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 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
def apply(self):
penalty = ["l1", "l2"][self.penalty_type]
learner = lr.LogisticRegressionLearner(
penalty=penalty,
dual=self.dual,
tol=self.tol,
C=self.C,
fit_intercept=self.fit_intercept,
intercept_scaling=self.intercept_scaling,
preprocessors=self.preprocessors
)
learner.name = self.learner_name
classifier = None
if self.data is not None:
self.error([0, 1])
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
elif len(np.unique(self.data.Y)) < 2:
self.error(1, "Data contains only one target value.")
else:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def main():
from Orange.classification import \
logistic_regression as lr, naive_bayes as nb
app = QtGui.QApplication([])
data = Orange.data.Table("iris")
w = OWTestLearners()
w.show()
w.set_train_data(data)
w.set_test_data(data)
w.set_learner(lr.LogisticRegressionLearner(), 1)
w.set_learner(nb.NaiveBayesLearner(), 2)
w.handleNewSignals()
return app.exec_()
def apply(self):
penalty = ["l1", "l2"][self.penalty_type]
learner = lr.LogisticRegressionLearner(
penalty=penalty,
dual=self.dual,
tol=self.tol,
C=self.C,
fit_intercept=self.fit_intercept,
intercept_scaling=self.intercept_scaling)
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 predict_discrete(predictor, data):
return predictor(data, Model.ValueProbs)
def predict_continuous(predictor, data):
values = predictor(data, Model.Value)
return values, [None] * len(data)
def is_discrete(var):
return isinstance(var, Orange.data.DiscreteVariable)
if __name__ == "__main__":
import Orange.classification.svm as svm
import Orange.classification.logistic_regression as lr
app = QtGui.QApplication([])
w = OWPredictions()
data = Orange.data.Table("iris")
svm_clf = svm.SVMLearner(probability=True)(data)
lr_clf = lr.LogisticRegressionLearner()(data)
w.setData(data)
w.setPredictor(svm_clf, 0)
w.setPredictor(lr_clf, 1)
w.handleNewSignals()
w.show()
app.exec_()
w.saveSettings()
