Package com.google.cloud.automl.v1beta1

Class TablesModelMetadata.Builder

    • Method Detail

      • getDescriptor

        public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • clear

        public TablesModelMetadata.Builder clear()
        Specified by:
        clear in interface com.google.protobuf.Message.Builder
        Specified by:
        clear in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        clear in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • getDescriptorForType

        public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
        Specified by:
        getDescriptorForType in interface com.google.protobuf.Message.Builder
        Specified by:
        getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
        Overrides:
        getDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • getDefaultInstanceForType

        public TablesModelMetadata getDefaultInstanceForType()
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

      • build

        public TablesModelMetadata build()
        Specified by:
        build in interface com.google.protobuf.Message.Builder
        Specified by:
        build in interface com.google.protobuf.MessageLite.Builder

      • buildPartial

        public TablesModelMetadata buildPartial()
        Specified by:
        buildPartial in interface com.google.protobuf.Message.Builder
        Specified by:
        buildPartial in interface com.google.protobuf.MessageLite.Builder

      • clone

        public TablesModelMetadata.Builder clone()
        Specified by:
        clone in interface com.google.protobuf.Message.Builder
        Specified by:
        clone in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        clone in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • setField

        public TablesModelMetadata.Builder setField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                            Object value)
        Specified by:
        setField in interface com.google.protobuf.Message.Builder
        Overrides:
        setField in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • clearField

        public TablesModelMetadata.Builder clearField​(com.google.protobuf.Descriptors.FieldDescriptor field)
        Specified by:
        clearField in interface com.google.protobuf.Message.Builder
        Overrides:
        clearField in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • clearOneof

        public TablesModelMetadata.Builder clearOneof​(com.google.protobuf.Descriptors.OneofDescriptor oneof)
        Specified by:
        clearOneof in interface com.google.protobuf.Message.Builder
        Overrides:
        clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • setRepeatedField

        public TablesModelMetadata.Builder setRepeatedField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                    int index,
                                                    Object value)
        Specified by:
        setRepeatedField in interface com.google.protobuf.Message.Builder
        Overrides:
        setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • addRepeatedField

        public TablesModelMetadata.Builder addRepeatedField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                    Object value)
        Specified by:
        addRepeatedField in interface com.google.protobuf.Message.Builder
        Overrides:
        addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • mergeFrom

        public TablesModelMetadata.Builder mergeFrom​(com.google.protobuf.Message other)
        Specified by:
        mergeFrom in interface com.google.protobuf.Message.Builder
        Overrides:
        mergeFrom in class com.google.protobuf.AbstractMessage.Builder<TablesModelMetadata.Builder>

      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • mergeFrom

        public TablesModelMetadata.Builder mergeFrom​(com.google.protobuf.CodedInputStream input,
                                             com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                      throws IOException
        Specified by:
        mergeFrom in interface com.google.protobuf.Message.Builder
        Specified by:
        mergeFrom in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        mergeFrom in class com.google.protobuf.AbstractMessage.Builder<TablesModelMetadata.Builder>
        Throws:
        IOException

      • hasOptimizationObjectiveRecallValue

        public boolean hasOptimizationObjectiveRecallValue()
         Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
 Must be between 0 and 1, inclusive.
        float optimization_objective_recall_value = 17;
        Specified by:
        hasOptimizationObjectiveRecallValue in interface TablesModelMetadataOrBuilder
        Returns:
        Whether the optimizationObjectiveRecallValue field is set.

      • getOptimizationObjectiveRecallValue

        public float getOptimizationObjectiveRecallValue()
         Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
 Must be between 0 and 1, inclusive.
        float optimization_objective_recall_value = 17;
        Specified by:
        getOptimizationObjectiveRecallValue in interface TablesModelMetadataOrBuilder
        Returns:
        The optimizationObjectiveRecallValue.

      • setOptimizationObjectiveRecallValue

        public TablesModelMetadata.Builder setOptimizationObjectiveRecallValue​(float value)
         Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
 Must be between 0 and 1, inclusive.
        float optimization_objective_recall_value = 17;
        Parameters:
        value - The optimizationObjectiveRecallValue to set.
        Returns:
        This builder for chaining.

