Remove usage of scikit-learn's LabelEncoder
#3161
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@@ Coverage Diff @@
## main #3161 +/- ##
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+ Coverage 99.7% 99.7% +0.1%
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Files 324 324
Lines 31233 31232 -1
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Hits 31128 31128
+ Misses 105 104 -1
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|
angela97lin
requested review from
bchen1116,
christopherbunn and
jeremyliweishih
jeremyliweishih
approved these changes
Dec 23, 2021
| @@ -167,7 +168,7 @@ def _encode_labels(self, y): | |||
| if not is_integer_dtype(y_encoded): | |||
| self._label_encoder = LabelEncoder() | |||
| y_encoded = pd.Series( | |||
| self._label_encoder.fit_transform(y_encoded), dtype="int64" | |||
| self._label_encoder.fit_transform(None, y_encoded)[1], dtype="int64" | |||
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do we need the pd.Series call here when we don't have it in catboost?
| @@ -103,7 +103,7 @@ def _convert_bool_to_int(X): | |||
| def _label_encode(self, y): | |||
| if not is_integer_dtype(y): | |||
| self._label_encoder = LabelEncoder() | |||
| y = pd.Series(self._label_encoder.fit_transform(y), dtype="int64") | |||
| y = pd.Series(self._label_encoder.fit_transform(None, y)[1], dtype="int64") | |||
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Same question here!
bchen1116
approved these changes
Dec 27, 2021
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LGTM! Agreed with @jeremyliweishih, the label encoder component already returns the y value as an int-encoded series, so I don't think we need to do the extra initialization and type-casting in lightgbm and xgboost
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Closes #3132