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我正在尝试使用 imblearn 绘制 ROC 曲线，但遇到了一些问题。

这是我的数据的截图

截屏

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()

我将原来的更改为X_train and y_train，X_resampled, y_resampled因为应该在重新采样的数据集上进行训练，并且需要在原始测试数据集上进行测试。但是我得到了以下回溯`

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

我添加了另一行来对 y_resampled 和原始 y 进行二值化，其他一切都保持不变，但我不确定我是否正在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。

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1 回答 1

1

首先让我们讨论一下错误。你正在这样做：

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

所以你n_classes实际上是3。

在后续部分中，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)

这里你用的是原装的y，不是y_resampled。因此，y_test当前是 shape 的一维数组(n_samples,)或 shape 的列向量(n_samples, 1)。

在 for 循环中，您开始从 0 迭代到 3 (n_classes)，这是不可能的y_test，因此您尝试访问的索引y_test不存在。

其次，您应该首先将数据拆分为训练和测试，然后仅对训练部分进行重新采样。

所以这段代码应该做你想做的事：

# 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])
于 2018-07-20T12:40:44.480 回答