如何用scikit学习多类情况绘制ROC曲线

Question

我想为我自己的数据集绘制多类情况的 ROC 曲线。通过文档，我读到标签必须是二进制的（我有 5 个标签从 1 到 5），所以我按照文档中提供的示例进行操作：

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()

这样做的问题是，这种无可奈何永远不会结束。知道如何为此数据集绘制此 ROC 曲线的任何想法？

Answer 1

此版本永远不会完成，因为以下行：

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

svm 分类器需要很长时间才能完成，请使用不同的分类器，如 AdaBoost 或您选择的其他分类器：

classifier = OneVsRestClassifier(AdaBoostClassifier())

请记住添加导入：

from sklearn.ensemble import AdaBoostClassifier

删除此代码，它没用：

# 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)]

相反，只需添加：

random_state = 0