      • clearOptimizationObjectiveRecallValue

        public TablesModelMetadata.Builder clearOptimizationObjectiveRecallValue()
         Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
 Must be between 0 and 1, inclusive.
        float optimization_objective_recall_value = 17;
        Returns:
        This builder for chaining.

      • hasOptimizationObjectivePrecisionValue

        public boolean hasOptimizationObjectivePrecisionValue()
         Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
 Must be between 0 and 1, inclusive.
        float optimization_objective_precision_value = 18;
        Specified by:
        hasOptimizationObjectivePrecisionValue in interface TablesModelMetadataOrBuilder
        Returns:
        Whether the optimizationObjectivePrecisionValue field is set.

      • getOptimizationObjectivePrecisionValue

        public float getOptimizationObjectivePrecisionValue()
         Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
 Must be between 0 and 1, inclusive.
        float optimization_objective_precision_value = 18;
        Specified by:
        getOptimizationObjectivePrecisionValue in interface TablesModelMetadataOrBuilder
        Returns:
        The optimizationObjectivePrecisionValue.

      • setOptimizationObjectivePrecisionValue

        public TablesModelMetadata.Builder setOptimizationObjectivePrecisionValue​(float value)
         Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
 Must be between 0 and 1, inclusive.
        float optimization_objective_precision_value = 18;
        Parameters:
        value - The optimizationObjectivePrecisionValue to set.
        Returns:
        This builder for chaining.

      • clearOptimizationObjectivePrecisionValue

        public TablesModelMetadata.Builder clearOptimizationObjectivePrecisionValue()
         Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
 Must be between 0 and 1, inclusive.
        float optimization_objective_precision_value = 18;
        Returns:
        This builder for chaining.

      • hasTargetColumnSpec

        public boolean hasTargetColumnSpec()
         Column spec of the dataset's primary table's column the model is
 predicting. Snapshotted when model creation started.
 Only 3 fields are used:
 name - May be set on CreateModel, if it's not then the ColumnSpec
        corresponding to the current target_column_spec_id of the dataset
        the model is trained from is used.
        If neither is set, CreateModel will error.
 display_name - Output only.
 data_type - Output only.
        .google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;
        Specified by:
        hasTargetColumnSpec in interface TablesModelMetadataOrBuilder
        Returns:
        Whether the targetColumnSpec field is set.

      • getTargetColumnSpec

        public ColumnSpec getTargetColumnSpec()
         Column spec of the dataset's primary table's column the model is
 predicting. Snapshotted when model creation started.
 Only 3 fields are used:
 name - May be set on CreateModel, if it's not then the ColumnSpec
        corresponding to the current target_column_spec_id of the dataset
        the model is trained from is used.
        If neither is set, CreateModel will error.
 display_name - Output only.
 data_type - Output only.
        .google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;
        Specified by:
        getTargetColumnSpec in interface TablesModelMetadataOrBuilder
        Returns:
        The targetColumnSpec.

      • setTargetColumnSpec

        public TablesModelMetadata.Builder setTargetColumnSpec​(ColumnSpec value)
         Column spec of the dataset's primary table's column the model is
 predicting. Snapshotted when model creation started.
 Only 3 fields are used:
 name - May be set on CreateModel, if it's not then the ColumnSpec
        corresponding to the current target_column_spec_id of the dataset
        the model is trained from is used.
        If neither is set, CreateModel will error.
 display_name - Output only.
 data_type - Output only.
        .google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

      • setTargetColumnSpec

        public TablesModelMetadata.Builder setTargetColumnSpec​(ColumnSpec.Builder builderForValue)
         Column spec of the dataset's primary table's column the model is
 predicting. Snapshotted when model creation started.
 Only 3 fields are used:
 name - May be set on CreateModel, if it's not then the ColumnSpec
        corresponding to the current target_column_spec_id of the dataset
        the model is trained from is used.
        If neither is set, CreateModel will error.
 display_name - Output only.
 data_type - Output only.
        .google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

      • mergeTargetColumnSpec

        public TablesModelMetadata.Builder mergeTargetColumnSpec​(ColumnSpec value)
         Column spec of the dataset's primary table's column the model is
 predicting. Snapshotted when model creation started.
 Only 3 fields are used:
 name - May be set on CreateModel, if it's not then the ColumnSpec
        corresponding to the current target_column_spec_id of the dataset
        the model is trained from is used.
        If neither is set, CreateModel will error.
 display_name - Output only.
 data_type - Output only.
        .google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

