本文整理汇总了Python中sklearn.preprocessing.LabelBinarizer.ravel方法的典型用法代码示例。如果您正苦于以下问题：Python LabelBinarizer.ravel方法的具体用法？Python LabelBinarizer.ravel怎么用？Python LabelBinarizer.ravel使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.preprocessing.LabelBinarizer的用法示例。
在下文中一共展示了LabelBinarizer.ravel方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: write_score
# 需要导入模块: from sklearn.preprocessing import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.LabelBinarizer import ravel [as 别名]
def write_score(name, gold_labels, pred_scores, classes, average_classes):
classes, average_classes = np.array(classes), np.array(average_classes)
gold_scores = LabelBinarizer().fit(classes).transform(gold_labels)
pred_labels = classes[np.argmax(pred_scores, axis=1)]
with closing(Tee('{}.txt'.format(name), 'w')):
precision, recall, fscore, _ = precision_recall_fscore_support(gold_labels, pred_labels, labels=classes)
for t in zip(classes, precision, recall, fscore):
print('{}: P={:.2f}, R={:.2f}, F1={:.2f}'.format(*t))
print('Accuracy: {:.4f}'.format(accuracy_score(gold_labels, pred_labels)))
print('F1 average: {:.4f}'.format(np.mean(fscore[LabelEncoder().fit(classes).transform(average_classes)])))
with PdfPages('{}.pdf'.format(name)) as pdf:
fpr = {}
tpr = {}
roc_auc = {}
for i in range(len(classes)):
fpr[i], tpr[i], _ = roc_curve(gold_scores[:, i], pred_scores[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
fpr['micro'], tpr['micro'], _ = roc_curve(gold_scores.ravel(), pred_scores.ravel())
roc_auc['micro'] = auc(fpr['micro'], tpr['micro'])
plt.figure()
plt.plot(fpr['micro'], tpr['micro'], label='micro-average (area = {:.2f})'.format(roc_auc['micro']))
for i in range(len(classes)):
plt.plot(fpr[i], tpr[i], label='{0} (area = {1:.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('ROC Curves')
plt.legend(loc='lower right')
pdf.savefig()