plot_n-gram_TFIDF.py

from sklearn.ensemble import RandomForestClassifier as RF
from sklearn import model_selection
from sklearn.metrics import confusion_matrix
import pandas as pd
import numpy as np
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
import matplotlib.pyplot as plt
import math

avg = lambda items: float(sum(items)) / len(items)
TPR = []
FPR = []
Score =[]
Acc = []
Gmean = []
subtrainLabel = pd.read_csv('subtrainLabels.csv')
for i in range(1,6):
    subtrainfeature = pd.read_csv("TFIDF_top50_{0}feature.csv".format(i))
    subtrain = pd.merge(subtrainLabel,subtrainfeature,on='Id')
    labels = subtrain.Class
    subtrain.drop(["Class","Id"], axis=1, inplace=True)
    subtrain = subtrain.values

#Develop test set and training set
    X_train, X_test, y_train, y_test = model_selection.train_test_split(subtrain,labels,test_size=0.5)

    srf = RF(n_estimators=10, n_jobs=-1)
    srf.fit(X_train,y_train)
    score = srf.score(X_test,y_test)
    print(score)
    Score.append(score)
    y_pred = srf.predict(X_test)
    CM = confusion_matrix(y_test, y_pred)
    #print(CM)
    FP = CM.sum(axis=0) - np.diag(CM)  
    FN = CM.sum(axis=1) - np.diag(CM)
    TP = np.diag(CM)
    TN = CM.sum() - (FP + FN + TP)
    FP = FP.astype(float)
    FN = FN.astype(float)
    TP = TP.astype(float)
    TN = TN.astype(float)
    tpr = TP/(TP+FN)
# Specificity or true negative rate
    tnr = TN/(TN+FP) 
# Precision or positive predictive value
    ppv = TP/(TP+FP)
# Negative predictive value
    npv = TN/(TN+FN)
# Fall out or false positive rate
    fpr = FP/(FP+TN)
# False negative rate
    fnr = FN/(TP+FN)
# False discovery rate
    fdr = FP/(TP+FP)
# Overall accuracy
    acc = (TP+TN)/(TP+FP+FN+TN)
    TPR.append(avg(tpr))
    FPR.append(avg(fpr))
    Acc.append(avg(acc))
    gmean=math.sqrt(avg(tnr)*avg(tpr))
    Gmean.append(gmean)
print(TPR,FPR,Score,Acc,Gmean)
array=[TPR,FPR,Acc,Gmean]
list1=[1,2,3,4,5]
name = ['TPR','FPR','Accuracy','Gmean']
import matplotlib.pyplot as plt

for i in range(4):
    plt.figure(i)
    x=[1,2,3,4,5]
    plt.plot(x,array[i],label = 'TFIDF')
    plt.xlabel('Opcode n-gram size')
    plt.ylabel(name[i])
    plt.tick_params(axis='both', which='major', labelsize=14)
    for a,b,c in zip(x,array[i],name[i]):
        str_label = c + '_' + str(b)
        plt.text(a,b,str_label, ha = 'center' ,va = 'bottom' ,fontsize=5)
    plt.legend(loc='best')
    plt.show()