help="path where trained classifier was saved")
parser.add_option(
"-r",
"--result",
action="store",
dest="result",
default="roc",
help="path of the saved roc figure, make sure the folder exists")
options, args = parser.parse_args(sys.argv)
with open(options.path, 'rb') as f:
classifier = pickle.load(f)
y_test_prob = []
y_test = []
for X, y in parse_svm_light_data(sys.stdin):
y_prob = classifier.predict_prob(X)
y_test.append(y)
y_test_prob.append(y_prob)
fpr, tpr, _ = roc_curve(y_test, y_test_prob)
roc_auc = auc(fpr, tpr)
# Plot of a ROC curve for a specific class
plt.figure()
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
if __name__ == '__main__':
parser = OptionParser()
parser.add_option("-m", "--model-path", action="store", dest="path",
default="models", help="path where trained classifiers are saved")
parser.add_option("-r", "--result", action="store", dest="result",
default="roc", help="name of the figure")
options, args = parser.parse_args(sys.argv)
files = [options.path + "/" +
filename for filename in os.listdir(options.path) if filename.startswith('part')]
classifiers = map(load_model, files)
y_test_prob = []
y_test = []
for X, y in parse_svm_light_data(sys.stdin):
y_prob = predict_prob(classifiers, X)
y_test.append(y)
y_test_prob.append(y_prob)
fpr, tpr, _ = roc_curve(y_test, y_test_prob)
roc_auc = auc(fpr, tpr)
# Plot of a ROC curve for a specific class
plt.figure()
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
