import json

import csv

import numpy as np

import os

import pandas as pd

import random

import tensorflow as tf

from functions import quan_detector, most_repeared_promoter,dataset

from sklearn.metrics import confusion_matrix

from sklearn import datasets, linear_model

from sklearn.metrics import mean_squared_error, r2_score

np.random.seed(42)

tf.set_random_seed(42)

random.seed(42)

labels_file = 'labes.csv'

labels_df = pd.read_csv(labels_file, index_col=0)

ids_csv = labels_df.FID.tolist()

promoters_list = range(1,2484)

dataset_X = []

for promoter_num in promoters_list:

    promoter_file = 'promoters/chr22_'+str(promoter_num)+'.json'

    # # read files

    with open(promoter_file) as json_data:

        ind_var = json.load(json_data)

    ids_json = ind_var.keys()

    var_num = []

    for i in ids_csv:

        id_name = str(i)

        temp = ind_var[id_name]

        var_seq = map(int, temp)

        var_num.append(var_seq)

    labels_df['vars'] = var_num

    lab_num = {1: [1, 0], # positive

               2: [0, 1]} # negative

    pheno_new = []

    for i in labels_df.Pheno.tolist():

        pheno_new.append(lab_num[i])

    d = {"Pheno": pheno_new, "Vars":labels_df.vars}

    dataset_ = pd.DataFrame(d)

    dataset_X .append(dataset_.Vars.tolist())

    dataset_Y = np.array(dataset_.Pheno.tolist())

dataset_X = np.array(dataset_X).reshape(11908,64*2580,1)

N = len(dataset_X)

# network accuracy

x_train, y_train,x_test,y_test = dataset(dataset_X,dataset_Y,test_ratio=0.1)

# Create linear regression object

regr = linear_model.LinearRegression()

# Train the model using the training sets

regr.fit(x_train, y_train)

# Make predictions using the testing set

y_pred = regr.predict(x_test)

y_pred = np.argmax(y_pred,axis=1)

y_test_num = np.argmax(y_test,axis=1)

tn, fp, fn, tp = confusion_matrix(y_test_num, y_pred).ravel()

acc = (tp+tn)*1./(tp+fp+tn+fn)

print acc

dataset_Y = np.argmax(dataset_Y,axis=-1)

x_train, y_train,x_test,y_test = dataset(dataset_X,dataset_Y,test_ratio=0.1)

logisticRegr = linear_model.LogisticRegression()

regr.fit(x_train, y_train)

# Make predictions using the testing set

y_pred = regr.predict(x_test)

y_test_num = np.argmax(y_test,axis=1)

tn, fp, fn, tp = confusion_matrix(y_test_num, y_pred).ravel()

acc = (tp+tn)*1./(tp+fp+tn+fn)

print "LogisticRegression acc is:",acc