* The collection of settings used by an AutoML job V2 for the TABULAR problem type.
*
* The configuration information of how model candidates are generated. *
*/ private CandidateGenerationConfig candidateGenerationConfig; private AutoMLJobCompletionCriteria completionCriteria; /** *
* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot
* job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
* You can also specify the data type of the feature (optional) in the format shown below: *
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
* These column keys may not include the target column. *
*
* In ensembling mode, Autopilot only supports the following data types: numeric,
* categorical, text, and datetime. In HPO mode, Autopilot can support
* numeric, categorical, text, datetime, and
* sequence.
*
* If only FeatureDataTypes is provided, the column keys (col1, col2,..)
* should be a subset of the column names in the input data.
*
* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys
* should be a subset of the column names provided in FeatureAttributeNames.
*
* The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...]
* are case sensitive and should be a list of strings containing unique values that are a subset of the column names
* in the input data. The list of columns provided must not include the target column.
*
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
* Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. *
*/ private Boolean generateCandidateDefinitionsOnly; /** ** The type of supervised learning problem available for the model candidates of the AutoML job V2. For more * information, see * Amazon SageMaker Autopilot problem types. *
*
* You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
*
* The name of the target variable in supervised learning, usually represented by 'y'. *
*/ private String targetAttributeName; /** ** If specified, this column name indicates which column of the dataset should be treated as sample weights for use * by the objective metric during the training, evaluation, and the selection of the best model. This column is not * considered as a predictive feature. For more information on Autopilot metrics, see Metrics and * validation. *
** Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than * others. Data points that have invalid or no weight value are excluded. *
** Support for sample weights is available in Ensembling * mode only. *
*/ private String sampleWeightAttributeName; /** ** The configuration information of how model candidates are generated. *
* * @param candidateGenerationConfig * The configuration information of how model candidates are generated. */ public void setCandidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig) { this.candidateGenerationConfig = candidateGenerationConfig; } /** ** The configuration information of how model candidates are generated. *
* * @return The configuration information of how model candidates are generated. */ public CandidateGenerationConfig getCandidateGenerationConfig() { return this.candidateGenerationConfig; } /** ** The configuration information of how model candidates are generated. *
* * @param candidateGenerationConfig * The configuration information of how model candidates are generated. * @return Returns a reference to this object so that method calls can be chained together. */ public TabularJobConfig withCandidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig) { setCandidateGenerationConfig(candidateGenerationConfig); return this; } /** * @param completionCriteria */ public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) { this.completionCriteria = completionCriteria; } /** * @return */ public AutoMLJobCompletionCriteria getCompletionCriteria() { return this.completionCriteria; } /** * @param completionCriteria * @return Returns a reference to this object so that method calls can be chained together. */ public TabularJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) { setCompletionCriteria(completionCriteria); return this; } /** *
* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot
* job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
* You can also specify the data type of the feature (optional) in the format shown below: *
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
* These column keys may not include the target column. *
*
* In ensembling mode, Autopilot only supports the following data types: numeric,
* categorical, text, and datetime. In HPO mode, Autopilot can support
* numeric, categorical, text, datetime, and
* sequence.
*
* If only FeatureDataTypes is provided, the column keys (col1, col2,..)
* should be a subset of the column names in the input data.
*
* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys
* should be a subset of the column names provided in FeatureAttributeNames.
*
* The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...]
* are case sensitive and should be a list of strings containing unique values that are a subset of the column names
* in the input data. The list of columns provided must not include the target column.
*
FeatureAttributeNames (optional) in JSON format as shown
* below:
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
* You can also specify the data type of the feature (optional) in the format shown below: *
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
* These column keys may not include the target column. *
*
* In ensembling mode, Autopilot only supports the following data types: numeric,
* categorical, text, and datetime. In HPO mode, Autopilot can support
* numeric, categorical, text, datetime, and
* sequence.
*
* If only FeatureDataTypes is provided, the column keys (col1, col2
* ,..) should be a subset of the column names in the input data.
