8000 WIP simplify naive Bayes parametrization by amueller · Pull Request #1525 · scikit-learn/scikit-learn · GitHub
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WIP simplify naive Bayes parametrization #1525

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WIP simplify naive Bayes parametrization #1525

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6f48d62
ENH simplify naive Bayes parametrization, make more consistent with r…
amueller Jan 6, 2013
35da4e4
ENH use compute_class_weight utility in NB.
amueller Jan 6, 2013
a30f3b5
misc
amueller Jan 6, 2013
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71 changes: 33 additions & 38 deletions sklearn/naive_bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@
from .preprocessing import binarize, LabelBinarizer
from .utils import array2d, atleast2d_or_csr
from .utils.extmath import safe_sparse_dot, logsumexp
from .utils import check_arrays
from .utils import check_arrays, compute_class_weight

__all__ = ['BernoulliNB', 'GaussianNB', 'MultinomialNB']

Expand Down Expand Up @@ -211,10 +211,6 @@ def fit(self, X, y, sample_weight=None, class_prior=None):
sample_weight : array-like, shape = [n_samples], optional
Weights applied to individual samples (1. for unweighted).

class_prior : array, shape [n_classes]
Custom prior probability per class.
Overrides the fit_prior parameter.

Returns
-------
self : object
Expand All @@ -225,7 +221,6 @@ def fit(self, X, y, sample_weight=None, class_prior=None):
labelbin = LabelBinarizer()
Y = labelbin.fit_transform(y)
self.classes_ = labelbin.classes_
n_classes = len(self.classes_)
if Y.shape[1] == 1:
Y = np.concatenate((1 - Y, Y), axis=1)

Expand All @@ -243,20 +238,22 @@ def fit(self, X, y, sample_weight=None, class_prior=None):
warnings.warn('class_prior is deprecated in fit function and will '
'be removed in version 0.15. Use the `__init__` '
'parameter class_weight instead.')
class_weight = class_prior
else:
class_prior = self.class_weight

if class_prior:
if len(class_prior) != n_classes:
raise ValueError("Number of priors must match number of"
" classes.")
self.class_log_prior_ = np.log(class_prior)
elif self.fit_prior:
# empirical prior, with sample_weight taken into account
y_freq = Y.sum(axis=0)
self.class_log_prior_ = np.log(y_freq) - np.log(y_freq.sum())
else:
self.class_log_prior_ = np.zeros(n_classes) - np.log(n_classes)
class_weight = self.class_weight

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

class_weight = compute_class_weight(class_weight, self.classes_, y)
class_weight /= class_weight.sum()
self.class_log_prior_ = np.log(class_weight)

# N_c_i is the count of feature i in all samples of class c.
# N_c is the denominator.
Expand Down Expand Up @@ -295,13 +292,12 @@ class MultinomialNB(BaseDiscreteNB):
Additive (Laplace/Lidstone) smoothing parameter
(0 for no smoothing).

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

class_weight : array-like, size=[n_classes,]
Prior probabilities of the classes. If specified the priors are not
adjusted according to the data.
class_weight : 'auto', None or array-like. default='auto'
Prior probabilities of the classes. 'auto' means class priors
are estimated from the data, None means uniform priors
over all classes.
If an array is given, it must have shape [n_classes,] and
explicitly specifies the class priors.

Attributes
----------
Expand All @@ -324,7 +320,7 @@ class MultinomialNB(BaseDiscreteNB):
>>> from sklearn.naive_bayes import MultinomialNB
>>> clf = MultinomialNB()
>>> clf.fit(X, Y)
MultinomialNB(alpha=1.0, class_weight=None, fit_prior=True)
MultinomialNB(alpha=1.0, class_weight='auto', fit_prior=None)
>>> print(clf.predict(X[2]))
[3]

Expand All @@ -335,7 +331,7 @@ class MultinomialNB(BaseDiscreteNB):
Tackling the poor assumptions of naive Bayes text classifiers, ICML.
"""

def __init__(self, alpha=1.0, fit_prior=True, class_weight=None):
def __init__(self, alpha=1.0, fit_prior=None, class_weight='auto'):
self.alpha = alpha
self.fit_prior = fit_prior
self.class_weight = class_weight
Expand Down Expand Up @@ -373,13 +369,12 @@ class BernoulliNB(BaseDiscreteNB):
Threshold for binarizing (mapping to booleans) of sample features.
If None, input is presumed to already consist of binary vectors.

