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Oct 29, 2019
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Assert on evaluation results after classification analysis in tests. #48626

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12 changes: 12 additions & 0 deletions ...icsearch/xpack/core/ml/dataframe/evaluation/classification/MulticlassConfusionMatrix.java
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Expand Up @@ -322,6 +322,18 @@ public ActualClass(StreamInput in) throws IOException {
this.otherPredictedClassDocCount = in.readVLong();
}

public String getActualClass() {
return actualClass;
}

public List<PredictedClass> getPredictedClasses() {
return predictedClasses;
}

public long getOtherPredictedClassDocCount() {
return otherPredictedClassDocCount;
}

@Override
public void writeTo(StreamOutput out) throws IOException {
out.writeString(actualClass);
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25 changes: 7 additions & 18 deletions ...ests/src/test/java/org/elasticsearch/xpack/ml/integration/ClassificationEvaluationIT.java
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Expand Up @@ -39,13 +39,8 @@ public void cleanup() {
}

public void testEvaluate_MulticlassClassification_DefaultMetrics() {
EvaluateDataFrameAction.Request evaluateDataFrameRequest =
new EvaluateDataFrameAction.Request()
.setIndices(List.of(ANIMALS_DATA_INDEX))
.setEvaluation(new Classification(ACTUAL_CLASS_FIELD, PREDICTED_CLASS_FIELD, null));

EvaluateDataFrameAction.Response evaluateDataFrameResponse =
client().execute(EvaluateDataFrameAction.INSTANCE, evaluateDataFrameRequest).actionGet();
evaluateDataFrame(ANIMALS_DATA_INDEX, new Classification(ACTUAL_CLASS_FIELD, PREDICTED_CLASS_FIELD, null));

assertThat(evaluateDataFrameResponse.getEvaluationName(), equalTo(Classification.NAME.getPreferredName()));
assertThat(evaluateDataFrameResponse.getMetrics().size(), equalTo(1));
Expand Down Expand Up @@ -104,13 +99,10 @@ public void testEvaluate_MulticlassClassification_DefaultMetrics() {
}

public void testEvaluate_MulticlassClassification_ConfusionMatrixMetricWithDefaultSize() {
EvaluateDataFrameAction.Request evaluateDataFrameRequest =
new EvaluateDataFrameAction.Request()
.setIndices(List.of(ANIMALS_DATA_INDEX))
.setEvaluation(new Classification(ACTUAL_CLASS_FIELD, PREDICTED_CLASS_FIELD, List.of(new MulticlassConfusionMatrix())));

EvaluateDataFrameAction.Response evaluateDataFrameResponse =
client().execute(EvaluateDataFrameAction.INSTANCE, evaluateDataFrameRequest).actionGet();
evaluateDataFrame(
ANIMALS_DATA_INDEX,
new Classification(ACTUAL_CLASS_FIELD, PREDICTED_CLASS_FIELD, List.of(new MulticlassConfusionMatrix())));

assertThat(evaluateDataFrameResponse.getEvaluationName(), equalTo(Classification.NAME.getPreferredName()));
assertThat(evaluateDataFrameResponse.getMetrics().size(), equalTo(1));
Expand Down Expand Up @@ -169,13 +161,10 @@ public void testEvaluate_MulticlassClassification_ConfusionMatrixMetricWithDefau
}

public void testEvaluate_MulticlassClassification_ConfusionMatrixMetricWithUserProvidedSize() {
EvaluateDataFrameAction.Request evaluateDataFrameRequest =
new EvaluateDataFrameAction.Request()
.setIndices(List.of(ANIMALS_DATA_INDEX))
.setEvaluation(new Classification(ACTUAL_CLASS_FIELD, PREDICTED_CLASS_FIELD, List.of(new MulticlassConfusionMatrix(3))));

