def eval():
start = time.clock()
print "Calculating predictions of ",len(testing_labels)," labels' sets..."
predicted_labels = jrs_multilabel_classifier.classify_multilabel(testing2training_distances, training_labels, multilabel_classifier)
accuracy, precision, recall, hammingloss, subset01loss, fmeasure = jrs_evaluation.jrs_evaluate(testing_labels, predicted_labels)
print " accuracy:", accuracy,"\n precision:", precision,"\n recall:", recall,"\n fmeasure:", fmeasure
lcount = [len(ll) for ll in predicted_labels]
print " avg labels in predicted:", float(sum(lcount))/(len(lcount))
print " done in", (time.clock() - start), "sec..."
return predicted_labels
def eval():
start = time.clock()
print "Calculating predictions of ", len(
testing_labels), " labels' sets..."
predicted_labels = jrs_multilabel_classifier.classify_multilabel(
testing2training_distances, training_labels,
multilabel_classifier)
accuracy, precision, recall, hammingloss, subset01loss, fmeasure = jrs_evaluation.jrs_evaluate(
testing_labels, predicted_labels)
print " accuracy:", accuracy, "\n precision:", precision, "\n recall:", recall, "\n fmeasure:", fmeasure
lcount = [len(ll) for ll in predicted_labels]
print " avg labels in predicted:", float(
sum(lcount)) / (len(lcount))
print " done in", (time.clock() - start), "sec..."
return predicted_labels
def eval():
start = time.clock()
print "Calculating predictions of ",len(testing_labels)," labels' sets..."
predicted_labels = jrs_multilabel_classifier.classify_multilabel(testing2training_distances, training_labels, multilabel_classifier)
try:
print testing_labels[:10],"\n", predicted_labels[:10]
accuracy, precision, recall, hammingloss, subset01loss, fmeasure = jrs_evaluation.jrs_evaluate(testing_labels, predicted_labels)
except:
print "[knn] Error in jrs_evaluation.jrs_evaluate(testing_labels, predicted_labels):",testing_labels, predicted_labels
print " accuracy:", accuracy,"\n precision:", precision,"\n recall:", recall,"\n fmeasure:", fmeasure
lcount = [len(ll) for ll in predicted_labels]
#print " avg labels in predicted:", float(sum(lcount))/(len(lcount))
#accuracy, precision, recall, hammingloss, subset01loss, fmeasure = jrs_evaluation.jrs_evaluate(filter_out_labels(testing_labels), filter_out_labels(predicted_labels))
#print " postfiltering-accuracy:", accuracy,"\n postfiltering-precision:", precision,"\n postfiltering-recall:", recall,"\n postfiltering-fmeasure:", fmeasure
print " done in", (time.clock() - start), "sec..."
return predicted_labels
def eval(testing2training_distances, training_labels, testing_labels, multilabel_classifier):
predicted_labels = jrs_multilabel_classifier.classify_multilabel(testing2training_distances, training_labels, multilabel_classifier)
accuracy, precision, recall, hammingloss, subset01loss, fmeasure = jrs_evaluation.jrs_evaluate(testing_labels, predicted_labels)
return fmeasure
