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+/*
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+ * Licensed to Elasticsearch under one or more contributor
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+ * license agreements. See the NOTICE file distributed with
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+ * this work for additional information regarding copyright
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+ * ownership. Elasticsearch licenses this file to you under
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+ * the Apache License, Version 2.0 (the "License"); you may
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+ * not use this file except in compliance with the License.
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+ * You may obtain a copy of the License at
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+ *
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+ * http://www.apache.org/licenses/LICENSE-2.0
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+ *
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+ * Unless required by applicable law or agreed to in writing,
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+ * software distributed under the License is distributed on an
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+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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+ * KIND, either express or implied. See the License for the
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+ * specific language governing permissions and limitations
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+ * under the License.
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+ */
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+package org.elasticsearch.client.ml.dataframe;
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+
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+import org.elasticsearch.common.Nullable;
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+import org.elasticsearch.common.ParseField;
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+import org.elasticsearch.common.Strings;
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+import org.elasticsearch.common.xcontent.ConstructingObjectParser;
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+import org.elasticsearch.common.xcontent.XContentBuilder;
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+import org.elasticsearch.common.xcontent.XContentParser;
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+
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+import java.io.IOException;
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+import java.util.Objects;
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+
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+public class Regression implements DataFrameAnalysis {
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+
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+ public static Regression fromXContent(XContentParser parser) {
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+ return PARSER.apply(parser, null);
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+ }
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+
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+ public static Builder builder(String dependentVariable) {
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+ return new Builder(dependentVariable);
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+ }
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+
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+ public static final ParseField NAME = new ParseField("regression");
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+
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+ static final ParseField DEPENDENT_VARIABLE = new ParseField("dependent_variable");
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+ static final ParseField LAMBDA = new ParseField("lambda");
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+ static final ParseField GAMMA = new ParseField("gamma");
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+ static final ParseField ETA = new ParseField("eta");
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+ static final ParseField MAXIMUM_NUMBER_TREES = new ParseField("maximum_number_trees");
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+ static final ParseField FEATURE_BAG_FRACTION = new ParseField("feature_bag_fraction");
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+ static final ParseField PREDICTION_FIELD_NAME = new ParseField("prediction_field_name");
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+ static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
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+
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+ private static final ConstructingObjectParser<Regression, Void> PARSER = new ConstructingObjectParser<>(NAME.getPreferredName(), true,
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+ a -> new Regression(
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+ (String) a[0],
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+ (Double) a[1],
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+ (Double) a[2],
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+ (Double) a[3],
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+ (Integer) a[4],
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+ (Double) a[5],
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+ (String) a[6],
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+ (Double) a[7]));
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+
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+ static {
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+ PARSER.declareString(ConstructingObjectParser.constructorArg(), DEPENDENT_VARIABLE);
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+ PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), LAMBDA);
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+ PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), GAMMA);
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+ PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), ETA);
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+ PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), MAXIMUM_NUMBER_TREES);
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+ PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_BAG_FRACTION);
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+ PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME);
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+ PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT);
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+ }
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+
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+ private final String dependentVariable;
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+ private final Double lambda;
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+ private final Double gamma;
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+ private final Double eta;
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+ private final Integer maximumNumberTrees;
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+ private final Double featureBagFraction;
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+ private final String predictionFieldName;
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+ private final Double trainingPercent;
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+
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+ private Regression(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
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+ @Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction, @Nullable String predictionFieldName,
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+ @Nullable Double trainingPercent) {
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+ this.dependentVariable = Objects.requireNonNull(dependentVariable);
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+ this.lambda = lambda;
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+ this.gamma = gamma;
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+ this.eta = eta;
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+ this.maximumNumberTrees = maximumNumberTrees;
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+ this.featureBagFraction = featureBagFraction;
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+ this.predictionFieldName = predictionFieldName;
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+ this.trainingPercent = trainingPercent;
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+ }
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+
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+ @Override
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+ public String getName() {
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+ return NAME.getPreferredName();
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+ }
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+
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+ public String getDependentVariable() {
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+ return dependentVariable;
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+ }
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+
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+ public Double getLambda() {
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+ return lambda;
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+ }
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+
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+ public Double getGamma() {
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+ return gamma;
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+ }
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+
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+ public Double getEta() {
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+ return eta;
