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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.inputs;
import com.pulumi.azurenative.machinelearningservices.enums.ClassificationPrimaryMetrics;
import com.pulumi.azurenative.machinelearningservices.enums.LogVerbosity;
import com.pulumi.azurenative.machinelearningservices.inputs.AutoNCrossValidationsArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.ClassificationTrainingSettingsArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.CustomNCrossValidationsArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.MLTableJobInputArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.TableVerticalFeaturizationSettingsArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.TableVerticalLimitSettingsArgs;
import com.pulumi.core.Either;
import com.pulumi.core.Output;
import com.pulumi.core.annotations.Import;
import com.pulumi.core.internal.Codegen;
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;
/**
* Classification task in AutoML Table vertical.
*
*/
public final class ClassificationArgs extends com.pulumi.resources.ResourceArgs {
public static final ClassificationArgs Empty = new ClassificationArgs();
/**
* Columns to use for CVSplit data.
*
*/
@Import(name="cvSplitColumnNames")
private @Nullable Output> cvSplitColumnNames;
/**
* @return Columns to use for CVSplit data.
*
*/
public Optional>> cvSplitColumnNames() {
return Optional.ofNullable(this.cvSplitColumnNames);
}
/**
* Featurization inputs needed for AutoML job.
*
*/
@Import(name="featurizationSettings")
private @Nullable Output featurizationSettings;
/**
* @return Featurization inputs needed for AutoML job.
*
*/
public Optional> featurizationSettings() {
return Optional.ofNullable(this.featurizationSettings);
}
/**
* Execution constraints for AutoMLJob.
*
*/
@Import(name="limitSettings")
private @Nullable Output limitSettings;
/**
* @return Execution constraints for AutoMLJob.
*
*/
public Optional> limitSettings() {
return Optional.ofNullable(this.limitSettings);
}
/**
* Log verbosity for the job.
*
*/
@Import(name="logVerbosity")
private @Nullable Output> logVerbosity;
/**
* @return Log verbosity for the job.
*
*/
public Optional>> logVerbosity() {
return Optional.ofNullable(this.logVerbosity);
}
/**
* Number of cross validation folds to be applied on training dataset
* when validation dataset is not provided.
*
*/
@Import(name="nCrossValidations")
private @Nullable Output> nCrossValidations;
/**
* @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);
}
/**
* Positive label for binary metrics calculation.
*
*/
@Import(name="positiveLabel")
private @Nullable Output positiveLabel;
/**
* @return Positive label for binary metrics calculation.
*
*/
public Optional> positiveLabel() {
return Optional.ofNullable(this.positiveLabel);
}
/**
* Primary metric for the task.
*
*/
@Import(name="primaryMetric")
private @Nullable Output> primaryMetric;
/**
* @return Primary metric for the task.
*
*/
public Optional>> primaryMetric() {
return Optional.ofNullable(this.primaryMetric);
}
/**
* Target column name: This is prediction values column.
* Also known as label column name in context of classification tasks.
*
*/
@Import(name="targetColumnName")
private @Nullable Output targetColumnName;
/**
* @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);
}
/**
* AutoMLJob Task type.
* Expected value is 'Classification'.
*
*/
@Import(name="taskType", required=true)
private Output taskType;
/**
* @return AutoMLJob Task type.
* Expected value is 'Classification'.
*
*/
public Output taskType() {
return this.taskType;
}
/**
* Test data input.
*
*/
@Import(name="testData")
private @Nullable Output testData;
/**
* @return Test data input.
*
*/
public Optional> testData() {
return Optional.ofNullable(this.testData);
}
/**
* 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.
*
*/
@Import(name="testDataSize")
private @Nullable Output testDataSize;
/**
* @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);
}
/**
* [Required] Training data input.
*
*/
@Import(name="trainingData", required=true)
private Output trainingData;
/**
* @return [Required] Training data input.
*
*/
public Output trainingData() {
return this.trainingData;
}
/**
* Inputs for training phase for an AutoML Job.
*
*/
@Import(name="trainingSettings")
private @Nullable Output trainingSettings;
/**
* @return Inputs for training phase for an AutoML Job.
*
*/
public Optional> trainingSettings() {
return Optional.ofNullable(this.trainingSettings);
}
/**
* Validation data inputs.
*
*/
@Import(name="validationData")
private @Nullable Output validationData;
/**
* @return Validation data inputs.
*
*/
public Optional> validationData() {
return Optional.ofNullable(this.validationData);
}
/**
* 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.
*
*/
@Import(name="validationDataSize")
private @Nullable Output validationDataSize;
/**
* @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);
}
/**
* 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.
