Forecasting Class

Package:
com.azure.resourcemanager.machinelearning.models
Maven Artifact:
com.azure.resourcemanager:azure-resourcemanager-machinelearning:1.1.0

public final class Forecasting
extends AutoMLVertical

Forecasting task in AutoML Table vertical.

Constructor Summary

Constructor Description
Forecasting()

Creates an instance of Forecasting class.

Method Summary

Modifier and Type Method and Description
List<String> cvSplitColumnNames()

Get the cvSplitColumnNames property: Columns to use for CVSplit data.
TableVerticalFeaturizationSettings featurizationSettings()

Get the featurizationSettings property: Featurization inputs needed for AutoML job.
ForecastingSettings forecastingSettings()

Get the forecastingSettings property: Forecasting task specific inputs.
static Forecasting fromJson(JsonReader jsonReader)

Reads an instance of Forecasting from the JsonReader.
TableVerticalLimitSettings limitSettings()

Get the limitSettings property: Execution constraints for AutoMLJob.
NCrossValidations nCrossValidations()

Get the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
ForecastingPrimaryMetrics primaryMetric()

Get the primaryMetric property: Primary metric for forecasting task.
TaskType taskType()

Get the taskType property: [Required] Task type for AutoMLJob.
MLTableJobInput testData()

Get the testData property: Test data input.
Double testDataSize()

Get the testDataSize property: The fraction of test dataset that needs to be set aside for validation purpose.
JsonWriter toJson(JsonWriter jsonWriter)
ForecastingTrainingSettings trainingSettings()

Get the trainingSettings property: Inputs for training phase for an AutoML Job.
void validate()

Validates the instance.
MLTableJobInput validationData()

Get the validationData property: Validation data inputs.
Double validationDataSize()

Get the validationDataSize property: The fraction of training dataset that needs to be set aside for validation purpose.
String weightColumnName()

Get the weightColumnName property: The name of the sample weight column.
Forecasting withCvSplitColumnNames(List<String> cvSplitColumnNames)

Set the cvSplitColumnNames property: Columns to use for CVSplit data.
Forecasting withFeaturizationSettings(TableVerticalFeaturizationSettings featurizationSettings)

Set the featurizationSettings property: Featurization inputs needed for AutoML job.
Forecasting withForecastingSettings(ForecastingSettings forecastingSettings)

Set the forecastingSettings property: Forecasting task specific inputs.
Forecasting withLimitSettings(TableVerticalLimitSettings limitSettings)

Set the limitSettings property: Execution constraints for AutoMLJob.
Forecasting withLogVerbosity(LogVerbosity logVerbosity)

Set the logVerbosity property: Log verbosity for the job.
Forecasting withNCrossValidations(NCrossValidations nCrossValidations)

Set the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
Forecasting withPrimaryMetric(ForecastingPrimaryMetrics primaryMetric)

Set the primaryMetric property: Primary metric for forecasting task.
Forecasting withTargetColumnName(String targetColumnName)

Set the targetColumnName property: Target column name: This is prediction values column.
Forecasting withTestData(MLTableJobInput testData)

Set the testData property: Test data input.
Forecasting withTestDataSize(Double testDataSize)

Set the testDataSize property: The fraction of test dataset that needs to be set aside for validation purpose.
Forecasting withTrainingData(MLTableJobInput trainingData)

Set the trainingData property: [Required] Training data input.
Forecasting withTrainingSettings(ForecastingTrainingSettings trainingSettings)

Set the trainingSettings property: Inputs for training phase for an AutoML Job.
Forecasting withValidationData(MLTableJobInput validationData)

Set the validationData property: Validation data inputs.
Forecasting withValidationDataSize(Double validationDataSize)

Set the validationDataSize property: The fraction of training dataset that needs to be set aside for validation purpose.
Forecasting withWeightColumnName(String weightColumnName)

Set the weightColumnName property: The name of the sample weight column.

Methods inherited from AutoMLVertical

fromJson logVerbosity targetColumnName taskType toJson trainingData validate withLogVerbosity withTargetColumnName withTrainingData

Methods inherited from java.lang.Object

clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Constructor Details

Forecasting

public Forecasting()

Creates an instance of Forecasting class.

Method Details

cvSplitColumnNames

public List<String> cvSplitColumnNames()

Get the cvSplitColumnNames property: Columns to use for CVSplit data.

Returns:

the cvSplitColumnNames value.

featurizationSettings

public TableVerticalFeaturizationSettings featurizationSettings()

Get the featurizationSettings property: Featurization inputs needed for AutoML job.

Returns:

the featurizationSettings value.

forecastingSettings

public ForecastingSettings forecastingSettings()

Get the forecastingSettings property: Forecasting task specific inputs.

Returns:

the forecastingSettings value.

fromJson

public static Forecasting fromJson(JsonReader jsonReader)

Reads an instance of Forecasting from the JsonReader.

Parameters:

jsonReader - The JsonReader being read.

Returns:

An instance of Forecasting if the JsonReader was pointing to an instance of it, or null if it was pointing to JSON null.

Throws:

IOException

 - If the deserialized JSON object was missing any required properties.

limitSettings

public TableVerticalLimitSettings limitSettings()

Get the limitSettings property: Execution constraints for AutoMLJob.

