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    Class Model (2.25.2) 

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

    Inheritance

    builtins.object > proto.message.Message > Model

    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. .. attribute:: log_likelihood

    Log-likelihood.

    :type: float

    ArimaForecastingMetrics

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

    Model evaluation metrics for ARIMA forecasting models. .. attribute:: non_seasonal_order

    Non-seasonal order.

    :type: Sequence[ google.cloud.bigquery_v2.types.Model.ArimaOrder ]

    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. .. attribute:: davies_bouldin_index

    Davies-Bouldin index.

    :type: google.protobuf.wrappers_pb2.DoubleValue

    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.

    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. .. attribute:: training_options

    Options that were used for this training run, includes user specified and default options that were used.

    :type: google.cloud.bigquery_v2.types.Model.TrainingRun.TrainingOptions

    Methods

    __delattr__

      __delattr__ 
 ( 
 key 
 )

    Delete the value on the given field.

    This is generally equivalent to setting a falsy value.

    __eq__

      __eq__ 
 ( 
 other 
 )

    Return True if the messages are equal, False otherwise.

    __ne__

      __ne__ 
 ( 
 other 
 )

    Return True if the messages are unequal, False otherwise.

    __setattr__

      __setattr__ 
 ( 
 key 
 , 
 value 
 )

    Set the value on the given field.

    For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.
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