skmultiflow.bayes.NaiveBayes

class skmultiflow.bayes.NaiveBayes(nominal_attributes=None)

Naive Bayes classifier.

Performs classic bayesian prediction while making naive assumption that all inputs are independent. Naive Bayes is a classifier algorithm known for its simplicity and low computational cost. Given n different classes, the trained Naive Bayes classifier predicts for every unlabelled instance the class to which it belongs with high accuracy.

Parameters

nominal_attributes: numpy.ndarray (optional, default=None)

List of Nominal attributes. If emtpy, then assume that all attributes are numerical.

Notes

The scikit-learn implementations of NaiveBayes are compatible with scikit-multiflow with the caveat that they must be partially fitted before use. In the scikit-multiflow evaluators this is done by setting pretrain_size>0.

Examples

>>> # Imports
>>> from skmultiflow.data import SEAGenerator
>>> from skmultiflow.bayes import NaiveBayes
>>>
>>> # Setup a data stream
>>> stream = SEAGenerator(random_state=1)
>>>
>>> # Setup Naive Bayes estimator
>>> naive_bayes = NaiveBayes()
>>>
>>> # Setup variables to control loop and track performance
>>> n_samples = 0
>>> correct_cnt = 0
>>> max_samples = 200
>>>
>>> # Train the estimator with the samples provided by the data stream
>>> while n_samples < max_samples and stream.has_more_samples():
>>>     X, y = stream.next_sample()
>>>     y_pred = naive_bayes.predict(X)
>>>     if y[0] == y_pred[0]:
>>>         correct_cnt += 1
>>>     naive_bayes.partial_fit(X, y)
>>>     n_samples += 1
>>>
>>> # Display results
>>> print('{} samples analyzed.'.format(n_samples))
>>> print('Naive Bayes accuracy: {}'.format(correct_cnt / n_samples))

Methods

fit(self, X, y[, classes, sample_weight])	Fit the model.
get_info(self)	Collects and returns the information about the configuration of the estimator
get_params(self[, deep])	Get parameters for this estimator.
partial_fit(self, X, y[, classes, sample_weight])	Partially (incrementally) fit the model.
predict(self, X)	Predict classes for the passed data.
predict_proba(self, X)	Estimates the probability of each sample in X belonging to each of the class-labels.
reset(self)	Resets the estimator to its initial state.
score(self, X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(self, **params)	Set the parameters of this estimator.

fit(self, X, y, classes=None, sample_weight=None)

Fit the model.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The features to train the model.

y: numpy.ndarray of shape (n_samples, n_targets)

An array-like with the class labels of all samples in X.

classes: numpy.ndarray, optional (default=None)

Contains all possible/known class labels. Usage varies depending on the learning method.

sample_weight: numpy.ndarray, optional (default=None)

Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.

Returns

self

get_info(self)

Collects and returns the information about the configuration of the estimator

Returns

string

Configuration of the estimator.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

partial_fit(self, X, y, classes=None, sample_weight=None)

Partially (incrementally) fit the model.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The features to train the model.

y: numpy.ndarray of shape (n_samples)

An array-like with the labels of all samples in X.

classes: numpy.ndarray, optional (default=None)

Array with all possible/known classes. Usage varies depending on the learning method.

sample_weight: numpy.ndarray of shape (n_samples), optional (default=None)

Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.

Returns

NaiveBayes

self

predict(self, X)

Predict classes for the passed data.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The set of data samples to predict the labels for.

Returns

A numpy.ndarray with all the predictions for the samples in X.

predict_proba(self, X)

Estimates the probability of each sample in X belonging to each of the class-labels.

Parameters

XNumpy.ndarray of shape (n_samples, n_features)

The matrix of samples one wants to predict the class probabilities for.

Returns

A numpy.ndarray of shape (n_samples, n_labels), in which each outer entry is associated

with the X entry of the same index. And where the list in index [i] contains

len(self.target_values) elements, each of which represents the probability that

the i-th sample of X belongs to a certain class-label.

reset(self)

Resets the estimator to its initial state.

Returns

self

score(self, X, y, sample_weight=None)

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

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

Xarray-like, shape = (n_samples, n_features)

Test samples.

yarray-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like, shape = [n_samples], optional

Sample weights.

Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns

self