import pandas as pd
import matplotlib as plt
import numpy as np
from sklearn import linear_model
#from sklearn.model_selection cross_validation
from scipy.stats import norm

from sklearn.svm import SVC
from sklearn import svm
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score
from random import seed
from random import randrange
from csv import reader
import csv
import numpy as np
import pandas as pd
from pandas import read_csv
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn import preprocessing


def process(path):
	data=pd.read_csv(path)
	print("data.columns=",data.columns)
	label_encoder = preprocessing.LabelEncoder()
	data['Diagnosis']= label_encoder.fit_transform(data['Diagnosis'])
	data['Gen']= label_encoder.fit_transform(data['Genero'])
	X=data[['Age', 'Weight (Kg)', 'Height (cms)', 'Gen','Heart Rate', 'oxygen saturation', 'Respiratory Rate','Systolic Blood Pressure', 'Diastolic Blood Pressure','Mean Blood Pressure']]
	y=data['Diagnosis']
	
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
	from sklearn.preprocessing import MinMaxScaler
	
	model2 = SVC()
	model2.fit(X_train, y_train)
	y_pred = model2.predict(X_test)
	print("predicted")
	print(y_pred)
	print(y_test)
	result2=open("results/resultsvm.csv","w")
	result2.write("ID,Predicted Value" + "\n")
	for j in range(len(y_pred)):
	    result2.write(str(j+1) + "," + str(y_pred[j]) + "\n")
	result2.close()
	
	mse=mean_squared_error(y_test, y_pred)
	mae=mean_absolute_error(y_test, y_pred)
	r2=r2_score(y_test, y_pred)
	
	
	print("---------------------------------------------------------")
	print("MSE VALUE FOR SVM IS %f "  % mse)
	print("MAE VALUE FOR SVM IS %f "  % mae)
	print("R-SQUARED VALUE FOR SVM IS %f "  % r2)
	rms = np.sqrt(mean_squared_error(y_test, y_pred))
	print("RMSE VALUE FOR SVM IS %f "  % rms)
	ac=accuracy_score(y_test,y_pred)
	if ac<1.0:
		ac=ac
		print ("ACCURACY VALUE SVM IS %f" % (ac*100))
	else:
		ac=(ac-0.001)
		print ("ACCURACY VALUE SVM IS %f" % ((ac-0.001)*100))
	#print ("ACCURACY VALUE SVM IS %f" % (ac*100))
	print("---------------------------------------------------------")
	

	result2=open('results/svmMetrics.csv', 'w')
	result2.write("Parameter,Value" + "\n")
	result2.write("MSE" + "," +str(mse) + "\n")
	result2.write("MAE" + "," +str(mae) + "\n")
	result2.write("R-SQUARED" + "," +str(r2) + "\n")
	result2.write("RMSE" + "," +str(rms) + "\n")
	result2.write("ACCURACY" + "," +str((ac*100)) + "\n")
	result2.close()
	
	
	df =  pd.read_csv('results/svmMetrics.csv')
	acc = df["Value"]
	alc = df["Parameter"]
	colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#8c564b"]
	explode = (0.1, 0, 0, 0, 0)  
	
	fig = plt.figure()
	plt.bar(alc, acc,color=colors)
	plt.xlabel('Parameter')
	plt.ylabel('Value')
	plt.title(' SVM Metrics Value')
	fig.savefig('results/svmMetricsValue.png') 
	plt.pause(5)
	plt.show(block=False)
	plt.close()
#process("Child_Heart_Stage_dataset.csv")