*
* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column
* keys should be a subset of the column names provided in FeatureAttributeNames.
*
* The key name FeatureAttributeNames is fixed. The values listed in
* ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique
* values that are a subset of the column names in the input data. The list of columns provided must not
* include the target column.
*/
public void setFeatureSpecificationS3Uri(String featureSpecificationS3Uri) {
this.featureSpecificationS3Uri = featureSpecificationS3Uri;
}
/**
*
* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot
* job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
* You can also specify the data type of the feature (optional) in the format shown below: *
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
* These column keys may not include the target column. *
*
* In ensembling mode, Autopilot only supports the following data types: numeric,
* categorical, text, and datetime. In HPO mode, Autopilot can support
* numeric, categorical, text, datetime, and
* sequence.
*
* If only FeatureDataTypes is provided, the column keys (col1, col2,..)
* should be a subset of the column names in the input data.
*
* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys
* should be a subset of the column names provided in FeatureAttributeNames.
*
* The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...]
* are case sensitive and should be a list of strings containing unique values that are a subset of the column names
* in the input data. The list of columns provided must not include the target column.
*
FeatureAttributeNames (optional) in JSON format as shown
* below:
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
* You can also specify the data type of the feature (optional) in the format shown below: *
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
* These column keys may not include the target column. *
*
* In ensembling mode, Autopilot only supports the following data types: numeric,
* categorical, text, and datetime. In HPO mode, Autopilot can
* support numeric, categorical, text, datetime, and
* sequence.
*
* If only FeatureDataTypes is provided, the column keys (col1, col2
* ,..) should be a subset of the column names in the input data.
*
* If both FeatureDataTypes and FeatureAttributeNames are provided, then the
* column keys should be a subset of the column names provided in FeatureAttributeNames.
*
* The key name FeatureAttributeNames is fixed. The values listed in
* ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique
* values that are a subset of the column names in the input data. The list of columns provided must not
* include the target column.
*/
public String getFeatureSpecificationS3Uri() {
return this.featureSpecificationS3Uri;
}
/**
*
* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot
* job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
* You can also specify the data type of the feature (optional) in the format shown below: *
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
* These column keys may not include the target column. *
*
* In ensembling mode, Autopilot only supports the following data types: numeric,
* categorical, text, and datetime. In HPO mode, Autopilot can support
* numeric, categorical, text, datetime, and
* sequence.
*
* If only FeatureDataTypes is provided, the column keys (col1, col2,..)
* should be a subset of the column names in the input data.
*
* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys
* should be a subset of the column names provided in FeatureAttributeNames.
*
* The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...]
* are case sensitive and should be a list of strings containing unique values that are a subset of the column names
* in the input data. The list of columns provided must not include the target column.
*
FeatureAttributeNames (optional) in JSON format as shown
* below:
*
* { "FeatureAttributeNames":["col1", "col2", ...] }.
*
* You can also specify the data type of the feature (optional) in the format shown below: *
*
* { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
*
* These column keys may not include the target column. *
*
* In ensembling mode, Autopilot only supports the following data types: numeric,
* categorical, text, and datetime. In HPO mode, Autopilot can support
* numeric, categorical, text, datetime, and
* sequence.
*
* If only FeatureDataTypes is provided, the column keys (col1, col2
* ,..) should be a subset of the column names in the input data.
*
* If both FeatureDataTypes and FeatureAttributeNames are provided, then the column
* keys should be a subset of the column names provided in FeatureAttributeNames.
*
* The key name FeatureAttributeNames is fixed. The values listed in
* ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique
* values that are a subset of the column names in the input data. The list of columns provided must not
* include the target column.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TabularJobConfig withFeatureSpecificationS3Uri(String featureSpecificationS3Uri) {
setFeatureSpecificationS3Uri(featureSpecificationS3Uri);
return this;
}
/**
*
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
AUTO. In AUTO
* mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and
* HYPERPARAMETER_TUNING for larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING
* mode.