Returns:

the limitSettings value.

nCrossValidations

public NCrossValidations nCrossValidations()

Get the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.

Returns:

the nCrossValidations value.

primaryMetric

public ForecastingPrimaryMetrics primaryMetric()

Get the primaryMetric property: Primary metric for forecasting task.

Returns:

the primaryMetric value.

taskType

public TaskType taskType()

Get the taskType property: [Required] Task type for AutoMLJob.

Overrides:

Forecasting.taskType()

Returns:

the taskType value.

testData

public MLTableJobInput testData()

Get the testData property: Test data input.

Returns:

the testData value.

testDataSize

public Double testDataSize()

Get the testDataSize property: 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.

Returns:

the testDataSize value.

toJson

public JsonWriter toJson(JsonWriter jsonWriter)

Overrides:

Forecasting.toJson(JsonWriter jsonWriter)

Parameters:

jsonWriter

Throws:

trainingSettings

public ForecastingTrainingSettings trainingSettings()

Get the trainingSettings property: Inputs for training phase for an AutoML Job.

Returns:

the trainingSettings value.

validate

public void validate()

Validates the instance.

Overrides:

Forecasting.validate()

validationData

public MLTableJobInput validationData()

Get the validationData property: Validation data inputs.

Returns:

the validationData value.

validationDataSize

public Double validationDataSize()

Get the validationDataSize property: 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.

Returns:

the validationDataSize value.

weightColumnName

public String weightColumnName()

Get the weightColumnName property: 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.

Returns:

the weightColumnName value.

withCvSplitColumnNames

public Forecasting withCvSplitColumnNames(List<String> cvSplitColumnNames)

Set the cvSplitColumnNames property: Columns to use for CVSplit data.

Parameters:

cvSplitColumnNames - the cvSplitColumnNames value to set.

Returns:

the Forecasting object itself.

withFeaturizationSettings

public Forecasting withFeaturizationSettings(TableVerticalFeaturizationSettings featurizationSettings)

Set the featurizationSettings property: Featurization inputs needed for AutoML job.

Parameters:

featurizationSettings - the featurizationSettings value to set.

Returns:

the Forecasting object itself.

withForecastingSettings

public Forecasting withForecastingSettings(ForecastingSettings forecastingSettings)

Set the forecastingSettings property: Forecasting task specific inputs.

Parameters:

forecastingSettings - the forecastingSettings value to set.

Returns:

the Forecasting object itself.

withLimitSettings

public Forecasting withLimitSettings(TableVerticalLimitSettings limitSettings)

Set the limitSettings property: Execution constraints for AutoMLJob.

Parameters:

limitSettings - the limitSettings value to set.

Returns:

the Forecasting object itself.

withLogVerbosity

public Forecasting withLogVerbosity(LogVerbosity logVerbosity)

Set the logVerbosity property: Log verbosity for the job.

Overrides:

Forecasting.withLogVerbosity(LogVerbosity logVerbosity)

Parameters:

logVerbosity

withNCrossValidations

public Forecasting withNCrossValidations(NCrossValidations nCrossValidations)

Set the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.

Parameters:

nCrossValidations - the nCrossValidations value to set.

Returns:

the Forecasting object itself.

withPrimaryMetric

public Forecasting withPrimaryMetric(ForecastingPrimaryMetrics primaryMetric)

Set the primaryMetric property: Primary metric for forecasting task.

Parameters:

primaryMetric - the primaryMetric value to set.

Returns:

the Forecasting object itself.

withTargetColumnName

public Forecasting withTargetColumnName(String targetColumnName)

Set the targetColumnName property: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.

Overrides:

Forecasting.withTargetColumnName(String targetColumnName)

Parameters:

targetColumnName

withTestData

public Forecasting withTestData(MLTableJobInput testData)

Set the testData property: Test data input.

Parameters:

testData - the testData value to set.

Returns:

the Forecasting object itself.

withTestDataSize

public Forecasting withTestDataSize(Double testDataSize)

Set the testDataSize property: 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.

Parameters:

testDataSize - the testDataSize value to set.

Returns:

the Forecasting object itself.

withTrainingData

public Forecasting withTrainingData(MLTableJobInput trainingData)

Set the trainingData property: [Required] Training data input.

Overrides:

Forecasting.withTrainingData(MLTableJobInput trainingData)

Parameters:

trainingData

withTrainingSettings

public Forecasting withTrainingSettings(ForecastingTrainingSettings trainingSettings)

Set the trainingSettings property: Inputs for training phase for an AutoML Job.

Parameters:

trainingSettings - the trainingSettings value to set.

Returns:

the Forecasting object itself.

withValidationData

public Forecasting withValidationData(MLTableJobInput validationData)

Set the validationData property: Validation data inputs.

Parameters:

validationData - the validationData value to set.

Returns:

the Forecasting object itself.

withValidationDataSize

public Forecasting withValidationDataSize(Double validationDataSize)

Set the validationDataSize property: 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.

Parameters:

validationDataSize - the validationDataSize value to set.

Returns:

the Forecasting object itself.

withWeightColumnName

public Forecasting withWeightColumnName(String weightColumnName)

Set the weightColumnName property: 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.

Parameters:

weightColumnName - the weightColumnName value to set.

Returns:

the Forecasting object itself.

Applies to