EvaluateDataFrameAction.Response evaluateDataFrameResponse =
client().execute(EvaluateDataFrameAction.INSTANCE, evaluateDataFrameRequest).actionGet();
evaluateDataFrame(
ANIMALS_DATA_INDEX,
new Classification(ACTUAL_CLASS_FIELD, PREDICTED_CLASS_FIELD, List.of(new MulticlassConfusionMatrix(3))));

assertThat(evaluateDataFrameResponse.getEvaluationName(), equalTo(Classification.NAME.getPreferredName()));
assertThat(evaluateDataFrameResponse.getMetrics().size(), equalTo(1));
Expand Down
82 changes: 35 additions & 47 deletions ...lti-node-tests/src/test/java/org/elasticsearch/xpack/ml/integration/ClassificationIT.java
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Expand Up @@ -16,9 +16,13 @@
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.action.support.WriteRequest;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.xpack.core.ml.action.EvaluateDataFrameAction;
import org.elasticsearch.xpack.core.ml.dataframe.DataFrameAnalyticsConfig;
import org.elasticsearch.xpack.core.ml.dataframe.analyses.BoostedTreeParamsTests;
import org.elasticsearch.xpack.core.ml.dataframe.analyses.Classification;
import org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.MulticlassConfusionMatrix;
import org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.MulticlassConfusionMatrix.ActualClass;
import org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.MulticlassConfusionMatrix.PredictedClass;
import org.junit.After;

import java.util.ArrayList;
Expand All @@ -28,6 +32,7 @@
import java.util.Map;
import java.util.function.Function;

import static java.util.stream.Collectors.toList;
import static org.hamcrest.Matchers.allOf;
import static org.hamcrest.Matchers.equalTo;
import static org.hamcrest.Matchers.greaterThan;
Expand All @@ -48,7 +53,7 @@ public class ClassificationIT extends MlNativeDataFrameAnalyticsIntegTestCase {
private static final List<Boolean> BOOLEAN_FIELD_VALUES = Collections.unmodifiableList(Arrays.asList(false, true));
private static final List<Double> NUMERICAL_FIELD_VALUES = Collections.unmodifiableList(Arrays.asList(1.0, 2.0));
private static final List<Integer> DISCRETE_NUMERICAL_FIELD_VALUES = Collections.unmodifiableList(Arrays.asList(10, 20));
private static final List<String> KEYWORD_FIELD_VALUES = Collections.unmodifiableList(Arrays.asList("dog", "cat"));
private static final List<String> KEYWORD_FIELD_VALUES = Collections.unmodifiableList(Arrays.asList("cat", "dog"));

private String jobId;
private String sourceIndex;
Expand Down Expand Up @@ -97,6 +102,7 @@ public void testSingleNumericFeatureAndMixedTrainingAndNonTrainingRows() throws
"Creating destination index [" + destIndex + "]",
"Finished reindexing to destination index [" + destIndex + "]",
"Finished analysis");
assertEvaluation(KEYWORD_FIELD, KEYWORD_FIELD_VALUES, "ml.keyword-field_prediction");
}

public void testWithOnlyTrainingRowsAndTrainingPercentIsHundred() throws Exception {
Expand Down Expand Up @@ -136,11 +142,13 @@ public void testWithOnlyTrainingRowsAndTrainingPercentIsHundred() throws Excepti
"Creating destination index [" + destIndex + "]",
"Finished reindexing to destination index [" + destIndex + "]",
"Finished analysis");
assertEvaluation(KEYWORD_FIELD, KEYWORD_FIELD_VALUES, "ml.keyword-field_prediction");
}

public <T> void testWithOnlyTrainingRowsAndTrainingPercentIsFifty(
String jobId, String dependentVariable, List<T> dependentVariableValues, Function<String, T> parser) throws Exception {
initialize(jobId);
String predictedClassField = dependentVariable + "_prediction";
indexData(sourceIndex, 300, 0, dependentVariable);

