019\n", + "df_ride_fare = glueContext.create_dynamic_frame_from_options(\n", + " connection_type=\"s3\", format=\"parquet\",\n", + " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-fare/year=2019/\"], \"recurse\": True}).toDF()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d63af3a3-358f-4c6e-97d4-97a1f1a552de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "| ride_id|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|total_amount|\n", + "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "|1400160115693| 2| 31.0| 0.0| 0.5| 0.0| 6.12| 40.42|\n", + "|3770982177323| 1| 4.5| 0.0| 0.5| 1.2| 0.0| 9.0|\n", + "|1400160115694| 1| 16.5| 1.0| 0.5| 4.16| 0.0| 24.96|\n", + "|3770982177324| 1| 18.0| 2.5| 0.5| 5.3| 0.0| 26.6|\n", + "|1400160115695| 1| 8.0| 2.5| 0.5| 1.13| 0.0| 12.43|\n", + "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "only showing top 5 rows\n" + ] + } + ], + "source": [ + "df_ride_fare.show(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "07a3baab-44b0-416a-b12e-049a270af8bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_joined = df_ride_info.join(df_ride_fare, [\"ride_id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2a456733-4533-4688-8174-368e50f4dd66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "| ride_id|vendor_id|passenger_count| pickup_at| dropoff_at|trip_distance|rate_code_id|store_and_fwd_flag|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|total_amount|\n", + "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "|51539607553| 1| 1|2019-04-21 17:20:19|2019-04-21 17:31:28| 2.7| 1| N| 1| 10.5| 2.5| 0.5| 3.45| 0.0| 17.25|\n", + "|51539607560| 2| 1|2019-02-21 22:49:59|2019-02-21 22:53:45| 0.62| 1| N| 2| 4.5| 0.5| 0.5| 0.0| 0.0| 8.3|\n", + "|51539607572| 1| 1|2019-02-21 22:19:08|2019-02-21 22:24:13| 0.6| 1| N| 1| 5.0| 3.0| 0.5| 1.75| 0.0| 10.55|\n", + "|51539607626| 2| 5|2019-02-21 22:18:33|2019-02-21 22:30:32| 2.0| 1| N| 1| 10.0| 0.5| 0.5| 2.76| 0.0| 16.56|\n", + "|51539607627| 2| 1|2019-04-21 17:21:49|2019-04-21 17:35:46| 2.72| 1| N| 1| 12.0| 0.0| 0.5| 2.3| 0.0| 17.6|\n", + "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "only showing top 5 rows\n" + ] + } + ], + "source": [ + "df_joined.show(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9a52a903-f394-4d00-a216-6af8c2132d83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "root\n", + " |-- ride_id: long (nullable = true)\n", + " |-- vendor_id: integer (nullable = true)\n", + " |-- passenger_count: byte (nullable = true)\n", + " |-- pickup_at: timestamp (nullable = true)\n", + " |-- dropoff_at: timestamp (nullable = true)\n", + " |-- trip_distance: float (nullable = true)\n", + " |-- rate_code_id: integer (nullable = true)\n", + " |-- store_and_fwd_flag: string (nullable = true)\n", + " |-- payment_type: integer (nullable = true)\n", + " |-- fare_amount: float (nullable = true)\n", + " |-- extra: float (nullable = true)\n", + " |-- mta_tax: float (nullable = true)\n", + " |-- tip_amount: float (nullable = true)\n", + " |-- tolls_amount: float (nullable = true)\n", + " |-- total_amount: float (nullable = true)\n" + ] + } + ], + "source": [ + "df_joined.printSchema()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c6bcc15f-8d41-4def-ae49-edaef4105343", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44200708\n" + ] + } + ], + "source": [ + "df_joined.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7d13d8d9-7eed-4efb-b972-601baf291842", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Drop duplicates in case there are any\n", + "df_no_dups = df_joined.dropDuplicates([\"ride_id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3e3e82a3-e3db-4752-8bab-f42cbbae4928", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44200708\n" + ] + } + ], + "source": [ + "df_no_dups.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9dc1d15f-53f6-404d-86fd-5a28f3792db8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_cleaned = df_joined.drop(\"pickup_at\", \"dropoff_at\", \"store_and_fwd_flag\", \"vendor_id\", \"payment_type\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "48382726-c767-4b0e-9336-decbf8184938", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_sample = df_cleaned.sample(False, 0.1, seed=0).limit(20000)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2bf2f181-0096-4044-8210-7d9de299d966", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20000\n" + ] + } + ], + "source": [ + "df_sample.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "13f80864-21ec-43c6-8cb3-517fcb438f4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "test_pandas = df_sample.toPandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a8b2f670-c5f9-4a01-8d9f-6a29a3dae660", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ride_id passenger_count ... tolls_amount total_amount\n", + "count 2.000000e+04 20000.000000 ... 20000.000000 20000.000000\n", + "mean 5.327415e+10 1.580700 ... 0.354632 18.917547\n", + "std 3.447216e+09 1.218221 ... 1.540669 14.226608\n", + "min 5.153961e+10 0.000000 ... 0.000000 -59.799999\n", + "25% 5.154042e+10 1.000000 ... 0.000000 11.300000\n", + "50% 5.154121e+10 1.000000 ... 0.000000 14.750000\n", + "75% 5.154202e+10 2.000000 ... 0.000000 20.299999\n", + "max 6.013019e+10 6.000000 ... 21.500000 242.300003\n", + "\n", + "[8 rows x 10 columns]\n" + ] + } + ], + "source": [ + "df_pandas = test_pandas\n", + "df_pandas.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "246c98e9-64bd-4644-a163-b86a943d6a09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset shape: (20000, 10)\n" + ] + } + ], + "source": [ + "print(\"Dataset shape: \", df_pandas.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c5b2727c-de75-4cc0-94e9-d254e235d003", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ride_id passenger_count ... tolls_amount total_amount\n", + "0 51539607572 1 ... 0.0 10.550000\n", + "1 51539607730 5 ... 0.0 17.299999\n", + "2 51539607857 2 ... 0.0 6.800000\n", + "3 51539607985 1 ... 0.0 7.300000\n", + "4 51539608203 1 ... 0.0 16.559999\n", + "\n", + "[5 rows x 10 columns]\n" + ] + } + ], + "source": [ + "df_pandas.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d69b48b6-98c2-4851-9c7a-f24f092bae41", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20000 entries, 0 to 19999\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ride_id 20000 non-null int64 \n", + " 1 passenger_count 20000 non-null int8 \n", + " 2 trip_distance 20000 non-null float32\n", + " 3 rate_code_id 20000 non-null int32 \n", + " 4 fare_amount 20000 non-null float32\n", + " 5 extra 20000 non-null float32\n", + " 6 mta_tax 20000 non-null float32\n", + " 7 tip_amount 20000 non-null float32\n", + " 8 tolls_amount 20000 non-null float32\n", + " 9 total_amount 20000 non-null float32\n", + "dtypes: float32(7), int32(1), int64(1), int8(1)\n", + "memory usage: 800.9 KB\n" + ] + } + ], + "source": [ + "df_pandas.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b7f3e4f7-e04e-41e1-b94b-b32eb3bc3bbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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" + }, + "metadata": { + "image/png": { + "height": 480, + "width": 640 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Perform exploratory data analysis (EDA) on a sample of the data\n", + "from pyspark.ml.stat import Correlation\n", + "from pyspark.ml.feature import VectorAssembler\n", + "import seaborn as sns \n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd # not sure how the kernel runs, but it looks like I have import pandas again after going back to the notebook after a while\n", + "\n", + "vector_col = 'corr_features'\n", + "assembler = VectorAssembler(inputCols=df_sample.columns, outputCol=vector_col)\n", + "df_vector = assembler.transform(df_sample).select(vector_col)\n", + "\n", + "matrix = Correlation.corr(df_vector, vector_col).collect()[0][0]\n", + "corr_matrix = matrix.toArray().tolist()\n", + "corr_matrix_df = pd.DataFrame(data=corr_matrix, columns=df_sample.columns, index=df_sample.columns) \n", + "\n", + "plt.figure(figsize=(16,10))\n", + "sns.heatmap(corr_matrix_df,\n", + " xticklabels=corr_matrix_df.columns.values,\n", + " yticklabels=corr_matrix_df.columns.values, cmap=\"Greens\", annot=True)\n", + "\n", + "%matplot plt" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6e207c64-2e22-468f-a0c7-948090bcfce2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Split the dataset into train, validation, and test sets\n", + "df_train, df_val, df_test = df_cleaned.randomSplit([0.7, 0.15, 0.15])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f16ea3a1-6d6d-4755-94ad-c743298bd130", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Define the S3 locations to store the datasets\n", + "import boto3\n", + "import sagemaker\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "s3_bucket = sagemaker_session.default_bucket()\n", + "train_data_prefix = \"sandbox/glue-demo/train\"\n", + "validation_data_prefix = \"sandbox/glue-demo/validation\"\n", + "test_data_prefix = \"sandbox/glue-demo/test\"\n", + "region = boto3.Session().region_name" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "64d7ae48-6158-4273-8bb3-2f00abb1c20c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_train.write.parquet(f\"s3://{s3_bucket}/{train_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "de3d1190-4717-4944-846d-0169c093cb90", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_val.write.parquet(f\"s3://{s3_bucket}/{validation_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9d18ef1c-fc2f-4e34-a692-4a6c48be7cba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_test.write.parquet(f\"s3://{s3_bucket}/{test_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a31b7742-93df-44c5-8674-b6355032c508", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-06-14 00:36:26 Starting - Starting the training job...\n", + "2024-06-14 00:36:46 Starting - Preparing the instances for training...\n", + "2024-06-14 00:37:14 Downloading - Downloading input data...\n", + "2024-06-14 00:37:34 Downloading - Downloading the training image...\n", + "2024-06-14 00:37:59 Training - Training image download completed. Training in progress...[2024-06-14 00:38:35.919 ip-10-0-229-197.ec2.internal:7 INFO utils.py:28] RULE_JOB_STOP_SIGNAL_FILENAME: None\n", + "[2024-06-14 00:38:35.939 ip-10-0-229-197.ec2.internal:7 INFO profiler_config_parser.py:111] User has disabled profiler.\n", + "[2024-06-14:00:38:36:INFO] Imported framework sagemaker_xgboost_container.training\n", + "[2024-06-14:00:38:36:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\n", + "Returning the value itself\n", + "[2024-06-14:00:38:36:INFO] No GPUs detected (normal if no gpus installed)\n", + "[2024-06-14:00:38:36:INFO] Running XGBoost Sagemaker in algorithm mode\n", + "[2024-06-14:00:38:36:INFO] Determined 0 GPU(s) available on the instance.\n", + "[2024-06-14:00:38:36:INFO] File path /opt/ml/input/data/train of input files\n", + "[2024-06-14:00:38:36:INFO] Making smlinks from folder /opt/ml/input/data/train to folder /tmp/sagemaker_xgboost_input_data\n", + "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00004-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00004-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-6099176745642522633\n", + "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00001-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00001-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet7068749022651873836\n", + "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00012-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00012-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet4480105206382563880\n", + "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00002-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00002-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet666710498772781167\n", + "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00006-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00006-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet2012959030070555737\n", + "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00003-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00003-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-7792575879923673435\n", + "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00000-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination 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/tmp/sagemaker_xgboost_input_data/part-00014-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet8311663450763339060\n", + "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00002-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00002-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet8238084367374917843\n", + "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00006-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00006-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet-2173537324320107354\n", + "[2024-06-14:00:38:40:INFO] files path: /tmp/sagemaker_xgboost_input_data\n", + "[2024-06-14:00:38:41:INFO] Single node training.\n", + "[2024-06-14:00:38:41:INFO] Train matrix has 30944499 rows and 9 columns\n", + "[2024-06-14:00:38:41:INFO] Validation matrix has 6630552 rows\n", + "[2024-06-14 00:38:41.080 ip-10-0-229-197.ec2.internal:7 INFO json_config.py:92] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\n", + "[2024-06-14 00:38:41.080 ip-10-0-229-197.ec2.internal:7 INFO hook.py:206] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\n", + "[2024-06-14 00:38:41.081 ip-10-0-229-197.ec2.internal:7 INFO hook.py:259] Saving to /opt/ml/output/tensors\n", + "[2024-06-14 00:38:41.081 ip-10-0-229-197.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\n", + "[2024-06-14:00:38:41:INFO] Debug hook created from config\n", + "[0]#011train-rmse:1830258878187.12842#011validation-rmse:1830650699152.31494\n", + "[2024-06-14 00:38:46.805 ip-10-0-229-197.ec2.internal:7 INFO hook.py:427] Monitoring the collections: metrics\n", + "[2024-06-14 00:38:46.807 ip-10-0-229-197.ec2.internal:7 INFO hook.py:491] Hook is writing from the hook with pid: 7\n", + "[1]#011train-rmse:1629960261821.84106#011validation-rmse:1630342677781.64697\n", + "[2]#011train-rmse:1487695600747.47461#011validation-rmse:1488064146076.08887\n", + "[3]#011train-rmse:1389026442545.81470#011validation-rmse:1389377954565.06006\n", + "[4]#011train-rmse:1321695765365.73877#011validation-rmse:1322031530771.13354\n", + "[5]#011train-rmse:1276740831629.93091#011validation-rmse:1277061863710.23682\n", + "[6]#011train-rmse:1247097412925.66821#011validation-rmse:1247401430743.64795\n", + "[7]#011train-rmse:1227747968014.05444#011validation-rmse:1228037163005.18335\n", + "[8]#011train-rmse:1215170724247.77222#011validation-rmse:1215448698513.50879\n", + "[9]#011train-rmse:1207046944746.58350#011validation-rmse:1207316233855.67749\n", + "[10]#011train-rmse:1201783443361.93457#011validation-rmse:1202043885587.02417\n", + "[11]#011train-rmse:1198401334034.30933#011validation-rmse:1198654899842.81396\n", + "[12]#011train-rmse:1196223057609.97485#011validation-rmse:1196472335408.23047\n", + "[13]#011train-rmse:1194793492477.64160#011validation-rmse:1195040826268.06714\n", + "[14]#011train-rmse:1193861428638.90527#011validation-rmse:1194109169621.48096\n", + "[15]#011train-rmse:1193230091247.77100#011validation-rmse:1193474801224.40454\n", + "[16]#011train-rmse:1192821546941.62378#011validation-rmse:1193068675122.69312\n", + "[17]#011train-rmse:1192561165347.32251#011validation-rmse:1192806237217.96143\n", + "[18]#011train-rmse:1192386477794.77588#011validation-rmse:1192628642455.83276\n", + "[19]#011train-rmse:1192270314999.13452#011validation-rmse:1192512558017.28052\n", + "[20]#011train-rmse:1192185331187.94312#011validation-rmse:1192428000382.65649\n", + "[21]#011train-rmse:1192113970087.07056#011validation-rmse:1192358025332.75098\n", + "[22]#011train-rmse:1192070221222.92139#011validation-rmse:1192315120608.84546\n", + "[23]#011train-rmse:1192036912041.30347#011validation-rmse:1192280233203.12524\n", + "[24]#011train-rmse:1192008426772.59277#011validation-rmse:1192252534585.66406\n", + "[25]#011train-rmse:1191984055285.96313#011validation-rmse:1192227766501.24878\n", + "[26]#011train-rmse:1191960405482.00928#011validation-rmse:1192204293324.09521\n", + "[27]#011train-rmse:1191945650115.00171#011validation-rmse:1192189907585.56787\n", + "[28]#011train-rmse:1191937076532.34546#011validation-rmse:1192182107256.42993\n", + "[29]#011train-rmse:1191911091380.20825#011validation-rmse:1192157949699.30249\n", + "[30]#011train-rmse:1191889211431.19482#011validation-rmse:1192136029069.66968\n", + "[31]#011train-rmse:1191878758489.41479#011validation-rmse:1192126105484.02148\n", + "[32]#011train-rmse:1191871084793.49341#011validation-rmse:1192117990930.42285\n", + "[33]#011train-rmse:1191850168213.44604#011validation-rmse:1192096702217.71631\n", + "[34]#011train-rmse:1191842445605.27563#011validation-rmse:1192088592129.65991\n", + "[35]#011train-rmse:1191825318352.25293#011validation-rmse:1192072497184.73462\n", + "[36]#011train-rmse:1191815568908.05786#011validation-rmse:1192063233346.56299\n", + "[37]#011train-rmse:1191807671488.23853#011validation-rmse:1192056982851.26904\n", + "[38]#011train-rmse:1191802078377.84863#011validation-rmse:1192051206608.39307\n", + "[39]#011train-rmse:1191791601237.45581#011validation-rmse:1192041999023.11670\n", + "[40]#011train-rmse:1191782542291.56982#011validation-rmse:1192032919271.50024\n", + "[41]#011train-rmse:1191776496494.06421#011validation-rmse:1192028434782.18604\n", + "[42]#011train-rmse:1191769742829.94604#011validation-rmse:1192020721371.07715\n", + "[43]#011train-rmse:1191760877562.48730#011validation-rmse:1192013123163.06396\n", + "[44]#011train-rmse:1191756403194.40674#011validation-rmse:1192009794212.31812\n", + "[45]#011train-rmse:1191749717552.74341#011validation-rmse:1192003290591.67969\n", + "[46]#011train-rmse:1191742470497.40967#011validation-rmse:1191997083425.68970\n", + "[47]#011train-rmse:1191730093274.09351#011validation-rmse:1191985279848.86987\n", + "[48]#011train-rmse:1191723680549.70190#011validation-rmse:1191980086431.17139\n", + "[49]#011train-rmse:1191709586099.72583#011validation-rmse:1191966703191.52051\n", + "\n", + "2024-06-14 00:41:28 Uploading - Uploading generated training model\n", + "2024-06-14 00:41:28 Completed - Training job completed\n", + "Training seconds: 255\n", + "Billable seconds: 255\n" + ] + } + ], + "source": [ + "from sagemaker import image_uris\n", + "from sagemaker.inputs import TrainingInput\n", + "\n", + "hyperparameters = {\n", + " \"max_depth\":\"5\",\n", + " \"eta\":\"0.2\",\n", + " \"gamma\":\"4\",\n", + " \"min_child_weight\":\"6\",\n", + " \"subsample\":\"0.7\",\n", + " \"objective\":\"reg:squarederror\",\n", + " \"num_round\":\"50\"}\n", + "\n", + "# Set an output path where the trained model is saved\n", + "prefix = 'sandbox/glue-demo'\n", + "output_path = f's3://{s3_bucket}/{prefix}/xgb-built-in-algo/output'\n", + "\n", + "# The following line automatically looks for the XGBoost image URI and builds an XGBoost container.\n", + "# Version 1.7-1 of the image URI is used. You can specify a version that you prefer.\n", + "xgboost_container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.7-1\")\n", + "\n", + "# construct a SageMaker estimator that calls the xgboost-container\n", + "estimator = sagemaker.estimator.Estimator(image_uri=xgboost_container,\n", + " hyperparameters=hyperparameters,\n", + " role=sagemaker.get_execution_role(),\n", + " instance_count=1,\n", + " instance_type='ml.m5.4xlarge',\n", + " output_path=output_path)\n", + "\n", + "content_type = \"application/x-parquet\"\n", + "train_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/train/\", content_type=content_type)\n", + "validation_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/validation/\", content_type=content_type)\n", + "\n", + "# Run the XGBoost training job\n", + "estimator.fit({'train': train_input, 'validation': validation_input})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0967a86-ccea-4992-8071-4e624e4d1865", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Glue PySpark and Ray", + "language": "python", + "name": "glue_pyspark" + }, + "language_info": { + "codemirror_mode": { + "name": "python", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "Python_Glue_Session", + "pygments_lexer": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b32ab2b2604b4cd826053bf711d5777963a2d3d6 Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Tue, 25 Jun 2024 13:19:10 +0200 Subject: [PATCH 02/13] move athena notebook into dedicated folder --- .../athena-tutorial-ux360-draft-processing-file(1).py | 0 .../athena-tutorial-ux360-jwos-first-revision.ipynb | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename use-cases/{ => athena-ml-workflow-end-to-end}/athena-tutorial-ux360-draft-processing-file(1).py (100%) rename use-cases/{ => athena-ml-workflow-end-to-end}/athena-tutorial-ux360-jwos-first-revision.ipynb (100%) diff --git a/use-cases/athena-tutorial-ux360-draft-processing-file(1).py b/use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-draft-processing-file(1).py similarity index 100% rename from use-cases/athena-tutorial-ux360-draft-processing-file(1).py rename to use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-draft-processing-file(1).py diff --git a/use-cases/athena-tutorial-ux360-jwos-first-revision.ipynb b/use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-jwos-first-revision.ipynb similarity index 100% rename from use-cases/athena-tutorial-ux360-jwos-first-revision.ipynb rename to use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-jwos-first-revision.ipynb From b2db0e824b3081a3639225c1b483b300c5331ec5 Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Tue, 25 Jun 2024 13:22:42 +0200 Subject: [PATCH 03/13] renamed athena end2end notebooks --- ...first-revision.ipynb => athena-ml-workflow-end-to-end.ipynb} | 2 +- ...360-draft-processing-file(1).py => processing_data_split.py} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename use-cases/athena-ml-workflow-end-to-end/{athena-tutorial-ux360-jwos-first-revision.ipynb => athena-ml-workflow-end-to-end.ipynb} (99%) rename use-cases/athena-ml-workflow-end-to-end/{athena-tutorial-ux360-draft-processing-file(1).py => processing_data_split.py} (100%) diff --git a/use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-jwos-first-revision.ipynb b/use-cases/athena-ml-workflow-end-to-end/athena-ml-workflow-end-to-end.ipynb similarity index 99% rename from use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-jwos-first-revision.ipynb rename to use-cases/athena-ml-workflow-end-to-end/athena-ml-workflow-end-to-end.ipynb index aa7adcb5fe..ef3d4dcdce 100644 --- a/use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-jwos-first-revision.ipynb +++ b/use-cases/athena-ml-workflow-end-to-end/athena-ml-workflow-end-to-end.ipynb @@ -1698,7 +1698,7 @@ "\n", "# Run the processing job\n", "sklearn_processor.run(\n", - " code='athena-tutorial-ux360-draft-processing-file.py', # Ensure this path is correct\n", + " code='processing_data_split.py', # Ensure this path is correct\n", " inputs=[ProcessingInput(\n", " source='s3://example-s3-bucket/query-id.csv', # use the output of the preceding cell as the source\n", " destination='/opt/ml/processing/input'\n", diff --git a/use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-draft-processing-file(1).py b/use-cases/athena-ml-workflow-end-to-end/processing_data_split.py similarity index 100% rename from use-cases/athena-ml-workflow-end-to-end/athena-tutorial-ux360-draft-processing-file(1).py rename to use-cases/athena-ml-workflow-end-to-end/processing_data_split.py From 5c0f7521c945c2cc3c9b4a12e1655d0497f5f57e Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Tue, 25 Jun 2024 13:24:16 +0200 Subject: [PATCH 04/13] moved pyspark notebook into dedicated directory --- .../pyspark-etl-training.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename use-cases/{pyspark-tutorial-ux360-jwos-feedback-first-revision.ipynb => pyspark_etl_and_training/pyspark-etl-training.ipynb} (100%) diff --git a/use-cases/pyspark-tutorial-ux360-jwos-feedback-first-revision.ipynb b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb similarity index 100% rename from use-cases/pyspark-tutorial-ux360-jwos-feedback-first-revision.ipynb rename to use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb From 2f33eec687f0309a1349291300cb035b8491659a Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Tue, 25 Jun 2024 13:26:18 +0200 Subject: [PATCH 05/13] minor change: consistent directory naming convention --- .../athena_ml_workflow_end_to_end.ipynb} | 0 .../processing_data_split.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename use-cases/{athena-ml-workflow-end-to-end/athena-ml-workflow-end-to-end.ipynb => athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb} (100%) rename use-cases/{athena-ml-workflow-end-to-end => athena_ml_workflow_end_to_end}/processing_data_split.py (100%) diff --git a/use-cases/athena-ml-workflow-end-to-end/athena-ml-workflow-end-to-end.ipynb b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb similarity index 100% rename from use-cases/athena-ml-workflow-end-to-end/athena-ml-workflow-end-to-end.ipynb rename to use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb diff --git a/use-cases/athena-ml-workflow-end-to-end/processing_data_split.py b/use-cases/athena_ml_workflow_end_to_end/processing_data_split.py similarity index 100% rename from use-cases/athena-ml-workflow-end-to-end/processing_data_split.py rename to use-cases/athena_ml_workflow_end_to_end/processing_data_split.py From a7d95a0a8a93b1fe736e05b771fbc046fda1972c Mon Sep 17 00:00:00 2001 From: parsash2 <60193914+parsash2@users.noreply.github.com> Date: Tue, 25 Jun 2024 18:10:53 -0400 Subject: [PATCH 06/13] Added overview, headers, and explantory text Tested the notebook end to end. Added more context for processing jobs and cleaning up. The output is visible in the cells. --- athena_ml_workflow_end_to_end.ipynb | 3173 +++++++++++++++++++++++++++ 1 file changed, 3173 insertions(+) create mode 100644 athena_ml_workflow_end_to_end.ipynb diff --git a/athena_ml_workflow_end_to_end.ipynb b/athena_ml_workflow_end_to_end.ipynb new file mode 100644 index 0000000000..c1555ce64b --- /dev/null +++ b/athena_ml_workflow_end_to_end.ipynb @@ -0,0 +1,3173 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ece13bd7-19b2-47b3-976d-cf636fa68003", + "metadata": {}, + "source": [ + "# Create an end to end machine learning workflow using Amazon Athena\n", + "Importing and transforming data can be one of the most challenging tasks in a machine learning workflow. We provide you with a Jupyter notebook that demonstrates a cost-effective strategy for an extract, transform, and load (ETL) workflow. Using Amazon Simple Storage Service (Amazon S3) and Amazon Athena, you learn how to query and transform data from a Jupyter notebook. Amazon S3 is an object storage service that allows you to store data and machine learning artifacts. Amazon Athena enables you to interactively query the data stored in those buckets, saving each query as a CSV file in an Amazon S3 location.\n", + "\n", + "The tutorial imports 16 CSV files for the 2019 NYC taxi dataset from multiple Amazon S3 locations. The goal is to predict the fare amount for each ride. From these 16 files, the notebook creates a single ride fare dataset and a single ride info dataset with deduplicated values. We join the deduplicated datasets into a single dataset.\n", + "\n", + "Amazon Athena stores the query results as a CSV file in the specified location. We provide the output to a SageMaker Processing Job to split the data into training, validation, and test sets. While data can be split using queries, a processing job ensures that the data is in a format that's parseable by the XGBoost algorithm.\n", + "\n", + "__Prerequisites:__\n", + "\n", + "The notebook must be run in the us-east-1 AWS Region. You also need your own Amazon S3 bucket and a database within Amazon Athena. You won't be able to access the data used in the tutorial otherwise.\n", + "\n", + "For information about creating a bucket, see [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). For information about creating a database, see [Create a database](https://docs.aws.amazon.com/athena/latest/ug/getting-started.html#step-1-create-a-database).\n", + "\n", + "Amazon Athena uses the AWS Glue Data Catalog to read the data from Amazon S3 into a database. You must have permissions to use Glue. To clean up, you also need permissions to delete the bucket you've created. For a quick guide to providing permissions, see [Setting up\n", + "](http://parsash-clouddesk-2024.aka.corp.amazon.com/sagemaker-dg/src/AWSIronmanApiDoc/build/server-root/sagemaker/latest/dg/create-end-to-end-ml-workflow-athena.html#setting-up)." + ] + }, + { + "cell_type": "markdown", + "id": "0b11693f-7c35-41cf-8e4b-4f86eea8f3b0", + "metadata": {}, + "source": [ + "## Solution overview\n", + "\n", + "To create the end to end workflow, we do the following:\n", + "\n", + "1. Create an Amazon Athena client within the us-east-1 AWS Region.\n", + "2. Define the run_athena_query function that runs queries and prints out the status in the following cell.\n", + "3. Create the `ride_fare` table within your database using all ride fare tables for the year 2019.\n", + "4. Create the `ride_info` table using ride info table for the year 2019.\n", + "5. Create the `ride_info_deduped` and `ride_fare_deduped` tables that have all duplicate values removed from the original tables.\n", + "6. Run test queries to get the first ten rows of each table to see whether they have data.\n", + "7. Define the `get_query_results` function that takes the query ID and returns comma separated values that can be stored as a dataframe.\n", + "8. View the results of the test queries within pandas dataframes.\n", + "9. Join the `ride_info_deduped` and `ride_fare_deduped` tables into the `combined_ride_data_deduped` table.\n", + "10. Select all values in the combined table.\n", + "11. Define the `get_csv_file_location` function to get the Amazon S3 location of the query results.\n", + "12. Download the CSV file to our environment.\n", + "13. Perform Exploratory Data Analysis (EDA) on the data.\n", + "14. Use the results of the EDA to select the relevant features in query.\n", + "15. Use the `get_csv_file_location` function to get the location of those query results.\n", + "16. Split the data into training, validation, and test sets using a processing job.\n", + "17. Download the test dataset.\n", + "18. Take a 20 row sample from the test dataset.\n", + "20. Create a dataframe with 20 rows of actual and predicted values.\n", + "21. Calculate the RMSE of the data.\n", + "22. Clean up the resources created within the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "54d7468c-c77b-4273-b02d-9e9c4e884d46", + "metadata": {}, + "source": [ + "### Define the run_athena_query function\n", + "\n", + "In the following cell, we define the `run_athena_query` function. It runs an Athena query and waits for its completion.\n", + "\n", + "It takes the following arguments:\n", + "\n", + "- query_string (str): The SQL query to be executed.\n", + "- database_name (str): The name of the Athena database.\n", + "- output_location (str): The S3 location where the query results are stored.\n", + "\n", + "\n", + "It returns the query execution ID string." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8ab1ff0e-fcde-4976-a1cd-51e75c18deb2", + "metadata": {}, + "outputs": [], + "source": [ + "# Import required libraries\n", + "import time\n", + "import boto3\n", + "\n", + "def run_athena_query(query_string, database_name, output_location):\n", + " # Create an Athena client\n", + " athena_client = boto3.client('athena', region_name='us-east-1')\n", + "\n", + " # Start the query execution\n", + " response = athena_client.start_query_execution(\n", + " QueryString=query_string,\n", + " QueryExecutionContext={'Database': database_name},\n", + " ResultConfiguration={'OutputLocation': output_location}\n", + " )\n", + "\n", + " query_execution_id = response['QueryExecutionId']\n", + " print(f\"Query execution ID: {query_execution_id}\")\n", + "\n", + " while True:\n", + " # Check the query execution status\n", + " query_status = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " state = query_status['QueryExecution']['Status']['State']\n", + "\n", + " if state == 'SUCCEEDED':\n", + " print(\"Query executed successfully.\")\n", + " break\n", + " elif state == 'FAILED':\n", + " print(f\"Query failed with error: {query_status['QueryExecution']['Status']['StateChangeReason']}\")\n", + " break\n", + " else:\n", + " print(f\"Query is currently in {state} state. Waiting for completion...\")\n", + " time.sleep(5) # Wait for 5 seconds before checking again\n", + "\n", + " return query_execution_id\n" + ] + }, + { + "cell_type": "markdown", + "id": "8df0da48-89b3-45c2-a479-af422a51b962", + "metadata": {}, + "source": [ + "### Create the ride_fare table\n", + "\n", + "We've provided you with the query. You most provide the name of the database you created within Amazon Athena and the Amazon S3 output location. If you're not sure about how to specify the output location, provide the name of the S3 bucket. After running the query, you should get a message that says \"Query executed successfully.\" and a 36 character string in single quotes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "64131b68-de28-4060-bb75-8148902846f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: cb929408-df15-408d-a776-a8963facbf80\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'cb929408-df15-408d-a776-a8963facbf80'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to create the 'ride_fare' table\n", + "create_ride_fare_table = \"\"\"\n", + "CREATE EXTERNAL TABLE `ride_fare` (\n", + " `ride_id` bigint, \n", + " `payment_type` smallint, \n", + " `fare_amount` float, \n", + " `extra` float, \n", + " `mta_tax` float, \n", + " `tip_amount` float, \n", + " `tolls_amount` float, \n", + " `total_amount` float\n", + ")\n", + "ROW FORMAT DELIMITED \n", + " FIELDS TERMINATED BY ',' \n", + " LINES TERMINATED BY '\\n' \n", + "STORED AS INPUTFORMAT \n", + " 'org.apache.hadoop.mapred.TextInputFormat' \n", + "OUTPUTFORMAT \n", + " 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'\n", + "LOCATION\n", + " 's3://dsoaws/nyc-taxi-orig-cleaned-split-csv-with-header-per-year-multiple-files/ride-fare/year=2019'\n", + "TBLPROPERTIES (\n", + " 'skip.header.line.count'='1', \n", + " 'transient_lastDdlTime'='1716908234'\n", + ");\n", + "\"\"\"\n", + "\n", + "# Athena database name\n", + "database = 'example-database-name'\n", + "\n", + "# S3 location for query results\n", + "s3_output_location = 's3://example-s3-bucket/example-s3-prefix'\n", + "\n", + "# Execute the query to create the 'ride_fare' table\n", + "run_athena_query(create_ride_fare_table, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "ebe5920a-4c36-48c0-9cb4-e418c738aa59", + "metadata": {}, + "source": [ + "### Create the ride fare table with the duplicates removed" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3d249cc5-2d53-4274-8f5e-6ab09ccd3ea6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 15337c2c-54e5-4e19-94a8-92d2faef2efd\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'15337c2c-54e5-4e19-94a8-92d2faef2efd'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to create a new table with duplicates removed\n", + "remove_duplicates_from_ride_fare = \"\"\"\n", + "CREATE TABLE ride_fare_deduped\n", + "AS\n", + "SELECT DISTINCT *\n", + "FROM ride_fare\n", + "\"\"\"\n", + "\n", + "# Run the preceding query\n", + "run_athena_query(remove_duplicates_from_ride_fare, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "2ac7fc34-37cb-4c46-993b-38f18576361c", + "metadata": {}, + "source": [ + "### Create the ride_info table" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2f9a68b9-bd11-49e9-ad72-b44b43d32e47", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: bc365d36-bbbb-4f33-a153-3192127a1069\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'bc365d36-bbbb-4f33-a153-3192127a1069'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to create the ride_info table\n", + "create_ride_info_table_query = \"\"\"\n", + "CREATE EXTERNAL TABLE `ride_info` (\n", + " `ride_id` bigint, \n", + " `vendor_id` smallint, \n", + " `passenger_count` smallint, \n", + " `pickup_at` string, \n", + " `dropoff_at` string, \n", + " `trip_distance` float, \n", + " `rate_code_id` int, \n", + " `store_and_fwd_flag` string\n", + ")\n", + "ROW FORMAT DELIMITED \n", + " FIELDS TERMINATED BY ',' \n", + " LINES TERMINATED BY '\\n' \n", + "STORED AS INPUTFORMAT \n", + " 'org.apache.hadoop.mapred.TextInputFormat' \n", + "OUTPUTFORMAT \n", + " 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'\n", + "LOCATION\n", + " 's3://dsoaws/nyc-taxi-orig-cleaned-split-csv-with-header-per-year-multiple-files/ride-info/year=2019'\n", + "TBLPROPERTIES (\n", + " 'skip.header.line.count'='1', \n", + " 'transient_lastDdlTime'='1716907328'\n", + ");\n", + "\"\"\"\n", + "\n", + "# Run the query to create the ride_info table\n", + "run_athena_query(create_ride_info_table_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "4c17ea01-2c1e-4c10-a539-0d00e6e4bb1d", + "metadata": {}, + "source": [ + "### Create the ride info table with the duplicates removed" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "263d883c-f189-43c0-9fbd-1a45093984e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 1946c89d-d1c3-449d-b7af-42521778c51c\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'1946c89d-d1c3-449d-b7af-42521778c51c'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to create table with duplicates removed\n", + "remove_duplicates_from_ride_info = \"\"\"\n", + "CREATE TABLE ride_info_deduped\n", + "AS\n", + "SELECT DISTINCT *\n", + "FROM ride_info\n", + "\"\"\"\n", + "\n", + "# Run the query to create the table with the duplicates removed\n", + "run_athena_query(remove_duplicates_from_ride_info, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "a19f8e17-42c5-4412-96a8-b7bc1a74c73c", + "metadata": {}, + "source": [ + "### Run a test query on ride_info_deduped" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6db6bb67-44a9-4ff4-b662-ad969a84d3d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: ab1e6968-e04c-47c0-94c7-03868d1d7fc1\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'ab1e6968-e04c-47c0-94c7-03868d1d7fc1'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_ride_info_query = '''\n", + "SELECT * FROM ride_info_deduped limit 10\n", + "'''\n", + "\n", + "run_athena_query(test_ride_info_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "b969d31f-e14a-473b-aefa-a1a19bc312f7", + "metadata": {}, + "source": [ + "### Run a test query on ride_fare_deduped" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "92d8be21-3f20-453d-8b84-516571d9854d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: caeedc97-8f55-4759-9380-8ced39fab414\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'caeedc97-8f55-4759-9380-8ced39fab414'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_ride_fare_query = '''\n", + "SELECT * FROM ride_fare_deduped limit 10\n", + "'''\n", + "\n", + "run_athena_query(test_ride_fare_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "c86acade-c4b9-4918-860e-11ee5e386a44", + "metadata": {}, + "source": [ + "### Define the `get_query_results` function\n", + "\n", + "In the following cell, we define the `get_query_results` function to get the query results in CSV format. The function gets the 36 character query execution ID string. The end of the output of the preceding cell is an example of a query execution ID string." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "50e87ba6-42e9-4d99-862e-7eae16ad810e", + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "def get_query_results(query_execution_id):\n", + " athena_client = boto3.client('athena', region_name='us-east-1')\n", + " s3 = boto3.client('s3')\n", + "\n", + " # Get the query execution details\n", + " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " s3_location = query_execution['QueryExecution']['ResultConfiguration']['OutputLocation']\n", + "\n", + " # Extract bucket and key from S3 output location\n", + " bucket_name, key = s3_location.split('/', 2)[2].split('/', 1)\n", + "\n", + " # Get the CSV file location\n", + " obj = s3.get_object(Bucket=bucket_name, Key=key)\n", + " csv_data = obj['Body'].read().decode('utf-8')\n", + " csv_buffer = io.StringIO(csv_data)\n", + "\n", + " return csv_buffer" + ] + }, + { + "cell_type": "markdown", + "id": "d3d2ed4f-d7e6-49dc-9ea1-0dc66f252c76", + "metadata": {}, + "source": [ + "### Read `ride_info_deduped` test query into a dataframe\n", + "\n", + "Specify the query execution ID string in the `get_query_results` function. The output is the head of the dataframe. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b04abae5-936b-4d96-98e8-d2e2b6a17b9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amount
02834679627591152.00.00.512.286.1273.70
11400160739953152.02.50.511.050.0066.35
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3133144095039414.01.00.51.660.009.96
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" + ], + "text/plain": [ + " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", + "0 2834679627591 1 52.0 0.0 0.5 12.28 \n", + "1 1400160739953 1 52.0 2.5 0.5 11.05 \n", + "2 2834679627600 2 7.0 0.0 0.5 0.00 \n", + "3 1331440950394 1 4.0 1.0 0.5 1.66 \n", + "4 2834679627624 1 4.5 0.0 0.5 1.06 \n", + "\n", + " tolls_amount total_amount \n", + "0 6.12 73.70 \n", + "1 0.00 66.35 \n", + "2 0.00 7.80 \n", + "3 0.00 9.96 \n", + "4 0.00 6.36 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "# Provide the query execution id of the test_ride_info query to get the query results\n", + "ride_info_sample = get_query_results('test_ride_info_query_execution_id')\n", + "\n", + "df_ride_info_sample = pd.read_csv(ride_info_sample)\n", + "\n", + "df_ride_info_sample.head()" + ] + }, + { + "cell_type": "markdown", + "id": "6d10ebe2-8c17-4f2b-97fe-a5f339cd89d7", + "metadata": {}, + "source": [ + "### Read `ride_fare_deduped` test query into a dataframe\n", + "\n", + "Specify the query execution ID string in the `get_query_results` function. The output is the head of the resulting dataframe. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "be89957f-31b1-4710-bfc2-178d6db18592", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amount
02834679627591152.00.00.512.286.1273.70
11400160739953152.02.50.511.050.0066.35
2283467962760027.00.00.50.000.007.80
3133144095039414.01.00.51.660.009.96
4283467962762414.50.00.51.060.006.36
\n", + "
" + ], + "text/plain": [ + " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", + "0 2834679627591 1 52.0 0.0 0.5 12.28 \n", + "1 1400160739953 1 52.0 2.5 0.5 11.05 \n", + "2 2834679627600 2 7.0 0.0 0.5 0.00 \n", + "3 1331440950394 1 4.0 1.0 0.5 1.66 \n", + "4 2834679627624 1 4.5 0.0 0.5 1.06 \n", + "\n", + " tolls_amount total_amount \n", + "0 6.12 73.70 \n", + "1 0.00 66.35 \n", + "2 0.00 7.80 \n", + "3 0.00 9.96 \n", + "4 0.00 6.36 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Provide the query execution id of the test_ride_fare query to get the query results\n", + "\n", + "ride_fare_sample = get_query_results('test_ride_fare_query_execution_id')\n", + "\n", + "df_ride_fare_sample = pd.read_csv(ride_fare_sample)\n", + "\n", + "df_ride_fare_sample.head()" + ] + }, + { + "cell_type": "markdown", + "id": "3867e94a-7c89-48ed-86aa-92b09d47740d", + "metadata": {}, + "source": [ + "### Join the deduplicated tables together" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b8a76635-3c09-4cbc-b1b4-9318dc611250", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: 8eb61f36-2e1b-43c7-9b33-61e7ce5d21bc\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'8eb61f36-2e1b-43c7-9b33-61e7ce5d21bc'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to join the tables into a single table containing all the data.\n", + "create_ride_joined_deduped = \"\"\"\n", + "CREATE TABLE combined_ride_data_deduped AS\n", + "SELECT \n", + " rfs.ride_id, \n", + " rfs.payment_type, \n", + " rfs.fare_amount, \n", + " rfs.extra, \n", + " rfs.mta_tax, \n", + " rfs.tip_amount, \n", + " rfs.tolls_amount, \n", + " rfs.total_amount,\n", + " ris.vendor_id, \n", + " ris.passenger_count, \n", + " ris.pickup_at, \n", + " ris.dropoff_at, \n", + " ris.trip_distance, \n", + " ris.rate_code_id, \n", + " ris.store_and_fwd_flag\n", + "FROM \n", + " ride_fare_deduped rfs\n", + "JOIN \n", + " ride_info_deduped ris\n", + "ON \n", + " rfs.ride_id = ris.ride_id;\n", + ";\n", + "\"\"\"\n", + "\n", + "# Run the query to create the ride_data_deduped table\n", + "run_athena_query(create_ride_joined_deduped, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "b2f9f6ca-f668-42ab-ac4a-371a82e1786d", + "metadata": {}, + "source": [ + "### Select all values from the deduplicated table" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b0791e57-4351-4f27-a8f9-ad741441d214", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: f303cff8-5369-409a-9c51-8c791d446fe3\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. 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Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'f303cff8-5369-409a-9c51-8c791d446fe3'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL query to select all values from the table and create the dataset that we're using for our analysis\n", + "ride_combined_full_table_query = \"\"\"\n", + "SELECT * FROM combined_ride_data_deduped\n", + "\"\"\"\n", + "\n", + "# Run the query to select all values from the combined_ride_data_deduped table\n", + "run_athena_query(ride_combined_full_table_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "4492eaa8-b0cc-4a4d-9810-e9f1a39f21c7", + "metadata": {}, + "source": [ + "### Define get_csv_file_location function and get Amazon S3 location of query results\n", + "\n", + "Specify the query ID from the preceding cell in the function call. The output is the Amazon S3 URI of the dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "97373c52-882b-4e44-8d75-a80d8d8c58df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s3://ux360-nyc-taxi-dogfooding/f303cff8-5369-409a-9c51-8c791d446fe3.csv'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Function to get the Amazon S3 URI location of Amazon Athena select statements\n", + "def get_csv_file_location(query_execution_id):\n", + " athena_client = boto3.client('athena', region_name='us-east-1')\n", + " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", + " s3_location = query_execution['QueryExecution']['ResultConfiguration']['OutputLocation']\n", + "\n", + " return s3_location\n", + "\n", + "# Provide the 36 character string at the end of the output of the preceding cell as the query.\n", + "get_csv_file_location('ride_combined_full_table_query_execution_id')" + ] + }, + { + "cell_type": "markdown", + "id": "c7bf4f25-dc86-4f1f-95de-967c20c5a7af", + "metadata": {}, + "source": [ + "### Download the dataset and rename it\n", + "\n", + "Replace the example S3 path in the following cell with the output of the preceding cell. The second command renames the CSV file it downloads to `nyc-taxi-whole-dataset.csv`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "954022d5-bdf9-4dbd-be2e-66d0009ce522", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "download: s3://ux360-nyc-taxi-dogfooding/f303cff8-5369-409a-9c51-8c791d446fe3.csv to ./f303cff8-5369-409a-9c51-8c791d446fe3.csv\n", + "mv: cannot stat 'query-id.csv': No such file or directory\n" + ] + } + ], + "source": [ + "# Use the S3 URI location returned from the preceding cell to download the dataset and rename it.\n", + "!aws s3 cp s3://example-s3-bucket/ride_combined_full_table_query_execution_id.csv .\n", + "!mv ride_combined_full_table_query_execution_id.csv nyc-taxi-whole-dataset.csv" + ] + }, + { + "cell_type": "markdown", + "id": "4d34ca22-8417-46f5-982f-dd22816f1d93", + "metadata": {}, + "source": [ + "### Get a 20,000 row sample and some information about it" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "79d2f2a5-5111-4fb8-90f3-67474f1072c1", + "metadata": {}, + "outputs": [], + "source": [ + "sample_nyc_taxi_combined = pd.read_csv('nyc-taxi-whole-dataset.csv', nrows=20000)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f9dececa-272d-458c-9f64-baa13eca0832", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset shape: (20000, 15)\n" + ] + } + ], + "source": [ + "print(\"Dataset shape: \", sample_nyc_taxi_combined.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1c117a0f-429e-4913-aded-c839675f9e17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amountvendor_idpassenger_countpickup_atdropoff_attrip_distancerate_code_idstore_and_fwd_flag
06013183901417.50.00.51.660.09.96212019-01-04T07:53:41.000Z2019-01-04T08:02:20.000Z1.451N
16013183907418.00.00.51.000.09.80222019-01-04T07:05:28.000Z2019-01-04T07:13:12.000Z1.911N
2139157156874018.50.00.52.360.014.16222019-02-05T10:59:56.000Z2019-02-05T11:10:40.000Z1.531N
36013183913018.00.00.51.760.010.56212019-01-04T07:12:07.000Z2019-01-04T07:20:07.000Z1.681N
4139157156891215.00.00.51.660.09.96212019-02-05T11:14:36.000Z2019-02-05T11:19:52.000Z0.651N
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" + ], + "text/plain": [ + " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", + "0 60131839014 1 7.5 0.0 0.5 1.66 \n", + "1 60131839074 1 8.0 0.0 0.5 1.00 \n", + "2 1391571568740 1 8.5 0.0 0.5 2.36 \n", + "3 60131839130 1 8.0 0.0 0.5 1.76 \n", + "4 1391571568912 1 5.0 0.0 0.5 1.66 \n", + "\n", + " tolls_amount total_amount vendor_id passenger_count \\\n", + "0 0.0 9.96 2 1 \n", + "1 0.0 9.80 2 2 \n", + "2 0.0 14.16 2 2 \n", + "3 0.0 10.56 2 1 \n", + "4 0.0 9.96 2 1 \n", + "\n", + " pickup_at dropoff_at trip_distance \\\n", + "0 2019-01-04T07:53:41.000Z 2019-01-04T08:02:20.000Z 1.45 \n", + "1 2019-01-04T07:05:28.000Z 2019-01-04T07:13:12.000Z 1.91 \n", + "2 2019-02-05T10:59:56.000Z 2019-02-05T11:10:40.000Z 1.53 \n", + "3 2019-01-04T07:12:07.000Z 2019-01-04T07:20:07.000Z 1.68 \n", + "4 2019-02-05T11:14:36.000Z 2019-02-05T11:19:52.000Z 0.65 \n", + "\n", + " rate_code_id store_and_fwd_flag \n", + "0 1 N \n", + "1 1 N \n", + "2 1 N \n", + "3 1 N \n", + "4 1 N " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = sample_nyc_taxi_combined\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d3c56da9-0a1c-4c58-93e3-77260dfff40b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20000 entries, 0 to 19999\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ride_id 20000 non-null int64 \n", + " 1 payment_type 20000 non-null int64 \n", + " 2 fare_amount 20000 non-null float64\n", + " 3 extra 20000 non-null float64\n", + " 4 mta_tax 20000 non-null float64\n", + " 5 tip_amount 20000 non-null float64\n", + " 6 tolls_amount 20000 non-null float64\n", + " 7 total_amount 20000 non-null float64\n", + " 8 vendor_id 20000 non-null int64 \n", + " 9 passenger_count 20000 non-null int64 \n", + " 10 pickup_at 20000 non-null object \n", + " 11 dropoff_at 20000 non-null object \n", + " 12 trip_distance 20000 non-null float64\n", + " 13 rate_code_id 20000 non-null int64 \n", + " 14 store_and_fwd_flag 20000 non-null object \n", + "dtypes: float64(7), int64(5), object(3)\n", + "memory usage: 2.3+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "dc25bcd9-a4b1-4491-867f-7534336d1ecd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amountvendor_idpassenger_counttrip_distancerate_code_id
count2.000000e+0420000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.0000020000.0000020000.00000020000.000000
mean1.818963e+121.28870012.9201551.0605400.4960252.1283920.37697618.4721391.624401.568452.9285301.054400
std1.210592e+120.47640711.8908781.2307330.0509592.6013791.63952814.6649320.484291.215523.8417760.363108
min5.153977e+101.000000-74.500000-4.500000-0.5000000.0000000.000000-76.3000001.000000.000000.0000001.000000
25%1.005022e+121.0000006.5000000.0000000.5000000.0000000.00000010.7900001.000001.000000.9400001.000000
50%1.400160e+121.0000009.0000000.5000000.5000001.7950000.00000014.1600002.000001.000001.6000001.000000
75%2.834679e+122.00000014.5000002.5000000.5000002.8600000.00000019.8000002.000002.000003.0000001.000000
max3.839702e+124.000000300.0000007.0000000.50000052.16000030.500000312.9600002.000006.0000070.8900005.000000
\n", + "
" + ], + "text/plain": [ + " ride_id payment_type fare_amount extra mta_tax \\\n", + "count 2.000000e+04 20000.000000 20000.000000 20000.000000 20000.000000 \n", + "mean 1.818963e+12 1.288700 12.920155 1.060540 0.496025 \n", + "std 1.210592e+12 0.476407 11.890878 1.230733 0.050959 \n", + "min 5.153977e+10 1.000000 -74.500000 -4.500000 -0.500000 \n", + "25% 1.005022e+12 1.000000 6.500000 0.000000 0.500000 \n", + "50% 1.400160e+12 1.000000 9.000000 0.500000 0.500000 \n", + "75% 2.834679e+12 2.000000 14.500000 2.500000 0.500000 \n", + "max 3.839702e+12 4.000000 300.000000 7.000000 0.500000 \n", + "\n", + " tip_amount tolls_amount total_amount vendor_id passenger_count \\\n", + "count 20000.000000 20000.000000 20000.000000 20000.00000 20000.00000 \n", + "mean 2.128392 0.376976 18.472139 1.62440 1.56845 \n", + "std 2.601379 1.639528 14.664932 0.48429 1.21552 \n", + "min 0.000000 0.000000 -76.300000 1.00000 0.00000 \n", + "25% 0.000000 0.000000 10.790000 1.00000 1.00000 \n", + "50% 1.795000 0.000000 14.160000 2.00000 1.00000 \n", + "75% 2.860000 0.000000 19.800000 2.00000 2.00000 \n", + "max 52.160000 30.500000 312.960000 2.00000 6.00000 \n", + "\n", + " trip_distance rate_code_id \n", + "count 20000.000000 20000.000000 \n", + "mean 2.928530 1.054400 \n", + "std 3.841776 0.363108 \n", + "min 0.000000 1.000000 \n", + "25% 0.940000 1.000000 \n", + "50% 1.600000 1.000000 \n", + "75% 3.000000 1.000000 \n", + "max 70.890000 5.000000 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "18bd92b1-962a-40f2-b15f-7351d869f390", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "vendor_id\n", + "2 12488\n", + "1 7512\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['vendor_id'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e4c4997f-85d8-4f57-a60c-51e3568cfe2e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "passenger_count\n", + "1 14030\n", + "2 3040\n", + "3 857\n", + "5 850\n", + "6 487\n", + "4 379\n", + "0 357\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['passenger_count'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "ae527104-9312-498c-b0ee-d1e2303bf500", + "metadata": {}, + "source": [ + "### View the distribution of fare amount values" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "641c278d-8fed-42b8-98d1-becba90d6259", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot to find the distribution of ride fare values\n", + "import matplotlib.pyplot as plt\n", + "plt.hist(df['fare_amount'], edgecolor='black', bins=30, range=(0,100))\n", + "plt.xlabel('Fare Amount')\n", + "plt.ylabel('Count')\n", + "plt.show" + ] + }, + { + "cell_type": "markdown", + "id": "65d141c4-95ba-4176-8794-1475cb8f2a62", + "metadata": {}, + "source": [ + "### Make sure that all rows are unique" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9d484f57-f150-45b5-9cc5-cc10a6e8e9f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20000" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['ride_id'].nunique()" + ] + }, + { + "cell_type": "markdown", + "id": "abc60782-4411-46e0-9d31-55adaa4dd1f5", + "metadata": {}, + "source": [ + "### Drop the store_and_fwd flag\n", + "\n", + "Determining its relevance isn't in scope for this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f627790e-8aed-48e3-9c5d-52775bbb124d", + "metadata": {}, + "outputs": [], + "source": [ + "df.drop('store_and_fwd_flag', axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "96fc51be-6a0f-44e6-abb8-2a6bf9188367", + "metadata": {}, + "source": [ + "### Drop the time series columns\n", + "\n", + "Analyzing the time series data also isn't in scope for this analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c359f4db-b503-4d80-bb4c-55dc411f9b5e", + "metadata": {}, + "outputs": [], + "source": [ + "# We're dropping the time series columns to streamline the analysis.\n", + "time_series_columns_to_drop = ['pickup_at','dropoff_at']\n", + "df.drop(columns=time_series_columns_to_drop, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "ad5d1df6-d418-483a-b06d-848205f3f8ed", + "metadata": {}, + "source": [ + "### Install seaborn and create scatterplots" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "05abe8af-bf44-471b-b130-19cee0dd822f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting seaborn\n", + " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.10/site-packages (from seaborn) (1.26.4)\n", + "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.10/site-packages (from seaborn) (2.1.4)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.10/site-packages (from seaborn) (3.8.4)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n", + "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.3)\n", + "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", + "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: seaborn\n", + "Successfully installed seaborn-0.13.2\n" + ] + } + ], + "source": [ + "!pip install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b6a10b9b-e916-48a9-88f5-ae94db2f6576", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create visualizations showing correlations between variables.\n", + "import seaborn as sns\n", + "target = 'fare_amount'\n", + "features = [col for col in df.columns if col != target]\n", + "\n", + "# Create a figure with subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=len(features), figsize=(50, 10))\n", + "\n", + "# Create scatter plots\n", + "for i, feature in enumerate(features):\n", + " sns.scatterplot(x=df[feature], y=df[target], ax=axes[i])\n", + " axes[i].set_title(f'{feature} vs {target}')\n", + " axes[i].set_xlabel(feature)\n", + " axes[i].set_ylabel(target)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "11c33316-1502-46b1-b265-6cf43d0d8f1d", + "metadata": {}, + "source": [ + "## Calculate the correlation coefficient between each feature and fare amount" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "d8dff114-adb5-4b34-a788-b93e42a2fee4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tip_amount 0.5743753694582684\n", + "tolls_amount 0.6327404045395644\n", + "extra -0.008246801964138361\n", + "mta_tax -0.1628089444699402\n", + "total_amount 0.9783791092253548\n", + "trip_distance 0.8848067140931489\n" + ] + } + ], + "source": [ + "# extra and mta_tax seem weakly correlated\n", + "# total_amount is almost perfectly correlated, indicating target leakage.\n", + "continuous_features = ['tip_amount', 'tolls_amount', 'extra', 'mta_tax', 'total_amount', 'trip_distance']\n", + "\n", + "for i in continuous_features:\n", + " correlation = df['fare_amount'].corr(df[i])\n", + " print(i, correlation)" + ] + }, + { + "cell_type": "markdown", + "id": "7ea2dc4f-c366-43f0-8a81-44ecd8289a3d", + "metadata": {}, + "source": [ + "### Calculate a one way ANOVA between the groups\n", + "\n", + "From running the ANOVA, `mta_tax` and `extra` have the most variance between the groups. We're using them as features to train our model." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "3e083025-3312-4fd9-8cd2-4c8e37db5859", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature: payment_type, F-statistic: 22.20, p-value: 0.00000\n", + "Feature: extra, F-statistic: 130.42, p-value: 0.00000\n", + "Feature: mta_tax, F-statistic: 999.42, p-value: 0.00000\n", + "Feature: vendor_id, F-statistic: 12.42, p-value: 0.00042\n", + "Feature: passenger_count, F-statistic: 2.57, p-value: 0.01744\n" + ] + } + ], + "source": [ + "# The mta tax and extra have the most variance between the groups\n", + "from scipy.stats import f_oneway\n", + "# Separate features and target variable\n", + "X = df[['payment_type', 'extra', 'mta_tax', 'vendor_id', 'passenger_count']]\n", + "y = df['fare_amount']\n", + "\n", + "# Perform one-way ANOVA for each feature\n", + "for feature in X.columns:\n", + " groups = [y[X[feature] == group] for group in X[feature].unique()]\n", + " if len(groups) > 1:\n", + " f_statistic, p_value = f_oneway(*groups)\n", + " print(f'Feature: {feature}, F-statistic: {f_statistic:.2f}, p-value: {p_value:.5f}')" + ] + }, + { + "cell_type": "markdown", + "id": "5b2f3d07-8010-43c4-873e-f462fd0bd94e", + "metadata": {}, + "source": [ + "### Run a query to get the dataset we're using for ML workflow\n", + "\n", + "The XGBoost algorithm on Amazon SageMaker uses the first column as the target column. `fare_amount` must be the first column in our query." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "0dbcf599-076c-468e-9e9b-2e0bd53c3fa7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: e9866ba2-8e0d-426f-a601-e6ca24890b71\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query is currently in RUNNING state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'e9866ba2-8e0d-426f-a601-e6ca24890b71'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Final select statement has tip_amount, tolls_amount, extra, mta_tax, trip_distance\n", + "ride_combined_notebook_relevant_features_query = \"\"\"\n", + "SELECT fare_amount, tip_amount, tolls_amount, extra, mta_tax, trip_distance FROM combined_ride_data_deduped\n", + "\"\"\"\n", + "\n", + "run_athena_query(ride_combined_notebook_relevant_features_query, database, s3_output_location)" + ] + }, + { + "cell_type": "markdown", + "id": "4bbfeb06-e0e2-4ce0-9e73-98894053592d", + "metadata": {}, + "source": [ + "### Get the Amazon S3 URI of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "624a7833-c815-480e-b1da-c29da3d02c76", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s3://ux360-nyc-taxi-dogfooding/e9866ba2-8e0d-426f-a601-e6ca24890b71.csv'" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_csv_file_location('ride_combined_notebook_relevant_features_query_execution_id')" + ] + }, + { + "cell_type": "markdown", + "id": "4632047c-eabc-495a-9758-b55b78937f73", + "metadata": {}, + "source": [ + "### Run a SageMaker processing job to split the data\n", + "\n", + "The code in `processing_data_split.py` splits the dataset into training, validation, and test sets. We use a SageMaker processing job to provide the compute needed to transform large volumes of data. For more information about processing jobs, see [Use processing jobs to run data transformation workloads](https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html). For more information about running sci-kit scripts, see [Data Processing with scikit-learn](https://docs.aws.amazon.com/sagemaker/latest/dg/use-scikit-learn-processing-container.html). \n", + "\n", + "For faster processing, we recommend using an `instance_count` of `2`, but you can use whatever value you prefer.\n", + "\n", + "For `source` within the `ProcessingInput` function, replace `'s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv'` with the output of the preceding cell. Within `processing_data_split.py`, you specify `/opt/ml/processing/input/query-id` as the `input_path`. The processing job is copying the query results to a location within its own container.\n", + "\n", + "For `Destination` under `ProcessingOutput`, replace `example-s3-bucket` with the Amazon S3 bucket that you've created." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "788cae3c-a34b-4ee0-899e-0a461e21b210", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to only available Python version: py3\n", + "INFO:sagemaker:Creating processing-job with name sagemaker-scikit-learn-2024-06-25-17-41-19-446\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", + " import imp\u001b[0m\n", + "\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", + " LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\u001b[0m\n", + "\u001b[35m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", + " import imp\u001b[0m\n", + "\u001b[35m/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", + " LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\u001b[0m\n", + "\u001b[34msys:1: DtypeWarning: Columns (0,1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\u001b[0m\n", + "\u001b[35msys:1: DtypeWarning: Columns (0,1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\u001b[0m\n", + "\u001b[35mTraining set: 30940496 samples\u001b[0m\n", + "\u001b[35mValidation set: 6630106 samples\u001b[0m\n", + "\u001b[35mTest set: 6630107 samples\u001b[0m\n", + "\u001b[34mTraining set: 30940496 samples\u001b[0m\n", + "\u001b[34mValidation set: 6630106 samples\u001b[0m\n", + "\u001b[34mTest set: 6630107 samples\u001b[0m\n", + "\n" + ] + } + ], + "source": [ + "import sagemaker\n", + "from sagemaker.sklearn.processing import SKLearnProcessor\n", + "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", + "\n", + "\n", + "\n", + "# Define the SageMaker execution role\n", + "role = sagemaker.get_execution_role()\n", + "\n", + "# Define the SKLearnProcessor\n", + "sklearn_processor = SKLearnProcessor(framework_version='0.20.0',\n", + " role=role,\n", + " instance_type='ml.m5.4xlarge',\n", + " instance_count=2)\n", + "\n", + "# Run the processing job\n", + "sklearn_processor.run(\n", + " code='processing_data_split.py', \n", + " inputs=[ProcessingInput(\n", + " source='s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv',\n", + " destination='/opt/ml/processing/input'\n", + " )],\n", + " outputs=[\n", + " ProcessingOutput(\n", + " source='/opt/ml/processing/output/train',\n", + " destination='s3://ux360-nyc-taxi-dogfooding/output/train'\n", + " ),\n", + " ProcessingOutput(\n", + " source='/opt/ml/processing/output/validation',\n", + " destination='s3://ux360-nyc-taxi-dogfooding/output/validation'\n", + " ),\n", + " ProcessingOutput(\n", + " source='/opt/ml/processing/output/test',\n", + " destination='s3://ux360-nyc-taxi-dogfooding/output/test'\n", + " )\n", + " ]\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "bc164657-fd8f-4f96-89ff-23e991945ea4", + "metadata": {}, + "source": [ + "### Verify that train.csv is in the location that you've specified" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "41cb0fb0-079d-421d-a4b8-005ee38fc472", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-06-25 17:49:51 794185864 train.csv\n" + ] + } + ], + "source": [ + "#Verify that train.csv is in the location that you've specified\n", + "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/train/train.csv" + ] + }, + { + "cell_type": "markdown", + "id": "d0d2ba3c-fd6d-4aa0-b75b-92ba5a70ad00", + "metadata": {}, + "source": [ + "### Verify that val.csv is in the location that you've specified" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ee3f29f1-a135-4bf6-bba5-595fb80c471d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-06-25 17:49:51 170183603 val.csv\n" + ] + } + ], + "source": [ + "#Verify that val.csv is in the location that you've specified\n", + "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/validation/val.csv" + ] + }, + { + "cell_type": "markdown", + "id": "c92d4b89-65a5-474b-aa22-dcb442c344b9", + "metadata": {}, + "source": [ + "### Specify `train.csv` and `val.csv` as the input for the training job" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "1e4e4113-b76c-49d5-a3b0-2327eb174fdf", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.session import TrainingInput\n", + "\n", + "bucket = 'example-s3-bucket'\n", + "\n", + "train_input = TrainingInput(\n", + " f\"s3://{bucket}/output/train/train.csv\", content_type=\"csv\"\n", + ")\n", + "validation_input = TrainingInput(\n", + " f\"s3://{bucket}/output/validation/val.csv\", content_type=\"csv\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "866262fe-5737-49af-9cde-af55575e07d1", + "metadata": {}, + "source": [ + "### Specify the model container and output location of the model artifact\n", + "\n", + "Specify the S3 location of the trained model artifact. You can access it later.\n", + "\n", + "It also gets the URI of the container image. We used version `1.2-2` of the XGBoost container image, but you can specify a different version. For more information about XGBoost container images, see [Use the XGBoost algorithm with Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "d5b6a9b2-54e5-4dfd-9a5e-3c7442f6d5af", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.2-2\n" + ] + } + ], + "source": [ + "# Getting the XGBoost container that's in us-east-1\n", + "prefix = \"training-output-data\"\n", + "region = \"us-east-1\"\n", + "\n", + "from sagemaker.debugger import Rule, ProfilerRule, rule_configs\n", + "from sagemaker.session import TrainingInput\n", + "\n", + "s3_output_location = f's3://{bucket}/{prefix}/xgboost_model'\n", + "\n", + "container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.2-2\")\n", + "print(container)" + ] + }, + { + "cell_type": "markdown", + "id": "d04e189b-6f38-44cf-a046-6791abd32c00", + "metadata": {}, + "source": [ + "### Define the model" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "44efb3a1-acf0-4193-987f-85025c7c3894", + "metadata": {}, + "outputs": [], + "source": [ + "xgb_model = sagemaker.estimator.Estimator(\n", + " image_uri = container,\n", + " role = role,\n", + " instance_count = 2,\n", + " region = region,\n", + " instance_type = 'ml.m5.4xlarge',\n", + " volume_size = 5, \n", + " output_path = s3_output_location,\n", + " sagemaker_session = sagemaker.Session(),\n", + " rules = [\n", + " Rule.sagemaker(rule_configs.create_xgboost_report()),\n", + " ProfilerRule.sagemaker(rule_configs.ProfilerReport())\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "44f1c8b1-7bf0-4381-9128-b00c2bfcf9f1", + "metadata": {}, + "source": [ + "### Set the model hyperparameters\n", + "\n", + "For the purposes of running the training job more quickly, we set the number of training rounds to 10." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "e28512bf-d246-4a46-a0c8-24d1a8ad65a8", + "metadata": {}, + "outputs": [], + "source": [ + "xgb_model.set_hyperparameters(\n", + " max_depth = 5,\n", + " eta = 0.2,\n", + " gamma = 4,\n", + " min_child_weight = 6,\n", + " subsample = 0.7,\n", + " objective = \"reg:squarederror\",\n", + " num_round = 10\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e5b6ed18-990f-4ec7-9d42-6965ec67e2ce", + "metadata": {}, + "source": [ + "### Train the model" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "58b77fc0-407d-4743-ae35-7bc7b04478e6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: latest.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: latest.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2024-06-25-18-20-44-522\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-06-25 18:20:45 Starting - Starting the training job...CreateXgboostReport: InProgress\n", + "ProfilerReport: InProgress\n", + "...\n", + "2024-06-25 18:21:29 Starting - Preparing the instances for training...\n", + "2024-06-25 18:22:09 Downloading - Downloading input data......\n", + "2024-06-25 18:23:12 Training - Training image download completed. Training in progress....\u001b[34m[2024-06-25 18:23:33.281 ip-10-2-65-56.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", + "\u001b[34mReturning the value itself\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:38.246 ip-10-2-111-68.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", + "\u001b[35mReturning the value itself\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:42:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] start listen on algo-1:9099\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9099}\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] Connected to RabitTracker.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] No data received from connection ('10.2.65.56', 37490). Closing.\u001b[0m\n", + "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:47:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:48:INFO] Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:48:INFO] Connected to RabitTracker.\u001b[0m\n", + "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", + "\u001b[35mtask NULL got new rank 0\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:48:INFO] No data received from connection ('10.2.111.68', 42310). Closing.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] Recieve start signal from 10.2.111.68; assign rank 0\u001b[0m\n", + "\u001b[34mtask NULL got new rank 1\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] Recieve start signal from 10.2.65.56; assign rank 1\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker All of 2 nodes getting started\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker All nodes finishes job\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker 0.1758573055267334 secs between node start and job finish\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] start listen on algo-1:9100\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9100}\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] Connected to RabitTracker.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] No data received from connection ('10.2.65.56', 38280). Closing.\u001b[0m\n", + "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:49:INFO] Failed to connect to RabitTracker on attempt 0\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:49:INFO] Sleeping for 3 sec before retrying\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] No data received from connection ('10.2.111.68', 60082). Closing.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Recieve start signal from 10.2.111.68; assign rank 0\u001b[0m\n", + "\u001b[34mtask NULL got new rank 1\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Recieve start signal from 10.2.65.56; assign rank 1\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Train matrix has 30940497 rows and 5 columns\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] @tracker All of 2 nodes getting started\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Validation matrix has 6630107 rows\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.600 ip-10-2-65-56.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.601 ip-10-2-65-56.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.601 ip-10-2-65-56.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.602 ip-10-2-65-56.ec2.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.602 ip-10-2-65-56.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Debug hook created from config\u001b[0m\n", + "\u001b[34m[18:23:52] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:52:INFO] Connected to RabitTracker.\u001b[0m\n", + "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", + "\u001b[35mtask NULL got new rank 0\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:52:INFO] Train matrix has 30940497 rows and 5 columns\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:52:INFO] Validation matrix has 6630107 rows\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.600 ip-10-2-111-68.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.601 ip-10-2-111-68.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.601 ip-10-2-111-68.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.602 ip-10-2-111-68.ec2.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.602 ip-10-2-111-68.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:52:INFO] Debug hook created from config\u001b[0m\n", + "\u001b[35m[18:23:52] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", + "\u001b[34m[2024-06-25 18:24:08.407 ip-10-2-65-56.ec2.internal:7 INFO hook.py:423] Monitoring the collections: labels, metrics, predictions, feature_importance, hyperparameters\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:08:INFO] [0]#011train-rmse:184.43744#011validation-rmse:135.48259\u001b[0m\n", + "\u001b[35m[2024-06-25 18:24:08.409 ip-10-2-111-68.ec2.internal:7 INFO hook.py:423] Monitoring the collections: predictions, labels, hyperparameters, feature_importance, metrics\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:20:INFO] [1]#011train-rmse:184.28534#011validation-rmse:135.24808\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:31:INFO] [2]#011train-rmse:184.18167#011validation-rmse:135.09784\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:43:INFO] [3]#011train-rmse:184.11903#011validation-rmse:134.99771\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:55:INFO] [4]#011train-rmse:184.07890#011validation-rmse:134.93574\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:07:INFO] [5]#011train-rmse:184.05234#011validation-rmse:134.89529\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:19:INFO] [6]#011train-rmse:184.03487#011validation-rmse:134.86635\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:30:INFO] [7]#011train-rmse:184.02385#011validation-rmse:134.84970\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:42:INFO] [8]#011train-rmse:184.01642#011validation-rmse:134.83659\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:54:INFO] [9]#011train-rmse:183.88487#011validation-rmse:134.82910\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:54:INFO] @tracker All nodes finishes job\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:54:INFO] @tracker 121.60369801521301 secs between node start and job finish\u001b[0m\n", + "\n", + "2024-06-25 18:26:11 Uploading - Uploading generated training model\n", + "2024-06-25 18:26:11 Completed - Training job completed\n", + "Training seconds: 520\n", + "Billable seconds: 520\n" + ] + } + ], + "source": [ + "xgb_model.fit({\"train\": train_input, \"validation\": validation_input}, wait=True)" + ] + }, + { + "cell_type": "markdown", + "id": "f0f8be08-10a5-4204-8f8b-60235d4b1f04", + "metadata": {}, + "source": [ + "### Deploy the model\n", + "\n", + "Copy the name of the model endpoint. We use it for our model evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "c1aa7bc3-feee-4602-a64c-8c1e08526d03", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating model with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n", + "INFO:sagemaker:Creating endpoint-config with name sagemaker-xgboost-2024-06-25-18-26-38-055\n", + "INFO:sagemaker:Creating endpoint with name sagemaker-xgboost-2024-06-25-18-26-38-055\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------!" + ] + } + ], + "source": [ + "xgb_predictor = xgb_model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')" + ] + }, + { + "cell_type": "markdown", + "id": "ddcf330c-8add-437d-af1f-687ed3ebc78d", + "metadata": {}, + "source": [ + "### Download the test.csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a9cc4eea-a6d0-418f-ab35-db437ce2a99d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "download: s3://ux360-nyc-taxi-dogfooding/output/test/test.csv to ./test.csv\n" + ] + } + ], + "source": [ + "!aws s3 cp s3://example-s3-bucket/output/test/test.csv ." + ] + }, + { + "cell_type": "markdown", + "id": "27b6cc9e-cb1c-43f6-99b8-fc26b38934c3", + "metadata": {}, + "source": [ + "### Create a 20 row test dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "953f9d9b-04d0-4398-8620-8f9ab4eb407b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4 5\n", + "0 7.5 1.08 0.0 0.0 0.5 0.97\n", + "1 10.0 0.00 0.0 0.5 0.5 2.60\n", + "2 6.0 1.00 0.0 1.0 0.5 0.82\n", + "3 23.5 5.45 0.0 3.0 0.5 7.40\n", + "4 53.5 8.36 10.5 0.0 0.0 12.68" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import boto3\n", + "import json\n", + "\n", + "test_df = pd.read_csv('test.csv', nrows=20)\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "a27e6c58-1abb-41db-ab45-263b97ee01ed", + "metadata": {}, + "source": [ + "### Get predictions from the test dataframe\n", + "\n", + "Define the `get_predictions` function to convert the 20 row dataframe to a CSV string and get predictions from the model endpoint. Provide the `get_predictions` function with the name of the model and the model endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "218e7887-f37d-42e1-8f6a-9ee97d3c75c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.515090465545654,10.813796043395996,6.515090465545654,22.628469467163086,49.72923278808594,8.302289962768555,7.602119445800781,6.515090465545654,7.602119445800781,12.309170722961426,16.632259368896484,28.30757713317871,10.813796043395996,37.56535339355469,10.813796043395996,12.309170722961426,6.515090465545654,14.130854606628418,10.813796043395996,6.515090465545654\n" + ] + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "\n", + "# Initialize the SageMaker runtime client\n", + "runtime = boto3.client('runtime.sagemaker')\n", + "\n", + "# Define the endpoint name\n", + "endpoint_name = 'sagemaker-xgboost-timestamp'\n", + "\n", + "# Function to make predictions\n", + "def get_predictions(data, endpoint_name):\n", + " # Convert the DataFrame to a CSV string and encode it to bytes\n", + " csv_data = data.to_csv(header=False, index=False).encode('utf-8')\n", + " \n", + " response = runtime.invoke_endpoint(\n", + " EndpointName=endpoint_name,\n", + " ContentType='text/csv',\n", + " Body=csv_data\n", + " )\n", + " \n", + " # Read the response body\n", + " response_body = response['Body'].read().decode('utf-8')\n", + " \n", + " try:\n", + " # Try to parse the response as JSON\n", + " result = json.loads(response_body)\n", + " except json.JSONDecodeError:\n", + " # If response is not JSON, just return the raw response\n", + " result = response_body\n", + " \n", + " return result\n", + "\n", + "# Drop the target column from the test dataframe\n", + "test_df = test_df.drop(test_df.columns[0], axis=1)\n", + "\n", + "# Get predictions\n", + "predictions = get_predictions(test_df, endpoint_name)\n", + "print(predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "a136ae86-efd3-4d4f-9966-6610f445d84c", + "metadata": {}, + "source": [ + "### Create an array from the string of predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "58b45ac2-8a18-4d27-8aff-57370696d58f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['6.515090465545654',\n", + " '10.813796043395996',\n", + " '6.515090465545654',\n", + " '22.628469467163086',\n", + " '49.72923278808594',\n", + " '8.302289962768555',\n", + " '7.602119445800781',\n", + " '6.515090465545654',\n", + " '7.602119445800781',\n", + " '12.309170722961426',\n", + " '16.632259368896484',\n", + " '28.30757713317871',\n", + " '10.813796043395996',\n", + " '37.56535339355469',\n", + " '10.813796043395996',\n", + " '12.309170722961426',\n", + " '6.515090465545654',\n", + " '14.130854606628418',\n", + " '10.813796043395996',\n", + " '6.515090465545654']" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions_array = predictions.split(',')\n", + "predictions_array" + ] + }, + { + "cell_type": "markdown", + "id": "20097b4e-d515-45cf-9677-bd12953b6912", + "metadata": {}, + "source": [ + "### Get the 20 row sample of the test dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "a5b69119-c58d-401d-a683-345a21451090", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4 5\n", + "0 7.5 1.08 0.0 0.0 0.5 0.97\n", + "1 10.0 0.00 0.0 0.5 0.5 2.60\n", + "2 6.0 1.00 0.0 1.0 0.5 0.82\n", + "3 23.5 5.45 0.0 3.0 0.5 7.40\n", + "4 53.5 8.36 10.5 0.0 0.0 12.68" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_with_target_column_values = pd.read_csv('test.csv', nrows=20)\n", + "df_with_target_column_values.head()" + ] + }, + { + "cell_type": "markdown", + "id": "85cd39f3-5f12-4cb1-aab2-6ca658e9d16e", + "metadata": {}, + "source": [ + "### Convert the values of the predictions array from strings to floats" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "75353856-df2f-4c45-9a9b-11e16a856aa6", + "metadata": {}, + "outputs": [], + "source": [ + "predictions_array = [float(x) for x in predictions_array]" + ] + }, + { + "cell_type": "markdown", + "id": "408a6da9-9a0c-4307-8966-acbcc11beacc", + "metadata": {}, + "source": [ + "### Create a dataframe to store the predicted versus actual values" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "9589000e-1ce0-4a08-9d9c-055d29e13639", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " predicted_values actual_values\n", + "0 6.515090 7.5\n", + "1 10.813796 10.0\n", + "2 6.515090 6.0\n", + "3 22.628469 23.5\n", + "4 49.729233 53.5\n", + "5 8.302290 9.0\n", + "6 7.602119 8.5\n", + "7 6.515090 2.5\n", + "8 7.602119 8.5\n", + "9 12.309171 17.5\n", + "10 16.632259 16.5\n", + "11 28.307577 32.5\n", + "12 10.813796 12.5\n", + "13 37.565353 52.0\n", + "14 10.813796 12.0\n", + "15 12.309171 13.5\n", + "16 6.515090 6.5\n", + "17 14.130855 26.5\n", + "18 10.813796 13.0\n", + "19 6.515090 10.5" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "column_to_add = df_with_target_column_values.iloc[:, 0]\n", + "\n", + "comparison_df['actual_values'] = column_to_add\n", + "\n", + "comparison_df" + ] + }, + { + "cell_type": "markdown", + "id": "a1ee137e-2706-4972-b70a-4d908bb0cb0a", + "metadata": {}, + "source": [ + "### Verify that the datatypes of both columns are floats" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "48f6f988-0de8-4c44-8c10-9845ef4d476d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "predicted_values float64\n", + "actual_values float64\n", + "dtype: object" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comparison_df.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "8c7cce0b-ce8b-4320-b9a4-9a50b2c732b3", + "metadata": {}, + "source": [ + "### Compute the RMSE" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "781fe125-4a2e-4527-8c45-fcd20558f4bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 4.833823838366928\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# Calculate the squared differences between the predicted and actual values\n", + "comparison_df['squared_diff'] = (comparison_df['actual_values'] - comparison_df['predicted_values']) ** 2\n", + "\n", + "# Calculate the mean of the squared differences\n", + "mean_squared_diff = comparison_df['squared_diff'].mean()\n", + "\n", + "# Take the square root of the mean to get the RMSE\n", + "rmse = np.sqrt(mean_squared_diff)\n", + "\n", + "print(f\"RMSE: {rmse}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "4a21cb4e-d9be-466c-869d-ac0be688700c", + "metadata": {}, + "source": [ + "### Clean up" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "9a6e651d-3e68-4c1b-8a28-3e15604b5ec1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "remove_bucket: parsa-machine-learning-exam\n" + ] + } + ], + "source": [ + "# Delete the S3 bucket\n", + "!aws s3 rb s3://example-s3-bucket --force" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "6c883864-e707-46d2-a183-76e5f2090368", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Deleting endpoint configuration with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n", + "INFO:sagemaker:Deleting endpoint with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n" + ] + } + ], + "source": [ + "# Delete the endpoint\n", + "xgb_predictor.delete_endpoint()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c3968d13b86c7785f517ed2a884b898055a4a0c4 Mon Sep 17 00:00:00 2001 From: parsash2 <60193914+parsash2@users.noreply.github.com> Date: Wed, 26 Jun 2024 07:20:18 -0400 Subject: [PATCH 07/13] Added overview, headers, explanatory text Also added troubleshooting note from further testing. --- pyspark-etl-training.ipynb | 850 +++++++++++++++++++++++++++++++++++++ 1 file changed, 850 insertions(+) create mode 100644 pyspark-etl-training.ipynb diff --git a/pyspark-etl-training.ipynb b/pyspark-etl-training.ipynb new file mode 100644 index 0000000000..2dc23d344c --- /dev/null +++ b/pyspark-etl-training.ipynb @@ -0,0 +1,850 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0a1828f9-efdc-4d12-a676-a2f3432e9ab0", + "metadata": {}, + "source": [ + "# Perform ETL and train a model using PySpark\n", + "\n", + "To perform extract transform load (ETL) operations on multiple files, we recommend opening a Jupyter notebook within Amazon SageMaker Studio and using the `Glue PySpark and Ray` kernel. The kernel is connected to an AWS Glue Interactive Session. The session connects your notebook to a cluster that automatically scales up the storage and compute to meet your data processing needs. When you shut down the kernel, the session stops and you're no longer charged for the compute on the cluster.\n", + "\n", + "Within the notebook you can use Spark commands to join and transform your data. Writing Spark commands is both faster and easier than writing SQL queries. For example, you can use the join command to join two tables. Instead of writing a query that can sometimes take minutes to complete, you can join a table within seconds.\n", + "\n", + "To show the utility of using the PySpark kernel for your ETL and model training worklows, we're predicting the fare amount of the NYC taxi dataset. It imports data from 47 files across 2 different Amazon Simple Storage Service (Amazon S3) locations. Amazon S3 is an object storage service that you can use to save and access data and machine learning artifacts for your models. For more information about Amazon S3, see [What is Amazon S3?](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html).\n", + "\n", + "The notebook is not meant to be a comprehensive analysis. Instead, it's meant to be a proof of concept to help you quickly get started.\n", + "\n", + "__Prerequisites:__\n", + "\n", + "This tutorial assumes that you've in the us-east-1 AWS Region. It also assumes that you've provided the IAM role you're using to run the notebook with permissions to use Glue. For more information, see [Providing AWS Glue permissions\n", + "](docs.aws.amazon.com/sagemaker/latest/dg/perform-etl-and-train-model-pyspark.html#providing-aws-glue-permissions)." + ] + }, + { + "cell_type": "markdown", + "id": "dffc1f72-88d2-442d-97ee-0d1c4e095ffb", + "metadata": {}, + "source": [ + "## Solution overview \n", + "\n", + "To perform ETL on the NYC taxi data and train a model, we do the following\n", + "\n", + "1. Start a Glue Session and load the SageMaker Python SDK\n", + "2. Set up the utilities needed to work with AWS Glue.\n", + "3. Load the data from the Amazon S3 into Spark dataframes.\n", + "4. Verify that we've loaded the data successfully.\n", + "5. Save a 20000 row sample of the Spark dataframe as a pandas dataframe.\n", + "6. Create a correlation matrix as an example of the types of analyses we can perform.\n", + "7. Split the Spark dataframe into training, validation, and test datasets.\n", + "8. Write the datasets to Amazon S3 locations that can be accessed by an Amazon SageMaker training job.\n", + "9. Use the training and validation datasets to train a model." + ] + }, + { + "cell_type": "markdown", + "id": "e472c953-1625-49df-8df9-9529344783ab", + "metadata": {}, + "source": [ + "### Start a Glue Session and load the SageMaker Python SDK" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "94172c75-f8a9-4590-a443-c872fb5c5d6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Welcome to the Glue Interactive Sessions Kernel\n", + "For more information on available magic commands, please type %help in any new cell.\n", + "\n", + "Please view our Getting Started page to access the most up-to-date information on the Interactive Sessions kernel: https://docs.aws.amazon.com/glue/latest/dg/interactive-sessions.html\n", + "Installed kernel version: 1.0.5 \n", + "Additional python modules to be included:\n", + "sagemaker\n" + ] + } + ], + "source": [ + "%additional_python_modules sagemaker" + ] + }, + { + "cell_type": "markdown", + "id": "725bd4b6-82a0-4f02-95b9-261ce62c71b0", + "metadata": {}, + "source": [ + "### Set up the utilities needed to work with AWS Glue\n", + "\n", + "We're importing `Join` to join our Spark dataframes. `GlueContext` provides methods for transforming our dataframes. In the context of the notebook, it reads the data from the Amazon S3 locations and uses the Spark cluster to transform the data. `SparkContext` represents the connection to the Spark cluster. `GlueContext` uses `SparkContext` to transform the data. `getResolvedOptions` lets you resolve configuration options within the Glue interactive session." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2ea1c3a4-8881-48b0-8888-9319812750e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trying to create a Glue session for the kernel.