mojolearn.BernoulliNB

class mojolearn.BernoulliNB(*, alpha=1.0, force_alpha=True, binarize=0.0, fit_prior=True, class_prior=None)

Bases: _DiscreteNB

Naive Bayes classifier for binary features.

Follows scikit-learn’s BernoulliNB. X is binarized at binarize (values above the threshold become 1), then fit counts how often each feature is 1 in each class, smooths the counts by alpha and scores each class with the Bernoulli likelihood, including the features that are 0. Computation is float32.

fit accepts sample_weight, which weights the counts. partial_fit(X, y, classes=None, sample_weight=None) adds each batch’s counts to the running ones; classes is required on the first call.

Parameters:

• alpha (float, default 1.0) – Additive smoothing. Must be greater than 0; alpha <= 0 raises NotImplementedError.

• force_alpha (bool, default True) – Accepted for scikit-learn compatibility.

• binarize (float or None, default 0.0) – Threshold for binarizing X; None means X is already binary.

• fit_prior (bool, default True) – Learn class priors from the data; when False, use a uniform prior.

• class_prior (array-like of shape (n_classes,), default None) – Given class priors (nonnegative); overrides fit_prior.

Variables:

• classes_ (array of shape (n_classes,)) – Class labels.

• class_count_ (array of shape (n_classes,)) – Number (or weighted number) of training rows per class.

• class_log_prior_ (array of shape (n_classes,)) – Log prior of each class.

• feature_count_ (array of shape (n_classes, n_features)) – Number of rows per class in which each feature is 1.

• feature_log_prob_ (array of shape (n_classes, n_features)) – Smoothed log probability that each feature is 1 given the class.

• n_features_in_ (int) – Number of columns seen in fit.

• numeric_mode_ (str) – Numeric mode the estimator was fitted in; prediction runs in the same mode.

Examples

>>> import numpy as np, mojolearn
>>> rng = np.random.default_rng(0)
>>> X = rng.integers(0, 2, (1000, 20)).astype(np.float32)
>>> y = rng.integers(0, 2, 1000)
>>> model = mojolearn.BernoulliNB().fit(X, y)

__init__(*, alpha=1.0, force_alpha=True, binarize=0.0, fit_prior=True, class_prior=None)

Methods

fit(X, y[, sample_weight])
fit_transform(X[, y])	Fit to X, then transform X.
get_params([deep])	Return the constructor parameters as a dict.
partial_fit(X, y[, classes, sample_weight])	Update the model with one batch of rows.
predict(X)	Predict the class of each row.
predict_joint_log_proba(X)	Return the unnormalized log score of each row and class.
predict_log_proba(X)	Return the log of the class probabilities for each row.
predict_proba(X)	Return class probabilities for each row.
score(X, y)	Return the mean accuracy on the given rows and labels.
set_params(**params)	Set constructor parameters.