为 DecisionTreeClassifier 绘制多类 ROC 曲线

Question

我试图用文档中提供的 svm.SVC 以外的分类器绘制 ROC 曲线。我的代码适用于 svm.SVC；但是，在我切换到 KNeighborsClassifier、MultinomialNB 和 DecisionTreeClassifier 之后，系统一直告诉我 check_consistent_length(y_true, y_score)andFound input variables with inconsistent numbers of samples: [26632, 53264] My CSV file looks like this

这是我的代码

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
import sys
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier
# Import some data to play with
df = pd.read_csv("E:\\autodesk\\Hourly and weather categorized2.csv")
X =df[['TTI','Max TemperatureF','Mean TemperatureF','Min TemperatureF',' Min Humidity']].values
y = df['TTI_Category'].as_matrix()
y=y.reshape(-1,1)
# Binarize the output
y = label_binarize(y, classes=['Good','Bad'])
n_classes = y.shape[1]

# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
                                                    random_state=0)

# Learn to predict each class against the other
classifier = OneVsRestClassifier(DecisionTreeClassifier(random_state=0))
y_score = classifier.fit(X_train, y_train).predict_proba(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"])
plt.figure()
lw = 1
plt.plot(fpr[0], tpr[0], color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[0])
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
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()

我怀疑错误发生在这一行fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])，但我是这个 ROC 曲线的初学者，所以有人可以指导我完成这个追溯。非常感谢您的时间和帮助。Here is another question regarding ROC curve from me 顺便说一下，这里是整个追溯。希望我的解释足够清楚。 `

Traceback (most recent call last):

  File "<ipython-input-1-16eb0db9d4d9>", line 1, in <module>
    runfile('C:/Users/Think/Desktop/Python Practice/ROC with decision tree.py', wdir='C:/Users/Think/Desktop/Python Practice')

  File "C:\Users\Think\Anaconda2\lib\site-packages\spyder\utils\site\sitecustomize.py", line 880, in runfile
    execfile(filename, namespace)

  File "C:\Users\Think\Anaconda2\lib\site-packages\spyder\utils\site\sitecustomize.py", line 87, in execfile
    exec(compile(scripttext, filename, 'exec'), glob, loc)

  File "C:/Users/Think/Desktop/Python Practice/ROC with decision tree.py", line 47, in <module>
    fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())

  File "C:\Users\Think\Anaconda2\lib\site-packages\sklearn\metrics\ranking.py", line 510, in roc_curve
    y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)

  File "C:\Users\Think\Anaconda2\lib\site-packages\sklearn\metrics\ranking.py", line 302, in _binary_clf_curve
    check_consistent_length(y_true, y_score)

  File "C:\Users\Think\Anaconda2\lib\site-packages\sklearn\utils\validation.py", line 173, in check_consistent_length
    " samples: %r" % [int(l) for l in lengths])

ValueError: Found input variables with inconsistent numbers of samples: [26632, 53264]

Answer 1

在原代码y_resampled = label_binarize(y_resampled, classes=['Good','Bad','Ok']) 中加入这一行即可解决问题

Answer 2

需要用到DecisionTreeClassifier的predict_proba函数：

示例：

import matplotlib.pyplot as plt
from sklearn import 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.tree import DecisionTreeClassifier

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

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

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)

classifier = OneVsRestClassifier(DecisionTreeClassifier(random_state=0))
y_score = classifier.fit(X_train, y_train).predict_proba(X_test)


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])
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()