[FIXED] Can I plot ROC curve for multiclass text classification problem, without using OneVsRestClassifier?

Issue

I have a pickle file that when loaded returns a trained RandomForest classifier. I want to plot the ROC curve for the classes, but from what I read online, the classifier must be wrapped in scikit learn's OneVsRestClassifier. The problem is that since I already have the trained model I cannot wrap it in it to fit the model again.

So I would like to know if there is some workaround to plot the ROC curve. From my trained model I have y_test, y_proba. I also have x_test values.

• The shape of my y_proba examples is: (6715, 5)

• The shape of y_test is (6715, 5)

This is the output of the code @dx2-66 suggested:

Solution

I assume your y_test is single column with class id, and your y_proba has as much columns as there are classes (at least that's what you'd usually get from predict_proba().

How about this? It should yield you OvR-style curves:

from sklearn.metrics import roc_curve
from sklearn.preprocessing import label_binarize
import matplotlib.pyplot as plt

classes = range(y_proba.shape[1])

for i in classes:
    fpr, tpr, _ = roc_curve(label_binarize(y_test, classes=classes)[:,i], y_proba[:,i])
    plt.plot(fpr, tpr, alpha=0.7)
    plt.legend(classes)

Update: solution for non-monotonic class labels:

classes = sorted(list(y_test['label'].unique()))

plt.plot([0, 1], linestyle='--')

for i in range(len(classes)):
    fpr, tpr, _ = roc_curve(label_binarize(y_test, classes=classes)[:,i], y_proba.values[:,i])
    plt.plot(fpr, tpr, alpha=0.7)
    plt.legend(['baseline']+classes) # Fixed the baseline legend

Answered By - dx2-66