* @see AutoMLMode
*/
public void setMode(String mode) {
this.mode = mode;
}
/**
*
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
AUTO. In AUTO
* mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and
* HYPERPARAMETER_TUNING for larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING
* mode.
* @see AutoMLMode
*/
public String getMode() {
return this.mode;
}
/**
*
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
AUTO. In AUTO
* mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and
* HYPERPARAMETER_TUNING for larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING
* mode.
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLMode
*/
public TabularJobConfig withMode(String mode) {
setMode(mode);
return this;
}
/**
*
* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot
* choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot
* chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for
* larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks
* directly from your dataset. This machine learning mode combines several base models to produce an optimal
* predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A
* multi-stack ensemble model can provide better performance over a single model by combining the predictive
* capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a
* model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best
* hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
*
AUTO. In AUTO
* mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and
* HYPERPARAMETER_TUNING for larger ones.
*
* The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and
* regression tasks directly from your dataset. This machine learning mode combines several base models to
* produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from
* contributing members. A multi-stack ensemble model can provide better performance over a single model by
* combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
*
* The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version
* of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO
* finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING
* mode.
* @return Returns a reference to this object so that method calls can be chained together.
* @see AutoMLMode
*/
public TabularJobConfig withMode(AutoMLMode mode) {
this.mode = mode.toString();
return this;
}
/**
*
* Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. *
* * @param generateCandidateDefinitionsOnly * Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. */ public void setGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) { this.generateCandidateDefinitionsOnly = generateCandidateDefinitionsOnly; } /** ** Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. *
* * @return Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. */ public Boolean getGenerateCandidateDefinitionsOnly() { return this.generateCandidateDefinitionsOnly; } /** ** Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. *
* * @param generateCandidateDefinitionsOnly * Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. * @return Returns a reference to this object so that method calls can be chained together. */ public TabularJobConfig withGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) { setGenerateCandidateDefinitionsOnly(generateCandidateDefinitionsOnly); return this; } /** ** Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. *
* * @return Generates possible candidates without training the models. A model candidate is a combination of data * preprocessors, algorithms, and algorithm parameter settings. */ public Boolean isGenerateCandidateDefinitionsOnly() { return this.generateCandidateDefinitionsOnly; } /** ** The type of supervised learning problem available for the model candidates of the AutoML job V2. For more * information, see * Amazon SageMaker Autopilot problem types. *
*
* You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
*
* You must either specify the type of supervised learning problem in ProblemType and provide
* the AutoMLJobObjective metric, or none at all.
*
* The type of supervised learning problem available for the model candidates of the AutoML job V2. For more * information, see * Amazon SageMaker Autopilot problem types. *
*
* You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
*
* You must either specify the type of supervised learning problem in ProblemType and provide
* the AutoMLJobObjective metric, or none at all.
*
* The type of supervised learning problem available for the model candidates of the AutoML job V2. For more * information, see * Amazon SageMaker Autopilot problem types. *
*
* You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
*
* You must either specify the type of supervised learning problem in ProblemType and provide
* the AutoMLJobObjective metric, or none at all.
*
* The type of supervised learning problem available for the model candidates of the AutoML job V2. For more * information, see * Amazon SageMaker Autopilot problem types. *
*
* You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
*
* You must either specify the type of supervised learning problem in ProblemType and provide
* the AutoMLJobObjective metric, or none at all.
