8000 moved class_prior in NB to __init__ by mrshu · Pull Request #1499 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

moved class_prior in NB to __init__ #1499

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 9 commits into from
Closed

moved class_prior in NB to __init__ #1499

Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
28f1284
moved class_prior in NB to __init__
mrshu Dec 30, 2012
cf9e1da
added deprecation warning to fit function
mrshu Dec 30, 2012
c002994
fixed docstring tests
mrshu Dec 30, 2012
623d0d4
fixed typos
mrshu Dec 30, 2012
d4f7504
added warnings
mrshu Dec 30, 2012
6316502
updated based on comments
mrshu Dec 30, 2012
073a40b
fixed local variables
mrshu Dec 31, 2012
9f7c230
renamed the new parameter to class_wieght
mrshu Jan 3, 2013
1844820
fixed docstring test
mrshu Jan 3, 2013
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 13 additions & 4 deletions sklearn/naive_bayes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@

import numpy as np
from scipy.sparse import issparse
import warnings

from .base import BaseEstimator, ClassifierMixin
from .preprocessing import binarize, LabelBinarizer
Expand Down Expand Up @@ -238,6 +239,12 @@ def fit(self, X, y, sample_weight=None, class_prior=None):
if sample_weight is not None:
Y *= array2d(sample_weight).T

if class_prior is not None:
warnings.warn('class_prior is deprecated in fit function and will '
'be removed in version 0.15. Use it in __init__ instead.')
else:
class_prior = self.class_weight

if class_prior:
if len(class_prior) != n_classes:
raise ValueError("Number of priors must match number of"
Expand Down Expand Up @@ -311,7 +318,7 @@ class MultinomialNB(BaseDiscreteNB):
>>> from sklearn.naive_bayes import MultinomialNB
>>> clf = MultinomialNB()
>>> clf.fit(X, Y)
MultinomialNB(alpha=1.0, fit_prior=True)
MultinomialNB(alpha=1.0, class_weight=None, fit_prior=True)
>>> print(clf.predict(X[2]))
[3]

Expand All @@ -322,9 +329,10 @@ class MultinomialNB(BaseDiscreteNB):
Tackling the poor assumptions of naive Bayes text classifiers, ICML.
"""

def __init__(self, alpha=1.0, fit_prior=True):
def __init__(self, alpha=1.0, fit_prior=True, class_weight=None):
self.alpha = alpha
self.fit_prior = fit_prior
self.class_weight = class_weight

def _count(self, X, Y):
"""Count and smooth feature occurrences."""
Expand Down Expand Up @@ -377,7 +385,7 @@ class BernoulliNB(BaseDiscreteNB):
>>> from sklearn.naive_bayes import BernoulliNB
>>> clf = BernoulliNB()
>>> clf.fit(X, Y)
BernoulliNB(alpha=1.0, binarize=0.0, fit_prior=True)
BernoulliNB(alpha=1.0, binarize=0.0, class_weight=None, fit_prior=True)
>>> print(clf.predict(X[2]))
[3]

Expand All @@ -395,10 +403,11 @@ class BernoulliNB(BaseDiscreteNB):
naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).
"""

def __init__(self, alpha=1.0, binarize=.0, fit_prior=True):
def __init__(self, alpha=1.0, binarize=.0, fit_prior=True, class_weight=None):
self.alpha = alpha
self.binarize = binarize
self.fit_prior = fit_prior
self.class_weight = class_weight

def _count(self, X, Y):
"""Count and smooth feature occurrences."""
Expand Down
0