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Merged
merged 9 commits into from
Dec 4, 2019
Merged

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

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6b789d3
maj
RobinVogel Nov 14, 2019
7d52b1c
Merge branch 'master' of http://github.com/RobinVogel/metric-learn in…
RobinVogel Nov 28, 2019
275c69a
added fit checks
RobinVogel Nov 28, 2019
1c28b56
maj
RobinVogel Nov 28, 2019
76ffccb
Added checks that the function was fitted.
RobinVogel Nov 28, 2019
c6d695f
check the input before if model is fitted
RobinVogel Nov 29, 2019
0a0a227
made more sensible checks.
RobinVogel Nov 29, 2019
658ef4a
added a test for a threshold
RobinVogel Nov 29, 2019
f6a1c96
added a test for the unset threshold
RobinVogel Nov 29, 2019
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12 changes: 10 additions & 2 deletions metric_learn/base_metric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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):
Expand Down Expand Up @@ -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_)


Expand Down Expand Up @@ -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):
Expand All @@ -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)
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down
19 changes: 17 additions & 2 deletions test/test_pairs_classifiers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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',
Expand Down Expand Up @@ -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()
Expand Down