How to build ROC curve with scikit for multiclass case?

Question

I would like to build an ROC curve for a multiclassical case for my own dataset. In the documentation, I read that labels must be binary (I have 5 labels from 1 to 5), so I followed the example provided in the documentation

print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.cross_validation import train_test_split from sklearn.preprocessing import label_binarize from sklearn.svm import SVC from sklearn.multiclass import OneVsRestClassifier from sklearn.feature_extraction.text import TfidfVectorizer import numpy as np tfidf_vect= TfidfVectorizer(use_idf=True, smooth_idf=True, sublinear_tf=False, ngram_range=(2,2)) from sklearn.cross_validation import train_test_split, cross_val_score import pandas as pd df = pd.read_csv('path/file.csv', header=0, sep=',', names=['id', 'content', 'label']) X = tfidf_vect.fit_transform(df['content'].values) y = df['label'].values # Binarize the output y = label_binarize(y, classes=[1,2,3,4,5]) n_classes = y.shape[1] # Add noisy features to make the problem harder random_state = np.random.RandomState(0) n_samples, n_features = X.shape X = np.c_[X, random_state.randn(n_samples, 200 * n_features)] # shuffle and split training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33 ,random_state=0) # Learn to predict each class against the other classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True, random_state=random_state)) y_score = classifier.fit(X_train, y_train).decision_function(X_test) # Compute ROC curve and ROC area for each class fpr = dict() tpr = dict() roc_auc = dict() for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) # Plot of a ROC curve for a specific class plt.figure() plt.plot(fpr[2], tpr[2], label='ROC curve (area = %0.2f)' % roc_auc[2]) plt.plot([0, 1], [0, 1], 'k--') 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 example') plt.legend(loc="lower right") plt.show() # Plot ROC curve plt.figure() plt.plot(fpr["micro"], tpr["micro"], label='micro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc["micro"])) for i in range(n_classes): plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Some extension of Receiver operating characteristic to multi-class') plt.legend(loc="lower right") plt.show() 

The problem is that this aproach never ends. Any idea on how to plot this ROC curve for this dataset ?

Answer 1

This version never ends because this line:

 classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True, random_state=random_state)) 

The svm classifier takes a very long time to use another classifier, for example AdaBoost or another one of your choice:

 classifier = OneVsRestClassifier(AdaBoostClassifier()) 

Do not forget to add import:

 from sklearn.ensemble import AdaBoostClassifier 

Remove this code, it is useless:

 # Add noisy features to make the problem harder random_state = np.random.RandomState(0) n_samples, n_features = X.shape X = np.c_[X, random_state.randn(n_samples, 200 * n_features)] 

Instead, just add:

 random_state = 0