- 1.28.0 (latest)
- 1.27.0
- 1.26.0
- 1.25.0
- 1.24.0
- 1.22.0
- 1.21.0
- 1.20.0
- 1.19.0
- 1.18.0
- 1.17.0
- 1.16.0
- 1.15.0
- 1.14.0
- 1.13.0
- 1.12.0
- 1.11.1
- 1.10.0
- 1.9.0
- 1.8.0
- 1.7.0
- 1.6.0
- 1.5.0
- 1.4.0
- 1.3.0
- 1.2.0
- 1.1.0
- 1.0.0
- 0.26.0
- 0.25.0
- 0.24.0
- 0.23.0
- 0.22.0
- 0.21.0
- 0.20.1
- 0.19.2
- 0.18.0
- 0.17.0
- 0.16.0
- 0.15.0
- 0.14.1
- 0.13.0
- 0.12.0
- 0.11.0
- 0.10.0
- 0.9.0
- 0.8.0
- 0.7.0
- 0.6.0
- 0.5.0
- 0.4.0
- 0.3.0
- 0.2.0
LinearRegression(
optimize_strategy: typing.Literal[
"auto_strategy", "batch_gradient_descent", "normal_equation"
] = "normal_equation",
fit_intercept: bool = True,
l2_reg: float = 0.0,
max_iterations: int = 20,
learn_rate_strategy: typing.Literal["line_search", "constant"] = "line_search",
early_stop: bool = True,
min_rel_progress: float = 0.01,
ls_init_learn_rate: float = 0.1,
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 "normal_equation"
The strategy to train linear regression models. Possible values are "auto_strategy", "batch_gradient_descent", "normal_equation". Default to "normal_equation". |
fit_intercept |
bool, default True
Default |
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. |
learn_rate_strategy |
str, default "line_search"
The strategy for specifying the learning rate during training. Default to "line_search". |
early_stop |
bool, default True
Whether training should stop after the first iteration in which the relative loss improvement is less than the value specified for min_rel_progress. Default to True. |
min_rel_progress |
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_learn_rate |
float, default 0.1
Sets the initial learning rate that learn_rate_strategy='line_search' uses. This option can only be used if line_search is specified. Default to 0.1. |
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. |