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[ML][HLRC] Add data frame analytics regression analysis #46024
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[ML][HLRC] Add data frame analytics regression analysis
dimitris-athanasiou fffc806
Fix unit test
dimitris-athanasiou f8b34b0
Fix spelling
dimitris-athanasiou 8f59877
Fix spelling
dimitris-athanasiou 297eb98
Merge branch 'master' into add-regression-to-hlrc
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242 changes: 242 additions & 0 deletions
242
client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/Regression.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,242 @@ | ||
| /* | ||
| * Licensed to Elasticsearch under one or more contributor | ||
| * license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright | ||
| * ownership. Elasticsearch licenses this file to you under | ||
| * the Apache License, Version 2.0 (the "License"); you may | ||
| * not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, | ||
| * software distributed under the License is distributed on an | ||
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| * KIND, either express or implied. See the License for the | ||
| * specific language governing permissions and limitations | ||
| * under the License. | ||
| */ | ||
| package org.elasticsearch.client.ml.dataframe; | ||
|
|
||
| import org.elasticsearch.common.Nullable; | ||
| import org.elasticsearch.common.ParseField; | ||
| import org.elasticsearch.common.Strings; | ||
| import org.elasticsearch.common.xcontent.ConstructingObjectParser; | ||
| import org.elasticsearch.common.xcontent.XContentBuilder; | ||
| import org.elasticsearch.common.xcontent.XContentParser; | ||
|
|
||
| import java.io.IOException; | ||
| import java.util.Objects; | ||
|
|
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| public class Regression implements DataFrameAnalysis { | ||
|
|
||
| public static Regression fromXContent(XContentParser parser) { | ||
| return PARSER.apply(parser, null); | ||
| } | ||
|
|
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| public static Builder builder(String dependentVariable) { | ||
| return new Builder(dependentVariable); | ||
| } | ||
|
|
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| public static final ParseField NAME = new ParseField("regression"); | ||
|
|
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| static final ParseField DEPENDENT_VARIABLE = new ParseField("dependent_variable"); | ||
| static final ParseField LAMBDA = new ParseField("lambda"); | ||
| static final ParseField GAMMA = new ParseField("gamma"); | ||
| static final ParseField ETA = new ParseField("eta"); | ||
| static final ParseField MAXIMUM_NUMBER_TREES = new ParseField("maximum_number_trees"); | ||
| 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"); | ||
|
|
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| private static final ConstructingObjectParser<Regression, Void> PARSER = new ConstructingObjectParser<>(NAME.getPreferredName(), true, | ||
| a -> new Regression( | ||
| (String) a[0], | ||
| (Double) a[1], | ||
| (Double) a[2], | ||
| (Double) a[3], | ||
| (Integer) a[4], | ||
| (Double) a[5], | ||
| (String) a[6], | ||
| (Double) a[7])); | ||
|
|
||
| static { | ||
| PARSER.declareString(ConstructingObjectParser.constructorArg(), DEPENDENT_VARIABLE); | ||
| PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), LAMBDA); | ||
| PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), GAMMA); | ||
| PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), ETA); | ||
| PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), MAXIMUM_NUMBER_TREES); | ||
| PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_BAG_FRACTION); | ||
| PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME); | ||
| PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT); | ||
| } | ||
|
|
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| private final String dependentVariable; | ||
| private final Double lambda; | ||
| private final Double gamma; | ||
| private final Double eta; | ||
| private final Integer maximumNumberTrees; | ||
| private final Double featureBagFraction; | ||
| private final String predictionFieldName; | ||
| private final Double trainingPercent; | ||
|
|
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| private Regression(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta, | ||
| @Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction, @Nullable String predictionFieldName, | ||
| @Nullable Double trainingPercent) { | ||
| this.dependentVariable = Objects.requireNonNull(dependentVariable); | ||
| this.lambda = lambda; | ||
| this.gamma = gamma; | ||
| this.eta = eta; | ||
| this.maximumNumberTrees = maximumNumberTrees; | ||
| this.featureBagFraction = featureBagFraction; | ||
| this.predictionFieldName = predictionFieldName; | ||
| this.trainingPercent = trainingPercent; | ||
| } | ||
|
|
||
| @Override | ||
| public String getName() { | ||
| return NAME.getPreferredName(); | ||
| } | ||
|
|
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| public String getDependentVariable() { | ||
| return dependentVariable; | ||
| } | ||
|
|
