From 78ce9d43bf75a18e47d8a5f2cf8666777f9543f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9B=84=E6=9C=A8?= Date: Thu, 19 Sep 2019 17:55:40 +0800 Subject: [PATCH 1/2] add tutorial creditcard fraud --- doc/examples/creditcard_samples.csv | 55 ++++++++++++ doc/examples/tutorial_creditcardfraud.md | 110 +++++++++++++++++++++++ 2 files changed, 165 insertions(+) create mode 100755 doc/examples/creditcard_samples.csv create mode 100644 doc/examples/tutorial_creditcardfraud.md diff --git a/doc/examples/creditcard_samples.csv b/doc/examples/creditcard_samples.csv new file mode 100755 index 0000000000..5a856dcbcf --- /dev/null +++ b/doc/examples/creditcard_samples.csv @@ -0,0 +1,55 @@ +"Time","V1","V2","V3","V4","V5","V6","V7","V8","V9","V10","V11","V12","V13","V14","V15","V16","V17","V18","V19","V20","V21","V22","V23","V24","V25","V26","V27","V28","Amount","Class" 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To train the model using the full dataset, you need to download the dataset manually and load the dataset into MySQL under below instructions. + +## Import the Dataset into MySQL + +Below SQL statements will create a table used to store the dataset: + +```sql +%%sqlflow +CREATE DATABASE IF NOT EXISTS creditcard; +CREATE TABLE IF NOT EXISTS creditcard.creditcard ( + time INT, + v1 float, + v2 float, + v3 float, + v4 float, + v5 float, + v6 float, + v7 float, + v8 float, + v9 float, + v10 float, + v11 float, + v12 float, + v13 float, + v14 float, + v15 float, + v16 float, + v17 float, + v18 float, + v19 float, + v20 float, + v21 float, + v22 float, + v23 float, + v24 float, + v25 float, + v26 float, + v27 float, + v28 float, + amount float, + class INT); +``` + +Then we could run a python script to insert values to that table: + +```python +from MySQLdb import connect +fn = open("creditcard_samples.csv", "r") +line_no = 0 +samples = [] +for line in fn: + if line_no == 0: + line_no += 1 + continue + l = line.replace("\n", "") + samples.append("(%s)" % l) + line_no += 1 +sql = "INSERT INTO creditcard.creditcard VALUES %s" % ",".join(samples) +conn = connect(user="root", passwd="root", db="creditcard", host="localhost", port=3306) +c = conn.cursor() +c.execute(sql) +conn.commit() +c.close() +conn.close() +``` + +You can verify the data in MySQL using: + +```sql +%%sqlflow +SELECT * from creditcard.creditcard limit 5; +``` + +## Train a DNN Model Using SQLFlow + +Once your dataset is prepared, you can run the below SQL statement to start training. +Note that SQLFlow will automatically split the dataset into training and validation +sets, the output of evaluation result is calculated using the validation set. + +```sql +%%sqlflow +SELECT * from creditcard.creditcard +TRAIN DNNClassifier +WITH model.n_classes=2, model.hidden_units=[128,32], train.epoch=100 +COLUMN time,v1,v2,v3,v4,v5,v6,v7,v8,v9,v10,v11,v12,v13,v14,v15,v16,v17,v18,v19,v20,v21,v22,v23,v24,v25,v26,v27,v28,amount +LABEL class +INTO creditcard.creditcard_deep_model; +``` + +## Run Predict + +We can use the trained model to predict new data, e.g. we can choose some positive sample in the dataset +to do predict: + +```sql +%%sqlflow +SELECT * from creditcard.creditcard +WHERE class=1 +PREDICT creditcard.predict.class +USING creditcard.creditcard_deep_model; +``` + +Then we can get the predict result using: + +```sql +%%sqlflow +SELECT * from creditcard.predict; +``` From 8d348114ed7476dd21f0bfd60eea35a67d4bd2b4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9B=84=E6=9C=A8?= Date: Fri, 20 Sep 2019 10:34:10 +0800 Subject: [PATCH 2/2] update --- Dockerfile | 1 + doc/examples/creditcard_samples.csv | 55 ------------ doc/examples/tutorial_creditcardfraud.md | 68 +------------- .../datasets/popularize_creditcardfraud.sql | 89 +++++++++++++++++++ 4 files changed, 92 insertions(+), 121 deletions(-) delete mode 100755 doc/examples/creditcard_samples.csv create mode 100644 example/datasets/popularize_creditcardfraud.sql diff --git a/Dockerfile b/Dockerfile index e6281ef302..9733198ae3 100644 --- a/Dockerfile +++ b/Dockerfile @@ -40,6 +40,7 @@ VOLUME /var/lib/mysql COPY example/datasets/popularize_churn.sql \ example/datasets/popularize_iris.sql \ example/datasets/popularize_boston.sql \ + example/datasets/popularize_creditcardfraud.sql \ example/datasets/create_model_db.sql \ /docker-entrypoint-initdb.d/ diff --git a/doc/examples/creditcard_samples.csv