Class TrainingOptions (3.0.1)

Version 3.0.1

• 3.27.0 (latest)
• 3.26.0
• 3.25.0
• 3.24.0
• 3.23.1
• 3.22.0
• 3.21.0
• 3.20.1
• 3.19.0
• 3.18.0
• 3.17.2
• 3.16.0
• 3.15.0
• 3.14.1
• 3.13.0
• 3.12.0
• 3.11.4
• 3.4.0
• 3.3.6
• 3.2.0
• 3.1.0
• 3.0.1
• 2.34.4
• 2.33.0
• 2.32.0
• 2.31.0
• 2.30.1
• 2.29.0
• 2.28.1
• 2.27.1
• 2.26.0
• 2.25.2
• 2.24.1
• 2.23.3
• 2.22.1
• 2.21.0
• 2.20.0
• 2.19.0
• 2.18.0
• 2.17.0
• 2.16.1
• 2.15.0
• 2.14.0
• 2.13.1
• 2.12.0
• 2.11.0
• 2.10.0
• 2.9.0
• 2.8.0
• 2.7.0
• 2.6.2
• 2.5.0
• 2.4.0
• 2.3.1
• 2.2.0
• 2.1.0
• 2.0.0
• 1.28.2
• 1.27.2
• 1.26.1
• 1.25.0
• 1.24.0
• 1.23.1
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0

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

Options used in model training.

Attributes

Name	Description
max_iterations	int The maximum number of iterations in training. Used only for iterative training algorithms.
loss_type	google.cloud.bigquery_v2.types.Model.LossType Type of loss function used during training run.
learn_rate	float Learning rate in training. Used only for iterative training algorithms.
l1_regularization	google.protobuf.wrappers_pb2.DoubleValue L1 regularization coefficient.
l2_regularization	google.protobuf.wrappers_pb2.DoubleValue L2 regularization coefficient.
min_relative_progress	google.protobuf.wrappers_pb2.DoubleValue When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
warm_start	google.protobuf.wrappers_pb2.BoolValue Whether to train a model from the last checkpoint.
early_stop	google.protobuf.wrappers_pb2.BoolValue Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.
input_label_columns	Sequence[str] Name of input label columns in training data.
data_split_method	google.cloud.bigquery_v2.types.Model.DataSplitMethod The data split type for training and evaluation, e.g. RANDOM.
data_split_eval_fraction	float The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
data_split_column	str The column to split data with. This column won't be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties
learn_rate_strategy	google.cloud.bigquery_v2.types.Model.LearnRateStrategy The strategy to determine learn rate for the current iteration.
initial_learn_rate	float Specifies the initial learning rate for the line search learn rate strategy.
label_class_weights	Sequence[google.cloud.bigquery_v2.types.Model.TrainingRun.TrainingOptions.LabelClassWeightsEntry] Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.
user_column	str User column specified for matrix factorization models.
item_column	str Item column specified for matrix factorization models.
distance_type	google.cloud.bigquery_v2.types.Model.DistanceType Distance type for clustering models.
num_clusters	int Number of clusters for clustering models.
model_uri	str Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
optimization_strategy	google.cloud.bigquery_v2.types.Model.OptimizationStrategy Optimization strategy for training linear regression models.
hidden_units	Sequence[int] Hidden units for dnn models.
batch_size	int Batch size for dnn models.
dropout	google.protobuf.wrappers_pb2.DoubleValue Dropout probability for dnn models.
max_tree_depth	int Maximum depth of a tree for boosted tree models.
subsample	float Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
min_split_loss	google.protobuf.wrappers_pb2.DoubleValue Minimum split loss for boosted tree models.
num_factors	int Num factors specified for matrix factorization models.
feedback_type	google.cloud.bigquery_v2.types.Model.FeedbackType Feedback type that specifies which algorithm to run for matrix factorization.
wals_alpha	google.protobuf.wrappers_pb2.DoubleValue Hyperparameter for matrix factoration when implicit feedback type is specified.
kmeans_initialization_method	google.cloud.bigquery_v2.types.Model.KmeansEnums.KmeansInitializationMethod The method used to initialize the centroids for kmeans algorithm.
kmeans_initialization_column	str The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
time_series_timestamp_column	str Column to be designated as time series timestamp for ARIMA model.
time_series_data_column	str Column to be designated as time series data for ARIMA model.
auto_arima	bool Whether to enable auto ARIMA or not.
non_seasonal_order	google.cloud.bigquery_v2.types.Model.ArimaOrder A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
data_frequency	google.cloud.bigquery_v2.types.Model.DataFrequency The data frequency of a time series.
include_drift	bool Include drift when fitting an ARIMA model.
holiday_region	google.cloud.bigquery_v2.types.Model.HolidayRegion The geographical region based on which the holidays are considered in time series modeling. If a valid value is specified, then holiday effects modeling is enabled.
time_series_id_column	str The time series id column that was used during ARIMA model training.
time_series_id_columns	Sequence[str] The time series id columns that were used during ARIMA model training.
horizon	int The number of periods ahead that need to be forecasted.
preserve_input_structs	bool Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b.
auto_arima_max_order	int The max value of non-seasonal p and q.
decompose_time_series	google.protobuf.wrappers_pb2.BoolValue If true, perform decompose time series and save the results.
clean_spikes_and_dips	google.protobuf.wrappers_pb2.BoolValue If true, clean spikes and dips in the input time series.
adjust_step_changes	google.protobuf.wrappers_pb2.BoolValue If true, detect step changes and make data adjustment in the input time series.

Inheritance

builtins.object > proto.message.Message > TrainingOptions

Classes

LabelClassWeightsEntry

LabelClassWeightsEntry(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.

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.