honeymoose
/
OpenSearch
mirror of https://github.com/honeymoose/OpenSearch.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Make num_top_classes parameter's default value equal to 2 (#48119) (#48201)

This commit is contained in:
Przemysław Witek 2019-10-17 18:43:15 +02:00 committed by GitHub
parent e0489fc328
commit 28f68fa221
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 96 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

29
client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/Classification.java
View File

@ -48,6 +48,7 @@ public class Classification implements DataFrameAnalysis {
static final ParseField FEATURE_BAG_FRACTION = new ParseField("feature_bag_fraction");
static final ParseField PREDICTION_FIELD_NAME = new ParseField("prediction_field_name");
static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
static final ParseField NUM_TOP_CLASSES = new ParseField("num_top_classes");
private static final ConstructingObjectParser<Classification, Void> PARSER =
new ConstructingObjectParser<>(
@ -61,7 +62,8 @@ public class Classification implements DataFrameAnalysis {
(Integer) a[4],
(Double) a[5],
(String) a[6],
(Double) a[7]));
(Double) a[7],
(Integer) a[8]));
static {
PARSER.declareString(ConstructingObjectParser.constructorArg(), DEPENDENT_VARIABLE);
@ -72,6 +74,7 @@ public class Classification implements DataFrameAnalysis {
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_BAG_FRACTION);
PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME);
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT);
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), NUM_TOP_CLASSES);
}
private final String dependentVariable;
@ -82,10 +85,11 @@ public class Classification implements DataFrameAnalysis {
private final Double featureBagFraction;
private final String predictionFieldName;
private final Double trainingPercent;
private final Integer numTopClasses;
private Classification(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
@Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction, @Nullable String predictionFieldName,
@Nullable Double trainingPercent) {
@Nullable Double trainingPercent, @Nullable Integer numTopClasses) {
this.dependentVariable = Objects.requireNonNull(dependentVariable);
this.lambda = lambda;
this.gamma = gamma;
@ -94,6 +98,7 @@ public class Classification implements DataFrameAnalysis {
this.featureBagFraction = featureBagFraction;
this.predictionFieldName = predictionFieldName;
this.trainingPercent = trainingPercent;
this.numTopClasses = numTopClasses;
}
@Override
@ -133,6 +138,10 @@ public class Classification implements DataFrameAnalysis {
return trainingPercent;
}
public Integer getNumTopClasses() {
return numTopClasses;
}
@Override
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
builder.startObject();
@ -158,6 +167,9 @@ public class Classification implements DataFrameAnalysis {
if (trainingPercent != null) {
builder.field(TRAINING_PERCENT.getPreferredName(), trainingPercent);
}
if (numTopClasses != null) {
builder.field(NUM_TOP_CLASSES.getPreferredName(), numTopClasses);
}
builder.endObject();
return builder;
}
@ -165,7 +177,7 @@ public class Classification implements DataFrameAnalysis {
@Override
public int hashCode() {
return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName,
trainingPercent);
trainingPercent, numTopClasses);
}
@Override
@ -180,7 +192,8 @@ public class Classification implements DataFrameAnalysis {
&& Objects.equals(maximumNumberTrees, that.maximumNumberTrees)
&& Objects.equals(featureBagFraction, that.featureBagFraction)
&& Objects.equals(predictionFieldName, that.predictionFieldName)
&& Objects.equals(trainingPercent, that.trainingPercent);
&& Objects.equals(trainingPercent, that.trainingPercent)
&& Objects.equals(numTopClasses, that.numTopClasses);
}
@Override
@ -197,6 +210,7 @@ public class Classification implements DataFrameAnalysis {
private Double featureBagFraction;
private String predictionFieldName;
private Double trainingPercent;
private Integer numTopClasses;
private Builder(String dependentVariable) {
this.dependentVariable = Objects.requireNonNull(dependentVariable);
@ -237,9 +251,14 @@ public class Classification implements DataFrameAnalysis {
return this;
}
public Builder setNumTopClasses(Integer numTopClasses) {
this.numTopClasses = numTopClasses;
return this;
}
public Classification build() {
return new Classification(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName,
trainingPercent);
trainingPercent, numTopClasses);
}
}
}

7
client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java
View File

@ -1296,8 +1296,7 @@ public class MachineLearningIT extends ESRestHighLevelClientTestCase {
.setDest(DataFrameAnalyticsDest.builder()
.setIndex("put-test-dest-index")
.build())
.setAnalysis(org.elasticsearch.client.ml.dataframe.Regression
.builder("my_dependent_variable")
.setAnalysis(org.elasticsearch.client.ml.dataframe.Regression.builder("my_dependent_variable")
.setTrainingPercent(80.0)
.build())
.setDescription("this is a regression")
@ -1331,9 +1330,9 @@ public class MachineLearningIT extends ESRestHighLevelClientTestCase {
.setDest(DataFrameAnalyticsDest.builder()
.setIndex("put-test-dest-index")
.build())
.setAnalysis(org.elasticsearch.client.ml.dataframe.Classification
.builder("my_dependent_variable")
.setAnalysis(org.elasticsearch.client.ml.dataframe.Classification.builder("my_dependent_variable")
.setTrainingPercent(80.0)
.setNumTopClasses(1)
.build())
.setDescription("this is a classification")
.build();

