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.linear_model import LogisticRegression

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)

    model2=LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,

          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,

          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,

          verbose=0, warm_start=False)

    model2.fit(X_train, y_train)

    y_pred = model2.predict(X_test)

    print("predicted")

    print(y_pred)

    print(y_test)

    result2=open("results/resultLR.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 Logistic Regression IS %f "  % mse)

    print("MAE VALUE FOR Logistic Regression IS %f "  % mae)

    print("R-SQUARED VALUE FOR Logistic Regression IS %f "  % r2)

    rms = np.sqrt(mean_squared_error(y_test, y_pred))

    print("RMSE VALUE FOR Logistic Regression IS %f "  % rms)

    ac=accuracy_score(y_test,y_pred)

    print ("ACCURACY VALUE Logistic Regression IS %f" % (ac*100))

    print("---------------------------------------------------------")

    result2=open('results/LRMetrics.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/LRMetrics.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('Logistic Regression Value')

    fig.savefig('results/LRMetricsValue.png') 

    plt.pause(5)

    plt.show(block=False)

    plt.close()

#process("Child_Heart_Stage_dataset.csv")