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| public Double getLambda() { | ||
| return lambda; | ||
| } | ||
|
|
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| public Double getGamma() { | ||
| return gamma; | ||
| } | ||
|
|
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| public Double getEta() { | ||
| return eta; | ||
| } | ||
|
|
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| public Integer getMaximumNumberTrees() { | ||
| return maximumNumberTrees; | ||
| } | ||
|
|
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| public Double getFeatureBagFraction() { | ||
| return featureBagFraction; | ||
| } | ||
|
|
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| public String getPredictionFieldName() { | ||
| return predictionFieldName; | ||
| } | ||
|
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| public Double getTrainingPercent() { | ||
| return trainingPercent; | ||
| } | ||
|
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| @Override | ||
| public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException { | ||
| builder.startObject(); | ||
| builder.field(DEPENDENT_VARIABLE.getPreferredName(), dependentVariable); | ||
| if (lambda != null) { | ||
| builder.field(LAMBDA.getPreferredName(), lambda); | ||
| } | ||
| if (gamma != null) { | ||
| builder.field(GAMMA.getPreferredName(), gamma); | ||
| } | ||
| if (eta != null) { | ||
| builder.field(ETA.getPreferredName(), eta); | ||
| } | ||
| if (maximumNumberTrees != null) { | ||
| builder.field(MAXIMUM_NUMBER_TREES.getPreferredName(), maximumNumberTrees); | ||
| } | ||
| if (featureBagFraction != null) { | ||
| builder.field(FEATURE_BAG_FRACTION.getPreferredName(), featureBagFraction); | ||
| } | ||
| if (predictionFieldName != null) { | ||
| builder.field(PREDICTION_FIELD_NAME.getPreferredName(), predictionFieldName); | ||
| } | ||
| if (trainingPercent != null) { | ||
| builder.field(TRAINING_PERCENT.getPreferredName(), trainingPercent); | ||
| } | ||
| builder.endObject(); | ||
| return builder; | ||
| } | ||
|
|
||
| @Override | ||
| public int hashCode() { | ||
| return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName, | ||
| trainingPercent); | ||
| } | ||
|
|
||
| @Override | ||
| public boolean equals(Object o) { | ||
| if (this == o) return true; | ||
| if (o == null || getClass() != o.getClass()) return false; | ||
| Regression that = (Regression) o; | ||
| return Objects.equals(dependentVariable, that.dependentVariable) | ||
| && Objects.equals(lambda, that.lambda) | ||
| && Objects.equals(gamma, that.gamma) | ||
| && Objects.equals(eta, that.eta) | ||
| && Objects.equals(maximumNumberTrees, that.maximumNumberTrees) | ||
| && Objects.equals(featureBagFraction, that.featureBagFraction) | ||
| && Objects.equals(predictionFieldName, that.predictionFieldName) | ||
| && Objects.equals(trainingPercent, that.trainingPercent); | ||
| } | ||
|
|
||
| @Override | ||
| public String toString() { | ||
| return Strings.toString(this); | ||
| } | ||
|
|
||
| public static class Builder { | ||
| private String dependentVariable; | ||
| private Double lambda; | ||
| private Double gamma; | ||
| private Double eta; | ||
| private Integer maximumNumberTrees; | ||
| private Double featureBagFraction; | ||
| private String predictionFieldName; | ||
| private Double trainingPercent; | ||
|
|
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| private Builder(String dependentVariable) { | ||
| this.dependentVariable = Objects.requireNonNull(dependentVariable); | ||
| } | ||
|
|
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| public Builder setLambda(Double lambda) { | ||
| this.lambda = lambda; | ||
| return this; | ||
| } | ||
|
|
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| public Builder setGamma(Double gamma) { | ||
| this.gamma = gamma; | ||
| return this; | ||
| } | ||
|
|
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| public Builder setEta(Double eta) { | ||
| this.eta = eta; | ||
| return this; | ||
| } | ||
|
|
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| public Builder setMaximumNumberTrees(Integer maximumNumberTrees) { | ||
| this.maximumNumberTrees = maximumNumberTrees; | ||
| return this; | ||
| } | ||
|
|
||
| public Builder setFeatureBagFraction(Double featureBagFraction) { | ||
| this.featureBagFraction = featureBagFraction; | ||
| return this; | ||
| } | ||
|
|
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| public Builder setPredictionFieldName(String predictionFieldName) { | ||
| this.predictionFieldName = predictionFieldName; | ||
| return this; | ||
| } | ||
|
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| public Builder setTrainingPercent(Double trainingPercent) { | ||
| this.trainingPercent = trainingPercent; | ||
| return this; | ||
| } | ||
|
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| public Regression build() { | ||
| return new Regression(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName, | ||
| trainingPercent); | ||
| } | ||
| } | ||
| } |
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Does it have to be fully-qualified?
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Yes, there is also the
Regressionobject from the evaluation API.