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BUG: XGBClassifier.feature_importances_ raises ValueError if input is pandas DataFrame

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sinhrks
2016-04-24 14:34:09 +09:00
parent 4149854633
commit c55cc809e5
2 changed files with 45 additions and 6 deletions
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19
python-package/xgboost/sklearn.py
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@@ -495,12 +495,19 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
feature_importances_ : array of shape = [n_features]
"""
fs = self.booster().get_fscore()
keys = [int(k.replace('f', '')) for k in fs.keys()]
fs_dict = dict(zip(keys, fs.values()))
all_features_dict = dict.fromkeys(range(0, self._features_count), 0)
all_features_dict.update(fs_dict)
all_features = np.fromiter(all_features_dict.values(), np.float32)
b = self.booster()
fs = b.get_fscore()
if b.feature_names is None:
keys = [int(k.replace('f', '')) for k in fs.keys()]
all_features_dict = dict.fromkeys(range(0, self._features_count), 0)
fs_dict = dict(zip(keys, fs.values()))
all_features_dict.update(fs_dict)
all_features = np.fromiter(all_features_dict.values(),
dtype=np.float32)
else:
all_features = [fs.get(f, 0.) for f in b.feature_names]
all_features = np.array(all_features, dtype=np.float32)
return all_features / all_features.sum()