int numTopClasses = 2;
Expand All @@ -166,7 +174,6 @@ public <T> void testWithOnlyTrainingRowsAndTrainingPercentIsFifty(
SearchResponse sourceData = client().prepareSearch(sourceIndex).setTrackTotalHits(true).setSize(1000).get();
for (SearchHit hit : sourceData.getHits()) {
Map<String, Object> resultsObject = getMlResultsObjectFromDestDoc(getDestDoc(config, hit));
String predictedClassField = dependentVariable + "_prediction";
assertThat(resultsObject.containsKey(predictedClassField), is(true));
T predictedClassValue = parser.apply((String) resultsObject.get(predictedClassField));
assertThat(predictedClassValue, is(in(dependentVariableValues)));
Expand Down Expand Up @@ -194,6 +201,10 @@ public <T> void testWithOnlyTrainingRowsAndTrainingPercentIsFifty(
"Creating destination index [" + destIndex + "]",
"Finished reindexing to destination index [" + destIndex + "]",
"Finished analysis");
assertEvaluation(
dependentVariable,
dependentVariableValues.stream().map(String::valueOf).collect(toList()),
"ml." + predictedClassField);
}

public void testWithOnlyTrainingRowsAndTrainingPercentIsFifty_DependentVariableIsKeyword() throws Exception {
Expand All @@ -219,51 +230,6 @@ public void testWithOnlyTrainingRowsAndTrainingPercentIsFifty_DependentVariableI
"classification_training_percent_is_50_boolean", BOOLEAN_FIELD, BOOLEAN_FIELD_VALUES, Boolean::valueOf);
}

public void testSingleNumericFeatureAndMixedTrainingAndNonTrainingRows_TopClassesRequested() throws Exception {
initialize("classification_top_classes_requested");
indexData(sourceIndex, 300, 50, KEYWORD_FIELD);

int numTopClasses = 2;
DataFrameAnalyticsConfig config =
buildAnalytics(
jobId,
sourceIndex,
destIndex,
null,
new Classification(KEYWORD_FIELD, BoostedTreeParamsTests.createRandom(), null, numTopClasses, null));
registerAnalytics(config);
putAnalytics(config);

assertIsStopped(jobId);
assertProgress(jobId, 0, 0, 0, 0);

startAnalytics(jobId);
waitUntilAnalyticsIsStopped(jobId);

client().admin().indices().refresh(new RefreshRequest(destIndex));
SearchResponse sourceData = client().prepareSearch(sourceIndex).setTrackTotalHits(true).setSize(1000).get();
for (SearchHit hit : sourceData.getHits()) {
Map<String, Object> destDoc = getDestDoc(config, hit);
Map<String, Object> resultsObject = getMlResultsObjectFromDestDoc(destDoc);

assertThat(resultsObject.containsKey("keyword-field_prediction"), is(true));
assertThat((String) resultsObject.get("keyword-field_prediction"), is(in(KEYWORD_FIELD_VALUES)));
assertTopClasses(resultsObject, numTopClasses, KEYWORD_FIELD, KEYWORD_FIELD_VALUES, String::valueOf);
}

assertProgress(jobId, 100, 100, 100, 100);
assertThat(searchStoredProgress(jobId).getHits().getTotalHits().value, equalTo(1L));
assertInferenceModelPersisted(jobId);
assertThatAuditMessagesMatch(jobId,
"Created analytics with analysis type [classification]",
"Estimated memory usage for this analytics to be",
"Starting analytics on node",
"Started analytics",
"Creating destination index [" + destIndex + "]",
"Finished reindexing to destination index [" + destIndex + "]",
"Finished analysis");
}

public void testDependentVariableCardinalityTooHighError() {
initialize("cardinality_too_high");
indexData(sourceIndex, 6, 5, KEYWORD_FIELD);
Expand Down Expand Up @@ -377,4 +343,26 @@ private static <T> void assertTopClasses(
// Assert that the top classes are listed in the order of decreasing probabilities.
assertThat(Ordering.natural().reverse().isOrdered(classProbabilities), is(true));
}

