def plot_precision_recall(predicted, event, against="true", max_angle=1.0e-2):
_, precision, recall = precision_recall(predicted, event, against, max_angle)
plt.figure()
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.05])
plt.plot(recall, precision)
plt.xlabel("Recall")
plt.ylabel("Precision")
return plt
def plot_precision_recall(predicted, event, against='true', max_angle=1.0e-2):
_, precision, recall = precision_recall(predicted, event, against,
max_angle)
plt.figure()
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.05])
plt.plot(recall, precision)
plt.xlabel("Recall")
plt.ylabel("Precision")
return plt
def test():
# test run using degree-3 polynomial kernel with C=1
# for the ten main topics in reuters
dm = data.create_data_manager()
print 'loading train data . . .'
X_train, Y_train = dm.load_data('train')
Y_train_slice = dm.slice_Y(Y_train, topics)
print 'training svm . . .'
K = len(topics)
learner = svm2.MultiSVM(K, 1.0, 'poly', 3)
learner.train(X_train, Y_train_slice)
print 'loading test data . . .'
X_test, Y_test = dm.load_data('test')
Y_gold = dm.slice_Y(Y_test, topics)
print 'predicting train . . .'
Y_pred = learner.batch_predict_classes(X_train)
print 'evaluating . . .'
precision, recall = evaluate.precision_recall(Y_pred, Y_train_slice)
f1 = evaluate.f_score(precision, recall)
print 'Precision: %.3f' % precision
print 'Recall: %.3f' % recall
print 'F1: %.3f' % f1
print 'predicting test . . .'
Y_pred = learner.batch_predict_classes(X_test)
print 'evaluating . . .'
precision, recall = evaluate.precision_recall(Y_pred, Y_gold)
f1 = evaluate.f_score(precision, recall)
print 'Precision: %.3f' % precision
print 'Recall: %.3f' % recall
print 'F1: %.3f' % f1