scikit-learn · rth · Dec 27, 2019 · Dec 21, 2019 · Dec 21, 2019 · Dec 22, 2019
diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
@@ -60,6 +60,14 @@ Changelog
 - |Fix| :func:`utils.check_array` now correctly converts pandas DataFrame with
   boolean columns to floats. :pr:`15797` by `Thomas Fan`_.
 
+- |Fix| :func:`utils.check_is_fitted` accepts back an explicit ``attributes``
+  argument to check for specific attributes as explicit markers of a fitted
+  estimator. When no explicit ``attributes`` are provided, only the attributes
+  ending with a single "_" are used as "fitted" markers. The ``all_or_any``
+  argument is also no longer deprecated. This change is made to
+  restore some backward compatibility with the behavior of this utility in
+  version 0.21. :pr:`15947` by `Thomas Fan`_.
+
 .. _changes_0_22:
 
 Version 0.22.0

diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py
@@ -1099,6 +1099,7 @@ def test_vectorizer_string_object_as_input(Vectorizer):
     assert_raise_message(
             ValueError, message, vec.fit_transform, "hello world!")
     assert_raise_message(ValueError, message, vec.fit, "hello world!")
+    vec.fit(["some text", "some other text"])
     assert_raise_message(ValueError, message, vec.transform, "hello world!")
 
 

diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
@@ -1498,7 +1498,11 @@ def transform(self, X, copy=True):
             X.data += 1
 
         if self.use_idf:
-            check_is_fitted(self, msg='idf vector is not fitted')
+            # idf_ being a property, the automatic attributes detection
+            # does not work as usual and we need to specify the attribute
+            # name:
+            check_is_fitted(self, attributes=["idf_"],
+                            msg='idf vector is not fitted')
 
             expected_n_features = self._idf_diag.shape[0]
             if n_features != expected_n_features:
@@ -1883,7 +1887,7 @@ def transform(self, raw_documents, copy="deprecated"):
         X : sparse matrix, [n_samples, n_features]
             Tf-idf-weighted document-term matrix.
         """
-        check_is_fitted(self, msg='The tfidf vector is not fitted')
+        check_is_fitted(self, msg='The TF-IDF vectorizer is not fitted')
 
         # FIXME Remove copy parameter support in 0.24
         if copy != "deprecated":

diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
@@ -5,6 +5,7 @@
 
 from tempfile import NamedTemporaryFile
 from itertools import product
+from operator import itemgetter
 
 import pytest
 from pytest import importorskip
@@ -14,7 +15,6 @@
 from sklearn.utils._testing import assert_raises
 from sklearn.utils._testing import assert_raises_regex
 from sklearn.utils._testing import assert_no_warnings
-from sklearn.utils._testing import assert_warns_message
 from sklearn.utils._testing import assert_warns
 from sklearn.utils._testing import ignore_warnings
 from sklearn.utils._testing import SkipTest
@@ -50,7 +50,6 @@
 import sklearn
 
 from sklearn.exceptions import NotFittedError, PositiveSpectrumWarning
-from sklearn.exceptions import DataConversionWarning
 
 from sklearn.utils._testing import assert_raise_message
 from sklearn.utils._testing import TempMemmap
@@ -678,6 +677,52 @@ def test_check_is_fitted():
     assert check_is_fitted(svr) is None
 
 
+def test_check_is_fitted_attributes():
+    class MyEstimator():
+        def fit(self, X, y):
+            return self
+
+    msg = "not fitted"
+    est = MyEstimator()
+
+    with pytest.raises(NotFittedError, match=msg):
+        check_is_fitted(est, attributes=["a_", "b_"])
    with pytest.raises(NotFittedError, match=msg):
+        check_is_fitted(est, attributes=["a_", "b_"], all_or_any=all)
+    with pytest.raises(NotFittedError, match=msg):
+        check_is_fitted(est, attributes=["a_", "b_"], all_or_any=any)
+
+    est.a_ = "a"
+    with pytest.raises(NotFittedError, match=msg):
+        check_is_fitted(est, attributes=["a_", "b_"])
+    with pytest.raises(NotFittedError, match=msg):
+        check_is_fitted(est, attributes=["a_", "b_"], all_or_any=all)
+    check_is_fitted(est, attributes=["a_", "b_"], all_or_any=any)
+
+    est.b_ = "b"
+    check_is_fitted(est, attributes=["a_", "b_"])
+    check_is_fitted(est, attributes=["a_", "b_"], all_or_any=all)
+    check_is_fitted(est, attributes=["a_", "b_"], all_or_any=any)
+
+
+@pytest.mark.parametrize("wrap",
+                         [itemgetter(0), list, tuple],
+                         ids=["single", "list", "tuple"])
+def test_check_is_fitted_with_attributes(wrap):
+    ard = ARDRegression()
+    with pytest.raises(NotFittedError, match="is not fitted yet"):
+        check_is_fitted(ard, wrap(["coef_"]))
+
+    ard.fit(*make_blobs())
+
+    # Does not raise
+    check_is_fitted(ard, wrap(["coef_"]))
+
+    # Raises when using attribute that is not defined
+    with pytest.raises(NotFittedError, match="is not fitted yet"):
+        check_is_fitted(ard, wrap(["coef_bad_"]))
+
+
 def test_check_consistent_length():
     check_consistent_length([1], [2], [3], [4], [5])
     check_consistent_length([[1, 2], [[1, 2]]], [1, 2], ['a', 'b'])

diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
@@ -852,18 +852,29 @@ def check_symmetric(array, tol=1E-10, raise_warning=True,
     return array
 
 
-def check_is_fitted(estimator, msg=None):
+def check_is_fitted(estimator, attributes=None, msg=None, all_or_any=all):
     """Perform is_fitted validation for estimator.
 
     Checks if the estimator is fitted by verifying the presence of
     fitted attributes (ending with a trailing underscore) and otherwise
     raises a NotFittedError with the given message.
 
+    This utility is meant to be used internally by estimators themselves,
+    typically in their own predict / transform methods.
+
     Parameters
     ----------
     estimator : estimator instance.
         estimator instance for which the check is performed.
 
+    attributes : str, list or tuple of str, default=None
+        Attribute name(s) given as string or a list/tuple of strings
+        Eg.: ``["coef_", "estimator_", ...], "coef_"``
+
+        If `None`, `estimator` is considered fitted if there exist an
+        attribute that ends with a underscore and does not start with double
+        underscore.
+
     msg : string
         The default error message is, "This %(name)s instance is not fitted
         yet. Call 'fit' with appropriate arguments before using this
@@ -874,6 +885,9 @@ def check_is_fitted(estimator, msg=None):
 
         Eg. : "Estimator, %(name)s, must be fitted before sparsifying".
 
+    all_or_any : callable, {all, any}, default all
+        Specify whether all or any of the given attributes must exist.
+
     Returns
     -------
     None
@@ -892,9 +906,13 @@ def check_is_fitted(estimator, msg=None):
     if not hasattr(estimator, 'fit'):
         raise TypeError("%s is not an estimator instance." % (estimator))
 
-    attrs = [v for v in vars(estimator)
-             if (v.endswith("_") or v.startswith("_"))
-             and not v.startswith("__")]
+    if attributes is not None:
+        if not isinstance(attributes, (list, tuple)):
+            attributes = [attributes]
+        attrs = all_or_any([hasattr(estimator, attr) for attr in attributes])
+    else:
+        attrs = [v for v in vars(estimator)
+                 if v.endswith("_") and not v.startswith("__")]
 
     if not attrs:
         raise NotFittedError(msg % {'name': type(estimator).__name__})