simpleml.models.classifiers.sklearn.naive_bayes

Wrapper module around sklearn.naive_bayes

Module Contents

Classes

SklearnBernoulliNB	No different than base model. Here just to maintain the pattern
SklearnGaussianNB	No different than base model. Here just to maintain the pattern
SklearnMultinomialNB	No different than base model. Here just to maintain the pattern
WrappedSklearnBernoulliNB	Naive Bayes classifier for multivariate Bernoulli models.
WrappedSklearnGaussianNB	Gaussian Naive Bayes (GaussianNB)
WrappedSklearnMultinomialNB	Naive Bayes classifier for multinomial models

simpleml.models.classifiers.sklearn.naive_bayes.LOGGER

Bernoulli

simpleml.models.classifiers.sklearn.naive_bayes.__author__ = Elisha Yadgaran

class simpleml.models.classifiers.sklearn.naive_bayes.SklearnBernoulliNB(has_external_files=True, external_model_kwargs=None, params=None, **kwargs)

Bases: simpleml.models.classifiers.sklearn.base_sklearn_classifier.SklearnClassifier

No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)

Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors

_create_external_model(self, **kwargs)

Abstract method for each subclass to implement

should return the desired model object

class simpleml.models.classifiers.sklearn.naive_bayes.SklearnGaussianNB(has_external_files=True, external_model_kwargs=None, params=None, **kwargs)

Bases: simpleml.models.classifiers.sklearn.base_sklearn_classifier.SklearnClassifier

No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)

Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors

_create_external_model(self, **kwargs)

Abstract method for each subclass to implement

should return the desired model object

class simpleml.models.classifiers.sklearn.naive_bayes.SklearnMultinomialNB(has_external_files=True, external_model_kwargs=None, params=None, **kwargs)

Bases: simpleml.models.classifiers.sklearn.base_sklearn_classifier.SklearnClassifier

No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)

Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors

_create_external_model(self, **kwargs)

Abstract method for each subclass to implement

should return the desired model object

class simpleml.models.classifiers.sklearn.naive_bayes.WrappedSklearnBernoulliNB(*, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None)

Bases: sklearn.naive_bayes.BernoulliNB, simpleml.models.classifiers.external_models.ClassificationExternalModelMixin

Naive Bayes classifier for multivariate Bernoulli models.

Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features.

Read more in the User Guide.

alphafloat, default=1.0

Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).

binarizefloat or None, default=0.0

Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.

fit_priorbool, default=True

Whether to learn class prior probabilities or not. If false, a uniform prior will be used.

class_priorarray-like of shape (n_classes,), default=None

Prior probabilities of the classes. If specified the priors are not adjusted according to the data.

class_count_ndarray of shape (n_classes)

Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.

class_log_prior_ndarray of shape (n_classes)

Log probability of each class (smoothed).

classesndarray of shape (n_classes,)

Class labels known to the classifier

coef_ndarray of shape (n_classes, n_features)

Mirrors feature_log_prob_ for interpreting BernoulliNB as a linear model.

feature_count_ndarray of shape (n_classes, n_features)

Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.

feature_log_prob_ndarray of shape (n_classes, n_features)

Empirical log probability of features given a class, P(x_i|y).

intercept_ndarray of shape (n_classes,)

Mirrors class_log_prior_ for interpreting BernoulliNB as a linear model.

n_features_int

Number of features of each sample.

>>> import numpy as np
>>> rng = np.random.RandomState(1)
>>> X = rng.randint(5, size=(6, 100))
>>> Y = np.array([1, 2, 3, 4, 4, 5])
>>> from sklearn.naive_bayes import BernoulliNB
>>> clf = BernoulliNB()
>>> clf.fit(X, Y)
BernoulliNB()
>>> print(clf.predict(X[2:3]))
[3]

C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234-265. https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html

A. McCallum and K. Nigam (1998). A comparison of event models for naive Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48.

