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 \ V1beta1Methods
__construct
Constructor.
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.
float
hasOptimizationObjectiveRecallValue
setOptimizationObjectiveRecallValue
Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
Must be between 0 and 1, inclusive.
var
float
$this
getOptimizationObjectivePrecisionValue
Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
Must be between 0 and 1, inclusive.
float
hasOptimizationObjectivePrecisionValue
setOptimizationObjectivePrecisionValue
Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
Must be between 0 and 1, inclusive.
var
float
$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.
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.
$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.
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.
$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).
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).
var
string
$this
getTablesModelColumnInfo
Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.
setTablesModelColumnInfo
Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.
$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.
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.
var
int|string
$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.
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.
var
int|string
$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.
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.
var
bool
$this
getAdditionalOptimizationObjectiveConfig
string