      • clearTargetColumnSpec

        public TablesModelMetadata.Builder clearTargetColumnSpec()
         Column spec of the dataset's primary table's column the model is
 predicting. Snapshotted when model creation started.
 Only 3 fields are used:
 name - May be set on CreateModel, if it's not then the ColumnSpec
        corresponding to the current target_column_spec_id of the dataset
        the model is trained from is used.
        If neither is set, CreateModel will error.
 display_name - Output only.
 data_type - Output only.
        .google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

      • getTargetColumnSpecBuilder

        public ColumnSpec.Builder getTargetColumnSpecBuilder()
         Column spec of the dataset's primary table's column the model is
 predicting. Snapshotted when model creation started.
 Only 3 fields are used:
 name - May be set on CreateModel, if it's not then the ColumnSpec
        corresponding to the current target_column_spec_id of the dataset
        the model is trained from is used.
        If neither is set, CreateModel will error.
 display_name - Output only.
 data_type - Output only.
        .google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;

      • getTargetColumnSpecOrBuilder

        public ColumnSpecOrBuilder getTargetColumnSpecOrBuilder()
         Column spec of the dataset's primary table's column the model is
 predicting. Snapshotted when model creation started.
 Only 3 fields are used:
 name - May be set on CreateModel, if it's not then the ColumnSpec
        corresponding to the current target_column_spec_id of the dataset
        the model is trained from is used.
        If neither is set, CreateModel will error.
 display_name - Output only.
 data_type - Output only.
        .google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;
        Specified by:
        getTargetColumnSpecOrBuilder in interface TablesModelMetadataOrBuilder

      • getInputFeatureColumnSpecsList

        public List<ColumnSpec> getInputFeatureColumnSpecsList()
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
        Specified by:
        getInputFeatureColumnSpecsList in interface TablesModelMetadataOrBuilder

      • getInputFeatureColumnSpecsCount

        public int getInputFeatureColumnSpecsCount()
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
        Specified by:
        getInputFeatureColumnSpecsCount in interface TablesModelMetadataOrBuilder

      • getInputFeatureColumnSpecs

        public ColumnSpec getInputFeatureColumnSpecs​(int index)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
        Specified by:
        getInputFeatureColumnSpecs in interface TablesModelMetadataOrBuilder

      • setInputFeatureColumnSpecs

        public TablesModelMetadata.Builder setInputFeatureColumnSpecs​(int index,
                                                              ColumnSpec value)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • setInputFeatureColumnSpecs

        public TablesModelMetadata.Builder setInputFeatureColumnSpecs​(int index,
                                                              ColumnSpec.Builder builderForValue)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • addInputFeatureColumnSpecs

        public TablesModelMetadata.Builder addInputFeatureColumnSpecs​(ColumnSpec value)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • addInputFeatureColumnSpecs

        public TablesModelMetadata.Builder addInputFeatureColumnSpecs​(int index,
                                                              ColumnSpec value)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • addInputFeatureColumnSpecs

        public TablesModelMetadata.Builder addInputFeatureColumnSpecs​(ColumnSpec.Builder builderForValue)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • addInputFeatureColumnSpecs

        public TablesModelMetadata.Builder addInputFeatureColumnSpecs​(int index,
                                                              ColumnSpec.Builder builderForValue)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • addAllInputFeatureColumnSpecs

        public TablesModelMetadata.Builder addAllInputFeatureColumnSpecs​(Iterable<? extends ColumnSpec> values)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • clearInputFeatureColumnSpecs

        public TablesModelMetadata.Builder clearInputFeatureColumnSpecs()
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • removeInputFeatureColumnSpecs

        public TablesModelMetadata.Builder removeInputFeatureColumnSpecs​(int index)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • getInputFeatureColumnSpecsBuilder

        public ColumnSpec.Builder getInputFeatureColumnSpecsBuilder​(int index)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • getInputFeatureColumnSpecsOrBuilder

        public ColumnSpecOrBuilder getInputFeatureColumnSpecsOrBuilder​(int index)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
        Specified by:
        getInputFeatureColumnSpecsOrBuilder in interface TablesModelMetadataOrBuilder

      • getInputFeatureColumnSpecsOrBuilderList

        public List<? extends ColumnSpecOrBuilder> getInputFeatureColumnSpecsOrBuilderList()
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
        Specified by:
        getInputFeatureColumnSpecsOrBuilderList in interface TablesModelMetadataOrBuilder

      • addInputFeatureColumnSpecsBuilder

        public ColumnSpec.Builder addInputFeatureColumnSpecsBuilder()
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • addInputFeatureColumnSpecsBuilder

        public ColumnSpec.Builder addInputFeatureColumnSpecsBuilder​(int index)
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • getInputFeatureColumnSpecsBuilderList

        public List<ColumnSpec.Builder> getInputFeatureColumnSpecsBuilderList()
         Column specs of the dataset's primary table's columns, on which
 the model is trained and which are used as the input for predictions.
 The

 [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
 as well as, according to dataset's state upon model creation,

 [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
 and

 [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
 must never be included here.

 Only 3 fields are used:

 * name - May be set on CreateModel, if set only the columns specified are
   used, otherwise all primary table's columns (except the ones listed
   above) are used for the training and prediction input.

 * display_name - Output only.

 * data_type - Output only.
        repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;

      • getOptimizationObjective

        public String getOptimizationObjective()
         Objective function the model is optimizing towards. The training process
 creates a model that maximizes/minimizes the value of the objective
 function over the validation set.

 The supported optimization objectives depend on the prediction type.
 If the field is not set, a default objective function is used.

 CLASSIFICATION_BINARY:
   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
                                 operating characteristic (ROC) curve.
   "MINIMIZE_LOG_LOSS" - Minimize log loss.
   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
                                   recall value.
   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
                                    precision value.

 CLASSIFICATION_MULTI_CLASS :
   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.


 REGRESSION:
   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
        string optimization_objective = 4;
        Specified by:
        getOptimizationObjective in interface TablesModelMetadataOrBuilder
        Returns:
        The optimizationObjective.

      • getOptimizationObjectiveBytes

        public com.google.protobuf.ByteString getOptimizationObjectiveBytes()
         Objective function the model is optimizing towards. The training process
 creates a model that maximizes/minimizes the value of the objective
 function over the validation set.

 The supported optimization objectives depend on the prediction type.
 If the field is not set, a default objective function is used.

 CLASSIFICATION_BINARY:
   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
                                 operating characteristic (ROC) curve.
   "MINIMIZE_LOG_LOSS" - Minimize log loss.
   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
                                   recall value.
   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
                                    precision value.

 CLASSIFICATION_MULTI_CLASS :
   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.


 REGRESSION:
   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
        string optimization_objective = 4;
        Specified by:
        getOptimizationObjectiveBytes in interface TablesModelMetadataOrBuilder
        Returns:
        The bytes for optimizationObjective.

      • setOptimizationObjective

        public TablesModelMetadata.Builder setOptimizationObjective​(String value)
         Objective function the model is optimizing towards. The training process
 creates a model that maximizes/minimizes the value of the objective
 function over the validation set.

 The supported optimization objectives depend on the prediction type.
 If the field is not set, a default objective function is used.

 CLASSIFICATION_BINARY:
   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
                                 operating characteristic (ROC) curve.
   "MINIMIZE_LOG_LOSS" - Minimize log loss.
   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
                                   recall value.
   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
                                    precision value.

 CLASSIFICATION_MULTI_CLASS :
   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.


 REGRESSION:
   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
        string optimization_objective = 4;
        Parameters:
        value - The optimizationObjective to set.
        Returns:
        This builder for chaining.

      • clearOptimizationObjective

        public TablesModelMetadata.Builder clearOptimizationObjective()
         Objective function the model is optimizing towards. The training process
 creates a model that maximizes/minimizes the value of the objective
 function over the validation set.

 The supported optimization objectives depend on the prediction type.
 If the field is not set, a default objective function is used.

