def test_generate_predictions(self):
# Test KNN regressor multiple predictions against sklearn regressor predictions
reg_obs = np.array([self.reg_obs1, self.reg_obs2, self.reg_obs3])
knn_reg_preds = gp.generate_predictions(self.knn_regressor, reg_obs,
'all')
test_generate_predictions.knn_reg_preds = knn_reg_preds
sklearn_reg_preds = self.sklearn_regressor.predict(reg_obs)
test_generate_predictions.sklearn_reg_preds = sklearn_reg_preds
self.assertTrue(np.mean(knn_reg_preds == sklearn_reg_preds))
# Check that length of inputted observations and results match
self.assertEqual(len(reg_obs), len(knn_reg_preds))
# Test KNN classifier multiple predictions against sklearn classifier predictions
clf_obs = np.array([self.clf_obs1, self.clf_obs2, self.clf_obs3])
knn_clf_preds = gp.generate_predictions(self.knn_classifier, clf_obs,
'all')
test_generate_predictions.knn_clf_preds = knn_clf_preds
sklearn_clf_preds = self.sklearn_classifier.predict(clf_obs)
test_generate_predictions.sklearn_clf_preds = sklearn_clf_preds
self.assertTrue(np.mean(knn_clf_preds == sklearn_clf_preds))
# Check that length of inputted observations and results match
self.assertEqual(len(clf_obs), len(knn_clf_preds))
def test_accuracy(self):
self.accuracy = 0.6666666666666666
TestClassificationMetrics.assert_accuracy = mm.model_accuracy(
['Iris-virginica', 'Iris-virginica', 'Iris-setosa'],
gp.generate_predictions(
TestClassificationMetrics.test_knn_classifier, self.to_predict,
'all'))
self.assertEqual(self.accuracy, self.assert_accuracy)
self.assertIsInstance(TestClassificationMetrics.assert_accuracy,
numbers.Number)
self.assertIsInstance(TestClassificationMetrics.assert_accuracy, float)
assert TestClassificationMetrics.assert_accuracy >= 0
def test_model_num_incorrect(self):
self.num_incorrect = 2
TestClassificationMetrics.assert_num_incorrect = mm.model_num_incorrect(
['Iris-virginica', 'Iris-virginica', 'Iris-setosa'],
gp.generate_predictions(
TestClassificationMetrics.test_knn_classifier, self.to_predict,
'all'))
self.assertNotEqual(self.assert_num_incorrect, self.num_incorrect)
self.assertIsInstance(TestClassificationMetrics.assert_num_incorrect,
numbers.Number)
self.assertNotIsInstance(
TestClassificationMetrics.assert_num_incorrect, float)
assert TestClassificationMetrics.assert_num_incorrect >= 0
def setUp(self):
self.actual_mpg=[19,18,23,28]
self.mpg_to_predict=np.array([[6,250,100,3282,15],[6,250,88,3139,14.5],[4,122,86,2220,14],[4,116,90,2123,14]])
self.predicted_mpg= gp.generate_predictions(TestRegressionMetrics.test_knn_regressor,self.mpg_to_predict,'train')
def setUp(self):
self.to_predict = np.array([[6.3, 2.3, 4.4, 1.3], [6.8, 3.2, 5.9, 2.3],
[4.3, 3, 1.1, 0.1]])
gp.generate_predictions(TestClassificationMetrics.test_knn_classifier,
self.to_predict, 'all')