b/doc/examples/creditcard_samples.csv deleted file mode 100755 index 5a856dcbcf..0000000000 --- a/doc/examples/creditcard_samples.csv +++ /dev/null @@ -1,55 +0,0 @@ -"Time","V1","V2","V3","V4","V5","V6","V7","V8","V9","V10","V11","V12","V13","V14","V15","V16","V17","V18","V19","V20","V21","V22","V23","V24","V25","V26","V27","V28","Amount","Class" 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-7519,1.23423504613468,3.0197404207034,-4.30459688479665,4.73279513041887,3.62420083055386,-1.35774566315358,1.71344498787235,-0.496358487073991,-1.28285782036322,-2.44746925511151,2.10134386504854,-4.6096283906446,1.46437762476188,-6.07933719308005,-0.339237372732577,2.58185095378146,6.73938438478335,3.04249317830411,-2.72185312222835,0.00906083639534526,-0.37906830709218,-0.704181032215427,-0.656804756348389,-1.63265295692929,1.48890144838237,0.566797273468934,-0.0100162234965625,0.146792734916988,1,"1" diff --git a/doc/examples/tutorial_creditcardfraud.md b/doc/examples/tutorial_creditcardfraud.md index 7ffda79303..03c07f68d8 100644 --- a/doc/examples/tutorial_creditcardfraud.md +++ b/doc/examples/tutorial_creditcardfraud.md @@ -1,72 +1,8 @@ # The Credit Card Fraud Detection Example -The `creditcard_samples.csv` file in this directory comes from [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud). To train the model using the full dataset, you need to download the dataset manually and load the dataset into MySQL under below instructions. +The sample data already loaded in MySQL comes from [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud). To train the model using the full dataset, you need to download the dataset and load the dataset into MySQL manually. -## Import the Dataset into MySQL - -Below SQL statements will create a table used to store the dataset: - -```sql -%%sqlflow -CREATE DATABASE IF NOT EXISTS creditcard; -CREATE TABLE IF NOT EXISTS creditcard.creditcard ( - time INT, - v1 float, - v2 float, - v3 float, - v4 float, - v5 float, - v6 float, - v7 float, - v8 float, - v9 float, - v10 float, - v11 float, - v12 float, - v13 float, - v14 float, - v15 float, - v16 float, - v17 float, - v18 float, - v19 float, - v20 float, - v21 float, - v22 float, - v23 float, - v24 float, - v25 float, - v26 float, - v27 float, - v28 float, - amount float, - class INT); -``` - -Then we could run a python script to insert values to that table: - -```python -from MySQLdb import connect -fn = open("creditcard_samples.csv", "r") -line_no = 0 -samples = [] -for line in fn: - if line_no == 0: - line_no += 1 - continue - l = line.replace("\n", "") - samples.append("(%s)" % l) - line_no += 1 -sql = "INSERT INTO creditcard.creditcard VALUES %s" % ",".join(samples) -conn = connect(user="root", passwd="root", db="creditcard", host="localhost", port=3306) -c = conn.cursor() -c.execute(sql) -conn.commit() -c.close() -conn.close() -``` - -You can verify the data in MySQL using: +You can verify the sample data content in MySQL using: ```sql %%sqlflow diff --git a/example/datasets/popularize_creditcardfraud.sql b/example/datasets/popularize_creditcardfraud.sql new file mode 100644 index 0000000000..6582191b7c --- /dev/null +++ b/example/datasets/popularize_creditcardfraud.sql @@ -0,0 +1,89 @@ +CREATE DATABASE IF NOT EXISTS creditcard; +CREATE TABLE IF NOT EXISTS creditcard.creditcard ( + time INT, + v1 float, + v2 float, + v3 float, + v4 float, + v5 float, + v6 float, + v7 float, + v8 float, + v9 float, + v10 float, + v11 float, + v12 float, + v13 float, + v14 float, + v15 float, + v16 float, + v17 float, + v18 float, + v19 float, + v20 float, + v21 float, + v22 float, + v23 float, + v24 float, + v25 float, + v26 float, + v27 float, + v28 float, + amount float, + class INT); + +INSERT INTO creditcard.creditcard VALUES +(0,-1.3598071336738,-0.0727811733098497,2.53634673796914,1.37815522427443,-0.338320769942518,0.462387777762292,0.239598554061257,0.0986979012610507,0.363786969611213,0.0907941719789316,-0.551599533260813,-0.617800855762348,-0.991389847235408,-0.311169353699879,1.46817697209427,-0.470400525259478,0.207971241929242,0.0257905801985591,0.403992960255733,0.251412098239705,-0.018306777944153,0.277837575558899,-0.110473910188767,0.0669280749146731,0.128539358273528,-0.189114843888824,0.133558376740387,-0.0210530534538215,149.62,"0"), 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