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,svm

from sklearn.metrics import mean_squared_error, r2_score

np.random.seed(42)

tf.set_random_seed(42)

random.seed(42)

out_put_header = ['Promoter region','Posotive_zeros','Negative_zeros','Sum_zeros',

                  'Positive_freq', 'Negative_freq','Sum_freq',

                  'Sum_all','Percent_all', 'Vector_freq',

                  "True positive", "False positive", "True negative", "False negative", "Accuracy",

                  '>50%']

output_file_name = 'output_svm.csv'

with open(output_file_name,'w') as f:

    writer = csv.writer(f)

    writer.writerow(out_put_header)

labels_file = 'labes.csv'

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

ids_csv = labels_df.FID.tolist()

promoters_list = range(100)

for promoter_num in promoters_list:

    print promoter_num

    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 = np.array(dataset_.Vars.tolist())

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

    t_idx = [int(line.strip()) for line in open("train_id.txt", 'r')]

    dataset_X= dataset_X[t_idx]

    dataset_Y = dataset_Y[t_idx]

    N = len(dataset_X)

    # repeat information

    per_zeros, p_zeros,n_zeros = quan_detector(dataset_X,dataset_Y)

    count_zeros = p_zeros+n_zeros # sum of individuals without any variants

    most_vector, max_count,count_vector = most_repeared_promoter(dataset_X,dataset_Y)

    _, p_count,n_count = count_vector

    vart_pos = []

    for i in range(len(most_vector)):

        if most_vector[i] != '0':

            vart_pos.append(i)

    np.random.seed(42)

    tf.set_random_seed(42)

    random.seed(42)

    # network accuracy

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

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

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

    # Create linear regression object

    lsvm = svm.SVC(kernel='rbf', gamma=0.7, C=1.0)

    # Train the model using the training sets

    lsvm.fit(x_train, y_train)

    # Make predictions using the testing set

    y_pred = lsvm.predict(x_test)

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

    y_test_num = y_test

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

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

    info = ['promoter '+str(promoter_num), p_zeros,n_zeros,count_zeros,

            p_count, n_count, max_count,

            max_count + count_zeros, (max_count + count_zeros)*1./N, vart_pos,

            tp, fp, tn, fn, acc, acc>0.5]

    with open(output_file_name,'a') as f:

        writer = csv.writer(f)

        writer.writerow(info)

print "Done"