Fix checking booster.

python-package/xgboost/sklearn.py

@@ -307,7 +307,7 @@ def get_params(self, deep=True):
  np.iinfo(np.int32).max)
 
  def parse_parameter(value):
- for t in (int, float):
+ for t in (int, float, str):
  try:
  ret = t(value)
  return ret
@@ -678,10 +678,10 @@ def feature_importances_(self):
  feature_importances_ : array of shape ``[n_features]``
 
  """
- if getattr(self, 'booster', None) is not None and self.booster not in {
-  'gbtree', 'dart'}:
- raise AttributeError('Feature importance is not defined for Booster type {}'
-  .format(self.booster))
+ if self.get_params()['booster'] not in {'gbtree', 'dart'}:
+ raise AttributeError(
+  'Feature importance is not defined for Booster type {}'
+ .format(self.booster))
  b = self.get_booster()
  score = b.get_score(importance_type=self.importance_type)
  all_features = [score.get(f, 0.) for f in b.feature_names]
@@ -703,11 +703,13 @@ def coef_(self):
  -------
  coef_ : array of shape ``[n_features]`` or ``[n_classes, n_features]``
  """
- if getattr(self, 'booster', None) is not None and self.booster != 'gblinear':
- raise AttributeError('Coefficients are not defined for Booster type {}'
- .format(self.booster))
+ if self.get_params()['booster'] != 'gblinear':
+ raise AttributeError(
+ 'Coefficients are not defined for Booster type {}'
+ .format(self.booster))
  b = self.get_booster()
- coef = np.array(json.loads(b.get_dump(dump_format='json')[0])['weight'])
+ coef = np.array(json.loads(
+ b.get_dump(dump_format='json')[0])['weight'])
  # Logic for multiclass classification
  n_classes = getattr(self, 'n_classes_', None)
  if n_classes is not None:
@@ -732,9 +734,10 @@ def intercept_(self):
  -------
  intercept_ : array of shape ``(1,)`` or ``[n_classes]``
  """
- if getattr(self, 'booster', None) is not None and self.booster != 'gblinear':
- raise AttributeError('Intercept (bias) is not defined for Booster type {}'
- .format(self.booster))
+ if self.get_params()['booster'] != 'gblinear':
+ raise AttributeError(
+ 'Intercept (bias) is not defined for Booster type {}'
+ .format(self.booster))
  b = self.get_booster()
  return np.array(json.loads(b.get_dump(dump_format='json')[0])['bias'])
 

tests/python/test_with_sklearn.py

@@ -193,6 +193,19 @@ def test_feature_importances_gain():
  np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
 
 
+def test_select_feature():
+ from sklearn.datasets import load_digits
+ from sklearn.feature_selection import SelectFromModel
+ digits = load_digits(2)
+ y = digits['target']
+ X = digits['data']
+ cls = xgb.XGBClassifier()
+ cls.fit(X, y)
+ selector = SelectFromModel(cls, prefit=True, max_features=1)
+ X_selected = selector.transform(X)
+ assert X_selected.shape[1] == 1
+
+
 def test_num_parallel_tree():
  from sklearn.datasets import load_boston
  reg = xgb.XGBRegressor(n_estimators=4, num_parallel_tree=4,