# to introduce the necessary library
The import pandas as pd
The import numpy as np
The import matplotlib. Pyplot as PLT
The from itertools import cycle
The from sklearn. Model_selection import train_test_split
The from sklearn. Naive_bayes import GaussianNB
The from sklearn. Metrics import roc_curve, auc
The from sklearn. Preprocessing import label_binarize
The from sklearn. Multiclass import OneVsRestClassifier
The from scipy import interp

# load data
Df=pd read_excel (' c: \ \ Users \ \ asus \ Desktop \ \ mm XLS ')
X=df. Iloc [: 142, 0:38]
Y=df [' y ']
X=np. Array (X.v alues)
Y=np. Array (y.v alues)
# label binarization
Y=label_binarize (y, classes=[2, 1, 0])
# set type
N_classes=y.s hape [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 the each class against the other
GNB=GaussianNB ()
GNB. Fit (X_train y_train)
Y_score=GNB. Predict (X_test)


# to calculate each kind of ROC
FPR=dict ()
TPR=dict ()
Roc_auc=dict ()
For I in range (n_classes) :
FPR [I], [I] TPR, _=roc_curve (y_test [, I], y_score [, I])
Roc_auc [I]=auc (FPR [I], [I] TPR)

# compute micro - business, ROC curve and ROC area
FPR [" micro "], [" micro "] TPR, _=roc_curve (y_test. Ravel (), y_score. Ravel ())
Roc_auc [" micro "]=auc (FPR [" micro "], TPR [" micro "])


# the plot all roc curves
Lw=2
PLT. Figure ()
PLT. The plot (FPR [" micro "], TPR, "micro" label='micro - business, ROC curve (area={0-0. 2 f})'
'. The format (roc_auc [" micro "]), color=deeppink ', graphics.linestyle=':' our linewidth=4)

Colors=cycle ([' aqua ', 'darkorange', 'cornflowerblue'])
For I, the color in the zip (range (n_classes), colors) :
PLT. The plot (FPR [I], [I], TPR color=color, lw=lw, label='ROC curve of class {0} (area={1-0. 2 f})'
"'. The format (I, roc_auc [I]))

PLT. The plot ([0, 1], [0, 1], "k", lw=lw)
PLT. Xlim ([0.0, 1.0])
PLT. Ylim ([0.0, 1.05])
PLT. Xlabel (' False Positive Rate ')
PLT. Ylabel (' True Positive Rate ')
PLT. Title (' Some extension of the Receiver operating characteristic to multi - class ')
PLT. Legend (loc="lower right")
plt.show()