*
* The name of the target variable in supervised learning, usually represented by 'y'. *
* * @param targetAttributeName * The name of the target variable in supervised learning, usually represented by 'y'. */ public void setTargetAttributeName(String targetAttributeName) { this.targetAttributeName = targetAttributeName; } /** ** The name of the target variable in supervised learning, usually represented by 'y'. *
* * @return The name of the target variable in supervised learning, usually represented by 'y'. */ public String getTargetAttributeName() { return this.targetAttributeName; } /** ** The name of the target variable in supervised learning, usually represented by 'y'. *
* * @param targetAttributeName * The name of the target variable in supervised learning, usually represented by 'y'. * @return Returns a reference to this object so that method calls can be chained together. */ public TabularJobConfig withTargetAttributeName(String targetAttributeName) { setTargetAttributeName(targetAttributeName); return this; } /** ** If specified, this column name indicates which column of the dataset should be treated as sample weights for use * by the objective metric during the training, evaluation, and the selection of the best model. This column is not * considered as a predictive feature. For more information on Autopilot metrics, see Metrics and * validation. *
** Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than * others. Data points that have invalid or no weight value are excluded. *
** Support for sample weights is available in Ensembling * mode only. *
* * @param sampleWeightAttributeName * If specified, this column name indicates which column of the dataset should be treated as sample weights * for use by the objective metric during the training, evaluation, and the selection of the best model. This * column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and * validation. ** Sample weights should be numeric, non-negative, with larger values indicating which rows are more * important than others. Data points that have invalid or no weight value are excluded. *
** Support for sample weights is available in Ensembling mode only. */ public void setSampleWeightAttributeName(String sampleWeightAttributeName) { this.sampleWeightAttributeName = sampleWeightAttributeName; } /** *
* If specified, this column name indicates which column of the dataset should be treated as sample weights for use * by the objective metric during the training, evaluation, and the selection of the best model. This column is not * considered as a predictive feature. For more information on Autopilot metrics, see Metrics and * validation. *
** Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than * others. Data points that have invalid or no weight value are excluded. *
** Support for sample weights is available in Ensembling * mode only. *
* * @return If specified, this column name indicates which column of the dataset should be treated as sample weights * for use by the objective metric during the training, evaluation, and the selection of the best model. * This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and * validation. ** Sample weights should be numeric, non-negative, with larger values indicating which rows are more * important than others. Data points that have invalid or no weight value are excluded. *
** Support for sample weights is available in Ensembling mode only. */ public String getSampleWeightAttributeName() { return this.sampleWeightAttributeName; } /** *
* If specified, this column name indicates which column of the dataset should be treated as sample weights for use * by the objective metric during the training, evaluation, and the selection of the best model. This column is not * considered as a predictive feature. For more information on Autopilot metrics, see Metrics and * validation. *
** Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than * others. Data points that have invalid or no weight value are excluded. *
** Support for sample weights is available in Ensembling * mode only. *
* * @param sampleWeightAttributeName * If specified, this column name indicates which column of the dataset should be treated as sample weights * for use by the objective metric during the training, evaluation, and the selection of the best model. This * column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and * validation. ** Sample weights should be numeric, non-negative, with larger values indicating which rows are more * important than others. Data points that have invalid or no weight value are excluded. *
