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[7.x][ML] Refactor DFA custom processor to cross validation splitter … #53956

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Merged
merged 1 commit into from
Mar 23, 2020
Merged

[7.x][ML] Refactor DFA custom processor to cross validation splitter … #53956

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9 changes: 5 additions & 4 deletions ...l/src/main/java/org/elasticsearch/xpack/ml/dataframe/process/AnalyticsProcessManager.java
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Expand Up @@ -31,8 +31,8 @@
import org.elasticsearch.xpack.ml.dataframe.DataFrameAnalyticsTask;
import org.elasticsearch.xpack.ml.dataframe.extractor.DataFrameDataExtractor;
import org.elasticsearch.xpack.ml.dataframe.extractor.DataFrameDataExtractorFactory;
import org.elasticsearch.xpack.ml.dataframe.process.customprocessing.CustomProcessor;
import org.elasticsearch.xpack.ml.dataframe.process.customprocessing.CustomProcessorFactory;
import org.elasticsearch.xpack.ml.dataframe.process.crossvalidation.CrossValidationSplitter;
import org.elasticsearch.xpack.ml.dataframe.process.crossvalidation.CrossValidationSplitterFactory;
import org.elasticsearch.xpack.ml.dataframe.process.results.AnalyticsResult;
import org.elasticsearch.xpack.ml.dataframe.stats.ProgressTracker;
import org.elasticsearch.xpack.ml.extractor.ExtractedFields;
Expand Down Expand Up @@ -208,7 +208,8 @@ private void processData(DataFrameAnalyticsTask task, ProcessContext processCont
private void writeDataRows(DataFrameDataExtractor dataExtractor, AnalyticsProcess<AnalyticsResult> process,
DataFrameAnalysis analysis, ProgressTracker progressTracker) throws IOException {

CustomProcessor customProcessor = new CustomProcessorFactory(dataExtractor.getFieldNames()).create(analysis);
CrossValidationSplitter crossValidationSplitter = new CrossValidationSplitterFactory(dataExtractor.getFieldNames())
.create(analysis);

// The extra fields are for the doc hash and the control field (should be an empty string)
String[] record = new String[dataExtractor.getFieldNames().size() + 2];
Expand All @@ -226,7 +227,7 @@ private void writeDataRows(DataFrameDataExtractor dataExtractor, AnalyticsProces
String[] rowValues = row.getValues();
System.arraycopy(rowValues, 0, record, 0, rowValues.length);
record[record.length - 2] = String.valueOf(row.getChecksum());
customProcessor.process(record);
crossValidationSplitter.process(record);
process.writeRecord(record);
}
}
Expand Down
6 changes: 3 additions & 3 deletions ...ess/customprocessing/CustomProcessor.java → ...ssvalidation/CrossValidationSplitter.java
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Expand Up @@ -3,12 +3,12 @@
* or more contributor license agreements. Licensed under the Elastic License;
* you may not use this file except in compliance with the Elastic License.
*/
package org.elasticsearch.xpack.ml.dataframe.process.customprocessing;
package org.elasticsearch.xpack.ml.dataframe.process.crossvalidation;

/**
* A processor to manipulate rows before writing them to the process
* Processes rows in order to split the dataset in training and test subsets
*/
public interface CustomProcessor {
public interface CrossValidationSplitter {

void process(String[] row);
}
12 changes: 6 additions & 6 deletions ...tomprocessing/CustomProcessorFactory.java → ...ation/CrossValidationSplitterFactory.java
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Expand Up @@ -3,7 +3,7 @@
* or more contributor license agreements. Licensed under the Elastic License;
* you may not use this file except in compliance with the Elastic License.
*/
package org.elasticsearch.xpack.ml.dataframe.process.customprocessing;
package org.elasticsearch.xpack.ml.dataframe.process.crossvalidation;

