bbai.glm package¶
Module contents¶
-
class
bbai.glm.
LogisticRegression
(fit_intercept=True, normalize=False, penalty='l2', active_classes='auto', C=None, tolerance=0.0001)¶ Bases:
object
Implements logistic regression with regularizers fit so as to maximize performance on approximate leave-one-out cross-validation.
See https://arxiv.org/abs/2011.10218 for background on the approach.
- Parameters
fit_intercept (bool, default=True) – Whether constant columns should be added to the feature matrix.
normalize (bool, default=False) – Whether to center and rescale the feature matrix columns.
penalty ({'l2'}, default='l2') –
Regularization function to use
’l2’ will use the function sum_i alpha |w_i|^2
C (float, default=None) – Inverse of regularization strength. If None, bbai will choose C so as to maximize performance on approximate leave-one-out cross-validation.
tolerance (float, default=0.0001) – The tolerance for the optimizer to use when deciding to stop the objective. With a lower value, the optimizer will be more stringent when deciding whether to stop searching.
Examples
>>> from sklearn.datasets import load_breast_cancer >>> from bbai.glm import LogisticRegression >>> X, y = load_breast_cancer(return_X_y=True) >>> model = LogisticRegression(normalize=True).fit(X, y) # Defaults to use the regularizer l2 and finds the # hyperparameters that maximize performance on # approximate leave-one-out cross-validation. >>> print(model.C_) # print out the hyperparameters
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fit
(X, y)¶ Fit the model to the training data.
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get_params
(deep=True)¶ Get parameters for this estimator.
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predict
(X)¶ Predict class labels for the given feature matrix.
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predict_log_proba
(X)¶ Predict class log probabilities for the given feature matrix.
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predict_proba
(X)¶ Predict class probabilities for the given feature matrix.
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set_params
(**parameters)¶ Set parameters for this estimator.
-
class
bbai.glm.
RidgeRegression
(fit_intercept=True, normalize=False, penalty='l2', alpha=None, tolerance=0.0001)¶ Bases:
object
Implements regularized regression with regularizers fit so as to maximize performance on leave-one-out cross-validation.
- Parameters
fit_intercept (bool, default=True) – Whether a constant column should be added to the feature matrix.
normalize (bool, default=False) – Whether to center and rescale the feature matrix columns.
alpha (float, default=None) – Regularization strength. If None, bbai will choose alpha so as to maximize performance on leave-one-out cross-validation.
tolerance (float, default=0.0001) – The tolerance for the optimizer to use when deciding to stop the objective. With a lower value, the optimizer will be more stringent when deciding whether to stop searching.
Examples
>>> from sklearn.datasets import load_boston >>> from bbai import RidgeRegression >>> X, y = load_boston(return_X_y=True) >>> model = RidgeRegression().fit(X, y) # Defaults to use the regularizer l2 and finds the # hyperparameters that maximize performance on # leave-one-out cross-validation. >>> print(model.alpha_) # print out the hyperparameters
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fit
(X, y)¶ Fit the model to the training data.
-
get_params
(deep=True)¶ Get parameters for this estimator.
-
predict
(X)¶ Predict target values.
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set_params
(**parameters)¶ Set parameters for this estimator.