numpy - ROC curve for binary classification in python

Question

I am tying to plot an ROC curve for Binary classification using RandomForestClassifier

I have two numpy arrays one contains predicted values and one contains true values as follows:

In [84]: test
Out[84]: array([0, 1, 0, ..., 0, 1, 0])

In [85]: pred
Out[85]: array([0, 1, 0, ..., 1, 0, 0])

How do I port ROC curve and obtain AUC (Area Under Curve) for this binary classification result in ipython?

Answer 1

You need probabilities to create ROC curve.

In [84]: test
Out[84]: array([0, 1, 0, ..., 0, 1, 0])

In [85]: pred
Out[85]: array([0.1, 1, 0.3, ..., 0.6, 0.85, 0.2])

Example code from scikit-learn examples:

import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(2):
    fpr[i], tpr[i], _ = roc_curve(test, pred)
    roc_auc[i] = auc(fpr[i], tpr[i])

print roc_auc_score(test, pred)
plt.figure()
plt.plot(fpr[1], tpr[1])
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.show()