 CLASSIFICATION_BINARY:
   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
                                 operating characteristic (ROC) curve.
   "MINIMIZE_LOG_LOSS" - Minimize log loss.
   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
                                   recall value.
   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
                                    precision value.

 CLASSIFICATION_MULTI_CLASS :
   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.


 REGRESSION:
   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
        string optimization_objective = 4;
        Returns:
        This builder for chaining.

      • setOptimizationObjectiveBytes

        public TablesModelMetadata.Builder setOptimizationObjectiveBytes​(com.google.protobuf.ByteString value)
         Objective function the model is optimizing towards. The training process
 creates a model that maximizes/minimizes the value of the objective
 function over the validation set.

 The supported optimization objectives depend on the prediction type.
 If the field is not set, a default objective function is used.

 CLASSIFICATION_BINARY:
   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
                                 operating characteristic (ROC) curve.
   "MINIMIZE_LOG_LOSS" - Minimize log loss.
   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
                                   recall value.
   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
                                    precision value.

 CLASSIFICATION_MULTI_CLASS :
   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.


 REGRESSION:
   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
        string optimization_objective = 4;
        Parameters:
        value - The bytes for optimizationObjective to set.
        Returns:
        This builder for chaining.

      • getTablesModelColumnInfoCount

        public int getTablesModelColumnInfoCount()
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
        Specified by:
        getTablesModelColumnInfoCount in interface TablesModelMetadataOrBuilder

      • getTablesModelColumnInfo

        public TablesModelColumnInfo getTablesModelColumnInfo​(int index)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
        Specified by:
        getTablesModelColumnInfo in interface TablesModelMetadataOrBuilder

      • setTablesModelColumnInfo

        public TablesModelMetadata.Builder setTablesModelColumnInfo​(int index,
                                                            TablesModelColumnInfo value)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • setTablesModelColumnInfo

        public TablesModelMetadata.Builder setTablesModelColumnInfo​(int index,
                                                            TablesModelColumnInfo.Builder builderForValue)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • addTablesModelColumnInfo

        public TablesModelMetadata.Builder addTablesModelColumnInfo​(TablesModelColumnInfo value)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • addTablesModelColumnInfo

        public TablesModelMetadata.Builder addTablesModelColumnInfo​(int index,
                                                            TablesModelColumnInfo value)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • addTablesModelColumnInfo

        public TablesModelMetadata.Builder addTablesModelColumnInfo​(TablesModelColumnInfo.Builder builderForValue)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • addTablesModelColumnInfo

        public TablesModelMetadata.Builder addTablesModelColumnInfo​(int index,
                                                            TablesModelColumnInfo.Builder builderForValue)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • addAllTablesModelColumnInfo

        public TablesModelMetadata.Builder addAllTablesModelColumnInfo​(Iterable<? extends TablesModelColumnInfo> values)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • clearTablesModelColumnInfo

        public TablesModelMetadata.Builder clearTablesModelColumnInfo()
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • removeTablesModelColumnInfo

        public TablesModelMetadata.Builder removeTablesModelColumnInfo​(int index)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • getTablesModelColumnInfoBuilder

        public TablesModelColumnInfo.Builder getTablesModelColumnInfoBuilder​(int index)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • addTablesModelColumnInfoBuilder

        public TablesModelColumnInfo.Builder addTablesModelColumnInfoBuilder()
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • addTablesModelColumnInfoBuilder

        public TablesModelColumnInfo.Builder addTablesModelColumnInfoBuilder​(int index)
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • getTablesModelColumnInfoBuilderList

        public List<TablesModelColumnInfo.Builder> getTablesModelColumnInfoBuilderList()
         Output only. Auxiliary information for each of the
 input_feature_column_specs with respect to this particular model.
        repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;

      • getTrainBudgetMilliNodeHours

        public long getTrainBudgetMilliNodeHours()
         Required. The train budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour.

 The training cost of the model will not exceed this budget. The final cost
 will be attempted to be close to the budget, though may end up being (even)
 noticeably smaller - at the backend's discretion. This especially may
 happen when further model training ceases to provide any improvements.

 If the budget is set to a value known to be insufficient to train a
 model for the given dataset, the training won't be attempted and
 will error.

