Computing scikit-learn multiclass ROC Curve with cross validation (CV)

Question

I want to evaluate my classification models with a ROC curve. I\'m struggling with computing a multiclass ROC Curve for a cross-validated data set. There is no division in t

Answer 1

You can use label_binarizer this way and get the desired plot as output.

Example using Iris data:

import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.metrics import roc_curve, auc
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import cross_val_predict
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

iris = datasets.load_iris()
X = iris.data
y = iris.target

# Binarize the output
y_bin = label_binarize(y, classes=[0, 1, 2])
n_classes = y_bin.shape[1]

pipe= Pipeline([('scaler', StandardScaler()), ('clf', LogisticRegression())])
# or
#clf = OneVsRestClassifier(LogisticRegression())
#pipe= Pipeline([('scaler', StandardScaler()), ('clf', clf)])
y_score = cross_val_predict(pipe, X, y, cv=10 ,method='predict_proba')

fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_bin[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
colors = cycle(['blue', 'red', 'green'])
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([-0.05, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic for multi-class data')
plt.legend(loc="lower right")
plt.show()