121 lines
3.2 KiB
Python
121 lines
3.2 KiB
Python
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#!/usr/bin/env python
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import os, sys, math, random
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from collections import defaultdict
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if sys.version_info[0] >= 3:
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xrange = range
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def exit_with_help(argv):
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print("""\
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Usage: {0} [options] dataset subset_size [output1] [output2]
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This script randomly selects a subset of the dataset.
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options:
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-s method : method of selection (default 0)
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0 -- stratified selection (classification only)
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1 -- random selection
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output1 : the subset (optional)
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output2 : rest of the data (optional)
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If output1 is omitted, the subset will be printed on the screen.""".format(argv[0]))
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exit(1)
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def process_options(argv):
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argc = len(argv)
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if argc < 3:
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exit_with_help(argv)
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# default method is stratified selection
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method = 0
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subset_file = sys.stdout
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rest_file = None
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i = 1
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while i < argc:
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if argv[i][0] != "-":
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break
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if argv[i] == "-s":
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i = i + 1
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method = int(argv[i])
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if method not in [0,1]:
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print("Unknown selection method {0}".format(method))
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exit_with_help(argv)
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i = i + 1
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dataset = argv[i]
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subset_size = int(argv[i+1])
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if i+2 < argc:
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subset_file = open(argv[i+2],'w')
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if i+3 < argc:
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rest_file = open(argv[i+3],'w')
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return dataset, subset_size, method, subset_file, rest_file
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def random_selection(dataset, subset_size):
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l = sum(1 for line in open(dataset,'r'))
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return sorted(random.sample(xrange(l), subset_size))
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def stratified_selection(dataset, subset_size):
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labels = [line.split(None,1)[0] for line in open(dataset)]
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label_linenums = defaultdict(list)
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for i, label in enumerate(labels):
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label_linenums[label] += [i]
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l = len(labels)
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remaining = subset_size
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ret = []
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# classes with fewer data are sampled first; otherwise
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# some rare classes may not be selected
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for label in sorted(label_linenums, key=lambda x: len(label_linenums[x])):
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linenums = label_linenums[label]
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label_size = len(linenums)
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# at least one instance per class
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s = int(min(remaining, max(1, math.ceil(label_size*(float(subset_size)/l)))))
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if s == 0:
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sys.stderr.write('''\
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Error: failed to have at least one instance per class
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1. You may have regression data.
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2. Your classification data is unbalanced or too small.
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Please use -s 1.
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''')
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sys.exit(-1)
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remaining -= s
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ret += [linenums[i] for i in random.sample(xrange(label_size), s)]
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return sorted(ret)
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def main(argv=sys.argv):
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dataset, subset_size, method, subset_file, rest_file = process_options(argv)
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#uncomment the following line to fix the random seed
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#random.seed(0)
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selected_lines = []
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if method == 0:
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selected_lines = stratified_selection(dataset, subset_size)
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elif method == 1:
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selected_lines = random_selection(dataset, subset_size)
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#select instances based on selected_lines
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dataset = open(dataset,'r')
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prev_selected_linenum = -1
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for i in xrange(len(selected_lines)):
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for cnt in xrange(selected_lines[i]-prev_selected_linenum-1):
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line = dataset.readline()
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if rest_file:
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rest_file.write(line)
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subset_file.write(dataset.readline())
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prev_selected_linenum = selected_lines[i]
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subset_file.close()
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if rest_file:
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for line in dataset:
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rest_file.write(line)
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rest_file.close()
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dataset.close()
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if __name__ == '__main__':
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main(sys.argv)
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