\n", + "Session Type: etl\n", + "Session ID: 11fe1ff7-3608-485f-a4a3-65392596dba0\n", + "Applying the following default arguments:\n", + "--glue_kernel_version 1.0.5\n", + "--enable-glue-datacatalog true\n", + "--additional-python-modules sagemaker\n", + "Waiting for session 11fe1ff7-3608-485f-a4a3-65392596dba0 to get into ready status...\n", + "Session 11fe1ff7-3608-485f-a4a3-65392596dba0 has been created.\n", + "\n" + ] + } + ], + "source": [ + "import sys\n", + "from awsglue.transforms import Join\n", + "from awsglue.utils import getResolvedOptions\n", + "from pyspark.context import SparkContext\n", + "from awsglue.context import GlueContext\n", + "from awsglue.job import Job\n", + "\n", + "glueContext = GlueContext(SparkContext.getOrCreate())" + ] + }, + { + "cell_type": "markdown", + "id": "e03664e5-89a2-4296-ba83-3518df4a58f0", + "metadata": {}, + "source": [ + "### Create the `df_ride_info` dataframe\n", + "\n", + "Create a single dataframe from all the ride_info Parquet files for 2019." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ba577de7-9ffe-4bae-b4c0-b225181306d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_ride_info = glueContext.create_dynamic_frame_from_options(\n", + " connection_type=\"s3\", format=\"parquet\",\n", + " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-info/year=2019/\"], \"recurse\": True}).toDF()" + ] + }, + { + "cell_type": "markdown", + "id": "b04ce553-bf3d-4922-bbb1-4aa264447276", + "metadata": {}, + "source": [ + "### Create the `df_ride_info` dataframe\n", + "\n", + "Create a single dataframe from all the ride_fare Parquet files for 2019." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6efc3d4a-81d7-40f5-bb62-cd206924a0c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_ride_fare = glueContext.create_dynamic_frame_from_options(\n", + " connection_type=\"s3\", format=\"parquet\",\n", + " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-fare/year=2019/\"], \"recurse\": True}).toDF()" + ] + }, + { + "cell_type": "markdown", + "id": "6c8664da-2105-4ada-b480-06d50c59e878", + "metadata": {}, + "source": [ + "### Show the first five rows of `dr_ride_fare`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d63af3a3-358f-4c6e-97d4-97a1f1a552de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "| ride_id|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|total_amount|\n", + "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "|1400160115693| 2| 31.0| 0.0| 0.5| 0.0| 6.12| 40.42|\n", + "|3770982177323| 1| 4.5| 0.0| 0.5| 1.2| 0.0| 9.0|\n", + "|1400160115694| 1| 16.5| 1.0| 0.5| 4.16| 0.0| 24.96|\n", + "|3770982177324| 1| 18.0| 2.5| 0.5| 5.3| 0.0| 26.6|\n", + "|1400160115695| 1| 8.0| 2.5| 0.5| 1.13| 0.0| 12.43|\n", + "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "only showing top 5 rows\n" + ] + } + ], + "source": [ + "df_ride_fare.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "688a17e8-0c83-485d-a328-e89344a0e8bf", + "metadata": {}, + "source": [ + "### Join df_ride_fare and df_ride_info on the `ride_id` column" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "07a3baab-44b0-416a-b12e-049a270af8bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_joined = df_ride_info.join(df_ride_fare, [\"ride_id\"])" + ] + }, + { + "cell_type": "markdown", + "id": "236c2efc-85f8-43f8-b6d3-7f0e61ccefb0", + "metadata": {}, + "source": [ + "### Show the first five rows of the joined dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2a456733-4533-4688-8174-368e50f4dd66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "| ride_id|vendor_id|passenger_count| pickup_at| dropoff_at|trip_distance|rate_code_id|store_and_fwd_flag|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|total_amount|\n", + "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "|51539607553| 1| 1|2019-04-21 17:20:19|2019-04-21 17:31:28| 2.7| 1| N| 1| 10.5| 2.5| 0.5| 3.45| 0.0| 17.25|\n", + "|51539607560| 2| 1|2019-02-21 22:49:59|2019-02-21 22:53:45| 0.62| 1| N| 2| 4.5| 0.5| 0.5| 0.0| 0.0| 8.3|\n", + "|51539607572| 1| 1|2019-02-21 22:19:08|2019-02-21 22:24:13| 0.6| 1| N| 1| 5.0| 3.0| 0.5| 1.75| 0.0| 10.55|\n", + "|51539607626| 2| 5|2019-02-21 22:18:33|2019-02-21 22:30:32| 2.0| 1| N| 1| 10.0| 0.5| 0.5| 2.76| 0.0| 16.56|\n", + "|51539607627| 2| 1|2019-04-21 17:21:49|2019-04-21 17:35:46| 2.72| 1| N| 1| 12.0| 0.0| 0.5| 2.3| 0.0| 17.6|\n", + "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", + "only showing top 5 rows\n" + ] + } + ], + "source": [ + "df_joined.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "1396f6ee-c581-4274-baf8-243d38ec000b", + "metadata": {}, + "source": [ + "### Show the data types of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9a52a903-f394-4d00-a216-6af8c2132d83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "root\n", + " |-- ride_id: long (nullable = true)\n", + " |-- vendor_id: integer (nullable = true)\n", + " |-- passenger_count: byte (nullable = true)\n", + " |-- pickup_at: timestamp (nullable = true)\n", + " |-- dropoff_at: timestamp (nullable = true)\n", + " |-- trip_distance: float (nullable = true)\n", + " |-- rate_code_id: integer (nullable = true)\n", + " |-- store_and_fwd_flag: string (nullable = true)\n", + " |-- payment_type: integer (nullable = true)\n", + " |-- fare_amount: float (nullable = true)\n", + " |-- extra: float (nullable = true)\n", + " |-- mta_tax: float (nullable = true)\n", + " |-- tip_amount: float (nullable = true)\n", + " |-- tolls_amount: float (nullable = true)\n", + " |-- total_amount: float (nullable = true)\n" + ] + } + ], + "source": [ + "df_joined.printSchema()" + ] + }, + { + "cell_type": "markdown", + "id": "18bb75a2-eba5-4d06-8a26-f30e31776a02", + "metadata": {}, + "source": [ + "### Count the number of rows" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c6bcc15f-8d41-4def-ae49-edaef4105343", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44200708\n" + ] + } + ], + "source": [ + "df_joined.count()" + ] + }, + { + "cell_type": "markdown", + "id": "d2daa67c-4b21-433a-b46e-eed518ba9ce7", + "metadata": {}, + "source": [ + "### Drop duplicates if there are any" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7d13d8d9-7eed-4efb-b972-601baf291842", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_no_dups = df_joined.dropDuplicates([\"ride_id\"])" + ] + }, + { + "cell_type": "markdown", + "id": "657e48dc-1f4a-4550-afe1-d9754e6d0e1e", + "metadata": {}, + "source": [ + "### Count the number of rows after dropping the duplicates\n", + "\n", + "In this case, there were no duplicates in the original dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3e3e82a3-e3db-4752-8bab-f42cbbae4928", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44200708\n" + ] + } + ], + "source": [ + "df_no_dups.count()" + ] + }, + { + "cell_type": "markdown", + "id": "ae4c0fc4-7cb5-4b70-8430-965b5fe4506e", + "metadata": {}, + "source": [ + "### Drop columns\n", + "Time series data and categorical data is outside of the scope of the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9dc1d15f-53f6-404d-86fd-5a28f3792db8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_cleaned = df_joined.drop(\"pickup_at\", \"dropoff_at\", \"store_and_fwd_flag\", \"vendor_id\", \"payment_type\")" + ] + }, + { + "cell_type": "markdown", + "id": "081c81f9-f052-4ddb-b769-4d41b6138f6a", + "metadata": {}, + "source": [ + "### Take a sample from the notebook and convert it to a pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "48382726-c767-4b0e-9336-decbf8184938", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_sample = df_cleaned.sample(False, 0.1, seed=0).limit(20000)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2bf2f181-0096-4044-8210-7d9de299d966", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20000\n" + ] + } + ], + "source": [ + "df_sample.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a8b2f670-c5f9-4a01-8d9f-6a29a3dae660", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ride_id passenger_count ... tolls_amount total_amount\n", + "count 2.000000e+04 20000.000000 ... 20000.000000 20000.000000\n", + "mean 5.327415e+10 1.580700 ... 0.354632 18.917547\n", + "std 3.447216e+09 1.218221 ... 1.540669 14.226608\n", + "min 5.153961e+10 0.000000 ... 0.000000 -59.799999\n", + "25% 5.154042e+10 1.000000 ... 0.000000 11.300000\n", + "50% 5.154121e+10 1.000000 ... 0.000000 14.750000\n", + "75% 5.154202e+10 2.000000 ... 0.000000 20.299999\n", + "max 6.013019e+10 6.000000 ... 21.500000 242.300003\n", + "\n", + "[8 rows x 10 columns]\n" + ] + } + ], + "source": [ + "df_pandas = df_sample.toPandas()\n", + "df_pandas.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "246c98e9-64bd-4644-a163-b86a943d6a09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset shape: (20000, 10)\n" + ] + } + ], + "source": [ + "print(\"Dataset shape: \", df_pandas.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c5b2727c-de75-4cc0-94e9-d254e235d003", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ride_id passenger_count ... tolls_amount total_amount\n", + "0 51539607572 1 ... 0.0 10.550000\n", + "1 51539607730 5 ... 0.0 17.299999\n", + "2 51539607857 2 ... 0.0 6.800000\n", + "3 51539607985 1 ... 0.0 7.300000\n", + "4 51539608203 1 ... 0.0 16.559999\n", + "\n", + "[5 rows x 10 columns]\n" + ] + } + ], + "source": [ + "df_pandas.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d69b48b6-98c2-4851-9c7a-f24f092bae41", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20000 entries, 0 to 19999\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ride_id 20000 non-null int64 \n", + " 1 passenger_count 20000 non-null int8 \n", + " 2 trip_distance 20000 non-null float32\n", + " 3 rate_code_id 20000 non-null int32 \n", + " 4 fare_amount 20000 non-null float32\n", + " 5 extra 20000 non-null float32\n", + " 6 mta_tax 20000 non-null float32\n", + " 7 tip_amount 20000 non-null float32\n", + " 8 tolls_amount 20000 non-null float32\n", + " 9 total_amount 20000 non-null float32\n", + "dtypes: float32(7), int32(1), int64(1), int8(1)\n", + "memory usage: 800.9 KB\n" + ] + } + ], + "source": [ + "df_pandas.info()" + ] + }, + { + "cell_type": "markdown", + "id": "34222bea-8864-4934-8c93-a71a7e72325b", + "metadata": {}, + "source": [ + "### Create a correlation matrix of the features\n", + "\n", + "We're creating a correlation matrix to see which features are the most predictive. This is an example of an analysis that you can use for your own use case." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b7f3e4f7-e04e-41e1-b94b-b32eb3bc3bbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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OVJEA8F8QALlDderUMfvHvW7duvr888+Vnp6uXbt2aeTIkdq9e7diYmKUkZEhKbNxDw8P18svv6yOHTtqx44deuSRR9S+fXs99NBDkjLTPDVv3lxlypRRy5Yt1bZtWz3ySOY/q7t371ZiYqK8vMyfIEtKStKRI0dM70NCQkzBD0ny8/MzpeA6evSoUlNTVatW9pMb7u7uKlOmjOn9nj17lJ6errCw7H8qpMy0WDd+toODgypVurMfQBcuXNCZM2dMQZeb7d+/X4GBgabghySFh4erSJEi2r9//78OgNzoxu23JlfiriojIyNHF2RXD1edP3kx12USYhJNqWFM5Yu4Kj469+7tQEFnyfNg3vjfFFo++IFO5RMfHS9Jcrt5f3rkvT8T465kHbOcy5w7kdlWx8fEy87eVoVcnM3KuHm4KD4mPtf1blq6RTWaVpODY/YNlJIVQtXrjac08b2flZqSqoz0DFWsW15d+nf8dxtawF0x7dObzwMXXTiZ+/UvISYhZ/kbzoPraU9ynisuSsgqc/lsZu/TZT/9rvYvtZOXr6f+mLVW414brzcnD1Vht0KSpNa9HlHpKqXk4OigA9sjNWvsPCVfS1HDx+v/xy2/f8VcjpEkeXgVMZtexLOIad7/IzAkQEV9i2rSuJ/16huvyMnZUfN/WahL5y8r+lL07Vdgha63Gbm1OXm1J3m1U24eLjqfdU7FRyfk3U79i+uJo7ODHn/pUZWsECKDwaBd6//W929P1vPv9rrvgyDxebUjHi6meTfLuz1zNdv3trns+8xrT+YxzbWNy6pHQnS8JPMASNylOO3felA93nzyX2yhdbiTfX6z+OiEXK792deHOzn2RqNRv3wyQ/Xb1VVQmUBdPvfgtVEF/fp9o81Lt6pak6pmAXbkH2t6SOR6+vjc3JhmXZLs7Ow0YsQI0wOtAJBfCID8R9euXVOLFi3UokUL/fLLLypatKhOnDihFi1aKCUlM6dzq1atdPz4cS1ZskQrVqxQ06ZN1adPH3322WeqVq2ajh07pqVLl2rlypV64okn1KxZM82ePVuJiYny8/PLcZGQMns0XGdvb/6jxGAwmIIwdyIxMVG2trbavn17jm6GLi7ZP7acnZ3v+MLs7Ox8+0K3YWNjkyPv4/UnHW/0X7f/uuTkZCUnJ5t/XnKq7PnRBzzw/tm4T4d2HdHgCf1vX9iKbF25XdNHzzK9f+Wj5yxYm2xH90bp3PHz6jnM/MbV2ahzmv31fLXq3lzhNcsqLjpe8779TdO/mKWnB3e1UG2tR0ZG5jX5kSebqkrWE6FPDe6it7u9r13rdqte28w0TS2ebm5aJqC0v1KupeiPmWseqADI6qVr9NWH403v3xkzPF8+x87OTm99+rq+fG+cujR5Sja2Nqpaq7JqPFRdRj0YubO3rdyu6V/MNr1/+cOC0U7lxcXdRU07NzS9Dy4bpLjL8Vr165r7LgCybeUO/XrDvn/xw2ctWJt/Z8vvf8nZxUmV6t1f+/x+tm7eBiUnJav5Db0fcG/c6fX7umP7onT+xAV1f73bPa8rAAD5gQDIHdqyZYvZ+82bN6t06dI6cOCALl++rFGjRpl6M/z11185li9atKh69uypnj17qkGDBho8eLA+++wzSZKbm5u6dOmiLl26qFOnTmrZsqWio6NVrVo1nTt3TnZ2dv/34N0lSpSQvb29tm3bpqCgIElSXFycIiMj9fDDmV1pq1atqvT0dF24cEENGjS41erumKurq0JCQrRq1So1btw4x/xy5crp5MmTOnnypGm/7du3T7GxsQoPD5eUuc/++cc8Z+yuXbtyBDxux97eXunp6bct99FHH+mdd94xm/ZkRBc9PbBg3DQr7F5INjY2OQbIS4jJ+UTWda4eLjkGM0yITXhg0/fg/mep8yBy12FdPhOtYY+NNJs+6Z2fVaJCqF4dnfsAxfe7Sg+VV0i5INP7tJTMtjQ+JsFs8NKEmAQFlMqZXkSSXNwLZx2zm45BTPYxcPNwU1pquq4mJpk94Rsfkyg3j5yDpG5cslkBpfwVFBZoNn35tFUqUT5Uzbtm3lzxL1lcDk4O+qL/OLV7prXVDLha2LRPbz4PcvZ2us7VwzVn+RvOA9OT0jcf29hE+ZfMTI/p7pVZxifYxzTfzsFO3n6eirkQm2d9g8sGafnUlUpLSZOdw4Px07P2w7VUpkJ2b9vrA9HGXI6Vp3d2up3Y6FiVCAv9T59VulwpjZs2RlcSrygtNU3uHu6K6DlIpcMfjLG9Kj5UXiHlstNPXR/vIcd3OSZBASX/XTsVH5OY3U55uubdTv3H31XBZYN0YHvkf1qHJVR8KFwh5Qaa3ue97xMVULJ4juWlW7VnCXL1zFyHm6er0nPZ95nXkcwyrh6uOn7gZI51SDKt5zqj0agty7apZvPqsrN/MNqkG93JPr+Zm6drjl48CTGJcjVdx/O4htxw7CN3Htaxfcc1sOXrZuv57OUvVaNpVT39ANxsv1+u35uWbJV/yeIKDAv4l1sIAEDBxCDod+jEiRMaOHCgDh48qOnTp+urr75S//79FRQUJAcHB3311Vc6evSoFi5cqPfee89s2bffflsLFizQ4cOHtXfvXi1atEjlymXmIx89erSmT5+uAwcOKDIyUrNmzZKvr6+KFCmiZs2aqW7dumrfvr1+//13RUVFaePGjXrzzTdzDbLkxtXVVT179tTgwYO1evVq7d27V88++6xsbGxMvTnCwsL01FNPqUePHpo7d66OHTumrVu36qOPPtLixYv/7302cuRIff755xo7dqwOHTqkHTt26KuvvpIkNWvWTBUrVtRTTz2lHTt2aOvWrerRo4caNmxoGiulSZMm+uuvv/TTTz/p0KFDGjFiRI6AyJ24Hog5d+6cYmLyTjMxbNgwxcXFmb269Ck4aVPs7O0UEOavQzsOm6ZlZGQocudhhYQH5bpMSHiwDu08Yjbt4PZDeZYHCjpLnQfNujbWkO8iNPjb/qaXJLV/uZ2eHGy9A6I7FXJSMf+ippdfiI/cPF11cMchU5mkK9cUtf9EruN0SJnHLDAswGyZjIwMHdxxSCWylgkKC5Ctna0O7si+AXj+xAXFXIhRaHnzvPrXkpK1Y81u1c1lUM7U5BQZbMx7Kl7PM3xzj8L7WeY+9VdkrudB7uMQhIYHK3LnIbNpmedBZnkvP0+5eboqcmf2Oq9duabj+0+YxjYILB0gO3s7XTiVnW4uPS1dl8/FyLOYR571PX3kjAq5Oj8wwQ9JKlS4kIoH+pleQSUC5eHlod3b/jaVuZp4VQf/iVS5imVusaY7V9ilsNw93HX6xBkd3n9EdRvWvivrLeicCjmpqL+36eUbnHc7ldf5YWqndpq3U5E7D5m+/0Glr7dT2WXOn8xqp/Jo/+7U6SNnTDfy7yd57fvIm/b98dvue3+z9ikjI0MHdx42a3ts7WzN1pu572NN6w0ND9aZY2fNglgHth+SU2En+d5w01eSDu8+oounLz2wgzvfyT6/WUh4sNn+l6QD2yNN5U3XkFsc+45922vodwM15LsBGvLdAL34UWaPoV7Dn1abZ1vd1W0sqO6H63dyUrJ2rf37gT0/LMVgMBTYFwBYgwfnP9H/qEePHkpKSlKtWrVka2ur/v3764UXXpDBYNDkyZP1xhtvaOzYsapWrZo+++wzPfroo6ZlHRwcNGzYMEVFRcnZ2VkNGjTQjBkzJGUGKD755BMdOnRItra2qlmzppYsWWK6YbNkyRK9+eab6t27ty5evChfX189/PDDpkGk7sTo0aP10ksvqW3btnJzc9OQIUN08uRJOTk5mcpMmjTJNGj56dOn5e3trTp16qht27b/9z7r2bOnrl27pi+++EKDBg2St7e3OnXqJCnzAr9gwQK9+uqrevjhh2VjY6OWLVuaAiSS1KJFCw0fPlxDhgzRtWvX9Mwzz6hHjx7as2fPv6rH559/roEDB+r777+Xv7+/oqKici3n6OhoGmz+Ovu4gpX+qlHHBpr2yUwFlglQUJkArZ27QSnXUlW7ZWbQaOqoX+Xu7aZ2z2X+E9GwQz19NfBbrZ61TuG1y2rH6t06GXlaXQZkB3auxF9VzIVYxV/OzKF8IWscBTdPV3qK3CWFnQqplH+I6X2ob6AqlwxXdHysTl48Y7mK3acscR7kdT54FCsiL78HZ/BUg8Ggxh0f1rKpK1TM31tefp5aNGmZ3L3dVLl+dhqRL18br8r1K6jR45m9Cpt2bqifRk1XUJlAhZQN0h9z1ir5WorqtMz859rZxVl1W9XWnG8WqrBrITkVdtLMsfMUGh6S48bijtU7lZGerlrNa+SoX4W65TXt85lat+BPUwqs2V/PV3DZIBXxds+/HWMBjTo+rF8++VVBZQIUVCZQa+euV8q1FNVumTmG1tRR0+Xu7a52z7WWJDXsUF9jB47XH7PWqnztctqxepdORp5SlwHZ1+WGHRro919Wqai/t7x8PbVk8nK5e7mZxr1xKuykeu3qaOmU3+VR1F0ePh76Y+ZaSVKVrMHo/9m0TwkxCQouFyx7Bzsd3B6pFdNXqfENKX8eRAaDQe27tdOMH2eqeKCffPx99PP4afIq6qm6jeqYyg17ebgealRH7bq0kSQlXU3SmZNnTfPPnz6vIwePytXdVcV8i0qS1q/8U+5F3FTUt6iiDh/Xt5//oDoNa6tanZwDTj8IDAaDGnd4WMt+WamiAd7y8vXS4klLc7RTYweNV+X6FdWwfWZqtiadHtbPH89QUFhmO7V6zrrMdqrFje1ULc0df72dctSsr+YpNDzY7KbxxdOXlJyUrPjoBKUmp+rU4dOSJN9gH9nZ22nz8m2ys7c19ZrbvX6PNi3bqidfe+Je7aJ8c70dWf7LKhUNKCovX08tzrpGVLph348bNEGV6lfQw1n7vnGnhpr68QwFhgUouGyQ1szJas9aZLZnzi7OqtOqluaNX6hCWdeI2V/NU8gN+75sjTLyDfbRz6Om67EX2io+OkG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" + }, + "metadata": { + "image/png": { + "height": 480, + "width": 640 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from pyspark.ml.stat import Correlation\n", + "from pyspark.ml.feature import VectorAssembler\n", + "import seaborn as sns \n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd # not sure how the kernel runs, but it looks like I have import pandas again after going back to the notebook after a while\n", + "\n", + "vector_col = 'corr_features'\n", + "assembler = VectorAssembler(inputCols=df_sample.columns, outputCol=vector_col)\n", + "df_vector = assembler.transform(df_sample).select(vector_col)\n", + "\n", + "matrix = Correlation.corr(df_vector, vector_col).collect()[0][0]\n", + "corr_matrix = matrix.toArray().tolist()\n", + "corr_matrix_df = pd.DataFrame(data=corr_matrix, columns=df_sample.columns, index=df_sample.columns) \n", + "\n", + "plt.figure(figsize=(16,10))\n", + "sns.heatmap(corr_matrix_df,\n", + " xticklabels=corr_matrix_df.columns.values,\n", + " yticklabels=corr_matrix_df.columns.values, cmap=\"Greens\", annot=True)\n", + "\n", + "%matplot plt" + ] + }, + { + "cell_type": "markdown", + "id": "cbde3b29-d37d-485a-a114-5313c5a702c7", + "metadata": {}, + "source": [ + "### Split the dataset into train, validation, and test sets" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6e207c64-2e22-468f-a0c7-948090bcfce2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_train, df_val, df_test = df_cleaned.randomSplit([0.7, 0.15, 0.15])" + ] + }, + { + "cell_type": "markdown", + "id": "01a4d181-e2f0-4743-ab35-dd1f68b0fd31", + "metadata": {}, + "source": [ + "### Define the Amazon S3 locations that store the datasets\n", + "\n", + "If you're getting a module not found error, restart the kernel and run all the cells again." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f16ea3a1-6d6d-4755-94ad-c743298bd130", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Define the S3 locations to store the datasets\n", + "import boto3\n", + "import sagemaker\n", + "\n", + "sagemaker_session = sagemaker.Session()\n", + "s3_bucket = sagemaker_session.default_bucket()\n", + "train_data_prefix = \"sandbox/glue-demo/train\"\n", + "validation_data_prefix = \"sandbox/glue-demo/validation\"\n", + "test_data_prefix = \"sandbox/glue-demo/test\"\n", + "region = boto3.Session().region_name" + ] + }, + { + "cell_type": "markdown", + "id": "8899a159-700c-403a-b4f5-a00c62b06e5a", + "metadata": {}, + "source": [ + "### Write the files to the locations" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "64d7ae48-6158-4273-8bb3-2f00abb1c20c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_train.write.parquet(f\"s3://{s3_bucket}/{train_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "de3d1190-4717-4944-846d-0169c093cb90", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_val.write.parquet(f\"s3://{s3_bucket}/{validation_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9d18ef1c-fc2f-4e34-a692-4a6c48be7cba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "df_test.write.parquet(f\"s3://{s3_bucket}/{test_data_prefix}\", mode=\"overwrite\")" + ] + }, + { + "cell_type": "markdown", + "id": "73c947e4-b4a9-4cc4-aefe-755aa0a713c8", + "metadata": {}, + "source": [ + "### Train a model\n", + "\n", + "The following code uses the `df_train` and `df_val` datasets to train an XGBoost model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a31b7742-93df-44c5-8674-b6355032c508", + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker import image_uris\n", + "from sagemaker.inputs import TrainingInput\n", + "\n", + "hyperparameters = {\n", + " \"max_depth\":\"5\",\n", + " \"eta\":\"0.2\",\n", + " \"gamma\":\"4\",\n", + " \"min_child_weight\":\"6\",\n", + " \"subsample\":\"0.7\",\n", + " \"objective\":\"reg:squarederror\",\n", + " \"num_round\":\"50\"}\n", + "\n", + "# Set an output path to save the trained model.\n", + "prefix = 'sandbox/glue-demo'\n", + "output_path = f's3://{s3_bucket}/{prefix}/xgb-built-in-algo/output'\n", + "\n", + "# The following line looks for the XGBoost image URI and builds an XGBoost container.\n", + "# We use version 1.7-1 of the image URI, you can specify a version that you prefer.\n", + "xgboost_container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.7-1\")\n", + "\n", + "# Construct a SageMaker estimator that calls the xgboost-container\n", + "estimator = sagemaker.estimator.Estimator(image_uri=xgboost_container,\n", + " hyperparameters=hyperparameters,\n", + " role=sagemaker.get_execution_role(),\n", + " instance_count=1,\n", + " instance_type='ml.m5.4xlarge',\n", + " output_path=output_path)\n", + "\n", + "content_type = \"application/x-parquet\"\n", + "train_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/train/\", content_type=content_type)\n", + "validation_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/validation/\", content_type=content_type)\n", + "\n", + "# Run the XGBoost training job\n", + "estimator.fit({'train': train_input, 'validation': validation_input})" + ] + }, + { + "cell_type": "markdown", + "id": "b1b1d546-1c7e-48f5-9262-939289ada936", + "metadata": {}, + "source": [ + "### Clean up\n", + "\n", + "To clean up, shut down the kernel. Shutting down the kernel, stops the Glue cluster. You won't be charged for any more compute other than what you used to run the tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e32c38c-719f-47bf-849f-54b63c39823b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Glue PySpark and Ray", + "language": "python", + "name": "glue_pyspark" + }, + "language_info": { + "codemirror_mode": { + "name": "python", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "Python_Glue_Session", + "pygments_lexer": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 0c3159b0eb039bf20e7e5a8932c378b86872c8d8 Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Wed, 26 Jun 2024 14:01:55 +0200 Subject: [PATCH 08/13] fix directory locations for new notebooks --- athena_ml_workflow_end_to_end.ipynb | 3173 ----------------- pyspark-etl-training.ipynb | 850 ----- .../athena_ml_workflow_end_to_end.ipynb | 1911 ++++++---- .../pyspark-etl-training.ipynb | 432 ++- 4 files changed, 1367 insertions(+), 4999 deletions(-) delete mode 100644 athena_ml_workflow_end_to_end.ipynb delete mode 100644 pyspark-etl-training.ipynb diff --git a/athena_ml_workflow_end_to_end.ipynb b/athena_ml_workflow_end_to_end.ipynb deleted file mode 100644 index c1555ce64b..0000000000 --- a/athena_ml_workflow_end_to_end.ipynb +++ /dev/null @@ -1,3173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ece13bd7-19b2-47b3-976d-cf636fa68003", - "metadata": {}, - "source": [ - "# Create an end to end machine learning workflow using Amazon Athena\n", - "Importing and transforming data can be one of the most challenging tasks in a machine learning workflow. We provide you with a Jupyter notebook that demonstrates a cost-effective strategy for an extract, transform, and load (ETL) workflow. Using Amazon Simple Storage Service (Amazon S3) and Amazon Athena, you learn how to query and transform data from a Jupyter notebook. Amazon S3 is an object storage service that allows you to store data and machine learning artifacts. Amazon Athena enables you to interactively query the data stored in those buckets, saving each query as a CSV file in an Amazon S3 location.\n", - "\n", - "The tutorial imports 16 CSV files for the 2019 NYC taxi dataset from multiple Amazon S3 locations. The goal is to predict the fare amount for each ride. From these 16 files, the notebook creates a single ride fare dataset and a single ride info dataset with deduplicated values. We join the deduplicated datasets into a single dataset.\n", - "\n", - "Amazon Athena stores the query results as a CSV file in the specified location. We provide the output to a SageMaker Processing Job to split the data into training, validation, and test sets. While data can be split using queries, a processing job ensures that the data is in a format that's parseable by the XGBoost algorithm.\n", - "\n", - "__Prerequisites:__\n", - "\n", - "The notebook must be run in the us-east-1 AWS Region. You also need your own Amazon S3 bucket and a database within Amazon Athena. You won't be able to access the data used in the tutorial otherwise.\n", - "\n", - "For information about creating a bucket, see [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). For information about creating a database, see [Create a database](https://docs.aws.amazon.com/athena/latest/ug/getting-started.html#step-1-create-a-database).\n", - "\n", - "Amazon Athena uses the AWS Glue Data Catalog to read the data from Amazon S3 into a database. You must have permissions to use Glue. To clean up, you also need permissions to delete the bucket you've created. For a quick guide to providing permissions, see [Setting up\n", - "](http://parsash-clouddesk-2024.aka.corp.amazon.com/sagemaker-dg/src/AWSIronmanApiDoc/build/server-root/sagemaker/latest/dg/create-end-to-end-ml-workflow-athena.html#setting-up)." - ] - }, - { - "cell_type": "markdown", - "id": "0b11693f-7c35-41cf-8e4b-4f86eea8f3b0", - "metadata": {}, - "source": [ - "## Solution overview\n", - "\n", - "To create the end to end workflow, we do the following:\n", - "\n", - "1. Create an Amazon Athena client within the us-east-1 AWS Region.\n", - "2. Define the run_athena_query function that runs queries and prints out the status in the following cell.\n", - "3. Create the `ride_fare` table within your database using all ride fare tables for the year 2019.\n", - "4. Create the `ride_info` table using ride info table for the year 2019.\n", - "5. Create the `ride_info_deduped` and `ride_fare_deduped` tables that have all duplicate values removed from the original tables.\n", - "6. Run test queries to get the first ten rows of each table to see whether they have data.\n", - "7. Define the `get_query_results` function that takes the query ID and returns comma separated values that can be stored as a dataframe.\n", - "8. View the results of the test queries within pandas dataframes.\n", - "9. Join the `ride_info_deduped` and `ride_fare_deduped` tables into the `combined_ride_data_deduped` table.\n", - "10. Select all values in the combined table.\n", - "11. Define the `get_csv_file_location` function to get the Amazon S3 location of the query results.\n", - "12. Download the CSV file to our environment.\n", - "13. Perform Exploratory Data Analysis (EDA) on the data.\n", - "14. Use the results of the EDA to select the relevant features in query.\n", - "15. Use the `get_csv_file_location` function to get the location of those query results.\n", - "16. Split the data into training, validation, and test sets using a processing job.\n", - "17. Download the test dataset.\n", - "18. Take a 20 row sample from the test dataset.\n", - "20. Create a dataframe with 20 rows of actual and predicted values.\n", - "21. Calculate the RMSE of the data.\n", - "22. Clean up the resources created within the notebook." - ] - }, - { - "cell_type": "markdown", - "id": "54d7468c-c77b-4273-b02d-9e9c4e884d46", - "metadata": {}, - "source": [ - "### Define the run_athena_query function\n", - "\n", - "In the following cell, we define the `run_athena_query` function. It runs an Athena query and waits for its completion.\n", - "\n", - "It takes the following arguments:\n", - "\n", - "- query_string (str): The SQL query to be executed.\n", - "- database_name (str): The name of the Athena database.\n", - "- output_location (str): The S3 location where the query results are stored.\n", - "\n", - "\n", - "It returns the query execution ID string." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8ab1ff0e-fcde-4976-a1cd-51e75c18deb2", - "metadata": {}, - "outputs": [], - "source": [ - "# Import required libraries\n", - "import time\n", - "import boto3\n", - "\n", - "def run_athena_query(query_string, database_name, output_location):\n", - " # Create an Athena client\n", - " athena_client = boto3.client('athena', region_name='us-east-1')\n", - "\n", - " # Start the query execution\n", - " response = athena_client.start_query_execution(\n", - " QueryString=query_string,\n", - " QueryExecutionContext={'Database': database_name},\n", - " ResultConfiguration={'OutputLocation': output_location}\n", - " )\n", - "\n", - " query_execution_id = response['QueryExecutionId']\n", - " print(f\"Query execution ID: {query_execution_id}\")\n", - "\n", - " while True:\n", - " # Check the query execution status\n", - " query_status = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", - " state = query_status['QueryExecution']['Status']['State']\n", - "\n", - " if state == 'SUCCEEDED':\n", - " print(\"Query executed successfully.\")\n", - " break\n", - " elif state == 'FAILED':\n", - " print(f\"Query failed with error: {query_status['QueryExecution']['Status']['StateChangeReason']}\")\n", - " break\n", - " else:\n", - " print(f\"Query is currently in {state} state. Waiting for completion...\")\n", - " time.sleep(5) # Wait for 5 seconds before checking again\n", - "\n", - " return query_execution_id\n" - ] - }, - { - "cell_type": "markdown", - "id": "8df0da48-89b3-45c2-a479-af422a51b962", - "metadata": {}, - "source": [ - "### Create the ride_fare table\n", - "\n", - "We've provided you with the query. You most provide the name of the database you created within Amazon Athena and the Amazon S3 output location. If you're not sure about how to specify the output location, provide the name of the S3 bucket. After running the query, you should get a message that says \"Query executed successfully.\" and a 36 character string in single quotes." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "64131b68-de28-4060-bb75-8148902846f7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: cb929408-df15-408d-a776-a8963facbf80\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'cb929408-df15-408d-a776-a8963facbf80'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# SQL query to create the 'ride_fare' table\n", - "create_ride_fare_table = \"\"\"\n", - "CREATE EXTERNAL TABLE `ride_fare` (\n", - " `ride_id` bigint, \n", - " `payment_type` smallint, \n", - " `fare_amount` float, \n", - " `extra` float, \n", - " `mta_tax` float, \n", - " `tip_amount` float, \n", - " `tolls_amount` float, \n", - " `total_amount` float\n", - ")\n", - "ROW FORMAT DELIMITED \n", - " FIELDS TERMINATED BY ',' \n", - " LINES TERMINATED BY '\\n' \n", - "STORED AS INPUTFORMAT \n", - " 'org.apache.hadoop.mapred.TextInputFormat' \n", - "OUTPUTFORMAT \n", - " 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'\n", - "LOCATION\n", - " 's3://dsoaws/nyc-taxi-orig-cleaned-split-csv-with-header-per-year-multiple-files/ride-fare/year=2019'\n", - "TBLPROPERTIES (\n", - " 'skip.header.line.count'='1', \n", - " 'transient_lastDdlTime'='1716908234'\n", - ");\n", - "\"\"\"\n", - "\n", - "# Athena database name\n", - "database = 'example-database-name'\n", - "\n", - "# S3 location for query results\n", - "s3_output_location = 's3://example-s3-bucket/example-s3-prefix'\n", - "\n", - "# Execute the query to create the 'ride_fare' table\n", - "run_athena_query(create_ride_fare_table, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "ebe5920a-4c36-48c0-9cb4-e418c738aa59", - "metadata": {}, - "source": [ - "### Create the ride fare table with the duplicates removed" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3d249cc5-2d53-4274-8f5e-6ab09ccd3ea6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: 15337c2c-54e5-4e19-94a8-92d2faef2efd\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'15337c2c-54e5-4e19-94a8-92d2faef2efd'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# SQL query to create a new table with duplicates removed\n", - "remove_duplicates_from_ride_fare = \"\"\"\n", - "CREATE TABLE ride_fare_deduped\n", - "AS\n", - "SELECT DISTINCT *\n", - "FROM ride_fare\n", - "\"\"\"\n", - "\n", - "# Run the preceding query\n", - "run_athena_query(remove_duplicates_from_ride_fare, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "2ac7fc34-37cb-4c46-993b-38f18576361c", - "metadata": {}, - "source": [ - "### Create the ride_info table" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2f9a68b9-bd11-49e9-ad72-b44b43d32e47", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: bc365d36-bbbb-4f33-a153-3192127a1069\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'bc365d36-bbbb-4f33-a153-3192127a1069'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# SQL query to create the ride_info table\n", - "create_ride_info_table_query = \"\"\"\n", - "CREATE EXTERNAL TABLE `ride_info` (\n", - " `ride_id` bigint, \n", - " `vendor_id` smallint, \n", - " `passenger_count` smallint, \n", - " `pickup_at` string, \n", - " `dropoff_at` string, \n", - " `trip_distance` float, \n", - " `rate_code_id` int, \n", - " `store_and_fwd_flag` string\n", - ")\n", - "ROW FORMAT DELIMITED \n", - " FIELDS TERMINATED BY ',' \n", - " LINES TERMINATED BY '\\n' \n", - "STORED AS INPUTFORMAT \n", - " 'org.apache.hadoop.mapred.TextInputFormat' \n", - "OUTPUTFORMAT \n", - " 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'\n", - "LOCATION\n", - " 's3://dsoaws/nyc-taxi-orig-cleaned-split-csv-with-header-per-year-multiple-files/ride-info/year=2019'\n", - "TBLPROPERTIES (\n", - " 'skip.header.line.count'='1', \n", - " 'transient_lastDdlTime'='1716907328'\n", - ");\n", - "\"\"\"\n", - "\n", - "# Run the query to create the ride_info table\n", - "run_athena_query(create_ride_info_table_query, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "4c17ea01-2c1e-4c10-a539-0d00e6e4bb1d", - "metadata": {}, - "source": [ - "### Create the ride info table with the duplicates removed" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "263d883c-f189-43c0-9fbd-1a45093984e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: 1946c89d-d1c3-449d-b7af-42521778c51c\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'1946c89d-d1c3-449d-b7af-42521778c51c'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# SQL query to create table with duplicates removed\n", - "remove_duplicates_from_ride_info = \"\"\"\n", - "CREATE TABLE ride_info_deduped\n", - "AS\n", - "SELECT DISTINCT *\n", - "FROM ride_info\n", - "\"\"\"\n", - "\n", - "# Run the query to create the table with the duplicates removed\n", - "run_athena_query(remove_duplicates_from_ride_info, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "a19f8e17-42c5-4412-96a8-b7bc1a74c73c", - "metadata": {}, - "source": [ - "### Run a test query on ride_info_deduped" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6db6bb67-44a9-4ff4-b662-ad969a84d3d8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: ab1e6968-e04c-47c0-94c7-03868d1d7fc1\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'ab1e6968-e04c-47c0-94c7-03868d1d7fc1'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_ride_info_query = '''\n", - "SELECT * FROM ride_info_deduped limit 10\n", - "'''\n", - "\n", - "run_athena_query(test_ride_info_query, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "b969d31f-e14a-473b-aefa-a1a19bc312f7", - "metadata": {}, - "source": [ - "### Run a test query on ride_fare_deduped" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "92d8be21-3f20-453d-8b84-516571d9854d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: caeedc97-8f55-4759-9380-8ced39fab414\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'caeedc97-8f55-4759-9380-8ced39fab414'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_ride_fare_query = '''\n", - "SELECT * FROM ride_fare_deduped limit 10\n", - "'''\n", - "\n", - "run_athena_query(test_ride_fare_query, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "c86acade-c4b9-4918-860e-11ee5e386a44", - "metadata": {}, - "source": [ - "### Define the `get_query_results` function\n", - "\n", - "In the following cell, we define the `get_query_results` function to get the query results in CSV format. The function gets the 36 character query execution ID string. The end of the output of the preceding cell is an example of a query execution ID string." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "50e87ba6-42e9-4d99-862e-7eae16ad810e", - "metadata": {}, - "outputs": [], - "source": [ - "import io\n", - "def get_query_results(query_execution_id):\n", - " athena_client = boto3.client('athena', region_name='us-east-1')\n", - " s3 = boto3.client('s3')\n", - "\n", - " # Get the query execution details\n", - " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", - " s3_location = query_execution['QueryExecution']['ResultConfiguration']['OutputLocation']\n", - "\n", - " # Extract bucket and key from S3 output location\n", - " bucket_name, key = s3_location.split('/', 2)[2].split('/', 1)\n", - "\n", - " # Get the CSV file location\n", - " obj = s3.get_object(Bucket=bucket_name, Key=key)\n", - " csv_data = obj['Body'].read().decode('utf-8')\n", - " csv_buffer = io.StringIO(csv_data)\n", - "\n", - " return csv_buffer" - ] - }, - { - "cell_type": "markdown", - "id": "d3d2ed4f-d7e6-49dc-9ea1-0dc66f252c76", - "metadata": {}, - "source": [ - "### Read `ride_info_deduped` test query into a dataframe\n", - "\n", - "Specify the query execution ID string in the `get_query_results` function. The output is the head of the dataframe. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "b04abae5-936b-4d96-98e8-d2e2b6a17b9c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amount
02834679627591152.00.00.512.286.1273.70
11400160739953152.02.50.511.050.0066.35
2283467962760027.00.00.50.000.007.80
3133144095039414.01.00.51.660.009.96
4283467962762414.50.00.51.060.006.36
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" - ], - "text/plain": [ - " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", - "0 2834679627591 1 52.0 0.0 0.5 12.28 \n", - "1 1400160739953 1 52.0 2.5 0.5 11.05 \n", - "2 2834679627600 2 7.0 0.0 0.5 0.00 \n", - "3 1331440950394 1 4.0 1.0 0.5 1.66 \n", - "4 2834679627624 1 4.5 0.0 0.5 1.06 \n", - "\n", - " tolls_amount total_amount \n", - "0 6.12 73.70 \n", - "1 0.00 66.35 \n", - "2 0.00 7.80 \n", - "3 0.00 9.96 \n", - "4 0.00 6.36 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "# Provide the query execution id of the test_ride_info query to get the query results\n", - "ride_info_sample = get_query_results('test_ride_info_query_execution_id')\n", - "\n", - "df_ride_info_sample = pd.read_csv(ride_info_sample)\n", - "\n", - "df_ride_info_sample.head()" - ] - }, - { - "cell_type": "markdown", - "id": "6d10ebe2-8c17-4f2b-97fe-a5f339cd89d7", - "metadata": {}, - "source": [ - "### Read `ride_fare_deduped` test query into a dataframe\n", - "\n", - "Specify the query execution ID string in the `get_query_results` function. The output is the head of the resulting dataframe. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "be89957f-31b1-4710-bfc2-178d6db18592", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amount
02834679627591152.00.00.512.286.1273.70
11400160739953152.02.50.511.050.0066.35
2283467962760027.00.00.50.000.007.80
3133144095039414.01.00.51.660.009.96
4283467962762414.50.00.51.060.006.36
\n", - "
" - ], - "text/plain": [ - " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", - "0 2834679627591 1 52.0 0.0 0.5 12.28 \n", - "1 1400160739953 1 52.0 2.5 0.5 11.05 \n", - "2 2834679627600 2 7.0 0.0 0.5 0.00 \n", - "3 1331440950394 1 4.0 1.0 0.5 1.66 \n", - "4 2834679627624 1 4.5 0.0 0.5 1.06 \n", - "\n", - " tolls_amount total_amount \n", - "0 6.12 73.70 \n", - "1 0.00 66.35 \n", - "2 0.00 7.80 \n", - "3 0.00 9.96 \n", - "4 0.00 6.36 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Provide the query execution id of the test_ride_fare query to get the query results\n", - "\n", - "ride_fare_sample = get_query_results('test_ride_fare_query_execution_id')\n", - "\n", - "df_ride_fare_sample = pd.read_csv(ride_fare_sample)\n", - "\n", - "df_ride_fare_sample.head()" - ] - }, - { - "cell_type": "markdown", - "id": "3867e94a-7c89-48ed-86aa-92b09d47740d", - "metadata": {}, - "source": [ - "### Join the deduplicated tables together" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b8a76635-3c09-4cbc-b1b4-9318dc611250", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: 8eb61f36-2e1b-43c7-9b33-61e7ce5d21bc\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'8eb61f36-2e1b-43c7-9b33-61e7ce5d21bc'" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# SQL query to join the tables into a single table containing all the data.\n", - "create_ride_joined_deduped = \"\"\"\n", - "CREATE TABLE combined_ride_data_deduped AS\n", - "SELECT \n", - " rfs.ride_id, \n", - " rfs.payment_type, \n", - " rfs.fare_amount, \n", - " rfs.extra, \n", - " rfs.mta_tax, \n", - " rfs.tip_amount, \n", - " rfs.tolls_amount, \n", - " rfs.total_amount,\n", - " ris.vendor_id, \n", - " ris.passenger_count, \n", - " ris.pickup_at, \n", - " ris.dropoff_at, \n", - " ris.trip_distance, \n", - " ris.rate_code_id, \n", - " ris.store_and_fwd_flag\n", - "FROM \n", - " ride_fare_deduped rfs\n", - "JOIN \n", - " ride_info_deduped ris\n", - "ON \n", - " rfs.ride_id = ris.ride_id;\n", - ";\n", - "\"\"\"\n", - "\n", - "# Run the query to create the ride_data_deduped table\n", - "run_athena_query(create_ride_joined_deduped, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "b2f9f6ca-f668-42ab-ac4a-371a82e1786d", - "metadata": {}, - "source": [ - "### Select all values from the deduplicated table" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "b0791e57-4351-4f27-a8f9-ad741441d214", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: f303cff8-5369-409a-9c51-8c791d446fe3\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. 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Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'f303cff8-5369-409a-9c51-8c791d446fe3'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# SQL query to select all values from the table and create the dataset that we're using for our analysis\n", - "ride_combined_full_table_query = \"\"\"\n", - "SELECT * FROM combined_ride_data_deduped\n", - "\"\"\"\n", - "\n", - "# Run the query to select all values from the combined_ride_data_deduped table\n", - "run_athena_query(ride_combined_full_table_query, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "4492eaa8-b0cc-4a4d-9810-e9f1a39f21c7", - "metadata": {}, - "source": [ - "### Define get_csv_file_location function and get Amazon S3 location of query results\n", - "\n", - "Specify the query ID from the preceding cell in the function call. The output is the Amazon S3 URI of the dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "97373c52-882b-4e44-8d75-a80d8d8c58df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'s3://ux360-nyc-taxi-dogfooding/f303cff8-5369-409a-9c51-8c791d446fe3.csv'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Function to get the Amazon S3 URI location of Amazon Athena select statements\n", - "def get_csv_file_location(query_execution_id):\n", - " athena_client = boto3.client('athena', region_name='us-east-1')\n", - " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", - " s3_location = query_execution['QueryExecution']['ResultConfiguration']['OutputLocation']\n", - "\n", - " return s3_location\n", - "\n", - "# Provide the 36 character string at the end of the output of the preceding cell as the query.\n", - "get_csv_file_location('ride_combined_full_table_query_execution_id')" - ] - }, - { - "cell_type": "markdown", - "id": "c7bf4f25-dc86-4f1f-95de-967c20c5a7af", - "metadata": {}, - "source": [ - "### Download the dataset and rename it\n", - "\n", - "Replace the example S3 path in the following cell with the output of the preceding cell. The second command renames the CSV file it downloads to `nyc-taxi-whole-dataset.csv`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "954022d5-bdf9-4dbd-be2e-66d0009ce522", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "download: s3://ux360-nyc-taxi-dogfooding/f303cff8-5369-409a-9c51-8c791d446fe3.csv to ./f303cff8-5369-409a-9c51-8c791d446fe3.csv\n", - "mv: cannot stat 'query-id.csv': No such file or directory\n" - ] - } - ], - "source": [ - "# Use the S3 URI location returned from the preceding cell to download the dataset and rename it.\n", - "!aws s3 cp s3://example-s3-bucket/ride_combined_full_table_query_execution_id.csv .\n", - "!mv ride_combined_full_table_query_execution_id.csv nyc-taxi-whole-dataset.csv" - ] - }, - { - "cell_type": "markdown", - "id": "4d34ca22-8417-46f5-982f-dd22816f1d93", - "metadata": {}, - "source": [ - "### Get a 20,000 row sample and some information about it" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "79d2f2a5-5111-4fb8-90f3-67474f1072c1", - "metadata": {}, - "outputs": [], - "source": [ - "sample_nyc_taxi_combined = pd.read_csv('nyc-taxi-whole-dataset.csv', nrows=20000)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "f9dececa-272d-458c-9f64-baa13eca0832", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset shape: (20000, 15)\n" - ] - } - ], - "source": [ - "print(\"Dataset shape: \", sample_nyc_taxi_combined.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "1c117a0f-429e-4913-aded-c839675f9e17", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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36013183913018.00.00.51.760.010.56212019-01-04T07:12:07.000Z2019-01-04T07:20:07.000Z1.681N
4139157156891215.00.00.51.660.09.96212019-02-05T11:14:36.000Z2019-02-05T11:19:52.000Z0.651N
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" - ], - "text/plain": [ - " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", - "0 60131839014 1 7.5 0.0 0.5 1.66 \n", - "1 60131839074 1 8.0 0.0 0.5 1.00 \n", - "2 1391571568740 1 8.5 0.0 0.5 2.36 \n", - "3 60131839130 1 8.0 0.0 0.5 1.76 \n", - "4 1391571568912 1 5.0 0.0 0.5 1.66 \n", - "\n", - " tolls_amount total_amount vendor_id passenger_count \\\n", - "0 0.0 9.96 2 1 \n", - "1 0.0 9.80 2 2 \n", - "2 0.0 14.16 2 2 \n", - "3 0.0 10.56 2 1 \n", - "4 0.0 9.96 2 1 \n", - "\n", - " pickup_at dropoff_at trip_distance \\\n", - "0 2019-01-04T07:53:41.000Z 2019-01-04T08:02:20.000Z 1.45 \n", - "1 2019-01-04T07:05:28.000Z 2019-01-04T07:13:12.000Z 1.91 \n", - "2 2019-02-05T10:59:56.000Z 2019-02-05T11:10:40.000Z 1.53 \n", - "3 2019-01-04T07:12:07.000Z 2019-01-04T07:20:07.000Z 1.68 \n", - "4 2019-02-05T11:14:36.000Z 2019-02-05T11:19:52.000Z 0.65 \n", - "\n", - " rate_code_id store_and_fwd_flag \n", - "0 1 N \n", - "1 1 N \n", - "2 1 N \n", - "3 1 N \n", - "4 1 N " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = sample_nyc_taxi_combined\n", - "\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "d3c56da9-0a1c-4c58-93e3-77260dfff40b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 20000 entries, 0 to 19999\n", - "Data columns (total 15 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 ride_id 20000 non-null int64 \n", - " 1 payment_type 20000 non-null int64 \n", - " 2 fare_amount 20000 non-null float64\n", - " 3 extra 20000 non-null float64\n", - " 4 mta_tax 20000 non-null float64\n", - " 5 tip_amount 20000 non-null float64\n", - " 6 tolls_amount 20000 non-null float64\n", - " 7 total_amount 20000 non-null float64\n", - " 8 vendor_id 20000 non-null int64 \n", - " 9 passenger_count 20000 non-null int64 \n", - " 10 pickup_at 20000 non-null object \n", - " 11 dropoff_at 20000 non-null object \n", - " 12 trip_distance 20000 non-null float64\n", - " 13 rate_code_id 20000 non-null int64 \n", - " 14 store_and_fwd_flag 20000 non-null object \n", - "dtypes: float64(7), int64(5), object(3)\n", - "memory usage: 2.3+ MB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "dc25bcd9-a4b1-4491-867f-7534336d1ecd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amountvendor_idpassenger_counttrip_distancerate_code_id
count2.000000e+0420000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.0000020000.0000020000.00000020000.000000
mean1.818963e+121.28870012.9201551.0605400.4960252.1283920.37697618.4721391.624401.568452.9285301.054400
std1.210592e+120.47640711.8908781.2307330.0509592.6013791.63952814.6649320.484291.215523.8417760.363108
min5.153977e+101.000000-74.500000-4.500000-0.5000000.0000000.000000-76.3000001.000000.000000.0000001.000000
25%1.005022e+121.0000006.5000000.0000000.5000000.0000000.00000010.7900001.000001.000000.9400001.000000
50%1.400160e+121.0000009.0000000.5000000.5000001.7950000.00000014.1600002.000001.000001.6000001.000000
75%2.834679e+122.00000014.5000002.5000000.5000002.8600000.00000019.8000002.000002.000003.0000001.000000
max3.839702e+124.000000300.0000007.0000000.50000052.16000030.500000312.9600002.000006.0000070.8900005.000000
\n", - "
" - ], - "text/plain": [ - " ride_id payment_type fare_amount extra mta_tax \\\n", - "count 2.000000e+04 20000.000000 20000.000000 20000.000000 20000.000000 \n", - "mean 1.818963e+12 1.288700 12.920155 1.060540 0.496025 \n", - "std 1.210592e+12 0.476407 11.890878 1.230733 0.050959 \n", - "min 5.153977e+10 1.000000 -74.500000 -4.500000 -0.500000 \n", - "25% 1.005022e+12 1.000000 6.500000 0.000000 0.500000 \n", - "50% 1.400160e+12 1.000000 9.000000 0.500000 0.500000 \n", - "75% 2.834679e+12 2.000000 14.500000 2.500000 0.500000 \n", - "max 3.839702e+12 4.000000 300.000000 7.000000 0.500000 \n", - "\n", - " tip_amount tolls_amount total_amount vendor_id passenger_count \\\n", - "count 20000.000000 20000.000000 20000.000000 20000.00000 20000.00000 \n", - "mean 2.128392 0.376976 18.472139 1.62440 1.56845 \n", - "std 2.601379 1.639528 14.664932 0.48429 1.21552 \n", - "min 0.000000 0.000000 -76.300000 1.00000 0.00000 \n", - "25% 0.000000 0.000000 10.790000 1.00000 1.00000 \n", - "50% 1.795000 0.000000 14.160000 2.00000 1.00000 \n", - "75% 2.860000 0.000000 19.800000 2.00000 2.00000 \n", - "max 52.160000 30.500000 312.960000 2.00000 6.00000 \n", - "\n", - " trip_distance rate_code_id \n", - "count 20000.000000 20000.000000 \n", - "mean 2.928530 1.054400 \n", - "std 3.841776 0.363108 \n", - "min 0.000000 1.000000 \n", - "25% 0.940000 1.000000 \n", - "50% 1.600000 1.000000 \n", - "75% 3.000000 1.000000 \n", - "max 70.890000 5.000000 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "18bd92b1-962a-40f2-b15f-7351d869f390", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "vendor_id\n", - "2 12488\n", - "1 7512\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['vendor_id'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e4c4997f-85d8-4f57-a60c-51e3568cfe2e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "passenger_count\n", - "1 14030\n", - "2 3040\n", - "3 857\n", - "5 850\n", - "6 487\n", - "4 379\n", - "0 357\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['passenger_count'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "ae527104-9312-498c-b0ee-d1e2303bf500", - "metadata": {}, - "source": [ - "### View the distribution of fare amount values" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "641c278d-8fed-42b8-98d1-becba90d6259", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot to find the distribution of ride fare values\n", - "import matplotlib.pyplot as plt\n", - "plt.hist(df['fare_amount'], edgecolor='black', bins=30, range=(0,100))\n", - "plt.xlabel('Fare Amount')\n", - "plt.ylabel('Count')\n", - "plt.show" - ] - }, - { - "cell_type": "markdown", - "id": "65d141c4-95ba-4176-8794-1475cb8f2a62", - "metadata": {}, - "source": [ - "### Make sure that all rows are unique" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "9d484f57-f150-45b5-9cc5-cc10a6e8e9f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "20000" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['ride_id'].nunique()" - ] - }, - { - "cell_type": "markdown", - "id": "abc60782-4411-46e0-9d31-55adaa4dd1f5", - "metadata": {}, - "source": [ - "### Drop the store_and_fwd flag\n", - "\n", - "Determining its relevance isn't in scope for this tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "f627790e-8aed-48e3-9c5d-52775bbb124d", - "metadata": {}, - "outputs": [], - "source": [ - "df.drop('store_and_fwd_flag', axis=1, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "id": "96fc51be-6a0f-44e6-abb8-2a6bf9188367", - "metadata": {}, - "source": [ - "### Drop the time series columns\n", - "\n", - "Analyzing the time series data also isn't in scope for this analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "c359f4db-b503-4d80-bb4c-55dc411f9b5e", - "metadata": {}, - "outputs": [], - "source": [ - "# We're dropping the time series columns to streamline the analysis.\n", - "time_series_columns_to_drop = ['pickup_at','dropoff_at']\n", - "df.drop(columns=time_series_columns_to_drop, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "id": "ad5d1df6-d418-483a-b06d-848205f3f8ed", - "metadata": {}, - "source": [ - "### Install seaborn and create scatterplots" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "05abe8af-bf44-471b-b130-19cee0dd822f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting seaborn\n", - " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", - "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.10/site-packages (from seaborn) (1.26.4)\n", - "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.10/site-packages (from seaborn) (2.1.4)\n", - "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.10/site-packages (from seaborn) (3.8.4)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", - "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n", - "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0)\n", - "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.3)\n", - "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", - "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", - "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: seaborn\n", - "Successfully installed seaborn-0.13.2\n" - ] - } - ], - "source": [ - "!pip install seaborn" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "b6a10b9b-e916-48a9-88f5-ae94db2f6576", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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88uSh+NHTY/FHv/IyRRByTI1vzVL3l1oJkH8ysXX2GUnSL0G+qPGtsb8A8iPpvlKNb439BYszOlGNkfFqHJ6cinWreqOytq8tc97RiWpcec8j8cC+kTnbhocqccelO8y9aUqdb81y7is6V2uyqpcAC5GJrbG/ID9O7DX7+1bEFedvjR2nro+IiNM29MfJ61bquVg0Nb519hlAfrivmC37a36epQSKTo1vzXL2l/uKlIF7xhSdXGyN/QUsh74hWUnPNxZb43uWNFooqZHxanz5hwfiyz88MO92hbAcjofg2ORUDKzujcoaIQjHqZUAAM0l0S+ZkwAA4DockDfzzVWzqEUj49WGD19EROzZN6JGQk44V+tOrJejE9V4/MC4638AAEt0Yq85UX0h7vzqYzN//so1r9ZfAQCl4b4ieeXYBGCx3FekaJby7z2yesYGACiu6YiIrqxHUXxZzTcs+gYtGJucarr98ALb6QxPHToS1+3eO6toDw9V4pZd22PL+tUZjgzyQa0EAGhuuf2SOQkAABGuwwH5kre5qhoJxeBcbSxvNRUAoIgOTlSbbi9rrwkAlJPrcOSVYxOAxZIZFIn7vQBAmvQayctqvtGdyneFDjWwqrfp9nULbKf4RieqcwIworY65/W798boAg8KQRmsXdl8Tdk1C2wHAOh0y5lbmpMAAHCc63BAXuRxruq+LhSDc3WuPNZUAICiGZ2oRvX5Y03fU8ZeEwAoL/cVySvHJgCL5b4iReF+LwCQJr1GOrKabyxp0beXvvSl8eyzz855/dChQ/HSl7502YOCvKqs7YvhoUrDbcNDlais7WvziGi3kfHqnAA8bs++kRgZF4JlIxPn6lvRHTu3bWy4bee2jdG3wpqzAJ1IJsLiLWduaU4CxSAXAWiHIlyHk4lQDnmcq7qvS97IxMacq3PlsaYCJE0uAmkbGa/Gw3//7LzXzl5V0l6T/JGJALSL+4rkVRGOTYB2k4mNua9IUbjfC8mRiQBz6TXSkdV8Y0lXvv7hH/4hXnjhhTmvHz16NP7xH/9x2YOCvBrs74tbdm2fc7IOD1Xi1l3bY7DfhYFONzY51XT74QW203lk4lyHjlTjXTu3zrn5tnPbxnjXzq0xekSzCNCJZCIs3nLmluYkUAxyEYB2KMJ1OJkI5ZDHuar7uuSNTGzMuTpXHmsqQNLkIpC2scmpuOvB/fNeO/vwW36hlL0m+SMTAWgX9xXJqyIcmwDtJhMbc1+RonC/F5IjEwHm0mukI6v5Rk8rb/6bv/mbmf//3//7f4/BwcGZP7/wwgvxla98Jc4444zEBgd5tGX96rjj0h0xMl6Nw5NTsW5Vb1TW9rkoUBIDq3qbbl+3wHY6h0yc39qVvXHpJ78RV5y/Na7YuTWOPn8sVvZ0xyNPHor33/NI/Jcrz896iAAkSCbC0ix1bmlOAvkmFwFopzxfh5OJUC55nau6r0seyMSFOVdny2tNBUiCXATaZWBVb0xUX4j33/NIw2tnkDWZCEC7ua9IXuX52ARoN5m4MPcVKQL3e2H5ZCLA/PQa6clivtHSom+XXHJJRER0dXXFZZddNmtbb29vnHHGGfHRj340scFBXg32uxBQVpW1fTE8VIk9+0bmbBseqkRlreOiLGTi/Cpr++Kc00+KO7/62JxtzhOAziMTYemWMrc0J4F8k4sAtFOer8PJRCiXPM9V3dclazJxcZyrdXmuqQDLJReBdnlxT3XitbPhoUr8xvlbMxoZ1MhEANrNfUXyKs/HJkC7ycTFcV+RvHO/F5ZPJgLMT6+RrnbPN7pbefOxY8fi2LFjcdppp8WBAwdm/nzs2LE4evRo/OhHP4qLL744rbECZG6wvy9u2bU9hocqs14fHqrErbu2u2BUIjJxfs4TgHKRidBeei3IN7kIQDvluTeUiVAuea5HkDWZSKvUVKCTyUWgXfRU5J1MBKDd8twfycVyy/OxCdBuMhE6g/4Glk8mAsxPr9FZuqanp6ezHkSejI2NxeDgYIyOjsbAwEDWwwFyanSiGiPj1Tg8ORXrVvVGZa3fEFAEanxrlru/nCcA+SUTW2efkTd6LUiOGt8a+wsgf5LqDdX41thfMJe5Kp1EnW+N/ZU8NRXICzW+NfYX5IueiiSp8a2zzwDyx33FbNhfC9O7A0WlxrfG/qJM9DeUkTrfGvsLWA69Rr4ttsb3LPUHfOUrX4mvfOUrM6ujvthdd9211G8LEVEvMGOTUzGwujcqaxQY8mWw3zFJnUxsbjoioivrUQDQDjIRWrOcua85CeSfXITic52aosh7bygTYWGdkjl5r0eQNZlIK9RUaF2n9FRlIReBdmjUU8kL8kYmApCFvD7fLxfJ67EJ0G4yEYovT/d7XROlyGQiFJ8cSkeeeg2WbkmLvn34wx+O3//9349zzjknNm/eHF1drqaRnKcOHYnrdu+NB/aNzLw2PFSJW3Ztjy3rV2c4MoC5ZGJjajlA+chEaI1+CTqbXITik9WQDJkIC5M5UA4yESBdeqpikYtAVuQFeSMTAWinvPdCcrG88n5sArSbTASSpNeiyGQiFJ8cgua6pqenp1v9os2bN8dHPvKR+LVf+7U0xpSpsbGxGBwcjNHR0RgYGMh6OKUzOlGNK+95ZFbRPm54qBJ3XLrDapPAkqVR42XiXGo5QP7JxNaZK5Ik/RLki1xsjUykDGQ1ZSUTWyMTSYLMgfxKus53ciZGyEUgW3qqdJkrtkYmQn7JC5ZLJrZOLgLkR9K9kFxsjUycnz4dKDqZ2BqZCO2l16LdPGvTGrlIp5NDlNlia3z3Ur55tVqN8847b8mDg/mMjFcbFu2IiD37RmJkvNrmEQE0JxPnUssBykkmwuLpl6DzyUUoNlkNyZGJ0JzMgfKQiQDp0VMVj1wEsiAvyCOZCEC7FKEXkovlVIRjE6DdZCKQFL0WRScTodjkECxsSYu+/fqv/3p89rOfTXosEGOTU023H15gO0C7ycS51HKAcpKJsHj6Jeh8chGKTVZDcmQiNCdzoDxkIkB69FTFIxeBLMgL8kgmAtAuReiF5GI5FeHYBGg3mQgkRa9F0clEKDY5BAvrWcoXTU5Oxn/8j/8xvvzlL8f27dujt7d31vbbbrstkcFRPgOreptuX7fAdoB2k4lzqeUA5SQTYfH0S9D55CIUm6yG5MhEaE7mQHnIRID06KmKRy4CWZAX5JFMBKBditALycVyKsKxCdBuMhFIil6LopOJUGxyCBa2pEXf9u7dGy9/+csjIuLRRx+dta2rq2vZg6K8Kmv7YnioEnv2jczZNjxUicravgxGBTA/mTiXWg5QTjIRFk+/BJ1PLkKxyWpIjkyE5mQOlIdMBEiPnqp45CKQBXlBHslEANqlCL2QXCynIhybAO0mE4Gk6LUoOpkIxSaHYGFd09PT01kPIk/GxsZicHAwRkdHY2BgIOvhlNJTh47E9bv3zirew0OVuHXX9ti8fnWGIwOKTo1vzXL2l1oOkG8ysXX2GUnTL0F+qPGtsb8oC1lNGanxrbG/SIrMgXxS51tjfwFZ01OlR41vjf0F+SYvWA41vnX2GUC+JNkLqfGtsb+a06cDRabGt8b+gvbTa9FO6nxr7C/KQA5RVout8T1tHBMsypb1q+OOS3fEyHg1Dk9OxbpVvVFZ2xeD/VbqBCgKtRwAoDn9EgDkm6wGoF1kDgDA8umpAFgMeQEAlJleiLxybAIApEevBUCW5BA0t6RF3y644ILo6uqad/tXv/rVJQ8IIiIG+xVqoBhk4vzUcoBykYnQOv0SdC65CJ1BVsPyyURYHJkDnU8mAqRPT1UcchHIkrwgT2QiAO2W515ILpZbno9NgHaTiUDS9FoUlUyEziCHYH5LWvTt5S9/+aw/T01Nxfe+97149NFH47LLLktiXABQCDIRAGpkIgDUyUUAqJGJAFAjEwGgTi4CQI1MBIA6uQgANTIRAGpkIgCdbkmLvt1+++0NX7/xxhtjfHx8WQMCgCKRiQBQIxMBoE4uAkCNTASAGpkIAHVyEQBqZCIA1MlFAKiRiQBQIxMB6HTdSX6zf/Nv/k3cddddSX5LACgkmQgANTIRAOrkIgDUyEQAqJGJAFAnFwGgRiYCQJ1cBIAamQgANTIRgE6R6KJv/+N//I9YtWpVkt8SAApJJgJAjUwEgDq5CAA1MhEAamQiANTJRQCokYkAUCcXAaBGJgJAjUwEoFP0LOWL3va2t8368/T0dDz99NPx7W9/Oz70oQ8lMjAAKAKZCAA1MhEA6uQiANTIRACokYkAUCcXAaBGJgJAnVwEgBqZCAA1MhGATrekRd8GBwdn/bm7uzt+7ud+Ln7/938/LrrookQGBnk2OlGNkfFqjE1OxcDq3qis6YvB/r6shwUdKe/nm0ycX94/OxbPZwkshkyE1v3T2GQcfK4aY5PPx8Dqnjipvy82DfhtM9AJ5CJ0BvNhiiLPx6pMbM6cAKA8ZGJzee5nyB/HCxSfXITOspRsludQIxMBaLc835uSi+WW52MToN1kYnOuK6XHvgXyRiYC0G7tvka1pEXfPv3pTyc9DiiMpw4diet2740H9o3MvDY8VIlbdm2PLetXZzgy6DxFON9kYmNF+OxYHJ8lsFgyEVrzxLPPxQ33fj8eeuzZmdfO37Yxbnrr2XHaxjUZjgxIglyE4jMfpijyfqzKxPmZEwCUi0ycX977GfLF8QKdQS5C51hKNstzqJOJALRT3u9NycXyyvuxCdBuMnF+riulx74F8kgmAtBOWVyj6pqenp5e6hd/5zvfiR/+8IfR1dUVZ555ZuzYsSPJsWVibGwsBgcHY3R0NAYGBrIeDjkzOlGNK+95ZNbE9bjhoUrccekOK5eXhFXr05fG+ZZmjZeJdWpl5/BZQueSia0zVyRJ/zQ2Gdf85fdmXQA67vxtG+Ojb395qr8BwHwGZpOLrZGJlMHoRDU+8J//Z/zLzQOx49T1cfT5Y7Gqd0V894mD8aOnx+KPfuVlspNcSPrajUxszXL21z+NTcYH/vJ78WBGcwIAFpZWLnZiJkYs776i3pvFcu8SsmGu2BrXTymTpfRy8pwik4mtW+4+82wDQHKSfl5NLrbGXHF+WT9L2U56G+hMMrE1y9lfriulx75tD70AZeBZm9aYK1IWMpAiyOr6ac9SBnvgwIH41V/91fj6178e69evj+np6RgdHY0LLrggPve5z8VLXvKSpXxbyL2R8WrDiWtExJ59IzEyXhUwJWDV+vYoyvkmE+cqymfHwnyWQCtkIizeweeqDS8ARUQ8+NizcfC5amoPKpnPQHvIRSi2Z5+rxq/+H6fFpx/aH3d+9bGZ13du2xjv2rk1nn3OfJh8KMK1G5nY2MGJasMF3yL+/znBRHpzAgCyIRMb03vTiiL0v8DiyEXoDEvp5eQ5zCYT5+fZBoBkZfm82mLJxXIqwrGZBL0N0AqZ2JjrSumxb9OnF4ClkYlQfDKQosjqGlX3Ur7oqquuirGxsfjBD34Q//zP/xwHDx6MRx99NMbGxuL9739/0mOE3BibnJr5//19K+LK126LT112TvzJv/7f467LXxnHpqczHB3tMDpRndNYRNQuXly/e2+MTlQzGlnnefH51sjhBba3i0yca2xyqmGNvPK126K/b0VuPjsWNnqkeU0bPeKzBOpkIize2OTzy9q+VOYz0D5yEYrt+WPT8emH9s+5afPQY8/Gpx/aHy8ccx2YfCjCNVSZ2NjhBXr+hbbDUoxOVOPxA+PxyBMH4/GfjpsDQpvJxMb03rSiCP0vsDhyETrDUnq5hfJ89MiUuSulIhMb82wDQPKyel6tFXKxnIpwbC6X3gZolUxszH2i9BR53xbhWRi9ACydTIRik4HpKkIfVCRZXaPqWcoX/bf/9t/iy1/+cvz8z//8zGtnnnlmfPzjH4+LLrooscFB3gys6o2I2oJvH7t0x5zfUPiqoUrcalXRjmbV+vY5fr7NZ90C29tFJs41uLq3YY3cuW1jfOzSHTGwOh+fHQvr72veKvb3rWjTSIAikImweAOrm2fsQtuXynwG2kcuQrEdOzY972/peeixZy08QW4U4RqqTGxs7crmPf9C26FVflsiZE8mNnZseoHe2y/e40WK0P8CiyMXoTMs5TrqQnk+OfVCvO0TD8/82dyVTicTG/NsA0DysnperRVysZyKcGwul94GaJVMbMx9ovQUdd8W5VkYvQAsnUyEYpOB6SlKH1QkA6sWuEa1wPal6l7KFx07dix6e+c26b29vXHs2LFlDwryqrK2L4aHKnHF+Vsb/obCB6wq2vGKvGp90Rw/3xoZHqpEZW0+mjiZONealT3z/hbXux/aH2v8g8XC6O7uip3bNjbctnPbxljR3dXmEQF5JhNh8QZW9cb582Ts+ds2LnjjdKnMZ6B95CIU20S1+W/hmai+0KaRQHNrV/U07SvXpnRzsRUysbEVXdH8uluX624kx29LhHyQiY09d7R5773QdsqlKM8QAAuTi9AZlnIdtVmen79tYzz897OfNzN3pdPJxMY82wCQvKyeV2uFXCynIhyby6W3AVolExtznyg9Rdy3RXoWRi8ASycTodhkYDqK1AcVyUlr+ppeozppTTo98ZIWfXvta18bv/mbvxlPPfXUzGv/+I//GL/1W78VF154YWKDg7wZ7O+LW3Ztj/NeunHe31B4fFVROlNRV60vouPn24kXjIaHKnHrru25WblXJs41Pvn8vDXywceejfFJ/zijKHq6u+JdO7fO+QeoO7dtjHft3GrRN2AWmQiLNzn1Qlw+T8ZevnNrTE6ls5CM+Qy0j1yEYhtc3fy60+BqmUk+PHf0+aZ9ZR4WSZGJjfWs6G563a1nhetuJGcxvy0RSJ9MbGxNX/NFahfaTrkU5RkCYGFyETrDUq6jzpfnrxqqxOU7t8ZdD+6f8zXmrnQymdiYZxsAkpfV82qtkIvlVIRjc7n0NkCrZGJj7hOlp4j7tkjPwugFYOlkIhSbDExHkfqgItk0sCpueuvZcxZ+O3/bxrjprWfHpoFVqfzcJT0deeedd8Yv//IvxxlnnBGnnnpqdHV1xRNPPBFnn312/Pmf/3nSY4Rc2bJ+dTwzeqTpe6wq2rmOr1q/p0EQ5nXV+iLbsn513HHpjhgZr8bhyalYt6o3Kmv7cnWhSCbOZeXlzrFxTV/c/MUfxo7TToordm6No88fi5U93fHIk4fi8998Iv7oV16W9RCBHJGJsHijR6bi/fc8Elecv3VOxr7/nkfis79+bio/13wG2kcuQrHJTIoiq76yFTKxMdfdaCfX7CEfZGJj3d1dsXNb41+6t3PbRr+AiDmK8AwBsDC5CJ1hqddRG+X5C9PTccnHH4qJauMFHcxd6VQysTH3aQCS574ieVWEY3O59DZAq2Ti/NwnSk/R9m2RnoXRC8DSyUQoNhmYjiL1QUVz2sY18dG3vzwOPleNscnnY2BVT5y0pi+1Bd8ilrjo26mnnhrf/e5347777ov/9b/+V0xPT8eZZ54Zr3vd65IeH+TSQr+h0Kqinev4qvXX7947q8HI86r1RTfYn9+LQxEysRErL3eOwf6++PAvnxXX794bd371sZnX1TygEZkIizewqjcmqi/MytcXS6tfMp+B9pGLUGwyk6LIqq9shUxszHU32sk1e8gHmdhYT3dXvGvn1oiIWQu/7dy2Md61c6tF32go788QAAuTi9AZlnMd9cQ8f/zA+LwLvkWYu9K5ZGJj7tMAJM99RfKqCMfmcultgFbJxObcJ0pPkfZtkZ6F0QvA0slEKDYZmI4i9UFFtGlgVaqLvJ1oSYu+Hff6178+Xv/61yc1FigMq4qWW9FWrac9ZGKdGtlZ1DygVTIRFpZlvyTbob3kIhSXzKQIinQdTibOpc7QLkWqFVAGMnG2jWv64uYv/jB2nHZSXLFzaxx9/lis7OmOR548FJ//5hPxR7/ysqyHCECK5CIUX1LXN8xdKTuZOJfrpwDJKlK/JRfLpUjH5nLobYClkIkwv6L1EHoBWB6ZCMUlA5NXtD6I5pa86Ns3v/nN+PrXvx4HDhyIY8eOzdp22223LXtgkGdWFaVIq9aTPpk4mxrZedQ8YLFkIixO1v2SbIf2kItQfDKTvMu6r1wsmTg/dYZ2KEqtgDKQiXMN9vfFh3/5rLh+996486uPzbyuRgF0PrkInSOJ6xvmrpSZTJyf66cAySlKvyUXy6cox2YS9DZAK2QiNFfEHkIvAEsjE6H4ZGCyitgHMb8lLfp20003xe/+7u/Gz/3cz8WmTZuiq6trZtuL/z90MquKAhEycT5qJED5yERojX4JOptcBKBd8t5XykTIh7zXCigDmTg/NQqgfOQi0Ii+kDKSiQC0U977LblYXnk/NgHaTSbC4ughoPPJRIDG9EGdY0mLvv2H//Af4q677orLL7884eFAsVhVFJCJ81MjAcpFJkLr9EvQueQiAO2U575SJkJ+5LlWQBnIxObUKIBykYvAfPSFlI1MBKDd8txvycVyy/OxCdBuMhEWTw8BnU0mAsxPH9QZupf0Rd3dsXPnzqTHAgCFIxMBoEYmAkCdXASAGpkIADUyEQDq5CIA1MhEAKiTiwBQIxMBoEYmAtDplrTo22/91m/Fxz/+8aTHAgCFIxMBoEYmAkCdXASAGpkIADUyEQDq5CIA1MhEAKiTiwBQIxMBoEYmAtDpepbyRddee2286U1vip/92Z+NM888M3p7e2dt/6u/+qtEBgcAeScTAaBGJgJAnVwEgBqZCAA1MhEA6uQiANTIRACok4sAUCMTAaBGJgLQ6Za06NtVV10VX/va1+KCCy6IjRs3RldXV9LjAoBCkIkAUCMTAaBOLgJAjUwEgBqZCAB1chEAamQiANTJRQCokYkAUCMTAeh0S1r07c/+7M9i9+7d8aY3vSnp8QBAochEAKiRiQBQJxcBoEYmAkCNTASAOrkIADUyEQDq5CIA1MhEAKiRiQB0uu6lfNGGDRviZ3/2Z5MeCwAUjkwEgBqZCAB1chEAamQiANTIRACok4sAUCMTAaBOLgJAjUwEgBqZCECnW9KibzfeeGP83u/9XkxMTCQ9HgAoFJkIADUyEQDq5CIA1MhEAKiRiQBQJxcBoEYmAkCdXASAGpkIADUyEYBO17OUL/rYxz4Wjz/+eGzatCnOOOOM6O3tnbX9u9/9biKDA4C8k4kAUCMTAaBOLgJAjUwEgBqZCAB1chEAamQiANTJRQCokYkAUCMTAeh0S1r07ZJLLkl4GABQTDIRAGpkIgDUyUUAqJGJAFAjEwGgTi4CQI1MBIA6uQgANTIRAGpkIgCdrmt6eno660HkydjYWAwODsbo6GgMDAxkPRwAEqTGt8b+Auhcanzr7DOAzqXGt8b+Auhcanxr7C+AzqbOt8b+Auhcanxr7C+AzqXGt84+A+hcanxr7C+AzqXGt8b+Auhs6nxr7C+AzrXYGt/dxjEBAAAAAAAAAAAAAAAAAAAAAAAAlE7PUr7ohRdeiNtvvz3+8i//Mp544omoVquztv/zP/9zIoMDgLyTiQBQIxMBoE4uAkCNTASAGpkIAHVyEQBqZCIA1MlFAKiRiQBQIxMB6HTdS/miD3/4w3HbbbfF29/+9hgdHY1rrrkm3va2t0V3d3fceOONCQ8RAPJLJgJAjUwEgDq5CAA1MhEAamQiANTJRQCokYkAUCcXAaBGJgJAjUwEoNMtadG3v/iLv4hPfvKTce2110ZPT09ceuml8ad/+qfx7//9v4+/+7u/S3qMAJBbMhEAamQiANTJRQCokYkAUCMTAaBOLgJAjUwEgDq5CAA1MhEAamQiAJ1uSYu+PfPMM3H22WdHRMTatWtjdHQ0IiIuvvji+Nu//dvkRgcAOScTAaBGJgJAnVwEgBqZCAA1MhEA6uQiANTIRACok4sAUCMTAaBGJgLQ6Za06NvP/MzPxNNPPx0REdu2bYsvfelLERHxrW99K1auXJnc6AAg52QiANTIRACok4sAUCMTAaBGJgJAnVwEgBqZCAB1chEAamQiANTIRAA63ZIWfXvrW98aX/nKVyIi4jd/8zfjQx/6UAwNDcW//bf/Nq644opEBwgAeSYTAaBGJgJAnVwEgBqZCAA1MhEA6uQiANTIRACok4sAUCMTAaBGJgLQ6bqmp6enl/tNvvGNb8RDDz0U27Zti7e85S1JjCszY2NjMTg4GKOjozEwMJD1cABIUDtqvEwEoAhkYuvkIkDnkoutkYkAnUsmtkYmAnS2tOt8J2VihFwE6GTmiq2RiQCdSya2Ti4CdC652BqZCNC5ZGJrZCJAZ/OsTWvkIkDnWmyN70nih5177rlx7rnnznn9TW96U/zpn/5pbN68OYkfAwC5JxMBoEYmAkCdXASAGpkIADUyEQDq5CIA1MhEAKiTiwBQIxMBoEYmAtBputP85nv27IkjR46k+SMAoBBkIgDUyEQAqJOLAFAjEwGgRiYCQJ1cBIAamQgAdXIRAGpkIgDUyEQAiqon6wFAUY1OVGNkvBpjk1MxsLo3Kmv6YrC/L+thQcdxrhXXP41NxsHnqjE2+XwMrO6Jk/r7YtPAqqyHBQCQG/olAMg316UoCsdqcfnsaAfHGQBF0EpeyTYAyFZaWSzjgROpCwDJU1vJK89SAkB76QvzyecCtELNoCgcqzA/i77BEjx16Ehct3tvPLBvZOa14aFK3LJre2xZvzrDkUFnca4V1xPPPhc33Pv9eOixZ2deO3/bxrjprWfHaRvXZDgyAIB80C8BQL65LkVROFaLy2dHOzjOACiCVvJKtgFAttLKYhkPnEhdAEie2kpeeZYSANpLX5hPPhegFWoGReFYhea6sx4AFM3oRHVOsERE7Nk3Etfv3hujE9WMRgadxblWXP80NjnnpltExIOPPRsfvPf78U9jkxmNDAAgH/RLAJBvrktRFI7V4vLZ0Q6OMwCKoJW8km0AkK20sljGAydSFwCSp7aSV56lBID20hfmk88FaIWaQVE4VmFhFn2DFo2MV+cEy3F79o3EyLhwgSQ414rr4HPVOTfdjnvwsWfj4HM+OwCg3PRLAJBvrktRFI7V4vLZ0Q6OMwCKoJW8km0AkK20sljGAydSFwCSp7aSV56lBID20hfmk88FaIWaQVE4VmFhFn2DFo1NTjXdfniB7cDiONeKa2zy+WVtBwDodPolAMg316UoCsdqcfnsaAfHGQBF0EpeyTYAyFZaWSzjgROpCwDJU1vJK89SAkB76QvzyecCtELNoCgcq7CwnjS/+Qc/+MHYsGFDmj8CUjM6UY2R8WqMTU7FwOreqKzpi8H+vhhY1dv069YtsB1YnE4718qUiQOreqK/b0Vccf7W2HHq+jj6/LFY1bsivvvEwbjrwf0xsCrV9gOAnCtTJsJ89EvAcXIR8mlgVW/TrC7adSk6VyddQy1bJnbSZ0d+Oc6SNd+9Y0ha2TIRWsmrMmWb3IEauQj5klYWL+f7ykzKomyZWKbeH6BdOukeeNlysdMt9KxkJz1Laf4CJE0mshRpzbnl3PK4FgLLU7ZMVDMoik66HgVp6V7qF37mM5+JnTt3xpYtW+LHP/5xRET88R//cXzhC1+Yec8NN9wQ69evX/Ygod2eOnQkrrznkbjwtvvjrX/ycFz40fvjqnseiacOHYnK2r4YHqo0/LrhoUpU1pqIQhKKdK7JxNk2rOmLT112TjzyxMF493/6drz3L74bV9z9rXjkiYPxqcvOiQ1r8vPZAZAsmQiLo1+CcpCLUFyVtX1x1+WvbJjVd13+ylxdl6LcinINVSbOVZTPjmJznCWn2b1jaIVMhLlayauyZJvcoSzkIhRPWlm81O8rM+kUMnGusvT+AO1UlHvgcrF8TlrTF+dv29hw2/nbNsZJHfIspfkL0CqZSFrSmHPLueVzLQTmJxPnUjMoiqJcj4IsLWnRt0984hNxzTXXxC/90i/FoUOH4oUXXoiIiPXr18cf//EfJzk+aLvRiWpct3tvPLBvZNbre/aNxPW798bk88fi/3rTz8enLjsn7rr8lXHla7dFf9+KGB6qxK27tlt9HBIy2N8Xt+zaPmfikbdzTSbOtbKnO/7ka4/FQ489O+v1hx57Nv7k64/Hyp4lrzkLQI7JRFg8/RJ0PrkIxffxrzbO6o9/7bGMRgRzFeEaqkxsbLC/L27dtT1uftvZ8anLzok/+df/e9x1+Svj5redHR/JyWdH8RWhRhTBQveORyeqGY2MopGJlNHoRDUePzAejzxxMB7/6XjDmtlKXpUh2+QOZSEXoViOZ/rfjzwXv/umM+Pmt50d/X0rZrYvN4uXkvEyk04hExsrQ+8PkIW83wOXi+W0aWBV3PTWs+NVJyz89qptG+Omt54dmwZWZTSy5Ji/AK2SibzYYu63tSLpObecS4ZrIdCYTGxMzaBI8n49Ck6UdP+9kJ6lfNEdd9wRn/zkJ+OSSy6JW265Zeb1c845J6699trEBgdZGBmvzplgHrdn30g8fmA83vmn35h57VVDlfji+18VJ/X3aoIgYVvWr447Lt0RI+PVODw5FetW9UZlbV+uzjWZONfIeDUeOKEBP+6BfSMxMl7N1WcIQDJkIiyefgk6n1yEYqtldeNrxLKavMn7NVSZOL/piPji3qdn1ZvhoUq8+n97SXaDouPkvUYUwUL3jvUFLJZMpGyeOnRkzj/wGB6qxC27tseW9atnvbeVvOr0bJM7lIVchOKYL9O/+P5XxdiRaqxZmUwWt5rxMpNOIRPn1xURbzx7c1x23hlx9PljsbKnOw4cPpr1sAAKqwj3wOViefWu6I5fOntLXL5z66zc713RGb881/wFaJVM5LhW7re1Isn7bXIuOZ1+HxSWQibOT82gCIpwPQpeLK3+u5klLfq2f//+2LFjx5zXV65cGc8999yyBwVZGpucarr90JHZ2x/YNxL//guPxh2Xzj0ngOUb7M/3JEMmzjV6pPmKtaNHmtdZAIpJJsLi6Zeg88lFKDZZTdHk+RqqTGxs5rfcPtb4t9zecemO3H6mFE+ea0QRLHTv+PAC2+E4mUiZzPQ6+xbf67SSV52cbXKHspCLUAzNMv34M7NJZnIrGS8z6RQysbHRiWr8ToP6E1H7xzWunwK0rgj9k1wspzLkfhHOPyBfZCIRS7vf1oqk7rfJuWR18n1QWAqZ2JyaQd7pEyiStPvv+SzpVx5s3bo1vve97815/b/+1/8aZ5555pIGsmfPnnjzm98cW7Zsia6urvjrv/7rWdunp6fjxhtvjC1btsTq1avjNa95TfzgBz+Y9Z6jR4/GVVddFZVKJdasWRNvectb4ic/+cmSxkN5Dazqbbp9Zc/c02bPvpF4emwyRiea/2NAoPPIxLn6+5qvKdvft6It4wCgvdLIxIji5yI0ol+CzmeuCMUmqyE55oqNLea33FJuoxPVePzAeDzyxMF4/Kfj7kFmaKF7x+sW2A7HycTm1L3OotdZOrlDWbh+CsXQaqa3s6eTmXQKc8XGzCkAkleE/slcsZzKkPtFOP+AfDFXJCKfGdno+p+cA9IkE6HY9AkUSVb995IWffvt3/7teN/73hef//znY3p6Or75zW/GH/7hH8YHP/jB+O3f/u0lDeS5556Ll73sZXHnnXc23P6Rj3wkbrvttrjzzjvjW9/6Vpxyyinx+te/Pg4fPjzznquvvjruvffe+NznPhcPPvhgjI+Px8UXXxwvvPDCksZEOVXW9sXwUKXhtp3bNsYjTx5quO3vf/pcXHXPI/HUoSMpjg7IG5k4V3d3V+zctrHhtp3bNsaK7q7UxwBA+6WRiRHFz0VoRL8Enc9cEYpNVkNyzBUb89vraOapQ0fiynseiQtvuz/e+icPx4Ufvd89yAw1u3c8PFSJylq/LZXFkYnzU/c6j15n6eQOZeH6KRRDK5ne7p5OZtIpzBUbM6cASF5lbV+8ap7+6VU56Z/MFcupDLlv/gK0ylyRiPxl5HzX/1b1dss5IDUyEYrNfJgiyar/7pqenp5eyhd+8pOfjD/4gz+IJ598MiIi/sW/+Bdx4403xrvf/e7lD6qrK+6999645JJLIqK2GuqWLVvi6quvjuuuuy4iaqufbtq0KW699dZ4z3veE6Ojo/GSl7wkPvOZz8Q73vGOiIh46qmn4tRTT40vfvGL8YY3vGFRP3tsbCwGBwdjdHQ0BgYGlv13oZieOnQkrt+9N/a8aCXGVw1V4rLzzoj33/NITFTnNlyfuuycePd/+nYMD1Xijkt3xGC/kIG8SavGy8TZHjtwOP7h2Yn49EP746HHnp15fee2jfGunVvjjI39se3kdYv+fgAkr4iZGFHMXIRG9EuQL0XMRZkI6ZLVlFURMzEiu1xczv56/MB4XHjb/fNu/8o1r46fPXltS9+TzjA6UY0r73mk4W+Lcw8yO43uHQ8PVeLWXdtj8/rVGY6MNKWRi52aiRFL31/qXmfS6yyP3CFvijhXLGImQh4tNtOz6ulkJu1WxEyMKGYumlMAJG90oho/fOZw3PHVfXPugV/12qH4+VPWtdSzFTEXi5iJZbDvnw7H62/fM+/2+35rOIY2Ff/5DPMX6FxFzMSIYj5rUzZ5mhs3u/73+p8/OX7vzb8QH7z3+3IO8KyNuSLMYT5MUSR9jWqxNb6npVFGxPPPPx9/8Rd/EW9+85vjN37jN2JkZCSOHTsWJ598cqvfatH2798fzzzzTFx00UUzr61cuTJe/epXx8MPPxzvec974jvf+U5MTU3Nes+WLVvirLPOiocffnjecDx69GgcPXp05s9jY2Op/T0oji3rV8cdl+6IkfFqHJ6cinWremPtqp743Xu/33DBt53bNsYjTx6KiIg9+0ZiZLzqwWMoAZnYWG93d3z2Gz+OHaedFFfs3BpHnz8WK3u645EnD8Vnv/Hj+L2Lf2FJ3xeA/MoiEyOKkYvQiH4JOpu5IhSfrIZkdNpcMclMPP7b6/bM84+h/fa68hoZrzZ8SDbCPcgsNbp3XFnb57Ng0TotEyOSy0V1rzPpdZZH7tDpXD+F4lhspmfV08lMis5ccX7mFADJGxmvxhV3fyuuOH/rnHvgV9z9rfgvV56faR9lrlhefSu6Y+e2jbMWIzxu57aN0beiO4NRJc/8BVisTpsrysSly9PcuNn1v/t+eCBu+KWfl3NA4jotEyPkIuVkPkxRZHWNquVF33p6euLf/bt/Fz/84Q8jIqJSqSQ+qBM988wzERGxadOmWa9v2rQpfvzjH8+8p6+vL0466aQ57zn+9Y3cfPPN8eEPfzjhEdMJBvvnhsWHf/msOPp8bSXR/r4VccX5W+P/fOnGWNHVFUemXogrX7st7npwfxyenMpo1EA7ycTGDh2pxq/94hnx9OiRmde6urpiy+CqeMVpJ8XokWpErFn2zwEgP7LIxIhi5CI0ol+CzmauCMV36Eg13nnu6fHph/bHnV99bOb1nds2xrt2bpXV5M7oRDVGxqsxNjkVA6t7o7ImHzfDO22umGQmDvb3xS27ts/72+vy8PmRjbEF7jG24x5kXmtK1hrdO4bF6rRMjEguF8cmp2aevdhx6vo4+vyxWNW7Ir77xEHPXhSYXmf55A6dzPVTKI7FZvqJc9kT+7vq8y/E6EQ6C7/JTIrMXHF+5hQAyWt0/6Grq2vm/2d9Hc5csbwOHanGr5//0njT2Ztj08CqmWvEz4weic2Dqzvq+QzzF2AxOm2uKBOX7vjc+Pe+8Gj83OaBmWttJ/X3xmkb+tuaKQs9yzJ2ZCpe+pK1cg5IVKdlYoRcpLzMhymCrK5RtbzoW0TEueeeG4888kicfvrpSY+nqRdfUI6ImJ6envPaiRZ6zw033BDXXHPNzJ/Hxsbi1FNPXd5A6VjHVxJ9ZmwyIiL+4P/+f+b8o7+PXbojBlb3ZjVEoM1k4lzrVvXGs89V44vffzoefNFqtq/atjHedf7WWLtKjQToRFllYkS+cxEa0S9B5zNXhGJbu7I3Lv3kNxr+lvP33/NI/Jcrz896iDDjqUNH4rrde2f9NtXhoUrcsmt7bFm/OsOR1XTSXDHpTPTb62hkYIH54LqU54t5rylQZJ2UiRHJ5eLg6t742KU7Gi647NmLYtPrAM24fgrFsZhMf/Fctr9vRcP+ztwSGjNXnJ85BUCyinAdzlyxnDxLCTBXJ80VZeLybFm/On7vzb8QN/zV3kyvtWX9LAtQXp2UiRFyESDPsrpGtaRF39773vfGBz7wgfjJT34Sr3jFK2LNmtmr0W3fvj2RwR13yimnRERt1dPNmzfPvH7gwIGZVVJPOeWUqFarcfDgwVmroh44cCDOO++8eb/3ypUrY+XKlYmOl8422N8Xk88fi2v+8nvx0ItO1oiIhx57Nroi4qNvf3kmYwPaTybOtWZlT3zqwf1zauQDjz0b06FGAnSqdmdiRDFyERrRL0HnM1eEYqus7YtzTj9p1oNSxw0PVaKy1j8oIh9GJ6pzFmeKiNizbySu37037rh0R+b/AK6T5oppZKLfXseJKmv7YnioEntOOK8j0s+gItQUKLJOysSIZO8rfvqhudfJPHvRGfQ6wHxcP4ViWSjTXzyXveL8rQ37O3NLaMxcsTlzCoDkFOE6nLliOXmWEmCuTporysTlGZ2oxg33fj8eyPhaW5bPsgDl1kmZGCEXAfIsq2tU3Uv5one84x2xf//+eP/73x87d+6Ml7/85bFjx46Z/03a1q1b45RTTon77rtv5rVqtRr333//TPC94hWviN7e3lnvefrpp+PRRx9tGo6wFOOTz885WY978LFnY3zy+TaPCMiKTJxLjQQop3ZnYkQxchEa0S9B5zNXhGIb7O+LW3Ztj+GhyqzXh4cqceuu7f6BEbkxMl6dszjTcXv2jcTIeLXNI5rLXBFak2UGFaGmQJHJxMZcJwMoJ9dPobO8eC6749T18/Z35pYwl7kiAO1ShOtw5orlVIRjE6DdzBU5Li/PcXieEsiKTASgXbK6RtWzlC/av39/0uOI8fHxeOyxx2b9jO9973uxYcOGOO200+Lqq6+Om266KYaGhmJoaChuuumm6O/vj3e+850RETE4OBjvfve74wMf+EBs3LgxNmzYENdee22cffbZ8brXvS7x8VJuY5NTTbcfXmA70Dlk4lxqJEA5pZGJEcXPRWhEvwSdz1wRim/L+tVxx6U7YmS8Gocnp2Ldqt6orO3zgBK5UoS+0lwRWpdVBhWhpkCRycTG1B6AcnL9FDrP8bns/3tgvOn79Hcwm7kiAO1ShOtw5orlVIRjE6DdzBU5Lk856XlKIAsyEYB2yar3XtKib6effnrS44hvf/vbccEFF8z8+ZprromIiMsuuyzuvvvu+J3f+Z04cuRIvPe9742DBw/GueeeG1/60pdi3bp1M19z++23R09PT7z97W+PI0eOxIUXXhh33313rFixIvHxUm4Dq3qbbl+3wHagc8jEudRIgHJKIxMjip+L0Ih+CTqfuSJ0hsF+DyWRb0XoK80VYWmyyKAi1BQoMpnYmNoDUE6un0JnGuzviw0LzGX1dzCbuSIA7VKE63DmiuVUhGMToN3MFTkubznpeUqg3WQiAO3y/7H354FtlXe++P/WvliWbaQkTWgMIjKErJgCZZCcEtp+B0qApBl6m3Y6JKbtTCGkd7YSKGELZWnpTCeB/u7tDAbm3pLOvU0TSsp07tDQwXY7LMXTLGwWmDg0IUGKJVnr0fb7w5FjWUfneNFyjvR+/ZX4WNZjSzqfZ/k8n6dWfW9NLpfLzfTBb7zxBoaHhyEIQsHXr7/++lk3rFbC4TBaWloQCoVgt9tr3RxSqFBMwG27BvDSoL/o2qoOJ3Zu6OTglUiBKnmPZ0w8g/dIIiLlY0ycPo4VqZzYXyJSFsbF6WFMJCJSjnL3KxkTp4cxkeoNx6pEhSp1n6/HmAhwXZGIqJ5xrDg9HCtSo2P/juoZY+L0MS4SESkH1xVrizGxNI4hiEjtGBOnhzFxehgniUhtmGszPYyLRETKUav5U/1MGvvee+9h3bp1OHjwIDQaDfJ14zQaDQAgk8nM5McSqUaL1YiH16/A1t0HCj60qzqceGT9Cg6UiRoIY2Ix3iOJiBoTYyLR1LG/RFT/GBeJiKga1NCvZEwkUg813FOI1IwxURzvPUREjYlxkah+sX9HND2MiUREVC1q6KcxLjYmNbw3iYiqjTGR8hgniajRMSYSEVG11KrvPaOib9/85jfhcrnwwgsv4LzzzsMrr7yCQCCAv/7rv8ajjz5a7jYSKdKCVgt2buiEPyJgNJFCs9kAp83IgTJRg2FMFMd7JBFR42FMJJoe9peI6hvjIhERVYvS+5WMiUTqovR7CpGaMSaWxnsPEVHjYVwkqm/s3xFNHWMiERFVk9L7aYyLjUvp700iompjTKSJGCeJqJExJhIRUTXVou89o6Jvv/3tb7F//37MmTMHWq0WWq0WXq8XDz30ELZs2YKBgYFyt5NIkVqsHBwTNTrGxNJ4jyQiaiyMiUTTx/4SUf1iXCQiompScr+SMZFIfZR8TyFSM8ZEabz3EBE1FsZFovrH/h3R1DAmEhFRtSm5n8a42NiU/N4kIqo2xkSajHGSiBoVYyIREVVbtfve2pk8KJPJwGazAQCcTieOHTsGADjnnHPw9ttvl691RERECseYSERENIYxkYiI6AzGRSIiojGMiURERGMYE4mIiM5gXCQiIhrDmEhERHQG4yIREdEYxkQiIqIxjIlERFTv9DN50LJly3DgwAGcd955+OQnP4nvfve7MBqN+NGPfoTzzjuv3G0kIiJSLMZEIiKiMYyJREREZzAuEhERjWFMJCIiGsOYSEREdAbjIhER0RjGRCIiojMYF4mIiMYwJhIREY1hTCQionqnneo3HjhwANlsFgBw1113IZfLAQAeeOABHDlyBF1dXXj++eexY8eOyrSUiIhIIRgTiYiIxjAmEhERncG4SERENIYxkYiIaAxjIhER0RmMi0RERGMYE4mIiM5gXCQiIhrDmEhERDSGMZGIiBqJJpePdDJ0Oh2OHz+OuXPn4rzzzsOrr74Kh8Mxfv3UqVNoa2uDRqOpWGOrIRwOo6WlBaFQCHa7vdbNoRoKxQT4IwLCiRTsFgOcTUa0WI21bhZRXav0565c93jGxKnhfbR+8LUkqj+MidPHsSJVQq1iLGM7USHGxelhTKRGwphJalGu9ypj4vSU4+91IpzASFRAOJGG3aJHm9WIeXZzmVtKREQzUY77fKPERIDrikSVxM8H1RrHitPD+VNSotnEEsYhojMYE6ePY0UiIuXhumJtcKworxHifiP8jkSNiDFxehgT1UkuhjHGEVEec22mh3GRGgX7CpXBv6uyTfUer5/qD2xtbcXQ0BDmzp2L999/f7xCat5ZZ50189YSKcyxYBy37z6A3kH/+NdWdTjx8PoVWNBqqWHLiOqXmj53jIny1PR6kjS+lkQkhTGRaOZqFWMZ24kqh3GRqL4wZpJaKPG9ypg4NcOBKO7YcxD9vsD417xuBx5ctxztjqYatoyIiMqFMXFqlNifIVIKfj6onjAuEtXGbGIJ4xBRZTAmTg3vQURE5afEeyvjIgHKfG+WWyP8jkQ0O4yJpFRyMYwxjojKjTGRqL6wr1AZ/LvWjykXfVu/fj0+9alPYf78+dBoNLjkkkug0+lEv/e9994rWwOJqi0UE4pucADw0qAfW3cfwM4NnaxwSVRmavvcMSZKU9vrSaXxtSQiOYyJRDNTqxjL2E5UWYyLRPWDMZPUQqnvVcZEeSfCiaKCbwDQ5wvgzj0H8f0vXIR5dnONWkfVxJP2iOobY6I8pfZnlIAxgvj5oHrDuEhUfbOJJVN5LAD2V4hmgDFRHvvCRETlp9R7K+MiKfW9WU6N8DsS0ewxJpISycWw7924cloxjuufRDQVjIlE9YPj4crg37W+TLno249+9CN8/vOfh8/nw5YtW/C1r30Nzc3NlWwbUU34I0LRDS7vpUE//BGBNzmiMlPb544xUZraXk8qja8lEclhTCSamVrFWMZ2ospiXCSqH4yZpBZKfa8yJsobiQpFBd/y+nwBjEQFFn1rADxpj6j+MSbKU2p/ptYYIwjg54PqD+MiUfXNJpbIPfbDcAIP/OJN9leIZoAxUR77wkRE5afUeyvjIin1vVlOjfA7EtHsMSaSEsnFsJHo1GMc1z+JaKoYE4nqB8fDlcG/a32ZctE3ALj66qsBAL/73e/wzW9+kwGS6lI4kZK8PipznYimLxQXZK4r73PHmFiaGl9PEseYSERTwZhINH21irHspxFVHuMiUX3geJjUQsn9O8ZEaeFEelbXSf140h5R42BMlMa+dzHGCMrj54PqEeMiUXXNJpbIPfaDkTj7K0SzwJgoTclz30REaqXkeQbGxcbWCHFfyZ8/IlIWxkRSGrkYJpfflI9xXP8kouliTCSqDxwPV0YjzKU0kmkVfct78skny90OogKhmAB/REA4kYLdYoCzyVi1QZvdbJC83jzhei3bSVRPrEbpcGQ16qrUkuljTCxmNephNerQ7XWhc2ErkukszAYdXh8eQU/fkKJfTyo0nZhIRMSYSDR1drNBsr9UqRir5n43kdowLhKpW61iNdF0qaF/x5gozm6Wfu3krtca18dmr55O2uP7gWhqGBPFcS2qWD3FiEpplNjDzwfVM8ZFouqYTSyReqzVqMOcZhOeuOmSorlT9leIpocxUZwa5r6JiNRGDWvgjIuNqRH2nnCej4imizGxspS0zqSktoiRi2Fy+U35GMf1TyKaKcZEInXjeLgyuIZSWSfCCYxEBYQTadgterRZjZhnN1fs+ZS9Y4Aa0rFgvKhq96oOJx5evwILWi0Vf36nzYhVHU68JDKIXNXhhNNmVEQ7ieqJVquBx+1Avy9QdM3jdkCn1dSgVTRTep0GPTddip0vDuKx/b7xr3vcDvTcdCn0Or6eajHVmEhERETT47QZ0bPxUuzcL9Jf2nhpxWIs+91ERERTU6tYTTRd7N+pV1uTEV63A30ir53X7UBbk3LvM1wfK496OcGQ7wcimi2nzYiuDqdokn9Xg65F1UuMqJRGij1cqyUiotmaTSwxG7SicxdWow49Gy/Fd3/5VsE1j9uBHRs6sWXXQMP3V4ho9nRaDbrcTvT6RMaKbifnvomIZoBr4KRUjbD3hPN8RETKoaR1JiW1pRS5GNbWNLUYx/VPIiKixsTxcGVw/0DlDAeiuGPPwYK/rdftwIPrlqPd0VSR59RW5KcSzVAoJhQNVIGxat1bdx9AKCZUvA0tViMeXr8CqzqcBV9f1eHEI+tXoMVqVEQ7ieqJXqvBJo8LHrej4OsetwObPC52LlTGpNPi8RcHizqL/b4AHn/RB5OO3Q+1mEpMJCIioulLpLN4fH+J/tJ+HxLpbEWel/1uIiKiqalVrCaaLvbv1Gue3YwH1y2Hd9Jrl18YruSJYLPB9bHyqYcTDPl+IKJyuXW1W7Q/c+tqd41aVFv1ECMqpdFiD9dqiYhotmYaS0IxAff8/DA2isw7bbv2Qjy+f7CoGFy/L4An+4fQ7XU1dH+FiMrDpNPiltWLRMeKt6x2MweViGiGHt/vK5nfT1QrjbD3hPN8RETKoKR1JiW1RYpcDJtnN08pxnH9k4iIqDFxPFwZ3D9QGSfCiaKCbwDQ5wvgzj0HcSKcqMjz6ivyU4lmyB8RRE9wBsYGrP6IUJWb94JWC3Zu6IQ/ImA0kUKz2QCnzTj+3EppJ1G9cDQZ8dDzb6KzvQ3dHheS6SxMei0GjgbxL68M49EbV9a6iTQNUSGDXpHqwADQ6/MjKmSq3CKaDbmYSERERNM3EhUk+0sjUaEiRR7Y7yYiIpqaWsVqouli/07d2h1N+P4XLsJIVEA4kYbdrEdbk1HR9xeuj5VPPZxgyPcDEZWDPyKg+6lX0e11FfVnup96Fc9t9jbcvaQeYkSlNGLs4VotERHN1kxiiT8i4IU3T+I37waK+mktFgN69xwSfVy/L4Bbr3Q3dH+FiMojmcni5qdfEx0r3vz0q3j2Vk+tm0hEpDr+iIBen/i8Sm+dzquQOjTK3hPO8xER1Z6S1pmU1BY5cjFsKjGO659ERESNi+Ph8uP+gcoYiQpFBd/y+nyBiu0hYtE3UpRwIiV5fVTmejm1WEsHCyW1k2ojFBPgjwgIJ1KwWwxwNrFzMRstViPuu2EZtu4+gMf2nzkpipV61SmcSMNq1KHb60LnwlYk01mYDTq8PjyCnr4hhBPpWjeRpkkqJhIREdH01aq/xH43ERHR1MjFYs5tkFK0WI24/4Zl+PU7H41/TaPR4OxWC/70snb271Rgnt087QXgWq5PcH2sfPInGG7dfaAgqVVN4zO+H4ioHMTuJRrNmdNGG/FeUg8xolIaNfZwrZaIiGZq8hyCy9k0pZiSj7kxIVOwpggAP/zyxZKPNRm0jFtENGuRZFr0HpQXTXKdhohousKJlGS+Wr3Oq5DyNdLeE87zERHVlpLWmZTUlqmQi2FTuV7p9U/u9yaiRsR7H6kFx8Plxf2hlVGrPUQs+kaKYjcbJK83y1yvFrW0kyrjWDCO23cfKKimv6rDiYfXr8CCVksNW6ZurNRbP1oseuzY0Ikn+4cKOosetwM7NnSixcLuBxERETW2WvaX2O8mIiKSZzdLx2K560TVlAPw/IHj6PUVzld/6vw5tWsUVUyt1ye4PlZeah+f8f1AROXQYjFIzpPZLY15L1F7jKgUxh4iIqKpm80cglTMNem1ko9ttTR2f4WIyoN9fyKi8uM8HCkV954QEVG1KGmsqaS2VEsl1z9rnU9FRFQLvPcRNTbmlpVfrfYQSa++E1WZ02bEqg6n6LVVHU44bcq4yailnVR+oZhQ1AkGgJcG/di6+wBCMaFGLasPLVYjFs214aL2Niyaa2PHQqWazQY82T+Efl+g4Ov9vgCe6h+qy4lHIiIioumodX+J/W4iIiJpbU1GeN0O0WtetwNtTYydpAzj89U+zlc3AiWsT3B9rPzUPD7j+4GIyqHJpJecJ2syNe6GPjXHiEph7CEiIpqa2c4hSMXck6NJxmMiqjj2/YmIyo/zcKRUtc6lJCKixqGksaaS2lJNlVj/VEI+FRFRtfHeR0QAc8vKrVZ7iFj0jRSlxWrEw+tXFA1YV3U48cj6FYq50ailnVR+/ohQ1AnOe2nQD3+EHWGiuJApWnTL6/MFEBcyVW4RERERkbKwv0RERKRs8+xmPLhuedGijdftwIPrlmOe3VyjlhEV4nx1Y1HC6831MZqI7wciKodIIi05TxZJpKvcIlIyxh4iIqKpme0cglTMXX3+HMZjIqo49v2JiMqP83CkVMylJCKialHSWFNJbVE7JeRTERFVG+99RETlV6s9RDyKgxSnyajD9huWISqkERMyaLEYMLfZpLiB6oJWC3Zu6IQ/ImA0kUKz2QCnzai4dlJ5hRMpyeujMteJGgE/J0RERETSytFfCsUE+CMCwokU7BYDnE0cjxIREZVTu6MJ3/2TlQjFU+Pzvy0WAxa0WmrdNKJxnIdrLEp5vbk+phxKGBfy/UBEs6WU+EbqwdjTGJTQzyEiUrNy9LHkYu5M4jHv70Q0Hez7ExGVF+fhSKn43qw/HPsRkZIpaayppLYo0VTjCfsSRNSIeO8jteE4kdSi3dGE73/hIoxEBYQTadjNerQ1GStW8A1g0TdSmGPBOG7ffaCguuyqDiceXr8CLdYaNmyCoqBiM2LRXFutm0VVYjcbJK83y1wnagT8nBARERFJm21/SWrszEI0RERE5cF4S2rAebjGoqTXu8XKhIupqlSyipLiFN8PRDQbSopvSsOEx9IYe+qbkvo5RERqVa4+1sR4G06kAM2Zr083HvP+TkQzwb4/EVH5cB6OlIrvzfrCsR8RqYGSxppKaouS/GEkhiOBGILxFMwGHX711km8fTyM+25YVhRP2JcgokbEex+pCceJlcPcssqYZzdXtMjbZNqqPRORjFBMKLphA8BLg35s3X0AoZhQo5adcSwYx+ZdA/j03/0H1v3wN/j09/8Dt+0awLFgvNZNoypx2oxY1eEUvbaqwwmnjYGQyGbWo8st/jnpcjthM7PmLBERETU2p82IrhLjii6ZcYUaxs5ERERqx3hLajGbfiWpD9cn1KdS64qMU0RUTxjfxDE3hRoV+zlEROVRrj5WufokvL8TERER1R7z+0mpuOZdPzj2IyKicvjgVAzf2n0AX/qnl3HLj19H91OvYmB4BP/tsnbc8+yhonjC9WYiakS895FacJxYOcwtqx8s+kaK4Y8IRTfsvJcG/fBHqnvTDsUEvHsygoHhEbz7UQQnwgkGFUKL1YiH168o6gyv6nDikfUrWP2UCEBSyOCW1YvgcTsKvu5xO3DLajeSQqZGLSMiIiJSjltXu0X7S7eudks+TmljZyIionrEeEtqMtN+JakP1yfUpdzJKhPXLY+HE1i5sBVWo67o+xiniEhtGN+KMeGRamFyjlSt3mccjxMRlUc5+liz7ZNwHEtERESkLMzvJyVrlDVvpczBVQrn9oiIlEWNcScUE3DHzw6g3xco+Hq/L4An+4dwwXx7UTzhejMRNSLe+0gtOE6sDOaWVVa1+9E8ioMUI5xISV4flbleTseC8aIb3TNf/aRsUGEnqDEsaLVg54ZO+CMCRhMpNJsNcNqMfP2JTosIadz89Gvo9rrQ7XEhmc7CpNdi4GgQNz/9Kn76F39U6yYSERER1ZQ/IqD7qVdF+0vdT72K5zZ7S44vlDR2JiIiqleMt6QWs+lXkjpxfUI9ppKsMtXXTWzd0uN2YMeGTmzZNYDYpI1YjFNEpDaMb4XKGUOIpkKsr7Gqw4mH16/AglZLVdvC8TgRUfnMto81mz4Jx7FEREREysP8flKqRlnzVtIcXKVwbo+ISDnUGnf8EQG9kwq+5fX7Auj2uETjCdebiagR8d5HasBxYmUwt6xyatGPZtE3Ugy72SB5vVnmermUqmwZjDOo0BkTA104kQI0xV8nalQxIYOYkMFj+30lr5O6hGIC/BEB4UQKdosBziYO/omIiGYjFBck+0shifGnUsbORERE9cxuNsBq1KHb60LnwlYk01mYDTq8PjyCnr4hxltSjNn0K0kZZjLv1mLl3JwalCtZpdS6Zf5U426vq+gewDhFRGrE+HYGEx7lce2yfORO/925obOqf1vOfxMRlZdYH2uqcXSmfRKOY4mIiIiUifn9pFThREryvVkP86FKm4OrFM7tEREpg5LiznTX9OTmJJPpbMl4wvVmImpEvPeR0nFPRmUwt6wyatWPZtE3UgynzYhVHU68JFJVclWHE05bdTodpSpbmvRayccxqDQWtVa7J6oGm0m6eyF3nZSF9zsiIqLysxql+0NWo67kNaWMnYmIiOqZ02ZEz8ZLsXP/YEFiscftQM/GSxlvSTFm06+k2uO8W30r16YGqRP58qcYT8RxIRGR+nFjnDT2ocpLaaf/cv6biKiyphNHZ9on4TiWiIiISJmY309K1QjzoUqbg6sUzu0RESmDUuLOTNb05PoFrRYD4wkREZGKcE9GZTTCXEot1KofLV3FiqiKWqxGPLx+BVZ1OAu+vqrDiUfWr6jaBGapypYDR4PwuB2i1zj52FjkqnSGYkKNWkakDFqtpuT90uN2QKfVVLlFNFO83xEREVWGVgPJ/pJWU7q/pJSxMxERUb17fL8P/b5Awdf6fQE8/qL46dJEtcB5OPXivFv9y29qEDOddcWpnGI88edyXEhEpH7liiH1iH2o8lPa6b+c/yYiqpzpxtGZ9kk4jiUiIiJSptnkqxFVks2sh7fEe9PrdsBmVn9BQqXNwVUK5/aIiJRBCXFnpmt6UnOSXrcD5zisjCdEREQqwz0Z5cfcssqoVT9a/TNfVFcWtFqwc0Mn/BEBo4kUms1jlberORArVdmyp28IOzZ0QqvRFFUX5+RjY1FKtXsipdJrNdh0+lTWiR1xj9uBTR4XN5uqCO93RERElZEDJPtLOeQkH6+EsTMREVE980cE9PrEx8O9HA+TgnAeTr0471b/8psatu4+UHCa/XTXFeVO5DvP2YS9t1zBcSERUR0pVwypR+xDlZ8ST//l/DcRUWVMN47OtE/CcSwRERGRMs02X42oUqLJNDZ6XMih+L250eNCNJmuXePKRIlzcJXCuT0iotpTQtyZ6ZpeqTnJrg4nHlq3HGe3WSvWZiIiIio/7smoDOaWVUat+tEs+kaK02Kt7WSe02bEZy+ciwvm29G5sBXJdBZmgw6vD49g7+sf4NEbVyKSSHPysYEpodo9kZI5moz4u//3Nro9Lmy9ZjEiiQxsZj1OhhPY8/oH+M665bVuIk0R73dERESVodFo8MzLR9DZ3oZujwvJdBYmvRYDR4N45uUjuOOaC2V/xmzGzqGYAH9EQDiRgt1igLOJ41oiIqKJwokUrEYdur2uojninr4hjodJMRxNRjz0/Jui/cp/eWUYj964stZNpBI479YYprupQWyslj+R7yWRZNhVHU7MbzFzPEdEVIe4MU5cI/WhqjWHK9fXqNXpv7XOHSMiqkcziaNT7ZNMjFs2kx4PfX45tu97AzEhU/B9HMcSERER1Y5Go8Hu3x0Vze//6e+O4q8+e0Gtm0gNKhRPYevuA3hk/Yqi9+btuw/gn/7sklo3cdaUOgdXKZzbIyKqrXLHnZmsWc1mTY/rpERERPWDezIqh32m8qvV/A2LvhFN0mI1YtuaJbhjz0E8tt83/nWv24EH1y3HPLsZ8+w1bCDVnBKq3RMpWYvViG9dvRh37DlYcNpS/j7KDqN68H5HRERUGXqtBl/65Dl4sn+oYNyZPzlVp9VU7LmPBeO4ffeBgtOzVnU48fD6FVjQaqnY8xIREalJi8WAHRs6RWP1jg2dsFs4HiZlaLEacd8Ny7B194GC9ypP6lI+zrs1jqluapAaqz2yfgVu54l8REQNhxvjijVKH6qac7g8/ZeIqHHMNI7K9UnE4lZXhxM9Gy9F91Ovjhd+Y2whIiIiqi2DVoP//pkLcP++w0X5/dvWLIW+gvlqRFJaLAY8vH4FevqHCt6bHrcDD69fURf5GZyDIyKiaipn3JnpmtVs1/S4TkpERFQfuCejsthnKq9azd+w6FuZVeukVaqcUEzA/fveQGd7G7o9roKKodv3vYFHb1yJFquRr3UDa7RTVoimKxQT8IMX3sHtVy+GXqfFaHzsPpnKZPGDF97BPdct5f1SJXi/IyIiqgxHkxE/euld3Hv9UqQzOYRP95f0Og2e6H0Pd1xzYUWeNxQTihZeAeClQT+27j6AnRs62U8jIiIC0GTSF51y3mzW40Q4gd2/O4p7rl9W6yYSjVvQasH3blyJkaiAcCINu0WPNqsR8+zmWjeNJDhtRqxZ/jGsu/jjmGs3Fdxn9rz+AefdGkgoJuDkaBLDp2LY5HFh5cJW9PQNISZkCsZqPJGPiKj+MQdFXr2uXU587W0mPV47MoLfHRkp+J5KzuFO9/RfvleJiJSt1H26VBy1GnXYtmYJsrkcBoZHpnVvPxFO4H1/FBsua8cmjwuvD4+gp28IvYN+aAD865YujMQEjmOJaMbY9yQiKh+rSY/v/fyQ6Br4P7zwNtfAqWYaJT+D6/pERFRNU137kRp3z3TfQSgmIJvL4YmbLoFGoxmfM5x4OES51/Q4f0BERKRMjTLmp/pRi/kbFn0ro2qetEqVE4gK+OJl7aIVQzd5XDgVFRAVMnytGxhPWSGSNhIVcNunO3DX3kNFJ4FtX7sMI1GBnxOV4P2OiIioMlqsRnzjSjfu3HOwqL/0nXXLKxZj/RGhaOE176VBP/wR9tOIiIgAIJZMS55yHkuma9g6okJcm1KnFqsRf3v14qqPCUhZxD6/+RMMt+waGC/85o8IWDTXxvcFEVEdY59uaupx7XIq/YG8Ss7hTvX0X75XiYiUTe4+PTmOWo069Gy8FI/v9+GOnx0UfYzkc/309+idMK8xMYa9NOhHOpvDRe1tFfhNiagRsO9JRFResWQa3+QaOClQo+RnsG9DRETVJrf2IxebZrLvQG7d65Jz2sq+pscYS0REpFyNMuan+lGLvqW2Ij+1AclVrQ7FhBq1jKYrnc3hyf6hgsABAP2+AJ7sH4JGA77WNF7t/ld/9SnsveUK/OqvPoWdGzoxnxMBRNBqNUUF3wCgzxfAtr2HoNVqatQymgne74iIiMrvWDBeVNwBGOsvfXvPQRwLxivyvOFESvL6qMx1IiKihqFB0eIiMBart+87DA2nNkghuDalXifCCckxwYlwokYto2op9fnNr0d2e13jX+NYjYiovrFPNz31tHY5nf5AXi37BXyvEhEp21Tu05Pj6L9u6cLj+33o9U3v3j7+XCVybPMxjONZIpop9j2JiCqAa+CkVA3w3mTfhoiIlGYqsWm6+w6k1r2e/s37+NctXWVf02OMJSIiUrgGGPNT/ahV31JfkZ/agGZStZqUKZvNFQWOvH5fAPFUlq81AZj6Scc0e6GYAH9kbLLIbjHA2cS/vZJFhUzJ+2ifL4DohNPISR0S6SxSmSyETA6pbBaJdBYttW4UERGRioXiKcn+Uiieqkj1f7vZIHm9WeY6ERFRo0ikspKxOp7KVrlFROK4NqVeI1FB8j4zEhUwz26ucquomqQ+v/2+ALo9Z4q8cKxGRI2gkdeD2aebvnrJ1ZhOfyCvlv0CvleJiJRDrO8UiE7tPj0xjr57MlJU8E3sMZNNNYZxPEtEM8W+JxFR+XENnJSqEd6b7NsQEZVXI68rlstUYtN09x1I/czeQT/S2VzZX6fpxli+d4io3vC+RkrXCGN+qh/+iIDfHRnB5qvc6FzYimQ6C7NBh9eHR9DTN1Sx+RsWfSuT6VatJuWKCWnJ63KvZTDO6t9E5XQsGC+qirqqw4mH16+oSCEMmr1wnDGxnhwNRNHr82Oe3YxkOotIMo2BIyPwup1Y6GiqdfOIiIhUqRz9pZlMzjttRqzqcOIlkcXNVR1OOG2c3CciIgLkY/FoQnoOmahawokUrEYdur0u0cVFzsMpV1jmPiJ3ndRPbm05mR5LZuFYjYgaQaOvB4vFhIl9vEBUAD6KMDm3Dk21P5BX634Bc+OIiJShVN/p3uuX4r9/pgPLz24pmiOKCRnR+/RM7+1TiWG1jltEpG7sexIRlR/XwEmpGuG9yb4NEVH5NPq6YrlMJTa5nE3T2ndQi3g38TnFcuiyudz4db53iKjeHA/G8et3PsLcZhOS6SxGYim8MnQKV54/B/N5XyOFaIQxP9WPSDKFHRs68WT/EB7b7xv/usftwI4NnYgmKzN/w6JvZTLdqtWkXC0W6UQTudfarNchFOMpG0TlEIoJRZMpwFil/a27D2Dnhk5+1hTIbmFMrBcnwwl8EIzjFwePF1TT9rgdONfZBJNBh7l2cw1bSEREpE6z7S/NdNGxxWrEw+tXYOvuAwULsKs6nHhk/Qr2rYmIiE6TmwO2m7m0QsrQbNZLLi7a+F5VLLtF+rWRu07qIFWsWy7WmPRajtWIqCFwPRiwmQrjvtWoE+3j1fumg0Y8gXoq/YE8JfQLmBtHRFR7pfpOrx0ZwclwEq+9fwo/eGFw/Ov5OaItuwZE79Ny9/Ymkx7vnowUxWe5x7VaDDWPW0SkbpPHSZM1yVwnIqJiXAMnpZLLpayHdVPOqxERlQfXFctnKrFpuvsOahHv8s9Zan2163Rbm4w63P7TA+j18b1DRPUhFBNw5FQM+w4cK9r37XI2wWrU8b5GisD5KFKTVosR3/23twvuqwDG///g2uUVeV5+CsrEaTNOq2o1KZfca9nWZERXh7NocgAY6wz9/oMgbCY9O0NEZeCPCKKfNWBsUsUfYYFFJWqxGOB1O9A3qVMDAF63Ay0yC3OkHNFkGo+96CvZQX3ghmW1aBYREZHqNRl1kv2lJqOu5GNnu2C9oNWCnRs64Y8IGE2k0Gw2wGmr/w2URERE0zGbWE1UTSadFk/2D4nO3WgAPLSuMouLNHtWg/R9xmrgfUbt5Ip1S61HdnU44Z5jY1IpETUErgcDRp0WHrdjvE/X7XWJ9vHqedPBTA+5UDu5/CT3HBv23nKFYuZwmRtHRFR7pfpO3V4Xdr44WDK/Z9uaJaL3abmx6WtHRnDHzw6Ofy0fn+Uet2iuDfN4iCQRzYJZr5WcPzVPKJBMRERTwzVwUqpGWDflvBoRUXlwXbF8phqbprPvoBbxLv+cKxa2iq6v9p5eX73v+qVFBd/y+N4hIjUKxlLYub/0utCDa5fzvkaK0NZklBzztzXxfUrKIWSyRffVvH5fAEImW5Hn5YpXmeSrVq/qcBZ8XQknrdaDUEzAuycjGBgewbsfRRCKCRV7LrnXcp7djPuvXwqP21Fw3eN2YJPHhQd+8SaiQrpi7SNqJKG49Gc9FE9VqSU0HU1GHR5Yuxxdk+6TXW4HHli7nIvCKhJLZSQ7qLFUpsotIiIiqg/+SAJ3X7dUtL9093VL4Y8kJR4rv2Atp8VqxKK5NlzU3oZFc22csyAiIpokGBckY7XcnBVRtYwm0yXnbvp8AYwmuVahVFEhjY0el+ha00aPi+tMVVbudUi5Yt2hmCC5Hvnd9StwjrOJYzUiagjhhPR676jM9XoQjAvYNKFf0LmwtWQfb6rzf2oylbgp9phq5RBVklx+0jnOJkXN4TI3joio9kr1naT6D/2+ADrbW0Xv06Xu7V0dTmxbswQLWizYfJUb1tO5Xvn4DEByTMuCb0Q0WxGZ+dMI50+JiKZtJJbE3dctxUPrluGJmy7BD798MXo2XoqH1i3DPdcvxYhK51dI/aJCGt1el2h+Rre3PtZNOa9GRFQeXFcsn+nEpvy+A5ezCQDwnj8quj5Xi3iXf84rznNIrq/KzSPwvUNEahMVSucO9/sCdTGOovowz27Gg+vE6008uG451xRJUSIy+y6iFdqXoa/IT21Q+arVwVgKUSGNqJBBq8UwnvBAM1OLE33lKpCfignobG9Dt8eFZDoLk16LgaNBbNk1gJiQQUxgERyicrAapcMU76/KFIgKOBaM43PL52PjhPvkydEkPhiJIZuzcGFKJWJJ6Xgmd52IiIjEWU0GHBfrL4UTOB6MY15L6UnLcCIFq1GHbq8LnQtbkUxnYTbo8PrwCHr6hrjoSEREVAZNJgP+MCIWq5M4EUrg7LbKzEsTTVdUZm5G7jrVTiSRwdbdB/DI+hXYes1iRBIZ2Mx6nAwncPvuA/j/ffkTtW5iw6jEOuRUT5eezonIRET1ym42SF5vlrleD2wmAzb848vo9rrQ7XHJrpHX2/zfVONmXjljdygmwB8REE6kYLcY4GyqfhxWW39Abe0lIqo3pfpOybT4qeL5NcVMNoeB4RHReDfx3h6MC0imsvjNewGsfbwfMSEDj9uBHRs6x3Nj8/F50VwbYwIRVUwkmcGWXQPj46TJufr/3H1ZrZtIRKQ6JoMeH4YSeP7gcfRO2Bje1eHE2W1WzLObatg6amSxZAY6jUY0l1Kn0dTNfgXOqxERzR7XFctrOrFJbH2uq8OJe69fCg0Ax+k5x1rEuwWtFnwYikt+j1wOHd87RKQ2zB0mNTHotPjc8gVF9SYMOm2tm0ZUoFbjDRZ9K7OokMFdzx6qaoGyeiZ3ou/ODZ0VG/C1WEsPJu1mA3r6hgo22V/c3gZ4gZ6+IbRYOMhrBEpIAK53Wq0GVy2egyULWooKWrxxLASdVlPrJpKITDaH//kf7xYsCOd1uZ24+7olNWgVzYRcPGO8IyIimhmrQYd/7H0PfSL9Je/p0ypKabEYsGNDJ57sH8Jj+33jX89vuLBPIT5zLENERCStyaTHj14qMbfR4cSjN66sQauIijWb9ZIFgZvNXAZUqlarAd/9kxXo6RsqGBd0uR347p+sQKuV827VUKl1yPzp0qU+n9HkmWI9UuuRRESNwGkzYlWHEy+JFP1a1eGE01b/90inzYhLzmkbn+t74qZLZPp49dVPCMsUsZtY5K6csbsWB1CWotT+wIlwAiNRAeFEGnaLHm1WI+bZzYptLxFRI5jcd8r3GRaeZcEPv3xxQZ8BgOia4qoOJx5ctxyJdAYjsRSajDo0mfSwmfW497nDRXG23xeAFhr8+KufxEgsVTCuZUwgokpptRgQEzIF96+JmLdIRDR9Jr0W/yiyBp7v/22/YWktmkWEs2xG/OCFd0ruPbl/bf29N3MAwK1QRETTxnXF6ZnKfoGpzO+VWp/rHfTj7mcPobO9DQeOBgvmHG0mHRxNRiTSWbznj8JuESq6X6HFIv1z9VoNPG4H+kvkg/K9Q0Rqw9xhUotQTMC3fnoAvb7i/ltXhxOPVbBWENF0OW1GfPbCubhgvr3o3vr28XDF+oy8Y5dRLQuU1avpnuhbTlKD2rnNJvRsvBQ79w8WbbLv2Xgp5jbzlJt6p6QE4Hpm0Gpw+9UX4v59hws+a163A9vWLIWeRd8UKZvLiS66AUCvz49sLlflFtFMzbOb0NXhFI3FXR1OnupGREQ0Q/FURrTgGwD0+QKIp0qfqtJk0uPJ/qGiRcd+XwAaAN//wkWSz82xDBERkbxwPFV6bmPQj3A8hXl2c5VbRVTMatSh56ZLsfNFkbWKmy6F1airYetIit1iwJPPDRWNC8buPRo8+gUWl6yGSq1D2s0GWI26kgW7/+Tij8+4zURE9abFasTD61dg6+4DBRs0VnU48cj6FQ2RYzP5b3DoWAhP3HQJHnvRJ5qPUs4EMiUcDjGdU1LLFbuZ3yVvOBDFHXsOFsxD5w8saXc01bBlRESNbWK/4bUjI5IHRR36Q0h0TfGlQT+2/uwALmo/U3TW43bgzmsuLBlne31+bPSci5uffo3jWiKqirnN0nmLzNMnIpq+ZDoruQaeTGer3CKiMYLUe9Pnh1An703mbRIRzR7XFaeunHFHan2u3xdAt8eFx/b7xucce/qGxuctJ85NVjLuSRUE7Opwos/nxyaPa7zNeR63A9tvWMb3DhGpDnOHSS1OjiZFC74BY/NRJ0eTjMOkGC1WI7atWYI79hwsqmvz4LrlFXuvsuhbGdWyQFm9ms6JvuV0LBjH3c8ewuLTVRiPhxI4aTWg/Swrzm6zIpHO4vH9g6Kb7LWnN+O0VKRlpARMAK4eq0mPu//PfxV91vp8AWzfd1i2oAXVxmgiPavrpCybV7uBSYX8utyOsa8TERHRjMj1hyIS1yOJtOgpU8BYPzmSSGOeXfyxHMsQERFNTSguPe8sd52oWkwGHR5/scRahUaDR29k4TClCsYEyc0LwZjA4pJVUI51SLFiOU7b2MJ/qYLddz97iOMvIqIJFrRa8L0bV2IkKiCcSMNu0aPNaqxaLFRC4bMFrRbs3NAJf0RANpfD/c8dLtnHe2xDZ1meUymbDKU2YazqcBYUuQvFBcmfNdWxGvO7pJ0IJ4oKvgFj88937jmI73/hIvZViYiqbGJ/pcViwPduXIm4kMa2vYdE+wwA8JefOR8/eGFQ9Of1+QLjmxzzj/kwnJBsQ74ICMe1RFQNLVYjHlm/omjM0tXhxHe5kZ+IaEaY309KFY5Lv/fCdfDeZN4mEVH51HpdUQ3KHXfkcmvy84b5Ocdur6v0YRQizx+KCQhEBaSzOWRzOcSSabRYjdNas5UqCPjguuW477nD+NFL76Hb60K3x4VkOguTXouTo0m0WaUPqCIiUiLmDpNaBGPS/Ygg92SQgoRiAr4tsv7e5wvgrr2VWx9n0bcyGolJJxdWqkBZPZvOib7lEooJuPvZQ/jiZe1FpyB63Q48/PkViCTTkptxRqLcjFPPmABcPVIb3+QKWlDtNJmkuxdy10k5/BEBm556Fd1eFzZOmNQcOBrEpqdexXObvbzfERERzYBcf8gqcX02RQk4liEiIpoazm2QWoTjqdJrFYN+hOMprlUolOzmBZnrVB6zXYcsVSznkfUrcHF7K+742UHRx3H8RURUqJbFx5RS+AwY24zQYjXi3ZMRyT5eOWKIkjYZSm3CeGRSMQerUWZedYqnVdfqAEq1GIkKkgePMCeKiKi6xPorXR1ObL16cck+Q78vgG9+Wvrn5jdjTpVJrx3/N8e1RFQNGgCfWz4fG684t2AzNhERzQzXwEmprCbpOb2pzvkpGfM2iYjKR0lre0pV7rgjl1szcd4wmc6ic2FrwZ58qec/FowX7OWfuD413dd14iFbo4kUms0GOG1j66/33bAMW3cfKGiX2FokEZFaMHeY1KIRxvxUP2o1f8NZ2TIJxQQIMkkQlShQVu+mc6JvufgjAhbPt4tWE+/zBXDHnoO47Sq35M+oh5NEqDQmAFdHKCbgg5G45Pfwb61MGgAet0M0EdzjdkBT/SbRDIXiAmJCpuRka4hVtImIiGZkNv2l2RQlCMali9XLXSciImoUnNsgtZCbm+HcjXLJJSrIJTpQecxmHVKqWM7tuw/gv3+mQ/K5ub5BRDSmlsXHlFT4rKBdMnN05ejjKW2TodQmjIm0Wo3kWE2nndporRYHUKqJXM4Tc6KIiKqnVH+ld9CPjVckJB9rNmglr0/cjAkAA0eD6HI7RDcJedwODBwNFnyN41oiqqRQTMC3RO5/wNi8Xa3Ga0REasY1cFIqs14n+d4069W/blqNOV8iokag1LU9pSn33mep3JrJ84YmvVb2sIn88+dfz5ULW0X38s/kdc0fsjXZVNciiYjUgrnDpBaNMOan+lGrGkLSq/o0Zf6IgN+8F4DH7RC93lWhAmX1Ln+i76oOZ8HXK1lFO5xI4eL2tpKn1vYO+mUTPO1m1lOsZ0wArpxQTMC7JyMYGB7B8XACLRb+rdVIowU2eVxFMdHjdmCTxwUNex+qYTVKxzNW0SYiIpohjXR/SSqLLr9wKkauKIF8bOdYloiICODcBqlHk0m6/yZ3nWrHYtSVXFP0uB2wGDjvVg2zWYecXCzHatRh81VuPHHTJfjiZe2wyIyvuL5BRDRmKsXH6vG5pVRjfU6JB921WI1YNNeGi9rbsGiuTTQO67UaybHaVIu+zWaOtR5NzNN496OIbM4Tc6KIiKpHqr8yFVJzD5OLuPX0DWHbmqXomhQj83G2p2+o4Osc1xJRJSl1vEZEpGZcAyel0mqAzavdou/Nzas7MMUpP0XjngwiovLgWHFqprP3efIaUShW/DfM59bIzRvm5xwnHzZR6vnzr2fnwtaSe/nL+bpOZS2SiEgtmDtMaqGTGfPr6mDMT/WjVjWEeMcuk3AihZ6+IezY0AkABYMMj9uB+65fykHADFW7inaLxYBkSrqauMWghdftQJ/IYNLrdqCtia91PZOqTt+ICcDlciwYLzpt4cF1y9DldqLXV/y3ZjFN5bLodfjJy8PobG9Dt8eFZDoLk16LgaNB/OTlYdx93ZJaN5GmSKvVSFbRnurGCSIiIipkNeiw6+Ujov2lXS8fwT3XLS352PzC6dbdBwrGJFMpSqDVoHT/2u2siwQpIiKicpCb27iHcxukEBpAcu6G3TvlMmg12LzaDaB4TXHz6g4Y2DmvmpmuQ04slmM16rBjQyee7B/CY/t9AIDNV7lLriVyLYmI6IxaFh9TYuEzoDrrc2o96M7RZMSDz78pPlZ7ZRjfv3HllH7ObOZY641YnsZPvn45c6KIiBRCqr8ycDSILrcDvSX6DC++fRKbPC5oNZqC+7zX7cBGjwtbdg0UPKazvRW/PxrErasXYeMV58Kg10Kn0eC37wWwZdcAYkJm/Hs5riWiSlPqeI2ISM2sXAMnhdJrNdBqgGuXzy94b54IJ6DVjF1XO+7JICIqD44Vp2aqe5/F1ohWdTjx8PoVWNBqKXjcglYLHrhhGd71R2E16pDJ5grmDSfOOXZ7XSXj3sTnz7+eybT0Xn6+rkRExZg7TGphNeklx/xWFigkBalVDSF+CsrEbjYgJmTGByWTJ8FpdlqslSvyNlmTSY9MLiH5Pbkc8OC65bhzz8GCJEev24EH1y3HPLu50s2kGmICcPmFYkLRJBEAPPCLN/HETZcAyBUkqHncDmy/YRn/1kqVzeGuNRdi295D4xvcAKDL7cD2tcuBbK6GjaPp0GrGTnUDijefbvK4oOHon4iIaEZS2SzuWrOkqL/kPd1fSmWlFy9nWpTApNPiltWLkEWuKLbfstoNk45HthIREQFjsfrbay7E3SXmNgSZWE1ULRqtzNwNu3eKlchkML/FjDWTEhlOhhOY32JGIpOR/yFUNjNZh5xYLKfb68KT/UMFn8OJh4VNXEvkWhIRUaFaFh9TauEzvVYj2ccrxwZANR9099Wu87Bz/2DBWM3jduC2qzqm9XOqfQClEpXK09j8zOv4ydf/CPf+/BBzooiIakyqv9LTN4S9t3hw377DBX2GLrcTN3nOxZZdA/AscuCR9Svwvj+KYDwFs0GHOTYj/uGFwYIibh63A7et7oBGA2x66lXEhMx4gfOB4ZGigm8c1xJRpSl1vEZEpGrZHLatWYK79h4sWgN/YB3z+6l2ktkM5tnNeO+jaMHXNQDm2c1IZtW/blqNOV8iokbAseLUTGXvc6k1opcG/di6+wB2bugsmv9rtRrwVP8QXjsygm6vC50LW/HojSsxz25Ck1GPR//tbcSEzHi+jAbS+TL519Okl05w4+tKRCRCZt83q76RUmgAOJtNomN+Z7OJb1VSlFrVEGLRtzKZmBA5cQIcGHsRv+Z11ahlNF2RRBq/fS9QssJtV4cTZzWNJXp+/wsXYSQqIJxIw27Wo63JyOTGBsEE4PLyR4SiSSIAiAkZ3Pz0a/jJ1y/HxtHkmY1vo0m0WTlho1SJTA43P/0qHv/yxbhDp8VoPIVmiwHpTBZfeeJlPHHTpbVuIk2RQavFMy8fET3V7ZmXj+CeNUtr3UQiIiJV0mu0+PsX3sa3rl6MOyf1l/7hhbfxl5+5QPZnzKQogZDJ4eanXxMtVn/z069i7y2emf5KREREdUULDf7hhXfwt1cvLprb+MELb+Obnz6/1k0kAgC0Woz43stvi87d/OTlYXzn88tr3UQqwazT4e/+/W1s8p4H/YT7zJxmE/7hhbfxV5+VHxNQbU1cG+5c2Fq0PjzxsLC7rl2CRCrDtSQiIhG1LD6m1MJnjiYjHnr+TdE+3r+8MoxHb1w56+dQ60F3/oiA7qdeFZ3f7H7qVTy32TuttlfzAEolKpWn4Y8I+OKPfos937gCUSHDnCgiohqS6q90trfiXw8fL+gztFgMSKQyuG3XAC45pw3337AMMSGDL/3Ty+OPsxp1+Pqq8/AXVy5COptDs1kPDYBsLocv/M//HC/wNvkQ7BaLAW1WI8e1RFQVSh2vERGpWTSTxZZnBvD4n16MdCY3vjal12nwtadfw44vdda6idSgdNDiBy+8g26RddN6yc+oxpwvEVEj4Fhx6uT2PpdaIwLGCr/5I0LRHODE9cWJOTLPfPWT+MoTvymYc7SZdLjn+qUIx1PI5SA6r5h/PQeOBkvu5efrSkQkzm42YNfLb4mOMXa9fATb1zJ3mJQhEBXwp0+8jEfWr8BcuwmRRAY2sw4A8N/+52/xv2/+JOYwD4UUpBY1hFj0rUzUmhCpBqGYAH9EQDiRgt1igLOprecZ1gABAABJREFUsh+KcCKFn7wyjJ6Nl2I0kUbo9AmHrw+P4I1jIdx17ZLxauYJIYNsLgeNBtBpNTDLVBWn+tLoCcDlFE6kSl6LCRl8MBLHLT9+HQDvq2oQFdKIpzL4aDSJuXYTsjkgm83ho9Ek4qkMYkK61k2kKQrGBXzl8nNxPBQf/5pGo8GCFjM+0d6GUFwA0FS7BhIREalUMC7g850LERPSaDLpx/tLMSGNz3cunFKMnclYOSqkERMyRcUI8thPIyIiGhOICliz8mw88su3RE9AC0QFuObUsIFEp82zm3HfdUsQFtLIQYPR+FjfcEGrGRs+8XEWZFCwYFzArZ/uQCqdRe701zQADDoNNn+6A6G4gIWcd1O0/NrwPc8egkEnvj6YH399ZvFcXNTeVuUWEhGpQ4vViEfWr8Cv3/kIc5tNSKazMBt0OBFOYPX5cyq6Jqy0PJ+J831/+8eL8bvhEdy2a2C88Eq526XGg+4yuRx2buhEMp2FRqPBG8fD6OkbGv8bjUrkHUxW7VwkJZLK0/BHBASiAvswREQ11mI14qHPL8eRQAzB03msB/8QxIIWC5bMt2N4JAaTXocDHwRxdqsF581pQjiexs9uuQJt1rFinQPDIwU/MyZk8IMXBvGDFwYBAHtvuQJz7Wa89WF4PKZO/N78uuLeW67Aorm26vziRNTwlDZeIyKqB+H4WH5/XMhAr9OOr0/FhQziqQzCceaNUW0EotK5lPWQn9FiNeK+G5YVFclh30a9GmF+uRF+R1IfjhWnR2rvs9QaEQCMxASEYsWF38TWF0NxoWjOcaL8vGIoJuDdk5GC+8oj61fg7mcP4ave83DDygVYPN8OYGxe0mLQYY7C1y6JiGplQasF9163FH8IxdFk0iOSyKDZrIfX7cB/+8THsaDVUusmEgEAwok0/BEBNz/9WsnrREqVA8aS+yuMRd/KSI0JkUp3LBjH7bsPFFQNX9XhxMPrV1Ssw9FiMeDh9SuKNvR1uZ346z++ABoAx4NxHDkVw879g4Xfc3qCgJ0houmxmw2S189zNmHvLVfwvqoSrRYDfvzVy3H/vsMF90iv24Eff/VyGHVV6OFQWTSbDQhEBTx/8Dj6CmKiA5u8LthkPrtEREQkzm42wGLU477nivtLd1+3FAatdH9ppmNluX53M2M7ERERAMBm0uPPel5Bt9dVdALall0D+Nk3rqh1E4nGpQE88Is3i/qV31nHk/qUrNlkQE4D3CPy2m1fuww2E/vmarCg1YJ7rluK4VMxye/jWIuISFoOwPMHjqPXVzjX9anzK7+TTyl5PqXm+57f0oVwXECTqTLtUtNBd8eCcWx/7jB6JxXm3rGhE1tOF8ebasytRS6SEnG+mIhI+Y4F49j6s4PjMctq1KFn46V4fL8Pf/vTA+Pf19XhxOUuBz7/w98UFIx9eP0KtFjk7/cLWi2yxVMZF4io2pQyXiMiqhdtTXr8882X4a69h4rWpv755suQzmZr2DpqZHaLHnaLoWQuJcZLFKob+zb1oxHmlxvhdyT14v20POTWiELxFG7bNSD6uZ+8vvjuyYj0c1kMkveVR29ciWAsBYtBW7ynn/v1iYhKygHYud