def test_softmax(self):
df = np.array([[2.75021367e+10, -8.83772371e-01, -2.20516715e+27],
[-2.10848072e+11, 2.35024444e-01, 5.20106536e+25]])
# With a numerically unstable softmax, the output would be something
# like this:
# [[ 0. 0. nan]
# [nan 0. 0.]]
probas = softmax(df)
expected = np.array([[1, 0, 0], [0, 0, 1]])
self.assertTrue((expected == probas).all())
df = np.array([[0.1, 0.6, 0.3], [0.2, 0.3, 0.5]])
probas = softmax(df)
expected = np.array([[0.25838965, 0.42601251, 0.31559783],
[0.28943311, 0.31987306, 0.39069383]])
assert_array_almost_equal(expected, probas)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
# return self.estimator.predict_proba(X)
decision = self.estimator.decision_function(X)
if len(self.estimator.classes_) > 2:
decision = _ovr_decision_function(decision < 0, decision, len(self.estimator.classes_))
return softmax(decision)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
if self.loss in ["log", "modified_huber"]:
return self.estimator.predict_proba(X)
else:
df = self.estimator.decision_function(X)
return softmax(df)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
# return self.estimator.predict_proba(X)
decision = self.estimator.decision_function(X)
if len(self.estimator.classes_) > 2:
decision = _ovr_decision_function(decision < 0, decision,
len(self.estimator.classes_))
return softmax(decision)
def test_softmax_binary(self):
df = np.array([-40.00643897, 34.69754581, 23.71181359 -29.89724287,
27.06071791, -37.78334103, -40.15812461, 40.16139229,
-27.85887801, 42.67404756, -36.89753589 -36.45148009,
54.68976306, 19.47886562, -49.99821027, -35.70205302,
-40.59639267, 32.96343916, -39.23777841, -37.86535019,
-33.10196906, 26.84144377, -36.8569686])
probas = softmax(df)
expected = [[1., 0.], [0., 1.], [0.99794501, 0.00205499],
[0., 1.], [1., 0.], [1., 0.], [0., 1.],
[1., 0.], [0., 1.], [1., 0.], [0., 1.],
[0., 1.], [1., 0.], [1., 0.], [1., 0.],
[0., 1.], [1., 0.], [1., 0.], [1., 0.],
[0., 1.], [1., 0.]]
assert_array_almost_equal(expected, probas)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
df = self.estimator.predict_proba(X)
return softmax(df)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
df = self.estimator.predict_proba(X)
return softmax(df)