60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
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'''
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To use: python with_true_label_train.py --starting 2011
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GOAL: generate the original detection model for the starting year (applying all data from the starting year as training data)
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'''
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import numpy as np
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import scipy
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from scipy.stats import logistic
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from scipy.special import expit
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from numpy import dot
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import sklearn
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from sklearn.datasets import load_svmlight_file
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import os
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import sys
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import string
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from decimal import *
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import collections
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from classifiers import *
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import time
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import random
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import argparse
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--starting', type=int, help='directory for initialization data') # to use = args.past
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args = parser.parse_args()
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starting_year = args.starting
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X_train,Y_train=load_svmlight_file(str(starting_year))
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print 'X_train data shape' , type(X_train), X_train.shape
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global clfs
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clfs = [PA1(), OGD(), AROW(), RDA(), ADA_FOBOS()]
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print 'model pool size: ', len(clfs)
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ori_train_acc = []
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directory = './' + str(starting_year) + 'train/'
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if not os.path.exists(directory):
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os.makedirs(directory)
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# training process of all models
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print 'All model initialization'
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for i in xrange(len(clfs)): # i = every model in model pool
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print clfs[i]
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print 'training'
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train_accuracy,data,err,fit_time=clfs[i].fit(X_train,Y_train, False)
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ori_train_acc.append(train_accuracy)
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clfs[i].save('./' + str(starting_year) + 'train/' + str(starting_year) + '_' + str(i) + '.model')
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print 'original model accuracy', ori_train_acc
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if __name__ == "__main__":
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main()
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