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Showing 2 of 2 files from the diff.

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src/papierstat/datasets/cat.py changed.

82.40% 100.00% -0.14%

...pierstat/mltricks/sklearn_base_learner_category.py changed.

@@ -73,7 +73,7 @@

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    test[label] = test[label].str.strip(' .')
    cols = train.select_dtypes(object).columns
    for c in cols:
        train[c] = train[c].str.strip()
        train[c] = train[c].str.strip()  # pylint: disable=E1136,E1137
    for c in cols:
        test[c] = test[c].str.strip()
    return train, test

@@ -115,12 +115,13 @@

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    # API scikit-learn
    ###################

    def fit(self, X, y=None, **kwargs):
    def fit(self, X, y=None, sample_weight=None, **kwargs):
        """
        Apprends un modèle pour chaque modalité d'une catégorie.

        @param      X               features
        @param      y               cibles
        @param      sample_weight   poids de chaque observation
        @return                     self, lui-même

        La fonction n'est pas parallélisée mais elle le pourrait.

@@ -131,7 +132,6 @@

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                raise ValueError(  # pragma: no cover
                    "One of the row has a missing category.")

        sample_weight = kwargs.get('sample_weight', None)
        res = {}
        for c in sorted(cats):
            _, xcat, ycat, scat = self._filter_cat(c, X, y, sample_weight)

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Hiding 1 contexual commits

93.90% ø ø

update travis link

sdpython

9047719

CI Passed

Files		•	•	•	Coverage
papierstat	-1	-1	ø	ø	^-0.01% 93.89%
Project Totals (21 files)	622	584	0	38	93.89%

115	115		# API scikit-learn
116	116		###################
117	117
118		-	def fit(self, X, y=None, **kwargs):
	118	+	def fit(self, X, y=None, sample_weight=None, **kwargs):
119	119		"""
120	120		Apprends un modèle pour chaque modalité d'une catégorie.
121	121
122	122		@param X features
123	123		@param y cibles
	124	+	@param sample_weight poids de chaque observation
124	125		@return self, lui-même
125	126
126	127		La fonction n'est pas parallélisée mais elle le pourrait.

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73	73		test[label] = test[label].str.strip(' .')
74	74		cols = train.select_dtypes(object).columns
75	75		for c in cols:
76		-	train[c] = train[c].str.strip()
	76	+	train[c] = train[c].str.strip() # pylint: disable=E1136,E1137
77	77		for c in cols:
78	78		test[c] = test[c].str.strip()
79	79		return train, test

131	132		raise ValueError( # pragma: no cover
132	133		"One of the row has a missing category.")
133	134
134		-	sample_weight = kwargs.get('sample_weight', None)
135	135		res = {}
136	136		for c in sorted(cats):
137	137		_, xcat, ycat, scat = self._filter_cat(c, X, y, sample_weight)

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