def main():
import sip
from PyQt4.QtGui import QApplication
from Orange.classification import logistic_regression, svm
from Orange.evaluation import testing
app = QApplication([])
w = OWLiftCurve()
w.show()
w.raise_()
data = Orange.data.Table("ionosphere")
results = testing.CrossValidation(
data,
[logistic_regression.LogisticRegressionLearner(penalty="l2"),
logistic_regression.LogisticRegressionLearner(penalty="l1"),
svm.SVMLearner(probability=True),
svm.NuSVMLearner(probability=True)
],
store_data=True
)
results.fitter_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 test_NuSVM(self):
n = int(0.7 * self.data.X.shape[0])
learn = svm.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 apply(self):
kernel = ["linear", "poly", "rbf", "sigmoid"][self.kernel_type]
common_args = dict(kernel=kernel,
degree=self.degree,
gamma=self.gamma,
coef0=self.coef0,
tol=self.tol,
probability=True,
preprocessors=self.preprocessors)
if self.svmtype == 0:
learner = svm.SVMLearner(C=self.C, **common_args)
else:
learner = svm.NuSVMLearner(nu=self.nu, **common_args)
learner.name = self.learner_name
classifier = None
sv = None
if self.data is not None:
classifier = learner(self.data)
classifier.name = self.learner_name
sv = self.data[classifier.skl_model.support_]
self.send("Learner", learner)
self.send("Classifier", classifier)
self.send("Support vectors", sv)
def main():
import gc
import sip
from PyQt4.QtGui import QApplication
from Orange.classification import logistic_regression, svm
app = QApplication([])
w = OWROCAnalysis()
w.show()
w.raise_()
# data = Orange.data.Table("iris")
data = Orange.data.Table("ionosphere")
results = Orange.evaluation.testing.CrossValidation(
data,
[logistic_regression.LogisticRegressionLearner(),
logistic_regression.LogisticRegressionLearner(penalty="l1"),
svm.SVMLearner(probability=True),
svm.NuSVMLearner(probability=True)],
k=5,
store_data=True,
)
results.fitter_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