def test_binary_probabilities(self):
inner_est = DummyProbEstimator(2)
est = BinaryProbabilities(inner_est)
# test attr wrap
self.assertEqual(est._coefs, inner_est._coefs)
preds = est.predict(self.data.values)
self.assertEqual(preds.shape, (10,))
def test_sklearn_probabilities(self):
# test multi-class
self.data['target'] = [0] * 5 + [1] * 3 + [2] * 2
inner_est = linear_model.LogisticRegression()
est = wrap_sklearn_like_estimator(inner_est)
x = self.data[['a', 'b']]
est.fit(x, self.data.target)
preds = est.predict(x)
self.assertEqual(preds.shape, (10, 3))
# test binary, single output
self.data['target'] = [0] * 5 + [1] * 5
est = BinaryProbabilities(inner_est)
x = self.data[['a', 'b']]
est.fit(x, self.data.target)
preds = est.predict(x)
self.assertEqual(preds.shape, (10, ))
def test_sklearn_probabilities(self):
# test multi-class
self.data['target'] = [0] * 5 + [1] * 3 + [2] * 2
inner_est = linear_model.LogisticRegression()
est = wrap_sklearn_like_estimator(inner_est)
x = self.data[['a', 'b']]
est.fit(x, self.data.target)
preds = est.predict(x)
self.assertEqual(preds.shape, (10, 3))
# test binary, single output
self.data['target'] = [0] * 5 + [1] * 5
est = BinaryProbabilities(inner_est)
x = self.data[['a', 'b']]
est.fit(x, self.data.target)
preds = est.predict(x)
self.assertEqual(preds.shape, (10, ))
