More systematic checks that an estimator was fit before using its parameters (#267)

* maj

* added fit checks

* maj

* Added checks that the function was fitted.

* check the input before if model is fitted

* made more sensible checks.

* added a test for a threshold

* added a test for the unset threshold

Author: RobinVogel

metric_learn/base_metric.py

@@ -240,12 +240,14 @@ def transform(self, X):
     X_embedded : `numpy.ndarray`, shape=(n_samples, n_components)
       The embedded data points.
     """
+    check_is_fitted(self, ['preprocessor_', 'components_'])
     X_checked = check_input(X, type_of_inputs='classic', estimator=self,
                             preprocessor=self.preprocessor_,
                             accept_sparse=True)
     return X_checked.dot(self.components_.T)
 
   def get_metric(self):
+    check_is_fitted(self, 'components_')
     components_T = self.components_.T.copy()
 
     def metric_fun(u, v, squared=False):
@@ -298,6 +300,7 @@ def get_mahalanobis_matrix(self):
     M : `numpy.ndarray`, shape=(n_features, n_features)
       The copy of the learned Mahalanobis matrix.
     """
+    check_is_fitted(self, 'components_')
     return self.components_.T.dot(self.components_)
 
 
@@ -333,7 +336,10 @@ def predict(self, pairs):
     y_predicted : `numpy.ndarray` of floats, shape=(n_constraints,)
       The predicted learned metric value between samples in every pair.
     """
-    check_is_fitted(self, ['threshold_', 'components_'])
+    if "threshold_" not in vars(self):
+      msg = ("A threshold for this estimator has not been set,"
+             "call its set_threshold or calibrate_threshold method.")
+      raise AttributeError(msg)
     return 2 * (- self.decision_function(pairs) <= self.threshold_) - 1
 
   def decision_function(self, pairs):
@@ -357,6 +363,7 @@ def decision_function(self, pairs):
     y_predicted : `numpy.ndarray` of floats, shape=(n_constraints,)
       The predicted decision function value for each pair.
     """
+    check_is_fitted(self, 'preprocessor_')
     pairs = check_input(pairs, type_of_inputs='tuples',
                         preprocessor=self.preprocessor_,
                         estimator=self, tuple_size=self._tuple_size)
@@ -599,7 +606,7 @@ def predict(self, quadruplets):
     prediction : `numpy.ndarray` of floats, shape=(n_constraints,)
       Predictions of the ordering of pairs, for each quadruplet.
     """
-    check_is_fitted(self, 'components_')
+    check_is_fitted(self, 'preprocessor_')
     quadruplets = check_input(quadruplets, type_of_inputs='tuples',
                               preprocessor=self.preprocessor_,
                               estimator=self, tuple_size=self._tuple_size)
@@ -628,6 +635,7 @@ def decision_function(self, quadruplets):
     decision_function : `numpy.ndarray` of floats, shape=(n_constraints,)
       Metric differences.
     """
+    check_is_fitted(self, 'preprocessor_')
     quadruplets = check_input(quadruplets, type_of_inputs='tuples',
                               preprocessor=self.preprocessor_,
                               estimator=self, tuple_size=self._tuple_size)

test/test_pairs_classifiers.py

@@ -73,7 +73,7 @@ def test_raise_not_fitted_error_if_not_fitted(estimator, build_dataset,
   estimator.set_params(preprocessor=preprocessor)
   set_random_state(estimator)
   with pytest.raises(NotFittedError):
-    estimator.predict(input_data)
+    estimator.decision_function(input_data)
 
 
 @pytest.mark.parametrize('calibration_params',
@@ -133,10 +133,25 @@ def fit(self, pairs, y):
     pairs, y = self._prepare_inputs(pairs, y,
                                     type_of_inputs='tuples')
     self.components_ = np.atleast_2d(np.identity(pairs.shape[2]))
-    self.threshold_ = 'I am not set.'
+    # self.threshold_ is not set.
     return self
 
 
+def test_unset_threshold():
+  # test that set_threshold indeed sets the threshold
+  identity_pairs_classifier = IdentityPairsClassifier()
+  pairs = np.array([[[0.], [1.]], [[1.], [3.]], [[2.], [5.]], [[3.], [7.]]])
+  y = np.array([1, 1, -1, -1])
+  identity_pairs_classifier.fit(pairs, y)
+  with pytest.raises(AttributeError) as e:
+    identity_pairs_classifier.predict(pairs)
+
+  expected_msg = ("A threshold for this estimator has not been set,"
+                  "call its set_threshold or calibrate_threshold method.")
+
+  assert str(e.value) == expected_msg
+
+
 def test_set_threshold():
   # test that set_threshold indeed sets the threshold
   identity_pairs_classifier = IdentityPairsClassifier()