Created
February 25, 2016 19:03
-
-
Save robert-spurrier/b3215825cb593e2f90ea to your computer and use it in GitHub Desktop.
Generate AUCROC in Python with scikit-learn (sklearn)
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| from sklearn.metrics import roc_curve, auc | |
| import matplotlib.pyplot as plt | |
| import random | |
| import csv | |
| actual = [] | |
| predictions = [] | |
| with open('scores_and_outcomes.tsv','rb') as tsvin: | |
| tsvin = csv.reader(tsvin, delimiter='\t') | |
| for row in tsvin: | |
| predictions.append(float(row[0])) | |
| actual.append(int(row[1])) | |
| false_positive_rate, true_positive_rate, thresholds = roc_curve(actual, predictions) | |
| roc_auc = auc(false_positive_rate, true_positive_rate) | |
| plt.title('Receiver Operating Characteristic') | |
| plt.plot(false_positive_rate, true_positive_rate, 'b', | |
| label='AUC = %0.2f'% roc_auc) | |
| plt.legend(loc='lower right') | |
| plt.plot([0,1],[0,1],'r--') | |
| plt.xlim([0.0,1.0]) | |
| plt.ylim([0.0,1.0]) | |
| plt.ylabel('True Positive Rate') | |
| plt.xlabel('False Positive Rate') | |
| plt.show() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment