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LinearRegression(
*,
optimize_strategy: typing.Literal[
"auto_strategy", "batch_gradient_descent", "normal_equation"
] = "auto_strategy",
fit_intercept: bool = True,
l1_reg: typing.Optional[float] = None,
l2_reg: float = 0.0,
max_iterations: int = 20,
warm_start: bool = False,
learning_rate: typing.Optional[float] = None,
learning_rate_strategy: typing.Literal["line_search", "constant"] = "line_search",
tol: float = 0.01,
ls_init_learning_rate: typing.Optional[float] = None,
calculate_p_values: bool = False,
enable_global_explain: bool = False
)
Ordinary least squares Linear Regression.
LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.
Parameters | |
---|---|
Name | Description |
optimize_strategy |
str, default "auto_strategy"
The strategy to train linear regression models. Possible values are "auto_strategy", "batch_gradient_descent", "normal_equation". Default to "auto_strategy". |
fit_intercept |
bool, default True
Default |
l1_reg |
float or None, default None
The amount of L1 regularization applied. Default to None. Can't be set in "normal_equation" mode. If unset, value 0 is used. |
l2_reg |
float, default 0.0
The amount of L2 regularization applied. Default to 0. |
max_iterations |
int, default 20
The maximum number of training iterations or steps. Default to 20. |
warm_start |
bool, default False
Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. Default to False. |
learning_rate |
float or None, default None
The learn rate for gradient descent when learning_rate_strategy='constant'. If unset, value 0.1 is used. If learning_rate_strategy='line_search', an error is returned. |
learning_rate_strategy |
str, default "line_search"
The strategy for specifying the learning rate during training. Default to "line_search". |
tol |
float, default 0.01
The minimum relative loss improvement that is necessary to continue training when EARLY_STOP is set to true. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue. Default to 0.01. |
ls_init_learning_rate |
float or None, default None
Sets the initial learning rate that learning_rate_strategy='line_search' uses. This option can only be used if line_search is specified. If unset, value 0.1 is used. |
calculate_p_values |
bool, default False
Specifies whether to compute p-values and standard errors during training. Default to False. |
enable_global_explain |
bool, default False
Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False. |
Methods
__repr__
__repr__()
Print the estimator's constructor with all non-default parameter values
fit
fit(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T
Fit linear model.
Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
Series or DataFrame of shape (n_samples, n_features). Training data. |
y |
bigframes.dataframe.DataFrame or bigframes.series.Series
Series or DataFrame of shape (n_samples,) or (n_samples, n_targets). Target values. Will be cast to X's dtype if necessary. |
Returns | |
---|---|
Type | Description |
LinearRegression | Fitted Estimator. |
get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]
Get parameters for this estimator.
Parameter | |
---|---|
Name | Description |
deep |
bool, default True
Default |
Returns | |
---|---|
Type | Description |
Dictionary | A dictionary of parameter names mapped to their values. |
predict
predict(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame
Predict using the linear model.
Parameter | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
Series or DataFrame of shape (n_samples, n_features). Samples. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values. |
register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T
Register the model to Vertex AI.
After register, go to Google Cloud Console (https://console.cloud.google.com/vertex-ai/models) to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options.
Parameter | |
---|---|
Name | Description |
vertex_ai_model_id |
Optional[str], default None
optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation. |
score
score(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame
Calculate evaluation metrics of the model.
Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
Series or DataFrame of shape (n_samples, n_features). Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape |
y |
bigframes.dataframe.DataFrame or bigframes.series.Series
Series or DataFrame of shape (n_samples,) or (n_samples, n_outputs). True values for |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | A DataFrame of the evaluation result. |
to_gbq
to_gbq(
model_name: str, replace: bool = False
) -> bigframes.ml.linear_model.LinearRegression
Save the model to BigQuery.
Parameters | |
---|---|
Name | Description |
model_name |
str
the name of the model. |
replace |
bool, default False
whether to replace if the model already exists. Default to False. |
Returns | |
---|---|
Type | Description |
LinearRegression | saved model. |