 The train budget must be between 1,000 and 72,000 milli node hours,
 inclusive.
        int64 train_budget_milli_node_hours = 6;
        Specified by:
        getTrainBudgetMilliNodeHours in interface TablesModelMetadataOrBuilder
        Returns:
        The trainBudgetMilliNodeHours.

      • setTrainBudgetMilliNodeHours

        public TablesModelMetadata.Builder setTrainBudgetMilliNodeHours​(long value)
         Required. The train budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour.

 The training cost of the model will not exceed this budget. The final cost
 will be attempted to be close to the budget, though may end up being (even)
 noticeably smaller - at the backend's discretion. This especially may
 happen when further model training ceases to provide any improvements.

 If the budget is set to a value known to be insufficient to train a
 model for the given dataset, the training won't be attempted and
 will error.

 The train budget must be between 1,000 and 72,000 milli node hours,
 inclusive.
        int64 train_budget_milli_node_hours = 6;
        Parameters:
        value - The trainBudgetMilliNodeHours to set.
        Returns:
        This builder for chaining.

      • clearTrainBudgetMilliNodeHours

        public TablesModelMetadata.Builder clearTrainBudgetMilliNodeHours()
         Required. The train budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour.

 The training cost of the model will not exceed this budget. The final cost
 will be attempted to be close to the budget, though may end up being (even)
 noticeably smaller - at the backend's discretion. This especially may
 happen when further model training ceases to provide any improvements.

 If the budget is set to a value known to be insufficient to train a
 model for the given dataset, the training won't be attempted and
 will error.

 The train budget must be between 1,000 and 72,000 milli node hours,
 inclusive.
        int64 train_budget_milli_node_hours = 6;
        Returns:
        This builder for chaining.

      • getTrainCostMilliNodeHours

        public long getTrainCostMilliNodeHours()
         Output only. The actual training cost of the model, expressed in milli
 node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
 to not exceed the train budget.
        int64 train_cost_milli_node_hours = 7;
        Specified by:
        getTrainCostMilliNodeHours in interface TablesModelMetadataOrBuilder
        Returns:
        The trainCostMilliNodeHours.

      • setTrainCostMilliNodeHours

        public TablesModelMetadata.Builder setTrainCostMilliNodeHours​(long value)
         Output only. The actual training cost of the model, expressed in milli
 node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
 to not exceed the train budget.
        int64 train_cost_milli_node_hours = 7;
        Parameters:
        value - The trainCostMilliNodeHours to set.
        Returns:
        This builder for chaining.

      • clearTrainCostMilliNodeHours

        public TablesModelMetadata.Builder clearTrainCostMilliNodeHours()
         Output only. The actual training cost of the model, expressed in milli
 node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
 to not exceed the train budget.
        int64 train_cost_milli_node_hours = 7;
        Returns:
        This builder for chaining.

      • getDisableEarlyStopping

        public boolean getDisableEarlyStopping()
         Use the entire training budget. This disables the early stopping feature.
 By default, the early stopping feature is enabled, which means that AutoML
 Tables might stop training before the entire training budget has been used.
        bool disable_early_stopping = 12;
        Specified by:
        getDisableEarlyStopping in interface TablesModelMetadataOrBuilder
        Returns:
        The disableEarlyStopping.

      • setDisableEarlyStopping

        public TablesModelMetadata.Builder setDisableEarlyStopping​(boolean value)
         Use the entire training budget. This disables the early stopping feature.
 By default, the early stopping feature is enabled, which means that AutoML
 Tables might stop training before the entire training budget has been used.
        bool disable_early_stopping = 12;
        Parameters:
        value - The disableEarlyStopping to set.
        Returns:
        This builder for chaining.

      • clearDisableEarlyStopping

        public TablesModelMetadata.Builder clearDisableEarlyStopping()
         Use the entire training budget. This disables the early stopping feature.
 By default, the early stopping feature is enabled, which means that AutoML
 Tables might stop training before the entire training budget has been used.
        bool disable_early_stopping = 12;
        Returns:
        This builder for chaining.

      • setUnknownFields

        public final TablesModelMetadata.Builder setUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        setUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>

      • mergeUnknownFields

        public final TablesModelMetadata.Builder mergeUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        mergeUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<TablesModelMetadata.Builder>