def test_KNN_regression(self):
learners = [KNNRegressionLearner(),
KNNRegressionLearner(metric="mahalanobis")]
cv = CrossValidation(k=3)
results = cv(self.housing, learners)
mse = MSE(results)
self.assertLess(mse[1], mse[0])
def test_abs_error_normalized(self):
tab = Table('housing')
normalizer = Normalize(zero_based=True,
norm_type=Normalize.NormalizeBySpan)
tab = normalizer(tab)
icr = InductiveRegressor(AbsError(LinearRegressionLearner()))
icr_knn = InductiveRegressor(AbsError(KNNRegressionLearner(4)))
icr_norm = InductiveRegressor(
AbsErrorNormalized(KNNRegressionLearner(4),
Euclidean,
4,
exp=False))
icr_norm_exp = InductiveRegressor(
AbsErrorNormalized(KNNRegressionLearner(4), Euclidean, 4,
exp=True))
icr_norm_rf = InductiveRegressor(
AbsErrorNormalized(KNNRegressionLearner(4),
Euclidean,
4,
rf=RandomForestRegressor()))
r, r_knn, r_norm, r_norm_exp, r_norm_rf = ResultsRegr(), ResultsRegr(
), ResultsRegr(), ResultsRegr(), ResultsRegr()
eps = 0.05
for rep in range(10):
for train, test in CrossSampler(tab, 10):
train, calibrate = next(
RandomSampler(train,
len(train) - 100, 100))
r.concatenate(run_train_test(icr, eps, train, test, calibrate))
r_knn.concatenate(
run_train_test(icr_knn, eps, train, test, calibrate))
r_norm.concatenate(
run_train_test(icr_norm, eps, train, test, calibrate))
r_norm_exp.concatenate(
run_train_test(icr_norm_exp, eps, train, test, calibrate))
r_norm_rf.concatenate(
run_train_test(icr_norm_rf, eps, train, test, calibrate))
print(r.median_range(), r.interdecile_mean(), 1 - r.accuracy())
print(r_knn.median_range(), r_knn.interdecile_mean(),
1 - r_knn.accuracy())
print(r_norm.median_range(), r_norm.interdecile_mean(),
1 - r_norm.accuracy())
print(r_norm_exp.median_range(), r_norm_exp.interdecile_mean(),
1 - r_norm_exp.accuracy())
print(r_norm_rf.median_range(), r_norm_rf.interdecile_mean(),
1 - r_norm_rf.accuracy())
self.assertGreater(r.accuracy(), 1 - eps - 0.03)
self.assertGreater(r_knn.accuracy(), 1 - eps - 0.03)
self.assertGreater(r_norm.accuracy(), 1 - eps - 0.03)
self.assertGreater(r_norm_exp.accuracy(), 1 - eps - 0.03)
self.assertGreater(r_norm_rf.accuracy(), 1 - eps - 0.03)
"""
def test_error_message_cleared_when_valid_learner_on_input(self):
# Disconnecting an invalid learner should use the default one and hide
# the error
self.send_signal("Learner", KNNRegressionLearner())
self.send_signal('Learner', None)
self.assertFalse(self.widget.Error.no_weight_support.is_shown(),
'Error message was not hidden on input disconnect')
# Connecting a valid learner should also reset the error message
self.send_signal("Learner", KNNRegressionLearner())
self.send_signal('Learner', RandomForestRegressionLearner())
self.assertFalse(
self.widget.Error.no_weight_support.is_shown(),
'Error message was not hidden when a valid learner appeared on '
'input')
def test_nan(self):
lrn1 = KNNRegressionLearner(n_neighbors=1)
lrn3 = KNNRegressionLearner(n_neighbors=3)
X = np.arange(1, 7)[:, None]
Y = np.array([np.nan, np.nan, np.nan, 1, 1, 1])
attr = (ContinuousVariable("Feat 1"), )
class_var = (ContinuousVariable("Class"), )
domain = Domain(attr, class_var)
data = Table(domain, X, Y)
clf = lrn1(data)
predictions = clf(data)
self.assertEqual(predictions[0], 1.0)
clf = lrn3(data)
predictions = clf(data)
self.assertEqual(predictions[3], 1.0)
def test_input_learner_disconnect(self):
"""Check base learner after disconnecting learner on the input"""
self.send_signal("Learner", KNNRegressionLearner())
self.assertIsInstance(self.widget.base_estimator, KNNRegressionLearner)
self.send_signal("Learner", None)
self.assertEqual(self.widget.base_estimator,
self.widget.DEFAULT_BASE_ESTIMATOR)
def test_input_learner_that_does_not_support_sample_weights(self):
self.send_signal("Learner", KNNRegressionLearner())
self.assertNotIsInstance(self.widget.base_estimator,
KNNRegressionLearner)
self.assertIsNone(self.widget.base_estimator)
self.assertTrue(self.widget.Error.no_weight_support.is_shown())