def test(self, labels, test_set): _,ts = helper.format_for_scikit(labels, test_set) predictions = self.classifier.predict(ts) if self.plot_roc: print("ROC curve plot unavailable for %s") % (str(self)) return helper.accuracy(labels, predictions), predictions
def test(self, labels, test_set): if self.classifier == None: return [] if self.plot_roc: print("ROC curve plot unavailable for %s") % (str(self)) predictions = [self.classifier] * len(test_set) return helper.accuracy(labels, predictions), predictions
def test(self, labels, test_set): l,ts = helper.format_for_scikit(labels, test_set) #pca = PCA(n_components='mle') #ts = pca.fit_transform(ts) predictions = self.classifier.predict(ts) if self.plot_roc: probas = self.classifier.predict_proba(ts) helper.roc(probas, l, str(self)) return helper.accuracy(labels, predictions), predictions
def test(self, labels, test_set): _,ts = helper.format_for_scikit(labels, test_set) predictions = self.classifier.predict(ts) if self.plot_roc: feat_list = test_set[0].keys() # FIXME: handle output file name outfile = '../data/dt.dot' print("ROC curve unavailable for this classifier.\n" + "Creating a Decision Tree plot instead in: %s") % (outfile) tree.export_graphviz(self.classifier, outfile, feat_list) return helper.accuracy(labels, predictions), predictions