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DOC Clarifies comments and docstrings in _BaseDiscreteNB #22565

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Merged
merged 9 commits into from
Feb 24, 2022
Merged

DOC Clarifies comments and docstrings in _BaseDiscreteNB #22565

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75ce0de
Add abstract methods to _BaseDiscreteNB and minor corrections in comm…
Feb 21, 2022
6f80025
Merge remote-tracking branch 'upstream/main' into naive-bayes-abstract
Feb 22, 2022
fb7112d
Rephrased docstring for _BaseNB._joint_log_likelihood
Feb 22, 2022
e623537
merge upstream/main
Feb 22, 2022
a2bb6f3
Correction in _BaseNB._joint_log_likelihood doctstring
Feb 22, 2022
b2c380b
Remove abstract methods, which will be in a separate PR
Feb 22, 2022
39e0789
Update sklearn/naive_bayes.py
avm19 Feb 22, 2022
c56d139
Update sklearn/naive_bayes.py
avm19 Feb 22, 2022
6328c2c
Merge remote-tracking branch 'upstream/main' into naive-bayes-abstract
Feb 23, 2022
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22 changes: 14 additions & 8 deletions sklearn/naive_bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,8 +53,8 @@ def _joint_log_likelihood(self, X):
I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of
shape (n_samples, n_classes).

Input is passed to _joint_log_likelihood as-is by predict,
predict_proba and predict_log_proba.
predict, predict_proba, and predict_log_proba pass the input through
_check_X and handle it over to _joint_log_likelihood.
"""

@abstractmethod
Expand Down Expand Up @@ -140,7 +140,7 @@ class GaussianNB(_BaseNB):
Parameters
----------
priors : array-like of shape (n_classes,)
Prior probabilities of the classes. If specified the priors are not
Prior probabilities of the classes. If specified, the priors are not
adjusted according to the data.

var_smoothing : float, default=1e-9
Expand Down Expand Up @@ -423,13 +423,13 @@ def _partial_fit(self, X, y, classes=None, _refit=False, sample_weight=None):
# Take into account the priors
if self.priors is not None:
priors = np.asarray(self.priors)
# Check that the provide prior match the number of classes
# Check that the provided prior matches the number of classes
if len(priors) != n_classes:
raise ValueError("Number of priors must match number of classes.")
# Check that the sum is 1
if not np.isclose(priors.sum(), 1.0):
raise ValueError("The sum of the priors should be 1.")
# Check that the prior are non-negative
# Check that the priors are non-negative
if (priors < 0).any():
raise ValueError("Priors must be non-negative.")
self.class_prior_ = priors
Expand Down Expand Up @@ -523,6 +523,12 @@ def _check_X_y(self, X, y, reset=True):
return self._validate_data(X, y, accept_sparse="csr", reset=reset)

def _update_class_log_prior(self, class_prior=None):
"""Update class log priors.

The class log priors are based on `class_prior`, class count or the
number of classes. This method is called each time `fit` or
`partial_fit` update the model.
"""
n_classes = len(self.classes_)
if class_prior is not None:
if len(class_prior) != n_classes:
Expand Down Expand Up @@ -733,7 +739,7 @@ class MultinomialNB(_BaseDiscreteNB):
If false, a uniform prior will be used.

class_prior : array-like of shape (n_classes,), default=None
Prior probabilities of the classes. If specified the priors are not
Prior probabilities of the classes. If specified, the priors are not
adjusted according to the data.

Attributes
Expand Down Expand Up @@ -988,7 +994,7 @@ class BernoulliNB(_BaseDiscreteNB):
If false, a uniform prior will be used.

class_prior : array-like of shape (n_classes,), default=None
Prior probabilities of the classes. If specified the priors are not
Prior probabilities of the classes. If specified, the priors are not
adjusted according to the data.

Attributes
Expand Down Expand Up @@ -1136,7 +1142,7 @@ class CategoricalNB(_BaseDiscreteNB):
If false, a uniform prior will be used.

class_prior : array-like of shape (n_classes,), default=None
Prior probabilities of the classes. If specified the priors are not
Prior probabilities of the classes. If specified, the priors are not
adjusted according to the data.

min_categories : int or array-like of shape (n_features,), default=None
Expand Down