import os
import sys
import time
import numpy
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from pprint import pprint
from scipy.io import matlab
import re
import math
from theano import shared
from collections import OrderedDict
from layers import *
from theano.misc.pkl_utils import dump
import numpy as np
from io_utils_MUV_ROC import *
from mol_graph import *
from process_poc_pretrain import *



def eval_ER_MUV(target,thres):
	
	ER_5pt=0
	ER_one=0
	ER_two=0
	ER_five=0
	total_count=0

	pro_name = target
	PDB_ID = PDB_dict[target]
			
	Weights_ID = 'MUV_fix0_assay_nodec_e_1_190000_drop_041519'
	y_true_4 = numpy.load('../MUV_results_0408/test_labels_'+Weights_ID+'_'+pro_name+'_'+PDB_ID+'.dat')
	y_prob_4 = numpy.load('../MUV_results_0408/test_probs_'+Weights_ID+'_'+pro_name+'_'+PDB_ID+'.dat')


	y_true = y_true_4
	y_prob = y_prob_4 #+ y_prob_3

	from sklearn import metrics
	from sklearn.metrics import roc_auc_score
	from sklearn.metrics import confusion_matrix

	pos = numpy.where(y_prob>=thres)
	y_pred = numpy.zeros(y_prob.shape)
	y_pred[pos]=1

	tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
	fpr = float(fp) / (fp+tn)
	tpr = float(tp) / (tp+fn)


	print (pro_name)
	print ("fpr")
	print (fpr)
	print ("tpr")
	print (tpr)
	print ("RE")
	print (tpr/fpr)

	
if __name__ == '__main__':
	fold_dict = {'846':0,'600':0,'692':1,'859':1,'548':2,'852':2,'466':4,'689':4,'832':5}
	PDB_dict = {'859':'5cxv','733':'1u9e','692':'1yow','832':'1au8','689':'2y6o','852':'4xe4','466':'3v2y','548':'3poo','713':'5tn7','600':'1yow','846':'5exm'}
	target = sys.argv[1]
	thres = float(sys.argv[2])
	eval_ER_MUV(target,thres)