9XNI56kLnDpCB2s3Q5K7nrRNVWi/Gw+LHjNGMtViMWzbXhovY2LJpr42BxFkIxoegDAQAvDfqxdfcBhGJCRZ63yaTHk/1DBZ0cAOj1+fHov70Fk0GHX7/zUVHBNwDorXDbiOqV02bEqg6n6LVVHU7MbzHzvqoiRr22qOAbAPT5Ati+7zCMenY/1KLJpMcTfUMFBd8AoNcXQE/fEJpMHFARERHNhFGvLUpSAsb6S/c/J91fms1YWa7f7bSxr01ERASMJRpd3N6Kx/b7cPPTr+GWH7+Om59+DY/t9+Hi9lbYZTZKElXLsWAcd+45KNqv/PaegzgWjNeoZSTHoNcWbaoBxl67bXsPwcA5VFUIxQTcsecgfvNeAB63Q/R7ONYiIpI2Ptflq25eyES1zvORmu+7+9lDOMfR1PDr5GfeJ4V9p35fAE/2D6Hb65pyzK1VLpIScb6YiEjZxGJWt9eFnfsHi/pOvYN+7HxxEN1e1/jX8rGtyaSXvd+HYgJeHw5ybEtEilPr8RoRUT2xGPSSa1MWA3PCqTaaRA7PBc7kUjYZ6+e9yb6N+jXC/HIj/I6kfryfzp7UGpHH7cDA0eCUP/dy601NJr3kfSWRzuI37wWw80Uf9+sTEU3RiXACd5TIHb5zz0GcCCdq1DKiQnaLAd4S649et4N7MkhRajUe5o4BUix/RCj6QOS9NOiHP1KZD0UkkS7q5OT1+QIIJ1KY22wq+T2VbBtRvWqxGvHw+hVFEzyrTlfj5+SbuoRl76PpKreIZkouJkb4WhIREc3IbPpLsxkrs99NREQ0NclUBtvWLC1aZMyfJJ1MZWrUMqJCoXhKsl8Ziqeq3CKaKs6h1of8+KynbwibPK6izfFdHGsREcmqVV6IkvBvIE/qb9TvC+CK8xxTjrn8e5/B+WIiImUTi1mdC1tLzif0+wLoXNha8LWXBv2IJNKy93t/RMD2fW+Ijm09bgfuv2EZ4wIRERGRyo3KrE2Ncm2KaoTvTVKTRphfboTfkYhKrxF53A5s8rjQ0zcEYGqfe7n1pkgiLXlfGYkK3K9PRDRNI1FBchw1EuV9k5SBezJITWo1Hq6f4w6o7oQT0puRRmWuV+p5w/E0kums5PdUqm2kLKGYAH9EQDiRgt1igLPJyOSmWVjQasHODZ3wRwSMJlJoNhvgtPFvqkZhmc2kvEeqR61iMRERUb2bTX9ptvGZ/W4iIiJ5wXgKt//0AB7/04uRzuQwGk+h2WKAXqfBrf/7dXz3T1bUuolEADgPp2Z87WanVuszk583k8vBatQhJmSwZdcAur0udHtcSKazMOm1aD/Livmtloq3i4hIzbgWVd9/gxPhBEaiAsKJNOwWPdqsRsyzm6f9c+T+RmaDbsoxt57/3jPB+WIiIuUKxYuTxuXyVsWujyZScNqM2H7DMkSFNGJCBi0WA+wWA6LJNAaGR2DSa9HtdWHr7gP44mXtBWPbgaNBhOMCgKZy/WpEREREVANcmyKlkjsMq54Oy+IeNPVrhPnlRvgdiWhMfo3oD8E4jpyKYY7NBKNei+OhBB770sV4fXgEPX1DJT/3E+Nai8WA7924EpFEumi9aWB4RLId4cT09+szphJRo2ukcRSpG/dkkJrUajzMom+kSKGYAItBhx9++WKYDbrxAWJMOFOts9lsqMhz22V+botFj2az9EenUm0j5TgejOPX73yEuc0mJNNZjMRSeGXoFK48fw438MxCi5UTLPXAbjHAatSh2+tC58JWJNPZgns575HqYTfztSQiIqqE2fSX5MasU4nPiXQWqUwWQiaHVDaLRDqLlmn/FkRERPWr1WLA4396MQKRJJpMemRyQCabQygu4PE/vRh6jabWTSQCMNavlMK5G+Xiazdzx4Jx3L77QMFpaqs6nHh4/QosqOD6jNjzdnU4sWND5/im+MnjO5dz6hvi5RJSmbBKRPWqHHNdaif3NzDotXjrw/CMC6bVynAgijv2HCw4XdrrduDBdcvR7phe0ZiJfyOxeVVH09RjotLec0qI8dPN01BCm4mI6t2xYByJVPFGR5NeK/k4sesWow6bdw2Mj2etRh16Nl6K+597A72+M2PcqxbPwZMbL4OQySCVzmFusw6ABhaDFq0WI949GeG9n4iqjn1PIqLyYX4/KZXdrJd8b9pl9u+pRa3WOKm8lDa/XAmN8DsS0RktViMCUQFmvQ7vfRTB4vl2zG8xIyZk8NkL5+GPl3wMNpNu/Pvz4/SRmIBUJov+dwPj+/7zcW3RXNv4973nj47NT17lxk9eGRbNrbFb9LIFNCbeexhTiYggO06ql3EUqR/3ZJCa1Go8zDs2KY7YoMvjdmDHhk5s2TUwPgB02iqzaOu0GbGqw4mXJjx/XleHE787MoJjoQQ8bkdBcmqe1+2AjZ2huhaKCThyKoZ9B44VvAc8bgdcziZYjTomFVBDazHr8cRNl+CxF314bL9v/OsetwNP3HQJWniPVI1mmddSrggqERERiWsx69Fz06XY+eJgUYztuelSyf6SzayH1+1A3wzHo+XcbElERFSvLAYdIkIaO/f7imLmvdcvhcWgk3g0UfXYZfqGTNxQLr52MxOKCUVriADw0qAfW3cfwM4NnRVZnyn1vL2Dfhh1GvzTTZfie//2VsH4zut24IuXLJzSz5dLSGXCKhHVs9nOddUDqRwVj9uBfz30IR7b71PVHN6JcKJoDhIA+nwB3LnnIL7/hYumVcAu/zd67cgIdmzoxJP9QwVxdzpxUervXclcJDFqjPFqbDMRkdrkx6ArF7YW5agOHA2WzFv1uB0YOBos+NqqDideHw4W3Le7vS7s3D9Y8DOsRh3+9PJz8N1fvoneSfmQt63uwIfhBDY+9er4oc289xNRNbDvSURUXnaZnHCuTVGttJj16Nl4KXbuF8ml3CidS6kWtVrjpPJT0vxypTTC70hEhWwmPaxGLRaeZcUjv3yrYN6wy+3Edz6/HID8vv+XBv2459lDuOe6pbhjz8GC7/O6HfjxVy/HI798UzS35uRosuS858R7D2MqEdGYFosBXR3OovshMFYLpUXmUGKiauGeDFKTWo2HpY99I6qyUEzA7T8tHnT1+wJ4sn8I3V4XVnU48cj6FRUbfLVYjbj/hmXwuB0FX+/qcOKWK9144BdvoqdvCJs8rqLv8bgd2OhxIZpMV6RtpAzBWKoo8QkYe5/u3D+IYEy6sjxNTygm4N2TEQwMj+DdjyIIxYRaN4lkZLM5/PBFn+hn5Ie/fhfZbK5GLaPpiqcypV/LF99FPJWpUcuIiIjULZPN4fEXxccUj7/oQ0aivxRNprFxhuNRuc2WJ8KJGfw2RERE9UfIZHHvzw+Lxsx7f34YQiZbo5YRTZLN4d7rl8I7qW84thi+DOA8nHLxtZsRf0QQTVQCxhI4/RHp9YOZrjdIPe+FC1rw/X97q+Q4S+455BJST4QTkte5ZkJEahdNpnGz9zw8uG4ZnrjpEvzwyxejZ+OleHDdMtzsPa8hci9arEY8vH4FVnU4C77ucTuwyeNCT98QAHXN4Y1EBdFNGcDY7zESLY5fUnE6/zfatmYJnuwfKvrZ04mLpf7elc5FmkyuD6DEGK/GNhMRqVF+DCqWo9rTN4TNq93ocp+JY1ajDg+tW4a7rl2CpQvs6Nl4KTZf5cZnL5yL+29Yhu373ij4+Z0LW4tiabd3rM/RK5YP+eIgfB9F0O11jX+d934iqrRQTMB3fvEGNl5xLp67zYNdX7sc+27z4qYrzsWDv3iD9x8iohnIyeT357g2RTWSyebweIn9WY/vl86lVAt/RMDvjoxg81XugnnwzVe58dqREdk1TlIOpcwvV1Ij/I5UX7jvdPbiQgbvfhTFYyJ9xV6fH98+vT4ptkY0cd8/AFww3447flb8fX2+ALbvO4wlC1qKvn7fc4exyu3EbVd1iO7pv/f6pQhEBYRiAk5FBaxc2FoUT61G3ZTyhoiI6kUmk8UtVy4S3Vd2y5VuZJjnTgohZLK4h3sySCVarEY8sn4FHvr88oL+5kOfX47vVnA8rP7jDhQoFBPgjwgIJ1KwWwxwNhk5oTFFH4YT6PWJb5ro9wWwbc0SfM3rqvjfMxQX0Nnehm6PC8l0Fia9Fi5nE9bs7Bs/rXDLrgF0e13j3+NyNuEXB49jy64BPPPVT1a0fVRbUSFdMkG53xdAVKj/xPNq4Ul96jQqZIqSAPN6B/0YFVgoTC2iyXTp19Lnb4iNNkRERJUQkeov+fyISPSXQvFU0XjUpNdi4GhQdjw6lc2W8+zm6f0yREREdSgqZNAnETOjnNsghQgLGXzliZfxyPoVuP2axYgkMrCZdTgZTuKLP/ot/tfNn8TZtW4kieJrNzPhhPShO6MS12ez3hBOpGA16tDtdaFzYSuS6SzMBh1eHx7Bxe1tBacQT5RPKJVa15QrZDcSlS90x3VoIlKz0UQKOeTw/MHjBX3wLrcDm7wuRGTu/fVCA+Ca5fNx0xXnotlswGgiNT7fF5sw/lDLHF44Ib2GOPn6VOL0glYLLjmnDXf87KDoz5xOXFzQasHODZ3wRwSMJlJoNhvgtFU3t2sqxWyVFuPV2GYiIjXKj31jQgZbdg3g66vOw13XLsGQPwqTXotX3j+FT5zbho2ec5HO5uByNGH7vsO4Y8+h8Z/R1eHEQ+uWYySWLOhLAEAynS0a4y48y1pybNvvC6Db4yrqf/DeT0SVdCoqYMunz8f9+wo3g3ndDmxbsxSnorz/EBFNF/P7Sakk35u++nhvRpIp7NjQiSf7hwrGXh63Azs2dCKabIx58HqhhPnlSmuE35HqA/edlkcslcb585px54T5xYl6ZXJX8vOHwNiBE6XmGft8AWzyuIq+/u9vnsQdn7sQF36sGQ+uXY6okEZUyMCg0+LXb58c39P/2Qvn4tvXLsHA8IhoPN2ya0Ayb4iIqJ6MChnc/PRrovvKbn76Vez+xhW1biIRgLE9GVL7GLkng5QmB+D5A8cLal6t6nDiU+fPqdhzsuhbmXGgOHP5YnlSYslMVSaIms2Ggv9rNBp8NFqcADNRLnfm9JDJj6f6IteBkHqf0NTJnRK9c0MnJ4wVKiKTxC93nZRD7n7G+x0REdHMzKa/ZDcbEBMyJRdEpcajcgUS5K4TERE1CtlYzSLopBDheAoxIYOBo8HxTbrxlA4DR4OICRkmsinYaIKv3UzYZdbfSo2HZrve0GIxlNyI4XU7Sz4OkC5EB0xlnCYdc/heISK1a7UY8d1/e1vk1PgAsgAeXLu8Ng2rolBMwLZnD2HxfDvmNpuQy+Wg0WhKfr9cbFACu1k6HW3i9enEabmx2HTiYou1thvUZlPMtlbU2GYiIjWaOPaNCRloNGO5qSa9Fsl0FsvPbsXrwyO47fQhUf/82/eL+1KDftyx5yAeXLsMVqOuIL/HatAVjXF/+OWLJduUTGdFv857PxFVjAZFBd+AsU1g2/cdxgNrl9WoYURE6hWOc1xPytQIe09KzYPn/19v8+D5/aHhRAp2iwHOpvorFlbr+eVqaITfkdQrFBMQjKVw196DRYVDue90+gw6HULxhOT3yK1P5ucPJ88jTj58Ym6zCZuvcqOnb6hgzjIcT+G8OTa0WI0IxQRs3jVQtHZ4wXw7vr33YMl42u11cV8/ETUMsTH+xDwTjvFJKeTei3yvkpKM57D5qlvbhkXfyogFimbHHxFgt0i/Ja1GXVXaYtRpiyp+/+s3uwraUWpzR8/GS+G08XWuZ60W6cF/i8x1mhqeEq1eNpkkfrnrpBw2k8xrKXOdiIiIxM2mv+S0GbGqw4mXRPrKqzqckuNRuYVMLnQSERGNkY3VHA+TQrRYSxei2rGhE3bOVSuWXaKIGF+70mY6HprtekOTSY8n+4dEE0dvvdIt2Wa5cZZcITu5ojkcxxGR2gmZbMkTXft9AQgZ8QIj9SQQFfDFy9pL9gu27Boo2PTQrIK11rYmI7xuB/pEXluv24G2pjNxdzpxWm4s1qSisdpMi9nWkhrbTESkRhPHvlajDtcsnY/t+w4XbGDN9xO0Gk3Jg6J6B/04OhJHz8ZL0f3Uq+P9iUwuVzTGNem1km0qdZ33fiKqlESq9FixzxdAPFX/Y0UionKTW3ti345qpRH2njTSPPixYLxob+2qDiceXr8CC1otNWwZEdWL/H1m4xXnFhV8y+O+06kLxQTc9/ND2OhxSX6fXO5Kfv5w4r5rqT34k9dAJ/ZFS60ddi5sLTkXms/f4b5+ImoUzB0mtZB7L/K9SkpSq9o20iv1NC1TeRGptHAihXQmB4/bIXrd43ZApy19mnG5hGIC7thTXPHbqNPCe7pt3V5Xyc0dj78oPnCk+jG32YSuDqfota4OJ+Y2m6rcovoUTqRgNeqw+So3nrjpEvzwyxejZ+Ol2HyVG1ajjtV7FUyrgeS9vAq3cioTrVZT87hMRERUj2bTX2qxGvHw+hVYNWlMsqrDiUfWr5CcPDLrz4xrJ/O6HTDLbOggIiJqFBqZWK3hcJgUosmgK7lW8VT/EJoM1TlIh6bPKvPaWav02oViAt49GcHA8Aje/SiCUEzZa5kzGQ+FYgJOyfxecusNkUS65EaM37wXQJdbfM1IrjA3cGYzf6nHtzVJX2fCKhGpnfyJrtKnxteDdLa48Aow1i94sn8I3d4zmyy8bgcsBuXP4c2zm/HguuVFc5FetwMPrluOeXbz+NfC0zjV16jTSo7VjDrl/23y5PoASozxamwzEZHahGIC/BEBWz7dgZ987XL85OuXFxV8A870E/QyeTvBeAqPv+jDtjVLxr+m0WiK+h0DR4PokoixJ8IJDBwNFnyd934iqiSOFYmIys9m1EnmjdmMXFek2miEvSeRpHTfJSpzXS1CMaGo4Bswtqd26+4Dil+LJiLlC8UE3P3sIaxc2Iomo3QRMu47nRp/RECvL4CBo8GS8Vgud8XjdmDgaBCrOpw4x2Ed33cttQd/4hroxHnGUExAMp0p2kcMAMm0dJFUk0HLQn9E1DCYO0xqYdZL5/lwHyMpSSguPW8RildmjKH+4w4UJF+gqNvrQufCViTTWZgNOrw+PIKeviEOFGXYzQYcPRXDptNVwfsnnUy4yeNCJpfDiXCiIAG03EoV7zsWjGPbmqXYvu+wZFXwXlaCr3stViMeWb8CW3cfwEuTTh+RK7JA0vKJa+FEClaTdDV/Vu9VrhwgeS/P1ahdNH0GrQb3XLcUv3v/FObazeN9mxOhOC5xnQU9d7kTERHNyGz7SwtaLXjo88sRTqQRjqfQYjGg2azHfJmTEFPZ7Pi4tm/C83rdDtx93VKksvVzYiQREdFs5HI56Vid4+wGKUM0lSlZiKrPF0A0lalyi2iqYoL0a5c/ybaS1HrK+oJWC3Zu6IQ/ImA0kUKz2QCnzSi6NjPxhGcpE08MFiNVjKanbwh7brkC9+97oyhmbF+7DIGogPf8UdgtBjibituZL2RXas1pnt0sev2zF87FvdcvhT8i/fOJiJTOapJOW7Ka6j8RNZvNlewX9PsCuP3qxbi4vW18fU4tw5F2RxO+/4WLMBIVEE6kYTfr0dZkLMr3scvE4YlxOhgXJMdqY8l3TeX7JSpIrg9QqZg+MSdjuv2HarR5Nu0jIlK7Y8E4bv/pAfT6ztxjn/nqJ4sKvuX1+wL49ueWiF7LM+m16B304+41S/Crv/oURhMppLLFnYmeviE89qVOAJqC5+9yO3H/DUvhjySx4uOt6FzYitt3H8CS+XbmSRJRRcnlB9st3AJDRDRdESGNjafz0ibPq2z0uBAR6qPoVL2q5zmTRth7YjcbJPebyq1VqkWp/ZjAWOG3etprWc+fSSIlC0QFfPGydjzZP4TOha2S31sv99ZKy+fD9PQN4fEvXYwbVi7A4vl2AGO5TRaDDnNsxpK5K10dTtx//VLoNBp8zetCi9WI7Tcsw7f3HpTcg9/vC6Db4xp7/A3LAIjnMeX3EW/ZNQCTTFGYVgvvw0TUOJg7TGoRiqfwta7zoBVZg7y5y4VwhYpoNQqOTcurVnmMXPEqoxaLgQWKZsFm1uN4KI5/f/MEOtvb0O1xIZnOwqTXYuBoEM+8fARLFrTgv4ZH8OC65Wh3VCZRs9TGjZffP4V3T45ik8clm3RaqSqNpBzT2VREUzN5YuaJmy4pWWlaA+D7X7io+o2kKdEAeOblIyXv5bdfvbjWTaQpajLpMeSP4hcHjxcUhulyO3DeHBtcTnVsmiAiIlKa2faXjgSiuHPPwYK+stftwHfWLcc5EmPlFosR9/38EDZ5XLj9msWIJDKwmXU4GU7iB//+Nu65flm5fkUiIiJVsxn1+Mkrw6Kx+ievDOOeNdKbKYmqJZKQ3nwhd51qp9anucudsr5zQ6ei1ztarPLrMRN/x5ULW+FxO0QTnSaeGFyK1LpgTMjgeDBREDPMBh2cNiO2P3cYL7z1UcFziRXVk1tzmnzdbjHAqNNi688Oqq5oHxHRZFaDDl63o2AdKs/rdsDaAKcPx2Q21H4wEsctP34dXW4nFs1txgXzbFVq2ezNs5tlD3V02oxY1eEs2CCSNzlO20wGbPjHl9HtdRWN1bbsGsBzm71l/x0qqdp5J+Uo+lvJNqu1KDERUTmEYkJRwTcACMrkoaazWXS5nUWPA8ZyewaOBgGMzTNc1N4GAHj3ZKToe2NCBpufGcDXV52Hb6+5EJFEGjazHq8fGcG1O/vGi9N73Q789C+uQJvVoOh5AyJSP7vZIDlWlMvjJyKiYtFkBlt2DZScV/nn7stq3UQqod7nTBph74nTZsSTGy/Fzv2DBftNu9wOPLnxUtm1SrUYO5RD6np97LWs988kkZKlM9nxvaad7W2zygOhMRPH11oNsPAsKx755VsFf9eu04f/5NeIPgwn8MFIHAAwcDSIa3f24RPntOHe65ciEBVgM+mxZsUCNBmly1c0mfRYubAVn9vRi21rluD5A8eL5jnz7ej2ujBwNFhyroCvORE1Grnc4Chzh0kh7GYDLEY9rln+MWz0nDs+5j8ZTmBBqwUGrabWTVQtjk3Lz6TTSq5NmXTSRYhnikXfysio17JA0SxEk2l8rMWCr3Wdh8de9BUVztvkcWHLrgHEhAzu3HMQ3//CRbLJoTNRaiG4p28IOzZ04qnfvI9vXX2B5M+wGus/8ZimtqmIpiYUE3D3s4ewcmErNl4x1mmbZzdjYDiIzVe5RU+ziSTSmGevdctJjEYDfOXyc3E8FJ/wNQ0WtJjxifY2aNgHV42YkMaO/YNFfZteXwBZAN9Zy8IwREREM5ED8KVPniNaNF7udMpjwTjue+5wUUGB14dHcP9zh7F97fKSk3Pz7GZsveZC3LnnYMEElNftwIPrlldkjE1ERKRWN3tdovPUm1d31LBVRIVsZr3kieA2M5cBlUrutWmq8Gvnjwj43ZGRkvPv9XDK+sST5PNrfAAK5jpXnU5MlftdpYrReNwOvHb675b/LOq1GkSSaSxe0ILfvHdqfHO8VFE9uTWniddDMQGbdw2otmgfEdFEsVQGG0/Ph028R3vcDmz0uBBrgNOHW2ROnc+fWt/r8wMa4LHTMa1etFiNeHj9CmzdfaAg1orFaafNiEvOaSsYp038fjVs5hA7ZXfRXFvBtff80SmdwDudE3vLWfS3Erkyai9KTEQ0Wx+GE6KF2/L9gFJORQTcsnoRgBx6J23E/Ns/vgDdT70KAGiekBNbaowbEzI4/IcQNlzWDi00ODoSw8daLOj2utDTN4SYkEGfL4C79o7l7rbM4vclIpITE9LSY0WZ4tlERFSsWWTtSTMhqV/sOtVeQ8yZaKRzKVEne08e2z9YtGm41xcANJq6mfO1yhT3qYe9lg3xmTxtOvPPRNWSyZ0ZI842D6QRiX2u83OFKxa24g/BOH5x8HjxHsIJ9zgAeOAXb6J30D+es7ZzQyeS6SxOhBOICxnsef0DfOvqxeOF4UqJJtPjsX9us0l0fhQYe327PS785JVhPLhuOe7ae0h2TZGIqN7ZTNK5w00mjvFJGUwGHe766e9FC/V63Q58909W1qBV6tdIY9NqSmaykmtTyUy2Is/LO3aZhGICjp6Kid5wAKDPF2CBIhmheAq3PvM6/vxT5+H2qxfDoNNiyB8tOD0mvymizxfASFSoyIZ0qaSWf3llGI/euBIjMaFkJXiP2wEdq4oSTUsgKuCLl7UXLNT8z698Ajs2dIou3uzY0Ilosj5OealHBq0WVqMOzx88XrAw1eV2YPNVHTBoK1PJlsovKmRK9m36fQFEhfrfaENERFQROenTKb/1x6VPpwwnUpJJTuFECgtQ+kQGo06LzVd14PZrFiOSyMBm1iGazMBYodMGiIiI1CgiZHDz06+JnnJ+89OvYvc3rqh1E4kAjPXtnrjpEtEChU/cdAn7eApm0Gol15nKPYc6OXEzm8vh8S9djH/qe69u598nniQfEzLYsmug4L5+rsOKs1stU0pskCpGc8tqNzY/87rkesbENc6XBv2zLqo3saDdZOX4+URE1RSOp4ru0RNzRJ7adGmtm1hxcsVFB44Gx//fW6f3+QWtFuzc0Al/RMBoIoVmswFOW/EGsharEQ+sXSZ6qMUDa5cp/u8idcquBsC3pnEC73RP7FV6/0Hp7SMiqqRQTCi5AXLgaFBy/iBfhPwnX78cG0eT432pBa0W7PjVO3h4/Qr8yyvDBYVRS41xP3vhXGxbswR/839/X3BPnjy2rWTuLhFR3khMeqz4z92X1bqJRESqw3VFdWqEg5z0Gq1kLuW3P7ek1k2ctZOjyYJC3RP1DvpxcjSp+tcRALRaTd3vtWyUeczpzj8TVUskeaYAuFgeyDkOKz4+xTyQRiP2uf7shXNx95oluGW1G5lsDolU6T2E+XscgPGCb2J5Ml1uJ27ynItHfvkWNl/VgS63U7SY2+Q10GRauoBGi8WAR29ciRarcUprikRE9U6rheQYn1v4SSnC8ZRk/aVwPMUxxgw0yti02kYTacm1qacrtDbFom9lcnI0iWBcegNESOZ6o7ObDYgJGfz9vw/i7/99ELu+djlu+fHrJb8/nKjMKV1SSaLb1izBPLsZ0WR67MQQFFdp3ORx1cVEJFE1pbM5PNk/VPB5mt9ixiO/fKuoM5f//4Nrl1e1jTR1Qjpb+iQiaHDXmgtr0zCatlhSuqib3HUiIiIqYTanU+ZQ1HcGzvSTt11bOskpFBPwtyInOQBjCRk8yYGIiGjMaDyFmJApiNMF1xOc6ydlSGWy+OGLPtG+oZbzcIoWjAmS60zBmFDqodMmlrjZ1eHELVcuwsBwsOB762X+/VgwjkSqMCF08n39V3/1qWmNf0oVo4kJGfz1/3eB5Dit2+sqeO7ZxpGwzOMZp4hITZpP54mU6ns3mw1VblH1lSq8ku8XbNk1UPD99Xqfb7HKb8gIxQTcv+8NXNTehk2Tkuu273tjfLOHEsmdsnvN8vlTPoF3Jif2Kr3/oPT2ERFVUn6zpJieviHs2NAJrUZTVIgt30+ICRl8MBIvyLX98Vc/iUVzm/FU/xAeWb+iKC6IjXFtZn1RwTdAfGxbqdxdIqK8JqNOcqxoNeqq3CIiIvVLprmuqEaRZEry4KF6OMgpGBMkcynLuW5aK3L7Setlv6leq6n7vZaNMI85k/lnomppNheWQ5g8bvzlf+/i+1NEqc/1BfPt2LrnIAaGg/gff/oJ2cJro4kUcqf/3e11iebJ9Pr8yCKHzvY2nAglcJPnXGSRE40LE9dATXrp6kRtE9YRp7KmSERU7wxareQY//4bltaoZUSF5MZQctdJXCOMTWuh2ayXyWOsTHk2Fn0rk2A8hTk2k+T3WE1cYJQy+fRim1n672Wv0IdiKkmijiYjHnr+TdGTRP7llWE8euPKirSNqF5lszkMDAcLTiDSaTUlq/f2+wIQMtITSVQ7WaD0SUQ+P7I50UukQM0W6Vgrd52IiIjENRl0+NnvjqLb48LWaxYjksjAZtbjZDiB3b87ijuuKZ1ElwMk+8lSXS2e5EBERDQ1zRbpwhKNUHiC1CGb4zycWjWZ9PhKzyslTwP72TeuKMvzlErc7B30I5vLFRUjA9Q//57/nVcubC15kvyqDiectumPfcQSR1uswCXntOGOnx0UfUy/L4Du05sb8mYbR+wyj2ecIiI1aTLq8OnFc3DhgpbxdWKzQYfXh0fw5rEQmhpkI//EwisjMQGheGq8XxATCg9hauT7vD8i4IU3T+KFN0+WvK7U+U25udmbrji35LXJv9dM5nmV3n9QevuIiCopcrpIwzNf/SSC8dR4X6inbwgxIYNnXj6Cu9cswfCpWNH8Qb6fMHFTpMftwG/fC6BzYSse2+8rKoqeN3mM++7JSMn4MnlsW6ncXSKivCaTXnqsaOJ9iIhounLguqIatVqM+O6/vV3y4CG1H+QEADaz9LrpnlvKs25aS3J9l3rp2zTCXstGmMdknjEpWbPMWLG5Tu6n5Vbqc52fPwTGDh6VK7w28R438bGT5ecSY6kM/ub//r4gxi88y4J/O3yiaA104Giw7Dk+RET1LJHOSo7xEzKFPImqRW6MVA9jqFpohLFpLTSb9PC6HegTub963Y6KjTc4iimTJqMO6Wyu5MDC43bAqJUe9DS6/OnF9zx7CBfMtwMAnrjpEmg0moIkFmDsQ9HWVJmBWj5J9DfvBtDtdY1PAFzc3gYACEQFnDfHhvtvWIZfv/PR+OM0Gg3ObrXgTy9r5+QV0TQlUumiE4h++OWLJR8TTfLEUKWSe2342qmHTgN0uZ3o9RVP7na5ndBp1H/aEhERUS3E0ml86+rF+O27Y/MHyXQW8VQGJ8NJ3H71YsTSpftLcn2pmFD6Ok9yICIimhqrTOEJa4MUniDl4zycemk1wCfOaRNNgOzqcKJch5xLJWQPDAdx+9WLi+5zPX1DVX3vnAgnMBIVEE6kYbfo0WY1Yp7dPOOfF4gKWLmwFRe3t8G7yIlbV7vxm3cD4+uMXR1OPLJ+RVnX8iIyf6+JpyGXIxl18kFaEzHZlYjUJpHJ4K41S3D33kMFcbHL7cT2tUuRyGQkHl1f8oVXQjEBt+0aqIv7fCgmwB8REE6kYLcY4GwqLqA6HWqe35Rre1Ii6Xry7zWTv4PS+w9Kbx8RUSW1iBRx8Lgd2LGhE8+8fARfufxcHB2J4anfvD8+xrcadeO5rQDQZjVi81VuvHEshC998hxs2TUwvpl+qvFxqrGqkrm7RER5iXQGt19zIV57/1TB189uMeP6lQuQSDfOWJGIqFy4rqhOQiYreUCsmg9yytNqgCsWOQq+pjm9R+GKRY6yrZvWkgaQ3G9aB78igLE57vtuWIatuw8UzPevqsD6bK00wjymmufhqf5FhTS2XnMh7n/ucNG64t3XLUFUIoe+lHKvZSlRqc/1xLWpgaNBLGgxT6nw2qoOp+S6Vv5nm/RaxIRMwWu1+So3fn80WHToVU/fEHo2XgqdRlNwj62nGEJEVE5y+Ypy14mqxazXShbRMssUnSVxjTA2rYVIMo2/WLUI3772QuSgwWh8bIwA5BAYFSp2b2XRtzJpMuox5I/iZq8LWhSegNLldmCT14VgXKhdA1ViQasF91y3FHf8rHCCL5/EsmXXAC5ub8W91y9DrkJHyYQTKViNuqICVPl2rOs8GwCQzeVwzlnWgtMV3z4exqfOn1ORdhHVszarCd/7f+8UTApN53QAUhabzGmuctdJOXRaLTZ5zwWQE+nbnAtdPayiEhER1YAWGnwYSmDfweNFGzhcc5rgaCrd122TWbRstZS+zpMciIiIpiYYTWLbmiX4zbuFC4xnt5jxJxefjWA0iXMcTTVqHdEZzTLzbHLXqXaMOi1uuXIRsrlc0ZjglivdMOrKk8hQKnEzvw72vV++VTDvl1+PG1ukrryjgSh6fX7Ms5uRTGcRSaYxcGQEXrcTC2d4n80BGBgeKUrw3XPLFTgeTODsNgvmt1pKPn4mCb1yY638eke5klHzB2lt3X2Aya5EpH454O69h4pOIO71+bHt2cPYtubCGjWsdurlPn88GMev3/kIc5tNSKazGIml8MrQKVx5/hzJWCzFbjYUFLmZXLhWyfObU+0viJn8e81knlfp7yult4+IqFJCMQHb9h4q2kjZ7wtACw26vecihxz+938ewU1XnItsLoeB4WBRbqvVqMO2a5fgc8vmY/hUDI996WI4bUY4bUa0WY1492REdpw7lVjldTvw4LrlsyrWTkQ0JTkgEE3iFyI5Fec6m3AWNywREU0b8/vVSW4zaT0U6zNqtdh6zWLc8/PCAj5etwP3Xr8MhjrYrqDVAvdevxSvDZ3C3NPromaDDidCcVziOgu5XGX2R9bCglYLdm7ohD8iYDSRQrPZAKetfgooNcI8JvOMSdFywP37DouuK96/7w1su3Z664rHgnHcvvtAwWGKqzqceHj9CiyY4VpWrUjlupT6XOfXpqxGHfRaDVZ8vBWL5thkD1d8eP0KvO+PSranxWLAb98rLu7S0zeE57d04e5nDxXcRy85pw3nnmWt6xhCRFROdpkxvNx1ompJZbO4R2I8nMqqv5h9LTTC2LQW4kIaZ59lwV2T1u+9bge2r12GUKwy9cJ4xy6TVqsBH2s143gwgWuWz8dGj2u8GvWJcAIaaKq2QULNQjEBd+w5WDTwziex/HyzB0cCMXzxR7/Fkvl27NzQWfabjt1sQLfXhSf7h0STaR56/k3cfbow3eSF5E0eF+559hAevXElb4Z1rhGq+FeT2AlEA0eDUzodgJTHatChy+1Er6+4QnCX2wmrQVeDVtFMOJqMeOj5N7Gyva2gbzNwNIhdLw+PnwpMRERE06PXarHzRZ/omBMAHrhhWcnHOm1GdHU4CxaY87pk+slOmxGfvXAuLphvL9oU+fbxMPvYREREp7VajTgeSohuJnI5bZjfYqph64jOMOq0kieCl6twGJWfkMnh5qdfQ7fXhe5J8243P/0q9t7iKcvzlErclFoH0wD4/hcuKsvzSzkZTuCDYLzkxk2TQYe509xEHooJuPfZ4s3y+QTfzvY2LDzLWvLxx4Jx3P3sISw+PWY6HkrgpNWA9rOsOLut9OOkTs3r6nCi/SwrfvVXnyprMmq9b5ggosaRy6EoPySvd9CPOtrrNi1qv8+HYgKOnIph34FjImOqJliNuhn9Lk6bEf/cfSnS2RyaTHpEEhk0m/Xwuh248nxl5xDInbJ7cjQp+jix3IiZntir9PeV0ttHRFQJ/oggml8FjI1lv/mZDvz9C2MHqf7ne6fQ7XXh9qsX43u/fGs8xk484PiOPQfHH9/lduAnX/8jfPeXb8E11yY7zpUb257nbML3v3ARC74RUVUYdVo8NsOcCiIiEmcz6CRzzmzM71ekRii+lAVwz88PF8X9Pl8A9/z8UF3Efatehz+czkHpm/B7drkdOG+ODWe31Nc4q8Va33N69T6POdP5Z6JqyEFmXXEaPysUE4oKvgHAS4N+bN19oCL71itlcvE6q1GHbWuW4OL2VsSEDGwmPR76/HJs3/cGYkJm/HsAYNfXPomzmox4YN8b+MELg+M/s6vDiZ9v9gLIYY7NVPC3WNBqgU6rKdm39LodOKvJiJ6+oaJrl5zThjarQfI+qpa/OxFRLZn00rnDUofOEVVTm8WIcCKF5w8eL+jHdXU4cZnrLNgtjPszVe9j01pwNJmwdc9B0TmqbXsP4aF1yyvyvCz6ViYtViMiyTS2970nGiC9bkdVNkionT8iiA70gLEklngqi4GjQcSEDF4a9MMfEcp+43HajLjiPEfBCSETLZ5vx7cnFXwDziwkd7a3VaRdpBz1VMVfKcROIOrpG8KODZ0AUPB5Y5VZ5Utnc7hl9SJkkStK4r9ltRvpbIPuzlChFqsR99+wDL9+56Pxr2k0GpzdasGfXtbOzyEREdEMxVIZ0bkDYKzvG0tlJB9/62o3srnivtatq92Sj2uxGrFtzRLcsedg0amYD65bzthORER0WjaHGRdoJaqmU1EBN3td0KIwqbHL7cAmrwunogJcc2rXPiotKhTPiWs0Z46pj4lcn4lSCdmdC1tLroP1+QKIJNKYZy9LE0qKJtNl37g5tlm+9Fjr1ivdJZPQQzEBdz97CF+8rB1P9g8VjZke/vwKfLxEwTi5U/PmV2jtqN43TBBRYwjH07Aadej2uooOKejpG0I4Xp6YqEZqvs8HYyns3D9YMs4/uHbmc5GtVhPu+Xnxiar3Xl/9cdp0DguU6y/k/z2VE3hnc2Kv0t9XSm8fEVG5RZIpbL7KLdoPigkZpLNn1gNjQgaP7fehc2Frwdi3VGH3Xl8A9/78EL519WI88su3ZMe5tRrbEhGJmW1OBRERFUtnc7j1ykXiOWdXMr9fqRqh+FIjxP1UNocdIvOlvb4AsmAOihrV8zzmbOafiSpNbt1wOuuKUvvYK7VvvRImF68rOCDiZxMOiOhwomfjpeh+6lUAGP+ei9rbMDA8UhyjBv24/7nDJYvfzbOb8dC65bhjz8GCv2NXhxMPrVsOvVaDS85pk7yPqOHvS0SkVKF4SjJ3OBxP1a5xRBNkAdz788NFeb29g37c+9wbePTGlbVpWJ2o57FpLUSE0nNUfb4AIkJl5qhY9K2MEqms9ItYhQ0SahdOSHcijp6KYWB4BDs2dGLLrgGMynz/TLRYjTBKVLCV2gjT7wug2+NCiJ2hulVPVfyVROwEopiQwZZdA+j2urDt2iVIpDKsMqsS8VQGNz/9Grq9LnR7XEimszDptRg4GsTNT7+K//sXf1TrJtI0pDJZPH/gWNHg37PIUcNWERERqVs0KT3JE5O47o8I6H7qVdG+VvdTr+K5zd6S/eVQTMC39x4SPXHgrr2HOJ4hIiI6rRGSiqk+WI06hBNpXLN8PjZO6BueCCeggQZWk67WTaQSms368QTKiWtOHrcDOzZ0wmYuzxJuqYRsOZVYf5usEvdauXVGk0FbcszjjwhYPN8uulG+zxcYK54tMWaayql50ykMQ0TUKGwmnUxMZH9GjaJCWjLOixXAnYpwPFVU8A0Yi9X3/nzsRNVqxdaZHBYo11+Yzgm8PLGXiKg+tFiMGBgeEe0Hbdk1AKuxuC+UTGcL/i9X2P0bieK4XGqcy/hCREohlTMxletERFQsns6iu0R+f/fTr2L3N66odRNJRCMUX4rJbJiNV2hDbTUxB4XUplHmB7h+rz5ic2UF16eRJyWXX1KNvJnZCsUEHA8lCtaqSh4QMeiHBsAvbvNiNJnG9375Fvp8AWzyuErOLUoVvzsWjOPe5w5j5cJWbLziXCTTWbRaDDjHYcXZbWMHTTTCfYSIqFaazQaciqZK5g7bRGo2ENVCMFb6IOfeQT+CMQHz7OYqt4pInFzBzEqNEVj0rYzqYaBXa2KFnyYy6bXjA85urwvNFep0tFhK/9zJSTNi17mRqn7VSxV/pSl1AlFMyODA0SC+5nXx76oisWRm/HTZUtdJHU6EE7hjz0HRU6Xu3HMQ3//CRRxQERERzYDdIj0d0yxxPRQXJPtaUkXIOZ4hIiKaGm4mIrWwmvT4p743RRPEvW4HvrNueQ1aRVNh1GlFkyz7fQFoADy4bjnePRkpS3KxWEJ2NpeTfEyl1t8mqsS9Vm6dsdVS+m8YTqQkN8r3TmHMJHVq3kwKwxARNQKrSS8ZExupP1NPm4uiMhsx5TZyllKrE1Unm81hgVL9hemewMsTe4mI1C0UE7BN5LCm/P+3rVkCm6l4zdA06UBjuXzWUmuHvYN+fBhOFMUSxhciUoJmmUMx5K4TEVGxSCItmXMWSc6sSD9VXr0XXxIb90zUJHNdDZiDQmpU7/MDXL9XJ4tRB4/bIbpW5HE7YDFMfU+3XH5JNfJmZiP/Ht5wWXvB16XyXl4a9ON4KIF4KjNefEVublGsJsLEdbIX3jxZcK2rw4kH1i5D64T9+TkA0EzhlyIioilrMunxT33vlcwd/v4XLqp+o4hEhOPS801y14mqyS5RYwqo3BhBK/8tNFVqH+gpQb7wkxiP24GBo0EAY4ktV5zngNNWmckjo04Lj9shek2qIFz+ulnPj1a9YnHHysifQDT5858/gSiZzuKt42G8MnQKb30YxolwokYtpamwySTUyF0n5RiJCpIbJ0aiQpVbREREVB+aDDp0ucXHvl1uJ5okFp2tRum+lNQpZhzPEBERTQ3nNkgtEhIngvf5AkjwRHDFisoULIkKGXz67/4D6374G3z6+/+B23YN4FgwPuPna7EasWiuDRe1t2HRXBvmNptKrset6nBWbP2toE1TWG+bLql1Rrnfy242zCiZdSrkCsOEYpxnJaLGxf7MmGPBODbvGiiK/+/7o/j90RG8+1FEVfGitQJx/lgwXrMTVSebyuEaREREck6OJtHrE48n/b4AVpzdguFArGhNceBosCC3dXIRuMmkrn8wEldVH4OIGofJoEVXiXm+rg4nTAbm6RMRTZfVJF0ERSrnjGpv8lpfPRVi0gAl9+953I66qBEjdQjwVK4TUXlx/V69zHotNq92F8UNj9uBzas7prWnezb5JbU28T083QMigvFUwffIzS2K1USQWifrHfTj3Y+iePPDUWx+5vWy5j4REdEZkURa+sC8BAtpkTLIzTfJzVeRtFBMwLsnIxgYVl9umRI1m/Xwlpij8rodFTuQiCteZaTmgZ5SlCr85HE7sMnjQk/f0PjXTAZtxSaqg3EBmzyuogmA/Iex1EJy/rrc4JjUi8UdKyd/AtGv/upT2HvLFfjVX30KOzd0Ip3J4i//z3/h6n/oxRf+529x9Q968df/578wHIjWuslUglmvlSxiwsKY6hGWGdzLXSciIiJx8UwG969diq5JY84utwPb1y5DPFN6M6tWI53kpNWUTnPieIaIiGhqOLdBasG5G/WSS6qZfP2lQT9uL2NysdxBLNXYKDLPbpLcuDnPbpr2z5zN7+W0GWUL1Mx0zMTCMEREpY3KxES56/VAanPRtr2HkM0BRwIxPH/oQxxXyUaIuc3ScX5u8/TifP5vVKsTVSfj4RpERDRbx4JxDJ+KSX7P8MjY9W1rlhSsDfb0DWHzavf4/OXkInATdbmd4wctl8IxKREpkQ6a0/e64pyKzavd0NVF+Rciouoy63WSOWdmPTfZUm3kkBPdv5ffR5hDrkYtK582q1EyB6Wtjor4EakB1+/VK5nOwmEzYc3y+Xjipkvwwy9fjCduugRrls+Hs9k4rT3dSsibmamJ7+GZHBAx8Xuk5hYn1kSYWFDklEzukl6nwc79g+idVIyIhRWJiMonGJe+l8pdJ6oWi1F6Pspi4HzUTJU6YJRFdmcunc5i+9plRYXfvG4Htq9djnSFakjxKIAyyg/0tu4+gJcmDPzVMNBTknzhp+OhBN7zR2HSazFwNIgtuwYQE85sfG+1VO7vaTMZsOEfX0a314VujwvJdBbnOprw/KHj6H7qVez62uW497nDBVVwu9xO/M0fX4Dup17Fj75yScXaRrWVL+74ksjkHos7lk8OADRAVMjgnkmfNWCs0vSdew7i+1+4CPPs5pq0kUoTsllsvsoNIFcwQdfldmDzVW6ksiyMqRYtMqdGyV0nIiIicaFoGn/1f/4Lj3/5Ytyh02I0nkKzxYB0JouvPPEy/u6/XVTysTkAmzwuACjoJ08lyclpM6KrwymarNDF8QwREdE4DSA5t8GtRKQUsiegyVyn2pE7nU7seu+gHydHk2Vbb8yvx/kjAkYTKTSbDXDajFVbz2yxGvHI+hVFRW66Opz47izWVWf6e7VYjTjHYYXX7UCfyCmYs1kDYmEYIqLS2J+R3lzU6/Njo+dc3Pz0a/C4HXA5m2A16hSff5SP8+XKn8r/jfRaTclY7XU7YDFUp0A3D9cgIqLZCMUE/Mc7H2HZ2XbJ7zPqtIilMvjXw8dx7fL56Pa4IGSymGMzwWzQ4m/++AJsvWYxEukMrl+5ANv3vVHQp1jV4cQDa5fhvucOi/58j9uBgaNBOJqU3a8gosYUEdLY9NSr6Pa6sPF0Hn9+P8Gmp17FT//ij2rdRCIi1dFogM2r3QCKc842r+6AxDmjRBVl1uvwzMtH0NneNr5/Lx/3n3n5CLZdu6TWTZy1eXYzvrNuGb6952BRDsp31i3jniiiKuP6vXoF4yl87Z9fwyPrV2Cu3YRIIgObeWwtccOP/hP/9GfT29Nd67yZmZr4Hu7pG8KODZ0Axvp4+SJuk/fiAmP5OPkDIvLfM/nxeRPX9I4F4wW5PU/cJP13brMaRJ8fOFNYUel/YyIipbMapfd1y10nqhYtpOejqpPlU3+kDhjduvsAdm7oZH9rBkbiKWzZNYDHv3wx7py03/fPnngZO0/3m8uNd+wyW9BqwfduXImRqIBwIg27RY82q5ETcNPUYjUikc7imV+8UTJZ02au3NvXaTNi25olmNtsQjKdhdmgQySZwmP7fQCAY8EELj33LPzlZ85HOpuD1aiDBhrsf/sEYkIGLTKnC5N6sbhj5UyeAALGJpNuuuJc/Od7pwqKPgJjhd9GogLvrwqUzeaQSGXwueXzCxJuToYTSKQyyGTZ/VALq0GHLrcTvT6RwjBuJ6ysok1ERDQjNrMegaiA//fGCXQubEUynUVUyOD14REEogJsptL9JY1GI5nkdMc1F0o+962r3cjmckWTpbeenkQlIiIiIIMcHDaj6NyGw2ZEpg5Okqb6YNbrSibqedwOmPWcu1Eqi16HqxbPwZIFLeNjArNBh9eHR/DGsVDJk3dD8fImF7dYa5usuqDVgsdmmUAbignwRwSEEynYLQY4m4wz/r3ObrPi4c+vwB17DhZtlJ/NGhALwxARlWYxSPdnGuFEV7nNRcnTp4Tm/0YPrl2uiryEcuZP5f9GHwYTuOe6pbj/ucNFmyPvvm4pclUaqjltRjz0+eUFOUWvD4+gp28Il5zTxsM1iIhI0kgshX0HjuEPwXjJnByP24FDx0L43PL5aD/LipiQQSabxcC7QfT0DSEmZMYPhMpkc/gfv34Xj964EpFEumh8ffeapRDShwqeJ//YLbsGsO6is6v56xMRTUk0mUFMyIzn7YtdJyKi6dEA0Gs1WHO6oPDENXC9Fjz4jGomlc3iz1ctwmP7Bwti/9ihfB1IZbM1bF35GHVa3HpVB751zeLxIkXRZAZGHbe4E1Ub1+/Vy242wB8RcPPTr4len8lrV+u8mZmY+B6OCRls2TWAP//Uebj96sXQaoC1F52N+547XJD38pkL5+Le65bi6EgM4UR67BCJ0+ttW3YNoNvrwq1XumEyaNFqMY7PLYoVFJEqLOdxO5DOSC/YsbAiEdHsaQDJezHH+KQUGeQwp9kkOh81p5l7MmZK6oBRFtmdObvZgKMjcVz/WL/o9UqNFVl1pczEihat6nDi4fUrsKDVUsOWqU80mcZGjws5FFfu3OhxIZpMV+65hQyeP3C8INHl2Vs94//+rw9G8EnXWfj7F94palvPxkuZwFnn1FrFX8lKVZTtHfQjm8uh2+sSTd4IJyp3H6CZs+h1MBu0RV3tHACzQQsLN5uqRiabwz3XL8G9Pz9cUITV63bgnuuXIJPlgIqIiGgmbEYdem66FDtfLExU8rgd6LnpUtiMpftLeq0GX/rkOXiyf6josZs8Lui0pafn/REB3adPw55cMK77qVfx3GYvxzVEREQA7CYDwknx5CKNZuw6kRJoNTInoDFzQ7F0GuDOzy3BPT8/VNCv97oduPf6ZUilxTctWiXGCmo1mwTaSqzLfvws66wL0U3mtBmxqsNZcJhQntftgNnAzRxE1LjYn5HfXDSxGGy/L4CooI418nLG6fzf6PUPRnCF0SFaoDsYE3D+vOay/g6liOUU5fOFzj3LyjlWIiIqKRQTsG3vQfT7AhgYDuKZr12OHHIFOTketwNf9Z4Hq1GH+ybl63S5ndhzyxU4HkzgteERbNk1gH/6s0tw/w3LMM9uxjx78XO2Wg343Ir52Og5t2BtcMuuAXyCxUqJSKGaLdJbXOSuExFRMS2As2wm5E5Giq6dZTOBKxVUKxotMM9uwjUic37z7Cakcuov+haKCfjbnx4QLfrd1eHEYxs6OadIVEVS6/erOpycK1Ewm1kPr9tRMF+W53U7YDM3xlhR7D28dEELHvnlW+j3BWA16tDtdeEvPrUIOq0GyVQWc5qNuONnB8djkdWow13XXohvXbMYH40mcXarBR+zm4vikVhBkZ6+IezY0AmgeH13k8eFUEy6qBsLKxIRlYEG2ORxARC/F7PqGymFs8kEf1QQvabRaOBsYt97JuQOGGWR3Zmp1XijMUYxVRKKCbhdZBLupUE/tu4+gJ2chJuSUEyAPyIgEBWg02jwVa8Ld1yzGNkcEEtmYNBr0Tv4EeZWaAJlvPjUpNfxVDQ5frJiLgf88EVfUQXcfl8AWo0Gj50etFL9mvhZDidS4x1gfsZnRqqibL8vgO7Tg4/J7A0yGac2eq0G//LKMDZ6z4Nep8VoPAW7xYA5zSY81TeEv/rs+bVuIk1RJgc8+PybuKi9DZsmFYZ58Pk38e3PLal1E4mIiFTrf/12CN0eF7aePrmx2azHiXAC/+u372PrNYtLPq7JpMeul4+gs72tqHDbrpeP4MHPryj5WLFJPY3mzGw+J/WIiIjGxFMZfOcXb2LJghbMtZsBjMXMP4QSePAXb+Ke65bWuIVEYzQAjDrgrmsvRA6a8Xk4IIdYMs28DQXTajW4b8/BonWmPl8A9/38MG6/unhM4HE70GQs75x4fk0unBh77zib1HPAzbFgHCNRAZtXu3Hn5y6ETqvBiVACL79/Cvc8ewiP3rhyxr9LOU9yDsXG1jzvu2Eptu09VLSRf6PHhXt/fnhW7SUiUrNEJotcNoftNyxDMp1F+HR/xqTX4sNgHImM+jf0yZHaXORxOzBwNFjwtZggXhxWSUod+jbT/Kn83yiXA3b8ahC9Isl1+c2RlVYqp6jfF4BOo8FO5gsREZEEf0QYj2MxIYOvPv0qejZeim8k0gjFU+NrfiPRJPyjOfzlZ8/Hlk/n0GTSw6TXIpnK4vdHgzgeTuCx/T50dTixaK4N807PYYpJprNwOazYOSnf1eN24NbTxXeJiJTGpNfiM4vnYvECOzoXtiKZzsJs0OH14RG8dSxcUBybiIimRqvV4O/+9U18/hMLMdduGs9XA4C/+7e3JfPViCrJrNXh3ucO48IFLeNjm3x+xvZ9b+DeOsjPODmaxJsfhvHETZcUfP5OhBO4ffcBnBxNcp1QZdS8zk5j+QAPr1+BrbsPFKzNrOpw4pH1K/haKlg0mcbXuxZh81VuNJn04/fTSDIFIZVDNFm5g5OU9Lmf/B7u9rrwZP/Q+NxfTMjgsf0+PLbfB4/bgW6PC995/s2CucGYkMGdew6hq8OJR29cWXJ+UWzvQUzIYMuuAXR7Xfj25y7E8VACAMYPmuj2uuBxOwoK0OXH9m1WQ8MU5yMiqiSTTouf/e6o6J603b87ir/9Y47xSRmiQgZ3P3uoKFcaGCui9d0/WYkWaw0apnJyB4yyyO7M1Gq8wd5xGX0YToieugCMJS76IwIH/TLETvrt6nDi1isXofvp18aTVz1uB9ZedHZF2lCq+JQGGmzyngsghxUfb8UPXhgUfXwvX+uGUM5TqWlsAmjyJE4+QaOnbwjJdHEyf5fbiTZW8FWkeCaD2z5zPu7aW9gR97od2L52GeIZ5W9EoDGJdAYHPgjhy588p2CBsXNhK37yyjASab6WREREMxFLZ7DlMxfg/n2Hi/pL29YsRUwixkaTaXz58nPwZN8QHtvvG/96l9uBTV6X5ARSi8WAHRs68WR/4WM9bgd2bOg8XSBEeZS0UE5ERI0hlsrgS588RzRmbvK4EEtxPEzKkEYWDpsF3957sKhf+cDa5Ujn+F5VqoiQES1WAgC9Pj+2TqrY53E7cNtVHWi1lq/PruZ1juFAFHfsKX7fb1uzFO+eHMV/u6wdgWjt1+om/o2f2nip6OEaW3YNICZkuLZIRA0rIaQxv81Scl0xHBc/7bWetFiNeGT9Cvz6nY8wt9mEZDqLJqMeJoMGNpMBR0di6Nl46fjaeYtC5/Amkjr0bSb5U/nNK+/7ozXP1Sn370ZERI0lkkxh81Xugvy4Pt9HWPwxO9rPsiIuZLBmxXyY9FrcvfdQwdyBx+3A5tVunOOworO9DQc/CGH7DcskC74dC8bxvj+Kr/7za+j2uooOlOp+6lU8t9nL2EVEihOMCbj9msW4/7nDRXkRd1+3FMFY/Y8ViYjKbTb5akSVFE1lsEEiPyNaB/kZkWQKP/7q5aKfvx9/9XJEkzysV03UvM4+HfWet7ug1YKdGzrhjwgYTaTQbDbAaauv37EeRZMpfKzVjPueK76f3n3dUsQqdD9V4ud+4ns4mc4UxNCJ+n0BbL1msWihFWBsfS2SSGOeXfx5xAqKTNwDHE6kMc9uxoEPgujpGwIA6LUa3HnNhfhoNIkFbZaisX2t/3ZERPUgjSy+dfVibJu0ltTldmL72qVI5er/gEVSh1A8VbIf0ucLIBRPsU8wA1IHjK7qcMJp47hmJmo13mDRtzI5EU7gg5G45PeMilS1pjNKnfTbO+hHNpdDt9c1Prjr9wVw97OHpn3671SIVR8Hxjb6/c3//T3+/FPnjZ9oUwpf6/oWigm4/afFJzfP9FRqki8+0WTUFXy/x+3ArVe5YeZpfYpk0umKNtwBYx3wbXsP4cF1y2vUMpouIZ2RXGCMCYx3REREM2HUanGXyEkVfb4Atu87jO03LCv52NFECjqNBp9bPh8bJ2zMOBlOQKfRICIxHm0y6QtO8srr9wWgAfD9L1w0m1+rIpS4UE5ERA0gh5IxEwC2XbukFq0iKmLS6rC1xDzcXXsP4iHOwymW3DpSVEjjiZsuOdPfH03i3LOsZVt7CMUE3P3sIaxc2IqNV5xbcBDLPc8ewqM3rpR8rlomeJ8IJ0rOP2/fdxibPC709A/h3uuWTrnNlfh9Jq97xlKlk2wBri0SUeM6y2oq2Z/ZtvdQw/RncgCeP3AcvT4/rEYddmzoxI/2F45JPG4HejZeirnNpto1dIqCMsX65K6LWdBqwYeh2udllcopqmYbiIhIvVosRgwMj+Cx/T5YjTr8+afOw+eWzcf9zx0e35Sz+So3fj88UlQsPt8vuHb5fLz3UQy3X7MYVqOu5Jg2Py7dcFk7YkLpMSljFxEpUYvFiHuePVR0L+z1BXD/c2/gvhuWlngkERGVMpt8NaKKaoD8jLOsJty5t/T65nfWNsY8eD0otfe13vYTHgvGi/ZN1mPebouVRd7UpsViLJkvcv9zhyuyX1ENn/tIMlNwgFRMKCyYGklIF1CVmh+cXFAkv445eQ9wV4cTz9/mRQbAvc8ewg9eGMTmq9wY6Huv6PVS0t+OiEitjFot7tojNn/qx7ZnD+OBtZw/JWUIx5ljUwliB4yaDTqcCCew+vw57GPNUC3GGwCLvpXNSFQ+IbFZpKo1nSF1Gm6/L4Buj6vga5U6IVes+jgAmE4Xl1p5diusBp3o9+Txta5vH4YT+N3wSNGpm/mJEZ7cPH1yxSe+dfXigg1uPGlU2aJCBgPDwZKfkaggPVlIyuFoMkluoGQBPyIioplJpLOS/aVEuvSpKq1WI3b+ahCLF7Rgrt0MANBoNPhDKIFfvXkS264rneQUSaQlT8iQOqmrFtSwUE5ERPUpB5SMmf2+AHLVbQ5RSRGZebgI5+EUq8UivY7UYjHA0WQaP1H6knPaytr3DUQFfPGydtGDWDZ5XAhES69z1LowczAmoLO9Dd2ni2BPfM/3+QK4/fQJxZnsmbt1qTY/sn4FckBFfp/J654mmUNsuLZIRI2K/ZkJc2CnN091e10l1861Gg0e29BZi2ZOi9UonY4md72UFot0f6ga8bRUTlE120BEROoUign47btjebB/9kfnwuVswu/eP4V7J50Ufkl7GwCMH/40sW80MY82mcriw3ACD/ziTdExbVzIoHfQj41XnCvZLsYuIlKiVCZbtGExr9fnRypTOqeCiIjEzSZfjaiSGiE/I5aSngePpep/HrxeSO19rdQ+12oLxYSigm/A2O93++4DeIx5u1RDUSEjmQdfif2KSv3ci+XBeNwO7NjQiS27BgoKv9nMZ/bCW406dHtdBfGoTaL9LVYjHvr8chwJxBCMp7CwzYpHfvlm0evQO+jHb94L4F8PHh8fz3cubC15EEW93DOJiGolkZKYPx30I5HiGJ+UwS6TK811ypmbeMBo3qoOJz51/pzaNUrlalUfhUXfyiScSGPgaBAet0N04NjV4YTTxgGIFLmTfJMiiwiVqN6Zrz7+2pGRggFsm9WI3X/xR4AGOPhBCA+uW4Z5dnPRh/UT57Txta5joZiA48GEaEX6/MRINMmqstMlV3yiOyLg5qdfK7rGCr7KFEmk+BmpEzGZDurk0z+IiIhoaqLJmfeXhHQWX778XBwPxQu+vqDFjE+0t0GQSMALy/Sflda/VupCORER1b9oMj2r60TVEk2k8MMvX4wnet8rPD3V7cQPv3wx5+EUzKDT4qrFc7BkQUvRvNsbx0Iw6LTI5cYW5aEp//Ons7mSxWRMei3uXrME756MIJxIwW4xwNk0dsJ1rQszh2ICdFotBoZHRMdSW3YNjJ9QnJ+7lGrzr9/5qCjpoVy/z+Txl9Q68iquIxNRA+O6YvEcWOfCVvT0DVXlELpQTIA/IhTF/NnSACXjnsftmHH3Jp/P85LInGG14qkS2kBEROo0EhPwi4PH0OcLYPNVbvzzb9/HX3QtQrfHha3XLEYkkUGLRQ+jXod/7Huv5Lg3n0cbSaYRTaZLjtG3fLoDAMejRKRO4bj0OozcdSIiKhZNcl2RlKkR8jNmky+qRpWad1YCteXgzsTJ0WTR+nle76AfJ0eTdfN6kvrIfcYq8RlU4ue+VB5Mfv6v2+sajzcetwMnw0l43A4MDAdF49HEgxEn38PNei3u+flh/ObdAP78U+fhXEcT+nwB0eJxTpsRf/fv74yvcVqNevRsvLTkHsR6uGcSEdVKOJ6G02bEI+tXYK7dhEgig2azHifCCdy++wDnT0kxmow6eN0O9ImsU3rdDjQZdSKPIjmTDxjNq1Yud72K1iiPkUXfysRu1qOnbwg7Tp+oOzFBwuN24P4blvKDIUPuJF+TXlv0tUpU72yxjnVyjpyKYef+wYIPpNftwPYblmGu3Ywnet8rqILrcTvwxE2XYL7dXPY2kXL4IwLOshnxyC/fEt0MBQAPrl1ei6apWqkJsPwEkNNmxA+/fHFRsSlW8FUmh82EH/xqsORnZPsNy2rRLJqBiMwCY6TOFhiJiIiq5awmE/7+hZn1l7LIwWzQ4hcHjxfNPWxe7UZO4mxLm0l63N0kc73alLhQTkREjUEuJiotZlLjcjSb8A97DxWd2Nfr8wMa4P4bltaoZSQnGEvi9qsvxP37DhetQ21bsxQj0STW/4//HP/6xATLcshmc6Kbva1GHb70yXOwbdL7Kv/8iVSm7IWZp5r0fywYx3+88xGeP3Cs5Fiq2+saP6G42azHuycjOBUTsMnjwsqFrfjJK8P44mXt40mnC8+y4o6fHSzr75Nnn7R+UWodeVWHE4+sX8F1ZCJqWFxXLJ4DS2dzVUkgOxaMF20IKVufQwNs8rgAFOdPbfK4ZlzUtsVqxMPrV2Dr7gMFRdeqGU9brEY8sHYZ7txzsCAp1et24IG1yxjTiYhIVCgmYNveQ+Ox4+L2NvzklWF83GHBXXsPjcfLzVe58fvhEclxbz6P1m4xQKvRYPc3rsB/vudHOpvDsgUt45ss8/m4pcajXRyPEpGCWU3SG73krhMRUTGHzYR/eIHriqQ8VpkN3nLX1WA2+aJqU9F5ZwWYvAY8WT3scQvGpdcgQjLXiSpJ7jNWic+gEj73k/Nqstlcydydfl8Af/mZ89G5sBUAMM9uxn+8cxJbrurAex9FRQ+IzBcHeejzy7H1ZwcLfrbX7cDN3vPwlcvPgVGvxfFQHFajTnQts8vtwI+/ejke+eWbJQ+0mFj4rR7umUREtdJs0eHHX70c9+87XHBf956+F+eQrWHriM5IZjLYvnZZwTopcLpmz9rlSGYyEo+mUiYfMDrRbHOfG9lZNhP+vgZ5jNyZVCZtTUZc3N6KLbsG0O11odvjQjKdhUmvxclwAk0yBc0I0GjGBnaTFxGAsYHdwNFgwde6KnjKYDaXw+P7iz+Qfb4A/vO9U+MnLk7U7wtAC+Db1y5BKJ7ijbBOhRMpZEpshgLG3gdChp3h6RKbACs1AZSf6PnJK8M8aVShUpms5Gckxc+IajTSAiMREVE1zaa/ZNBq8diLvhnFZ6NOC4/bIfrcHrcDRl1xsfVaUsJCORERNSatBpIxUzvDAgVE5Saks6JrKsDYCctCmvNwStVqNWHbs4dE16Hu33e4qF9f7tPXYoL4SY7dXpdkgudda5ZI/tzpFmY+Hozj1+98hLnNJiTTWYzEUnhl6BSuPH8O5k9I+s+fSrfxinNLvuf7fQHccqUbJ8NJdHU48dqRkYKCbh6R5NIffvnisv4+EzltRqzqcI4XpIkJmfF15FuvdMNs0KHFYoDTVj8n2xMRzQTXFYsPapjfYpY8hO47a2e/Pjd+4utgZU58Neu1+NnvjqLb48LWaxYXnCq9+3dH8a2rF8/4Z2sAXLN8Pm664twzeVmjyRn/vOkKxQTcv+8NXNTehk0TcsMGjgaxfd8bePTGlYztRERU5MNwomA8q9dq8Mj6FQUF3wCgc2FrQZ7cRPlx75FAFACwoNWClwY/Qk/fED5xThtuuXIRbn76tfGNkw+tW4YutxO9Pn9RXnOrxYBFc22YxwOOiUihrAad5DqN1aD+4i9ERNXGdUVSqmaTHl63o2iPHjC2Cby5Dg7lE2TmwetlD1ql552VYPIa8ESrKrjPtZqaGqAQI6mXWa+VjBlmffnz4Gv9uRcrpvnETZdIPiYUT+Hmp18b/3+X24mujjm44GPNuGOPeDx6adCPI4FY0T28zxfA55bPhwbAvoPH0e1xlcwt6vUFsH3fYVzU3ob9b300/vWJB1rk5z4rWRuAiKgRNBn02LrnoGj+6fZ9h/HQuuU1ahlRIQ2AUDSJ+29YhmQ6i9F4Cs0WA0x6LULRBGwW9gdmYvIBo5PNJve5kdVq/kb9M18KMc9uxkPrlqPP58dcu3n8tLwToTi8bifmMjliSratWYr7JlWV7XI7cctqN25++tXxr3ndDjy0bnlFJvtCMWFsgFriAznXbhKdGADGBqaZXA7RCRXHqb7YzQa8c3JU8nuiSfHNUlSa2ARYqQmgfl8AGowVWCRlCselPwPhBD8jatEoC4xERETVJtcfkroeT2Uk43M8VXo8OhJLYpPHNf69eR63A5s8LozEBJyDJsm2VZPNLJ3QZTNzWouIiCpHKmYSKYXsPJzMdaqdmcy7lfP0tZYSiRJSm8tfGvQjm81J/tzpFGYOxQQcORXDvgPHiu61LmcTrEbd+O+aP5Vuw2Xtkj/TqNfimZeP4NbVbnQ/9WrBtX5fAA/sewMbPefiP987hZiQgUkm4Xc2haZbrEY8vH4Ftu4+UFD47cDRIL58WXtBUTsiokbGdUXAoNMUFDMQ0tL9hEQZNuBW+sRXPTT41tWLsW3voYLcm67TJ/XOdFYvFBPwLZFNg8DYxppqbBr0RwS88OZJvPDmyZLX1b5xkYiIyisUExCMFSbWtzWNjTcnx/ykTJzXazVYNMeGmJDBiVACA8Mj2LGhE1t2DeCHL/rw9VXn4QcvDAIAtv/izdMbQHPo9QXGx/urOpx4ZP0KFnwjIsXbvNoNoHidZvPqjlo1iYhI1biuSEoVTWWw0eNCDsVxf6PHhahEPqRajMrMc8tdV4tKzzsrgdgaMHBmrK323w8Amox6yQLMTUbm7VLtCNkstq1Ziu37DhfklnvdDtx93VII2fLvcavl575UMU2NRvrE2snXe31+ZH+Zw5+vWiT5uGBcvDhIfh6x3xdAZ3sb/ug8R8ncoj5fQDS/st8XQPfpr1eyNgARUaOICKX3lfX5AoiwzgkphEmnQyCTwt3PHirov3W5Hdh8VQccuvIX7W0Edpnc5tnkPjeyWs3fcJRdRjqdFs8f/BC9vjODqK4OJz51wdwatko9tAD+6+gIrl0+H90eF4RMFh9vs+DNY2G88n4AOzd0jp8yeI7DirPbrBVphz8ilBygAvKJNaOJNLTS42ZSMafNiBNhBsJyE5sAk9pc1ucL4HgoAaNOywkeBbKaeLJLvQhLxMOpXCciIiJxcv0hqeuxpPTke0xict5s0GPLrlfQ7XWh2+NCMp2FSa/FwNEgtuwawM++cYV0w6ssmkxLJ3RVuOB2KCbAHxEQTqRgtxjgbDJy/EFE1CCMOi12vXwEne1tRTFz18tHcBcPIyCFkJ2Hk7lOtTPTebdynb5W6iRiuTWwmJAp2wnGwVgKO/cPih78AgAPrj2T4DkSEwBAtkibxaDD3/x/F+DanX2iY6Nenx9/ceWi8Q3xA0eDJRPWy3Gq8IJWC3Zu6IQ/ImA0kUKz2QCnjeMKIqKJuK4IBKJCQdHpjyJJye8vxwbcSp/4mkauqOAbMHaQ4ra9h7B97dIZ/VwlbBqs5N+O85FERPXJHxFgMepgNerQ7XWhc2Er9BotQiJjf7lxr91iwP9740P8/mgQSxa0jI9nu70uPLbfh29dsxg/euk9xIQMYkIGNz/9Gn7y9cuxcTSJZDqLcx1WnN1qYXwhIsULRJJIpLLjewry6zQnwgkkUhkEokm45thq3UwiIlXhuiIpVTiewpZdAyVzGp/adGmtmzhrNpP09l2562oRTqQKxr7JdBZmgw6vD4+gp2+obGvdtVbva8CtVgNuu2qs0PLkvN3brupAq5V7Jql2NDng7194G5s8Ltx+zWJEEhnYzDqcDCfx9//+Nv7yMxdU5Hlr9bkvtS6Wy+UkD1XP5YoPdOz3BWRzHj/eZkHPxkvH79v5vJuJuUQ9fUPwLnJK/pxSuUdWox6/uM2LVquhYrUBiIgahVz+ab30vUn90tkcHts/WNRvGcsn+v+z9/fxTdf3/vj/SN7JO5dNWxJaClIMplJoixZRmGlR0E0RUBhnOyuec4DqtjNFtrOzCSrI5RB3dfYF3PmdbeDY5yie2xkHRbzY5nCT1olX3YAK2gijKBelpUmay3fyTn5/pAlJ3xcppWmT9Hm/3bzdJO9cvJM079fV8/V8KrDxvoHFD410UjHgwJXHcpPLCtLMz6Q7PlD5MSuUBVw+Dqt+eyQl4RsAHGrrxKq9R7BjCKrJ5joVo8QYkw6WAhZPvXoChxydicm+WyeawaqUKNazGR8QxycZpaQLrDFqVGAo61veKtSzmGDWS06MUEM4cH0nwEKRqOyEfzDM08AjS+nUjGxlF52aFoVzRboklpTkkhBCCBmYq+kvmXTyUzkmrfRxpVKB2nLx5Mp2mznrxrKuNAFdzz84I2OvfdbpF1RHm1VhwdbFUzG2SJex1yWEEJIdgnwEDTMm4NnmUyntpt1mxnK7FUF+8KuCEjIQNA+Xu9LNq0kF9g/WfJxUJeIinfzzF+rU2LywGo/vOyqomrx5YbVg/U4ucYmXC0tWm2x2dMHLhRPPwfUGhMolabPbzDj6uQsVJUbZZNgufwjPHT6NxjordjWdwraG2sRrJj/XpvuE72cgCvWZD/SlBDGEkFymT9Of0Y+A/oxOzeCfkwo1jC7QyN5fbv6vvzJd8TUQiggSvsUdcnQiEBrYmCrTyer6I1Of3TmnH3/65CJKCjQIhiPo9oXw7qlLuP360Sij+UhCCMlp7kAIakaBnUunY8ebDuw46ICeZfBbkWJM6ca9rxw9h5b2biy3W7FyTwuA2Hi2sTeBbIc7mEgAB8SSt3/W7cdDz30IPcvgtZX16PRwONnppfEjISSrGbRq/Muz76GxzopSkzZx+1lXAJtfOY69WVbQjhBCcgGtK5JsVaBVw8fxojGN8eO5TqtSot5mEew5BYB6mwXaNPsUc0WhTo0dS2qxqyk11qbeZsaOJbUwpVmLziVDsQY8XAr1LCaM0mP+1LEpcbsdPUFcO0qft++b5AYVo8Tim8ZjV/MpQYzHcrsVKiZzcfDD8buXWhdjFArZouqMQvxzUCogmRzEbjPj9x9dwI6DDtht5kQxRR/Hp+yn93E8fCH5AvZS+++j0SgsBZqUcT4hhJCBSde3zodxFMkPgXCa+KE0haqJOKkY8FkVFjy9eCqN2wZIxzKy8ze6DBWvpaRvg6SjJyj65QGxxG8dPUH6caRhNrC42BPET37/MW4oL8Iy+7UIhiMo0qkxysCirFA7JJ9hoU4No1YlmtRLzzIwaFR4/sEZcPpDKQmofBwPu82MSDSKQpZ+WvlsXLEeW788FY/tOypIAEAN4dVJngA7edGDbQ21optrtzXUoqxQA52afmvZKByJYOWcCiiBlM54vc2MFXMqEI5QJzxXmCTaQyC2kXMwNpUQQgghI9HV9Jd0aka2fZYLwFMpFVjeu+lDbLE725K+mYYpoMvl4wQJ3wDgrbZOrN57BNspsT8hhOS9nkBYNvHobxpvGe5TJAQAwCoVsv1KNsv6d+QyI8vgjsrRmDy2UFD05PhZF/iIsOruYBedEatEbNSqZKu/GbUqPLHvKG4sL44lwUy6Pm468BF+/JUbEn3ldImUvTKJ2QAkErd1eji8fbILdptZMklbvc2Mh2ZXIBDi4Q6EZZ9Xo1ImNsT7OD5xvV89txJnLvkTAevFOVKhnBJWE0JynVqpwIrZNgDC+aoVsyugHgH9GQOrwsyJoxL/dvpCeP7rM/D2p12JWJS4OpsZxYar7w9kuuJrT5r2ON1xKZlOVtcfmfjsXD4Opy/5cODIWcHvwGoxQM8yNB9JCCE5zKRVo/2SF7uaYptR40VQ+UgEO5dOh0KhSMSgxse9CiBlLbC+woI18ybjnDMAAIkNl3HBpA0RteOLUl5fo1JCzzLYtexmrHnxWEqcM40fCSHZysAymCZR0K7OZoYhQxtrCCEknykB2XXF/Eg5RXKRVqWUXTfNh4RojFKBFXNsAKIivz9b1sVtDpRBo8KzTacEsa2x96zAj796w/CcGLliZUU63FM9JiWWYPqEYpqnJsPObGDx1KvHUVteLIjp+5932/Hjr+TXdUZqXcwX4vG9//2bZGyj1OfAKBWiyUHi+wiSi0wAwDdvm4gQH4XZoIGXCyfWL49+Ll24os5mRssZp+jtE8x6SvhGCCGDxMjK7ysz0vwpyRKeNPFB6Y4TaWIx4BZj/iYoHwpFOjUeuaMCAFLW1OttFqy8oyJtYfWBokwdg8TpDyWCMfpOMu5qOgWXP/PVZPPBjoMOHHJ04o8nLqbcXl9hweaF1UNSYdCgUeHkRQ8eqJuIe2rKUGrSIhiOQKdmMMakwU9+/zHeSDq/eAKq5w+fxpIZE6BRKymh0QhQoFVh833V8HJh+DgehTo1Sgo01BBeJZePQ6eHgzsQQrFejd19Ki8AsYkjBYAVc2ywGGmiJ1uNMrJ4eI4Nj86thCfAw6hVwRsMYZSRRTQq3LBIslMwHJGt/hGkLNqEEELIgESjUdn+UiQq3cb6w7xs++wPSydOyLXF7kxvPpXS6eEECd/i3mrrRKeHo7EfIYTkOQPLyCYe1dNiOMkS/giPMSaNaL9yjEkDf0Q+qRYZPlwkgkfvnoyNB1pTrjV1NjPWzq+C2x9MuX+mis6IVSKWq/7mDYbxxvEOvHG8Q/T54n3l/iRSTrf4Xdh73B0IJSV7O5VI0vZA3URoVcpE4ptAiMfhU11QKCAZXGpPCi6Nz236OB5/a+8GEFunzKUCP5SwmhCSD3q4MAKhCObVlKXMV11wBxAI8ejh8j+4T88yeGLeZBw+eQkA4AmGoVUzGFeoxTNLpuHh5z+Ej+NRZzNjy6KaQdkMkemKrwaN/Jgp3XEpFiOLL04uwaQykyA26+Nz7ozNFybLxGfn9IWw/WCbaGwEAGxZWENtOiGE5CiXj4OaUaCkQItDvQnf5IqgrtzTgj2HT2PTwmqc7vLBH+JRZtLBpFOhrcMDNSOe7ECjUibGvFPKTCnP23LGibXzp+CZ3tjcZDR+JIRkq2gkivX3VmH9/taUjYt1NjPW31uNqEjRDEIIIfK4aER2XTFIe6DIMOERwdr5U/D2p6lzY+MKtfiHaeMQlomlzBVBnoelgMU9NWWJPRgalRId7gAsBRoE+fxY13f7QylJ7ZIdcnTC7Q/lTbKf5L1vmd7nOlzEYgkIGW6FehYb7qvG6r1HUubWBjPOI5t+31Jx9BqVUja2USOSMLW+woICrRqlJm0iOUi3j4PLH0oki0suMtHS7sS6+VXY0Ceu6Y7K0fjOndfjjspSXHAHUgpa1JYX4ckFVdj62nHBaz+1qAbjivVX83EQQghJ4ud5PLmgCh/8/RJKevOgaNUMLrj8mG4dBX+ejDFI7tOniQ9Kd5zIo3Hb4CrUsygzaTC3ZgyW2a9Nmb8ZY8pcHiFK+jZIjBr5YIyBBiyOJJ0eDoccnSnJ8zg+gpICLdSMAhdcAXg4Hn880YGPz7mx4b7qjFQY9ATCcAfCUDMKvHr0nGCxeJndirdPXkoMYpsdXVBCgWX2a7Hn3XasnT8ZtN6R3845/fjTJxdRUqBJdITbOjy4/frRKKS5hwE76/SnbE7auXS65IR/k6MLjXUT4QmEUWoSvQsZRjoVg8+cfux40yFIRLJitg3XUHXYnOHyh7B67xE8vXgqVvcu8BdoVbjgDmDV3iP41b9MH+5TJIQQQnKSXq3CWVcAOw46Usac8cqpYwulA1vcvrBs+/yf/3ST5GOHYrF7MGV686kUd0A+cX9PmuOEEEJyn1Gjwp2TS1ApkkjgxDk3jBpaWiHZIRIBznoCkvNwo4Yg6QUZGD4CPP16akLm+HXm6dePY/Xdk/HH7942LNXX5Kq/tfQmR5MS7yv3J5FySYEG9RUW0fvVV1hQUqCBy8dBp2bw46/cAEahQKPdCqZOgSAfwTVFOmw80NpnTGXB9+6ahNprigEIE2UnVyaOB7nabWZsXFiNHn8Ii24cl1OV7ihhNSEkH3gCPL7/27/h6cVTUWLSJDabAsD3f/s32bmufOEP8bjoDuKVo+cEbdcjcyqw76FbEYkCJq1qUDdDZLLiq4FVYU7laEwZWygYU3101gUDO7AxVaGexdr5U/DYvqOCxLlbFg1dYrTB/uy8XFg0YS0Q6894R0DyQ0IIyUfnnH6c7vJi+5sO3D9jAgCgsc6KZ5OKoCbHygLAvoduhV7NIByJwMAyKB+lx7qXjqXE0NXbzNixpBYrno9txLTbzLjgDiTGvNsbanvvZ8G6e6ugVAB8JIrH/u+o6HnS+JEQko38fASXeoLYeF81guEI3P7YZnuNSonzTj9GFWiG+xQJISTnsAplLF5NYl2xLE8SMZHco1EyOOsKCPbv1dvMmDjaKBtLmSsiUeBcdwDW0QYYNKreeXAGBg2Dzy/5UVqUH30bp18+rtKV5niu6Lv3DYjFtG5dPDUj+1xJ5mRTci/SfwoAc2vKsPTWpCQMPcG0j+uPbPt9S8XRX/JyeOrLNSl7m+OJ124qL8YFdyDleeptFjxQZ8WafUex8b7qRMHbSBRQKBSir91YZ8WmA60p/UY9y6BhxgRsff1Eyu31NjNefMiO11rPYckv38HXbinH/TMmwKRTY5Sezak4HEIIyRU9/jC8QR6vHj2XuoZUYcG4Yj3ltSFZw5gmfsg4wPghEkNjmsHl8nF4fN9R0fw29RUW7MhQITX6FQwSI6tKCcaIa3Z0QQHgqUU1w3NiOcQdCElWMqy3WbBm/mT4QxGMLdTipvJirHvpGH78lRsG/YfhDoRQVqjF030Gn0As0VQUsUFr8vkdcnTiX2+/DqvnVqLDHcAEs3FQz4lkD5ePw+lLPhw4claw2GW1GKBnGWoMB8Dl4wSTYsGwfPbEYJinZAtZKhyJChaEgcsb7DbfVz0cp0UGwKBhsHXxVOzq08ex28zYungqZdHOMRfcAXR7ObgDYZh0KhTr2byplkUIIbkmHI1ix8G2lCAlAL2TQgpsXFgl+dgCnUq2fS7Qyk/1jC3SYeviqXD5Q3D7QyjUqWHSqVGWpcEmmdx8KsWkVcseL0hznBBCSO5jGSVWz63Euv2tgkQC6++tBssIq2ESMhxYpZLm4XJUJBrFkhkTRItJLbdbEUEU15cUDNv5SVV/629fuT+JlK8rMcaSWUskefZxPB797REcclw+Vm8zY3mdFcfPufGbv/xd8Ld/yNGJKKJYdXclbrGOwuq5lThzyQ+NSplSmbjeZsEoA4udS6ej5YwTPf4Qpo4vTvu5ZBtKWE0IyQcmvfxcl0mX/2FNYT6K7TJ9ulV3V+LeHc2JxGblZsOgvXamKr4W6dV4/J4pWLf/mOiYqkg/sPk1l4/DEy8eE43lWfPiMWzPUHCdmMH87LycfJVtX5rjhBBCso/Lx+FPH1/EgaOxGMNGuxUAUDu+KNE2SsXK2m1mPDLbhgKtWpDwDbi8nviNWRPxwd+7sXbBFLx27Bw2v3IcN5UXp4x3lQpg4mhjv5O4E0JItghwYVhMWqx9KbX/X2cz48kFVfAF6bpFCCFXKgrQuiLJSuHIwGMpc4UCCvz6L6dSNrn7uNj65UdnXXhs7uThPsVBYWDl95bo0xzPBWJ734BYQvXVe48M6Rw1uTrZltyL9I/Lx+FRkd8gEPv+ruY3mK2/775x9CadGiyjxGP/dzQlnsZuM2Pn0ungwhFccAfw23/9AtyBEEYXaMAoFPAEwvjnmdei28fh0d+eEDx2W0NtIqYGSJ3HjOtb0CLukKMLGw60ora8GJ0eLvG4//3XL+C6EtpnTwghmTDKwOJnfxBZQ2rrBKLIi3EUyQ+Zih8iNKbJhPPugGjCNyB2fT3vDlDSt2wWCEckq642OboQSJO8iMQ2ikgP/Dqx8cBHqC0vRkt7N1bMtqH6msKMVBg0adVw+UOyVXTjQTjJ+GgUW149jjsnl6KkgC6E+crpC+EXb32K2vJiNNqtKRllf/HWp1g3v4omaAeg08Phg9PdWDHHlljEKB+lx4o5NuxqOiUaxHxNsW7AFcBJZvlCPFranSnfZ3LVCF+IgtJzhU7FUFLbPNHe5cVj+44KguAGe3MQIYSQ/gmEIvhApr8UCEnPIRjUDJ4/fFp0TLLn8GmsXyA/OZ+LbUKmNp9KsRhZzKqwpCSeiJtVYYHFSGM+QgjJd/4Qj3X7W0UTCazbf4wC3knWoHm43KUAJOfdAODJ+VOG5DyutEhAf/vK/U0OJ5XkGQBWPN+CQ45O6FkGjXXWxN+4Ts3gjskl+NkbbaLP3eTowrcCYYT4KFhGCZc/hJICDaaUmbBjyTScd/lRVqjD/b86nFh7WHTjuPQfVhaihNWEkHygUzHY+8EZNNqtWD23Ep4AjwKtChfcAez94AxW58lmNzn+sHyfLq7J0YXH9x3FT756Y04U9dkgMaba8HIrdjTUDug5Oz2c6EYeILbpJhMxREOhSCffZhemOU4IIST7nHcHUFakTbSFLWecmFM5GuqkYhLJsbJ9x75RxDbDf9DuFH3+Q45OPHbPZMypLMHCZ5rh43jYbWYstV+bGO/OqrDg63WxOFcaPxJCcs0og0YQ1wDExhQbX27FFopbJISQK0briiRbBcIR6Q21jk7ZWMpcoVYq8Ng9k9HlCcKgUSXmwetsZiysHQsGiuE+xUFhYFWYUzk6Jbld/Drz0VlXXuz/ytc5ajEuH4dODwd3b5Ipi2Fo43gzKVuTe5H0MvkbzNRzX+1vqe/jDRoVntiXmvANiMUbKQEsuGEsRhk12Nl0Et+5cxI2Hri8Xrdijg0t7d2SsUqNdVZBordkYongkp/jgbqJKbcZNbl/3SeEkGzFheX3pHGU14ZkkUzED410NKbJDLc/fFXHB4p6zYPEE5T/grxpjpPYRpFbJ5plB36N9ssDx1V3V2akwqDFyOKSl5O9T1CksxMKR3DwxEXcP2MCvBx93/nKFwpjyYwJohU2l9ut8IXoux8ITzAkWrm0zmbG81+fCaeXgy/Ep0z4H/vMhTmTS4fxrImUAMdLVqLd1lCLAC0K5wwPx8smtfVQVfmccMEdkAyCy6XNQYQQkk98wbBsf8knM4fgDfGyYxKvTF+L2oT+KdSz2Lp4KlbvPZKSzGJWhQVPL55KE5+EEDIC+ELS4+FmRxcFvJOsQfNwuSsShex1JhLN/DkMJCF0f/vKV5JIWSzJ8yfnexIJ38T+xncunS773lz+EG6daIZRo8KrR86lBLvW28xYXne5uFMuJ3amhNWEkHzgC4UFGw6AWJu0dn7ViFh/v5I+XZOjC91eLuvn8Do9nGCzSdyhq9gY404TI5SJGKKhUFKgQX2FRXQzUX2FBSUFmmE4K0IIIQN1wR3AOWcA+qRNjS+8247nHpyJTk8wcVt8k6TU2LfeZsG2hlqs3NMiWjC1JxDCGJMW/++BWxAMRfD2ya7Efa9mnE4IIdnAmyZu0Utxi4QQcsVoXZFkK09Afg443fFcYNSo4AqEsf2gQzAPvv7eahi1+bG9t0ivxuP3TMG6/ccE+8LW31uNIn3uJxzP1znqvs45/fjTJxdRUqBBMBxBty+Ed09dwu3Xj0ZZkW64T++qjaTkffnG6Y/t+e5bQCG+39QdkN8TLicTv++zTr8gGcesCgu2Lp6Ksf34LYk9vr7CgqW3Xou3P+0SzBkecnThsXsmo8MdxD/cNF6w/pouaVuj3Zo4x2uKhecntqc+mYq5nMTUbjODUeRHUlNCCMlGnjR70tLlvSFkqGQqfmikozFNZuhZRv64Rv74QOXHrFAWoEp4V69Qz4JVKWXvEx8YNju6oEDmPtcQLz8A1fQ5T7vNjJYzTgCxc/QGacEjXykUikSFzWTxfz85f8pwnFbOK9Kx+OHvPhZNPgGcwI3lxYmBRzzA//5fvYNbrGaUmobhhImsUQYWP33jE8nfyeb7qofjtMgA9PhHxqJUvuv2crJBcLmwOYgQQvJNkV6+v7TxXpn+UhR4/vBp1JYXo9FuTVmsfv7waTx6V6XkQ6lN6L+xRTpsb6hFp4dDTyCEAq0aFmP+VCokhBAiz5dmfldskyUhw4Hm4XLXcG9euJqE0P3pK19JIuW+VYm1KiVcvYG6jXVW0TWZdDQqJViVEuv2twoCRg45uhDpfe4jZ5w5ndiZElYTQvKBhmGw6ZWPROe6fvj6cayZl//r7+n6dH1jENJtOskGmdr4lq+xWYV6Fk9Tm04IIXmj28thlJFNGdt/7ZZybDzQitryYthtZjQ7uhCORLFijg1fmlKKH71+QtAXOOToRARRNNZZRTdjGrUqTLAYMMFigMsXW+O7s7LkqsfphBCSDShukRBCBh+tK5Jspdcwksl7djWdytiG2qEUCPFYt/+Y6Nrs+v3H8NSimmE6s8G3YX+r6Pvc8HIrdjTUDtNZDZ58naNO5vJxOH3JhwNHzqZ8l3abGVaLAXqWyfl5hJGSvC8f6VmVZAEFu82MxbXjBvzcg/37dvk4QcI2IJaEY/XeI9jeUCv7W5J6/KG2TkSi0nOGf+/y4aHnPsSuZTcLrsfpkrYVaFX443dvg1GrQreXQ53NjCZHV6KdHj9Kh5/fPy2lnU6OoyzuTe4ZL2SfnASOEELI4BqlZ/HTP9AYn2Q/6ntnBn2umaFnGdTbLKKJCuttFujVlPQtq1mMLP5/S6aietwo9HA83P7YxgQjy0ALgNXl/qTNUCjQMrAYY8GMJSYNPAEeBVoVLrgDWLX3CCaM0uP1b9dDp2bgCQYzUmGw08Ph3b9fwtemj8MDs65DmI8mvs9ClQK8UgkPx+N/vjEz8R0jwkOhZLB02jX4u9uHgjypskFERIFSoxpN378dnhCPHn8YBg0DnZqBAkAY8pMfRBzHR0Q3belZBjeWF2NeTRnqbZbEb04V4fH9L15PnY4sFeQjaGl3YsUcm+jCWzBNYk2SPQp0amxeMAm3VZbB06d/8+cT5/JiUWokcKfZJJzuOCGEkMEXisj3l0IRmf6SAlgyY4LoYvVyuxWQWR91B8KyY+5sbRM+6/ahJxCG2x9CoU4No1aFa4r1GX/dQv3wJHkbrvebi+izIoRkikknP79rovlfkiVoHi53GbUq2Xk3o1aFTzs8iURoFoN43zSeMM0TjPWHAuEI3P5YIjYjy8TWtvQsPu/2wZ3Ub4ICsgmhL3k5cCFecG4qAL5IGBqlChwfAcdHEI5EcMnL4cwlD0w6DbxJj9m8sBqIRsEDCIQi8ATC6PZx8Id4sEoFFADcXBhRKMCFI4n/Sgs0+L9vzUSxXoP5U8vg9oVg0qtRoGYQAeDlePzvN78Ak04FNaPEJU8QRq0aWrUS/+/tv+P6EiM8HI+v10/E9740CQaWQTDCo9sThlGrgkIBqBngazddA6c/hPZLvlh/kmXgCfFw+WLnb2AZqAHwkSgYlRKhSBQ+joc7EIaxd21Ip2YwJqkCc9/vJBiOwJ20lqRUAE5/CEbt5e81+TFFehZcOAJPMCz73cdRwmpCSK4L8hE8elcFWFX8mhm7Bn+5dhy4mpIR0Z8RWytP3uTo8ofx2rfrYWQZnOxwCtbn+iZQTdd2DIWBbIy54A6g28vBHQjDpFOhWM8KktBajCxmVVhSktXEzaqwZCSGaKhQmz78svG3RAjJTSpVGEZWAy2rxJv/fhsC4Qh6AiHMrymDkWXwjzddkxhvz6spAxBLUB7XN9mB1WLA/JoyKBSAQc2Aj0TgC0fREwjhxDk3DCwDpz8IVqVCoU4dC/LvXS9Mvo5JtTUA+jUHQQghQ6lAp8auf74BFWOKBXOUbee7KW6REEIGICixXwOIrRmNhHm4XJbPMVJ6NYODj8xESKlOaff/8aZr8E+1pfAj95O+eTge15l12LrodkHf5pdvOeDJk8KDnR5OdGM0EEtS1Onhcn68aTGy+NrN1+CB+okp+01VjAI7D53M6TnqOKcvhI8+v4Sti2oEf69/+OgsykzanP8eTVo1ri8x4pl/mib4Hh/+7w9pvJHFFAB++U834RqzHmvmTYEnEEaBTgUNo0QgHEGnh0Mo0oPSAg2A2HXJ5eeg16igVCigUipgNsRiMvwhHl6OR48/hEK9GgaNCs8/OANOfygRe/X7Y+fx03+8AXpWBT/H471Tl2DSqWDSqqFnGVxwB+HyhxLxIAaNCiW9a1udHk6QsC3urd5rIgC4fCGEI1H4wzx8HI8inRolBRrZxzc7utBotwIQn0s8+O+3QatU4K3v3x57j73zfkY1gz/9+22IKgAuHEFPIAyjRgWNSolQNIIIH1u3PHPJB6NWhS2LauAL8Yn+h5pRorRAA71GhXk1Y3B3VSm+9d8f4h+mX4O7poyBmlHilZV18AV5mHQqqJQKnL7oQYCPvZaeZaBXM1ApFfCFInAHQjBoYjE8KqUCo40a0SKS8e+QUSigUykTcUPx9yW2ptj3OdyBEIwaFVhGCaefS4nZIYSQXERj/MzK5zH4UBsJibOHA32umcEqFXho9nWIICpIgv7QbBtYZWaSGtPOpEFSqGcxedworNp3NOULrLOZsXlhDXh/iAYA/WBSq/DCN74gqGBRZzPjhW98AX89fQnf23s08blm4jP1BEOYeW0xxhSNwxMvXv4+vzZ9LP719uvxhMR3/Idjn+NLVeNwbSE12vlMpQrj23dW4rF9R1OCrew2M1bMtmFcoU7m0USKWPI2qcoL8d/cnOssCEajQ3mapJ/8wbBk1YxtDbXwB7MzmQgRsrAM6iaVYbVE22egohs5IV0yAkpWQAghQ+9q+ksKAM82n5KsyPLk/CmSjy3Sq/DcgzOx8UCroG1/7sGZUCiyr399usuLx0X6Ij9YVIMJZsMwnllmjLT3ezXosyKEZBLLKGG3mUUXxO02M1hGOQxnRYgQzcPlrvTzblHc/KM/J26fVWHB1sVTMTYpudhZpx+r9h7BB6e7sa2hFj/83ceC59pwbzVc/hAe6/M6u5ZOlz0/bzCMza98JOxrLayBWqESrIfOrS7Fo3dXCl4n/n5+/PpxvHLsQuJ2u82MzfdNhkLBYPMrx0UeUw2NisGaly6vF+pZBs8uuxk7DrYJ1meW2634l2ffw7TyIvxgUQ2eeu04Xk16vfh5bDrwNxz53I251aVYdfdk0XXdZXYrVu5pgY/jUWczY/291dApFfCGItiw/5jgtR+ZUwGOj6DcbEj7ndhtZjwyuwLBMI+GXx6G/Toz1s6fgidePCb5GLHvvq/hSlhNCCGDwaQGuCibEpsBXL52s4r82Owmx9OnEEO6dfLk9bl425O86aM/bUemXWlytvYur2g/YsuiGpQnzfMU6llsXliNx/cdRZNI/yHX20Nq04dPtv6WCCG5Sa3Q4KnXjmPlnZOw5sDlca3FyOKFb8zEuv2p63Q7k8boUv2A+Nh3z+HTeOyeKfjnne8kNobG+wjf+M37GG3SYLndioZfHsb0CcWC61jftoauf4SQbGVhGehKiylukRBCBpE3TTHQdMfJ8Mn3GCmjUgFvVC25V8+YB+2+WhVCY71Nsm8TjgaG8ewGj1tkX1gysX1juaZQz+Jbt9skf5P5ML+riIYxZ/JYyb9XZTT32wuLkcUv/mW66NrUL/5lOor1lCAhW5lVgHaUHk/0rhMlz6Ulf5f1FRasmG3D8l+/B19vYk27zYwH6yai2xtEoZ5NzNHFn+PXzadS1p7mVpfil/8yHWddfjz12gnB8z8yx4Zlz6Y+/4rZNgRDPMabDWmviU4/hy5vEHwkih1vOgTP/+07KmQfHwxHJOcS51SOxpPzp2DNi8dS3tOdlSV47J7JWP9SauzLHZWj8ejdk7HpFeH+guQYmvgc5T/vehc3lRdjxRwbftN4Czo9QXT7OOH7sFmwaWEVHtz9Ps50+6FnGexcOh0/f9MhGvez9bUT2HhfNcYW6UTnLedVj8H3756EJ14U5h7ou6YIiM99xl9Lav6UEEJyRY9fvk/WQ2P8Acv3MfhQy+fijsOJPtfM8IR4PLD7fTTWWdFotyIYjkCjUqLljBMP7H4P+x66NSOvSzuTBsln3T7BBRwAmhxdWPPiUYR670OkfdbtAw8IEr4Bsc9x/f5jmDHRnPj3mhePZuQzNWnVKCnUCSZuHp59veC25HO5uzqWJC4AIJihLI1k+GmUGqx5MTXhGxBLsLDjTQeaP+2k3/oAGDTChEONdVbRZBbx31xAAUBFzVg2KtKzkolInm0+haI8WMgYKYJKhWzbR+1dbig2sKizmUWP1dnMKDbQb5IQQoba1fSXIlEIHpf8+IhM3jY9qxIkfANibfumA63Qs9mVCFRuruWJfZmZExhOI+39Xg36rAghmXbJy2G53Qp7n7FUPOjnko8bpjMjJBXNw+WuICA/74bUebe32jqxeu8RuHzxSr9cIjBRbi59/cutaHJ0Co6ZdPJByiqlUvLcmj8VPt8/3DQea14UX99b8+JRfPmm8Sm3Nzu6oFKqBIGY8cesffEYjnzmSjnWWGfF9j4J3+LP9WzzKTTWWRP9wcV9Xi9+HtsaahPnK/X5x58r/u/1+48hGI0KEr7FX3v7wTY0O7rwebcv7XfS7OjC9jfbcNbpR2OdFZPKTLEiQzKP6fvdE0JIvuHByLaJPJhhOrOhY+xTnCfdOnl8fS65P5AsG9qOQj2LrYunYlaFJeX2WRUWPL14asrGtwvugCDhGxB7v4/vO4oL7ssbHl0+DhsPfIQby4tjm0Lun4adS6fjxvJibDrwEbWXZECy+bdECMk9n3X7EuPgvmtyTy+eKkj41pfcePLZ5lOYPLYQ6/cfw9OLpyaOxfsIz/zTtJQxcrrrGF3/CCHZLP38KSGEkCvVdw7qSo+T4TESYqQCkG/38yEdmkaplX2PGqV2mM5scOlZ+fl8XZrjuWAk/CYVSpXs3yuUud9e9ATDsu+xhwosZq2gKvb3GU9kJjWXdqitEzsOtiViP4DY/No5lx+hSDRlji7+HE0OYRxM86edgkRm8eff/keH4Pl3vOlAk6MTF9wBmLTycTmMUglHh0fy+blwRPbxhTq15PufMrYQT/RJ+AYAlWNNWC8S+zJ5bKHk/oLkGJrkucdDjthnfKEnIP0+HJ1Y++IxPHP/NACxz3pHn4Rvyc9bWWbC6r1HcMEdEJ23/PJN10jGGYmtKYo9x5XMnxJCSDZLO8YXydlA0hsJ/f2hdiXxQ6T/6HPNDLc/DB/HY8dBBx7Y/T4eeu5DPLD7few46ICP4+FOk3BzoChbziDpCYQlgzGaHF3wcDxlRU2jJxBGD8en/RyT/52JzzTIRxAMRwTn4enHudF3nf88HC+YWIhrdnShxKSl738A1IxSsIm2dnwRXVdzVJAXXkPjmh1dCPLyE48ke6Tr39BvMDeUmrTYsqhGkPgtXs2k1JQfC8WEEJJLrqa/5E0TTOCTOZ5rbXuune/VGmnv92rQZ0UIyTQdy2DlnhbU9kkkUFtejJV7WqBT534gKskPNA+Xu9Kth/UkrYfFvdXWiU5PLNCw08MlAhPl5tIPtXWKzv2wKuGcfJzdZoZSYgX5UO86SF8lJo3s+ykxaQS3y627ib1O7fgiQUBqXLOjC7Xji2Rfr8nRBX8okvZ8k58r/rhgOJJmbUgDdyDcr+8kvpZUO74o5X5yj0n+7gkhJN/0Jw4j3/VdK0+3Th6f90juD/SVDW3H2CIdtjfU4o/fvQ0vPnQr/vjd27C9oRZlRbqU+3V7Odn32+29/D46PRzeON4hGlz3h+Mdw/6eSW7K9t8SISR3nHP60RMI945pheNOqbFoyxlnoi+QbjwZHxv3Hfc2OboQ5qMp9wPkr2N0/SOEZLOBzJ8SQgiRJ7ZfI85uM0PN0PbCbDQSYqRGwhzxSHiPwMi4ztBvMj/+XkfC95iv+n53svEyfWI/gNjeKoNG1a/nKDFpUGrSyjx/p+D54/Eg3V4OFiMrSMIRV19hgVIB2ed/+2QX6iUeb7eZEQjxuKuqVPTxUu+pdnyRaOxLf+Ykxf59yNEFg0aFEtnPqQuq3ut/f17nrbZOdHvF5y3TxSb1XVOUmvvs7/wpIYRkM4UCsn1vhUL0EEmD+omZ0d/4IXJl6HMdfHqN/B6hdMcHitJ0DhK3PyR7vMcfAqiBlJXuMwR6P8fkfwfSP+aKz8MXQlTs9v58xxk6J5I90v0dBMMR+hsYgC5PEMvtl7P+A7HPUg5dV7OX2yf/G+jP9Z5kh7RtH13vcka52YCffPVGdHs5uANhmLQqFBtYSvhGCCHD5Gr6S+kqshhkjuda255r53u1Rtr7vRr0WRFCMk3PMripvBg7DjoEx+ptlrTViQkZKjQPl7sG2p+J3+5OOp5uLl3s+DlXQDAnD8QCfpbbrTjnCggeI/d8noB8YLfY8f6st6R7XanjUucTX8tLd759X6vv+qTY/ZPfz0C+k7RrItTHJYTkKRrjC9fK+9smuNN8Ntnw2RXq2bTVY91pgmKTj+fCeya5h/6uCCGDweXj0OToxLVmAwDxcafUWHRX0ylsa6iFEv0fT4o9V4/EuFTqOkbXP0JINqOxIiGEDD6x/RrA5bWhS94gJo42DtfpEQkjoU2k95gf7xEYGdeZkfBd0nvMj/eYr/p+d1camxEMRwTzalLP4QnwA479cAfCqCxjsXXxVKzeewRvJSUfm1Vhwbp7q+Do8ICPiO1ij9nVdAovP1KHDftbUx4fv6Y+sqcFP/7KDf0+r4HcLnW8b3xOv/YAX8HrSK0dpov1uZI1xf7MnxJCSLaT63uTgaF+Yub0J36IXDn6XAeXXs2g3mbBIYcweXC9zQK9OjN7iHImRf769euhUChS/hszZkzieDQaxfr16zF27FjodDrcfvvtaG1tHbLzM+nUsscLdGoUaOXvM9KZdOp+fY4p/87AZ1ogcR79PbcCLX3X+Szd34FGpaTvfwCMWjVW7mlBbXkxdi6djp/fPw3XWgyyjxnp19Vsbhf7XqsFx0fw95Zr0rZ99F3mlFKTFpVlJtxiHYXKMhMlfCN5I5vbREKkXE1/iU1TDZGVqYaYa237YJyvy8fh0w4PWtq78elFD1y+7K3GlWvfz3Ciz0oatYuEDA6VQoEVc67DlkXVibmqXctuxpZF1VgxxwYVlUAjWYLm4aRle5s40P5M/Haj5nKyZ41KfrlX7LhKqRDMye9cOh215cVYuacFKqX0dU7s+Yxa+YVsseP9WW9J97pSx6XOJ/6bSXe+fV8r3W9No1KmvJ/+nGv8v/4+ZiT/ngkhVydf28R80net/Jpi+Yqr8c/ElOazyYbPrj9zc6Y0RS6Sj5u0auhZBivm2FLGaivm2KBnmax4zyT35MJviQyebG8XSe7q9HCYNKYgUbxJbNwpNRb1cTxW7mnB9++uxPhR8v2A+NhR7LkKJMalUtcxuv4RMrJle5tIY0VCCBl8Yvs1kteGDJqRe23N5nZxJLSJ9B7z4z0CI+M6MxK+S3qP+fEeByqb20RA+N1dabyMRqUUzKuJPYeeZTC6gO33XF3f2+JrW2OLdNjeUIs/fvc2vPjQrfjjd2/D9oZaKBCL25E7fx/HQwGkPP61b9cnrqk+jpd8/GDdLnW8b3xO2niX3u+tv68jtXaYLtan75pif14LGNm/eUKItGxvE1lGiecPnxbtez9/+LTsvjIijfqJhIxsWjUju4dIm6Gkb/KRc1mmqqoKb7zxRuLfDHP5Q/nhD3+In/70p/j1r3+N66+/Hps3b8YXv/hFfPzxxygoKMj4uRVoVaizmdGUlA01rs5mhpHNzBeYTwq0KkQj0X5/jnU2MwrSBH8O9Dy4cERwHkaWQZ3NjA/bnWiss6J2fBGC4Qi0agYXXH4U9B6n7zq/GVnpDJ12mxkd7gAmjcn8NSffFLAMppUXYcdBR+K2nUunp70ejPTttdnaLsavh1LfXQFdJ3OGMc13SW1ebnH5OHR6OLgDIZh0algMlMWb5I9sbRMJkXI1/aVuL4cH6yZiXk0ZSk3axLj0vMuPskIdur0cMFr8sYU6Ne6oHI3JYwtTxrQftnfj+FkXCtNMUA+1dHMt6eYEzjr9WPXbIynjt1kVFmxdPBVji+QX4ofD1b7fkYQ+K3nULhJy9Xq4MLwcj1ePnku51tTbzFheZ4WHE68mSchQo3k4edncJg5k3m1WhQUWI4uzTj/eP90Nu82MZkcXWs44E//fV32FBR3ugOD2ljNOwZx84jE2Mzp6gqLnXd+7DtJXhzso+3463MLnK0iz7nbJk5oUpuWME/U2Mw6JvIbdZkbLGafs69XZzNCplWnPN/m54o/TqJSyr93hDqJyTAFmVVjwVlun7HcSX0v63BVIPH9Tmu8x/t0TQshAZXubKDdfNRLWovqula+YY5NsE5LnPSxGNtH29JUNbcdZpx+r9h7BoTb5ubliAyvbjyg2XH4fFiOLXctuxvaDbSn9GLvNjF3Lbh7290xyU7b/lsjgy+Z2keQudyAERqmAQnF5nNi3fetwByX7PR+ddeH3H10AANnxZMsZp+i4t85mhopRpNwPkL+O0fWPEJLNbSLNfRNCyOArYBl8YeKolNsUvcXOvjBx1Ii/tmZruzgSYqRGQrs/Et4jEPt7lVqDzpu/15Hwm6T3mBfv8Wpka5sICGNtZONl+sR+AMAFdwAGjfxz6FkG2xpqsWF/K24oL5Z5fovg+ePxINXjChO3FeqF+7YC4Qg6eoKIRqOy8T6sSpny+LNOP/7a3g0fx0Pf23bUV1jwwenulLibIj2LpxZVY9Mrx+Hj+MRzxuJuhPui43OOYr+JepsZJQVarJhjw66mU6gtL0q873qbGd5gGB3ugOz3EOYjop9138+u5YwTsyosKDaIz1umi03qu6YoNffZ3/lTQgjJ5jbR6eOw7AtWsGoFDBoVPAE+0ce5ZcIoOEUK8pH0qJ9IyMjmD/Gye4j8IV7m0QOXU2k6VSoVxowZk/hv9OjYLuZoNIqf/exneOKJJ/DlL38Z1dXV2L17N3w+H55//vkhObdrivX4wcIa1NnMKbfX2cz4waIaqAFEItRAyrmmWA91FNgs8zluePlYyr+vKdZn5Dw0SoXgPJ558xP8YGENdi6djpb2bjyw+3089NyHaPz1e3j12Hl0+0P4waIahCI8tPRd5y2TTo3Ni6pRX2FJud1uM2PF7ArYbRYUaKjTdqU0kajgGrpq7xGsv7dK8nqgBaAZ4dmms7Vd1ICXbRM1yEynhgw+VYSXbZdVEfouc8VZpx8r9rTgjp/+GYt+/jbu+Mmf8cieFpx1+of71AgZFNnaJhIiRQP5Nlauv6TXMFAogNeOnksZl7529DwUithxKWOLdHhyQZVgTPvX9m48uaAq6xKhXVOsxw8WSX9OcnMCLh8nSPgGAG+1dWLV3iNwZeEixtW835GGPit51C4ScvWUCgV2Np0SLNoecnRhZ9OpRPA7IcNNEwnLz8NFRnaCwmxuE1WRcJp5t9TvblaFBU8vngogNne+6cBHWG63xhKcNJ1K/H/f51p/bxXsNovgdU529Ej2pzYvqsGtE0dJHhN7vt9+cAabFlaLP2ZhDf7vg89SbrfbzPAEA9iySHrdrXZCMV74xgysmGODnmWwq+kUVsypEF2fWW63YlfTqcTnt/eDM6Kf68o9LYnzlfr84891+TOshkapwLp7q1FvE772I3MqYLeZMa5Yj62Lp2JWhUXyO7HbzHhkdgXGFumwq+kUPj7nTnwPUo+Jf/dUvIEQcjWyuU2MRsJ4cr70fFV0BPRn+q6Vx9sEyT5e73ikUM8m2p5k2dB2uHycIOEbEJubW91nbq7UpMUWiX7JlkU1KDVpU25/5qBDsBmk2dGFZ94UbiTs77l+2uFBS3s3Pr3oycp5Q5JZ2fxbIpmRze0iyV0mrRq+II+uniCW26347Qdn8OSC1Li39ftbsXb+FNF+z+P3TMEL77ZL9gPiY9/jZ11Yf281Vu09kjgWH68//N8fpoyR013H6PpHCMnmNlGDaJqYiuiQnAchhOQTTZp5OFpXzM52cSTESGkiafae5MF+BQ2jlO/b5Mm+qJHw91qgUcl+l/mwn3AkfI8j4T1ejWxtE4HYmtrmfqyp1VdYsGJORSL2A4jNr5UV6qBWKrD+3mrJ52iss+LZ5lM45OiSjOOor7DgkTuEz79idgXqbBbB2lays04/nth3FGNMWlw32oAVs23C57dZsNx+Lda9dAyfXfIlbh9bpMMPFtXgzsoSbGuoxXOHT+Mb9ROxa9nNKX28xf/5Nl49dh7PLpuO79xZgZ1Lp+Pn909Dvc2MjQurBHE3x8+6sHZ+lWiMy1K7Ff/4i7+gpb0bO5dOx4N1E7Gr6RTqbbHPeHSBBrYSo+T72LSwBg8/92His14x2yYae7PcbsWJc248vXgqSk1a0XnL//vgM8lrcN81Ram5zyuZPyWEkGxuEw1aBmVFWjxz0IEF25vR8Mt3MH97E5456EBZkRYGbX4klh5q1E8kZGQL8xHZPURhPjNrU4poNJoTq17r16/Hj370IxQWFkKj0WDGjBnYsmULJk6ciJMnT+K6667Dhx9+iNra2sRj7rvvPhQVFWH37t2SzxsMBhEMXq5653a7MX78eLhcLphMpn6f3wV3AO6AD1pGAw/HoycQQoFWDSPLQAvg/73XjntuGIfKsv4/50h0sdsHRKIIAILPkYuE0OHmYdQy6HAHse/Dz/CDRTWDPqhy+TgcO3MJ11kKEOpzHkUsg0f3HcUhiezpX51+DULhCKZNKMa1FuOgnhfJHp9ddANKBp4Qjx5/GHoNA52agRLAN/7fB/jPf7oJ15XQ938lXD4Ofz3diYklhbHfnD+EAp0aJpbB+Z4AWDUDTyD2+/cGeejUCuhZBnxUgcox/b+uut1uFBYWXvE1Phtlol0crDaxvcuLjS+3ojKpMq1GpUTLGSdOnHPjyflTUG42DPzNkyFzpsuLP398HrdNGpPy2zSyTOL28fRdZj2Xj8OKPS2CjTVALFh3e0MtTVKPQNQmDt1YkRAxx8+5oVMFwSj1gjaWj/jgD2swWWL+4LMuLx6TGpfaLHhqUTWukWifzzr9ePS3f5OsOvLDf7gh6xK/XXT6EeAjgs9JyygxWuZc2y704Iv/8Zbk8T/82yxUlGa+gs5AfNbtQ08gnJiLKNCqaHFAwmB9VtQuDs1YkZBccvycG3P/v0OSx1/7dr1kW03IUPqsy4v1MvNw6+dPkewbiqE2cejaxJMXPXi77YLkvJu9ohSRKBL9HIsxVrn30w4P7vjpnwHEqgvHK/WGI1GUFWrBqpRw+y+vrZl0ahTqWXze7YM7qd9kZBmsf7kVkyX+dlbNnQQtwwjW6tQAvJEwNEoVenqPmbRqaFRKeIMhmHQsvEmPMbAMEI0iAiAQiqAncHlNhVUqwACS6252mxm15cX42xkn1i2oAh+JwKhmEAFirxEMo0CrAssocckbhEETO8dg2AeNSjjWCkZ4dHvCMGpVUCqAaDQCA6tOeY8FbGztJ/4ZGnrf8zef/xDfv6sS5WY9/ByfeB9aFQNGAejUDEp7xycuH4dOD9f7eagRDEfg7l1L0qsZKBWAyx+CQRP7Xi95Oext+Tzle+TCEVz0BMEySthGGzHBQnOwhAyHfGkXs33+VHauq8KCpxZKz3Xli3NOP1ThMAIKZaL9MunVMKgZeDke7uT4o4gPXbwmZY083vb07TcMp+Q+i5g/fvc2QUzFBXcA3V4O7kAYJq0KxQZWsClmIM8r56zTL0hON6vCgq2Lp2bdPCnJvGz8LWWLfGkTgewfK5Lc5fJxONXlwxvHL+Cjsy5MGVuIe6pL4Q6EYdCo4AnwGF3AYsP+Vok1PjOeXFAFlz8Eo1YFvZqBj+PhCcYerwAABWBUM4giCg8XSRmvO/1BqFUqaBglurxBFGjVGGPS9us6Rtc/QvqP2sShGyt+fN6NaCgIg144z+f1+aBQazDpCmKHCSGExGLCH5eZh9uysPqKYsKpXRzasWI+x5Od6fJig8ya97r5U3J+v8KnHR6EOT/0OmHfxuf3QcXq8moPWj7/vbZd6MHDz32IZ/5pGsJ8NPFdqhgFHv7vD/HM/dOyNi61v1w+Dvs+bMcdk8sEf69/PH4Oi6aV5828AcWfCmV7m+jycXj5r2cSsTaeQBgFuticWCAcgTcYRqFOjZICDQCg08PB5Q9BzzJglAowSgXMBhZcOAJ/iE/Et5h0ahRoVAiEIgiGedyzrSnxmsmxOcFwBFaLAUU6NfQsgwvuYOz5e+NBDBoVSmQSviXv7dKzDL5520TMn1oGRVQBbygMb5CHSqlAk6MTv3jrJHwcj/oKC3Yk7fn6vPfv9nOnv7dwbRS7RBJT6FkGu5bdjGcOtqX0/+6cXIIn50+BPxSLfTFoVNCqlAhFI4hE0PsafgBAyxkndjWdgo+LJWCtr7BgzbzJiEQALasEH4kgCkDHMAhHouCjQCDMwxfkUaCLzXFGI1EE+Fi8kI6NfU5qpQK+UATuQAgGTSyGh1EqMNqoSbm+xOctk79DHaNERIHL351WLbqm2Pc5enpfi2WUcPm5RMxOvlzPCMkm+dIuZvv86dXsKyPp5fOYhhAibbD3EPW3TcyZ9PEzZszAb37zG1x//fW4cOECNm/ejFtvvRWtra04f/48AKC0tDTlMaWlpTh9+rTs8z711FPYsGHDVZ9ft5fDgaMXEwEbteOL0OnhoFUz+LC9GyfOe7CgVnHVr5PPXD4OJ7v92JE0kNy5dDq++l/vSD7m3744adAHVp0eDl9/7q/Y1lCLZ5tPpVQIfulhu2gHCAAOtXXisbmT8S+7DuO/H5wxqOdEsstFP49FP5e+YPcEQkN4NvmhUM/CWlKYspC4f4Udj+77SFClG4ht9lp/bxXcvpH7WWeiXRysNtHH8XjjxEW8ceKi6PHvfWnSVb8GGRoeLoxyiwmPv3gsZbNDfYUFX6+fCC83squ65YpODyea8A0A3mrrRKeHo4lqktOyfaxIiBg/F8YFdxQ7D6VOstfbLHig3gqTVrqN9YR46XGpoxOekHRlS5c/JJrwDQCaHF1w+UNZtZnR5ePwaZcP299sSxkX2G1mPDKnAizLSLZhzt4F3uSF9vgcza6mU3D5s3csQYsB/UeflVA2jxUJySWeoPx4N91xQoaKJ5RmHu6ukTsPl+1tYoiPYNLYYtF5t5V3VIDjI6KbFt1J6w8+jseOg46U4y8+dCtusZr7PgzjivUYl/Tvj8668McTF/FHib+d79x5PV7662eJvrQnyOPD9m58fM6NH3/lhkGZS2rv8qK92y85vml2dKHRbsWOgw5s2N+Ke2rG4LF9xwT3e+7BGbj/V4clX+f1b9cjEI7gi/8RW9fRs4zoGmC88m/lGFMi6POSl0MoEsWRz934513vij7/z++fhhfebU8UVoj/118nO72C7zHZiw/digmgACxCyMBl+/yp7FxXm/xcV74IhHiseak1pV2Kz2t9YaIZfCQKTzCA8y4/ygp1grnDK217hoI7TcyEWExFqUkruSHjap5XisvHCRK+AbG1s9V7j1DRpBEoG39LZPBl+1iR5K5CPQtLMIx51WOw8MZx2PByK2rHF+GB3e8n7rNz6XSZNb4utF/yYVfzKayZNwWzfvSnlON2mxnzasowfcIoLPx5c2KjJRAblz703Icp/972Rwe2N9SiP+j6R8jIlO1jRbc/jG8991c8vXgqSkwa8FGAj0TxSYcHq/YewX/ef9NVvwYhhIw03jTzcN4RMA8nJRfGivkcI+UdAXtPXH4O/7Tzg5RYRg/HJ2IZ//uB/NqDmM9/r05/CJ90ePDFn4oXJM7muNT+6vRwWH/gY6w/8LHo8frrx+TNPEI+/60OVLa3iYV6FnOmjMXqvUfwVp+CPk8vnipIwDCQv9WW9u6Uf/eNzXnxoVtR1vs6V/r8yXu7fByP//hDGypKCqBRKVPmEZMdStrzdbo3iW/ymuJzD84Q3RvQWGfF9oNtgn25bxzvQDAcwdr5U+AN8vjTJxfxwrvt+Not5biragyAaNpzMWpUWLztbdw0oRhPLarBNaMy81sajHlL8eegGBxCSHrZPn96NfvKSHrUTyRkZBquPUQ5k/Rt7ty5if+vqanBF77wBVx33XXYvXs3Zs6cCQC9makvi0ajgtv6euyxx/Dd73438e94RtQr5Q6E8Ptj5/GrpdNxptsPlz+U2Ex8/KwLa+dXAdHoFT/vSOL0hbCtz0CS4yOyj8lEci13IIRvzJqIX/fZ7KFnGXBh+fM57/Zj6+Kp8ARo018+M2nVsscL0hwnQi4fhyf2HRMMMsQSvsVv58IRmLQ504wNuky0i4PXJspfA3voGpkzuDCP8lE6zK0eg2W3XpuonNXhDqB8lA5uPzfcp0j6YTA3wBCSjbJ9rEiImGIDi//4wyeC/u8hRyegADbeVyX52B5/mr6WzPF01/xsaxO6fZwg4RtweZzwg4XVkou5Rs3lRA7Ji+12mxnbGmph0DCZO3FChlE2jxUJySUFaeac0h0nZKi4r6JvmO+yvU1kmChKjBrRebdSowYRRRSfdnjg7q0obDHEghAHa33Cy8kH9fhDPMYWpiZdGVuoxU3lxejyXn0BgQvuAJ7YdxQNMybI3i/Yuy53yNGJZfZrRe+TLnA+EOKRvELbWGcVJHwDYomwH993FD/56o0oNWkT7/HTDo/s82tUyqsqrEBrToSQTMv2+VPqzwC+EC+ITZGa11ox24ZRhuzfTJWp9m0wn5eKJhEyMmX7WJHkrviGy5Z2J75520R8/65JCIRSY02DaWJPg+EImh1domHG8cTomw60orHOmtJH0KiUKfe92nEqIWRkyPaxYoGOwXMPzsTGA62Cwg3PPTgTUchfUwkhhAjRPJw0GisOr5Gw98SgUVEsY54wsPLflT7N8VxAe09GtlxoE8cW6bC9oRadHg49gRAKtGpYjINX1CCTMRxivy+NSolwJIoVc2yiRc59HI+eQAhnnX5BwjdAOmamdnyRZAHCQ22duNgTRCgSwacdPYnx946DDvz8/mmy70HNKPHA7vfg43gcauvE4/uOUiElQkheyvb5UxrjE0LI4CvQpNlDlOb4QOXsziSDwYCamhq0tbVh4cKFAIDz58+jrKwscZ+Ojg5BltS+NBoNNBrNVZ+PSafGjiXT8FifgZPdZsby3mCLTQtrrvp18pmXCwsCWa8p1sk+JhMbHQp1atwxuQS/eOtkymC51KQFk6azFY0Czzafwub7qgf9vEj2MGpVqLOZRbPg19nMMNKmzyvW0RPEB+3dKb+5voFvffmCPMrNlC05bjDaxcFqE9MtOulpUSpnjNJrsO7lVkwZW4hSU2yTp0KhwOeuADYd+AjrF0gnpCHZgzaOkpEm28aKhIjhwhEcP9+DnUuno8SkgSfAo0CrwgV3AKv2HpFNOJ6uLyV3PN01P9vaBC/HyyaClktUYWRVookcmh1dUAB4ahHN0ZCRIZvGioTkkmI9Kzv/V0xBSiRL0Dxc/2Vbm6hRqrDlteP4h5vGp4wJFArgQo8f2/7oSEkSPavCgq2Lp8JiZDGrwpJSsTj5PhZj/65PRTr5vr9ezeCVo+cEa54rZtsQGYQiV90+DoccXVhmt8reL3njutTm+L6b2/vqO86RC2xtcnSh28sl5kIByH7mdpsZLWecAAYe3D5Y3ykhhPRXts2fUn8G8PWZ42qss+L5w6dRW16MRrs1ZXPHLw+dxNp5U4bpTPsvU+3bYD4vbVwjhADZN1Ykuemzbh8e33cUH5/vwQvfmImeQBifdftRPkqPFXNsic2Z6cav8eNSFcKD4YhgLJ08Lu37b2rLCCFXIuvGimoVVotsZm9ydGHTgVZa7yeEkAGgebj+o7Hi0BoJf5salVI2lnHzQtqDmCsMrAp2m1k0rtVuM8PA5v5+Qtp7QpJla5tYqB+8JG99ZTKGQ+z3deysC/dUl+E3f/m7aGLQlXtaYNKpEQjxaLRbcf+MCSlJ4aTmHNMVoGCUCigAPDy7Ah+cvoSWdieA9DE4nmAYnR4u8e/k4hMuH4dODycocEkIIfkg6+ZPR8A4ihCSHvW/BpeBZWT3EKVLBD9Q8j3wLBYMBnH8+HGUlZXBarVizJgx+MMf/pA4znEc/vznP+PWW28dkvMxqBls6lNNCohNwD3bfAqTxxbCnabS/EjXd7N2Y50VRz9zwW4zi96/vsKSkeRaBo0KXT0ctjXUoqW9Gw/sfh8PPfchFv/n2/jkghv1FRbJ8zn6uQvNji5wPFUQy2feYBjL7FbB36bdZsYyuxVeicArIs0dCAl+c0r5HIso0KpSNl6NdNnULurVjOS1224zQ6+mAWOu8IZ4LJkxIeW32fjr99DS3o2GGRPgDUknWiHZI77oIIY2jpJ8lE1tIiFSfFwYzz04E7uaT2HB9mY0/PIdzN/ehGebT+G5B2fCz0mPKa6mr6XvnXwSU2czZ12VQV9Qvq8hd9wfikgmjGtydMGfJsk0IfmC2kVCBqbUpMWWRTWCdrPOZsaWRTU0J0Wyhl7NoN4msWZhs9A8XJJsaxN9YR7fuXOSYEywq+kUig0aHD/fk3L/t9o6sXrvEQDA1sVTBXM9syoseHrx1H4v1pcUaGTXu/72mVN0zXPHm460BZL6I15J8ujnLsm/4b4b16UCS1vOOCWfIz73lTw/li6w1R1IHY8V6llsXTxV8HnFC3/tajoFYODB7fHnv9rvlBBC+ivb2kTqzwDGPlVAp5cXS67PLZkxAYFw9q/PZap9G8znpY1rhBAg+9pFkntcPg4uXwgt7U78aunNePr1E7j/V4fx0HMfYv72Jvy1vRvbGmqhZxm0nHHKrvHFx8CMRFR3fFwcH9fW9RmXDtY4lRAyMmVbm+iRKRDX5OiCR6ZAHCGEEHE0D9d/2dYu5ruR8LcZDMvHMqZbvyTZo0ivxiNzKkT3Ez4ypwJF+twfixu1Ktk440zs5SXZayS2iZmM4RDb2xWNAhtfls5LsOHeKrCMEuteOiZYN9zWUItjZ8X336dL3uYNhvHA7vcxf3sTXjl6rt9zmB+2dwtu7wmEcNbpx4o9Lbjjp3/Gop+/jTt+8mc8sqcFZ53+dB8LIYTkhGxrE0fCOIoQIo/6X4PP6eewdn6V6B6iJxdUweXnJB55dXJmlP29730PCxYsQHl5OTo6OrB582a43W4sXboUCoUC3/nOd7BlyxZUVFSgoqICW7ZsgV6vx5IlS4bk/AK9VfTENDu60Gi3CjYJkFRFutSJrdrxRXhkTwu2NdQCQMrA1W4zY+N9VfBlILmW2x/CKCOLp18/IRgsn3MH8NDt1yESjQrO5+HbbTj899htPfRd5zWXj0PrWRdW3V0JIFZ5W6VUoMnRiZV7WvD8gzOG+QxzT7GOxU//8EnK76rJ0Yl6mwWHHMLKCLHBSBQX3IERu8k2m9tFtVKBR2ZXABBeux+ZXQF1uox+JHtEIVlVCgDWzpsyHGdFrlB80WH13iMp1WZo4yjJF9ncJhIiZZRBg8dlqlL/QKYqtUqpwIrZNgDCvtaK2RVQyfS1ujwBrFtQhY0vt6bMYdT3Tj51eQKYYDYM9G0NugKd/LSV3HFXQD7xvjvNcUJyFbWLhAyecrMBP/nqjej2cnAHwjBpVSg2sCN2LopkJ7VSgYdmX4cIhGsWD822jeh5uGxvE1mlEmteOiY6Jli/vxVPL56KB3a/n3IsXh3XYmSx6b5qeLkwfByPQp0aJQWaK5rjKdSzeHrxVKzaewSHkuaL6iss2HhvFeZtbxJ9XLOjC/6rLATh8nGJQHCFApJ/w8vtVqzc05I4r9EFGuxadnOiWrGvd2Pn8bMubF5UjbUvHpOd+9ra+37TBbaaRILUxxbp8OOv3IBPOzxw+kPQqJRoOePEyj0t8HH8VRdWGFukw/aGWnR6OPQEQijQqmExUsU9QsjgyPY2kfozsfZwTuVoTBlbiNrxRSg1abH51Y9yfn0uU+3bYD1vfGNNcv8hjoomEZK/sr1dJLmny8vBEwyjsc6Kn/xOGG8arwLeWBdLxratoRZKIGWdLnkMXF9hgUqpFIx/k5PCTTDr8cfv3gYdy6C9y4sff+WGQR+nEkLyX7a3iW6//Hp+D633E0LIFaN5OGnZ3i7mu5Hwt5luPyntN80dhXoWE0bpMX/qWDTarQiGI9ColOjoCeLaUfq8WN/1BsNYZrciCmGM8jK7Fd4M7OUl2YPaxJhMrXGJ7e2qGVeIn73RJnr/ZkcXNt9Xjcf2HRXkLoj/Pm+9zozv31UJpeLjlPifC+6A5L7cvkUYm0XmMJNvjz8mOY4nmUGjEsQfAZcLXG5vqM2L6yMhZGTJ9jZxJIyjCCHSXD6O+l8ZoFWr0PDLd2Lx9XMr4QnwMGoZdLiDWPLLd7Dn6zMz8ro5k/Tts88+Q0NDAzo7OzF69GjMnDkT77zzDiZMmAAAePTRR+H3+/HQQw+hu7sbM2bMwO9//3sUFBQMyfl50kxYBMMRjKVM9rJKCjSor7AkLi7xShVSybXOuwKwGDWDfh4ufwgKBUSraFSPLcQDu99HY501ZXKu5YwTjbvfw44l0wAAepYy4OYrl49DoZ7FaKMGF3uCCIYj0KoZnHf5ccM1RQBiExXkyoQiwso1v3jrJHYsqQUQFSSkWDHHhrfaLmLW9SUjdqNtNreLnhCPYJjH/Jqy1IUMdwDBMA/PVW4KJEOn70JNsmZHF6JDezrkKtDGUZLPsrlNJESKL01Vap9MVWoPF0YgFMG8Pn2tC+4AAiEeHk56fsKgVQMK4J6aMizr00+Dovd4FinWs7KJoItl2jFDmnF5f8btLh+HTg8HdyAEk04Ni4HaTpL9qF0kZHCVmrQjdu6J5IYejpdcs3hg93vY+638qaR7pbK9TQykqea+au5k0WNOP4f1L7emLNTPqrBg6+KpKNRf2TkoANxTXYZlt16bEoze7eNkxyRemWPpuHwcLvk4KBALMLphXBHe/fslrLq7EgrEPpdQOIK3T3YlNqrX2cxYeuu1+Nov3klsdN/WUIuVe1pw04RibLi3GteM0qed+xpbpMPa+VMQCPGot5lFi3nV2ywoNoj3+UtNWvCRaMYKKxTqabxBCMmMbG8TqT8DMEpg1d2TsfFAK3YcdODAI3Wy63O5JFPt22A8LxVNImRkyvZ2keQeRqGASafGl6aUYsdBh+h9YuP8SkwpM0GlUKCxzopH51aiwx0EgESytpsmFGPt/Cl47dg5/NefT6K2vAjbGmrxwuHT+NqMCVi5pwV2mxlqpRLXlRgTz//zP1FbRgi5ctneJpp08rELBVkW20AIIbmA5uGkZXu7COR3HNtI+NtMF8uY7niuueAOXC6wqFOhWJ9fBRbLinS4p3pMytr09AnFefObdPlDWLmnRfQ3uXJPC55/cMZwnyLJoFxoE4fKQNei0rXZ8b1dTl8IXi4MTzAsWgAxzh/iBck84podXVgzbwqOfubEqrsrsezWQOI3e+ysC8vrrk1bhDH5uRrtVvg4PuUaUKhTo1CnxvunuxNxPMlmVVjAMkrJc4wXuMyXayQhZOTI9jZxJIyjCCHSOj0c9b8ygGWUmDSmQFC0HYj1o1lGvuj4QOVMZqIXXnhB9rhCocD69euxfv36oTmhPoxpkjwV6tTQqTPzJeaLQj2LjfdVYc2Lx9Ds6IJezWBbQy2ebT6F//jD5Wzl8YElF46Cjwx+uhmjVoWLPUHRY8FwBD6OlwzQ0bMM5lSOhoHNmZ8WuUIuXwgd7gBeOXpOMOGxYrYN6xdMydgFO5/1iFSm8XE8VjwfmyRafc9knO7yJZJZAMDtk0rhHcHV+rK5XXT7Q/jWcx+isc6KkqTFmc9dAWx65Th+vfzmIT8nMjDpqvBQlZ7cQhtHSb7K5jaRECnpqk7LHfcEeDz8fKyvlRwIc9YVwOZXjmN34y2Sj9WrGKzed1R0c2idzYynFtX04+yHTqlJix8sqsYTfSqU1dvM+MGiatlAIAOrgt1mFn2vdps57bj9rNMvqHoRT6Yxtkg3gHdDyNCgdpEQQkYWtz8ku2aRrt+Zz7K9TUz33fgk5t2CocigVGZz+Tg8+tsjogmW0wVpFwywyNVZpx9//vgiDhw9i8fnVuLBuokYW6TDL5tOJtYB9SyDNfMm466qUlSNNeGaYh1+13ohJXC02dEFpUKB11bWo0ivTrzn/sx9MQoFvvaLd7CtoRYRCKsSb1xYJTvOoMIKhJBclAtt4kjvz7BKJZ7YfyzRLqUrOpnuOOk/atsJGXmyvV0kueV0lxePvxhbd/v5/dNk73vmkh8PPfdh4t96lsHzX58BRqlA7fgibG+oRcsZJxY+05xI9rZyTwuUir9juf1arHi+BbXlRVhut0LFKBLPQ20ZIWSgsr1NNLIM6mxmNEnENhjzLDEKIYQMBVpXlJbt7WK+x7GNhL9NPcvIFr/tTwHbXNHe5cVjfWJU62xmbFlUg3KzYRjPbHDl874Mk1Yt+5ukBMz5LdvbxLhsTYba3zbby/FY89KxlPslF0BMTqwmtuc22alOL15vPQ+rxShITqFnmUQyID2rgkHD4PcfXRBN3gbE9s4DSFwDZlVYErFIBo0Kr00oFi0+0dETkD3HfGjLCSEjT7a3iSNhHEUIkea+in2pRJrTz2G53QpAGF++3G6Fy88BGPy5DcpMNUhYRik5AVdnM2NskRbRwc9PlldcPg6dPUHUlhej0W7F2GIdNrzcKtig3ezoggLA8t7M4YNNwyhhNmjEj6nkk3nxkSi+f1clivQ0gZWvwpEotr/pEP27BIAN91Zl7IKdzwwSiTPjg455NWWwGDUwsAyuNRtw5pIPu9/+GKvunjzEZ0r6w6STn+RPV4WRZA9jmg2c6Y4TQgghRFy6oAe54wU6VZqACun22cPxoknQAKDJ0QVPBsbYV6vcbMDT/3ADXP5QYrNKoU6dNmBNxzJYOacCSkCQMG7FnAroZAKlXD5OsOgODCyZBiGEEEJIJqWbZ6Ng2+yV7rsR+27rKyx4+6R4f/79091w+kKSQa19A155Piq6pgkAb5/sQn2FRbQKXL3NAm2atTIxLh+HJ186hm/fcT1KTBooFUp0eoLY1XQyZYzi43g8vu8Y7DYzasuLwTJK0bHPobZO+MM8inBlf+MWI4vpE4pFK5N39AQxqh/9/HwO4CeEkOGQrj8zEtYVA+FISiKDwjTvmdbnBhe17YQQQgbigjuANUmb2NPFlfY97uN4hPkofvS7jyXjEBvrrNhx0IHv3zUpkRTuf95tx4+/ckPK/aktI4Tko2gkis0Lq7HmxWMp46U6mxmbF9YgmoGi8YQQku9oXTE3jYQ4tpHwt8koFHho9nWIICrYNPzQbBsYhULm0bnjgjuA9S+3JvaFBsMRaNUMPmzvxoaXW7Hly1NlC3CR7GAxsphVYUlJ7BQ3q8ICizG3rzkk92VrMtT+ttlS9+s7JwjExsCMUr6N0KiUmDK2EBwfERxL3m+wc+l0RKJRyf0H8eeKq+9N6BbvZ8gVn0i3xz8f2nJCCMk2I2EcRQiRZrqKfalEmlGjRsMvDwviy1vOOLFyTwteXlGXkdelSMBB4vRz+Pe7JiGKaMriYn2FBWvnT8H2P7Zh3YKqYTzD7Nfp4aBRM4mB46+X34yWdidWzLGhdnxRymTfrqZTeLBekTbYdSAueTkYNCrYbWZBQE3LGadk5TC7zYy/nOzCgqllOT9pTqT5w9JJEpodXfCHeBg01BBeKQWAOZWjMWVsoeD3/tFZFxQAvvpff0ncv85mxtr5VQjw2ZeUgsQSocp9nyxz5ZsCyfBQK5Wi7SEQa/dYJX2XhBBCyEDo01SllqvcqFRAOgFDhQVya7s9gVCiapfYODsbKzmcdfqx6rdHUhJS9Gdx3hsMo9jAYm5NGZYlJ3JwB1BsYOENSlc+6/Rwop8vEFt87/RwNO4nhBBCSFa4mn4lGV56tfx3p1OnzrvNqrBg3b1VWLC9SfhcLINtDbVY8+LRlITH8X6zAsCjfQJGdy6dLnluu5pOYd9Dt2LzgY8ECZSX112bqOx7Jbq9HFbPrUQwFIGeVYGPRjF9QjHW7W8VvX+zowsP327D2ye7JMcwnZ4gfvK7j7Hhvup+B+4W6llsXTwVq/ceSQlkndUnWJUQQsjQ0TDya1GaEbCu6PanzlOxaT4TNa3PEUIIIcOu28vhg6TY1lEGVrJodL3NjJICLVbMsWFX0yn4OD4Wc6NSysYhNvZWEf+s24+HnvuQxq6EkBGly8fBpFdj433VCIYj6PGHUKBTQ6NSIhKNoNsfQvlwnyQhhOQYWlfMTSMhjm0k/G32BMN4ZE8Lnl48FavnVsIT4FGgVeGCO4BH9nyI3zTeMtynOCicPg5LZkzAs82nUtZi7TYzltutcPo4SvqWA5LX1N/qk1SL5iXIcMvmZKj9bbPF7pccF2NgVZhWXoyuniAqy0xQKCA572i3mdFyxona8UX4y8kuyfXFepsFlzxBzJhoxvMPzoDTH0rZP+DjeNTbzJhg1uN/vjETGjWDskKt4JotVXyCkkUSQsjQGwnjKEKINOp/ZYbFyMJ+nTnlNkVvon77deaMfa6U9G2QGDVq/OMv/oJdy27GtwJhuPyhRNa+ra8dx5Pzq2hSIw1PMJSSbE2lVGBbQ63oZN+2hlpoVcqM/DC0LINwJIIH6yZiXk0ZSk3axCYSp5fDP0y7BmtePCqoHPbkgios+eU7mDOpZNDPiWQPf5rM8wEugvJi+q1fKYUSWHX3ZGw80Jrye48nd4Miij1fn5lY2Fi19wg2HWjFpvuqh/GsiRSXj5P9Pl0+bhjPjlwJd4DDugVV2Phyq2i75w7Qd0kIIYQMxCVPAJsWVmOtSFXqTQtr0O0NYILZIPpYllHi4duvQyQqrPr48O022QS7Jp1adpydrtrLUHP5OEHCNyC2+L1q7xHskFmcD0Wi2PByq+Tm2PUyifndaZLfZWNyPEIIIYSMTC5vAJsX1oiuWWxeVAOXLwBI9CvJ8HL6grJjApc/iD9+97aU6rhdXk60Om5jnRXPNp8S9H3jQa1za8okA0vF+Dge55wB3FBenJJAueWMEyueb8F/PzDjyt+wQoF1Lx1LOcd6mwXbGmqxck+L6PtiVUq88G675BjmvhvGYsmMCVj30jH8+Cs39HsdVq76MCGEkKHXE+CwWaZN7PEHh/HshoZekxps2+3jRONVzrv8KCvUwUlrrYQQQsiw8wbDKePVeEL2vkWj7TYzltqt+Mdf/AU3lRdhx5JaPPfOaTTMmIBzroDsa8STrl9r1uOP372Nxq6EkBGlSM/ioieI7X9s61OYwoJH7rBhtFEzjGdHCCG5idYVc9NIiGPr9g48ljJXBEI8ti6eil191nTtNjO2Lp6KQEh+n1quiEYhum4d//faeVOG47TIAIwt0uFHX7kB3V4O7kAYJp0KxXqWkvaRYZfNyVD722bH7xdP9DatvBh6NYMIonj70y7sajoFANi17Gb88PXj+KDdiW0NtYhAuG9gud2KlXta8OOv3IBdTaewraEWAAT3W39fFTzBMB7bd1RwbFtDLV443I7vful6/P6j87itogRmw5X93ilZJCGEDD0a4xMyslH/KzMK9SzWzp+Cx/YdFeRH2bKoJmOfKyV9GyRGrQqVYwrwtV+8k1JtvnZ8EcYVaikjaj8U6VgcPtWFFbNtAIAxhTqs239McrJv033VGflh6NUMAmEeepbBq0fPpXR2frS4BpsPtOLG8mIs77PRZetrx/G1W8ph0NB3nc8MGvnLplGrooZwAPQqRjBxBABNji5sOtCKpxbVgNNG0BMIY0yhFv/3rVvx4O73EegNbiPZpUjPYs1Lwut3k6MLGw+0YjMl68sZRToWP3jlI9F274evncAT8yYP9ykSQgghOalIr8EPDoi3sT848JFsGxsMR9C4+3001lnR2Oexjbvfw76HbpV8rEHN4NcSQTUKAE8tqhmstzgoOnqC+KC9Gyvm2BLzLMmVxTp6gpLjr0g0ipZ2p+Rj+WhU8nVNWnVK1bS+jy3QZldyPEIIIYSMXEUGLda/LL5msenlVqyTSXRLhpdJz8qPCeZPhtViFDxOrDLbtPLilMXlZG+1dWKZ3Sq4/cR5N+orLKLBsPUVFvz1M6fkcxbKJIt2+Th0eji4AyGYdGpYDCx6gmE88aJw/v+QoxMRRNFYZxV9LR3LYPfyW6BQALdYR6Gl3ZlIDtfs6MLGl1sxt6YMk8pMVxy4K1V9mBBCyNAr1GuwQaI/s/lAK54cAf0ZvZrBnMrRmDK2ELXji2DUquANhqHocz8FAKUCMOoo1IsQQggZTh3uAIoNLH76xieJsa6P47FyTwsa66z43pcq0eWNJa5tOeNMJDs/5OgCFAqsXzAFn3UHUGaSH5dqVErUV1gwrkhHY1hCyMijAH7x509TClPE1+x/8eeTeGI+xS0SQsiVonXF3GRKE6eWD3FshbqBx1LmiiI9i5/84RPZ/ZH5IAqIFulF7+3SEZsk25x1+rFq75GUeIJZFRZsXTwVY4t0w3hmg0ssvoHmYLKbOxCSje8ezmSoUm12/Hy1agYt7d3QsQz+7YsVqBlXiJ1N4kXcj33uwvaDbYlranzesbE3/qdQp8afPrmYmHfUqJQp85Px/QV6NROLl48CP379hGg7pFQo8OSCKbhvRzN+9S/TYSnQDCjBIxVgJISQoUVjfEII9b8Gn8vH4YkXxfOjrHnxGLY31Gbk86VIwEHiDYaxzG5FFMKB1nK7FZ5gGCXDeH65gOMjWP/yR/jP+6dhfk0ZQnxEdrIvGI7A5Rv87OsRRKFVM9jyyvGUhG96lkHlWBO+v/co3jhxUfSxD9ZNhE5NSd/ymUIR+12L/W3abWYo+kZek37xhfiU31uyJkcXPnP60fDLw4nb6mxm/Ne/3IRub/5Xds9FwXTXb56S9eWKIB/BGycuSrZ737t70hCfESGEEJIfuDRt7Pdl2li3Pwwfx0smYHAHwpKP9abpd3uzrGKkOxDCz++fhp2HTqa833qbBT+/f5rs4ryfC2NbQy2ebRZfEPdz0p+Txchi17Kbsf1gm+Cxu5bdDIuRJkAJIYQQkh28HI+DJy7ioNTczV3Z1b8jlwXDVz7vJlWZTaNSyr6WiklduNCzDOorRuOWa0fhnuoxKDFpE4GwF9wBVJQYcOqiV/S56mxmGLXiy8tigd9PfbkGU8cVys4Xx4NSkwNzASAQiuDNjzvw0VkXvl4/EVPHFeHh5z9MJH475OjCMrsVpSbtsAbuEkIIuTo+6s/AqFFh3fwqNH8aa0NVSgVYlRIHjp5LaUPtNjNWzLahgKVQL0IIIWQ4+YJhcGFhbFR8/a52fBEe2P2+6GMPtXXCFQgjEo2iJxjCcw/OgMsfStmc6uN41Nss6PIE8VQGK4YTQkg2C4Yj+NqMCaLr/fFNjIQQQq4MrSvmJouRFS0KBcQSMOVDHNvVxFLmCi7N/houT/bX+LgwLEYWTy+eihKTBp4AjwKtChfcAazaewQ+mZhNkj1cPk6w7g/ECs6t3nskY5vch9pISWyXbwp1atnYcJNMEcNME2uz9Swjer5PLarGs03iRdyVUODRuZPwszfaErf33Tewa9nNKf8+dtaFpxZVJ+J/FAoF2jp6UDOuELuaT6HRbo0VpBBxqK0TLl8oljxOrRxQwrc4KsBICCFDh8b4hBCA+l+DrdPD4YPT3VgxxyaaZPpKC5T3F0UCDhKXP4QN+1vxzP3ToGKU6PHHMryH+Agefu5DbG+oHe5TzHrxDRmRWPJweIPyk3neYBiXvIP/w1ArlPCFeMFAtrHOig63fIIpFaNAJEK1J/JZNBrFN+uvw7fvqIBRo4YnGIZBw4BllODCEYBqjwxIj0xiCgDo9qVu2GpydGHti8fwg0U1mTwtMkCeNN9nuuMke/T45b+rdL9dQgghhIhzp2lj5Y7rNYxslTI9K52IPNf6aaP0LP7zTYdo9e7/fuc0nrhHuopnkY7Fj38vXR1zs0x1zEA4gmeSKqQlP1YJBX781RtQeBXvixBCCCFksKRLdEWJsLLXQPvmYpXZQmk2ARTrU4NaG+us+K8/OfCdL4lskIhGYTZoYNAwggI4dpsZy+xW0fU7scBvPcvg+lKjbGLq5PtKBeYut1vxy0MncefkUjTWWVOOxzd3FiRVTHb5OHR5OYQjUUSiUfiCYRTqWdGq3FS5mxBChl+Pn/ozYT6Ccy4/XulN8nbgkTrseNMxoHktQgghhGReVAF87vRLHk+XiIjno7FxcJ+1qPjm1BcOn8aD9ddh/CgdxhXrB+28CSEk1+z94Awa7VasnluZkjDktx+cwbfvvH64T48QQnIOrSvmJqmiULMqLHh68dS8WNdKG0uZZTGNA5FrcZsDVaRX47kHZ2LjgdaU8W6dzYznHpwJlXRoK8kinR5OkPAt7q22zoxtch9KIyWxXT4yaFR4tlk8WZoCwE++euOwnBcg3mY31llFz7fEpJVOwuboxIqgTfa1TFoVvn/X9XjmzU+hZxncNWUMLnk5OJOKS5QX6xKJ5e6fMUH2+S56Ynvmi3T0d08IIbmCxviEEDL4PMGQbJJpbzAz11ZK+jZIinRq/Grpzdh0oBVNSQOuepsFuxtvgXIYzy1X6FkVvjFrInY2nUSTowuvfbte9v4GjQoKxeCfRyAcgUdkw0rt+KK0jzVq1PCHeLh8uT+BRcQZ1SqwxQzW7z+WMrlit5nxyJwKlF1FNvuRzKCRb440KuFVtMnRBR9H2aazUbrvM91xkj2MWvnvykjfJSGEEDIgadtYmeM6NYNnl92M7QfbUiaQ6m1mPLvsZujU0pExudZPC0Xkq3eHItKbZoJh+eqYchtuur2c7GJ6t5e7qkpmhBBCCCGDJTnR1UCOk+FzNX3zvpXZPrnQI0jQFme3mRHtU6umdnwRppcXo7MniAO9yWWS73+txYDRBRrUlhejsTf5skalRMsZJ1buacHzD84QvE6Xl8MN44uw7NZrEQxHoFMzsBg18HFhGLXy0ftjC3XYv8KODftbJZPb1JYXo9SkFfTDtWoGY0waRKJRtLR3w8Cq8LfPnDAbWezsUxW5b1VuqtxNCCHZoUBH/ZlAOILtfZK8yc1r+UK0Rk4IIYQMF5ePw5MvHsMyu1XyPmJxbsn0LIOnXjsuWXzoyXun4L4dzXh5Rd2gnDMhhOQihQL4zp2TRBOGrJ1fBSioQDchhFwpWlfMXWJFoSzG/ClkNBL2K+Ra3OZAsUolnnzpqGC82+TowqYDrXhqUc0wnRm5Eu4RkEBkJCS2y1eeQFhyDa3J0QVPIIxS0xCfVJK+bbZWzaTEv8elKxqhkykAH3/8KIMGLz5sh0qpwNqXjglif+6qKsWje48CSD9fyTJKzKqwwGKkv3tCCMkVNMYnhJDBV6Rj8cPffSwZy75lYWbmNSgX2SBhVUpBwjcgthn4yZeOgVFmIDtZnmEYBe6aUorp147Cb//1C1AgNsAUY7eZoUAs+HWwebmQ6IRpMBxByxmn5DnV28wAolAoFOj2cYN+XiQ7MCqlIOEbELtYbz/YhrdPduGCOzBMZ5e7tCol6mR+7y1nnKLHevKkok2+USrkr9/UJOYOjUop+12mm/gl2cXl4/Bphwct7d349KIHLuqvEELIsLmaNlatVGDHwTaR+Ycu7HjTAbVMZ6s/4+xsEolAsiLbs82nIJPzTTSZe3+Pp6sOmg/VQwkhhBCSH+TmVetsZmhp7iZrDWbfXKVUYLndKni+eLJkrToWnBkXDEdwzSgddvRJLgPE+to73nRAqVBgx0EHHtj9Ph567kM8sPt97DjogI/jRQOCogBa2rsT91/+6/fw/73xCQp1anS4g7J/p6FIBB09QcnEy82OLtSOL0IwHEkJfLXbzCgzafDUqyfwxf94C/f/6jD2HzmLqrGmRLXiZPGq3C4fl7ZyN82bEULI0DGwjGw7YUizqSEf+EJ8SruVrvCZL0hJ3wghhJDhct4dwCFHl2wc6QV3oDeWVMhuM0OjVgrW+eIOOTrh53j4OB4uf+5voiaEkIFilUpBwjfgcsIQVklz34QQcqVoXTG3FepZXFdixI3lxbiuxJhXyYhGwn6FtGvD2Ra4OUAejpdPxpRm7ptkB9MISCAyEhLb5atc+O6S22y/RCGn/rRt9TaL6O12mxl/OdmFkgINPvj7Jazrk/ANiMXZdLiDiX+n2xPf0RPE04un5lX/ghBC8h2N8QkhZPBxfES2UCvHD35uKwDIj1IAWcAdCEsGYjQ5unCm2w+TTk0DHxkaRoloFPjg75fwszfa8Nq367G8tyJi30zjy+1WKBSx7OyDzazXwBfiYbeZU15Xo1JiV9MpbGuoFT2ntQuqcKrTi/JReskBOcl9bn9IduNRo92Kbi+HUpN2iM8st3F8BMvsVkSR+tuqt1nwvbsmodvL4ef3T4NWzeDD9m7sajrVu7mMmrFsFAVkr99UYzF3dPs42e/SSZsfc8ZZp1+wkXVWhQVbF0/F2CLdMJ4ZIYSMTN0+Dg/UWaEEUsYX9TYzltfJt7HeEC85JjnU1gmv3HhUId9Py7asb7HEEU6smGNLJHlIHhPI9SvTVgCVOW7SyT823XFCCCGEkKES5HmsnV8lKEpUZzPjyQVVCPK0VpGtooimmUPt/yyq2cDiqVePo7a8GI12K4LhCDQqJVrOOPE/77bjx1+5AT/6yg3o9nJwB8Io0KoQDMkvTEutc4lV9nX5OKzvE0SqZxl8bUY5jn7mxF8+7cLG+6qx7qVWHHJcnpuqs5mx8b5qfN7th5qRDzKKv6e4epsFj91TiZ/8/hMccnRCzzLY1lCLZ5tPoXZ8keSYKV6VGwBV7iaEkCzR5QnK9me6PEGUmw3DeIaZ5wvy0LMMGuusqB1fBINGPtFdunkvQgghhGSO2x+LV5WLIy0r1GF5nRURCOPgHpptgzdN4aJ4Alj9CEh+SwghUgLhiGysQCaKxhNCSL6jdUWSra4mljJnpIvbzBPpki1lQzImkp7FyKK+wiK6nl4vEi+Qi0ZCYrt8ZdKqU9bU+o4Vs+27i/+t9T3nkgIt6m1m0dgWu82MNz/uwJr5k7HxwEd95hcv72EvKdBgdIEGm145nvY8pOYy6yssWLdgCkxaNUpoLzQhhOQUGuMTQsjg8wTDsuONdOv8A0WRgIMk3cST0x+iDQJpRKJRbHq5NTFY7XAH8MK77aKbVF54tx3/ettEjDJoBv08wtEootGoYNK45YwTN5UXY/XeI3h68VSsnlsJTyCWdMoTDOGN4+dROaYQ3V4Oowz0PeerdBUBguEI3BlIRpjvPMGw4Ldl0qnAMko89dpxHDxxMXFfu82MbQ21eOFw+4io7J6LFAD2ffAZGu3WlGvlBXcA//fBZ1h5Z8VwnyLpJ62awco976Kxzipoi1fuacH//usXhvsUST+4fJwg4RsQ28C6eu8RbG+opT4qIYQMMT3LwBvg8fAcGx7tM7bkQlHoZPq5PQH5CSS55OgaRok9h0+LjrP3HD6NNfOmZOLtDpifCycSN+w46EjcHh8T+Dnp96pWKgXJ3JMfr5ap/F2gUeGOytGYPLZQ8BkfP+tCgYam0wghhBCSHViGwY4/tuHRuyvxOKNEjz8Ek06NEB/B/+9NB1bcQfNw2UqjYvDCYYk1sMPteHze5H4/V6GexYb7qrF675GUfvOsCgueXjwVPo7Ho0lzQyvm2HBbxWjZcUUgxOOpL9egpECTOHbBHcDs60cL5pE6PVxKIKqeZbC9oRZaNQNtsQ7fvP06fPD3S7jp2mIss1+LYDiCIp0a44p1eOT5D/HUl6fC6ZdfeynUqdHe5UNlWQFefsQOllGCUSrwxokOAEBjnRXPNp9Cs6ML/zRzguxz9QRCaVPq0YYDQggZOqyKwf2/egdPL56KVb3zZEYtgw53EEt++Q52N94y3KeYcQU6FXYsqcWuptgc2Io5NsypHI0pInNTJ866oVVTRWZCCCFkOHx+yZdIxObjeKzc04JvzJqI1XMr0eEOolCnxicXevDw8x8CAL5520SsursSAODneBi1KkQigNMrn7BApVSgvsICRpll1ZoIIWQIeYMhPLNkGs65/Cm3jy3U4pkl0+AN0vwdIYRcKVpXJNlKzzJw+8OYW1OGZUnrphfcASigkI2lzCV7Pzgjur/mtx+cwXfuvH64T29QpEukle44yR6PzLFhbvUYlJq0iTWK8y4/bCXG4T61QWExsrhzcgkqy0zCdZhz7rxIbJevLEYWu5bdjO0H2wRx5buW3Zx1353FyOKLk0vwj7eUp8TC61kGO5dOBxSKlH1e9TYL1syfjPOuILzBcCKmiOMjuKZYh+Nn3Xj16DnUjCvEmW4/ivVqvPCNmWj89XuJAohxLWecicRy8bnM+N5AACgxaaCAApe8HAwsxcUTQkiuUSsZbD/4iegY/+dvtuGROfkxxiCEkKFUqFPL7mM16TIzr0G98UGSLgu4RqWkDQJp+Dg+ZVPIsbMuPFhnxfY3HYIfxSOzK3D41CXcMbl00M/DH+KBaBQGlkmZNNarGSyqHYsOdxA73nQIqjCumGODQcNAqVDEMh6RvNSf37qJKmtfMaNGha2Lp2JX76YsANi/wo71r7YKkjQ0O7qgALBlUQ1CEco2nY20KiX+7UvXY93+1O+vzmbG+nurQfsQcodOzaC2vCilHY6z28zQqfNjETXfdXo40UpLQCzxGyUmJoSQoWdkVXCqQth+0CGo3Lhitg1GmcVTk1YlO4FUIDMeCfI8Hr17MjYdaE15bLZWcynWs/jJHz4RHRMAwOb7qiUf6/RxstUx5SqAekNhPHr3ZGwU+ZzWzq+CN0SJvgkhhBCSHVilAg/NsUnOw7G0OThrqZQKPFBvxQ6RNbAVsyugusLvbmyRDtsbatHp4dATCKFAq04Es67Y05IyN7Sr6RTuvaFMclyxY0ktzAYWrx45h0OOy4+bVWHBbdePFrx2crEcPctgW0Mtft18KmXNL94Pf2RPC3wcn7ht86IaAMBfTnZJJm2us5kxSs+CC0ew/WAbpowtxKIbx6W8bu342BymnmVwTbFO9rPqT2XnbKv+TAgh+UzHMpg0pgAP7H5fcGykrEXp1QyebTqVqL78wrvteO7BmaJzU+vvrYYyXfZSQgghhAw6l49DkI9tAI6PX30cj3Akiq2vnUCzoysxJq4tL0JLuxNVYwvx9OsnBOtUT9wzGXdWjsYbSUVQ4+orLDh50YP1C6qgoGkdQsgIZjZoEOIDeOXoOdGYCnMGisYTQki+o3VFkq30rAq/ajouuU74g971xFymUADfuXMSNh4Q/v7Wzq8CFPkx6WtgGdTZzIm57mR1NjMMeZLAL991eTl4gmG8evRcyndZbzNjeZ0VXd782Huyeu5krNt/THQdhmS3Z/rE3gOxOHGlQoEdDbXDdFbiCvUs1s6fgsf2HU05Zx/H44Hd72PtvMn43pcmwR/iwUei+MvJLiz6+dvwcTyeWlSNv51xJgpG/e/7Z7BkxgS89Lez+NkbbYnnqrOZ8aulN2PJL99JxOMAsdiglx62Y+PLrYnEbzsOOmC3mbFuQRUWPtOM3zTegmXPvofpE4qxvaE2L37bhBAyUqiUwIo512Pd/mOiY3wV7eEnhJArplUziQLkyeK5bX74Dzdk5HUpM9Eg0asZ1NssKZsf4uptFpw478aXpowZhjPLHT6Oh55l0FhnRe34IhhYFfwhHo12K75eNxG+EA+NSomWM0407n4P2xtq4fYPfiI9X5BHoV6FHa85UjakAMD377oe73zaJfihHnJ0Agpg4Y1jMa5Yh2Ia4OYtI8skstz3ZbeZ0dETRPW4wmE4s9zGqpR4/vDpRAWCYDgCRqlAS7sTK+bYBJUzdjWdgi/EQz8CgvxzUSQKwYIwADQ5urBu/zHZ5Bwku7BKBVbOqYASSLnu1dvMWDGnghb4c4Q7TeJhSkxMCCFDLxyJCpKJA/1LZqZVMZJVH/d+cAar7p4s+Vi1ksGPf38Cy+1WrOp9rFHLoMMdxH/84WN870uVg/MGB0kgHBEN5gJin1UwHJF8bIFWjW/+9wd4evFUwee0au8RPP/gTMnHRnhg0yvi/dmNB1qxdt6Ugb0hQgghOcfl49Dp4eAOxCqgWQwsBTeRrBKKRGkeLkf5Q7EAzngV3XjF+pYzTjyw+z3se+hWycdKXZvi/yX7tMMjKAbg43hoGOmFaSWAe2rKBGueb7V1YvXeIymBni4fB52awc/vnwatmkE0GsVzh0/jhvLiRFGl+Lz+84dPo7HOmgiabnZ0wRMIIxyJYlfTKWzrDb7tW3Rp48IqvHbsHN7/ezeWzJiA/3m3HZY6K6KIJtYP9KwKu5bdjGg0iuNn3ZIJ5GZVWBLJ8GZVWPCWSKGE5PsQQgjJvNhalA3zaspQatIm2o7zLj+uG20cEWtR/lBqgcSv3VIu2PwHxPp4G/a3Ys186fk/kttoDEoIIdnL5Q9hyysf4TtfvB6PzK4AEBu/Jichb6yzQqlQ4JuzrsO4Ih3W99loE3/MU6+ewKN3T0KQj6aM2ettZqxfUIUDR8/is24fbhxfRG0DIWRE+81f/i4aF/Gbv/wdj96VXbENhBCSC2hdkWSrQIjHx+d7sHPpdJSYNII4v0AouwrZDoRWxeCJPgl/gNjvb9OB1rxIbAcAwUgE6++txvr9x1KShdXZzNhwbzW4iHS8J8keYT6CnU3CWIJDji5EAKybXzU8JzaInL6QIEEKcLlN3LKwhuZfslSnhxPdvw8Ah9o60ekZ/qSEfefzgiFeNH7Fx/F4bN8xvPnvt+GHvzshuM+mV45j19LpQDRWCBGAaJxPk6MLCsXHeOEbM/G504+SAi3UjAKXPBz4SBTr7q2G28/B6Q/BpFXjkws9eO3YOdSWF+FPn1yEj+PxVpZ8doQQQvqPUSiwZr/4GGPDy614aiGN8Qkh5Eq5AyHJfaxNji64AyGMhXxh8oGgpG+D5JI3iI33VeHJ/a0pgRh2mxkPzbahxKQBS6X3ZBm1KmxrqMWzzadSsuTbbWYst1vxvf/9W0q28WA4grFa9aCfR6FOjVA4IprUa3JZIX70u09EH3eorROr7q5El4dDJD+KbBARJp0a6+6txob9rSmTRHabGY/MrsC4Ii1KTdphPMPcxPER/PPMa3HO5U/cFokC//ONL+D4OVfKfccWavHMkmno8YcQpd9aVvKHeNmEff48WHgbKfwRHqUmDebWlCU2aWpUSnS4Ayg1aeCP0HeZC0xp+ksFGehPEUIIkedL01/yyfSX/OEw/u2Lk/D+3y8BiI2N/SEeHe4AvvPFSfCHw5KPVSqAZbdaseNgm2hC12zbQ+sJSr+XdMdLTRrsWDIN2w+2CSp/71gyDaUm6crfUUA22RwNQwghZGQ46/Rj1d4jKfP9syos2Lp4KsYWDf5iDSEDQfNwucsdCCeq6EodF3Ol1yapYgAeLizZ5z3k6MIyu1X0WHKg51mnH6t+eyRlrWTP12dgyYwJkmt9qt610vhmeKNWFfsclkzDkc+cuMU6Co12K8KRKMoKteDCEVzs4TCrogSTSk3Y9+Fn2HhfNQr1LJy+EFrau1Nep95mwfK6a/Fg3UQAEIwFNtxbhS4vB7OBxdOLp2LV3iMpid9mVVjw9OKpFMhKCCFDyBsOo9SkxcmL3pTbFYjN73hl5rryhdsfFhRInFZejNry4thcYVKczCFHJ8Wj5CkagxJCSHbzh3l8bcYE/PQPn+CfZ16LeTVlaLRboWdV0LOMIO5117KbU9biktv6YDgChUKBtfOn4Gy3P6UY8mfdfvzHH9rw+3+rh5fjqW0ghIxYQZ7Hd+6cJEiIXWczY+38KgR5mvsmhJArdTXxamT45XNCaB8XxvNfnykaD/n812fCG8z94ubeYDglCVqyJkcXvGniJHNFNBoFq1RgxRxbSlFibzAMlRLgaHI7J/BRyLYXfB5s5PPKxEs0O7rg4/LjN5mPpGJg4nrSHM+0s04/nnzpGCrLTKgdX4RzrgAsRo1gbjD5N9UTFP49xu/vC0Ww8s4KGFgGowtKsavplOjrHmrrxLJbr8W3/vtDAJdjdL78n2+jtrwIK2bboIACYT6CEpMWB493YLndipV7WhLPMdyfHSGEkCvTw/H4QKbP1sPRGJ8QQq6U2y8/Fkx3fKAo6dsg0WtU+Kzbj3uqx2DZrdcmkqK0nHHigd3vYVp5ETZTVlRZGkYpmm08/u/GOmvKxo0inRqFugwkfdOrcbrLJ3pMlWYHvMsfgkmnypsJVyLO5Qvi23fa8Pi8yfAEwtBrGOjUDJQAfOEwXD7KbH+llAC0aiVeOXou5RpQb7PgodnX4YHd7yeC2e02M1bMtsFs1KDTExymMyZyAiFeMonntobavKi2NFJEeOCJA8IKNkDs+3xy/pRhOCtypYxaFepsZtHF4jqbGUYtDQkIIWSo+bk0/SWZCXYtw+CcOyDoO9ttZlxrMaBMJgk1H43AUiCe0NVSoAEfza5KiqY0bVRBmuPPHHSIzjEoFQrsaKiVfJwvzZieAjoIIST/uXycYEMlEEt2tHrvEWxvqKX5P5IVaB4udxk18n1ZsePprk0/+soN8ATCqZs9JNbRfEH5v41gWHps0BMIxc6lT8I3ANCqGex4U7wfDgDf+9Ik6FkGO5bUYldT6t9tfYUF37trElY89yHW3VuFp18/IVgv2LyoGmVFOlxwB7DmRWGlytj5RPHo3ZPxzVnX4bG5kxHiI3D5Qni/vRufu/z4zgt/xY4l01CoU2PFHBsev2cytGololFgVB5tkCGEkFyhUTI45wrgwADmuvKFUSveNsb7dCv3tKQkfsvXeJR83riaDo1BCSEku33e7UMwFEnEtr5z8hIa66woNWlh0MQ2YCbHvepZBno1k3i8WFI4IDYOfqDOiraOHlSPLUTt+CIYtSo89eUaFOpYfO9//0ZtAyFkxNIwDNa+JIxbbHJ0YdOBVmy6j/ZkEELIlUq3rhikdcWsdc7px58+uYiSAg2C4Qi6fSG8e+oSbr9+NMryICG02aDB506/ZDzkuDx4j940a7PePEnIoGEYPP3acXz5pvEw9K53K6CAN8jjh69/jFVzK4f5DEl/+LiwbHuRD/GzvjS/uXz5TeYjk1Z+L3lBmuOZ5PJxePKlY/jaLeV4/vBpAEDt+CIgGsWLD9ux8eXWtHsG9CyDb942EXOryrDpQGufIoji64ZxyXE+Yvvx59WUYVp5MaLg0DCjHI/0eZ7h/OwIIYRcOX+aPps/D/pshBAy1NLtY013fKAow8MgKdKzCPNR/POud0WPNzm64A9l1+bpbBMIR0STyuhZBrXlxbirqhRTykzQqhlccPlRXqzLSMVCd2/iNjHFBvnBa/xxuqSgHZJfOj0cPjjTjTmTSrFhf2tKRU67zYxHZldAx4QoqOoKMUql6EawQ45ORBBNmWSK32fzfdU0oZSlivUsfvKHTyQ39m2mgJucEYlCtoINFVvKDd5gGMvsVkQBwWL4Mrs1bzcHEUJINjMbWPzHGwPrL0UA2SQKcsHNGobB1t6gmhKTBp4An0ic9tPff4zVd2dXUA3LKGG3mSUT0LKMUvKxnR5OkIAi7lBbJzo90sm60yVETZeggxBCSO7r9HCCDZVxb6VpRwgZSjQPl7s0Kvm+rkYl7OumuzZ92uHBkl8dTtw2q8KCLYtq8MXJJfjD8Y6U+0utgyWfn5QCrRodPUFBf1vPMtCoGNn5xNVzFfjGrIl4tumUoEDBobZOIAo8u/wWrNsv3NB5yNGJJ/YdxdbFU9He5UtZn0m9XxeW9QTwwO73AVyuYLyr6RTurhqD5x6ciY0HWlOev85mxpZFNXRtJ4SQYXA1c12DIRsSjRlYlWjbKFUgMR+L+Zx1+gVJz2ZVWLB18dSMxAVlGxqDEkJIdnMHwlAoLrfNPo5PtM0r5tjwhYnmlLa6sc4KPhpN+bdYMeT4tX/B1LLEGBaIJYObVl5EbQMhZEQLhCOiBU6B2J6MgEzRCkIIIeJG6Vn8lNYVc47Lx+H0JR8OHDkriAG3WgzQs0zOjw3CkeiwzhEPhXRrs5naNDzUAjyPlXdOEl2LXTu/CgE+fxJpZcPaQqaMhPbCkCYGON1xMnwsRhazKix4S2TebFaFBRbj8P0OOz0cqscVYs/h01gyY0IiCc+KOTa0tHeL/qYUANbOnwLgcuGIDncAG/pcR4FYLEwEwnXDuL5xPs2OLjTarSn/H0UsqWHfxHHD/dkRQgi5chQ7TAghg8/AMqizmUXXp+psZhjYzOSQohHoIFEgfZZ3DyXUkCWW6V+uyuFtk0oych6eQAhFBhb1Notgw0qYj8puwgnzUfg4nga5ecwTDOGLlWOwRqSCXGJR4V7qDF8pf4hHS7sTK+bYUDu+CMFwBFo1gyOfOaFQAHMqSxJJHz9s78auplPwhXiYaCI1KwXDEXx8vgc7l05PSSZywR3Aqr1HUqpHkOzmCcj3XdIdJ9nB5Q9h5Z4WNNZZ0Wi3IhiOQKNSouWMEyv3tOD5B2cM9ykSQsiIw/HiSc+B2LiC46X7S1J953g/2S9TddWfJqjGn2VBNV3eIB6sm4h5NWUoNWkT7/W8y4+yQh26vEFYRxtFH+vyc7LP7fKHJI+pGSXmVI7GlLGFgs/4o7MuqGWSzRFCCMkP7kAIepZBY51VtL3tCUi3I4QMJZqHy13dXg7Lk4Ir4+IJyrq9HDA69THuNNceZ58+7lttnXh831E89eUaBMORlKBXvZrBnZUlqBxrElznTpxzo6MnKPoa8UDPTzo8KbePL9Zh59KbcabbJ/++fSHU2Sz42RttoscPOTrBSRSJAmIbOnv8IehYBjuXThdcn+NrtWIVjNfMmwydmsEPXv0IteXFiXmy+OM3vNyKLV+eilKTVvY9EEIIGVz+EC87TyY313W1siXRmC/E44M+8306NYNINApGoYBWzWBaeTE+bO/G8bMu2eSsucjl4wTfAxDry6zeewTbG2rzZrOclHT9PBqDEkLI8OoJhBCQKPj8wrvtuK3i8gBezzL40pRScOEIdi6dDoVCAYuRxa6mU5LzjbaS1LWuQ22d+Kzbn/acCCEkn3kCYdl1GtqTQQghVy4YjsjGnNG6YnZy+kLYfrBNcu/SloW5X9TIzw08HjJXaNXym4a16sxsGh5qaoUSaw4I99o1Obqw6UBrXiTwA7JnbSFTOF6+vZCLb84VCkB2f7Bi6E+J9FOhnsXWxVOxeu+RlBiYWRUWPL146rC2iZ5gCHdMLkE4Ek0pAFE7vkg0SRsQuz76uDC2LKrG9aUFcPlDmHpNETp6grjFOgrVYwsFv8F4IrdkdpsZLWecgtuT+3fBcATeYBj/8267IOHbcH92hBBCrhzFDhNCyOBz+jk8uaAKm15uTSlMXm8zY+2CKrj8HMbDMOivS9lyBsklL5e2mq6RkhPJMmnVggXaUpMWH593o6XdmXLfQ22dWLX3CHZkILhzlFGDi24/Ni6swpMvHkv5QQZDvOwmHJcvBEuBBlHBs5J8UaRj8bnTLx94Hs79RYWhFgjx4gkebRY8NPs6/ON/vZOYULLbzNjWUIsAx8OgyY/FjXzj40J47sGZoslEnntwJrxB+QQcJHuk7dvkSVWpfGfSqlMqTfdVoFUP8RkRQghxp0mcKnc8wIn3nRP9ZJkgp1wLqinQquEOhPHq0XMpAU/1NjOW11ll2zA9K99P0ctUV3D6glh192RsPNCa8hnHk+M5fUEA4snmCCGE5IdCnVq2vTXpaBxFsgPNw+UuvUaFf971rmSS/r3fulXwGFOaORyxBDBvtXUiEIpge0MtOj0cegIhFGjV8Id5PDq3EhteFvZ5N95XDUYBQXXk5EDP5GplFiOL3zTegjUvHRMNLk02Ss+mLaIlt3FdzzJQqxhsefV4yvpd/Pocr0YsVsF49dxKMApg9d2TsalPXz++zuf0cZT0jRBChpgvKN8upGs3BiqbEo15A2HR8UedzYxldisad78HH8cn5qZCkfwKzu30cILvIe6ttk50eri832iSrp9Ha3mEEDJ8Pr/kQ4FWDU8wIHr8a7eUJ9rmeIHjH71+QhAEvmNJLRRQ4FdNJwXj0YU3joOeZa6o30NtAyEk3xXoVLLrNAUUt0gIIVfMEwzJXls9QUosnI28XFh275KXy/1EqFJ7iZL3DeU6HxfGMrsVUQj3Ii6zW+HLg+8RAAJpinsF8iDxRDatLWSKNyi+ZhH/TXrzIAGzQgHZ/cEKyvqW1cYW6QQxMBYjO+y/vSIdC8dFjyDJm1zSHT3LQM+q8NrRc3h837HE7fH9tA/sfl+wn5bp8wca/7tduadF8PzJsTMalRLFehY//soNWffZEUIIuXIUO0wIIYPPoFHj824/Hp5jw6NzK+EJ8DBqVfAGQ7jgCmBccWYSvdOK1yDxBEMYbdTiqUXVKDFpBRm0a8uLoKQBvywNo8TOpdOx402H6KRQfKNG3KG2TnT0BAd9UOkP8fByEXx+xolNC6vhD0XQ4w+hQKeGmlHgV68eR215sWATzvOHT+OLU8ZgTKEGhjypskGEOD4CV5pKmekC04lQsZ7FT//wiWCC/4P2bpzq9OI3jbegoyeYuK4+f/g01sybgqBEFVUyvMxGLTa83JpyrYx/dz98/TieXFA13KdI+snAMrijcjQmjy0UVOk5ftaVsrmTZC+LkRVs0o2bVWGBxUgT9IQQMtTkEo6lOz7KwOKZPzlE+1rPHz6NtfOmSD4214JqdGoGO5tOCc75kKMLUQBbFtVIPlahSFOJT2aOplivwQ9/dwKNditW907Sxave/OyNj/HoXZUDfUuEEEJyhEGjSqm4Gdfs6IICwE++euOwnBchfdE8XO4ysAy+MHFUym2K3k7qFyaOEp13k5vjkaraC8SSqF1XYkxZT2vv8uKJfUdFE0I/+dIx/GBRDbYungq3PwR3IAyTToViPZtIiGZgVbHXbHdi17Kb4eN4NDu6cIt1FLYsqkapxFppNApo1cLkdMnkNq431lmxYX9qwSbgciB2Y50VLe3dop+FP8jj2GcuHDh6TvT6DkB2PEUIISQzCnQqQXHA5PYjUxv5synRWJGexU/fEK6VN/XOgTXWWbHjoANNji5sPNCad+2VO038hVxC2HxBa3mEEJKdXD4Onzt9iAAoK9TgqUU1KDFpUvor08uL8fanXZhTORr3z5gArZpBw4wJWF43MdGfiY9h59aUiY5HN7zcmmjv41rOOFFfYRHtr1DbQAgZCTSMUnadJtsK2hFCSC4YZdDg53/6VDLmbE2ezTnlC2+ahGeZKpoxlK4mHjJXeAI8Vu5pkSwItnv5LcN9ioMi3VxuPsz1ZtPaQqYU6Vn8RGR/X/zf+dAXL9Kz+OHrJ0T3B+85fBpbvjx1uE+RpFGoz75EZaFIBIU6NTp6gim3ixVwjGuss2LzgY8EMTCHHJ2IIJoyZ9jS7sS8mgCmTSjGC1+fiQKdCiqlAn/rbUv69gmS44jsNjM6eoKYPqE4Kz87QgghV45ihwkhZPAZNSqoGAW2H3QIEoSvmG2DUZOZOEZK+jZILAYNzrsDePXoOUFl+Z1Lp8OoUSESjQ7jGWa/IB/Bz990SE4K9Q1sAQCXf/An/DyBMMaP0oNVKfHEi8dSzmde9RismV+FNS8eFVRYXn9vFZw+Dh+f78HU8UWDfl4kO3iCIVyTJgunkSrIXbGgSPKJePXTZ5tPpVQriFcgCPER9ARyv0JIPvKHeCyZMUG0sstyuxX+UO4vLo4U4QiPJ+dXofnT1IWpcYVafPWmaxCO0HeZCwr1LLYunorVe4+kbBaZVWHB04un0oQ9IYQMA52KkU1IplNJJ30L8hHZvlaQl07clmtBNT1B6UqlTY4u9MhVDExTiQ8ySd/4aBTfuXOSaNWbtfOrwNP8DiGE5D1PQL4N8gTCKDUN8UkRIoLm4XIXE4nKzrsxIn1OqTme+goLlt56rWjVXkCYRK29y4sz3X5B0Ghc/Dq39bXjKfeZVWHB1sVTMbZIhyK9Gv/+xevB8VH86PUTaJgxAXqWQc24QjybtKEdiP097lo6Hf5QBC4/B5NOLTseUqsUqLdZcMghDJb/wkSzYK0wrtnRhYdut+HG8UWin0U4EkWJSSt5fW/uTaxDhpfLx6HTw8EdCMGkU8NioIBjQvIdowR2LbsZ2w+2Cfozu5bdDEY+V+iAZVOisRAvXaih2dGFxt45rvi/8629MskkfAXkE8LmC1rLI4SQ7OTyhcBHgV8eOol/mjkBrx49mzLenVM5Gotrx4FRKrDwxnGCJOXJxY4PObqwLKlNT3aorRPLbr025bZdTafwyiN1eHJ/a8pmcmobCCEjRa4VtCOEkFwQjsjHnIUjdG3NRkU6tWzRjEJd7s+dcWniITmZeMhcUaBTwcfxkuucBbr82IOWbi43H+Z6s2ltIVPE9vfFNTu6EMyDvnipSYt1C6rw+D7h/uAti2oSxfAIuRIKhQKfXOjBtWZDyu0tZ5ySMTLpYmDia4Rye2yfnF+FmRNH4eCJiym3L7dbsXJPS2+CigqUF+toTpEQQvIIxQ4TQsjg83M8dsjku9qysCYjr5sfs0JZIApg28E20S9QCQWemFcJrVp60zYB+EhUcoNJ30DWuExkQxxlYHH4ZBcOHD0n+D6tJUZsPNCKG8uLY5vpkzL5b331OKqvKcKXpoxBgDpDeatIx+K9v1/CnZWjUTm2ULBwcuKcG1p1hiLP85hHJFlDY51VslIfAKydNwWGDGVEJVcpirTfHckNrJLB565YUtumpO+z3mbGxNFGjCukxYxcMbZIh+0Ntej0cOgJhFCgVcNipA2ThBAyXDSMAhvurcL6/a0pbWw8obiGkclIhjR9rfnSfa1cC6rxBtNUKpU5rlYq8fzh06KV+NJVp2UUCqw7cEzwGTc5urDpQGteVCokhBAibyQEaZI8QfNwOSuqVOD8AObdxOZ4jFoV1uw7KqjaC8Q2g1uMbCKRFh+JYNOBj9AwY4Ls+Z13BwRrdm+1dWL13iPY3lALACgxavDYvqM45OjC8rqJaKyzYmeT+N+jUqHATROKccfkEhw80YFHZtsSx+LigaZHzzjx73dNQgTRlOP1NrNsBeQ4sQrG9RUWvH2yC1PK5DN2+jgqNDOczjr9WLX3iCChQTzZICEkP2kYBs8c/Egy1uYHizIzD5NNicbE1sqT9d1A5U1z/1xjMbKYVWFJSXYWF+/LjAS0lkcIIdknHI3iVKcX37nzevzo9RMp42SLkcXKO67Hky8dww3lxWhp705b7FhuU3TfY7XlRdh/5CxuGF+Eb912HTRqJYp0LLUNhJARI924J9/GRYQQMhSiadYVn5SJOSPDp1Cnxq6lN2P7myJFM5benBdJ36IYeDxkrjCoGdTZzCnrwnF1NjMMebLf1MjKv08jm/vv09Q7b/v04qkoMWngCfAo0KpwwR3Aqr1Hsi4GdyDSrVmkO54rWEaJFXMqsGpuJTwBHkYtA2+QB5upakQkr7l8HNa/dAzHz/fg/751a0qhw11Np7CtN86mbwwMmyYGJj5nKLfHduOBVjTarbh/xgRwfASjjRqwKiVC4Qj+55szoVeroFMrMbZYP5hvmRBCyHCj2GFCCBl0Xi6MlnYnVsyxiRZf8GYoxpyy5QwSX4jHx+d7sHPpdNFJG0ABA0sft5x0Gyn6Vuew93Nzx5UK8RGUFupEs6fXji/CjoOOlMznyZbMvBY9gRCK9Lk/SUfEcZEIxhbq8MS8KVjz0jFBRYfNC2ugls/PQESYtMLrY/z3JqbZ0QU+EoUqTTIMMjyigOg1FMjP6vP5LBSJYsfBNsHCWyyQVYGN91UNz4mRASnUU/AvIYRkCy4SxdZXj0skFD+BJ+ZNlnxsNJqmryXT2SpgGdxTXYrFN40XzF3s/eAMCrIsqMaUpoKlXIVLt5/D6rmTsenl1pRxRb3NjLULquD2c5KPparhhBBCsikBBCFyaB4ud13NvJvYHM+m+6rxQL0PBg2T6Od7gmFcN0oPTyCMt9ouosSkhYFVYXndRNE5+f54q60T590B/Oh3H+N7d03CIUcX9CwDi5GVrUB8qK0Ty269FmE+ir+dceLGa4owv2ZsSoLmC+4AGCVQbNDgwd3v4d+/OAnf/eL18HE8LEYNQnwESoX8moDZyOKm8uJE8CwQr2w8Bfc905xIWCelSEdzZ8PF5eMECd+A1GSDNLdJSH7ycrxkccBDjk54RZKaXo14IlSXn8Oer89A86dd2NV0KiVh6FAnGitI0y73jYvJt8JohXoWWxdPxeq9R1ISv82qsODpxVNH1PWf1vIIISR7uHyxdaRXjp5DqUmb0l/Rswx2LbsZT79+As2OLiyzW2Vj3OLFjuViXZOP1dssWGq/NpHUfMdBB2ZVWGhcRAgZUYxpxknpjhNCCBGKpIk5i9DCYlbyh3g882abZNGMzRkqmjGUriYeMlf4QjyW2a2C9X27zYxldit8ocGdBx8u/jCPJxdUYePLwmLITy6ogj+c++/TYmTxwje+gA37j6XMFdTbzHjhG1/IiyIm6WIJBhprkE1cPg7fF1mbBkBzMGRAOj0cPmh3YseSWjz92nH8+12TAMTWOn0cj5V7WrB+wRSsmTcFZ51+AEDLGSd6AvL76eNzhun22DbarXhg9/uJ2+psZqy8owImrRpFOjX9PRNCSB6i2GFCCBl8fo7HtoZaPNt8SlB8YVtDLfwZmr/J/VF2luBCPJ57cCY2HmhNaSTrbGY89+BMdLgC0OVJ5YVMMWqEG+X0LIPGOitqxxehQKvCrmU348P2bnx01oUlMyag28fBOsjn4QmEoZf4ruSqLcbPN4oo1JTVP6+ZjSzWvnRM0CFucnRh7UvHsPFeSoJ0pVhGCbvNnPKZpvu9+UM8jAw1Y9mIqizmj0A4IrvRhhKeEEIIIQPjC/F448RFvCGRUPzfvnS95GPT9aV8MsejkSgevXsynnjxqGDuYvPCGkSzLHrPmKbCpVFmnqVQx6KjJ4C5NWVY1ieRRLc3iJICreRjewIh2fNKd5wQQkjusxhZzKqwpCQciBvqBBCEyKF5uNzVn3m3Tzs8cAdCMOnUsBjkE4BwfATbD7YJ+vlbFtXgvDuAA0fPpRz74eIa1FdYRIOY6yssOPq5S/K1zjoD+M6dFbjkiW2Ab6yz4mdvfIJv1F8n+56D4QhcvhCWzJiA3X/5O6ZeU4SKUiPCkSgMGgYTzHpwoQiWPfseasuLcNO1Rfis249nm04lPqsVc2ySY4R6mxnRSBRP3jsFn3f74eP4RHLt+L9bzjgFaxFxdH0fXp0eTvTvEYglfuv0cBSITEie6vEP3TzMWadfkGCyrjcoK55UZTgSjYmtlcfZbWa0nHEm/l1vs0CZobpo8YR4/e1/DKaxRTpsb6hFp4dDTyCEAq0aFiMlQCOEEDJ8evwhrOuND7x/xoSUY411VvQEwom2O12MWzAcQb3NjAvugOjxepsZows0+Pn901CoUyMQ4vFIb98kjsZFhJCRRsMoZWMFNBSnTwghV8xD64o5yRsM44N2J1bMsaF2fBGC4Qi0agYftndjV9OpvPjeRsKad8//n717D4yqPvPH/z7nzH0mk4QZQIIEBiYaSAJGUdCZUEFbRZDL0u7XSFtIsNYq4na3CiogAlps7bYF6m5bgdKu4P62FAS8tCpuJVjxQraECJrRVFAQSEgymeuZOef8/pjMkJOZOcMlyVzyvP5oJSeTOXM9n8/zeT7PEwhjyfZ61DptsqZY9SfasWR7PX5fe0O6T7FXdAbC8AYELJ5mx9LppfAEBJh0HLzBML5qD8Coy/59tYGwGFfwDYg0d3tyTyOe/dYE5Kfp3HqLOsWaRS7smW3x8Pjw87ak360UgyEXKrq21urlUeu04YV3P0f1pJFY/+YnuG5UIf7l6yWQJECrZnHGHcTalz+SfbYWT7Nf0BrhhcQfo5x2C9bOrYBRzWGwOXmuPCGEkOw2EOZRhBDS3wYZNfj3Nz5J2HwBANbO7pvmC1Qtp5dYTFo8urMhYRGoNXsb8cSsMnh5ukAqUXOMbJJq0HAJKyFW2a34t9uuxj1b38cfFk3q9fMoNGpwqiNxgo1St0UAEEUJZp26z6o0kvRTsyxOdQYSJhIAwP6mlpzpstKf2nw8arq6mka/A1J93kw6FXIgVpyTUnWjT3WcZA5Piq4hqY4TQgghJLHumzQS8SscT9Wx2qhwXGSYuIJvQCR2sXxXA56aU6H4t/ubN6zc4dKr0PlRArB+nyvpQvgahUCbWRdflP5ijhNCCMl++QYN1s0bj2U7DssKv6WjAAQhSgwa5aToVMdJ+lxI3O1bv/5b7N9TSqxYN288igr0cb972h1IukZ5wNWCV3oUfAOAU+4A7r95DERJihtrPzDVjoPNiddAAGBYvg4qjoHbH3kM0Y7C3548SvExaVUsPuhq7DSuKB8Vw/NxpjMIrYpFnasFx065sXhaCTZUV6L+RDv4kIRNdc2y89tc14z11ZWxxxdVVWLFmtnlMGg4PL6zAa8fPSO772ixuO637/536fs9/dxUfJuQAStPrxxnyeulOEyHj48r+AZEricMw+ClBxxgGSYthcYSrZUDkU0ZCx02LNleDyBynb5/qr1PihskKoinNP7oC/kGKvJGCCEkc3hCQmwDd88ctsoRBejoVrg2VY5bvl6Nh28vRauHj9vEWWW3YoFjFO76zbu4bmQhFtw0KlaMtieaFxFCBhJeFBVzBXiRmtUSQsjFMtK6Ylbyh4SE++ocXc0sAjmwd2kgrHkbtSr4eEH2GvY8ngsGGTT499ePJM3Z7KvN0f2p3ccnb+7W1IJ2H4+hWV5k6Zw3mHDNwmG3oMZhwzlvEKMHm9J1er3CEwwpfrd6gxSDIal1X1vbtGAiKkcUAAC2HGhG/fF23D1pJD7+qhMvN5xCbYLPFBDJgdn2vclgcCwuB+b+m+1YtPV9AKnjj6OsRmz/3uSuQpsCCvVqWnMjhJAcNxDmUYQQ0t8CYTFhTAOIjOUDKYoxX6rciAplAC8vJH0B61yt4MNiyk3dA905rzyRtdZpw5YDzXHP635XC0RI+NevX9UnHYx5QcTfPmtNWCW94csO3Dp2CEqHmeMq+X90sgPvfNaKGeXDUnbBIdnLHxJkSVsJf4c+6xdNr1Fh2R8+xDPzxmNZV0eXwXlaVNmt2O9qift9h90CNctCzfVRG3NyWQZCZ5eBwqBNMflPcZwQQgghiZm0Khg0HGqdtoRd4pSSeDQpxloahbGWLySgXqHzZ6YVsHb7lDtcbq1J3uHSH0oepzngalUs1m7UcIpdw1MlQRJCCMkNRQV6bKiuRIuHR2cghDydOi0FIAhRolNzimNDnZrGLZnqYuNubze1YNmOw9hQXRn3PdTm5ZOOfYeYdQmTzsuL8rFo6wcJx9q1v3sfz393YsK/V2W3wh0I4dMzHpQNz0eV3RLrGHzoeFvS92NViRU2ixHDCvS4+arBCAkSDnzags11zQCAFTPG4bayofiizQ+tisPIQQao1Uzc3/LxQmyOsHR6KU6c82OUxYDhBfrY8/Lk7HIEw/KinR+fcuPpuRVYvuuIbI4BAFcW6nGFWUff72mWqrh2bxV9IoRkHp2KVYzD6FJsYrhQLR4+ruAbEEl0nTCiAIIowRMKA13Lz/15XdBrVFiy6T3Zddmg4TDIqAHHAlsWXg+jVoXT7gAe3H4IL9zTu80RkxXEUxp/EEIIIbnsZLsfJ9v8sX/Xn2iXzXejc+hkx7urKrFiSJ4WIUHE379oxw22QbHr/YhBeqhYFqc7Ath5/03QqTlM/+X+pHnGNC8ihAwkKXMFapPnChBCCEmM1hWz0yCDBr96y4XK4sLYNTGa77ft4OdYMWNcuk/xsg2E9ybLQPEx9sXeyHQIptgcHeyjzdH9yZ2iuVtniuPZwKhV47ub3086Ft/xg5vSfYqXLV+vxk/+/HHc+zX676fmZH+BQtI3Onw8Wr08WCayf3jp7aV46BYBQ/K0aDrjwaSRg1A5ogDD7tDjVIcfFVcW4LGdR3D9qEGoGJ4f9/d8vIB2L4+JowbhX269CoIoIU+ngk7NoaUziP/v+zciJIhQsyyq7JaE+T9VdgteaTiFjftccNotWPdP1PCQEEIGgoEwjyKEkP6WsqF7H9WQoqJvvaQzRREotz+MQiMlXSjRaTgs2fweFk8bg1WzyhAWJDjGWPH4HWMRFiWc8/Lw8UIsQD1hRD6kPjiPTn8Ym+uasb66EsD5gI1Bw8ExZhBmjh+GJ3Y3yir5O+0WrLyzDHf/9l1MKRkMs54+WrnKFxRSDnZzpctKf2IZYEN1JTa+5ZJ95jYtiGws6174LdodpN3Hw6ij5zoTdfiCWDunHCt2HZFt0HDaLVgzpwIdvgCA7O7sMlDoU0z+9TT5zyodPh4tHh7uQAhmvRpWIxUrIISQdGEZYNOCidj4liuuS9ymBRPBKuxlbetRML37bWscNrR5eWBw4tt6gyFsvLsSm+vk3emq7BZsvDvzutMZExTCYBhG8XiUL6hcwE6pML+HDyt2Dffw2Z+YQwgh5OJIQKz4AyGZRJBEPDWnHMsTxOHWzqlASMysor7kvEuJu73d1IIWDx8Xz3EHko/jkyXOB7uaVSXrJq9WsXFNWapKrFg9uwyiJOHTM5HiOQ9MLUGeToXF0+x48b3jWDdvPAD5OLrKbsWKmeOw9pWPsO/YWdnf2/XATQAYrNnTiEd3NsiOXTeyEL/+znVQc+z5QtW8EDvvccPMuP+FQ3jtoSrZcxIt2tnuC8HLh+HlBRTo1cjXq6mYZwazmjSYUmKVFeuLmlJihfAyzpAAAQAASURBVNVErxMhuSooCFgxswxr9jbGjWdW3lmGoNA745lE10uDhsP66kpsOSCPlU0psWLdvPEoKtD3yn0r6fDxYABUFhfEziF6Xs+8diwuNrVu3vheTyBLVhAPSD7+IIQQQnJZMCTgim7jgGguKQsG+10tsY3O0Xn95rpmbLz7/PEoh92C+2+244s2Px7YdgjXFRegxmnD4m318PEC/uPb12KUxYjK4gLkGzTo8PGYOLKQ5kWEEILETTO65woYqFEbIYRcNFpXzE4hUcTdk0bGxTCjuYIhMfuLaA2E96YE4B7naMyoGIahZl2seN9XHX4My9f3yd7IdFBat76Q49nAlGK/YC7sJzRqONw02iL7WXQsftPo3GiaPBAKFOa6dOyNOtnux8qXjmD+pJHQq1lseMuF+uPt+M/518GnDaOoQA9vMAxOYPDKkVPYXNeMZ781AVaTBreUDoWKY/Dc/Gvlzdp5AUFBRPnwfDz3vy7cPWkk1u9rilsfvMc5GjVOG0TE5+OsnlOGT057sO2eSRhpMWB4oaFPnwdCCCGZYSDMowghpL+lqhFl7qO6NtkfScgQeXrlgm5mnarXOhDnKoOaw9eusuLWsVdg9e5G7He1ypJse05WZ00oAtMHoU2DloOPF+I6hF1ZqEfjlx3Y2+CKC+zUuVqxZk8j7rqhGEYtR691Dss3qKBTJ+82XlVipdf/Eqg5Fs+9Jf9s+XgBi7Z+gBUzxuFfvl6C0+6grDvI9u9NRs6sbuSYQSYdntzTiGuKC1HTo7PL2r2NWHlnWbpPkVygkChi8VQ7gPiCJ4unliCcAwvFA8XJdj+W7jgs27TTn5uWCCGEyGlU8eNfIHK9ZcFg9Zzk46VowfRkXfT+574bk97WYtTil280xc1lIp2/GKyenVnjNLNWpVgcz6yQIJOfIk6Tr0t+3BsUsGzHYTwzbzyWTS+FJyDApFPhjDuApTsO4z+/fd3FPxhCCCFZ52S7H0v/eFi2UZPmUSTTaDkOq5LE4dbsbcQTFIfLWOFLjLt1BkJxyat5CmNbbZL1imQ/P38/YUwoLsB9N48BxzLw8QJOd/ihYRic6Ahgb8Mp2Xk7u4rQLNtxGHfdUBybq+Tr1cjTqbDu1aOygm8AsL+pBR/+ow2vNJyK60a8v6kFK146gsriQmzc54LDbsH66kos2V4fK+CsVbFw2C3gWHlVzg4fjzZfCCt2Ncj+bvQ7fMwQakiSifINmth76O0eMcxn5lEnakJymVbF4dnXPkaNw4alsTgMhzPuIH75+if40e1X98r9mBNcL2udtrhcFCBS6GzZjsPYUF3Zp98/0bWb5TPHYmW3wnfJziv67zWzy3v1PFJttOvMgY14hBBCyIX64pwPK186ggnFhbGibtFc0hfumYSFjlEYZNTg2Em3rElTw5cdmF5xBRY6RsniM4u2vo/K4gLUOm1da10M7p0yGr94ownFhQaoOSY23qB5ESGEnGdUc4q5AkZqVksIIReN1hWzkyRBMVa4cua4dJxWrxoI700dx8Kg4fBKwylZ7maV3YLF00qg43JjD5pZp4ZBw6HWaUPliIJYcbtocaNEcfpswwCKzd2YHGgoGRQELLujFE/sbpSNxZ12C1bNKu+1Zj3p5A4oN9dJdZykVzr2RnX4eCzdcRgTRhTgVIcfbxw9jcriQvzb169Cnk6N1XsaZfkp0RwXNcvi+QXXY91rR+Nyg6I5MMPydXjmtWOoLC5UvObfYBuEGRXDsGx6Kb5o80PDsRiWrwMjAaMsBlxh1lH8kBBCBpCBMI8ihJD+puHiG6ZHVdmt0PRR/IaKvvWSPA2HW0oHY2xRflxg6ujJDujUHFV5T0GrYvHIbaVY+dKR2CRXKZl1zd6P8PSc3k1mBSLF56IBuO7BqU0LJmKIWZe0kv9+Vyvun2qHQcMhKNBrnat0Kg4/efUYFjpskIC4TU2PTi/NiSBtf+MFMW5TFxAp/PbozgZsWjAR979wKPZzp92CVm8Qw/Jpg20m8vEC9h07G7eJL+pHt2V/kH+gkCQgEBIxo2KYrKjMaXcAgZAAUcr+hbeBILrAsL9HN+j+2rRECCEkXjCcePwLAPtdLYrxA4OaQ2VxgWy+GuWwW2BQSG5ONu6O3i+fYXPZkCgpF8dTKFKnVSsH2rTq5IE2k06FdfPGY3OCAvTr5o2HqY86MxBCCMkcHT4eK3cdwYTigthGzWi8f+VLR/Czb02geRTJCF6Kw2Ut8RLjbnoNh8Xb67G/qSWWMP9PlcOTjn3PuAMJj9WfaE/a3MZht+DQ8TZs3OeKFVyrddgwxKxDUJTwWYsXtQ5bV/diDqIkgWMYCJKEX39nIt76+Awe3F6P60YWYsFNke/QZO/RIWZd0jnKAVcrarttoAcQ2yRfZbdgWL4OD04tgShJ6PDxyDdocLLdj79+chZ7D59MWwEfcumKCvTYUF2JFg+PzkAIeTo1rKa+78hNCEkvNcNgoWMUXGc8ACJxM3+Iwxl3AAsdo6DupcV3q0mDKSVWWQGVyhGJY2xA5LrR4uH77Duo+9qNXiVPxh1q1iU9rwOu1l6P4aXaaKdUYJYQQgjJJR0+Hqt2H8F1owZhSokVc64Zjif3NGJ/Uwt8vIAOXwj1J9oxsbgQD99+NX762seoLC5Ebdf1e+aGuoR/t/v8dr+rBQ/dWoL3/3EOh7/owE12i+x3aV5ECCERKpZRzBX48dze3z9ACCG5jtYVs5MoIeketgOuVohSP59QHxgI700JwMZ92dOs91KZNMqFe02a7C/cy7CQFYGPctgtqHHYcmI/IcewWPHSkbjvnjpXK57YfaTXG9OkQ55OpVigMI9yhDNWOvZGdfh4fNUZwGPTxwIM4A2GMXHUIKze0wgAqD/ehgOu1rj3lEHDYXCeFqt3NyYt5FbrtMWu9bUOm+L64GN3jMWrR77C2pePxpolvvpQFXQqFqMGU/NDQggZaAbCPIoQQvrbOS+P700ZDTCQzTmqSqz4XtVonPPxsPXB/dIMtLeIElbMLMPjuxriqtg/NbcCy3cexr98vXc6EOcqbzgMQYBsc4dSku3+phZ4Q70/6AhLIp6cVY5Vu4/EBVRTFe5Tcyw4AGJm7ZMnvagzGMYbx87inc/OodZpk23Gqj/RjjPuIK4o0KX7NLOOJ0UXjO6fPYfdgoUOG1gw8Aape0YmStXtnbrBZxEJ+MO7/8C4onwMNZ//bjvZEcAbR0/jkdtK03hy5EK1ePi4RY2ovt60RAghJDG3X3k8lOr44ql2APFJI4unlij/3SzrTucLCYpF6nwKMYEOXxAr7hyH1XsaZXN7p92CFXeOQ4ePByzGhLfVcmzSAvQMkBNJK4QQQpSd9QRx16RibDnQHJeIWuOw4awnSPMokhEoDpfdFONut8fH3aaUWHHoeHus4Nv66kpsOdCMzXXNWF9dCRFSXKOaibZBmDhqEJ7sMS4+dsqNVbPK8cTuIwmT0Zdsr4/97ICrFfffbMf3fv8B9j7oxMsNp+LuZ2HXbXy8gCq7Bbvud0CEhH967h08+60JSZ+DVOtu3Y9Hk1yrSqx44GY7vvmff4slsk4pseLpuRVYtacR1TcUJ938QrGwzJdvoGIGhAw03rCAfIMGrzScksWBquwWrLizDN5w7+SE5Bs0WDdvPJbtOBwr/JbqOtSRIkZ3Obqv3fhD8mTc//j2tYq3TRU7vFiJCuJFTSmxwmqi72VCCCEDw1lPEHdPHoktdc34xRtNsY2aP/jaGKg5BoPzdPht3WfYuM8VO3bjaAs4loGfVx6zdB93CKIUm3u/9IAj7ndpXkQIIUAnr5wr0Jnie5cQQkg8WlfMTqn22aQ6ng0GwnvTnyIP0t8HeyPT4XKa/GYLFgxePHg8VgS++37CFw8ex9Lp2b+/xh8SFItN5sL7VcOxigUKNVzyptIkvfp7b9TJdj9W7joSyyM84GrF4ml2fHSyA9cUF+K2sqEYN8yMe6vGIN+gxk//fEz2ntp2zyTFRoiPTR+Lcz4eQOp1y+PnfHHvV5YBrig09MIjJYQQkm0GwjyKEEL6m1HHIU+vxvTyK7Cwq+m5VsXijDuAK/J1EKW+KSJFRd96icQyWL6zIWEV+8d3NmDtnIqUE6+BTg0W7QFe9rNUz1lfBKhVDItn/3wM9zhHY9WsMgRDIry8gDydCifb/Yq31as5hCVQIaoc5gtGgpM+XkhYkPC5+dei00+v/8UyJemCEU2QG2U1Yvv3JiNPp8JpdwBLdxzG6tnlGGLW9vOZkguRp1On6HpC3eCzBcMCd08amXSTO0NrGVnBTUEcQgjJOGa98njJrE8+Xmr1BBEIiZhRMUyWNHLaHUAgJKDVG4QtSccuk1Y5DJTqeH+Lzr8u5XieToOf/PkYahw2LJ1eCk9AgEmnwhl3AD9//WPF4rWBsIj64+1YPM2e8PUJUHyHEEJyXliUkhYABYCVM8el47QIiUNxuOzFMCnibj06gE8psWL17HJ88z/fweJpdnxj3FD89LVjse+lJdvrY41qAGBYvh5GDYeXj5zEB/9owzXFhajpNn8YZTHgqw4/ZlYUodZhg0Gjgo8Po/5Ee6x4W3cd/hBqnTY88dKRhGPl0x1+/H/fn4zj5/zQqTl8+Pk5TB5twbPfmoARg5InmGpVygHGnseNWhUenV4qK/gGRJJ3H93ZgAld56SEYmGEEJJZNCyLtS9/hAnFhVjYda2KjmeeefUols/ovbF3UYEeG6or0eLh0ebjYUwRCzNouV677566r930bMSQajNRb4/xEhXEAyLjj2fmjaeiM4QQQgYMQZSwua45VjQ9mh+4cZ8LP55bjvVvNsXNiYNhEafdAVxzZb7i3+4+vzXpVKj53fvw8QLlmRJCSBKpil1TjI8QQi4erStmp2T7bC70eDYYCO/NnmuvF3s8W1xOk99swbEM7p5cjC118nX+KrsFNU4bOJZRuHV2SJm3mwPv15AgKhYoXD5zbJrOjKTSm3ujOnw8Wjw83IEQzHo1rMbzjRg6fDzafSEs39WACcWFsjzCicWFuGZEQaxJY63ThpuvGoxWTxCLnKNRWVyIzXXN8PEC2lPMbY+3+WLrgqnyZ7qvH1bZLVjgsEFNBQoJIWTAGgjzKEII6W9GtQrLEtQMAyIN0n88t6JP7jf7o3sZwsMLsWSPnupcrfCHROjVNIlSIiJ+g3mqyWqqJNxLERJFfHNiMbRqFk/sbox9KBdPs2N4vh4OuyXhB7XKbgEviEAoUqiK5KZUiyJaFdunyd+5SsOxcZ8tg4bD+urKhJve1s0bD5OGg15Nz3UmMmo4xa4nRvqOzBo6FYftBz9P2Ilo+8HPsfLO7O+2NBCYUwRpKIhDCCH9T5uiS5xWYRHWqFPjvhcO4Zl54zHErIUnEClSDgAP//Hv+MOiScnvVxU/7u5+36nm4P3NrFeefykdD4ki5l03Apt7FOyJFtEIickLQfj5cNK5yPrqSvh52oBDCCG5TpKg2LVWlPr5hAhJguJw2UvDstjx4QnUOmxY1lWkONr05I8fnsAjt5XizX/9GjoDIeTp1LCaNPi81YN188Zjy4FmVI4okCXM92xUs+dBBzoCIq65shA/ee0T7Dt2Vnb/W2uuh07NQkLkC82o5VD923eTnq9BzeHmqwZjc11z0rHydSMH4Uf/83f4eEH271qnDU67JeE66pnOIKrsloTJ/w67BfUn2mU/E0UJrxz5Cj5eSJi0ZDVp0OLh4/5WdxQLI4SQzBIURMyfPBKbk2yQCgq9W3w/3xDZsFF/vA0qjlGMlWnYvouVdV+76ZknU3+iPel5Oe0WmPpgjNe9IF738QcVfCOEEDKQiBJwKElToGH5enx4vD3pnHiybRBuLR2CN46difu73ee3DrsFKpaJbZCmOSohhCSm1CgPoO9PQgi5FLSumJ2MGi7pOpvTbsmJ120gvDdNOpViQYZcKN4HXF6T32yhU3N44d3PZY1sovtrXnj3c6yZ0zcbwPtTXoq83bwceL+KEhQLFFJeWObqrb1RJ9v9WLrjMPb3aIa0bt54MAAe2XEYC28ahf2uVix02GTXp3yDGs/+5WPUK8QK11dXYsn2+pR5+VcW6vH2J2dR1RU/VNq3PjhPixfvnQxvMIzBeVqsf7MJP/vWhAt6vIQQQnLPQJhHEUJIf/PwQtI9RHWuVnj6qAh69s+yM4QnoLzhtzMQhk5NyZBK/CEBHCNPqlWarDrsFjB90ABBlIBTHX683HBKdr+b65qx50EHJo8ehJUvNWK/6/yk3mm3YMWdZTjdHoBOw2GoWdv7J0Yygk7NoqrEKgvqRDnsFpxxBzHaakzDmWW3Ni+PGocNwPkNtbVOm6wTQtQBVysYAD+eW4HMKklBokRJSt71hGGwZjYVCssWPj6Mb08ehVMd/tjPGIZBUb4O1xUXwkcFT7KC1aTBlBIr3k5w7ZpSYoXVRGNUQgjpb/xldInL03B47u5r4TrrAQAEwyL8IQGn3QE8d/e1yFMIzicadwPnC6G1eXlg8OU8st6lVbGKSWtKi+GSBGzrUbw2mii17eDnWHp7adLbFho0+NnrnyR8fQBg7ezyS3xEhBBCsoU3qDzfTXWckP6SMg43h+Jwmardx+OHX78aH/zjHIDz4/oz7gB++PWr0e7jUTlykOw2eV4NnvlzZJw6f9JIAEi6OeBkewCP72zAf3z7uqTnsHGfK5bIvHiaPel63LTSwSgwatDqCeLeKaPxuyRx+zV7G1HrjCS7dv93tFAcANnYvqrEism2QV1raoxs3S06R1myvf7879utGFagw+a65qRNY6rsFjx8e2nSx5IsFqbUwXkgoeeBEJIODIAtdc1x8Z/INUo5TnY5TFoV2jzKsbJ2v3Ih0cvRfe2G6brP6Dl0v3Z2Py+n3YJVs8oR7qPdRtGCeIQQQshA1OHj0eHjsb66EtsOfg4AqBxRgEBIwE1jLBhm1uGhW0qSzolXvnQEj90xFsGwINs03H1+67Bb8OC0EgiChOfmX4tCgzpnNvYTQkhvy9NwuKV0MMYW5cfFPo+e7FDMiyCEEJJYMCQoriuu7KM4HLk8kihh1axyrNp9RBZDjcYKpRyoTBRI8d5ckQPvTU2KJsEahSbB2SRlsbAUx7NBZzCE7944Cifb5ftrhufrcP3IQnQGQwD06TvBXqBVJd83WVVizbjm0peC8sKyV2/sjerw8XEF3wDg7aYWLNtxGNMrhmF/Uwu+c+NILJ5mx+A8LZ6bf21sTqrmWBxwtWLxNHvSfa8sgBfvnYx2XwjbvjcJ73zais11zbFGEAYNh+UzxkIUgUk2C24ruwLrXjmadN1ygcOGX7zxCcYV5WPjPhc2LZiIx+8YS+t6hBAygFHuMCEEoJzf3tYZCF3W8UuV/dGSDJGq60KeToVAqHc7EOcaX1CAScfJJqfRZFYG8o0g0WQYtg+KvkkSMNSsS7gZRBSBZ18/hoWOUXhk+tXwBASYdSpo1Cye29eEmROGY4heC38o+7tPkMTUDIPVs8uwcldj3AakxVNLwDGAui/emDnOoFXhO5vfQ63TFivIUDzIIFvQ6K7O1QpfSIA2RxY3ck0gLCbvetLUgkCYrofZwhcUIEHCKw2n5Bsy7RbUOG050W1pIMg3aLBu3ngs23FYtrgxpcSKZ+aNp0kcIYSkweV0iZO6/W/y44klGndHuwwu2V6PHT+46UJOv98EBRG1ThsYyJ+v6FgkKCQfV0oA7p40MmEHtRqHTfF5CoTFpJ0ZDrhaaTxLCCEDQKqutLnQtZbkhpRxOFqXylj5Bg3Odgbifi4BaPMGMThPh2On3HAHwjDrVSg0aBAUzo9TtSo2aeEzh92CeZXD8ew3J4APCbL1y7AooShfDxXHoHrSSNQ4R+PQ8Ta8+N5xrJs3HkBkzBu9zU2jLSg0aPD0Kx/hnqoxuGXsEPzijaaEj2m/qxUP3XqVbJ10eIEOL753HEu216PWacNjM8bBFwzDpFVBw7Hw8WG88G6kKPOjLIOv3H4MztOi4YsOLNleH0t4ddoteGLWOHzR6oePF5Imz+53tUL9xid4+LZSAMdkx5PFwpQ6OBcVZHdC/sWg54EQki4SlONkfbVlUcOx0Kg4LPnd+0ljZS894Oije5ev3bAscG/VaMysGIYhZh2CYREcw+DBaXY8dkcpfEERJp0KIUFEzZb38J/fSV7UlRBCCCGX5nRnAIPzdPjNKx8lXF+6o3woltx6FX786rGEt9/vakW4qxhDu59HWJCgVXFgWeBURwAbqitxxh3AFXla3LGhLjbfpXkXIYQk5g0LeOT2sVi9t1H2fey0W7BiZhm8YcpbJISQiyVAIQ7X1AIh+2uH5aSz3iD8vIjF0+xYOr0UnkBk3503GMapdj/0GhYjB5vSfZqXRVDKpWxqgUKKYNYIXUaT4GwyEIqF+QJhFBXqZUXfoooK9fD4+2YDeH/iwyIWT7UDkhSXt7t4qh18DuTPmnXqyzpO0qc39ka1ePiE31NApPBbrcOGh2+7CiWDTdj6zj/i8nGmlES6q1eOKEi673W/qxULO4N4sCtX5sbRFjjHWOEPCfj7F+24abQFG/Y14bGdRwCcLwI3LF+Lh2+7GsumM3D7w9CpWbz18VlsO/g57p40MtY40WLUgmNo/zIhhAxklDtMCDnZ7sfSPx6W1buhtefLY9anmCumOH6paGdSL+FYKHZdYBnA7acq70rMehVCgoQXDx5HZXFhLKlWxTCocdjwwFQ71BwLrZrDGXcAf/zwRNemjd7lCYbBJ4gKPzB1DJ559SjmTx6FUx2R4FwwLMIfEnC6w48a52j4+TAMag6n3PEbdUhu4AUJZzwBTB4zCI9Mvxp+XoRRw0Gr5vDFOR9+9Me/44V7JqX7NLMOywCVxfJg1+9rb1C8jdsfvqAODKT/eQLK17tUx0nmGGTU4N/f+CTh5kkRwNrZ5ek5MXLRGADTK4ZhwU2jYpuWznQG031ahBAyYF1Ol7iwKEGQgJcbTsV18lo81Y6wQsW4PA2H64oLEy4yV9mtGdcN2xsIgwGD6RXDsLDbxtvT7gAYMPAqjSslYNvBz2XxhWhx/m0HP8cjCvEET4rXJ9VxQggh2U+rYuG0W2QF0KOcdktOJKKS3EBxuOwV+RZhEjZbWDytBCyA23+5P/Zzp92Cf7n1qti/60+0Y/mMsUm7Bq/c3YjH7iiFQcVhy8LrsX5fU6zR0rrXjspuU2W34oV7JuGMO4hahw33Vo3BlYP0eO+zVvhDAlh/CLXO0Rhl0eOLduX1rw5/CIu2ftDjb0/G/OffxcZ9LlSOKMDgPG3ssd1aOgQrZo7Dp2e9WLrjMJ5fcD1++XoTSovM2FBdiWBYRL5ejeEFenzzP9/BXTcUw2G3KCbP7jt2Ft+dPCo2FwCAKwv1uMKsi0vwVerg/MRLR7B2bgU8gXDOd8FL1cl6Q3VlTj5uQkhmSNd4pt3Pg2PZ5LGyEiuG5Gn75L6jigr02FBdiXYfj+GFBjy//7O4DVRr5pSjw+fHqY4wvurwY/XscgR4Km5ACCGE9KYOHw+GieSv3X+zPTK3dY5GZXEhNtc1AwDuu9mOVg+v+HdOtPlx/wuH4LBbcI9zNP72WSuuHzUIeg0HFcNg8hhLXPdvmncRQkhikgj85M9HE673P/PaUcX1fkIIIYnRumJ2MmnV+O7md2INnoJhET4+0rhic10z/pRhTV4vxeXkUmaLy2kSnE0YCfiXW0oAQLbuWFVixb/cUgImBx6nxajFlx1+7E2QvzvKasTw/OzfWC9KwAPbDuGZeePxSKzYpApn3AE8sO0Q/rAo+/dMalUsppUOxriifFlTu0PH2/DRyQ7KC8tw0fW1Fg+PzkAIeTo1rKYLz+VwB5IXZzRoOAwr0OFUhx8rXzqSMB/n/psj+86DKQogGjUq7HrAgdV7GrG5rjl2La8Yng+jToXrRg3Ch8fb4eMF+HgBj+08EsmH6bZ2uXuxA5UjCgAg1jixym6BVs0i30DFCQkhZCCjOT4hA1uHj48r+AZE1p6X7jiMjbT2fEm0HAuH3RI3DwAi834t1zdzRSr61ktULKvYdWHVrHEwajNr83SmibzJBXxvymhs3NckS6yNbnLRqlic7QzijDuAH379arT7ggB6tyuJSctBp44Psn3tqsEIixJ0ajbh5nqb1YRCowZhUUKBkSbNuSoQFhAWJFx9hRnrXj0W9z5YN298Tiwq9DsGqOnafBV9TgcZlQcTBg3XZ53dyeUx6ZSHF6mOk8zBC2LCwSkQ+awmKpJKMk+Hj8cjCTZtApHK3ZQ8TAgh/S8vxXhI6bgoARuTxB8AYPWs5EVZw5KEB6aOgQgpbi7zwDQ7wlJmjbALDBr87PX4ArRA5JzXKBWgZYC7J43ElgPNccX5axy2SEXUJMwpXp9UxwkhhGS/YFjEQocNEhB3zYwWIiUkE1AcLnuJADbua4orLhlJ9mfw5Owy2c/rXK14YOr5757Ndc347+9PjnX97Wl/UwvOeUPQ5XP47duf4oCrFYun2RMWidvvasHqvR+hsrgQ9cfb8Nj0Upx1B2SJ6gYNh92LHRf9OPe7WrBmbyOemTcei7Z+gCvMOrxx7HTs+BvHzkCEhEduL0WLh8fdv31XtmlFq2Lxt89aMaP8CrR4+FjhulR0Gg7jhpkxLF+H4QX6pLGvZB2cDRoO/++GYvzo//s/2QaMXO2Cl6qTdYuHp/ghIaTPGLXK45VUxy+VSavG//vN37Bp4fXAn3tsQOuKO/XHd1++QYNzPh5P7I7fOLLf1Yrlu46gxmHDoq0fxJo+WFKsoxNCCCHk4rR5eahZDit2NcjmgA67BeurK/HxV274LiAfMLohOHpNn1kxDMPydTje6sM7x89hc10zrisuxPrqytgmTYDmXYQQktBlrPcTQghJjNYVs1OhUYNrixM3Q3LaLSjMgVjhQHhvDoTCdgDgDvCwmDSYXn4FFnZvVO8OwGLSwO1XLqaeDcKSpJi/q5jTmiX8fBjr5o3H5h65BdE9k34++9+v7gCPpbePxeq9jbLvV6fdghUzy9AZ4AEY03eCJKV8w6U37DPrku/7rnXasHbvR1josCUt1vm3z1pRZbemLA6o4hg8uacR9cfbsb66Mm5+W9UVe+weJzzgao01NwSAs51BeePFEisenV6KfK2KYomEEDLADYR5FCEkuTOdwbiCb1H7m1pwpjNI48VLcM7HY5HTBhaIa9xa47ShzcfDlvzml4y+sXsJL4iKXRd4QeqzZNRcwQsiDp/owCirAQ9Ms8u6AXiDIXAsIHZtQJcAtHmDGJqn6/XzMGpUqHO1xFVh9PMipl49BM+8dkxxc30gLMKkoQJ/ucrHCxhWoMOH/2jDD2+9Cg9OlboKOjLY9/FpbDv4OZbPGJfu08w+EvDiweOyrnwGNQen3RK36Q2IBFI1Khb+EHUxz0T6FK+dXk3fkdmiM0VF91THSWagTZuEEJJ5dCnGSzqF8VIgLCgWZQ2Ek4+RvbyAh/94GL+afy1UHItOfwhmvRohQcQDLxzCf37nuot/MH0oGFYuQKtUcIcBEha0iP575czk8zYNx6LKbk0YAK2yW6Hpo84MhBBCMoc3GMaS7fWoddpisSqtKtIte8n2evxXDnStJbmB4nDZKxhWXldMNNb18ULs9fbxAr5o8yveR4c/hP/830+x0DEK1ZNGYnCeNuGGEOB84ujGfS4wDIMNPRLVa502PLm7EROKC5N2MauyW1B/oj3u53WuVjx6x1jcOnYIJAC//utnsuP7jp3F43eMQ1WJFfubWuLO0Wm3YPaEothzsGR7PX5fe4PiY/cGw7j/hUN481+/phj3StbBudZpSzifeLupBct2HM65JgpKnawBoDPFcUIIuRxGjfJ4xthHuRdWkwbjhplR/ZtIwVHZBrTOIAoN/dfoLxBKHgOrc7Vi6fRSAOfjWmtzYNPY5erw8Wjx8HAHIvFNq/HSN/cQQgghLMvgsZ0NceOR7mtK0WLpFzonPuBqxQ9vvQrHW31Y+Lv3Yz/f72qBCAm1Tpts/kvzLkIIkbuc9X5CCCGJaTg26XjWYbdQPlSGGmrW4em5FXFzFqfdgqfnVmCouff31PW3gbDmbUgR5051PFvkG7R4bGdDwu8Zp92Cp+ZWpOGsepc/pJy/mwt73AoNGmx8yyXb36dTczh0vA3bDn6OFTmwZ9Ks0+CxXfHv1TpXK1bvbcTTc7L/vZrrLnadqPvvDzJq8PWxQ3D1MHOsIWH0PT7FbgEQ2V+ezIvvHcf/fP9GvNt8DlV2S8LcH0fXvlflBo2tEIG4OGH3fKFh+Xq8vMQJXzCyx16rYqFXsRhWaLjQp4oQQkiOGgjzKEJIcu1+5bXljhTHSWJGrQpufxjTK4ZhYbc9RKfdATBgYOijemFUhayXeFIUPfEEwmCoq5QiQQIG5+ng40WcbPdjiFmHYFiEPyTgdIcfRQV6SJKA2t+9H+tg3Bd8IQFrXz6KX919LWZUDMPQrvMwaDkIopRyc70vmP0BOpKcSacCCwZ7/n4yrrPng1PtuObKAvBC8sIDJDEJQI1jFDiOhVHLwRMQwIsiFjpskIC47iALHTaEJQkBnj5vmcjHhxVfO18OdHYZKFIVrKWCttmBNm0SQkjm8QUvfbyUas7pUxgj8yEBmxZejw/+cS421/XykTn3poXXw5th1wRPIAyDhkOt0xa3sL65rlkxFiNKUJy/i1Ly+23z8Vh55zis3tMY15lh5Z3j+qwzAyGEkMyRr9fAxwtJiyPl6/uvAAQhSigOl70uZF2xJ45hZK+30uYbg4bDiEIDHrq1BKIE6NUs9BoOP/x6CX79188SzhuiiaPhHmthBg2Hm68ajI37XPiwqwMxIH/PVdmtWDWrDCfO+fDc/Gtl43YfL6AzEMaKmePw7ecPJrxv11kPFtw0CpIkxW1YWTWrHHoViyklVrzd1AIfL+B/PzmrsNHeivoT7ZhSYoXVpFz8JVkH58oRBUmvAbnYREGpkzUA5KU4Tgghl8MXEpTHM320QSrfoMG6eeOxbMdh2Xf+lBIrnpk3vl+/5ztTJNr5ggI2L7z+/LU1zZvG0l1w7WS7H0t3HJY1PJpSYsW6eeNRVKDvt/MghBCSO7y8kHBTDBAZn4SFyDy5PtmcuMSKJ2eVoemMR3bNPtMZTPo3ax3ylSaadxFCiNzlrPcTQghJrM3Lo6ZrHNozDlfjsKHNywOD03V2REmxxYhn5o2HOxCGu6vJq1mnwvAcKfgyENa89WoOt44dgtIEBYaOnXLnTEEGH5+8IFq0sVm2S5m/mwN7SnlBxN2TRmLLgWbZ+kn0epELeyZ5QbkhdS48xlx2setEPX/fatLgxXtvxBO7j8je47eUDsY3r70S9cfbUDmiIOF9W00aPL/gejz18keoGFGAJ2eXY8VLRxKOrU51BAAo558kihNqVZFcoKoSK4JhARoVizy9Cno1iwI9NUEihBASMRDmUYSQ5FI1Uc2V4vL9TcuxeL7us6TF7Nf0UaNWqtTRSy6k64I3SBdIJd5gGIVGNXy8gL0Np+IGGYun2mPPc/TY6lm9/8Fw+yIJtSqWQfc6fToVhxZP4kScKG9QgFHLwUevdc7ScixWvnQkrgp/9D05s6IIV+Rnf7ec/mZSc9AX6nHg09ZY8QmGAZZsr0et0xbrDqJVsag/0Y4l2+vx2+9ORBE91xmpMyAovnZba25I9ymSC8QAil3dqJ5tdqBNm4QQknk8wUsfL5l0yqEck0JRVqtJiy/a/Xg5wZx7lNWIKzNsU6TZoMLGuyuxuU6ePFJlt2Dj3ZUwG5I/1lQxGKXjgwwanO4MJOzMcM4bxNA8mocQQkius5o0seJCPV1IESFC+gvF4bKXQZtiXVHLYdOCibKE/6Ag4of//X+x13uQUYMquxX7XfLvKoMmctufvHY0rnnN4ql2jB9egIf/+HfcdUOxbFOB1aSB1aSBmmNj961Xc7CYNDjbtVHdxyd+zw3O08IfCkME8NEpNzbXNaOyuADrqyuxZHs9jFoOy3cdwezK4QmTWVUsgwe7/u4PbrZDo2Jh0qqg5Vjo1ZEuxdHCPG83tWBzXTPWV1eCAeKKxP3otquxcV/TBRXsSfZ9371zciK51kSBrnuEkHRy+0OK45nf1VzfZ/ddVKDHhupKtHh4dAZCyNOpYTX1/2aJvBRFpT3BMBZt/QAOuwXrqyvT2hgt3QXXOnx83P0DkaKsy3YcxobqStrsQggh5KJ81e5PvabUde1NNiceXqCHBODYV278+q+fxebDKoaBICWuStR93knzLkIIiXc56/2EEEIS02k4LNn8XtI43P/cd2O6T5EkcaLVi/2uFlmT1/877ofTbsUIizHdp3fZBsKat5ZlsGx6KZ7Y3ShbK402wNKyubErI1WDk1xYY03VJDJXmkhuOdAct4co+u+VM8el45R6lTvFezHVcZI+F7tO1P33o03Ib75qMD5v9WKRczQqiwtjzQzHFuXj8V0NOOBqxQ22QXh6bnns2qtTczj2VQe+Me4KnHYH8U/XjYBOzeHLc36smVWOM51BsCzQGQjHrl8buppHpMo/6X7cYbeg/kQ7HHYLls8YizYvD5NWBaOGQ1GOFHslhBDSOwbCPIoQkpxRo1Ks/WDUUBmxSxEMJy8QXudqTTm2v1T0avUSvZpDVYk1bsIIRIJwLMPAYqTEDCVGrQpqjsFP/vxx0sDQk7PKZD8LhHs/mTVPr8YDU8dAzTGy4nOv/2sVCgypiqaooFWxOdNlg8QLhMW4gm9R0er61EDu4jEATnYEZMUnNi2YCB8vJO1mEAyL1D0jQ5n1KsXXLk9Pw49swbBQ7OrGsOk6M3IxaNMmIYRknlTjJbPCeEmnYhMWdgCAKrsVOlXyC3RIlLDxLVfSOXdfdRy4VEYVhy11zbIiDgC65mQMfjw3+fkaFYrfpTouAVi/L/55AiLjoLUZ9jwRQgjpffkGjay4UNSUEusFFREipL9QHC576dWc4oK7Xs1h0dYPZD+bPaEIAGKvt0HDYX11JcBAtj65fMZYPPeWK2nzmtkTivD8guvx0z8fk713bi0djG3fm4w1explt3XaLVg6vTT270TvuT0POvDPv34XPl6IFaRZsr0eQDNWzBiLM+4g9je1oMZhw+Jpdlmxua86/DhysiP2dzfuc2HPgw4wDGDpVnine2Gedj+PUFhEjcOGWudoBEICCvRqDC/UQxQlPPutCRf0XZ3s+74gRTJ+XzRR6PDxaPHwcAdCMOvVsBr7r+gQXfcIIemUp1Mrj2f6uHFNvqH/i7z1ZNIkzzeqKrHi2FduAOev5emKTWVCwbUWD5/weYqeR4uHT/vrSQghJLuEBDFlrmdet4ZMicYtf7zvRvz85U9ihdYf2HYIDJqxeJo9bo0rStu1nkfzLkIISSwvRTO8VMcJIYTEM6g5VBYXJIzDOewWGGgPVEY64w4oNnnVqjkMMWd3A9OBsOYdFCU8sbsxbm24ztWKJ3YfyZl8xFQNTnKhUb1Jp1LM303V1DkbiBKSbnI/4GqFmAObJg0pCiCkOk7S52LXiaK/H82v2XKgGZvrmlHrtGFicSG+MW4oZlQMQ4c/hHy9GreUDsF9U8IwG9R49rVjsdwZg4bDH++7EU/ulufT/GReBTiOgT8kQNcjzydavE2rkNcPnI8TVtmteGJWGdx+HgBwzstjWL4Oapalgm+EEELiDIR5FCEkuQKDGg9OKwEQX/vhwWklKWsikcR8vHLDIV8fNWqlb+xewjKRzdHLdzXEdZRfeWcZfv76x3jktlKFv0BULBAMJa9+eMDVikBIXuCpLz4YJg2Hb4y7Aqv2yAOqX7YFcM4TVEy21alYhEQxJwJYJLFU3eGCYRGdAeogd7GCCYpPRINbyTa9HTrehjzd4P48TXKBjGpOcSHDSIvCWUPDsth28HNUFhfGVXzfdvBzrJiR/V16BgLatEkIIZnHoObgtFsSbvRwpkiiU7EMFk+zA5BkC8dVdgsWT7NDpdD10R8SFOfc/lDfBJ8ulSckJC26vd/VAo/C+bIMFOcTSs0xfSmeJ1+GPU+EEEL6RvfiQp2BEPJ0alhN6S8IQUh3FIfLXiyAxVPtAOIX3BdPLUHPlM8Drlas3vsRls8Yi8d2HgEQWSNbsr0erz1UhRNtfnT4Q9CqWOTr1bHf6emAqxU/vPUq/OzPx+LGvKVF+XhyT+LNBnd80YEquyXh+LzKbsGbR8/E1uyit6912rBxnwsrZozDtzcdBAAYNRzqj7fJko2qSqxYMXMcSq8wQ82xOHS8DQFeAKeNH7R3L8wTLZIW+Y42XPJ3dKLve5NO1a9NFE62++OK6EwpsWLdvPEoKtD36n0lQ9c9Qki6GDXKcTKjJvfHMwyA+28eA1GS4sYF999sR7BbLOqAqxXBNDVGy4SCa+5ASPF4Z4rjhBBCSE/ekABeEJOuKVWVWPFVh18x/lLnaonddkbFsNh8+NHpY7G5rjnh3yweZMCb//o1mncRQkgSao5VXO9Xc9StlhBCLpaKZRTXppRyzkj6eINhxSavuVAszJRizduUA2veAyUf0aRJ8VrmQLy/MxBCjXMUEuXv1jhHdcWo+2d9t694UuyJTHU8G+hUrOLalFLzbZJeF7tOFP39WqcNWw40o/54O9ZXV2Lbwc9xzYgCPPPasbhx0dLbS/GT147J3h+1Tht+/MrRuJwZi0mLdn8IIwbp8efG07J57Oa6ZqyvrsRpd0Ax9jg4T4s/3ncjmk578OfGUyi9woz/O96Gf7pmODiGwfBBVPCNEEJIPModJmRgyzdoMHKQATPHF8lqP5zpDGLUIAOtP1+ifL3y85afotj9paKib71ElICnXm7ENcWFqOlRFGXdq0cxrig/Z4JwfUWn4tDmCyr+jp8XsHiaHZvrmuHjhT7p1CWKEnghvvicKAHLX2rExrsrASk+OLd6dhk4CWBYFoE0JdmSvmfUKr/ndGoOhhwIRPc3Px+/iBENbgHxC4s1DhuWbK/HjIph/Xqe5MJ4Q2GsuHMc1uxpjPuuXHHnOHhD2R/kHyjOeXl8e/JIbKlrlm/EtFtQ47ThnI+HLY3nRy4cbdokhGSL6KZ9dyAEs14NqzE3v6u8IQELHTZIiB/rLnTY4FWIH5z1BDDIpMEdFcOwsHtgzh35+VlPACMsxoS39QWV4xKpjve3Tr/yuFHxOAPUOCIjlUTzCSgVfQsKMGg41DptqBxRgGBYhE7N4dDxtlg8ghBCyMDQvbgQIZmI4nDZKyAIsOZpMbNimHzB3R3A4DwNAkL8mHN/Uwseuf1qWXMiHy+gzRvA8AI9/uN/XahzteK5+dcq3ndYlGTdiKPj3sF52qSdH9e+fBS77nfgyb3yonBVdgsWdMXruzvgakVt13j8H60+tHj42H33XAvY39SCJ/c0orK4EBv3ueCwW/BPlcMxc0MdJo4sTFr4rDe/oxP9rf5qotDh4+MKvgGR4jnLdhzGhurKfrsW0XWPEJIOLZ4AVswsw5q9jQkbLLZ4AihOEuvqDZkQj+zkBSza+gFqnba4JkyLtr6P//7+ZPnvp2lDVSYUXDPrlJPn8lIcJ4QQQrr7stWLVg+PUFjEIqcNLMPI5mYOuwXLZ4zF/OcP4iffHI9kG7oXb4vMiaNz4aFmHYBIM6bK4gLZPDg6rxzWTwW+CSEkW7V5ecX1/jYvD1DPaEIIuSgePoxASIwUKu4WgzrtDiAQEuDhaV0xEw2EYmHesKC85h3OgceYZXmbl0oQJTwwdQxExDc4eWCqHYIopfHsekeHP4zF2+pR67TJ8nfrT7Rj8bZ6/K7mhnSf4mUzpdirm+p4NuBFUTGHmxdpX3Cmuth1oujvV44owMZ9LiyeZseWA82oLC7ElgPNCYuqdgbCqHO1XlA+TTAswqDm4AsKcftgo40cV905Dmtml+OJl47IrnMOuwUrZo7DnF8dwPPfnYih+VqsefkjbKiuxKpZ5fCHwmCY7P+8EUII6RuUO0wIGVagxx3lV8hqBkwcWUg5wJfBpFMpFgjvq/kwjfp7iS8k4J3PzqG0KD/2M4aJ7CB+97NzmD9pZM4E4fqKlxeQqvGWUavC8Hw9Xrx3Mn7910/RF81kfGER3gQbvA0aDj5eSBqcO+fhwTJAgUEDKfvjkCQJBpBtqOquym5BUb4WOhUVfbtYiYomRINbtU4bHrtjLDyBMMx6NUKCiAdeOITrigv75DuAXD63X8D9L7yHZ+aNxyPTS+EJCDDpOJxxB3H3b9/Ff8y/Lt2nSC6QQcvBHQhjeo+iMqfdATBgqMhllpIAxUI3hBCSLifb/XEb3aeUWJNu8M9mHb5QbKzbcyPnku31+F3N9Ulva9Cq0eIOYNJoC4JhEW5/ZEOqzWrE6Q4/Ck3apLfN0yuHgVId728GrfJYQ+m4UcXhlcMnsfT2Uqg4Fp1dz1NIEPG7A83411uvSnpbs0GF9dWV2HJAXvjWYbdgfXUlzBn2PBFCCOk7mVAAghAlFIfLXlqOgwTANtgIo1YVe+2MWg4sw0DLJR7rnmwP4NHppVh4UwC8IMJmMUKv5rB67/nGVIPzks8JAMRiegYNFxv3bq5rxq+/k/z94uMFNLd6UVlcKJvDDM7T4q7fvJswxh8MRxKh1Vwkjrhi5jgUGtR48d7JUHMs9jedxW/e/gy+rqYwtd02cT7xUiNqnTZs3Ofq98JnUf3VRKHFwydccwIihd9aPDxdewghOU2vUWP+8+/imXnjsTTBeOYPiyb12X1nSjyyMxCCjxeSFl/1BOTX2b5ojnghMqHgmtWkwZQSq6woa9SUEiusJrpmEkIIuTBn3QGEJAlGjQrvfHEWU68egjvKr0CNwwYVx6DQoAYkBhIi87ZozujDt5fiiza/bEN39zlxdC4MRHJdZ1YMw+N3jMM/Wr0YbTViWL6O5niEEHIBdBoOSza/lzSn4n/uuzHdp0gIIVnHExDwwLZDqHWeL1QMACc7Alj78lFszYFCRbkoZZPXHGhe2uEN4f5th5KueadqeJUNUuUbZlre5qXyhgQs3l6PZ+aNx7LYa6nCGXcAi7cfwtba7P+eMetUivF8cw4URDNpOOVN7jmwh8jtCyvmcOfCezVXXew6UfT3o3vDvzFuqGIRN4OGg0HDYfPC62FQcxAh4b3mc5h69WBsWjAxrom4UaNCvkENNcvI9sFG31dXFurxl49O4yd//hvuuqE4bj96c4sXPl5AWJJiccbhhXr85aNTqP+8Hc9+a0KfPZeEEEKyG+UOE0IAavTc2zoDIcUC4Z2BkCyu2luyP5KQIQIhQXFDcFiUciYI11c6A2F8/JU7eUGtEiv+9mkL9n18BqOsBiy7vbRPujx0BkLI16vjXs/NCyOb7pMF56aXXQG9hoMvJMCfA4FzkgQjYc3sMqzY1Yj9LnlnzwemlqAzEEaemT7rF8usV8UVWuweBLutbCj+32/eBRAJEm+puR7tXh6aVJUiSVqY9ZGFjPoT7bHX0x/iUH+iHT5eoOthFjFoVHi+7mjCDmFOuwVPza1Iw1mRS5EpG5fI5aOiEyRXdfj4uO8pILLBPV0b/PuSWR+/ATJaOB5Q3iBpVnNQmXVY8dIR2TXaabdg1axyGLjklT21HKs459Zm2Phar+bgsFsSjkUcdgv06uTJI4IoYcktJXh8V/zztHZOhWI8waDiknZwYwD8mMZAhBAyIJxq9+N/PzmLIXlaBMMi2nwhvNd8DjdfNRjDaB5FMgTF4bKXimXw6M6GpInSycacxYP0UHEshhXo0ekPAQAEScLfPjuHN4+dBQAsnmZHld0i6+AYVWW3wKiJvC9qnTZsOdCM+uPt+NXd12JQ15wrWaxey7Gy9bEquxUTiguSbibRqlg47BY0fNmBzQsm4ldvufDonxpkt994d2UscbX7xvj9rhYsdIwCkN7CZ/2REOEOhBSPd6Y4Tggh2S5Pw+GaEQUJxzPXjChAXh9tHsqkeGSqYmkm3fnnwGm3wKAQE+tLfV1w7ULWP/INGqybNx7LdhyWnceUEiuemTc+p2LIhBBC+lY4JCAQEpCvV2Hq1UNxst2Pofn6WK6axajBf90zCW3eEDYtmAiGYXDoeBvOeXjc/8KhpH9Xq4qstTnsFjAAbrRb8Z3nD+JEmx+77r+JrlWEEHKBDGoOlcUFCfP1HWmcFxFCSDZLVHSqe74aNcHMTCmbvOZAgak8vVp5zbsfmk30NaOaU8zbNObI2CbAC1g3bzw298i9dNgtWDdvPAI5sNcyT6dSLIiWC59JX0jA96eMweJp9lgDuzydCp5gCKGwBF8o+19Hg5ZTLN5nyIHCdrnqYteJor9/vNWLjXdX4qevHcN+Vyt+/Z3rsHiaXZYXc/RUB5wlg/Fs1+9EVdmtmGwbhAe31wOI5NrcONqCKrsVBQY1VCwLlkHsu6H7+2rTgomxfyd6v21aMBEAEAqL8PECqkqsePPoaRz87BytexFCCFFEucOEENL72n0hPLm7Eb+afy1UHItOfySHLSSIeOCFQ/j5/7umT+6XvrF7ySCDBv/++icJNwQDwON3jEVmbZ3OPGaDCjdfNRj2ISaIkhQX4Htgqh1mrQprXj4KIPKcmrS9/xbO06mhVbFxG7wlScKtY4egdJg5bqPLRyc7cPiLDkyyDYIvGOlEQXKTUa2Clw/jjoorsNAxSlZdv3br+7iuuBCrZ5el+zSzjlbFYvPC67FhX5MsiDWtdDD2LnYiKIj473snw6xXQ8Ux+O3bn+K28mHIS1Asg6RfnprD1prr4Trjkf18eL4OW2uuR16OLEoNBIGQkLDICgDUuVoRyIEFm4EgkzYukctDxftILmvx8AkTWoD0bvDvK3kaDlsWTsSnZ72ynxfl67Bl4UTFzaxhAD/7y8eoddhi3RDzdCqcdgfw7385hmXTxya9rYoB1swux/Jd8uISkUJo5WCl3i+sfjk0LIMHp5YAiO+Q8ODUEmjY5AXuwDBYvbcRlcWFsY5p0Tn8mr2NWDkz+bzNwwuoP94et6Af3ejjyYHkI0IIIco6fDw+P+fD3sMn465BNqsRBg2XU2MTkr0oDpe9vLyQMBEciMTdvAnGnNNKB0OvVmHFroa4BNONd1ei4csOlBflIyxKmD2hCKv3fiSbZ1XZLVhxZxlOnPPBabegckRk0+TiaXac6vDjVIcf08uH4v6b7XAHwujwh2Lj4GMnO/CNcUNh0ESSn512C9bOLceaPY0JH4PDbsGZziBWzixDSBDwzKvH4orQRRrrSNhQXQkAMGpU2Lzw+ti4u3sRuFwufGZOsVElFzayEEJIKo/dMRYHP5NfJ4YX6PFPlcP77D4zKR5p0nCKm8TOuIMAzncN9YfTE5vqy4JrF7P+UVSgx4bqSrR4eHQGQsjTqWE1UYMcQgghF67Dx4OXAHcgjA1vHpXNVx12C37z7etQbDHgizY/2mVzYze+MW4oppUOxr6uwuvdOewWnHYHwAB44s4yiJKIe7d+gBNtfgA0vyOEkIvBAFgyrQQs0CMWasHiaSVQyBQghBCShDHFumKuFJ3KNSwDxWJhSulz2SJPk2LNOweKL/nCYayeXYYVu47E5W2unl0OXzicxrPrPYVGDf79jeR7a9fMLk/HafUqXziMNXPKE76Wa+ZU5MRrGeDDGDFIjxW7jsSNxdfMKUeHj0/j2fUOg5rDtNLBGFeUn3CPMBWZzmwMgOkVw7DgpvP7es90BpP+fnSdaekf/479rlYYNBxsFiN+/7d/yPaw/nhuBTa88QkmFBfinqoxsJo0YFkGnmAYJq0Kf/rBTeA4Bk/uboxrmPjI7aX48dwKPLbzSFcuTMQZdyDpddxht6D+RHvs/512C9bMLocoSfj2pJG07kUIIUQR5Q4TQkjvMxtUeH7B9Vi9t1EW23DaLXh+wfVg2L7Zd0uVqXpJUBCTFkWJ/JyBlAPB1L5kUHHw8AJqt36AWqcttik7VlDrd+/jT/ffhMXT7Nhc14ywKCXc9HK5jBoOXj6+yI2GZbF8xjg8vqtBNjF32i1YMbMM859/F39YNAl5ehUtJucwLy+AYYGy4fmyCp2D87R4qf5L7He1INBtIxK5MLwg4vm3P5UVZDBqVMg3qLFyd3wwfO2cCgSFEDr92R8Qz0UCJIgS8HLDKdlrF024EZBZxURIcp2BMAwaDrVOW+KCJwH6DGaDTNq4RC4dFe8juc6dYgN/rm3wD0sSpK7xUlwxs2klCCsUX/OHBfzLrVcnDCCtmFmmuOmTYRg891YTnphVhrAgwd01n1FxkZ9HC6xlirAogWUkzKgYJosRnHYHwDKR48n4wwK+M3kUTnX4ZT8vytfhuuJCxefJEwhhfXUlthxols3/HXYL1ldXwhvMrfcjIYSQeO2+ELa+05ywyOrWd5qx7PaxNP4mGYHicNkrVdyts0fczWG34Ie3XhVX8A1ALGH0gWlj4A0KEEQJJ9sDeOgWOx6fMRZfdm0uP3KyA680nMINtkF46JaroNdwWDzNjonFhRAkCat2N+IPiybh8V0NcfOUGocN699sws77b4IoAVqOxRm3H4uqRsMflq+RVtmtWDW7DB/+4xxeazyF8qL8uHM+f+6tuO9mO+Y/f1B2f+urK6Fm2Vgh5pAg4dOzHliNuVfQxWrSYEqJVVY8J2pKiRVWU249XkII6UmEhLOdwYTjmdFWI4bla/vkfjMpHunlw1gxswxr9jbGPQer55SjpTOIvQ86cdodwNIdh/Ef86/rt3PrqS8Krl3K+ke+IffGBIQQQvqP1x+CLxTGhjeb4uarZ91BjLAY8PjOhrhicNG58ao7yxASjsiuXdGGRZY8DYIhAc+8ehQr7yzDJ12bbWh+RwghF6cjwOMKsxYPTLPjka51GpNOBW8whCvMWnT4s7/QBCGE9DdaV8xOKpbFQ7fYcUf5MAwxa2Nriqc7AigZaoSaZdN9ipdtILw3NSyHn7x2DDUOG5Z2G9uccQfw09eO4uHbStN9ir2CT7G3lheyf6+dEAZ+9ddP8MjtpXis257CkCDil298jPu+Zk/3KV62QUYtntzTiAnFhVjYo+Hy2r1H8cSd49J9ipeNRaQhUXzxLguemFWO7P9mzV0dPh6P7DiMDz9vQ63ThmuLC6FiGVQMz8dZTxBePozBJm3cGpI3GI7F+mqdtrgcfAAYlq/DXZNGYtvBz3HNiAI8/erRHrkwFtw/1Y5Dx9sBQJbzc6YzAI1Kj6XTS7GMkeD2CzDrVDBpOVw5yABRkhLm4bz43nGsnDkObV4eMyuGQcsxGFZo7KNnjxBCSC4ZCPMoQgjpb3qOw9N/OZpwD9Ev3/gYj04f2yf3S0Xfeonbr5zw+ZU7gJGDDP10NtnJwwvwBMLw8YIsYNJdpz+M+uNtWF9diUBIQLAPimsFRSFhEZs8gworemx0AYA6VytW723EXTcUwxMII9+gyonAOUksEAqhwKDD6j0NcQUWfr/oBnx303tUBOkSSADumjRSVlRh8TQ76o+3JfzMLd/VgB/PrYCoTU8Xc6JMkICN+5riutHv7yqCunpOWXpOjFw0k06lWPDEqKOhZDbIpI1L5NJR8T6S68wpOtvnpTiebSQJ2PCWK2lHw7UKHQ01LIvlLx1JOE5es7dRsRuiTxDwg5vtWL7rSNx8Zs2ccviEzBpfe0MCFv4uUhh+qFkX+/nJjgDWvnwU/3PfjUlvywDQqdmEhfUWT7UrFmu3mrT4xZtNl/T6EEIIyQ3+cDhFkVWK/5HMQHG47JWnV4675elV2LRgoqw5Ei+ICsXTWnDfzWOwaOsHsZ89PbccrzacinUqjt7fL95okt3fneOH4R+tPqyaVZZwHSz678riQoRFCRzDgGGAX7zpQv3x9rhGTqfdAfj5MB7Z0RB7DEo6eqyxHnC1ggHwxKwy1O9vkz0/U0qsWDdvfKwTcy7IN2iwbt54LNtxWFb4bUqJFc/MG0/xHkJIzkvXeCaT4pGdQQE/+K/38My88Vg6vRTegIACgxr1x9swY30dfF3NEB12C9bNGw+TLr0dmXu74BqtfxBCCOlvvASc84bi5thWkwa/XTARj+1Unhv7QgKuG1mIh24pQViUYNBErs1vfXwGv/7rZ3j2WxPw5rGzePi2yDWc5neEEHLxBhm0+LLdH5dXES2yObxAp3BrQgghiYi0rpiVwpIIs16DVxrkRaur7BasGFWGkJT9RbQGwnszKIiYd90IbD7QnLDoTzAHiqEBSLmXLif22jHAzAnD8cxrxxK+loqJqVnCFxbi9vcB5x+jT6HhcrYICiLW7P0oQcO9Vjy5pxErZvTNRn5y+Vo8PD78vC1pvk2Nw4ZnXj2GJ2eXy/JK2rvlpVSOKEi4dz3foMazf/kYlcWF2NLj+xqIvD9ERIrGba5rTngOVXYLFjhsWLK9Hj5egNNuwSO3l+L6UYPw2B1j0erlUWhQIyxI6PCF8KNvXI1T7QGMGGSAmmMwrJDqDxBCCLkwlDtMCCG9zxcWFPcQ9dV8mCp19JI8nVpWnbt7FfvNdc0YnKdBu4+6Silx+0MwpSgeY9BysQ/Iypnj+qTOrAosDNr4JFk1xybdTHPA1Ypahw0GLQcVy+ZE9wmSmMWow7IEiV11rlas2HUEv5p/LTg2B6K0/U1CXEAsWRANiDzfHl4APdWZKRBS3nwYCNF3ZLYwqLmEweroxsun51ak58TIRcmkjUvk0lHxPpLrrCYNppRYZRvco6aU5F7ne19IUOxo6AslDwIFwsm7Ida5WhFQKKig4zg8qjCfybRru48XFAvD+/nkz5OaY7FRobCeUnG8gdBxkhBCiDItx11ykVVC+hPF4bKXlmMV425rZpfLCrgBQJXdqrgW2bN42lCzTtapONn9rd17FA/ffjUYhkm5DuYLCsjTq+APi5g/aSQWOUfj0PE2PNiVsBq1e7EDABAMi8jXK8eetKr4Rkp1rlac7QzGne/bTS1YtuMwNlRX5tRm+aICPTZUV6LFw6MzEEKeTg2rqXcL6hBCSKZK13gmk+KRZp0KLR4+du1P1hwtV9fnaP2DEEJIf/qizYfHdzVg/qSRcceemTceX7b5FdeIah02dPrD+MUbTbKi6t1F57mdwTDe/Nev0fyOEEIuQViULrmRHiGEkMRoXTE7qRkWK3YfSbiRf/We3MhdGBDvzQT7pYDzY5sVM8al46x6nUmrvCc01fFsofRarpyZ/a+lJOb++1UEkn/vNLUgB751cpY7EFLMfwEiTRt65pUYNef3ivdsXBjNw9GquVj8L1neevR4snPoXhhu4z4X6lytYF77GBOKCyBKEu77w4ey3Jq9Dzrx27rPsGLmOLAMbZIlhBBy4QbEPIoQQvqZhmXTsocoN6IlGSBPw2HzwuuxYV9TXIXwzQsm4u1PzmJa6dA0nmHmM+vV0HAsHHZLwsQZh90CnSoywT7gakUgJPZJwC8QFqFXc3Hn4Q1GJtTJNtRwDAODhkMwJMLH50D3CZKQh09enKHO1YrHOTbhBiWiTJQQ97z2DKL11BkIwZxisxhJjwHRoWeA8PIC6o+3Y/E0e8KNpF6FQiskc2TSxiVy6ah4H8l1+QYN1s0bj2U7Dsu+r6aU5Gbn++j8MhmfwvFUmxyVjntTzGcy7dpu0qoUi1oYFWICgRSF9QIKhfXc/rDi/bppPEsIITnvcoqsEtKfKA6XvQJhUTHuFgiL2LRgYuznpzv8yDeosPHuSmyui+8UvPHuSrA92oZ3L1as1GRlv6sFP5KuRlBhjAxEYvYGLQdIwKl2f+znRfk6/Orua/HAtkOx5NRASMDiaXaMHGSAjxew7XuT8M6nrdhc1yxLYK2yW1B/oj3h/bX7Es9t3m5qQYuHz7l5Yr6BigAQQgamdI1nMikeqe6RJ1M5ogCb65qTjhN8GRbDu1y0/kEIIaQ/dQbC5xv89lgLKirQo7nFq3j76Nx404KJYBgm7vrs6DbPHWTQYMwQU18/JEIIyUm+kICPv+rEpgUTMcSshScgIE+nwml3AEt3HFZspEcIISQxWlfMToGwGFfwLSpXchcGwntTQvx+qagDrlZI/Xs6fUppT2hOkKC4zi/lwIs5EN6vnX7l75XOHPjeyVVmnVox/6V70bYznUG0+0Lw8mH4eAH/fe8keIIChph1eG7+tdCpOTR82Y6K4fnYVNeMG0YNApB6PysviLj5qsGK57DIOTr27/2uFtx38xg0fNGB9dWVWNLVVNFht4BjgBqHDe1eHmOHmS/xWSGEEDIQDYR5FCGE9Ld07SGiom+9RALwq31NCSuEswyDb147HDo1FYJSkqfhwIsSlkyzY0bFMAw162LBr686/Bgz2AQw50NDgZCAsNj7HwxvMASOVeOhaSWy88jTRTabr6+uxJYDzXHF/eZVDoeGYdDGh1FImzJyltufosCCPwTWQEnHFyvRBCJV8bw8nRo5ES3OQQYtd1nHSebwBkOKG0m9QeXvRJIZMmnjErl0VLyPDARFBXpsqK5Ei4dHZyCEPJ0aVlNubno365XDMXkKx1NtclQ6fjkF49JBr2KxJUGB/Sq7BVsWXg+9wpwh1eZXpeMmXfK5//rqSphoPEsIITkv9TWTFsNJZqA4XPbyBcOKcTcfH8airR+c/3mJFU67FVvqmuM2dkQ6NjKodY6S/XywSRv771RJqSFBREhQ/p18vRoGNYeVLx2RdYl02C1YPNWO739tNH7+ehMMGg4Woxb1x9tkj83ZNZ6OJrBWlVhR4xiFxdvqE96f0hpBps1dCCGEXLp0jmcyJR55zhtEjcMGIJJjFBYlxdhUrjUhpPUPQggh/Sma+3fkZEfcGtRz869Nma+Wr1dDEKXYnN3Rba57XXEBFjhsWLK9HlV0DSOEkMsSDAl44Z7JWL23UbY3w2m34IV7JsNHeYuEEHLRaF0xO3mCKTbypzieDQbCe9Ob4nVKdTxbSJBw35QxmFlRhCFmbbcGZwFcWaiHlAMbwHx8WDF+78+B+L03qNw0ORferym/dzTZ/72Tq6wmDf7RmrppAwCc8/JY37Xfv/ue8O5zzKfnlmPbu5+jsrgQg4yRWJ5Orfz6X1moxxl3UPF3DBoOBg0Xy1XnWAZrXz6KyuIC1DptqD/ehgenleBMZxAvHvwcT84uz8n9EoQQQvrOQJhHEUJIf0vXvlsq+tZLvCFBtsGhu/1NLVg+YyxUTD+fVJZhAHAALCYtNu5zyZ7PKrsFT8wqRzB0PjBk1Kqg6YMndZBRi7AoYohZiw37mmLn8ea/fg3LZ4yNm9wDkcTblbsbsXpWGRV8y3FmfaSAQrIAptmgzoEwdP8z61Vxz2mBQYOn55Zj7ctH4woyOO0WmDQcfGHq1peJDGoO00oHY1xRftxn5KOTHTCkCICSzGExavHLN5qSbiRdPbssPSdGLlqmbFwil46K95GBIt8wML6bjGoOVXYr9rviNzJW2a0wKoyXjBoOt5QOxtgEY62jJztgVEg2MOvViskY0flOppAYYOO+JGMRRnksYtQqh7yUjhs1qqRzfwbA03MrUp88IYSQrGZOUWTVrKOlFZIZKA6XvQYZNfiP/3XhmuJC1Dhsstdu27vH8fiMsQDkaxGeoMJapKsFj80Yiz0POuAJCMjTqWDUcLi1dDDeOHY25aZ1Pizinc9aUWW3JLwPp90Ci1GDs54gFjpsqJ40UjaX2PiWC0tvL8XPX2/CihnjsGr3kbjxdJ2rFQzD4E8/uAlgIvOiJ/d+lLAgc5XdivoT7UnPN1UxbEIIIdkj3eOZTIhHGrVq3Pdfh/DMvPFYNr0UBrUKKxNcS6P/XjO7PB2n2Wdo/YMQQkh/+arNh3yDGoun2fGNsUPxfyfaUeOw4e6uOa7VpMG+Y2eSjk2OnXRjkEGDvxz9KvY3D7hawQLYvdgBHy/grt+8i8riAqyhjZqEEHJZrCYtHt3ZkDDGuGZvI63ZE0LIJUh3HI5cmrwUuQmpjmeDgfDevJxcxmxiVHHQD9LjN29/2mNPqBVr5pSBzYHNdgUGDX72+ic5Hb8361WKhe1SNb3OBno1B4fdEvc6ApHHqc+B751clW/Q4MpCveLvRPNjwqIUe41rnbaEeeEjCgxYcutV6AyEcc7LY9v3JsGkVcFpt8TlrgPArWOHwBsMY4hZi+fmXyvLm+me+6JTRXJ9op8hHy/Axws44GrFsumlGF6gRygs4g/vfo5Vs8oxvNBwWc8LIYSQgWcgzKMIIaS/pcpN76vc9eyfZWeITr9ylfp2XwiGfF0/nU12CooS2n08fv76J3EbSva7WvHknkbcOyXS3bjKbsHRk25MHj2o18+DF0RoVSx++toxLHTY8Mj0UngCAjr8PMZfWYDHdh5JeLv9TS3whQSoOAaimAORSJKQqavAQvWkkXEBTKfdgm9deyUg0et/sTQci00LJmLjWy7Zc1pVYsWmBROxaOsHseCX027ByjvLEBAEaFjljWokPTgAK2eW4fFdDXGfkbVzKsDSZyRr8IKouJGUF8R+PiNyOTJh4xK5PFS8j5Dc4eHDqHGOAiDFFTyvcY6CR6HbX0gUsGLmODy+60jCsVZITF4YWZdk3O2wW7BpwUTouMwaXwdCCmORphYEQsnHIgwDxaQMRqGGvD8kJLwdEEki94eo+DQhhOS6QqMmafKW025BoZHG4CQzsABWJh0bloOhMFzGCoki7kqwzuCwW1DjsCEkirJOwxv3ufD/fX+y4t90+0P4f795N/bvqhIrnpxVBgkfof5Ee9LxcVWJFe981hop3nZ3JQBGVqC6ym7FYzNKoVdzWPPyR7K/EU3sXrK9Pva3KkcW4NGdDQnPcX9TCwRJAscw0HIsVs8uBx+WF3hx2C340W1X45dvfpLwb0wpscJqou/h/tLh49Hi4eEOhGDWq2E1UiyKENK7aDwD5Gk4bKiuxMa3XDjgasUrS5xJY1MHXK0IhnNvfY7WPwghhPQHQZSgU3H46GQH5l4zHHsbTsmuuVV2C5bcYscdFcPw5J7GhOtwX7b58Ou/fib7u/u71o4EUcKG6kqc6QyCBVB/vI3mUYQQcom8vPKavTdBIwlCCCHKKL8/O6lZVjEHTp0D+2kGQoyYwaXnMmYTlmXw+M6GBHtCW7DipUb8eE72F0QLhsWcj98bVJxi0+R1OVCAOSSIWDzVDgBx+Q+Lp5YgRPukMtoVZh2mlFhleSZRDrsF9SfaUWW34m+fnX9tK0cUyK4xQKQJY9EgPVa+JG/EdGvpEKyYWYbVextlP59WOhiPzxiL5buOJM2b8fECHHYLWDZyn9Hjh463xX4/EBIxfng+VCoGP/vWBIobEkIIuSQDYR5FCCH9zajhFPcQGTV9U1CTir71EtMFdM/w0qZgRX5egFrFKm7ofuT2q+G0W/DErDKcag/AF+7959QTCIPVq7Dk1qtlk/NXH6pCZ0C5uJ+fF5CnU4HNlYgriSOKEp64syxpB7nHdzVQB7lLwAsinutKYu9uf1MLIEl48d7J+KLND62KRf2Jdqx79Sgeua0U/jCfpjMmSgQAK3Yl/ows39WQE91rBgp3iqK27hTXRZJZaINmbqDifYTkBk9AwOJt9ah12rDQYUMwLMbGuou31WNr7Q1Jb8uCxeM9FoyB8/ORtQpjrWCScfcBVytYMFg+c+zlPbBe5kkx1lA6zjJAjSNSOL7n4nqNwwZWYdpOYyBCCCFDzTo8PbcCj+1skC3aOO0WPD23AkPN1OCFZAYRwIokY8Plu45QHC6DiSKSJkoDwIqZ4+I6DRs1ymuR+h6LyfubWvDE7kbUOEaBBYM7xw/D2r1HZQXdnHYLVt1Zhjs31sHHR+Yp904ZjYduLUFYlGDQcDBqVAiLIpYniPl274ocDImYMKIAJ9v9iufpCwq4aqgpFt+IFnhp9/MIhkS881krFm19H+vmjQcfFmXfw1NKrHhm3niKjfSTk+1+LN1xOLJW02VKiRXr5o1HUYFy92xCCLlQNJ6JPAfdY3apYk+eYG7Gpmj9gxBCSF+L5jNNHmPBqt3x44/9rlbcUVGEVxri19LqXK1YsasB140aFGtc2l2AF7Fgy3u4bmQhHphqx+3r98d+j+ZRhBBy8Tr9IeXjAeXjhBBC4lF+f3Zq9/GKOXDtvuzfTzMgYsSMhJUzy7Bmb2NcDsrKO8sgSrlRYMoTEhT3hHpyYF9tqvi8Nwfi950pCjB35kABZgmRwlszKoahtlsO92l3AIGQAAnqdJ8iUZBv0GDdvPFYtiO+wWCNw4YXD36O5TPHYu5z78SOJSrIWOu0YdVL8defN46dgQgJtQ6b7P0BACsTXK+65838/XgbFjhsONURgCBKsXOKNlIEAJNWBRXLoGRo3uU/GYQQQgasATGPIoSQfqZXc1g1qwyrdsfHb1bNKodeTUXfMppOxaLKbpVtlIiqsluhVbE456UFRiU+XkCqwrGBkIhrigvx9CtH8fWxV2CIWdvr52HWq6BmWazoMWnXq1l0+EQsnmZH5YgCBMMidGoOh463YXNdM3y8AINWBQnImS4bJJ5fEIAwQx3kepkoIXlw39WKhZ1B3P/CIdnPH76tFAWG3v8OIJcv0GMjXnd1rlYEcqB7zUBh0CoPQA19VJWY9D7aoEkIIZklT6+CjxfiuobFjisUlveFkidUHHC1wqeQGKM87m6BmGHdXAxaDgYNh1qnLeE8XGmsolVx2H7wc1QWF8oW3etPtGP7wc+x8s6ypLdNVdjfpKVwGiGEDATFFiN+9s/XoM3Lwx0Iw6xTodCooYJvJKNQHC57SYDiuF6S4jsNa9SsYgf4RPY3teDR6aUIhkUIooSn5pbjRJsfHf4QRlr0EMRIU5ZnvzUhNtb+zduf4RdvNMX+xqsPVYFhks8lDrha8cNbr4Jew2LjPhc2LZio+Njz9WpZQZfuBV46fDyGmnW4tXQIzHo1fvbP18ATCKMzEEKeTg2riYrB9JcOHx8XTwSAt5tasGzHYWyorqTXghDSK2g8A3h5+UY4lVK3AgB5FJsihBBCLtqZNh/8IQEfHm/Ho3eMw0///EnsWPe1qMF5Wjy6syHh39jvasXCrmILPeXpVNh5/004dLwdtb97X1YYjuZRhBBy8fL0asVcgTwdFWEghJCLRXG47GTSqfCdze+h1mmLy4Fbsr0eO++/Kd2neNkGwntTr1Jh3WtHUeOwYen0UngCAvJ0Kpx2B/CL1z/G0tszq1nvpeq8jCa/2SJV7qgxB+L3qQos50IBZqOaw3+9+w+MLcqX5YGd7AjgzaOnsUohv5hkhqICfazBYIc/FNvb9lVHAEtuvQqn2gOy+Fy0aFt3PXNyutt37CzmTxqJB7dHGrxfW1yIIXlaxbyZZdNL8Y1xQ3HXb97FhupKXFmgR63Dhge318fOparECpYBWNp8Tggh5DINhHkUIYT0t85gGCfbAlg8zR6L35h0HLzBME62+cEO0mNwH9xv9kcSMkRIFHH/1DEQIcV1z7h/qh28IMKYonDKQGfWRwqmKRVVM2lVscn0d28clbJI3KXQqTh4eSFusOMPCdBpVKg/3iab0DvsFqyvrsSLB48jFBbBC6LiBn2S3bQchzOdQcXfyYUAZn9L1c0kUUeFXOiAkqsGQpB/oNCrOcWNpH1VlZj0LtqgSQghmUfDKRdr0HDxi8tR3qBykWmfwvFUY+hMG2Mb1Bw2L7geG95qipuHb15wPQwKYxFfMIzqSSOx5UBz3G1rHDb4+OSPVatSfn0SLf4TQgjJTToVCzXHQs0xUKtY6OgaQDIMxeGy14V0AO8ZF5ckCYun2gEgbi1y8dQSnPPw2LRgYtz6mjsQxoZ9TTjgasWeBx2Y//xBjCjU4/eLbsDKXUdkSanRNa8lXUmnDrsFBjWHU+6A4vl2+EOwmkz49Xeuw5A8HapKrHGxKCCSwDq0R0OnDh+PFg8PdyAEsz5S2G3MEFPs+FCz4l2TPtLi4RO+hkAkrtji4SmmSAjpFTSeATq7NqNEixmoOTb5tdRupSaEhBBCyCXwixK+aPPj+18bLZuTGzQc1ldXxtaTnpt/reLfSZTDVlVihUmrQjAs4tE/JS4YR/MoQgi5OCaNcq6AiZrVEkLIRaM4XHbSqzlcW5y4KI0zR3L5B8J7MyAImHfdCGw+0By3zlvjsCEgKOeEZouBUBCNZaCYW5qip0tWMKcowGzWZ38BZl9YwCO3j8WavY2y71en3YKVd5bBF86Nz2Qu655nkm9Qw2rUoMXDY+Hv3sfiaXYU5etkn9X6E+1xn91Ecb7uwqJ0UXHDM+4g6k+0o7K4AKfdgch/jyg4X/DNbsXqWWWoP9GOW0qHXOYzQAghZKAbCPMoQgjpbx3+EO574UPZfNjHR5ovbK5rxrZ7JvXJ/WZ/tCRDCCKwaOsHCbtnLNr6Pv54303Io6JvikxqDiEJSYuqbTv4ObQqFoun2bG5rhk6NdcnXR58ISHh32XA4Kd/PhoXmDvgagUDYM3scvhDArRqFaS+qEZHMkJQEJFvUA5QmqmD3EUzpSiUmKioglGroolHhkoVxM+FIP9AERJELJlWAhaQbf6ssluweFoJQiJVfM8GtEGTEEIyT7uPxyKnLeE1tsZpQ7uPT3pbs1557JyncNyQIvE51fH+pmIZ/OqtpoTzcBYMnp5bnvS2nqCAJV0d1hJ1Od1ac0PS27b7eNQ4bLH7ioomWSm9PoQQQnLHyXZ/XAHtKSVWrJs3HkUF+jSeGSHnURwue11IwrvWL49/82ERLAPMqBgmG+OecQdhzdPgx68cxb5jZ2O/H11fk6TzDas8AQFWkwa/r70BK3YdiWuAFP29WqcN9cfb8OSsMqzZ24jqSSNTPqYVuxowobgQP/zv/8PmBdcDgOw7tKrEiqfmlMviUPRdm7nclBhGCOknNJ4BzAa1bNOI1aTBtu9Nxuo9jbJrtdNuwYo7xwEMJaQQQgghF+PLNh+W72zAIudo3FE+DCHh/LW01mnDlm6b7lM1/snvMTapKrFi7ZxyXDnIgPrjbYq3pXkUIYRcOAlQzBV4SiFXgBBCSGJ5Kfa4pDpO0sPLh7HQYYOE+Dy2hQ4bvAqNT7PFgIgRS5DNPaOi/145c1w6zqrXMVAuiJYD9dAgAYq5pbkQvddyLDYtmIiNb7ni9vduWjARWoWm1tnC7Q9j/Rsf48dzx8PDC3D7Iw3qTBoOj+08jCW3Xp3uUyQKkuWZPP1PFXDaLdhc14xf3X0tFk+1Q6tiMa4oH9ePHIQZ5cMgShJavTwEUUJeir2sIwr1eOqVoxccNxxi1uKjkx1YPLUEgZCAtS8fxe9qbsBz86+FVsVilMWIsCTiptEW2r9FCCHksg2IeRQhhPQzs04NHy8kbL4A9F38lIq+9RJPMKz4Anr5MNjsj2n0KV6SsHLXkaRF1ZbeXop2H4/6421YX12JAoO6b4qrSYkLUPUMkndX52qFPyTAHQijKF+HM53BPjgxkhEkQJQkxUB0qiAOiaflWMXntP5Eu+xnVXYrOJYWFzNVqtczF4L8AwYDWPI0eGzGWEhg0Nm1mAFIUKtYCGIuLEvlPncgBKtJg2fmjccQsxaegIA8nQqn3QEs3XGYEouzzMl2Pzr8Ibj9IeTr1TDr1bQJmpAsZNSqEOAFPDm7HMGwGEsY0KpYfNXuV+xoaFJzuLV0CEqLzHFd9I6ddMOk0L0zT6uC026JK+wARDaN5mVYJ0UPL8iK4nW339UCD5+8m55Zr1KM0ygVz9OpOSzb8SGemTcey6aXxl07f1+bvGAcIYSQ3NDh4/Hsn4/h4duuxqN3jI3Nh0OCiGf/fAxP3FlGyVckI1AcLnvp1CyqSqwJC/VXlVhh1HAYlq/HpgUTwTAMDh1vw1fuIHQci8riAlmszj7EhF+91YRxRfmYP2mkbI6w/eDnuLtbwTaTjsMz88bjbGcQ1xQXoqareFz3ztwHXK14bPpYzKi4AuteOYo3jp1FaVF+yhj+flcrFjps8PECare+j+UzxuLfvn41eCFSrK7O1YI1ez/Cs9+agHyDBh0+Pi4RF4g0KVi24zA2VFfSd20apWpwROszhJDeQuMZwKjmZBv+7rqhGOtePSq7VkebGTzz6lGsurMszWdMCCGEZJfOYBj7Xa2472tjoGIZcCyDH8+twBCzFkPNWgCIFVcfkqdDld2ScH2qym5Fnk6FzQuvhyRJuMKsg1mnwgiLEQDNowghpDd5eQEfHm/H4mn2uLyIzXXN8CrkChBCCEnMpOEU88ZMGdYslER4Apfe+DRbDIQYsSgBH3/ViU0LJibM5c+VLRkMq1wQjcn+lxKQgG0HP0dlcWHcZ3Lbwc/xyG2l6T7Dy8YLIp57y5W0APPymWPTdGa9x2JUYe2c8XhsZ0Nc4+61c8ZDkGi+kak6fDxWvnQEE0YUYOFNo8ALkXiemmPg9oe6CqU24+E//h1/qL0eK2eWYe3eRlwzogBrX/ko9r42aDhsXng9bh07BKXDzJhYXIh8gxoqjkWbl4eaY6FXc6g/3h677/oT7UmvV5E8HxV+eOvV2Pfxafz6r5/BxwtgGeD+Fw6hym7B03MrYNbrsjIPpsPHo8XDwx2I5ClZjZqsfByEEJJLBsI8iuSW0+4A2rw83IEwzHoVCg0aDDXr0n1ahMiYdCrcUjoYY4vy49amjp7sSFiDqjdk1m7eLGZIEeDWqVm4/VRQQ0kwJCbdzF3nasUyhoFBq4oVgXtqbgX8od4PokhI3F3CG1TugOLjBQhhET5eUNygT7KbBOBEm18xEH3Ox8OWpvPLVm4/j7VzyrFi15G4buWrZpXhrt+8K/vZE7PKEBYFmDT0WctEbb4gVs4sw5q98d3nV95ZhjZfEDaY0niG5EIZVByCgoi1Lx+Vfd9FP5sGFS3wZ4MCvRov3DMZq/c2xr2OL9wzGRouF/pmDQyft3rx2M6GuNfxqbkVGNmVSE4IyQ5GNQdtoR6P9yh87rRbsHZOOVRM8u9mvyhg+cxxeHxXg6ygWeS2FfCLyefJ3pCg3PmzD+bYl6MzRRxFqXCpNkURDa06+SKGQcNh493XYsO+prjnaePd16aMARFCCMl+bV4eD95SguUJrtVr5pSjzctT4hLJCBSHy15qhsGa2WVYniAmvmZ2OeqPt+Gh//577OcOuwWzrykCJGDVnvgYz5o55Viz96O4Tts1DhtU3bpSnXEHMbxABxXHof54W9zvr6+uxJLt9fj8nA+jLEa8cewsAGBzXTPWV1eCBWRredH7WLK9HgAQDIsAImtmj+08gj0POrDw+ffg67YJs8UT+Q5t8fAJx+tApPBb9PdIelhNGkwpseLtBK/RlBIrrCZ6bQghvSPVeKbdFwT6eDyTzo0KHT4eHl6QXdsrRxRg4z4X9nVdh3t6+PbMiuERQgghmeysOwBPIIwRhXoMLzRg9d5GfHvySLzScBIfHm/Hzvtvks2PDRoOmxZMBMBgv+v8fKjKbsECxyjc9Zt3Y3PcVx+qihV8A2geRQghvckbDGF9dSW2HGhOGMP0BmlPBiGEXKyQKGDtnAos39UQF4d7am4FQgo5ZyR9TDrlxqd9tdm0P53z8Tm/PysYCivm8vv53Bjb6FUcXjx4PGFBtBcPHsfKO8el+xQvHwPcPWlkwnFqjcMW2ZCa5UQJis2ac6FIoYbl8GiPgm9A5HEv33UEP55bnqYzI6m0ennMnzQSpzr8AABJAjr8IXzV4cfowSYs2V6P739tNJ64swwf/uMc9jacQmVxoaz5kkHDYX11Jba+04yHbyvFM68dxTUjCvDsXz6WfUdXdcuh8fFCLG8GQI/fs+JH37gaX57z4ztb3pP9vM7VgqoSK56aW4ERgwz98RT1upPt/riGjlNKrFg3bzyKCvRpPDNCCBnYKHeYZJPjrV48mmBP9NNzK1BMe6JJBvEFw1h2x1i833xO9vPh+TrMvqYIvhT1pi5V9kf3MoRezSlWRJUk6tSXiifFm9ztD2FYfqRiZ52rNVLtvA8KzXqCYZi0XFzQOFUht2BYxOB8Hdz+EPJyIHBOEvMGw1CxDB5U6Jaz4wc3pfs0s06BQYsn9zYm7Fa+7pWj2LzwenzR5pf97Ee3XZ1xRSlIRKFBizUvf5T49Xz1KFbMyIHFmgFClIAndjfGjW/qXK1YtbsRT82pSNOZkYuhVXNY/tLfE76Oa/Y24iffnJCmMyMX42S7P67gGxB5HR/f2YCffHMCLZoQkkUkIK7gGxD5TEcSBpJfY4UwsOrlJN8HuxqwYmbysZbbH1Ls/Pm7musv63H1tjy9chxFMc4iAg/dYscd5cMwxKyNdVc43RFAyVATICa/qYph8KseBd+A850KKaGDEEJyH8sycYuLQOR6u2LXETytcK0mpD9RHC57CRKwem/i127N3kYsu0PeHfuAqxUf/KMNrxw+mfS76ZriQllxmOjvPXzb1Vg8zY7KEQVgGQYGjQqPJ/iOi/671mmDVsXC023jpI8XsGR7PV68dzIWdgbj5hLRDe9alXzh7os2v6zgG3C+eLNboYhz998j6ZFv0GDdvPFYtuOwrGDBlBIrnpk3ngryEUJ6TbrHM+neqNDi4eOuedEiqsl4An2TQEYIIYTkomBIAB8W8dz8a/H4rgZUFhdiU11kk+fiaXas3fuRbH7s4wUs2voBls8Yi4duLUFYlOANhjE4Tysr+AbEz1tpHkUIIb1nkFGLn7+ReM0eANbMpjV7Qgi5WGqWw6o9ifdrrN7TiFV3lqX7FEkCOjWLKrtVVpQ6qspuhU6h8Wm2MGpV+M6m95LmNP7p/uzfn1Vo1CbNAV+ztzFnclACYQGPzijFe5+d3xzNMAyG5+swt3I4AuHs3/9lUHP404cnUOuwYdn0UngCAvJ0Kpx2B7DjwxNYNn1s6j+S4bwp9vemOp4NPCFBsbCdh/YqZixBkqBTs3i54VRcodDHuj5/E4YXICyImDCiEI/uPIJah01WpLHWacOWA82oLC7E6r2NcUXhova7WiF2/f7Gfa5Y3kz0emXUqmRxw2e/dX5fVrR5Y1AQ8M/XXYmiwuws+Nbh4+PWUYFII8dlOw5jQ3UlxTwJISRN0p1rQ8iFOu0OJN2T8djOBvzsn6/BULMuTWdHiJwICa2dwYTzDdtgE4bk9c3YlypT9RINy2DN7HKc6QzAqFXFgjaeYAhD8nRoPNmB8qL8dJ9mRktVVM2sVyPQLbm1MxCC1ajt/fPQcNCpOGw7+LmsuwTLAFUl1rhJKhD5oP7ts1bMqBgGo1aFHGhaQJIwalX430/OorK4IGG3nKoSa9ymJpKaLyRg37GzSbuVV08aiftfOCT72b/ddjW8tOkrIwUFUfH1fOR25Y0KJHP4QkLCgrZAVwFWWszICm5/SPF1dPtDVCwsC3SkeB076HUkJKt4eOVrrIdPfo2VgKS3PeBqhaQwIc3TqRU7f2ZasXqjhlNMWjNquKS35UURPl7EKw0nZckZVSVWfK9wNHh98jFpJ6+c0NGp8PoQQgjJDd4U12ovXQtIhqA4XPZKFRP/t2+Uxv1sSJ426Ti1ztUaa2bU3QFXK5bP4FCUrwPHMBhWoMcXbf6kf+eAqxX332zH3z5rxW1lV8jPmRfwl49Oo/54W9ImWPUn2mU/03Dx6yXReYc5xfwj0+YnA1FRgR4bqitjBYnydGpYTRpK2iWE9Kp0jmcyYaOCu+v7tbtU+QapcmsIIYQQcl4nL+CdrrzOA65W2SbPyhGJ8/98vIDHdh7BC/dMwt8+a0X98TZUFhfGFTVPNK+leRQhhPSOkCAq5kWEBIp9E0LIxfLwAt48dhZvJonD/eg2WgPPSBKweJodgCTPgbNbIj/Pgc1rGo5Nuj/LYbckXG/MNr4BkoMiSsBXbQG80nAqLmdzeKEBQ8y9vwe0vzGihEduH4vHd8mLFjjtFqydUwFGzP4PZao1iFxYo3D7lQvXdaY4TtJHxTDY+JYrYYHwxpMd+F3N9RBFCU+9chTzJ40EEN9sKRoTjMYJexaF6/l3a7vl4kRz8B12CyqLC2VxwxGD9Hhu/rWxgjtr9zZi9ZyKrN7j0+LhE+6lByLrqS0enuKehBCSJpQ7TLJFm5dXnA+3eXkq+kYyBgsGG5LMNwBgbR81JMr+WXaGYBAJTm3Y54oL2qyaVY5rryyAX6QJvxKdioXTbkFdgi9up90CjYpFh/98gac8nRptfr7Xz8OkVSEQFnBv1RhseKspNmn/33/7GlbMGIcn9zbGVWascdiwZHs9ppRYYdapoc2BoDJJjGGAj0524B7naMyoGIahZh2CYRE6NYfTHX7cOMaCc97ef1/muk6/cvG2RN3MOwMhWE3ZH/TPRe4Ur2eq4yRzpPps9uxaTDKTO6A8Bk11nGSGVJ83+jwSkl1SjYeUPtOeFF3ylI6nmnfrMqyAdTAs4IGpYyBCipuHPzDVjqBC50cGwO/facaE4kIs7CrmrlNzOHS8Db//2z+w9Park96WxkCEEELoWkCyhdtHcbhsdSnfM4ni5N2pVSw2LZgoG/turmuGNxjGG0dPY/yVBdhU9xmquxJbk+FYBsdOulE5ogAOu0U2Ft9c14z11ZVgANm8ovtaWfef9SwCN6XECqspkvBpNWkwpcSKtxMkiHb/PZJe+QYqTkAI6VvpHM9kwkYFs04NUZJkMbv6E+1x1+CoKrsVLNOnp0QIIYTkjNPtfniDYbxU/yWmlFgByOfWqebZHMvg6MmOuPkuEFlXKzQmHifQPIoQQi4f5bsRQkjvu5x8NZI+LZ4gfLyA6RXDYjlwWhWL0+4AfLyAVm8QtsGmdJ/mZWnz8rHmVon26rX5sn9/1kD5/DEAfvv2p3ENyPY3tQASsHzm2PScWC8KA1jRo+AbEFk7X76rAWvm9M0G8P7EMsC00sEYV5SPyhEFsvyDj0525MQahVGbvOE0ABhSHCfp4w8lL6J5siMAQZSwt+GUrFhbz2ZL0Zhgz/+/UFV2K35029X4zV9dsbihw27BnxtPxxWPezTLi3q6aQ8TIYRkLModJtmCYv0km/gU5hsHXK3whfpmfE9F33pJGMATu48kDNqs2n0Eq2eVQ8vR060kKIhY6LBBQnygdqHDhpAgIE8XeQ6r7FacdgdwRX7vV+4MhAVIiHQJm1lRhNquwHggLOKrjgAqiwtjP4tWXl+yvR4+XkCeTg2WjTwWkpsYBvjO5FEwaDi80nBKtqmpqsSKyWMsyNPTZ/1i5enjO592l6ibeZ5OTd36MlTPbvQXe5xkjlSfTXots4M5xXUp1XGSGei7lZDcYr6Ma6xZl+J7XeF4SBKxYmYZ1uxtlM1lnHYLVt5ZhpCUWePrsAjUbv0AtU5b3Dy8duv72PGDm5LeVgJw16SR2HKgWbaIHk0GU+qnSGMgQgghdC0g2YLeq9kr1WtnSjCu16mVE4s5hsF3t34Q+7fDbsH66kpoOBbfmTwKBQY1fvP2Z3jo1qsU/44kSXjwlhIs2vo+NlZXgmWYWEEcHy/gxYOfY82ccrj9YXQGQsg3qNHwRUdsrQyIrJfcf7Mdi7a+H/u7U0qseGbe+NjG93yDBuvmjceyHYdlhd96/h4hhJDcls7xTKqNCq1eHjjrgdXYd4VbrCYNPjntxso7y7B6TyRmp1Rk9f6pdmpCSAghhFwgXhDBCyKenlsRG1N0z0FLlI/WXb5ejWV3jMW6V47G5rtAZF3tqbkV1HWeEEL6kEmrnBeR6jghhJB4l5OvRtLHpFPjBy8cwjPzxmOIWQtPQIitIz78x7/jD4smpfkML59Bq8J3Nr+XMEdwyfZ6xRzBbDFQPn8SgA+Pt2PxNHtcsbDNdc2KOZvZIhAWcUjhMQZCmZWDeylYFlh6+1is3tsoy7112i1YMbMMLJv9r6RBw6HKbsV+V3xjoCq7FQYNFX3LVF6FImoVw/MBnN+XXn+iHVVdzQq7v97RmGDP/08mX6+ONWAcMUiPPzeexoZ9TXhwWiSvprK4IGHTCCD7i6KZaQ8TIYRkLModJtnicvZAEtLffEHlom6pjl8q+hT0Eh+fvGpfnasVgbCIPnoNc4YnEMaS7fVJA7W//e5EDMvXwWG34IFpdrR0BpBnNfbJeZh0KujVLGyDDTBqVfAEBLgDIfz9y3YUdSs0xzDn2xNU2a0wajgIkoRWT/Z3EiGJqRgWZzsD2P33k7IEayDSfWTlS414Ogc6c/S3vBQB04YvO2Q/c9otMKg5nO4M9NcpkougU7GybvTdOe0W6FIEREnm0KmVX0u9ml7LbJCnVSm+jnmUBJcV6LuVkNxi1HC4pXQwxibohHf0ZAeMCgkDejWX4vqc/LaMBPzyzU9Q47Bh6fTSriQwDmfcQfzyjU/wL7eW9Mrj6y2eQBg+XojrfNb9eDKiCGw50BwXq4n+e8WMcUlvq1OxivMT+s4lhJDcZ9QoX2+VrtWE9Cd6r2avVPP80+5I7Nug4VDrtKFyRAFGDjIo3uZvn8WPfRkAi6fZYdRwMGk5/PG+mxAWxeTj3RIrrizQ4yt3AM9+cwICIRGPTi/F0tsBbzAMg5bDm0fPYMb6OlxXXIga5yjUbHkfd91QjA3VlQCA4YV6vHn0NN77Ryue/+5EaNUsCvQaWE3xBXOKCvTYUF2JFg+PzkAIeTp1wt8jhBCSu9I5nkm1UaEzEMI///pvmFJixbp541FUoO/1c8g3aHBFvgH//pdjeOT2UjzGsfAGwvAGw6hx2FDrHI1ASIjlziza+j523e/o9fMghBBCcpFfEDC8QAdRiqwbObs2eU4rHYxxRfkYnKdFld2C/QnGIVUlVpg0HI6f8+GhW6/CD79+NTzBMMw6Ncw6Fa4cZEjDIyKEkIGDY6C4Zs8xCW5ECCFEkSlFvpqJ1hUzUp6Gw3Pzr8WGN5tkc5cquxXPzb8WeTnwunEMcF1xYcIcwVy57ptSxMFz5fPnDYSx8e5KbK6TN+qtsluw8e5KeIPJ8z2zhTcQSvEYs7vAEwBoOA6P726Iy72tc7Vizd5GPDW3Ik1n1ntYAIunjcH0iisw1KyLXRO/6vBjzGATKEM4cxUoFLjhBXnRxc11zdh1vwPrXjuK5TPHYvXej3DA1YojJzvw9NxyFBgixdyG5OmSxggddgv+95Ozsc/7c/Ovjf13MCziv++9ETv/70tZk8Tusr3gjtWkwZQSq6yRY9SUEiusJsrtIYSQdKHcYZItCo0axfdqoZHGEyRz5OmVay2kOn6pqMJDL0lVdduTA0GbvmbSqhQ3cw8yatDiCeJfv3419jedxfSKKxAWe787QKFBAwaAKEn47Kw3FrwZM8SIiuH52FLXLJvEO+wWbFowEdY8LXwhAQwQ65xCck+7j0f58AI8sqMh4fH9TS3whqjC48USJAkPTB0DEZIsMOywW/DAVDtMOg5XDc2DTs3hdIcfE22D4A+Hsz74las4AGvnlGP5riOygbjTbsHaORVgpezv7DJQMBKwalY5ntx9pMdCsQWrZpUjJ9otDQBePowVM8uwZm9j3Gdy5Z1l8PLZv4A6EAQFQfF1DAo0/iAkq4gSVswch8d3HYnrhLd2TgWgMNf1hZS/132h5N/rKpbFwptGwXXGAyCy4OwPcTjjDmDhTaOgYjMrVSHV3FrpuASg/hK7RjIMks9PptnB5EAiGSGEEGX5ejVWzSrHqt3xsY1Vs8qRn6JDGiH9RpIU43CgOFzGCokinrizDKv3NMbF3dbOqYAnGMbeB53w8QIEUcKBT1tgMaqx0GGDBMjGqVV2KxY4RiXsGlznasX3p4yBxaTG6j0fYb+rFQYNh/XVlZAgyd43VSVWPHFnGe5+/iDWzC5HWJJkhZQNGg7LZ4zFLWOH4Oor8qBVRZKev3PjSPz89SY47ZZIgrcowTHGesEF3PINVOQtU3T4eLR4eLgDIZj1aliN9NoQQvpBGsczShsVHF1FYQDg7aYWLNtxGBuqK/vkezEsiLjv5hKs3tuIA65WPDf/Wvzof/4eK/wKyBsS+mhdhxBCCEnprDsAHcfhK3cAgihBr2bx9NwKfNXhxzevvRKP72rA5rpmrK+uhIie82wLVs8uwxlPELwg4S8fncaxU24smz4WBjWLosLzBd9oHkUIIX1DzbG4P8ma/f1T7VBzmZXbQAgh2SAoRnJQH9/VkDBfLShSDmom4gUhruAbgEhhVAZ48s6xaTqz3qPhWMW9RJocuO7zooC1cyqwYldD3NrwmjkV4HPk81do1OAXb3wSt5k/8pgZPDm7LD0n1ossJi1++WZT0se4OgceoycYTliQAYjkH3hyoHifPyzCywt4peGUPGfCbkGN0wZ/mPLCMtWQPC2qSqzYn2Btb5hZj7B4vvCbjxfQ3OrFuKJ8CKKEyuJC3Fs1BsML9Xhy9xE8tvMIgEguzKYFEwEwssLjVSVWLJ8xDl91BLB4mh2b65qh7dY0fH9TC9p8PP5+vD1hwbdcKIqWb9Bg3bzxWLbjsGw9dUqJFc/MG09xUEIISSfKHSZZYqhZh6fnVuCxnQ1x79Wn51ZgqFmXxrMjRI5N0ZCI7aP9pFSZqpeYU2zyosJEFybZpNtpt+DlhlOxxQWH3YKZ44vA9sFO66AgQsexECTg5YZTsaDx2w/fjE11zXGdCg64WsECqHXakKdTw6hVUQXcHGbUqdDuUy7i2OnP/gBmf/OFRNRu/QC1ThtqHTYEw2KsW3nt1vexoboS979wCEAkiDp6sAmDjGqocmABJxcJANbs/QjXFBeipsfruXpvI1bMGJfuUyQXKCCIOOsO4o6KYVjY7bU84w7iZLsfg83adJ8iuQCiCPzkz0cTfibXvXoUj9xWmu5TJBdAr1Jh3WtHUeOwYen0UngCAkw6DmfcQfz89Y+x7PbsT9wgZCARACzfdSRhJ7zHdzVg7exyhVszeOY1he/125N/r/OiiM5gGC8nSVQwGzIrdnE5HS79fBjrqyux5YC8o6LDbsH66kr4FTbHBsMK85PfvY+d9990eQ+MEEJIxss3aODnBSyeViIbf3uDAvI0HCUskYwhSMCalykOl400LIuznvi42zlPELwg4ulXj8ZtaphzzXDc8/sPcdcNxbJx6iCjBvOfP5gwiRQArHlaPP3yR7ENBD5ewJLt9ah12vCDm+0QJAmhsIgrC/XoDPBYN288Cg1q/PiVo7KCb9HxdTTxNXpea2aX4xvjhsKgVqFAr0a+QYORffjckb5xst2PpTsOy9Zpp5RYsW7eeBQV6NN4ZoSQXJfO8UyyjQoOuwU1DpusoOrbTS1o8fB9MhcIiVKs4BsAGDUqxbiWQUv5KIQQQkgqfEiAJxjGhjeb8OHxdqyvrsRP//IJKosL8ffjbbHrbnR+XOuwAYg0gzBoOOz++0n8/PUmAJEE7idmlSFfp8LgbhsPaB5FCCF9JxgWsSjJmv2ire/jTz+gNXtCCLlYHMNixa6GhPlqK146khOFinJRSASOftWJTQsmYohZC09AQJ5OhdPuAJbuOIyQmPpvZLqAoLyX6E85kKsnSIAkiQn3ZIRFETnwMgIAeEGMK1AYtd/VAl7I/kc6EB6jL6hchDDV8WyRaI/wflcrRAArZ1KuTabKN2jwTIK1vaoSK/INKvh5AQ67Jfbaarv2nWpULP5+vB2VIwrwxG55/r6PF7Bo6wdYMWMsHr1jLFq8QYQFCVeYtTjZ7scD2w6hsrgAmxZMxHv/OCc7H3cghCdnl+GJ3Y1xMcJcKYpWVKDHhupKtHh4dAZCF9wAkhBCSN+i3GGSTYotRvzsn69Bm5eHOxCGWadCoVFDBd9IxmEZBjXOUQCkuKL9Nc5RfVLbCqCib71Gy7GyCWF3DrsFGo6Fhuuj0n05QsL5qrLdJ7lOuwULeyTUHnC14sk9jXhqjtJG+EvTGQhDbdBg41uuuAl8otcXiAR1apyjYdSq4OfDoFc6d2lVLIwpkqgpyfrieQJh+HhBlrTeXTB8PvAdDaKunV2OcM4sb+QWX0jAvmNnse/Y2YTH/+0buRHkHwhYAL95+9OEC1NVditWzKQiU9lAAhQ/kw9T0besUGBQY8FNNmzY1xS36fvBaSUoyLBCTYQQZb5Q8vnlAVcrfKHk4yVJuvTvdUlSTlRYkWGJCr6wgIUOGyQg7rtvocMGXzj581Rg0OBnr3+SsHA7AKxRKKzn9ivPT9wBKvRNCCEDwRUFeug1HFo8PARBgkGtQnGhgRKWSEahOFz2Coki1r/ZFBd3WzzNjl1/P5lwHPvknkbcdUNx3Dh104KJSQu+AZF5QM/7iY53N+5zYdOCiVi09QNsXjgRJq0KD71Yjy0118tuU+u0YcuBxI2RnnjpCFbeWQYVx9B3ZJbq8PFxhQqASIGjZTsOY0N1Jb22hJA+k+7xTPeNCq3eyGaF+hPtWLK9Pu762hlQbg53qURRkl1jBxnVWPfasYTXXQbAj+dW9Ml5EEIIIbniqzYfBABPdRUzXzzNHpvT1jpssnl1z/WgTQsmIixK+PVfP4v9bL+rBU/ubsTabrmqNI8ihJC+5Qkqr9l7grRmTwghFysYVihU1NQi269BMocvGMYL90yWNY0AIvvsXrhnMnx838Qs+1OqvUSeHMjVUzMslr8U3yQYiLyWSrmM2aTTr/xadebAa+lO8RhzIbc0T6+83TzV8WwgSVDM4Ralfj4hclESFSEz6SJ7ur3BMO6bMgYzxxdhWL4ORq0Kt40bChbA4zPHQhCkhK+9jxfw6M4jsfwZAHhu/rV44eDnqHVG4oksGFw3qlB2u2H5enAAVs8qQ1iU4A2Gc7IoWr4htx4PIYTkgnTn2hBysYaadVTkjWQ8jmHwX+9+jgnFhbKi/fUn2vFf737eZwU1s3+WnSHO+XjUdHX767kRucZhQ7ufxxAjTWyUGDUqSKKEx+8oRSAkIRAWYNap8cqRUwkTavc3tShuYrlUZp0KgXD8BvxUwUVJksAxQIFeg3M+vtfPi2QGRgIMapVikUedioq+XSyTTvlypFWxsn9HC2Hka+kylokGSmeXgUBE/IbQqP2uFiq7mCW8KZLcUh0nmSHfoMHIQQbMHF8k62R3pjOIUYOo8AQh2eZyxkupkpeVvtdTJSpIGZao0OELY9mOw5GubNNL4QkIMOlUONPVqfQ/5l+X9LbBsKj4WJUSFVMV8jZoaM5HCCEDjQSAOn2QTERxuOwlJCjEBgCVIwqSbmjY39SC+742Ju54/Yl2OO0W1CVq3FBihY9XnkNEx8aD87T45etN2LLw+riEfMXzcrVClCQMLzSgw8ejxcPDHQjBrFfDaqTEz2zQ4uHjChVEvd3UghYPT68jIaTPZMJ4JrZR4YwH//zrvyX9vTxd3zRf8fa4Vqs4Nmlcq87VCq9CwwhCCCGEALwo4USbP3Y97T6nTVXIIhgWwTBCfK6qq0V2zaZ5FCGE9C1jitzgVMcJIYTES1WoKFWxJpIeg4xarN7biMriwljusE7N4dDxNvzktaNYObMs3ad42QbCdT+gkMtY52pFIEeKLg6EvMuB8Bi1HJs0/8Bpt0DLsQlulV1of032616E7Ms2H1o6g+A4BnxYxPBCPba+04zSonxcW1wIQc3BEwzjwKetKC/KV/y73WOHWhUbayIBROKDCx2jYserSqzQqVkwYGBQsRhaoO/9B0oIIYQkkQm5NoQQkmvOeXksvNEGjZqBUauCJyAgT6eC027BDSMH4ZyXh21w799v9ke+MoRezeE7299DrdMmK8IQ7QD8x/tuREjKjSBcX+FFASqwaPOFsPEtFw64WvHc/GuTbiIB+qbLg4Zj0RmIL9qWKjA3vFAPDcciKIgpC1iR7BUQREiShMfvGIsfv3IM+13nk7ccdgsenFoCjjaAXjQGwLTSwRhXlI/KEQWxxajDX7SjKF+HfL0az82/NrZAtbmuGb6gAI6lJzsTmXQqWE0aPDNvPIaYtbFBzemu4hz0HZk9BkK3pYEg1eKhMQcWFweKYQV63FF+hawr0cSRhZQsTkgWMutVMGg41DptsvFvdKxrVuiEl+p7Xa9wPNsSFfJ0HH76zQk41eEHEFlM94cEnHYH8NNvToBJl/yxpiqOp3TcoOFQVWJNuFmnqsSaE4k5hBBCUjvZ7sfSHYdl14MpJVasmzceRZSkRTIExeGyV7K4W6rN5yqWiWtK89HJDqyaVY4ndzfK1ixuHTsE//b1qxGWJFl8/cX3juOuG4pjc5HiQQb8+J8q8LdPW/DGsTMICiIeu2PsRZ2XnxfoezOLuQMhxeOdKY4TQsjlyKTxjNWkwZQSK95OEBOaUmKF1dQ3sfiemxY9KdbfaAMuIYQQktyXrV580eZHh//8PCYYFmHQcPj+10bjqqEm7F7sgI8XoOZY7G86i9+8/VmsyFuBXo13Pku8Eb97ITiaRxFCSN9KllN86HgbPjrZAYZShwkh5KIZtZxivlqq/VIkPQJhAdWTRmLLgWbZ3jqH3YIahw2BcPZv5GcATC8fim9eNyIuRvzHD0/kRH++VHPEXJlDGtRc3Fp2lMNugUGd/d8zJo1KsSCaSZP9ORpBQcRChw0S5E2mHXYLFjpsCArZvz/alKKYZKrjJHN8cc6HR/90GB8eb8fva2/AlYV6PLn3I9zdde3cXNeMe6eMhtNuRZXdijydCnsWO7Hv49P49V8/i2v8oFVFiho67BbUn2gHIM+Xif53VYkVq2eXQcUw0HIshlBODCGEkH6WSbk2hBCSK4w6Dma9Gqv3NMoau1fZLVh5ZxmEPqoXRt/YvcSkVeG64sKEBcqq7FaYtKqcCDT2JQ4sBAmxgm/A+YlyMn2xsMCHxYSdQHQqDlV2i+wDGlVlt6Dhiw5MHFUIdyCMwUYqvJGrWABt/jB++/anmFBcgIWOUQiGReTr1RhkUEOv5kAf9ovHsMDS28di9d5G2fdold2KyVPH4Lub34sF0hx2C9ZXV8JsUMHto4T2TGRQs3jx3hvxxO4jsiC/027Bi/feCHX2N3YZMAZCJ6KBwKhVocpulW36jaqyW2GgRams0r0rESEkexnUHDYvvB4b9jX9/+zde3xU9Z0//tc5Z+6ZTBJmAEEJBCcVCEGDWNEkVGi33kBh2e4W/LVcbG2LSHf324pWQQS8tdvdrlC7rYp1W6F7YaWKl1aLXQKt92y5KxEEFAQSkkzmembOOb8/hoRMZuYMIZnMzMnr+Xh0V3IymU8yM+fz+bw/78/7k5SMtWHhVbrJLfYMiTF2ncdm6rvzrW8vtpjQZo7ipd0nkpJHlk73olgnQcaVYZFC77pJELDkukuhalrS8y65zgsTM8iJiAyvPSgnFS4CgO0Hm3HP5l1YN6+G43LKCw5Thjgchy15K13cLdO6WJHVhJurRyQcQHXKFwYEDbMuH9G1ZnGRywqbWcLal/clvDdmjBuK574xNTkWX+nBipkT8Ivth9FwsBlRRU1IVj+fdvG+WbhcNrPu9eIM14mI+iKfxjMlDgsenTsJ92zelVD4bVqlB4/NnZS1vsxhlhL63Yzrc9yAS0RElFJ7UIZPVtAWisLWbb3MYZbw0/mTYTOLeOCFvQljjnqvB+vn12DpxkZMLi/F0GIrNuw4nPLnl9jPzY04jyIiyi5JSp1TXOd1Y8XMKoiilsPWEREVpiKzhKcXTMH6N5qS8tWeXjAFRQYoxmRUz+w8nJQr2PnvlTMn5KJJ/UoyAXffMA73b0mOEa+ZPRGxLG2oHUguu/4cMdP1QrJ0uhdAcrGwpdMrc9WkfhVWFN2CaGGl8AsxdoRiWLapEYvrKhLyEhqPtWHZpkY8u/jzuW5in1lNom4edqb8CMoP7UEZ9/7PLjQ0tWDpDC/KHBYEZBUTRpbgmZ2H0Xi0Devn1+CZHYfxk9cPdj2uMwd90sWluHPj+wn7VRuPtXUVVl22qRFAYr7MJWV2vPLdelglEWZBgAlgwTciIsqJfMq1ISIyiiKzCT94fndSPamGphasfnEfHp4zMSvPyxloP1FUDUumX4parzvh67VeN5ZM90JRNfjlwg/cZFM4piIcUxIGF50T5VTqKz1ZOeVBxbmTQp5eMAUv3lWLTd+cClEAVt06MeVrvKC2Ag+8sBfhaPxUSC4lG5dZEvH4toN4/cBprN/WhNuffRdLnnsftz31Fh5+eT80AeAboPccJglrtu5NCpg2NDVj3RtNWFxX0fW1nU0t+OXOw3CYJCa05ylREJImiwCwo6kFq17YA5FFMgpG50lEqRjlJKLBwG6WsHSGF/U9Xst6rxtLZ3h1iwNR/mkPyvjolB+NR1vx0Wk/2oNyrptERBdABfDTbQdTJmP9dFsT9NKUoqqKZTMqU97Xl82oRExN/+giq37fnqoAei5FVS2hMHynnU0tWP9GE6Jq+smX7exm2VTqvO6EzT49+WUFtz/7LmrKy/D0gil44rbJeHrBFNSUl+H2Z99hfIeIaBBo9stJhYs6bT/YjGY/x+GUH0QxQxxOZBwuX6WLuzUea0s7jq31uvH6/pMAgGEuKwQh/v9HlNjx/setcDutiMRU2MwSiqwmPPTy/qT3xoSRJVidKhZ/sBmrX9yLx+ZOAgCcaA9jYW1FV1v01uumVXpgkUTeNwuYx2nBtEpPymvTKj3wOFmwj4iyJ9/GMyNL7Vg3rwZ/+McvYMuSa/GHf/wC1s2rwYgsbtgIRuObxDr7WptJStvv1nrdsJm4rkNERJSKLxRFRzh6diOm1jWnVTQNJ9pDKdecGpqa8cyOw1hx8wQ8NKcaP/tjU9dGz+7qKz0YVmzt+jfnUURE2WWVUucU72hqwZqte2GVOC8iIuotSRTwRJo8rCfe+AgS1xXzkqohZVEiIP51nfS5gmETpaSCb0C831+xZQ9sYuH3+xZJ1I35WiRjbO89E5ARjqq4uXpEQt7lzdUjEI4qOGOAnHff2YJoqXJLl21qhC8Uy3UT+6zYboLDIqFmVCmGuawoc1gw3GVDzahSOCwSijMcyFwIWoMybq+rSJmHfXtdBVoN8F4dDJr9Mt472oa//1Ilbpp4EWKqCkkEbpo4AjubWrC4rgIbdhxOKtjQmYN+oj3UtV+13uvBypkTMKW8rOvzHJSVrkJwQPx+vffTduz+pA0Q4oUZLnIXDewvTUREdFa+5doQERlBIKokzR86NTQ1IxDNzn7Swp9l54lgNL4hOFUV+9uffQf/9e1rEIgUfuAmmzrCUQgQ4LBIWFxXgZpRpYipGm69YiTePXwGw1y2rs0qn7WHcO2l7qwU1wpEYii2S0knhfz+H+rREYqhprws5UkFQVmBPxyD22lBMEsfWMq9YDRemLD7+7Tzffn+0VaEowriZQOpN/yy0nVyeU87m1qwuLYi4Ws7mlrglxU4WagoL/llJe3iYudrR4VhMJxENBj4IzEEZQU3Vo/Awm5jmJO+cHz8EolhWK4bSefleFsIyzfvSthEPa3Sg0fnTsJInhBEVFCCsn4QKNVmkk4CgCFOC+6c4cXdN46DP6yg2GaCPxLFEKcFmpZ+ohyK6vftoTybywZ1xpU7m1p0595BOab7uwbl9DGajlAUQVlJONU24Xo4en6/ABERFSxfhns9+wLKF4zDFa50cbd9x9uxYmYV1m7dh4amc/P/+koPVs2qwietIbxz5AzWvrQfQVnBz792JSrcRXhyxyE0Hm07t76maCnfGzWjStOOc3c0tWD5jeMBACZRwF1nT+9efuM4HG8L45ZJI7HmpX1JcYnH5k7CqY6w7u/L+2Z+K3FY8OjcSbhn8y5sT/H6ljhYrICIsicfxzMlDsuA3vt8oSiWne13F9dWAIKGpdO9AJLjWkunV4K5uURERGmoClx2M/58qAVfHj8ct9eNxU3VIzDcZQOQvlBCQ1ML7ps5AZqqYsl0L073OBAi1dyI8ygiouzqiMTS5hTvaGpBB/dkEBH1mj9DvhrXFfOTP6zf52W6XgjyMUbc31oDMhad3RPVM+a7qNY4BaYcVhO+9ev38NjcSRjmssIfVuA8WyDs+//9F/zq9qtz3MK+c1pNurmlzjw7ePlC2CURv7ljKh54IbEIc53Xjd/cMRVmofAXKRxWE9pDsZT7awQIcBjgdRwM/JEo1s+vwTM7DuPXbx7Bf3zrGpz2hRE422/o5cd07lW9/JJS3FB1EYosEh56eT9e33+q63s679HLNjUmrNEJACyigJEs+EZERDk0GOZRREQDLVMh944sFXrnDLSfBCOKbtAmGFFQYjcPcKsKS7HNDEkEHp9Xg2d2Hsb6bU1wWCSsn1+Dl3efSFhgqPe6Uev1oC0U6fd2FFklOEwm3PP87oQBj6YBDqsEsyRg6NlTG4OygumXDYNJFPCL7YfgsEqwiAJaIhwMGVVQVuBxWrBh4VXoCMfQHop2FXzbd7wdc664GO1ZeF8aXaYNX5GYmvIxxWeT8ii/dIS4MdooOs6eRJSqqO2yTY3498Wfz3UT6TzEVA1P7TiUMpBT63Vj1ayqHLSKeqs9KCcVfAOA7Qebcc/mXVg3r4aJ40QFJNN4SO+6wyTh0/YQDp0OYPjZ4uihqILP2kMQAFxckr4IpD9cWH27XvE7IB5rSccfVvD46x/i8Xk1CEVVdISiKLabYTeLWLapEStmpu//ijPEb4ptjO8QERmdK8O9nn0B5QvG4QqXXtzttqfexK9vvxpRVUVrMIqYouFkewhnAhHsOd4GAFg3rwaRmIpLhzrx4At78MFnHfjNHVO71i3SbXpMFWvvLhiJdZ1U3LnuOWGEC0ueex8Oi4SX7qrDSV8EJkmA02qCKAoIRRU4LPpLzrxv5r+RpXasm1eDZr8cX3+xmeFxDmzRIyIanDieAVx2c0K+0T/8VSWmVgzBzdUjEsYJJ31hiAIgGWBDFRERUTaIooTdH7dgZIkdzR0yPE4LLCYRHeFYxvmwLxTFqx+14Mbqi7By5niEYyoiURVlDkvauRHnUURE2RPIkIevlytARESp9SVfjXKns2DWhV4vBL5BECN2WE342oa30+Ztbv7OtbluYr+QROBnt9VA6XZusYD4Ps2f3VYDScxZ0/qNzSTii+OGYvzIEtSMKkUkpnbtKdx/vB02U+H/kiqQVPANiBcPWfXCXqy5dWJuGtaPrJKYdn9NnddtiN9xMCi1W/Cj332AHU0t+OWiq/D2oRa8tv8k7jx7sFKmeGAkpqIlKOPbv3oPU8cOwcqZVfjOdZfCF4rBU2yBzSTBF4riv751DSxmEZGogv3HO3D12CEZ88yJiIiyjbk2RET9r8gq6V53ZLh+oQo/upcniu0mOCwSFtdVJAVtNuw4jGK7CVYjRKeyqMgiQQJwyhfG4toK3Hb1aIwqc+CxV/cnndbV0NSC+7fswUOz+z+IUmwxoSNFhVuLJEISBPzV+OF45OX9iUXoKj345aKr4DBLCCoKiu38aBlVsc2EpxZchcdePZDyhJVHXtmPlTMn5LCFhSnThi9risC3y25GB6tN5yUWyTCOogwnERXxBJuCoGpa2sr9O5taoGhaymuUX5p7nCbe3faDzWj2y0weJyogmcZDetdjqgZVA17afSJpTrJ0uhcxNf193WnLcMpgniWBuTLMrfWuD3Ga8Pi8ybhvy+6k0wYfnzcZipZ+LlFskXQTc4ot2QnSERFR/vA4LZhW6cH2FGPwaZUeeJwce1N+YByucKWLu3WuNaoAjp0JJaw1XllehgdvrcKbh86Nb+WYiveOtmHjN6cmrFs8vWBKyudNFWvvzmU3Y/kN49Dsl/HEbZNhM0vwOC1wWCRcOboMf/mkDTXlZfjZH5vwm3c+6XrcI39djfpKT8rYRar7ZntQRrNfhi8chctuhqeIm+LzQYmDrwMRDTyOZ4Biqwn1Xg8amuL96H+/+wlmXz4SGuJjBn9YQbHNhCKrhBEuG0ocxv+bEBERXQh/TMFwlx3DS60wiyJW/HYPdja14OkFUzLOh4ttZky6pATQALNZxBNvNOGemyZgeIYDSTmPIiLKjky5AszTJyLqPVeGOFym65QbTouUdg2uvtIDpwHy2DK994wQI7aZRVw71p3wNeHs4R7XjnXDZjbGflOrKKKsyJpUMKzO68aqWybCbIDzTEwAVsyswn1bdies9dd53Vg7uxqSAfZlhGNq2r0nO5paEM5QSKsQDIbf0ehO+sIIRpWuPecXldgADZh/9WgoqoYvjhuKYcVW3Z9hNcX3ij8+ryZ+mPiWPbix+iIMc9kQCMfgKjXDXWRBIKqgNRCFy27CNWOHwCqJjAcSEVHOMdeGiKj/OcwSar3ulPPFWq8bDjOLvuU1kwhsWHgV1m07mBC0qfW6sWHhVTCJQEThhF9PRFFgEsSEjetPL5iC94+2YekMb8pieoEsFHzyR5WUJ4UomopIDHjo5f1JH9TOAPrqW6sAFYY4mYFSc5glPLR1X9J7oPPfNeVlCEZZiKy3bCYRdV53UoFHIH4fbTzWlvQ1qyTitD8yQC2k3nBapLSvZ53XbYjFxcFCFIAZ44ZiQoqCJ/uOt0M0wMLbYBCIxHSLEwcisVw3kc6Dj6csEhmK3vi3zuvWnVOqGvBkwyHUlJd1nfrYeV9/suEQ7r8pfRHqIrP+OK0oS8GnC1Vk1k9a02uvRZRwz/O7U542eP+W3XhkTnXax0oAVs6sws6PEp/34hIb/vbKS5BffyUiIsqGEocFj86dhHs270oo/Dat0oPH5k5i4hblDcbhCpeA5LhbkcWEEocZ//S7A0lrjZ1JppGYmrCO9rP/bzIW11Xgx7+LF3zrjAENLbai3utOOMAIABqPtem+ZxwWCT94PnENpP7sWudFxVacCco43RHGX024CF+uGoFwVIHNLGHXJ224a4YXApB033x4TjVaAjIONQdQYjfDIom49/ndCeP8aZUePDp3EkaW2vvpL0xERIWC4xkgFlPxwC0TsOqFvdjR1II1t07EZ74I1m9rSvi71HvdWDqjErZB8DchIiLqreOtQUSiKj5pC2KYy4IHXtzTNU8GgFO+SNoE7XqvGy/tPtE1F6/3urFiZhVzKYiIcshplhKKY3dX7/XAmWe5DUREhcCRIW8sW5sWqW9kVcEDs87FDjvVed14YFYVZLXw9y4NihixBtxz03g88MKepEJhq26ZCBR+nbA4UcADz+9JmbO56oU9eEgnZ7NQaKKAFWnyUlf8dg8emj0xRy3rP5n2JBhhz0KmmA9jQvntaEsA9z6/G7ddPRoAMKrMjmAkhpFldjT7IxAFAffdPAFvHWpJmTcDxPNwTvkiGOayYuNbR7C4rgLrtzVhYe0YbNh5GPffPAFHm4MY6rLiK//2Z1w5ugxrZlfBapYwNMMhEURERANhUMyjiIgGWJHVhLumVwJAwry/1uvGXdMrUWTNTnk2Fn3rJxZJwlPbD6TcdP1UwyGsnDkBAaXwg6nZJAkiVv42MbinaBoen1eDZ3YeTrnBJRDp/0CRLxSDy25OKo4iCSJOBsJpK/k3HGxGJKZ2VXknYwrISspgDxC/ed9eNxYdIQb3eiumqVgzeyJWbNmTmLhe6cGS67y4/dl3ur5W53Vj5awqtIdkVpvOU8FYLOXrWed1Y83sagRj/IwUDAFYfsN4rN66N2mBccXMKkAwygqjsTmtJt3xlDNLEw3qXy5b8vi0e/E+9olEhSWiKFgxswprtiYnY62cVYWITvwgoiiYf/XolPf1RbUVuo8NxhQ8MKsKq1/cl5AcXe/1YOWsCQjG8ituEVM1LLnuUqialhQsW3KdFzE1/VjELyu6J/H5dYrIxwAcbw8lFNPofN4xniJcwkIURESDwshSO370lcvRGpDhC8fgsptQ5rBgOBO3KI8wDle4BDE57rZ0hheNR1vTHjqzuK4CwUgMy28YB7NJhKDF8/9HlTmwflsTHBapKwa0YcdhPD6vBioSF54PnPBh7exq3L9ld9J7Zu2caqx5cW/ywUdn/33lmCH4xfZDXc/Rc6w86ZISrL11IqKqho5wFMU2M2xmEQ+8sBev7z+l+ztuP9iMezbvwrp5NSysSUQ0yAyW8Ux7UEazX4YvHIXLboanyNLV58U04OGX9+OK8jIsqq1A+RAHVryQvDGuoakFKoC1txb+pjEiIqL+1iHHIIkCRpU6IAgCGs4WfHt8Xg2ee+sIvjZ1DJZO9wJInCfXV3qwqHYMlm5s7PpaQ1ML1ry0Dytnpj9oiYiIsiumarhz+qVQkZwrcOd0/VwBIiJKLRiN6earBaPGiMMZjQgRP/z9ASyqrcDyG8fBH1bgtJlwyhfGj39/AHdfPy7XTeyzwRAjlhUVa7fuS1ko7MEX9uL+meNz1LL+FcyQsxnUydksFMFo+j2FDQebEYwW/u+YaU+CEfYsOG0m3X0ZThv31+Srk74w7n1+NxqPtuEfvvQ5OCwSnl38eQgAVv52DxqaWrryUhqPtmH9/BoAQkLOfK3XjaXTKxGOKvjVmx9j/tWjYTq7FzwSU7GzqQUxVYWsqoiqGq4cXYbVt1ZB1AQWfCMiorwxGOZRREQDbZjLBjmq4K4ZXtzTLQ4XiERxSYkNw7I0H+AMtJ8Eowq+qrPpOhhVWOU9g1CKwNfoIQ6sThHY7Px3NpJZi6wSnBYJTy+YgvVvNHW9ni8tq0N7KKob1OkIx6CxcIqh+cP6n2OTJMBhZQXk3rKIElZv3duVyB6JqajwFGHPJ214++MWrJtX01VUsfFYG374ygHcd7MxFjaMSFMF/OsbH+LuG8bhB5KIjlB880JUUfGvr3+Ab1/nzXUT6TxZRBErfpv6tKU1W/diDTeVFASLJCZtxAXi4ykB4OtYIDxOC55ZeBXWbTuYMN+o97rxzMKr4HFyQzRRIbFIEv7tf5vwwC1ViCkaOkJRFNvNMEkCntz+Eb79hfTjJQECNr51JGXR+Y1vHcHdN6RP5BKF+P9urL4IC2vHdI2xT/nCEEUB0PIrMToYVXD7s+9icV1F1+/aOSe4/dl38N/fvibtY32hCz9tMKpqWP9GU9pYBPtOIqLB4XhbCMs370LDwXNJX9MqPXh07iSMZAFQyhOMwxWuVHG3mlGlCXP+7nY2teCbdWMxxGnBQ1v3da1J7mxqwRO3TQYQLwrXPQa0bFNj11gaAEaU2CEKwIm2YNJ7BtAQCEfx+oHTKZ+/oakF3/3S5yAISBtnAoBVs6pQObwYQLy4zdJNjQn3Ub3fcfvBZjT7ZRZ9IyIaZAbDeCbT3CIcU/DmoTOYMLIEQDx3Jt3GuJ1NLYbYNEZERNSfTrcEYBJEtIbCuMRtxylfBEB8nrzxrSOYMLIEGgBF1bD6lomIxBR0hGMosZsRiamY9+SbSRvPGw42Q2FBISKinAnFVCxOkyuw+Nl3sPk71+a6iUREhUcT8JM/fJCyeNi/vPYBvvulz+W6hZRCOKZg7pWjsCHFgUyLaisQzrNDXi9ExhixTi5lodCA9IXCmpphlNmnXk7m+VwvBB0h/T2FHRn2HBYCm0lEndedUDykU53XDZtJzEGr+pdNErFhwVVY98bBpH3gGxZcBZtU+L+jUbUGZDQebcPj82pw6LQfv1r8ebx1qAUv7z7RdZ+dXF527vDFjY24c/qluG/meHSEYiiymiAg3r++dbgFbx46g0hMxfe+fBkAwHr2/e0LxdB4rA3TKofi3hvHARpQUlT4BQ+JiMg4BkOuDRFRLoiSiJ9u+yihcHR9pQePzZ2Utedkdar+oulvclgxcwKKWAxMVzCSHGxWVOgms0YUtd/b4TBLCMcUPNFjg3cwosBhjp/+mKq43+PzauC0SvGiYEK/N4vyRKbTGtwOC+xmFn3rrYAcwx8OnMYfum0oe3lZPb6/eXfax3z/hsugGWZ5w2AEYOblF+OxVw+kXFykwhFR1JSLNUC88Fs2+mHqf+GYisajbVg6w5uyaG04xtexUDy5/aOEAqmdr+OTDYfw469cnuvmEVEvmAAsua4SK7bsTkjmqT97qoqoV3xNAObrFJ3XY5Uk3Pv87pTz7DqvGw/Pqe7175JNqU52FARB93qneOGK9PROG8y0sTbEjbVERIbXHpSTijIA8YJE92zehXXzaliUiPID43AFK6KoeL9HvGZYsRVLZ3ixYcfhlGNdt9OClVv24PLysoQ1yc6k054F1YKykvDvl5fVQRAFDHPZseqFPT3mIh48cEsVHBYp7Ti7PRTFjVUj8PP/PZTy+s6mloQN8c1+Oek+GskQh8qnRP/2oIxmvwxfOJ4Q5Smy8N5PRJQNBh/PnM/cIiwrCXkov/nmVN2fmSq3hoiIaLBqD8qIAHj7cAtqvW5ElXNrRFPKy3DFqNK0a2pFVgnN/kjaebDeOhQREWWXP5JcLKN7rkAgxXUiIspAgG7xMMpPAgT9vYo3T8hFs/pXphixAfbn+TMUAst0vVDo5WSez/VC4LDq7xd0WAp/P2FEUbBiZhXWbN2bsJeozuvGyllViCgGiJeIwE/fOJjy3ioKAtbMrspRwygTXziGO6aNxS93HkZ95VD85PUPsahubEL+i1kS4LBIWFxXgZpRpbCaRFglEWv+8GFSP/P4vBos29QIkySg3utB47E2AECJ3YwNOw6j9lI3BJhgMYnMFyEiovxi8FwbIqJc6Mrza0rM82vI8h4iViHrJxr0i5NpmiHijFnlsie/HX0h/c0dma5fCEkUEI4ln6JRbDehIxJNGzAXgG4b5PlqG5UAYMa4oZgwsiSpcM6+4+2QJAFmka9/bwUiSkJALRJTMyZl+MIxDC1iwCwfCdAvhLpypgEWFweJwbLAaHQhOaZbtDYk83UsBKf9EcyfOhobdiS+jvVeNxbVVeC0P8KFJKICIokC7nt+d9K8s6GpBSu27MGjcybqPv5Cx1oBWdEtBBrIs80rTpsJP50/GSfaQwlfH1liw0/nT9Ytyu20SLqnDTp1kmsybZzlJh8iIuNLVaio0/aDzWj2yxx/U15gHK5wBSP68ZplmxqTxp0mSURDUwsW1lYkPGbP8XY8PGciiiz6y74n2sOoHOZMWQi6oakZD76wF4vrEn92T2u26n9P9zb7UhRws2Y49TtfEv2Pt4WSCvRMq/Tg0bmTMLLUnsOWEREZj9HHM+cztxhSZME/v35uo4lJ0s83KE6RW0NERDRYBUNRRFUN2w+exjWXerDqhd34Zv1YPPLX1RjusmHty/t0iyN0buRMpSjDRm4iIsqe4j7kChARUWpGj8MZVca9igPbnKwYDO9Np82UtF+qe96mUcY2RRlyNosMUBDNZpJQ63Wn/FzWet2wmQr/d3SYTfiX1z7A3TeMww8kER2hKIrtZsQUFT974yD+4a8uy3UT+ywcVZPytzs1HGxGOKp/mB3lTondhC+OH4afvH4Q99w4Dk82HEKx9dw91GGRMKzYmjCfLLKasPK3e9L2M4vrKuALxbDqlirMWr8D9ZUevL7/JCaXl2J4sQ0WScDFZY6B+yWJiIjOw2CYRxERDbRc7SEyRlQoD2QqThSIxAwThMsWu1lCfaUn4YOQ6W+Wjc0foaiS8oSwIrMEd5E1bcB8R1MLQlEFTosJMdUIoXNKSdCw/IbxWL11b8KmpjqvGytmVkFVFQT7vxah4bkcpqQNbk8vmKL7GIdFgsQCe3lJ1fQXF3mLLByZ+mGObQpDqcOC9W80oaa8DItrKxIWije+dQT3G+GUt0FAUTU8s+Nw0kJ4fMFRwIqZ43PTMCK6IB1RJX3CQFMzOqLpi4ppfRhrBSJR3cToQCS/JjM2SYTDkroohMMiwialLxghAlg7eyLu37In6bTBtbOroVdqItPG2WKOgYiIDK89JGe4nl99Jg1ejMMVrjKHBT9+7cOUSTdWk4h182oAoCuOc7I9jODZwv2R2LkEY4dFQvXFJXhmx2EsPI8TGo+1htK+ZxqamvGd6y5NWdCt1utG47G2rqJz6ZTYz63buVKs4TUea0ubBD+t0gOPM3ExvD0oo9kvwxeOwmU3w1NkycqCeffncVpNePdIK9470prwPduzfFIbEdFgZfTxTKoiqN11hKOwWaSEv8GOpmbUez1Jp4YCQH2lJ2MRVSIiosEkCmDlb/fgm9PGYu3WvbhyzBAMK7EhqmrQhMzFEQ6c8KW8Xut14/2jbbjIZeMckIgoB/qSK0BERKkZPQ5nVIFITLdYWKa9jIVgMLw3HSYJTy+YgvVvNCUdCPb0gilwGKBQGADIioJVt0zEqheSczZX3TIRslL4h+1KAvCQTl4qtMJ/w4YVBUtmVKLFH0GR1QRVA1RVQ1COYcmMSoQN8Dp2hPTvnZmuU+4U28zYf8IHj9OCaEzFxm9OhXK2o3BYJKybV4OYqsFhkTDGU4Q/fdSCyeVlaXP2dza1YHFtBZxWE17dewJXji7DqllVeOSV/VgzeyLsJgkX8WBAIiLKQ4NhHkVENNBytYeIu1T7yfkURbFygVFXQI7hgVlVeOCFc5XTVU3TPeXBmYVTHjrCMbhSvJ6BqJKwkSYVXygGVQME1qEyLLvZhPue3500GN7R1II1W/figVuq0Bbgps/espukpKrSuz9tT5/M7vXAbpYQ0CmGQblzPoVQqTBYJFH3c2jh2KYgyIqK+VePTiisCcQXihfVVkBWeBJRIdAA3QJRfBWJCouvDwkDqYqUd6c31vIUWRFVwnhp94mEsXet142l073wFFl1f/ZA0xBfjEjXXr11CJ+s4I5/fxc/vW1y0mmDX3v6Lfzi61NwcZrHWk1iUlH6TtxYS0Q0ODgs+vF+hwFOHyZjYByucEUUNWXSjcMiYf7Vo/HLnYcT4gD1Xg+mjq2CwyIljEcX11Xg6R3x2Prl5WW6p4o3HmvDhBEu3XaZU4yFO2NIyzY16j62Z9E2j9OCaZUebO/2szbsOIzH59VAABLW/qZVevDY3EkJG+mPt4WwfPOuhLZMq/Tg0bmTMLIfk2pTPU+t143H59Vg2aZGBOVz6xDZPKmNiGiwMvp4JlUR1O6KbWa0BBITxn795hFs/OZUrH5xb9KmsQdmVSHKdR0iIiIAwKetQXzSGsJ7R9twv8uGr56dT//k9YMAgH9f/HndxwcjMdx743hEompCXky914NFdWOwdGMjPj9mCOeAREQ50JdcASIiSq0vOWeUO06bCY/Pq0mZA/74vBpDHOA+GN6biqbhiTeaUh4IJgoCVt9SlaOW9S9FA062hbB0hhfLbxwHf1iB0yYhEInheGsIw0vzKz/1QkiigAde3IsrysuwqLYCkZgKq0lE47E2rNm6F6tmGeO1FACs25b4no0X7zPG7+ew6ud9ZbpOueMPxyAJAn656CoUW81498gZVI104Uvjh+Grny9PyrWp9bpRd6kn488ttZvxpQnDAQBRVcXS6V6YBIEF34iIKG8ZPdeGiCgXcrWHqPCje3nCIom6GykskghZZeKlHn9YQYlDwFVjhuAfvvQ5xFQNFpOIhbUV0ICkRduFtRVZKfhUZDXBZpaSis21B6OwZ/ggOqwSOsJRiKz6ZljhqJKyCCEQ36DkC0UZ3LsAAVlJun8KArBk+qVQoSV9/pdM98IsCmjxc+KRj86nECoVhqiiYumMS3Fj9UUY7rJ1nQ72WXsIlw51clNJodCQVFgTODe2WnHzhFy0inop44lSYfaJRIWkyCrpnsCpN6co7sNYSwOwPk3yEACsvXXi+f8SA0DV9Nu7+pb07fWHY2gJyPj9vpNdf+OArOD9o61oCcj6yWIasHT6pbhxYuoxEDPIiYgGAQG68X6GfylfMA5XuDpCsZRzAk3T8NxbR5IKvzc0NWPlC3ux4uYJaDzWhnqvGw1NLagZVdq1waOzoBqQvKbWWbRt3dnr6TitJnz/+suw8NoxCQnq3YufDXNZk9bQUhVtK3FY8OjcSbhn866uwm9BWcF/vH0Uj82dhHBURUc4imKbGR6nJeGx7UE5qRAbEC+6ds/mXVg3r6ZfNt2ne57Ov9/iuoqEDTQA0BHmwT9ERP3J6OOZVEVQO3UWTO253vbVz5fj0Vf2p9w09ujL+3HfzeMHqvlERER5rSMSQzCq4Dd3TIUgAKd8YSyqrcD8q0fDZpZQlCGHTxQFyIqKm6pH4HvXXwZZUdERjuGz9hB2f9qOoKxwDkhElCN9yRUgIqLU+pJzRrnjMEv4ZZoccAHAw3Oqc9OwfpRpw2ymfXyFIBxT0x/8fbAZ4Zgx9mRoKvDzho/S5tqsmFn4exb8soJtB05j24HTKa9/7/r+3+c60KyShAdf3Iua8jIsPrtG0Znf/MjL+7HSAIXtnFZTUs5DpzqvG04r+8R85QtHoWgaREFAMKpg3Ihi2MwS/t+XL8PxthAW1Y3F5eVl2LDjMIJn96ouuc6r+zMvLrPjvSNncKwthHcOn8Gtk0bCaZZQbNc/2ImIiCiXjJ5rQ0SUK3p7iJClPUS8Y/eTM4EIFtVWAEi9keJMIAIhW6+iQRTbTLCJIm69fCRCZzd6dIRiWLapEYvrKroCRd03mDyb4TTGCyEACMqxpGJzJQ4zrJKIR+ZUY5jLmrQp/8ryMjjMEgu+GZwvQ0GV9lAUQ3i6Z6/5U/xdJ44swe3Pvpvy83/7s+/g+SXXssBenjKL+oVQzaKYg1bRhdAAhKNq0ghGQLwIpgYGsQtBz+K53e1samHdmgKR8UQpAyQ1EA2E9qCMZr8MXzgKl90MT5GlXwoF9FaRWcK/L74KB0/6E75+cYkd/774KhSZ03+mrZKIeq8HDU3JG0XrvR5YpfRjrWA0udhyp51NLQhmobB6X4Rj+u0Nx9K3t9iuf8qpXiJjOKYgICt4efeJhKSOeq8bi+oqEI4xnEZEZHSapuEbdWNxc/WIpAKgI0rsUDXOpCg/WCQRXxo3DONGupKKCR847oNFZ2xIuVVsN+Gn8yfjRHso4eunfBF8beoYvHnoTFeRtU4NB5tx303j8YfffYYVs6rw4It7Eem2ASAoK0lrapeU2fH7fSe7irbFC8alnk/Ued0wSwJ+v+8U3v24NakQGhAfEzvMEhbVVmBx3VhomoZLyuy4yGVLObcaWWrHunk1aPbLaQu8pdLsl1M+PxAv/Nbsl/tlLqf3PDubWrD47Npvd8U2xkSJiPqT0cczqYqgAokFU8MxNWFzUWdR13Sbxu6+YdyAtJ2IiCjfKbEYPjfMiVMdEcgxFWM8RfjTRy1dmzofnjMx/ZpapQdNp/zwOC249/ndeHrBFJTYzVj8y3cAAE8vmAKAc0AiolwJxxQ0Hm3D0hnelAfp6eUKEBFRag6zhPpKT+r1n0oPHDr5apQ7AVlJWZQIAHY0tSAgF36f6LBIujFiI+RHp9ov1ZvrhSLjngUDpNp0hKK6Bz4boXh8KKpg/tWjU+beLqqtQCjP8owvRCSmJO0bBuK/48LaCkQ438hbRVYTAnIMMVVDJBrDkCIrVmzZg/eOtnV9LmtGleI/v3UNXt9/Er/Yfgh/PtSiGyP0h6NwOSzYtfsEHppTDQmASRJzsr+AiIjofBk914aIKBc0aLijfixmVo/AsG57iE62hzCy1A4tS4EN7lLtJ0VWM76+4Z20xck2f+daOEzsIPWUWCRENOD+Lbu7AiZPL5iCoKwkBIm6c2Wj0qwA+EPJG2OKzRKiGvDy7uMJJ2zUet14esEUiEK8rJ/TIuXdRnnqP5lOOBrussEqsvBfb6WqGh2Jqbqff184Bk8RA2j5qC0o6xZCbQvJuWoa9ZIoABaTiK27TyS9lkune8HbXWEIRPQXgjNdp/zgMEu6BTWZcEOU2fG2EJZv3pWQuDat0oNH507CyFL7gLbFLAqIKVrKPvauoZUw63SyrUEZi+rGANAS5qbxgmRj0BqUkVyWIC4Y0Z+r9iwqkWt9aa9VEvGMzimna25Nf/K3AAFP70h+bENTC1QAK24u/BMniYhIX5HZBIcllrIA6NIZlSgyc2mF8kNMVXHPTePwwAt7E+KodV43Vt0yETHNGCeCG5FNEmEzi3gpTdztW18Yi3957WDS4w63BDBj/HAEIlGsmlUFucep7z1j6k8vmJLw733H2/H/rr8MKrSE5633urFiVhX+5fcfYEFtBWZffjHu27I74R5Y63VjyfRKbN19IqFt0yo9WDevJu3vWuLofbFtX4Zk+P5Kls/0PJEef99plR54nFyXICLqT/k2nsnGoRGZiqAOd9nw8Jxq/OD5eN/bs//pKShzXYeIiAgAnHZrV//ZqfPwn2WbGrH2pf1ni7f1WFOr9GDtrRMRVVV89RdvAojP/077I13fE4mpnAMSEeVQWFZ0D3kLM0+fiKjXNE3DndddClXTktam7pzuhcYjpPPSYCgWZhEE3RixRSj8DQtFGQ7+znS9UPgHwZ4Fl8OsO0512Q1QPF5D2txbwBj5s+3BWNK+4e77wJ9d/PlcN5HScJhEOCwmhKMKPEVWPPBCvOBbqs9l/dnP5T2bd+G5b1yN1Vv3JebJVHqwdvZERBQFTpsZq2ZVIaYqcBSlPvSQiIgon+Rbrg0RkRHYTBIuLrPjqYZDSXt2H7ilClKWYlTcmdRPnBYJV5aXpSxOVO/1wGmAkyWyTQFwX7eCbwDQeKwtbXGLOq8b9iwUt7CbJQhI3hiz/fvXYcWW3QkfUCAetBIBXDlmCG6YeBHMiG8uJ2OySmLaE47qvG4UW02Iqlzw6i2rScSMcUMxYWRJV1XpUUMcuo9xWk3gJy0/OW0mfOvX7+GxuZNwz43j4A8rcNpMOOULY/nZYCkVBpMoYv0bTWkXbPSKpVD+KLLqD/kzXaf8IABYOt0LILmg5tLplSj8lAai7GoPykkF3wBg+8Fm3LN5F9bNqxnQBVpZ1bBOp49dq9PHFllNuONX8bHW3T3GWnf/t/5Yq9iuf8/PVOR6oPWlveGYmvbUyB1NLQjrbJ7NeOKkbquIiMgITCYR67cdTDoxu6GpBRAEPDZ3Uo5aRpTIJIpY8ds9SWOXHU0teOCFPYzd5DEV0I27Lb9hXMqib1aThOPtYVw5ugyhqAJJFPDcN66GHFPx3tnTwzuLI9d53Wg81tb12HqvB9+svxTfePYdfPXz5Vh89uCOi0vtONEewvwn34wXuYkoeHT2RCydUYnFdWMRjiqwmkSc9IURjir4+f8eSmjT9oPNaPbL/Tqnctn0k+GLM1zvr+exdjvQa1qlB4/NncTkXiKifpZP45lsHhqRqQhqubsIP/7bK9AakKFkOBW0v/pBIiKiQvZJaxD39yj4BpybVy+uq8D6bU24/dl38R93XIO7BQ2nfBFcUmaHwyxBUTV89RfxeTCQOP8DgFK7mXNAIqIcGlJkwT+//uEF5VQQEVFq4ZiKxc++m7LAzeJfvoPnl1yb6yZSCs4M+XyZrheCiKrhgRf25kWMOFuKrSbUed1Jc1jg3D40I8iUf2qE92uRWdI9jPiROdW5aVg/Ggz5s6nei0K3zftOg3wmjUgQBQiaCo/ThmBUQUNTC5bO8Kb8XMb3gMfz/E61R1BTXoY7r/PCbBLhC0Ux3GVDKKrgR68ewMqZVQgrCoY5WfCNiIgKQz7l2hARGYVJFHD/88n31oamFqx6YW/W5vycgfYTRdWwZPqlUJF86smS6V4oqoawwqqoejpkJekDsGHHYfx0/mTcXD0Cw102RGIqbGYJn7WHMKLEjkAWTjCWEC/81jOgGpCVpIJvnRqaWrCwtgIdoRguclkRY9Evw5IVFatmVeGBF/YknRK6sLYCD764FytnVeWwhYXJF5Kx/IbxWL31XFXppTO8aRc2ar1uiAIgq7yv5iOrJGL9/MlYt+1gUp+4fv5kFsYsIKFoct/caWdTC0I8MbMgiALwpXHDMG6kq6uwps0s4f2jrThw3AeR1cIKQktARjiq4ubqEQkJN50brluCMsbkupFEeazZL6cs3Axkp0BBJsEMfWxQp4+1SCJ+Or8GH50OAAAiMRWhqIKTvjB+Or8GFp2xloB4oYeGpuS/Rb3Xk3cFJPvS3oynnOqcGpnpREkjnDhJRET6fOFo+ljwwWb4wlGMRN+KPhD1B8ZuClem1y6VWq8bHqcF+46344pRpUlJq52nhy/b1Igry8vwvesvgyQCE0a4MMZdBFXV0OyP4Id/cznCUQV2s4QRJTZ85ed/7trkDsTvcx1RBeMvKkazX0ZHOArL2U0/a1/a31VUrruOcLSPf5FEHqcF0yo92J5iHjet0gOPs3/mb5mexzvUiS1LrkWxzQyPU79YDxERXZh8Gc/kw6ERw102DHfZ0B6U0x5EV9+P/SAREVEh6wjH0sbvdja1dBU6D8oKgnIMR1uCuHJMGdqCERSbJczpNheu9bpx0hfG8fYwgHh/e+kwJ4a7bAPzyxARURK9Q952ZjjkjYiIUvOFYwjKStdejVTXKf9Ign7+nJRvCX8XIJRiL2Eno6x5B6IKFtZWJBXT6tyHFjDA7wjE9xF9cdxQjB9ZkrRnYf/xdkPsI9LL/d2RIfe3UAyG/FmTCDy76Co0nfInfP3iEhueXXQVTIX/VjWsUEzBUJcda17cizu+4AUA1IwqTTu+aWhqxpLpl8JuMeHA8XZMLi+FSRJw+7Pv4onbJqPUbsaKWVUwAbBbzMwJISKigpEvuTZEREbi14lR7WhqgT9F/nx/YNG3fhKIKrg9zakntz/7Dv7r29dAEgwQTc0iXyj1hhANGl7efSKh8FO9141FdRUpN5b0VUzVEFFVrJxVhfc+PoNhZ4vNdW4Yd1gkLK6rSApAxlQNDquEqKpxMGRkAhDTVFxRXoZFPT7ryzY1Iigr+N71fP17q8Ruwf09qkpv2HEYj8+rAYCkz/+KWVUANEgiI6n5SAPw1PaPUFNe1tUndt4rn9p+CPfdPD7XTaTzFIzo388yXaf8YJVE3HPTeDzwwp6ExYw6rxurbpkIE4eoBcFmkfC1DW9jcV1FQnL58fYw1r60H//17Wty2Dqi/OfLUICgvwsUZBKMKGnnlht2HNbtY6OKCk0DXtp9IikB6K7plYjqFEbWACyqGwNAS9gIE59jj8m7E/hUTcPt9fFNOd0T1+q9HtxeXwFVS9/iYrt+yEvvVMlMJ05muk5ERIUvY/FQJrxTnsg4rszS4iL1Xaoxf/fXMiQr2LDwqq7XcnJ5KVbfOhFrX9qHCSNL0p4eLgL4zR1Toaga5j35JtbNq8GS597H0wum4PZn3+363nqvB4vqxuDomVBCwbdOHaEYRrji/60BEDOcGlBsM/f6b6CnxGHBo3Mn4Z7NuxIKsk2r9OCxuZP6LdE20/OMKLVjNIr65bmIiCi1fBnP5NuhEXdO90LVkg+dvHO6d8DaQERElK8+aw12He6jl8vZSVE1eIotOHjKj/IhDviVaELBt6XTKxGOKlj70v6u+SALvhER5VZfDnkjIqLUim0m3Tgc86Hyk0USsXSGF6ny/ZbO8OoeEFsoMsWAjbDm3R6MYtmmxpR7TpdtasQvF12V6yb2i6im4v6ZE3D/luQ9C2tmT9TNbS0UmQpkGqGAZpFVv78oshZ+f2EzSVBUGVtT5WHPqITNJOWwdaTHIor4pDWEcSNLoJ6N/0W6FQVP9d4tdViwfttBzLt6NJZubMRv7pgKALCaRAxxWhBTFZhFEcNKeQAsEREVjnzJtSEiMpJM+4uztf+48GfZeSLV5gyhW5G3YESBk0FwXS578oaQxXUVeHpH8saVhqYWqADW3jqx39sRiqmQRKA1IOOlbsXmXv37ejgsEh6fV4Nndh5OCEDWet2YfflImEQBgaiCMIu+GZZFFHGkJZjwNaFHQceBLhhhBBEl+VS+oKx0LWzce9N4HGkJwmoScdIXRmsggotL7Ghn4kZeiigqvnr16JT3ykW1FYgohb9YM1i4MhRLyXSd8oMmAA/2KKwJxAtqPvjCXqyeXZWjllFv2EwSaspTn0JU63VzcZEoA1eGAgT9XaAgE5fDlHZu+fi8Grgc6ftYkyRi3RtNKQs8AMAanXmywyzhxf/7FN+/YRzulUR0hKJw2c2IKiqe2XEI//BXl/XxN+tfdpMEu1nEjdUXYWHtmK5kp5O+MOxmEXade1+RWUKd151QQLpTndeNInP6x5olEbVed8rTGWq9bpgNkCxHRET6MiUoGiGBkYwh47iSsZu81fO1Sbf+VO91Y8uSWryy9wRO+sLYduA0brt6tM4pxS1Y2BEBANSUl6LxWBsAYESJHRsWXgWn1YSOcBRDi634yesfYsLIkpQ/x2GVsHRTY0Lxm7qz76vOA3A6Tav0wOPs/yI4I0vtWDevBs1+GR3hKIptZnicln4vuDOy1I4ffeVytAZk+MIxuOwmlDks3OBPRDRA8mU8k+1DI9qDMpr9MnzheDzOU5S+T2v2y1j8y3dSbgBc/Mt38OLSugEtQEdERJRvYqoGiyTq5nLeMmkkHJb4GvufDrWgZlQpiq0mOC0SoqqI//rWNXBYJTjMEsyigLaQjBeX1mVl3klERL2XMW+RezKIiHrNYZbw9IIpWP9GU9L4+ekFU+DQyaWi3JFVFUFZwY3VI7CwW6zwpC+MoKxANkARrb4c7looXHYzgrKSdo13oPNXs8UsiFixJfWehRVb9ujmthYKZ4aCaE4D5BNJInT7CyOkz8ZUTTcPOxv7lal/hGMqfOEorho9BEOLraiv9MBqir8p0+fdePD/rr8MbQEZNeWlAOLv55O+MMqHOBAIKyguYTyQiIgKS77k2hARGUmm+Ey24je8Y/cTlz1z5xhgcSJdRZbkDdmTy8vSBjV3NrVkpXBQIBLFkCIrHt92MCF4YxZF3H/zeDyzM7kI3c6mFjy4dR8emj0R4aiCIUz8MSxZVVHuduCpHYcSA0CVHmy6Yypu/+U7GQtKULKOUOr7Y+fCxrRKD4B4gb3j7WG8vv8k7r95QsqCm5QHNKS9VwLAipsn5KJVdAEsJhH1Xg8ampqTrtV7PbCYDLBiMwiEo2rC6W7dNTQ1Ixwt/AX/wUAUgKXTvQCQdKLU0umVEIV0jyQiAPA4LZhW6cH2g8l9WrYKFOixiGLa8ZIA/cJtoaiSshhZ5+NDOkXIfaEI/v5Ll2HnR80Y7rIhElMRkBWcbA/h7790GXyhCICiC/mVskIUBTy5/RDGjSzpKrjQOSfYduAUVs5MP64MxhSsmFmFNVv3JsQZ6rxurJxVhWAs/d/pTCCCRbUVAJLvuYtqK3AmEMHYoc6+/npERJTHBEC3ACiH35QvbCZJd1z50Jzq3DSMMuoZd1tcV5HytWxoasGarfvwj1/+XNfpxN1PKU7npC+MRbUVWLapEbVeN/5yrBWftodRe6kHtz/7Lp5eMAXfrL8Utz/7TtJja71u2M1SQsE3AF3j6sV1FV3rI9MqPXhs7qQ+bYjXK4JT4sj+ZvvjbSEs37wr4fedVunBo3MnYSRPciYiyrp8Gc9k89CI3vY1vnBUdwMgD6EjIqLB7ERrEIqmwWU3YcXMCWnHEWu27sWKmydgqMuKZZsasW5eDUaV2dEeisBsMuErP/9z1/dzDkhElH+sJhH1lZ6kGCWAhE39RER0/mKqhifSFLgRIeDBW3mAdD7SNOCpHYfS5i7o5c8VCgHQ3a9ghF6/OMV+yU51XjeKLcYouhiOqSl/RyC+1hw+j3XufGc3idiw4Cqse+Ng0v7hDQuugt0A41SzKOr2F6sN0F8EM+RhB3XysCm3OsJRDC+2wmE1QRSAB2ZNwDuHW1HrdaOmvCxN3k28f/n+DZdh6fRKfHDCh7tmVEIE4DCJsJlEHgpIREQFJ19ybYiIjMRmEnXjN7YszflZ9K2fOMz6neMjc6phEgs/cJNNLf5I0oZss6S/fc4f7v9Cem6nFUE5OXjTFpRRfUkJfvD8npSPazjYjIAcL/gmZ6EYHeUHiyhi5W/3JN2sGw42Axrw69uvztoN28icGU7f6QjHsOS59wGcK7QQiSmsNp2nNEA3AK4NbHOoD1r8ESyqGwNASygaVu91Y1HdGLT4I6jwsOBJvstUeJiFiQuDSRQgCsDN1SOwuMdpfaIQv05E6ZU4LHh07iTcs3lXQuG3/ihQcCHCMTXteClTckumwsd61112Kz5tC+Gl3SeSipmN8RTh4jzbzBKMKvjq1aNTFthfVFuhm1ihqcAPf7cfV5SXnZ0/xO+bjcfa8Ogr+3H39ePSPrbYZsbXN7yDxXUVCffcxmNtWLapEc8vubZff08iIso/ggDdAqACh9+UJ/yRmO640s85f97qGXerGVWatrBLQ1MzvvulShRZ48n+mTY0Xlxqx57j7Vj70n7UlJd2FX9bN68GTlv8Z5Q6zPCFYriyvCxhA0W9142lMyqhaiqWzvCmPJ38/psn4EvjhqHYZobH2beibLkuuNYelJOeHwC2H2zGPZt3Yd28mgGfLxIRDTb5Mp7J1qERF9LXZLMAHRERUaFTVA1hRYFNMqFmVCnu/Z/dKb+voakF9940Hn/zb39GUFZwcZkd/ogMs8mEV/d+lvC9nAMSEeWfqKpiza0Tcf+W3UmHvK25dSKiKoswEBH1Viiq6B4grXfQKOWOqunvy1CNsDFDgO5+Bc0A+RmKqmHVLVVY9ULyAbarbpkIxRAvZOY9nkbIX9AAPNXwEWrKy7pySzvX0p9qOIT7Zo7PdRP7LBxTdfsLIxTvC/QhD5tyq9hmhs0kQhQERFQFJkHEVRVluHrsEHzWHtbNu7lXHI8RJSaMKLFiz6ftuPySUlhEAUPLHAP8WxAREfVdvuTaEBEZTc96V0A8frNyVvYKoLNaTj/xpygS1mlHUwv8ssKNYBlYzBK+8ew7+Oltk/EDSURHKAqXXT9Ztcja/29hOaYiKCtwWCQsrqvo2tDitJlwqDmg+9j4IMjE4ikGFlEyBy9Zc6X3JCH96Ty1Xjcaj7V1/bvzXrvi5glwmI1xoo3RsMCUcTi7FTxZ2KPgydKNjfgfFjwpCJnGS9kYT1H/OxOMYIjTikOnE8ejAoAhTgvOBCMY5S7KTeOICsTIUjvWzatBs19GRzjaLwUKLlRfxkvFNlPSfLV7AYZinYLKMVXD+jQn8AHAmlsn9uK3GAAasPGtIykTZDa+dUS3cJsGYNuB09h24HTK69/XeaxFEnHtWHfC14SzQZ1rx7phkVjom4jI6EodFvzw1QMJfVDnfHjTW0fw8F9PynUTiQCcR0FgmYmo+apn3M1h0Y/PyDEVxTYTXryrFiFZxW/vrMUbH5zCL7YfSnid670eaJqG2ks9mDiypKtwcef3nPJFUOt1440PTmPDjsP4729fg4Xt4a773ClfGEOLrZAEAf93tDUhIbbe68b6+TWQYwquKC/r898gHwquNfvlpOfv3o5mv8wN/0REWZYv45lsHRpxIX1NtgrQERERGUFM1WAWJdy/ZTeWzqjU/V5fOIagrKC+0oN9x9uhqMC1FUPw8/89lPS9nAMSEeUXiyThX177AHffMC5hT0FUUfGvr3+Av/+ry3LdRCKigpMvcTjqnUwb9Y2wL8NhkrD1/47j+zeMw709+v1f7jiMf/irz+W6iX3mjyr49q/eS9gvWWw3I6aoWPTM2/i3r12Z6yb2C6dO7ioAOA2wZyGiqLoHGUcMUBAt033HCAVEXHb992JxhuuUOyUWCRFNw5qte/HgrCr45Bg6wlF4nDaYMuR2B+UYTCYT7IKIKy8phQiw4BsRERUszvGJiPrfkCIL7nt+NxbVVmD5jePgDytw2iSc8kXw+Osf4qE51Vl5Xs5A+0lHKKp/PRyFyKpvulxWE370lcvx2KsHujacv/LdetR63SkL6tV63cjGX9QXiqHYbsLj82oSgnBvfO8LKM1QhK7YakJHJKa7yZ4Kmy+kH5wMRxVYTSwA0FtWScQDt0xIeXLNyllVmP/kmwnf33lPMMqJNkaT6R7Ie2ThsEoiriwvS3naSb3XAysLnhQEUYDueIrFSguDxWTCvF+8icfmTsIwl7VrwggA837xJn51+9U5biFRYShx5KbIW08Zk1t0rhdZJGxYcBXWvXEwKWlkw4KrUGRJXxg5FE1fsH5nU0v+ndgqAPN1EmT0ggJ9KawXVVXcc9M4PPDC3oTn7TxZM6oWfmIOERHpG+6y4YFZVfjB87uT+oKH51RjuMuWw9YRnZMpEdXFOFzecpilhLjb0wum6H5/id2M1Vv3JYzn670erJ9fg6UbG7s2sd95nRef+SK4qMSKv/m3Pyf8jIvL7Fj3+odYVFuBZZsaMbm8FC/v+Swp9vfwnIl4ZfeJhFg9gLMH4ghYO6d/ikXnQ8E1Xzjz+i4REWVXPo1nsnFoxIX0NdkqQEdERFToPm0NQoGGsKxi/2cdKHXo53KaJQH1lR4sne7FRS4bRAH4w4cn02504RyQiCh/hGMKvnNdJVZv3ZsQE63zurFiZhXCsTzLbSAiKgD5FIej8+fQyQUEAHuG64VAUzUs+1Il7tuyJ6nfXzu7GpoB9gyFZAUP3FKVsF8SiOdBPnBLFcIGKcggDIY9CxrwzM7DaQ9eXnHzhFy0ql9l6g+M0F84zBLqvR40NCXnK9R7PXCYC//ealQKgJVb9mD6uKFQAfhCUagasPK3e+J55TrsZgk2SYIGDZIkYlipfUDaTERElA2c4xMR9b8ShyV+GNHzu5Pq3Tw8pzprOXu8Y/eT4kzFwGz61wmIaRqe2v4RasrLsLjb6QbLbxiHn7z+IbYdON31vZ0bvDX0f/DWYZFglUT8skcQTlZUXOSyod7rPruxJVGd1x0PmAuAWWQRHKMqskpwWCQsrqtAzahSRGIqbGYJ7x9txYYdh+GwSIY4LWegqQB+/PsPEiqfFttMOOkL419f+wAbFl6FT1pDCX9rfyQGs8SE9nzkMEuo87qTNgUC8XslA+CFI6qqWPZFL26qvgjDXLaue97J9hC8w5wseFJAOhcwei4UZ1rYoPwhALjsomLc/uy7SdeyVQyZiLLHLIq6yS16c8qYpuGphsS5c+c4+amGQ7hv5vi0jw1GFN35TKbTXgaaAP0EmZUz0yfIFGU4FVLvukkUseK3e5Ked0dTCx54YQ/W3No/RS6IiCi/lbuL8OO/vQKtARm+cAwumwllRRYWfKO8wjhc4YqpWkLcbUiRRSep2I2/fNKGnU0tSeN5u1nCi0vrEI7G4LCYcKQliDs3vo+nF1yV+DMqPbCZRVRe5MKyTY24cnQZFlw7Bss2NSY933CXLeU6GAA0NDUjEu2fmGA+FFxzZVi/5fouEVH25dt4pr8PjbjQviYbBeiIiIgKnT8Sg0kUcbojgmcWXgVRENLOpeu8bgx1WrH61ipIggATAEHT8OgrH6b9+ZwDEhHlD4so4v40a/Zrtu7lmj0R0QWwZ4jD2bmumJfsZkk3z9AIr5smCnjslf1YXFuBe3rsJfrhq/ux/Mb0+ZCFwl1kwb+8/mHaPMi1Bhrb6O1ZKPzyfYAG6B68bITfcTD0F+GYggdumYAHX9ibkBtR31mIkUWm85ZfVtDQ1IKH5lTDH4nh09YQRpbZsfzGcTAJIuorPSkPH6z3euKFVFUNkiiw4BsRERW8fMu1ISIygvagjNVb9+GK8jIsOrtn12oS0XisDWu27sM/feXyrOTusehbP7GZRN3O0WYSu4qYUWrhqIL5U0djw47DWL+tqevrdV43vnf9OHx96hgEo0rXB2PjW0dwfxZOQHBYJIRjatJrqWmAAg0P3FKFVS/sTarOuHJWFULRGKyShDMBud/bRfmhyCJhw4KrsO6Ngwnv01qvGxsWXAWnVUJEYtmV3grHFPztlHKcaA8BACIxFaGogpO+MP5mSjnO+GUsee59APG/9ePzauC0mSArDKTmo2BMwYqZVVizNfW9MsgAeMHQAJQ6LHh594mkxYyVs6qgGmJZyvhMooiNbx1JKA7UfTx1302Ff6LUYCAIwO11FRCBpM/joroKCBx+EBWU9pCMB2ZVYfWLyeOlB2ZVwRdKP6cMRxV89erReGbn4aQ5yaLaCoSj6cdaJQ4THp9Xk/Kxj8+rQYkjv8JEqqafIKN3iKfVpF9Yz2pKX1gvFFV0nzek8zcmIiJjGe6yscgb5bWQomDN7IlYsWVP0rhyzexqhBg/zVuyoiTE3RwWCY/Pq4EGLeG1rK/0YMXMCZj9051d39NzPF/v9eCBWRMAaFiy8X0EZQVOm5TwM1bNqkJUUTHuIhfWzavBxaV2/PXP/oRgilPjM61nZirWdr7yoeCax2nBtEoPtqdI+p1W6YHHycI6RETZZvTxTF/6mv4uQEdERFToQnIM7iIrxniKsGLLbuz/rAPPfWNq+vwkOQZRFGA2SQipCloDMdSUl6ZcA+IckIgov4Rjato1+x1NLQhzTwYRUa+FYjH9OFwslsPWUToigLumVwJILqJ11/RKpM+AKxzhmIK//9JlWL11b8LvWOd1Y8VMYxRfiigqPvisA08vmIJhLmtCYbvlm3chohhjbGOR9PcsZGMP6EALRPTvlZmuF4KAHMPCs0X6et53FtZWICAX/u+oqMD8p9/EY3Mn4e6zxSadNgmnfBF89Rd/xq9uvzrXTaQ0fKF4vkoopkAyCagZXYb7t+zpOkTx8Xk10LQeeTdeD+69aRysYnzDzUh3UU7aTkRE1J+MnmtDRJQLzX4Zr+8/hdf3n0p7nUXf8lhE0S9wE1EUCCIrMegRBQHP7DicVGwt/u8DuKK8rGsTS63XjaXTKxHNQmDTLApoSRVk04B3Dp3Ba/tPpqzO+MNX9uN7N4xDe0iG08aPllGZBAE/feNgyhNWRAi47+ZxcFr4+veWKAqwmUW8tPtEUlB46XRv/DSFs3Y2tUAAsObWiYZZ3DAaTQV++Lv9Ke+Vj76yH3dfPy7XTaTzlO7EzIamFjz4Ik/MLBS+kIzlN4zHmq17kwrrrpxVBV+YxWoLQZnDAl9Ixp0zvF2Li8U2E/yRKCQhfp2ICkdZkQUPpan8/9irB3DfzelPpxQg4Jmdh9Oe+rhCJzHGIolpHysAWDs7v/r2viTItAVl3VMj24Lp+79gRH+BI9N1IiIiooFiFkSsSTOuXLt1ryGSpo3KKkkJcbegrGDZpkYsrqvAd67zwmoSISsqhjgsONwcQFBWsHSGN+V4vqGpGQ9u3Yfb6yoQlBXUed1wWc34z29dA6c1Hjs43haC22mBqmkAhK7nTNk2nQLJABLi9X2RDwXXShwWPDp3Eu7ZvCuhHdMqPXhs7iQW2iEiGgBGH8+wryEiIuofJ1uDKHNY4Y/EsPbl/djZ1IKlM7x47FWd/KQb4vlJYVXBidYIVGgp147YLxMR5Z+ODAdPZLpORETJLKJk6DicUUUUFZIIzKwekVBE65QvDEmEIfbTpNuvsKOpBWu2GmO/QigSw3PfmJqysN1z35iKoGyMsU1bUMZ8nQON9XI2C0Vxhv2ima4XAn/4XO5Cz+J9yzY14tlFn891E/ssEImh2S/j9mffTXud8pPLbobDIsEiiYipWlfBNyA570YSBRRZJZTazBAQL6R6EQu+ERGRQRg914aIKBcyHYqerbWpwo8k5AmrJOGHvzuARbUVWN6jwvu/vPYB7r5+HEws+qZLQ7yITCo7mlpw703jMWGEC1aTiJO+MMJRBRrM/d6ODllBkTX5o6EBGOayYduB09h24HTKx/6/68fBZbfAzNfasDpkJe37tKGpGVEVCKTZKEXpmUUR699oSlu44sFbqhK+vqOpBRFFhaYNWBOpFzRA9175fRZ9Kxg8MdMYSh0WrHkpdRDn0Vf26xYHovxhNYlQVGBdj/6y1uvGXTMqM27IJqL8IsdUvH7gNF5PO166LO1je56e193OphboDZFDUf2+PRTNr7491dz8fK/bLSYse/rttIknm79zbdrHFtszJOZkuE5EREQ0UMIxVTcO973r82t8R+ekirsFZQXrtzVh/bYmvPLdehRpGs4E5K71xZpRpQnJ8d01HGzGotoK1HndePCWiRABNPsjKLJK+NNHLdj9SRvGjyzpevx/fmsq6r0eNDQlF1w75YugvtKDhhTF2Gq9bhT108E3+VIEZ2SpHevm1aDZL6MjHEWxzQyP08LN/kREA2QwjGfY1xAREfVdRNWw86NmjHYXdc2nO+fJevlJx84EUeIw45PWID5tD2PDjsNYMXMCVs6cgJCssF8mIspTLrv+/oBM14mIKNlgiMMZkSAA/oiSlBOoIf71Ekfh94mDYb/CEKcVP3h+d9rCdg/Nqc5Ry/pXX3I2C4VFElHrdad8z9Z63bBIhZ/PX2w3deUupLte6JwZcpMzXafccVmkeGxvyx7ce/ME3bybF++qhVkUIYEF34iIyHg4xyci6n8um36crTjD9QvFGWg/MYsCvn7NmKSiRbVeN5ZOr4RZFOCPshCUno6QfhX8QOTc3+94exh/2H8Sq2ZV6TziwvhCUZTYzUlBOH8khkiGYLE/EgNggsyab4blC+lX4PSHYzBAjHbAhaKKbuGKcIriE/4wT87IV5lONeGpJ4WjI8M9jydmFoZIhiDO3QziFIS2YBS/3/sZHplTjYCswBeKwmU3o8gi4Zc7D2OEy8ZEdKIC4ssw/9W73pexVqGN00QBugkyevXWBQA15amLYtR63dCbtgtA2gIY9V6P7mOJiIiIBlKm2AxjN/nrfOJuDosJfzrUgpElNtR63RnXqIqtJnz3S5X4tDWEIU4znnvrCGrKy/CXY21YMXMCooqC2ks9cNok+AJRrJk9ESu27E446Kbe68a1l7ox9dIhCSciA+cKz5f24+aRfCmCU+LI/Qb/9qCMZr8MXzge8/EU5b5NREQDYbCMZ/KhrzES9ptERIPLidYgArKC4S4b2rvNpzPNkwORGD43vBiBaAzXjHWjLRTFnCsuhscZ7zM6+5LOhR/2JURE+WMwFNMgIhpogyUOZzRmScRzbx7BuJEuDHfZur5+vD2MbftP4b6Z43PYuv4xGN6bQTn9fqkdTS0IysbYb9qXnM1C0RKQsai2AgCS1tIX1VbgTEBGxdBcta5/iNDPnzXKSFxvvkH5q/M+c+//7MbSDHk3gYiCS0rMkABEVBkfnda4nnQBuCZHRJSfBsM8iohooHmcFkyr9CQcZN5pWqWnK8+gv7HoWz/xRxWEoypurh6RcBrBSV8Y4agCf1TJWCxqsHNYJd3rNrOIJc+9DwCo87qxdk41zgQjGNXPVdZddjMgICkI57BIsJr0Q1NOqwn+cAxa0jkqZBSZTodz2iSYRKOEMAdO96KOqfhTFJ8ospogGCHqb0AOi/79PNN1yh/FGe552apKTP2rI0MBn0zXKT9EtRi+fm0F7u1x0lud1421s6sR1fg6EhWSjOMlnflxUYYT5PSu9+WxuaBqmm6CjKrpzL1TzOu7P1Yvg0jVNCyqGwNASyqAsahujP7zEhEREQ2gTLEZxm7y1/nE3T5uCWDDjsP46fzJWDrdCzXDMLTIZsLLu09gw47DeGbhVVh9y0R81h7CX9dcjIde2oc/dDsQ4JE5E/HMnw7j8vIyLOxxyvqal/ZhzS1VmDlpZMKa56mOCMYMcfR78iaL4ADH20JYvnkXGrolCkyr9ODRuZMwstSew5YREWUfxzPUW+w3iYgGn4iiwReKds1PO2XM5bSZ8OdDp/H7faexcuYEXFFeBoB9CRFRIWjNUEyjNSADBV5Mg4hooDEOV5jOBGTMn1qOZ3YcTiikFc9jO1tgypPDBvaDwfDePJ8DwQyhDzmbhcJukfD1DW9jcV1Fwlp647E2LNvUiP/69jW5bmKfqciQP2uAPbMa9HOTuS84f3VEFXScPVjdkSHnvcRuhhnA5r98gs+NKMWyTW9hyugyxgB7gXFUIqL8NRjmUUREA63EYcGjcyfhns27Egq/Tav04LG5k7KW655fu3kLWEc4hjs3vo/FdRVJp2esfWk//n3x59lBZmAzSbpV8p1WE/7zW1Nht0gIRGJQVQ1Wc/+/hZ1nN99veusIasrLuoJwDrOEk75w2jbWV3rgsEgQBID7v43LaZFQ53VjR4r3QJ3XjUBEwcgSftZ7y2XX/yybxMTofq3XDUEAiswsHpaP7Gb9+7mdr1vBcGS457GAX2Fw2fTvsZmuU36wiibc06PgGxA/4e3+LbvxyJzqHLWMiC6E3XLh4yUB+ifM6eXFiIL+Y8U8S6qxSBI29pibdybIbHzrCO67aULax1olMWle3/nYTW8dwf03p3+szSTh128eSVkA49dvHsEKnccSERERDaSiDLGbIsZu8tb5xN0uKbMjKCu4c+P7+NYXxuKm6hGor/QkJFN2f8yJtlDXZo8Shxnf+fV7GOay4qE51Xj4ryehNSDDF47BZYsfqHLv83vwerdCcN0tv2Ecbpp4EZr9MjrCURTbzJgyumzQF2fLhvagnJQkCwDbDzbjns27sG5eDf/uRGRoHM9Qb7DfJCIafD5pDeK+Lbtx300T4I/E0HisrWutq/t/91Rf6UGRWcLdm/cCAOSYinXzagCAfQkRUQEospnwNZ1iGv+z5NpcN5GIqOAwDleYnFYTvvZ0vE/smce2dGMjnjdAn5hpj5bTAO/N8zkQzAiKzJJuzuaqWVW5bmKfOcwSaspLE4owdqr1uuEwwF4pqyThuTT5s8+9qZ97WyisJv3cZCP8jkbVEY7BeXb/0ylfOGP/EQUQjAHP7DyMxXUVWL+tiTHA88Q1OSKi/MY5PhFRdowstWPdvJqE/HmPM7uHm7PCQz8ptpkQlJWUQRsgfmqgVdI/WXCwEwVg6XQvgOQq+UunV0IEYJYEPPbKAay6ZSL+72gbrhpT1u/taA9FYLOacP/MCVixZU/Xa9rw/evgHeZM2cZ6rwerb62CCYDTLLGev4FFVQUPza7GfVt2JwyG67xurLplIiQB4Ce99xxmCfVeDxqakjer1Xs92NHt650nZ4gC4JdjA9lMOk+CANw1vRJA8v38rumVEPKsmAilJwFYdUsVVr2wN/U9L3dNo14wS6JugR8zx6gFwS8rKV9DIF74zS8rA9wiIuoLiyjozn8tOtXXRFH/NERR77ZeYCcpSgLwzfqxWP9GU0K8pfPvJOm0N6qpuPuG8VizdW/CY+u8bqycVYWopqZ9rADgjmmXYt22g0nPe9eMynz7MxEREdEgFo/dTMSqF/YwdlNgMsXdRA343d6TqPe60dDUgn957SB+/r+H8Pi8GqialjCe7xzjzn/yTQDxmPqJthCGuaxYOasKrYEIrigfknBo1Tsfn9FtX3soisrhxUzQHADNfjllIT8gnizb7Jf5OhCRoXE8Q73BfpOIaPDpCMews6kFZpOAU74w9h1v71rr2rDjMB4/W8gtIZez0oOHZk9EazDY9bXOfgIA+xIiogLgNEu4srws5b6Meq8HTgMU0yAiGmgi9ONwzCTOTxZJ1C0wZTFCDriqYe3satyfYo/WQ3OqAbXwd+jZTKJuQQabyQCvI+L77VbMrML9W3Yn5Ww+NKcaUbXwc93NooC7ZqTZKzWjEuZ8O3n5AlhEAd/UyZ/Vy28uFCKAO+ovxbo3UvyOZ/cwU34qspogCvH43/LNu/DcN6ZizdbkvJuH5lTDCuAXOw/hmsphWL+tCYvPxhQZAzw/XJMjIspvzLUhIsqeEkd2i7z1xKJv/aTILKG+0pNyItN5amBYKfzgVDZZRAGSKODm6hEJVfJP+sKQBOD3+z/DtMphWDrDi7ZABFeUl2blDWwymXCkOYRVL+zFT2+bjB9IIjpCUQgAPE4r3jvSiuU3jAMABGUFNpMEl90EsyBAAqCd/R8ZkySK+NWfPsYjc6rhl5WuCp1FFgmtgTBKHdZcN7EgRRQFq2dXYeWWPWhIKKjoxoO3VuHYmRCeuG1y18kZv3n7KOq9HkwYWZLDVlM6UVWFu9iCmT3u56d8YbiLLYiq6QtsUH7RoEEUBCyd4cXyG8fBH1bgtEkIRGIQhfh1yn8t/ohugZ8zgQjGDnXmqnl0nnyhqO71jrD+dSLKL2FVwcgSe8rx0sWlNoR1kluKTBJ+8/bRlCfM/ebto3hwZvoT5gQN+M1baR771lHcfcNl2fh1L5gGNX2cQIxfT8chSXjktQNYVFuRMI455YvgX1//EPeendenYxKBmdUje7w+ERgkv4qIiIgMQoUWLyicInZjEQWojN3krUxxt9cOfIYNOw5j/fwaAAIampoRlBUs29SIFTdPwP03T0AgEoPDIuFEexjzn3wTzX4Z9V431syeiFA0hkW1FZj/5JvY9M2pSc+f6SRHB096HDC+DDEdxnyIyOg4nqHeYL9JRDT4dK6Tv7bvM9xUNQIVniL8ouFQ11pXTNVw303jIYoC/OH4PLnYaoJF07D42f9L+Fkd4WjGkQX7EiKi/CABWDN7IlZs2Z2UU7xmNjctEhFdCFXTIApIuzalaozD5aMzQVk/BzwooyJXjesnJlFASyCM1bdORCSmoiMURbHdDKtJRGsgjOFFtsw/JM9FFAUrZ1Vh9YvJhYlWzqpCxCD7TWUF8AWC5/bbnX0tnRYJJ9qCcBUV/n47AcCwYitmTuqRW9oRwbBiqyEOFBYAjHTZUubPjiyxGeJ3lFUVkoiUOdySGL9O+Uk4+3/uuWEcHn31AG576k08NncSlt84DoGIgpKz9xwrgJ2HTuGpPx3F5AoPACASO/e6MgaYGdfkiIjyG3NtiIiMg0Xf+okZwKpZVVj1QnLBolW3VOGDkx0YN7w4dw0sALKq4CKXDR+d8iddC8gK3jp0BjdWjYBJsuJMQIZZFOCwm/u9HQ6zBLtZwrHWEG5Zv7Pr66/+fS3sJhMqPA6YJRH+cAzFNhPsZgkWUYCIeODAryqwsqa/YcmKituuGY0Ht+7DuBEu1IwqRbNfRqndjItcNqgaYDZCBHOAmQQR614/iO/fMA73ni206LKbYTeL+NGrB/DSnpNd31tf6cG3po3FHb96D/+z5NoctprScUoS1r56AH89+RIMc1m7JowA8JPXPsT9GQpsUP4IxtSzpw31vLEJADQEY5z8FwKnzYwFz7yDxXUVSQV+lm1qxObv8F5aCFwZxr3Ftv4fFxNR9kgQ8bM/NuH2aWMRU7Su5JZytwNPvNGEb33h0rSPNQHxIg9b9iScMFfv9WDtnIm6gR5NAOZPLcczOw73eKwbi+oqoOXZXCaqAoqqQhASGyYIAhRVRVQnr8IqCrj7+nG4P0US+No51bDqnTYoAKqaXOBWgwZVRfLQiIiIDOuT1iA6wjH4QtF4UpjNhEvKHLluFlEXDUBbKIISuwUaAFFQIIkiSuwWtIUiKOWJqnlLL+7WFpTxT78/iKCsYOnGRtwxbSy++6VKKKoGp80Eh1mCJArY+ObHmD91DC4us+OJ2yajyGKCy2bCnRvfx65PfQDi8fRhxcnJ80UWE2q97oTNIZ1qvW4UWbiEPFBcGWI6jPkQkdFxPEO9wX7TmNqDMpr9MnzheJ6Ip2hgT8wlovzWuU6+fttHmDSyFKPdDqy4eQKCsoKArMBplaCqwN5P2zF5TClsoggzNMx9+m00++WEn1VsM8MkCnh6wRREYipsZgnvH23Fhh2HEZSVru8hIqLcC6oKrIKEH9w8HhqErpwKARpMQvw6ERH1jgqgLSgj3drUkCLOxfNRkcWErz39dtoc8P8xQA54SFUwxGHDzkMtGFZsjRd+i8RwqiOCukvdCBmg37dIEn70u9QH2P7Lax/g+9cbY3+N1STiFw0fY9zI+F67ztey8VgbDhz34b6Z43PdxD6LqvHxaIXHgSKrqeu1LLJKMAkComrh76+Jj8VFXD12SEIhxgpPEUQNCGqF/5kEAH9ESSqFop39eomD8aG8JQCKouGJPzbh3hvHQdPi+85FCPA4LTgTkFFskbDz0Cn8w+Z9AOL3pu7/H2AM8HxwTS43uGZGROeLuTZERNkz0HuIDJmx/8QTT+BHP/oRTpw4gaqqKvzkJz9BfX19Vp9TMkuwQMGdMypxd0JFVAUdIRkXuWwozkKBMiMxm0x4sfEYZl5+CY62hrqqpx9vD+MP+09i5awqtPjDsFrMGOGywSoKWZmwmEUBF5faUed1J5yg8WlrBFv/cgi318c332vQIAoCJEHAKX8YFzltePJPH+PvPj8KkoVF34zKYTbhx7//AD+4cRxkRUPH2RNCbWYJZ4IRDC+2waRXPIBSsphE/N3ny/GjVw8kFGT40rih+N714/Cd6ZU4eibYtUB1x6/ew5XlZXBaDdmN9atc9IkmUcDy68fhvt/uQcPB5q6v11d68PDsifyMFJAiiwkPv7QPc68chSLrufMxA5EY/ulPh/GDmyfksHV0voptJkwuL00o7tOpzutGsY330kJQbDMljU878XU8f7noF4lSsZhEzJl8CVa/sDepINnSGZWwmNLPKQVRgEnR8OCtVUknW5rOXk/HaTFh41tHcXl5GRb2SALb+NZRPHhLVX/+mn3mtJjwwx0fYNwIV0Khik/bQvjD/pNYrdPeoWUOnGgJ4OEUp0aazl5Px2ExQRAiKcveCkL8OlGhY59IlNmRlgB+8PzuhIJIdV43HppTjdHuohy2jOgcSRShQcD/HWvrSoD3n02AnzCiGJLItYrzkYt+MV3cLSTHYLabceXoMjQcbEZQVvCT1w/ivSOtWDWrCjFNRVRREYpqWFw3FkL8bAaIDjPOBKJY8My5Te3TKj14bO6klGtppQ4z7ppRCQAJ97larxt3zahEKZOYB4zHacG0Sg+2d4uld5pW6YHHyQQsIho4Ocm14XiGeoH9pvEcbwth+eZdCXkF0yo9eHTuJIwsteewZTTYMX6aH460BOC0SF3r5Hf8+j38/LYrMWqIHbs+bcewYiua/RFYTSJimgbp7CT5pp/9Oang27RKD2xmEfds3o2GpnP3nFqvG4/Pq8GyTY2YMrqMfQkRUQq56BetJhN+/scm3F4/FqGois7jM+xmCT/f/hG+dZ03q89PRGREZkmEqmk4fDqAYa54HC4oizjli6ByeBHMEuNwmeSiTywrsujmgJcZoFifxWTCr//0MW6bOjohz+9zw5x47s0j+P+uHZPrJvaZ1STi69eMwfo3mpLWZpdOr0woRFTIhjgs+Oa0sVi/7WDSocRLZ1RiiAEKT5QVWfDgi3uxsLYCkih2FddwWEz4yR8+xAOz8isH90IUWy147JX9WFhbAZMkQkNnMbQYfrnzMJbfWPjF+5xWM557K56bPNxl6/r68fYwth04hdW3Tsxh6wpHLvpFpzWea7NkeiX++bUPMW5EvMhksz+CUrsZo8rseP/j010F32q9bjQea+v6/wDXk84X1+QGHtfMiAoXc22IiIwjF3uIBE3TCr+EfDf/8R//ga997Wt44oknUFtbi5///Od46qmnsG/fPpSXl2d8vM/nQ0lJCdrb2+FyuXr13CdaAogB8SBjOIYiiwSTJGDvcR+uHevGRRxYZ3SkJYCfvPYBFtWNhVkS0RGOothmht0s4nBzEKqm4ZQvglqvG+VZ3Fj3SUsAYUXDgy/u7ZokeZwW/OaOa7DqhT0JxTbqvG6snV2NDTsO4atXj0axRYLLbmYFbYNqD8poC0Wx+sW9GD+ypOv0kVK7GUOLrfjZH5uw/MbxCUE/yqw9KKMtGMWfPmrGMJetq/jEKV8YtV4PHntlP17ac7Lr++u9bqy9gM6xL/f4QpSrPrE9KOO/3vkY10+8OKnAxu/2HsdXpozmPbJAnG4LIRhTcd+W3Ul930NzquGQRAzl+CbvnfaFEZAV3J/udTRLGMp+K++d9oXRHo5i1Qt7k17HVbdMRInN1KvXcbD1iUBu54pEPZ1uDSKkaNiZZvxrFwXdomTHWwIIq1pS0TebKGBkhjHykZYA7n9+d1KxuQsZXw+EIy0B3L8luZjw2tkTz6u9nacrdMYXis/zdIVjLQHs+Ojc6aFWk9h1euioPPw7Ud8Mtns8+0SizD5pDWL55l0JizWd6rxuPDp3UlZP6yHqjaMtAexsSj2u7O06ymC8x/elX+zL3+t0axBBRUsZd3t4TjUEaPDLatc41mmREIgq8IXi/3ZYJIgARADOs2tSnae9dj7G49Q/7fVEWwh//PB00ph3+ueGck1zgB1vC+GezbsSkmU7i/aN4GtBlDODrV/M1Vyxr3EyGnzYbxpHe1DG0k2NCbHfTtMqPVg3r4Z5BXmCfSLjp7nQGZ8ToWHN7Eld+Q4Oi4Sfzp+Mz9pDiWOHs2s4qqbF15W6zbWnVXrw8JxqrHpxL17ffyrpuWq9bsycNBLXfW4o+xIi0jUY7/G5nCumi58+NKcaDs4ViYh67bQvjJCspI/D9TKXeLD1i7ncl3HaL6fcx/bgLRMzrgcWgsHS7/fnun4+Gwxr0IMhRn20JYD7nt+TUDi/3uvBQ3MmGub92p+51IOtTwRyn2uzemvivt4SuxnlZXa0dgRxy7+9DSDej3z/+nH41z98iPlXj+469MFIn9VsGwz3u3zBNTMyksHWL+YsftrPc3wiIur/PUTne483XNG3q6++GpMnT8bPfvazrq+NHz8es2fPxiOPPJL0/ZFIBJFIpOvfPp8Po0aNuuDBxClfGIFIDMGogmBEQYndjOEuKwfU5+nwaT8OtwQw3GWDPxxDaZEZDpMEvxzfyOKym1FiNw9IZeoTrUGEY2rXa1lsN8EuCYAoIigrXZtmbCYREUWBVZJgFgXc+/wurLqlGpcOc2a9jTTwPjrlx/G2EN76+ExXYMhqEtF4rA0bdhxGUFbw6nfrMW6E8Scj/emjU36semE3Hp4zKalIWESNwSqazhbU7F2hhp4G24QxV33iR6f8+OI//2/a63/4xy/wHlkg9p/wYeeHn6Uu4LfnU9R+7iKM5/0u7x044cO6P3yAe26ckPQ6PvrKPtz1xcvYbxWAAyd8+P+efguPzZ2EYS4r/GEFTpuEU74Ilm/ehV/ffnWvXsfB1icCuZ8rEnW3/4QP63XuzUu/eFnGPvZCi5n19bG58GlrEL5u7XXZTLh4ANp70hdGa0CGLxyDy2ZCWZGFBb4NarD1i+wTiTLbf8KHG/+1Ie31V75bz/kw5ZX+Gt8Ntj4R6F2/2J994v4TPmx68zC+Oc2bNCd4cnsT5k2tQJFFQqBbXNyZhUOHelsojrKHrwVR/hls/WKu5or7T/jw7M6PcOf0zyX1iT9940MsqL2UY29Kwn7TGJhXUDjYJzJ+mgvd43P13iHncsrC8RzSIrOEjkgMgYgCl90Ep1mCLyLDajajyGqCv1ucxOO0oCUgY8aP099zXvuHaagcXjxQvx4RFajB1icCuZ0rvvD+McyfOiZprrjxzY9xy+RRnCsSEfXSgRM+PPTS3pT7NX7w/C7cd3MVc1B15HJfxt/94s9pc4f/445rCj5+sv+EDw/8djf+6StXxNdGz743iywSvvdf/4cHb602TL9faHmbF2owxG8Hw+84GPJnj7eF0B6Kdr2OF7pnebD1iUBuc2301hW/XnspPm4OdO31vbn6IkQVDSfawxg9xIERJTbDfVazbTDc7/IB18zISAZbv5irueKBEz5s1Mk/nT+1gvuFiYh6qb/3EJ1vn2jqVSvznCzLeO+993DPPfckfP3LX/4y/vSnP6V8zCOPPIIHH3yw39owzGDBi4HWFopi8S/fTXt9y5JrMXKABhkjegRO3z58Bjf8/M9pv3/TN6di3pNvAgA6wtGsto1yxxeOoiMSw/ptTTrfExvAFhmDLxxFQ9MZ1P/ojymvb1lyLa4oLxvYRhW4XPaJvgz3QN4jC4cvFMXaVw5i7SsHU17/z3L3ALeILoQvHMNLe07hpT3JJ1YDwILaSwe4RXQhfOEYmv0ybn829ViZ4w99+TBXJOrOF4pmuDePzfgz+pLsU2iJQheXOXBxDp53uMtmuCQVIvaJROfHF2JsgwpLoY3v8kVv+8X+7BN9oSj+/a1P8O9vfZLy+swrLhmQzQslDiZj5gu+FkSUSzldVwxF8Zt3j+M37x5Pef2vr8x88jENPuw3jYF5BZSPGD/NH93jc6lyyv7zW1Px+YrEnJWLUdT138N7TKkPNQd0ny8Q4Xo7EVFPuZ4r/qzhY/ys4eOU16dPuKjPz0FENNj4wjHd/RrMQU0v1/sy9HKHjRA/8YWiePvjNkxL8940wu/YabCs6w+G+O1g+B0HQ/7syFL7BRV5G+xynWujt654a005ljz3fte/J4xwdf17y5JrDf+5zYbBcL/LB1wzIypMuZ0rxjLkn47q83MQEQ02udpDJGblp+ZIc3MzFEXB8OHDE74+fPhwfPbZZykfc++996K9vb3rf8eOHRuIplIaLptZ93pxhuvZ5LLp10h02qSu/85lOym7XDYzrCb9W2em9woly+fPfqHKZZ/I19M4XHa+lkaQqV9iv1UY+Dr2DeeKlG/YxxJRrrBPJDo/7KuJBofe9ov92SfyPkNERPkkp+uK7BOJBi3mFVA+Yvw0f/T3GIH3HCKi3uNckYjIWJiDeuG4LyO72O8TERWWfM616b6/G0DCHmD2J5TPBsOYj8iIcjtX5ByfiKi/5SpGZaiib50EQUj4t6ZpSV/rZLVa4XK5Ev7XF+1BGUeaA9h3vB3vfHwGB092oD0o9+lnDiYepwXTKj0pr02r9MDjzF1V8LIiC+q87pTX6rxunPJFAOS+nZRdHqcFpzoiqE3zXqj3ulFWxNe/t/L5s1/octEn8vU0jhK7WbfvK8kwgKX8kGkMw36rMPB17B+5nCtSfmsPyvjolB+NR1vx0Wl/1ufxxTaT7me6mAF2IsqyXPaJJ31hHDjhw9uHz+DAZz6c9IX79POIsoF9NdHgcr79Yn/2ibzPEBFRPsrFXJF9ItHgxbwCymdcUxx4nWt1fznWiiMtATgtUr+OEXjPISK6cJwrEhEZA3NQ+477MrKD/T4R5cpA544bTT7m2nTu7waAWq8bjcfaABinzyTjGgxjPiIjy8VckXN8IqL+l6sYlaGKvnk8HkiSlFT99NSpU0lVUrPhs7YQjreH8UlbCB+3BNERjuG3fzmO//dff8HxtlDWn98IShwWPDp3UtIEZVqlB4/NnYQSR+4GGcNdNjw8pzrpg1rndWPN7GpYJBGP/HU1fpjjdlJ2lTgsmP65objvpvGo9ya+T+srPXhoTjWGu2w5al3hyufPfqHKZZ/I19M4Rpba8VCavu+hOdUYWWrPUcuoN4a7bLqvI/utwjDcZcMjc6rxyJyJeHrBFDxx22RsWHgVHpkzEY/wdcwo13NFym/H20JYuqkRX/zn/8WcJ/6EL/74f3HXpsbznscfbwth/wkf3jrUggMnfOf1uEvKHLr35kvKHBf0uxARZZLrPvHTlgDOBCJQEV/Q1DTgTCCCT1sCWX9uot5gX000OOSyX+y8z9T3uM/U8z5DREQ5wD6RiHKBeQWUj3IdPx2sTp7Nu40oKsJRFUFZgV+O4aHZ/Ref4z2HiKj38mGuyHUaIqL+05lLnC4OxxzU9LgvI7vY7xsPC2lRITjRFsLLez7Dxy0BnGgP40hLEC/v+QwnuAc8o3yYK6Yaz6ycVYXlm3cBiBd8W1RbgQ07DhuqzyTjGgxjPiIjymWf2DXHr2SdCyKi/pKrGJWhjjuwWCy48sor8dprr2HOnDldX3/ttddw6623ZvW524MyzgRlPPTyfuxsaun6eucE8YHf7sE/feVyDq7Pw8hSO9bNq0GzX0ZHOIpimxkepyUv/nYWScTSGZVYfuM4+MMKnDYJgUgMx1qC+PZz72HK6DJ84XNDc91MyjIVwD///kNcXl6KhbVjEImpKLWbUT7EgUuGcEHhQuXzZ78Q5bJPBPh6GslodxF++DeXoz0U7XotS+xmFnwrICd9YTz44l5cUV6GRbUViMRUWE0iGo+1YfWLe/HwX09iIKdAqABe3n0CDd3mG/VeD671pj7Rhc7Jdb9I+as9KGP55l1oONic8PXtB5txz+ZdWDevRnf8cqQlgB88vzshDtAZyBntLtJ9Xl8wjIfmVCMoK+gIRVFsN8NhkeALhtFuN3PcRERZkcs+8bQvjKimYe1L+5Pum2tnT8RpXxhDOS6lPDLaXYRH505CRzjWNR8utpmYUExkILnuF6FpuKl6BBZ2i9ec6ogAmsZ+kYiIBhT7RCLKFeYVUL7hmuLAaw/KCMVUrH1pX/J62+yJSWtpfYnP8Z5DRNQ7ue4XuU5DRNS/0sbhzn6dcbj0ct0nGn0u0x6U0RYIY/WtExGJqV3zP6tJRFsgjFLmUhaU422hpJzcaZUePDp3EvefUN5oD8o4ciaIrbuOJ+0Dr/AUwWGReN/Rka/rihZJwJNfvxJOqxkWSUR7SMaLS+sM1WeSsRl9zEdkRPnQJ9448SIsvHYM5/hERP0kF2tThir6BgD/+I//iK997WuYMmUKrrnmGvziF7/A0aNH8e1vfzurz9sWjOKRHgXfAHT9u6a8DM1+mQPsXtIAQMjd87cHZTT7ZfjCUTitJrx7pBVrtu5DUFYSvq/W68biugqs39Z0XoUBqHB1Lwrx+oFTCdemVXr42vdRiYOBiP6Uqz6xE19P4xhZauciWwFrDcjY9Uk7brt6NIa5rPCHFRTbTKgZVYrfvH0UrQGZRd8KwPG2EO57fjd29JhvNDQ1477nd+OHf3M5P6cZ5LpfpPzU7Jfx3pFWLJ3hRc2oUkRiKmxmCe8fbcWGHYd15/HH20JY/eJe1JSXYfHZhePOx65+cS/WzK5O+7lsC0bx6O8OJsUQgPj88uHZ1RxHEVHW5KpPDEcVrNm6L+V9c83WfVg1qyqrz090IbhxiMj4ctkvrt66D+NHlmDY2biMIAj4tC2E1ewXiYgoB9gnElGuMK+A8g3XFAeWLxTFii270Xi0LWm97k8fteDyS0px07odAIA//OMX+hyv4z2HiKh3ct0vcp2GiKj/pI3DtYcZhzsPue4TjTyXaQ3K+Lfth/E3V47CMJcVigYoqobDzQH893vHsPyGcYb53bvvUXTZzfAUGet17eshzEQDpS0YxbptyTncnf9mDndmuVxX/MGWPdjZ1AKHRcLiugrUjCpFid2ME+1hXFJqx6iuQ9vTH95OlK+MPOYjMqp8yLUZzjk+EVG/Gui1KcMVffu7v/s7tLS0YPXq1Thx4gQmTpyIl19+GaNHj87q8wbkGBp6TBa7b1ycUl6GjnA0q20wihNtIfzxg9MY5rIiElPRGozi7cNncO1YN9pCMpy2gQlsHm8LYfl/70JD07lgY73XjfXza7B0Y2NC4bedTS1YXFsBIB6MZIE/42r2y2g42Jz2s94S4GtP+SNXfSIR5ZegHMWmO6bincNnAACRmIpQVMEpXxib7piKjpCc4xbS+WgPRfF+ikT3zsJU7aEoi75lwH6RUvFHonh8Xg2e2XkY67c1dX291uvG4/NqEIikn8f7wlHcNnU0NuxIfGy9141FdRXwhaMYidSfy4AcwwefdeDpBVMSCnKe9IWxfPMuBORY//2SREQ95Cx+GlXw9WvG4HhbKOHrF5fYcNXoMgSiSppHEhERZQ/7RSIiojj2iURERHFcUxw4x9tC+LQthKmXunHvTRNwoj0EQRCw74QPG3YcRk15KaZe6obHaUGzX2b+LRFRDrBfJCIyDsbh+oZ9YvaEYwr+8cuXpcxz/8cvX4ZwzBjvzRNtIfzxw9MYVpy4V/K6zw3FCIPkf/flEGaigRSQYykP7Qbie4SZw51ZrvpFv6x0FXxbP78Gz715BABQM6oUrcFzscNzhd+IiIiyi7k2RETUV4Yr+gYAS5YswZIlSwb0OQOyAodFSrtZfNakETBL4oC2qRC1B2UcaQli6+7jCcGTWq8bFe4ivHf0DH70uw8xrdKDR+dOyloWPHvMAACB+UlEQVRhi/agnFTwDQAamloACLhj2lj85PWDCdciMbXrv5lgZFztIRkOi4Sfzp+ME+2Jg+GRJbYctYoovVz0iUSUX4Y6bTjeo88CAA3AGX8EI0uMsVBsdMFITLcwVTDCxcXzwX6Reiq1W/DD332ge1pbOgKAjW8ewRXlZVhUW5GQoLLxzaP4/g2XpX1sJKbguW9MxeqtexOeu87rxnPfmIqQzDklEWVXLvpESQDsZhFaj69rAOxmCZIwoM0hIiLqwn6RiIgojn0iERFRHNcUs689KOOhrXvxvevHoWZUGT5uCXSts+073o7H59Vg2aZGrNyyF4/NnYTbn30XxTZzrptNRDQosV8kIjIGxuH6jn1idtgkCSd8Yby0+0TSPsIxniKMcBX+Pq32oIxjrUFoWuInUNM0HGsNwmGRDFEMrS+HMBMNpICsXwQlmOE6xeWiX/SF4veRO6aNxeZ3j+G7X/ocOsIxtIeisJkl7PyoBfuPt2PlrCqMZuE3IiIaIMy1ISKivjBk0bdcKLWbsbiuAs/sPJxys/jarfvxT397eY5aVzhagzLWvXEw7Yb7lTMn4Ee/+xDbDzbjns27sG5eTVYCm6c6IkkF3zo1NDXju1+qTCr6ZjWdK+pXZOVHy6gcFhO+9YWxsJnFlIsKY4c64S6yGCLgTkRExhBTNagaUvZbS6d7EVN7hncoH5UVWfDPr3+Ydpy85taJuWgWUcGTFVX3tDZZUVNeAwAIwFevHp0yQWVRbUW8Klwa7iIr7n1+d9Jz72hqwZqte/HwnPTF5oiICpVZFKHojEvNIg/MICKiwYP9IhERURz7RCIiosHpZEcY3/yCF/f/dk/SGGBRbQU2vnUEi+sqsH5bE+6+8TJMq/TA42Q+HhERERHRhWIcjvKVCmD9G02Gzo9uD0ahqFraz197MGqIPWh9OYSZaCCV2vUPFijJcJ1yx3X2tZlW6cF1lw3DY68eSBlbXLN1H1bfOhEjS+25aioREVFWcY5PRGQcvGP3k2HFVlw71p12s3hDUzP84dgAt6rwBGRFd8N9TDlXlGT7wWY0++WstKMtpH96RM/iKLVeNxqPtXX9t0XiR8uoRFHAX40fnnZRYd22g2gL8vSR/tQelPHRKT8aj7bio9N+tAez87knIjIqVUu/GL7+jSaw5lthiMT0C1NFYjqFqYgoLX9Ef54e0LmuqUhb+P2ZnYeh6Xwsgzpz3x1NLTwljogMSckwLlU4LiUiokGE/SIREVEc+0QiIqLBSVWBH//uQNp1tgkjS1AzqhQAEIgoeGzuJENsgCciIiIiyhXG4ShfhTLsIwxFCz+XMqZqup8/oxzg3qdDmIkG0LBiK+orPSmv1Vd6MKzYOsAtovPlsplQ7/XAbjbpxhbHjXChPcP+cCIiokLGOT4RkXGwMlU/KXFYYDHp/zk7wpwoZhKM6AdjAz02vmfrb1pkkXSvO7pd76wAv2HH4a7/bg+xKJVRmUQBmgbdQHRAZoHH/nK8LYSlmxrxxX/+X8x54k/44o//F3dtasTxtlCum0ZEVDDCMf3F8HCs8BfDB4NMBaQzFa4iotRcNv3T2Ip1rmvQnxfoxch9GT7Tma4TERUijkuJiIjOYb9IREQUxz6RiIhocNIANOiMAWpGlXYdfFZqN2NEqX0AW0dEREREZDyMw1G+ynRAbqZ9hoUglOHzFzLI568vhzATDaQShwWPzZ2EaT0Kv02r9OCHPHggr11c5sDq2VXQhMyxRe7lJyIiI+Mcn4jIOEy5boCRlGWY0OttFqe4Yrv+W9JpTbyerb9pkcWEWq875YCn1uuG02rCz/6/yRjqtMJiEnGiPYx182rQeKwNyzY14rd31malXZR77iILDnR06H5PpkUHOj/tQRnLN+9Cw8HmhK9vP9iMezbvwrp5NQykEhGdh0yL3ey3CoMrwzjZZePUjuhCOG0m1Hnd2JFi7lfndcOp89nKlICidz3jZzrDdSKiQsRxKRER0TnsF4mIiOLYJxIREQ0+7UE54zpbJKbCahJR7/WgrIj5YUREREREfcU4HOWrwZBLOVg+fz33fPZUlOE60UAaWWrHunk1aPbL6AhHUWwzw+O0cJ9iARjisODDU37d74nEVFxsM6M9KPM1JSIiQxoscwwiosGA0ZJ+5HFaMK3Sg+09ChQB8UrvHicniJmUOSyo93rQ0JT8N6z3enDSF+r6dzb/piZJwNLpXgBIKPxW63Vj6fRKWEQBv37zSNqicBZJzEq7KPdKHBaUOvSLDbpY4LFfNPvlpIJvnbYfbEazn4E3IqLzwWJhxuAwS7rjZIdZykGriApfIBLDwtoKaEie+y2srdDdcFKc4f6pd91lM+sWm+OcgoiMiONSIiKic9gvEhERxbFPJCIiGnzaglHIMVX3e0rsZhxtCWDtnIkY7rINUMuIiIiIiIyLcTjKV0VWk25+tBEKhRVn+PxlykUtFBZJRK3Xzb2WVDBKHCzyVohKHJaM45ZSuxmNR9tgv9TN15iIiAyJc3wiIuNgtKQflTgseHTuJEyr9CR8fVqlB4/NncQJ4nkY7rLhoTkTUe91J3y93uvGilkT8P3/3gUgu3/T9qCMY2eCCEdV3Fw9Ak8vmIInbpuMpxdMwc3VIxCOKuiQY1hUW4H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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create visualizations showing correlations between variables.\n", - "import seaborn as sns\n", - "target = 'fare_amount'\n", - "features = [col for col in df.columns if col != target]\n", - "\n", - "# Create a figure with subplots\n", - "fig, axes = plt.subplots(nrows=1, ncols=len(features), figsize=(50, 10))\n", - "\n", - "# Create scatter plots\n", - "for i, feature in enumerate(features):\n", - " sns.scatterplot(x=df[feature], y=df[target], ax=axes[i])\n", - " axes[i].set_title(f'{feature} vs {target}')\n", - " axes[i].set_xlabel(feature)\n", - " axes[i].set_ylabel(target)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "11c33316-1502-46b1-b265-6cf43d0d8f1d", - "metadata": {}, - "source": [ - "## Calculate the correlation coefficient between each feature and fare amount" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "d8dff114-adb5-4b34-a788-b93e42a2fee4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tip_amount 0.5743753694582684\n", - "tolls_amount 0.6327404045395644\n", - "extra -0.008246801964138361\n", - "mta_tax -0.1628089444699402\n", - "total_amount 0.9783791092253548\n", - "trip_distance 0.8848067140931489\n" - ] - } - ], - "source": [ - "# extra and mta_tax seem weakly correlated\n", - "# total_amount is almost perfectly correlated, indicating target leakage.\n", - "continuous_features = ['tip_amount', 'tolls_amount', 'extra', 'mta_tax', 'total_amount', 'trip_distance']\n", - "\n", - "for i in continuous_features:\n", - " correlation = df['fare_amount'].corr(df[i])\n", - " print(i, correlation)" - ] - }, - { - "cell_type": "markdown", - "id": "7ea2dc4f-c366-43f0-8a81-44ecd8289a3d", - "metadata": {}, - "source": [ - "### Calculate a one way ANOVA between the groups\n", - "\n", - "From running the ANOVA, `mta_tax` and `extra` have the most variance between the groups. We're using them as features to train our model." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "3e083025-3312-4fd9-8cd2-4c8e37db5859", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Feature: payment_type, F-statistic: 22.20, p-value: 0.00000\n", - "Feature: extra, F-statistic: 130.42, p-value: 0.00000\n", - "Feature: mta_tax, F-statistic: 999.42, p-value: 0.00000\n", - "Feature: vendor_id, F-statistic: 12.42, p-value: 0.00042\n", - "Feature: passenger_count, F-statistic: 2.57, p-value: 0.01744\n" - ] - } - ], - "source": [ - "# The mta tax and extra have the most variance between the groups\n", - "from scipy.stats import f_oneway\n", - "# Separate features and target variable\n", - "X = df[['payment_type', 'extra', 'mta_tax', 'vendor_id', 'passenger_count']]\n", - "y = df['fare_amount']\n", - "\n", - "# Perform one-way ANOVA for each feature\n", - "for feature in X.columns:\n", - " groups = [y[X[feature] == group] for group in X[feature].unique()]\n", - " if len(groups) > 1:\n", - " f_statistic, p_value = f_oneway(*groups)\n", - " print(f'Feature: {feature}, F-statistic: {f_statistic:.2f}, p-value: {p_value:.5f}')" - ] - }, - { - "cell_type": "markdown", - "id": "5b2f3d07-8010-43c4-873e-f462fd0bd94e", - "metadata": {}, - "source": [ - "### Run a query to get the dataset we're using for ML workflow\n", - "\n", - "The XGBoost algorithm on Amazon SageMaker uses the first column as the target column. `fare_amount` must be the first column in our query." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "0dbcf599-076c-468e-9e9b-2e0bd53c3fa7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: e9866ba2-8e0d-426f-a601-e6ca24890b71\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'e9866ba2-8e0d-426f-a601-e6ca24890b71'" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Final select statement has tip_amount, tolls_amount, extra, mta_tax, trip_distance\n", - "ride_combined_notebook_relevant_features_query = \"\"\"\n", - "SELECT fare_amount, tip_amount, tolls_amount, extra, mta_tax, trip_distance FROM combined_ride_data_deduped\n", - "\"\"\"\n", - "\n", - "run_athena_query(ride_combined_notebook_relevant_features_query, database, s3_output_location)" - ] - }, - { - "cell_type": "markdown", - "id": "4bbfeb06-e0e2-4ce0-9e73-98894053592d", - "metadata": {}, - "source": [ - "### Get the Amazon S3 URI of the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "624a7833-c815-480e-b1da-c29da3d02c76", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'s3://ux360-nyc-taxi-dogfooding/e9866ba2-8e0d-426f-a601-e6ca24890b71.csv'" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_csv_file_location('ride_combined_notebook_relevant_features_query_execution_id')" - ] - }, - { - "cell_type": "markdown", - "id": "4632047c-eabc-495a-9758-b55b78937f73", - "metadata": {}, - "source": [ - "### Run a SageMaker processing job to split the data\n", - "\n", - "The code in `processing_data_split.py` splits the dataset into training, validation, and test sets. We use a SageMaker processing job to provide the compute needed to transform large volumes of data. For more information about processing jobs, see [Use processing jobs to run data transformation workloads](https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html). For more information about running sci-kit scripts, see [Data Processing with scikit-learn](https://docs.aws.amazon.com/sagemaker/latest/dg/use-scikit-learn-processing-container.html). \n", - "\n", - "For faster processing, we recommend using an `instance_count` of `2`, but you can use whatever value you prefer.\n", - "\n", - "For `source` within the `ProcessingInput` function, replace `'s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv'` with the output of the preceding cell. Within `processing_data_split.py`, you specify `/opt/ml/processing/input/query-id` as the `input_path`. The processing job is copying the query results to a location within its own container.\n", - "\n", - "For `Destination` under `ProcessingOutput`, replace `example-s3-bucket` with the Amazon S3 bucket that you've created." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "788cae3c-a34b-4ee0-899e-0a461e21b210", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker.image_uris:Defaulting to only available Python version: py3\n", - "INFO:sagemaker:Creating processing-job with name sagemaker-scikit-learn-2024-06-25-17-41-19-446\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...........\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", - " import imp\u001b[0m\n", - "\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\u001b[0m\n", - "\u001b[35m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", - " import imp\u001b[0m\n", - "\u001b[35m/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\u001b[0m\n", - "\u001b[34msys:1: DtypeWarning: Columns (0,1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\u001b[0m\n", - "\u001b[35msys:1: DtypeWarning: Columns (0,1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\u001b[0m\n", - "\u001b[35mTraining set: 30940496 samples\u001b[0m\n", - "\u001b[35mValidation set: 6630106 samples\u001b[0m\n", - "\u001b[35mTest set: 6630107 samples\u001b[0m\n", - "\u001b[34mTraining set: 30940496 samples\u001b[0m\n", - "\u001b[34mValidation set: 6630106 samples\u001b[0m\n", - "\u001b[34mTest set: 6630107 samples\u001b[0m\n", - "\n" - ] - } - ], - "source": [ - "import sagemaker\n", - "from sagemaker.sklearn.processing import SKLearnProcessor\n", - "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", - "\n", - "\n", - "\n", - "# Define the SageMaker execution role\n", - "role = sagemaker.get_execution_role()\n", - "\n", - "# Define the SKLearnProcessor\n", - "sklearn_processor = SKLearnProcessor(framework_version='0.20.0',\n", - " role=role,\n", - " instance_type='ml.m5.4xlarge',\n", - " instance_count=2)\n", - "\n", - "# Run the processing job\n", - "sklearn_processor.run(\n", - " code='processing_data_split.py', \n", - " inputs=[ProcessingInput(\n", - " source='s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv',\n", - " destination='/opt/ml/processing/input'\n", - " )],\n", - " outputs=[\n", - " ProcessingOutput(\n", - " source='/opt/ml/processing/output/train',\n", - " destination='s3://ux360-nyc-taxi-dogfooding/output/train'\n", - " ),\n", - " ProcessingOutput(\n", - " source='/opt/ml/processing/output/validation',\n", - " destination='s3://ux360-nyc-taxi-dogfooding/output/validation'\n", - " ),\n", - " ProcessingOutput(\n", - " source='/opt/ml/processing/output/test',\n", - " destination='s3://ux360-nyc-taxi-dogfooding/output/test'\n", - " )\n", - " ]\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "bc164657-fd8f-4f96-89ff-23e991945ea4", - "metadata": {}, - "source": [ - "### Verify that train.csv is in the location that you've specified" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "41cb0fb0-079d-421d-a4b8-005ee38fc472", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-06-25 17:49:51 794185864 train.csv\n" - ] - } - ], - "source": [ - "#Verify that train.csv is in the location that you've specified\n", - "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/train/train.csv" - ] - }, - { - "cell_type": "markdown", - "id": "d0d2ba3c-fd6d-4aa0-b75b-92ba5a70ad00", - "metadata": {}, - "source": [ - "### Verify that val.csv is in the location that you've specified" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "ee3f29f1-a135-4bf6-bba5-595fb80c471d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-06-25 17:49:51 170183603 val.csv\n" - ] - } - ], - "source": [ - "#Verify that val.csv is in the location that you've specified\n", - "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/validation/val.csv" - ] - }, - { - "cell_type": "markdown", - "id": "c92d4b89-65a5-474b-aa22-dcb442c344b9", - "metadata": {}, - "source": [ - "### Specify `train.csv` and `val.csv` as the input for the training job" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "1e4e4113-b76c-49d5-a3b0-2327eb174fdf", - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.session import TrainingInput\n", - "\n", - "bucket = 'example-s3-bucket'\n", - "\n", - "train_input = TrainingInput(\n", - " f\"s3://{bucket}/output/train/train.csv\", content_type=\"csv\"\n", - ")\n", - "validation_input = TrainingInput(\n", - " f\"s3://{bucket}/output/validation/val.csv\", content_type=\"csv\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "866262fe-5737-49af-9cde-af55575e07d1", - "metadata": {}, - "source": [ - "### Specify the model container and output location of the model artifact\n", - "\n", - "Specify the S3 location of the trained model artifact. You can access it later.