def apply(self):
learner = KNNLearner(n_neighbors=self.n_neighbors,
metric=self.metrics[self.metric_index],
preprocessors=self.preprocessors)
learner.name = self.learner_name
classifier = None
if self.data is not None:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def apply(self):
learner = KNNLearner(
n_neighbors=self.n_neighbors, metric=self.metrics[self.metric_index], preprocessors=self.preprocessors
)
learner.name = self.learner_name
classifier = None
if self.data is not None:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
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", KNNLearner())
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", KNNLearner())
self.send_signal('Learner', RandomForestLearner())
self.assertFalse(
self.widget.Error.no_weight_support.is_shown(),
'Error message was not hidden when a valid learner appeared on '
'input')
def test_predict_single_instance(self):
data = Table('iris')
learn = KNNLearner()
clf = learn(data)
for ins in data[::20]:
clf(ins)
val, prob = clf(ins, clf.ValueProbs)
def test_KNN(self):
table = Table('iris')
learn = KNNLearner()
results = CrossValidation(table, [learn], k=10)
ca = CA(results)
self.assertGreater(ca, 0.8)
self.assertLess(ca, 0.99)
def test_input_learner_disconnect(self):
"""Check base learner after disconnecting learner on the input"""
self.send_signal("Learner", KNNLearner())
self.assertIsInstance(self.widget.base_estimator, KNNLearner)
self.send_signal("Learner", None)
self.assertEqual(self.widget.base_estimator,
self.widget.DEFAULT_BASE_ESTIMATOR)
def apply(self):
learner = KNNLearner(
n_neighbors=self.n_neighbors,
metric=self.metrics[self.metric_index],
preprocessors=self.preprocessors
)
learner.name = self.learner_name
classifier = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def test_random(self):
nrows, ncols = 1000, 5
x = np.random.random_integers(-20, 50, (nrows, ncols))
y = np.random.random_integers(-2, 2, (nrows, 1))
x1, x2 = np.split(x, 2)
y1, y2 = np.split(y, 2)
attr = (ContinuousVariable('Feature 1'),
ContinuousVariable('Feature 2'),
ContinuousVariable('Feature 3'),
ContinuousVariable('Feature 4'),
ContinuousVariable('Feature 5'))
class_vars = (DiscreteVariable('Target 1'),)
domain = Domain(attr, class_vars)
t = Table(domain, x1, y1)
learn = KNNLearner()
clf = learn(t)
z = clf(x2)
correct = (z == y2.flatten())
ca = sum(correct)/len(correct)
self.assertTrue(0.1 < ca < 0.3)
def test_random(self):
nrows, ncols = 1000, 5
x = np.random.randint(-20, 51, (nrows, ncols))
y = np.random.randint(0, 9, (nrows, 1))
x1, x2 = np.split(x, 2)
y1, y2 = np.split(y, 2)
attr = (ContinuousVariable('Feature 1'),
ContinuousVariable('Feature 2'),
ContinuousVariable('Feature 3'),
ContinuousVariable('Feature 4'),
ContinuousVariable('Feature 5'))
class_vars = (DiscreteVariable('Target 1', values=list("abcdefghij")),)
domain = Domain(attr, class_vars)
t = Table(domain, x1, y1)
lrn = KNNLearner()
clf = lrn(t)
z = clf(x2)
correct = (z == y2.flatten())
ca = sum(correct) / len(correct)
self.assertGreater(ca, 0.1)
self.assertLess(ca, 0.3)
def test_reprs(self):
lr = LogisticRegressionLearner(tol=0.0002)
m = MajorityLearner()
nb = NaiveBayesLearner()
rf = RandomForestLearner(bootstrap=False, n_jobs=3)
st = SimpleTreeLearner(seed=1, bootstrap=True)
sm = SoftmaxRegressionLearner()
svm = SVMLearner(shrinking=False)
lsvm = LinearSVMLearner(tol=0.022, dual=False)
nsvm = NuSVMLearner(tol=0.003, cache_size=190)
osvm = OneClassSVMLearner(degree=2)
tl = TreeLearner(max_depth=3, min_samples_split=1)
knn = KNNLearner(n_neighbors=4)
el = EllipticEnvelopeLearner(store_precision=False)
srf = SimpleRandomForestLearner(n_estimators=20)
learners = [lr, m, nb, rf, st, sm, svm,
lsvm, nsvm, osvm, tl, knn, el, srf]
for l in learners:
repr_str = repr(l)
new_l = eval(repr_str)
self.assertEqual(repr(new_l), repr_str)
def test_random(self):
nrows, ncols = 1000, 5
x = np.random.randint(-20, 51, (nrows, ncols))
y = np.random.randint(-2, 3, (nrows, 1))
x1, x2 = np.split(x, 2)
y1, y2 = np.split(y, 2)
attr = (
ContinuousVariable("Feature 1"),
ContinuousVariable("Feature 2"),
ContinuousVariable("Feature 3"),
ContinuousVariable("Feature 4"),
ContinuousVariable("Feature 5"),
)
class_vars = (DiscreteVariable("Target 1"),)
domain = Domain(attr, class_vars)
t = Table(domain, x1, y1)
lrn = KNNLearner()
clf = lrn(t)
z = clf(x2)
correct = z == y2.flatten()
ca = sum(correct) / len(correct)
self.assertGreater(ca, 0.1)
self.assertLess(ca, 0.3)
def test_KNN(self):
results = CrossValidation(self.iris, [KNNLearner()], k=3)
ca = CA(results)
self.assertGreater(ca, 0.8)
self.assertLess(ca, 0.99)
from Orange.data import Domain, Table
from Orange.classification import LogisticRegressionLearner
from Orange.classification import NaiveBayesLearner
from Orange.classification import TreeLearner
from Orange.classification import RandomForestLearner
from Orange.classification import KNNLearner
from Orange.classification import SVMLearner
### create models ###
models = [
LogisticRegressionLearner(),
NaiveBayesLearner(),
TreeLearner(),
RandomForestLearner(),
KNNLearner(),
SVMLearner(),
]
### read train data ###
train = Table.from_file('train.csv')
# move `sex` from X to Y (from attributes/features to class_var/target)
domain = Domain(train.domain.attributes[1:], train.domain.attributes[0])
train = train.transform(domain)
print('\n=== train.X ===')
print(train.X)
print('\n=== train.Y ===')
print(train.Y)
def test_input_learner_that_does_not_support_sample_weights(self):
self.send_signal("Learner", KNNLearner())
self.assertNotIsInstance(self.widget.base_estimator, KNNLearner)
self.assertIsNone(self.widget.base_estimator)
self.assertTrue(self.widget.Error.no_weight_support.is_shown())
def test_predict_single_instance(self):
lrn = KNNLearner()
clf = lrn(self.iris)
for ins in self.iris[::20]:
clf(ins)
val, prob = clf(ins, clf.ValueProbs)
def test_KNN_mahalanobis(self):
learners = [KNNLearner(metric="mahalanobis")]
results = CrossValidation(self.iris, learners, k=3)
ca = CA(results)
self.assertGreater(ca, 0.8)
def setUpClass(cls):
cls.iris = Table('iris')
cls.learn = KNNLearner()