** Support for sample weights is available in Ensembling mode only. * @return Returns a reference to this object so that method calls can be chained together. */ public TabularJobConfig withSampleWeightAttributeName(String sampleWeightAttributeName) { setSampleWeightAttributeName(sampleWeightAttributeName); return this; } /** * Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be * redacted from this string using a placeholder value. * * @return A string representation of this object. * * @see java.lang.Object#toString() */ @Override public String toString() { StringBuilder sb = new StringBuilder(); sb.append("{"); if (getCandidateGenerationConfig() != null) sb.append("CandidateGenerationConfig: ").append(getCandidateGenerationConfig()).append(","); if (getCompletionCriteria() != null) sb.append("CompletionCriteria: ").append(getCompletionCriteria()).append(","); if (getFeatureSpecificationS3Uri() != null) sb.append("FeatureSpecificationS3Uri: ").append(getFeatureSpecificationS3Uri()).append(","); if (getMode() != null) sb.append("Mode: ").append(getMode()).append(","); if (getGenerateCandidateDefinitionsOnly() != null) sb.append("GenerateCandidateDefinitionsOnly: ").append(getGenerateCandidateDefinitionsOnly()).append(","); if (getProblemType() != null) sb.append("ProblemType: ").append(getProblemType()).append(","); if (getTargetAttributeName() != null) sb.append("TargetAttributeName: ").append(getTargetAttributeName()).append(","); if (getSampleWeightAttributeName() != null) sb.append("SampleWeightAttributeName: ").append(getSampleWeightAttributeName()); sb.append("}"); return sb.toString(); } @Override public boolean equals(Object obj) { if (this == obj) return true; if (obj == null) return false; if (obj instanceof TabularJobConfig == false) return false; TabularJobConfig other = (TabularJobConfig) obj; if (other.getCandidateGenerationConfig() == null ^ this.getCandidateGenerationConfig() == null) return false; if (other.getCandidateGenerationConfig() != null && other.getCandidateGenerationConfig().equals(this.getCandidateGenerationConfig()) == false) return false; if (other.getCompletionCriteria() == null ^ this.getCompletionCriteria() == null) return false; if (other.getCompletionCriteria() != null && other.getCompletionCriteria().equals(this.getCompletionCriteria()) == false) return false; if (other.getFeatureSpecificationS3Uri() == null ^ this.getFeatureSpecificationS3Uri() == null) return false; if (other.getFeatureSpecificationS3Uri() != null && other.getFeatureSpecificationS3Uri().equals(this.getFeatureSpecificationS3Uri()) == false) return false; if (other.getMode() == null ^ this.getMode() == null) return false; if (other.getMode() != null && other.getMode().equals(this.getMode()) == false) return false; if (other.getGenerateCandidateDefinitionsOnly() == null ^ this.getGenerateCandidateDefinitionsOnly() == null) return false; if (other.getGenerateCandidateDefinitionsOnly() != null && other.getGenerateCandidateDefinitionsOnly().equals(this.getGenerateCandidateDefinitionsOnly()) == false) return false; if (other.getProblemType() == null ^ this.getProblemType() == null) return false; if (other.getProblemType() != null && other.getProblemType().equals(this.getProblemType()) == false) return false; if (other.getTargetAttributeName() == null ^ this.getTargetAttributeName() == null) return false; if (other.getTargetAttributeName() != null && other.getTargetAttributeName().equals(this.getTargetAttributeName()) == false) return false; if (other.getSampleWeightAttributeName() == null ^ this.getSampleWeightAttributeName() == null) return false; if (other.getSampleWeightAttributeName() != null && other.getSampleWeightAttributeName().equals(this.getSampleWeightAttributeName()) == false) return false; return true; } @Override public int hashCode() { final int prime = 31; int hashCode = 1; hashCode = prime * hashCode + ((getCandidateGenerationConfig() == null) ? 0 : getCandidateGenerationConfig().hashCode()); hashCode = prime * hashCode + ((getCompletionCriteria() == null) ? 0 : getCompletionCriteria().hashCode()); hashCode = prime * hashCode + ((getFeatureSpecificationS3Uri() == null) ? 0 : getFeatureSpecificationS3Uri().hashCode()); hashCode = prime * hashCode + ((getMode() == null) ? 0 : getMode().hashCode()); hashCode = prime * hashCode + ((getGenerateCandidateDefinitionsOnly() == null) ? 0 : getGenerateCandidateDefinitionsOnly().hashCode()); hashCode = prime * hashCode + ((getProblemType() == null) ? 0 : getProblemType().hashCode()); hashCode = prime * hashCode + ((getTargetAttributeName() == null) ? 0 : getTargetAttributeName().hashCode()); hashCode = prime * hashCode + ((getSampleWeightAttributeName() == null) ? 0 : getSampleWeightAttributeName().hashCode()); return hashCode; } @Override public TabularJobConfig clone() { try { return (TabularJobConfig) super.clone(); } catch (CloneNotSupportedException e) { throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e); } } @com.amazonaws.annotation.SdkInternalApi @Override public void marshall(ProtocolMarshaller protocolMarshaller) { com.amazonaws.services.sagemaker.model.transform.TabularJobConfigMarshaller.getInstance().marshall(this, protocolMarshaller); } }