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+ }
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+
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+ public Integer getMaximumNumberTrees() {
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+ return maximumNumberTrees;
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+ }
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+
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+ public Double getFeatureBagFraction() {
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+ return featureBagFraction;
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+ }
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+
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+ public String getPredictionFieldName() {
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+ return predictionFieldName;
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+ }
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+
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+ public Double getTrainingPercent() {
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+ return trainingPercent;
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+ }
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+
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+ @Override
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+ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
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+ builder.startObject();
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+ builder.field(DEPENDENT_VARIABLE.getPreferredName(), dependentVariable);
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+ if (lambda != null) {
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+ builder.field(LAMBDA.getPreferredName(), lambda);
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+ }
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+ if (gamma != null) {
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+ builder.field(GAMMA.getPreferredName(), gamma);
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+ }
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+ if (eta != null) {
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+ builder.field(ETA.getPreferredName(), eta);
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+ }
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+ if (maximumNumberTrees != null) {
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+ builder.field(MAXIMUM_NUMBER_TREES.getPreferredName(), maximumNumberTrees);
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+ }
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+ if (featureBagFraction != null) {
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+ builder.field(FEATURE_BAG_FRACTION.getPreferredName(), featureBagFraction);
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+ }
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+ if (predictionFieldName != null) {
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+ builder.field(PREDICTION_FIELD_NAME.getPreferredName(), predictionFieldName);
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+ }
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+ if (trainingPercent != null) {
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+ builder.field(TRAINING_PERCENT.getPreferredName(), trainingPercent);
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+ }
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+ builder.endObject();
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+ return builder;
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+ }
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+
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+ @Override
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+ public int hashCode() {
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+ return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName,
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+ trainingPercent);
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+ }
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+
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+ @Override
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+ public boolean equals(Object o) {
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+ if (this == o) return true;
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+ if (o == null || getClass() != o.getClass()) return false;
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+ Regression that = (Regression) o;
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+ return Objects.equals(dependentVariable, that.dependentVariable)
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+ && Objects.equals(lambda, that.lambda)
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+ && Objects.equals(gamma, that.gamma)
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+ && Objects.equals(eta, that.eta)
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+ && Objects.equals(maximumNumberTrees, that.maximumNumberTrees)
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+ && Objects.equals(featureBagFraction, that.featureBagFraction)
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+ && Objects.equals(predictionFieldName, that.predictionFieldName)
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+ && Objects.equals(trainingPercent, that.trainingPercent);
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+ }
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+
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+ @Override
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+ public String toString() {
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+ return Strings.toString(this);
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+ }
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+
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+ public static class Builder {
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+ private String dependentVariable;
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+ private Double lambda;
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+ private Double gamma;
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+ private Double eta;
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+ private Integer maximumNumberTrees;
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+ private Double featureBagFraction;
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+ private String predictionFieldName;
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+ private Double trainingPercent;
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+
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+ private Builder(String dependentVariable) {
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+ this.dependentVariable = Objects.requireNonNull(dependentVariable);
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+ }
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+
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+ public Builder setLambda(Double lambda) {
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+ this.lambda = lambda;
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+ return this;
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+ }
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+
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+ public Builder setGamma(Double gamma) {
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+ this.gamma = gamma;
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+ return this;
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+ }
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+
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+ public Builder setEta(Double eta) {
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+ this.eta = eta;
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+ return this;
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+ }
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+
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+ public Builder setMaximumNumberTrees(Integer maximumNumberTrees) {
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+ this.maximumNumberTrees = maximumNumberTrees;
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+ return this;
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+ }
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+
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+ public Builder setFeatureBagFraction(Double featureBagFraction) {
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+ this.featureBagFraction = featureBagFraction;
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+ return this;
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+ }
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+
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+ public Builder setPredictionFieldName(String predictionFieldName) {
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+ this.predictionFieldName = predictionFieldName;
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+ return this;
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+ }
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+
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+ public Builder setTrainingPercent(Double trainingPercent) {
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+ this.trainingPercent = trainingPercent;
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+ return this;
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+ }
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+
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+ public Regression build() {
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+ return new Regression(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName,
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+ trainingPercent);
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+ }
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+ }
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+}
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Dimitris Athanasiou