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)