Module gcp_bigquery_client::model [−][src]
All the object definitions used by the BigQuery REST API.
Modules
| aggregate_classification_metrics | Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows. |
| argument | Input/output argument of a function or a stored procedure. |
| arima_coefficients | Arima coefficients. |
| arima_fitting_metrics | ARIMA model fitting metrics. |
| arima_forecasting_metrics | Model evaluation metrics for ARIMA forecasting models. |
| arima_model_info | Arima model information. |
| arima_order | Arima order, can be used for both non-seasonal and seasonal parts. |
| arima_result | (Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results. |
| arima_single_model_forecasting_metrics | Model evaluation metrics for a single ARIMA forecasting model. |
| audit_config | |
| audit_log_config | |
| bigquery_model_training | |
| bigtable_column | |
| bigtable_column_family | |
| bigtable_options | |
| binary_classification_metrics | Evaluation metrics for binary classification/classifier models. |
| binary_confusion_matrix | Confusion matrix for binary classification models. |
| binding | |
| bqml_iteration_result | |
| bqml_training_run | |
| bqml_training_run_training_options | |
| categorical_value | Representative value of a categorical feature. |
| category_count | Represents the count of a single category within the cluster. |
| cluster | Message containing the information about one cluster. |
| cluster_info | Information about a single cluster for clustering model. |
| clustering | |
| clustering_metrics | Evaluation metrics for clustering models. |
| confusion_matrix | Confusion matrix for multi-class classification models. |
| connection_property | |
| csv_options | |
| data_format_options | |
| data_split_result | Data split result. This contains references to the training and evaluation data tables that were used to train the model. |
| dataset | |
| dataset_reference | |
| datasets | |
| destination_table_properties | |
| dimensionality_reduction_metrics | Model evaluation metrics for dimensionality reduction models. |
| encryption_configuration | |
| entry | A single entry in the confusion matrix. |
| error_proto | |
| evaluation_metrics | Evaluation metrics of a model. These are either computed on all training data or just the eval data based on whether eval data was used during training. These are not present for imported models. |
| explain_query_stage | |
| explain_query_step | |
| explanation | Explanation for a single feature. |
| expr | |
| external_data_configuration | |
| feature_value | Representative value of a single feature within the cluster. |
| field_type | |
| get_iam_policy_request | |
| get_policy_options | |
| get_query_results_parameters | |
| get_query_results_response | |
| get_service_account_response | |
| global_explanation | Global explanations containing the top most important features after training. |
| google_sheets_options | |
| hive_partitioning_options | |
| information_schema | |
| iteration_result | Information about a single iteration of the training run. |
| job | |
| job_cancel_response | |
| job_configuration | |
| job_configuration_extract | |
| job_configuration_load | |
| job_configuration_query | |
| job_configuration_table_copy | |
| job_list | |
| job_list_jobs | |
| job_reference | |
| job_statistics | |
| job_statistics2 | |
| job_statistics3 | |
| job_statistics4 | |
| job_statistics_reservation_usage | |
| job_status | |
| list_models_response | |
| list_routines_response | |
| materialized_view_definition | |
| model | |
| model_definition | |
| model_definition_model_options | |
| model_reference | |
| multi_class_classification_metrics | Evaluation metrics for multi-class classification/classifier models. |
| policy | |
| principal_component_info | Principal component infos, used only for eigen decomposition based models, e.g., PCA. Ordered by explained_variance in the descending order. |
| project_list | |
| project_reference | |
| query_parameter | |
| query_parameter_type | |
| query_parameter_type_struct_types | |
| query_parameter_value | |
| query_request | |
| query_response | |
| query_timeline_sample | |
| range_partitioning | |
| range_partitioning_range | |
| ranking_metrics | Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit. |
| regression_metrics | Evaluation metrics for regression and explicit feedback type matrix factorization models. |
| routine | A user-defined function or a stored procedure. |
| routine_reference | |
| row | A single row in the confusion matrix. |
| row_access_policy | Represents access on a subset of rows on the specified table, defined by its filter predicate. Access to the subset of rows is controlled by its IAM policy. |
| row_access_policy_reference | |
| row_level_security_statistics | |
| script_stack_frame | |
| script_statistics | |
| set_iam_policy_request | |
| snapshot_definition | |
| standard_sql_data_type | The type of a variable, e.g., a function argument. Examples: INT64: {type_kind=“INT64”} ARRAY: {type_kind=“ARRAY”, array_element_type=“STRING”} STRUCT>: {type_kind=“STRUCT”, struct_type={fields=[ {name=“x”, type={type_kind=“STRING”}}, {name=“y”, type={type_kind=“ARRAY”, array_element_type=“DATE”}} ]}} |
| standard_sql_field | A field or a column. |
| standard_sql_struct_type | |
| streamingbuffer | |
| table | |
| table_cell | |
| table_data_insert_all_request | |
| table_data_insert_all_request_rows | |
| table_data_insert_all_response | |
| table_data_insert_all_response_insert_errors | |
| table_field_schema | |
| table_field_schema_categories | |
| table_field_schema_policy | |
| table_list | |
| table_list_tables | |
| table_list_view | |
| table_reference | |
| table_row | |
| table_schema | |
| test_iam_permissions_request | |
| test_iam_permissions_response | |
| time_partitioning | |
| training_options | Options used in model training. |
| training_run | Information about a single training query run for the model. |
| transaction_info | |
| user_defined_function_resource | |
| view_definition |