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