1
client/rest-high-level/src/test/java/org/elasticsearch/client/documentation/MlClientDocumentationIT.java
View File

@ -2951,6 +2951,7 @@ public class MlClientDocumentationIT extends ESRestHighLevelClientTestCase {
.setFeatureBagFraction(0.4) // <6>
.setPredictionFieldName("my_prediction_field_name") // <7>
.setTrainingPercent(50.0) // <8>
.setNumTopClasses(1) // <9>
.build();
// end::put-data-frame-analytics-classification

1
client/rest-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/ClassificationTests.java
View File

@ -34,6 +34,7 @@ public class ClassificationTests extends AbstractXContentTestCase<Classification
.setFeatureBagFraction(randomBoolean() ? null : randomDoubleBetween(0.0, 1.0, false))
.setPredictionFieldName(randomBoolean() ? null : randomAlphaOfLength(10))
.setTrainingPercent(randomBoolean() ? null : randomDoubleBetween(1.0, 100.0, true))
.setNumTopClasses(randomBoolean() ? null : randomIntBetween(0, 10))
.build();
}

1
docs/java-rest/high-level/ml/put-data-frame-analytics.asciidoc
View File

@ -118,6 +118,7 @@ include-tagged::{doc-tests-file}[{api}-classification]
<6> The fraction of features which will be used when selecting a random bag for each candidate split. A double in (0, 1].
<7> The name of the prediction field in the results object.
<8> The percentage of training-eligible rows to be used in training. Defaults to 100%.
<9> The number of top classes to be reported in the results. Defaults to 2.
===== Regression

12
x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/analyses/Classification.java
View File

@ -67,6 +67,12 @@ public class Classification implements DataFrameAnalysis {
.flatMap(Set::stream)
.collect(Collectors.toSet()));
/**
* As long as we only support binary classification it makes sense to always report both classes with their probabilities.
* This way the user can see if the prediction was made with confidence they need.
*/
private static final int DEFAULT_NUM_TOP_CLASSES = 2;
private final String dependentVariable;
private final BoostedTreeParams boostedTreeParams;
private final String predictionFieldName;
@ -87,7 +93,7 @@ public class Classification implements DataFrameAnalysis {
this.dependentVariable = ExceptionsHelper.requireNonNull(dependentVariable, DEPENDENT_VARIABLE);
this.boostedTreeParams = ExceptionsHelper.requireNonNull(boostedTreeParams, BoostedTreeParams.NAME);
this.predictionFieldName = predictionFieldName;
this.numTopClasses = numTopClasses == null ? 0 : numTopClasses;
this.numTopClasses = numTopClasses == null ? DEFAULT_NUM_TOP_CLASSES : numTopClasses;
this.trainingPercent = trainingPercent == null ? 100.0 : trainingPercent;
}
@ -107,6 +113,10 @@ public class Classification implements DataFrameAnalysis {
return dependentVariable;
}
public int getNumTopClasses() {
return numTopClasses;
}
public double getTrainingPercent() {
return trainingPercent;
}

66
x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/dataframe/analyses/ClassificationTests.java
View File

