write_result.py

# Copyright (c) 2014, Tu Dinh Nguyen (nguyendinhtu@gmail.com)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# - Redistributions of source code must retain the above copyright notice,
#   this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
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#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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"""
write results produced by ConvNet to CSV file to submit
"""

import sys
import os
import csv
import cPickle as pickle
import numpy as np


INPUT_PATH = "./RUN/model/ConvNet__2014-03-29_07.47.35_182.59/feat_62_140_fc37"


def refine_result(res):
    # all values > 1 should be = 1
    res[res > 1] = 1
    # al values < 0 should = 0
    res[res < 0] = 0


def main():
    start_batch = int(sys.argv[1])
    end_batch = int(sys.argv[2])

    res_total = np.zeros((0, 37), dtype=np.float32)

    label_data = np.genfromtxt("./raw_data/kaggle_submission.csv",
                               dtype=np.int32, delimiter=',', skip_header=1, usecols=0)
    label_data = label_data.reshape(len(label_data), 1)

    r = csv.reader(open("./raw_data/kaggle_submission.csv", 'rb'), delimiter=",")
    h = r.next()

    for inp in INPUT_PATH:
        res_each_inp = np.zeros((0, 37), dtype=np.float32)
        for i in range(start_batch, end_batch+1):
            res_each = pickle.load(open(os.path.join(inp, "data_batch_{}".format(i)), 'rb'))
            res_each_inp = np.vstack((res_each_inp, res_each['data'].T))
        if res_total.shape[0] == 0:
            res_total = res_each_inp
        else:
            res_total += res_each_inp

    res_total /= len(INPUT_PATH)

    refine_result(res_total)

    with open('./RUN/avg_res/0.07939.csv', 'wb') as f_out:
        w = csv.writer(f_out, delimiter=",")
        w.writerow(h)
        for i in range(label_data.shape[0]):
            w.writerow(np.hstack([label_data[i, 0], res_total[i, :]]).astype(np.single))


if __name__ == '__main__':
    main()