mojolearn.ComplementNB#

class mojolearn.ComplementNB(*, alpha=1.0, force_alpha=True, fit_prior=True, class_prior=None, norm=False)[source]#

Bases: _DiscreteNB

Complement naive Bayes classifier, suited to imbalanced text data.

Follows scikit-learn’s ComplementNB. Fit learns, for each class, the smoothed feature counts of all the other classes and stores their negated log share (normalized when norm=True). As in scikit-learn, the class prior is used only when there is a single class. X must be nonnegative (ValueError otherwise). 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.
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.
norm (bool, default False) – Normalize the weights of each class.

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)) – Sum of each feature per class.
feature_log_prob_ (array of shape (n_classes, n_features)) – Class weights computed from the complement counts.
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, 5, (1000, 20)).astype(np.float32)
>>> y = rng.integers(0, 3, 1000)
>>> model = mojolearn.ComplementNB().fit(X, y)

__init__(*, alpha=1.0, force_alpha=True, fit_prior=True, class_prior=None, norm=False)[source]#

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.