def test_stacked_featurizer(self):
data = self.make_test_data()
data['y'] = [1, 2, 3]
# Test for a regressor
model = DummyRegressor()
model.fit(self.multi.featurize_many(data['x']), data['y'])
# Test the predictions
f = StackedFeaturizer(self.single, model)
self.assertEquals([2], f.featurize(data['x'][0]))
# Test the feature names
self.assertEquals(['prediction'], f.feature_labels())
f.name = 'ML'
self.assertEquals(['ML prediction'], f.feature_labels())
# Test classifier
model = DummyClassifier("prior")
data['y'] = [0, 0, 1]
model.fit(self.multi.featurize_many(data['x']), data['y'])
# Test the prediction
f.model = model
self.assertEquals([2. / 3], f.featurize(data['x'][0]))
# Test the feature labels
self.assertRaises(ValueError, f.feature_labels)
f.class_names = ['A', 'B']
self.assertEquals(['ML P(A)'], f.feature_labels())
# Test with three classes
data['y'] = [0, 2, 1]
model.fit(self.multi.featurize_many(data['x']), data['y'])
self.assertArrayAlmostEqual([1. / 3] * 2, f.featurize(data['x'][0]))
f.class_names = ['A', 'B', 'C']
self.assertEquals(['ML P(A)', 'ML P(B)'], f.feature_labels())
def test_stacked_featurizer(self):
data = self.make_test_data()
data['y'] = [1, 2, 3]
# Test for a regressor
model = DummyRegressor()
model.fit(self.multi.featurize_many(data['x']), data['y'])
# Test the predictions
f = StackedFeaturizer(self.single, model)
self.assertEqual([2], f.featurize(data['x'][0]))
# Test the feature names
self.assertEqual(['prediction'], f.feature_labels())
f.name = 'ML'
self.assertEqual(['ML prediction'], f.feature_labels())
# Test classifier
model = DummyClassifier("prior")
data['y'] = [0, 0, 1]
model.fit(self.multi.featurize_many(data['x']), data['y'])
# Test the prediction
f.model = model
self.assertEqual([2. / 3], f.featurize(data['x'][0]))
# Test the feature labels
self.assertRaises(ValueError, f.feature_labels)
f.class_names = ['A', 'B']
self.assertEqual(['ML P(A)'], f.feature_labels())
# Test with three classes
data['y'] = [0, 2, 1]
model.fit(self.multi.featurize_many(data['x']), data['y'])
self.assertArrayAlmostEqual([1. / 3] * 2, f.featurize(data['x'][0]))
f.class_names = ['A', 'B', 'C']
self.assertEqual(['ML P(A)', 'ML P(B)'], f.feature_labels())