idf_ setter for TfidfTransformer. Fixes scikit-learn#7102

sklearn/feature_extraction/tests/test_text.py

@@ -942,6 +942,16 @@ def test_pickling_transformer():
         orig.fit_transform(X).toarray())
 
 
+def test_transformer_idf_setter():
+    X = CountVectorizer().fit_transform(JUNK_FOOD_DOCS)
+    orig = TfidfTransformer().fit(X)
+    copy = TfidfTransformer()
+    copy.idf_ = orig.idf_
+    assert_array_equal(
+        copy.fit_transform(X).toarray(),
+        orig.fit_transform(X).toarray())
+
+
 def test_non_unique_vocab():
     vocab = ['a', 'b', 'c', 'a', 'a']
     vect = CountVectorizer(vocabulary=vocab)

sklearn/feature_extraction/text.py

@@ -1062,6 +1062,12 @@ class TfidfTransformer(BaseEstimator, TransformerMixin):
     sublinear_tf : boolean, default=False
         Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
 
+    Attributes
+    ----------
+    idf_ : array, shape = [n_features], or None
+        The learned idf vector (global term weights)
+        when ``use_idf`` is set to True, None otherwise.
+
     References
     ----------
 
@@ -1157,6 +1163,13 @@ def idf_(self):
         # which means hasattr(self, "idf_") is False
         return np.ravel(self._idf_diag.sum(axis=0))
 
+    @idf_.setter
+    def idf_(self, value):
+        value = np.asarray(value, dtype=np.float64)
+        n_features = value.shape[0]
+        self._idf_diag = sp.spdiags(value, diags=0, m=n_features,
+                                    n=n_features, format='csr')
+
 
 class TfidfVectorizer(CountVectorizer):
     """Convert a collection of raw documents to a matrix of TF-IDF features.