bigframes.ml.linear_model.LogisticRegression#

class bigframes.ml.linear_model.LogisticRegression(*, optimize_strategy: Literal['auto_strategy', 'batch_gradient_descent'] = 'auto_strategy', fit_intercept: bool = True, l1_reg: float | None = None, l2_reg: float = 0.0, max_iterations: int = 20, warm_start: bool = False, learning_rate: float | None = None, learning_rate_strategy: Literal['line_search', 'constant'] = 'line_search', tol: float = 0.01, ls_init_learning_rate: float | None = None, calculate_p_values: bool = False, enable_global_explain: bool = False, class_weight: Literal['balanced'] | Dict[str, float] | None = None)[source]#

Logistic Regression (aka logit, MaxEnt) classifier.

Examples:

>>> from bigframes.ml.linear_model import LogisticRegression
>>> import bigframes.pandas as bpd
>>> X = bpd.DataFrame({                 "feature0": [20, 21, 19, 18],                 "feature1": [0, 1, 1, 0],                 "feature2": [0.2, 0.3, 0.4, 0.5]})
>>> y = bpd.DataFrame({"outcome": [0, 0, 1, 1]})
>>> # Create the LogisticRegression
>>> model = LogisticRegression()
>>> model.fit(X, y)
LogisticRegression()
>>> model.predict(X)
    predicted_outcome   predicted_outcome_probs feature0        feature1        feature2
0       0       [{'label': 1, 'prob': 3.1895929877221615e-07} ...       20      0       0.2
1       0       [{'label': 1, 'prob': 5.662891265051953e-06} ...        21      1       0.3
2       1       [{'label': 1, 'prob': 0.9999917826885262} {'l...        19      1       0.4
3       1       [{'label': 1, 'prob': 0.9999999993659574} {'l...        18      0       0.5
4 rows × 5 columns

[4 rows x 5 columns in total]

>>> # Score the model
>>> score = model.score(X, y)
>>> score
    precision   recall  accuracy        f1_score        log_loss        roc_auc
0       1.0     1.0     1.0     1.0     0.000004        1.0
1 rows × 6 columns

[1 rows x 6 columns in total]

Parameters:

  • optimize_strategy (str, default "auto_strategy") – The strategy to train logistic regression models. Possible values are “auto_strategy” and “batch_gradient_descent”. The two are equilevant since “auto_strategy” will fall back to “batch_gradient_descent”. The API is kept for consistency. Default to “auto_strategy”.

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

  • class_weight (dict or 'balanced', default None) – Default None. Weights associated with classes in the form {class_label: weight}.If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)). Dict isn’t supported.

  • 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

__init__(*[, optimize_strategy, ...])

fit(X, y[, X_eval, y_eval])

Fit the model according to the given training data.

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict class labels for samples in X.

predict_explain(X, *[, top_k_features])

Explain predictions for a logistic regression model.

register([vertex_ai_model_id])

Register the model to Vertex AI.

score(X, y)

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

to_gbq(model_name[, replace])

Save the model to BigQuery.
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