使用imblearn绘制ROC曲线

Question

我正在尝试使用imblearn绘制ROC曲线，但是遇到了一些问题。

这是我的数据的屏幕截图

from imblearn.over_sampling import SMOTE, ADASYN
from collections import Counter
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 ml.csv")
# X and y are different columns of the input data. Input X as numpy array
X = df[['TTI','Max TemperatureF','Mean TemperatureF','Min TemperatureF',' Min Humidity']].values
# # Reshape X. Do this if X has only one value per data point. In this case, TTI.

# # Input y as normal list
y = df['TTI_Category'].as_matrix()

X_resampled, y_resampled = SMOTE().fit_sample(X, y)

y_resampled = label_binarize(y_resampled, classes=['Good','Bad','Ok'])
n_classes = y_resampled.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_resampled, y_resampled).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()

我将原始X_train and y_train更改为X_resampled, y_resampled，因为训练应该在重新采样的数据集上进行，而测试则需要在原始测试数据集上进行。但是我得到以下回溯`

runfile('E:/autodesk/SMOTE with multiclass.py', wdir='E:/autodesk')
Traceback (most recent call last):

  File "<ipython-input-128-efb16ffc92ca>", line 1, in <module>
    runfile('E:/autodesk/SMOTE with multiclass.py', wdir='E:/autodesk')

  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 "E:/autodesk/SMOTE with multiclass.py", line 51, in <module>
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])

IndexError: too many indices for array

我添加了另一行以对y_resampled和原始y进行二值化，其他所有内容都保持不变，但是我不确定是否要拟合重新采样的数据并测试原始数据

X_resampled, y_resampled = SMOTE().fit_sample(X, y)

y_resampled = label_binarize(y_resampled, classes=['Good','Bad','Ok'])

y = label_binarize(y, classes=['Good','Bad','Ok'])
n_classes = y.shape[1]

非常感谢您的帮助。

Answer 1

首先让我们讨论该错误。您正在这样做：

y_resampled = label_binarize(y_resampled, classes=['Good','Bad','Ok'])
n_classes = y_resampled.shape[1]

所以您的n_classes实际上是3。

在随后的部分中，您执行了以下操作：

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

这里您使用的是原始的y，而不是y_resampled。因此，y_test当前是形状为(n_samples,)的一维数组，或者可能是形状为(n_samples, 1)的列向量。

在for循环中，您开始从0循环到3（n_classes），这对于y_test是不可能的，因此不会出现您在y_test中尝试访问的索引的错误。

第二，您应该首先将数据分为训练和测试，然后仅对训练部分进行重新采样。

因此，此代码应执行您想要的操作：

# First divide the data into train test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
                                                    random_state=0)

# Then only resample the training data
X_resampled, y_resampled = SMOTE().fit_sample(X_train, y_train)

# Then label binarize them to be used in multi-class roc
y_resampled = label_binarize(y_resampled, classes=['Good','Bad','Ok'])

# Do this to the test data too
y_test = label_binarize(y_test, classes=['Good','Bad','Ok'])

y_score=classifier.fit(X_resampled, y_resampled).predict_proba(X_test)

# Then you can do this and other parts of code
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])