roc_Y = [] for alpha in alpha_list: positive_count = sum([1 for x in X_positive if x >= alpha]) positive_rate = positive_count / float(len(X_positive)) negative_count = sum([1 for x in X_negative if x >= alpha]) negative_rate = negative_count / float(len(X_negative)) roc_X.append(negative_rate) roc_Y.append(positive_rate) #print roc_X plt.plot(roc_X, roc_Y, color="green") classify = IterativeEM() classify.__classify__(vote_list, 2) estimates = classify.__getEstimates__() X_positive = [] X_negative = [] for subject_index, zooniverse_id in enumerate(big_subjectList): probability = estimates[subject_index] wreness_condor = gold_condor[subject_index] if wreness_condor == 0: X_negative.append(probability) else: X_positive.append(probability) alpha_list = X_negative[:] alpha_list.extend(X_positive)