Cloud AutoML V1beta1 Client - Class TablesModelMetadata (1.6.5)

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• 1.6.5
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• 1.4.17

Beta

This library is covered by the Pre-GA Offerings Terms of the Terms of Service. Pre-GA libraries might have limited support, and changes to pre-GA libraries might not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

Reference documentation and code samples for the Cloud AutoML V1beta1 Client class TablesModelMetadata.

Model metadata specific to AutoML Tables.

Generated from protobuf message google.cloud.automl.v1beta1.TablesModelMetadata

Namespace

Google \ Cloud \ AutoMl \ V1beta1

Methods

__construct

Constructor.

Parameters
Name	Description
data	array Optional. Data for populating the Message object.
↳ optimization_objective_recall_value	float Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". Must be between 0 and 1, inclusive.
↳ optimization_objective_precision_value	float Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". Must be between 0 and 1, inclusive.
↳ target_column_spec	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.
↳ input_feature_column_specs	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	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).
↳ tables_model_column_info	array<Google\Cloud\AutoMl\V1beta1\TablesModelColumnInfo> Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.
↳ train_budget_milli_node_hours	int|string 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	int|string 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.
↳ disable_early_stopping	bool 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.

getOptimizationObjectiveRecallValue

Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".

Must be between 0 and 1, inclusive.

Returns
Type	Description
float

hasOptimizationObjectiveRecallValue

setOptimizationObjectiveRecallValue

Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".

Must be between 0 and 1, inclusive.

Parameter
Name	Description
var	float

Returns
Type	Description
$this

getOptimizationObjectivePrecisionValue

Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".

Must be between 0 and 1, inclusive.

Returns
Type	Description
float

hasOptimizationObjectivePrecisionValue

setOptimizationObjectivePrecisionValue

Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".

Must be between 0 and 1, inclusive.

Parameter
Name	Description
var	float

Returns
Type	Description
$this

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.

Returns
Type	Description
Google\Cloud\AutoMl\V1beta1\ColumnSpec|null

hasTargetColumnSpec

clearTargetColumnSpec

setTargetColumnSpec

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.

Parameter
Name	Description
var	Google\Cloud\AutoMl\V1beta1\ColumnSpec

Returns
Type	Description
$this

getInputFeatureColumnSpecs

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
Type	Description
Google\Protobuf\Internal\RepeatedField

setInputFeatureColumnSpecs

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.

Parameter
Name	Description
var	array<Google\Cloud\AutoMl\V1beta1\ColumnSpec>

Returns
Type	Description
$this

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).

Returns
Type	Description
string

setOptimizationObjective

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).

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getTablesModelColumnInfo

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

Returns
Type	Description
Google\Protobuf\Internal\RepeatedField

setTablesModelColumnInfo

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

Parameter
Name	Description
var	array<Google\Cloud\AutoMl\V1beta1\TablesModelColumnInfo>

Returns
Type	Description
$this

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.

Returns
Type	Description
int|string

setTrainBudgetMilliNodeHours

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.

Parameter
Name	Description
var	int|string

Returns
Type	Description
$this

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.

Returns
Type	Description
int|string

setTrainCostMilliNodeHours

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.

Parameter
Name	Description
var	int|string

Returns
Type	Description
$this

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.

Returns
Type	Description
bool

setDisableEarlyStopping

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.

Parameter
Name	Description
var	bool

Returns
Type	Description
$this

getAdditionalOptimizationObjectiveConfig

Returns
Type	Description
string