6.数据降维--KNN--feature-selection

代码：

from sklearn.neighbors import KNeighborsClassifier

from sklearn.feature_selection import SelectFromModel

from sklearn import datasets

X,y = datasets.load_wine(True)

model = SelectFromModel(estimator= KNeighborsClassifier(),threshold='median')

model.fit(X,y)

SelectFromModel(estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,
                                               metric='minkowski',
                                               metric_params=None, n_jobs=None,
                                               n_neighbors=5, p=2,
                                               weights='uniform'),
                max_features=None, norm_order=1, prefit=False,
                threshold='median')

model.transform(X)

-------------------------------------------------------

ValueError            Traceback (most recent call last)

<ipython-input-6-5431fe5f687b> in <module>
----> 1 model.transform(X)


d:\python3.7.4\lib\site-packages\sklearn\feature_selection\_base.py in transform(self, X)
     75         X = check_array(X, dtype=None, accept_sparse='csr',
     76                         force_all_finite=not tags.get('allow_nan', True))
---> 77         mask = self.get_support()
     78         if not mask.any():
     79             warn("No features were selected: either the data is"


d:\python3.7.4\lib\site-packages\sklearn\feature_selection\_base.py in get_support(self, indices)
     44             values are indices into the input feature vector.
     45         """
---> 46         mask = self._get_support_mask()
     47         return mask if not indices else np.where(mask)[0]
     48 


d:\python3.7.4\lib\site-packages\sklearn\feature_selection\_from_model.py in _get_support_mask(self)
    176                              ' "prefit=True" while passing the fitted'
    177                              ' estimator to the constructor.')
--> 178         scores = _get_feature_importances(estimator, self.norm_order)
    179         threshold = _calculate_threshold(estimator, scores, self.threshold)
    180         if self.max_features is not None:


d:\python3.7.4\lib\site-packages\sklearn\feature_selection\_from_model.py in _get_feature_importances(estimator, norm_order)
     30             "`feature_importances_` attribute. Either pass a fitted estimator"
     31             " to SelectFromModel or call fit before calling transform."
---> 32             % estimator.__class__.__name__)
     33 
     34     return importances


ValueError: The underlying estimator KNeighborsClassifier has no `coef_` or `feature_importances_` attribute. Either pass a fitted estimator to SelectFromModel or call fit before calling transform.