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A native Pulumi package for creating and managing Azure resources.
// *** WARNING: this file was generated by pulumi-java-gen. ***
// *** Do not edit by hand unless you're certain you know what you are doing! ***
package com.pulumi.azurenative.machinelearningservices.outputs;
import com.pulumi.azurenative.machinelearningservices.outputs.AutoNCrossValidationsResponse;
import com.pulumi.azurenative.machinelearningservices.outputs.CustomNCrossValidationsResponse;
import com.pulumi.azurenative.machinelearningservices.outputs.MLTableJobInputResponse;
import com.pulumi.azurenative.machinelearningservices.outputs.RegressionTrainingSettingsResponse;
import com.pulumi.azurenative.machinelearningservices.outputs.TableVerticalFeaturizationSettingsResponse;
import com.pulumi.azurenative.machinelearningservices.outputs.TableVerticalLimitSettingsResponse;
import com.pulumi.core.Either;
import com.pulumi.core.annotations.CustomType;
import com.pulumi.exceptions.MissingRequiredPropertyException;
import java.lang.Double;
import java.lang.String;
import java.util.List;
import java.util.Objects;
import java.util.Optional;
import javax.annotation.Nullable;
@CustomType
public final class RegressionResponse {
/**
* @return Columns to use for CVSplit data.
*
*/
private @Nullable List cvSplitColumnNames;
/**
* @return Featurization inputs needed for AutoML job.
*
*/
private @Nullable TableVerticalFeaturizationSettingsResponse featurizationSettings;
/**
* @return Execution constraints for AutoMLJob.
*
*/
private @Nullable TableVerticalLimitSettingsResponse limitSettings;
/**
* @return Log verbosity for the job.
*
*/
private @Nullable String logVerbosity;
/**
* @return Number of cross validation folds to be applied on training dataset
* when validation dataset is not provided.
*
*/
private @Nullable Either nCrossValidations;
/**
* @return Primary metric for regression task.
*
*/
private @Nullable String primaryMetric;
/**
* @return Target column name: This is prediction values column.
* Also known as label column name in context of classification tasks.
*
*/
private @Nullable String targetColumnName;
/**
* @return AutoMLJob Task type.
* Expected value is 'Regression'.
*
*/
private String taskType;
/**
* @return Test data input.
*
*/
private @Nullable MLTableJobInputResponse testData;
/**
* @return The fraction of test dataset that needs to be set aside for validation purpose.
* Values between (0.0 , 1.0)
* Applied when validation dataset is not provided.
*
*/
private @Nullable Double testDataSize;
/**
* @return [Required] Training data input.
*
*/
private MLTableJobInputResponse trainingData;
/**
* @return Inputs for training phase for an AutoML Job.
*
*/
private @Nullable RegressionTrainingSettingsResponse trainingSettings;
/**
* @return Validation data inputs.
*
*/
private @Nullable MLTableJobInputResponse validationData;
/**
* @return The fraction of training dataset that needs to be set aside for validation purpose.
* Values between (0.0 , 1.0)
* Applied when validation dataset is not provided.
*
*/
private @Nullable Double validationDataSize;
/**
* @return The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
*
*/
private @Nullable String weightColumnName;
private RegressionResponse() {}
/**
* @return Columns to use for CVSplit data.
*
*/
public List cvSplitColumnNames() {
return this.cvSplitColumnNames == null ? List.of() : this.cvSplitColumnNames;
}
/**
* @return Featurization inputs needed for AutoML job.
*
*/
public Optional featurizationSettings() {
return Optional.ofNullable(this.featurizationSettings);
}
/**
* @return Execution constraints for AutoMLJob.
*
*/
public Optional limitSettings() {
return Optional.ofNullable(this.limitSettings);
}
/**
* @return Log verbosity for the job.
*
*/
public Optional logVerbosity() {
return Optional.ofNullable(this.logVerbosity);
}
/**
* @return Number of cross validation folds to be applied on training dataset
* when validation dataset is not provided.
*
*/
public Optional> nCrossValidations() {
return Optional.ofNullable(this.nCrossValidations);
}
/**
* @return Primary metric for regression task.
*
*/
public Optional primaryMetric() {
return Optional.ofNullable(this.primaryMetric);
}
/**
* @return Target column name: This is prediction values column.
* Also known as label column name in context of classification tasks.
*
*/
public Optional targetColumnName() {
return Optional.ofNullable(this.targetColumnName);
}
/**
* @return AutoMLJob Task type.
* Expected value is 'Regression'.
*
*/
public String taskType() {
return this.taskType;
}
/**
* @return Test data input.
*
*/
public Optional testData() {
return Optional.ofNullable(this.testData);
}
/**
* @return The fraction of test dataset that needs to be set aside for validation purpose.
* Values between (0.0 , 1.0)
* Applied when validation dataset is not provided.
*
*/
public Optional testDataSize() {
return Optional.ofNullable(this.testDataSize);
}
/**
* @return [Required] Training data input.
*
*/
public MLTableJobInputResponse trainingData() {
return this.trainingData;
}
/**
* @return Inputs for training phase for an AutoML Job.
*
*/
public Optional trainingSettings() {
return Optional.ofNullable(this.trainingSettings);
}
/**
* @return Validation data inputs.
*
*/
public Optional validationData() {
return Optional.ofNullable(this.validationData);
}
/**
* @return The fraction of training dataset that needs to be set aside for validation purpose.
* Values between (0.0 , 1.0)
* Applied when validation dataset is not provided.
