from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
try:
	import cPickle as pickle 
except:
	import _pickle as pickle
import logging
import os
import numpy as np
import time
import os
import math
import pickle as pkl 

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt  
from matplotlib.patches import Rectangle 
from sklearn.preprocessing import scale, MinMaxScaler, StandardScaler
from sklearn.metrics import accuracy_score, precision_score, recall_score
from scipy.stats import pearsonr
from scipy.stats import kurtosis, skew
from scipy.spatial.distance import pdist
from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
from sklearn.metrics import precision_recall_curve, roc_curve, auc, average_precision_score


color_dict = {
	'ml_loo': 'red',
}

linestyles = {
	'ml_loo': '-',
}

labels = {
	'ml_loo': 'ML-LOO',
}

labels_attack = { 
	'cw': 'C&W', 
}

labels_data = {
	'cifar10': 'CIFAR-10',
}

labels_model = {
	'resnet': 'ResNet',
}


def load_data(args, attack, det, magnitude = 0.0):
	x, y = {}, {}
	for data_type in ['train', 'test']:
		if det == 'ml_loo':
			data_ori = np.load('{}/data/{}_{}_{}_{}_{}.npy'.format(
				args.data_model,
				args.data_sample,
				data_type,
				'ori',
				attack, 
				'ml_loo'))

			data_adv = np.load('{}/data/{}_{}_{}_{}_{}.npy'.format(
				args.data_model,
				args.data_sample,
				data_type,
				'adv',
				attack, 
				'ml_loo'))
			d = len(data_ori)
			print('detect using {}'.format(det))
			print('using adv only')
			print('data_ori', data_ori.shape) 
			# Use only IQR features (5th dimension).
			data_ori = data_ori[:, [5], :]
			data_adv = data_adv[:, [5], :]
			data_ori = data_ori.reshape(d, -1)
			data_adv = data_adv.reshape(d, -1)
			# (200, 1)

		d = len(data_ori)
		x[data_type] = np.vstack([data_ori, data_adv])
		y[data_type] = np.concatenate((np.zeros(d), np.ones(d)))


	idx_train = np.random.permutation(len(x['train']))
	x['train'] = x['train'][idx_train]  
	y['train'] = y['train'][idx_train]
	return x, y


def train_and_evaluate(args, detections, attack, fpr_upper = 1.0):
	plt.figure(figsize = (10, 8))
	font = {'weight': 'bold', 'size': 16}
	matplotlib.rc('font', **font)

	auc_dict = {}
	tpr1 = {}
	tpr5 = {}
	tpr10 = {}

	for det in detections:
		# Load data
		x, y = load_data(args, attack, det)
		x_train, y_train = x['train'], y['train']
		x_test, y_test = x['test'], y['test']
		x_train = x_train.reshape(len(x_train), -1)
		x_test = x_test.reshape(len(x_test), -1)
		# Train
		lr = LogisticRegressionCV(n_jobs=-1).fit(x_train, y_train) 
		# Predict
		pred = lr.predict_proba(x_test)[:, 1] 
		pred = lr.predict_proba(x_test)[:, 1] 
		# Evaluate.
		fpr, tpr, thresholds = roc_curve(y_test, pred)
		def find_nearest(array, value):
			array = np.asarray(array)
			idx = (np.abs(array - value)).argmin()
			return idx
		auc_dict[det] = auc(fpr, tpr)
		tpr1[det] = tpr[find_nearest(fpr, 0.01)]
		tpr5[det] = tpr[find_nearest(fpr, 0.05)]
		tpr10[det] = tpr[find_nearest(fpr, 0.10)]
		plt.plot(
			fpr, tpr,
			label="{0} (AUC: {1:0.3f})".format(labels[det], auc(fpr, tpr)),
			color=color_dict[det], 
			linestyle=linestyles[det], 
			linewidth=4)

	plt.xlim([0.0, fpr_upper])
	plt.ylim([0.0, 1.0])
	plt.xlabel('False Positive Rate', fontsize = 32)
	plt.ylabel('True Positive Rate', fontsize = 32)
	plt.title('{} ({}, {})'.format(labels_attack[attack], labels_data[args.dataset_name], labels_model[args.model_name]), fontsize = 32) 
	plt.legend(loc="lower right", fontsize = 22)
	plt.show()
	figure_name = '{}/figs/mad_transfer_roc_{}_{}_{}.pdf'.format(args.data_model, args.data_sample, attack, attack)
	plt.savefig(figure_name)

	return auc_dict, tpr1, tpr5, tpr10 


if __name__ == '__main__':
	import argparse
	parser = argparse.ArgumentParser()
	parser.add_argument('--dataset_name', type = str, 
		choices = ['cifar10'], 
		default = 'cifar10')
	parser.add_argument('--model_name', type = str, 
		choices = ['resnet'], 
		default = 'resnet') 
	parser.add_argument('--data_sample', type = str, 
		choices = ['x_val200'], 
		default = 'x_val200') 

	args = parser.parse_args()
	dict_a = vars(args) 
	args.data_model = args.dataset_name + args.model_name

	train_and_evaluate(args, ['ml_loo'], 'cw', fpr_upper = 1.0)







