Module linear_model (2.29.0)

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Linear models. This module is styled after scikit-learn's linear_model module: https://scikit-learn.org/stable/modules/linear_model.html.

Classes

LinearRegression

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.

Examples:

>>> from bigframes.ml.linear_model import LinearRegression
>>> 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 linear model
>>> model = LinearRegression()
>>> model.fit(X, y)
LinearRegression()

>>> # Score the model
>>> score = model.score(X, y)
>>> print(score) # doctest:+SKIP
    mean_absolute_error  mean_squared_error  mean_squared_log_error          0             0.022812            0.000602                 0.00035
    median_absolute_error  r2_score  explained_variance
0               0.015077  0.997591            0.997591

LogisticRegression

LogisticRegression(
    *,
    optimize_strategy: typing.Literal[
        "auto_strategy", "batch_gradient_descent"
    ] = "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,
    class_weight: typing.Optional[
        typing.Union[typing.Literal["balanced"], typing.Dict[str, float]]
    ] = None
)

Logistic Regression (aka logit, MaxEnt) classifier.

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) # doctest:+SKIP 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 # doctest:+SKIP 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]