private void assertEvaluation(String dependentVariable, List<String> dependentVariableValues, String predictedClassField) {
EvaluateDataFrameAction.Response evaluateDataFrameResponse =
evaluateDataFrame(
destIndex,
new org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.Classification(
dependentVariable, predictedClassField, null));
assertThat(evaluateDataFrameResponse.getEvaluationName(), equalTo(Classification.NAME.getPreferredName()));
assertThat(evaluateDataFrameResponse.getMetrics().size(), equalTo(1));
MulticlassConfusionMatrix.Result confusionMatrixResult =
(MulticlassConfusionMatrix.Result) evaluateDataFrameResponse.getMetrics().get(0);
assertThat(confusionMatrixResult.getMetricName(), equalTo(MulticlassConfusionMatrix.NAME.getPreferredName()));
List<ActualClass> actualClasses = confusionMatrixResult.getConfusionMatrix();
assertThat(actualClasses.stream().map(ActualClass::getActualClass).collect(toList()), equalTo(dependentVariableValues));
for (ActualClass actualClass : actualClasses) {
assertThat(actualClass.getOtherPredictedClassDocCount(), equalTo(0L));
assertThat(
actualClass.getPredictedClasses().stream().map(PredictedClass::getPredictedClass).collect(toList()),
equalTo(dependentVariableValues));
}
assertThat(confusionMatrixResult.getOtherActualClassCount(), equalTo(0L));
}
}
10 changes: 10 additions & 0 deletions .../java/org/elasticsearch/xpack/ml/integration/MlNativeDataFrameAnalyticsIntegTestCase.java
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Expand Up @@ -18,6 +18,7 @@
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.search.sort.SortOrder;
import org.elasticsearch.xpack.core.ml.action.DeleteDataFrameAnalyticsAction;
import org.elasticsearch.xpack.core.ml.action.EvaluateDataFrameAction;
import org.elasticsearch.xpack.core.ml.action.GetDataFrameAnalyticsAction;
import org.elasticsearch.xpack.core.ml.action.GetDataFrameAnalyticsStatsAction;
import org.elasticsearch.xpack.core.ml.action.PutDataFrameAnalyticsAction;
Expand All @@ -28,6 +29,7 @@
import org.elasticsearch.xpack.core.ml.dataframe.DataFrameAnalyticsSource;
import org.elasticsearch.xpack.core.ml.dataframe.DataFrameAnalyticsState;
import org.elasticsearch.xpack.core.ml.dataframe.analyses.DataFrameAnalysis;
import org.elasticsearch.xpack.core.ml.dataframe.evaluation.Evaluation;
import org.elasticsearch.xpack.core.ml.inference.TrainedModelConfig;
import org.elasticsearch.xpack.core.ml.inference.persistence.InferenceIndexConstants;
import org.elasticsearch.xpack.core.ml.job.persistence.AnomalyDetectorsIndex;
Expand Down Expand Up @@ -143,6 +145,14 @@ protected GetDataFrameAnalyticsStatsAction.Response.Stats getAnalyticsStats(Stri
return stats.get(0);
}

protected EvaluateDataFrameAction.Response evaluateDataFrame(String index, Evaluation evaluation) {
EvaluateDataFrameAction.Request request =
new EvaluateDataFrameAction.Request()
.setIndices(List.of(index))
.setEvaluation(evaluation);
return client().execute(EvaluateDataFrameAction.INSTANCE, request).actionGet();
}

protected static DataFrameAnalyticsConfig buildAnalytics(String id, String sourceIndex, String destIndex,
@Nullable String resultsField, DataFrameAnalysis analysis) {
DataFrameAnalyticsConfig.Builder configBuilder = new DataFrameAnalyticsConfig.Builder();
Expand Down