def main():
import sip
from PyQt4.QtGui import QApplication
from Orange.classification import (LogisticRegressionLearner, SVMLearner,
NuSVMLearner)
app = QApplication([])
w = OWCalibrationPlot()
w.show()
w.raise_()
data = Orange.data.Table("ionosphere")
results = Orange.evaluation.CrossValidation(data, [
LogisticRegressionLearner(penalty="l2"),
LogisticRegressionLearner(penalty="l1"),
SVMLearner(probability=True),
NuSVMLearner(probability=True)
],
store_data=True)
results.learner_names = ["LR l2", "LR l1", "SVM", "Nu SVM"]
w.set_results(results)
rval = app.exec_()
sip.delete(w)
del w
app.processEvents()
del app
return rval
def main():
import gc
import sip
from PyQt4.QtGui import QApplication
from Orange.classification import (LogisticRegressionLearner, SVMLearner,
NuSVMLearner)
app = QApplication([])
w = OWROCAnalysis()
w.show()
w.raise_()
# data = Orange.data.Table("iris")
data = Orange.data.Table("ionosphere")
results = Orange.evaluation.CrossValidation(
data,
[LogisticRegressionLearner(),
LogisticRegressionLearner(penalty="l1"),
SVMLearner(probability=True),
NuSVMLearner(probability=True)],
k=5,
store_data=True,
)
results.learner_names = ["Logistic", "Logistic (L1 reg.)", "SVM", "NuSVM"]
w.set_results(results)
rval = app.exec_()
w.deleteLater()
sip.delete(w)
del w
app.processEvents()
sip.delete(app)
del app
gc.collect()
return rval
def test_NuSVM(self):
n = int(0.7 * self.data.X.shape[0])
learn = NuSVMLearner(nu=0.01)
clf = learn(self.data[:n])
z = clf(self.data[n:])
self.assertTrue(
np.sum(z.reshape((-1, 1)) == self.data.Y[n:]) > 0.7 * len(z))
def results_for_preview(data_name=""):
from Orange.data import Table
from Orange.evaluation import CrossValidation
from Orange.classification import \
LogisticRegressionLearner, SVMLearner, NuSVMLearner
data = Table(data_name or "heart_disease")
results = CrossValidation(data, [
LogisticRegressionLearner(penalty="l2"),
LogisticRegressionLearner(penalty="l1"),
SVMLearner(probability=True),
NuSVMLearner(probability=True)
],
store_data=True)
results.learner_names = ["LR l2", "LR l1", "SVM", "Nu SVM"]
return results
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_NuSVM(self):
learn = NuSVMLearner(nu=0.01)
cv = CrossValidation(k=2)
res = cv(self.data, [learn])
self.assertGreater(CA(res)[0], 0.9)
def test_NuSVM(self):
learn = NuSVMLearner(nu=0.01)
res = Orange.evaluation.CrossValidation(self.data, [learn], k=2)
self.assertGreater(Orange.evaluation.CA(res)[0], 0.9)