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

class_weight : array-like, size=[n_classes,]
Prior probabilities of the classes. If specified the priors are not
adjusted according to the data.
class_weight : 'auto', None or array-like. default='auto'
Prior probabilities of the classes. 'auto' means class priors
are estimated from the data, None means uniform priors
over all classes.
If an array is given, it must have shape [n_classes,] and
explicitly specifies the class priors.

Attributes
----------
Expand All @@ -397,7 +392,7 @@ class BernoulliNB(BaseDiscreteNB):
>>> from sklearn.naive_bayes import BernoulliNB
>>> clf = BernoulliNB()
>>> clf.fit(X, Y)
BernoulliNB(alpha=1.0, binarize=0.0, class_weight=None, fit_prior=True)
BernoulliNB(alpha=1.0, binarize=0.0, class_weight='auto', fit_prior=None)
>>> print(clf.predict(X[2]))
[3]

Expand All @@ -415,8 +410,8 @@ 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,
class_weight=None):
def __init__(self, alpha=1.0, binarize=.0, fit_prior=None,
class_weight='auto'):
self.alpha = alpha
self.binarize = binarize
self.fit_prior = fit_prior
Expand Down
3 changes: 1 addition & 2 deletions sklearn/tests/test_common.py
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Expand Up @@ -721,8 +721,7 @@ def test_class_weight_auto_classifies():
continue

if name.endswith("NB"):
# NaiveBayes classifiers have a somewhat differnt interface.
# FIXME SOON!
# naive bayes classifiers don't work on this kind of data :(
continue

with warnings.catch_warnings(record=True):
Expand Down
9 changes: 4 additions & 5 deletions sklearn/tests/test_naive_bayes.py
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Expand Up @@ -138,11 +138,10 @@ def test_discretenb_predict_proba():

def test_discretenb_uniform_prior():
"""Test whether discrete NB classes fit a uniform prior
when fit_prior=False and class_prior=None"""
when class_weight=None"""

for cls in [BernoulliNB, MultinomialNB]:
clf = cls()
clf.set_params(fit_prior=False)
clf = cls(class_weight=None)
clf.fit([[0], [0], [1]], [0, 0, 1])
prior = np.exp(clf.class_log_prior_)
assert_array_equal(prior, np.array([.5, .5]))
Expand All @@ -152,8 +151,8 @@ def test_discretenb_provide_prior():
"""Test whether discrete NB classes use provided prior"""

for cls in [BernoulliNB, MultinomialNB]:
clf = cls()
clf.fit([[0], [0], [1]], [0, 0, 1], class_prior=[0.5, 0.5])
clf = cls(class_weight=[.5, .5])
clf.fit([[0], [0], [1]], [0, 0, 1])
prior = np.exp(clf.class_log_prior_)
assert_array_equal(prior, np.array([.5, .5]))

Expand Down
13 changes: 8 additions & 5 deletions sklearn/utils/class_weight.py
6D40
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Expand Up @@ -37,17 +37,20 @@ def compute_class_weight(class_weight, classes, y):
weight = np.array([1.0 / np.sum(y == i) for i in classes],
dtype=np.float64, order='C')
weight *= classes.shape[0] / np.sum(weight)
else:
elif isinstance(class_weight, dict):
# user-defined dictionary
weight = np.ones(classes.shape[0], dtype=np.float64, order='C')
if not isinstance(class_weight, dict):
raise ValueError("class_weight must be dict, 'auto', or None,"
" got: %r" % class_weight)
for c in class_weight:
i = np.searchsorted(classes, c)
if classes[i] != c:
raise ValueError("Class label %d not present." % c)
else:
weight[i] = class_weight[c]

else:
# user-specified array or list
weight = np.array(class_weight)
if len(weight) != len(classes):
raise ValueError("The number of entries in class_weight is %d, "
"which does not match the number of classes %d."
% (len(weight), len(classes)))
return weight
0