\n", - "\n", - "It also gets the URI of the container image. We used version `1.2-2` of the XGBoost container image, but you can specify a different version. For more information about XGBoost container images, see [Use the XGBoost algorithm with Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). " - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "d5b6a9b2-54e5-4dfd-9a5e-3c7442f6d5af", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.2-2\n" - ] - } - ], - "source": [ - "# Getting the XGBoost container that's in us-east-1\n", - "prefix = \"training-output-data\"\n", - "region = \"us-east-1\"\n", - "\n", - "from sagemaker.debugger import Rule, ProfilerRule, rule_configs\n", - "from sagemaker.session import TrainingInput\n", - "\n", - "s3_output_location = f's3://{bucket}/{prefix}/xgboost_model'\n", - "\n", - "container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.2-2\")\n", - "print(container)" - ] - }, - { - "cell_type": "markdown", - "id": "d04e189b-6f38-44cf-a046-6791abd32c00", - "metadata": {}, - "source": [ - "### Define the model" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "44efb3a1-acf0-4193-987f-85025c7c3894", - "metadata": {}, - "outputs": [], - "source": [ - "xgb_model = sagemaker.estimator.Estimator(\n", - " image_uri = container,\n", - " role = role,\n", - " instance_count = 2,\n", - " region = region,\n", - " instance_type = 'ml.m5.4xlarge',\n", - " volume_size = 5, \n", - " output_path = s3_output_location,\n", - " sagemaker_session = sagemaker.Session(),\n", - " rules = [\n", - " Rule.sagemaker(rule_configs.create_xgboost_report()),\n", - " ProfilerRule.sagemaker(rule_configs.ProfilerReport())\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "44f1c8b1-7bf0-4381-9128-b00c2bfcf9f1", - "metadata": {}, - "source": [ - "### Set the model hyperparameters\n", - "\n", - "For the purposes of running the training job more quickly, we set the number of training rounds to 10." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "e28512bf-d246-4a46-a0c8-24d1a8ad65a8", - "metadata": {}, - "outputs": [], - "source": [ - "xgb_model.set_hyperparameters(\n", - " max_depth = 5,\n", - " eta = 0.2,\n", - " gamma = 4,\n", - " min_child_weight = 6,\n", - " subsample = 0.7,\n", - " objective = \"reg:squarederror\",\n", - " num_round = 10\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "e5b6ed18-990f-4ec7-9d42-6965ec67e2ce", - "metadata": {}, - "source": [ - "### Train the model" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "58b77fc0-407d-4743-ae35-7bc7b04478e6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: latest.\n", - "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", - "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: latest.\n", - "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", - "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2024-06-25-18-20-44-522\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-06-25 18:20:45 Starting - Starting the training job...CreateXgboostReport: InProgress\n", - "ProfilerReport: InProgress\n", - "...\n", - "2024-06-25 18:21:29 Starting - Preparing the instances for training...\n", - "2024-06-25 18:22:09 Downloading - Downloading input data......\n", - "2024-06-25 18:23:12 Training - Training image download completed. Training in progress....\u001b[34m[2024-06-25 18:23:33.281 ip-10-2-65-56.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", - "\u001b[34mReturning the value itself\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:38.246 ip-10-2-111-68.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", - "\u001b[35mReturning the value itself\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:42:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] start listen on algo-1:9099\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9099}\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] Connected to RabitTracker.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] No data received from connection ('10.2.65.56', 37490). Closing.\u001b[0m\n", - "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:47:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:48:INFO] Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:48:INFO] Connected to RabitTracker.\u001b[0m\n", - "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35mtask NULL got new rank 0\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:48:INFO] No data received from connection ('10.2.111.68', 42310). Closing.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] Recieve start signal from 10.2.111.68; assign rank 0\u001b[0m\n", - "\u001b[34mtask NULL got new rank 1\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] Recieve start signal from 10.2.65.56; assign rank 1\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker All of 2 nodes getting started\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker All nodes finishes job\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker 0.1758573055267334 secs between node start and job finish\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] start listen on algo-1:9100\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9100}\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] Connected to RabitTracker.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] No data received from connection ('10.2.65.56', 38280). Closing.\u001b[0m\n", - "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:49:INFO] Failed to connect to RabitTracker on attempt 0\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:49:INFO] Sleeping for 3 sec before retrying\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] No data received from connection ('10.2.111.68', 60082). Closing.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Recieve start signal from 10.2.111.68; assign rank 0\u001b[0m\n", - "\u001b[34mtask NULL got new rank 1\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Recieve start signal from 10.2.65.56; assign rank 1\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Train matrix has 30940497 rows and 5 columns\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] @tracker All of 2 nodes getting started\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Validation matrix has 6630107 rows\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.600 ip-10-2-65-56.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.601 ip-10-2-65-56.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.601 ip-10-2-65-56.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.602 ip-10-2-65-56.ec2.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.602 ip-10-2-65-56.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Debug hook created from config\u001b[0m\n", - "\u001b[34m[18:23:52] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:52:INFO] Connected to RabitTracker.\u001b[0m\n", - "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35mtask NULL got new rank 0\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:52:INFO] Train matrix has 30940497 rows and 5 columns\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:52:INFO] Validation matrix has 6630107 rows\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.600 ip-10-2-111-68.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.601 ip-10-2-111-68.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.601 ip-10-2-111-68.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.602 ip-10-2-111-68.ec2.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.602 ip-10-2-111-68.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:52:INFO] Debug hook created from config\u001b[0m\n", - "\u001b[35m[18:23:52] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", - "\u001b[34m[2024-06-25 18:24:08.407 ip-10-2-65-56.ec2.internal:7 INFO hook.py:423] Monitoring the collections: labels, metrics, predictions, feature_importance, hyperparameters\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:08:INFO] [0]#011train-rmse:184.43744#011validation-rmse:135.48259\u001b[0m\n", - "\u001b[35m[2024-06-25 18:24:08.409 ip-10-2-111-68.ec2.internal:7 INFO hook.py:423] Monitoring the collections: predictions, labels, hyperparameters, feature_importance, metrics\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:20:INFO] [1]#011train-rmse:184.28534#011validation-rmse:135.24808\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:31:INFO] [2]#011train-rmse:184.18167#011validation-rmse:135.09784\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:43:INFO] [3]#011train-rmse:184.11903#011validation-rmse:134.99771\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:55:INFO] [4]#011train-rmse:184.07890#011validation-rmse:134.93574\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:07:INFO] [5]#011train-rmse:184.05234#011validation-rmse:134.89529\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:19:INFO] [6]#011train-rmse:184.03487#011validation-rmse:134.86635\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:30:INFO] [7]#011train-rmse:184.02385#011validation-rmse:134.84970\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:42:INFO] [8]#011train-rmse:184.01642#011validation-rmse:134.83659\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:54:INFO] [9]#011train-rmse:183.88487#011validation-rmse:134.82910\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:54:INFO] @tracker All nodes finishes job\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:54:INFO] @tracker 121.60369801521301 secs between node start and job finish\u001b[0m\n", - "\n", - "2024-06-25 18:26:11 Uploading - Uploading generated training model\n", - "2024-06-25 18:26:11 Completed - Training job completed\n", - "Training seconds: 520\n", - "Billable seconds: 520\n" - ] - } - ], - "source": [ - "xgb_model.fit({\"train\": train_input, \"validation\": validation_input}, wait=True)" - ] - }, - { - "cell_type": "markdown", - "id": "f0f8be08-10a5-4204-8f8b-60235d4b1f04", - "metadata": {}, - "source": [ - "### Deploy the model\n", - "\n", - "Copy the name of the model endpoint. We use it for our model evaluation." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "c1aa7bc3-feee-4602-a64c-8c1e08526d03", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Creating model with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n", - "INFO:sagemaker:Creating endpoint-config with name sagemaker-xgboost-2024-06-25-18-26-38-055\n", - "INFO:sagemaker:Creating endpoint with name sagemaker-xgboost-2024-06-25-18-26-38-055\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------!" - ] - } - ], - "source": [ - "xgb_predictor = xgb_model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')" - ] - }, - { - "cell_type": "markdown", - "id": "ddcf330c-8add-437d-af1f-687ed3ebc78d", - "metadata": {}, - "source": [ - "### Download the test.csv file" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "a9cc4eea-a6d0-418f-ab35-db437ce2a99d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "download: s3://ux360-nyc-taxi-dogfooding/output/test/test.csv to ./test.csv\n" - ] - } - ], - "source": [ - "!aws s3 cp s3://example-s3-bucket/output/test/test.csv ." - ] - }, - { - "cell_type": "markdown", - "id": "27b6cc9e-cb1c-43f6-99b8-fc26b38934c3", - "metadata": {}, - "source": [ - "### Create a 20 row test dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "953f9d9b-04d0-4398-8620-8f9ab4eb407b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 0 1 2 3 4 5\n", - "0 7.5 1.08 0.0 0.0 0.5 0.97\n", - "1 10.0 0.00 0.0 0.5 0.5 2.60\n", - "2 6.0 1.00 0.0 1.0 0.5 0.82\n", - "3 23.5 5.45 0.0 3.0 0.5 7.40\n", - "4 53.5 8.36 10.5 0.0 0.0 12.68" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import boto3\n", - "import json\n", - "\n", - "test_df = pd.read_csv('test.csv', nrows=20)\n", - "test_df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "a27e6c58-1abb-41db-ab45-263b97ee01ed", - "metadata": {}, - "source": [ - "### Get predictions from the test dataframe\n", - "\n", - "Define the `get_predictions` function to convert the 20 row dataframe to a CSV string and get predictions from the model endpoint. Provide the `get_predictions` function with the name of the model and the model endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "218e7887-f37d-42e1-8f6a-9ee97d3c75c4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.515090465545654,10.813796043395996,6.515090465545654,22.628469467163086,49.72923278808594,8.302289962768555,7.602119445800781,6.515090465545654,7.602119445800781,12.309170722961426,16.632259368896484,28.30757713317871,10.813796043395996,37.56535339355469,10.813796043395996,12.309170722961426,6.515090465545654,14.130854606628418,10.813796043395996,6.515090465545654\n" - ] - } - ], - "source": [ - "import json\n", - "import pandas as pd\n", - "\n", - "# Initialize the SageMaker runtime client\n", - "runtime = boto3.client('runtime.sagemaker')\n", - "\n", - "# Define the endpoint name\n", - "endpoint_name = 'sagemaker-xgboost-timestamp'\n", - "\n", - "# Function to make predictions\n", - "def get_predictions(data, endpoint_name):\n", - " # Convert the DataFrame to a CSV string and encode it to bytes\n", - " csv_data = data.to_csv(header=False, index=False).encode('utf-8')\n", - " \n", - " response = runtime.invoke_endpoint(\n", - " EndpointName=endpoint_name,\n", - " ContentType='text/csv',\n", - " Body=csv_data\n", - " )\n", - " \n", - " # Read the response body\n", - " response_body = response['Body'].read().decode('utf-8')\n", - " \n", - " try:\n", - " # Try to parse the response as JSON\n", - " result = json.loads(response_body)\n", - " except json.JSONDecodeError:\n", - " # If response is not JSON, just return the raw response\n", - " result = response_body\n", - " \n", - " return result\n", - "\n", - "# Drop the target column from the test dataframe\n", - "test_df = test_df.drop(test_df.columns[0], axis=1)\n", - "\n", - "# Get predictions\n", - "predictions = get_predictions(test_df, endpoint_name)\n", - "print(predictions)" - ] - }, - { - "cell_type": "markdown", - "id": "a136ae86-efd3-4d4f-9966-6610f445d84c", - "metadata": {}, - "source": [ - "### Create an array from the string of predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "58b45ac2-8a18-4d27-8aff-57370696d58f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['6.515090465545654',\n", - " '10.813796043395996',\n", - " '6.515090465545654',\n", - " '22.628469467163086',\n", - " '49.72923278808594',\n", - " '8.302289962768555',\n", - " '7.602119445800781',\n", - " '6.515090465545654',\n", - " '7.602119445800781',\n", - " '12.309170722961426',\n", - " '16.632259368896484',\n", - " '28.30757713317871',\n", - " '10.813796043395996',\n", - " '37.56535339355469',\n", - " '10.813796043395996',\n", - " '12.309170722961426',\n", - " '6.515090465545654',\n", - " '14.130854606628418',\n", - " '10.813796043395996',\n", - " '6.515090465545654']" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictions_array = predictions.split(',')\n", - "predictions_array" - ] - }, - { - "cell_type": "markdown", - "id": "20097b4e-d515-45cf-9677-bd12953b6912", - "metadata": {}, - "source": [ - "### Get the 20 row sample of the test dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "a5b69119-c58d-401d-a683-345a21451090", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 0 1 2 3 4 5\n", - "0 7.5 1.08 0.0 0.0 0.5 0.97\n", - "1 10.0 0.00 0.0 0.5 0.5 2.60\n", - "2 6.0 1.00 0.0 1.0 0.5 0.82\n", - "3 23.5 5.45 0.0 3.0 0.5 7.40\n", - "4 53.5 8.36 10.5 0.0 0.0 12.68" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_with_target_column_values = pd.read_csv('test.csv', nrows=20)\n", - "df_with_target_column_values.head()" - ] - }, - { - "cell_type": "markdown", - "id": "85cd39f3-5f12-4cb1-aab2-6ca658e9d16e", - "metadata": {}, - "source": [ - "### Convert the values of the predictions array from strings to floats" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "75353856-df2f-4c45-9a9b-11e16a856aa6", - "metadata": {}, - "outputs": [], - "source": [ - "predictions_array = [float(x) for x in predictions_array]" - ] - }, - { - "cell_type": "markdown", - "id": "408a6da9-9a0c-4307-8966-acbcc11beacc", - "metadata": {}, - "source": [ - "### Create a dataframe to store the predicted versus actual values" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "9589000e-1ce0-4a08-9d9c-055d29e13639", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " predicted_values actual_values\n", - "0 6.515090 7.5\n", - "1 10.813796 10.0\n", - "2 6.515090 6.0\n", - "3 22.628469 23.5\n", - "4 49.729233 53.5\n", - "5 8.302290 9.0\n", - "6 7.602119 8.5\n", - "7 6.515090 2.5\n", - "8 7.602119 8.5\n", - "9 12.309171 17.5\n", - "10 16.632259 16.5\n", - "11 28.307577 32.5\n", - "12 10.813796 12.5\n", - "13 37.565353 52.0\n", - "14 10.813796 12.0\n", - "15 12.309171 13.5\n", - "16 6.515090 6.5\n", - "17 14.130855 26.5\n", - "18 10.813796 13.0\n", - "19 6.515090 10.5" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "column_to_add = df_with_target_column_values.iloc[:, 0]\n", - "\n", - "comparison_df['actual_values'] = column_to_add\n", - "\n", - "comparison_df" - ] - }, - { - "cell_type": "markdown", - "id": "a1ee137e-2706-4972-b70a-4d908bb0cb0a", - "metadata": {}, - "source": [ - "### Verify that the datatypes of both columns are floats" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "48f6f988-0de8-4c44-8c10-9845ef4d476d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "predicted_values float64\n", - "actual_values float64\n", - "dtype: object" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comparison_df.dtypes" - ] - }, - { - "cell_type": "markdown", - "id": "8c7cce0b-ce8b-4320-b9a4-9a50b2c732b3", - "metadata": {}, - "source": [ - "### Compute the RMSE" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "781fe125-4a2e-4527-8c45-fcd20558f4bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 4.833823838366928\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "# Calculate the squared differences between the predicted and actual values\n", - "comparison_df['squared_diff'] = (comparison_df['actual_values'] - comparison_df['predicted_values']) ** 2\n", - "\n", - "# Calculate the mean of the squared differences\n", - "mean_squared_diff = comparison_df['squared_diff'].mean()\n", - "\n", - "# Take the square root of the mean to get the RMSE\n", - "rmse = np.sqrt(mean_squared_diff)\n", - "\n", - "print(f\"RMSE: {rmse}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "4a21cb4e-d9be-466c-869d-ac0be688700c", - "metadata": {}, - "source": [ - "### Clean up" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "9a6e651d-3e68-4c1b-8a28-3e15604b5ec1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "remove_bucket: parsa-machine-learning-exam\n" - ] - } - ], - "source": [ - "# Delete the S3 bucket\n", - "!aws s3 rb s3://example-s3-bucket --force" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "6c883864-e707-46d2-a183-76e5f2090368", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Deleting endpoint configuration with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n", - "INFO:sagemaker:Deleting endpoint with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n" - ] - } - ], - "source": [ - "# Delete the endpoint\n", - "xgb_predictor.delete_endpoint()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pyspark-etl-training.ipynb b/pyspark-etl-training.ipynb deleted file mode 100644 index 2dc23d344c..0000000000 --- a/pyspark-etl-training.ipynb +++ /dev/null @@ -1,850 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0a1828f9-efdc-4d12-a676-a2f3432e9ab0", - "metadata": {}, - "source": [ - "# Perform ETL and train a model using PySpark\n", - "\n", - "To perform extract transform load (ETL) operations on multiple files, we recommend opening a Jupyter notebook within Amazon SageMaker Studio and using the `Glue PySpark and Ray` kernel. The kernel is connected to an AWS Glue Interactive Session. The session connects your notebook to a cluster that automatically scales up the storage and compute to meet your data processing needs. When you shut down the kernel, the session stops and you're no longer charged for the compute on the cluster.\n", - "\n", - "Within the notebook you can use Spark commands to join and transform your data. Writing Spark commands is both faster and easier than writing SQL queries. For example, you can use the join command to join two tables. Instead of writing a query that can sometimes take minutes to complete, you can join a table within seconds.\n", - "\n", - "To show the utility of using the PySpark kernel for your ETL and model training worklows, we're predicting the fare amount of the NYC taxi dataset. It imports data from 47 files across 2 different Amazon Simple Storage Service (Amazon S3) locations. Amazon S3 is an object storage service that you can use to save and access data and machine learning artifacts for your models. For more information about Amazon S3, see [What is Amazon S3?](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html).\n", - "\n", - "The notebook is not meant to be a comprehensive analysis. Instead, it's meant to be a proof of concept to help you quickly get started.\n", - "\n", - "__Prerequisites:__\n", - "\n", - "This tutorial assumes that you've in the us-east-1 AWS Region. It also assumes that you've provided the IAM role you're using to run the notebook with permissions to use Glue. For more information, see [Providing AWS Glue permissions\n", - "](docs.aws.amazon.com/sagemaker/latest/dg/perform-etl-and-train-model-pyspark.html#providing-aws-glue-permissions)." - ] - }, - { - "cell_type": "markdown", - "id": "dffc1f72-88d2-442d-97ee-0d1c4e095ffb", - "metadata": {}, - "source": [ - "## Solution overview \n", - "\n", - "To perform ETL on the NYC taxi data and train a model, we do the following\n", - "\n", - "1. Start a Glue Session and load the SageMaker Python SDK\n", - "2. Set up the utilities needed to work with AWS Glue.\n", - "3. Load the data from the Amazon S3 into Spark dataframes.\n", - "4. Verify that we've loaded the data successfully.\n", - "5. Save a 20000 row sample of the Spark dataframe as a pandas dataframe.\n", - "6. Create a correlation matrix as an example of the types of analyses we can perform.\n", - "7. Split the Spark dataframe into training, validation, and test datasets.\n", - "8. Write the datasets to Amazon S3 locations that can be accessed by an Amazon SageMaker training job.\n", - "9. Use the training and validation datasets to train a model." - ] - }, - { - "cell_type": "markdown", - "id": "e472c953-1625-49df-8df9-9529344783ab", - "metadata": {}, - "source": [ - "### Start a Glue Session and load the SageMaker Python SDK" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "94172c75-f8a9-4590-a443-c872fb5c5d6e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Welcome to the Glue Interactive Sessions Kernel\n", - "For more information on available magic commands, please type %help in any new cell.\n", - "\n", - "Please view our Getting Started page to access the most up-to-date information on the Interactive Sessions kernel: https://docs.aws.amazon.com/glue/latest/dg/interactive-sessions.html\n", - "Installed kernel version: 1.0.5 \n", - "Additional python modules to be included:\n", - "sagemaker\n" - ] - } - ], - "source": [ - "%additional_python_modules sagemaker" - ] - }, - { - "cell_type": "markdown", - "id": "725bd4b6-82a0-4f02-95b9-261ce62c71b0", - "metadata": {}, - "source": [ - "### Set up the utilities needed to work with AWS Glue\n", - "\n", - "We're importing `Join` to join our Spark dataframes. `GlueContext` provides methods for transforming our dataframes. In the context of the notebook, it reads the data from the Amazon S3 locations and uses the Spark cluster to transform the data. `SparkContext` represents the connection to the Spark cluster. `GlueContext` uses `SparkContext` to transform the data. `getResolvedOptions` lets you resolve configuration options within the Glue interactive session." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2ea1c3a4-8881-48b0-8888-9319812750e7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Trying to create a Glue session for the kernel.\n", - "Session Type: etl\n", - "Session ID: 11fe1ff7-3608-485f-a4a3-65392596dba0\n", - "Applying the following default arguments:\n", - "--glue_kernel_version 1.0.5\n", - "--enable-glue-datacatalog true\n", - "--additional-python-modules sagemaker\n", - "Waiting for session 11fe1ff7-3608-485f-a4a3-65392596dba0 to get into ready status...\n", - "Session 11fe1ff7-3608-485f-a4a3-65392596dba0 has been created.\n", - "\n" - ] - } - ], - "source": [ - "import sys\n", - "from awsglue.transforms import Join\n", - "from awsglue.utils import getResolvedOptions\n", - "from pyspark.context import SparkContext\n", - "from awsglue.context import GlueContext\n", - "from awsglue.job import Job\n", - "\n", - "glueContext = GlueContext(SparkContext.getOrCreate())" - ] - }, - { - "cell_type": "markdown", - "id": "e03664e5-89a2-4296-ba83-3518df4a58f0", - "metadata": {}, - "source": [ - "### Create the `df_ride_info` dataframe\n", - "\n", - "Create a single dataframe from all the ride_info Parquet files for 2019." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ba577de7-9ffe-4bae-b4c0-b225181306d9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_ride_info = glueContext.create_dynamic_frame_from_options(\n", - " connection_type=\"s3\", format=\"parquet\",\n", - " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-info/year=2019/\"], \"recurse\": True}).toDF()" - ] - }, - { - "cell_type": "markdown", - "id": "b04ce553-bf3d-4922-bbb1-4aa264447276", - "metadata": {}, - "source": [ - "### Create the `df_ride_info` dataframe\n", - "\n", - "Create a single dataframe from all the ride_fare Parquet files for 2019." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6efc3d4a-81d7-40f5-bb62-cd206924a0c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_ride_fare = glueContext.create_dynamic_frame_from_options(\n", - " connection_type=\"s3\", format=\"parquet\",\n", - " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-fare/year=2019/\"], \"recurse\": True}).toDF()" - ] - }, - { - "cell_type": "markdown", - "id": "6c8664da-2105-4ada-b480-06d50c59e878", - "metadata": {}, - "source": [ - "### Show the first five rows of `dr_ride_fare`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d63af3a3-358f-4c6e-97d4-97a1f1a552de", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "| ride_id|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|total_amount|\n", - "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "|1400160115693| 2| 31.0| 0.0| 0.5| 0.0| 6.12| 40.42|\n", - "|3770982177323| 1| 4.5| 0.0| 0.5| 1.2| 0.0| 9.0|\n", - "|1400160115694| 1| 16.5| 1.0| 0.5| 4.16| 0.0| 24.96|\n", - "|3770982177324| 1| 18.0| 2.5| 0.5| 5.3| 0.0| 26.6|\n", - "|1400160115695| 1| 8.0| 2.5| 0.5| 1.13| 0.0| 12.43|\n", - "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "only showing top 5 rows\n" - ] - } - ], - "source": [ - "df_ride_fare.show(5)" - ] - }, - { - "cell_type": "markdown", - "id": "688a17e8-0c83-485d-a328-e89344a0e8bf", - "metadata": {}, - "source": [ - "### Join df_ride_fare and df_ride_info on the `ride_id` column" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "07a3baab-44b0-416a-b12e-049a270af8bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_joined = df_ride_info.join(df_ride_fare, [\"ride_id\"])" - ] - }, - { - "cell_type": "markdown", - "id": "236c2efc-85f8-43f8-b6d3-7f0e61ccefb0", - "metadata": {}, - "source": [ - "### Show the first five rows of the joined dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2a456733-4533-4688-8174-368e50f4dd66", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "| ride_id|vendor_id|passenger_count| pickup_at| dropoff_at|trip_distance|rate_code_id|store_and_fwd_flag|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|total_amount|\n", - "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "|51539607553| 1| 1|2019-04-21 17:20:19|2019-04-21 17:31:28| 2.7| 1| N| 1| 10.5| 2.5| 0.5| 3.45| 0.0| 17.25|\n", - "|51539607560| 2| 1|2019-02-21 22:49:59|2019-02-21 22:53:45| 0.62| 1| N| 2| 4.5| 0.5| 0.5| 0.0| 0.0| 8.3|\n", - "|51539607572| 1| 1|2019-02-21 22:19:08|2019-02-21 22:24:13| 0.6| 1| N| 1| 5.0| 3.0| 0.5| 1.75| 0.0| 10.55|\n", - "|51539607626| 2| 5|2019-02-21 22:18:33|2019-02-21 22:30:32| 2.0| 1| N| 1| 10.0| 0.5| 0.5| 2.76| 0.0| 16.56|\n", - "|51539607627| 2| 1|2019-04-21 17:21:49|2019-04-21 17:35:46| 2.72| 1| N| 1| 12.0| 0.0| 0.5| 2.3| 0.0| 17.6|\n", - "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "only showing top 5 rows\n" - ] - } - ], - "source": [ - "df_joined.show(5)" - ] - }, - { - "cell_type": "markdown", - "id": "1396f6ee-c581-4274-baf8-243d38ec000b", - "metadata": {}, - "source": [ - "### Show the data types of the dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9a52a903-f394-4d00-a216-6af8c2132d83", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "root\n", - " |-- ride_id: long (nullable = true)\n", - " |-- vendor_id: integer (nullable = true)\n", - " |-- passenger_count: byte (nullable = true)\n", - " |-- pickup_at: timestamp (nullable = true)\n", - " |-- dropoff_at: timestamp (nullable = true)\n", - " |-- trip_distance: float (nullable = true)\n", - " |-- rate_code_id: integer (nullable = true)\n", - " |-- store_and_fwd_flag: string (nullable = true)\n", - " |-- payment_type: integer (nullable = true)\n", - " |-- fare_amount: float (nullable = true)\n", - " |-- extra: float (nullable = true)\n", - " |-- mta_tax: float (nullable = true)\n", - " |-- tip_amount: float (nullable = true)\n", - " |-- tolls_amount: float (nullable = true)\n", - " |-- total_amount: float (nullable = true)\n" - ] - } - ], - "source": [ - "df_joined.printSchema()" - ] - }, - { - "cell_type": "markdown", - "id": "18bb75a2-eba5-4d06-8a26-f30e31776a02", - "metadata": {}, - "source": [ - "### Count the number of rows" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c6bcc15f-8d41-4def-ae49-edaef4105343", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "44200708\n" - ] - } - ], - "source": [ - "df_joined.count()" - ] - }, - { - "cell_type": "markdown", - "id": "d2daa67c-4b21-433a-b46e-eed518ba9ce7", - "metadata": {}, - "source": [ - "### Drop duplicates if there are any" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7d13d8d9-7eed-4efb-b972-601baf291842", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_no_dups = df_joined.dropDuplicates([\"ride_id\"])" - ] - }, - { - "cell_type": "markdown", - "id": "657e48dc-1f4a-4550-afe1-d9754e6d0e1e", - "metadata": {}, - "source": [ - "### Count the number of rows after dropping the duplicates\n", - "\n", - "In this case, there were no duplicates in the original dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3e3e82a3-e3db-4752-8bab-f42cbbae4928", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "44200708\n" - ] - } - ], - "source": [ - "df_no_dups.count()" - ] - }, - { - "cell_type": "markdown", - "id": "ae4c0fc4-7cb5-4b70-8430-965b5fe4506e", - "metadata": {}, - "source": [ - "### Drop columns\n", - "Time series data and categorical data is outside of the scope of the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9dc1d15f-53f6-404d-86fd-5a28f3792db8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_cleaned = df_joined.drop(\"pickup_at\", \"dropoff_at\", \"store_and_fwd_flag\", \"vendor_id\", \"payment_type\")" - ] - }, - { - "cell_type": "markdown", - "id": "081c81f9-f052-4ddb-b769-4d41b6138f6a", - "metadata": {}, - "source": [ - "### Take a sample from the notebook and convert it to a pandas dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "48382726-c767-4b0e-9336-decbf8184938", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_sample = df_cleaned.sample(False, 0.1, seed=0).limit(20000)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2bf2f181-0096-4044-8210-7d9de299d966", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20000\n" - ] - } - ], - "source": [ - "df_sample.count()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a8b2f670-c5f9-4a01-8d9f-6a29a3dae660", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ride_id passenger_count ... tolls_amount total_amount\n", - "count 2.000000e+04 20000.000000 ... 20000.000000 20000.000000\n", - "mean 5.327415e+10 1.580700 ... 0.354632 18.917547\n", - "std 3.447216e+09 1.218221 ... 1.540669 14.226608\n", - "min 5.153961e+10 0.000000 ... 0.000000 -59.799999\n", - "25% 5.154042e+10 1.000000 ... 0.000000 11.300000\n", - "50% 5.154121e+10 1.000000 ... 0.000000 14.750000\n", - "75% 5.154202e+10 2.000000 ... 0.000000 20.299999\n", - "max 6.013019e+10 6.000000 ... 21.500000 242.300003\n", - "\n", - "[8 rows x 10 columns]\n" - ] - } - ], - "source": [ - "df_pandas = df_sample.toPandas()\n", - "df_pandas.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "246c98e9-64bd-4644-a163-b86a943d6a09", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset shape: (20000, 10)\n" - ] - } - ], - "source": [ - "print(\"Dataset shape: \", df_pandas.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c5b2727c-de75-4cc0-94e9-d254e235d003", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ride_id passenger_count ... tolls_amount total_amount\n", - "0 51539607572 1 ... 0.0 10.550000\n", - "1 51539607730 5 ... 0.0 17.299999\n", - "2 51539607857 2 ... 0.0 6.800000\n", - "3 51539607985 1 ... 0.0 7.300000\n", - "4 51539608203 1 ... 0.0 16.559999\n", - "\n", - "[5 rows x 10 columns]\n" - ] - } - ], - "source": [ - "df_pandas.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d69b48b6-98c2-4851-9c7a-f24f092bae41", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 20000 entries, 0 to 19999\n", - "Data columns (total 10 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 ride_id 20000 non-null int64 \n", - " 1 passenger_count 20000 non-null int8 \n", - " 2 trip_distance 20000 non-null float32\n", - " 3 rate_code_id 20000 non-null int32 \n", - " 4 fare_amount 20000 non-null float32\n", - " 5 extra 20000 non-null float32\n", - " 6 mta_tax 20000 non-null float32\n", - " 7 tip_amount 20000 non-null float32\n", - " 8 tolls_amount 20000 non-null float32\n", - " 9 total_amount 20000 non-null float32\n", - "dtypes: float32(7), int32(1), int64(1), int8(1)\n", - "memory usage: 800.9 KB\n" - ] - } - ], - "source": [ - "df_pandas.info()" - ] - }, - { - "cell_type": "markdown", - "id": "34222bea-8864-4934-8c93-a71a7e72325b", - "metadata": {}, - "source": [ - "### Create a correlation matrix of the features\n", - "\n", - "We're creating a correlation matrix to see which features are the most predictive. This is an example of an analysis that you can use for your own use case." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b7f3e4f7-e04e-41e1-b94b-b32eb3bc3bbf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - }, - { - "data": { - "image/png": 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" - }, - "metadata": { - "image/png": { - "height": 480, - "width": 640 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from pyspark.ml.stat import Correlation\n", - "from pyspark.ml.feature import VectorAssembler\n", - "import seaborn as sns \n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd # not sure how the kernel runs, but it looks like I have import pandas again after going back to the notebook after a while\n", - "\n", - "vector_col = 'corr_features'\n", - "assembler = VectorAssembler(inputCols=df_sample.columns, outputCol=vector_col)\n", - "df_vector = assembler.transform(df_sample).select(vector_col)\n", - "\n", - "matrix = Correlation.corr(df_vector, vector_col).collect()[0][0]\n", - "corr_matrix = matrix.toArray().tolist()\n", - "corr_matrix_df = pd.DataFrame(data=corr_matrix, columns=df_sample.columns, index=df_sample.columns) \n", - "\n", - "plt.figure(figsize=(16,10))\n", - "sns.heatmap(corr_matrix_df,\n", - " xticklabels=corr_matrix_df.columns.values,\n", - " yticklabels=corr_matrix_df.columns.values, cmap=\"Greens\", annot=True)\n", - "\n", - "%matplot plt" - ] - }, - { - "cell_type": "markdown", - "id": "cbde3b29-d37d-485a-a114-5313c5a702c7", - "metadata": {}, - "source": [ - "### Split the dataset into train, validation, and test sets" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6e207c64-2e22-468f-a0c7-948090bcfce2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_train, df_val, df_test = df_cleaned.randomSplit([0.7, 0.15, 0.15])" - ] - }, - { - "cell_type": "markdown", - "id": "01a4d181-e2f0-4743-ab35-dd1f68b0fd31", - "metadata": {}, - "source": [ - "### Define the Amazon S3 locations that store the datasets\n", - "\n", - "If you're getting a module not found error, restart the kernel and run all the cells again." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f16ea3a1-6d6d-4755-94ad-c743298bd130", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "# Define the S3 locations to store the datasets\n", - "import boto3\n", - "import sagemaker\n", - "\n", - "sagemaker_session = sagemaker.Session()\n", - "s3_bucket = sagemaker_session.default_bucket()\n", - "train_data_prefix = \"sandbox/glue-demo/train\"\n", - "validation_data_prefix = \"sandbox/glue-demo/validation\"\n", - "test_data_prefix = \"sandbox/glue-demo/test\"\n", - "region = boto3.Session().region_name" - ] - }, - { - "cell_type": "markdown", - "id": "8899a159-700c-403a-b4f5-a00c62b06e5a", - "metadata": {}, - "source": [ - "### Write the files to the locations" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "64d7ae48-6158-4273-8bb3-2f00abb1c20c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_train.write.parquet(f\"s3://{s3_bucket}/{train_data_prefix}\", mode=\"overwrite\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "de3d1190-4717-4944-846d-0169c093cb90", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_val.write.parquet(f\"s3://{s3_bucket}/{validation_data_prefix}\", mode=\"overwrite\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "9d18ef1c-fc2f-4e34-a692-4a6c48be7cba", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "df_test.write.parquet(f\"s3://{s3_bucket}/{test_data_prefix}\", mode=\"overwrite\")" - ] - }, - { - "cell_type": "markdown", - "id": "73c947e4-b4a9-4cc4-aefe-755aa0a713c8", - "metadata": {}, - "source": [ - "### Train a model\n", - "\n", - "The following code uses the `df_train` and `df_val` datasets to train an XGBoost model. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a31b7742-93df-44c5-8674-b6355032c508", - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker import image_uris\n", - "from sagemaker.inputs import TrainingInput\n", - "\n", - "hyperparameters = {\n", - " \"max_depth\":\"5\",\n", - " \"eta\":\"0.2\",\n", - " \"gamma\":\"4\",\n", - " \"min_child_weight\":\"6\",\n", - " \"subsample\":\"0.7\",\n", - " \"objective\":\"reg:squarederror\",\n", - " \"num_round\":\"50\"}\n", - "\n", - "# Set an output path to save the trained model.\n", - "prefix = 'sandbox/glue-demo'\n", - "output_path = f's3://{s3_bucket}/{prefix}/xgb-built-in-algo/output'\n", - "\n", - "# The following line looks for the XGBoost image URI and builds an XGBoost container.\n", - "# We use version 1.7-1 of the image URI, you can specify a version that you prefer.\n", - "xgboost_container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.7-1\")\n", - "\n", - "# Construct a SageMaker estimator that calls the xgboost-container\n", - "estimator = sagemaker.estimator.Estimator(image_uri=xgboost_container,\n", - " hyperparameters=hyperparameters,\n", - " role=sagemaker.get_execution_role(),\n", - " instance_count=1,\n", - " instance_type='ml.m5.4xlarge',\n", - " output_path=output_path)\n", - "\n", - "content_type = \"application/x-parquet\"\n", - "train_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/train/\", content_type=content_type)\n", - "validation_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/validation/\", content_type=content_type)\n", - "\n", - "# Run the XGBoost training job\n", - "estimator.fit({'train': train_input, 'validation': validation_input})" - ] - }, - { - "cell_type": "markdown", - "id": "b1b1d546-1c7e-48f5-9262-939289ada936", - "metadata": {}, - "source": [ - "### Clean up\n", - "\n", - "To clean up, shut down the kernel. Shutting down the kernel, stops the Glue cluster. You won't be charged for any more compute other than what you used to run the tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e32c38c-719f-47bf-849f-54b63c39823b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Glue PySpark and Ray", - "language": "python", - "name": "glue_pyspark" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "Python_Glue_Session", - "pygments_lexer": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb index ef3d4dcdce..c1555ce64b 100644 --- a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb +++ b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb @@ -8,15 +8,18 @@ "# Create an end to end machine learning workflow using Amazon Athena\n", "Importing and transforming data can be one of the most challenging tasks in a machine learning workflow. We provide you with a Jupyter notebook that demonstrates a cost-effective strategy for an extract, transform, and load (ETL) workflow. Using Amazon Simple Storage Service (Amazon S3) and Amazon Athena, you learn how to query and transform data from a Jupyter notebook. Amazon S3 is an object storage service that allows you to store data and machine learning artifacts. Amazon Athena enables you to interactively query the data stored in those buckets, saving each query as a CSV file in an Amazon S3 location.\n", "\n", - "The tutorial imports 29 CSV files for the 2019 NYC taxi dataset from multiple Amazon S3 locations. The goal is to predict the fare amount for each ride. From those 29 files, the notebook creates a single ride fare dataset and a single ride info dataset with deduplicated values. We join the deduplicated datasets into a single dataset.\n", + "The tutorial imports 16 CSV files for the 2019 NYC taxi dataset from multiple Amazon S3 locations. The goal is to predict the fare amount for each ride. From these 16 files, the notebook creates a single ride fare dataset and a single ride info dataset with deduplicated values. We join the deduplicated datasets into a single dataset.\n", "\n", - "Amazon Athena stores the query results as a CSV file in the specified location. This CSV file is provided to a SageMaker Processing Job to split the data into training, validation, and test sets. While data can be split using queries, a processing job ensures that the data is in a format that's parseable by the XGBoost algorithm.\n", + "Amazon Athena stores the query results as a CSV file in the specified location. We provide the output to a SageMaker Processing Job to split the data into training, validation, and test sets. While data can be split using queries, a processing job ensures that the data is in a format that's parseable by the XGBoost algorithm.\n", "\n", - "__Important__\n", + "__Prerequisites:__\n", "\n", "The notebook must be run in the us-east-1 AWS Region. You also need your own Amazon S3 bucket and a database within Amazon Athena. You won't be able to access the data used in the tutorial otherwise.\n", "\n", - "For information about creating a bucket, see [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). For information about creating a database, see [Create a database](https://docs.aws.amazon.com/athena/latest/ug/getting-started.html#step-1-create-a-database)." + "For information about creating a bucket, see [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html). For information about creating a database, see [Create a database](https://docs.aws.amazon.com/athena/latest/ug/getting-started.html#step-1-create-a-database).\n", + "\n", + "Amazon Athena uses the AWS Glue Data Catalog to read the data from Amazon S3 into a database. You must have permissions to use Glue. To clean up, you also need permissions to delete the bucket you've created. For a quick guide to providing permissions, see [Setting up\n", + "](http://parsash-clouddesk-2024.aka.corp.amazon.com/sagemaker-dg/src/AWSIronmanApiDoc/build/server-root/sagemaker/latest/dg/create-end-to-end-ml-workflow-athena.html#setting-up)." ] }, { @@ -24,58 +27,64 @@ "id": "0b11693f-7c35-41cf-8e4b-4f86eea8f3b0", "metadata": {}, "source": [ - "## Code overview\n", + "## Solution overview\n", + "\n", + "To create the end to end workflow, we do the following:\n", + "\n", + "1. Create an Amazon Athena client within the us-east-1 AWS Region.\n", + "2. Define the run_athena_query function that runs queries and prints out the status in the following cell.\n", + "3. Create the `ride_fare` table within your database using all ride fare tables for the year 2019.\n", + "4. Create the `ride_info` table using ride info table for the year 2019.\n", + "5. Create the `ride_info_deduped` and `ride_fare_deduped` tables that have all duplicate values removed from the original tables.\n", + "6. Run test queries to get the first ten rows of each table to see whether they have data.\n", + "7. Define the `get_query_results` function that takes the query ID and returns comma separated values that can be stored as a dataframe.\n", + "8. View the results of the test queries within pandas dataframes.\n", + "9. Join the `ride_info_deduped` and `ride_fare_deduped` tables into the `combined_ride_data_deduped` table.\n", + "10. Select all values in the combined table.\n", + "11. Define the `get_csv_file_location` function to get the Amazon S3 location of the query results.\n", + "12. Download the CSV file to our environment.\n", + "13. Perform Exploratory Data Analysis (EDA) on the data.\n", + "14. Use the results of the EDA to select the relevant features in query.\n", + "15. Use the `get_csv_file_location` function to get the location of those query results.\n", + "16. Split the data into training, validation, and test sets using a processing job.\n", + "17. Download the test dataset.\n", + "18. Take a 20 row sample from the test dataset.\n", + "20. Create a dataframe with 20 rows of actual and predicted values.\n", + "21. Calculate the RMSE of the data.\n", + "22. Clean up the resources created within the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "54d7468c-c77b-4273-b02d-9e9c4e884d46", + "metadata": {}, + "source": [ + "### Define the run_athena_query function\n", + "\n", + "In the following cell, we define the `run_athena_query` function. It runs an Athena query and waits for its completion.\n", "\n", - "The following code uses the boto3 client to set up an Athena client within the us-east-1 AWS Region. It defines the run_athena_query function that runs queries and checks their status. Afterwards, it uses the function to create the ride fare and ride info table in Amazon Athena using all the CSV files from the year 2019.\n", + "It takes the following arguments:\n", "\n", - "The code creates a separate ride fare and ride info table with all of the duplicate values removed. Amazon Athena saves the query results of the select statements as a CSV string. The get_query_results functions saves the CSV string as a CSV file. We use the function to read the results of our test queries into the notebook as pandas dataframes and verify that we're able to get our data successfully. \n", + "- query_string (str): The SQL query to be executed.\n", + "- database_name (str): The name of the Athena database.\n", + "- output_location (str): The S3 location where the query results are stored.\n", "\n", - "We join the deduplicated tables into a single dataset that we use for our exploratory data analysis. We perform our exploratory data analysis and run a query to select the final set of features we're using for the analysis. We run the SageMaker processing job using the processing-file.py file. Afterwards, we define our model, train our model, and evaluate it on a test set of 20 samples." + "\n", + "It returns the query execution ID string." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "8ab1ff0e-fcde-4976-a1cd-51e75c18deb2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: cb779f49-17e5-49fd-91f9-0fbbf62cb9bb\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'cb779f49-17e5-49fd-91f9-0fbbf62cb9bb'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Import required libraries\n", "import time\n", "import boto3\n", "\n", "def run_athena_query(query_string, database_name, output_location):\n", - " \"\"\"\n", - " Function to execute an Athena query and wait for its completion.\n", - "\n", - " Args:\n", - " query_string (str): The SQL query to be executed.\n", - " database_name (str): The name of the Athena database.\n", - " output_location (str): The S3 location where the query results will be stored.\n", - "\n", - " Returns:\n", - " str: The query execution ID.\n", - " \"\"\"\n", " # Create an Athena client\n", " athena_client = boto3.client('athena', region_name='us-east-1')\n", "\n", @@ -104,8 +113,46 @@ " print(f\"Query is currently in {state} state. Waiting for completion...\")\n", " time.sleep(5) # Wait for 5 seconds before checking again\n", "\n", - " return query_execution_id\n", + " return query_execution_id\n" + ] + }, + { + "cell_type": "markdown", + "id": "8df0da48-89b3-45c2-a479-af422a51b962", + "metadata": {}, + "source": [ + "### Create the ride_fare table\n", "\n", + "We've provided you with the query. You most provide the name of the database you created within Amazon Athena and the Amazon S3 output location. If you're not sure about how to specify the output location, provide the name of the S3 bucket. After running the query, you should get a message that says \"Query executed successfully.\" and a 36 character string in single quotes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "64131b68-de28-4060-bb75-8148902846f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution ID: cb929408-df15-408d-a776-a8963facbf80\n", + "Query is currently in QUEUED state. Waiting for completion...\n", + "Query executed successfully.\n" + ] + }, + { + "data": { + "text/plain": [ + "'cb929408-df15-408d-a776-a8963facbf80'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# SQL query to create the 'ride_fare' table\n", "create_ride_fare_table = \"\"\"\n", "CREATE EXTERNAL TABLE `ride_fare` (\n", @@ -134,7 +181,7 @@ "\"\"\"\n", "\n", "# Athena database name\n", - "database = 'database_name'\n", + "database = 'example-database-name'\n", "\n", "# S3 location for query results\n", "s3_output_location = 's3://example-s3-bucket/example-s3-prefix'\n", @@ -143,9 +190,17 @@ "run_athena_query(create_ride_fare_table, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "ebe5920a-4c36-48c0-9cb4-e418c738aa59", + "metadata": {}, + "source": [ + "### Create the ride fare table with the duplicates removed" + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "3d249cc5-2d53-4274-8f5e-6ab09ccd3ea6", "metadata": {}, "outputs": [ @@ -153,7 +208,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: e07f4538-b44b-4dc8-923d-6758a4e99913\n", + "Query execution ID: 15337c2c-54e5-4e19-94a8-92d2faef2efd\n", "Query is currently in QUEUED state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", @@ -164,10 +219,10 @@ { "data": { "text/plain": [ - "'e07f4538-b44b-4dc8-923d-6758a4e99913'" + "'15337c2c-54e5-4e19-94a8-92d2faef2efd'" ] }, - "execution_count": 12, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -185,9 +240,17 @@ "run_athena_query(remove_duplicates_from_ride_fare, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "2ac7fc34-37cb-4c46-993b-38f18576361c", + "metadata": {}, + "source": [ + "### Create the ride_info table" + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "id": "2f9a68b9-bd11-49e9-ad72-b44b43d32e47", "metadata": {}, "outputs": [ @@ -195,7 +258,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: d8128d6d-c3d7-4c44-99ed-b533f69c3cfa\n", + "Query execution ID: bc365d36-bbbb-4f33-a153-3192127a1069\n", "Query is currently in QUEUED state. Waiting for completion...\n", "Query executed successfully.\n" ] @@ -203,10 +266,10 @@ { "data": { "text/plain": [ - "'d8128d6d-c3d7-4c44-99ed-b533f69c3cfa'" + "'bc365d36-bbbb-4f33-a153-3192127a1069'" ] }, - "execution_count": 14, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -243,9 +306,17 @@ "run_athena_query(create_ride_info_table_query, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "4c17ea01-2c1e-4c10-a539-0d00e6e4bb1d", + "metadata": {}, + "source": [ + "### Create the ride info table with the duplicates removed" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "263d883c-f189-43c0-9fbd-1a45093984e9", "metadata": {}, "outputs": [ @@ -253,7 +324,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: 9f4f8ff3-3c76-4ff4-a848-3d834e848cf7\n", + "Query execution ID: 1946c89d-d1c3-449d-b7af-42521778c51c\n", "Query is currently in QUEUED state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", @@ -264,10 +335,10 @@ { "data": { "text/plain": [ - "'9f4f8ff3-3c76-4ff4-a848-3d834e848cf7'" + "'1946c89d-d1c3-449d-b7af-42521778c51c'" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -285,9 +356,17 @@ "run_athena_query(remove_duplicates_from_ride_info, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "a19f8e17-42c5-4412-96a8-b7bc1a74c73c", + "metadata": {}, + "source": [ + "### Run a test query on ride_info_deduped" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "id": "6db6bb67-44a9-4ff4-b662-ad969a84d3d8", "metadata": {}, "outputs": [ @@ -295,7 +374,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: 0f66a43e-cde0-4361-a050-09a2616ffefa\n", + "Query execution ID: ab1e6968-e04c-47c0-94c7-03868d1d7fc1\n", "Query is currently in QUEUED state. Waiting for completion...\n", "Query executed successfully.\n" ] @@ -303,10 +382,10 @@ { "data": { "text/plain": [ - "'0f66a43e-cde0-4361-a050-09a2616ffefa'" + "'ab1e6968-e04c-47c0-94c7-03868d1d7fc1'" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -319,9 +398,17 @@ "run_athena_query(test_ride_info_query, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "b969d31f-e14a-473b-aefa-a1a19bc312f7", + "metadata": {}, + "source": [ + "### Run a test query on ride_fare_deduped" + ] + }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "id": "92d8be21-3f20-453d-8b84-516571d9854d", "metadata": {}, "outputs": [ @@ -329,7 +416,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: 1d0f08ba-c579-4ff1-8188-4a7b87043d07\n", + "Query execution ID: caeedc97-8f55-4759-9380-8ced39fab414\n", "Query is currently in QUEUED state. Waiting for completion...\n", "Query executed successfully.\n" ] @@ -337,10 +424,10 @@ { "data": { "text/plain": [ - "'1d0f08ba-c579-4ff1-8188-4a7b87043d07'" + "'caeedc97-8f55-4759-9380-8ced39fab414'" ] }, - "execution_count": 21, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -353,30 +440,25 @@ "run_athena_query(test_ride_fare_query, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "c86acade-c4b9-4918-860e-11ee5e386a44", + "metadata": {}, + "source": [ + "### Define the `get_query_results` function\n", + "\n", + "In the following cell, we define the `get_query_results` function to get the query results in CSV format. The function gets the 36 character query execution ID string. The end of the output of the preceding cell is an example of a query execution ID string." + ] + }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "50e87ba6-42e9-4d99-862e-7eae16ad810e", "metadata": {}, "outputs": [], "source": [ "import io\n", "def get_query_results(query_execution_id):\n", - " \"\"\"\n", - "\n", - " Function to retrieve the results of an Athena query execution.\n", - "\n", - "\n", - " Args:\n", - "\n", - " query_execution_id (str): The ID of the query execution.\n", - "\n", - "\n", - " Returns:\n", - "\n", - " io.StringIO: A file-like object containing the query results in CSV format.\n", - "\n", - " \"\"\"\n", " athena_client = boto3.client('athena', region_name='us-east-1')\n", " s3 = boto3.client('s3')\n", "\n", @@ -395,9 +477,19 @@ " return csv_buffer" ] }, + { + "cell_type": "markdown", + "id": "d3d2ed4f-d7e6-49dc-9ea1-0dc66f252c76", + "metadata": {}, + "source": [ + "### Read `ride_info_deduped` test query into a dataframe\n", + "\n", + "Specify the query execution ID string in the `get_query_results` function. The output is the head of the dataframe. " + ] + }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "id": "b04abae5-936b-4d96-98e8-d2e2b6a17b9c", "metadata": {}, "outputs": [ @@ -423,92 +515,92 @@ " \n", " \n", " ride_id\n", - " vendor_id\n", - " passenger_count\n", - " pickup_at\n", - " dropoff_at\n", - " trip_distance\n", - " rate_code_id\n", - " store_and_fwd_flag\n", + " payment_type\n", + " fare_amount\n", + " extra\n", + " mta_tax\n", + " tip_amount\n", + " tolls_amount\n", + " total_amount\n", " \n", " \n", " \n", " \n", " 0\n", - " 1005024574809\n", + " 2834679627591\n", " 1\n", - " 1\n", - " 2019-05-15T12:11:17.000Z\n", - " 2019-05-15T12:48:59.000Z\n", - " 3.40\n", - " 1\n", - " N\n", + " 52.0\n", + " 0.0\n", + " 0.5\n", + " 12.28\n", + " 6.12\n", + " 73.70\n", " \n", " \n", " 1\n", - " 944895157463\n", - " 2\n", - " 2\n", - " 2019-06-18T22:11:43.000Z\n", - " 2019-06-18T22:29:46.000Z\n", - " 1.92\n", + " 1400160739953\n", " 1\n", - " N\n", + " 52.0\n", + " 2.5\n", + " 0.5\n", + " 11.05\n", + " 0.00\n", + " 66.35\n", " \n", " \n", " 2\n", - " 944895157471\n", - " 1\n", + " 2834679627600\n", " 2\n", - " 2019-06-18T22:29:47.000Z\n", - " 2019-06-18T22:37:08.000Z\n", - " 1.00\n", - " 1\n", - " N\n", + " 7.0\n", + " 0.0\n", + " 0.5\n", + " 0.00\n", + " 0.00\n", + " 7.80\n", " \n", " \n", " 3\n", - " 1005024574929\n", - " 2\n", + " 1331440950394\n", " 1\n", - " 2019-05-15T12:26:17.000Z\n", - " 2019-05-15T12:33:01.000Z\n", - " 0.95\n", - " 1\n", - " N\n", + " 4.0\n", + " 1.0\n", + " 0.5\n", + " 1.66\n", + " 0.00\n", + " 9.96\n", " \n", " \n", " 4\n", - " 1005024574951\n", - " 2\n", - " 2\n", - " 2019-05-15T12:51:35.000Z\n", - " 2019-05-15T13:30:12.000Z\n", - " 2.65\n", + " 2834679627624\n", " 1\n", - " N\n", + " 4.5\n", + " 0.0\n", + " 0.5\n", + " 1.06\n", + " 0.00\n", + " 6.36\n", " \n", " \n", "\n", "" ], "text/plain": [ - " ride_id vendor_id passenger_count pickup_at \\\n", - "0 1005024574809 1 1 2019-05-15T12:11:17.000Z \n", - "1 944895157463 2 2 2019-06-18T22:11:43.000Z \n", - "2 944895157471 1 2 2019-06-18T22:29:47.000Z \n", - "3 1005024574929 2 1 2019-05-15T12:26:17.000Z \n", - "4 1005024574951 2 2 2019-05-15T12:51:35.000Z \n", + " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", + "0 2834679627591 1 52.0 0.0 0.5 12.28 \n", + "1 1400160739953 1 52.0 2.5 0.5 11.05 \n", + "2 2834679627600 2 7.0 0.0 0.5 0.00 \n", + "3 1331440950394 1 4.0 1.0 0.5 1.66 \n", + "4 2834679627624 1 4.5 0.0 0.5 1.06 \n", "\n", - " dropoff_at trip_distance rate_code_id store_and_fwd_flag \n", - "0 2019-05-15T12:48:59.000Z 3.40 1 N \n", - "1 2019-06-18T22:29:46.000Z 1.92 1 N \n", - "2 2019-06-18T22:37:08.000Z 1.00 1 N \n", - "3 2019-05-15T12:33:01.000Z 0.95 1 N \n", - "4 2019-05-15T13:30:12.000Z 2.65 1 N " + " tolls_amount total_amount \n", + "0 6.12 73.70 \n", + "1 0.00 66.35 \n", + "2 0.00 7.80 \n", + "3 0.00 9.96 \n", + "4 0.00 6.36 " ] }, - "execution_count": 19, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -516,16 +608,26 @@ "source": [ "import pandas as pd\n", "# Provide the query execution id of the test_ride_info query to get the query results\n", - "ride_info_sample_1 = get_query_results('0f66a43e-cde0-4361-a050-09a2616ffefa')\n", + "ride_info_sample = get_query_results('test_ride_info_query_execution_id')\n", "\n", - "df_ride_info_sample_1 = pd.read_csv(ride_info_sample_1)\n", + "df_ride_info_sample = pd.read_csv(ride_info_sample)\n", "\n", - "df_ride_info_sample_1.head()" + "df_ride_info_sample.head()" + ] + }, + { + "cell_type": "markdown", + "id": "6d10ebe2-8c17-4f2b-97fe-a5f339cd89d7", + "metadata": {}, + "source": [ + "### Read `ride_fare_deduped` test query into a dataframe\n", + "\n", + "Specify the query execution ID string in the `get_query_results` function. The output is the head of the resulting dataframe. " ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 13, "id": "be89957f-31b1-4710-bfc2-178d6db18592", "metadata": {}, "outputs": [ @@ -563,58 +665,58 @@ " \n", " \n", " 0\n", - " 60130304733\n", + " 2834679627591\n", " 1\n", - " 5.0\n", - " 2.5\n", + " 52.0\n", + " 0.0\n", " 0.5\n", - " 2.05\n", - " 0.00\n", - " 10.35\n", + " 12.28\n", + " 6.12\n", + " 73.70\n", " \n", " \n", " 1\n", - " 1391571259067\n", + " 1400160739953\n", " 1\n", - " 16.5\n", - " 1.0\n", + " 52.0\n", + " 2.5\n", " 0.5\n", - " 4.16\n", + " 11.05\n", " 0.00\n", - " 24.96\n", + " 66.35\n", " \n", " \n", " 2\n", - " 1391571259101\n", + " 2834679627600\n", " 2\n", - " 8.0\n", - " 1.0\n", + " 7.0\n", + " 0.0\n", " 0.5\n", " 0.00\n", " 0.00\n", - " 12.30\n", + " 7.80\n", " \n", " \n", " 3\n", - " 60130304799\n", + " 1331440950394\n", " 1\n", - " 6.5\n", - " 0.0\n", + " 4.0\n", + " 1.0\n", " 0.5\n", - " 1.96\n", + " 1.66\n", " 0.00\n", - " 11.76\n", + " 9.96\n", " \n", " \n", " 4\n", - " 60130304800\n", + " 2834679627624\n", " 1\n", - " 39.5\n", - " 3.5\n", + " 4.5\n", + " 0.0\n", " 0.5\n", - " 9.90\n", - " 5.76\n", - " 59.46\n", + " 1.06\n", + " 0.00\n", + " 6.36\n", " \n", " \n", "\n", @@ -622,21 +724,21 @@ ], "text/plain": [ " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", - "0 60130304733 1 5.0 2.5 0.5 2.05 \n", - "1 1391571259067 1 16.5 1.0 0.5 4.16 \n", - "2 1391571259101 2 8.0 1.0 0.5 0.00 \n", - "3 60130304799 1 6.5 0.0 0.5 1.96 \n", - "4 60130304800 1 39.5 3.5 0.5 9.90 \n", + "0 2834679627591 1 52.0 0.0 0.5 12.28 \n", + "1 1400160739953 1 52.0 2.5 0.5 11.05 \n", + "2 2834679627600 2 7.0 0.0 0.5 0.00 \n", + "3 1331440950394 1 4.0 1.0 0.5 1.66 \n", + "4 2834679627624 1 4.5 0.0 0.5 1.06 \n", "\n", " tolls_amount total_amount \n", - "0 0.00 10.35 \n", - "1 0.00 24.96 \n", - "2 0.00 12.30 \n", - "3 0.00 11.76 \n", - "4 5.76 59.46 " + "0 6.12 73.70 \n", + "1 0.00 66.35 \n", + "2 0.00 7.80 \n", + "3 0.00 9.96 \n", + "4 0.00 6.36 " ] }, - "execution_count": 22, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -644,16 +746,24 @@ "source": [ "# Provide the query execution id of the test_ride_fare query to get the query results\n", "\n", - "ride_fare_sample_1 = get_query_results('1d0f08ba-c579-4ff1-8188-4a7b87043d07')\n", + "ride_fare_sample = get_query_results('test_ride_fare_query_execution_id')\n", "\n", - "df_ride_fare_sample_1 = pd.read_csv(ride_fare_sample_1)\n", + "df_ride_fare_sample = pd.read_csv(ride_fare_sample)\n", "\n", - "df_ride_fare_sample_1.head()" + "df_ride_fare_sample.head()" + ] + }, + { + "cell_type": "markdown", + "id": "3867e94a-7c89-48ed-86aa-92b09d47740d", + "metadata": {}, + "source": [ + "### Join the deduplicated tables together" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "id": "b8a76635-3c09-4cbc-b1b4-9318dc611250", "metadata": { "scrolled": true @@ -663,7 +773,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: 2ba0fd2b-030f-4e32-8acb-ec0d802b994f\n", + "Query execution ID: 8eb61f36-2e1b-43c7-9b33-61e7ce5d21bc\n", "Query is currently in QUEUED state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", @@ -682,10 +792,10 @@ { "data": { "text/plain": [ - "'2ba0fd2b-030f-4e32-8acb-ec0d802b994f'" + "'8eb61f36-2e1b-43c7-9b33-61e7ce5d21bc'" ] }, - "execution_count": 23, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -723,9 +833,17 @@ "run_athena_query(create_ride_joined_deduped, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "b2f9f6ca-f668-42ab-ac4a-371a82e1786d", + "metadata": {}, + "source": [ + "### Select all values from the deduplicated table" + ] + }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 15, "id": "b0791e57-4351-4f27-a8f9-ad741441d214", "metadata": {}, "outputs": [ @@ -733,7 +851,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: 08185c50-d51f-4a5f-b82f-e9de593c6b9b\n", + "Query execution ID: f303cff8-5369-409a-9c51-8c791d446fe3\n", "Query is currently in QUEUED state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", @@ -778,10 +896,10 @@ { "data": { "text/plain": [ - "'08185c50-d51f-4a5f-b82f-e9de593c6b9b'" + "'f303cff8-5369-409a-9c51-8c791d446fe3'" ] }, - "execution_count": 25, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -796,19 +914,29 @@ "run_athena_query(ride_combined_full_table_query, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "4492eaa8-b0cc-4a4d-9810-e9f1a39f21c7", + "metadata": {}, + "source": [ + "### Define get_csv_file_location function and get Amazon S3 location of query results\n", + "\n", + "Specify the query ID from the preceding cell in the function call. The output is the Amazon S3 URI of the dataset. " + ] + }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 16, "id": "97373c52-882b-4e44-8d75-a80d8d8c58df", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'s3://parsa-ux360-burner-account-bucket/08185c50-d51f-4a5f-b82f-e9de593c6b9b.csv'" + "'s3://ux360-nyc-taxi-dogfooding/f303cff8-5369-409a-9c51-8c791d446fe3.csv'" ] }, - "execution_count": 47, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -823,12 +951,22 @@ " return s3_location\n", "\n", "# Provide the 36 character string at the end of the output of the preceding cell as the query.\n", - "get_csv_file_location('query-id-from-preceding-cell')" + "get_csv_file_location('ride_combined_full_table_query_execution_id')" + ] + }, + { + "cell_type": "markdown", + "id": "c7bf4f25-dc86-4f1f-95de-967c20c5a7af", + "metadata": {}, + "source": [ + "### Download the dataset and rename it\n", + "\n", + "Replace the example S3 path in the following cell with the output of the preceding cell. The second command renames the CSV file it downloads to `nyc-taxi-whole-dataset.csv`." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 17, "id": "954022d5-bdf9-4dbd-be2e-66d0009ce522", "metadata": {}, "outputs": [ @@ -836,19 +974,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "download: s3://parsa-ux360-burner-account-bucket/08185c50-d51f-4a5f-b82f-e9de593c6b9b.csv to ./08185c50-d51f-4a5f-b82f-e9de593c6b9b.csv\n" + "download: s3://ux360-nyc-taxi-dogfooding/f303cff8-5369-409a-9c51-8c791d446fe3.csv to ./f303cff8-5369-409a-9c51-8c791d446fe3.csv\n", + "mv: cannot stat 'query-id.csv': No such file or directory\n" ] } ], "source": [ "# Use the S3 URI location returned from the preceding cell to download the dataset and rename it.\n", - "!aws s3 cp s3://example-s3-bucket/query-id.csv .\n", - "!mv query-id.csv nyc-taxi-whole-dataset.csv" + "!aws s3 cp s3://example-s3-bucket/ride_combined_full_table_query_execution_id.csv .\n", + "!mv ride_combined_full_table_query_execution_id.csv nyc-taxi-whole-dataset.csv" + ] + }, + { + "cell_type": "markdown", + "id": "4d34ca22-8417-46f5-982f-dd22816f1d93", + "metadata": {}, + "source": [ + "### Get a 20,000 row sample and some information about it" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "id": "79d2f2a5-5111-4fb8-90f3-67474f1072c1", "metadata": {}, "outputs": [], @@ -858,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 21, "id": "f9dececa-272d-458c-9f64-baa13eca0832", "metadata": {}, "outputs": [ @@ -876,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 22, "id": "1c117a0f-429e-4913-aded-c839675f9e17", "metadata": {}, "outputs": [ @@ -921,91 +1068,91 @@ " \n", " \n", " 0\n", - " 3839702413301\n", + " 60131839014\n", " 1\n", - " 29.5\n", - " 2.5\n", + " 7.5\n", + " 0.0\n", " 0.5\n", - " 7.75\n", - " 6.12\n", - " 46.67\n", - " 1\n", + " 1.66\n", + " 0.0\n", + " 9.96\n", + " 2\n", " 1\n", - " 2019-04-19T13:23:59.000Z\n", - " 2019-04-19T13:45:15.000Z\n", - " 10.10\n", + " 2019-01-04T07:53:41.000Z\n", + " 2019-01-04T08:02:20.000Z\n", + " 1.45\n", " 1\n", " N\n", " \n", " \n", " 1\n", - " 51541365988\n", - " 2\n", - " 4.0\n", - " 1.0\n", + " 60131839074\n", + " 1\n", + " 8.0\n", + " 0.0\n", " 0.5\n", - " 0.00\n", - " 0.00\n", - " 8.30\n", + " 1.00\n", + " 0.0\n", + " 9.80\n", " 2\n", " 2\n", - " 2019-02-25T17:01:30.000Z\n", - " 2019-02-25T17:03:53.000Z\n", - " 0.49\n", + " 2019-01-04T07:05:28.000Z\n", + " 2019-01-04T07:13:12.000Z\n", + " 1.91\n", " 1\n", " N\n", " \n", " \n", " 2\n", - " 3770983743101\n", - " 2\n", - " 7.0\n", - " 0.5\n", + " 1391571568740\n", + " 1\n", + " 8.5\n", + " 0.0\n", " 0.5\n", - " 0.00\n", - " 0.00\n", - " 8.30\n", + " 2.36\n", + " 0.0\n", + " 14.16\n", " 2\n", - " 1\n", - " 2019-03-30T20:43:40.000Z\n", - " 2019-03-30T20:52:18.000Z\n", - " 1.15\n", + " 2\n", + " 2019-02-05T10:59:56.000Z\n", + " 2019-02-05T11:10:40.000Z\n", + " 1.53\n", " 1\n", " N\n", " \n", " \n", " 3\n", - " 3770983743148\n", - " 2\n", - " 6.0\n", - " 0.5\n", + " 60131839130\n", + " 1\n", + " 8.0\n", + " 0.0\n", " 0.5\n", - " 0.00\n", - " 0.00\n", - " 7.30\n", + " 1.76\n", + " 0.0\n", + " 10.56\n", " 2\n", " 1\n", - " 2019-03-30T20:15:08.000Z\n", - " 2019-03-30T20:19:32.000Z\n", - " 1.12\n", + " 2019-01-04T07:12:07.000Z\n", + " 2019-01-04T07:20:07.000Z\n", + " 1.68\n", " 1\n", " N\n", " \n", " \n", " 4\n", - " 3839702413585\n", + " 1391571568912\n", " 1\n", - " 14.5\n", - " 2.5\n", + " 5.0\n", + " 0.0\n", " 0.5\n", - " 3.55\n", - " 0.00\n", - " 21.35\n", - " 1\n", + " 1.66\n", + " 0.0\n", + " 9.96\n", + " 2\n", " 1\n", - " 2019-04-19T13:10:55.000Z\n", - " 2019-04-19T13:32:34.000Z\n", - " 1.90\n", + " 2019-02-05T11:14:36.000Z\n", + " 2019-02-05T11:19:52.000Z\n", + " 0.65\n", " 1\n", " N\n", " \n", @@ -1015,25 +1162,25 @@ ], "text/plain": [ " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", - "0 3839702413301 1 29.5 2.5 0.5 7.75 \n", - "1 51541365988 2 4.0 1.0 0.5 0.00 \n", - "2 3770983743101 2 7.0 0.5 0.5 0.00 \n", - "3 3770983743148 2 6.0 0.5 0.5 0.00 \n", - "4 3839702413585 1 14.5 2.5 0.5 3.55 \n", + "0 60131839014 1 7.5 0.0 0.5 1.66 \n", + "1 60131839074 1 8.0 0.0 0.5 1.00 \n", + "2 1391571568740 1 8.5 0.0 0.5 2.36 \n", + "3 60131839130 1 8.0 0.0 0.5 1.76 \n", + "4 1391571568912 1 5.0 0.0 0.5 1.66 \n", "\n", " tolls_amount total_amount vendor_id passenger_count \\\n", - "0 6.12 46.67 1 1 \n", - "1 0.00 8.30 2 2 \n", - "2 0.00 8.30 2 1 \n", - "3 0.00 7.30 2 1 \n", - "4 0.00 21.35 1 1 \n", + "0 0.0 9.96 2 1 \n", + "1 0.0 9.80 2 2 \n", + "2 0.0 14.16 2 2 \n", + "3 0.0 10.56 2 1 \n", + "4 0.0 9.96 2 1 \n", "\n", " pickup_at dropoff_at trip_distance \\\n", - "0 2019-04-19T13:23:59.000Z 2019-04-19T13:45:15.000Z 10.10 \n", - "1 2019-02-25T17:01:30.000Z 2019-02-25T17:03:53.000Z 0.49 \n", - "2 2019-03-30T20:43:40.000Z 2019-03-30T20:52:18.000Z 1.15 \n", - "3 2019-03-30T20:15:08.000Z 2019-03-30T20:19:32.000Z 1.12 \n", - "4 2019-04-19T13:10:55.000Z 2019-04-19T13:32:34.000Z 1.90 \n", + "0 2019-01-04T07:53:41.000Z 2019-01-04T08:02:20.000Z 1.45 \n", + "1 2019-01-04T07:05:28.000Z 2019-01-04T07:13:12.000Z 1.91 \n", + "2 2019-02-05T10:59:56.000Z 2019-02-05T11:10:40.000Z 1.53 \n", + "3 2019-01-04T07:12:07.000Z 2019-01-04T07:20:07.000Z 1.68 \n", + "4 2019-02-05T11:14:36.000Z 2019-02-05T11:19:52.000Z 0.65 \n", "\n", " rate_code_id store_and_fwd_flag \n", "0 1 N \n", @@ -1043,7 +1190,7 @@ "4 1 N " ] }, - "execution_count": 34, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1056,7 +1203,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 23, "id": "d3c56da9-0a1c-4c58-93e3-77260dfff40b", "metadata": {}, "outputs": [ @@ -1095,7 +1242,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 24, "id": "dc25bcd9-a4b1-4491-867f-7534336d1ecd", "metadata": {}, "outputs": [ @@ -1145,114 +1292,114 @@ " 20000.000000\n", " 20000.000000\n", " 20000.000000\n", - " 20000.000000\n", + " 20000.00000\n", " 20000.00000\n", " 20000.000000\n", " 20000.000000\n", " \n", " \n", " mean\n", - " 1.519688e+12\n", - " 1.286600\n", - " 12.963254\n", - " 1.080586\n", - " 0.495847\n", - " 2.146389\n", - " 0.362381\n", - " 18.521490\n", - " 1.622900\n", - " 1.56580\n", - " 2.945799\n", - " 1.055550\n", + " 1.818963e+12\n", + " 1.288700\n", + " 12.920155\n", + " 1.060540\n", + " 0.496025\n", + " 2.128392\n", + " 0.376976\n", + " 18.472139\n", + " 1.62440\n", + " 1.56845\n", + " 2.928530\n", + " 1.054400\n", " \n", " \n", " std\n", - " 1.068094e+12\n", - " 0.474312\n", - " 12.006646\n", - " 1.240546\n", - " 0.053405\n", - " 2.680182\n", - " 1.585315\n", - " 14.706571\n", - " 0.484672\n", - " 1.21846\n", - " 3.797848\n", - " 0.369014\n", + " 1.210592e+12\n", + " 0.476407\n", + " 11.890878\n", + " 1.230733\n", + " 0.050959\n", + " 2.601379\n", + " 1.639528\n", + " 14.664932\n", + " 0.48429\n", + " 1.21552\n", + " 3.841776\n", + " 0.363108\n", " \n", " \n", " min\n", " 5.153977e+10\n", " 1.000000\n", - " -52.000000\n", + " -74.500000\n", " -4.500000\n", " -0.500000\n", " 0.000000\n", " 0.000000\n", - " -57.300000\n", - " 1.000000\n", + " -76.300000\n", + " 1.00000\n", " 0.00000\n", " 0.000000\n", " 1.000000\n", " \n", " \n", " 25%\n", - " 9.534837e+11\n", + " 1.005022e+12\n", " 1.000000\n", " 6.500000\n", " 0.000000\n", " 0.500000\n", " 0.000000\n", " 0.000000\n", - " 10.800000\n", - " 1.000000\n", + " 10.790000\n", + " 1.00000\n", " 1.00000\n", - " 0.980000\n", + " 0.940000\n", " 1.000000\n", " \n", " \n", " 50%\n", - " 1.322852e+12\n", + " 1.400160e+12\n", " 1.000000\n", - " 9.500000\n", + " 9.000000\n", " 0.500000\n", " 0.500000\n", - " 1.835000\n", + " 1.795000\n", " 0.000000\n", " 14.160000\n", - " 2.000000\n", + " 2.00000\n", " 1.00000\n", - " 1.610000\n", + " 1.600000\n", " 1.000000\n", " \n", " \n", " 75%\n", - " 1.417341e+12\n", + " 2.834679e+12\n", " 2.000000\n", " 14.500000\n", " 2.500000\n", " 0.500000\n", " 2.860000\n", " 0.000000\n", - " 20.160000\n", - " 2.000000\n", + " 19.800000\n", + " 2.00000\n", " 2.00000\n", - " 3.050000\n", + " 3.000000\n", " 1.000000\n", " \n", " \n", " max\n", - " 3.839703e+12\n", + " 3.839702e+12\n", " 4.000000\n", - " 412.230000\n", + " 300.000000\n", " 7.000000\n", - " 1.440000\n", - " 61.500000\n", - " 26.000000\n", - " 412.530000\n", - " 2.000000\n", - " 8.00000\n", - " 44.500000\n", + " 0.500000\n", + " 52.160000\n", + " 30.500000\n", + " 312.960000\n", + " 2.00000\n", + " 6.00000\n", + " 70.890000\n", " 5.000000\n", " \n", " \n", @@ -1262,36 +1409,36 @@ "text/plain": [ " ride_id payment_type fare_amount extra mta_tax \\\n", "count 2.000000e+04 20000.000000 20000.000000 20000.000000 20000.000000 \n", - "mean 1.519688e+12 1.286600 12.963254 1.080586 0.495847 \n", - "std 1.068094e+12 0.474312 12.006646 1.240546 0.053405 \n", - "min 5.153977e+10 1.000000 -52.000000 -4.500000 -0.500000 \n", - "25% 9.534837e+11 1.000000 6.500000 0.000000 0.500000 \n", - "50% 1.322852e+12 1.000000 9.500000 0.500000 0.500000 \n", - "75% 1.417341e+12 2.000000 14.500000 2.500000 0.500000 \n", - "max 3.839703e+12 4.000000 412.230000 7.000000 1.440000 \n", + "mean 1.818963e+12 1.288700 12.920155 1.060540 0.496025 \n", + "std 1.210592e+12 0.476407 11.890878 1.230733 0.050959 \n", + "min 5.153977e+10 1.000000 -74.500000 -4.500000 -0.500000 \n", + "25% 1.005022e+12 1.000000 6.500000 0.000000 0.500000 \n", + "50% 1.400160e+12 1.000000 9.000000 0.500000 0.500000 \n", + "75% 2.834679e+12 2.000000 14.500000 2.500000 0.500000 \n", + "max 3.839702e+12 4.000000 300.000000 7.000000 0.500000 \n", "\n", - " tip_amount tolls_amount total_amount vendor_id \\\n", - "count 20000.000000 20000.000000 20000.000000 20000.000000 \n", - "mean 2.146389 0.362381 18.521490 1.622900 \n", - "std 2.680182 1.585315 14.706571 0.484672 \n", - "min 0.000000 0.000000 -57.300000 1.000000 \n", - "25% 0.000000 0.000000 10.800000 1.000000 \n", - "50% 1.835000 0.000000 14.160000 2.000000 \n", - "75% 2.860000 0.000000 20.160000 2.000000 \n", - "max 61.500000 26.000000 412.530000 2.000000 \n", + " tip_amount tolls_amount total_amount vendor_id passenger_count \\\n", + "count 20000.000000 20000.000000 20000.000000 20000.00000 20000.00000 \n", + "mean 2.128392 0.376976 18.472139 1.62440 1.56845 \n", + "std 2.601379 1.639528 14.664932 0.48429 1.21552 \n", + "min 0.000000 0.000000 -76.300000 1.00000 0.00000 \n", + "25% 0.000000 0.000000 10.790000 1.00000 1.00000 \n", + "50% 1.795000 0.000000 14.160000 2.00000 1.00000 \n", + "75% 2.860000 0.000000 19.800000 2.00000 2.00000 \n", + "max 52.160000 30.500000 312.960000 2.00000 6.00000 \n", "\n", - " passenger_count trip_distance rate_code_id \n", - "count 20000.00000 20000.000000 20000.000000 \n", - "mean 1.56580 2.945799 1.055550 \n", - "std 1.21846 3.797848 0.369014 \n", - "min 0.00000 0.000000 1.000000 \n", - "25% 1.00000 0.980000 1.000000 \n", - "50% 1.00000 1.610000 1.000000 \n", - "75% 2.00000 3.050000 1.000000 \n", - "max 8.00000 44.500000 5.000000 " + " trip_distance rate_code_id \n", + "count 20000.000000 20000.000000 \n", + "mean 2.928530 1.054400 \n", + "std 3.841776 0.363108 \n", + "min 0.000000 1.000000 \n", + "25% 0.940000 1.000000 \n", + "50% 1.600000 1.000000 \n", + "75% 3.000000 1.000000 \n", + "max 70.890000 5.000000 " ] }, - "execution_count": 36, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1302,7 +1449,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 25, "id": "18bd92b1-962a-40f2-b15f-7351d869f390", "metadata": {}, "outputs": [ @@ -1310,12 +1457,12 @@ "data": { "text/plain": [ "vendor_id\n", - "2 12458\n", - "1 7542\n", + "2 12488\n", + "1 7512\n", "Name: count, dtype: int64" ] }, - "execution_count": 37, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1326,7 +1473,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 26, "id": "e4c4997f-85d8-4f57-a60c-51e3568cfe2e", "metadata": {}, "outputs": [ @@ -1334,18 +1481,17 @@ "data": { "text/plain": [ "passenger_count\n", - "1 14091\n", - "2 2931\n", - "3 851\n", - "5 832\n", - "6 485\n", - "4 433\n", - "0 376\n", - "8 1\n", + "1 14030\n", + "2 3040\n", + "3 857\n", + "5 850\n", + "6 487\n", + "4 379\n", + "0 357\n", "Name: count, dtype: int64" ] }, - "execution_count": 38, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1354,9 +1500,17 @@ "df['passenger_count'].value_counts()" ] }, + { + "cell_type": "markdown", + "id": "ae527104-9312-498c-b0ee-d1e2303bf500", + "metadata": {}, + "source": [ + "### View the distribution of fare amount values" + ] + }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 27, "id": "641c278d-8fed-42b8-98d1-becba90d6259", "metadata": {}, "outputs": [ @@ -1366,13 +1520,13 @@ "" ] }, - "execution_count": 39, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1390,9 +1544,17 @@ "plt.show" ] }, + { + "cell_type": "markdown", + "id": "65d141c4-95ba-4176-8794-1475cb8f2a62", + "metadata": {}, + "source": [ + "### Make sure that all rows are unique" + ] + }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 28, "id": "9d484f57-f150-45b5-9cc5-cc10a6e8e9f1", "metadata": {}, "outputs": [ @@ -1402,7 +1564,7 @@ "20000" ] }, - "execution_count": 40, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1411,9 +1573,19 @@ "df['ride_id'].nunique()" ] }, + { + "cell_type": "markdown", + "id": "abc60782-4411-46e0-9d31-55adaa4dd1f5", + "metadata": {}, + "source": [ + "### Drop the store_and_fwd flag\n", + "\n", + "Determining its relevance isn't in scope for this tutorial." + ] + }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 29, "id": "f627790e-8aed-48e3-9c5d-52775bbb124d", "metadata": {}, "outputs": [], @@ -1421,9 +1593,19 @@ "df.drop('store_and_fwd_flag', axis=1, inplace=True)" ] }, + { + "cell_type": "markdown", + "id": "96fc51be-6a0f-44e6-abb8-2a6bf9188367", + "metadata": {}, + "source": [ + "### Drop the time series columns\n", + "\n", + "Analyzing the time series data also isn't in scope for this analysis." + ] + }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 30, "id": "c359f4db-b503-4d80-bb4c-55dc411f9b5e", "metadata": {}, "outputs": [], @@ -1434,19 +1616,17 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "05abe8af-bf44-471b-b130-19cee0dd822f", + "cell_type": "markdown", + "id": "ad5d1df6-d418-483a-b06d-848205f3f8ed", "metadata": {}, - "outputs": [], "source": [ - "!pip install seaborn" + "### Install seaborn and create scatterplots" ] }, { "cell_type": "code", - "execution_count": 43, - "id": "b6a10b9b-e916-48a9-88f5-ae94db2f6576", + "execution_count": 31, + "id": "05abe8af-bf44-471b-b130-19cee0dd822f", "metadata": {}, "outputs": [ { @@ -1470,14 +1650,25 @@ "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: seaborn\n", "Successfully installed seaborn-0.13.2\n" ] - }, + } + ], + "source": [ + "!pip install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b6a10b9b-e916-48a9-88f5-ae94db2f6576", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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mip91Rfn1mZgiU/3Opam8+tPN7f1Axof5O2/qXK/01ddtzLS5rptTPWnTJjeX+ilIm95c6sgccbxU/vK6V2FuoqOjkZycjClTphg6FIPL7mv76quvULZsWfj4+GDs2LF4/vy5oUMzmAYNGuDmzZvYtm0bhBD4559/8Mcff6Bt27aGDk0vzGFsuKljG6xwyGVcoybj6A1FTuMn5dAvL9dxmhkZGVixYgX69esHCwsLQ4eTS6NGjbB3715cvnwZAHD69GkcPHgQbdq0MXBkpArzYsGY6/0WTfE+lWZMecyRrpnyGCYyLnJs5xt6HrCc+k/kMkdeLnPd5TJfXU7zzrXpFzBE3WrSJ6DvOrUu8BFMzN27d5GZmYkyZcooLC9Tpgxu376tdJ/bt28r3f7169e4e/cu3N3d9RZvNm3idnd3x08//YTatWvj5cuXWL58OZo1a4b9+/ejcePGeo9ZU8ZQz9owpnoWQmD06NFo1KgRqlevnud2xlTX6sZsLPV89uxZBAcH48WLF3BycsKGDRvg5+endFtjqWdNYjaWel69ejVOnTqFEydOqLW9sdS1uTKH+jeWz4a25JgfNCW3fKIJOeYeTckxV2mKuY1UEUIgIiICgwYNQlBQEFJTUw0dUqFJTk7GggULEBUVZehQCkybtrupUDcXy5mm13I5u3r1KhYuXIjRo0fj008/xfHjxzF8+HDY2dkhPDzc0OGRAd2+fRulS5fOtbx06dL5Xudat26NLl26wNPTEykpKZg8eTKaNm2KuLg42NnZ4fbt27C1tUXx4sUV9ivo9VPbeN/24sULTJgwAT179kTRokWl5WFhYahYsSLc3Nxw7tw5TJw4EadPn8bu3bvVOq6++qnz2qageUgXOS4qKgrp6eno2rWrtKxq1apYunQp/P398fjxY3z77bdo2LAhTp8+jSpVqhRqvOq0NYy1ftPS0rB9+3b89ttvCsv1Vb/aMOT7VxcK8/2rLUO+h+k/5tQmyC6PsrJeu3bNECEViLI2jdzLmFefW/aAQbmWK1t+bTS5/9vVq1cPv/76K3x8fPDPP//giy++QIMGDXD+/HnZl01Ve1Pu5SNF5pQX85PfZ7pkyZKGDs8g+Fn/j6o+E1Njit+5tJFXf7q5vR/IODF/502d65W++rqNlbbXdXOop4K0yc2hfgrapjeHOjJXHC+VP2X3KsxNUlISJkyYgNjYWFhbc6rU1atXcfDgQdjb22PDhg24e/cuBg8ejPv372PJkiWGDs8gGjRogJUrV6Jbt2548eIFXr9+jQ4dOmDBggWGDk3nzGFsuDlgG0z/5DCuUZNx9IYkp/GTcumXl+s4zY0bN+Lhw4eIiIgwdChKffLJJ3j06BGqVq0KKysrZGZmYsaMGejRo4ehQyMVmBcLxhzvt2iK96nUZ+pjjnTNlMcwkfGRUzvfGOYBy6n/RA5z5OUy111O89XlNO9c01gNVbea9gnou055JyMPOZ8iL4TI98nyyrZXtlzfNInb19cXvr6+0uvg4GDcuHEDc+bMMdoHYhhLPWvCmOp56NChOHPmDA4ePKhyW2Opa3VjNpZ69vX1RUJCAh4+fIh169ahT58+iImJyTPRG0M9axKzMdTzjRs3MGLECOzatQv29vZq72cMdW3OTL3+jeGzURByzA+akls+0YQcc4+m5JarNMXcZt4iIyMxderUfLc5ceIEDh8+jMePH2PixImFFJnuqVvWoKAg6fXff/+NVq1aoUuXLvjwww/1HWKh0bTPwRRo8n1DjrS9lstVVlYWgoKC8OWXXwJ486sa58+fx8KFC416MBFpT91rOKD8u4iq61y3bt2k/69evTqCgoLg6emJrVu3onPnznnul9dx9R1vtlevXqF79+7IysrCDz/8oLBuwIAB0v9Xr14dVapUQVBQEE6dOoXAwECVx86mj35qfeYhbY+9atUqREZG4s8//1R4EF/9+vVRv3596XXDhg0RGBiIBQsWYP78+YUar7ptDWOs36VLl6JYsWLo1KmTwnJ916+mDP3+1Zah3r+aMob3MP3HnOrZVMqaX5tGrmXMq88tm1zLBajfRpNrGVu3bi39v7+/P4KDg1G5cmUsW7ZMyj1yLZu67U25lo+UM/d/z/w+06NHjzZgZIZn7u8NQPs+E7kyxe9c2sirHszt/UDGzZw+k5rSpm5Mtf50fV03pXrSR5vcVOpHn216U6kjc8fxUsrlda/CnGRmZqJnz56YOnUqfHx8DB2OUcjKyoKFhQVWrlwJFxcXAMA333yD//3vf/j+++/h4OBg4AgL34ULFzB8+HB8/vnnCA0NRVpaGsaNG4dBgwZh8eLFhg5Pp8xhbLg5YRtMf+QwrlHTuQKGILfxk3Lpl5frOM3FixejdevW8PDwMHQoSq1ZswYrVqzAb7/9hnfeeQcJCQkYOXIkPDw80KdPH0OHR2pgXiwY1l/eeJ9KfaY85kjXTH0MExknubTzjWEesJz6T+QwR14uc93lMl9dTvPOtYnVUHWrTZ+APuvUssBHMDGlSpWClZVVrid737lzJ9fT97K5ubkp3d7a2rrQnu6vTdzK1K9fH0lJSboOTyeMoZ51xRD1PGzYMGzatAn79u1DuXLl8t3WWOpak5iVMUQ929rawtvbG0FBQZg5cyZq1qyJb7/9Vum2xlLPmsSsTGHXc1xcHO7cuYPatWvD2toa1tbWiImJwfz582FtbY3MzMxc+xhLXZsrc61/Y86pb5NjftCUHPOJJuSYezQlt1ylKeY28zZ06FAkJibm+1e9enX89ddfOHr0KOzs7GBtbQ1vb28AQFBQkGxuMKtb1mx///033nvvPQQHB+Onn34yYOS6o6u2u9wUNBfLgTbXcjlzd3fP1ZldrVo1XL9+3UARkb6pew13c3PDP//8k2v/f//9V6PrnLu7Ozw9PaXvcG5ubsjIyMCDBw8Utsvr+lkY8b569Qpdu3ZFSkoKdu/ejaJFi+a7fWBgIGxsbNT+Xqqvfuq8tiloHipIjluzZg369++PtWvXonnz5vlua2lpiTp16hT4+72++tONsX6FEFiyZAl69+4NW1vbfLfVVf1qw5Dv34IwxPtXlwrrPUz/Mac2gZubGwCYRFnzatPIvYx59bnJvVyA6jZadjnkXMa3FSlSBP7+/khKSpL9v5+q9qbcy0eKzCkvauLtz7S54mc9bzn7TEyJqX7n0pQm/emm/H4g48X8nTd1rlea9nXLWUGu6+ZQTwVpk5t6/eiiTW/qdWTOOF5KOU3uVZiyJ0+e4OTJkxg6dKh0/Zg2bRpOnz4Na2tr/PXXX4YOsdC5u7ujbNmy0gPfgDd9bUII3Lx504CRGc7MmTPRsGFDjBs3DjVq1EBoaCh++OEHLFmyBGlpaYYOT2fMYWy4uWAbTL/kMq6xoOPoC4Pcx08aa7+8HMdpXrt2DXv27DHqH/oeN24cJkyYgO7du8Pf3x+9e/fGqFGjMHPmTEOHRiowLxaMud1v0RTvU2nGlMcc6Zq5jWEiw5N7O78w5wHLqf9ELnPk5TLXXS7z1eU071xX/QKGeBaAqj4BfdcpH/qWg62tLWrXro3du3crLN+9ezcaNGigdJ/g4OBc2+/atQtBQUGwsbHRW6xv0yZuZeLj4+Hu7q7r8HTCGOpZVwqznoUQGDp0KNavX4+//voLFStWVLmPoetam5iVMYb3sxACL1++VLrO0PWcl/xiVqaw67lZs2Y4e/YsEhISpL+goCCEhYUhISEBVlZWufYx1ro2F+Za/8ZwDcqPHPODpkwpn2hCjrlHU8aeqzTF3GbeSpUqhapVq+b7Z29vj/nz5+P06dPSe2Tbtm0A3gzcnDFjhoFLoR51ywoAt27dQpMmTRAYGIjo6GhYWppG94Wu2u5yoatcLAfaXMvlrGHDhrh06ZLCssuXL8PT09NAEZG+qXsNDw4OxqNHj3D8+HFp32PHjuHRo0caXefu3buHGzduSN/hateuDRsbG4XrZ1paGs6dO6f0uPqON/uBb0lJSdizZ49aHfXnz5/Hq1ev1P5eqq9+6ry2KWge0jbHrVq1ChEREfjtt9/Qtm1blecRQiAhIaHA3+/11Z9ubPULADExMbhy5Qr69++v8jy6ql9tGPL9qy1DvX91qbDew/Qfc2oTVKxYEW5ubgplzcjIQExMjGzKqqpNYwplfFt2n5splEtVG61SpUqyL+PbXr58icTERLi7u8v+309Ve1Pu5SNF5pQXNfH2Z9pc8bOet5x9JqbA3L5z5UWb/nRTfD+Q8WP+zps61ytN+7rlSBfXdXOop5w0aZObev3ook1v6nVkzjheKjdN71WYsqJFi+a6fgwaNAi+vr5ISEhAvXr1DB1ioWvYsCH+/vtvPH36VFp2+fJlWFpaGvXDjfTp2bNnucahZY/pEUIYIiSdMoex4eaGbTD9kPu4Rk3H0RcGuY+fNNZ+eTmO04yOjkbp0qWN+rtpXt8HsrKyDBQRqYt5sWDM5X6LpnifSjdMacyRrpnbGCYyPLm38wtjHrCc+k/kPkdeLnPdjXW+upzmneuqX8AQ71VVfQJ6r1NBuaxevVrY2NiIxYsXiwsXLoiRI0eKIkWKiNTUVCGEEBMmTBC9e/eWtr969apwdHQUo0aNEhcuXBCLFy8WNjY24o8//jDquOfOnSs2bNggLl++LM6dOycmTJggAIh169YVSrxPnjwR8fHxIj4+XgAQ33zzjYiPjxfXrl1TGq+x1LOmcRu6nj/++GPh4uIi9u/fL9LS0qS/Z8+eSdsYW11rE7Oh61kIISZOnCgOHDggUlJSxJkzZ8Snn34qLC0txa5du5TGbOh61iZmY6hnZUJCQsSIESOk18ZY16ZErtdvTcjtWq8NOeYHTck1n2hCjrlHU6aSqzTF3EaqpKSkCAAiPj7e0KHo3K1bt4S3t7do2rSpuHnzpsI13BSoarubEnVysSnLeS03JcePHxfW1tZixowZIikpSaxcuVI4OjqKFStWGDo0MgKtWrUSNWrUEEeOHBFHjhwR/v7+ol27dgrb+Pr6ivXr1wsh3rS/xowZIw4fPixSUlLEvn37RHBwsChbtqx4/PixtM+gQYNEuXLlxJ49e8SpU6dE06ZNRc2aNcXr168LNd5Xr16JDh06iHLlyomEhASF69vLly+FEEJcuXJFTJ06VZw4cUKkpKSIrVu3iqpVq4qAgACN4tVHP/WhQ4eElZWVmDVrlkhMTBSzZs0S1tbW4ujRo1rXobbx/vbbb8La2lp8//33CvX48OFDaZvIyEixY8cOkZycLOLj40Xfvn2FtbW1OHbsWKHHq05bw5jqN1uvXr1EvXr1lB5Tn/Wrj/4jfdavpvEa+v2rTcyGfg/Tf0ypTaDqfThr1izh4uIi1q9fL86ePSt69Ogh3N3dFXK8MVOnTSPXMqrqc5NrufKTs40m5zKOGTNG7N+/X1y9elUcPXpUtGvXTjg7O0vXETmXTZ32ppzLR7mZUl7UlqrPtKky9e8R6sqvHtTtM5E7U/7OpQlV9WAu7weSB3PO37rIX/rq6zYWurqum3I96aJNbsr1o4w2bXpzqyO5MoexqJrQx70KudO0TnKaMmWKqFmzZiFFq3+a1seTJ09EuXLlxP/+9z9x/vx5ERMTI6pUqSI+/PBDQxVB5zStk+joaGFtbS1++OEHkZycLA4ePCiCgoJE3bp1DVUEnTKHseHmSE5tMFWfSWMhp3GNqtoPxsyYx0/KpV9ebuM0MzMzRYUKFcQnn3xi6FDy1adPH1G2bFmxZcsWkZKSItavXy9KlSolxo8fb+jQSA1yyouGwPtvmuN9Ks2Z45gjXTOlMUykf3LqQ5XLvHw59Z/IaY68XOa6y32+upzmnauK1VB1q6pPoLDrlA99y8P3338vPD09ha2trQgMDBQxMTHSuj59+oiQkBCF7ffv3y8CAgKEra2t8PLyEgsXLizkiN/QJO7Zs2eLypUrC3t7e1G8eHHRqFEjsXXr1kKLdd++fQJArr8+ffoojVcI46hnTeM2dD0rixWAiI6OlrYxtrrWJmZD17MQQvTr10/6/Lm6uopmzZopdKQbWz1rE7Mx1LMyOZO+Mda1KZHr9VsTcrvWa0OO+UFTcs0nmpBj7tGUqeQqTTG3kSqm/NC36OjoPK/hpiK/trspUScXmzJjHrSkC5s3bxbVq1cXdnZ2omrVquKnn34ydEhkJO7duyfCwsKEs7OzcHZ2FmFhYeLBgwcK27x9LXj27Jlo2bKlcHV1FTY2NqJChQqiT58+4vr16wr7PH/+XAwdOlSUKFFCODg4iHbt2uXapjDizc7Byv727dsnhBDi+vXronHjxqJEiRLC1tZWVK5cWQwfPlzcu3dP4/j00U/9+++/C19fX2FjYyOqVq2q05simsQbEhKSb9tbCCFGjhwpKlSoILUHWrZsKQ4fPmyQeNVtaxhL/QohxMOHD4WDg0Oe12h91q+++o/0Vb+axmsM71999V/p8z1M/zGVNoGq92FWVpaYMmWKcHNzE3Z2dqJx48bi7Nmzhg1aA+q0aeRaRlV9bnItV35yttHkXMZu3boJd3d3YWNjIzw8PETnzp3F+fPnpfVyLpsQqtubci8f5WYqeVFbqj7TpsrUv0eoK796ULfPRO5M+TuXJlTVg7m8H0g+zDV/6yJ/6auv21jo6rpuyvWkiza5KdePMtq06c2tjuTKHMaiakIf9yrkTpv3yNtM7aFv2tRHYmKiaN68uXBwcBDlypUTo0ePNsqHGmlLmzqZP3++8PPzEw4ODsLd3V2EhYWJmzdvFn7weqDNuGkhTPvaairk0gZT9Zk0FnIa16iq/WDMjHn8pJz65eU0TnPnzp0CgLh06ZKhQ8nX48ePxYgRI0SFChWEvb29qFSpkvjss8+kH3wl4yeXvGgIvP+mOd6n0pw5jjnSNVMaw0T6J6c+VLnMy5dT/4k2sRqqXuUy113u89XlNO9cVayGqltVfQKFXacWQggBIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLSC0tDB0BEREREREREREREREREREREREREREREREREREREREREREREREREZMr40DciIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIj3iQ9+IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiPSID30jIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItIjPvSNiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEiP+NA3IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJIX7cNAAAtzElEQVQiIiIiIiI94kPfiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIj0iA99IyIivTlw4ADat28PDw8PWFhYYOPGjRrt/+LFC0RERMDf3x/W1tbo1KlTrm3Wr1+PFi1awNXVFUWLFkVwcDB27typmwIQEREREREREREREREREREREREREREREREREREREREREREREekAH/pGJDOqHpyVmpoKCwsLJCQk6OR8TZo0wciRI/PdxsvLC/PmzdPJ+ci0pKeno2bNmvjuu++02j8zMxMODg4YPnw4mjdvrnSbAwcOoEWLFti2bRvi4uLw3nvvoX379oiPjy9I6ERERERERERERERERAa1f/9+WFhY4OHDh4YOhYiIyOjkHM/CsStERETqi4iIUPoDrERERKS+wui/VWduCPuRiYiIiIiIcouMjEStWrX0eg7eryQiItIvXfd9qpOrVT3Ph0iX+NA3IplJS0tD69atC+1869evx/Tp0wvtfGRaWrdujS+++AKdO3dWuj4jIwPjx49H2bJlUaRIEdSrVw/79++X1hcpUgQLFy7EgAED4ObmpvQY8+bNw/jx41GnTh1UqVIFX375JapUqYLNmzfro0hEZEa0aQyq87BUIiIiY8QBoEREZIr0kd90/aMbREREb8vZv9igQQOkpaXBxcXFcEEZkaVLl6JYsWKGDoOIiPSA99h0h3VJRCRf2lzDed1XjQ+4IyIiOStfvjzS0tJQvXp1Q4dCRERk9jjxn4jIOKjbJzp27Fjs3btX/wG95cSJExg4cKBa2/IBcUREZEjmOhZTk1xNVBisDR0AEakvIyMjzwdf6UuJEiUK9XxkXvr27YvU1FSsXr0aHh4e2LBhA1q1aoWzZ8+iSpUqWh0zKysLT5484XuXiIiIiEgPMjIyYGtra+gwiIiIiIiIzIKtrW2h3xskIiIiIiIiIiIiosKhahyOlZUV+4iJiMikZWZmwsLCApaWloYOhYiITIQQApmZmXBycoKTk1OhntvV1bVQz0dERKQM5/7ljbmajA17Q4iMWJMmTTB06FCMHj0apUqVQosWLXL9KsTx48cREBAAe3t7BAUFIT4+PtdxLly4gDZt2sDJyQllypRB7969cffuXbVjePup53fu3EH79u3h4OCAihUrYuXKlQUtJpmp5ORkrFq1Cr///jveffddVK5cGWPHjkWjRo0QHR2t9XGjoqKQnp6Orl276jBaIspLdq4aOnQoihUrhpIlS2LSpEkQQgAAVqxYgaCgIDg7O8PNzQ09e/bEnTt3ALzpRPX29sacOXMUjnnu3DlYWloiOTkZwJtfRFq0aBHatWsHR0dHVKtWDUeOHMGVK1fQpEkTFClSBMHBwdL22TZv3ozatWvD3t4elSpVwtSpU/H69WtpvYWFBX755Re8//77cHR0RJUqVbBp0yYAQGpqKt577z0AQPHixWFhYYGIiIh86yIiIgIxMTH49ttvYWFhAQsLC6SkpKhdxoULF6J169ZSjv39998V9rl16xa6deuG4sWLo2TJkujYsSNSU1NV/RMREZEZEULgq6++QqVKleDg4ICaNWvijz/+gBACzZs3R6tWraQc/fDhQ1SoUAGfffZZvnlPWbsUAL755hv4+/ujSJEiKF++PAYPHoynT58apNxERGQ+mjRpgmHDhmHkyJEoXrw4ypQpg59++gnp6eno27cvnJ2dUblyZWzfvj3f/LZjxw40atRIase2a9cuV5syLxUrVgQABAQEwMLCAk2aNAHw5lefWrRogVKlSsHFxQUhISE4deqUtN/+/ftha2uL2NhYaVlUVBRKlSqFtLQ0HdQOERHJnbL+xaVLl8LCwgIPHz4E8N+vK27cuBE+Pj6wt7dHixYtcOPGDbXOkZycjI4dO6JMmTJwcnJCnTp1sGfPHoVtvLy88MUXXyA8PBxOTk7w9PTEn3/+iX///RcdO3aEk5MT/P39cfLkSYX91q1bh3feeQd2dnbw8vJCVFSUwvqc9zgBoFixYli6dCmAN32yFhYWWL9+Pd577z04OjqiZs2aOHLkCIA3ubRv37549OiRVD+RkZHqVS4RERk1ZTkwNTUVMTExqFu3Luzs7ODu7o4JEyYo3OtTJTIyEhUqVICdnR08PDwwfPhwtfbL7/4m8CYnWVhYYOfOnQgICICDgwOaNm2KO3fuYPv27ahWrRqKFi2KHj164NmzZ9J+L1++xPDhw1G6dGnY29ujUaNGOHHihLRe2a8ob9y4ERYWFgplqlWrFpYvXw4vLy+4uLige/fuePLkSb51SURExk+bfJjXPpmZmejfvz8qVqwIBwcH+Pr64ttvv9U6NlX9qdntubVr1+Ldd9+Fg4MD6tSpg8uXL+PEiRMICgqCk5MTWrVqhX///VfaLysrC9OmTUO5cuVgZ2eHWrVqYceOHdL67Jyb3SYGgISEBIX8lp0/d+7ciWrVqknnye5zjYyMxLJly/Dnn39KdbR//36t64KIiIzTokWLULZsWWRlZSks79ChA/r06QOgYONJs23btg0+Pj5wcHDAe++9p7S9paqfNLv/NSIiAi4uLhgwYEC+ZcvOswkJCRrFQURExqkgcy8A4MGDBwgLC4OrqyscHBxQpUoVaf5VRkYGhg4dCnd3d9jb28PLywszZ86U9n306BEGDhyI0qVLo2jRomjatClOnz4trVfV9wgAT548QVhYGIoUKQJ3d3fMnTs317zDjIwMjB8/HmXLlkWRIkVQr149hXZYdjtuy5Yt8PPzg52dHa5du6ay7pYsWSLlWHd3dwwdOlRad/36dek+ZtGiRdG1a1f8888/0vqIiAh06tRJ4XgjR46Uxvxk/9sMHz4c48ePR4kSJeDm5qZwL9LLywsA8P7778PCwkJ6TUREhSu/sTU7d+5EUFAQ7OzsEBsbK+W2t/ft1KkTpk6dKuXDjz76CBkZGWqdOz09XRpL4+7unqu9B7zJF/PmzZNe53W/skmTJrh27RpGjRollQMA7t27hx49eqBcuXJwdHSEv78/Vq1apXAOVTkLeDNXZODAgShTpgzs7e1RvXp1bNmyRVp/+PBhNG7cGA4ODihfvjyGDx+O9PR0teqBiIiMj7K5f/nN+8tvLKaqNp0qhw4dQkhICBwdHVG8eHGEhobiwYMHAFSPmwHU6/ssSB7LmauTkpLQuHFj2Nvbw8/PD7t371a7rES6wIe+ERm5ZcuWwdraGocOHcKiRYsU1qWnp6Ndu3bw9fVFXFwcIiMjMXbsWIVt0tLSEBISglq1auHkyZPYsWMH/vnnH60fiBUREYHU1FT89ddf+OOPP/DDDz8odCATqevUqVMQQsDHx0d6ar6TkxNiYmLUnmSb06pVqxAZGYk1a9agdOnSOo6YiPKSnauOHTuG+fPnY+7cufjll18AvGngTZ8+HadPn8bGjRuRkpIiTbK3sLBAv379cj3occmSJdLDILNNnz4d4eHhSEhIQNWqVdGzZ0989NFHmDhxojS58O0bdzt37kSvXr0wfPhwXLhwAYsWLcLSpUsxY8YMhXNNnToVXbt2xZkzZ9CmTRuEhYXh/v37KF++PNatWwcAuHTpEtLS0lQOgP32228RHByMAQMGIC0tDWlpaahQoYLaZZw8eTI++OADnD59Gr169UKPHj2QmJgIAHj27Bnee+89ODk54cCBAzh48KA0UFXdzmUiIjJ9kyZNQnR0NBYuXIjz589j1KhR6NWrFw4cOIBly5bh+PHjmD9/PgBg0KBBKFOmDCIjI1XmPWXtUktLS8yfPx/nzp3DsmXL8Ndff2H8+PGFX2giIjI7y5YtQ6lSpXD8+HEMGzYMH3/8Mbp06YIGDRrg1KlTCA0NRe/eveHq6ppnfktPT8fo0aNx4sQJ7N27F5aWlnj//fdzTQRR5vjx4wCAPXv2IC0tDevXrwfwZmBrnz59EBsbi6NHj6JKlSpo06aNNPg1e5Br79698ejRI5w+fRqfffYZfv75Z7i7u+ujqoiISGaU9S+WL18+13bPnj3DjBkzsGzZMhw6dAiPHz9G9+7d1TrH06dP0aZNG+zZswfx8fEIDQ1F+/btcf36dYXt5s6di4YNGyI+Ph5t27ZF7969ER4ejl69euHUqVPw9vZGeHi4NAElLi4OXbt2Rffu3XH27FlERkZi8uTJ0gPdNPHZZ59h7NixSEhIgI+PD3r06IHXr1+jQYMGmDdvHooWLSrVT877okREJE/KcqCNjQ3atGmDOnXq4PTp01i4cCEWL16ML774Qq1j/vHHH5g7dy4WLVqEpKQkbNy4Ef7+/mrtm9/9zbdFRkbiu+++w+HDh3Hjxg107doV8+bNw2+//YatW7di9+7dWLBggbT9+PHjsW7dOixbtkzKp6Ghobh//75acWVLTk7Gxo0bsWXLFmzZsgUxMTGYNWsWAPW/TxARkfHRJh/mdd3PyspCuXLlsHbtWly4cAGff/45Pv30U6xdu1ar2NTtT50yZQomTZqEU6dOwdraGj169MD48ePx7bffIjY2FsnJyfj8888VyhwVFYU5c+bgzJkzCA0NRYcOHZCUlKRRfM+ePcOcOXOwfPlyHDhwANevX5fai2PHjkXXrl2lB8GlpaWhQYMGWtUDEREZry5duuDu3bvYt2+ftOzBgwfYuXMnwsLCCjyeFABu3LiBzp07o02bNkhISMCHH36ICRMmKOyvbj/p119/jerVqyMuLg6TJ0/WqKzqxEFERMZN27kXwJv5BhcuXMD27duRmJiIhQsXolSpUgCA+fPnY9OmTVi7di0uXbqEFStWSA8mE0Kgbdu2uH37NrZt24a4uDgEBgaiWbNmCv2T+fU9AsDo0aNx6NAhbNq0Cbt370ZsbKzCDyICQN++fXHo0CGsXr0aZ86cQZcuXdCqVSuFtt6zZ88wc+ZM/PLLLzh//rzK+V8LFy7EkCFDMHDgQJw9exabNm2Ct7e3VLZOnTrh/v37iImJwe7du5GcnIxu3bpp9W9TpEgRHDt2DF999RWmTZsmTfjPfhBBdHQ00tLScj2YgIiICkd+98LGjx+PmTNnIjExETVq1FC6/969e5GYmIh9+/Zh1apV2LBhA6ZOnarWuceNG4d9+/Zhw4YN2LVrF/bv34+4uLg8t8/vfuX69etRrlw5TJs2TSoHALx48QK1a9fGli1bcO7cOQwcOBC9e/fGsWPHFI6dX87KyspC69atcfjwYaxYsQIXLlzArFmzYGVlBQA4e/YsQkND0blzZ5w5cwZr1qzBwYMHFeZlEhGR/OSc+5ffvL/8xmKq06bLS0JCApo1a4Z33nkHR44cwcGDB9G+fXtkZmYCUD1uRp2+T13msaysLHTu3BlWVlY4evQofvzxR3zyyScaH4eoQAQRGa2QkBBRq1YthWUAxIYNG4QQQixatEiUKFFCpKenS+sXLlwoAIj4+HghhBCTJ08WLVu2VDjGjRs3BABx6dIltWIYMWKEEEKIS5cuCQDi6NGj0vrExEQBQMydO1fzApJZefu9K4QQq1evFlZWVuLixYsiKSlJ4S8tLS3X/n369BEdO3bM8/irV68WDg4OYsuWLXqInojyEhISIqpVqyaysrKkZZ988omoVq2a0u2PHz8uAIgnT54IIYT4+++/hZWVlTh27JgQQoiMjAzh6uoqli5dKu0DQEyaNEl6feTIEQFALF68WFq2atUqYW9vL71+9913xZdffqlw7uXLlwt3d/c8j/v06VNhYWEhtm/fLoQQYt++fQKAePDggUb1kZ03s6lbxkGDBinsV69ePfHxxx8LIYRYvHix8PX1Vajnly9fCgcHB7Fz50614yMiItP19OlTYW9vLw4fPqywvH///qJHjx5CCCHWrl0r7OzsxMSJE4Wjo6NCmzCvvKesXarM2rVrRcmSJQteECIionyEhISIRo0aSa9fv34tihQpInr37i0tS0tLEwDEkSNH1G7X3blzRwAQZ8+eVRlDSkqKQv9rXl6/fi2cnZ3F5s2bpWUvX74UAQEBomvXruKdd94RH374ocrzERGRecnZv5gzl0VHR+d5ry67/1FTfn5+YsGCBdJrT09P0atXL+l1dm6dPHmytCy7jzb7fk7Pnj1FixYtFI47btw44efnJ73OeZ9ICCFcXFxEdHS0EOK/HPvLL79I68+fPy8AiMTERKn8Li4uWpWTiIiMW84c+Omnn+a6N/b9998LJycnkZmZqXQfT09PaexKVFSU8PHxERkZGQWOLef9zez8vGfPHmmbmTNnCgAiOTlZWvbRRx+J0NBQIcSb/lsbGxuxcuVKaX1GRobw8PAQX331lRBCeZ7bsGGDeHt435QpU4Sjo6N4/PixtGzcuHGiXr160mtl9yuJiEgedJEP8zJ48GDxwQcfSK9VjcXLT87+VGXtuVWrVgkAYu/evdKymTNnCl9fX+m1h4eHmDFjhsKx69SpIwYPHiyEUH7/Mj4+XgAQKSkpQoj/2slXrlyRtvn+++9FmTJldFJWIiKSjw4dOoh+/fpJrxctWiTc3NzE69evdTKedOLEiUrHyr6dq9TpJ/X09BSdOnVSu1w5702qEwcRERmvgs69aN++vejbt6/SbYcNGyaaNm2qcOxse/fuFUWLFhUvXrxQWF65cmWxaNEiIYTqvsfHjx8LGxsb8fvvv0vrHz58KBwdHaV26ZUrV4SFhYW4deuWwnmaNWsmJk6cKIT4rx2XkJCgtBzKeHh4iM8++0zpul27dgkrKytx/fp1aVn2Pcbjx48LIZS3C0eMGCFCQkKk1znHJAnxpo36ySefSK+V3e8kIqLCl9fYmo0bNypsN2XKFFGzZk3pdZ8+fZTOx3+7vzUvT548Eba2tmL16tXSsnv37gkHBwet71e+vW1+2rRpI8aMGSO9VpWzdu7cKSwtLfN8fkDv3r3FwIEDFZbFxsYKS0tL8fz5c5XxEBGR8VFn7l/OeX/Kxqio06bLT48ePUTDhg2VrlNn3Iw6fZ8FzWNv59+dO3cKKysrcePGDWn99u3b2fajQmWpjwfJEZHuBAUF5bkuMTERNWvWhKOjo7QsODhYYZu4uDjs27cPTk5O0l/VqlUBvPkFDk0kJibC2tpaIaaqVauiWLFiGh2HCAACAgKQmZmJO3fuwNvbW+HPzc1No2OtWrUKERER+O2339C2bVs9RUxEealfvz4sLCyk18HBwUhKSkJmZibi4+PRsWNHeHp6wtnZGU2aNAEAXL9+HQDg7u6Otm3bYsmSJQCALVu24MWLF+jSpYvCOd7+hY0yZcoAgPTrFtnLXrx4gcePHwN4k/+mTZumkP+yf8Xj2bNnSo9bpEgRODs7486dO7qoFom6ZcyZw4ODg5GYmCiV58qVK3B2dpbKU6JECbx48ULjfE5ERKbpwoULePHiBVq0aKGQ/3799VcpV3Tp0gWdO3fGzJkzERUVBR8fH7WOraxdum/fPrRo0QJly5aFs7MzwsPDce/ePaSnp+u0XERERDm93Y6zsrJCyZIlc7UPAeTbtktOTkbPnj1RqVIlFC1aFBUrVgTwX1tVG3fu3MGgQYPg4+MDFxcXuLi44OnTpwrHtLW1xYoVK7Bu3To8f/4c8+bN0/p8RERkvvK6V5fdl5if9PR0jB8/Hn5+fihWrBicnJxw8eLFXDlQnf5Y4L98m5iYiIYNGyoco2HDhlI/sSbePre7u7vCeYiIyHwkJiYiODhY4R5kw4YN8fTpU9y8eVPl/l26dMHz589RqVIlDBgwABs2bMDr16/VOreq+5vZcuZLR0dHVKpUSWFZdg5LTk7Gq1evFPKljY0N6tatq1YOf5uXlxecnZ2l1+7u7syVREQmqiD58Mcff0RQUBBcXV3h5OSEn3/+Wev+T3X7U9VpS2bnrMePH+Pvv/9W2pbUNDc6OjqicuXK0mvmRiIi8xQWFoZ169bh5cuXAICVK1eie/fusLKy0sl40sTERKVjZd+mbj9pfvNDVFEnDiIiMm4FmXvx8ccfY/Xq1ahVqxbGjx+Pw4cPS8eJiIhAQkICfH19MXz4cOzatUtaFxcXh6dPn6JkyZIK+TAlJUVhLkJ+fY9Xr17Fq1evULduXWm9i4sLfH19pdenTp2CEAI+Pj4K54mJiVE4j62trULezc+dO3fw999/o1mzZkrXJyYmonz58ihfvry0LPteqKbty5wxsX1JRCQv6rS1lM3Hf/r0KW7cuJHvfsnJycjIyFBof5UoUUIhD+akzf3KzMxMzJgxAzVq1JDy9q5du/LtiwUUc1ZCQgLKlSuX51yRuLg4LF26VCFXh4aGIisrCykpKfnGR0RExitnHtRm3p+6bbq8JCQk5Nl2U2fcjDp9n7rMY4mJiahQoQLKlSuX5/mI9M3a0AEQUf6KFCmS5zohhMr9s7Ky0L59e8yePTvXuuyJEurKPt/biZIoP0+fPsWVK1ek1ykpKUhISECJEiXg4+ODsLAwhIeHIyoqCgEBAbh79y7++usv+Pv7o02bNgDePLwiIyMD9+/fx5MnT5CQkAAAqFWrFoA3D3wLDw/Ht99+i/r16+P27dsAAAcHB7i4uBRqeYlI0YsXL9CyZUu0bNkSK1asgKurK65fv47Q0FBkZGRI23344Yfo3bs35s6di+joaHTr1k2hAxV403jLlp2HlC3LysqS/jt16lR07tw5V1z29vZKj5t9nOxj6JI6ZVTm7XLVrl0bK1euzLWNq6urzuMlIiL5yc5fW7duRdmyZRXW2dnZAQCePXuGuLg4WFlZISkpSe1j52yXXrt2DW3atMGgQYMwffp0lChRAgcPHkT//v3x6tWrApaEiIgof8racfm1D5Vp3749ypcvj59//hkeHh7IyspC9erVFdqqmoqIiMC///6LefPmwdPTE3Z2dggODs51zOwBt/fv38f9+/fz7f8lIiLKi7J7dercvxs3bhx27tyJOXPmwNvbGw4ODvjf//6XK19p2h8rhMh1/pz3MS0sLHItU9aG1DSvExGRacovt6iT88qXL49Lly5h9+7d2LNnDwYPHoyvv/4aMTExudqVb0tPT1fr/iaQO2fld98xr9jfLqelpaXGuTLneYiIyLRomw/Xrl2LUaNGISoqCsHBwXB2dsbXX3+NY8eOaRWHuv2p6rQlc+YsVbkxe1k2dXOjOmNriYjItLRv3x5ZWVnYunUr6tSpg9jYWHzzzTcAdDOeVJ3cok4/KZD//BB1zkFERKZJnbkXrVu3xrVr17B161bs2bMHzZo1w5AhQzBnzhwEBgYiJSUF27dvx549e9C1a1c0b94cf/zxB7KysuDu7o79+/fnOm+xYsWk/9e2jzNbVlaW9MBVKysrhe2cnJyk/3dwcFB7bqKDg0O+65Xl35zL2fdKRGQeCtLWUpWXtGmLaXO/MioqCnPnzsW8efPg7++PIkWKYOTIkfn2xWbHn52zVOXOrKwsfPTRRxg+fHiudRUqVNCkiEREZETezoPazvtTt02Xl/xykDrjZtR9do6u8piy8/E5OlTY+NA3Ihnz8/PD8uXL8fz5cykJHj16VGGbwMBArFu3Dl5eXrC2LthHvlq1anj9+jVOnjwp/TLHpUuX8PDhwwIdl0zXyZMn8d5770mvR48eDQDo06cPli5diujoaHzxxRcYM2YMbt26hZIlSyI4OFh64BsAtGnTBteuXZNeBwQEAPjvi9SiRYvw+vVrDBkyBEOGDJG2yz4HEelfztxz9OhRVKlSBRcvXsTdu3cxa9Ys6ZeTTp48mWv/Nm3aoEiRIli4cCG2b9+OAwcOFDimwMBAXLp0Cd7e3lofw9bWFgAUfmFRnX2Uba9OGY8ePYrw8HCF19nXvMDAQKxZswalS5dG0aJFNS0KERGZAT8/P9jZ2eH69esICQlRus2YMWNgaWmJ7du3o02bNmjbti2aNm0KQLO8d/LkSbx+/RpRUVHSZIu1a9fqqCRERES6oyy/3bt3D4mJiVi0aBHeffddAMDBgwcLdEwAiI2NxQ8//CD1a924cQN3795V2CY5ORmjRo3Czz//jLVr1yI8PBx79+6V8ikREVFe/Ytvy+teXdWqVVUePzY2FhEREXj//fcBvPnxntTU1ALH7efnlyufHj58GD4+PtLgH1dXV6SlpUnrk5KS8OzZM43Oo079EBGRPOW8xvv5+WHdunUKgzsPHz4MZ2fnXD96kRcHBwd06NABHTp0wJAhQ1C1alWcPXsWgYGBee6j7v1NTXl7e8PW1hYHDx5Ez549AbyZVHjy5EmMHDkSwJtc+eTJE6Snp0uDcbN/FE8TzJdERPKlTT5Udt2PjY1FgwYNMHjwYGlZcnKyVjEVtD81L0WLFoWHhwcOHjyIxo0bS8sPHz4stXezfwQxLS0NxYsXB8DcSEREeXNwcEDnzp2xcuVKXLlyBT4+PqhduzYA3Ywn9fPzw8aNGxWW5Rw7q04/aUGpEwcRERm3gs69cHV1RUREBCIiIvDuu+9i3LhxmDNnDoA3ba1u3bqhW7du+N///odWrVrh/v37CAwMxO3bt2FtbQ0vLy+t4q5cuTJsbGxw/PhxKb7Hjx8jKSlJGrMaEBCAzMxM3LlzR2pDFpSzszO8vLywd+9ehflp2fz8/HD9+nXcuHFDiuvChQt49OgRqlWrBuBNnZ07d05hv4SEhHx/IEQZGxsbti+JiIxAQfr7Tp8+nWs+vpOTE8qVK5fvft7e3rCxscHRo0elB8o8ePAAly9fznPuBpD//cq8+nY7duyIXr16AXjzYJukpCQpp6mjRo0auHnzJi5fvgwfH59c6wMDA3H+/PkCtZGJiMi4qTPvT1keKmibrkaNGti7dy+mTp2aa50642bU6fvUZR7Lbk/+/fff8PDwAAAcOXKkwMcl0gRnEhHJWM+ePWFpaYn+/fvjwoUL2LZtm9RRm23IkCG4f/8+evTogePHj+Pq1avYtWsX+vXrp3HD1tfXF61atcKAAQNw7NgxxMXF4cMPP1T55G8yX02aNIEQItdf9sPYbGxsMHXqVKSkpCAjIwNpaWlYv349/P39pWOkpqYqPUa2/fv353sOItK/GzduYPTo0bh06RJWrVqFBQsWYMSIEahQoQJsbW2xYMECXL16FZs2bcL06dNz7W9lZYWIiAhMnDgR3t7eCA4OLnBMn3/+OX799VdERkbi/PnzSExMxJo1azBp0iS1j+Hp6QkLCwts2bIF//77L54+fapyHy8vLxw7dgypqam4e/eu9CsZ6pTx999/x5IlS3D58mVMmTIFx48fx9ChQwEAYWFhKFWqFDp27IjY2FikpKQgJiYGI0aMwM2bN9UuExERmS5nZ2eMHTsWo0aNwrJly5CcnIz4+Hh8//33WLZsGbZu3YolS5Zg5cqVaNGiBSZMmIA+ffrgwYMHADTLe5UrV8br16+lHL98+XL8+OOPhVVUIiIitSnLb8WLF0fJkiXx008/4cqVK/jrr7+kHypQR+nSpeHg4IAdO3bgn3/+waNHjwC8uRG5fPlyJCYm4tixYwgLC1PoN83MzETv3r3RsmVL9O3bF9HR0Th37hyioqJ0Xm4iIpKvvPoX32ZjY4Nhw4bh2LFjOHXqFPr27Yv69etLk+Lz4+3tjfXr1yMhIQGnT59Gz549dfLr9GPGjMHevXsxffp0XL58GcuWLcN3332HsWPHSts0bdoU3333HU6dOoWTJ09i0KBBGk+k8PLywtOnT7F3717cvXtX44fGERGR8cqZAwcPHowbN25g2LBhuHjxIv78809MmTIFo0ePVuvB2UuXLsXixYtx7tw5qQ/TwcEBnp6e+e6n7v1NTRUpUgQff/wxxo0bhx07duDChQsYMGAAnj17hv79+wMA6tWrB0dHR3z66ae4cuUKfvvtN63GPajzfYKIiIyTNvlQ2XXf29sbJ0+exM6dO3H58mVMnjwZJ06c0Cqmgvan5mfcuHGYPXs21qxZg0uXLmHChAlISEjAiBEjALxpw5YvXx6RkZG4fPkytm7dqlV/qpeXF86cOYNLly7h7t27ePXqlU7iJyIi4xMWFiaNj8meIA/oZjzpoEGDkJycLI2VVdZmU6eftKDUiYOIiIxbQeZefP755/jzzz9x5coVnD9/Hlu2bJEeAjN37lysXr0aFy9exOXLl/H777/Dzc0NxYoVQ/PmzREcHIxOnTph586dSE1NxeHDhzFp0iS1f/TC2dkZffr0wbhx47Bv3z6cP38e/fr1g6WlpfSgch8fH4SFhSE8PBzr169HSkoKTpw4gdmzZ2Pbtm1a11lkZCSioqIwf/58JCUl4dSpU1iwYAEAoHnz5qhRowbCwsJw6tQpHD9+HOHh4QgJCUFQUBCAN/cpT548iV9//RVJSUmYMmVKrofAqSP74XO3b9+Wxt4SEVHhK8i9sIyMDGk+/vbt2zFlyhQMHTpU5f1HJycn9O/fH+PGjcPevXtx7tw5RERE5LufqvuVXl5eOHDgAG7duiX9wLC3tzd2796Nw4cPIzExER999BFu376tdvkAICQkBI0bN8YHH3yA3bt3IyUlBdu3b8eOHTsAAJ988gmOHDmCIUOGICEhAUlJSdi0aROGDRum0XmIiMh4qTPvT9lYzIK26SZOnIgTJ05g8ODBOHPmDC5evIiFCxfi7t27ao2bUafvU5d5rHnz5vD19UV4eDhOnz6N2NhYfPbZZxofh6gg+NA3IhlzcnLC5s2bceHCBQQEBOCzzz7D7NmzFbbx8PDAoUOHkJmZidDQUFSvXh0jRoyAi4uLWgNhc4qOjkb58uUREhKCzp07Y+DAgShdurSuikRERDIUHh6O58+fo27duhgyZAiGDRuGgQMHwtXVFUuXLsXvv/8OPz8/zJo1K9fDSbP1798fGRkZ6Nevn05iCg0NxZYtW7B7927UqVMH9evXxzfffKNyIsfbypYti6lTp2LChAkoU6aM9AC2/IwdOxZWVlbw8/ODq6srrl+/Lq1TVcapU6di9erVqFGjBpYtW4aVK1fCz88PAODo6IgDBw6gQoUK6Ny5M6pVq4Z+/frh+fPnKFq0qNplIiIi0zZ9+nR8/vnnmDlzJqpVq4bQ0FBs3rwZXl5e6N+/PyIjIxEYGAgAmDJlCjw8PDBo0CAAmuW9WrVq4ZtvvsHs2bNRvXp1rFy5EjNnziyUMhIREWlCWX6ztLTE6tWrERcXh+rVq2PUqFH4+uuv1T6mtbU15s+fj0WLFsHDwwMdO3YEACxZsgQPHjxAQEAAevfujeHDhyv0m86YMQOpqan46aefAABubm745ZdfMGnSJCQkJOi03EREJF/59S9mc3R0xCeffIKePXsiODgYDg4OWL16tVrHnzt3LooXL44GDRqgffv2CA0NldqJBREYGIi1a9di9erVqF69Oj7//HNMmzYNERER0jZRUVEoX748GjdujJ49e2Ls2LFwdHTU6DwNGjTAoEGD0K1bN7i6uuKrr74qcOxERGQccubAV69eYdu2bTh+/Dhq1qyJQYMGoX///mpPyC9WrBh+/vlnNGzYUPol4c2bN6NkyZL57qfJ/U1NzZo1Cx988AF69+6NwMBAXLlyBTt37kTx4sUBACVKlMCKFSuwbds2+Pv7Y9WqVYiMjNT4POp8nyAiIuOkTT5Udt0fNGgQOnfujG7duqFevXq4d+8eBg8erFVMBe1Pzc/w4cMxZswYjBkzBv7+/tixYwc2bdqEKlWqAHjz0PNVq1bh4sWLqFmzJmbPno0vvvhC4/MMGDAAvr6+CAoKgqurKw4dOqST+ImIyPg0bdoUJUqUwKVLl9CzZ09puS7Gk1aoUAHr1q3D5s2bUbNmTfz444/48ssvFbZRp5+0oNSJg4iIjFtB5l7Y2tpi4sSJqFGjBho3bgwrKyvpPqGTkxNmz56NoKAg1KlTB6mpqdi2bZv0ULZt27ahcePG6NevH3x8fNC9e3ekpqaiTJkyasf+zTffIDg4GO3atUPz5s3RsGFDVKtWDfb29tI20dHRCA8Px5gxY+Dr64sOHTrg2LFjKF++vNZ11qdPH8ybNw8//PAD3nnnHbRr1w5JSUkAAAsLC2zcuBHFixdH48aN0bx5c1SqVAlr1qyR9g8NDcXkyZMxfvx41KlTB0+ePEF4eLjGcURFRWH37t0oX748AgICtC4PEREVTEHuhTVr1gxVqlRB48aN0bVrV7Rv317t+3Fff/01GjdujA4dOqB58+Zo1KgRateunef2qu5XTps2DampqahcuTJcXV0BAJMnT0ZgYCBCQ0PRpEkTuLm5oVOnTmqXL9u6detQp04d9OjRA35+fhg/fjwyMzMBADVq1EBMTAySkpLw7rvvIiAgAJMnT4a7u7vG5yEiIuOkzry/vMZiFqRN5+Pjg127duH06dOoW7cugoOD8eeff8La2hqA6nEz6vR96jKPWVpaYsOGDXj58iXq1q2LDz/8EDNmzND4OEQFYSGEEIYOgoiIiIjkqUmTJqhVqxbmzZtXoOMcOnQITZo0wc2bNzW6cSgn+ZXRwsICGzZs0KojloiIiIiIiIiIiMzD0qVLMXLkSDx8+NDQoRARERERERERERERERGRBnQ198JYpKeno2zZsoiKikL//v0NHQ4REVG+IiIi8PDhQ2zcuNHQoRARERERAQCsDR0AEREREZmvly9f4saNG5g8eTK6du1qkg98M4cyEhERERERERERERERERERERERERERERGRaYqPj8fFixdRt25dPHr0CNOmTQMAdOzY0cCRERERERERERHJj6WhAyAiw7l+/TqcnJzy/Lt+/bqhQyQiIhO3atUq+Pr64tGjR/jqq68MHU6+tM2bciojERERERGRufvyyy/zbPe1bt3a0OERERHl65133skzj61cudLQ4RERERlcbGxsvvf7iIiIzA3HkBIRERkW700SEZHczJkzBzVr1kTz5s2Rnp6O2NhYlCpVqkDHzK9dGhsbq6PIiYiI8sZ+UiIiItVat26dZ6788ssvDR0exwSRLFkIIYShgyAiw3j9+jVSU1PzXO/l5QVra+vCC4iIiMiIMW8SERERERGZvvv37+P+/ftK1zk4OKBs2bKFHBEREZH6rl27hlevXildV6ZMGTg7OxdyRERERMbl+fPnuHXrVp7rvb29CzEaIiIiw+NYGCIiIsPivUkiIiLgypUrea4rW7YsHBwcCjEaIiIyR+wnJSIiUu3WrVt4/vy50nUlSpRAiRIlCjkiRRwTRHLEh74REREREREREREREREREREREREREREREREREREREREREREREREREemRpaEDICIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyZXzoGxERERERERERERERERERERERERERERERERERERERERERERERERGRHvGhb0REREREREREREREREREREREREREREREREREREREREREREREREREesSHvhERERERERERERERERERERERERERERERERERERERERERERERERER6REf+kZEREREREREREREREREREREREREREREREREREREREREREREREREpEd86BsRERERERERERERERERERERERERERERERERERERERERERERERERkR7xoW9ERERERERERERERERERERERERERERERERERERERERERERERERERHr0fwF/sgQB3XC9AAAAAElFTkSuQmCC", 