scikit-learn
diff --git a/‎sklearn/decomposition/pca.py
Lines changed: 74 additions & 0 deletions b/‎sklearn/decomposition/pca.py
Lines changed: 74 additions & 0 deletions
diff --git a/‎sklearn/decomposition/tests/test_pca.py
Lines changed: 16 additions & 0 deletions b/‎sklearn/decomposition/tests/test_pca.py
Lines changed: 16 additions & 0 deletions
@@ -278,6 +278,80 @@ def __init__(self, n_components=None, copy=True, whiten=False,
         self.iterated_power = iterated_power
         self.random_state = random_state
 
+    def get_feature_names(self, input_features=None, show_coef=False):
+        """
+        Return dominant feature names for each component
+
+        Parameters
+        ----------
+        input_features : list of string, shape (n_features), optional
+            String names for input features if available. By default,
+            "x0", "x1", ... "xn_features" is used.
+
+        show_coef : boolean or integer, default False
+            When it is "True", return the principal components as the
+            combination of the input features. If "False", will be just
+            the component names. If it is an integer n, it returns the
+            sorted top n contributions to each component.
+
+
+        Returns
+        -------
+        output_feature_names : list of string, shape (n_components)
+            When show_coef is "True", it is represented by the contribution
+            of input features and show_coef is "False", it just represents
+            the component names
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from sklearn.decomposition import PCA
+        >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
+        >>> pca = PCA(n_components=2).fit(X)
+        >>> pca.get_feature_names(show_coef=True)
+        ['0.84*x0 + 0.54*x1', '0.54*x0 - 0.84*x1']
+        >>> pca.get_feature_names(show_coef=1)
+        ['0.84*x0', '-0.84*x1']
+        >>> pca.get_feature_names()
+        ['pc0', 'pc1']
+        """
+        check_is_fitted(self, 'components_')
+
+        n_features = self.components_.shape[1]
+        components = self.components_
+
+        if input_features is None:
+            input_features = ['x%d' % i for i in range(n_features)]
+        else:
+            if len(input_features) != n_features:
+                raise ValueError("Length of input_features is {0} but it must"
+                    "equal number of features when fitted: {1}.".format
+                    (len(input_features), n_features))
+
+        def name_generator(coefficients, names):
+            yield "{0:.2g}*{1}".format(coefficients[0], names[0])
+            for c, n in zip(coefficients[1:], names[1:]):
+                yield "{0:s} {1:.2g}*{2}".format('-' if c < 0 else '+', abs(c), n)
+
+        if show_coef is True:
+            feature_names = [' '.join(name_generator(components[i],input_features))
+                  
8000
          for i in range(self.n_components)]
+        elif show_coef is False:
+            feature_names = ['pc{0}'.format(i) for i in range(self.n_components)]
+        elif isinstance(show_coef, six.integer_types):
+            if show_coef < 0 or show_coef > n_features:
+                raise ValueError("show_coef is {0} but it must be between 1 and"
+                    "number of features {1}".format(show_coef, n_features))
+            contribution = np.argsort(np.abs(components), axis=1)[:, ::-1]
+            required = contribution[:,:show_coef]
+            input_features = np.asarray(input_features)
+            feature_names = [' '.join(name_generator(components[i][required[i]], input_features[required[i]]))
+                            for i in range(self.n_components)]
+        else:
+            raise ValueError("show_coef must be integer or boolean")
+        return feature_names
+        
+
     def fit(self, X, y=None):
         """Fit the model with X.
 
 
@@ -11,6 +11,7 @@
 from sklearn.utils.testing import assert_warns_message
 from sklearn.utils.testing import ignore_warnings
 from sklearn.utils.testing import assert_less
+from sklearn.utils.testing import assert_array_equal
 
 from sklearn import datasets
 from sklearn.decomposition import PCA
@@ -507,3 +508,18 @@ def fit_deprecated(X):
     assert_warns_message(DeprecationWarning, depr_message, fit_deprecated, X)
     Y_pca = PCA(svd_solver='randomized', random_state=0).fit_transform(X)
     assert_array_almost_equal(Y, Y_pca)
+
+
+def test_get_feature_names():
+    X1 = np.array([[-1, -1, 3], [-2, -1, 1], [-3, -2, -1], [1, 1, 2]])
+    pca = PCA(n_components=2).fit(X1)
+    assert_array_equal(pca.get_feature_names(), ['pc0', 'pc1'])
+    assert_array_equal(pca.get_feature_names(show_coef=True),
+        ['-0.66*x0 - 0.46*x1 - 0.59*x2', '0.38*x0 + 0.47*x1 - 0.79*x2'])
+    assert_array_equal(pca.get_feature_names(show_coef=1),
+        ['-0.66*x0', '-0.79*x2'])
+    # Raise error when len(input_features) != n_features
+    assert_raises(ValueError, pca.get_feature_names, ['a']);
+    # Raise error when show_coef is greater than n_features
+    assert_raises(ValueError, pca.get_feature_names, None, 4)
+