*
*/
public Optional validationDataSize() {
return Optional.ofNullable(this.validationDataSize);
}
/**
* @return The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
*
*/
public Optional weightColumnName() {
return Optional.ofNullable(this.weightColumnName);
}
public static Builder builder() {
return new Builder();
}
public static Builder builder(RegressionResponse defaults) {
return new Builder(defaults);
}
@CustomType.Builder
public static final class Builder {
private @Nullable List cvSplitColumnNames;
private @Nullable TableVerticalFeaturizationSettingsResponse featurizationSettings;
private @Nullable TableVerticalLimitSettingsResponse limitSettings;
private @Nullable String logVerbosity;
private @Nullable Either nCrossValidations;
private @Nullable String primaryMetric;
private @Nullable String targetColumnName;
private String taskType;
private @Nullable MLTableJobInputResponse testData;
private @Nullable Double testDataSize;
private MLTableJobInputResponse trainingData;
private @Nullable RegressionTrainingSettingsResponse trainingSettings;
private @Nullable MLTableJobInputResponse validationData;
private @Nullable Double validationDataSize;
private @Nullable String weightColumnName;
public Builder() {}
public Builder(RegressionResponse defaults) {
Objects.requireNonNull(defaults);
this.cvSplitColumnNames = defaults.cvSplitColumnNames;
this.featurizationSettings = defaults.featurizationSettings;
this.limitSettings = defaults.limitSettings;
this.logVerbosity = defaults.logVerbosity;
this.nCrossValidations = defaults.nCrossValidations;
this.primaryMetric = defaults.primaryMetric;
this.targetColumnName = defaults.targetColumnName;
this.taskType = defaults.taskType;
this.testData = defaults.testData;
this.testDataSize = defaults.testDataSize;
this.trainingData = defaults.trainingData;
this.trainingSettings = defaults.trainingSettings;
this.validationData = defaults.validationData;
this.validationDataSize = defaults.validationDataSize;
this.weightColumnName = defaults.weightColumnName;
}
@CustomType.Setter
public Builder cvSplitColumnNames(@Nullable List cvSplitColumnNames) {
this.cvSplitColumnNames = cvSplitColumnNames;
return this;
}
public Builder cvSplitColumnNames(String... cvSplitColumnNames) {
return cvSplitColumnNames(List.of(cvSplitColumnNames));
}
@CustomType.Setter
public Builder featurizationSettings(@Nullable TableVerticalFeaturizationSettingsResponse featurizationSettings) {
this.featurizationSettings = featurizationSettings;
return this;
}
@CustomType.Setter
public Builder limitSettings(@Nullable TableVerticalLimitSettingsResponse limitSettings) {
this.limitSettings = limitSettings;
return this;
}
@CustomType.Setter
public Builder logVerbosity(@Nullable String logVerbosity) {
this.logVerbosity = logVerbosity;
return this;
}
@CustomType.Setter
public Builder nCrossValidations(@Nullable Either nCrossValidations) {
this.nCrossValidations = nCrossValidations;
return this;
}
@CustomType.Setter
public Builder primaryMetric(@Nullable String primaryMetric) {
this.primaryMetric = primaryMetric;
return this;
}
@CustomType.Setter
public Builder targetColumnName(@Nullable String targetColumnName) {
this.targetColumnName = targetColumnName;
return this;
}
@CustomType.Setter
public Builder taskType(String taskType) {
if (taskType == null) {
throw new MissingRequiredPropertyException("RegressionResponse", "taskType");
}
this.taskType = taskType;
return this;
}
@CustomType.Setter
public Builder testData(@Nullable MLTableJobInputResponse testData) {
this.testData = testData;
return this;
}
@CustomType.Setter
public Builder testDataSize(@Nullable Double testDataSize) {
this.testDataSize = testDataSize;
return this;
}
@CustomType.Setter
public Builder trainingData(MLTableJobInputResponse trainingData) {
if (trainingData == null) {
throw new MissingRequiredPropertyException("RegressionResponse", "trainingData");
}
this.trainingData = trainingData;
return this;
}
@CustomType.Setter
public Builder trainingSettings(@Nullable RegressionTrainingSettingsResponse trainingSettings) {
this.trainingSettings = trainingSettings;
return this;
}
@CustomType.Setter
public Builder validationData(@Nullable MLTableJobInputResponse validationData) {
this.validationData = validationData;
return this;
}
@CustomType.Setter
public Builder validationDataSize(@Nullable Double validationDataSize) {
this.validationDataSize = validationDataSize;
return this;
}
@CustomType.Setter
public Builder weightColumnName(@Nullable String weightColumnName) {
this.weightColumnName = weightColumnName;
return this;
}
public RegressionResponse build() {
final var _resultValue = new RegressionResponse();
_resultValue.cvSplitColumnNames = cvSplitColumnNames;
_resultValue.featurizationSettings = featurizationSettings;
_resultValue.limitSettings = limitSettings;
_resultValue.logVerbosity = logVerbosity;
_resultValue.nCrossValidations = nCrossValidations;
_resultValue.primaryMetric = primaryMetric;
_resultValue.targetColumnName = targetColumnName;
_resultValue.taskType = taskType;
_resultValue.testData = testData;
_resultValue.testDataSize = testDataSize;
_resultValue.trainingData = trainingData;
_resultValue.trainingSettings = trainingSettings;
_resultValue.validationData = validationData;
_resultValue.validationDataSize = validationDataSize;
_resultValue.weightColumnName = weightColumnName;
return _resultValue;
}
}
}
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