Delete model_pool_construction.py

model_pool_construction.py

@@ -1,56 +0,0 @@
-'''
-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()