8000 fix linting issues · scikit-learn/scikit-learn@3af9b04 · GitHub
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fix linting issues
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‎sklearn/feature_selection/_mutual_info.py

Lines changed: 8 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -350,13 +350,15 @@ def mutual_info_regression(X, y, *, discrete_features='auto', n_neighbors=3,
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For example, pixel intensities of an image are discrete features
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(but hardly categorical) and you will get better results if mark them
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as such. Also note, that treating a continuous variable as discrete and
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vice versa will usually give incorrect results, so be attentive about that.
353+
vice versa will usually give incorrect results, so be attentive about
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that.
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2. True mutual information can't be negative. If its estimate turns out
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to be negative, it is replaced by zero.
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References
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----------
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.. [1] `Mutual Information <https://en.wikipedia.org/wiki/Mutual_information>`_
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.. [1] `Mutual Information
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<https://en.wikipedia.org/wiki/Mutual_information>`_
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on Wikipedia.
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.. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
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information". Phys. Rev. E 69, 2004.
@@ -428,13 +430,15 @@ def mutual_info_classif(X, y, *, discrete_features='auto', n_neighbors=3,
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For example, pixel intensities of an image are discrete features
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(but hardly categorical) and you will get better results if mark them
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as such. A 10000 lso note, that treating a continuous variable as discrete and
431-
vice versa will usually give incorrect results, so be attentive about that.
433+
vice versa will usually give incorrect results, so be attentive about
434+
that.
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2. True mutual information can't be negative. If its estimate turns out
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to be negative, it is replaced by zero.
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References
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----------
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.. [1] `Mutual Information <https://en.wikipedia.org/wiki/Mutual_information>`_
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.. [1] `Mutual Information
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<https://en.wikipedia.org/wiki/Mutual_information>`_
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on Wikipedia.
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.. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
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information". Phys. Rev. E 69, 2004.

‎sklearn/feature_selection/_univariate_selection.py

Lines changed: 20 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -425,7 +425,8 @@ class SelectPercentile(_BaseFilter):
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SelectFpr: Select features based on a false positive rate test.
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SelectFdr: Select features based on an estimated false discovery rate.
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SelectFwe: Select features based on family-wise error rate.
428-
GenericUnivariateSelect: Univariate feature selector with configurable mode.
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GenericUnivariateSelect: Univariate feature selector with configurable
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mode.
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"""
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@_deprecate_positional_args
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def __init__(self, score_func=f_classif, *, percentile=10):
@@ -507,11 +508,13 @@ class SelectKBest(_BaseFilter):
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chi2: Chi-squared stats of non-negative features for classification tasks.
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f_regression: F-value between label/feature for regression tasks.
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mutual_info_regression: Mutual information for a continuous target.
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SelectPercentile: Select features based on percentile of the highest scores.
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SelectPercentile: Select features based on percentile of the highest
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scores.
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SelectFpr: Select features based on a false positive rate test.
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SelectFdr: Select features based on an estimated false discovery rate.
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SelectFwe: Select features based on family-wise error rate.
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GenericUnivariateSelect: Univariate feature selector with configurable mode.
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GenericUnivariateSelect: Univariate feature selector with configurable
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mode.
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"""
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@_deprecate_positional_args
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def __init__(self, score_func=f_classif, *, k=10):
@@ -586,11 +589,13 @@ class SelectFpr(_BaseFilter):
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mutual_info_classif:
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f_regression: F-value between label/feature for regression tasks.
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mutual_info_regression: Mutual information between features and the target.
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SelectPercentile: Select features based on percentile of the highest scores.
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SelectPercentile: Select features based on percentile of the highest
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scores.
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SelectKBest: Select features based on the k highest scores.
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SelectFdr: Select features based on an estimated false discovery rate.
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SelectFwe: Select features based on family-wise error rate.
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GenericUnivariateSelect: Univariate feature selector with configurable mode.
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GenericUnivariateSelect: Univariate feature selector with configurable
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mode.
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"""
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@_deprecate_positional_args
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def __init__(self, score_func=f_classif, *, alpha=5e-2):
@@ -652,11 +657,13 @@ class SelectFdr(_BaseFilter):
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chi2: Chi-squared stats of non-negative features for classification tasks.
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f_regression: F-value between label/feature for regression tasks.
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mutual_info_regression: Mutual information for a contnuous target.
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SelectPercentile: Select features based on percentile of the highest scores.
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SelectPercentile: Select features based on percentile of the highest
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scores.
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SelectKBest: Select features based on the k highest scores.
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SelectFpr: Select features based on a false positive rate test.
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SelectFwe: Select features based on family-wise error rate.
659-
GenericUnivariateSelect: Univariate feature selector with configurable mode.
665+
GenericUnivariateSelect: Univariate feature selector with configurable
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mode.
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"""
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@_deprecate_positional_args
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def __init__(self, score_func=f_classif, *, alpha=5e-2):
@@ -715,11 +722,13 @@ class SelectFwe(_BaseFilter):
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f_classif: ANOVA F-value between label/feature for classification tasks.
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chi2: Chi-squared stats of non-negative features for classification tasks.
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f_regression: F-value between label/feature for regression tasks.
718-
SelectPercentile: Select features based on percentile of the highest scores.
725+
SelectPercentile: Select features based on percentile of the highest
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scores.
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SelectKBest: Select features based on the k highest scores.
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SelectFpr: Select features based on a false positive rate test.
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SelectFdr: Select features based on an estimated false discovery rate.
722-
GenericUnivariateSelect: Univariate feature selector with configurable mode.
730+
GenericUnivariateSelect: Univariate feature selector with configurable
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mode.
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"""
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@_deprecate_positional_args
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def __init__(self, score_func=f_classif, *, alpha=5e-2):
@@ -783,7 +792,8 @@ class GenericUnivariateSelect(_BaseFilter):
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chi2: Chi-squared stats of non-negative features for classification tasks.
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f_regression: F-value between label/feature for regression tasks.
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mutual_info_regression: Mutual information for a continuous target.
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SelectPercentile: Select features based on percentile of the highest scores.
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SelectPercentile: Select features based on percentile of the highest
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scores.
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SelectKBest: Select features based on the k highest scores.
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SelectFpr: Select features based on a false positive rate test.
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SelectFdr: Select features based on an estimated false discovery rate.

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