'''
GOAL: generate the initial detection model for the starting year
'''
import numpy as np
import scipy
from scipy.stats import logistic
from scipy.special import expit
from numpy import dot
import sklearn
from sklearn.datasets import load_svmlight_file
import os
import sys
import string
from decimal import *
import collections
from classifiers import *
import time
import random
import argparse

def main():

	parser = argparse.ArgumentParser()
	parser.add_argument('--starting', type=int, help='directory for initialization data')
	args = parser.parse_args()

	starting_year = args.starting
	
	X_train,Y_train=load_svmlight_file(str(starting_year))
	print 'X_train data shape' , type(X_train), X_train.shape

	global clfs

	clfs = [PA1(), OGD(), AROW(), RDA(), ADA_FOBOS()]

	print 'model pool size: ', len(clfs)

	ori_train_acc = []

	directory = './' + str(starting_year) + 'train/' 
	if not os.path.exists(directory):
		os.makedirs(directory)

	# training process of all models 
	print 'All model initialization'
	for i in xrange(len(clfs)): # i = every model in model pool
		print clfs[i]
		print 'training'
		train_accuracy,data,err,fit_time=clfs[i].fit(X_train,Y_train, False)
		ori_train_acc.append(train_accuracy)
		clfs[i].save('./' + str(starting_year) + 'train/' + str(starting_year) + '_' + str(i) + '.model')

	print 'original model accuracy', ori_train_acc

if __name__ == "__main__":
	main()