import org.elasticsearch.xpack.core.ml.dataframe.analyses.Classification;
import org.elasticsearch.xpack.core.ml.dataframe.analyses.DataFrameAnalysis;
Expand All @@ -12,23 +12,23 @@
import java.util.List;
import java.util.Objects;

public class CustomProcessorFactory {
public class CrossValidationSplitterFactory {

private final List<String> fieldNames;

public CustomProcessorFactory(List<String> fieldNames) {
public CrossValidationSplitterFactory(List<String> fieldNames) {
this.fieldNames = Objects.requireNonNull(fieldNames);
}

public CustomProcessor create(DataFrameAnalysis analysis) {
public CrossValidationSplitter create(DataFrameAnalysis analysis) {
if (analysis instanceof Regression) {
Regression regression = (Regression) analysis;
return new DatasetSplittingCustomProcessor(
return new RandomCrossValidationSplitter(
fieldNames, regression.getDependentVariable(), regression.getTrainingPercent(), regression.getRandomizeSeed());
}
if (analysis instanceof Classification) {
Classification classification = (Classification) analysis;
return new DatasetSplittingCustomProcessor(
return new RandomCrossValidationSplitter(
fieldNames, classification.getDependentVariable(), classification.getTrainingPercent(), classification.getRandomizeSeed());
}
return row -> {};
Expand Down
10 changes: 5 additions & 5 deletions ...sing/DatasetSplittingCustomProcessor.java → ...dation/RandomCrossValidationSplitter.java
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Expand Up @@ -3,7 +3,7 @@
* or more contributor license agreements. Licensed under the Elastic License;
* you may not use this file except in compliance with the Elastic License.
*/
package org.elasticsearch.xpack.ml.dataframe.process.customprocessing;
package org.elasticsearch.xpack.ml.dataframe.process.crossvalidation;

import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper;
import org.elasticsearch.xpack.ml.dataframe.extractor.DataFrameDataExtractor;
Expand All @@ -12,19 +12,19 @@
import java.util.Random;

/**
* A processor that randomly clears the dependent variable value
* in order to split the dataset in training and validation data.
* A cross validation splitter that randomly clears the dependent variable value
* in order to split the dataset in training and test data.
* This relies on the fact that when the dependent variable field
* is empty, then the row is not used for training but only to make predictions.
*/
class DatasetSplittingCustomProcessor implements CustomProcessor {
class RandomCrossValidationSplitter implements CrossValidationSplitter {

private final int dependentVariableIndex;
private final double trainingPercent;
private final Random random;
private boolean isFirstRow = true;

DatasetSplittingCustomProcessor(List<String> fieldNames, String dependentVariable, double trainingPercent, long randomizeSeed) {
RandomCrossValidationSplitter(List<String> fieldNames, String dependentVariable, double trainingPercent, long randomizeSeed) {
this.dependentVariableIndex = findDependentVariableIndex(fieldNames, dependentVariable);
this.trainingPercent = trainingPercent;
this.random = new Random(randomizeSeed);
Expand Down
23 changes: 13 additions & 10 deletions ...DatasetSplittingCustomProcessorTests.java → ...n/RandomCrossValidationSplitterTests.java
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Expand Up @@ -3,7 +3,7 @@
* or more contributor license agreements. Licensed under the Elastic License;
* you may not use this file except in compliance with the Elastic License.
*/
package org.elasticsearch.xpack.ml.dataframe.process.customprocessing;
package org.elasticsearch.xpack.ml.dataframe.process.crossvalidation;

import org.elasticsearch.test.ESTestCase;
import org.elasticsearch.xpack.ml.dataframe.extractor.DataFrameDataExtractor;
Expand All @@ -20,7 +20,7 @@
import static org.hamcrest.Matchers.is;
import static org.hamcrest.Matchers.lessThan;

public class DatasetSplittingCustomProcessorTests extends ESTestCase {
public class RandomCrossValidationSplitterTests extends ESTestCase {