@ -19,6 +19,8 @@ import static org.hamcrest.Matchers.nullValue;
public class ClassificationTests extends AbstractSerializingTestCase<Classification> {
private static final BoostedTreeParams BOOSTED_TREE_PARAMS = new BoostedTreeParams(0.0, 0.0, 0.5, 500, 1.0);
@Override
protected Classification doParseInstance(XContentParser parser) throws IOException {
return Classification.fromXContent(parser, false);
@ -43,32 +45,68 @@ public class ClassificationTests extends AbstractSerializingTestCase<Classificat
return Classification::new;
}
public void testConstructor_GivenTrainingPercentIsNull() {
Classification classification = new Classification("foo", new BoostedTreeParams(0.0, 0.0, 0.5, 500, 1.0), "result", 3, null);
assertThat(classification.getTrainingPercent(), equalTo(100.0));
}
public void testConstructor_GivenTrainingPercentIsBoundary() {
Classification classification = new Classification("foo", new BoostedTreeParams(0.0, 0.0, 0.5, 500, 1.0), "result", 3, 1.0);
assertThat(classification.getTrainingPercent(), equalTo(1.0));
classification = new Classification("foo", new BoostedTreeParams(0.0, 0.0, 0.5, 500, 1.0), "result", 3, 100.0);
assertThat(classification.getTrainingPercent(), equalTo(100.0));
}
public void testConstructor_GivenTrainingPercentIsLessThanOne() {
ElasticsearchStatusException e = expectThrows(ElasticsearchStatusException.class,
() -> new Classification("foo", new BoostedTreeParams(0.0, 0.0, 0.5, 500, 1.0), "result", 3, 0.999));
() -> new Classification("foo", BOOSTED_TREE_PARAMS, "result", 3, 0.999));
assertThat(e.getMessage(), equalTo("[training_percent] must be a double in [1, 100]"));
}
public void testConstructor_GivenTrainingPercentIsGreaterThan100() {
ElasticsearchStatusException e = expectThrows(ElasticsearchStatusException.class,
() -> new Classification("foo", new BoostedTreeParams(0.0, 0.0, 0.5, 500, 1.0), "result", 3, 100.0001));
() -> new Classification("foo", BOOSTED_TREE_PARAMS, "result", 3, 100.0001));
assertThat(e.getMessage(), equalTo("[training_percent] must be a double in [1, 100]"));
}
public void testConstructor_GivenNumTopClassesIsLessThanZero() {
ElasticsearchStatusException e = expectThrows(ElasticsearchStatusException.class,
() -> new Classification("foo", BOOSTED_TREE_PARAMS, "result", -1, 1.0));
assertThat(e.getMessage(), equalTo("[num_top_classes] must be an integer in [0, 1000]"));
}
public void testConstructor_GivenNumTopClassesIsGreaterThan1000() {
ElasticsearchStatusException e = expectThrows(ElasticsearchStatusException.class,
() -> new Classification("foo", BOOSTED_TREE_PARAMS, "result", 1001, 1.0));
assertThat(e.getMessage(), equalTo("[num_top_classes] must be an integer in [0, 1000]"));
}
public void testGetNumTopClasses() {
Classification classification = new Classification("foo", BOOSTED_TREE_PARAMS, "result", 7, 1.0);
assertThat(classification.getNumTopClasses(), equalTo(7));
// Boundary condition: num_top_classes == 0
classification = new Classification("foo", BOOSTED_TREE_PARAMS, "result", 0, 1.0);
assertThat(classification.getNumTopClasses(), equalTo(0));
// Boundary condition: num_top_classes == 1000
classification = new Classification("foo", BOOSTED_TREE_PARAMS, "result", 1000, 1.0);
assertThat(classification.getNumTopClasses(), equalTo(1000));
// num_top_classes == null, default applied
classification = new Classification("foo", BOOSTED_TREE_PARAMS, "result", null, 1.0);
assertThat(classification.getNumTopClasses(), equalTo(2));
}
public void testGetTrainingPercent() {
Classification classification = new Classification("foo", BOOSTED_TREE_PARAMS, "result", 3, 50.0);
assertThat(classification.getTrainingPercent(), equalTo(50.0));
// Boundary condition: training_percent == 1.0
classification = new Classification("foo", BOOSTED_TREE_PARAMS, "result", 3, 1.0);
assertThat(classification.getTrainingPercent(), equalTo(1.0));
// Boundary condition: training_percent == 100.0
classification = new Classification("foo", BOOSTED_TREE_PARAMS, "result", 3, 100.0);
assertThat(classification.getTrainingPercent(), equalTo(100.0));
// training_percent == null, default applied
classification = new Classification("foo", BOOSTED_TREE_PARAMS, "result", 3, null);
assertThat(classification.getTrainingPercent(), equalTo(100.0));
}
public void testFieldCardinalityLimitsIsNonNull() {
assertThat(createTestInstance().getFieldCardinalityLimits(), is(not(nullValue())));
}

4
x-pack/plugin/ml/qa/native-multi-node-tests/src/test/java/org/elasticsearch/xpack/ml/integration/ClassificationIT.java
View File

@ -83,7 +83,7 @@ public class ClassificationIT extends MlNativeDataFrameAnalyticsIntegTestCase {
assertThat((String) resultsObject.get("keyword-field_prediction"), is(in(KEYWORD_FIELD_VALUES)));
assertThat(resultsObject.containsKey("is_training"), is(true));
assertThat(resultsObject.get("is_training"), is(destDoc.containsKey(KEYWORD_FIELD)));
assertThat(resultsObject.containsKey("top_classes"), is(false));
assertTopClasses(resultsObject, 2, KEYWORD_FIELD, KEYWORD_FIELD_VALUES, String::valueOf);
}
assertProgress(jobId, 100, 100, 100, 100);
@ -120,7 +120,7 @@ public class ClassificationIT extends MlNativeDataFrameAnalyticsIntegTestCase {
assertThat((String) resultsObject.get("keyword-field_prediction"), is(in(KEYWORD_FIELD_VALUES)));
assertThat(resultsObject.containsKey("is_training"), is(true));
assertThat(resultsObject.get("is_training"), is(true));
assertThat(resultsObject.containsKey("top_classes"), is(false));
assertTopClasses(resultsObject, 2, KEYWORD_FIELD, KEYWORD_FIELD_VALUES, String::valueOf);
}
assertProgress(jobId, 100, 100, 100, 100);

2
x-pack/plugin/src/test/resources/rest-api-spec/test/ml/data_frame_analytics_crud.yml
View File

@ -1810,7 +1810,7 @@ setup:
"maximum_number_trees": 400,
"feature_bag_fraction": 0.3,
"training_percent": 60.3,
"num_top_classes": 0
"num_top_classes": 2
}
}}
- is_true: create_time