Cloud AutoML V1beta1 API - Class Google::Cloud::AutoML::V1beta1::TablesModelMetadata (v0.6.0)

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Reference documentation and code samples for the Cloud AutoML V1beta1 API class Google::Cloud::AutoML::V1beta1::TablesModelMetadata.

Model metadata specific to AutoML Tables.

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#disable_early_stopping

def disable_early_stopping() -> ::Boolean
Returns
  • (::Boolean) — 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.

#disable_early_stopping=

def disable_early_stopping=(value) -> ::Boolean
Parameter
  • value (::Boolean) — 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.
Returns
  • (::Boolean) — 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.

#input_feature_column_specs

def input_feature_column_specs() -> ::Array<::Google::Cloud::AutoML::V1beta1::ColumnSpec>
Returns
  • (::Array<::Google::Cloud::AutoML::V1beta1::ColumnSpec>) —

    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 as well as, according to dataset's state upon model creation,

    weight_column, and

    ml_use_column 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.

#input_feature_column_specs=

def input_feature_column_specs=(value) -> ::Array<::Google::Cloud::AutoML::V1beta1::ColumnSpec>
Parameter
  • value (::Array<::Google::Cloud::AutoML::V1beta1::ColumnSpec>) —

    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 as well as, according to dataset's state upon model creation,

    weight_column, and

    ml_use_column 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.

Returns
  • (::Array<::Google::Cloud::AutoML::V1beta1::ColumnSpec>) —

    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 as well as, according to dataset's state upon model creation,

    weight_column, and

    ml_use_column 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.

#optimization_objective

def optimization_objective() -> ::String
Returns
  • (::String) — 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).

#optimization_objective=

def optimization_objective=(value) -> ::String
Parameter
  • value (::String) — 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).

Returns
  • (::String) — 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).

#optimization_objective_precision_value

def optimization_objective_precision_value() -> ::Float
Returns
  • (::Float) — Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive.

#optimization_objective_precision_value=

def optimization_objective_precision_value=(value) -> ::Float
Parameter
  • value (::Float) — Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive.
Returns
  • (::Float) — Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive.

#optimization_objective_recall_value

def optimization_objective_recall_value() -> ::Float
Returns
  • (::Float) — Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive.

#optimization_objective_recall_value=

def optimization_objective_recall_value=(value) -> ::Float
Parameter
  • value (::Float) — Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive.
Returns
  • (::Float) — Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive.

#tables_model_column_info

def tables_model_column_info() -> ::Array<::Google::Cloud::AutoML::V1beta1::TablesModelColumnInfo>
Returns

#tables_model_column_info=

def tables_model_column_info=(value) -> ::Array<::Google::Cloud::AutoML::V1beta1::TablesModelColumnInfo>
Parameter
Returns

#target_column_spec

def target_column_spec() -> ::Google::Cloud::AutoML::V1beta1::ColumnSpec
Returns
  • (::Google::Cloud::AutoML::V1beta1::ColumnSpec) — 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.

#target_column_spec=

def target_column_spec=(value) -> ::Google::Cloud::AutoML::V1beta1::ColumnSpec
Parameter
  • value (::Google::Cloud::AutoML::V1beta1::ColumnSpec) — 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.
Returns
  • (::Google::Cloud::AutoML::V1beta1::ColumnSpec) — 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.

#train_budget_milli_node_hours

def train_budget_milli_node_hours() -> ::Integer
Returns
  • (::Integer) — 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.

#train_budget_milli_node_hours=

def train_budget_milli_node_hours=(value) -> ::Integer
Parameter
  • value (::Integer) — 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.

Returns
  • (::Integer) — 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.

#train_cost_milli_node_hours

def train_cost_milli_node_hours() -> ::Integer
Returns
  • (::Integer) — 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.

#train_cost_milli_node_hours=

def train_cost_milli_node_hours=(value) -> ::Integer
Parameter
  • value (::Integer) — 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.
Returns
  • (::Integer) — 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.