Class Model (3.33.0)

  Model 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Attributes

Name

Description

etag

str
Output only. A hash of this resource.

model_reference

 google.cloud.bigquery_v2.types.ModelReference

Required. Unique identifier for this model.

creation_time

int
Output only. The time when this model was created, in millisecs since the epoch.

last_modified_time

int
Output only. The time when this model was last modified, in millisecs since the epoch.

description

str
Optional. A user-friendly description of this model.

friendly_name

str
Optional. A descriptive name for this model.

labels

Mapping[str, str]
The labels associated with this model. You can use these to organize and group your models. Label keys and values can be no longer than 63 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter and each label in the list must have a different key.

expiration_time

int
Optional. The time when this model expires, in milliseconds since the epoch. If not present, the model will persist indefinitely. Expired models will be deleted and their storage reclaimed. The defaultTableExpirationMs property of the encapsulating dataset can be used to set a default expirationTime on newly created models.

location

str
Output only. The geographic location where the model resides. This value is inherited from the dataset.

encryption_configuration

 google.cloud.bigquery_v2.types.EncryptionConfiguration

Custom encryption configuration (e.g., Cloud KMS keys). This shows the encryption configuration of the model data while stored in BigQuery storage. This field can be used with PatchModel to update encryption key for an already encrypted model.

model_type

 google.cloud.bigquery_v2.types.Model.ModelType

Output only. Type of the model resource.

training_runs

Sequence[ google.cloud.bigquery_v2.types.Model.TrainingRun 
]

Output only. Information for all training runs in increasing order of start_time.

feature_columns

Sequence[ google.cloud.bigquery_v2.types.StandardSqlField 
]

Output only. Input feature columns that were used to train this model.

label_columns

Sequence[ google.cloud.bigquery_v2.types.StandardSqlField 
]

Output only. Label columns that were used to train this model. The output of the model will have a predicted_ prefix to these columns.

best_trial_id

int
The best trial_id across all training runs.

Classes

AggregateClassificationMetrics

  AggregateClassificationMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

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.

ArimaFittingMetrics

  ArimaFittingMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

ARIMA model fitting metrics.

ArimaForecastingMetrics

  ArimaForecastingMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Model evaluation metrics for ARIMA forecasting models.

ArimaOrder

  ArimaOrder 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Arima order, can be used for both non-seasonal and seasonal parts.

BinaryClassificationMetrics

  BinaryClassificationMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Evaluation metrics for binary classification/classifier models.

ClusteringMetrics

  ClusteringMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Evaluation metrics for clustering models.

DataFrequency

  DataFrequency 
 ( 
 value 
 )

Type of supported data frequency for time series forecasting models.

DataSplitMethod

  DataSplitMethod 
 ( 
 value 
 )

Indicates the method to split input data into multiple tables.

DataSplitResult

  DataSplitResult 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Data split result. This contains references to the training and evaluation data tables that were used to train the model.

DistanceType

  DistanceType 
 ( 
 value 
 )

Distance metric used to compute the distance between two points.

EvaluationMetrics

  EvaluationMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

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.

This message has oneof _ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeedbackType

  FeedbackType 
 ( 
 value 
 )

Indicates the training algorithm to use for matrix factorization models.

GlobalExplanation

  GlobalExplanation 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Global explanations containing the top most important features after training.

HolidayRegion

  HolidayRegion 
 ( 
 value 
 )

Type of supported holiday regions for time series forecasting models.

KmeansEnums

  KmeansEnums 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

API documentation for bigquery_v2.types.Model.KmeansEnums class.

LabelsEntry

  LabelsEntry 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

The abstract base class for a message.

Parameters

Name

Description

kwargs

dict

Keys and values corresponding to the fields of the message.

mapping

Union[dict, .Message 
]

A dictionary or message to be used to determine the values for this message.

ignore_unknown_fields

Optional(bool)

If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.

LearnRateStrategy

  LearnRateStrategy 
 ( 
 value 
 )

Indicates the learning rate optimization strategy to use.

LossType

  LossType 
 ( 
 value 
 )

Loss metric to evaluate model training performance.

ModelType

  ModelType 
 ( 
 value 
 )

Indicates the type of the Model.

MultiClassClassificationMetrics

  MultiClassClassificationMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Evaluation metrics for multi-class classification/classifier models.

OptimizationStrategy

  OptimizationStrategy 
 ( 
 value 
 )

Indicates the optimization strategy used for training.

RankingMetrics

  RankingMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Evaluation metrics used by weighted-ALS models specified by feedback_type=implicit.

RegressionMetrics

  RegressionMetrics 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Evaluation metrics for regression and explicit feedback type matrix factorization models.

SeasonalPeriod

  SeasonalPeriod 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

API documentation for bigquery_v2.types.Model.SeasonalPeriod class.

TrainingRun

  TrainingRun 
 ( 
 mapping 
 = 
 None 
 , 
 * 
 , 
 ignore_unknown_fields 
 = 
 False 
 , 
 ** 
 kwargs 
 )

Information about a single training query run for the model.

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