How to plot a ROC curve using dataframe converted from CSV file

问题

I was trying to plot a ROC curve by using the documentation provided by sklearn. My data is in a CSV file, and it looks like this.It has two classes 'Good'and 'Bad'

screenshot of my CSV file

And my code looks like this

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

# Import some data to play with
df = pd.read_csv("E:\\autodesk\\TTI ROC curve.csv")
X =df[['TTI','Max TemperatureF','Mean TemperatureF','Min TemperatureF',' Min Humidity']].values
y = df['TTI_Category'].as_matrix()
# 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(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"])
plt.figure()
lw = 2
plt.plot(fpr[2], tpr[2], color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
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()enter code here

If i run this code the system told me random_state is not defined. so I changed it to random_state=true. Then the system told me

plt.plot(fpr[2], tpr[2], color='darkorange', KeyError: 2 <matplotlib.figure.Figure at 0xd8bff60>

if I print out n_classes. The system told me it's "1", and if I print out the n_classes in the documentation it says 3. I'm not sure if that's where the problem is. Does anyone have answer to this traceback?

回答1:

Looks like you simply don't understand how your data is structured and how your code should work.

LabelBinarizer will return a one-v-all encoding, meaning that for two classes you will get the following mapping: ['good', 'bad', 'good'] -> [[1], [0], [1]], s.t. n_classes = 1.

Why would you expect it to be 3 if you have 2 classes? Simply change plt.plot(fpr[2], tpr[2], color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2]) to plt.plot(fpr[0], tpr[0], color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[0]) and you should be good.

回答2:

Just look in tpr and fpr dictionaries and you will see that you don't have a tpr[2] or fpr[2]. n_classes = y.shape[1] shows how many classes you have (= 2), which means that you have keys of 0 and 1 in your tpr and fpr dictionaries.

You are overcomplicating things by using a multi-class approach when you only have 2 classes (binary classification). I think you are using this tutorial.

I would advise replacing the following:

# 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 = 2
plt.plot(fpr[2], tpr[2], color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])

With something like:

fpr, tpr = roc_curve(y_test.values, y_score[:,1])
roc_auc = auc(fpr,tpr)
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)

来源：https://stackoverflow.com/questions/51346750/how-to-plot-a-roc-curve-using-dataframe-converted-from-csv-file

标签

python

dataframe

machine-learning

scikit-learn

roc