Class Model (2.16.1) 

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

Attributes

NameDescription
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.

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.

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.

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
NameDescription
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.

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.