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" ] @@ -1506,9 +1697,17 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "11c33316-1502-46b1-b265-6cf43d0d8f1d", + "metadata": {}, + "source": [ + "## Calculate the correlation coefficient between each feature and fare amount" + ] + }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 33, "id": "d8dff114-adb5-4b34-a788-b93e42a2fee4", "metadata": {}, "outputs": [ @@ -1516,12 +1715,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "tip_amount 0.5490632216119445\n", - "tolls_amount 0.6102905023696504\n", - "extra -0.014430938533029767\n", - "mta_tax -0.15051466243094883\n", - "total_amount 0.9774147114602906\n", - "trip_distance 0.8802845818094683\n" + "tip_amount 0.5743753694582684\n", + "tolls_amount 0.6327404045395644\n", + "extra -0.008246801964138361\n", + "mta_tax -0.1628089444699402\n", + "total_amount 0.9783791092253548\n", + "trip_distance 0.8848067140931489\n" ] } ], @@ -1535,9 +1734,19 @@ " print(i, correlation)" ] }, + { + "cell_type": "markdown", + "id": "7ea2dc4f-c366-43f0-8a81-44ecd8289a3d", + "metadata": {}, + "source": [ + "### Calculate a one way ANOVA between the groups\n", + "\n", + "From running the ANOVA, `mta_tax` and `extra` have the most variance between the groups. We're using them as features to train our model." + ] + }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 34, "id": "3e083025-3312-4fd9-8cd2-4c8e37db5859", "metadata": {}, "outputs": [ @@ -1545,11 +1754,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Feature: payment_type, F-statistic: 14.30, p-value: 0.00000\n", - "Feature: extra, F-statistic: 105.47, p-value: 0.00000\n", - "Feature: mta_tax, F-statistic: 630.56, p-value: 0.00000\n", - "Feature: vendor_id, F-statistic: 8.74, p-value: 0.00312\n", - "Feature: passenger_count, F-statistic: 5.69, p-value: 0.00000\n" + "Feature: payment_type, F-statistic: 22.20, p-value: 0.00000\n", + "Feature: extra, F-statistic: 130.42, p-value: 0.00000\n", + "Feature: mta_tax, F-statistic: 999.42, p-value: 0.00000\n", + "Feature: vendor_id, F-statistic: 12.42, p-value: 0.00042\n", + "Feature: passenger_count, F-statistic: 2.57, p-value: 0.01744\n" ] } ], @@ -1568,9 +1777,19 @@ " print(f'Feature: {feature}, F-statistic: {f_statistic:.2f}, p-value: {p_value:.5f}')" ] }, + { + "cell_type": "markdown", + "id": "5b2f3d07-8010-43c4-873e-f462fd0bd94e", + "metadata": {}, + "source": [ + "### Run a query to get the dataset we're using for ML workflow\n", + "\n", + "The XGBoost algorithm on Amazon SageMaker uses the first column as the target column. `fare_amount` must be the first column in our query." + ] + }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 35, "id": "0dbcf599-076c-468e-9e9b-2e0bd53c3fa7", "metadata": {}, "outputs": [ @@ -1578,7 +1797,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: e7fbec48-e870-4d00-bb8e-ef1b64851e27\n", + "Query execution ID: e9866ba2-8e0d-426f-a601-e6ca24890b71\n", "Query is currently in QUEUED state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", "Query is currently in RUNNING state. Waiting for completion...\n", @@ -1597,90 +1816,107 @@ { "data": { "text/plain": [ - "'e7fbec48-e870-4d00-bb8e-ef1b64851e27'" + "'e9866ba2-8e0d-426f-a601-e6ca24890b71'" ] }, - "execution_count": 46, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Dropping passenger count and total_amount from dataset\n", "# Final select statement has tip_amount, tolls_amount, extra, mta_tax, trip_distance\n", "ride_combined_notebook_relevant_features_query = \"\"\"\n", "SELECT fare_amount, tip_amount, tolls_amount, extra, mta_tax, trip_distance FROM combined_ride_data_deduped\n", "\"\"\"\n", "\n", - "# Run the query to create the dataset that we're using to train our model\n", "run_athena_query(ride_combined_notebook_relevant_features_query, database, s3_output_location)" ] }, + { + "cell_type": "markdown", + "id": "4bbfeb06-e0e2-4ce0-9e73-98894053592d", + "metadata": {}, + "source": [ + "### Get the Amazon S3 URI of the dataset" + ] + }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 36, "id": "624a7833-c815-480e-b1da-c29da3d02c76", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'s3://parsa-ux360-burner-account-bucket/e7fbec48-e870-4d00-bb8e-ef1b64851e27.csv'" + "'s3://ux360-nyc-taxi-dogfooding/e9866ba2-8e0d-426f-a601-e6ca24890b71.csv'" ] }, - "execution_count": 48, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# For the athena-tutorial-ux360-draft processing script, you're specifying /opt/ml/processing/input/query-id-from-preceding-cell.csv\n", - "get_csv_file_location('e7fbec48-e870-4d00-bb8e-ef1b64851e27')" + "get_csv_file_location('ride_combined_notebook_relevant_features_query_execution_id')" + ] + }, + { + "cell_type": "markdown", + "id": "4632047c-eabc-495a-9758-b55b78937f73", + "metadata": {}, + "source": [ + "### Run a SageMaker processing job to split the data\n", + "\n", + "The code in `processing_data_split.py` splits the dataset into training, validation, and test sets. We use a SageMaker processing job to provide the compute needed to transform large volumes of data. For more information about processing jobs, see [Use processing jobs to run data transformation workloads](https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html). For more information about running sci-kit scripts, see [Data Processing with scikit-learn](https://docs.aws.amazon.com/sagemaker/latest/dg/use-scikit-learn-processing-container.html). \n", + "\n", + "For faster processing, we recommend using an `instance_count` of `2`, but you can use whatever value you prefer.\n", + "\n", + "For `source` within the `ProcessingInput` function, replace `'s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv'` with the output of the preceding cell. Within `processing_data_split.py`, you specify `/opt/ml/processing/input/query-id` as the `input_path`. The processing job is copying the query results to a location within its own container.\n", + "\n", + "For `Destination` under `ProcessingOutput`, replace `example-s3-bucket` with the Amazon S3 bucket that you've created." ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 42, "id": "788cae3c-a34b-4ee0-899e-0a461e21b210", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml\n" + "INFO:sagemaker.image_uris:Defaulting to only available Python version: py3\n", + "INFO:sagemaker:Creating processing-job with name sagemaker-scikit-learn-2024-06-25-17-41-19-446\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:sagemaker:Creating processing-job with name sagemaker-scikit-learn-2024-06-17-14-01-30-730\n" - ] - }, - { - "ename": "ResourceLimitExceeded", - "evalue": "An error occurred (ResourceLimitExceeded) when calling the CreateProcessingJob operation: The account-level service limit 'ml.m5.4xlarge for processing job usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances. Please use AWS Service Quotas to request an increase for this quota. If AWS Service Quotas is not available, contact AWS support to request an increase for this quota.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mResourceLimitExceeded\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[49], line 17\u001b[0m\n\u001b[1;32m 11\u001b[0m sklearn_processor \u001b[38;5;241m=\u001b[39m SKLearnProcessor(framework_version\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0.20.0\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 12\u001b[0m role\u001b[38;5;241m=\u001b[39mrole,\n\u001b[1;32m 13\u001b[0m instance_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mml.m5.4xlarge\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 14\u001b[0m instance_count\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Run the processing job\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[43msklearn_processor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mathena-tutorial-ux360-draft-processing-file.py\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Ensure this path is correct\u001b[39;49;00m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mProcessingInput\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms3://parsa-ux360-burner-account-bucket/e7fbec48-e870-4d00-bb8e-ef1b64851e27.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mdestination\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/opt/ml/processing/input\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mProcessingOutput\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/opt/ml/processing/output/train\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[43mdestination\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms3://parsa-ux360-burner-account-bucket/output/train\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mProcessingOutput\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/opt/ml/processing/output/validation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[43m \u001b[49m\u001b[43mdestination\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms3://parsa-ux360-burner-account-bucket/output/validation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[43m \u001b[49m\u001b[43mProcessingOutput\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/opt/ml/processing/output/test\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43mdestination\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms3://parsa-ux360-burner-account-bucket/output/test\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/workflow/pipeline_context.py:346\u001b[0m, in \u001b[0;36mrunnable_by_pipeline..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m context\n\u001b[1;32m 344\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _StepArguments(retrieve_caller_name(self_instance), run_func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrun_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/processing.py:680\u001b[0m, in \u001b[0;36mScriptProcessor.run\u001b[0;34m(self, code, inputs, outputs, arguments, wait, logs, job_name, experiment_config, kms_key)\u001b[0m\n\u001b[1;32m 670\u001b[0m normalized_inputs, normalized_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_normalize_args(\n\u001b[1;32m 671\u001b[0m job_name\u001b[38;5;241m=\u001b[39mjob_name,\n\u001b[1;32m 672\u001b[0m arguments\u001b[38;5;241m=\u001b[39marguments,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 676\u001b[0m kms_key\u001b[38;5;241m=\u001b[39mkms_key,\n\u001b[1;32m 677\u001b[0m )\n\u001b[1;32m 679\u001b[0m experiment_config \u001b[38;5;241m=\u001b[39m check_and_get_run_experiment_config(experiment_config)\n\u001b[0;32m--> 680\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlatest_job \u001b[38;5;241m=\u001b[39m \u001b[43mProcessingJob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_new\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 681\u001b[0m \u001b[43m \u001b[49m\u001b[43mprocessor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 682\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalized_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 683\u001b[0m \u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalized_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 684\u001b[0m \u001b[43m \u001b[49m\u001b[43mexperiment_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexperiment_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 685\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 686\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjobs\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlatest_job)\n\u001b[1;32m 687\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wait:\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/processing.py:916\u001b[0m, in \u001b[0;36mProcessingJob.start_new\u001b[0;34m(cls, processor, inputs, outputs, experiment_config)\u001b[0m\n\u001b[1;32m 913\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOutputs: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, process_args[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_config\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOutputs\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 915\u001b[0m \u001b[38;5;66;03m# Call sagemaker_session.process using the arguments dictionary.\u001b[39;00m\n\u001b[0;32m--> 916\u001b[0m \u001b[43mprocessor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_session\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprocess_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 918\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(\n\u001b[1;32m 919\u001b[0m processor\u001b[38;5;241m.\u001b[39msagemaker_session,\n\u001b[1;32m 920\u001b[0m processor\u001b[38;5;241m.\u001b[39m_current_job_name,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 923\u001b[0m processor\u001b[38;5;241m.\u001b[39moutput_kms_key,\n\u001b[1;32m 924\u001b[0m )\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/session.py:1497\u001b[0m, in \u001b[0;36mSession.process\u001b[0;34m(self, inputs, output_config, job_name, resources, stopping_condition, app_specification, environment, network_config, role_arn, tags, experiment_config)\u001b[0m\n\u001b[1;32m 1494\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprocess request: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, json\u001b[38;5;241m.\u001b[39mdumps(request, indent\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msagemaker_client\u001b[38;5;241m.\u001b[39mcreate_processing_job(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrequest)\n\u001b[0;32m-> 1497\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_intercept_create_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_request\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/session.py:6458\u001b[0m, in \u001b[0;36mSession._intercept_create_request\u001b[0;34m(self, request, create, func_name)\u001b[0m\n\u001b[1;32m 6441\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_intercept_create_request\u001b[39m(\n\u001b[1;32m 6442\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 6443\u001b[0m request: typing\u001b[38;5;241m.\u001b[39mDict,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6446\u001b[0m \u001b[38;5;66;03m# pylint: disable=unused-argument\u001b[39;00m\n\u001b[1;32m 6447\u001b[0m ):\n\u001b[1;32m 6448\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This function intercepts the create job request.\u001b[39;00m\n\u001b[1;32m 6449\u001b[0m \n\u001b[1;32m 6450\u001b[0m \u001b[38;5;124;03m PipelineSession inherits this Session class and will override\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6456\u001b[0m \u001b[38;5;124;03m func_name (str): the name of the function needed intercepting\u001b[39;00m\n\u001b[1;32m 6457\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 6458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sagemaker/session.py:1495\u001b[0m, in \u001b[0;36mSession.process..submit\u001b[0;34m(request)\u001b[0m\n\u001b[1;32m 1493\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating processing-job with name \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, job_name)\n\u001b[1;32m 1494\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprocess request: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, json\u001b[38;5;241m.\u001b[39mdumps(request, indent\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m))\n\u001b[0;32m-> 1495\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msagemaker_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_processing_job\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 550\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 551\u001b[0m )\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1005\u001b[0m error_code \u001b[38;5;241m=\u001b[39m error_info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQueryErrorCode\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m error_info\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 1006\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1007\u001b[0m )\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1011\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n", - "\u001b[0;31mResourceLimitExceeded\u001b[0m: An error occurred (ResourceLimitExceeded) when calling the CreateProcessingJob operation: The account-level service limit 'ml.m5.4xlarge for processing job usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances. Please use AWS Service Quotas to request an increase for this quota. If AWS Service Quotas is not available, contact AWS support to request an increase for this quota." + "...........\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", + " import imp\u001b[0m\n", + "\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", + " LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\u001b[0m\n", + "\u001b[35m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", + " import imp\u001b[0m\n", + "\u001b[35m/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", + " LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\u001b[0m\n", + "\u001b[34msys:1: DtypeWarning: Columns (0,1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\u001b[0m\n", + "\u001b[35msys:1: DtypeWarning: Columns (0,1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\u001b[0m\n", + "\u001b[35mTraining set: 30940496 samples\u001b[0m\n", + "\u001b[35mValidation set: 6630106 samples\u001b[0m\n", + "\u001b[35mTest set: 6630107 samples\u001b[0m\n", + "\u001b[34mTraining set: 30940496 samples\u001b[0m\n", + "\u001b[34mValidation set: 6630106 samples\u001b[0m\n", + "\u001b[34mTest set: 6630107 samples\u001b[0m\n", + "\n" ] } ], "source": [ - "# Run the processing job to create separate datasets from different files\n", "import sagemaker\n", "from sagemaker.sklearn.processing import SKLearnProcessor\n", "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", @@ -1698,31 +1934,39 @@ "\n", "# Run the processing job\n", "sklearn_processor.run(\n", - " code='processing_data_split.py', # Ensure this path is correct\n", + " code='processing_data_split.py', \n", " inputs=[ProcessingInput(\n", - " source='s3://example-s3-bucket/query-id.csv', # use the output of the preceding cell as the source\n", + " source='s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv',\n", " destination='/opt/ml/processing/input'\n", " )],\n", " outputs=[\n", " ProcessingOutput(\n", " source='/opt/ml/processing/output/train',\n", - " destination='s3://example-s3-bucket/output/train'\n", + " destination='s3://ux360-nyc-taxi-dogfooding/output/train'\n", " ),\n", " ProcessingOutput(\n", " source='/opt/ml/processing/output/validation',\n", - " destination='s3://example-s3-bucket/output/validation'\n", + " destination='s3://ux360-nyc-taxi-dogfooding/output/validation'\n", " ),\n", " ProcessingOutput(\n", " source='/opt/ml/processing/output/test',\n", - " destination='s3://example-s3-bucket/output/test'\n", + " destination='s3://ux360-nyc-taxi-dogfooding/output/test'\n", " )\n", " ]\n", ")\n" ] }, + { + "cell_type": "markdown", + "id": "bc164657-fd8f-4f96-89ff-23e991945ea4", + "metadata": {}, + "source": [ + "### Verify that train.csv is in the location that you've specified" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 43, "id": "41cb0fb0-079d-421d-a4b8-005ee38fc472", "metadata": {}, "outputs": [ @@ -1730,19 +1974,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-06-13 20:40:41 794188811 fourth-train.csv\n", - "2024-06-12 00:14:24 794186734 train.csv\n" + "2024-06-25 17:49:51 794185864 train.csv\n" ] } ], "source": [ "#Verify that train.csv is in the location that you've specified\n", - "!aws s3 ls s3://example-s3-bucket/output/train/" + "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/train/train.csv" + ] + }, + { + "cell_type": "markdown", + "id": "d0d2ba3c-fd6d-4aa0-b75b-92ba5a70ad00", + "metadata": {}, + "source": [ + "### Verify that val.csv is in the location that you've specified" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 44, "id": "ee3f29f1-a135-4bf6-bba5-595fb80c471d", "metadata": {}, "outputs": [ @@ -1750,33 +2001,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-06-13 20:40:41 170181422 fourth-val.csv\n", - "2024-06-12 00:14:24 170183095 val.csv\n" + "2024-06-25 17:49:51 170183603 val.csv\n" ] } ], "source": [ "#Verify that val.csv is in the location that you've specified\n", - "!aws s3 ls s3://example-s3-bucket/output/validation/" + "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/validation/val.csv" + ] + }, + { + "cell_type": "markdown", + "id": "c92d4b89-65a5-474b-aa22-dcb442c344b9", + "metadata": {}, + "source": [ + "### Specify `train.csv` and `val.csv` as the input for the training job" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 45, "id": "1e4e4113-b76c-49d5-a3b0-2327eb174fdf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml\n" - ] - } - ], + "outputs": [], "source": [ - "# Specify the input data sources for a training\n", "from sagemaker.session import TrainingInput\n", "\n", "bucket = 'example-s3-bucket'\n", @@ -1789,17 +2037,36 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "866262fe-5737-49af-9cde-af55575e07d1", + "metadata": {}, + "source": [ + "### Specify the model container and output location of the model artifact\n", + "\n", + "Specify the S3 location of the trained model artifact. You can access it later.\n", + "\n", + "It also gets the URI of the container image. We used version `1.2-2` of the XGBoost container image, but you can specify a different version. For more information about XGBoost container images, see [Use the XGBoost algorithm with Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html). " + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 46, "id": "d5b6a9b2-54e5-4dfd-9a5e-3c7442f6d5af", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.2-1\n" + "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.2-2\n" ] } ], @@ -1811,21 +2078,27 @@ "from sagemaker.debugger import Rule, ProfilerRule, rule_configs\n", "from sagemaker.session import TrainingInput\n", "\n", - "# S3 location to store the trained model artifact, so that it can be accessed later\n", "s3_output_location = f's3://{bucket}/{prefix}/xgboost_model'\n", "\n", - "container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.2-1\")\n", + "container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.2-2\")\n", "print(container)" ] }, + { + "cell_type": "markdown", + "id": "d04e189b-6f38-44cf-a046-6791abd32c00", + "metadata": {}, + "source": [ + "### Define the model" + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 47, "id": "44efb3a1-acf0-4193-987f-85025c7c3894", "metadata": {}, "outputs": [], "source": [ - "# Define the model\n", "xgb_model = sagemaker.estimator.Estimator(\n", " image_uri = container,\n", " role = role,\n", @@ -1842,14 +2115,23 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "44f1c8b1-7bf0-4381-9128-b00c2bfcf9f1", + "metadata": {}, + "source": [ + "### Set the model hyperparameters\n", + "\n", + "For the purposes of running the training job more quickly, we set the number of training rounds to 10." + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 48, "id": "e28512bf-d246-4a46-a0c8-24d1a8ad65a8", "metadata": {}, "outputs": [], "source": [ - "# Set the hyperparameters for the model\n", "xgb_model.set_hyperparameters(\n", " max_depth = 5,\n", " eta = 0.2,\n", @@ -1861,9 +2143,17 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "e5b6ed18-990f-4ec7-9d42-6965ec67e2ce", + "metadata": {}, + "source": [ + "### Train the model" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 49, "id": "58b77fc0-407d-4743-ae35-7bc7b04478e6", "metadata": {}, "outputs": [ @@ -1871,120 +2161,131 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2024-06-13-21-09-20-115\n" + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: latest.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: latest.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2024-06-25-18-20-44-522\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2024-06-13 21:09:20 Starting - Starting the training job...\n", - "2024-06-13 21:09:44 Starting - Preparing the instances for trainingCreateXgboostReport: InProgress\n", + "2024-06-25 18:20:45 Starting - Starting the training job...CreateXgboostReport: InProgress\n", "ProfilerReport: InProgress\n", "...\n", - "2024-06-13 21:10:08 Downloading - Downloading input data......\n", - "2024-06-13 21:11:13 Training - Training image download completed. Training in progress..\u001b[35m[2024-06-13 21:11:20.271 ip-10-2-118-110.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", - "\u001b[35mINFO:sagemaker-containers:Imported framework sagemaker_xgboost_container.training\u001b[0m\n", - "\u001b[35mINFO:sagemaker-containers:Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", - "\u001b[35mReturning the value itself\u001b[0m\n", - "\u001b[35mINFO:sagemaker-containers:No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[35mINFO:sagemaker_xgboost_container.training:Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", - "\u001b[35mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-13 21:11:21.431 ip-10-2-116-62.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", - "\u001b[34mINFO:sagemaker-containers:Imported framework sagemaker_xgboost_container.training\u001b[0m\n", - "\u001b[34mINFO:sagemaker-containers:Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", + "2024-06-25 18:21:29 Starting - Preparing the instances for training...\n", + "2024-06-25 18:22:09 Downloading - Downloading input data......\n", + "2024-06-25 18:23:12 Training - Training image download completed. Training in progress....\u001b[34m[2024-06-25 18:23:33.281 ip-10-2-65-56.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", "\u001b[34mReturning the value itself\u001b[0m\n", - "\u001b[34mINFO:sagemaker-containers:No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[34mINFO:sagemaker_xgboost_container.training:Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", - "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35mINFO:root:Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", - "\u001b[35mINFO:RabitContextManager:Failed to connect to RabitTracker on attempt 0\u001b[0m\n", - "\u001b[35mINFO:RabitContextManager:Sleeping for 3 sec before retrying\u001b[0m\n", - "\u001b[34mINFO:root:Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:start listen on algo-1:9099\u001b[0m\n", - "\u001b[34mINFO:RabitContextManager:Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9099}\u001b[0m\n", - "\u001b[34mINFO:RabitContextManager:Connected to RabitTracker.\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:No data received from connection ('10.2.116.62', 50370). Closing.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:38.246 ip-10-2-111-68.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", + "\u001b[35mReturning the value itself\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:42:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] start listen on algo-1:9099\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9099}\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] Connected to RabitTracker.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:43:INFO] No data received from connection ('10.2.65.56', 37490). Closing.\u001b[0m\n", "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35mINFO:RabitContextManager:Connected to RabitTracker.\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:47:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:48:INFO] Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:48:INFO] Connected to RabitTracker.\u001b[0m\n", "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:No data received from connection ('10.2.118.110', 60326). Closing.\u001b[0m\n", - "\u001b[34mtask NULL got new rank 0\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:Recieve start signal from 10.2.116.62; assign rank 0\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:Recieve start signal from 10.2.118.110; assign rank 1\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:@tracker All of 2 nodes getting started\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:@tracker All nodes finishes job\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:@tracker 0.1758744716644287 secs between node start and job finish\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:start listen on algo-1:9100\u001b[0m\n", - "\u001b[34mINFO:RabitContextManager:Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9100}\u001b[0m\n", - "\u001b[34mINFO:RabitContextManager:Connected to RabitTracker.\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:No data received from connection ('10.2.116.62', 54686). Closing.\u001b[0m\n", + "\u001b[35mtask NULL got new rank 0\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:48:INFO] No data received from connection ('10.2.111.68', 42310). Closing.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] Recieve start signal from 10.2.111.68; assign rank 0\u001b[0m\n", + "\u001b[34mtask NULL got new rank 1\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] Recieve start signal from 10.2.65.56; assign rank 1\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker All of 2 nodes getting started\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker All nodes finishes job\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker 0.1758573055267334 secs between node start and job finish\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] start listen on algo-1:9100\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9100}\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] Connected to RabitTracker.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:49:INFO] No data received from connection ('10.2.65.56', 38280). Closing.\u001b[0m\n", "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35mtask NULL got new rank 1\u001b[0m\n", - "\u001b[35mINFO:RabitContextManager:Failed to connect to RabitTracker on attempt 0\u001b[0m\n", - "\u001b[35mINFO:RabitContextManager:Sleeping for 3 sec before retrying\u001b[0m\n", - "\u001b[35mINFO:RabitContextManager:Connected to RabitTracker.\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:49:INFO] Failed to connect to RabitTracker on attempt 0\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:49:INFO] Sleeping for 3 sec before retrying\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] No data received from connection ('10.2.111.68', 60082). Closing.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Recieve start signal from 10.2.111.68; assign rank 0\u001b[0m\n", + "\u001b[34mtask NULL got new rank 1\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Recieve start signal from 10.2.65.56; assign rank 1\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Train matrix has 30940497 rows and 5 columns\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] @tracker All of 2 nodes getting started\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Validation matrix has 6630107 rows\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.600 ip-10-2-65-56.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.601 ip-10-2-65-56.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.601 ip-10-2-65-56.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.602 ip-10-2-65-56.ec2.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", + "\u001b[34m[2024-06-25 18:23:52.602 ip-10-2-65-56.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", + "\u001b[34m[2024-06-25:18:23:52:INFO] Debug hook created from config\u001b[0m\n", + "\u001b[34m[18:23:52] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:52:INFO] Connected to RabitTracker.\u001b[0m\n", "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35mtask NULL got new rank 1\u001b[0m\n", - "\u001b[35m[2024-06-13 21:11:37.262 ip-10-2-118-110.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", - "\u001b[35m[2024-06-13 21:11:37.263 ip-10-2-118-110.ec2.internal:7 INFO hook.py:199] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", - "\u001b[35m[2024-06-13 21:11:37.263 ip-10-2-118-110.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", - "\u001b[35m[2024-06-13 21:11:37.264 ip-10-2-118-110.ec2.internal:7 INFO hook.py:253] Saving to /opt/ml/output/tensors\u001b[0m\n", - "\u001b[35m[2024-06-13 21:11:37.264 ip-10-2-118-110.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", - "\u001b[35mINFO:root:Debug hook created from config\u001b[0m\n", - "\u001b[35mINFO:root:Train matrix has 30940497 rows and 5 columns\u001b[0m\n", - "\u001b[35mINFO:root:Validation matrix has 6630107 rows\u001b[0m\n", - "\u001b[35m[21:11:37] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:No data received from connection ('10.2.118.110', 59212). Closing.\u001b[0m\n", - "\u001b[34mtask NULL got new rank 0\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:Recieve start signal from 10.2.116.62; assign rank 0\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:Recieve start signal from 10.2.118.110; assign rank 1\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:@tracker All of 2 nodes getting started\u001b[0m\n", - "\u001b[34m[2024-06-13 21:11:37.262 ip-10-2-116-62.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", - "\u001b[34m[2024-06-13 21:11:37.263 ip-10-2-116-62.ec2.internal:7 INFO hook.py:199] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", - "\u001b[34m[2024-06-13 21:11:37.263 ip-10-2-116-62.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", - "\u001b[34m[2024-06-13 21:11:37.263 ip-10-2-116-62.ec2.internal:7 INFO hook.py:253] Saving to /opt/ml/output/tensors\u001b[0m\n", - "\u001b[34m[2024-06-13 21:11:37.264 ip-10-2-116-62.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", - "\u001b[34mINFO:root:Debug hook created from config\u001b[0m\n", - "\u001b[34mINFO:root:Train matrix has 30940497 rows and 5 columns\u001b[0m\n", - "\u001b[34mINFO:root:Validation matrix has 6630107 rows\u001b[0m\n", - "\u001b[34m[21:11:37] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", - "\u001b[35m[2024-06-13 21:11:52.675 ip-10-2-118-110.ec2.internal:7 INFO hook.py:413] Monitoring the collections: predictions, feature_importance, labels, hyperparameters, metrics\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[0]#011train-rmse:255.88295#011validation-rmse:68.20912\u001b[0m\n", - "\u001b[34m[2024-06-13 21:11:52.675 ip-10-2-116-62.ec2.internal:7 INFO hook.py:413] Monitoring the collections: labels, hyperparameters, predictions, metrics, feature_importance\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[1]#011train-rmse:250.89801#011validation-rmse:71.52632\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[2]#011train-rmse:250.13692#011validation-rmse:71.59752\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[3]#011train-rmse:247.76843#011validation-rmse:79.49778\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[4]#011train-rmse:245.87282#011validation-rmse:84.14578\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[5]#011train-rmse:245.98055#011validation-rmse:84.03645\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[6]#011train-rmse:245.69582#011validation-rmse:84.06477\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[7]#011train-rmse:243.92581#011validation-rmse:84.02535\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[8]#011train-rmse:243.96504#011validation-rmse:83.95972\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:[9]#011train-rmse:241.88516#011validation-rmse:77.56747\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:@tracker All nodes finishes job\u001b[0m\n", - "\u001b[34mINFO:RabitTracker:@tracker 112.72817921638489 secs between node start and job finish\u001b[0m\n", + "\u001b[35mtask NULL got new rank 0\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:52:INFO] Train matrix has 30940497 rows and 5 columns\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:52:INFO] Validation matrix has 6630107 rows\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.600 ip-10-2-111-68.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.601 ip-10-2-111-68.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.601 ip-10-2-111-68.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.602 ip-10-2-111-68.ec2.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", + "\u001b[35m[2024-06-25 18:23:52.602 ip-10-2-111-68.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", + "\u001b[35m[2024-06-25:18:23:52:INFO] Debug hook created from config\u001b[0m\n", + "\u001b[35m[18:23:52] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", + "\u001b[34m[2024-06-25 18:24:08.407 ip-10-2-65-56.ec2.internal:7 INFO hook.py:423] Monitoring the collections: labels, metrics, predictions, feature_importance, hyperparameters\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:08:INFO] [0]#011train-rmse:184.43744#011validation-rmse:135.48259\u001b[0m\n", + "\u001b[35m[2024-06-25 18:24:08.409 ip-10-2-111-68.ec2.internal:7 INFO hook.py:423] Monitoring the collections: predictions, labels, hyperparameters, feature_importance, metrics\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:20:INFO] [1]#011train-rmse:184.28534#011validation-rmse:135.24808\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:31:INFO] [2]#011train-rmse:184.18167#011validation-rmse:135.09784\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:43:INFO] [3]#011train-rmse:184.11903#011validation-rmse:134.99771\u001b[0m\n", + "\u001b[34m[2024-06-25:18:24:55:INFO] [4]#011train-rmse:184.07890#011validation-rmse:134.93574\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:07:INFO] [5]#011train-rmse:184.05234#011validation-rmse:134.89529\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:19:INFO] [6]#011train-rmse:184.03487#011validation-rmse:134.86635\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:30:INFO] [7]#011train-rmse:184.02385#011validation-rmse:134.84970\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:42:INFO] [8]#011train-rmse:184.01642#011validation-rmse:134.83659\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:54:INFO] [9]#011train-rmse:183.88487#011validation-rmse:134.82910\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:54:INFO] @tracker All nodes finishes job\u001b[0m\n", + "\u001b[34m[2024-06-25:18:25:54:INFO] @tracker 121.60369801521301 secs between node start and job finish\u001b[0m\n", "\n", - "2024-06-13 21:13:47 Uploading - Uploading generated training model\n", - "2024-06-13 21:13:47 Completed - Training job completed\n", - "Training seconds: 440\n", - "Billable seconds: 440\n" + "2024-06-25 18:26:11 Uploading - Uploading generated training model\n", + "2024-06-25 18:26:11 Completed - Training job completed\n", + "Training seconds: 520\n", + "Billable seconds: 520\n" ] } ], "source": [ - "# Train the model on new data\n", "xgb_model.fit({\"train\": train_input, \"validation\": validation_input}, wait=True)" ] }, + { + "cell_type": "markdown", + "id": "f0f8be08-10a5-4204-8f8b-60235d4b1f04", + "metadata": {}, + "source": [ + "### Deploy the model\n", + "\n", + "Copy the name of the model endpoint. We use it for our model evaluation." + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 50, "id": "c1aa7bc3-feee-4602-a64c-8c1e08526d03", "metadata": {}, "outputs": [ @@ -1992,9 +2293,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:sagemaker:Creating model with name: sagemaker-xgboost-2024-06-13-21-14-15-341\n", - "INFO:sagemaker:Creating endpoint-config with name sagemaker-xgboost-2024-06-13-21-14-15-341\n", - "INFO:sagemaker:Creating endpoint with name sagemaker-xgboost-2024-06-13-21-14-15-341\n" + "INFO:sagemaker:Creating model with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n", + "INFO:sagemaker:Creating endpoint-config with name sagemaker-xgboost-2024-06-25-18-26-38-055\n", + "INFO:sagemaker:Creating endpoint with name sagemaker-xgboost-2024-06-25-18-26-38-055\n" ] }, { @@ -2006,13 +2307,20 @@ } ], "source": [ - "# Deploy the model so that we can get predictions from it\n", "xgb_predictor = xgb_model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')" ] }, + { + "cell_type": "markdown", + "id": "ddcf330c-8add-437d-af1f-687ed3ebc78d", + "metadata": {}, + "source": [ + "### Download the test.csv file" + ] + }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 51, "id": "a9cc4eea-a6d0-418f-ab35-db437ce2a99d", "metadata": {}, "outputs": [ @@ -2020,18 +2328,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "download: s3://ux360-nyc-taxi-dogfooding/output/test/fourth-test.csv to ./fourth-test.csv\n" + "download: s3://ux360-nyc-taxi-dogfooding/output/test/test.csv to ./test.csv\n" ] } ], "source": [ - "# Download the test csv file\n", "!aws s3 cp s3://example-s3-bucket/output/test/test.csv ." ] }, + { + "cell_type": "markdown", + "id": "27b6cc9e-cb1c-43f6-99b8-fc26b38934c3", + "metadata": {}, + "source": [ + "### Create a 20 row test dataframe" + ] + }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 53, "id": "953f9d9b-04d0-4398-8620-8f9ab4eb407b", "metadata": {}, "outputs": [ @@ -2056,6 +2371,7 @@ " \n", " \n", " \n", + " 0\n", " 1\n", " 2\n", " 3\n", @@ -2066,58 +2382,63 @@ " \n", " \n", " 0\n", - " 3.45\n", - " 0.00\n", + " 7.5\n", + " 1.08\n", + " 0.0\n", " 0.0\n", " 0.5\n", - " 1.06\n", + " 0.97\n", " \n", " \n", " 1\n", - " 0.00\n", + " 10.0\n", " 0.00\n", " 0.0\n", " 0.5\n", - " 1.00\n", + " 0.5\n", + " 2.60\n", " \n", " \n", " 2\n", - " 0.00\n", - " 6.12\n", + " 6.0\n", + " 1.00\n", + " 0.0\n", " 1.0\n", " 0.5\n", - " 15.20\n", + " 0.82\n", " \n", " \n", " 3\n", - " 1.50\n", - " 0.00\n", + " 23.5\n", + " 5.45\n", " 0.0\n", + " 3.0\n", " 0.5\n", - " 1.34\n", + " 7.40\n", " \n", " \n", " 4\n", - " 0.00\n", - " 0.00\n", + " 53.5\n", + " 8.36\n", + " 10.5\n", " 0.0\n", - " 0.5\n", - " 3.86\n", + " 0.0\n", + " 12.68\n", " \n", " \n", "\n", "" ], "text/plain": [ - " 1 2 3 4 5\n", - "0 3.45 0.00 0.0 0.5 1.06\n", - "1 0.00 0.00 0.0 0.5 1.00\n", - "2 0.00 6.12 1.0 0.5 15.20\n", - "3 1.50 0.00 0.0 0.5 1.34\n", - "4 0.00 0.00 0.0 0.5 3.86" + " 0 1 2 3 4 5\n", + "0 7.5 1.08 0.0 0.0 0.5 0.97\n", + "1 10.0 0.00 0.0 0.5 0.5 2.60\n", + "2 6.0 1.00 0.0 1.0 0.5 0.82\n", + "3 23.5 5.45 0.0 3.0 0.5 7.40\n", + "4 53.5 8.36 10.5 0.0 0.0 12.68" ] }, - "execution_count": 23, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -2126,15 +2447,23 @@ "import boto3\n", "import json\n", "\n", - "# Create a small test dataframe to test predictions\n", "test_df = pd.read_csv('test.csv', nrows=20)\n", - "test_df = test_df.drop(test_df.columns[0], axis=1)\n", "test_df.head()" ] }, + { + "cell_type": "markdown", + "id": "a27e6c58-1abb-41db-ab45-263b97ee01ed", + "metadata": {}, + "source": [ + "### Get predictions from the test dataframe\n", + "\n", + "Define the `get_predictions` function to convert the 20 row dataframe to a CSV string and get predictions from the model endpoint. Provide the `get_predictions` function with the name of the model and the model endpoint." + ] + }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 54, "id": "218e7887-f37d-42e1-8f6a-9ee97d3c75c4", "metadata": {}, "outputs": [ @@ -2142,12 +2471,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "6.515090465545654,6.515090465545654,38.16786193847656,7.602119445800781,13.685721397399902,9.086471557617188,9.086471557617188,6.515090465545654,7.602119445800781,6.515090465545654,10.813796043395996,6.962556838989258,7.602119445800781,9.086471557617188,7.602119445800781,7.602119445800781,27.497194290161133,22.18333625793457,6.962556838989258,8.302289962768555\n" + "6.515090465545654,10.813796043395996,6.515090465545654,22.628469467163086,49.72923278808594,8.302289962768555,7.602119445800781,6.515090465545654,7.602119445800781,12.309170722961426,16.632259368896484,28.30757713317871,10.813796043395996,37.56535339355469,10.813796043395996,12.309170722961426,6.515090465545654,14.130854606628418,10.813796043395996,6.515090465545654\n" ] } ], "source": [ - "import boto3\n", "import json\n", "import pandas as pd\n", "\n", @@ -2155,7 +2483,7 @@ "runtime = boto3.client('runtime.sagemaker')\n", "\n", "# Define the endpoint name\n", - "endpoint_name = 'sagemaker-xgboost-2024-06-13-21-14-15-341'\n", + "endpoint_name = 'sagemaker-xgboost-timestamp'\n", "\n", "# Function to make predictions\n", "def get_predictions(data, endpoint_name):\n", @@ -2189,19 +2517,16 @@ ] }, { - "cell_type": "code", - "execution_count": 38, - "id": "1562ca50-b9ea-402b-991f-4a037c972159", + "cell_type": "markdown", + "id": "a136ae86-efd3-4d4f-9966-6610f445d84c", "metadata": {}, - "outputs": [], "source": [ - "# Create an array from the single string of predictions\n", - "predictions_array = predictions.split(',')" + "### Create an array from the string of predictions" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 55, "id": "58b45ac2-8a18-4d27-8aff-57370696d58f", "metadata": {}, "outputs": [ @@ -2209,39 +2534,48 @@ "data": { "text/plain": [ "['6.515090465545654',\n", + " '10.813796043395996',\n", " '6.515090465545654',\n", - " '38.16786193847656',\n", + " '22.628469467163086',\n", + " '49.72923278808594',\n", + " '8.302289962768555',\n", " '7.602119445800781',\n", - " '13.685721397399902',\n", - " '9.086471557617188',\n", - " '9.086471557617188',\n", " '6.515090465545654',\n", " '7.602119445800781',\n", + " '12.309170722961426',\n", + " '16.632259368896484',\n", + " '28.30757713317871',\n", + " '10.813796043395996',\n", + " '37.56535339355469',\n", + " '10.813796043395996',\n", + " '12.309170722961426',\n", " '6.515090465545654',\n", + " '14.130854606628418',\n", " '10.813796043395996',\n", - " '6.962556838989258',\n", - " '7.602119445800781',\n", - " '9.086471557617188',\n", - " '7.602119445800781',\n", - " '7.602119445800781',\n", - " '27.497194290161133',\n", - " '22.18333625793457',\n", - " '6.962556838989258',\n", - " '8.302289962768555']" + " '6.515090465545654']" ] }, - "execution_count": 39, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "predictions_array = predictions.split(',')\n", "predictions_array" ] }, + { + "cell_type": "markdown", + "id": "20097b4e-d515-45cf-9677-bd12953b6912", + "metadata": {}, + "source": [ + "### Get the 20 row sample of the test dataframe" + ] + }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 56, "id": "a5b69119-c58d-401d-a683-345a21451090", "metadata": {}, "outputs": [ @@ -2277,48 +2611,48 @@ " \n", " \n", " 0\n", - " 10.5\n", - " 3.45\n", - " 0.00\n", + " 7.5\n", + " 1.08\n", + " 0.0\n", " 0.0\n", " 0.5\n", - " 1.06\n", + " 0.97\n", " \n", " \n", " 1\n", - " 5.0\n", - " 0.00\n", + " 10.0\n", " 0.00\n", " 0.0\n", " 0.5\n", - " 1.00\n", + " 0.5\n", + " 2.60\n", " \n", " \n", " 2\n", - " 52.0\n", - " 0.00\n", - " 6.12\n", + " 6.0\n", + " 1.00\n", + " 0.0\n", " 1.0\n", " 0.5\n", - " 15.20\n", + " 0.82\n", " \n", " \n", " 3\n", - " 10.0\n", - " 1.50\n", - " 0.00\n", + " 23.5\n", + " 5.45\n", " 0.0\n", + " 3.0\n", " 0.5\n", - " 1.34\n", + " 7.40\n", " \n", " \n", " 4\n", - " 14.0\n", - " 0.00\n", - " 0.00\n", + " 53.5\n", + " 8.36\n", + " 10.5\n", " 0.0\n", - " 0.5\n", - " 3.86\n", + " 0.0\n", + " 12.68\n", " \n", " \n", "\n", @@ -2326,49 +2660,52 @@ ], "text/plain": [ " 0 1 2 3 4 5\n", - "0 10.5 3.45 0.00 0.0 0.5 1.06\n", - "1 5.0 0.00 0.00 0.0 0.5 1.00\n", - "2 52.0 0.00 6.12 1.0 0.5 15.20\n", - "3 10.0 1.50 0.00 0.0 0.5 1.34\n", - "4 14.0 0.00 0.00 0.0 0.5 3.86" + "0 7.5 1.08 0.0 0.0 0.5 0.97\n", + "1 10.0 0.00 0.0 0.5 0.5 2.60\n", + "2 6.0 1.00 0.0 1.0 0.5 0.82\n", + "3 23.5 5.45 0.0 3.0 0.5 7.40\n", + "4 53.5 8.36 10.5 0.0 0.0 12.68" ] }, - "execution_count": 32, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Get a 20 row sample from the test dataframe\n", "df_with_target_column_values = pd.read_csv('test.csv', nrows=20)\n", "df_with_target_column_values.head()" ] }, + { + "cell_type": "markdown", + "id": "85cd39f3-5f12-4cb1-aab2-6ca658e9d16e", + "metadata": {}, + "source": [ + "### Convert the values of the predictions array from strings to floats" + ] + }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 57, "id": "75353856-df2f-4c45-9a9b-11e16a856aa6", "metadata": {}, "outputs": [], "source": [ - "# Convert values from strings to floats\n", "predictions_array = [float(x) for x in predictions_array]" ] }, { - "cell_type": "code", - "execution_count": 49, - "id": "4b8dd2e5-8341-4aa4-88c9-21b10d25fd2e", + "cell_type": "markdown", + "id": "408a6da9-9a0c-4307-8966-acbcc11beacc", "metadata": {}, - "outputs": [], "source": [ - "# Create a dataframe to store the predicted versus actual values\n", - "comparison_df = pd.DataFrame(predictions_array, columns=['predicted_values'])" + "### Create a dataframe to store the predicted versus actual values" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 58, "id": "9589000e-1ce0-4a08-9d9c-055d29e13639", "metadata": {}, "outputs": [ @@ -2403,27 +2740,27 @@ " \n", " \n", " 1\n", - " 6.515090\n", + " 10.813796\n", " \n", " \n", " 2\n", - " 38.167862\n", + " 6.515090\n", " \n", " \n", " 3\n", - " 7.602119\n", + " 22.628469\n", " \n", " \n", " 4\n", - " 13.685721\n", + " 49.729233\n", " \n", " \n", " 5\n", - " 9.086472\n", + " 8.302290\n", " \n", " \n", " 6\n", - " 9.086472\n", + " 7.602119\n", " \n", " \n", " 7\n", @@ -2435,47 +2772,47 @@ " \n", " \n", " 9\n", - " 6.515090\n", + " 12.309171\n", " \n", " \n", " 10\n", - " 10.813796\n", + " 16.632259\n", " \n", " \n", " 11\n", - " 6.962557\n", + " 28.307577\n", " \n", " \n", " 12\n", - " 7.602119\n", + " 10.813796\n", " \n", " \n", " 13\n", - " 9.086472\n", + " 37.565353\n", " \n", " \n", " 14\n", - " 7.602119\n", + " 10.813796\n", " \n", " \n", " 15\n", - " 7.602119\n", + " 12.309171\n", " \n", " \n", " 16\n", - " 27.497194\n", + " 6.515090\n", " \n", " \n", " 17\n", - " 22.183336\n", + " 14.130855\n", " \n", " \n", " 18\n", - " 6.962557\n", + " 10.813796\n", " \n", " \n", " 19\n", - " 8.302290\n", + " 6.515090\n", " \n", " \n", "\n", @@ -2484,54 +2821,49 @@ "text/plain": [ " predicted_values\n", "0 6.515090\n", - "1 6.515090\n", - "2 38.167862\n", - "3 7.602119\n", - "4 13.685721\n", - "5 9.086472\n", - "6 9.086472\n", + "1 10.813796\n", + "2 6.515090\n", + "3 22.628469\n", + "4 49.729233\n", + "5 8.302290\n", + "6 7.602119\n", "7 6.515090\n", "8 7.602119\n", - "9 6.515090\n", - "10 10.813796\n", - "11 6.962557\n", - "12 7.602119\n", - "13 9.086472\n", - "14 7.602119\n", - "15 7.602119\n", - "16 27.497194\n", - "17 22.183336\n", - "18 6.962557\n", - "19 8.302290" + "9 12.309171\n", + "10 16.632259\n", + "11 28.307577\n", + "12 10.813796\n", + "13 37.565353\n", + "14 10.813796\n", + "15 12.309171\n", + "16 6.515090\n", + "17 14.130855\n", + "18 10.813796\n", + "19 6.515090" ] }, - "execution_count": 50, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "comparison_df = pd.DataFrame(predictions_array, columns=['predicted_values'])\n", "comparison_df" ] }, { - "cell_type": "code", - "execution_count": 51, - "id": "adf4f58c-f21c-4abf-b14c-2802cbd399b3", + "cell_type": "markdown", + "id": "e0652e07-1677-4fd4-b099-ccc2b1029cfd", "metadata": {}, - "outputs": [], "source": [ - "# Extract the target column from df_with_target_column_values_dataframe\n", - "column_to_add = df_with_target_column_values.iloc[:, 0]\n", - "\n", - "# Add the extracted column to df_target with the new header 'actual_values'\n", - "comparison_df['actual_values'] = column_to_add" + "### Add the actual values to the comparison dataframe" ] }, { "cell_type": "code", - "execution_count": 52, - "id": "1efe7090-97ce-4772-996f-b86d5432c28c", + "execution_count": 60, + "id": "adf4f58c-f21c-4abf-b14c-2802cbd399b3", "metadata": {}, "outputs": [ { @@ -2563,102 +2895,102 @@ " \n", " 0\n", " 6.515090\n", - " 10.5\n", + " 7.5\n", " \n", " \n", " 1\n", - " 6.515090\n", - " 5.0\n", + " 10.813796\n", + " 10.0\n", " \n", " \n", " 2\n", - " 38.167862\n", - " 52.0\n", + " 6.515090\n", + " 6.0\n", " \n", " \n", " 3\n", - " 7.602119\n", - " 10.0\n", + " 22.628469\n", + " 23.5\n", " \n", " \n", " 4\n", - " 13.685721\n", - " 14.0\n", + " 49.729233\n", + " 53.5\n", " \n", " \n", " 5\n", - " 9.086472\n", - " 10.0\n", + " 8.302290\n", + " 9.0\n", " \n", " \n", " 6\n", - " 9.086472\n", - " 10.5\n", + " 7.602119\n", + " 8.5\n", " \n", " \n", " 7\n", " 6.515090\n", - " 4.0\n", + " 2.5\n", " \n", " \n", " 8\n", " 7.602119\n", - " 7.5\n", + " 8.5\n", " \n", " \n", " 9\n", - " 6.515090\n", - " 6.5\n", + " 12.309171\n", + " 17.5\n", " \n", " \n", " 10\n", - " 10.813796\n", - " 13.0\n", + " 16.632259\n", + " 16.5\n", " \n", " \n", " 11\n", - " 6.962557\n", - " 7.5\n", + " 28.307577\n", + " 32.5\n", " \n", " \n", " 12\n", - " 7.602119\n", - " 8.0\n", + " 10.813796\n", + " 12.5\n", " \n", " \n", " 13\n", - " 9.086472\n", - " 9.5\n", + " 37.565353\n", + " 52.0\n", " \n", " \n", " 14\n", - " 7.602119\n", - " 9.0\n", + " 10.813796\n", + " 12.0\n", " \n", " \n", " 15\n", - " 7.602119\n", - " 7.0\n", + " 12.309171\n", + " 13.5\n", " \n", " \n", " 16\n", - " 27.497194\n", - " 33.0\n", + " 6.515090\n", + " 6.5\n", " \n", " \n", " 17\n", - " 22.183336\n", - " 21.5\n", + " 14.130855\n", + " 26.5\n", " \n", " \n", " 18\n", - " 6.962557\n", - " 7.0\n", + " 10.813796\n", + " 13.0\n", " \n", " \n", " 19\n", - " 8.302290\n", - " 9.0\n", + " 6.515090\n", + " 10.5\n", " \n", " \n", "\n", @@ -2666,40 +2998,52 @@ ], "text/plain": [ " predicted_values actual_values\n", - "0 6.515090 10.5\n", - "1 6.515090 5.0\n", - "2 38.167862 52.0\n", - "3 7.602119 10.0\n", - "4 13.685721 14.0\n", - "5 9.086472 10.0\n", - "6 9.086472 10.5\n", - "7 6.515090 4.0\n", - "8 7.602119 7.5\n", - "9 6.515090 6.5\n", - "10 10.813796 13.0\n", - "11 6.962557 7.5\n", - "12 7.602119 8.0\n", - "13 9.086472 9.5\n", - "14 7.602119 9.0\n", - "15 7.602119 7.0\n", - "16 27.497194 33.0\n", - "17 22.183336 21.5\n", - "18 6.962557 7.0\n", - "19 8.302290 9.0" + "0 6.515090 7.5\n", + "1 10.813796 10.0\n", + "2 6.515090 6.0\n", + "3 22.628469 23.5\n", + "4 49.729233 53.5\n", + "5 8.302290 9.0\n", + "6 7.602119 8.5\n", + "7 6.515090 2.5\n", + "8 7.602119 8.5\n", + "9 12.309171 17.5\n", + "10 16.632259 16.5\n", + "11 28.307577 32.5\n", + "12 10.813796 12.5\n", + "13 37.565353 52.0\n", + "14 10.813796 12.0\n", + "15 12.309171 13.5\n", + "16 6.515090 6.5\n", + "17 14.130855 26.5\n", + "18 10.813796 13.0\n", + "19 6.515090 10.5" ] }, - "execution_count": 52, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "column_to_add = df_with_target_column_values.iloc[:, 0]\n", + "\n", + "comparison_df['actual_values'] = column_to_add\n", + "\n", "comparison_df" ] }, + { + "cell_type": "markdown", + "id": "a1ee137e-2706-4972-b70a-4d908bb0cb0a", + "metadata": {}, + "source": [ + "### Verify that the datatypes of both columns are floats" + ] + }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 61, "id": "48f6f988-0de8-4c44-8c10-9845ef4d476d", "metadata": {}, "outputs": [ @@ -2711,19 +3055,26 @@ "dtype: object" ] }, - "execution_count": 53, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Verify that the datatypes of both columns are floats\n", "comparison_df.dtypes" ] }, + { + "cell_type": "markdown", + "id": "8c7cce0b-ce8b-4320-b9a4-9a50b2c732b3", + "metadata": {}, + "source": [ + "### Compute the RMSE" + ] + }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 62, "id": "781fe125-4a2e-4527-8c45-fcd20558f4bb", "metadata": {}, "outputs": [ @@ -2731,7 +3082,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RMSE: 3.6295376259632905\n" + "RMSE: 4.833823838366928\n" ] } ], @@ -2750,58 +3101,48 @@ "print(f\"RMSE: {rmse}\")\n" ] }, + { + "cell_type": "markdown", + "id": "4a21cb4e-d9be-466c-869d-ac0be688700c", + "metadata": {}, + "source": [ + "### Clean up" + ] + }, { "cell_type": "code", - "execution_count": 51, - "id": "d90f9ba9-0a80-4f0c-8b47-94fb7bed01f6", + "execution_count": 71, + "id": "9a6e651d-3e68-4c1b-8a28-3e15604b5ec1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Query execution ID: 9ecad177-c46b-4ec8-b387-20d099fb30de\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" + "remove_bucket: parsa-machine-learning-exam\n" ] - }, - { - "data": { - "text/plain": [ - "'9ecad177-c46b-4ec8-b387-20d099fb30de'" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" } ], - "source": [ - "# Delete the database\n", - "delete_database = \"\"\"\n", - "DROP DATABASE mydatabase\n", - "\"\"\"\n", - "\n", - "run_athena_query(delete_database, database, s3_output_location)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a6e651d-3e68-4c1b-8a28-3e15604b5ec1", - "metadata": {}, - "outputs": [], "source": [ "# Delete the S3 bucket\n", - "!aws s3 rb s3://example-s3-bucket --force " + "!aws s3 rb s3://example-s3-bucket --force" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "6c883864-e707-46d2-a183-76e5f2090368", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Deleting endpoint configuration with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n", + "INFO:sagemaker:Deleting endpoint with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n" + ] + } + ], "source": [ "# Delete the endpoint\n", "xgb_predictor.delete_endpoint()" diff --git a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb index 2376903772..2dc23d344c 100644 --- a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb +++ b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb @@ -7,49 +7,85 @@ "source": [ "# Perform ETL and train a model using PySpark\n", "\n", - "To perform extract transform load (ETL) operations on multiple files, we recommend opening a Jupyter notebook within Amazon SageMaker Studio and using the PySpark Kernel. The PySpark kernel is connected to an AWS Glue Interactive Session. The session connects your notebook to a cluster that automatically scales up the storage and compute to meet your data processing needs. When you shut down the kernel, the session stops and you're no longer charged for the compute on the cluster.\n", + "To perform extract transform load (ETL) operations on multiple files, we recommend opening a Jupyter notebook within Amazon SageMaker Studio and using the `Glue PySpark and Ray` kernel. The kernel is connected to an AWS Glue Interactive Session. The session connects your notebook to a cluster that automatically scales up the storage and compute to meet your data processing needs. When you shut down the kernel, the session stops and you're no longer charged for the compute on the cluster.\n", "\n", "Within the notebook you can use Spark commands to join and transform your data. Writing Spark commands is both faster and easier than writing SQL queries. For example, you can use the join command to join two tables. Instead of writing a query that can sometimes take minutes to complete, you can join a table within seconds.\n", "\n", - "To show the utility of using the PySpark kernel for your ETL and model training worklows, you can use the NYC taxi fare prediction notebook (link to notebook). The notebook uses the NYC taxi dataset to predict the fare amount. It imports data from multiple files across different Amazon Simple Storage Service (Amazon S3) locations. Amazon S3 is an object storage service that you can use to save and access data and machine learning artifacts for your models. For more information about Amazon S3, see What is Amazon S3?\n", + "To show the utility of using the PySpark kernel for your ETL and model training worklows, we're predicting the fare amount of the NYC taxi dataset. It imports data from 47 files across 2 different Amazon Simple Storage Service (Amazon S3) locations. Amazon S3 is an object storage service that you can use to save and access data and machine learning artifacts for your models. For more information about Amazon S3, see [What is Amazon S3?](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html).\n", "\n", - "__Important:__\n", + "The notebook is not meant to be a comprehensive analysis. Instead, it's meant to be a proof of concept to help you quickly get started.\n", "\n", - "This tutorial assumes that you've in the us-east-1 AWS Region. It also assumes that you've provided the IAM role you're using to run the notebook with permissions to use Glue. For more information, see [Setting up](docs.aws.amazon.com/sagemaker/latest/dg/create-end-to-end-ml-workflow-athena.html#setting-up)." + "__Prerequisites:__\n", + "\n", + "This tutorial assumes that you've in the us-east-1 AWS Region. It also assumes that you've provided the IAM role you're using to run the notebook with permissions to use Glue. For more information, see [Providing AWS Glue permissions\n", + "](docs.aws.amazon.com/sagemaker/latest/dg/perform-etl-and-train-model-pyspark.html#providing-aws-glue-permissions)." + ] + }, + { + "cell_type": "markdown", + "id": "dffc1f72-88d2-442d-97ee-0d1c4e095ffb", + "metadata": {}, + "source": [ + "## Solution overview \n", + "\n", + "To perform ETL on the NYC taxi data and train a model, we do the following\n", + "\n", + "1. Start a Glue Session and load the SageMaker Python SDK\n", + "2. Set up the utilities needed to work with AWS Glue.\n", + "3. Load the data from the Amazon S3 into Spark dataframes.\n", + "4. Verify that we've loaded the data successfully.\n", + "5. Save a 20000 row sample of the Spark dataframe as a pandas dataframe.\n", + "6. Create a correlation matrix as an example of the types of analyses we can perform.\n", + "7. Split the Spark dataframe into training, validation, and test datasets.\n", + "8. Write the datasets to Amazon S3 locations that can be accessed by an Amazon SageMaker training job.\n", + "9. Use the training and validation datasets to train a model." + ] + }, + { + "cell_type": "markdown", + "id": "e472c953-1625-49df-8df9-9529344783ab", + "metadata": {}, + "source": [ + "### Start a Glue Session and load the SageMaker Python SDK" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "94172c75-f8a9-4590-a443-c872fb5c5d6e", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "You are already connected to a glueetl session ec5c76e1-bd30-493a-9370-5a33b8bb3474.\n", - "\n", - "No change will be made to the current session that is set as glueetl. The session configuration change will apply to newly created sessions.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "Welcome to the Glue Interactive Sessions Kernel\n", + "For more information on available magic commands, please type %help in any new cell.\n", + "\n", + "Please view our Getting Started page to access the most up-to-date information on the Interactive Sessions kernel: https://docs.aws.amazon.com/glue/latest/dg/interactive-sessions.html\n", + "Installed kernel version: 1.0.5 \n", "Additional python modules to be included:\n", "sagemaker\n" ] } ], "source": [ - "# Load the SageMaker Python SDK into the kernel\n", "%additional_python_modules sagemaker" ] }, + { + "cell_type": "markdown", + "id": "725bd4b6-82a0-4f02-95b9-261ce62c71b0", + "metadata": {}, + "source": [ + "### Set up the utilities needed to work with AWS Glue\n", + "\n", + "We're importing `Join` to join our Spark dataframes. `GlueContext` provides methods for transforming our dataframes. In the context of the notebook, it reads the data from the Amazon S3 locations and uses the Spark cluster to transform the data. `SparkContext` represents the connection to the Spark cluster. `GlueContext` uses `SparkContext` to transform the data. `getResolvedOptions` lets you resolve configuration options within the Glue interactive session." + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "2ea1c3a4-8881-48b0-8888-9319812750e7", "metadata": {}, "outputs": [ @@ -57,12 +93,20 @@ "name": "stdout", "output_type": "stream", "text": [ + "Trying to create a Glue session for the kernel.\n", + "Session Type: etl\n", + "Session ID: 11fe1ff7-3608-485f-a4a3-65392596dba0\n", + "Applying the following default arguments:\n", + "--glue_kernel_version 1.0.5\n", + "--enable-glue-datacatalog true\n", + "--additional-python-modules sagemaker\n", + "Waiting for session 11fe1ff7-3608-485f-a4a3-65392596dba0 to get into ready status...\n", + "Session 11fe1ff7-3608-485f-a4a3-65392596dba0 has been created.\n", "\n" ] } ], "source": [ - "# Set up the utilities needed to work with AWS Glue.\n", "import sys\n", "from awsglue.transforms import Join\n", "from awsglue.utils import getResolvedOptions\n", @@ -73,9 +117,19 @@ "glueContext = GlueContext(SparkContext.getOrCreate())" ] }, + { + "cell_type": "markdown", + "id": "e03664e5-89a2-4296-ba83-3518df4a58f0", + "metadata": {}, + "source": [ + "### Create the `df_ride_info` dataframe\n", + "\n", + "Create a single dataframe from all the ride_info Parquet files for 2019." + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "ba577de7-9ffe-4bae-b4c0-b225181306d9", "metadata": {}, "outputs": [ @@ -88,15 +142,24 @@ } ], "source": [ - "# Import all ride info parquet files for 2019.\n", "df_ride_info = glueContext.create_dynamic_frame_from_options(\n", " connection_type=\"s3\", format=\"parquet\",\n", " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-info/year=2019/\"], \"recurse\": True}).toDF()" ] }, + { + "cell_type": "markdown", + "id": "b04ce553-bf3d-4922-bbb1-4aa264447276", + "metadata": {}, + "source": [ + "### Create the `df_ride_info` dataframe\n", + "\n", + "Create a single dataframe from all the ride_fare Parquet files for 2019." + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "6efc3d4a-81d7-40f5-bb62-cd206924a0c9", "metadata": {}, "outputs": [ @@ -109,15 +172,22 @@ } ], "source": [ - "# Import all ride fare parquet files for the year 2019\n", "df_ride_fare = glueContext.create_dynamic_frame_from_options(\n", " connection_type=\"s3\", format=\"parquet\",\n", " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-fare/year=2019/\"], \"recurse\": True}).toDF()" ] }, + { + "cell_type": "markdown", + "id": "6c8664da-2105-4ada-b480-06d50c59e878", + "metadata": {}, + "source": [ + "### Show the first five rows of `dr_ride_fare`" + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "id": "d63af3a3-358f-4c6e-97d4-97a1f1a552de", "metadata": {}, "outputs": [ @@ -142,6 +212,14 @@ "df_ride_fare.show(5)" ] }, + { + "cell_type": "markdown", + "id": "688a17e8-0c83-485d-a328-e89344a0e8bf", + "metadata": {}, + "source": [ + "### Join df_ride_fare and df_ride_info on the `ride_id` column" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -160,9 +238,17 @@ "df_joined = df_ride_info.join(df_ride_fare, [\"ride_id\"])" ] }, + { + "cell_type": "markdown", + "id": "236c2efc-85f8-43f8-b6d3-7f0e61ccefb0", + "metadata": {}, + "source": [ + "### Show the first five rows of the joined dataframe" + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "id": "2a456733-4533-4688-8174-368e50f4dd66", "metadata": {}, "outputs": [ @@ -187,9 +273,17 @@ "df_joined.show(5)" ] }, + { + "cell_type": "markdown", + "id": "1396f6ee-c581-4274-baf8-243d38ec000b", + "metadata": {}, + "source": [ + "### Show the data types of the dataframe" + ] + }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "id": "9a52a903-f394-4d00-a216-6af8c2132d83", "metadata": {}, "outputs": [ @@ -220,9 +314,17 @@ "df_joined.printSchema()" ] }, + { + "cell_type": "markdown", + "id": "18bb75a2-eba5-4d06-8a26-f30e31776a02", + "metadata": {}, + "source": [ + "### Count the number of rows" + ] + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "id": "c6bcc15f-8d41-4def-ae49-edaef4105343", "metadata": {}, "outputs": [ @@ -238,9 +340,17 @@ "df_joined.count()" ] }, + { + "cell_type": "markdown", + "id": "d2daa67c-4b21-433a-b46e-eed518ba9ce7", + "metadata": {}, + "source": [ + "### Drop duplicates if there are any" + ] + }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "id": "7d13d8d9-7eed-4efb-b972-601baf291842", "metadata": {}, "outputs": [ @@ -253,13 +363,22 @@ } ], "source": [ - "# Drop duplicates in case there are any\n", "df_no_dups = df_joined.dropDuplicates([\"ride_id\"])" ] }, + { + "cell_type": "markdown", + "id": "657e48dc-1f4a-4550-afe1-d9754e6d0e1e", + "metadata": {}, + "source": [ + "### Count the number of rows after dropping the duplicates\n", + "\n", + "In this case, there were no duplicates in the original dataframe." + ] + }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "id": "3e3e82a3-e3db-4752-8bab-f42cbbae4928", "metadata": {}, "outputs": [ @@ -275,9 +394,18 @@ "df_no_dups.count()" ] }, + { + "cell_type": "markdown", + "id": "ae4c0fc4-7cb5-4b70-8430-965b5fe4506e", + "metadata": {}, + "source": [ + "### Drop columns\n", + "Time series data and categorical data is outside of the scope of the notebook." + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "id": "9dc1d15f-53f6-404d-86fd-5a28f3792db8", "metadata": {}, "outputs": [ @@ -293,9 +421,17 @@ "df_cleaned = df_joined.drop(\"pickup_at\", \"dropoff_at\", \"store_and_fwd_flag\", \"vendor_id\", \"payment_type\")" ] }, + { + "cell_type": "markdown", + "id": "081c81f9-f052-4ddb-b769-4d41b6138f6a", + "metadata": {}, + "source": [ + "### Take a sample from the notebook and convert it to a pandas dataframe" + ] + }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "id": "48382726-c767-4b0e-9336-decbf8184938", "metadata": {}, "outputs": [ @@ -313,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "id": "2bf2f181-0096-4044-8210-7d9de299d966", "metadata": {}, "outputs": [ @@ -331,25 +467,7 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "13f80864-21ec-43c6-8cb3-517fcb438f4b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "test_pandas = df_sample.toPandas()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "a8b2f670-c5f9-4a01-8d9f-6a29a3dae660", "metadata": {}, "outputs": [ @@ -372,13 +490,13 @@ } ], "source": [ - "df_pandas = test_pandas\n", + "df_pandas = df_sample.toPandas()\n", "df_pandas.describe()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "id": "246c98e9-64bd-4644-a163-b86a943d6a09", "metadata": {}, "outputs": [ @@ -396,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "id": "c5b2727c-de75-4cc0-94e9-d254e235d003", "metadata": {}, "outputs": [ @@ -421,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "id": "d69b48b6-98c2-4851-9c7a-f24f092bae41", "metadata": {}, "outputs": [ @@ -453,9 +571,19 @@ "df_pandas.info()" ] }, + { + "cell_type": "markdown", + "id": "34222bea-8864-4934-8c93-a71a7e72325b", + "metadata": {}, + "source": [ + "### Create a correlation matrix of the features\n", + "\n", + "We're creating a correlation matrix to see which features are the most predictive. This is an example of an analysis that you can use for your own use case." + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "id": "b7f3e4f7-e04e-41e1-b94b-b32eb3bc3bbf", "metadata": {}, "outputs": [ @@ -469,7 +597,7 @@ }, { "data": { - "image/png": 