private List<String> fields;
private int dependentVariableIndex;
Expand All @@ -40,7 +40,7 @@ public void setUpTests() {
}

public void testProcess_GivenRowsWithoutDependentVariableValue() {
CustomProcessor customProcessor = new DatasetSplittingCustomProcessor(fields, dependentVariable, 50.0, randomizeSeed);
CrossValidationSplitter crossValidationSplitter = new RandomCrossValidationSplitter(fields, dependentVariable, 50.0, randomizeSeed);

for (int i = 0; i < 100; i++) {
String[] row = new String[fields.size()];
Expand All @@ -50,15 +50,16 @@ public void testProcess_GivenRowsWithoutDependentVariableValue() {
}

String[] processedRow = Arrays.copyOf(row, row.length);
customProcessor.process(processedRow);
crossValidationSplitter.process(processedRow);

// As all these rows have no dependent variable value, they're not for training and should be unaffected
assertThat(Arrays.equals(processedRow, row), is(true));
}
}

public void testProcess_GivenRowsWithDependentVariableValue_AndTrainingPercentIsHundred() {
CustomProcessor customProcessor = new DatasetSplittingCustomProcessor(fields, dependentVariable, 100.0, randomizeSeed);
CrossValidationSplitter crossValidationSplitter = new RandomCrossValidationSplitter(
fields, dependentVariable, 100.0, randomizeSeed);

for (int i = 0; i < 100; i++) {
String[] row = new String[fields.size()];
Expand All @@ -68,7 +69,7 @@ public void testProcess_GivenRowsWithDependentVariableValue_AndTrainingPercentIs
}

String[] processedRow = Arrays.copyOf(row, row.length);
customProcessor.process(processedRow);
crossValidationSplitter.process(processedRow);

// We should pick them all as training percent is 100
assertThat(Arrays.equals(processedRow, row), is(true));
Expand All @@ -78,7 +79,8 @@ public void testProcess_GivenRowsWithDependentVariableValue_AndTrainingPercentIs
public void testProcess_GivenRowsWithDependentVariableValue_AndTrainingPercentIsRandom() {
double trainingPercent = randomDoubleBetween(1.0, 100.0, true);
double trainingFraction = trainingPercent / 100;
CustomProcessor customProcessor = new DatasetSplittingCustomProcessor(fields, dependentVariable, trainingPercent, randomizeSeed);
CrossValidationSplitter crossValidationSplitter = new RandomCrossValidationSplitter(
fields, dependentVariable, trainingPercent, randomizeSeed);

int runCount = 20;
int rowsCount = 1000;
Expand All @@ -92,7 +94,7 @@ public void testProcess_GivenRowsWithDependentVariableValue_AndTrainingPercentIs
}

String[] processedRow = Arrays.copyOf(row, row.length);
customProcessor.process(processedRow);
crossValidationSplitter.process(processedRow);

for (int fieldIndex = 0; fieldIndex < fields.size(); fieldIndex++) {
if (fieldIndex != dependentVariableIndex) {
Expand Down Expand Up @@ -124,7 +126,8 @@ public void testProcess_GivenRowsWithDependentVariableValue_AndTrainingPercentIs
}

public void testProcess_ShouldHaveAtLeastOneTrainingRow() {
CustomProcessor customProcessor = new DatasetSplittingCustomProcessor(fields, dependentVariable, 1.0, randomizeSeed);
CrossValidationSplitter crossValidationSplitter = new RandomCrossValidationSplitter(
fields, dependentVariable, 1.0, randomizeSeed);

// We have some non-training rows and then a training row to check
// we maintain the first training row and not just the first row
Expand All @@ -139,7 +142,7 @@ public void testProcess_ShouldHaveAtLeastOneTrainingRow() {
}

String[] processedRow = Arrays.copyOf(row, row.length);
customProcessor.process(processedRow);
crossValidationSplitter.process(processedRow);

assertThat(Arrays.equals(processedRow, row), is(true));
}
Expand Down