Class Model (2.8.0)

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

Attributes

Name	Description
etag	`str` Output only. A hash of this resource.
model_reference	`.gcb_model_reference.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[`.gcb_model.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	`.encryption_config.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	`.gcb_model.Model.ModelType` Output only. Type of the model resource.
training_runs	Sequence[`.gcb_model.Model.TrainingRun`] Output only. Information for all training runs in increasing order of start_time.
feature_columns	Sequence[`.standard_sql.StandardSqlField`] Output only. Input feature columns that were used to train this model.
label_columns	Sequence[`.standard_sql.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.

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.

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.