BernoulliNB

class ibex.sklearn.naive_bayes.BernoulliNB(alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None)

Bases: sklearn.naive_bayes.BernoulliNB, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute coef_.

Example:

>>> import numpy as np
>>> from sklearn import datasets
>>> import pandas as pd
>>>
>>> iris = datasets.load_iris()
>>> features, targets, iris = iris['feature_names'], iris['target_names'], pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=iris['feature_names']+['class'])
>>> iris['class'] = iris['class'].map(pd.Series(targets))
>>>
>>> iris.head()
                sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)             0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2

    class
0  setosa
1  setosa
2  setosa
3  setosa
4  setosa

>>>
>>> from ibex.sklearn import naive_bayes as pd_naive_bayes
>>>
>>> clf =  pd_naive_bayes.BernoulliNB().fit(iris[features], iris['class'])
>>>
>>> clf.coef_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute intercept_.

Example:

>>> import numpy as np
>>> from sklearn import datasets
>>> import pandas as pd
>>>
>>> iris = datasets.load_iris()
>>> features, targets, iris = iris['feature_names'], iris['target_names'], pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=iris['feature_names']+['class'])
>>> iris['class'] = iris['class'].map(pd.Series(targets))
>>>
>>> iris.head()
                sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)             0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2

    class
0  setosa
1  setosa
2  setosa
3  setosa
4  setosa

>>> from ibex.sklearn import naive_bayes as pd_naive_bayes
>>>
>>> clf = pd_naive_bayes.BernoulliNB().fit(iris[features], iris['class'])
>>>
>>> clf.intercept_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64

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.

alpha : float, optional (default=1.0)
Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
binarize : float or None, optional (default=0.0)
Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.
fit_prior : boolean, optional (default=True)
Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
class_prior : array-like, size=[n_classes,], optional (default=None)
Prior probabilities of the classes. If specified the priors are not adjusted according to the data.
class_log_prior_ : array, shape = [n_classes]
Log probability of each class (smoothed).
feature_log_prob_ : array, shape = [n_classes, n_features]
Empirical log probability of features given a class, P(x_i|y).
class_count_ : array, shape = [n_classes]
Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.
feature_count_ : array, 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.
>>> import numpy as np
>>> X = np.random.randint(2, 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(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)
>>> 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. http://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).

fit(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit Naive Bayes classifier according to X, y

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
sample_weight : array-like, shape = [n_samples], (default=None)
Weights applied to individual samples (1. for unweighted).
self : object
Returns self.
partial_fit(X, y, classes=None, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Incremental fit on a batch of samples.

This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning.

This is especially useful when the whole dataset is too big to fit in memory at once.

This method has some performance overhead hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
classes : array-like, shape = [n_classes] (default=None)

List of all the classes that can possibly appear in the y vector.

Must be provided at the first call to partial_fit, can be omitted in subsequent calls.

sample_weight : array-like, shape = [n_samples] (default=None)
Weights applied to individual samples (1. for unweighted).
self : object
Returns self.
predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Perform classification on an array of test vectors X.

X : array-like, shape = [n_samples, n_features]

C : array, shape = [n_samples]
Predicted target values for X
predict_log_proba(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Return log-probability estimates for the test vector X.

X : array-like, shape = [n_samples, n_features]

C : array-like, shape = [n_samples, n_classes]
Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.
predict_proba(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Return probability estimates for the test vector X.

X : array-like, shape = [n_samples, n_features]

C : array-like, shape = [n_samples, n_classes]
Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
score : float
Mean accuracy of self.predict(X) wrt. y.