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0xo0bBwCoVasWFixYAH19fbx69QqvXr3CmDFjAABDhw7F5cuXsX37dty+fRudO3dGixYt8OjRv9/lHBYWhsaNG8PT0xOXL1/GhQsX0LZtW+TnS37wHjduHHbv3o2NGzfixo0bcHFxQfPmzZGYKJk58OzZM3To0AFt27ZFWFgY+vfvjwkTJsi8RmRkJFq0aIGOHTvi9u3b2LFjBy5cuIChQ4d+8jErKChAhw4doK6ujqtXr2LFihUYP378J5ejCAmvEpGamIbyVdyk27R0teDgYYfocPnTz/Ny8/Ds4XO4Vym6C1EoFMK9ipt0cCPm0XPk5+XD3beoXEs7CxiZG0l/dL8f+hDiAjGS41Mxre9s/Or/O9b+vglJb4qmp9u62UAoFODKsesoyC9ApigT14JD4V7FVenCarzti3eP2du+eHvM3ve2L8q/k0coFKK8rxuiCvPEPJT0RXl5fXFPfh9fPfEP1DXUULm+t3SbYwUHJMUl4+6VcIjFYqQmpuHmuVuoUN3jP7RaOagIVOCkb4c7CRHSbWKIcSc+Am5GTiXm6+TaGqk5aQh5fqnENERllapQBW7GjgiNLRrAFUOMf2LvooKpa4n5tFQ1sev7hQj6fjFm1AuAg4H1l6humRf/KgEpianw9C2azaetqwUnDwdE3pMfwiQvNw9PH8bAw9dduk0oFMLTtzwi7xX9GJydlYNV09aj+0h/GJgYyC3n/VmK6hrqyM3JxdMHHCQHgBfPXyA+Ph7VaxbdWKCnpwcv74q4HXb7g3lnTJ+JevXrokatGh/1WiJRGnR1daCqyon378vPy0dSdBIsKlhItwmEAph7miPhccLHlZGdD3G+GOo6klny4gIxXt16BV1LXZydcxb7h+xH8JRgvAh9USptUBYqAhU46NniXuID6TYxxAhPfAAXA4ePKsPD2A2WOuZ4kPQYgOTGEm/TCojNeIPRlQdjYf0Z+NVvNCqbef9LSd82VaEK3IwcEfpa9nod+vouKpiUfL3uXaEDkrNTcCTqrNz99xIeoXa5KjDVMgIAVDb3hK2eJa7H3vm8DSAiIvoK8ZP4V87V1RVz5syRPnd3L/pS7ODggOnTp2PgwIFYtmwZ1NXVYWBgAIFAAEtLS2m6mJgYrF+/HjExMShXrhwAYMyYMTh27BjWr1+PGTNmfLAOc+bMQdWqVbFs2TLptgoVKgAA0tPTsXz5cmzYsAEtW7YEAKxevRonT57E2rVrMXbsWCxfvhzOzs6YN2+etA137tzB7NmzpeXNnDkT3bt3x8iRI6XtXrRoEerXr4/ly5dDU/Pj79gKDg7G/fv3cfz4cWl7Z8yYIa3f1yw1SXJnrd57YS70jPSk+94nSklHQUFBsTz6Rrp4/Uxy909qYhpU1VSgratVLE1qoiQcUPyrBIjFYpzYGoyOQ9pBS0cLh9YfxZJxKxG4egxU1VRhamWCIbN/xrrfN2H7X0EoKCiAo6c9Bs0c8Fna/zVJTZQc7/dDjugZ6UmP2ftK6gs9Iz3ExhT2RVJqyX1RQh9fPnoVVRtXgbpGUQg854qO6DOxO9ZN+xu5ObkoyC+AV80KMuu1fKv01XWhIlQpFiIgOTsN5XQt5eZxN3JGI5vaGHdh+peoItEXZ6ChB1WhChKzUmS2J2WlwF6/nNw8MWmvMOvqKkQmxUBXXQtdPdpgedOp6HV4HOIyE+XmIYmURMlx1jeWnSGgb6Qvvb68Ly1FJFmrwOj9PHp4FfNa+nzHkiC4VHRC5To+csup4OeBk0GncTX4Oqo19EVKYioObDwiqVdCitw835r4eMksPxNT2fXLTExMEB9f8g/vR48cQ0T4fWzdufmjXicpKQmrlq9Gx868NsuTk5YDcYEYGvqyIcc0DTSR9qrkcJXvur3jNjSNNKWDKFmpWcjLysP9Q/dRsVNFePt7I/Z2LC4uuogGgQ1gXv7Da/B8q/TUdaAiVEFqjuz7U0pOGix1LErIJRkkn193OlSFqhCLC/D3/Z0ILxxE0VPXhZaqJlo7NsWex4ex89F+eJl6YqhPP8wJXSwdKCFZBuqS63VSset1KuxKuF57mbqhlVMD9D8WWGK5C0M3Yky1ftj9/RLkFeShQCzGn9fX4HZc8XBZ9OXJC2lJRESfDwdAvnK+vr4yz4ODgzFz5kzcv38fqampyMvLQ1ZWFjIyMqCtrS23jDt37iA/Px9ubm4y27Ozs2FiIn+BwXeFhYWhc+fOcvdFRkYiNzcXtWvXlm5TU1ODn58fIiIkd19HRESgevXqMvlq1pSdSn3r1i3cvn0bW7ZskW4Ti8UoKCjAkydP4OHx8Xe1R0REwNbWVjr4Ie/15MnOzkZ2drbMtpzsXKiXEKf7c7geHIptfwVJnw+a0b/UXuvfiAvEyM/LR6eh7eFRVTLQ1ueXHpjYeQoehj2GZ7XySE1MxdZ5O1G9WVX4NqqM7MxsHNpwHGunbsTQOT+X6Q9u14JDsW3+LunzwTMV1xfviroXjdinr9E78AeZ7a+iYxG0dB9a9mwKz2rlkZKYir0rD2LbX7vQY2xXBdW2bNJU0cAwn75YeXcz0nI/Luwe0bfgXvwj3Isvmil6J+4RNreZi+9cG2Pt7V0fyPntuXLyGjbN2yZ9PmLWoFJ5nbCLtxFx4wEmryn5R66K1TzReWAH/D1/G9bM2AhVNVW07dUSj24/hkBYdq/T/8Xhg0cwbUrRAPeSFYs+uYzYV7GYM3MuVq5Z/q9rhACASCTC0IHD4eTshIFDfv7k16N/F3EwAs+uPkODwAZF63sULlNkXcUa7i0kn2eN7I2Q8DgBkacjOQDymWXlZWPylVnQUNGAp4k7urq1x5vMeDxIegxh4feCm2/u4ESMJLTcM9ELuBg6ooFNHQ6AfCZaqpr4pcYg/Hl9DVJySg4t1sG1GTxNXBB47k/EpsfDx7w8Rvr2QXxmEkJf3/uCNSYiIvryOADyldPR0ZH+Pzo6Gm3atMGgQYPwxx9/wNjYGBcuXEC/fv2Qk5NT4gCISCSCiooKQkNDoaIiG6ZIV1f3X+ugpaX1r2n+K5FIhJ9//hnDhw8vts/Ozq7UXx+QzEKZOnWqzLYeo7qhV8APJeT477xqVYCDh730eV6uJD50WlIaDEyK7gBNS0qDjbP8sCO6BjoQCoVIS5K9Uy41SQR9Y8lMBH1jPeTl5iNDlCkz80AmTeHrWdoX3eWlZ6gLXX0dJL1JBgCc238RWrpaaPdzW2ma3oE/4Leu0xAdEQNHz6K2lDXetSrAwaPoby0vRxLiLVVeX7h8Wl+kJaUVHWcj/ZL74r27fgHg0pErsHGxhp2brcz241tPwamCI5p2bQQAsHYuB3VNdfw1Ygna/thKps7fmtQcEfIL8mGoIXsMDDX05C4caaFtBnNtU4z3HSzd9nYwb1uLpRh5bjJec00QKuNSstOQV5APY03ZkElGmgZIyEr+qDLyxfl4lPQUNrol3w38rfKp7Y3JHg7S52+v56mJqTB8J0xValIqbF1s5JahZ6ALoYqw2GzA1KQ0GBTOJIm48QBxL+MxrM0YmTTLJq2Gm7cLxi0cBQBo7t8Yzbo0QnJCCnT0tBH/KgG7V+2HmZXpf25rWdSgUX14eRetaZaTI1mAOSE+EWZmZtLtCQkJcC/vXiw/AITfi0BiQiK6dir6XJifn4/Qf25g+9YduB52Vfo5Oz09HYN/GgIdHW38tXg+1NRK72aaskxdTx0CoQDZqbI3IGWlZEHT4MOzv+8fuY/7h++j/rj6MLQzlC1TRQB9a9nPAHrl9BD/kNfykqTlpCO/IB/66rLHzUBdD6kfWHRbDDHeZEqO6zPRC5TTsUAbh2Z4kPQYaTnpyCvIx8v0WJk8r0SxcDXiuhMlScmRXK+Nil2v9ZGYWXwWn7WuBax0zTGj7mjptreDT6e6bELPI2MQn5mEAd7++PXCX7jyKgwAEJXyDC6G9vAv35oDIEREpPQ4AFKGhIaGoqCgAPPmzYNQKFm+ZefOnTJp1NXVpWtzvFW5cmXk5+fjzZs3qFu37ie/rre3N06dOlVscAAAnJ2dpWuU2NtLfvzOzc3F9evXpeGsPDw8cODAAZl8V65ckXlepUoVhIeHw8XF5ZPr9z4PDw88e/YMr169gpWVldzXkycwMBABAQEy287HnfrP9fkQTW1NaGoXfcETi8XQN9bDgxuPpD+yZ6ZnIToiBnXayl+8WVVNFbZuNnhw8xF86ngBkKyD8vDmI9RrJ5mZY+dqAxVVFTy48QiV60ni7r5+9gZJb5Lg6OkAAHCq4AgAePPsDYzMDAEA6akZEKWmw9hCEis2Jyu32CyPt3+LYnHBfz0cCvWhvrB9ry/qfldbbhnSvrgh2xcPbjxC/XZ1AAB2bm/74iEq15OEL3kdU9gXFWQHkLIys3HjzC18179VsdfKzc6B8L0BzaK+EP8/h0Bp5IvzEZUag4om5XH9tWSxYQEEqGhSHseenimW/mV6LEaf+11mW1e376CpqokN4TsRn5lULA9RWZNXkI+HiU/ga1EB55//A0ByXvhaVsCehyc+qgyhQAAnA1vpjydUREtbE1rvXUMMjPURceMB7FwlA9iZ6ZmIiohGg+/ryS1DVU0V9m52iAh9gCp1KwGQXEMibjxAo/aShW9b/dAMdVvLXoMm952OrkM6wae2l8x2gUAAI1NDAMC1U//A2NwI9m5f5qaSr42Ojo7MTUVisRimpqa4euUqyntIBjxEIhHu3L6Lzl3lz3quXtMPQftlZz5N/mUyHBwd0bd/H+ngh0gkwqABg6Guro6FSxd81GyRb5WKqgqMHIzw+t5rWPtKPmu9XdDcpUnJ3wnuH76PiAMRqDe2HoydZMOYqaiqwNjRuFgILVGsCDomOiD58sX5iE57Bk9jN9yMk6yDI4AAHsZuOPXs/EeXI4AAqkLVojJTn8JSW3bWjYWOORIYRrFEeQX5eJgkuV5feBEKQHJcq1hUxN5Hxa/XMakv0eeo7HqX/bw6Q1tNE4tv/I03GQlQF6pDTUUVYsh+RygQF0Ao4LKwX4OyHEmBiKgs4ABIGeLi4oLc3FwsXrwYbdu2lS6M/i4HBweIRCKcOnUKPj4+0NbWhpubG7p3745evXph3rx5qFy5MuLi4nDq1Cl4e3ujdevWH3zdwMBAeHl5YfDgwRg4cCDU1dUREhKCzp07w9TUFIMGDcLYsWNhbGwMOzs7zJkzBxkZGejXrx8AYODAgZg3bx7Gjh2L/v37IzQ0FBs2bJB5jfHjx6NGjRoYOnQo+vfvDx0dHYSHh+PkyZNYsmTJJx2nJk2awM3NDb1798bcuXORmpqKX3755V/zaWhoFPuSqp76Ze/YEwgEaNihHo5tCYaZjSlMLE1weP1RGJjqw6dO0Z2Li8Ysh08dL+mP6o061cPfs7fDzs0WDuXtELL7HLKzclCjuR8AyeLdNVv6Yc/yA9DR04amjgZ2Ld4LR0976awNC1szeNeqgKCl+9EtoBM0tTVxYM0RWNiaw62S5EtohRoeCNl9Dkc3nZCEwMrIxoF1R2BsYQSbEu5oLasEAgEadqyHY5tPwtzaFCZWxji0/lixvlg4ejl86lREg/aSwcXGnetj06xtsHOX9MXp3WclfdHi3b6ojt3L3vaFJnYu2gtHTwfpYNRbN0JuoiA/H35NqxarX8WaFbB13k6c239RGgIraOk+2Je3g6Fp8UVxvzWHngRjiHcfRKU8xePkaLRybAQNVXWcKVzgfIh3HyRmJ2Pbg33ILcjDM9FLmfzpeZkAUGw7fT46mtpwsXaQPne0tIWPsycSU5PxLI7HvTTsuH8EE2sOxP3EKEQkRKKze0toqWpKF0z9peYgxGckYuWtHQCAPhXb4178YzxPew09dW1082gDSx1THHocoshmlAkCgQBNOjfCoU1HYWFjDlNLE+xddxCGJgao8s7aHXNHLUSVuj5o3KEBAKBZl0ZYO3MTHMrbw7G8PYKDQpCdmY3aLSWhPA1MDOQufG5sYSQzu+PYtpOo6OcJgVCAG+fCcGTrCQyc0g9CFf7QBUj6p3uvH7B65RrY29vB2sYaSxctg5m5GRo1bihNN6Dvz2jUpCG6de8KHR0duLrK/iivpaUFQ0MD6XaRSISB/QcjKysLM2b/gXRROtJFktCKRsZGxWZiE+DWwg3XVl+DsaMxjJ2M8fDEQ+Rl58GxnuTGnKsrr0LLSAveXSQ38EQcisC9PfdQY1ANaJtqIzNZcr1W1VSFmqbkc7t7K3dcWXoFpu6mMPc0R+ztWLy8+RINAhsopI1lxYmnIehfoQeiU2MQlfoUzewaQENFAxdeSm4k61+hJ5KzkxH0+CAAoLVDUzxJjUFcZjxUharwNq2AmlZ++Pv+DmmZR6NPYZB3XzxIjsT9xIfwMvVEJdOKmB366WHoviU77x9FYI2fcT/xCe4nRqKTWwtoqWrgaOH1emL1gYjLTMLq2zuQU5CLJynPZfKLcjMAQLo9ryATN9+EY6BPN2Tn5yA2PR6VzD3Q3KEuloZ93JpGREREZRkHQMoQHx8fzJ8/H7Nnz0ZgYCDq1auHmTNnolevXtI0tWrVwsCBA+Hv74+EhARMnjwZU6ZMwfr16zF9+nSMHj0aL168gKmpKWrUqIE2bdr86+u6ubnhxIkTmDhxIvz8/KClpYXq1aujW7duAIBZs2ahoKAAPXv2RFpaGqpWrYrjx4/DyEgya8DOzg67d+/GqFGjsHjxYvj5+WHGjBn48ccfpa/h7e2Ns2fP4pdffkHdunUhFovh7OwMf3//Tz5OQqEQe/fuRb9+/eDn5wcHBwcsWrQILVq0+OSyFKFJ14bIzsrBtvlByBRlwtnLEYNn/gQ19aLBmPiXCRClFK1V4NuwMkQp6Ti84TjSklJh7WyNIbMGSMMuAUDHwd9DIBBgzdQNyMvNh0dVd/iP6CDz2j0n/IA9y/Zj+cS1EAgEcPVxxpBZA6CiKvnC7l7ZFb0ndkfwjhCc3BECdU11OHraY/CsAaW6VoqiNO3aCDlZOdg6f5e0L4bMer8v4pH+Xl+kJYtwaP2xor6Y/ZNMX3Qa8j2EQgFWT3mnL0YWXyD10pFr8KnrXWzBdACo2cIP2RnZOLvvAvasOABtXS24VXZBuwH/fk5/Cy6/CoW+uh66uLWFobo+otOeY8a1xUjJkdwRaqplXOwuOPqyqrr54My8orup/xo0BQCw4cRO9J0bUEIu+i9Ox1yBoaY++nl3grGmIR4nPcWYkFlIypKEN7HQNpGZzaenroNx1fvDWNMQaTnpeJj4BINOTkZ06gtFNaFMadmtKXIys7Hxz63IEGXA1csZo+YOhdo718u4l3EQpRTFbPdrVBVpySLsW3cIqYmScFmj5g6VhsD6WHeu3sOhzceQl5MHWxdrDPtjILxqVPhsbVMGffv1QWZmJn6fPB1paWmoXKUSlq1aKnMzzPNnz5CclPzRZUaE38ed23cAAG1afCez78jJw7C2lr+A8bfMroYdstOycXfPXWSlZMHQzhD1xtaThsDKSMiQuTM68nQkCvIKcGnxJZlyPNt5omIHyQ0qNlVtUKVPFdw/dB9hm8OgZ6WHWsNqwczdDFSya69vQE9dF+2cW8NAQw8xaS8w/8YypBZ+djLRNJL57KShoo5eHl1gpGGInIJcxKa/xuq7m3Dt9Q1pmhtxt7EpYgdaOzZFd/eOiM14g6W31+JRctQXb19ZEvLsCgw19fCjVycYaxrgcfJTjD0zG0mF4cjMdUxQ8ImfY3+/tAQ/efvj1xqDoa+ui9iMeKy5sxP7H5duxAMiIqKvgUD8rcdKIfqAk88PKboKVIjTgr8eq24dVHQV6B27/uL71NeiTl/5oY3oy5vZqJ+iq0CFqprVVHQVqNAf/8xUdBXoHc9TkxVdBSoUlcCQXF+Ls123KLoKClFvW3dFV6FE57p9m31CRMqF8+CJiIiIiIiIiIiIiEjpcACE0LJlS+jq6sp9zJgxQ9HVw/nz50usn66urqKrR0RERERERERERERfIa4BQlizZg0yMzPl7jM2Nv7CtSmuatWqCAsLU3Q1iIiIiIiIiIg+K0Z7JiIqXRwAIVhbWyu6Ch+kpaUFFxcXRVeDiIiIiIiIiIiIiMoQhsAiIiIiIiIiIiIiIiKlwxkgRERERERERERECiBgDCwiolLFGSBERERERERERERERKR0OABCRERERERERERERERKhyGwiIiIiIiIiIiIFIEhsIiIShVngBARERERERERERERkdLhAAgRERERERERERERESkdhsAiIiIiIiIiIiJSAAFDYBERlSrOACEiIiIiIiIiIiIiIqXDARAiIiIiIiIiIiIiIlI6DIFFRERERERERESkAEJGwCIiKlWcAUJEREREREREREREREqHAyBERERERERERERERKR0GAKLiIiIiIiIiIhIAQQCxsAiIipNnAFCRERERERERERERERKhwMgRERERERERERERESkdBgCi4iIiIiIiIiISAGEDIFFRFSqOAOEiIiIiIiIiIiIiIiUDgdAiIiIiIiIiIiIiIhI6TAEFhERERERERERkQIIGAKLiKhUcQYIEREREREREREREREpHQ6AEBERERERERERERGR0mEILCIiIiIiIiIiIgXgnclERKWL77NERERERERERERERKR0OABCRERERERERERERERKhyGwiIiIiIiIiIiIFEAoECi6CkRESo0zQIiIiIiIiIiIiIiISOlwAISIiIiIiIiIiIiIiJQOQ2AREREREREREREpgIAhsIiIShUHQIioTMgryFd0FajQrp0hiq4CvaNO33qKrgIVurD+nKKrQIUKGvZVdBWo0KOUCEVXgQoduXNP0VWgd+Rk5yq6ClTo7qFbiq4CvdVV0RUgIiJlxBBYRERERERERERERESkdDgDhIiIiIiIiIiISAGEDIFFRFSqOAOEiIiIiIiIiIiIiIiUDgdAiIiIiIiIiIiIiIhI6TAEFhERERERERERkQIIGAKLiKhUcQYIEREREREREREREREpHQ6AEBERERERERERERGR0mEILCIiIiIiIiIiIgXgnclERKWL77NERERERERERERERKR0OABCRERERERERERERERKhyGwiIiIiIiIiIiIFEAoECi6CkRESo0zQIiIiIiIiIiIiOg/Wbp0KRwcHKCpqYnq1avj2rVrH5Vv+/btEAgEaNeuXelWkIi+SRwAISIiIiIiIiIiov/bjh07EBAQgMmTJ+PGjRvw8fFB8+bN8ebNmw/mi46OxpgxY1C3bt0vVFMi+tZwAISIiIiIiIiIiEgBBALBV/v4FPPnz8eAAQPQt29feHp6YsWKFdDW1sa6detKzJOfn4/u3btj6tSpcHJy+q+HkohILg6AEBERERERERERkYzs7GykpqbKPLKzs4uly8nJQWhoKJo0aSLdJhQK0aRJE1y+fLnE8n///XeYm5ujX79+pVJ/IiKAAyBERERERERERET0npkzZ8LAwEDmMXPmzGLp4uPjkZ+fDwsLC5ntFhYWiI2NlVv2hQsXsHbtWqxevbpU6k5E9JaqoitARERERERERET0LRJ+YqipLykwMBABAQEy2zQ0NP5zuWlpaejZsydWr14NU1PT/1weEdGHcACEiIiIiIiIiIiIZGhoaHzUgIepqSlUVFTw+vVrme2vX7+GpaVlsfSRkZGIjo5G27ZtpdsKCgoAAKqqqnjw4AGcnZ3/Y+2JiCQYAouIiIiIiIiIiIj+L+rq6vD19cWpU6ek2woKCnDq1CnUrFmzWPry5cvjzp07CAsLkz6+++47NGzYEGFhYbC1tf2S1SciJccZIERERERERERERArw9QbA+jQBAQHo3bs3qlatCj8/PyxYsADp6eno27cvAKBXr16wtrbGzJkzoampiYoVK8rkNzQ0BIBi24mI/isOgBAREREREREREdH/zd/fH3FxcZg0aRJiY2NRqVIlHDt2TLowekxMDIRCBqIhoi+PAyBERERERERERET0nwwdOhRDhw6Vu+/MmTMfzLthw4bPXyEiInAAhIiIiIiIiIiISCGEAmUJgkVE9HXi3DMiIiIiIiIiIiIiIlI6HAAhIiIiIiIiIiIiIiKlwxBYRERERERERERECsAQWEREpYszQIiIiIiIiIiIiIiISOlwAISIiIiIiIiIiIiIiJQOQ2AREREREREREREpgIAhsIiIShVngBARERERERERERERkdLhAAgRERERERERERERESkdhsD6ionFYvz8888ICgpCUlISbt68iUqVKim6WlTKxGIxDm84jktHriBTlAmnio7wH9ER5jZmH8x3dt8FnNp5BqmJabB2LofOw9rDobyddH9uTi72LD+A0JAw5OXmwaOaO/yHd4S+sR4A4HnkS5zcdgqRd58gPSUdxpbGqNOmJhp2rCctI/JOFPavPozYmDfIzc6BsYURarepiUad6pfOwVCw8/sv4vTOs4XH1Aodh7aD/TvH9H03z97CkQ3HkRibBDNrU7Qd0AoVqntI94vFYhzdeAKXj1xFpigTjhUc0HlEh2J9e+9KBI5vPomXUa+gqq4GF28n9P+9j3T/iCZji71271+6o0rDSv+5zWXV4MY/YGzLfrA0MMOtmPsYtnkarj+5U2L6Ec16Y1DDbrAzsUJ8WhKC/jmOwKB5yM7NAQAIBUJMaT8MPWp+B0sDU7xMfoMNF/Zi+oFlX6pJSqW9a1N082gDYy0DRCbFYEHoRkQkRMpN29KxHibWHCizLTs/B0129PkCNf121fWqjrGdB8LXzQvlTCzRbnI/7L90XNHVUipisRgH1h/G+UOXkCHKhEtFJ3QP8IeFjfkH84XsPYvj208hJTEVti7W6Da8Mxw9HOSWv2j8cty9Fo7B0wagcl2fUmqJchCLxdixOgjBB0KQkZYOd283/DTuR1jZWpWYJ/xmBPZvOYSoB0+QFJ+McbNGwa9+NZk0O9YE4eLJy0h4kwhVNRU4uTui20B/uFVwKe0mlVmdPVqgl3c7mGgZ4lFiNOZcXoN7cY/lpm3r2hBT6g+T2Zadl4NaG7pKn0+pNxRt3RrJpLn07CaGHZ/2+SuvZLpWbIU+lTvAVNsIDxKeYOa5lbj75pHctN+Xb4zpjUfKbMvOy0HVlR2lz020DDGqZh/UtKsEPXVdhL68i5nnVyIm5VVpNkMpDG7TC2M7/QxLIzPciorAsOWTcP3hLblpVVVUEeg/BL2bdIK1iQUePI/C+HUzcTz0rDSNUCjElO6j0KNRe1gameNl4mtsOLkL07ct+lJNog8QMgQWEVGp4gDIV+zYsWPYsGEDzpw5AycnJ5iamiq6SmWSQCDA3r170a5dO0VX5aMEbw/B2b3n0XN8N5hYGuPQhmNYOmEVfl03DmrqanLzhIbcxN4VB+A/shMcytshZM95LB2/CpM2jIeekWSAY/ey/bh3NQL9JveClo4mdi7aizVTNiBgkeRL5LOHz6BnqIfegd1hZGaIqHvR2PbXLghVhKjfrg4AQF1THfXa1Ya1Uzmoa6oj8s4TbF8QBHVNddRpU/PLHKAv5EZIGPauOIguIzrCwcMOZ3afx/IJa/DL+nHQM9Itlv7JvWhs+mMr2vRriQo1PBB6+ibWTt6IMctHopyjJQDg1I4zOLf3ArqP84exlTGOrD+OFRPWIHDdGGnfhp27jR1/BaH1jy3hVtkFBfkFePUkttjr/TC2CzyquUufa+lqldKR+Pp18WuJ+V0DMXDjZFyNuoWRzXrj+Ji1cJ/QAnFpicXSd6vRBrM6j8aPayfi0uObcLNwwIb+syAWizF6+ywAwPjWAzCoYTf0XjMe9148RlWHiljfbyZSMtKwOPjvL93EMq2RXQ0MrdID866vQ3j8Y3Qu3xLzGk7ADwdHIzk7VW4eUU4Guh8aLX0uhvhLVfebpaOpjVtR4Vh3fAf2Tlmj6OoopWPbgnFq91n8GNgTplYm2LfuEBaMXYrfN/wKNQ351/frp0Oxc9le9Ajwh6OHA4KDQrBg7FJM+3sS9Auv728FB4UA/P3ko+3bfBBHdh3H0N8GwrycObav2oVpI2dhwda5UNdQl5snKysbDq72aNSmAeYG/iU3TTlbK/Qf3QcW1ubIyc7Foe1HMH3ETCze9RcMjPRLs0llUlOn2gio0RczLqzE3biH+KFiGyxpMQkddg1DUlaK3DyinHR02FU0CCLvGnHx2Q1MPbdE+jwnP/fzV17JNHepg7F1+mPamaW4/fohevp8h5Vtf0fbrQORmCm/L9Ky09F26zs3LbzXFQtb/YK8gjwMP/IH0nMy0KtSO6z+fjrabR2MzLzsUmxN2dalXlvM/+k3DFw8EVcfhGFku344Pn0z3Ac0QFxKQrH003uPRY+G7TFg0XjcfxaJ5r71sPe31ag1uj3CIu8BAMZ3HoRBrXui97wA3Hv6EFXdvLF+1J9ISU/D4gPrv3QTiYiIviiGwPqKRUZGwsrKCrVq1YKlpSVUVT99vCo3lx/2yxKxWIyQPefQvEcTeNeuCGvncug1vhtS4lNx68LdEvOdDjqHWq1qoGYLP1g5WKLryI5Q11DD5WPXAACZokxcPnoNHQZ+B/fKrrBzs0WPcf6IuheNJ+FPAQA1W1ZHp6Ht4OrjDNNyJvBr6osazavh1vmiu+htXW1QtVEVWDlYwsTSGH5NfeFR1R2Rd56U7oFRgDO7z6FWq+qo0aIaLO0t0GVkB6hrqOFK4TF939k9F1C+mjsa+zeApb0FWvdtARsXa5zffxGApG/P7jmPZt0bw6t2RVg7lUOP8V2RkpCKOxclX0zy8/OxZ9kBfPdTG9RpWxPmNmawtLdA5QbF7+LV0tWCvrG+9FHS4Ni3IKB5X6w+uxMbLuxBxMtIDNw4GRk5WfixXke56Wu5VMbFRzew7cohPI1/gZP3LmLb1UPwc/KWSbP/5ikcuXUWT+NfYPc/x3Hi3gWZNPRx/Mu3wsHIEByJOovo1Bf489paZOVlo7VzyTPHxBAjMStF+kjKkj9QQp/Psesh+G3DXOy7eEzRVVFKYrEYp4JC0Lpnc1Sq4w0bZ2v8GNgLyfEpuHlB/h29AHBy12nUbV0LtVvWRDkHK/QI6Ap1TXVcPHJZJl3Mo+c4seM0+ozrUdpNUQpisRiHdxxDxz7t4FevKhxc7DBs0iAkxSfj2rl/SsxXpWYldPu5C6o3qFZimrrNa8PbzwsW1hawdbJB7xE9kJGeiaePY0qjKWVej4ptsff+SRx8dBpPkp9jxoWVyMrLxvfvzeB4l1gMJGQmSx/yfpzPzc+VSZOWk16azVAKvSq1w+57x7Hv/ilEJT3D72eWITMvG+09mpaYRwwxEjKSix6ZydJ99gbl4GNZHtPOLse9N48QnfwC084sg4aKOlq6Kufs8c8loH1/rD66DRtO7kJEzCMMXByIjOxM/NjMX276no06YMaOJTh6PQRPYmOw4vBmHLl+GqM7DJCmqeVRFfuvnMCR66fx9M1z7L5wBCdunIOfO2cLEhGR8uMAyFeqT58+GDZsGGJiYiAQCODg4IBjx46hTp06MDQ0hImJCdq0aYPIyKIQItHR0RAIBNixYwfq168PTU1NbNmyBQCwZs0aeHh4QFNTE+XLl8eyZR8fxmX8+PFwc3ODtrY2nJyc8Ntvv8kMrEyZMgWVKlXCunXrYGdnB11dXQwePBj5+fmYM2cOLC0tYW5ujj/++EOm3JiYGHz//ffQ1dWFvr4+unTpgtevX8scg/dnbYwcORINGjSQPm/QoAGGDx+OcePGwdjYGJaWlpgyZYp0v4ODAwCgffv20uP4NUt4lYjUxDSUr+Im3aalqwUHDztEFw5UvC8vNw/PHj6HexVX6TahUAj3Km7SwY2YR8+Rn5cPd9+ici3tLGBkboQn4dEl1icrPQvaetol7n/26Dmi7kXD1dvpY5tYJkiO6Qu4vXdM3aq4ltgPT8KfyvQBAJSv5iZN/7Zv3y1TS1cL9h520n56/ugFUuJTIBAIMOfnv/Bbl9+xInANXsqZARK0aC8mdpiMeUMW4crRaxCLv8075NVU1ODrUAHB4Zek28RiMYLvXUJN58py81x6fBO+DhVQzdELAOBoZoNW3vVx5PZZmTSNPWvA1cIBAOBt6446rr44eudc6TVGCakKVeBm7IjQ2KIBXDHE+Cf2LiqYupaYT0tVE7u+X4ig7xdjRr0AOBhYf4nqEpWa+FcJSElMhYdveek2bV0tOHk6IKqE63Bebh6ePngGD9+i2X5CoRAevu6IDC+68SA7Kwdrpm9A95FdYGDCGQYf483LN0hOSIZ3tYrSbTq62nD1dMbDu/LD/fw/cnPzcHLfaWjrasPBteQQmt8qVaEqyps649rL29JtYohx7cVteFm4l5hPS00Th/xX4nDXVZjXdAKcDG2LpfG1qoiT3ddjd6fFCKz9Eww0is/epSKqQlV4mrngyvOiAVkxxLjyPAw+liX3hbaaFo73WouTvdZhUatf4Gxc9HeuriK5OSc7L0emzNz8XFSx8iyFVigHNVU1+Lp6ITjsgnSbWCxGcNgF1PSoIjePhpo6snJkZ9Rk5mShToWiwdpLEf+gcaXacLV2BAB4O3qgToVqOPrPmc/fCPpkgq/4QUSkDBgC6yu1cOFCODs7Y9WqVbh+/TpUVFRw7tw5BAQEwNvbGyKRCJMmTUL79u0RFhYGobBoLGvChAmYN28eKleuLB0EmTRpEpYsWYLKlSvj5s2bGDBgAHR0dNC7d+9/rYuenh42bNiAcuXK4c6dOxgwYAD09PQwbtw4aZrIyEgcPXoUx44dQ2RkJDp16oSoqCi4ubnh7NmzuHTpEn788Uc0adIE1atXR0FBgXTw4+zZs8jLy8OQIUPg7++PM2fOfNKx2rhxIwICAnD16lVcvnwZffr0Qe3atdG0aVNcv34d5ubmWL9+PVq0aAEVFZVPKvtLS02S3OWs915YCz0jPem+94lS0lFQUFAsj76RLl4/eyMpNzENqmoq0H4vTJK+kS5SE9Pklht17wlCz4Rh0Iz+xfb96v87RCki5OcXoFWv5qjVusbHNbCMSJceU9kvy3pGunhTeEzfl5aUVjy9oZ70+KYlpRWW8V7fGuoirTBNwitJuKZjm06g3cC2MLE0xuldZ7Fk9HL8smE8dPQlg1Gt+jSDayUXqGuo437oQ+xatBfZWTmo377Of2x52WOqZwRVFVW8fi8cwOvUBJS3kj8wt+3KIZjqGuHCL1shgABqqmpYfnobZh5aKU0z6/Aq6Gvp4v7Mo8gvyIeKUAW/7P4LWy8fLNX2KBsDDT2oClWQ+F4Yk6SsFNjrl5ObJybtFWZdXYXIpBjoqmuhq0cbLG86Fb0Oj0NcZvGQZkRlQUqi5Br+dt2tt/SM9KT73idKEaGgoKBYHn0jfcTGFN0wsnPpbjhXcESlOpyh9rGSEiTvSYbGBjLbDYwNkJwgP9TPp/jnwg0smLQY2Vk5MDIxxKSFgdA35ODU+ww1JdeId2cNAEBCVjIcDOUPfEenvMDv55biUWI0dNW10dP7e6z/bgY6B43EmwzJZ4FLz2/idPRVvEx7DRt9Swyp2h2Lmv+GvgcDUSAuKO1mlUlGmvqSvshIktmekJEMRyMbuXmik55j0umFeJgQDT11HfSu1B5/d5iD9tuG4HV6Ap4kP8fLtDcYWbM3fj+zBBm52ejl8z0s9cxgqmP0JZpVJpnqG0s+2ybFy2x/nRSP8jbOcvMcDz2LgA4DcO7uVUS+eorGleqgQ62WUFEp+o1g1s5l0NfWw/1VIUWfbTfOxdaQfaXZHCIioq8CB0C+UgYGBtDT04OKigosLSXrB3TsKBvOZd26dTAzM0N4eDgqViy6g23kyJHo0KGD9PnkyZMxb9486TZHR0eEh4dj5cqVHzUA8uuvv0r/7+DggDFjxmD79u0yAyAFBQVYt24d9PT04OnpiYYNG+LBgwc4cuSIZDaCuztmz56NkJAQVK9eHadOncKdO3fw5MkT2NpK7tratGkTKlSogOvXr6NatZJDC7zP29sbkydPBgC4urpiyZIlOHXqFJo2bQozM8ni0oaGhtLj+DW5HhyKbX8FSZ/LG2xQhJdPXmHVb+vRqlczeFQtftfXyAVDkJ2Zg+iIp9i/+jDMrE1QtZH8O5Lo4xUUSGZxNPuhMSrVk/yQ1X2sPyZ1m46wc7dQu3CdleY9ikIR2LhaIycrB6d3nvkmB0D+H/XL+2Fi258xeNNUXI26DRdzOyzs/gt+/W6wdJHzLn4t0b1GW/ywcjTuvXiMSnYeWPBDIF4mv8Gmi/sU2wAldy/+Ee7FF92BfSfuETa3mYvvXBtj7e1dCqwZ0ce7cvI6Ns/bJn0+bNagUnmdsIu3cf/GQ/y2ekKplK8szh2/gFWz10qfB/457gOp/7uKvp6Yu3Em0lLSELw/BPN/XYSZa36HwXsDLvTp7rx5iDtvHkqf3379AEGdFqGjRzMsD5WccyeiLkr3P06KwaPEpzjgvxy+VhVw/eWdYmXS/+fW6we49fqB9HlYbAT2/7AMnSu0wJJrW5BXkI9RR2dgaqPhuNh/O/IK8nHlWRjOP/0HAt5X/lmNWDkFq4fPxv1VIRBDjMhXT7H+5E6ZkFld6rVB94bt8MOcYbj39CEqOVXAgp8n42Xia2wKDvpA6URERGUfB0DKkEePHmHSpEm4evUq4uPjUVAguYMpJiZGZgCkatWq0v+np6cjMjIS/fr1w4ABRTFA8/LyYGDwcV/CduzYgUWLFiEyMhIikQh5eXnQ15e9i83BwQF6ekV3KFpYWEBFRUVmZoqFhQXevJHcPR8REQFbW1vp4AcAeHp6wtDQEBEREZ88APIuKysr6et8iuzsbGRny04dzsnOhXoJC5N+Dl61KsDBw176PC83D4BktsC7YSzSktJg4yz/TjhdAx0IhULpDIO3UpNE0rtG9Y31kJebjwxRpswskHfTvPUqOhaLx6xArdY10KKH/Ji/plYmAABrJyukJaXhyMYTSjUAoiM9piKZ7WlJomIzON7SM9Irnj45TXp83+Yr1rfJIlg7S+6ENzCRpLGwt5DuV1VXhamVMZLeJJdYX/vydji+ORh5OXlQVf+23tbj05KQl58HCwMTme0W+iaITYmXm2da+xH4+9IBrD0n+bJ39/lD6GhoY1Wf3/HHweUQi8WY22UcZh1ZhR1Xj0jT2JuUQ2CbnzkA8glSstOQV5APY03Z642RpgESspI/qox8cT4eJT2Fja7Fvycm+kpUqu0FJw8H6fPcwut7amIaDE2Kzoe0pDTYusi/u1rXQBdCobDYTM3UpFToG0uuI/dvPETcy3iMaDNWJs3yyWvg6uWMsQtHfobWlH3V6vjC1dNF+vzt563kxBQYmRbdiZ6SmAIHN/ti+T+VppYmrGwtYWVrCbeKrhjaeRROHTyDDr2//89lK5PkLMk1wkTLUGa7iaYh4t+bFVKSPHE+HiQ8gY1+yTc6vUh7jaTMFNjqW3EApARJWamSvtCWnZlhom1YbFZISfIK8nE/Lgq2BlbSbeFxkei8YwR01bWhJlRFUlYqtnT6E+FvHn/W+iuT+NREyWdbI1OZ7RZGpohNipOfJyUR7acNgIaaBkz0DfEy4TVm/RiIqNiitYfm9vsFs3Yuw46zktnMd6MfwN7cGoFdBnMA5CsgFHBQkIioNHENkDKkbdu2SExMxOrVq3H16lVcvXoVAJCTkyOTTkdHR/p/kUjyg+zq1asRFhYmfdy9exdXrlz519e8fPkyunfvjlatWuHQoUO4efMmfvnll2KvqaYmO0ggEAjkbns7aPMxhEJhsXUN5C3q/l9f562ZM2fCwMBA5rF9aenebayprQkza1Ppw9LeAvrGenhwo+ju58z0LERHxMDBU/4XclU1Vdi62eDBzaI8BQUFeHjzERwL89i52kBFVUWm3NfP3iDpTRIcPR2k215Fx2LR6OWo3qwqvuvX6qPaIC4QS39IUBaSY2qNhzeKvpxJjunjEvvB0dMeD2/Kxg1/EPpImt7Eyhj6xnp4eLOozKz0LDyNiJH2k62rDVTVVPHmedGXm/y8fCTEJsHYvORQAS8iX0JbT+ubG/wAJIuchkbfQ2PPmtJtAoEAjT1r4nLkTbl5tDU0i71H5BfkS/IW3pEoSSMuloZfTj5NXkE+HiY+ga9FBek2AQTwtawgM8vjQ4QCAZwMbD96wIToa6CprQlzGzPpo5yDJQyM9XH/RtHd0pnpmYgKj4bTO9fhd6mqqcLe3RYR7+QpKChAROhDOHtKYri3/KEZJq8NxKQ1E6QPAPAf0hF9JnBB9Le0dLSkAxJWtpawcbSGoYkh7vxzT5omIz0Dj8Ij4Vax5PWJ/l9isVjuZ9hvXV5BHu7HR6JauaKbmQQQoJq1N+68M7PgQ4QCIVyM7RD/gR/pzbVNYKCp98E037q8gjyExz1GdRvZvqhh44NbsR/fF64mDnKPsygnA0lZqbAzsEIFMxecfnL1s9Vd2eTm5SL00R00rlRbuk0gEKBxpdq4HHHjg3mzc7PxMuE1VFVU0bF2S+y/fEK6T1tDq1gIuPyCAggF/EmIiIiU37f3a1kZlZCQgAcPHmD16tWoW7cuAODChQv/kksy66JcuXKIiopC9+7dP/l1L126BHt7e/zyyy/SbU+fyl8E+lN4eHjg2bNnePbsmXQWSHh4OJKTk+HpKVkUz8zMDHfv3pXJFxYWVmzA49+oqakhPz//X9MFBgYiICBAZtv5uFOf9Fr/lUAgQMMO9XBsSzDMbExhYmmCw+uPwsBUHz51imb5LBqzHD51vFC/nSTkUaNO9fD37O2wc7OFQ3k7hOw+h+ysHNRo7gdAsth2zZZ+2LP8AHT0tKGpo4Fdi/fC0dNe+uP7yyevsGjMCnhUdUejzvWRWhiTXCAUQs9QsrbF2X0XYGxuBAs7cwDA49tROLXrDOq3r/vFjtGX0qBjPWyZswN27jawc7fF2T3nkZOVg+otJLOTNs/aBgNTA7TtLxkoqt+hDhYFLMfpXWdRoboHboSE4dnD5/Af1QmApG/rd6iLE1tOwczaFCaWxjiy4TgMTPThVVvy47CmjiZqt62BoxtPwMjMAEYWRji9U7Iwd6X6ki+kdy+HIy0pDfYe9lBTV8WD0Ic4ue0UGnau/6UP0Vdj/vH12DhgNv55chfXom5jZLPe0NHQwvrzewAAGwfMxouk15gYNB8AcDAsBAHN++JmTDiuRt6Gi4UdpnUYgYNhIdIvhgfDQvBL24GISXyJey8eo7KdBwKa98W687sV1s6yasf9I5hYcyDuJ0YhIiESnd1bQktVE0eiJH/bv9QchPiMRKy8tQMA0Kdie9yLf4znaa+hp66Nbh5tYKljikOPQxTZDKWno6kNF2sH6XNHS1v4OHsiMTUZz+JeKq5iSkIgEKBxp4Y4/PcxmNuYwdTKBPvXHoahqQEq1/GRppsXsAiV6/igUQfJe3rTzo2wbubfcHC3g6OHA4KDQpCTlY3aLSVrbxmY6Mtd+NzY3AhmVqbFtpOEQCBAa/8W2L1hL6xsLWFuZYbtq3fByNQQfvWKZlJPGfoHqtevipadmwMAMjOyEPs8Vrr/9cs4PHkYDV19XZhZmiIrMwu7N+xDtbq+MDIxRGpKGo4FnURiXBJqNVKu9dI+l813D2JqvWGIiH+Mu3GP8EOFttBS1cCBR6cBAFPrD0dcegKW/LMFADCgcmfcefMQz1Jjoaeug57e38NS1wz7HgQDALRUNfFTlS449eQKEjKTYKNviRF+vfAsNRaXn8u/MYIkNoXtwx+NR+Hem8e48+Yhevp8Dy1VTeyLkBzbPxqPwpv0BCy8sgkAMLBqV9x6/QDPUl5CT10XfSq3h5WeGXaHF/3o3sy5NhIzUxArioOriQPG1xmA00+u4vIz9sWHzN+7BhtHz8M/j+7g2oMwjGzXDzoa2lh/cicAYOPov/AiIRYTN8wGAPi5V4K1iSXCosJhbWKJKT1GQSgQYk7QCmmZB68G45euwxDz5iXuPX2Iyi4VENChP9ad2KmQNhIREX1JHAApI4yMjGBiYoJVq1bBysoKMTExmDDh4+I9T506FcOHD4eBgQFatGiB7Oxs/PPPP0hKSir2g//7XF1dERMTg+3bt6NatWo4fPgw9u7d+5/b06RJE3h5eaF79+5YsGAB8vLyMHjwYNSvX18awqtRo0aYO3cuNm3ahJo1a2Lz5s24e/cuKleu/Emv5eDggFOnTqF27drQ0NCAkZH8O+k1NDSgoaEhs009tfTCX5WkSdeGyM7Kwbb5QcgUZcLZyxGDZ/4ENfWiusS/TIAoJV363LdhZYhS0nF4w3GkJaXC2tkaQ2YNkAlv1XHw9xAIBFgzdQPycvPhUdUd/iOK1oq5ee42RMkiXA8OxfXgUOl2Ywsj/L5Vsg6MWCzGgbVHkBCbCKGKEKZWJvh+QBvUbqN8X+qrNKwEUUo6jmw4jtSkNNg4l8PAmf2hXxjKKulNMgTCotkAjhUc0GviDziy/jgOrTsKM2tT9JvaG+Uci0IyNPZvgJysHOz4KwiZoiw4VXTAwFn9Zfr2+5/aQKgixN+ztiM3Jxf25e0w9M+foa0nWQBdRUWI8/svYe/ygxCLxTCzNkG7gW1Rs1X1L3Rkvj47rx2FmZ4xfm8/HJYGZgiLiUCLef3xJlWyGKqdiZXMHW/TD0jCXE3vMBLWRhaIS0uUDHjs/kuaZtjm6ZjWYQSW9ZwMc30TvEx+g5VnduD3/Uu/ePvKutMxV2CoqY9+3p1grGmIx0lPMSZkFpKyJIOsFtomEL/TP3rqOhhXvT+MNQ2RlpOOh4lPMOjkZESnvlBUE74JVd18cGZe0azHvwZNAQBsOLETfed++LMCfZwW3ZogJysbf/+5DRmiTLh6OWPEnMFQeyfUZtyLeIhSisIpVmvki7RkEfavP4zUxDTYulhjxJwh0hBY9P9r16MtsjOzsXLWGqSLMlDe2w2//jUB6hrq0jSvX7xGakpRCLLI+1GYMmS69PnGRZsBAA1a1cPQ3wZCKBTixdNXOHtkAVJT0qBnoAtnD2dMWz4Jtk7yQ519605GXYSRpj4GVukGE21DPEx4gmHHpiExU7IYvaWu6XvXCF38WmcwTLQNkZotwv34KPx4cCKeJD8HABSIC+BqbI82rg2hp66NuIwkXHkRhuWh25BboFwzlj+3448vwFjLAEOqd4epthHux0dh4KHJ0kXqrfTMZGbn62voYkrDoTDVNkJqtgjhbx6j5+5xiEp6Jk1jqmOMsbX7wUTbEHEZSTh4/zRW/LPjSzetzNl57iDMDIzxe48AWBqbISwyHC1+64k3yZLwrnbm5WQ+22qqa2B677FwsrSFKDMDR66HoOfckUhJT5WmGbZ8Eqb1GoNlQ6bD3NAULxNfY+WRLfh968Iv3j4qjrPMiYhKl0D8fowh+mosWLAACxYsQHR0NAAgODgYw4cPR1RUFNzd3bFo0SI0aNAAe/fuRbt27RAdHQ1HR0fcvHkTlSpVkilr69atmDt3LsLDw6GjowMvLy+MHDkS7du3/9d6jBs3DuvWrUN2djZat26NGjVqYMqUKUhOTgYATJkyBfv27UNYWJg0T58+fZCcnIx9+/ZJtzVo0ACVKlXCggULAEjWLhk2bBhOnToFoVCIFi1aYPHixbCwKIrzPnnyZKxcuRJZWVn48ccfkZubizt37uDMmTNyywSAdu3awdDQEBs2bAAAHDx4EAEBAYiOjoa1tbX0eH6Mk88PfXRaKl354k8Pa0alo+VvY/89EX0xdZr5KroKVOjC+nOKrgIVOrtpvaKrQIWMNIwVXQUq1GfPH4quAr0jJ5th0b4Wdw/dUnQVqJD4aMy/J1JC/YKHK7oKJVrbZJGiq0BE9J9xAIToAzgA8vXgAMjXgwMgXxcOgHw9OADy9eAAyNeDAyBfDw6AfF04APL14ADI14MDIF8fDoAQkTJgCCwiIiIiIiIiIiIFEDAEFhFRqRIqugKkWDNmzICurq7cR8uWLRVdPSIiIiIiIiIiIiKi/wtngHzjBg4ciC5dusjdp6Wl9YVrQ0RERERERERERET0eXAA5BtnbGwMY2PGZiYiIiIiIiIi+tKEDIFFRFSqGAKLiIiIiIiIiIiIiIiUDgdAiIiIiIiIiIiIiIhI6TAEFhERERERERERkQIwABYRUeniDBAiIiIiIiIiIiIiIlI6HAAhIiIiIiIiIiIiIiKlwxBYRERERERERERECiAUMAgWEVFp4gwQIiIiIiIiIiIiIiJSOhwAISIiIiIiIiIiIiIipcMQWERERERERERERArAEFhERKWLM0CIiIiIiIiIiIiIiEjpcACEiIiIiIiIiIiIiIiUDkNgERERERERERERKYCAIbCIiEoVZ4AQEREREREREREREZHS4QAIEREREREREREREREpHYbAIiIiIiIiIiIiUgDemUxEVLr4PktEREREREREREREREqHAyBERERERERERERERKR0GAKLiIiIiIiIiIhIAQQCgaKrQESk1DgDhIiIiIiIiIiIiIiIlA4HQIiIiIiIiIiIiIiISOkwBBYREREREREREZECCBkCi4ioVHEGCBERERERERERERERKR0OgBARERERERERERERkdJhCCwiIiIiIiIiIiIFYAgsIqLSxRkgRERERERERERERESkdDgAQkRERERERERERERESochsIiIiIiIiIiIiBRAwBBYRESlijNAiIiIiIiIiIiIiIhI6XAAhIiIiIiIiIiIiIiIlA5DYBF9gLqKuqKrQIVsdO0UXQUqdG7WUkVXgd4hAKfMfy0KGvZVdBWoUP1e7Iuvxc6VsxVdBSo0q8WPiq4CvUNFqKLoKlAhcTuxoqtA3zghP88TEZUqzgAhIiIiIiIiIiIiIiKlwwEQIiIiIiIiIiIiIiJSOgyBRUREREREREREpAACAUNgERGVJs4AISIiIiIiIiIiIiIipcMBECIiIiIiIiIiIiIiUjoMgUVERERERERERKQAQobAIiIqVZwBQkRERERERERERERESocDIEREREREREREREREpHQYAouIiIiIiIiIiEgBBGAILCKi0sQZIEREREREREREREREpHQ4AEJEREREREREREREREqHIbCIiIiIiIiIiIgUQCBgCCwiotLEGSBERERERERERERERKR0OABCRERERERERERERERKhyGwiIiIiIiIiIiIFEDIEFhERKWKM0CIiIiIiIiIiIiIiEjpcACEiIiIiIiIiIiIiIiUDkNgERERERERERERKYCA9yYTEZUqvssSEREREREREREREZHS4QAIEREREREREREREREpHYbAIiIiIiIiIiIiUgChQKDoKhARKTXOACEiIiIiIiIiIiIiIqXDARAiIiIiIiIiIiIiIlI6DIFFRERERERERESkAAKGwCIiKlWcAUJEREREREREREREREqHAyBERERERERERERERKR0GAKLiIiIiIiIiIhIAQRgCCwiotLEGSBERERERERERERERKR0OABCRERERERERERERERKhyGwiIiIiIiIiIiIFEAoYAgsIqLSxBkgRERERERERERERESkdDgAQkRERERERERERERESochsKjMOHPmDBo2bIikpCQYGhoqujpfjFgsxoH1R3D+0CVkijLhXNER3QP8YWFj/sF8IXvP4cT2U0hJTIWNizW6De8ERw8H6f6/521HROgDpMSnQENLA84VHdHhp+9gZW9ZrCxRSjp+7zcLyfHJWHBwNrT1tD93M8sssViMzSu34tjek0gXpcPTpzyGTBgEa7tyJea5c+Medv+9F48jHiMxPgm//hmIWg1qyKRJSkjG+sUbcePKTaSnpaNilQoYOPanD5b7rRGLxdi/7jDOH7qIDFEmXLyc0COg67+eG6f3nsXx7cFISUyFrbM1uo3oAqd3zo13y184bhnuXgvHkOk/oXJdH+m+/vWHFEv/06S+8Gtc9T+3qyyS9MUhnHunL3oGdPuovji2/WRhX9jghw/0xYJxS6V9UaVuJem+8ND72Lf2IJ5HvYSGlgZqNa+ODv2/g4qqymduZdkkuYYcxvlDlyR9U9HpI68hZ3G88Bpi62KNbsM7y1xD3i1/0fjluHstHIOnDZA5T+j/U9erOsZ2HghfNy+UM7FEu8n9sP/ScUVXS6lcOXgdF4IuQZQkgqWTBdoMagkbd+sS0989H47gTSFIfp0ME2sTNOvbGO5+rtL9v7b8XW6+5v2aoG6nWp+9/mWJWCzG4Q3HcenIFWSKMuFU0RH+IzrC3Mbsg/nO7ruAUzvPIDUxDdbO5dB5WHs4lLeT7s/NycWe5QcQGhKGvNw8eFRzh//wjtA31gMAPI98iZPbTiHy7hOkp6TD2NIYddrURMOO9aRlRN6Jwv7VhxEb8wa52TkwtjBC7TY10ahT/dI5GF8hsViMQ+uP4sLhov75YVTnf+2fM3vP4+SO00hNTIONczn4D+8IBw976f7zBy/h+qlQPHv0HFkZ2Zh3cAa0dWW/P7x+9gZ7VhxA5N0nyM/Lg7VTObT9sRXcK7u+/3LfBLFYjEMbjuHi4cvIFGXBqaIDuo389744u++CTF90GdZBpi8uHLqE66duSPvizwMzoK2rJVPG8l/W4HnkC6QliaCtp4XyVdzQ7qe2MDQ1KJW2UnEChsAiIipVnAFCSicnJ0fRVfisjm8LxundZ9EjwB+By0dDQ0sDC8cuQ252bol5rp8Oxa5le9GmT0v8unocbJ2tsXDsMqQmpUnT2LvZos/47pi68ReMmDtY8gPj2GUoyC8oVt7GOVtg48wf3uUJ2rgHB7YfxtDAQfhrw1xoamrit2FTkJNd8t9hVmYWHF0dMHj8z3L3i8ViTBszA69exGLSvF+weMtfMLc0x8TBk5CVmVVaTSlzjm07iVN7zqDH6K6YuGIsNDTV8deYJR88N66dDsXOpXvQtncrTFo9AbbONlgwZonMufHWyV0hwAe+i/Sd0APz9syQPirX+XZ/+D267SSC95xBz9Hd8MuKsdDQ1MD8MYv/pS/+wY6lu/Fd79aYvDoQts7W+GvM4hL64rTcL4bPHj/HwvHLUNGvAiavCcTAyT/i1sXbCFq173M2r0w7ti0Yp3afRY+Arpi4fAzUtdSxYOzSf72G7Fy2F237tMRvq8fDxtkaC8Yulds3wUEfPk/o0+loauNWVDiGLP5V0VVRSnfO3sPRVSfQsHt9DF78EywdLbHh1y0QJafLTR8T/gw7Z+2Gb/PKGLzkJ3jUdMfWaTvwOvqNNM34LQEyj/ajvoNAAFSo7fGlmvXVCt4egrN7z6PryE4Ys2QE1DXVsXTCKuTmlPweFBpyE3tXHEDLXs0wfsUoWDuXw9Lxq5D2znvQ7mX7cfdKOPpN7oWRfw1GSnwq1kzZIN3/7OEz6BnqoXdgd/yydhya/9AEB9Yewdl9F6Rp1DXVUa9dbYxaMAS/rh+P5t2b4tD6Y7hw6HKpHIuv0YntpxCy5xx+GNUZ45aNgoamOhaNW/HB/vnn9A3sXr4PrXu3wMRVY2DjbI1F41bIXCNysnNQwc8DLbo3LbGcZRNXoyC/ACPnD0HgyjGwdrbGsomrkZKY+lnbWFac3H4aZ/acQ7dRnTF26UhoaGpg8fh/6YuQm5K+6NUcgStHw9q5HBaPXylzruRk5cKzWnk0/6FJieW4VXJB/0m9MXljIAZM6Yu4lwlY/c75REREVNZxAIS+qIKCAsycOROOjo7Q0tKCj48PgoKCIBaL0aRJEzRv3hxisRgAkJiYCBsbG0yaNAnR0dFo2LAhAMDIyAgCgQB9+vQBADRo0ABDhw7FyJEjYWpqiubNmwMA5s+fDy8vL+jo6MDW1haDBw+GSCRSSLv/X2KxGMFBZ9C6Z3NUquMNG2dr9A3sieT4FNy8cLvEfCd3haBO65qo3bIGyjlYoXuAP9Q11XHxSNEXunpta8PNxwWmViawd7NFu35tkPQmCfGxCTJlndl/HpmiTDTzb1Rq7SyrxGIx9m07iK79OqNmg+pwdHXA6N9HIiEuEZfPXCkxX7Xavug9uAdqNawpd/+LmJe4f+cBhk4YBLcKrrBxsMGQwIHIyc7BmePnSqs5ZYpYLEbwrhC06dkClev4wNbZGj9O7I3khBTcvHCrxHwnd55C3Ta1UKdVTZRzsEKP0V2hrqmOC0dkf+yIefQMJ3eeQt/xPUosS1tXGwYmBtKHmobaZ2tfWSLpi9Pv9IUN+hX2xY0P9MWJnadRr01taV/0HN2tsC8uyaSLefQMJ0roi2unQ2HjVA7f9WkFCxtzuFdyQ6eB7RGy9xwyMzhYKBaLcSooROYa8mNgr8JryAfOk12nUbd1LdRuWXieBHQtdg0BgJhHz3Fix2n0GVfyeUKf7tj1EPy2YS72XTym6KoopYt7L6NqyyrwbVYJ5vZm+G5Ya6hpqCH0xE256S/tvwrXqi6o26kWzO3M0KRXQ1g5W+HKwevSNHrGujKP+1cewNHbAcZWRl+qWV8lsViMkD3n0LxHE3jXrghr53LoNb4bUuJTcevC3RLznQ46h1qtaqBmCz9YOVii68iOUNdQw+Vj1wAAmaJMXD56DR0Gfgf3yq6wc7NFj3H+iLoXjSfhTwEANVtWR6eh7eDq4wzTcibwa+qLGs2r4db5O9LXsXW1QdVGVWDlYAkTS2P4NfWFR1V3RN55UroH5ishFotxOugcWvZsBp86XrBxLoc+gd2REp+CsAt3Ssx3atcZ1G5dE7VaVoeVgyW6BXSGuqY6Lh+9Kk3TuFMDNP+hCRw97eWWIUoR4c3zODT7oTFsnMvB3MYM7X9qg5ysHLx88uqzt/VrJxaLcXr3WbTo0Qw+tSV90XvCD4XnSsl9cXrXGdRuVRM13/bFqM5Q11DHpXf6olGn+oV94VBiOY07N4CjpwNMLI3hXNERzbs1RnTEU+Tn5X/OZhIRESkMB0Doi5o5cyY2bdqEFStW4N69exg1ahR69OiBc+fOYePGjbh+/ToWLVoEABg4cCCsra0xadIk2NraYvfu3QCABw8e4NWrV1i4cKG03I0bN0JdXR0XL17EihUrAABCoRCLFi3CvXv3sHHjRpw+fRrjxo378o3+D+JfJSA1MRUevu7Sbdq6WnD0dEBUuPwvZ3m5eYh58Ewmj1AohIevO6LCo+Xmyc7MxsWjV2BqZQJj86Iv6y+jX+HQxmPoO7EnBAK+Xbwv9sVrJCUkoZJf0Z3/Oro6cK/ohog7D/7vcnNzJXd6qb/zg7pQKISauirCwyL+/workfhXCUiRc244eTgg8l7J58bTh8/g6Vteuk1ybpRH1L0o6bbsrBysnrYBP4zsAgOTkqf+b1mwAyO/G4fpP8/BhcOXpIO335q3ffHucS3qiyi5eSR9EVPsfcrTt7xM/2Vn5WDVtPXoPtJfbl/k5eZBTV124EldQx25Obl4+iDmvzatzCs6T97rG0+HEq8Hebl5eFrCNSQyXLZv1kzfgO4ju8DARL/U2kD0OeXl5uPlo1dwruQo3SYUCuBcyRHPIp7LzfMs4rlMegBw9XUuMb0oSYQH1x7Bt3nlz1fxMirhVSJSE9NQvoqbdJuWrhYcPOwQXThQ8b683Dw8e/gc7lWKwiAJhUK4V3GTDm7EPHqO/Lx8uPsWlWtpZwEjcyM8KeG9DQCy0rM+GMb12aPniLoXDVdvp49tYpn29ntGeV/Z/nH0sMeTe9Fy8+Tl5iHm4XOZPEKhEOWruCGqhDzy6OjrwMLWHFdPXEd2Zjby8/Nx/uAl6Bnpws7N9v9tUpmV8CpBcq74vn+u2H/weh3z8LnMeSAUClHe11V6rvw/0lPTce1UKJwqODCc6Bck/Ir/EREpA64BQl9MdnY2ZsyYgeDgYNSsKbnz3cnJCRcuXMDKlSuxdetWrFy5Er169UJsbCyOHDmCmzdvQlVV8mdqbGwMADA3Ny+2BoirqyvmzJkjs23kyJHS/zs4OGD69OkYOHAgli1bVnqN/MxSC6eA6xXGM35L30hPuu99opR0FBQUQN9Y9gcpPSM9vIp5LbPtzL5z2L1iP7KzcmBha46Rfw6BqprkeOfm5GLNtA3oNLAdTCyMEf9SdmYIAUkJSQAAIxNDme2GxobSff8PWwcbmFmaYf2SvzFs4mBoamlg35YDiH+dgMT4xP9SZaXxNjzC+3/n+kZ6JYZOEKWIUJBfAH2j4udTbEys9PmOJUFwruj0wZBW3//YBuWruEFDQx33/onA5gU7kJWZjSadGv6/TSqzUhJTAMjrC/0S36fSpH1RvP/efZ/asSQILh/oiwp+HjgZdBpXg6+jWkNfpCSm4sDGI5J6JaT8321SFkXniezfvN6/nScFBcXy6BvpI/advtm5dDecKziiUh3vz1xrotKTkZqBggIxdI10ZLbrGukg/nm83DyiJBF0jHSLpU9Lkj+r+GbwLWhoqcOT4a+QmlT4Odao+HvQ233ve/s59v08+ka6eP1MEnYsNTENqmoqxdYx0DfSRWpi8VB9ABB17wlCz4Rh0Iz+xfb96v87RCki5OcXoFWv5qjVuoacEpTP22P1/ucivY/5niHns9Tr975nfIhAIMCIeYOx4te1GNV6AgQCAfSMdDFs9kDofINrDaZI+0L2veZDf9Ml9YWekR5ex7yRm+dD9q46iLP7LiAnKweOnvYY9MeATy6DiIjoa8UBEPpiHj9+jIyMDDRtKhsLNicnB5UrS+6S69y5M/bu3YtZs2Zh+fLlcHX9uEXwfH19i20LDg7GzJkzcf/+faSmpiIvLw9ZWVnIyMiAtnbxD9bZ2dnIzs6WrVt2DtQ11D+2if/Z1ZPXsXnedunzobMGlurr+TWpBo+q5ZGSkIoTO05h1dT1GL94FNQ01LB39UFY2lmiRrNqpVqHsiTk6BksnrFc+nzqgt9K5XVUVVXx69wJWDhtCfwbdYdQRYjKfj6oWssXYnybswyunLyGv+dtkz4fPmtwqbxO2MXbuH/jISatmfDBdG17t5T+387NFtmZOTi+PfibGAC5cvIaNr3TFyNmDSqV1wm7eBsRNx5g8prAEtNUrOaJzgM74O/527Bmxkaoqqmiba+WeHT7MQTCb29hiisnr2PzO30zrBT75v6Nh/ht9YfPE6JvUeiJMPg09IKa+rf3Net6cCi2/RUkfS5vsEERXj55hVW/rUerXs3gUdW92P6RC4YgOzMH0RFPsX/1YZhZm6BqoyoKqGnpunbyH2ydv1P6fPDMnxRWF7FYjO0LgqBnpIvRC4dBTUMNFw9fwbKJqzFhRcAHZ+Aqg2vBodj2Tl8Mmqn4wYam/g1Rq2V1JL5OwpFNx7Fx1hYMnjGAi3MTEZFS+PY+mZPCvF1/4/Dhw7C2tpbZp6GhAQDIyMhAaGgoVFRU8OjRo48uW0dH9k6+6OhotGnTBoMGDcIff/wBY2NjXLhwAf369UNOTo7cAZCZM2di6tSpMtt6B/RA3zE9P7oe/5VPbS84ejhIn+fl5gEA0hLTYPjOF4HUpDTYuli/nx0AoGugA6FQWOzOrbSkNBi8d4e2tq4WtHW1YGFjDidPB4xsOx43L9yCX+OquH/jIV48eYmBjcIAQPrDe8D3gWjVsxm+69v6vza3zKlezw/uFYu+OL9dlDApIRnGpsbS7cmJyXBycyyW/1O4erhgydYFSBelIy83DwZGBhjZewxcPV3+U7llVaXa3nLPjdTEVDnnho3cMnQNdCFUERZbyDn1nXPj/o2HiHsZj+FtxsqkWTZpNVy9XTBu4Ui5ZTt5OuDQpqPIzcktFpJJ2fjU9sbkj+qL1BL7Qk/aF7LvU+/2RcSNB4h7GY9hbcbIpFk2aTXcvF0wbuEoAEBz/8Zo1qURkhNSoKOnjfhXCdi9aj/MrEz/c1vLmkq1veD0Tt/kSvtG9hqS9m/niVBY7I7T1KRU6Syft+fJiPfOk+WT18DVyxljSzhPiBRNW18bQqEAoiTZBc9FSenQfe/O67d0jXSR/t5sD1FSOvTkpI+++xTxzxPgH9jx81W6DPGqVQEOHkVrPkg/xyalyYTKS0tKg43zhz/HphW7VoukM9P0jfWQl5uPDFGmzCyQd9O89So6FovHrECt1jXQoof8BblNrUwAANZOVkhLSsORjSeUcgDEu3ZFOLyzJkdeTuE1IilNZsAhLSkNNv/2PUPOZ6n3Z4J+yIMbj3Dnyj3MOzATWjqaACQ3lESEPsCV49c/uGC3MvCuVQEOHkWfb4r6QiTTF6lJIti4lJNbRkl9kfaJfVFUni50DXRhYWsOS3sL/OI/FU/Cn8KpgsMnl0WfjgNNRESliwMg9MV4enpCQ0MDMTExqF+/vtw0o0ePhlAoxNGjR9GqVSu0bt0ajRpJFt9WV5fMxMjP//fF2EJDQ1FQUIB58+ZBKJTErdy5c+cH8wQGBiIgIEBm29XEL7vgtKa2JjS1NaXPxWIx9I31EXHjAWxdJT9WZaZn4kl4NOp/V0duGapqqrBzt8X9Gw9Rua4kbExBQQEiQh+iYfu6Jb62WCyGWCyWfgAf+Hs/5GbnSvdHP4jBxtlbMHbxSJiV+/Z+WAQAbR1taOsUDZ6JxWIYmRjh1vXbcHaXxIvOEGXgwd2HaN2xxWd5TR1dyeDei5iXeBwRiV6Dun+WcssaeeeGQeG5YecqiRWdmZ6JqIhoNPhe/t+5qpoq7Au/XL97bty/8QAN20vek1r+0BR1W9eSyTe57x/wH9IRPrW9SqxfzOPn0NbTVvrBDwDQ0taE1kf3RT25ZUj6wg4RoQ9QpW4lAIXvUzceoFFhX7T6oRnqtq4tk29y3+noOqRTsb4QCAQwMjUEAFw79Q+MzY1g72b3OZpbppR0nty/8QB271xDov7lGmLvbouIGw+KXUMatZf0Z8sfmhU7T6b8OAP+QzrCu1bF0mga0WehqqaCcq5WiAp7As9akrVxCgrEiAp7gurfyZ/xauthg8iwJ6jVvigs0uObUbD1KD6IGHo8DOVcrWDlZFk6DfjKyf8cq4cHNx5Jf1DPTM9CdEQM6rStJbcMVTVV2LrZ4MHNR/CpI3mvLygowMObj1CvneSaYOdqAxVVFTy48QiV60nC8L1+9gZJb5JkFnp+FR2LRaOXo3qzqviuX6uPaoO4QCwduFE2JX3PeHDjkXRQPDM9C08inqLu97XllqGqpgo7Nxs8uPFIGgKxoKAAD248RIMPfM94X052DgAUm60pEApQUKD8s51LPlceSm9yk5wrT1Hvu5LPFUlfPESld86VBzceoX47+df4jyUu7ANlPReIiOjbwwEQ+mL09PQwZswYjBo1CgUFBahTpw5SUlJw8eJF6Ovrw9TUFOvWrcPly5dRpUoVjB07Fr1798bt27dhZGQEe3t7CAQCHDp0CK1atYKWlhZ0deXfrefi4oLc3FwsXrwYbdu2lVkcvSQaGhrSmShvqad/ufBX8ggEAjTp1ABH/j4OcxtzmFqZYP/aQzA0NUDld+Kuzw9YjEp1vNGog+SHw6adG2L9zM2wd7eDo4c9goPOICcrG7VbSr68x72Mxz8hN+BZtTx0DXWRHJeMo1tPQl1DDRVrVAAAmFubydRFlCK5W9LKzuKDC0h+SwQCAdp1a4vta3einK0VLKwt8PfyrTAxM0bNBkU/lAQO+g21GtRAW3/JrJnMjEy8fPZKuv/1i9eIfBAFPQM9mFtKjvv54IswMNSHmaUZoh8/xcp5a1CjfnVUqcFFVYHCc6NzQxzedAwWNuYwtTTBvnWHYGhiILNexJ+jFqJKXR806tAAANC0S2Osm7kJ9uXt4FjeAcFBp5GdWXRuGJgYyA27YGJhLJ1REHbxDlKTUuHs6QhVdVWE/3MfRzYfR3P/xqXf8K+QpC8a4dCmo9K+2LvuIAxNDFDlnb6YW9gXjQv7olmXRlg7cxMcytvDsbw9goNCCvtCskZUSX1hbGEkM7vj2LaTqOjnCYFQgBvnwnBk6wkMnNIPQhUu2igQCNC4U0Mc/vsYzG3MCq8hhwuvIUV9My9gESrX8XnnGtII62b+DQd3Ozh6OCA4KETmGmJgoi934XNjc6NvcubN56ajqQ0Xawfpc0dLW/g4eyIxNRnP4l4qrmJKonb7mtg9bx/KuZaDjXs5XNp3FTnZufBtWgkAEPTnPuib6KFZX8l7eq3vq2PNuI24sPsy3P1ccfvsXbx89BLthreRKTcrPRt3z4ej5QD5swy+RQKBAA071MOxLcEwszGFiaUJDq8/CgNTffjUKRosXTRmOXzqeEl/tG3UqR7+nr0ddm62cChvh5Dd55CdlYMazf0ASBaHrtnSD3uWH4COnjY0dTSwa/FeOHraw7FwhsPLJ6+waMwKeFR1R6PO9aUzowVCIfQMJd8fzu67AGNzI1jYmQMAHt+OwqldZ1D/E37IL8sEAgEadaqHI3+fgJm1GUytjHFw3REYmBpIf1AHgAUBS1Gprrd0gKNx5wbYOGurpH887HA66Cyys3JQs0V1aZ6UxFSkJqbizQvJ2jovol5BU1sDxuZG0NHXgVMFB2jramPjzC1o3as51DTUcOHwZSS8SoRXDc8veyC+AgKBAI061sfRzSdhbm0GEytjHJSeK0V9sXD0MvjU8ZL2RaPODbBp1lbYu9vCvrw9QnaX1BdpiCvsi5dRL6GhrQljc0Po6OvgScRTPL0fA2cvJ2jraiH+ZQIOrj8Cs3KmMgOKREREZRkHQOiLmjZtGszMzDBz5kxERUXB0NAQVapUQWBgIPz9/TFlyhRUqSKZcj516lScOHECAwcOxI4dO2BtbY2pU6diwoQJ6Nu3L3r16oUNGzbIfR0fHx/Mnz8fs2fPRmBgIOrVq4eZM2eiV69eX7C1n0fzbk2QnZWDzX9uQ4YoEy5eThgxZzDUNIruNI97ES8doACAao18kZYswoH1h5GaKJnGPnzOYOl0aDV1NTy6HYngoDPISMuAvpEeXH1cMH5JQLGF9OjDOvXugKysLCyesQyitHRUqOSB3xdNllk75tXzWKQkF4X6eRT+GBMG/ip9vvqvdQCAJm0aIWDKCABAYnwiVv+1FskJKTAyNULj1g3RrX+XL9SqsqFFt6bIzszBpj+3IkOUCVcvZ4ycO0T23HgZj7R3zg2/Rr4QJadh/7pDSE2UhJIbOXdIsfBwH6KqKkTI3nPYsWQ3ADHMrc3gP6QD6raRf7fkt6Blt6bIyczGxj+3IkOUAVcvZ4yaO/S9voiDKKUojIxfo6pISxZh37pDSE2UhMsaNXfoJ/UFANy5eg+HNh9DXk4ebF2sMeyPgfAqHMgloEW3JsjJysbfhdcQVy/nEq4hRX3z9hqyv/AaYutijRFzhvxfITXo01V188GZebukz/8aNAUAsOHETvSdG1BCLvpYXvUrID0lHac2n4EoUQQrZwv0nvaDNARW8psUmVAkdp626DK+A4I3huDkhtMwsTbGD7/5w8LBXKbcO2fvAhDDuwFnQb2rSdeGyM7Kwbb5QcgUZcLZyxGDZ/4kM2My/mWCzOdY34aVIUpJx+ENx5GWlAprZ2sMmTVAJrxVx8HfQyAQYM3UDcjLzYdHVXf4j+gg3X/z3G2IkkW4HhyK68Gh0u3GFkb4favkM5hYLMaBtUeQEJsIoYoQplYm+H5AG9Ru820sgg4Azbo2Rk5mDrbO24EMUSacvZwwbPbPMv0T91L2GlG1URWIUtJxaMNRpCamwsbZGsNm/yzTP+cPXMThjcelz+ePWAwA6DW+G2q2qA5dA10Mm/Mz9q85jAWjlyI/Lx9WDpYYOL1fieG3lF3Tro2QnZWDrfN3FvaFI4bOktcXRedK1YaVIUoW4dD6Y0hNkvTF0GJ9cQlHNr3TFyOXAAB6juuGmi38oK6hhrDzt3F44zFkZ+bAwEQfntXKo2WPpt/kWkaKwhBYRESlSyAWi5V/jinR/+nsqxOKrgIVstH99sLZfK1epj9XdBXoHQLwC9PXokBcoOgqUKH6vfoqugpUaOfK2YquAhUyVFfuhaXLGhWhiqKrQIX4k8jXo7H1x4WrUzazb85SdBVKNL7yhE9Kv3TpUsydOxexsbHw8fHB4sWL4efnJzft6tWrsWnTJty9excA4OvrixkzZpSYnojo/8X4EERERERERERERPR/27FjBwICAjB58mTcuHEDPj4+aN68Od68eSM3/ZkzZ9CtWzeEhITg8uXLsLW1RbNmzfDixYsvXHMiUnYcACEiIiIiIiIiIlIAIQRf7eNTzJ8/HwMGDEDfvn3h6emJFStWQFtbG+vWrZObfsuWLRg8eDAqVaqE8uXLY82aNSgoKMCpU6c+x2ElIpLiAAgRERERERERERH9X3JychAaGoomTZpItwmFQjRp0gSXL1/+qDIyMjKQm5sLY2Pj0qomEX2juKoVERERERERERERycjOzkZ2drbMNg0NDWhoaMhsi4+PR35+PiwsLGS2W1hY4P79+x/1WuPHj0e5cuVkBlGIiD4HzgAhIiIiIiIiIiJSAIFA8NU+Zs6cCQMDA5nHzJkzP/sxmDVrFrZv3469e/dCU1Pzs5dPRN82zgAhIiIiIiIiIiIiGYGBgQgICJDZ9v7sDwAwNTWFiooKXr9+LbP99evXsLS0/OBr/Pnnn5g1axaCg4Ph7e393ytNRPQezgAhIiIiIiIiIiIiGRoaGtDX15d5yBsAUVdXh6+vr8wC5m8XNK9Zs2aJ5c+ZMwfTpk3DsWPHULVq1VJpAxERZ4AQEREREREREREpgFAgUHQVPouAgAD07t0bVatWhZ+fHxYsWID09HT07dsXANCrVy9YW1tLQ2jNnj0bkyZNwtatW+Hg4IDY2FgAgK6uLnR1dRXWDiJSPhwAISIiIiIiIiIiov+bv78/4uLiMGnSJMTGxqJSpUo4duyYdGH0mJgYCIVFgWiWL1+OnJwcdOrUSaacyZMnY8qUKV+y6kSk5DgAQkRERERERERERP/J0KFDMXToULn7zpw5I/M8Ojq69CtERAQOgBARERERERERESmEAMoRAouI6GvFRdCJiIiIiIiIiIiIiEjpcACEiIiIiIiIiIiIiIiUDkNgERERERERERERKYBQwHuTiYhKE99liYiIiIiIiIiIiIhI6XAAhIiIiIiIiIiIiIiIlA5DYBERERERERERESmAQCBQdBWIiJQaZ4AQEREREREREREREZHS4QAIEREREREREREREREpHYbAIiIiIiIiIiIiUgABGAKLiKg0cQYIEREREREREREREREpHQ6AEBERERERERERERGR0mEILCIiIiIiIiIiIgUQChgCi4ioNHEGCBERERERERERERERKR0OgBARERERERERERERkdJhCCwiIiIiIiIiIiIFEIAhsIiIShNngBARERERERERERERkdLhAAgRERERERERERERESkdhsAiIiIiIiIiIiJSAKGAIbCIiEoTZ4AQEREREREREREREZHS4QAIEREREREREREREREpHYbAIiIiIiIiIiIiUgCBgPcmExGVJr7LEhERERERERERERGR0uEMEKIPyMrPVnQVqFBUymNFV4EKGWkaK7oK9I6KRpUUXQUq9CglQtFVoEI7V85WdBWoUJefxyu6ClTo8pYtiq4CvcNU00zRVaBC6Xnpiq4CERERlSIOgBARERERERERESmAAAJFV4GISKkxBBYRERERERERERERESkdDoAQEREREREREREREZHSYQgsIiIiIiIiIiIiBRAKGAKLiKg0cQYIEREREREREREREREpHQ6AEBERERERERERERGR0mEILCIiIiIiIiIiIgUQMAQWEVGp4gwQIiIiIiIiIiIiIiJSOhwAISIiIiIiIiIiIiIipcMQWERERERERERERAogBENgERGVJs4AISIiIiIiIiIiIiIipcMBECIiIiIiIiIiIiIiUjoMgUVERERERERERKQAAgFDYBERlSbOACEiIiIiIiIiIiIiIqXDARAiIiIiIiIiIiIiIlI6DIFFRERERERERESkAAIB700mIipNfJclIiIiIiIiIiIiIiKlwwEQIiIiIiIiIiIiIiJSOgyBRUREREREREREpABCCBRdBSIipcYZIEREREREREREREREpHQ4AEJEREREREREREREREqHIbCIiIiIiIiIiIgUQCBgCCwiotLEGSBERERERERERERERKR0OABCRERERERERERERERKhyGwiIiIiIiIiIiIFEAAhsAiIipNnAFCRERERERERERERERKhwMgRERERERERERERESkdBgCi4iIiIiIiIiISAEEAobAIiIqTZwBQkRERERERERERERESocDIEREREREREREREREpHQYAouIiIiIiIiIiEgBhGAILCKi0sQBECIFOrfvIk7vPIPUxDRYO1uh07D2sC9vV2L6m2dv4fD6Y0iMTYKZjSm+G9AaFap7SPeLxWIc2XAcl49cRaYoE44VHdFlRAeY25hJ06SnZiBoyV7cvRwOoUAAn7re6Dj0e2hoaQAAXj97gx1/7cbrp6+RmZ4FA1N9+DaqjJa9mkFFVaVYnUJP38TGP7bAq1YFDJjW9zMeHcUTi8U4vOE4Lh25gkxRJpwqOsJ/REeZ4ynP2X0XcErar+XQeVh7OLzTr7k5udiz/ABCQ8KQl5sHj2ru8B/eEfrGetI0u5bsRdTdaLyKfgULOwsErhot8xoJsYmY3P2PYq89evFwOHra/8eWf/3EYjF2r9mHkINnkZ6WATdvV/w4picsbS0/mO/E7lM4vPUoUhJTYOdih96jusPZ00m6//XzN9i6dAce3H6I3Jw8+NTwQu9R3WFgbAAAiHsVj70bDiA8NALJCSkwMjVE7eY10a53W6iq8ZL6llgsxrIly7Fn116kpaWhUmUf/DJpIuwdPu5vc+3qdVj012J07/kDxgWOBQCkJKdg2ZLluHzpCmJfxcLIyAgNGzfAkOGDoaen9y8lfrvEYjF2rA5C8IEQZKSlw93bDT+N+xFWtlYl5gm/GYH9Ww4h6sETJMUnY9ysUfCrX00mzY41Qbh48jIS3iRCVU0FTu6O6DbQH24VXEq7SWXWlYPXcSHoEkRJIlg6WaDNoJawcbcuMf3d8+EI3hSC5NfJMLE2QbO+jeHu5yrd/2vL3+Xma96vCep2qvXZ6/8tqutVHWM7D4SvmxfKmVii3eR+2H/puKKrVaaJxWLsXXsAZw6eR4YoE65ezug9ujssbS0+mC94TwiObjuBlMQU2DrboMfIbnD2dJRJ8/huJIJW70Nk+BMIhULYudpi7LwRUNdQBwCM7hyI+NgEmTydf26PNj1aft5GlmFisRibV27D8X3BSBelw8O7PIZM+BnWduVKzHP3xj3s/nsfHt+PRGJ8En6dOwE1G1SXSZOZkYkNS/7G5bPXkJaSBoty5vjOvzVadWxR2k0qs8RiMXau3o1TB0KQnpaB8t5u6D+uL6w+8Fk3/OZ9HNhyGE8Kr99jZo2EX/2q0v15eXnYvjIINy+F4c3LOGjrasGrakX8MNgfxmZGX6JZRERECsEQWPTZnDlzBgKBAMnJyZ+tzOjoaAgEAoSFhX22Mr8WN0LCsHfFAbTo1RRjV4yEtXM5LBu/GmlJaXLTR92LxsbpW1CzpR/GrRwF79oVsWbSBrx88kqaJnh7CM7tvYAuIzsiYMlwqGuqY/mE1cjNyZWm2TRjC2KjX2PInJ/w0x/9EHknCtvnB0n3q6iowK+ZLwbP+Qm/bhyHDoO/x+XDV3FkQ/Ev/Amxidi38hCcvRyL7VMGwdtDcHbveXQd2QljloyAuqY6lk5YJXM83xcachN7VxxAy17NMH7FKFg7l8PS8atk+nX3sv24eyUc/Sb3wsi/BiMlPhVrpmwoVlaNFtVQpUGlD9Zx2NyfMWPXZOnDzs3m/21umXJoyxEcDzqJvmN74ffVv0FDUx2zAuYjJ7vkvrkcfBVbFm9Hhx+/x/R1U2DnYotZAfOQkpQKAMjKzMasUX8CACYuGofJKyYiLzcPf45biIKCAgDAy6evIC4Q48exvTFn83T0GN4Np/adwY6VQSW+7rdo/doN2LZ5G36dPBGbt2+ClpYWBv00BNnZ2f+a9+6dewjauRtu7q4y29/ExSEuLg4BY0dh9/5d+H3GVFy8cAlTfptaWs1QCvs2H8SRXcfx07gfMWPtNGhoaWLayFnIyc4pMU9WVjYcXO3Rf3TJg9rlbK3Qf3QfzN88C9NXTIG5lRmmj5gpPZ9I1p2z93B01Qk07F4fgxf/BEtHS2z4dQtEyely08eEP8POWbvh27wyBi/5CR413bF12g68jn4jTTN+S4DMo/2o7yAQABVqe8gtkz6djqY2bkWFY8jiXxVdFaVxZOtxnNx9Gn3G9MCklYHQ0NLAn6MXfvD6ffXUdWxbsgvf92mDqWt+ha2LLf4cvRCp77zfPL4biT/HLETFap6YvGoipqyeiCYdGhZbWLhDv++wcN9c6aNpx0al1tayKGjTXhzccRhDAn/G/PWzoamlgd+G/f7ha0ZmFhzdHDBo3E8lpln913qEXr6JMb+PxIqdi/F917ZYPnc1rpy9VhrNUAr7Nx/C0V0nMGDcj5ixdio0tDTwx8jZH+yL7KxsOLjaod/o3nL352Tl4MmDaHTs2w6zN0zD6Jkj8TLmFeaMm19azSAiIvoqcACESEFCgs6iVqvqqNHCD1YOlugysiPUNdRw5dh1uenP7jkPj2ruaOzfEJb2FmjdtwVsXK1xft9FAJK7hM7uOY9mPZrAu3ZFWDuXQ8/xXZESn4rbF+4CAGKfvkbE9QfoNrozHDzs4ezliI5D2+FGSBhS4lMAAKblTFCjhR+sncvB2MIYXrUqwLdxFUTeeSJTn4L8AmyasRWtejeDiZVJKR4pxRCLxQjZcw7N3zmevcZ3Q0p8Km4VHk95TgedQ61WNVCzsF+7Fvbr5WOSL3iZokxcPnoNHQZ+B/fKrrBzs0WPcf6IuheNJ+FPpeV0Htoe9dvV+ddjq6OvA31jfelD3iwdZSMWi3Fs50m0690WVetWgZ2LLQb9NgDJ8UkIPX+jxHxHd5xAw7b1UL91Xdg4WuPHsb2goaGOs4fOAwAe3n6EuNh4/Pxrf9g528LO2RYDf+2PJ/ejER4aAQDwqeGFn3/pB+/qFWFubQ7fupXRulsLXD9b8ut+a8RiMbZs2ooBPw9Aw8YN4ebuhumzpiHuTRxOnwr5YN6M9AwEjpuIyVN/g76+vsw+V1cXzF84Dw0a1oetnS2q1/DDsBFDcTbkHPLy8kqzSWWWWCzG4R3H0LFPO/jVqwoHFzsMmzQISfHJuHbunxLzValZCd1+7oLqDaqVmKZu89rw9vOChbUFbJ1s0HtED2SkZ+Lp45jSaEqZd3HvZVRtWQW+zSrB3N4M3w1rDTUNNYSeuCk3/aX9V+Fa1QV1O9WCuZ0ZmvRqCCtnK1w5WPQZQc9YV+Zx/8oDOHo7wNiKd/F+Lseuh+C3DXOx7+IxRVdFKYjFYhzfGYy2vVqjSt1KsHOxwU+/9EVyQjJunJd/LgDAsR0nUb9tHdRrXRvWjuXQZ0x3qGuq49zhi9I0WxfvRNNOjdGmR0vYOJaDlZ0lqjeqCjV1NZmyNLU1YWhiIH28nQFNkv7Zv+0Q/H/sjJr1q8PR1QGjp45AYnwiLp+9WmK+qrV90WtQd9RqWKPENPdv30fj1g3h7VsRFuXM0bJDMzi6OuBh+KPSaEqZJxaLcWTHMXTo8z2q1fOFvYsdhk4aiKT45P+xd99xTV1tHMB/CXtv2Utkqqg4ce+99x5V66irbm0drbbqW7dWrRNrte5V994bt4Aggih7k7Ahef9Ag5HgaMVA/H37yed9c3POzTnnGm5yn3ueg9uX/IutV823CnqP6IFaxZy/dfV1MWvldNRtXgc2jjZwq1QB30waiOdBYUiISSip7tBHEAiEpfZBRKQK+NeMitW4cWOMHTsWEyZMgImJCSwtLbFhwwakp6djyJAhMDAwQIUKFXD8+HGEh4ejSZMmAAATExMIBAIMHjwYAHDixAnUr18fxsbGMDMzQ/v27REaGvpRbXB2LphZUK1aNQgEAjRu3BgAcPv2bbRo0QLm5uYwMjJCo0aNcPdu4QXICxcuQFNTE5cvX5Zt+9///ody5cohNjb2M4zOf5OXm4eXwZFw93GTbRMKhXD3cZW7CP628IAXcKsuf0e0Zw13WfnE6CSkJYng7lNYRkdfB46eDgh/XSYs4AV09HXg4G4vK+Ne3RUCgQDhQYovWsVHJiDwdhAqVCkvt/3EttMwMNaHb9vaCuuVdW/G0+OtY6SjrwOnt8bzXQXH9ZXcMSg4rm6y4xQR8gr5eflwr164XysHS5iUM0FYQPgnt/OPWZsxvdscLB2/Cg+vFR+YUSXxUfFISUxFxRoVZdt09XXh4uWCkMfPFNbJy81D2NNwVKpZWEcoFKJSDS9ZnbzcPAgEAmi8lcpKQ1MDAqEATx8W/wM9Iz0D+gZ6/7VbKiPyVSQSEhJQ27fwb4OBgQEqe1fCw/sP31v31/kL0LBRA9SpW/xFlLeJxSLo6+tBXZ3pxxSJi4pDSmIKvGtWkm3T09eFq5cLgh9/votOubl5OH3wHHT1deHkWnwax69VXm4+okKi4VK1cLakUCiAS1VnvAx8pbDOy8BXcuUBwLW6S7HlxcliPL0Vguqtqn2+hhN9ZvHRCUhNSkPFGoWzlHT1dVHe0xnPnjxXWCcvNw/hwRGoWL2wjlAoRMUanrI6aclpCA0Ig6GxAeaNWoixHSfh1zG/IVjBufvo9hMY3e57zPpmHo7tOIn8vPzP3MuyKyYyFsmJyahaq4psm56+HtwruiLo4dP/tG8Pbw/cvHQbCXGJkEqleHDnEaIiouBTu+p/bLVqinv9Xfft87euvi4qfObzNwBkiDMhEAiga6D7WfdLRERUmjAAQu+1detWmJub49atWxg7dixGjRqFHj16oG7durh79y5atmyJAQMGwMLCAvv27QMAPH36FNHR0VixYgUAID09HRMnTsSdO3dw9uxZCIVCdOnSRZZS5n1u3Sq4a/7MmTOIjo7G/v37AQAikQiDBg3ClStXcOPGDbi6uqJt27YQiQrSDDVu3BgTJkzAgAEDkJqainv37mHWrFnYuHEjLC3fn2P4S0hPTYdEIoGBib7cdgMTA4iSFKcPSUsSwdDE4J3y+hAlFfQ57XWKJQMFZd68JkoSwcBY/j3V1NSga6iDtCT51FtLx67CxNbTMW/gQrhULo+2g1vJXgt9FIbrx2+h96QeH9vlMudNWoWi42kgl3LhbWLZcZWvY2iiLxvftCQR1DXUoKuvU2yZj6Glo4kuIzti6OyBGPXLULhUcsaG2X5fRRAkJalgtpKRqfwMASNTQ6QkpiqsI0oRQZIvKVLH0NQIqa8/cxUqloeWthZ2rtmD7KxsZGVmY8fqXZDkS5CSmKJwvzGvYnFq71k07dz4v3VKhSQkFNxBaGZuKrfdzMwMCQmJiqoAAI4fO4HAgCCM+37sR71PcnIy1q/dgG49uv37xqq45NefB+PXa9i8YWRqVOxn5VPcuXIX/ZsOQd9Gg3B053HMXjEDhsaGH674lclIy4BEIoW+iXygVN9ED+JkscI64mQx9N75jqBvogdRMeXvnXkALR1NeDH9FZViqYkF51ujd78nmRrKzsXvEqWKFZ6/jUwMkPr671hcVMF558CWf9CofQNMXjwejm4OWDRhGWJeFt741KJbU4yaOxzTV0xCk44N8c+249i1dt9n619Zl/z6u46Jmfw5w9jMWPbavzVqynA4lLfDoHbD0Mm3B2aP+xmjpn6LSj4VP1z5K/Tme+enfNf9N3Kyc7B9zU7Ua+ELXT0GQIiISHXxlkl6rypVquDHHwvyHs+YMQMLFy6Eubk5hg8fDgCYPXs21q5di0ePHsHUtOBiV7ly5WBsbCzbR7du8henNm/eDAsLCwQEBKBSpUp4HwuLgsWmzczMYGVVuOBb06by+XrXr18PY2NjXLx4Ee3btwcAzJ8/H6dPn8a3336Lx48fY9CgQejYseO/GIWv05BZA5CVkYWo59E4+McRnNt9Ec17N0FWRha2LdyBPhO7Q99Ide56v33GH38vK1zHYdSvw5TYmg/TN9JHsx6NZM8dPRyQmpiGs7suwLvu+z9XZc3Vk9ex6betsudTfptQIu9jaGKIcfNGY8viP3Fy7xkIhAL4Nq8NJ3dHhdO/k+KT8b+JS1G7SQ007dhIwR6/Dkf/OYZ5c+fLnq9et/KT9xETHYP/LfgNf2xcCy2tD6cjEYvFGDNyHMq7lMfI70Z88vupqksnr2D9ok2y5zMWTy3R96tU3Qu/bV0AUaoIZw6dx9IfV2LBxp9h9E7AhUqe/6n7qNKkMjQ0+dWeSo9rp27Cb/FfsucTF40pkfeRSqQAgCYdG6Jhu3oAAEc3BwT4B+HS0avoObIrAKB17xayOg4V7KCuoQa/3/5CjxFdiqTK+hqcP34Rqxeskz2fu+yHEnuvw7uOIuhRMGYvmYly1hZ4fC8Aa/+3HqbmpqhWu8qHd6DiLp+8ivWLNsuez1g8ucTfMy8vD8t+XAVIpRg2dXCJvx+9nwCCDxciIqJ/jb+S6L28vb1l/19NTQ1mZmaoXLmybNub2RRxcXFF8rW/ERISgtmzZ+PmzZtISEiQzfyIiIj4YACkAfJ17gAAxo1JREFUOLGxsfjxxx9x4cIFxMXFIT8/HxkZGYiIKEzjpKmpie3bt8Pb2xuOjo5YtmzZe/eZnZ1dZIHenOxcaGp9/h9EekZ6EAqFRe7kFCWLYGCqeBwNTQ1kMzkKy4thYFpwF92b2SGiZBGMzAzlyti52AAADEwNIEqRf8/8/HxkpGXC0FT+bjyTcsYAAGsnK0jyJdi5bC+a9miEhKhEJMUkY/2PW2RlpdKCH54TWkzFD1unwsLG/KPGoTSpXLcinDwdZc/zcgvWFCg6niLYudgq3Ie+7LjKH6e0ZLFsfA1NDZCXm48McabcLJC3y/xbjh4OCPIP/k/7KI186leFS8XCFGx5OQXHJjUpDSbmxrLtqUlpcHS1f7c6AMDA2ABCNWGRO0zTklLl7q7zrl0Jy/b8D6IUEYRqatAz0MXoDuNRrpmFXL3k+GT8MnYRXCtXwNBpg/9jD8u2xk0bobJ34d/ynJyChWwTE5JkQWwASExMhLuHu8J9BDwJRFJiEnp37yvblp+fD/87d7Fzxy7cvn8TamoF69ukp6dj9LffQU9PF8tWLYWGxtd30ao4NetXh6tXBdnzN3/HUpJSYWJeuC5EalIqnNwci9T/VNo62rC2t4K1vRXcKrliTI/vcfafC+g6qNN/3rcq0TXUhVAogDhZfsFzcXI69N+Z5fGGvok+0t/5jiBOTi8ycxQAwh+/QMKrRPSawdlQVLpUq18FLl6FqdxyX/9NSk0Wwfit83daUhocijt/G+krPH+nJotg9HqmgvHr/7VxspYrY+NkjaS4pGLbV96rPPLzJUiISYS1g1Wx5VRV7Ya14F6pMCVr7uvzd3JiKkzfmsWZkpiC8m7ORep/rOysbPy5Zjt++G0aatWvAQBwdnXC8+Aw7P/rEAMgAGrU94Grl4vsueyzkpT2zvk7DU5u/z3VZF5eHpb9sAoJMYmYvXoGZ38QEZHKYwoseq93LywV5MfXkHsO4L3prDp06ICkpCRs2LABN2/exM2bBYvo5eTk/Ot2DRo0CPfv38eKFStw7do13L9/H2ZmZkX2ee3aNQBAUlISkpKK/wEEAAsWLICRkZHcY9fve/51G99HXUMd9m62CL5XmMNVIpHg6b1ncPZSfFHKycsRwXflc74G+QfLyptZm8LQ1ECuTGZ6Fl4ERsDpdRlnL0dkijMREVyYQzz43jNIpVI4eRT/ZVoqlSI/Lx9SqRSWDuUwfeMkTF3/vexRydcLrlVdMHX99zCxMP7k8SgNtHW1YWFrLntYOVrC0NQAT98Zz/C3xvNdBcfVDk/fOa7B90Jkx8nB1Q5q6mpy+419GYfkuGQ4ezn9pz5EhkbBsJgAWlmmo6cDKztL2cPW2QbGZkZ44h8gK5ORnonQgFC4VqqgcB/qGupwdnfCkzuFdSQSCR77ByqsY2BsAD0DXTzxD0Basgg+9avKXkuKT8b8sYvg7O6EETOHQij8uk+lenp6cHB0kD1cKpSHubk5bt4oXDBVLBbj0cPH8K7qrXAftX1rYe+hPdi1f6fsUbGSF9q2b4td+3fKgh9isRgjh42ChoYGVvy+/KNmi3xNdPR0ZAEJa3sr2DnbwtjMGI/uPJGVyUjPQEhAKNwqub5nT/+OVCpFbm7uZ99vWaeuoQYbV2s8vx8m2yaRSPH8fhjsPe0U1rH3tEPoW+UB4Nm95wrL+5+8DxtXa1iX//ou4FLppqOrDUu7crKHrZM1jEwNEeAfKCuTmZ6J54FhqFCxvMJ9qGuow+n1bI43JBIJAvwDZXXMrc1gbG4sl+4KAGJexsLM0qzY9kWEvIRAKCiSYvZroaunAxt7a9nDobw9TMxM8OB24XpdGeIMPH0SAg9vxTcwfIz8vHzk5eVBKJC/w10oFEIq/XBK5K+Bjp4OrOytZI+C87dRkfP3s89w/n4T/Ih5FYtZK6fDwOjr/PdPRERfF84Aoc9GU1MTQMFdu28kJibi6dOn2LBhAxo0aAAAuHLlyn/aJwBcvXoVa9asQdu2bQEAL1++lOWdfyM0NBTff/89NmzYgF27dmHQoEE4c+ZMsRcrZ8yYgYkTJ8ptuxh/5qPb+qmadG+EvxbthL2bHRw9HHBh32XkZOWgdquaAIBtC/+GkbkROg4r6GOjrg2w8vs1OLf7AirW8YL/+Xt4GfwKvSd2B1AQjGrUtQFObj8LCzsLmFmZ4uiWEzAyN4R3/YK7s60cLeFZ0x07l+xBr++7IT8vH3tXHoBPk6owMi+4e+72mbtQUxfCxtka6hrqiAh+iX82HoNP46pQU1eDGtRg4yx/h53O65kM724vywQCAZp0bYgT28/Aws4cZlZmOLrlOIzMDVGlfuHd7isnr0WV+pXRqHN9AEDT7g2xbdFOOLjZw8nDAef3XUJ2Vg7qtKoFoGCsfNvUwv61h6FnoAttPS3sWXUAzl6OcsGv+MgEZGdmIy1JhNzsXLx6Fgmg4Biqa6jjxsnbUNdQg12FgtkoDy4/wvUTt9B3Us8vNURKIxAI0LpnCxzc+g+s7CxhYWOOvRsOwNjcBNUb+MjK/Truf6jR0ActuzcHALTp1RJ//LIRzh5OcPEqjxO7TyE7KxuN2tWX1bl49DJsHG1gaGyAkCfPsG35DrTu1RI2jgX/tpPikzF/zEKYW5mj75heSEspnO1jbMa0P0DB8ek3sC82/LERjo4OsLWzxe8r18CinAWaNmsiKzd8yAg0bd4Effr1hp6eHlxd5QNROjo6MDY2km0vCH6MRlZWFn5d9AvSxelIFxfcUW9iaiILklAhgUCAdr1aY5/fAVjbW6GctQV2btgDE3Nj1GpYQ1Zu7phfULtRDbTpUbDWU2ZGFmJexchej42KR1hwOPQN9WFhZY6szCzs8zuImg2qw8TMGGmpIpzYexpJ8cmo2/TjFrD/2tTr4ot9Sw7CxtUGdu42uHbwJnKyc1G9RVUAwN7FB2FoZoCWQ5oBAOp2qo2NU7fiyr7rcK/liocXHyMqJAqdx7WX229WejYeXw5Am+Et3n1L+gz0tHVRwdZJ9tzZyh5VXLyQlJaCl/FRymtYGSUQCNCqZ3Mc3noMlnblYGFtjv0bD8HYzBg+DarJyi0avxQ+DauiRbeClLete7XAhl+3wNnDEeU9nXFyzxlkZ+agQdt6sv227dMSBzYfhoOLHRxc7XHlxHVEv4jBmHkFaRKfPQ5FaEAYPH3coa2rjWePn2PHqt2o27IO9AxUJ6XrfyEQCNCpT3vs3LwHNvbWsLK1xLZ1O2BqbgrfRrVl5WaOmg3fJnXQoWfBb5TMjExEvSw8Z8RExSL0aRgMjPRRzsoCuvq6qOxTEZtXboWmthbKWVng0d0nOHfsAoZNGPLF+1kWCAQCtO3VGvv9DsLa3hLlrMth54a9MDE3Rs2G1WXlfh7zK2o1qoHWPVoCALIyshDzqjAQGBcVj/DgF9A31IO5lTny8vKwdOZKhD0Nx7TFkyCRFK5zp2+oD3UNXh5SFoGAKbCIiEoSz3D02Tg6OkIgEODIkSNo27YtdHR0YGJiAjMzM6xfvx7W1taIiIjA9OnTP3qf5cqVg46ODk6cOAE7Oztoa2vDyMgIrq6u2LZtG2rUqIG0tDRMmTIFOjqF6YTy8/PRv39/tGrVCkOGDEHr1q1RuXJlLFmyBFOmTFH4XlpaWkXuJtZMK7nUKj5NqkKcKsYxv5NISxbBzsUGoxYOk6VBSo5LlvsiVL6iEwb90A9HN5/AP5uPo5ytOYb9PFgu6NC8dxPkZOVg59K9yBRnonxlZ4xaMFwur/HAmf2wd9UBrJ78BwRCAao0qIzuYzrLXldTE+LMzvOIf5UAqVQKU0sTNOhcD026NyyxsSitmvduguysHPz9ejxdKjtj9IJv5cYzISoR4tTCtCbVm1SDODUdR/1OQpScBlsXW3y3cLhceqtuoztBIBBg409+yMvNh2cNd/Qa31Xuvbcv2Y1nD0JlzxeOWAoA+Gn7DzCzKkhLcGLbGSTFJUOoJoSlfTl88+MAVGv0daQRaN+vLbIzc7Dpf37IEGfAzdsN05ZMlEtZFxsZB1FqYQoZ3+a1IUoRYe/Gg0hNSoWjqwOmLZkot15BdEQMdq3bC3FaOiyszdFpUAe06dVS9vqjW08Q+yoOsa/iMLazfMB0+9UtoAJDhg5GZmYmfp4zHyKRCNV8qmLN+t/l/sa+evkSKckpH73PwIAgPHr4CADQvrX8ek7HTh+Fra3NZ2m7quncvwOyM7Pxx8KNSBdnwMPbDT8umw5NLU1ZmdjIWKSlFgbzQoOeY+53heu6bF1ZkMO/cduGGDNrJIRCISJfROPiseVISxXBwEgfLp4umLd2NuzLK57R8LWr3Kgi0lPTcfavCxAniWHtYolB8/rKUmClxKXKnfMdvOzRc1pXnNl6Hqf9zsHM1hR9Z/WCpVM5uf0+uvgYgBTejVVr7afSooZbFVxYUjgbeNmouQAAv1O7MeS3icXUovdp27cVsjOz4ffbX8gQZ8C1cgVMXjxe7vwdFxUP8Vvn79rNaiItRYT9mw4jNSkNDhXsMHnxOLkUlq16NkduTi52rN4NcVo6HCrYYeqyCbC0LfjMqGuo4+bZ2zi45R/k5uTBwtocrXo2R+tezb9c58uA7gO7ICszC6t+XYt0cTq8qnhi3spZcueM6MgYpKUUpiQLCQzFjJGzZM83Liv4PtSsXRNMnDsOADD1l0nY+vtfWDxrGURpYpSzssDAUX3RtlurL9SzsqdT//avz9+bkfH6/D1z2dR3zt9xRc7fP333q+z5nyu3AwAatW2A72aNQFJ8Mu5cvgsAmDpQfs2XOb/PREUfr5LsEhERkdIIpG+S9xO9o3HjxqhatSqWL18u2+bk5IQJEyZgwoQJsm0CgQAHDhxA586dMW/ePKxZswaxsbEYOHAg/Pz8cObMGYwbNw7Pnz+Hu7s7Vq5cicaNG8vqfMjGjRvx888/IzIyEg0aNMCFCxdw79492eLm9vb2+PXXXzF58mRZ237++WesW7cOjx49gplZwdT3/fv3o0+fPrh16xaqVPm4i8QnX/3zKUNGJUjIheFKDRNt0w8Xoi+mkklVZTeBXgtJDfxwIfoiglKCPlyIvoieI6Ypuwn02vXt25XdBHqLubbFhwvRF5Gel/7hQvRFVDGtqewmKMWe56X373OP8v2U3QQiov+MARCi92AApPRgAKT0YACkdGEApPRgAKT0YACk9GAApPRgAKR0YQCk9GAApPT4WgMge5/vUHYTitW9fF9lN4GI6D/7ulduJSIiIiIiIiIiIiIilcQACCnVr7/+Cn19fYWPNm3aKLt5RERERERERERERFRGcRF0UqqRI0eiZ8+eCl97e1FzIiIiIiIiIiJVIxAw3TMRUUliAISUytTUFKamXE+AiIiIiIiIiIiIiD4vpsAiIiIiIiIiIiIiIiKVwxkgRERERERERERESiAEU2AREZUkzgAhIiIiIiIiIiIiIiKVwwAIERERERERERERERGpHKbAIiIiIiIiIiIiUgKBgCmwiIhKEmeAEBERERERERERERGRymEAhIiIiIiIiIiIiIiIVA5TYBERERERERERESmBgPcmExGVKP6VJSIiIiIiIiIiIiIilcMACBERERERERERERERqRymwCIiIiIiIiIiIlICgUCg7CYQEak0zgAhIiIiIiIiIiIiIiKVwwAIERERERERERERERGpHKbAIiIiIiIiIiIiUgIBmAKLiKgkcQYIERERERERERERERGpHAZAiIiIiIiIiIiIiIhI5TAFFhERERERERERkRIIBUyBRURUkjgDhIiIiIiIiIiIiIiIVA4DIEREREREREREREREpHKYAouIiIiIiIiIiEgJBGAKLCKiksQZIEREREREREREREREpHIYACEiIiIiIiIiIiIiIpXDFFhERERERERERERKIBAwBRYRUUniDBAiIiIiIiIiIiIiIlI5DIAQEREREREREREREZHKYQosIiIiIiIiIiIiJRDw3mQiohLFv7JERERERERERET0n/z+++9wcnKCtrY2ateujVu3br23/J49e+Dh4QFtbW1UrlwZx44d+0ItJaKvCQMgRERERERERERE9K/t2rULEydOxJw5c3D37l1UqVIFrVq1QlxcnMLy165dQ58+fTB06FDcu3cPnTt3RufOnfH48eMv3HIiUnUMgBARERERERERESmBQCAotY9PsXTpUgwfPhxDhgyBl5cX1q1bB11dXWzevFlh+RUrVqB169aYMmUKPD09MW/ePPj4+GD16tWfY1iJiGS4BgjRewjxaSd8KjkSSJXdBHpt9D9Lld0EeksrL09lN4FeO/boibKbQK8tbP2NsptAr13fvl3ZTaDXfPv1U3YT6C0tR7dQdhPotccBYcpuAr0W+cMFZTeB/qWcnBz4+/tjxowZsm1CoRDNmzfH9evXFda5fv06Jk6cKLetVatWOHjwYEk2lYi+QgyAEBERERERERERkZzs7GxkZ2fLbdPS0oKWlpbctoSEBOTn58PS0lJuu6WlJYKCghTuOyYmRmH5mJiYz9ByIqJCTIFFRERERERERESkBEIISu1jwYIFMDIyknssWLBA2UNGRPRJOAOEiIiIiIiIiIiI5MyYMaNImqp3Z38AgLm5OdTU1BAbGyu3PTY2FlZWVgr3bWVl9UnliYj+Lc4AISIiIiIiIiIiIjlaWlowNDSUeygKgGhqaqJ69eo4e/asbJtEIsHZs2fh6+urcN++vr5y5QHg9OnTxZYnIvq3OAOEiIiIiIiIiIhICQQCgbKb8FlMnDgRgwYNQo0aNVCrVi0sX74c6enpGDJkCABg4MCBsLW1laXQGj9+PBo1aoQlS5agXbt22LlzJ+7cuYP169crsxtEpIIYACEiIiIiIiIiIqJ/rVevXoiPj8fs2bMRExODqlWr4sSJE7KFziMiIiAUFiaiqVu3Lnbs2IEff/wRM2fOhKurKw4ePIhKlSopqwtEpKIYACEiIiIiIiIiIqL/ZMyYMRgzZozC1y5cuFBkW48ePdCjR48SbhURfe0YACEiIiIiIiIiIlICAVQjBRYRUWnFRdCJiIiIiIiIiIiIiEjlMABCREREREREREREREQqhymwiIiIiIiIiIiIlEAgYAosIqKSxBkgRERERERERERERESkchgAISIiIiIiIiIiIiIilcMUWEREREREREREREog4L3JREQlin9liYiIiIiIiIiIiIhI5TAAQkREREREREREREREKocpsIiIiIiIiIiIiJRAKBAouwlERCqNM0CIiIiIiIiIiIiIiEjlMABCREREREREREREREQqhymwiIiIiIiIiIiIlEAApsAiIipJnAFCREREREREREREREQqhwEQIiIiIiIiIiIiIiJSOUyBRUREREREREREpAQCAVNgERGVJM4AISIiIiIiIiIiIiIilcMACBERERERERERERERqRymwCIiIiIiIiIiIlICAZgCi4ioJHEGCBERERERERERERERqRwGQIiIiIiIiIiIiIiISOUwBRYREREREREREZESCARMgUVEVJI4A4SIiIiIiIiIiIiIiFQOAyBERERERERERERERKRyGABRQRcuXIBAIEBKSoqym0JERERERERERMUQluL/iIhUAdcAUQGNGzdG1apVsXz5cgBA3bp1ER0dDSMjI+U2rJTw8/PDhAkTykxASCqV4qjfSVw7dgOZ4kyUr+SMXuO7oZydxXvrXTx4BWd3X0Bakgi2LjboMbYLnDwcZK/n5uRi/9rD8D9/H3m5efCs6Y5e47rB0NRAVmbP6gN4/jgc0eHRsHSwxIz1k4q8T8DtIBzbehLR4bHQ0FSHi3d5dB3ZEWZWpp9vEJRAKpXimN9JXD92E5niTDhXckbP8V0/OO6XDl7FOdm4W6P72C5wfGfcD6z9B3ffGvce47rKjXtSbDJ2r9iHkPuh0NLRRK2WNdBhWFuoqakBAP5atBO3Tt0p8t5WjpaYuXkKAODZw1Cc3XUBL0MikZaYhmE/DYZ3/UqfY2jKjO4erdG/UkeY6RgjJPkFFt/YhICEZwrLtqvQGHMajJHblp2Xgwbb+iosP933W3T1aImlN7dgZ8DRz952VRNyJgRPjz1FVmoWjO2NUW1ANZi5mCksG3o+FC+uvkDqq1QAgImTCSr3qFykfFpkGh7ufoj4oHhI8iUwtDVE3bF1oWeuV+L9Kct6eLbGQO/OBZ+LpHD87/pGPIlX/Lno4NoEcxuNlduWnZeDun69Zc/nNhyDDm5N5cpce3kPY0/O+/yNL2OUdf5+FRqF03+fRejjMKSnpsPUyhT12/uiSbeGsn2EPnqOQxuOIiYiDrnZOTC1NEG99r5o2r1RyQxGKSOVSnFg02Fc+OcyMsSZcK3sgkGT+sHK3vK99c7sP4/jf59CalIq7F3s0H9CH7h4OcuVefY4FHs3HERoQBiEQiEcXO0xZcl4aGppAgAm9ZiBhJhEuTo9RnRB+/5tPm8nVVyDyrUxpcdIVHerDBszK3SeMxSHrp1UdrNUTgfnpuju2gam2kZ4nhqBNQ+342ly2AfrNbKthZm1RuFa1F38dHOVbPskn6Fo6Vhfruyd2Ef44drSz952VTOoemeMqtMbFvqmCIh9hlmnVuJ+VFCx5Q219DGt8VC08WgIY20DRKbGYs7p1TgXehMAoKepg6mNhqK1e32Y6ZrgSWwIZp9ahQfRT79Ul4iIiJSGARAVpKmpCSsrK2U3g/6lMzvP4+KByxgwrQ/MrExxxO8Efp++Hj9ungoNTQ2FdfzP38OBdYfRa0J3OHk44Pz+y/h92nrM9psGA5OCCyT71hzCk5uBGDpnIHT0tLF75QFsnOuHiSvlL3bVaV0TL4IiEPk8usj7JEQnYv2sLWjavREGzeyHLHEW9q09hA1z/DD9j4mffzC+oDM7z+PSgSvoN603zKxMcdTvJNZO34CZm6cUO+53z99/Pe7d4OjhgIv7L2PNtA340W+qbNz3rzmMgJuB+GbOAGjr6WDvygPYNHcrvl9ZcPFdki/BHz9sgqGJAb5fOQZpiWnYtmgn1NTU0GFYWwBAt+86oePwtrL3zc+XYNHwpajayFu2LSczB7YuNqjTphY2zdlaUsNUajV3rosJtQZh4bX1eBIfgt4V22Flyx/RY/84JGelKawjzklHj/3jZc+lUqnCco0daqGShSvi0hMVvk7yIm5E4MGOB6g+uDpMXUwRcjIEl367hDb/awNtQ+0i5eOD4uFQxwFmrmZQ01BD0NEgXPrtElr92gq6proAAHGsGOfmn4NzI2dU7FIRGjoaSI1MhZqm2pfuXpnSonw9TKwzBL9e+QOP44PRt1J7rG49G133jEVyVqrCOuKcdHTdU3hekKLo5+Lqy7v46dJq2fOc/NzP3/gySFnn75fBL2FgbIBBM/rBxMIYz5+E4+9leyBUE6JR54ILj5rammjYuR5sy9tAU1sToY/CsHP5Xmhqa6J+e98vM0BKdGzHSZzedw7DZw6BubU59m86hMWTVuDXbT9BU0vxsbl59jb+Xr0Hgyb1g4uXM07uOYvFk1Zg0Y6fYWhiCKAg+LF48gq0798G/Sf0gZqaEBHPXhVZzLbr0I5o1KGB7LmObtG/hfR+etq6ePA8AJtP7sKBuRuV3RyV1Mi2Fr6t3Bur7v+JoOTn6OLSAr/UnYShp2cgNUdUbD1LXTMMr9wLjxIUX0i/HfMQS+5ukj3PleR99rarmo6eTTCn+WhMP74U96ICMaxWd2zv/RsarhuAxIyUIuU1hOr4u+9iJGYk49t9cxAjSoCdkSXSssSyMovbTYG7hTPGHfoVseJEdK3UAjv7LkGT9YMRI0r4gr0jIiL68jifrYwbPHgwLl68iBUrVkAgEEAgEMDPz08uBZafnx+MjY1x8OBBuLq6QltbG61atcLLly8/6j1CQ0PRqVMnWFpaQl9fHzVr1sSZM2fkyjg5OWH+/PkYOHAg9PX14ejoiMOHDyM+Ph6dOnWCvr4+vL29ceeO/F3s+/btQ8WKFaGlpQUnJycsWbJE7nWBQICDBw/KbTM2Noafnx8AIDw8HAKBAPv370eTJk2gq6uLKlWq4Pr16wAK0oENGTIEqampsvGZO3fuxw2uEkilUpzffwmt+jeHd71KsHWxwcBpfZCakIYHVx4XW+/c3kuo27YOfFvXgrWTFXpP6AZNLQ1cP3ELAJApzsT147fQdWRHuFdzhYObPfpP7YXnT8IRFvBCtp8eY7qgUef6MLNWfKf2y+BXkEgkaP9Na1jYmMPezQ7NejRGZGgU8vPyP+9gfEFSqRQX919Gy7fGfcC03khNSMPD94z7+b0XUbdtbdR5Pe49X4/7jRO3ARSM+43jt9B5ZAe4VXOFg5sd+k3thbC3xj3ozlPEvIjFgBl9YVfBFl61PdFuSCtcPnwNebkFPxB19HVgaGooe7x8+gqZ4kzUaV1T1hav2p5o/00bVKlfuQRHqvTqW7EDDgafwZFn5xGW+goLr61HVl42Org2LbaOVAokZqbIHkkKLghb6JpiUp2hmH1pBfIkZfff+JcUfCIY5RuXh3NDZxjZGqH64OpQ11JH2EXFd5DWGVUHFZpXgImjCQxtDFFjaA1IJVLEBcTJyjza+wjWVaxRpXcVmDiZQN9SH7Y+tgoDKlSof6UOOBB0Gv+EnENYyiv8euUPZOVlo5PbJ3wuMot+LnLzc+XKiHLSS7IbZYIyz9++bWqj+5jOcK3iAnMbM9RqUR11WtXEg8uPZO9j72qHGk19YO1kBTMrU9RqUR2eNdwR+ujDd3aXdVKpFCd3n0GHge3g06AqHCrY4dsfhiAlMQV3L98rtt6JXafRqEN9NGxXD7bONhg8uR80tTVx6ehVWZkdq3ajRfdmaN+/DeycbWDtYIXaTWsUCXhp62rD2MxI9tDS0Sqx/qqqE7fPY5bfbzh49YSym6KyulZoiRPhl3Aq4goiRFFYef9PZOfnoJVTg2LrCCHAtBojsC3wIKLT4xWWyZXkITk7TfYQ52aUVBdUxvDaPbDj/lHsfngCIQkvMP3YUmTmZaF3lbYKy/eu2hbGOgb4Zs+PuPPqMV6lxuBGxAMExIUCALTVNdHWoxF+OfcHbr58iPDkSCy97Ifw5EgM9On0JbtGxXhzraI0PoiIVAEDIGXcihUr4Ovri+HDhyM6OhrR0dGwt7cvUi4jIwO//PIL/vzzT1y9ehUpKSno3bu3gj0WJRaL0bZtW5w9exb37t1D69at0aFDB0RERMiVW7ZsGerVq4d79+6hXbt2GDBgAAYOHIj+/fvj7t27cHFxwcCBA2V3Wfv7+6Nnz57o3bs3Hj16hLlz52LWrFmy4Man+OGHHzB58mTcv38fbm5u6NOnD/Ly8lC3bl0sX74choaGsvGZPHnyJ+//S0mMTkJakggePm6ybTr6OnDydED4W4GKt+Xl5uFl8Cu4+7jKtgmFQrj7uMkujkSEvEJ+Xj7cqxfu18rBEiblTBAWEP7R7bN3s4NQKMCNE7chyZcgU5yJW2f84e7jCjX1snsn9ptxf3sMdfR14PjBcY+E+1vHqmDcXWXj/lLBuFs6lINJOWPZfsMCXsDG2VouJZZnDXdkpWchOjxW4XtfP34Tbj6uMLUs22nHPhd1oTo8zMrjdtRD2TYppLgd/QiVy7kXW09HQxuHeqzFPz3X4bdm01De2E7udQEE+KnhWPz1+BCep7wqsfarkvy8fCSHJ8OyYmFaGYFQgHJe5ZD47ONm0ORn50OaL4WmXkH6GKlEiugH0dC30sfF/13Eoe8O4czcM4j0jyyRPqgKdaE6PMxdcOudz8WtyIeobPn+z8WRXn/gaO/1WNJiOsobF/1OUd26Ek7324J93VdhRr1vYaSlXyJ9KEtK2/k7Kz0Luga6xb7+MuQVnj8Jh6t3+Y/tYpkVH52A1KQ0VKzhKdumq6+L8p7OePbkucI6ebl5CA+OQMXqhXWEQiEq1vCU1UlLTkNoQBgMjQ0wb9RCjO04Cb+O+Q3BD0OK7O/o9hMY3e57zPpmHo7tOFmmbxoh1aQuUIOrsRPuxj+RbZNCinvxAfAyrVBsvX4enZCSnYaTLy4XW8bb3AO72q7Axua/YmyVATDQZOrK99EQqsPb2h2Xw/xl26SQ4kqYP6rbeSms08K1LvxfBeCX1hNwf/x+nB2+BWPr9oNQUHC5R02oBnWhGrLzcuTqZeXloKb913nzFBERfV2YAquMMzIygqamJnR1dWVpr4KCiuYGzc3NxerVq1G7dm0AwNatW+Hp6Ylbt26hVq1a732PKlWqoEqVKrLn8+bNw4EDB3D48GGMGVOYQ79t27YYMWIEAGD27NlYu3YtatasiR49egAApk2bBl9fX8TGxsLKygpLly5Fs2bNMGvWLACAm5sbAgIC8Ntvv2Hw4MGfNA6TJ09Gu3btAAA//fQTKlasiGfPnsHDwwNGRkYQCARlIi1YWnJBqp43aS/eMDAxkL32LnFqOiQSSZE6hib6iH1ZcAd1WpII6hpq0NXXKVImLan4Ke3vMrc2w3eLRmDzz39i57K9kEgkcPZyxKgFwz96H6VRWnLBGBQdd33Za+9Kl427/jt1DOTGXU3BuBuYGCAtqeB4ipJFCvcBAKKkNAC2cq+lJqQi8NZTDPxB8VoVXyNjLQOoC9WK3KmelJkCRyNbhXUiUqMw/8oahCS/gL6GLvpX6oiN7X5B7wPfIy4jCQAwsHJn5Ekk2BVwrMT7oCpyRDmQSqTQMpS/u1nbSBui6I/7W/Nw10Nom2jLgihZaVnIy8pD0JEgVOpeCd69vBHzMAZXV15F4xmNUc6j3Gfvhyow1i74XCRmpshtT8xKgZOx4s9FeGokfr70O0KSwqGvqYsB3p2wpeOv6LF3AuIyCgJY117dw7nwm4gSxcLO0Arf1eiHla1mYcg/MyCRSkq6W6VWaTp/P38SBv8L9zHq12FFXvux188Qp4qRny9B24GtULddnY/rYBmWmlgw/kbvjrOpIVKTFB8bUaoYknwJjEwN5bYbmRgg+kVBitC4qIKUMQe2/IPeo7vD0dUeV05cx6IJy/DL1jmy9UVadGsKR3cH6Bno4dnjUOz54wBSElPRd2zPz9pPov/CUMsAakI1pGTLfyaSs1Jhr6/4N1RFM1e0cmqA0efmFLvfO7GPcDXKHzEZCbDWs8AQr274xXciJlycD4mCFIsEmOoaQV2ohoT0JLnt8enJcDFzUFjH0dgG9ZyscODxaQzYNR3OJrb4tfUEqKupY9nlrUjPycSdV48xvv5AhCS8QHx6MjpXbIbqtl4IT+YNJUREpPoYAPlKqKuro2bNwnQ5Hh4eMDY2RmBg4AcDIGKxGHPnzsXRo0cRHR2NvLw8ZGZmFpkB4u1duB6BpWXBj77KlSsX2RYXFwcrKysEBgaiUyf5Kbf16tXD8uXLkZ+fL1sA+mO8/d7W1tay9/Hw8PjofWRnZyM7O1tuW052brG5oT+H22f88feyvbLnii5WlCZpSWnYsWQ3aresgepNqyE7MxtH/E5i009bMeZ/I8rMFNnbZ+5i11vjPuLXoUpszae5eeoOdPS14V3v61rg/HN7FB+MR/HBsucP455id9fl6OLeEn/c2wkPs/Lo7dUWAw5PVWIrvz6B/wTi5c2XaDyjceH6Hq+vj9j62MK9dcHMBRNHEyQ+S0TouVAGQD6jR3HBeBT31uci9in2dl+Jbp4tsdb/bwDAqeeF6X+eJUcgJOkFDvdai+rWFXE76lGRfaqq0nr+jgqLxvpZW9B2YEt41ig602fC8u+QnZmD8MAXOLThKCxszVCjqY8SWlpyrp26Cb/Ff8meT1w05j2l/z2ppOCPU5OODdGwXT0AgKObAwL8g3Dp6FX0HNkVANC6dwtZHYcKdlDXUIPfb3+hx4guxa4NQ1Ta6ahrY2r14Vh+zw9pOeJiy12MvCX7/+FprxCW+gpbW/0P3hYeuB8f+CWa+lUQCgRITE/G1GNLIJFK8CgmGFYG5hjp2xvLLhesDTju0K9Y0n4q7o7fhzxJPh7FBOPgk3Pwtnb7wN6JiIjKPgZA6IMmT56M06dPY/HixahQoQJ0dHTQvXt35OTIT6HV0Cj8EffmQriibRLJx98hKhAIiixMnJtbdLHV//o+ALBgwQL89NNPctv6f98HAyeW3J32letWhJOno+z5mzUfRMkiGJkV3nUoShbBzkXxHbv6RnoQCoUQvTNTIS1ZLEurZGhqgLzcfGSIM+XuIn27zMe4dOgqdPR10HlEB9m2QTP6YlbveQgPjICzl+N7apcelet6wcmzcNH24sddDDsXG4X70JONu/yPPlGyCAav7xg1NDVAvoJxFyWLYPi6jIGJAV4EvSyyDwCy/bwhlUpx88Rt1GxRHeoa/PP9Rkq2CHmSfJjqGMltN9UxLnL3e3HypfkITgyHnWHBXY5VLT1homOEwz3XycqoC9UwvuZA9PZqh857R3+29qsSTQNNCIQCZKfJB5OzUrOgbfT+9TqCjgUh6GgQGk1tBGMHY/l9qglgaCv/eTCwMUBCMBftLE5KVsHnwkzHWG67mbYxEj7yc5EnzcfTxDDZ50KRSFEskjNTYW9o/VUFQErj+Ts6PAarJq9D3XZ10Lp/Cyhi/nqNL9vy1hAli3Bs6ymVC4BUq18FLl7Osue5r49NarIIxubGsu1pSWlwcC2a4g0ADIz0IVQTFpkhkposgpFZwbnG+PX/2jhZy5WxcbJGUpz8ndtvK+9VHvn5EiTEJMLaofTPTqavQ1q2CPmSfBhryZ9rTbSNkJxddKaUtZ4FrPQs8HOd8bJtb36DHeu0EUPPzFC4JkhMRjxSskWw0bNkAKQYSRmpyJPkw1xPPtWthZ4J4tMV/22JFSciT5IvNxMzJPEFLPXNoCFUR64kDy9SotD9rwnQ0dCGgZYu4sRJWNtlNiJSokq0P/RxBCgbNxISEZVVXANEBWhqaiI///25hPPy8uQWIH/69ClSUlLg6en5nloFrl69isGDB6NLly6oXLkyrKysEB4e/l+bDU9PT1y9elVu29WrV+Hm5iab/WFhYYHo6GjZ6yEhIcjI+LSF8z5mfABgxowZSE1NlXv0/q7HJ73Xp9LW1YaFrbnsYeVoCUNTAzy9W5g/OjM9C+GBEXAqJrigrqEOezc7PL1XWEcikSD4XogsIOHgagc1dTW5/ca+jENyXDKcvZw+ur05WblFZnkIhQV/RqRlKPVJceMe/M64v/jguNsi+J1xf3rvmWzc7V+Pe3CRcU+R7dfZyxFRYdFyF8CC/EOgracNK8fCdRQA4NmDUMRHJqBOm/fP2vra5EnyEJT4HDWtC2ecCSBADevKeBT39KP2IRQI4WLigMSMZADA8dCL6HtwEvofmix7xKUn4q/HhzHu1PwS6YcqUFNXg4mTCWKfFK5f82ZBc7MKZsXWCzoahMBDgWg4uSFMy8v/4FdTV4Ops2mRFFriGDH0zJhHvDh5kjwEJYSipk3hDEkBBKhp641HsR//uahg6oCE158LRcrpmsFI2+C9ZVRRaTt/R4fHYOWktajdsgY6DlW8SO67pBKpLHCjSnR0tWFpV072sHWyhpGpIQL8Cy+2ZqZn4nlgGCpUVLwGirqGOpxez+Z4QyKRIMA/UFbH3NoMxubGiHkpv15XzMtYmFkW//cuIuQlBEIBDE0+/gYUopKWJ81HSEo4qlkUrjEhgABVLTwRkPSsSPmXomh8e+ZHjDo3R/a4EX0fD+KDMOrcHMRnKL5Qb65tAkNNPSRlpZRUV8q8XEkeHkY/RX2nwuC0AALUd6oO/1cBCuvcefUYTia2chfRy5vaI0aUgFyJ/N/5zNwsxImTYKStj0bla+Fk8NV3d0dERKRyeAuxCnBycsLNmzcRHh4OfX19hTMfNDQ0MHbsWKxcuRLq6uoYM2YM6tSp88H0VwDg6uqK/fv3o0OHDhAIBJg1a9Ynz65QZNKkSahZsybmzZuHXr164fr161i9ejXWrFkjK9O0aVOsXr0avr6+yM/Px7Rp0+Rme3wMJycniMVinD17FlWqVIGuri50dYsuDKqlpQUtLfm89ZppXzY1gUAgQJOuDXFi+xlY2JnDzMoMR7cch5G5IarUL0x5tHLyWlSpXxmNOtcHADTt3hDbFu2Eg5s9nDwccH7fJWRn5aBOq4Ljq6OvA982tbB/7WHoGehCW08Le1YdgLOXo9ysjfjIBGRnZiMtSYTc7Fy8elaQE9bK0RLqGuqoWMcT5/ddwvE/TxWkwMrIxuHNx2BqaQK7CvILSJclAoEAjbo2wMntZ2FhZwEzK1Mc3XICRuaG8H5r3FdPXgfv+pXQ8PW4N+neCH8t2gl7Nzs4ejjgwr7LyMnKQe1WBenmdPR1UKdNLRxYexi6BrrQ1tPG3lUH4PTWuHvUcIeVoyW2Lfwbnb5tj7QkEY5uOY4GHetCQ1P+T/T147fg6OkAG2f5u00BIDszG/GRhXfDJ8Yk4dWzSOga6MLU0uSzj1lps+PJP5hTfwwCE0PxJP4ZeldsBx11LRwJOQ8AmNtgLOIyErHGfwcAYGiV7ngcH4KXadEw0NJD/0qdYKVvjkPBZwEAqdlipGbLz+7Jk+QjMTMFEWm8U+593Fq74daGWzB1NoVpeVMEnwpGXnYenBsW3JF984+b0DHRgXfPggvzgUcC8WT/E9QZVQe65rrITMkEAKhrq0NDu+BvsHtbd9z4/QbM3c1RzqscYh7GIOpeFBrPaKyUPpYVfz3+Bz81HIvAhGd4HB+CvhU7QEddC4dDzgEAfmo0DvHpiVh9ZzsAYHi1HngUF4yXaTEw0NTDAO9OsNK3wMGnZwAUpDz51qcnzobdQGJmMuwMrTC+1kC8TIvB9Vf3lNbP0kCZ5++osGisnLwOnjXc0bRHI9kaUwKhEAbGBWtMXTx4BablTGDpUJAy7tnD5zi75wIadWnwxcZIWQQCAVr1bI7DW4/B0q4cLKzNsX/jIRibGcOnQTVZuUXjl8KnYVW06NYUANC6Vwts+HULnD0cUd7TGSf3nEF2Zg4atK0n22/bPi1xYPNhOLjYweH1GiDRL2IwZl7BmnjPHociNCAMnj7u0NbVxrPHz7Fj1W7UbVkHegYM4H4KPW1dVLB1kj13trJHFRcvJKWl4GU8z8ufw/5npzC5+jAEp4TjafJzdHFpCW01LZx6cQUAMKX6MCRkpmBLwN6CGQUi+bUjxLkFN6m92a6tpoX+np1wJfIOkrNTYa1XDsMq9kRUehz84x5/2c6VMRtu7sGyjjPwMPop7kUFYnit7tDR0Mauh8cBACs6zEC0KAELL2wAAPzpfwiDa3TBzy3HYsud/XA2tcPYuv2w+c5+2T4bla8JAQQITYyAk6ktZjUbhdDECOx6cFwpfSQiIvqSGABRAZMnT8agQYPg5eWFzMxMbNmypUgZXV1dTJs2DX379kVkZCQaNGiATZs2fdT+ly5dim+++QZ169aFubk5pk2bhrQ0xYtGfgofHx/s3r0bs2fPxrx582BtbY2ff/5ZbgH0JUuWYMiQIWjQoAFsbGywYsUK+Pv7f9L71K1bFyNHjkSvXr2QmJiIOXPmYO7cuf+5/SWlee8myM7Kwd9L9yJTnAmXys4YveBbuTzRCVGJEKemy55Xb1IN4tR0HPU7CVFyGmxdbPHdwuFy6TG6je4EgUCAjT/5IS83H5413NFrfFe5996+ZDeePQiVPV84YikA4KftP8DMyhTu1VwxaGY/nNl1Hqd3nYemtiacvRwxeuHwEl0r5Uto3rsJcrJysPP1uJev7IxRC4a/d9x9mlSFOFWMY34nkZYsgp2LDUYtHCY37l1Hd4RAIMDmn7YiLzcPHjXc0fOtcReqCTHil2+we/l+LB27CpramqjdsgbaDmkl175McSYeXH6Ebt/Jr5vzRsTTl1g1qTBd04G1hwEAtVrWQP9pvf/b4JQBZ8KuwUTbEN9W6w0zHWMEJ4Vj/KlfkJRVsDC6pZ65XFoAQy19zKw3EmY6xhBlixGY+BzDjv6IsNRXyuqCynCo44BsUTYe73+MrNQsGDsYo+GUhrIUWBmJGXIzyULPhUKSJ8G1Vdfk9uPV2QuVuhZcOLarYQefwT4IOhKE+3/dh4G1AeqOrQsLd4sv17Ey6PTzqzDRNsRInz4w0zVGcGIYxp6Yh6TMgs+Flb653Ow9A019/Fh/NMx0jZGWLUZQwnN8889MhKUUfC4kUglcTR3R3rUJDDR1EZ+RjBuR97HW/+8id5h+jZR1/r536SHEKWLcPuOP22cKvyOZWprg5x0/AihIoXh40zEkxiRBqCaEubUZOg1vj3rtVX8RdABo27cVsjOz4ffbX8gQZ8C1cgVMXjxe7rtLXFQ8xKmFge/azWoiLUWE/ZsOIzUpDQ4V7DB58Ti5hdFb9WyO3Jxc7Fi9G+K0dDhUsMPUZRNgaVsQaFLXUMfNs7dxcMs/yM3Jg4W1OVr1bI7WvZp/uc6riBpuVXBhyR7Z82Wj5gIA/E7txpDfJhZTiz7FxchbMNIywEDPzjDRMsLz1Aj8cG2pbGF0Cx0zSKQfv3C5RCqBs6E9WjjUg56GLhIzU3A37jG2Bh7gOeMDDgeeh6meMSY3GgILPVM8iX2G/junIiG9YLaljZGl3LGIEsWj399TMLfFGJwevhkxonhsur0Pv1//W1bGUEsP05sMh7WBBVKyRDgWdAmLLmxEnuTDmRKo5JWVtTSJiMoqgfTdBRZI5fj5+WHChAlISUlRdlPKnNOvjii7CfSaBPxTVVrMOr1V2U2gt7Ty+nAqQ/oyjj16ouwm0GsLW3+j7CbQawaaTPVUWvj266fsJtBbWo5WvFYPfXmPA8KU3QR6LfKHC8puglLcir+s7CYUq5aF6s8WJSLVxzVAiIiIiIiIiIiIiIhI5TAAQqhYsSL09fUVPrZv367s5hERERERERERqSRBKf6PiEgVcA2Qr8DgwYPl1tV417Fjx5Cbm6vwNUtLyxJqFRERERERERERERFRyWEAhODo6KjsJhARERERERERERERfVYMgBARERERERERESkBU00REZUsrgFCREREREREREREREQqhwEQIiIiIiIiIiIiIiJSOUyBRUREREREREREpAwCpsAiIipJnAFCREREREREREREREQqhwEQIiIiIiIiIiIiIiJSOUyBRUREREREREREpAQCMAUWEVFJ4gwQIiIiIiIiIiIiIiJSOQyAEBERERERERERERGRymEKLCIiIiIiIiIiIiUQCJgCi4ioJHEGCBERERERERERERERqRwGQIiIiIiIiIiIiIiISOUwBRYREREREREREZESCMAUWEREJYkzQIiIiIiIiIiIiIiISOUwAEJERERERERERERERCqHKbCIiIiIiIiIiIiUgCmwiIhKFmeAEBERERERERERERGRymEAhIiIiIiIiIiIiIiIVA5TYBERERERERERESmBQMAUWEREJYkzQIiIiIiIiIiIiIiISOUwAEJERERERERERERERCqHKbCIiIiIiIiIiIiUQACmwCIiKkmcAUJERERERERERERERCqHARAiIiIiIiIiIiIiIlI5TIFFRERERERERESkBEyBRURUsjgDhIiIiIiIiIiIiIiIVA4DIEREREREREREREREpHKYAouIiIiIiIiIiEgJBAKmwCIiKkmcAUJERERERERERERERCqHM0CI3kNNqKbsJtBrEkmesptAr6WmiJXdBHrLq7QUZTeBXsvJzlV2E+g1nr9LD3NtC2U3gV5rObqFsptAbzm15rSym0CvOTRzU3YTiIiIqAQxAEJERERERERERKQEAjAFFhFRSWIKLCIiIiIiIiIiIiIiUjkMgBARERERERERERERkcphCiwiIiIiIiIiIiIlEAiYAouIqCRxBggREREREREREREREakcBkCIiIiIiIiIiIiIiEjlMAUWERERERERERGREgjAFFhERCWJM0CIiIiIiIiIiIiIiEjlMABCREREREREREREREQqhymwiIiIiIiIiIiIlIApsIiIShZngBARERERERERERERkcphAISIiIiIiIiIiIiIiFQOU2AREREREREREREpgUDAFFhERCWJM0CIiIiIiIiIiIiIiEjlMABCREREREREREREREQqhymwiIiIiIiIiIiIlEAApsAiIipJnAFCREREREREREREREQqhwEQIiIiIiIiIiIiIiJSOUyBRUREREREREREpARMgUVEVLI4A4SIiIiIiIiIiIiIiFQOAyBERERERERERERERKRymAKLiIiIiIiIiIhICQQCpsAiIipJnAFCREREREREREREREQqhwEQIiIiIiIiIiIiIiJSOUyBRUREREREREREpBRMgUVEVJI4A4SIiIiIiIiIiIiIiFQOAyBERERERERERERERKRyGAAhIiIiIiIiIiJSAoFAUGofJSUpKQn9+vWDoaEhjI2NMXToUIjF4veWHzt2LNzd3aGjowMHBweMGzcOqampJdZGIlIdDIAQERERERERERHRF9GvXz88efIEp0+fxpEjR3Dp0iV8++23xZaPiopCVFQUFi9ejMePH8PPzw8nTpzA0KFDv2Criais4iLoREREREREREREVOICAwNx4sQJ3L59GzVq1AAArFq1Cm3btsXixYthY2NTpE6lSpWwb98+2XMXFxf88ssv6N+/P/Ly8qCuzsubRFQ8zgAhIiIiIiIiIiJSAkEp/q8kXL9+HcbGxrLgBwA0b94cQqEQN2/e/Oj9pKamwtDQkMEPIvog/pVQIY0bN0bVqlWxfPlyAICTkxMmTJiACRMmKLVd9HlJpVIc2XIcV47eQKY4E+UrOaPv9z1Qzs7ivfUuHLiM07vOIS1JBDsXG/Qa1w1Ono4AgPS0dBzxO4GAO0FIjk2BvrEeqtSrjI7ftIWOvs6X6FaZcOngVZzbfQFpSSLYulij+9gucPRwKLb8vYsPcHTLCSTFJMPCzhwdh7dDxdqestelUimO+Z3E9WM3kSnOhHMlZ/Qc31XhsczNycPSMSsRGRqFqX98D7sKtiXSx7Ksb5UOGFq9Oyz0TBAU/xzzzq/Bo9jgYssbaOnh+7qD0cK1Hoy19BEpisOvF/7ApfDbAIA+3u3Qx7s9bA3LAQBCEiOw5uZ2XAq/80X6U5Y1tWuANk7NYKRpiAhxJLYH7UVY2guFZauXq4J2zi1hqWMONaEaYjPiceLFOVyPvi1XzlrPEj1cO8HduALUhEJEiWOw+uEmJGUlf4kulVm9K7XF4GpdYa5rgqeJYVhw6Q88jgtRWLaTRzPMbzZBblt2Xg5q/NFN9txMxxjf+w6Gr0NVGGjqwz/qMRZc/gMRqdEl2Y0yqSTO1wBw+Z9ruH3WHy9DXiErIxtL/vkVuvq6cvuIfRmH/esOI/RxGPLz8mBb3gYdvmkL92quJdLXskgqleKvP/7GyYNnkC5Oh6e3B76bPgK2DkXvOn3j8d0n2LftIJ4FhSIpIRk//jYdvo1ry5XJzMiE3+ptuH7xFkSpIljalEPHXu3Qtlvrku5SmdXBuSm6u7aBqbYRnqdGYM3D7XiaHPbBeo1sa2FmrVG4FnUXP91cJds+yWcoWjrWlyt7J/YRfri29LO3/WvUoHJtTOkxEtXdKsPGzAqd5wzFoWsnld0slTPQpxO+rd0LFnqmCIwLxZzTq/AgOqjY8oZaepjScChauzeAkbYBItNi8fOZNTj/vOBi8pVRO2BvZFWk3p/+BzHr9MoS6weVfdnZ2cjOzpbbpqWlBS0trX+9z5iYGJQrV05um7q6OkxNTRETE/NR+0hISMC8efPemzaLiOgNzgAppRo3bszAxWeiamN5audZnN9/CX2/74Gpa76HlrYmVk5dh9yc3GLr3Dl3F/vWHkS7Qa0xc/1k2LnYYuXUdUhLFgEAUhLTkJKQim4jO2HW5mkYOK0vAm4HYdtvO79Ut0q9u+fv48C6w2g9sAWmrJsAWxcbrJm2AaLXY/iu50/CsXX+dvi2qYWpf3wP73qVsHG2H6LCCi8Sntl5HpcOXEHPCd0wcfU4aGprYu30DQqP5eH1R2BkZlhi/Svr2rg1xIyGw/H7jb/QZfsYBCU8x6auv8BUx0hheQ2hOrZ0XQBbQ0uMPzIfrbcOx6zTKxArTpCViREnYPGVzei6Yyy67RiHGy/v4/eOc1DBzFHhPqlALUsf9HbvgkPPj2Puzf/hpSgSk3xGw0BDX2F5cW46jjw/ifm3l2LW9YW4EnkDQ736oZKZh6yMhY45Ztb4HtHpsVjkvxKzri/E4bATyM0v/u8eAa0q1MeU+sOw7vbf6Ll7AoITwvBHh5+L/VwAgCg7HY23DJA9Wv0pn1d5RdsfYGdkiXHHfkHP3eMRLY7Hhk7zoaP+738Eq6qSOF8DQE52DirW8kTrfi2K3c+amRsgyZdgwtLvMOOPybB1scWamRuQmpT2WftYlu398wD+2XUU380YgaVbFkFbRwuzxv6MnOycYutkZWbB2c0Jo6YWf7Flw7It8L9+D5N/noB1u1ehU+8OWPvbBty4eKskulHmNbKthW8r98b2oEP47vxcPE99iV/qToKRpsF761nqmmF45V54lPBU4eu3Yx6i97HxsseC2+tKovlfJT1tXTx4HoDvVv2o7KaorPYejfFj01FYceVPtN8yAoFxodjWaxHMdI0VltcQquOv3r/BzsgKow7MRdMNgzD9+BLEiONlZTr6jUKNVd1kj75/TwYAHH168Ut0icqwBQsWwMjISO6xYMEChWWnT5/+wUXVg4KKD+R9rLS0NLRr1w5eXl6YO3fuf94fEak+BkCIyhCpVIpzey+hzYCWqFK/MuxcbDB4Rj+kJqTi/pVHxdY7u+cC6rXzRd02tWHtZIU+E3tAU1sT148X3BFk62yNET9/A++6lWBhaw4PHzd0HNoOj64/Rn5+/pfqXql2fu9F1G1bG3Va14K1kxV6TugGTS0N3DhxW2H5i/svw7OmO5r1agIrR0u0G9Iadq62uHzwKoCCY3lx/2W07N8c3vUqwdbFBgOm9UZqQhoeXnkst6+Am4EI8g9GpxHtS7yfZdUQn67Y/fgE9gecRmhSBOacWYWsvGx0q9RKYflulVrCSFsf3/3zE+5GBSAyLRa3Ix/haULhHafnn9/EpfDbeJEShfCUSCy/thUZuVmoauWhcJ9UoKVjE1x6dR1Xom4iKj0GfwbuQk5+DhrY+ios/zT5Ge7GP0R0eiziMxNw+uVFvBJHwdXYRVamW4X2eJjwBHtCDiFC9ArxmQm4H/8Yolzxl+pWmTSwamfse3ISB4PO4nnyS/x8YQ0y87LRxbP4C+dSSJGYkVL4yEyRveZoZIMqVh6Yd3EtnsSFIDwlEvMurIGWmibauDb6Aj0qO0rqfA0Azbo3Rqu+zeHspTgYK04VI+5VPFr2bQY7FxuUs7NAl2/bIycrRy4I/zWTSqU49PcR9PqmB3wb1YazqxMm/TQeSQlJuH6x+NQbNepVx8BR/VC3SZ1iywQ9DEKzdk3gXb0SLG3KoU3XlnB2dUJwgOKZV1+7rhVa4kT4JZyKuIIIURRW3v8T2fk5aOXUoNg6QggwrcYIbAs8iOj0eIVlciV5SM5Okz3EuRkl1YWvzonb5zHL7zccvHpC2U1RWcNq9cDOB8ew59EJhCS+wMwTy5CZm42e3m0Ulu/p3QbG2oYYvn8W7kQ+wavUWNx8+RCBcc9lZZIyUxGfnix7NKvgi/DkSNyIePClukXvoew0V+/7b8aMGUhNTZV7zJgxQ2E/Jk2ahMDAwPc+ypcvDysrK8TFxcnVzcvLQ1JSEqysis5UeptIJELr1q1hYGCAAwcOQEND47MdByJSXQyAlEKDBw/GxYsXsWLFClmUPDw8HBcvXkStWrWgpaUFa2trTJ8+HXl5eR+1T6lUirlz58LBwQFaWlqwsbHBuHHjPqrutm3bUKNGDRgYGMDKygp9+/aVO1lduHABAoEAJ0+eRLVq1aCjo4OmTZsiLi4Ox48fh6enJwwNDdG3b19kZBT++MjOzsa4ceNQrlw5aGtro379+rh9u/Bisp+fH4yNjeXacvDgQQgEhXko586di6pVq2Lbtm1wcnKCkZERevfuDZFI9N6xLKsSohORlpQGj+pusm06+jpw9nRE2JNwhXXycvMQEfxKro5QKISHjxueF1MHADLTM6Gtqw01NbXP1fwyKy83Dy+DI+HuIz+G7j6uCAtQnNYnPOAF3KrLpxrxrOEuK58YnYS0JBHcfQrL6OjrwNHTAeFv7TMtSYS/l+7FgOl9oKmt+Tm7pTI0hOqoaOmKaxH3ZNukkOJaxD1Us/ZUWKdp+Tq4Hx2E2U2/w9Vv/8Y/A9ZhRM1eEAoUnxaFAiHaujWCrroW7kUHlkg/VIGaQA1OBvZ4klR4R64UUgQkPUUFI6eP2oenqRus9MrhafIzAAU/CL3NKyImIw6Tqo3Gika/4sdak1DNwrskuqAy1IXq8LKogBuvCi9sSCHFjVf3UcXKvdh6uho6ODlwE04P3IyVbX+Ai2lhmj9NtYIfmNl5hXfISyFFbn4ufKy9SqAXZdeXPF+/S89QD5b25XDz1G1kZ2YjPz8fl/+5BgMTfTi42f/bLqmUmMhYJCcmo2qtKrJtevp6cK/oiqCHimcUfCwPbw/cvHQbCXGJkEqleHDnEaIiouBTu+p/bLXqUReowdXYCXfjn8i2SSHFvfgAeJlWKLZeP49OSMlOw8kXl4st423ugV1tV2Bj818xtsoAGGjqfda2E5UUDaE6Klu54Uq4v2ybFFJcCfeHj63ic20L17q4G/kE81qOx52xe3Fq6CZ859u32O+1GkJ1dKnYHLsfHi+RPpBq0dLSgqGhodyjuPRXFhYW8PDweO9DU1MTvr6+SElJgb9/4b/zc+fOQSKRoHbt2gr3DRTM/GjZsiU0NTVx+PBhaGtrf/b+EpFqYgCkFFqxYgV8fX0xfPhwREdHIzo6GhoaGmjbti1q1qyJBw8eYO3atdi0aRPmz5//Ufvct28fli1bhj/++AMhISE4ePAgKleu/FF1c3NzMW/ePDx48AAHDx5EeHg4Bg8eXKTc3LlzsXr1aly7dg0vX75Ez549sXz5cuzYsQNHjx7FqVOnsGpVYX7eqVOnYt++fdi6dSvu3r2LChUqoFWrVkhKSvqodr0RGhqKgwcP4siRIzhy5AguXryIhQsXAlA8lvb2ZffHf1pSQWDH0EQ+LYCBiQHSiklrIU5Nh0QiKVLH8L11xDi+7RTqt6/7GVpd9qW/HkMDE/kUPgYmBhAVM4ZpSSIFx0kfotfH8E06EwMFZd68JpVKsf1/O1G/gy8c3Mvuv9uSZqJjCHWhGhIzUuS2J2akwFzXRGEdeyNrtHKtDzWBGr49OAtrbu7AkOrdMKp2H7lybmZOuPvdATwa9w9+ajYW3/0zD6FJESXVlTLPQFMPakI1pOXIfy5Sc0Qw1Co+hZuOujbWNlmMDc2W4/uqI7E9aC8CXgdRDDT1oaOujXbOLfAoMRCL/X/H3biHGFNlKNxNir9A9rUz0X7zuZBfIyUxIwVmxXwuwpNfYfa5FRh3bD5mnFkKAYTY1vV/sNQzAwCEpbxClCgOE3wHwVBLD+pCdXxTrRusDCxgrqd4n1+rL3W+VkQgEGD8ktF4GRKJ79tNx7iWU3B2zwWMXTQSega6H97BVyA5MQUAYGImnw7O2MxY9tq/NWrKcDiUt8OgdsPQybcHZo/7GaOmfotKPhX/035VkaGWAdSEakjJlv/3nZyVCpNizhkVzVzRyqkBlt/zK3a/d2If4Tf/DZh25TdserIHlc3d8YvvRAhLaDFfos/JRNcI6kI1JKTLn78T0pNhoWeqsI69sTXaeDSCmkCIwbtnYOXVbRheqwfG1u2vsHxLt3ow1NbHnkdcu4WUw9PTE61bt8bw4cNx69YtXL16FWPGjEHv3r1hY1OwFldkZCQ8PDxw61ZBCsk3wY/09HRs2rQJaWlpiImJQUxMDLNWENEHcRH0UsjIyAiamprQ1dWVTf/74YcfYG9vj9WrV0MgEMDDwwNRUVGYNm0aZs+eDaHw/bGsiIgIWFlZoXnz5tDQ0ICDgwNq1ar1Ue355ptvZP+/fPnyWLlyJWrWrAmxWAx9/cILwvPnz0e9evUAAEOHDsWMGTMQGhqK8uXLAwC6d++O8+fPY9q0aUhPT8fatWvh5+eHNm0KpvJu2LABp0+fxqZNmzBlypSPHi+JRAI/Pz8YGBRcMBgwYADOnj2LX375ReFYliW3Tt/BjqW7Zc9HLyj5Bb4y07Pw+/T1sHK0RPvBXLBTmS4duILszGy06NNU2U1ROQKBAIkZKZh1ZgUkUgmexD2Dpb45htbojt9vbJeVC0t+hc5/jYaBlh5auTbAolaT0H/PVAZBPrOsvGzMubEQWmpa8DJzR2+3LojLTMDT5GcQvp71dy/uEU5FnAcAvBRHooKxMxrb1ZfNFKH/7kHsUzyILbz7/X5MIA71XYMeFVtj9a3tyJPk4/vjv+KnpuNwddhO5EnycePlfVx+cQeCr/zCojLO18WRSqXYuXwvDEz0MWnFWGhoaeDq0RtYM3MDpq+bCCOz4teAUVXnj1/E6gWFa0DMXfZDib3X4V1HEfQoGLOXzEQ5aws8vheAtf9bD1NzU1SrXeXDO6Bi6ahrY2r14Vh+zw9pOcWnQLwYWbjeSnjaK4SlvsLWVv+Dt4UH7sdzFiepHqFAgMT0ZEw/sRQSqQSPY0NgZWCOEbV7YcXVP4uU7+XdFhee30KcOFEJrSVF3s5y8bXYvn07xowZg2bNmkEoFKJbt25YuXKl7PXc3Fw8ffpUlkXk7t27uHmzIE1lhQryN0GFhYXBycnpi7WdiMoeBkDKiMDAQPj6+sqdGOvVqwexWIxXr17BwcHhPbWBHj16YPny5Shfvjxat26Ntm3bokOHDlBX//A/AX9/f8ydOxcPHjxAcnIyJBIJgIKgipdX4TRcb+/CdCSWlpbQ1dWVBT/ebHsTvQ8NDUVubq4sYAIAGhoaqFWrFgIDP+2HiZOTkyz4AQDW1tZF8kl+jOzsbGRnZ8tty8nOhaaW8nJKeterBKe3cnzn5RSkPEtLFsldwBAli2BXwVbhPvSN9CAUCuUWUH2zD0NT+bvrsjKysHraOmjpamPkvKFQU2f6KwDQez2GomT5H9uiZBEMTBXfoWhoalBkzEXJYhiYFvxbfXOHryhZJLe4uShZDDuXgrtegu89Q1jAC0xsPV1uP4tHrUCNZtXQf7r8bIWvVXJmGvIk+UUWhjTTNUbCO3e/vxGfnoQ8ST4kUols2/OkCJTTM4WGUB25koLPWq4kDxGpBTnzn8Q9Q2UrNwys1hlzzq5UuN+vnSgnHfmSfBhqyn8ujDQNkJZd/B3sUkgRl1mwAP1LcSRs9CzR3qklniY/gygnHXmSfESlx8jViRbHwNXERdHuCEBy1pvPhfzMDDNd4yKzQoqTJ8lHUPxz2BtZy7YFxIeix67x0NfUhYZQHclZadjefTEC4r7uQNSXPl+/z9O7IXh04wmWHF4AHb2C1BAObvYI9H+KGydvo1Xf5h+9L1VRu2EtuFcqTC32ZiH65MRUmJoX3lGdkpiC8m7O//p9srOy8eea7fjht2moVb8GAMDZ1QnPg8Ow/69DDIC8Iy1bhHxJPozfme1hom2EZAXnDGs9C1jpWeDnOuNl2978NjrWaSOGnpmhcE2QmIx4pGSLYKNnyQAIlXrJGanIk+QXmVlprmeC+HTFmRLixEnIk+TJfa99lhiBcvpmct9rAcDW0BL1nXww4sCckukA0UcyNTXFjh07in3dyckJUqlU9rxx48Zyz4mIPgVTYH0l7O3t8fTpU6xZswY6OjoYPXo0GjZsiNzc3PfWS09PR6tWrWBoaIjt27fj9u3bOHDgAAAgJydHruzbi08JBIIii1EJBAJZ8ORjCIXCIic4Re39r+/zxoIFC2BkZCT3+Hv1rk/ez+ekrauNcrYWsoe1kxUMTQ3x9G7hQpqZ6VkIC3wB54pOCvehrqEOBzc7uToSiQRP7waj/Ft1MtOzsHLKWqipq2H0L8OgocnFxN5Q11CHvZstgu+9M4b3nhW7CK2TlyOC78oveBrkHywrb2ZtCkNTA7kymelZeBEYIbuI1m1MZ0xbPxFT13+Pqeu/x4gFQwEAg2f1R7uhihdB/BrlSvLwJDYEvvZVZdsEEMDXvmqx63XcjQqAg5GN3F3rTia2iBMnyv1IfJcQAtk6CFRUvjQf4aKX8DItvNAogACepm54lhr+0fsRQAB1oXrhPtNewEq3nFwZS71ySMz8tJSJX5M8SR4C4p+htl3hzQkCCFDHrgoexHzcGgdCgRCuZk4KA4ninAwkZ6XBwcgaFS0q4FxY8QtHfw2+5Pn6Q3KyC76fCYTyd5MKhAJIJF/nhQNdPR3Y2FvLHg7l7WFiZoIHtx/KymSIM/D0SQg8vItfI+dD8vPykZeXJ5u59kbBd9pP/26q6vKk+QhJCUc1i8IbqgQQoKqFJwKSigZVX4qi8e2ZHzHq3BzZ40b0fTyID8Koc3MQn6H4nGCubQJDTT0kZaWUVFeIPptcSR4exQSjnpOPbJsAAtRz9MHdyACFde68egxHE1u577XOpnaIFSUU+V7bw7s1EjNScO7ZjZLpABERUSnEGSCllKamplweQ09PT+zbtw9SqVR2p9PVq1dhYGAAOzu7j9qnjo4OOnTogA4dOuC7776Dh4cHHj16BB8fn2LrBAUFITExEQsXLpStnXHnzp3/0LMCLi4u0NTUxNWrV+HoWHCxNzc3F7dv38aECRMAFCygJRKJkJ6eDj29goUL79+//8nv9e5YFmfGjBmYOHGi3LZriRc++f1KkkAgQNPuDXFs2ylY2FrA3NoU/2w+BiNzI1StX7imy/KJv6NqA2807tIAANCsR2NsXbgDDm72cPJ0wLm9F5GdlQPf1gULjL0JfuRm52DIzAHIzMhCZkYWAMDASB9CNcZKm3RvhL8W7YS9mx0cPRxwYd9l5GTloHarmgCAbQv/hpG5EToOawsAaNS1AVZ+vwbndl9AxTpe8D9/Dy+DX6H3xO4ACo5lo64NcHL7WVjYWcDMyhRHt5yAkbkhvOtXAgCYWsrf+aWlU7DYnLmNGUwsjL9Qz8uGLXf3Y1GryXgcF4KHMU8xqFoX6GhoY/+TUwCARa0mI1aciKVXtwAA/n5wBP2rdMAPjUfir/uH4WhiixE1e2Pb/UOyfU6sNwSXwm8jWhQPPQ0dtPdoglr23hi6v+RSp6iCUy/OY1jF/ghPi8DztBdo6dAYWmpauBJV8EN7WMUBSMlOwd5n/wAA2jm1QFhaBOIzE6AuVIe3eUX4WtfCtqDCAPTx8LMY5T0ET1NCEZQUjMrmXqhqXgmL/DkT533+vH8QvzT7Hk/inuFRXDAGVOkEHXVtHAw8AwD4pdn3iEtPxIobBekxRtbojQexT/EyNQoGmvoYXK0LrA0ssC/glGyfLV3qISkzFTHieLiaOWFa/eE4F3YT11/eU0ofS6uSOl8DQGpSGtKS0hAXWTBrKvJ5NLR1tWBazgR6hnooX9EJuvq62LpgO9oNbAUNLQ1cOXodidFJqFyHi9UDBcenU5/22Ll5D2zsrWFla4lt63bA1NwUvo0Kx3rmqNnwbVIHHXoWnNszMzIR9bJwNlpMVCxCn4bBwEgf5awsoKuvi8o+FbF55VZoamuhnJUFHt19gnPHLmDYhCFfvJ9lwf5npzC5+jAEp4TjafJzdHFpCW01LZx6cQUAMKX6MCRkpmBLwF7kSvLwQhQpV1+cW5Aa5c12bTUt9PfshCuRd5CcnQprvXIYVrEnotLj4B/3+Mt2TkXpaeuigq2T7LmzlT2quHghKS0FL+OjlNcwFbLx1h4saT8dD6Of4kF0EL6p0Q26mtrY8/AEAGBp++mIESXgfxc3AgD+uncYg6p3xtwWY+B35wCcTW3xnW9f+N05ILdfAQToUbk19j46hXwGZUuVrz2VKBFRSWMApJRycnLCzZs3ER4eDn19fYwePRrLly/H2LFjMWbMGDx9+hRz5szBxIkTP7j+BwD4+fkhPz8ftWvXhq6uLv766y/o6OjIgg/FcXBwgKamJlatWoWRI0fi8ePHmDdv3n/un56eHkaNGoUpU6bA1NQUDg4O+N///oeMjAwMHVpwl/ubts6cORPjxo3DzZs34efn98nv9e5YmpqaKhwzLS0taGlpyW3TFJe+O71b9m6GnMwc7FiyCxniTLhULo+xi0bIzdiIj0qAOLUwXVONpj4Qp6bjiN9xpCWlwc7FFmMXjYDh63RML0NeIjzwBQBgdv/5cu83/+9ZMLMy+wI9K918mlSFOFWMY34nkZYsgp2LDUYtHCYbw+S4ZLkUdeUrOmHQD/1wdPMJ/LP5OMrZmmPYz4Nh41yYSqZ57ybIycrBzqV7kSnORPnKzhi1YDhn3/wLx4MvwVTHCON8B8BC1wSB8c8x7MCPsoXRrQ3KQfLWjLIYcQKGHvgRMxp9i8MD1iJWnIA/7x3Ehjt7ZGXMdI2xqNUUlNMzgSgnA08TwjB0/w+4FsELve9zK/YuDDT10dmlHYy0DBAhisTSu2uQllOQ1sdM2wRSFB4LLTVNDPTsCRMtY+RIchGTHosNj//Erdi7sjJ34x/iz8BdaOfcAv3cuyEmIw6/P9yEkJTnX7x/ZcnJZ1dgqmOE72r3g7muCYISnmPkkTlIzEwBAFgbWMjNtDTU0sfcJmNgrmuCtGwxAuKeYcC+qXie/FJWxlzPFFPqDYWZrjHiM5LxT9A5rLuj3NmSpVVJnK8B4PLhqzi6tXDh2qXjVwEABk7rA9/WtaFvpI+x/xuBQxuPYvmk35Gflw9rJyuMnD+02PRbX6PuA7sgKzMLq35di3RxOryqeGLeylnQ1NKUlYmOjEFaSmEqppDAUMwYOUv2fOOygqB6s3ZNMHHuOADA1F8mYevvf2HxrGUQpYlRzsoCA0f1Rdturb5Qz8qWi5G3YKRlgIGenWGiZYTnqRH44dpS2cLoFjpmcufvD5FIJXA2tEcLh3rQ09BFYmYK7sY9xtbAA++d4Ukfr4ZbFVxYUvh9admouQAAv1O7MeS3icXUok9xJOgCzHSNMbHBEFjomSAgLhQDd02Tzci0MSwnl+4qWhSPgbumYVaz0TgxdCNiRQnYcmc/1t7YKbff+k7VYWdkid0Pj3/R/hARESmbQMokeqVScHAwBg0ahAcPHiAzMxNhYWF48eIFpkyZggcPHsDU1BSDBg3C/PnzZet4NG7cGFWrVsXy5csBFFz4nzBhAiZMmICDBw9i4cKFCAwMRH5+PipXroz58+ejWbNmH2zL33//jZkzZyI6Oho+Pj6YMWMGOnbsiHv37qFq1aq4cOECmjRpguTkZBgbGwMoCLhMmDABKSkpsv3MnTsXBw8elM3iyMrKwtSpU/H3339DJBKhRo0aWLZsGWrWrCmrc/DgQUyZMgWRkZFo1qwZOnbsiG+//VZ2webdfQLA8uXLsXz5coSHhxc7lh+7QNa5KH45LC34o7X0GLfnd2U3gd5St5KrsptAr90JDld2E+i1FV1GK7sJ9JqD/vtvtqEv57uzvym7CfSWU2tOK7sJ9JpDM7cPF6Iv4sX0c8puglK8EJfetdQc9St8uBARUSnHAAjRezAAUnowAFJ6MABSujAAUnowAFJ6MABSejAAUnowAFK6MABSejAAUnp8rQGQCHGosptQLAd9F2U3gYjoP2NifyIiIiIiIiIiIiIiUjkMgHzlLl++DH19/WIfRERERERERERERERlERdB/8rVqFFDbv0MIiIiIiIiIiL6MgQCgbKbQESk0hgA+crp6OigQgUuakVEREREREREREREqoUpsIiIiIiIiIiIiIiISOVwBggREREREREREZESCMAUWEREJYkzQIiIiIiIiIiIiIiISOUwAEJERERERERERERERCqHKbCIiIiIiIiIiIiUQCBgCiwiopLEGSBERERERERERERERKRyGAAhIiIiIiIiIiIiIiKVwxRYRERERERERERESiAAU2AREZUkzgAhIiIiIiIiIiIiIiKVwwAIERERERERERERERGpHKbAIiIiIiIiIiIiUgqmwCIiKkmcAUJERERERERERERERCqHARAiIiIiIiIiIiIiIlI5TIFFRERERERERESkBEyARURUsjgDhIiIiIiIiIiIiIiIVA4DIEREREREREREREREpHKYAouIiIiIiIiIiEgJBAImwSIiKkmcAUJERERERERERERERCqHARAiIiIiIiIiIiIiIlI5TIFFRERERERERESkFEyBRURUkjgDhIiIiIiIiIiIiIiIVA4DIEREREREREREREREpHKYAouIiIiIiIiIiEgJmACLiKhkcQYIERERERERERERERGpHAZAiIiIiIiIiIiIiIhI5TAFFhERERERERERkVIwCRYRUUniDBAiIiIiIiIiIiIiIlI5DIAQEREREREREREREZHKYQosIiIiIiIiIiIiJRAImAKLiKgkMQBC9B75knxlN4Fek0qlym4CvRb8IEzZTaC3WFmbKbsJ9NrjIw+U3QR6TdqZ54zSIj0vXdlNoNceB/D8XZo4NHNTdhPotYizwcpuAr0xXdkNICIiVcQUWEREREREREREREREpHIYACEiIiIiIiIiIiIiIpXDAAgREREREREREREREakcBkCIiIiIiIiIiIiIiEjlcBF0IiIiIiIiIiIiJRBAoOwmEBGpNM4AISIiIiIiIiIiIiIilcMACBERERERERERERERqRymwCIiIiIiIiIiIlICpsAiIipZnAFCREREREREREREREQqhwEQIiIiIiIiIiIiIiJSOQyAEBERERERERERERGRymEAhIiIiIiIiIiIiIiIVA4DIEREREREREREREREpHLUld0AIiIiIiIiIiKir5FAIFB2E4iIVBpngBARERERERERERERkcphAISIiIiIiIiIiIiIiFQOAyBERERERERERERERKRyGAAhIiIiIiIiIiIiIiKVwwAIERERERERERERERGpHHVlN4CIiIiIiIiIiOhrJIBA2U0gIlJpnAFCREREREREREREREQqhwEQIiIiIiIiIiIiIiJSOUyBRUREREREREREpBRMgUVEVJI4A4SIiIiIiIiIiIiIiFQOAyBERERERERERERERKRymAKLiIiIiIiIiIhICZgAi4ioZHEGCBERERERERERERERqRwGQIiIiIiIiIiIiIiISOUwBRYREREREREREZESCARMgkVEVJI4A4SIiIiIiIiIiIiIiFQOAyBERERERERERERERKRyGAD5Sg0ePBidO3dWdjOIiIiIiIiIiL5iglL8ICIq+7gGSCnSuHFjVK1aFcuXLy/ROl+bwYMHIyUlBQcPHlR2Uz6KVCrFUb+TuHbsBjLFmShfyRm9xndDOTuL99a7ePAKzu6+gLQkEWxdbNBjbBc4eTjIXs/NycX+tYfhf/4+8nLz4FnTHb3GdYOhqYHcfm6cuIVzey8h7lU8tPW0Ua2hN3qN71bk/eIjE7BwxFIIhQL8dviXz9P5Uubyoas4t/vi6zG1RrcxneH41pi+697FBzjmdxJJMcmwsDVHh+FtUbG2p+x1qVSK41tP4fqxm8gUZ8K5ohN6jO9a5Ng+uRGIk3+dRtTzaKhraqCCd3kM+3kwAODmydvY8dtuhe8/f88cGJjo//eOl0Gjm/XFlDZDYWVkgQcRQRj71zzcDntUbPnxLQdhVJM+cDCzRoIoGXvvnMSMvUuQnZsDABAKhJjbZSz6+3aElZE5olLi4HflAOYfXvOlulSmda7QAr0928FU2wihKRFY4b8VQUnPP1ivqUMdzKk7Fpdf3cGPV5bJtuuoa+Fb796ob1cDRpr6iE6Px77gkzgcerYku6ESRrcfiCndR8DKxAIPngdi7NrZuB38QGFZdTV1zOj1HQY17w5bM0s8ffUc0zYvwEn/i7IyQqEQc/t9j/5Nu8DKpByikmLhd3oP5v+98kt1qcyQSqU44ncCV49eR6Y4C+UrOaHPhB4fdT4/vesc0pJEsHOxQc+xXeHk6Sh7/cqRa7h99i5ehrxCVkY2Fh/+Fbr6OnL7WPvDRrwKjYQoWQxdAx14+Lih87cdYGxuVCJ9LYukUil2b9iHs4fPI12UAQ9vNwybOgTW9lbF1gm4F4TD248i7GkYkhNSMHnhBNRqVEP2el5eHnb+sRf3rt1HXFQ8dPV1ULlGJfQd3QumFiZfoltlzqDqnTGqTm9Y6JsiIPYZZp1aiftRQcWWN9TSx7TGQ9HGoyGMtQ0QmRqLOadX41zoTQCAnqYOpjYaitbu9WGma4InsSGYfWoVHkQ//VJdKtMG+nTCt7V7wULPFIFxoZhzehUeRL/veOhhSsOhaO3eAEbaBohMi8XPZ9bg/POC43Fl1A7YGxX9TP3pfxCzTvO88Tk0qFwbU3qMRHW3yrAxs0LnOUNx6NpJZTeLiIioVGEAhKiUObPzPC4euIwB0/rAzMoUR/xO4Pfp6/Hj5qnQ0NRQWMf//D0cWHcYvSZ0h5OHA87vv4zfp63HbL9pMDApCHDsW3MIT24GYuicgdDR08bulQewca4fJq4cK9vP2T0XcW7PBXQe0QFOng7IycxBYmxSkffLz8vHll/+gktlZ4Q9CS+RcVC2u+fv48C6f9BzfDc4eTrgwr7LWDt9I37YMlVhkCHsSTj+/GUH2g9tg4p1POF/7h42zdmKyWsnwMa54Iff2V0XcOnAFfSb2gum1qY4tuUk1k3fiBmbJ8uO7f1LD7Fr2V60+6YN3KpVgCRfguiwGNn7VGtcFZ413eXee/v/diEvJ++rDX70rNUGS3vPwMitc3Dz+QNMaDkIJydvgvv01ogXFf3326dOeyzsMQnfbJqJa8/uwc3SCX7DFkIqlWLSzoUAgGnthmNUkz4YtHEankQ+Qw2nStgydAFSM0RYdWbbl+5imdLEvg6+q9YPS+9sRkBiKHq4t8bixtPR/+hkpGSnFVvPSs8co6r2w4O4ohdavqvWH9XKeeGXG2sQkx6PmlaVMaH6ECRkJuNa1N2S7E6Z1rNhByz9dhZGrpqJm0/vY0LnoTg5/y+4D2+M+NTEIuXnD5qC/k26YPjKaQh6GYpW1RviwKwNqDupC+6HPgEATOsxCqPaDcCgJRPx5EUwarh5Y8v3i5GaLsKqw1u+dBdLtdM7z+HC/ksYOL0vzKzMcGTLcayatg6zt0wv9nx+5/w97Ft7EH0m9ICTpyPO7buIVdP+wNytM2Tn85ysXHjV9IBXTQ8c2nhU4X7cqlZA637NYWhqiJSEVOxfdxgb5vphyurxJdbfsubQX0dwfM8pfDdrBMrZWGDX+r34ZcIiLN2xCJpamgrrZGdlw8nVAU3bN8TiGSuKvJ6TlYOwp+HoNqQznFwdIBZlwG/ZNvxv6lIs3DKvpLtU5nT0bII5zUdj+vGluBcViGG1umN779/QcN0AJGakFCmvIVTH330XIzEjGd/um4MYUQLsjCyRliWWlVncbgrcLZwx7tCviBUnomulFtjZdwmarB+MGFHCF+xd2dPeozF+bDoKP5xcjvtRgfimZjds67UITdYPKvZ4/NX7NySmp2DUgbmIESfA1tASadmFx6Oj3yioCQuTTriZO2NHn8U4+vRikf3Rv6OnrYsHzwOw+eQuHJi7UdnNISIiKpWYAquUGDx4MC5evIgVK1ZAIBBAIBAgPDwcFy9eRK1ataClpQVra2tMnz4deXl5762Tn5+PoUOHwtnZGTo6OnB3d8eKFUV/pH2sEydOoH79+jA2NoaZmRnat2+P0NBQ2evh4eEQCATYvXs3GjRoAB0dHdSsWRPBwcG4ffs2atSoAX19fbRp0wbx8fGyehKJBD///DPs7OygpaWFqlWr4sSJE7LXL1y4AIFAgJSUFNm2+/fvy/oJAH5+fjA2NsbJkyfh6ekJfX19tG7dGtHR0QCAuXPnYuvWrTh06JBsjC5cuPCvx6KkSaVSnN9/Ca36N4d3vUqwdbHBwGl9kJqQhgdXHhdb79zeS6jbtg58W9eCtZMVek/oBk0tDVw/cQsAkCnOxPXjt9B1ZEe4V3OFg5s9+k/thedPwhEW8AIAkCHKwJEtxzFweh/UbOYDCxtz2LrYwLtupSLv98/m47C0LwefRlVKZiBKgQv7LqFu29qo07omrBwt0XNCV2hqaeDG6zF918X9V+BR0x3NejWGlaMl2g1pDbsKtrh86CqAgmN7cf9ltOzXDJXrVYJteRv0n9YbqYlpeHS14MJifn4+9q85jI7ftkf9Dr4oZ2cBK0dLVGtcOM6aWhowNDWUPYRCIULuh6JOm1olPyil1MRWQ7Dh4m74XdmPwKhQjNw6Bxk5WfimYdGZSwBQt0I1XA25i79vHMGLhEicfnIVf988glrlveXKHLp3FsceXMSLhEjsu3MSp55ckStDivX0aIMjoedxPOwSXqRFYsntzcjKy0bb8o2KrSMUCPBjne+w5fFeRKXHFXm9opkrToZfxv24QMSkJ+Cf0PMITYmAp5lLSXalzJvYZRg2HP8bfqf3IDAiBCNXzUBGdia+adlLYfkBTbvi112rcfz2eYTFRGDd0b9w7PY5TOo6XFamrmcNHLpxCsdun8OLuFfYd+UYTt29hFruqns++DekUinO7buI1v1bokq9yrBzscGg6X1fn8+Ln512bs8F1GvrC982tWHtZIU+3/eAppYmrh2/KSvTtHsjtOrbHM5eTsXup1mPxnD2coKZlSlcKjmjVZ9mCA98gfy8/M/ZzTJLKpXi2K4T6Dq4E2o2rA7HCg4YM3skkhNScPuSf7H1qvlWQe8RPVCrcU2Fr+vq62LWyumo27wObBxt4FapAr6ZNBDPg8KQEMOL7+8aXrsHdtw/it0PTyAk4QWmH1uKzLws9K7SVmH53lXbwljHAN/s+RF3Xj3Gq9QY3Ih4gIC4gt8l2uqaaOvRCL+c+wM3Xz5EeHIkll72Q3hyJAb6dPqSXSuThtXqgZ0PjmHPoxMISXyBmSeWITM3Gz292ygs39O7DYy1DTF8/yzciXyCV6mxuPnyIQLjCmd8JmWmIj49WfZoVsEX4cmRuBGheCYifboTt89jlt9vOHj1xIcLU6ml7CRXTIBFRKqOAZBSYsWKFfD19cXw4cMRHR2N6OhoaGhooG3btqhZsyYePHiAtWvXYtOmTZg/f36xdezt7SGRSGBnZ4c9e/YgICAAs2fPxsyZM7F7t+K0OR+Snp6OiRMn4s6dOzh79iyEQiG6dOkCiUQiV27OnDn48ccfcffuXairq6Nv376YOnUqVqxYgcuXL+PZs2eYPXu2XJ+XLFmCxYsX4+HDh2jVqhU6duyIkJCQT2pfRkYGFi9ejG3btuHSpUuIiIjA5MmTAQCTJ09Gz549ZUGR6Oho1K1b91+Nw5eQGJ2EtCQRPHzcZNt09HXg5OmA8NeBinfl5ebhZfAruPu4yrYJhUK4+7jJghsRIa+Qn5cP9+qF+7VysIRJOROEBYQDAIL8gyGVSJGSkIZ5Qxbhx14/Y9PPfyI5Llnu/Z7eC8G9Sw/Qc1zXz9XtUqdgTCPh9s6Yuvm4FnscwgJeyB0DAPCo6SYr/+bYvr1PHX0dOHo6yI7Tq5BIpCakQiAQ4H8jlmFWz5+xbsZGRL01A+Rdt077Q1NLA1Uafp0X5jXUNFDdqSLOBFyTbZNKpTjz5Bp8XaoprHPt2T1Ud6qIms6VAQDOFnZo690Ixx5elCvTzKsOXC2dAADe9u6o71odxx9dKrnOqAB1oRrcTJzhH1sYsJVCCv/Yx6ho5lpsvUEVuyIlOxXHniu+I/RJYgjq2fjAXKcghUy1cl6wN7DC7ZjiLyR/7TTUNVDdtTLO3L8i2yaVSnHm/hX4evoorKOloYmsnGy5bZk5WahfsfBi77XAO2hWtR5cbZ0BAN7OnqhfsSaO37nw+TtRhiVGJxacz6u/ez53xPPX59135eXmISL4ldy5WigUwqO6q+w88W+kp6Xj1ll/lK/oBDV1tX+9H1USFxWPlMRUeNcsvMlDV18XFbxcEPz4076HfkiGOBMCgQC6Brqfdb9lnYZQHd7W7rgcVhhwkkKKK2H+qG7npbBOC9e68H8VgF9aT8D98ftxdvgWjK3bD0JBwU9aNaEa1IVqyM7LkauXlZeDmvaVS64zKkBDqI7KVm64Ev7O8Qj3h49t8cfjbuQTzGs5HnfG7sWpoZvwnW9f2fFQ9B5dKjbH7ofHS6QPRERERMVhCqxSwsjICJqamtDV1YWVVUG6nB9++AH29vZYvXo1BAIBPDw8EBUVhWnTpmH27NkK6wCAmpoafvrpJ9lzZ2dnXL9+Hbt370bPnj0/uW3dusnfRb1582ZYWFggICAAlSoV/nCcPHkyWrVqBQAYP348+vTpg7Nnz6JevXoAgKFDh8LPz09WfvHixZg2bRp69+4NAFi0aBHOnz+P5cuX4/fff//o9uXm5mLdunVwcSm4E3jMmDH4+eefAQD6+vrQ0dFBdna23BiVVmnJBelh3qS5eMPAxED22rvEqemQSCRF6hia6CP2ZcGd1GlJIqhrqBXJEW5ooo+0JBEAICE6EVKpFKd2nEG37zpDR08HR7Ycx+qpf2DGhslQ11CHODUdf/1vJwbN6AsdPe3P0ufSKF02pvIppQxM9BH3sujd6QAgShYVLW9sIBtfUbLo9T7eObbG+hC9LpMYXZCu6cSfp9B5ZAeYWZni3J6LWD1pLX7wmwY9w6IXT24cvwWfptWgqaU4nYqqMzcwgbqaOmLfSecTm5YID+vyCuv8feMIzPVNcOWHHRBAAA11Daw99zcWHPlDVmbh0fUw1NFH0ILjyJfkQ02ohh/2LcOO6/+UaH/KOiNNA6gL1ZCclSq3PTkrDQ6GNgrrVDZ3Q9vyjTHsxIxi97vCfysm1xyKfZ1WI0+SB4lUisW3N+JhfPF5yb925oamBZ+NZPm7zmOTE+Bhp3jmzEn/i5jYdTguPb6J0OgXaFa1PrrWbQM1tcKLWQt3r4GhrgGC1p8v/Gxs/Q07zh8sye6UOamv/64bvnNeePu8+64353NDBd8BYiMUn3ve58D6f3Dx4BXkZOXA2csRo34Z/uFKX4mUxBQAgJGpodx2I1NDpCSmKqjx7+Rk52D7mp2o18IXunoMgLzNVNcI6kI1JKTLp6qMT0+Gi5ni9dYcjW1Qz8kKBx6fxoBd0+FsYotfW0+Aupo6ll3eivScTNx59Rjj6w9ESMILxKcno3PFZqhu64Xw5Mgv0a0yy0R2PORvfEp4z/GwN7aGr2M1HHpyBoN3z4CTiS3mtxoPdaE6Vlz9s0j5lm71YKitjz2PuD4FERERfVkMgJRigYGB8PX1hUBQOPGwXr16EIvFePXqFRwcil+M+ffff8fmzZsRERGBzMxM5OTkoGrVqv+qHSEhIZg9ezZu3ryJhIQE2cyPiIgIuQCIt3fhHeiWlpYAgMqVK8tti4t7fUE+LQ1RUVGy4Mjb/Xvw4NOmROvq6sqCHwBgbW0te59PkZ2djexs+Ttfc7JzS/TC8u0z/vh72V7Z81G/Diux9/oQqUSK/Lx8dB/TBZ41CtaYGPxDf8zsMRfB95/Bq6YH/l66BzWaVkMFb6adKQkSiRQA0LJvM1R9PaOj35RemN1nPu5feoB67X3lyocFhCM2Ig4Dpvf54m0tyxp51MLMDiMw+s+fcPP5Q1Qo54AV/X7Ajx1HyxY571mrDfrV6YC+f0zCk8hnqOrgieV9ZyAqJQ5/Xj2o3A6oEB11bfxQZxQW396I1BxxseW6uraEl1kFzLi0GDHpCahSzgMTqg9GQmYy/GOffMEWq7bxf8zFhnGLELT+PKSQIjT6Bbac3i2XMqtnw/bo16Qz+v5vLJ68CEbV8hWxfMQcRCXF4s8ze9+zd9V264w//l5aONN21ALlBxta9GqCum1qIyk2Gcf+PImtC7dj9K/D5b5Xfi0un7yK9Ys2y57PWDy5xN8zLy8Py35cBUilGDZ1cIm/39dAKBAgMT0ZU48tgUQqwaOYYFgZmGOkb28su7wVADDu0K9Y0n4q7o7fhzxJPh7FBOPgk3Pwtnb7wN7pU705HtNPLIVEKsHj2BBYGZhjRO1eCgMgvbzb4sLzW4gTF12Dioi+vnMzEdGXxACICtq5cycmT56MJUuWwNfXF/9v787Doqz6/4G/h132RQZFEAZB3AAXwtTczbTcW0xzyT1LQVBSc7c0UkE0TSs3NDee1NInd1EsNBUFRFGRRXFBQVAQUFlmfn8QoyNo9v09zJm436/r4rqYc98D7+FcM8Ocz33OsbCwwOLFi3Hq1Km/v3MVevfuDRcXF/z4449wdHSEUqlEs2bNUFysOb3c0PBpoaDiw/Xzbc8vm/Uyen9tmKdSqdRtJSUllc579ndU/J5n7/Oqvv76a42ZMwAwJHAQhgUN/sc/61V5tW0K18Yu6tulJeX7uzy8/xBWdk+vSnx4/yGcGtSr8meYW5lBT09PPcOgQv79Aljall9FamlrgdKSMhQVPNKYBaJxzl+/r46Lg/q4hbU5zC3NcD/rAQAgOe4qEk9cxJHI8mVqVFBBpVTB/81gDAp6D216tv4//R10jZn6b6o5IPvwfkGlGRwVLGwsKp//4KH671txv0p9+6AA9RqUXxlvZVd+jsMzfWBgZIDadW3VffCsk3tPo14DRzg3dPqHj7DmuPfwPkrLSuFgZafR7mBphzt5Va+3/mX/AGw6sRtrj5cP1l64mQwzY1P88PF8LNizCiqVCos/+Bwhe3/A9lN71ee42Dlieq9xLIC8RF7xQ5Qqy2BjYqXRbmNiidxHla+qrmfugLrmcixsP1ndpvfX+8eRDzZi6N4puPfoPsZ4D8TMP5biz8x4AEBa3g24W7tgYKN3WAB5gXv5ueXPDZvaGu0ONrVx53521ffJy0X/L8fA2NAYdpbWuJ1zFyEjpyPtTob6nMWjZiAk8jtsjy6fDXXh2hW4yOth+gefSroA4t22KVwbPx1ULy0ufz/Pv18AK7unz4f8+wVwcq96NlTF+3n+c+/nD+8/hOVzMxVehbmVOcytzOHgLEcdFwfMGDgP6UnX4dbU9R//rH873zdawqPJ04s3Sv76fysvNx82tW3U7Xm5+XBt+OILjF5VaWkpls74Fvfu5GD2iumc/VGF3KI8lCrLUNvMVqPd3swG2c/NCqlwtyAHpcoyKFVPP09czbkOB3M7GOoZoERZiusPbuO9nyahlqEJLIxNkVWQi1X9ZyPjwe1qfTz/dvfV/WGj0V77Jf2RVZD716zMp/2RkpMB+TP9UaGepQPecG2JcbvmVM8DICIiInoJ7gGiQ4yMjFBW9nRzysaNG+PkyZMag/kxMTGwsLCAk5NTlfepOKdt27b49NNP0aJFC7i7u2tsWv5P5OTk4MqVK5g5cya6du2Kxo0b4/79+39/x79haWkJR0dHxMTEVMrepEn5OrP29vYAoN7QHCjfBP2fqupvVJXp06cjLy9P4+vDz97/x7/vnzAxNYF9vdrqrzouDrC0tcCVc0/Xn35U+BjXLmXAtYlLlT/DwNAAzg2dcCXu6X2USiWS465C8dd96ns4Qd9AX+Pn3r2RhftZ99WbqLo1LV/P/dklngrzi1CQXwhbh/IPQ5O/9ce0H4LUX+983AMmpsaY9kMQfN6oOWsrl/9N6yH5XIq6rfxvmvLCflA0cUFynOa64VfOXlWfb1fXFpa2FkiOe/ozHxc+xvVLGep+cvZwgoGhAbJuPh2cLCstQ86d+7CVa34gffLoCeKjz0t683MAKCkrwdlrF9G1ydPZMTKZDF2btMHJ1Lgq72NqbFKpGFumLH+NkP119VX5OapK5+hJ8Mrpf6JUWYbk++lo5dBU3SaDDC0dmuFiTuV19TPyb+PjfVMx+sAX6q+YW+cQl5WE0Qe+QFZRDgxkBjDUN4AKmv2hVClfuM44ASWlJTh7NRFdmz+daSmTydC1eTucvHTupfd9UvIEt3PuwkDfAO+264lfTx5UHzM1rqUx2AUAZUr2hYmpCeT17NVfdV3r/PV+nqw+p/z9/DrcXrB5uYGhAeo3dNK4j1KpxJVzT9/P/69Uf72eVVxoITW1zGqhjnMd9ZeToh6s7ayQGPu0gFpUWISUpFQ0bPbi/YpeRUXx487Nu5i1fBosrKq+cELqSpSlOJ95BW+4Pt2TSAYZ3nBthbM3k6q8T+zNC3C1qad+rwYAN1tn3Hl4T2OwHQAelTxGVkEurEzM0dHNDweSY57/cfSMEmUpEu8ko91z/dHOpSXO3Xpxf7g81x8KWyfcraI/3vfugZyiB4hK+bN6HgARERHRS3AGiA5xdXXFqVOncO3aNZibm+PTTz9FeHg4Jk6ciAkTJuDKlSuYM2cOgoKC1LMjnr+Pra0tPDw8sHHjRhw4cAAKhQKbNm3CmTNnoFAo/nEmGxsb2NnZ4YcffkDdunWRkZGBadOm/U8eb3BwMObMmYMGDRqgefPmWL9+PeLj47F582YAgLu7O5ydnTF37lwsWLAAycnJCA0N/ce/x9XVFQcOHMCVK1dgZ2cHKyurSrNGAMDY2BjGxsYabUb52t1XQSaTofOADti/+TDsnWrDro4dflu/D1a1LeHzxtPlxpZPWQWfN7zQsd8bAIAu73XApm+2oX5DZ7g2qo+jO47jyeNivP5W+eB4LfNaaNPTDztX7YaZhSlMzIzxn293QdHERT2o4uBsD++2TfHzyl8xKOg9mJiaYPeavXBwlqNhc3cAmrNDACDjyg3IZDI4Kupq48+jVZ3e7YDNi7ajvqcT6ns6I3rn7yh+XIzWPco3A/4pZCusaluh9+i3AQAdB7yB5UGrEPWfaDRt3RjnjsbjRvJNDAx8D0B533Yc0B4HNx+Bfb3asKtji70bDsDKzhJe7coHi03MTNCu9+vYF3EQNvZWsHGwQdRfs22ad9Tc5PzcsQQoy8rg263qzYylJOzAekSM+Qax6RdwOu08JnUfDjPjWlj/+04AQMSYb3Dr/l188XMYAGBP/FEEvTUCcRlJOJV6Hu4O9fHlgADsiT+qHtjdE38UM3p/gozc27h4KwUt6jdG0FsjsO73HcIe579F5OV9mP76OFzOTcfl3FS817AHahkYY99fG5x/0foTZD+6jx/Pb0exsgTpeTc17l9QUgQA6vZS5SPEZSXhE59BeFJWjDuF99Bc3hhvubbHyviftPvg/mXCdq1BxORQxF5NxOkr8ZjUbxTMjE2x/lD5Uk0Rk5fiVs4dfLHhGwCAn2dz1LOrg/i0JNSzq4O5QwKhJ9PDop9Xq3/mnlOHMePDicjIuo2L15PRwr0pggaMxrqDkVVmkCqZTIYu73bEvp8OQV7PHnZ1bbFH/X7+9IKBZZO/g88bXujUvz0AoMv7nbAxZAtcPJ3h0sgFR3dE48njYrTp8XSGZV5uPvJzHyL7Vvkst9tpt2FsagJbuTXMLM2Qfuk6rl/OQAMvN5ia18K92znYs34v7B1rqy96kDqZTIa3B/bAzg2/oK6zA+R15dj248+wqW2N1zq0Up83f8JC+HX0RY/3uwMAHhc9xp2bd9XHs25n41rydZhbmqF2ndooLS1F2BfLkX7lGqYumQylUqneb8Tc0hwGhvzo9awfT/0HS/tMx/nMK4i7fQlj/N5DLUMTbP9rk+xlvacj8+E9hBz7EQCw8eyv+Ni3P+Z3n4j1sTuhsHXCxLYfYV3sTvXP7Oj2GmSQITUnA6629TCr63ik5mRgewI33v47a07/B6G9puF85hUkZF7GSN93YWpkgv+c3w8ACOs1DXce3sOi6DUAgJ/idmN4q36Y++YEbIjdBYVtPXzWZjA2xO7S+LkyyPC+Vw/8nHgQZapXXw2AXo2ZiSnc67mqbyvqOMOnQRPk5j/AjWzOfPq3kOLylERE2sT/wnXIlClTMHz4cDRp0gSPHj1Ceno69u7di+DgYPj4+MDW1hajRo3CzJkzX3qfcePGIS4uDgMHDoRMJsOgQYPw6aefYt++f/6Pv56eHrZt2wZ/f380a9YMnp6eWL58OTp16vT//Xj9/f2Rl5eHyZMnIysrC02aNMHu3bvh4VF+5Z2hoSG2bt2K8ePHw9vbG6+99hq++uorvP/+P5uVMWbMGBw7dgy+vr4oKCjA0aNH/yf5q0u3DzvjyeNibA37GY8KHqGBlwKffj0WhkZPizH3buegIK9QfbtV5xYoyCvEbxsO4OH9fNRrUA+fhYxRL78EAO9+2hcymQxr5m1AaUkZGvt6YmDAAI3fPXTaYOz87les+mItZDIZPHwa4LOQMdA30K/+B65jWnZujoK8QuzdcAD59x/CqYEjPvl6tHpz2vtZDyDTe+aKt6auGPbFYOxdfwD/XbcP9vVqY9S84XBU1FGf03VgJxQ/Lsb2pT/jUcFjuDVzxSchozX6tu/YXtDT18OmkG0oKS6BS6P6mLBkHEwtNJfP+HPfaXi/4VVpY3spijy9D/YWtpjf3x91rOwRn3EJPUJHIyu/fI3p+nZ1Na5Y/2p3+TJXXw2YhHo2Dsh+mFte8NixVH3OxJ++wpcDAvDd0DmQW9rh9oMsfH9sO+b/ulLrj+/f5uiNP2FtYoGRXu/B1sQKKQ+uI/jYN7j/JB8AIDezgxL/bJnC+SdWYKz3QMx8/VNYGpnjTtE9rEmMxK8pR6rjIdQYkcf3wN7KFvOHBKGOrT3iU5PQY9ZQZD0oHzivL3fUeG6YGBnjq+HBcKvjjIJHRdh75iiGLp6EvMJ89TkTV83Gl8Om4LvPvoLcujZu597F93s3Y/6WZVp/fLruzQ+74MnjYmwJi0TRX+/nE0LGabzmZ9++p/F+7tu5BQoeFOC/6/cj/34+nBrUw4Rvxmm8n/+++wT2bny6iXDYpBUAgKGfD0KbHn4wMjZE/O/n8VvEfjx5VAwrO0s0ea0Reg55E4ZG/Ne/Qt8hvfDk0RN8H7IORQVFaOTdEF8s/RxGxkbqc+7eykJ+3tMlyVIvp2HeZwvVtzcuL79op+Pb7fHZrHHIzb6P2N/LZ1h9PmyGxu+bs/ILNG3ZpDof0r/O7ktHYWtmjSkdR8DezBYX76ZgyLbP1RtxO1o5QPnMTPjbD7Px0dZgzH1zAg6NWYc7D7Ox9swOrDy5VX2OpbEZpnUeg7oW9njw+CH2Xj6Ob46tQany72eDS91/Lx+Dnak1gtqPgL2ZDZKyUjFs+1TcK/qrPyzlGu8ZmQ+zMWz7VMzq+in2j1qDuw/vYX3sTqz6c5vGz33DtRWcrBwQeZ5FqOrg29AHx0L/o769dPxcAMCGg5EYsThIUCoiIiLdIlP9XzZLIJKIQzf/KzoC/YVXjOmOnrOCRUegZ3To4Ss6Av3leMTvoiPQXw6vWf33J5FW1K5lLzoC/eXtVXz/1iUG+tJeOlCXZBxJ/vuTSCtUh27+/Uk1UH7J//8y49XF0tDm708iItJx/K+LiIiIiIiIiIiIiIhqHBZAJC4jIwPm5uYv/MrIyBAdkYiIiIiIiIiIiIjoH+NCwBLn6OiI+Pj4lx4nIiIiIiIiIiIiIvq3YQFE4gwMDODu7i46BhERERERERGR5MggEx2BiKhG4xJYRERERERERERERERU47AAQkRERERERERERERENQ6XwCIiIiIiIiIiIhKCS2AREVUnzgAhIiIiIiIiIiIiIqIahwUQIiIiIiIiIiIiIiKqcbgEFhERERERERERkQBcAIuIqHpxBggREREREREREREREdU4LIAQEREREREREREREVGNwyWwiIiIiIiIiIiIBJDJuAgWEVF14gwQIiIiIiIiIiIiIiKqcVgAISIiIiIiIiIiIiKiGocFECIiIiIiIiIiIiFkOvxVPXJzc/HRRx/B0tIS1tbWGDVqFAoKCl7pviqVCj179oRMJsMvv/xSbRmJqOZgAYSIiIiIiIiIiIi04qOPPsLFixdx6NAh/Pe//8Xx48cxduzYV7pveHg4900hon+Em6ATERERERERERFRtbt06RL279+PM2fOwNfXFwDw7bff4u2338aSJUvg6Oj4wvvGx8cjNDQUsbGxqFu3rrYiE9G/HGeAEBERERERERERCSB6kSttL4B18uRJWFtbq4sfANCtWzfo6enh1KlTL7xfUVERBg8ejJUrV6JOnTrVlI6IaiLOACEiIiIiIiIiIiINT548wZMnTzTajI2NYWxs/H/+mXfu3IFcLtdoMzAwgK2tLe7cufPC+wUGBqJt27bo27fv//l3E5E0cQYIERERERERERERafj6669hZWWl8fX1119Xee60adMgk8le+nX58uX/U47du3cjKioK4eHh/x+PhoikijNAiIiIiIiIiIiIhNDdDb2nT5+OoKAgjbYXzf6YPHkyPv7445f+PDc3N9SpUwdZWVka7aWlpcjNzX3h0lZRUVFITU2FtbW1Rvu7776L9u3b49ixYy/9vUQkbSyAEBERERERERERkYZ/styVvb097O3t//a8Nm3a4MGDBzh79ixatWoFoLzAoVQq0bp16yrvM23aNIwePVqjzcvLC0uXLkXv3r1fKR8RSRcLIERERERERERERFTtGjdujB49emDMmDFYvXo1SkpKMGHCBHz44YdwdHQEANy6dQtdu3bFxo0b4efnhzp16lQ5O6R+/fpQKBTafghE9C/DAggREREREREREZEAMpnuLoFVXTZv3owJEyaga9eu0NPTw7vvvovly5erj5eUlODKlSsoKioSmJKIagoWQIiIiIiIiIiIiEgrbG1tsWXLlhced3V1hUqleunP+LvjREQV9EQHICIiIiIiIiIiIiIi+l9jAYSIiIiIiIiIiIiIiGocFkCIiIiIiIiIiIiIiKjGYQGEiIiIiIiIiIiIiIhqHG6CTkREREREREREJIAMMtERiIhqNM4AISIiIiIiIiIiIiKiGocFECIiIiIiIiIiIiIiqnFkKpVKJToEEVWfJ0+e4Ouvv8b06dNhbGwsOo6ksS90B/tCd7AvdAf7QrewP3QH+0J3sC90B/tCd7AviIiIXo4FEKIaLj8/H1ZWVsjLy4OlpaXoOJLGvtAd7Avdwb7QHewL3cL+0B3sC93BvtAd7Avdwb4gIiJ6OS6BRURERERERERERERENQ4LIEREREREREREREREVOOwAEJERERERERERERERDUOCyBENZyxsTHmzJnDDfF0APtCd7AvdAf7QnewL3QL+0N3sC90B/tCd7AvdAf7goiI6OW4CToREREREREREREREdU4nAFCREREREREREREREQ1DgsgRERERERERERERERU47AAQkRERERERERERERENQ4LIEREREREREREREREVOOwAEJEREREREREL5WRkQGVSlWpXaVSISMjQ0Ai6Tp+/DhKS0srtZeWluL48eMCEhEREekuFkCIiEgyiouLceXKlSo/MBJJkZubG3Jyciq1P3jwAG5ubgISEQA8fvwY+fn5Gl9EUjR//nwUFRVVan/06BHmz58vIJG0KRQKZGdnV2rPzc2FQqEQkEi6OnfujNzc3ErteXl56Ny5s4BEREREukumquoSDiL611m+fPkrn+vv71+NSehZbm5uOHPmDOzs7DTaHzx4gJYtWyItLU1QMmkpKirCxIkTERERAQBITk6Gm5sbJk6ciHr16mHatGmCE0rD7t27X/ncPn36VGMSqqCnp4c7d+5ALpdrtN+9exf169fHkydPBCWTnqKiInz++eeIjIyssihVVlYmIJV0ZWdnw97evspjiYmJ8PLy0nIiadLX10dmZmal16icnBzI5XI+L7RMT08Pd+/erfTcuH79Opo0aYLCwkJByaTnRX2RnJwMX19fFs6JiIieYSA6ABH9byxdulTjdnZ2NoqKimBtbQ2gfMDd1NQUcrmcBRAtunbtWpUfzp88eYJbt24JSCRN06dPR0JCAo4dO4YePXqo27t164a5c+eyAKIl/fr107gtk8k0ltKQyWTq7zmoVb2eLUYdOHAAVlZW6ttlZWU4cuQIXF1dBSSTruDgYBw9ehSrVq3C0KFDsXLlSty6dQvff/89QkJCRMeTHC8vL6xduxbvvPOORvuSJUswa9YsPHr0SFAyaVGpVBrvDRUSEhJga2srIJE0BQUFASh/n541axZMTU3Vx8rKynDq1Ck0b95cUDppGTBgAIDyvvj4449hbGysPlZWVobz58+jbdu2ouIRERHpJBZAiGqI9PR09fdbtmzBd999h7Vr18LT0xMAcOXKFYwZMwbjxo0TFVFSOLioW3755Rds374dr7/+usZAStOmTZGamiowmbQolUr194cPH8bUqVOxcOFCtGnTBgBw8uRJzJw5EwsXLhQVUTIqilEymQzDhw/XOGZoaAhXV1eEhoYKSCZde/bswcaNG9GpUyeMGDEC7du3h7u7O1xcXLB582Z89NFHoiNKSlBQEN59912MGDECYWFhyM3NxbBhw5CYmIgtW7aIjlfj2djYQCaTQSaToWHDhpUK5AUFBfjkk08EJpSWuLg4AOUFqcTERBgZGamPGRkZwcfHB1OmTBEVT1IqPlOoVCpYWFigVq1a6mNGRkZ4/fXXMWbMGFHxiIiIdBKXwCKqgRo0aICff/4ZLVq00Gg/e/Ys3nvvPY1iCVUPPb3yLZaev8Id0Bxc7NWrl4h4kmNqaooLFy7Azc0NFhYWSEhIgJubGxISEtChQwfk5eWJjig5zZo1w+rVq/HGG29otP/+++8YO3YsLl26JCiZtCgUCpw5cwa1a9cWHUXyzM3NkZSUhPr168PJyQk7d+6En58f0tPT4eXlhYKCAtERJScuLg5Dhw7FkydPkJubi9atW2PdunWoU6eO6Gg1XkREBFQqFUaOHInw8HCNC0mMjIzg6uqqLp6T9owYMQLLli2DpaWl6CiSN2/ePEyZMgVmZmaioxAREek8zgAhqoEyMzOr3OS5rKwMd+/eFZBIeiqudOfgom7w9fXFb7/9hokTJwJ4utTSmjVrOIAiSGpqqnqJvmdZWVnh2rVrWs8jVSyI6w43Nzekp6ejfv36aNSoESIjI+Hn54c9e/ZU+Vyh6ufu7o5mzZphx44dAICBAwey+KElFTPTFAoF2rZtC0NDQ8GJCADWr18vOgL9Zc6cOaIjEBER/WtwBghRDdS7d2/cunULa9asQcuWLQGUz/4YO3Ys6tWr9482IiaqCf744w/07NkTQ4YMwYYNGzBu3DgkJSXhxIkTiI6ORqtWrURHlJwOHTrAxMQEmzZtgoODA4DyjbeHDRuGx48fIzo6WnBC6Thy5AiOHDmCrKwsjWXKAGDdunWCUknP0qVLoa+vD39/fxw+fBi9e/eGSqVCSUkJwsLCEBAQIDqipMTExGDIkCGwtbXFTz/9hJiYGAQFBaFnz55YvXo1bGxsREeUDKVSiZSUlCpfozp06CAolTQVFhYiJCTkhe8ZaWlpgpJJz927dzFlyhR1Xzw/rMO91IiIiJ5iAYSoBsrOzsbw4cOxf/9+9RVzpaWleOutt7BhwwbI5XLBCaWFg4u6ITU1FSEhIUhISEBBQQFatmyJqVOnwsvLS3Q0SUpJSUH//v2RnJwMZ2dnAMCNGzfg4eGBX375Be7u7oITSsO8efMwf/58+Pr6om7dupU2G961a5egZHT9+nWcPXsW7u7u8Pb2Fh1HcoyNjREYGIgvv/xS/b9UamoqhgwZghs3buDmzZuCE0rDn3/+icGDB+P69euVBnhlMhkHebVs0KBBiI6OxtChQ6t8z2ChVnt69uyJjIwMTJgwocq+6Nu3r6BkREREuocFEKIaLDk5GZcvXwYANGrUCA0bNhScSHo4uEj0YiqVCocOHVK/TjVu3BjdunWr9Dyh6lO3bl0sWrQIQ4cOFR1F0kpKStCjRw+sXr0aHh4eouMQgOjoaHTs2LFSu1KpxIIFCzBr1iwBqaSnefPmaNiwIebNm1fl/1HP7g1C1c/a2hq//fYb2rVrJzqK5FlYWOD3339H8+bNRUchIiLSeSyAEBFVIw4u6oa9e/dCX18fb731lkb7gQMHoFQq0bNnT0HJiMSys7PD6dOn0aBBA9FRJM/e3h4nTpxgAYToGWZmZkhISOCsQB2hUCiwd+9eNG7cWHQUyWvSpAk2b96MFi1aiI5CRESk81gAIaohgoKC8OWXX8LMzAxBQUEvPTcsLExLqYiDi7rB29sbISEhePvttzXa9+/fj6lTpyIhIUFQMmlZvnw5xo4dCxMTEyxfvvyl5/r7+2splbRNnToV5ubmvJpdBwQGBsLY2BghISGio9BfCgsLER0djYyMDBQXF2sc42uUdnTp0gWff/45evToIToKAfjpp5/w66+/IiIiAqampqLjSNrBgwcRGhqK77//Hq6urqLjEBER6TQWQIhqiM6dO2PXrl2wtrZG586dX3ieTCZDVFSUFpNJGwcXdUOtWrVw6dKlSh8Qr127hqZNm6KwsFBMMIlRKBSIjY2FnZ0dFArFC8+TyWTcSFVLAgICsHHjRnh7e8Pb21u910EFFsy1Z+LEidi4cSM8PDzQqlUrmJmZaRxnX2hXXFwc3n77bRQVFaGwsBC2tra4d+8eTE1NIZfL+RqlJbt27cLMmTMRHBwMLy+vSq9R3B9Hu1q0aIHU1FSoVCq4urpW6o9z584JSiY9NjY2KCoqQmlpKUxNTSv1RW5urqBkREREusdAdAAi+t84evRold+/zM2bN+Ho6Ag9Pb3qiiV5jx8/xg8//IDDhw9zcFEgKysrpKWlVSqApKSkVBpkpOqTnp5e5fckzvnz59Xrh1+4cEHjGPdi0a4LFy6gZcuWAMr38CKxAgMD0bt3b6xevRpWVlb4888/YWhoiCFDhnCjZy169913AQAjR45Ut8lkMqhUKm6CLkC/fv1ER6C/hIeHi45ARET0r8EZIEQSZmlpifj4eLi5uYmOUmNxNo5uGDduHE6ePIldu3aplyNLSUnBu+++i9deew1r1qwRnJBehK9TRCSCtbU1Tp06BU9PT1hbW+PkyZNo3LgxTp06heHDh+Py5cuiI0rC9evXX3rcxcVFS0mIiIiI6N+KM0CIJIz1z+r3qrNxqHotWrQIPXr0QKNGjeDk5ASgfAZU+/btsWTJEsHp6GX4OkVSMXLkSCxbtgwWFhYa7YWFhZg4cSLWrVsnKJk0GRoaqmfIyuVyZGRkoHHjxrCyssKNGzcEp5MOFjiIqpaRkfHS4/Xr19dSEiIiIt3HGSBEEmZhYYGEhAReWU2SoFKpcOjQISQkJKBWrVrw9vZGhw4dRMeiv8HXqerVuXPnly51xVlq2qOvr4/MzEzI5XKN9nv37qFOnTooLS0VlEyaunfvjo8//hiDBw/GmDFjcP78efj7+2PTpk24f/8+Tp06JTqiJGzcuPGlx4cNG6alJAQAenp6L33P4JJk2sO+ICIienWcAUJEVI04uKg7ZDIZunfvju7du4uOQqQzKvb/qFBSUoL4+HhcuHABw4cPFxNKYvLz86FSqaBSqfDw4UOYmJioj5WVlWHv3r2ViiJU/RYuXIiHDx8CABYsWIBhw4Zh/Pjx8PDwwNq1awWnk47n91spKSlBUVERjIyMYGpqygKIlu3atUvjdklJCeLi4hAREYF58+YJSiVNcXFxGrcr+iIsLAwLFiwQlIqIiEg3sQBCRFSNOLioO44cOYIjR44gKysLSqVS4xiXliGpWrp0aZXtc+fORUFBgZbTSJO1tTVkMhlkMhkaNmxY6bhMJuPAogC+vr7q7+VyOfbv3y8wjXTdv3+/UtvVq1cxfvx4BAcHC0gkbX379q3U9t5776Fp06bYvn07Ro0aJSCVNPn4+FRq8/X1haOjIxYvXowBAwYISEVERKSbuAQWkYRxc2FxKgYXuf+EdsybNw/z58+Hr68v6tatW2lWzvNXNJLu4OuUGCkpKfDz80Nubq7oKDVedHQ0VCoVunTpgh07dsDW1lZ9zMjICC4uLnB0dBSYUJq6dOmCnTt3wtraWqM9Pz8f/fr14wxOwWJjYzFkyBBuRq8j0tLS4O3tzcK5DkhJSYGPjw8KCwtFRyEiItIZnAFCJGGsf4ozZMgQ+Pn5sQCiJatXr8aGDRswdOhQ0VHoH+LrlBgnT57UWIqJqk/Hjh0BAOnp6XB2dlZvvE1iHTt2DMXFxZXaHz9+jN9//11AInqWgYEBbt++LToGAXj06BGWL1+OevXqiY4iKfn5+Rq3VSoVMjMzMXfuXHh4eAhKRUREpJtYACGqwVJSUpCamooOHTqgVq1aUKlUGle+JyUl8apSQTi4qF3FxcVo27at6BhUheLiYqSnp6NBgwYwMKj8b8m+ffs4qFKNnl8io2IAJTY2FrNmzRKUSprWr1+P2bNnV2rPy8vDJ598gq1btwpIJT3nz59Xf5+UlIQ7d+6ob5eVlWH//v18TdKi3bt3a9yueI1asWIF2rVrJyiVdNnY2Gh8lqjYu8jU1BQ//fSTwGTSU7F84rNUKhWcnZ2xbds2QamIiIh0E5fAIqqBcnJyMHDgQERFRUEmk+Hq1atwc3PDyJEjYWNjg9DQUNERJePvBhfnzJkjKJm0TJ06Febm5hzQ1SFFRUWYOHEiIiIiAADJyclwc3PDxIkTUa9ePUybNk1wQmkYMWKExm09PT3Y29ujS5cu6N69u6BU0uTs7AxnZ2f89NNP6iXfjh07hmHDhqFOnTo4ffq04ITSoKenpx5UrOpjUq1atfDtt99i5MiR2o4mSc/PiJLJZOrXqNDQUNStW1dQMmmqeM+uUPGe0bp1a9jY2AhKJU3R0dEatyv6wt3dvcoLSoiIiKSMBRCiGmjYsGHIysrCmjVr0LhxYyQkJMDNzQ0HDhxAUFAQLl68KDqiZHBwUTcEBARg48aN8Pb2hre3NwwNDTWOh4WFCUomXQEBAYiJiUF4eDh69OiB8+fPw83NDb/++ivmzp2LuLg40RGJtOr+/fsYN24c9u/fj9DQUCQnJ2PZsmUIDg7GvHnzOKClJdevX4dKpYKbmxtOnz4Ne3t79TEjIyPI5XLo6+sLTEhERERERP8ECyBENVCdOnVw4MAB+Pj4wMLCQl0A4QaFJFWdO3d+4TGZTMbNbAVwcXHB9u3b8frrr2u8TqWkpKBly5aV1ram6nX27FlcunQJANC0aVO0aNFCcCLp+uKLLxASEgIDAwPs27cPXbt2FR2JXuKdd97BmjVrOBNBCyo+tj6/7A9p14MHD7B27VqN94yRI0fCyspKcDLpSU1NRXh4uLovmjRpgoCAADRo0EBwMiIiIt3CS8mIaqDCwkKYmppWas/NzYWxsbGARMTBRbGOHj0qOgI9Jzs7G3K5vFJ7YWEhB7e0KCsrCx9++CGOHTsGa2trAOWDW507d8a2bds0rn6n6vftt99i2bJlGDRoEM6ePQt/f39s2bIFPj4+oqPRCxw/fhyPHj0SHaNG27hxIxYvXoyrV68CABo2bIjg4GAMHTpUcDLpiY2NxVtvvYVatWrBz88PQPks2gULFuDgwYNo2bKl4ITSceDAAfTp0wfNmzdX74cTExODpk2bYs+ePXjzzTcFJyQiItIden9/ChH927Rv3x4bN25U35bJZFAqlVi0aNFLr4Sn/72srCx06dIFr732Gvz9/eHv749WrVqha9euyM7OFh2PSBhfX1/89ttv6tsVRY81a9agTZs2omJJzsSJE/Hw4UNcvHgRubm5yM3NxYULF5Cfnw9/f3/R8SSlR48emDt3LiIiIrB582bExcWhQ4cOeP3117Fo0SLR8YiECAsLw/jx4/H2228jMjISkZGR6NGjBz755BMsXbpUdDzJCQwMRJ8+fXDt2jXs3LkTO3fuRHp6Onr16oVJkyaJjicp06ZNQ2BgIE6dOoWwsDCEhYXh1KlTmDRpEqZOnSo6HhERkU7hElhENdCFCxfQtWtXtGzZElFRUejTp496cCsmJobTorVo4MCBSEtLw8aNG9G4cWMAQFJSEoYPHw53d3ds3bpVcELpiI2NRWRkJDIyMlBcXKxxbOfOnYJSSdcff/yBnj17YsiQIdiwYQPGjRuHpKQknDhxAtHR0WjVqpXoiJJgZWWFw4cP47XXXtNoP336NLp3744HDx6ICSZBb775JiIiIuDo6KjR/ttvv2H06NHIzMwUlIxe5tkl/Oh/T6FQYN68eRg2bJhGe0REBObOnYv09HRByaSpVq1aiIuLQ6NGjTTak5KS4Ovri6KiIkHJpMfExASJiYnw8PDQaE9OToa3tzceP34sKBkREZHu4QwQohqoWbNmSE5OxhtvvIG+ffuisLAQAwYMQFxcHIsfWrZ//35899136uIHUL4+78qVK7Fv3z6ByaRl27ZtaNu2LS5duoRdu3ahpKQEFy9eRFRUFNesFuSNN95AfHw8SktL4eXlhYMHD0Iul+PkyZMsfmiRUqmEoaFhpXZDQ0MolUoBiaTr0KFDSE1NxZAhQ9CmTRvcunULQPnylZGRkYLTEYmRmZmJtm3bVmpv27Yti4ICWFpaIiMjo1L7jRs3YGFhISCRdNnb2yM+Pr5Se3x8fJVLjBIREUkZ9wAhqqGsrKwwY8YM0TEkj4OLumHhwoVYunQpPvvsM1hYWGDZsmVQKBQYN24cN64VqEGDBvjxxx9Fx5C0Ll26ICAgAFu3blXPPLh16xYCAwO5+baW7dixA0OHDsVHH32EuLg4PHnyBACQl5eHr7/+Gu3btxeckEj73N3dERkZiS+++EKjffv27ZWufKfqN3DgQIwaNQpLlixRF6ZiYmIQHByMQYMGCU4nLWPGjMHYsWORlpam0RfffPMNgoKCBKcjIiLSLVwCi6iGOH/+/Cuf6+3tXY1J6Fl9+/bFgwcPKg0ufvTRR7CxscGuXbsEJ5QGMzMzXLx4Ea6urrCzs8OxY8fg5eWFS5cuoUuXLryKVEvy8/Nf+VxLS8tqTEIVbty4oV4m0dnZWd3WrFkz7N69G05OToITSkeLFi0QGBiIYcOGaSyrFBcXh549e+LOnTuiI1IVuARW9dqxYwcGDhyIbt26aWz0fOTIEURGRqJ///6CE0pLcXExgoODsXr1apSWlgIov6hn/PjxCAkJgbGxseCE0qFSqRAeHo7Q0FDcvn0bAODo6Ijg4GD4+/ur91YjIiIiFkCIagw9PT3IZDKoVCqNf3grnuLPtpWVlWk9n1RxcFE3ODk5Yd++ffDy8oK3tzemT5+OQYMG4eTJk+jRowfy8vJER5SEitepV8HXKe1RqVQ4fPgwLl++DABo3LgxunXrJjiV9JiamiIpKQmurq4ag+ppaWlo0qQJ13PXUV9//TXGjx8Pa2tr0VFqrLNnz2Lp0qW4dOkSgPLXqMmTJ6NFixaCk0lXUVERUlNTAZTP5jQ1NRWcSNoePnwIAFyGjIiI6AW4BBZRDfHsJpBxcXGYMmUKgoOD0aZNGwDAyZMnERoaikWLFomKKEnOzs44d+4cBxcF69ChAw4dOgQvLy+8//77CAgIQFRUFA4dOsRlfrTo6NGj6u+vXbuGadOm4eOPP9Z4nYqIiMDXX38tKqIkyWQyvPnmm3jzzTdFR5G0OnXqICUlBa6urhrtf/zxB2cXCJSUlISMjAwUFxdrtPfp0wcAMH36dBGxJKVVq1b46aefRMegZ5iamsLLy0t0DPoLCx9EREQvxxkgRDWQn58f5s6di7ffflujfe/evZg1axbOnj0rKBmRGLm5uXj8+DEcHR2hVCqxaNEinDhxAh4eHpg5cyZsbGxER5Scrl27YvTo0ZXWDN+yZQt++OEHHDt2TEwwCTpz5gyOHj2KrKysSnsThYWFCUolPV9//TV++uknrFu3Dm+++Sb27t2L69evIzAwELNmzcLEiRNFR5SUtLQ09O/fH4mJieoZtsDTGbWcpaZdWVlZVb5GcVlX7Xr8+DG+/fbbF75nnDt3TlAy6cnJycHs2bNf2Be5ubmCkhEREekezgAhqoESExOhUCgqtSsUCiQlJQlIJF3+/v5wd3eHv7+/RvuKFSuQkpKC8PBwMcEkxtbWVv29np4epk2bJjANAeWzPVavXl2p3dfXF6NHjxaQSJoWLlyImTNnwtPTEw4ODhpLlHH9cO2aNm0alEolunbtiqKiInTo0AHGxsaYMmUKix8CBAQEQKFQ4MiRI1AoFDh9+jRycnIwefJkLFmyRHQ8yTh79iyGDx+OS5cu4fnr9mQyGQtRWjZq1CgcPHgQ7733Hvz8/Pg+IdDQoUORkpKCUaNGVXr/JiIiIk2cAUJUA7Vs2RLNmjXDmjVrYGRkBKB808LRo0fjwoULvDpLi+rVq4fdu3ejVatWGu3nzp1Dnz59cPPmTUHJpEVfXx+ZmZmQy+Ua7Tk5OZDL5RxAEcDT0xN9+/attCzf559/jl9//RVXrlwRlExaHBwc8M033+Djjz8WHYX+UlxcjJSUFBQUFKBJkyYwNzcXHUmSateujaioKHh7e8PKygqnT5+Gp6cnoqKiMHnyZMTFxYmOKAk+Pj5o0KABpk6dWuUgr4uLi6Bk0mRlZYW9e/eqN6QncSwsLPDHH3/Ax8dHdBQiIiKdxxkgRDXQ6tWr0bt3bzg5OamXBjh//jxkMhn27NkjOJ205OTkwMrKqlK7paUl7t27JyCRNL2o1v/kyRN1kZC0a+nSpXj33Xexb98+tG7dGgBw+vRpXL16FTt27BCcTjr09PQ4kKVjjIyM0KRJE9ExJK+srEy9rn7t2rVx+/ZteHp6wsXFhQVaLUpLS8OOHTvg7u4uOgqh/MIe7jehGxo1aoRHjx6JjkFERPSvwAIIUQ3k5+eHtLQ0bN68Wb3x9sCBAzF48GCYmZkJTict7u7u2L9/PyZMmKDRvm/fPm5qqwXLly8HUL5Mxpo1azSupC4rK8Px48fRqFEjUfEk7e2338bVq1fx3XffqV+nevfujU8++QTOzs6C00lHYGAgVq5cyeX4iJ7TrFkzJCQkQKFQoHXr1li0aBGMjIzwww8/8P1bi7p27YqEhAQWQHREaGgopk6ditWrV3P2jWDfffcdpk2bhtmzZ6NZs2YwNDTUOG5paSkoGRERke7hElhERNVo3bp1mDBhAoKDg9GlSxcAwJEjRxAaGorw8HCMGTNGcMKarWIvnOvXr8PJyQn6+vrqY0ZGRnB1dcX8+fPVMxCIpEapVOKdd95BcnIymjRpUmkAZefOnYKSEYl14MABFBYWYsCAAUhJSUGvXr2QnJwMOzs7bNu2DV27dhUdURLu3buH4cOHw8/Pr8pB3j59+ghKJk3Z2dn44IMPcPz4cZiamlbqD268rT1Xr17F4MGDKy1trFKpuD8OERHRc1gAIaohdu/ejZ49e8LQ0BC7d+9+6bn8sKhdq1atwoIFC3D79m0AgKurK+bOnYthw4YJTiYdnTt3xs6dO2FjYyM6Cj3jwYMHWLt2LS5dugQAaNq0KUaOHFnlsnFUPSZMmIA1a9agc+fOVa6vv379ekHJiHRPbm4ubGxsuNmwFu3ZswdDhw5Ffn5+pWMc5NW+bt26ISMj44Ubbw8fPlxQMunx8/ODgYEBAgICquyLjh07CkpGRESke1gAIaoh9PT0cOfOHcjlcujp6b3wPH5YFCc7Oxu1atWqckPbmJgY+Pr6wtjYWEAy6SkrK0NiYiJcXFxYFBEkNjYWb731FmrVqgU/Pz8AwJkzZ/Do0SMcPHgQLVu2FJxQGiwsLLBt2za88847oqMQ6ZSRI0di2bJllfY7KCwsxMSJE7Fu3TpByaTF1dUVvXr1wqxZs+Dg4CA6juSZmpri5MmT3HhbB5iamiIuLg6enp6ioxAREem8F4+SEtG/ilKphFwuR0lJCTp16oTLly9DqVRW+mLxQxx7e/sqix8A0LNnT9y6dUvLiaRj0qRJWLt2LYDy4keHDh3QsmVLODs749ixY2LDSVRgYCD69OmDa9euYefOndi5cyfS09PRq1cvTJo0SXQ8ybC1tUWDBg1ExyDSOREREVVuMPzo0SNs3LhRQCJpysnJQWBgIIsfOoIbb+sOX19f3LhxQ3QMIiKifwUWQIhqGENDQyQmJr50FgjpHk7Gq17/+c9/1Fcr7tmzB9euXcPly5cRGBiIGTNmCE4nTbGxsZg6dSoMDAzUbQYGBvj8888RGxsrMJm0zJ07F3PmzEFRUZHoKEQ6IT8/H3l5eVCpVHj48CHy8/PVX/fv38fevXshl8tFx5SMAQMG4OjRo6Jj0F9CQkIwefJkHDt2DDk5ORrPj6qWKaPqM3HiRAQEBGDDhg04e/Yszp8/r/FFRERET3EJLKIaKDAwEMbGxggJCREdhV6RhYUFEhIS4ObmJjpKjWRiYoKUlBQ4OTlh7NixMDU1RXh4ONLT0+Hj48MP7QI4ODhg06ZN6N69u0b7gQMHMGzYMNy9e1dQMmlp0aIFUlNToVKp4OrqWmlD2+c3VyWq6fT09F66x4dMJsO8efNYPNeSBQsWIDw8HO+88w68vLwqvUb5+/sLSiZNFRdYPf8c4cbb2lfVxW4ymYx9QUREVAWDvz+FiP5tSktLsW7dOhw+fBitWrWCmZmZxvGwsDBByYjEcHBwQFJSEurWrYv9+/dj1apVAICioiLo6+sLTidNAwcOxKhRo7BkyRK0bdsWQPleOMHBwRg0aJDgdNLRr18/0RGIdMrRo0ehUqnQpUsX7NixA7a2tupjRkZGcHFxgaOjo8CE0rJmzRqYm5sjOjoa0dHRGsdkMhkLIFrG2Ti6Iz09XXQEIiKifw3OACGqgTp37vzCYzKZDFFRUVpMQ6+CM0Cq19y5cxEeHo66deuiqKgIycnJMDY2xrp16/Djjz/i5MmToiNKTnFxMYKDg7F69WqUlpYCKF/Cb/z48QgJCYGxsbHghEQkZdevX4dcLkdiYiKysrKgVCrVx2QyGXr37i0wHRERERERvSoWQIiIdIClpSXi4+NZAKlGP//8M27cuIH3338fTk5OAMo3ubW2tkbfvn0Fp5OuoqIipKamAgAaNGgAU1NTwYmIiMqX4xs6dCju3btX6RiXlyGpKyoqQkZGBoqLizXavb29BSWSrqSkpCr7ok+fPoISERER6R4WQIiIqolKpcKNGzcgl8thYmLy0nM5A4SkJi8vD2VlZRrLywBAbm4uDAwMYGlpKSiZtJSVlWHp0qWIjIyscgAlNzdXUDIisTw8PNC9e3fMnj0bDg4OouNI2s2bN7F79+4qX6O4rKt2ZWdnY8SIEdi3b1+Vx1kY1J60tDT0798fiYmJ6r0/gKf7s7AviIiInuIeIERE1USlUsHd3R0XL16Eh4fHS899+PChllJJx/LlyzF27FiYmJhg+fLlLz2Xa4hr34cffojevXvj008/1WiPjIzE7t27sXfvXkHJpGXevHlYs2YNJk+ejJkzZ2LGjBm4du0afvnlF8yePVt0PCJh7t69i6CgIBY/BDty5Aj69OkDNzc3XL58Gc2aNcO1a9egUqnQsmVL0fEkZ9KkSXjw4AFOnTqFTp06YdeuXbh79y6++uorhIaGio4nKQEBAVAoFDhy5AgUCgVOnz6NnJwcTJ48GUuWLBEdj4iISKdwBggRUTVq2rQp1q5di9dff110FMlRKBSIjY2FnZ0dFArFC8+TyWRIS0vTYjICAFtbW8TExKBx48Ya7ZcvX0a7du2Qk5MjKJm0NGjQAMuXL8c777wDCwsLxMfHq9v+/PNPbNmyRXREIiFGjhyJdu3aYdSoUaKjSJqfnx969uyJefPmqWfLyuVyfPTRR+jRowfGjx8vOqKk1K1bF7/++iv8/PxgaWmJ2NhYNGzYELt378aiRYvwxx9/iI4oGbVr10ZUVBS8vb1hZWWF06dPw9PTE1FRUZg8eTLi4uJERyQiItIZnAFCRFSNQkJCEBwcjFWrVqFZs2ai40hKenp6ld+Tbnjy5Il68/NnlZSU4NGjRwISSdOdO3fg5eUFADA3N0deXh4AoFevXpg1a5bIaERCrVixAu+//z5+//13eHl5wdDQUOM4Zw5qx6VLl7B161YAgIGBAR49egRzc3PMnz8fffv2ZQFEywoLCyGXywEANjY2yM7ORsOGDeHl5YVz584JTictZWVlsLCwAFBeDLl9+zY8PT3h4uKCK1euCE5HRESkW1gAISKqRsOGDUNRURF8fHxgZGSEWrVqaRzn+vokVX5+fvjhhx/w7bffarSvXr0arVq1EpRKepycnJCZmYn69eujQYMGOHjwIFq2bIkzZ87A2NhYdDwiYbZu3YqDBw/CxMQEx44dU6+rD5TPHGQBRDvMzMzU+37UrVsXqampaNq0KQBUuUE9VS9PT09cuXIFrq6u8PHxwffffw9XV1esXr0adevWFR1PUpo1a4aEhAQoFAq0bt0aixYtgpGREX744QfuKUhERPQcFkCIiKpReHi46AiSFRQU9MrnchNV7fvqq6/QrVs3JCQkoGvXrgDK13o/c+YMDh48KDiddPTv3x9HjhxB69atMXHiRAwZMgRr165FRkYGAgMDRccjEmbGjBmYN28epk2bBj09PdFxJOv111/HH3/8gcaNG+Ptt9/G5MmTkZiYiJ07d3J5UQECAgKQmZkJAJgzZw569OiBzZs3w8jICBs2bBAbTmJmzpyJwsJCAMD8+fPRq1cvtG/fHnZ2dti+fbvgdERERLqFe4AQEVGN1LlzZ43b586dQ2lpKTw9PQEAycnJ0NfXR6tWrRAVFSUiouTFx8dj8eLFiI+PR61ateDt7Y3p06fDw8NDdDTJ+vPPP3HixAl4eHigd+/eouMQCWNra4szZ86gQYMGoqNIWlpaGgoKCuDt7Y3CwkJMnjxZ/RoVFhYGFxcX0RElraioCJcvX0b9+vVRu3Zt0XEkLzc3FzY2Nhoz1m7evAlHR0cWcomISNJYACEiqmapqalYv349UlNTsWzZMsjlcuzbtw/169dXL+NA1SssLAzHjh1DREQEbGxsAAD379/HiBEj0L59e0yePFlwQnqRkJAQfPLJJ7C2thYdRdLeeecdrFmzhkuckGQEBgbC3t4eX3zxhego9Aq2bt2KPn36wMzMTHQUAmBpaYn4+HguxaQD2BdEREQsgBARVavo6Gj07NkT7dq1w/Hjx3Hp0iW4ubkhJCQEsbGx+Pnnn0VHlIR69erh4MGDlQpOFy5cQPfu3XH79m1Byejv8IO7brCwsEBCQgL7gSTD398fGzduhI+PD7y9vSttgs6lE3UL3yt0C98zdAf7goiIiHuAEBFVq2nTpuGrr75CUFAQLCws1O1dunTBihUrBCaTlvz8fGRnZ1dqz87OxsOHDwUkolfF6zSISITExES0aNECQHmx/FnPLi9DuoHvFURERET0IiyAEBFVo8TERGzZsqVSu1wux7179wQkkqb+/ftjxIgRCA0NhZ+fHwDg1KlTCA4OxoABAwSnIyIiXXP06FHREYiIiIiI6H+ABRAiompkbW2NzMxMKBQKjfa4uDjUq1dPUCrpWb16NaZMmYLBgwejpKQEAGBgYIBRo0Zh8eLFgtMREREREREREVF10BMdgIioJvvwww8xdepU3LlzBzKZDEqlEjExMZgyZQqGDRsmOp5kmJqa4rvvvkNOTg7i4uIQFxeH3NxcfPfddxobpt68eRNKpVJgUiIiIqJ/Ny4TpzvYF0RERCyAEBFVq4ULF6JRo0ZwdnZGQUEBmjRpgg4dOqBt27aYOXOm6HiSY2ZmBm9vb3h7e2sUPio0adIE165d034wIiIiohqCe7LoDvYFERERl8AiIqpWRkZG+PHHHzFr1ixcuHABBQUFaNGiBTw8PERHoyrwQ6Luad++PWrVqiU6huR98cUXsLW1FR2DiKhKLi4uMDQ0FB1DcsrKypCYmAgXFxfY2Nio2/ft28elXnVEUlISHB0dRccgIiISSqbiaA8REREAwMLCAgkJCXBzcxMdRRJSU1Oxfv16pKamYtmyZZDL5di3bx/q16+Ppk2bio4nGZs2bcLq1auRnp6OkydPwsXFBeHh4VAoFOjbt6/oeEREiI2NxaVLlwAAjRs3hq+vr+BE0jRp0iR4eXlh1KhRKCsrQ8eOHXHixAmYmpriv//9Lzp16iQ6Yo02YMCAVz53586d1ZiEiIjo34UzQIiIqlFQUFCV7TKZDCYmJnB3d0ffvn15ZTVJTnR0NHr27Il27drh+PHjWLBgAeRyORISErB27Vr8/PPPoiNKwqpVqzB79mxMmjQJCxYsQFlZGQDA2toa4eHhLIAQkVA3b97EoEGDEBMTA2trawDAgwcP0LZtW2zbtg1OTk5iA0rMzz//jCFDhgAA9uzZg/T0dFy+fBmbNm3CjBkzEBMTIzhhzWZlZSU6AhER0b8SZ4AQEVWjzp0749y5cygrK4OnpycAIDk5Gfr6+mjUqBGuXLkCmUyGP/74A02aNBGcljgDRHvatGmD999/H0FBQRp/99OnT2PAgAG4efOm6IiS0KRJEyxcuBD9+vXT6IcLFy6gU6dOuHfvnuiIRCRhPXr0wIMHDxAREaH+P+rKlSsYMWIELC0tsX//fsEJpcXExAQpKSlwcnLC2LFjYWpqivDwcKSnp8PHxwf5+fmiIxIRERFVwk3QiYiqUd++fdGtWzfcvn0bZ8+exdmzZ3Hz5k28+eabGDRoEG7duoUOHTogMDBQdFRC+cwc0o7ExET079+/UrtcLueguxalp6ejRYsWldqNjY1RWFgoIBER0VPR0dFYtWqVuvgBAJ6envj2229x/PhxgcmkycHBAUlJSSgrK8P+/fvx5ptvAgCKioqgr68vOB0RERFR1bgEFhFRNVq8eDEOHToES0tLdZuVlRXmzp2L7t27IyAgALNnz0b37t0FpqQKnBSpPdbW1sjMzIRCodBoj4uL48apWqRQKBAfHw8XFxeN9v3796Nx48aCUhERlXN2dkZJSUml9rKyMm7sLMCIESPwwQcfoG7dupDJZOjWrRsA4NSpU2jUqJHgdNLz888/IzIyEhkZGSguLtY4du7cOUGpiIiIdA9ngBARVaO8vDxkZWVVas/OzlYvE2BtbV3pQwtVnxs3buDGjRtVHktKSqo0EEzV48MPP8TUqVNx584dyGQyKJVKxMTEYMqUKRg2bJjoeJIRFBSEzz77DNu3b4dKpcLp06exYMECTJ8+HZ9//rnoeEQkcYsXL8bEiRMRGxurbouNjUVAQACWLFkiMJk0zZ07F2vWrMHYsWMRExMDY2NjAIC+vj6mTZsmOJ20LF++HCNGjICDgwPi4uLg5+cHOzs7pKWloWfPnqLjERER6RTuAUJEVI0++ugjnDx5EqGhoXjttdcAAGfOnMGUKVPQtm1bbNq0Cdu2bcOSJUs0PtzT/1ZpaSnmzZuH5cuXo6CgAABgbm6OiRMnYs6cOTA0NBScUHqKi4vx2WefYcOGDSgrK4OBgQHKysowePBgbNiwgUtpaNHmzZsxd+5cpKamAgAcHR0xb948jBo1SnAyIpI6GxsbFBUVobS0FAYG5YsXVHxvZmamcW5ubq6IiERCNGrUCHPmzMGgQYM09vCaPXs2cnNzsWLFCtERiYiIdAYLIERE1aigoACBgYHYuHEjSktLAQAGBgYYPnw4li5dCjMzM8THxwMAmjdvLi5oDTd+/Hjs3LkT8+fPR5s2bQAAJ0+exNy5c9GvXz+sWrVKcELpunHjBhITE1FQUIAWLVrAw8NDdCTJKC0txZYtW/DWW2/BwcEBRUVFKCgogFwuFx2NiAgAEBER8crnDh8+vBqTSNfy5ctf+Vx/f/9qTELPMjU1xaVLl+Di4gK5XI5Dhw7Bx8cHV69exeuvv46cnBzREYmIiHQGCyBERFpQUFCAtLQ0AICbmxvMzc0FJ5IWKysrbNu2rdKSAHv37sWgQYOQl5cnKJl0zZ8/H1OmTIGpqalG+6NHj7B48WLMnj1bUDJpeXYAhYiI6HnP79X1IjKZTP2/LlU/Nzc37NixAy1atICvry/GjBmDcePG4eDBg/jwww85I4qIiOgZLIAQEVGNJ5fLER0dXWlT50uXLqFDhw7Izs4WlEy69PX1kZmZWWm2QU5ODuRyOcrKygQlk5ZOnTph0qRJ6Nevn+goREQAgPz8fFhaWqq/f5mK84ikZvTo0XB2dsacOXOwcuVKBAcHo127doiNjcWAAQOwdu1a0RGJiIh0BgsgRETVqLCwECEhIThy5AiysrKgVCo1jvNKOe2YP38+Ll++jPXr16s37Hzy5AlGjRoFDw8PzJkzR3BC6dHT08Pdu3dhb2+v0R4VFYWBAweyKKUlkZGRmD59OgIDA9GqVatKa+p7e3sLSkZEUvVsgVxPTw8ymazSOSqVCjKZjMVykiylUgmlUqneG2fbtm04ceIEPDw8MG7cOBgZGQlOSEREpDtYACEiqkaDBg1CdHQ0hg4dirp161b6EB8QECAombT0798fR44cgbGxMXx8fAAACQkJKC4uRteuXTXO3blzp4iIkmFjYwOZTIa8vDxYWlpqPCfKyspQUFCATz75BCtXrhSYUjr09PQqtclkMg4uEpEw0dHRaNeuHQwMDBAREQFnZ2fo6+trnKNUKpGRkcF9P7QgKCjolc8NCwurxiT0rIyMDDg7O1f6bKFSqXDjxg3Ur19fUDIiIiLdwwIIEVE1sra2xm+//YZ27dqJjiJpI0aMeOVz169fX41JKCIiAiqVCiNHjkR4eDisrKzUx4yMjODq6qreqJ6q3/Xr1196nHuDEJFIXC5RvM6dO7/SeTKZDFFRUdWchirwuUFERPTqDEQHICKqyWxsbGBrays6huSxqKE7Kq7WVSgUaNu2LQwNDQUnkjYWOIhIl1XMRnteQUEBTExMBCSSnqNHj4qOQFXgc4OIiOjVsQBCRFSNvvzyS8yePRsREREwNTUVHYdIZ3Ts2FH9/ePHj1FcXKxxnBvbaldSUhIyMjIq9UOfPn0EJSIiKatYdkkmk2HWrFka/0OVlZXh1KlTaN68uaB0BAA3b94EADg5OQlOIi18bhAREf1zLIAQEVWj0NBQpKamwsHBAa6urpWudj937pygZDVfy5YtceTIEdjY2KBFixZVXiVXgf2gfUVFRfj8888RGRmJnJycSse5dIN2pKWloX///khMTFTv/QFA/XxhPxCRCHFxcQDKr3JPTEzU2NDZyMgIPj4+mDJliqh4kqVUKvHVV18hNDQUBQUFAAALCwtMnjwZM2bMqHJfKfrf4nODiIjon2MBhIioGvXr1090BMnq27cvjI2NAbAfdFFwcDCOHj2KVatWYejQoVi5ciVu3bqF77//HiEhIaLjSUZAQAAUCgWOHDkChUKB06dPIycnB5MnT8aSJUtExyMiiapYdmnEiBFYtmwZZwXqiBkzZmDt2rUICQlR72/3xx9/YO7cuXj8+DEWLFggOGHNx+cGERHRP8dN0ImIqEYrKytDTEwMvL29YW1tLToO/aV+/frYuHEjOnXqBEtLS5w7dw7u7u7YtGkTtm7dir1794qOKAm1a9dGVFQUvL29YWVlhdOnT8PT0xNRUVGYPHmy+kpTIiIiR0dHrF69utLyiL/++is+/fRT3Lp1S1AyaeNyZERERC/HOapERNXswYMHWLNmDaZPn47c3FwA5Usu8UOidujr66N79+64f/++6Cj0jNzcXLi5uQEo3++j4rnxxhtv4Pjx4yKjSUpZWRksLCwAlBdDbt++DaB8c/QrV66IjEZERDomNzcXjRo1qtTeqFEj9fs4aYdSqcT8+fNhZWUFFxcXuLi4wNraGl9++SWUSqXoeERERDqFBRAiomp0/vx5NGzYEN988w2WLFmCBw8eAAB27tyJ6dOniw0nIc2aNUNaWproGPQMNzc3pKenAygfOImMjAQA7NmzhzN1tKhZs2ZISEgAALRu3RqLFi1CTEwM5s+fry5QERERAYCPjw9WrFhRqX3FihXw8fERkEi6ZsyYgRUrViAkJARxcXGIi4vDwoUL8e2332LWrFmi4xEREekULoFFRFSNunXrhpYtW2LRokWwsLBAQkIC3NzccOLECQwePBjXrl0THVES9u/fj+nTp+PLL79Eq1atYGZmpnGc6ydr39KlS6Gvrw9/f38cPnwYvXv3hkqlQklJCcLCwhAQECA6Yo11/vx5NGvWDHp6ejhw4ACKiorQv39/pKSkoFevXkhOToadnR22b9+OLl26iI5LREQ6Ijo6Gu+88w7q16+PNm3aAABOnjyJGzduYO/evWjfvr3ghNLB5ciIiIheHQsgRETVyMrKCufOnUODBg00CiDXr1+Hp6cnHj9+LDqiJOjpPZ3wKJPJ1N+rVCrIZDKUlZWJiCVZJSUl6NGjB1avXg0PDw8AwPXr13H27Fm4u7vD29tbcMKaTV9fH5mZmZDL5XBzc8OZM2dgZ2enPp6bmwsbGxuN5woREVFGRgYMDAywcuVKXL58GQDQuHFjfPrppygtLUX9+vUFJ5QOExMT9UzzZ125cgXNmzfHo0ePBCUjIiLSPQaiAxAR1WTGxsbIz8+v1J6cnAx7e3sBiaRp/fr1cHZ2hr6+vka7UqlERkaGoFTSZWhoiPPnz2u0VaxfTdXP2toa6enpkMvluHbtWqW1wm1tbQUlIyIiXaZQKJCZmYkFCxZotOfk5MDZ2ZkXlGhRxXJky5cv12jncmRERESVcQYIEVE1Gj16NHJychAZGQlbW1ucP38e+vr66NevHzp06IDw8HDRESXh2Sven5WTkwO5XM4P7AIEBgbC2NgYISEhoqNIztixY7Fx40bUrVsXGRkZcHJyqlQcrMC9c4iIqIKenh7u3LlT6f+p69evo0mTJigsLBSUTHq4HBkREdGr4wwQIqJqFBoaivfeew9yuRyPHj1Cx44dcefOHbRp06bS1XNUfSqWunpeQUEBTExMBCSi0tJSrFu3DocPH65yX5awsDBByWq+H374AQMGDEBKSgr8/f0xZswYWFhYiI5FREQ6KigoCED5MqKzZ8+Gqamp+lhZWRlOnTqF5s2bC0onTQqFAsnJyRrLkQ0YMEC9HBkRERE9xRkgRERaEBMTg4SEBBQUFKBly5bo1q2b6EiSUPGBfdmyZRgzZkyVH9j19fURExMjKqJkde7c+YXHZDIZoqKitJhGukaMGIHly5ezAEJERC9U8Z4dHR2NNm3awMjISH3MyMgIrq6umDJlinpfL6p+nN1MRET06lgAISLSsgcPHsDa2lp0DEngB3YiIiKi/40RI0Zg2bJlsLS0FB1F8rgcGRER0atjAYSIqBp98803cHV1xcCBAwEAH3zwAXbs2IE6depg79693KRQS/iBnYiIiIj+7Ti7mYiI6J9jAYSIqBopFAps3rwZbdu2xaFDh/DBBx9g+/btiIyMREZGBg4ePCg6IhERERER/QtwdjMREdE/xwIIEVE1qlWrFpKTk+Hs7IyAgAA8fvwY33//PZKTk9G6dWvcv39fdEQiIiIiIvoX4exmIiKiV6cnOgARUU1mY2ODGzduAAD279+v3vxcpVJxc0IiIiIiIvrH1q9fz+IHERHRKzIQHYCIqCYbMGAABg8eDA8PD+Tk5KBnz54AgLi4OLi7uwtOR0REREREREREVHOxAEJEVI2WLl0KV1dX3LhxA4sWLYK5uTkAIDMzE59++qngdERERERERERERDUX9wAhIiIiIiIiIiIiIqIah3uAEBFVo4iICPz222/q259//jmsra3Rtm1bXL9+XWAyIiIiIiIiIiKimo0FECKiarRw4ULUqlULAHDy5EmsXLkSixYtQu3atREYGCg4HRERERERERERUc3FJbCIiKqRqakpLl++jPr162Pq1KnIzMzExo0bcfHiRXTq1AnZ2dmiIxIREREREREREdVI/w/vT75XJ9lvpQAAAABJRU5ErkJggg==" }, "metadata": { "image/png": { @@ -481,7 +609,6 @@ } ], "source": [ - "# Perform exploratory data analysis (EDA) on a sample of the data\n", "from pyspark.ml.stat import Correlation\n", "from pyspark.ml.feature import VectorAssembler\n", "import seaborn as sns \n", @@ -504,9 +631,17 @@ "%matplot plt" ] }, + { + "cell_type": "markdown", + "id": "cbde3b29-d37d-485a-a114-5313c5a702c7", + "metadata": {}, + "source": [ + "### Split the dataset into train, validation, and test sets" + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "id": "6e207c64-2e22-468f-a0c7-948090bcfce2", "metadata": {}, "outputs": [ @@ -519,13 +654,22 @@ } ], "source": [ - "# Split the dataset into train, validation, and test sets\n", "df_train, df_val, df_test = df_cleaned.randomSplit([0.7, 0.15, 0.15])" ] }, + { + "cell_type": "markdown", + "id": "01a4d181-e2f0-4743-ab35-dd1f68b0fd31", + "metadata": {}, + "source": [ + "### Define the Amazon S3 locations that store the datasets\n", + "\n", + "If you're getting a module not found error, restart the kernel and run all the cells again." + ] + }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "f16ea3a1-6d6d-4755-94ad-c743298bd130", "metadata": {}, "outputs": [ @@ -550,9 +694,17 @@ "region = boto3.Session().region_name" ] }, + { + "cell_type": "markdown", + "id": "8899a159-700c-403a-b4f5-a00c62b06e5a", + "metadata": {}, + "source": [ + "### Write the files to the locations" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "id": "64d7ae48-6158-4273-8bb3-2f00abb1c20c", "metadata": {}, "outputs": [ @@ -588,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "id": "9d18ef1c-fc2f-4e34-a692-4a6c48be7cba", "metadata": {}, "outputs": [ @@ -604,134 +756,22 @@ "df_test.write.parquet(f\"s3://{s3_bucket}/{test_data_prefix}\", mode=\"overwrite\")" ] }, + { + "cell_type": "markdown", + "id": "73c947e4-b4a9-4cc4-aefe-755aa0a713c8", + "metadata": {}, + "source": [ + "### Train a model\n", + "\n", + "The following code uses the `df_train` and `df_val` datasets to train an XGBoost model. " + ] + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "a31b7742-93df-44c5-8674-b6355032c508", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-06-14 00:36:26 Starting - Starting the training job...