V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with naive Bayes – Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).

get_feature_metadata(self, features, **kwargs)

By default nothing is implemented

class simpleml.models.classifiers.sklearn.naive_bayes.WrappedSklearnGaussianNB(*, priors=None, var_smoothing=1e-09)

Bases: sklearn.naive_bayes.GaussianNB, simpleml.models.classifiers.external_models.ClassificationExternalModelMixin

Gaussian Naive Bayes (GaussianNB)

Can perform online updates to model parameters via partial_fit(). For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf

Read more in the User Guide.

priorsarray-like of shape (n_classes,)

Prior probabilities of the classes. If specified the priors are not adjusted according to the data.

var_smoothingfloat, default=1e-9

Portion of the largest variance of all features that is added to variances for calculation stability.

New in version 0.20.

class_count_ndarray of shape (n_classes,)

number of training samples observed in each class.

class_prior_ndarray of shape (n_classes,)

probability of each class.

classesndarray of shape (n_classes,)

class labels known to the classifier

epsilon_float

absolute additive value to variances

sigma_ndarray of shape (n_classes, n_features)

variance of each feature per class

theta_ndarray of shape (n_classes, n_features)

mean of each feature per class

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> Y = np.array([1, 1, 1, 2, 2, 2])
>>> from sklearn.naive_bayes import GaussianNB
>>> clf = GaussianNB()
>>> clf.fit(X, Y)
GaussianNB()
>>> print(clf.predict([[-0.8, -1]]))
[1]
>>> clf_pf = GaussianNB()
>>> clf_pf.partial_fit(X, Y, np.unique(Y))
GaussianNB()
>>> print(clf_pf.predict([[-0.8, -1]]))
[1]

get_feature_metadata(self, features, **kwargs)

By default nothing is implemented

class simpleml.models.classifiers.sklearn.naive_bayes.WrappedSklearnMultinomialNB(*, alpha=1.0, fit_prior=True, class_prior=None)

Bases: sklearn.naive_bayes.MultinomialNB, simpleml.models.classifiers.external_models.ClassificationExternalModelMixin

Naive Bayes classifier for multinomial models

The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.

Read more in the User Guide.

alphafloat, default=1.0

Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).

fit_priorbool, default=True

Whether to learn class prior probabilities or not. If false, a uniform prior will be used.

class_priorarray-like of shape (n_classes,), default=None

Prior probabilities of the classes. If specified the priors are not adjusted according to the data.

class_count_ndarray of shape (n_classes,)

Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.

class_log_prior_ndarray of shape (n_classes, )

Smoothed empirical log probability for each class.

classesndarray of shape (n_classes,)

Class labels known to the classifier

coef_ndarray of shape (n_classes, n_features)

Mirrors feature_log_prob_ for interpreting MultinomialNB as a linear model.

Deprecated since version 0.24: coef_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).

feature_count_ndarray of shape (n_classes, n_features)

Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.

feature_log_prob_ndarray of shape (n_classes, n_features)

Empirical log probability of features given a class, P(x_i|y).

intercept_ndarray of shape (n_classes,)

Mirrors class_log_prior_ for interpreting MultinomialNB as a linear model.

Deprecated since version 0.24: intercept_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).

n_features_int

Number of features of each sample.

>>> import numpy as np
>>> rng = np.random.RandomState(1)
>>> X = rng.randint(5, size=(6, 100))
>>> y = np.array([1, 2, 3, 4, 5, 6])
>>> from sklearn.naive_bayes import MultinomialNB
>>> clf = MultinomialNB()
>>> clf.fit(X, y)
MultinomialNB()
>>> print(clf.predict(X[2:3]))
[3]

For the rationale behind the names coef_ and intercept_, i.e. naive Bayes as a linear classifier, see J. Rennie et al. (2003), Tackling the poor assumptions of naive Bayes text classifiers, ICML.

C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 234-265. https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html

get_feature_metadata(self, features, **kwargs)

By default nothing is implemented