*
*/
@Import(name="weightColumnName")
private @Nullable Output weightColumnName;
/**
* @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);
}
private ClassificationArgs() {}
private ClassificationArgs(ClassificationArgs $) {
this.cvSplitColumnNames = $.cvSplitColumnNames;
this.featurizationSettings = $.featurizationSettings;
this.limitSettings = $.limitSettings;
this.logVerbosity = $.logVerbosity;
this.nCrossValidations = $.nCrossValidations;
this.positiveLabel = $.positiveLabel;
this.primaryMetric = $.primaryMetric;
this.targetColumnName = $.targetColumnName;
this.taskType = $.taskType;
this.testData = $.testData;
this.testDataSize = $.testDataSize;
this.trainingData = $.trainingData;
this.trainingSettings = $.trainingSettings;
this.validationData = $.validationData;
this.validationDataSize = $.validationDataSize;
this.weightColumnName = $.weightColumnName;
}
public static Builder builder() {
return new Builder();
}
public static Builder builder(ClassificationArgs defaults) {
return new Builder(defaults);
}
public static final class Builder {
private ClassificationArgs $;
public Builder() {
$ = new ClassificationArgs();
}
public Builder(ClassificationArgs defaults) {
$ = new ClassificationArgs(Objects.requireNonNull(defaults));
}
/**
* @param cvSplitColumnNames Columns to use for CVSplit data.
*
* @return builder
*
*/
public Builder cvSplitColumnNames(@Nullable Output> cvSplitColumnNames) {
$.cvSplitColumnNames = cvSplitColumnNames;
return this;
}
/**
* @param cvSplitColumnNames Columns to use for CVSplit data.
*
* @return builder
*
*/
public Builder cvSplitColumnNames(List cvSplitColumnNames) {
return cvSplitColumnNames(Output.of(cvSplitColumnNames));
}
/**
* @param cvSplitColumnNames Columns to use for CVSplit data.
*
* @return builder
*
*/
public Builder cvSplitColumnNames(String... cvSplitColumnNames) {
return cvSplitColumnNames(List.of(cvSplitColumnNames));
}
/**
* @param featurizationSettings Featurization inputs needed for AutoML job.
*
* @return builder
*
*/
public Builder featurizationSettings(@Nullable Output featurizationSettings) {
$.featurizationSettings = featurizationSettings;
return this;
}
/**
* @param featurizationSettings Featurization inputs needed for AutoML job.
*
* @return builder
*
*/
public Builder featurizationSettings(TableVerticalFeaturizationSettingsArgs featurizationSettings) {
return featurizationSettings(Output.of(featurizationSettings));
}
/**
* @param limitSettings Execution constraints for AutoMLJob.
*
* @return builder
*
*/
public Builder limitSettings(@Nullable Output limitSettings) {
$.limitSettings = limitSettings;
return this;
}
/**
* @param limitSettings Execution constraints for AutoMLJob.
*
* @return builder
*
*/
public Builder limitSettings(TableVerticalLimitSettingsArgs limitSettings) {
return limitSettings(Output.of(limitSettings));
}
/**
* @param logVerbosity Log verbosity for the job.
*
* @return builder
*
*/
public Builder logVerbosity(@Nullable Output> logVerbosity) {
$.logVerbosity = logVerbosity;
return this;
}
/**
* @param logVerbosity Log verbosity for the job.
*
* @return builder
*
*/
public Builder logVerbosity(Either logVerbosity) {
return logVerbosity(Output.of(logVerbosity));
}
/**
* @param logVerbosity Log verbosity for the job.
*
* @return builder
*
*/
public Builder logVerbosity(String logVerbosity) {
return logVerbosity(Either.ofLeft(logVerbosity));
}
/**
* @param logVerbosity Log verbosity for the job.
*
* @return builder
*
*/
public Builder logVerbosity(LogVerbosity logVerbosity) {
return logVerbosity(Either.ofRight(logVerbosity));
}
/**
* @param nCrossValidations Number of cross validation folds to be applied on training dataset
* when validation dataset is not provided.
*
* @return builder
*
*/
public Builder nCrossValidations(@Nullable Output> nCrossValidations) {
$.nCrossValidations = nCrossValidations;
return this;
}
/**
* @param nCrossValidations Number of cross validation folds to be applied on training dataset
* when validation dataset is not provided.
*
* @return builder
*
*/
public Builder nCrossValidations(Either nCrossValidations) {
return nCrossValidations(Output.of(nCrossValidations));
}
/**
* @param nCrossValidations Number of cross validation folds to be applied on training dataset
* when validation dataset is not provided.
*
* @return builder
*
*/
public Builder nCrossValidations(AutoNCrossValidationsArgs nCrossValidations) {
return nCrossValidations(Either.ofLeft(nCrossValidations));
}
/**
* @param nCrossValidations Number of cross validation folds to be applied on training dataset
* when validation dataset is not provided.
*
* @return builder
*
*/
public Builder nCrossValidations(CustomNCrossValidationsArgs nCrossValidations) {
return nCrossValidations(Either.ofRight(nCrossValidations));
}
/**
* @param positiveLabel Positive label for binary metrics calculation.
*
* @return builder
*
*/
public Builder positiveLabel(@Nullable Output positiveLabel) {
$.positiveLabel = positiveLabel;
return this;
}
/**
* @param positiveLabel Positive label for binary metrics calculation.
*
* @return builder
*
*/
public Builder positiveLabel(String positiveLabel) {
return positiveLabel(Output.of(positiveLabel));
}
/**
* @param primaryMetric Primary metric for the task.
