i want this python code in C#

import csv import pandas as pd import numpy as np from sklearn.naive_bayes import GaussianNB from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.metrics import confusion_matrix, f1_score, roc_curve, auc import matplotlib.pyplot as plt from itertools import cycle from scipy import interp

#converting txt file to csv file with open('heartdisease.txt', 'r') as in_file: stripped = (line.strip() for line in in_file) lines = (line.split(",") for line in stripped if line) with open('heartdisease.csv', 'w') as out_file: writer = csv.writer(out_file) writer.writerow(('age', 'sex', 'cp', 'restbp', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca','thal', 'num')) writer.writerows(lines) # reading CSV using Pandas and storing in dataframe df = pd.read_csv('heartdisease.csv', header = None)

training_x=df.iloc[1:df.shape[0],0:13] #print(training_set)

training_y=df.iloc[1:df.shape[0],13:14] #print(testing_set)

# converting dataframe into arrays x=np.array(training_x) y=np.array(training_y)

for z in range(5): print(" Test Train Split no. ",z+1," ") x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=None) # Gaussian function of sklearn gnb = GaussianNB() gnb.fit(x_train, y_train.ravel()) y_pred = gnb.predict(x_test) print(" Gaussian Naive Bayes model accuracy(in %):", metrics.accuracy_score(y_test, y_pred)*100) # convert 2D array to 1D array y1=y_test.ravel() y_pred1=y_pred.ravel() print(" Confusion Matrix") cf_matrix=confusion_matrix(y1,y_pred1) print(cf_matrix) print(" F1 Score") f_score=f1_score(y1,y_pred1,average='weighted') print(f_score) # Matrix from 1D array y2=np.zeros(shape=(len(y1),5)) y3=np.zeros(shape=(len(y_pred1),5)) for i in range(len(y1)): y2[i][int(y1[i])]=1 for i in range(len(y_pred1)): y3[i][int(y_pred1[i])]=1 # ROC Curve generation n_classes = 5 fpr = dict() tpr = dict() roc_auc = dict() for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(y2[:, i], y3[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area fpr["micro"], tpr["micro"], _ = roc_curve(y2.ravel(), y3.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) # Compute macro-average ROC curve and ROC area print(" ROC Curve") # First aggregate all false positive rates lw=2 all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) # Then interpolate all ROC curves at this points mean_tpr = np.zeros_like(all_fpr) for i in range(n_classes): mean_tpr += interp(all_fpr, fpr[i], tpr[i]) # Finally average it and compute AUC mean_tpr /= n_classes fpr["macro"] = all_fpr tpr["macro"] = mean_tpr roc_auc["macro"] = auc(fpr["macro"], tpr["macro"]) # Plot all ROC curves plt.figure() plt.plot(fpr["micro"], tpr["micro"], label='micro-average (area = {0:0.2f})' ''.format(roc_auc["micro"]), color='deeppink', linestyle=':', linewidth=4) plt.plot(fpr["macro"], tpr["macro"], label='macro-average (area = {0:0.2f})' ''.format(roc_auc["macro"]), color='navy', linestyle=':', linewidth=4) colors = cycle(['aqua', 'darkorange', 'cornflowerblue','red','black']) for i, color in zip(range(n_classes), colors): plt.plot(fpr[i], tpr[i], color=color, lw=lw, label='ROC of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.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('Receiver operating characteristic for multi-class') plt.legend(loc="lower right") plt.show()