From e1d3fa1a34499691915ccc4eb4da22836947dfc3 Mon Sep 17 00:00:00 2001 From: MDouriez Date: Sat, 2 Nov 2019 12:02:52 -0700 Subject: [PATCH 1/3] wip --- sklearn/naive_bayes.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py index be9450d7334f0..b5a2a152b7487 100644 --- a/sklearn/naive_bayes.py +++ b/sklearn/naive_bayes.py @@ -31,7 +31,7 @@ from .utils.extmath import safe_sparse_dot from .utils.fixes import logsumexp from .utils.multiclass import _check_partial_fit_first_call -from .utils.validation import check_is_fitted, check_non_negative, column_or_1d +from .utils.validation import check_is_fitted, check_non_negative, column_or_1d, _check_sample_weight __all__ = ['BernoulliNB', 'GaussianNB', 'MultinomialNB', 'ComplementNB', 'CategoricalNB'] @@ -359,8 +359,8 @@ def _partial_fit(self, X, y, classes=None, _refit=False, """ X, y = check_X_y(X, y) if sample_weight is not None: - sample_weight = check_array(sample_weight, ensure_2d=False) - check_consistent_length(y, sample_weight) + sample_weight = _check_sample_weight(sample_weight, X, + dtype=np.float64) # If the ratio of data variance between dimensions is too small, it # will cause numerical errors. To address this, we artificially From d5f8e61ca7eeb33d29674ab58224222fd05b9f00 Mon Sep 17 00:00:00 2001 From: MDouriez Date: Sat, 2 Nov 2019 12:16:53 -0700 Subject: [PATCH 2/3] flake8 --- sklearn/naive_bayes.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py index b5a2a152b7487..f80929827e2ee 100644 --- a/sklearn/naive_bayes.py +++ b/sklearn/naive_bayes.py @@ -21,17 +21,17 @@ import numpy as np -from scipy.sparse import issparse from .base import BaseEstimator, ClassifierMixin from .preprocessing import binarize from .preprocessing import LabelBinarizer from .preprocessing import label_binarize -from .utils import check_X_y, check_array, check_consistent_length +from .utils import check_X_y, check_array from .utils.extmath import safe_sparse_dot from .utils.fixes import logsumexp from .utils.multiclass import _check_partial_fit_first_call -from .utils.validation import check_is_fitted, check_non_negative, column_or_1d, _check_sample_weight +from .utils.validation import check_is_fitted, check_non_negative, column_or_1d +from .utils.validation import _check_sample_weight __all__ = ['BernoulliNB', 'GaussianNB', 'MultinomialNB', 'ComplementNB', 'CategoricalNB'] From 89e92bfbbfbd22451ec39136c1d572173f9dd3bd Mon Sep 17 00:00:00 2001 From: MDouriez Date: Sat, 2 Nov 2019 12:31:28 -0700 Subject: [PATCH 3/3] remove dtype --- sklearn/naive_bayes.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py index f80929827e2ee..86a09737addd8 100644 --- a/sklearn/naive_bayes.py +++ b/sklearn/naive_bayes.py @@ -359,8 +359,7 @@ def _partial_fit(self, X, y, classes=None, _refit=False, """ X, y = check_X_y(X, y) if sample_weight is not None: - sample_weight = _check_sample_weight(sample_weight, X, - dtype=np.float64) + sample_weight = _check_sample_weight(sample_weight, X) # If the ratio of data variance between dimensions is too small, it # will cause numerical errors. To address this, we artificially