*
* @return builder
*
*/
public Builder primaryMetric(@Nullable Output> primaryMetric) {
$.primaryMetric = primaryMetric;
return this;
}
/**
* @param primaryMetric Primary metric for the task.
*
* @return builder
*
*/
public Builder primaryMetric(Either primaryMetric) {
return primaryMetric(Output.of(primaryMetric));
}
/**
* @param primaryMetric Primary metric for the task.
*
* @return builder
*
*/
public Builder primaryMetric(String primaryMetric) {
return primaryMetric(Either.ofLeft(primaryMetric));
}
/**
* @param primaryMetric Primary metric for the task.
*
* @return builder
*
*/
public Builder primaryMetric(ClassificationPrimaryMetrics primaryMetric) {
return primaryMetric(Either.ofRight(primaryMetric));
}
/**
* @param targetColumnName Target column name: This is prediction values column.
* Also known as label column name in context of classification tasks.
*
* @return builder
*
*/
public Builder targetColumnName(@Nullable Output targetColumnName) {
$.targetColumnName = targetColumnName;
return this;
}
/**
* @param targetColumnName Target column name: This is prediction values column.
* Also known as label column name in context of classification tasks.
*
* @return builder
*
*/
public Builder targetColumnName(String targetColumnName) {
return targetColumnName(Output.of(targetColumnName));
}
/**
* @param taskType AutoMLJob Task type.
* Expected value is 'Classification'.
*
* @return builder
*
*/
public Builder taskType(Output taskType) {
$.taskType = taskType;
return this;
}
/**
* @param taskType AutoMLJob Task type.
* Expected value is 'Classification'.
*
* @return builder
*
*/
public Builder taskType(String taskType) {
return taskType(Output.of(taskType));
}
/**
* @param testData Test data input.
*
* @return builder
*
*/
public Builder testData(@Nullable Output testData) {
$.testData = testData;
return this;
}
/**
* @param testData Test data input.
*
* @return builder
*
*/
public Builder testData(MLTableJobInputArgs testData) {
return testData(Output.of(testData));
}
/**
* @param testDataSize 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.
*
* @return builder
*
*/
public Builder testDataSize(@Nullable Output testDataSize) {
$.testDataSize = testDataSize;
return this;
}
/**
* @param testDataSize 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.
*
* @return builder
*
*/
public Builder testDataSize(Double testDataSize) {
return testDataSize(Output.of(testDataSize));
}
/**
* @param trainingData [Required] Training data input.
*
* @return builder
*
*/
public Builder trainingData(Output trainingData) {
$.trainingData = trainingData;
return this;
}
/**
* @param trainingData [Required] Training data input.
*
* @return builder
*
*/
public Builder trainingData(MLTableJobInputArgs trainingData) {
return trainingData(Output.of(trainingData));
}
/**
* @param trainingSettings Inputs for training phase for an AutoML Job.
*
* @return builder
*
*/
public Builder trainingSettings(@Nullable Output trainingSettings) {
$.trainingSettings = trainingSettings;
return this;
}
/**
* @param trainingSettings Inputs for training phase for an AutoML Job.
*
* @return builder
*
*/
public Builder trainingSettings(ClassificationTrainingSettingsArgs trainingSettings) {
return trainingSettings(Output.of(trainingSettings));
}
/**
* @param validationData Validation data inputs.
*
* @return builder
*
*/
public Builder validationData(@Nullable Output validationData) {
$.validationData = validationData;
return this;
}
/**
* @param validationData Validation data inputs.
*
* @return builder
*
*/
public Builder validationData(MLTableJobInputArgs validationData) {
return validationData(Output.of(validationData));
}
/**
* @param validationDataSize 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.
*
* @return builder
*
*/
public Builder validationDataSize(@Nullable Output validationDataSize) {
$.validationDataSize = validationDataSize;
return this;
}
/**
* @param validationDataSize 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.
*
* @return builder
*
*/
public Builder validationDataSize(Double validationDataSize) {
return validationDataSize(Output.of(validationDataSize));
}
/**
* @param weightColumnName 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.
*
* @return builder
*
*/
public Builder weightColumnName(@Nullable Output weightColumnName) {
$.weightColumnName = weightColumnName;
return this;
}
/**
* @param weightColumnName 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.
*
* @return builder
*
*/
public Builder weightColumnName(String weightColumnName) {
return weightColumnName(Output.of(weightColumnName));
}
public ClassificationArgs build() {
$.logVerbosity = Codegen.stringProp("logVerbosity").left(LogVerbosity.class).output().arg($.logVerbosity).def("Info").getNullable();
$.primaryMetric = Codegen.stringProp("primaryMetric").left(ClassificationPrimaryMetrics.class).output().arg($.primaryMetric).def("AUCWeighted").getNullable();
$.taskType = Codegen.stringProp("taskType").output().arg($.taskType).require();
if ($.trainingData == null) {
throw new MissingRequiredPropertyException("ClassificationArgs", "trainingData");
}
return $;
}
}
}
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