\n", - "2024-06-14 00:36:46 Starting - Preparing the instances for training...\n", - "2024-06-14 00:37:14 Downloading - Downloading input data...\n", - "2024-06-14 00:37:34 Downloading - Downloading the training image...\n", - "2024-06-14 00:37:59 Training - Training image download completed. Training in progress...[2024-06-14 00:38:35.919 ip-10-0-229-197.ec2.internal:7 INFO utils.py:28] RULE_JOB_STOP_SIGNAL_FILENAME: None\n", - "[2024-06-14 00:38:35.939 ip-10-0-229-197.ec2.internal:7 INFO profiler_config_parser.py:111] User has disabled profiler.\n", - "[2024-06-14:00:38:36:INFO] Imported framework sagemaker_xgboost_container.training\n", - "[2024-06-14:00:38:36:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\n", - "Returning the value itself\n", - "[2024-06-14:00:38:36:INFO] No GPUs detected (normal if no gpus installed)\n", - "[2024-06-14:00:38:36:INFO] Running XGBoost Sagemaker in algorithm mode\n", - "[2024-06-14:00:38:36:INFO] Determined 0 GPU(s) available on the instance.\n", - "[2024-06-14:00:38:36:INFO] File path /opt/ml/input/data/train of input files\n", - "[2024-06-14:00:38:36:INFO] Making smlinks from folder /opt/ml/input/data/train to folder /tmp/sagemaker_xgboost_input_data\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00004-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00004-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-6099176745642522633\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00001-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00001-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet7068749022651873836\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00012-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00012-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet4480105206382563880\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00002-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00002-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet666710498772781167\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00006-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00006-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet2012959030070555737\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00003-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00003-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-7792575879923673435\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00000-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00000-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet1554580869360746365\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00013-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00013-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-3923144601956882519\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00007-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00007-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-6701620939578787966\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00005-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00005-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet5010314801406155242\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00008-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00008-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-6499940601498548870\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00010-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00010-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet3597535567109828643\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00014-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00014-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet495707717602281052\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00015-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00015-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet3829572270789775756\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00009-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00009-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-2865524942059150049\n", - "[2024-06-14:00:38:36:INFO] creating symlink between Path /opt/ml/input/data/train/part-00011-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00011-76ccc3b9-b09d-4c8f-b1a2-7fdb7ca14f4c-c000.snappy.parquet-4033658725503876771\n", - "[2024-06-14:00:38:36:INFO] files path: /tmp/sagemaker_xgboost_input_data\n", - "[2024-06-14:00:38:40:INFO] File path /opt/ml/input/data/validation of input files\n", - "[2024-06-14:00:38:40:INFO] Making smlinks from folder /opt/ml/input/data/validation to folder /tmp/sagemaker_xgboost_input_data\n", - "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00013-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00013-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet-4748847473618110904\n", - "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00010-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00010-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet5148602013217595227\n", - "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00008-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00008-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet5363507096728416946\n", - 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"[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00007-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00007-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet2408066730278722615\n", - "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00012-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00012-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet-2047781405163644280\n", - "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00014-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00014-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet8311663450763339060\n", - "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00002-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00002-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet8238084367374917843\n", - "[2024-06-14:00:38:40:INFO] creating symlink between Path /opt/ml/input/data/validation/part-00006-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet and destination /tmp/sagemaker_xgboost_input_data/part-00006-87332dd1-983b-478b-b967-822807750254-c000.snappy.parquet-2173537324320107354\n", - "[2024-06-14:00:38:40:INFO] files path: /tmp/sagemaker_xgboost_input_data\n", - "[2024-06-14:00:38:41:INFO] Single node training.\n", - "[2024-06-14:00:38:41:INFO] Train matrix has 30944499 rows and 9 columns\n", - "[2024-06-14:00:38:41:INFO] Validation matrix has 6630552 rows\n", - "[2024-06-14 00:38:41.080 ip-10-0-229-197.ec2.internal:7 INFO json_config.py:92] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\n", - "[2024-06-14 00:38:41.080 ip-10-0-229-197.ec2.internal:7 INFO hook.py:206] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\n", - "[2024-06-14 00:38:41.081 ip-10-0-229-197.ec2.internal:7 INFO hook.py:259] Saving to /opt/ml/output/tensors\n", - "[2024-06-14 00:38:41.081 ip-10-0-229-197.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\n", - "[2024-06-14:00:38:41:INFO] Debug hook created from config\n", - "[0]#011train-rmse:1830258878187.12842#011validation-rmse:1830650699152.31494\n", - "[2024-06-14 00:38:46.805 ip-10-0-229-197.ec2.internal:7 INFO hook.py:427] Monitoring the collections: metrics\n", - "[2024-06-14 00:38:46.807 ip-10-0-229-197.ec2.internal:7 INFO hook.py:491] Hook is writing from the hook with pid: 7\n", - "[1]#011train-rmse:1629960261821.84106#011validation-rmse:1630342677781.64697\n", - "[2]#011train-rmse:1487695600747.47461#011validation-rmse:1488064146076.08887\n", - "[3]#011train-rmse:1389026442545.81470#011validation-rmse:1389377954565.06006\n", - "[4]#011train-rmse:1321695765365.73877#011validation-rmse:1322031530771.13354\n", - "[5]#011train-rmse:1276740831629.93091#011validation-rmse:1277061863710.23682\n", - "[6]#011train-rmse:1247097412925.66821#011validation-rmse:1247401430743.64795\n", - "[7]#011train-rmse:1227747968014.05444#011validation-rmse:1228037163005.18335\n", - "[8]#011train-rmse:1215170724247.77222#011validation-rmse:1215448698513.50879\n", - "[9]#011train-rmse:1207046944746.58350#011validation-rmse:1207316233855.67749\n", - "[10]#011train-rmse:1201783443361.93457#011validation-rmse:1202043885587.02417\n", - "[11]#011train-rmse:1198401334034.30933#011validation-rmse:1198654899842.81396\n", - "[12]#011train-rmse:1196223057609.97485#011validation-rmse:1196472335408.23047\n", - "[13]#011train-rmse:1194793492477.64160#011validation-rmse:1195040826268.06714\n", - "[14]#011train-rmse:1193861428638.90527#011validation-rmse:1194109169621.48096\n", - "[15]#011train-rmse:1193230091247.77100#011validation-rmse:1193474801224.40454\n", - "[16]#011train-rmse:1192821546941.62378#011validation-rmse:1193068675122.69312\n", - "[17]#011train-rmse:1192561165347.32251#011validation-rmse:1192806237217.96143\n", - "[18]#011train-rmse:1192386477794.77588#011validation-rmse:1192628642455.83276\n", - "[19]#011train-rmse:1192270314999.13452#011validation-rmse:1192512558017.28052\n", - "[20]#011train-rmse:1192185331187.94312#011validation-rmse:1192428000382.65649\n", - "[21]#011train-rmse:1192113970087.07056#011validation-rmse:1192358025332.75098\n", - "[22]#011train-rmse:1192070221222.92139#011validation-rmse:1192315120608.84546\n", - "[23]#011train-rmse:1192036912041.30347#011validation-rmse:1192280233203.12524\n", - "[24]#011train-rmse:1192008426772.59277#011validation-rmse:1192252534585.66406\n", - "[25]#011train-rmse:1191984055285.96313#011validation-rmse:1192227766501.24878\n", - "[26]#011train-rmse:1191960405482.00928#011validation-rmse:1192204293324.09521\n", - "[27]#011train-rmse:1191945650115.00171#011validation-rmse:1192189907585.56787\n", - "[28]#011train-rmse:1191937076532.34546#011validation-rmse:1192182107256.42993\n", - "[29]#011train-rmse:1191911091380.20825#011validation-rmse:1192157949699.30249\n", - "[30]#011train-rmse:1191889211431.19482#011validation-rmse:1192136029069.66968\n", - "[31]#011train-rmse:1191878758489.41479#011validation-rmse:1192126105484.02148\n", - "[32]#011train-rmse:1191871084793.49341#011validation-rmse:1192117990930.42285\n", - "[33]#011train-rmse:1191850168213.44604#011validation-rmse:1192096702217.71631\n", - "[34]#011train-rmse:1191842445605.27563#011validation-rmse:1192088592129.65991\n", - "[35]#011train-rmse:1191825318352.25293#011validation-rmse:1192072497184.73462\n", - "[36]#011train-rmse:1191815568908.05786#011validation-rmse:1192063233346.56299\n", - "[37]#011train-rmse:1191807671488.23853#011validation-rmse:1192056982851.26904\n", - "[38]#011train-rmse:1191802078377.84863#011validation-rmse:1192051206608.39307\n", - "[39]#011train-rmse:1191791601237.45581#011validation-rmse:1192041999023.11670\n", - "[40]#011train-rmse:1191782542291.56982#011validation-rmse:1192032919271.50024\n", - "[41]#011train-rmse:1191776496494.06421#011validation-rmse:1192028434782.18604\n", - "[42]#011train-rmse:1191769742829.94604#011validation-rmse:1192020721371.07715\n", - "[43]#011train-rmse:1191760877562.48730#011validation-rmse:1192013123163.06396\n", - "[44]#011train-rmse:1191756403194.40674#011validation-rmse:1192009794212.31812\n", - "[45]#011train-rmse:1191749717552.74341#011validation-rmse:1192003290591.67969\n", - "[46]#011train-rmse:1191742470497.40967#011validation-rmse:1191997083425.68970\n", - "[47]#011train-rmse:1191730093274.09351#011validation-rmse:1191985279848.86987\n", - "[48]#011train-rmse:1191723680549.70190#011validation-rmse:1191980086431.17139\n", - "[49]#011train-rmse:1191709586099.72583#011validation-rmse:1191966703191.52051\n", - "\n", - "2024-06-14 00:41:28 Uploading - Uploading generated training model\n", - "2024-06-14 00:41:28 Completed - Training job completed\n", - "Training seconds: 255\n", - "Billable seconds: 255\n" - ] - } - ], + "outputs": [], "source": [ "from sagemaker import image_uris\n", "from sagemaker.inputs import TrainingInput\n", @@ -745,15 +785,15 @@ " \"objective\":\"reg:squarederror\",\n", " \"num_round\":\"50\"}\n", "\n", - "# Set an output path where the trained model is saved\n", + "# Set an output path to save the trained model.\n", "prefix = 'sandbox/glue-demo'\n", "output_path = f's3://{s3_bucket}/{prefix}/xgb-built-in-algo/output'\n", "\n", - "# The following line automatically looks for the XGBoost image URI and builds an XGBoost container.\n", - "# Version 1.7-1 of the image URI is used. You can specify a version that you prefer.\n", + "# The following line looks for the XGBoost image URI and builds an XGBoost container.\n", + "# We use version 1.7-1 of the image URI, you can specify a version that you prefer.\n", "xgboost_container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.7-1\")\n", "\n", - "# construct a SageMaker estimator that calls the xgboost-container\n", + "# Construct a SageMaker estimator that calls the xgboost-container\n", "estimator = sagemaker.estimator.Estimator(image_uri=xgboost_container,\n", " hyperparameters=hyperparameters,\n", " role=sagemaker.get_execution_role(),\n", @@ -769,10 +809,20 @@ "estimator.fit({'train': train_input, 'validation': validation_input})" ] }, + { + "cell_type": "markdown", + "id": "b1b1d546-1c7e-48f5-9262-939289ada936", + "metadata": {}, + "source": [ + "### Clean up\n", + "\n", + "To clean up, shut down the kernel. Shutting down the kernel, stops the Glue cluster. You won't be charged for any more compute other than what you used to run the tutorial." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "e0967a86-ccea-4992-8071-4e624e4d1865", + "id": "5e32c38c-719f-47bf-849f-54b63c39823b", "metadata": {}, "outputs": [], "source": [] From dad39e884ad1b7625aaf0f444d18ef568c03297a Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Wed, 26 Jun 2024 14:03:37 +0200 Subject: [PATCH 09/13] clear notebook outputs --- .../athena_ml_workflow_end_to_end.ipynb | 1979 +---------------- .../pyspark-etl-training.ipynb | 490 ++-- 2 files changed, 249 insertions(+), 2220 deletions(-) diff --git a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb index c1555ce64b..4fe6b17021 100644 --- a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb +++ b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "8ab1ff0e-fcde-4976-a1cd-51e75c18deb2", "metadata": {}, "outputs": [], @@ -128,30 +128,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "64131b68-de28-4060-bb75-8148902846f7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: cb929408-df15-408d-a776-a8963facbf80\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'cb929408-df15-408d-a776-a8963facbf80'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# SQL query to create the 'ride_fare' table\n", "create_ride_fare_table = \"\"\"\n", @@ -200,33 +180,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "3d249cc5-2d53-4274-8f5e-6ab09ccd3ea6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: 15337c2c-54e5-4e19-94a8-92d2faef2efd\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'15337c2c-54e5-4e19-94a8-92d2faef2efd'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# SQL query to create a new table with duplicates removed\n", "remove_duplicates_from_ride_fare = \"\"\"\n", @@ -250,30 +207,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "2f9a68b9-bd11-49e9-ad72-b44b43d32e47", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: bc365d36-bbbb-4f33-a153-3192127a1069\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'bc365d36-bbbb-4f33-a153-3192127a1069'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# SQL query to create the ride_info table\n", "create_ride_info_table_query = \"\"\"\n", @@ -316,33 +253,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "263d883c-f189-43c0-9fbd-1a45093984e9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: 1946c89d-d1c3-449d-b7af-42521778c51c\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'1946c89d-d1c3-449d-b7af-42521778c51c'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# SQL query to create table with duplicates removed\n", "remove_duplicates_from_ride_info = \"\"\"\n", @@ -366,30 +280,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "6db6bb67-44a9-4ff4-b662-ad969a84d3d8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: ab1e6968-e04c-47c0-94c7-03868d1d7fc1\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'ab1e6968-e04c-47c0-94c7-03868d1d7fc1'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "test_ride_info_query = '''\n", "SELECT * FROM ride_info_deduped limit 10\n", @@ -408,30 +302,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "92d8be21-3f20-453d-8b84-516571d9854d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: caeedc97-8f55-4759-9380-8ced39fab414\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'caeedc97-8f55-4759-9380-8ced39fab414'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "test_ride_fare_query = '''\n", "SELECT * FROM ride_fare_deduped limit 10\n", @@ -452,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "50e87ba6-42e9-4d99-862e-7eae16ad810e", "metadata": {}, "outputs": [], @@ -489,122 +363,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "b04abae5-936b-4d96-98e8-d2e2b6a17b9c", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amount
02834679627591152.00.00.512.286.1273.70
11400160739953152.02.50.511.050.0066.35
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" - ], - "text/plain": [ - " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", - "0 2834679627591 1 52.0 0.0 0.5 12.28 \n", - "1 1400160739953 1 52.0 2.5 0.5 11.05 \n", - "2 2834679627600 2 7.0 0.0 0.5 0.00 \n", - "3 1331440950394 1 4.0 1.0 0.5 1.66 \n", - "4 2834679627624 1 4.5 0.0 0.5 1.06 \n", - "\n", - " tolls_amount total_amount \n", - "0 6.12 73.70 \n", - "1 0.00 66.35 \n", - "2 0.00 7.80 \n", - "3 0.00 9.96 \n", - "4 0.00 6.36 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "# Provide the query execution id of the test_ride_info query to get the query results\n", @@ -627,122 +389,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "be89957f-31b1-4710-bfc2-178d6db18592", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amount
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11400160739953152.02.50.511.050.0066.35
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4283467962762414.50.00.51.060.006.36
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" - ], - "text/plain": [ - " ride_id payment_type fare_amount extra mta_tax tip_amount \\\n", - "0 2834679627591 1 52.0 0.0 0.5 12.28 \n", - "1 1400160739953 1 52.0 2.5 0.5 11.05 \n", - "2 2834679627600 2 7.0 0.0 0.5 0.00 \n", - "3 1331440950394 1 4.0 1.0 0.5 1.66 \n", - "4 2834679627624 1 4.5 0.0 0.5 1.06 \n", - "\n", - " tolls_amount total_amount \n", - "0 6.12 73.70 \n", - "1 0.00 66.35 \n", - "2 0.00 7.80 \n", - "3 0.00 9.96 \n", - "4 0.00 6.36 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Provide the query execution id of the test_ride_fare query to get the query results\n", "\n", @@ -763,43 +413,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "b8a76635-3c09-4cbc-b1b4-9318dc611250", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: 8eb61f36-2e1b-43c7-9b33-61e7ce5d21bc\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'8eb61f36-2e1b-43c7-9b33-61e7ce5d21bc'" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# SQL query to join the tables into a single table containing all the data.\n", "create_ride_joined_deduped = \"\"\"\n", @@ -843,67 +462,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "b0791e57-4351-4f27-a8f9-ad741441d214", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: f303cff8-5369-409a-9c51-8c791d446fe3\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. 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Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'f303cff8-5369-409a-9c51-8c791d446fe3'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# SQL query to select all values from the table and create the dataset that we're using for our analysis\n", "ride_combined_full_table_query = \"\"\"\n", @@ -926,21 +488,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "97373c52-882b-4e44-8d75-a80d8d8c58df", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'s3://ux360-nyc-taxi-dogfooding/f303cff8-5369-409a-9c51-8c791d446fe3.csv'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Function to get the Amazon S3 URI location of Amazon Athena select statements\n", "def get_csv_file_location(query_execution_id):\n", @@ -966,19 +517,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "954022d5-bdf9-4dbd-be2e-66d0009ce522", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "download: s3://ux360-nyc-taxi-dogfooding/f303cff8-5369-409a-9c51-8c791d446fe3.csv to ./f303cff8-5369-409a-9c51-8c791d446fe3.csv\n", - "mv: cannot stat 'query-id.csv': No such file or directory\n" - ] - } - ], + "outputs": [], "source": [ "# Use the S3 URI location returned from the preceding cell to download the dataset and rename it.\n", "!aws s3 cp s3://example-s3-bucket/ride_combined_full_table_query_execution_id.csv .\n", @@ -995,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "79d2f2a5-5111-4fb8-90f3-67474f1072c1", "metadata": {}, "outputs": [], @@ -1005,196 +547,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "f9dececa-272d-458c-9f64-baa13eca0832", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset shape: (20000, 15)\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Dataset shape: \", sample_nyc_taxi_combined.shape)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "1c117a0f-429e-4913-aded-c839675f9e17", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amountvendor_idpassenger_countpickup_atdropoff_attrip_distancerate_code_idstore_and_fwd_flag
06013183901417.50.00.51.660.09.96212019-01-04T07:53:41.000Z2019-01-04T08:02:20.000Z1.451N
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2139157156874018.50.00.52.360.014.16222019-02-05T10:59:56.000Z2019-02-05T11:10:40.000Z1.531N
36013183913018.00.00.51.760.010.56212019-01-04T07:12:07.000Z2019-01-04T07:20:07.000Z1.681N
4139157156891215.00.00.51.660.09.96212019-02-05T11:14:36.000Z2019-02-05T11:19:52.000Z0.651N
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ride_idpayment_typefare_amountextramta_taxtip_amounttolls_amounttotal_amountvendor_idpassenger_counttrip_distancerate_code_id
count2.000000e+0420000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.00000020000.0000020000.0000020000.00000020000.000000
mean1.818963e+121.28870012.9201551.0605400.4960252.1283920.37697618.4721391.624401.568452.9285301.054400
std1.210592e+120.47640711.8908781.2307330.0509592.6013791.63952814.6649320.484291.215523.8417760.363108
min5.153977e+101.000000-74.500000-4.500000-0.5000000.0000000.000000-76.3000001.000000.000000.0000001.000000
25%1.005022e+121.0000006.5000000.0000000.5000000.0000000.00000010.7900001.000001.000000.9400001.000000
50%1.400160e+121.0000009.0000000.5000000.5000001.7950000.00000014.1600002.000001.000001.6000001.000000
75%2.834679e+122.00000014.5000002.5000000.5000002.8600000.00000019.8000002.000002.000003.0000001.000000
max3.839702e+124.000000300.0000007.0000000.50000052.16000030.500000312.9600002.000006.0000070.8900005.000000
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0.000000 14.160000 2.00000 1.00000 \n", - "75% 2.860000 0.000000 19.800000 2.00000 2.00000 \n", - "max 52.160000 30.500000 312.960000 2.00000 6.00000 \n", - "\n", - " trip_distance rate_code_id \n", - "count 20000.000000 20000.000000 \n", - "mean 2.928530 1.054400 \n", - "std 3.841776 0.363108 \n", - "min 0.000000 1.000000 \n", - "25% 0.940000 1.000000 \n", - "50% 1.600000 1.000000 \n", - "75% 3.000000 1.000000 \n", - "max 70.890000 5.000000 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.describe()" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "18bd92b1-962a-40f2-b15f-7351d869f390", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "vendor_id\n", - "2 12488\n", - "1 7512\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df['vendor_id'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "e4c4997f-85d8-4f57-a60c-51e3568cfe2e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "passenger_count\n", - "1 14030\n", - "2 3040\n", - "3 857\n", - "5 850\n", - "6 487\n", - "4 379\n", - "0 357\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df['passenger_count'].value_counts()" ] @@ -1510,31 +617,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "641c278d-8fed-42b8-98d1-becba90d6259", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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"execution_count": null, "id": "05abe8af-bf44-471b-b130-19cee0dd822f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting seaborn\n", - " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", - "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.10/site-packages (from seaborn) (1.26.4)\n", - "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.10/site-packages (from seaborn) (2.1.4)\n", - "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.10/site-packages (from seaborn) (3.8.4)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create visualizations showing correlations between variables.\n", "import seaborn as sns\n", @@ -1707,23 +744,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "d8dff114-adb5-4b34-a788-b93e42a2fee4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tip_amount 0.5743753694582684\n", - "tolls_amount 0.6327404045395644\n", - "extra -0.008246801964138361\n", - "mta_tax -0.1628089444699402\n", - "total_amount 0.9783791092253548\n", - "trip_distance 0.8848067140931489\n" - ] - } - ], + "outputs": [], "source": [ "# extra and mta_tax seem weakly correlated\n", "# total_amount is almost perfectly correlated, indicating target leakage.\n", @@ -1746,22 +770,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "3e083025-3312-4fd9-8cd2-4c8e37db5859", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Feature: payment_type, F-statistic: 22.20, p-value: 0.00000\n", - "Feature: extra, F-statistic: 130.42, p-value: 0.00000\n", - "Feature: mta_tax, F-statistic: 999.42, p-value: 0.00000\n", - "Feature: vendor_id, F-statistic: 12.42, p-value: 0.00042\n", - "Feature: passenger_count, F-statistic: 2.57, p-value: 0.01744\n" - ] - } - ], + "outputs": [], "source": [ "# The mta tax and extra have the most variance between the groups\n", "from scipy.stats import f_oneway\n", @@ -1789,41 +801,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "0dbcf599-076c-468e-9e9b-2e0bd53c3fa7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query execution ID: e9866ba2-8e0d-426f-a601-e6ca24890b71\n", - "Query is currently in QUEUED state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query is currently in RUNNING state. Waiting for completion...\n", - "Query executed successfully.\n" - ] - }, - { - "data": { - "text/plain": [ - "'e9866ba2-8e0d-426f-a601-e6ca24890b71'" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Final select statement has tip_amount, tolls_amount, extra, mta_tax, trip_distance\n", "ride_combined_notebook_relevant_features_query = \"\"\"\n", @@ -1843,21 +824,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "624a7833-c815-480e-b1da-c29da3d02c76", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'s3://ux360-nyc-taxi-dogfooding/e9866ba2-8e0d-426f-a601-e6ca24890b71.csv'" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "get_csv_file_location('ride_combined_notebook_relevant_features_query_execution_id')" ] @@ -1880,42 +850,10 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "788cae3c-a34b-4ee0-899e-0a461e21b210", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker.image_uris:Defaulting to only available Python version: py3\n", - "INFO:sagemaker:Creating processing-job with name sagemaker-scikit-learn-2024-06-25-17-41-19-446\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...........\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", - " import imp\u001b[0m\n", - "\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\u001b[0m\n", - "\u001b[35m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", - " import imp\u001b[0m\n", - "\u001b[35m/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\u001b[0m\n", - "\u001b[34msys:1: DtypeWarning: Columns (0,1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\u001b[0m\n", - "\u001b[35msys:1: DtypeWarning: Columns (0,1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\u001b[0m\n", - "\u001b[35mTraining set: 30940496 samples\u001b[0m\n", - "\u001b[35mValidation set: 6630106 samples\u001b[0m\n", - "\u001b[35mTest set: 6630107 samples\u001b[0m\n", - "\u001b[34mTraining set: 30940496 samples\u001b[0m\n", - "\u001b[34mValidation set: 6630106 samples\u001b[0m\n", - "\u001b[34mTest set: 6630107 samples\u001b[0m\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import sagemaker\n", "from sagemaker.sklearn.processing import SKLearnProcessor\n", @@ -1966,18 +904,10 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "41cb0fb0-079d-421d-a4b8-005ee38fc472", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-06-25 17:49:51 794185864 train.csv\n" - ] - } - ], + "outputs": [], "source": [ "#Verify that train.csv is in the location that you've specified\n", "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/train/train.csv" @@ -1993,18 +923,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "ee3f29f1-a135-4bf6-bba5-595fb80c471d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-06-25 17:49:51 170183603 val.csv\n" - ] - } - ], + "outputs": [], "source": [ "#Verify that val.csv is in the location that you've specified\n", "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/validation/val.csv" @@ -2020,7 +942,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "1e4e4113-b76c-49d5-a3b0-2327eb174fdf", "metadata": {}, "outputs": [], @@ -2051,25 +973,10 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "d5b6a9b2-54e5-4dfd-9a5e-3c7442f6d5af", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.2-2\n" - ] - } - ], + "outputs": [], "source": [ "# Getting the XGBoost container that's in us-east-1\n", "prefix = \"training-output-data\"\n", @@ -2094,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "44efb3a1-acf0-4193-987f-85025c7c3894", "metadata": {}, "outputs": [], @@ -2127,7 +1034,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "e28512bf-d246-4a46-a0c8-24d1a8ad65a8", "metadata": {}, "outputs": [], @@ -2153,122 +1060,10 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "58b77fc0-407d-4743-ae35-7bc7b04478e6", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: latest.\n", - "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", - "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: latest.\n", - "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", - "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2024-06-25-18-20-44-522\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-06-25 18:20:45 Starting - Starting the training job...CreateXgboostReport: InProgress\n", - "ProfilerReport: InProgress\n", - "...\n", - "2024-06-25 18:21:29 Starting - Preparing the instances for training...\n", - "2024-06-25 18:22:09 Downloading - Downloading input data......\n", - "2024-06-25 18:23:12 Training - Training image download completed. Training in progress....\u001b[34m[2024-06-25 18:23:33.281 ip-10-2-65-56.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", - "\u001b[34mReturning the value itself\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:33:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:38.246 ip-10-2-111-68.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Failed to parse hyperparameter objective value reg:squarederror to Json.\u001b[0m\n", - "\u001b[35mReturning the value itself\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:38:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:42:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] start listen on algo-1:9099\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9099}\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] Connected to RabitTracker.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:43:INFO] No data received from connection ('10.2.65.56', 37490). Closing.\u001b[0m\n", - "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:47:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:48:INFO] Distributed node training with 2 hosts: ['algo-1', 'algo-2']\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:48:INFO] Connected to RabitTracker.\u001b[0m\n", - "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35mtask NULL got new rank 0\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:48:INFO] No data received from connection ('10.2.111.68', 42310). Closing.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] Recieve start signal from 10.2.111.68; assign rank 0\u001b[0m\n", - "\u001b[34mtask NULL got new rank 1\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] Recieve start signal from 10.2.65.56; assign rank 1\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker All of 2 nodes getting started\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker All nodes finishes job\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] @tracker 0.1758573055267334 secs between node start and job finish\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] start listen on algo-1:9100\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] Rabit slave environment: {'DMLC_TRACKER_URI': 'algo-1', 'DMLC_TRACKER_PORT': 9100}\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] Connected to RabitTracker.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:49:INFO] No data received from connection ('10.2.65.56', 38280). Closing.\u001b[0m\n", - "\u001b[34mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:49:INFO] Failed to connect to RabitTracker on attempt 0\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:49:INFO] Sleeping for 3 sec before retrying\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] No data received from connection ('10.2.111.68', 60082). Closing.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Recieve start signal from 10.2.111.68; assign rank 0\u001b[0m\n", - "\u001b[34mtask NULL got new rank 1\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Recieve start signal from 10.2.65.56; assign rank 1\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Train matrix has 30940497 rows and 5 columns\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] @tracker All of 2 nodes getting started\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Validation matrix has 6630107 rows\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.600 ip-10-2-65-56.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.601 ip-10-2-65-56.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.601 ip-10-2-65-56.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.602 ip-10-2-65-56.ec2.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", - "\u001b[34m[2024-06-25 18:23:52.602 ip-10-2-65-56.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", - "\u001b[34m[2024-06-25:18:23:52:INFO] Debug hook created from config\u001b[0m\n", - "\u001b[34m[18:23:52] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:52:INFO] Connected to RabitTracker.\u001b[0m\n", - "\u001b[35mtask NULL connected to the tracker\u001b[0m\n", - "\u001b[35mtask NULL got new rank 0\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:52:INFO] Train matrix has 30940497 rows and 5 columns\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:52:INFO] Validation matrix has 6630107 rows\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.600 ip-10-2-111-68.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.601 ip-10-2-111-68.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.601 ip-10-2-111-68.ec2.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.602 ip-10-2-111-68.ec2.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", - "\u001b[35m[2024-06-25 18:23:52.602 ip-10-2-111-68.ec2.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", - "\u001b[35m[2024-06-25:18:23:52:INFO] Debug hook created from config\u001b[0m\n", - "\u001b[35m[18:23:52] WARNING: ../src/gbm/gbtree.cc:129: Tree method is automatically selected to be 'approx' for distributed training.\u001b[0m\n", - "\u001b[34m[2024-06-25 18:24:08.407 ip-10-2-65-56.ec2.internal:7 INFO hook.py:423] Monitoring the collections: labels, metrics, predictions, feature_importance, hyperparameters\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:08:INFO] [0]#011train-rmse:184.43744#011validation-rmse:135.48259\u001b[0m\n", - "\u001b[35m[2024-06-25 18:24:08.409 ip-10-2-111-68.ec2.internal:7 INFO hook.py:423] Monitoring the collections: predictions, labels, hyperparameters, feature_importance, metrics\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:20:INFO] [1]#011train-rmse:184.28534#011validation-rmse:135.24808\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:31:INFO] [2]#011train-rmse:184.18167#011validation-rmse:135.09784\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:43:INFO] [3]#011train-rmse:184.11903#011validation-rmse:134.99771\u001b[0m\n", - "\u001b[34m[2024-06-25:18:24:55:INFO] [4]#011train-rmse:184.07890#011validation-rmse:134.93574\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:07:INFO] [5]#011train-rmse:184.05234#011validation-rmse:134.89529\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:19:INFO] [6]#011train-rmse:184.03487#011validation-rmse:134.86635\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:30:INFO] [7]#011train-rmse:184.02385#011validation-rmse:134.84970\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:42:INFO] [8]#011train-rmse:184.01642#011validation-rmse:134.83659\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:54:INFO] [9]#011train-rmse:183.88487#011validation-rmse:134.82910\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:54:INFO] @tracker All nodes finishes job\u001b[0m\n", - "\u001b[34m[2024-06-25:18:25:54:INFO] @tracker 121.60369801521301 secs between node start and job finish\u001b[0m\n", - "\n", - "2024-06-25 18:26:11 Uploading - Uploading generated training model\n", - "2024-06-25 18:26:11 Completed - Training job completed\n", - "Training seconds: 520\n", - "Billable seconds: 520\n" - ] - } - ], + "outputs": [], "source": [ "xgb_model.fit({\"train\": train_input, \"validation\": validation_input}, wait=True)" ] @@ -2285,27 +1080,10 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "c1aa7bc3-feee-4602-a64c-8c1e08526d03", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Creating model with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n", - "INFO:sagemaker:Creating endpoint-config with name sagemaker-xgboost-2024-06-25-18-26-38-055\n", - "INFO:sagemaker:Creating endpoint with name sagemaker-xgboost-2024-06-25-18-26-38-055\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------!" - ] - } - ], + "outputs": [], "source": [ "xgb_predictor = xgb_model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')" ] @@ -2320,18 +1098,10 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "a9cc4eea-a6d0-418f-ab35-db437ce2a99d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "download: s3://ux360-nyc-taxi-dogfooding/output/test/test.csv to ./test.csv\n" - ] - } - ], + "outputs": [], "source": [ "!aws s3 cp s3://example-s3-bucket/output/test/test.csv ." ] @@ -2346,103 +1116,10 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "953f9d9b-04d0-4398-8620-8f9ab4eb407b", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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predicted_valuesactual_values
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- "dtype: object" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "comparison_df.dtypes" ] @@ -3074,18 +1310,10 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "781fe125-4a2e-4527-8c45-fcd20558f4bb", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 4.833823838366928\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -3111,18 +1339,10 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "9a6e651d-3e68-4c1b-8a28-3e15604b5ec1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "remove_bucket: parsa-machine-learning-exam\n" - ] - } - ], + "outputs": [], "source": [ "# Delete the S3 bucket\n", "!aws s3 rb s3://example-s3-bucket --force" @@ -3130,19 +1350,10 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "6c883864-e707-46d2-a183-76e5f2090368", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:sagemaker:Deleting endpoint configuration with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n", - "INFO:sagemaker:Deleting endpoint with name: sagemaker-xgboost-2024-06-25-18-26-38-055\n" - ] - } - ], + "outputs": [], "source": [ "# Delete the endpoint\n", "xgb_predictor.delete_endpoint()" diff --git a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb index 2dc23d344c..87042b4580 100644 --- a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb +++ b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb @@ -51,24 +51,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "94172c75-f8a9-4590-a443-c872fb5c5d6e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Welcome to the Glue Interactive Sessions Kernel\n", - "For more information on available magic commands, please type %help in any new cell.\n", - "\n", - "Please view our Getting Started page to access the most up-to-date information on the Interactive Sessions kernel: https://docs.aws.amazon.com/glue/latest/dg/interactive-sessions.html\n", - "Installed kernel version: 1.0.5 \n", - "Additional python modules to be included:\n", - "sagemaker\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "%additional_python_modules sagemaker" ] @@ -85,27 +75,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "2ea1c3a4-8881-48b0-8888-9319812750e7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Trying to create a Glue session for the kernel.\n", - "Session Type: etl\n", - "Session ID: 11fe1ff7-3608-485f-a4a3-65392596dba0\n", - "Applying the following default arguments:\n", - "--glue_kernel_version 1.0.5\n", - "--enable-glue-datacatalog true\n", - "--additional-python-modules sagemaker\n", - "Waiting for session 11fe1ff7-3608-485f-a4a3-65392596dba0 to get into ready status...\n", - "Session 11fe1ff7-3608-485f-a4a3-65392596dba0 has been created.\n", - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "import sys\n", "from awsglue.transforms import Join\n", @@ -129,18 +106,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "ba577de7-9ffe-4bae-b4c0-b225181306d9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_ride_info = glueContext.create_dynamic_frame_from_options(\n", " connection_type=\"s3\", format=\"parquet\",\n", @@ -159,18 +132,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "6efc3d4a-81d7-40f5-bb62-cd206924a0c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_ride_fare = glueContext.create_dynamic_frame_from_options(\n", " connection_type=\"s3\", format=\"parquet\",\n", @@ -187,27 +156,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "d63af3a3-358f-4c6e-97d4-97a1f1a552de", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "| ride_id|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|total_amount|\n", - "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "|1400160115693| 2| 31.0| 0.0| 0.5| 0.0| 6.12| 40.42|\n", - "|3770982177323| 1| 4.5| 0.0| 0.5| 1.2| 0.0| 9.0|\n", - "|1400160115694| 1| 16.5| 1.0| 0.5| 4.16| 0.0| 24.96|\n", - "|3770982177324| 1| 18.0| 2.5| 0.5| 5.3| 0.0| 26.6|\n", - "|1400160115695| 1| 8.0| 2.5| 0.5| 1.13| 0.0| 12.43|\n", - "+-------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "only showing top 5 rows\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_ride_fare.show(5)" ] @@ -222,18 +178,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "07a3baab-44b0-416a-b12e-049a270af8bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_joined = df_ride_info.join(df_ride_fare, [\"ride_id\"])" ] @@ -248,27 +200,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "2a456733-4533-4688-8174-368e50f4dd66", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "| ride_id|vendor_id|passenger_count| pickup_at| dropoff_at|trip_distance|rate_code_id|store_and_fwd_flag|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|total_amount|\n", - "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "|51539607553| 1| 1|2019-04-21 17:20:19|2019-04-21 17:31:28| 2.7| 1| N| 1| 10.5| 2.5| 0.5| 3.45| 0.0| 17.25|\n", - "|51539607560| 2| 1|2019-02-21 22:49:59|2019-02-21 22:53:45| 0.62| 1| N| 2| 4.5| 0.5| 0.5| 0.0| 0.0| 8.3|\n", - "|51539607572| 1| 1|2019-02-21 22:19:08|2019-02-21 22:24:13| 0.6| 1| N| 1| 5.0| 3.0| 0.5| 1.75| 0.0| 10.55|\n", - "|51539607626| 2| 5|2019-02-21 22:18:33|2019-02-21 22:30:32| 2.0| 1| N| 1| 10.0| 0.5| 0.5| 2.76| 0.0| 16.56|\n", - "|51539607627| 2| 1|2019-04-21 17:21:49|2019-04-21 17:35:46| 2.72| 1| N| 1| 12.0| 0.0| 0.5| 2.3| 0.0| 17.6|\n", - "+-----------+---------+---------------+-------------------+-------------------+-------------+------------+------------------+------------+-----------+-----+-------+----------+------------+------------+\n", - "only showing top 5 rows\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_joined.show(5)" ] @@ -283,33 +222,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "9a52a903-f394-4d00-a216-6af8c2132d83", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "root\n", - " |-- ride_id: long (nullable = true)\n", - " |-- vendor_id: integer (nullable = true)\n", - " |-- passenger_count: byte (nullable = true)\n", - " |-- pickup_at: timestamp (nullable = true)\n", - " |-- dropoff_at: timestamp (nullable = true)\n", - " |-- trip_distance: float (nullable = true)\n", - " |-- rate_code_id: integer (nullable = true)\n", - " |-- store_and_fwd_flag: string (nullable = true)\n", - " |-- payment_type: integer (nullable = true)\n", - " |-- fare_amount: float (nullable = true)\n", - " |-- extra: float (nullable = true)\n", - " |-- mta_tax: float (nullable = true)\n", - " |-- tip_amount: float (nullable = true)\n", - " |-- tolls_amount: float (nullable = true)\n", - " |-- total_amount: float (nullable = true)\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_joined.printSchema()" ] @@ -324,18 +244,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "c6bcc15f-8d41-4def-ae49-edaef4105343", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "44200708\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_joined.count()" ] @@ -350,18 +266,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "7d13d8d9-7eed-4efb-b972-601baf291842", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_no_dups = df_joined.dropDuplicates([\"ride_id\"])" ] @@ -378,18 +290,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "3e3e82a3-e3db-4752-8bab-f42cbbae4928", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "44200708\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_no_dups.count()" ] @@ -405,18 +313,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "9dc1d15f-53f6-404d-86fd-5a28f3792db8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_cleaned = df_joined.drop(\"pickup_at\", \"dropoff_at\", \"store_and_fwd_flag\", \"vendor_id\", \"payment_type\")" ] @@ -431,64 +335,42 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "48382726-c767-4b0e-9336-decbf8184938", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_sample = df_cleaned.sample(False, 0.1, seed=0).limit(20000)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "2bf2f181-0096-4044-8210-7d9de299d966", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20000\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_sample.count()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "a8b2f670-c5f9-4a01-8d9f-6a29a3dae660", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ride_id passenger_count ... tolls_amount total_amount\n", - "count 2.000000e+04 20000.000000 ... 20000.000000 20000.000000\n", - "mean 5.327415e+10 1.580700 ... 0.354632 18.917547\n", - "std 3.447216e+09 1.218221 ... 1.540669 14.226608\n", - "min 5.153961e+10 0.000000 ... 0.000000 -59.799999\n", - "25% 5.154042e+10 1.000000 ... 0.000000 11.300000\n", - "50% 5.154121e+10 1.000000 ... 0.000000 14.750000\n", - "75% 5.154202e+10 2.000000 ... 0.000000 20.299999\n", - "max 6.013019e+10 6.000000 ... 21.500000 242.300003\n", - "\n", - "[8 rows x 10 columns]\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_pandas = df_sample.toPandas()\n", "df_pandas.describe()" @@ -496,77 +378,42 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "246c98e9-64bd-4644-a163-b86a943d6a09", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset shape: (20000, 10)\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "print(\"Dataset shape: \", df_pandas.shape)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "c5b2727c-de75-4cc0-94e9-d254e235d003", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ride_id passenger_count ... tolls_amount total_amount\n", - "0 51539607572 1 ... 0.0 10.550000\n", - "1 51539607730 5 ... 0.0 17.299999\n", - "2 51539607857 2 ... 0.0 6.800000\n", - "3 51539607985 1 ... 0.0 7.300000\n", - "4 51539608203 1 ... 0.0 16.559999\n", - "\n", - "[5 rows x 10 columns]\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_pandas.head()" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "d69b48b6-98c2-4851-9c7a-f24f092bae41", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 20000 entries, 0 to 19999\n", - "Data columns (total 10 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 ride_id 20000 non-null int64 \n", - " 1 passenger_count 20000 non-null int8 \n", - " 2 trip_distance 20000 non-null float32\n", - " 3 rate_code_id 20000 non-null int32 \n", - " 4 fare_amount 20000 non-null float32\n", - " 5 extra 20000 non-null float32\n", - " 6 mta_tax 20000 non-null float32\n", - " 7 tip_amount 20000 non-null float32\n", - " 8 tolls_amount 20000 non-null float32\n", - " 9 total_amount 20000 non-null float32\n", - "dtypes: float32(7), int32(1), int64(1), int8(1)\n", - "memory usage: 800.9 KB\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_pandas.info()" ] @@ -583,31 +430,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "b7f3e4f7-e04e-41e1-b94b-b32eb3bc3bbf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - }, - { - "data": { - "image/png": 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" - }, - "metadata": { - "image/png": { - "height": 480, - "width": 640 - } - }, - "output_type": "display_data" + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "from pyspark.ml.stat import Correlation\n", "from pyspark.ml.feature import VectorAssembler\n", @@ -641,18 +471,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "6e207c64-2e22-468f-a0c7-948090bcfce2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_train, df_val, df_test = df_cleaned.randomSplit([0.7, 0.15, 0.15])" ] @@ -669,18 +495,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "f16ea3a1-6d6d-4755-94ad-c743298bd130", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "# Define the S3 locations to store the datasets\n", "import boto3\n", @@ -704,54 +526,42 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "64d7ae48-6158-4273-8bb3-2f00abb1c20c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_train.write.parquet(f\"s3://{s3_bucket}/{train_data_prefix}\", mode=\"overwrite\")" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "de3d1190-4717-4944-846d-0169c093cb90", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_val.write.parquet(f\"s3://{s3_bucket}/{validation_data_prefix}\", mode=\"overwrite\")" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "9d18ef1c-fc2f-4e34-a692-4a6c48be7cba", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "metadata": { + "vscode": { + "languageId": "python_glue_session" } - ], + }, + "outputs": [], "source": [ "df_test.write.parquet(f\"s3://{s3_bucket}/{test_data_prefix}\", mode=\"overwrite\")" ] @@ -770,7 +580,11 @@ "cell_type": "code", "execution_count": null, "id": "a31b7742-93df-44c5-8674-b6355032c508", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, "outputs": [], "source": [ "from sagemaker import image_uris\n", @@ -823,7 +637,11 @@ "cell_type": "code", "execution_count": null, "id": "5e32c38c-719f-47bf-849f-54b63c39823b", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "python_glue_session" + } + }, "outputs": [], "source": [] } From 81a7661b1c64096ca371cfb33c9baacce4ace3b7 Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Wed, 26 Jun 2024 14:14:07 +0200 Subject: [PATCH 10/13] added integration for ci test results --- .../athena_ml_workflow_end_to_end.ipynb | 56 ++++++++++++++++- .../pyspark-etl-training.ipynb | 63 +++++++++++++++---- 2 files changed, 107 insertions(+), 12 deletions(-) diff --git a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb index 4fe6b17021..081d9cd388 100644 --- a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb +++ b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb @@ -2,10 +2,24 @@ "cells": [ { "cell_type": "markdown", - "id": "ece13bd7-19b2-47b3-976d-cf636fa68003", + "id": "9fbac6ee", "metadata": {}, "source": [ "# Create an end to end machine learning workflow using Amazon Athena\n", + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \\n\",\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "ece13bd7-19b2-47b3-976d-cf636fa68003", + "metadata": {}, + "source": [ "Importing and transforming data can be one of the most challenging tasks in a machine learning workflow. We provide you with a Jupyter notebook that demonstrates a cost-effective strategy for an extract, transform, and load (ETL) workflow. Using Amazon Simple Storage Service (Amazon S3) and Amazon Athena, you learn how to query and transform data from a Jupyter notebook. Amazon S3 is an object storage service that allows you to store data and machine learning artifacts. Amazon Athena enables you to interactively query the data stored in those buckets, saving each query as a CSV file in an Amazon S3 location.\n", "\n", "The tutorial imports 16 CSV files for the 2019 NYC taxi dataset from multiple Amazon S3 locations. The goal is to predict the fare amount for each ride. From these 16 files, the notebook creates a single ride fare dataset and a single ride info dataset with deduplicated values. We join the deduplicated datasets into a single dataset.\n", @@ -1358,6 +1372,46 @@ "# Delete the endpoint\n", "xgb_predictor.delete_endpoint()" ] + }, + { + "cell_type": "markdown", + "id": "cd9140e5", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + " \n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/use-cases|athena_ml_workflow_end_to_end|athena_ml_workflow_end_to_end.ipynb)" + ] } ], "metadata": { diff --git a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb index 87042b4580..2a05c442cf 100644 --- a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb +++ b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb @@ -2,11 +2,24 @@ "cells": [ { "cell_type": "markdown", - "id": "0a1828f9-efdc-4d12-a676-a2f3432e9ab0", + "id": "3ff2d442", "metadata": {}, "source": [ "# Perform ETL and train a model using PySpark\n", + "---\n", + "\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \\n\",\n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/use-cases|pyspark_etl_and_training|pyspark-etl-training)\n", "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "0a1828f9-efdc-4d12-a676-a2f3432e9ab0", + "metadata": {}, + "source": [ "To perform extract transform load (ETL) operations on multiple files, we recommend opening a Jupyter notebook within Amazon SageMaker Studio and using the `Glue PySpark and Ray` kernel. The kernel is connected to an AWS Glue Interactive Session. The session connects your notebook to a cluster that automatically scales up the storage and compute to meet your data processing needs. When you shut down the kernel, the session stops and you're no longer charged for the compute on the cluster.\n", "\n", "Within the notebook you can use Spark commands to join and transform your data. Writing Spark commands is both faster and easier than writing SQL queries. For example, you can use the join command to join two tables. Instead of writing a query that can sometimes take minutes to complete, you can join a table within seconds.\n", @@ -634,16 +647,44 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "5e32c38c-719f-47bf-849f-54b63c39823b", - "metadata": { - "vscode": { - "languageId": "python_glue_session" - } - }, - "outputs": [], - "source": [] + "cell_type": "markdown", + "id": "99668011", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + " \n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/use-cases|pyspark_etl_and_training|pyspark-etl-training)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/use-cases|pyspark_etl_and_training|pyspark-etl-training)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/use-cases|pyspark_etl_and_training|pyspark-etl-training)\n", + "\n", + "![This ca-central-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/use-cases|pyspark_etl_and_training|pyspark-etl-training)" + ] } ], "metadata": { From 4db2a798e88e04e2f96fa4821ba3bea07892f961 Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Wed, 26 Jun 2024 14:15:59 +0200 Subject: [PATCH 11/13] updated formatting with black-nb --- .../athena_ml_workflow_end_to_end.ipynb | 233 ++++++++++-------- .../pyspark-etl-training.ipynb | 85 ++++--- 2 files changed, 179 insertions(+), 139 deletions(-) diff --git a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb index 081d9cd388..27547fe764 100644 --- a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb +++ b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb @@ -98,36 +98,39 @@ "import time\n", "import boto3\n", "\n", + "\n", "def run_athena_query(query_string, database_name, output_location):\n", " # Create an Athena client\n", - " athena_client = boto3.client('athena', region_name='us-east-1')\n", + " athena_client = boto3.client(\"athena\", region_name=\"us-east-1\")\n", "\n", " # Start the query execution\n", " response = athena_client.start_query_execution(\n", " QueryString=query_string,\n", - " QueryExecutionContext={'Database': database_name},\n", - " ResultConfiguration={'OutputLocation': output_location}\n", + " QueryExecutionContext={\"Database\": database_name},\n", + " ResultConfiguration={\"OutputLocation\": output_location},\n", " )\n", "\n", - " query_execution_id = response['QueryExecutionId']\n", + " query_execution_id = response[\"QueryExecutionId\"]\n", " print(f\"Query execution ID: {query_execution_id}\")\n", "\n", " while True:\n", " # Check the query execution status\n", " query_status = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", - " state = query_status['QueryExecution']['Status']['State']\n", + " state = query_status[\"QueryExecution\"][\"Status\"][\"State\"]\n", "\n", - " if state == 'SUCCEEDED':\n", + " if state == \"SUCCEEDED\":\n", " print(\"Query executed successfully.\")\n", " break\n", - " elif state == 'FAILED':\n", - " print(f\"Query failed with error: {query_status['QueryExecution']['Status']['StateChangeReason']}\")\n", + " elif state == \"FAILED\":\n", + " print(\n", + " f\"Query failed with error: {query_status['QueryExecution']['Status']['StateChangeReason']}\"\n", + " )\n", " break\n", " else:\n", " print(f\"Query is currently in {state} state. Waiting for completion...\")\n", " time.sleep(5) # Wait for 5 seconds before checking again\n", "\n", - " return query_execution_id\n" + " return query_execution_id" ] }, { @@ -175,10 +178,10 @@ "\"\"\"\n", "\n", "# Athena database name\n", - "database = 'example-database-name'\n", + "database = \"example-database-name\"\n", "\n", "# S3 location for query results\n", - "s3_output_location = 's3://example-s3-bucket/example-s3-prefix'\n", + "s3_output_location = \"s3://example-s3-bucket/example-s3-prefix\"\n", "\n", "# Execute the query to create the 'ride_fare' table\n", "run_athena_query(create_ride_fare_table, database, s3_output_location)" @@ -299,9 +302,9 @@ "metadata": {}, "outputs": [], "source": [ - "test_ride_info_query = '''\n", + "test_ride_info_query = \"\"\"\n", "SELECT * FROM ride_info_deduped limit 10\n", - "'''\n", + "\"\"\"\n", "\n", "run_athena_query(test_ride_info_query, database, s3_output_location)" ] @@ -321,9 +324,9 @@ "metadata": {}, "outputs": [], "source": [ - "test_ride_fare_query = '''\n", + "test_ride_fare_query = \"\"\"\n", "SELECT * FROM ride_fare_deduped limit 10\n", - "'''\n", + "\"\"\"\n", "\n", "run_athena_query(test_ride_fare_query, database, s3_output_location)" ] @@ -346,20 +349,22 @@ "outputs": [], "source": [ "import io\n", + "\n", + "\n", "def get_query_results(query_execution_id):\n", - " athena_client = boto3.client('athena', region_name='us-east-1')\n", - " s3 = boto3.client('s3')\n", + " athena_client = boto3.client(\"athena\", region_name=\"us-east-1\")\n", + " s3 = boto3.client(\"s3\")\n", "\n", " # Get the query execution details\n", " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", - " s3_location = query_execution['QueryExecution']['ResultConfiguration']['OutputLocation']\n", + " s3_location = query_execution[\"QueryExecution\"][\"ResultConfiguration\"][\"OutputLocation\"]\n", "\n", " # Extract bucket and key from S3 output location\n", - " bucket_name, key = s3_location.split('/', 2)[2].split('/', 1)\n", + " bucket_name, key = s3_location.split(\"/\", 2)[2].split(\"/\", 1)\n", "\n", " # Get the CSV file location\n", " obj = s3.get_object(Bucket=bucket_name, Key=key)\n", - " csv_data = obj['Body'].read().decode('utf-8')\n", + " csv_data = obj[\"Body\"].read().decode(\"utf-8\")\n", " csv_buffer = io.StringIO(csv_data)\n", "\n", " return csv_buffer" @@ -383,8 +388,9 @@ "outputs": [], "source": [ "import pandas as pd\n", + "\n", "# Provide the query execution id of the test_ride_info query to get the query results\n", - "ride_info_sample = get_query_results('test_ride_info_query_execution_id')\n", + "ride_info_sample = get_query_results(\"test_ride_info_query_execution_id\")\n", "\n", "df_ride_info_sample = pd.read_csv(ride_info_sample)\n", "\n", @@ -410,7 +416,7 @@ "source": [ "# Provide the query execution id of the test_ride_fare query to get the query results\n", "\n", - "ride_fare_sample = get_query_results('test_ride_fare_query_execution_id')\n", + "ride_fare_sample = get_query_results(\"test_ride_fare_query_execution_id\")\n", "\n", "df_ride_fare_sample = pd.read_csv(ride_fare_sample)\n", "\n", @@ -509,14 +515,15 @@ "source": [ "# Function to get the Amazon S3 URI location of Amazon Athena select statements\n", "def get_csv_file_location(query_execution_id):\n", - " athena_client = boto3.client('athena', region_name='us-east-1')\n", + " athena_client = boto3.client(\"athena\", region_name=\"us-east-1\")\n", " query_execution = athena_client.get_query_execution(QueryExecutionId=query_execution_id)\n", - " s3_location = query_execution['QueryExecution']['ResultConfiguration']['OutputLocation']\n", + " s3_location = query_execution[\"QueryExecution\"][\"ResultConfiguration\"][\"OutputLocation\"]\n", "\n", " return s3_location\n", "\n", + "\n", "# Provide the 36 character string at the end of the output of the preceding cell as the query.\n", - "get_csv_file_location('ride_combined_full_table_query_execution_id')" + "get_csv_file_location(\"ride_combined_full_table_query_execution_id\")" ] }, { @@ -556,7 +563,7 @@ "metadata": {}, "outputs": [], "source": [ - "sample_nyc_taxi_combined = pd.read_csv('nyc-taxi-whole-dataset.csv', nrows=20000)" + "sample_nyc_taxi_combined = pd.read_csv(\"nyc-taxi-whole-dataset.csv\", nrows=20000)" ] }, { @@ -608,7 +615,7 @@ "metadata": {}, "outputs": [], "source": [ - "df['vendor_id'].value_counts()" + "df[\"vendor_id\"].value_counts()" ] }, { @@ -618,7 +625,7 @@ "metadata": {}, "outputs": [], "source": [ - "df['passenger_count'].value_counts()" + "df[\"passenger_count\"].value_counts()" ] }, { @@ -638,9 +645,10 @@ "source": [ "# Plot to find the distribution of ride fare values\n", "import matplotlib.pyplot as plt\n", - "plt.hist(df['fare_amount'], edgecolor='black', bins=30, range=(0,100))\n", - "plt.xlabel('Fare Amount')\n", - "plt.ylabel('Count')\n", + "\n", + "plt.hist(df[\"fare_amount\"], edgecolor=\"black\", bins=30, range=(0, 100))\n", + "plt.xlabel(\"Fare Amount\")\n", + "plt.ylabel(\"Count\")\n", "plt.show" ] }, @@ -659,7 +667,7 @@ "metadata": {}, "outputs": [], "source": [ - "df['ride_id'].nunique()" + "df[\"ride_id\"].nunique()" ] }, { @@ -679,7 +687,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.drop('store_and_fwd_flag', axis=1, inplace=True)" + "df.drop(\"store_and_fwd_flag\", axis=1, inplace=True)" ] }, { @@ -700,7 +708,7 @@ "outputs": [], "source": [ "# We're dropping the time series columns to streamline the analysis.\n", - "time_series_columns_to_drop = ['pickup_at','dropoff_at']\n", + "time_series_columns_to_drop = [\"pickup_at\", \"dropoff_at\"]\n", "df.drop(columns=time_series_columns_to_drop, inplace=True)" ] }, @@ -731,7 +739,8 @@ "source": [ "# Create visualizations showing correlations between variables.\n", "import seaborn as sns\n", - "target = 'fare_amount'\n", + "\n", + "target = \"fare_amount\"\n", "features = [col for col in df.columns if col != target]\n", "\n", "# Create a figure with subplots\n", @@ -740,7 +749,7 @@ "# Create scatter plots\n", "for i, feature in enumerate(features):\n", " sns.scatterplot(x=df[feature], y=df[target], ax=axes[i])\n", - " axes[i].set_title(f'{feature} vs {target}')\n", + " axes[i].set_title(f\"{feature} vs {target}\")\n", " axes[i].set_xlabel(feature)\n", " axes[i].set_ylabel(target)\n", "\n", @@ -765,10 +774,17 @@ "source": [ "# extra and mta_tax seem weakly correlated\n", "# total_amount is almost perfectly correlated, indicating target leakage.\n", - "continuous_features = ['tip_amount', 'tolls_amount', 'extra', 'mta_tax', 'total_amount', 'trip_distance']\n", + "continuous_features = [\n", + " \"tip_amount\",\n", + " \"tolls_amount\",\n", + " \"extra\",\n", + " \"mta_tax\",\n", + " \"total_amount\",\n", + " \"trip_distance\",\n", + "]\n", "\n", "for i in continuous_features:\n", - " correlation = df['fare_amount'].corr(df[i])\n", + " correlation = df[\"fare_amount\"].corr(df[i])\n", " print(i, correlation)" ] }, @@ -791,16 +807,17 @@ "source": [ "# The mta tax and extra have the most variance between the groups\n", "from scipy.stats import f_oneway\n", + "\n", "# Separate features and target variable\n", - "X = df[['payment_type', 'extra', 'mta_tax', 'vendor_id', 'passenger_count']]\n", - "y = df['fare_amount']\n", + "X = df[[\"payment_type\", \"extra\", \"mta_tax\", \"vendor_id\", \"passenger_count\"]]\n", + "y = df[\"fare_amount\"]\n", "\n", "# Perform one-way ANOVA for each feature\n", "for feature in X.columns:\n", " groups = [y[X[feature] == group] for group in X[feature].unique()]\n", " if len(groups) > 1:\n", " f_statistic, p_value = f_oneway(*groups)\n", - " print(f'Feature: {feature}, F-statistic: {f_statistic:.2f}, p-value: {p_value:.5f}')" + " print(f\"Feature: {feature}, F-statistic: {f_statistic:.2f}, p-value: {p_value:.5f}\")" ] }, { @@ -843,7 +860,7 @@ "metadata": {}, "outputs": [], "source": [ - "get_csv_file_location('ride_combined_notebook_relevant_features_query_execution_id')" + "get_csv_file_location(\"ride_combined_notebook_relevant_features_query_execution_id\")" ] }, { @@ -874,38 +891,38 @@ "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", "\n", "\n", - "\n", "# Define the SageMaker execution role\n", "role = sagemaker.get_execution_role()\n", "\n", "# Define the SKLearnProcessor\n", - "sklearn_processor = SKLearnProcessor(framework_version='0.20.0',\n", - " role=role,\n", - " instance_type='ml.m5.4xlarge',\n", - " instance_count=2)\n", + "sklearn_processor = SKLearnProcessor(\n", + " framework_version=\"0.20.0\", role=role, instance_type=\"ml.m5.4xlarge\", instance_count=2\n", + ")\n", "\n", "# Run the processing job\n", "sklearn_processor.run(\n", - " code='processing_data_split.py', \n", - " inputs=[ProcessingInput(\n", - " source='s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv',\n", - " destination='/opt/ml/processing/input'\n", - " )],\n", + " code=\"processing_data_split.py\",\n", + " inputs=[\n", + " ProcessingInput(\n", + " source=\"s3://example-s3-bucket/ride_combined_notebook_relevant_features_query_execution_id.csv\",\n", + " destination=\"/opt/ml/processing/input\",\n", + " )\n", + " ],\n", " outputs=[\n", " ProcessingOutput(\n", - " source='/opt/ml/processing/output/train',\n", - " destination='s3://ux360-nyc-taxi-dogfooding/output/train'\n", + " source=\"/opt/ml/processing/output/train\",\n", + " destination=\"s3://ux360-nyc-taxi-dogfooding/output/train\",\n", " ),\n", " ProcessingOutput(\n", - " source='/opt/ml/processing/output/validation',\n", - " destination='s3://ux360-nyc-taxi-dogfooding/output/validation'\n", + " source=\"/opt/ml/processing/output/validation\",\n", + " destination=\"s3://ux360-nyc-taxi-dogfooding/output/validation\",\n", " ),\n", " ProcessingOutput(\n", - " source='/opt/ml/processing/output/test',\n", - " destination='s3://ux360-nyc-taxi-dogfooding/output/test'\n", - " )\n", - " ]\n", - ")\n" + " source=\"/opt/ml/processing/output/test\",\n", + " destination=\"s3://ux360-nyc-taxi-dogfooding/output/test\",\n", + " ),\n", + " ],\n", + ")" ] }, { @@ -923,7 +940,7 @@ "metadata": {}, "outputs": [], "source": [ - "#Verify that train.csv is in the location that you've specified\n", + "# Verify that train.csv is in the location that you've specified\n", "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/train/train.csv" ] }, @@ -942,7 +959,7 @@ "metadata": {}, "outputs": [], "source": [ - "#Verify that val.csv is in the location that you've specified\n", + "# Verify that val.csv is in the location that you've specified\n", "!aws s3 ls s3://ux360-nyc-taxi-dogfooding/output/validation/val.csv" ] }, @@ -963,14 +980,10 @@ "source": [ "from sagemaker.session import TrainingInput\n", "\n", - "bucket = 'example-s3-bucket'\n", + "bucket = \"example-s3-bucket\"\n", "\n", - "train_input = TrainingInput(\n", - " f\"s3://{bucket}/output/train/train.csv\", content_type=\"csv\"\n", - ")\n", - "validation_input = TrainingInput(\n", - " f\"s3://{bucket}/output/validation/val.csv\", content_type=\"csv\"\n", - ")" + "train_input = TrainingInput(f\"s3://{bucket}/output/train/train.csv\", content_type=\"csv\")\n", + "validation_input = TrainingInput(f\"s3://{bucket}/output/validation/val.csv\", content_type=\"csv\")" ] }, { @@ -999,7 +1012,7 @@ "from sagemaker.debugger import Rule, ProfilerRule, rule_configs\n", "from sagemaker.session import TrainingInput\n", "\n", - "s3_output_location = f's3://{bucket}/{prefix}/xgboost_model'\n", + "s3_output_location = f\"s3://{bucket}/{prefix}/xgboost_model\"\n", "\n", "container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.2-2\")\n", "print(container)" @@ -1021,18 +1034,18 @@ "outputs": [], "source": [ "xgb_model = sagemaker.estimator.Estimator(\n", - " image_uri = container,\n", - " role = role,\n", - " instance_count = 2,\n", - " region = region,\n", - " instance_type = 'ml.m5.4xlarge',\n", - " volume_size = 5, \n", - " output_path = s3_output_location,\n", - " sagemaker_session = sagemaker.Session(),\n", - " rules = [\n", + " image_uri=container,\n", + " role=role,\n", + " instance_count=2,\n", + " region=region,\n", + " instance_type=\"ml.m5.4xlarge\",\n", + " volume_size=5,\n", + " output_path=s3_output_location,\n", + " sagemaker_session=sagemaker.Session(),\n", + " rules=[\n", " Rule.sagemaker(rule_configs.create_xgboost_report()),\n", - " ProfilerRule.sagemaker(rule_configs.ProfilerReport())\n", - " ]\n", + " ProfilerRule.sagemaker(rule_configs.ProfilerReport()),\n", + " ],\n", ")" ] }, @@ -1054,13 +1067,13 @@ "outputs": [], "source": [ "xgb_model.set_hyperparameters(\n", - " max_depth = 5,\n", - " eta = 0.2,\n", - " gamma = 4,\n", - " min_child_weight = 6,\n", - " subsample = 0.7,\n", - " objective = \"reg:squarederror\",\n", - " num_round = 10\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.7,\n", + " objective=\"reg:squarederror\",\n", + " num_round=10,\n", ")" ] }, @@ -1099,7 +1112,7 @@ "metadata": {}, "outputs": [], "source": [ - "xgb_predictor = xgb_model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')" + "xgb_predictor = xgb_model.deploy(initial_instance_count=1, instance_type=\"ml.m4.xlarge\")" ] }, { @@ -1138,7 +1151,7 @@ "import boto3\n", "import json\n", "\n", - "test_df = pd.read_csv('test.csv', nrows=20)\n", + "test_df = pd.read_csv(\"test.csv\", nrows=20)\n", "test_df.head()" ] }, @@ -1163,34 +1176,34 @@ "import pandas as pd\n", "\n", "# Initialize the SageMaker runtime client\n", - "runtime = boto3.client('runtime.sagemaker')\n", + "runtime = boto3.client(\"runtime.sagemaker\")\n", "\n", "# Define the endpoint name\n", - "endpoint_name = 'sagemaker-xgboost-timestamp'\n", + "endpoint_name = \"sagemaker-xgboost-timestamp\"\n", + "\n", "\n", "# Function to make predictions\n", "def get_predictions(data, endpoint_name):\n", " # Convert the DataFrame to a CSV string and encode it to bytes\n", - " csv_data = data.to_csv(header=False, index=False).encode('utf-8')\n", - " \n", + " csv_data = data.to_csv(header=False, index=False).encode(\"utf-8\")\n", + "\n", " response = runtime.invoke_endpoint(\n", - " EndpointName=endpoint_name,\n", - " ContentType='text/csv',\n", - " Body=csv_data\n", + " EndpointName=endpoint_name, ContentType=\"text/csv\", Body=csv_data\n", " )\n", - " \n", + "\n", " # Read the response body\n", - " response_body = response['Body'].read().decode('utf-8')\n", - " \n", + " response_body = response[\"Body\"].read().decode(\"utf-8\")\n", + "\n", " try:\n", " # Try to parse the response as JSON\n", " result = json.loads(response_body)\n", " except json.JSONDecodeError:\n", " # If response is not JSON, just return the raw response\n", " result = response_body\n", - " \n", + "\n", " return result\n", "\n", + "\n", "# Drop the target column from the test dataframe\n", "test_df = test_df.drop(test_df.columns[0], axis=1)\n", "\n", @@ -1214,7 +1227,7 @@ "metadata": {}, "outputs": [], "source": [ - "predictions_array = predictions.split(',')\n", + "predictions_array = predictions.split(\",\")\n", "predictions_array" ] }, @@ -1233,7 +1246,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_with_target_column_values = pd.read_csv('test.csv', nrows=20)\n", + "df_with_target_column_values = pd.read_csv(\"test.csv\", nrows=20)\n", "df_with_target_column_values.head()" ] }, @@ -1270,7 +1283,7 @@ "metadata": {}, "outputs": [], "source": [ - "comparison_df = pd.DataFrame(predictions_array, columns=['predicted_values'])\n", + "comparison_df = pd.DataFrame(predictions_array, columns=[\"predicted_values\"])\n", "comparison_df" ] }, @@ -1291,7 +1304,7 @@ "source": [ "column_to_add = df_with_target_column_values.iloc[:, 0]\n", "\n", - "comparison_df['actual_values'] = column_to_add\n", + "comparison_df[\"actual_values\"] = column_to_add\n", "\n", "comparison_df" ] @@ -1332,15 +1345,17 @@ "import numpy as np\n", "\n", "# Calculate the squared differences between the predicted and actual values\n", - "comparison_df['squared_diff'] = (comparison_df['actual_values'] - comparison_df['predicted_values']) ** 2\n", + "comparison_df[\"squared_diff\"] = (\n", + " comparison_df[\"actual_values\"] - comparison_df[\"predicted_values\"]\n", + ") ** 2\n", "\n", "# Calculate the mean of the squared differences\n", - "mean_squared_diff = comparison_df['squared_diff'].mean()\n", + "mean_squared_diff = comparison_df[\"squared_diff\"].mean()\n", "\n", "# Take the square root of the mean to get the RMSE\n", "rmse = np.sqrt(mean_squared_diff)\n", "\n", - "print(f\"RMSE: {rmse}\")\n" + "print(f\"RMSE: {rmse}\")" ] }, { diff --git a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb index 2a05c442cf..300260080f 100644 --- a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb +++ b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb @@ -129,8 +129,15 @@ "outputs": [], "source": [ "df_ride_info = glueContext.create_dynamic_frame_from_options(\n", - " connection_type=\"s3\", format=\"parquet\",\n", - " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-info/year=2019/\"], \"recurse\": True}).toDF()" + " connection_type=\"s3\",\n", + " format=\"parquet\",\n", + " connection_options={\n", + " \"paths\": [\n", + " \"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-info/year=2019/\"\n", + " ],\n", + " \"recurse\": True,\n", + " },\n", + ").toDF()" ] }, { @@ -155,8 +162,15 @@ "outputs": [], "source": [ "df_ride_fare = glueContext.create_dynamic_frame_from_options(\n", - " connection_type=\"s3\", format=\"parquet\",\n", - " connection_options={\"paths\": [\"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-fare/year=2019/\"], \"recurse\": True}).toDF()" + " connection_type=\"s3\",\n", + " format=\"parquet\",\n", + " connection_options={\n", + " \"paths\": [\n", + " \"s3://dsoaws/nyc-taxi-orig-cleaned-split-parquet-per-year-multiple-files/ride-fare/year=2019/\"\n", + " ],\n", + " \"recurse\": True,\n", + " },\n", + ").toDF()" ] }, { @@ -335,7 +349,9 @@ }, "outputs": [], "source": [ - "df_cleaned = df_joined.drop(\"pickup_at\", \"dropoff_at\", \"store_and_fwd_flag\", \"vendor_id\", \"payment_type\")" + "df_cleaned = df_joined.drop(\n", + " \"pickup_at\", \"dropoff_at\", \"store_and_fwd_flag\", \"vendor_id\", \"payment_type\"\n", + ")" ] }, { @@ -454,22 +470,26 @@ "source": [ "from pyspark.ml.stat import Correlation\n", "from pyspark.ml.feature import VectorAssembler\n", - "import seaborn as sns \n", + "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd # not sure how the kernel runs, but it looks like I have import pandas again after going back to the notebook after a while\n", + "import pandas as pd # not sure how the kernel runs, but it looks like I have import pandas again after going back to the notebook after a while\n", "\n", - "vector_col = 'corr_features'\n", + "vector_col = \"corr_features\"\n", "assembler = VectorAssembler(inputCols=df_sample.columns, outputCol=vector_col)\n", "df_vector = assembler.transform(df_sample).select(vector_col)\n", "\n", "matrix = Correlation.corr(df_vector, vector_col).collect()[0][0]\n", "corr_matrix = matrix.toArray().tolist()\n", - "corr_matrix_df = pd.DataFrame(data=corr_matrix, columns=df_sample.columns, index=df_sample.columns) \n", + "corr_matrix_df = pd.DataFrame(data=corr_matrix, columns=df_sample.columns, index=df_sample.columns)\n", "\n", - "plt.figure(figsize=(16,10))\n", - "sns.heatmap(corr_matrix_df,\n", - " xticklabels=corr_matrix_df.columns.values,\n", - " yticklabels=corr_matrix_df.columns.values, cmap=\"Greens\", annot=True)\n", + "plt.figure(figsize=(16, 10))\n", + "sns.heatmap(\n", + " corr_matrix_df,\n", + " xticklabels=corr_matrix_df.columns.values,\n", + " yticklabels=corr_matrix_df.columns.values,\n", + " cmap=\"Greens\",\n", + " annot=True,\n", + ")\n", "\n", "%matplot plt" ] @@ -604,36 +624,41 @@ "from sagemaker.inputs import TrainingInput\n", "\n", "hyperparameters = {\n", - " \"max_depth\":\"5\",\n", - " \"eta\":\"0.2\",\n", - " \"gamma\":\"4\",\n", - " \"min_child_weight\":\"6\",\n", - " \"subsample\":\"0.7\",\n", - " \"objective\":\"reg:squarederror\",\n", - " \"num_round\":\"50\"}\n", + " \"max_depth\": \"5\",\n", + " \"eta\": \"0.2\",\n", + " \"gamma\": \"4\",\n", + " \"min_child_weight\": \"6\",\n", + " \"subsample\": \"0.7\",\n", + " \"objective\": \"reg:squarederror\",\n", + " \"num_round\": \"50\",\n", + "}\n", "\n", "# Set an output path to save the trained model.\n", - "prefix = 'sandbox/glue-demo'\n", - "output_path = f's3://{s3_bucket}/{prefix}/xgb-built-in-algo/output'\n", + "prefix = \"sandbox/glue-demo\"\n", + "output_path = f\"s3://{s3_bucket}/{prefix}/xgb-built-in-algo/output\"\n", "\n", "# The following line looks for the XGBoost image URI and builds an XGBoost container.\n", "# We use version 1.7-1 of the image URI, you can specify a version that you prefer.\n", "xgboost_container = sagemaker.image_uris.retrieve(\"xgboost\", region, \"1.7-1\")\n", "\n", "# Construct a SageMaker estimator that calls the xgboost-container\n", - "estimator = sagemaker.estimator.Estimator(image_uri=xgboost_container,\n", - " hyperparameters=hyperparameters,\n", - " role=sagemaker.get_execution_role(),\n", - " instance_count=1,\n", - " instance_type='ml.m5.4xlarge',\n", - " output_path=output_path)\n", + "estimator = sagemaker.estimator.Estimator(\n", + " image_uri=xgboost_container,\n", + " hyperparameters=hyperparameters,\n", + " role=sagemaker.get_execution_role(),\n", + " instance_count=1,\n", + " instance_type=\"ml.m5.4xlarge\",\n", + " output_path=output_path,\n", + ")\n", "\n", "content_type = \"application/x-parquet\"\n", "train_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/train/\", content_type=content_type)\n", - "validation_input = TrainingInput(f\"s3://{s3_bucket}/{prefix}/validation/\", content_type=content_type)\n", + "validation_input = TrainingInput(\n", + " f\"s3://{s3_bucket}/{prefix}/validation/\", content_type=content_type\n", + ")\n", "\n", "# Run the XGBoost training job\n", - "estimator.fit({'train': train_input, 'validation': validation_input})" + "estimator.fit({\"train\": train_input, \"validation\": validation_input})" ] }, { From a314946593dc729555297ea0fd0ef3e0e33d0dfd Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Wed, 26 Jun 2024 20:04:28 +0200 Subject: [PATCH 12/13] update athena notebook: fix parse predictions --- .../athena_ml_workflow_end_to_end.ipynb | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb index 27547fe764..e723f14c7e 100644 --- a/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb +++ b/use-cases/athena_ml_workflow_end_to_end/athena_ml_workflow_end_to_end.ipynb @@ -1217,7 +1217,9 @@ "id": "a136ae86-efd3-4d4f-9966-6610f445d84c", "metadata": {}, "source": [ - "### Create an array from the string of predictions" + "### Create an array from the string of predictions\n", + "\n", + "The notebook uses the newline character as the separator, so we use the following code to create an array of predictions." ] }, { @@ -1227,7 +1229,8 @@ "metadata": {}, "outputs": [], "source": [ - "predictions_array = predictions.split(\",\")\n", + "predictions_array = predictions.split(\"\\n\")\n", + "predictions_array = predictions_array[:-1]\n", "predictions_array" ] }, From 3f0023b5c1a318ab7d941309751a93e7ddfacd54 Mon Sep 17 00:00:00 2001 From: Janosch Woschitz Date: Thu, 27 Jun 2024 11:22:33 +0200 Subject: [PATCH 13/13] fixed ci integration for pyspark-etl-training notebook --- .../pyspark-etl-training.ipynb | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb index 300260080f..d441ff4ac6 100644 --- a/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb +++ b/use-cases/pyspark_etl_and_training/pyspark-etl-training.ipynb @@ -8,9 +8,9 @@ "# Perform ETL and train a model using PySpark\n", "---\n", "\n", - "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \\n\",\n", + "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n", "\n", - "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/use-cases|pyspark_etl_and_training|pyspark-etl-training)\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", "\n", "---" ] @@ -680,35 +680,35 @@ " \n", "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", "\n", - "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/use-cases|pyspark_etl_and_training|pyspark-etl-training)\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/use-cases|pyspark_etl_and_training|pyspark-etl-training.ipynb)\n", "\n", - "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/use-cases|pyspark_etl_and_training|pyspark-etl-training)\n", + "![This us-east-2 badge failed to load. 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