Class LogisticRegression (0.4.0) 

LogisticRegression(fit_intercept: bool = True, auto_class_weights: bool = False)

Logistic Regression (aka logit, MaxEnt) classifier.

Parameters
Name Description
fit_intercept default True

Default True. Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
auto_class_weights default False

Default False. If True, balance class labels using weights for each class in inverse proportion to the frequency of that class.

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 the model according to the given training data.

Parameters
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series

Series or DataFrame of shape (n_samples, n_features). Training vector, where n_samples is the number of samples and n_features is the number of features.
y bigframes.dataframe.DataFrame or bigframes.series.Series

DataFrame of shape (n_samples,). Target vector relative to X.
Returns
Type Description
LogisticRegression 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 True. If True, will return the parameters for this estimator and contained subobjects that are estimators.
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 class labels for samples in X.

Parameter
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series

Series or DataFrame of shape (n_samples, n_features). The data matrix for which we want to get the predictions.
Returns
Type Description
bigframes.dataframe.DataFrame DataFrame of shape (n_samples,), containing the class labels for each sample.

register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Register the model to Vertex AI.

After register, go to 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

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series

DataFrame of shape (n_samples, n_features). Test samples.
y bigframes.dataframe.DataFrame or bigframes.series.Series

DataFrame of shape (n_samples,) or (n_samples, n_outputs). True labels for X.
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.LogisticRegression

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
LogisticRegression saved model.