subset.py

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#!/usr/bin/env python
from sys import argv, exit, stdout, stderr
from random import randint

method = 0
global n
global dataset_filename
subset_filename = ""
rest_filename = ""

def exit_with_help():
        print("""\
Usage: {0} [options] dataset number [output1] [output2]

This script selects a subset of the given dataset.

options:
-s method : method of selection (default 0)
     0 -- stratified selection (classification only)
     1 -- random selection

output1 : the subset (optional)
output2 : rest of the data (optional)
If output1 is omitted, the subset will be printed on the screen.""".format(argv[0]))
        exit(1)

def process_options():
        global method, n
        global dataset_filename, subset_filename, rest_filename
        
        argc = len(argv)
        if argc < 3:
                exit_with_help()

        i = 1
        while i < len(argv):
                if argv[i][0] != "-":
                        break
                if argv[i] == "-s":
                        i = i + 1
                        method = int(argv[i])
                        if method < 0 or method > 1:
                                print("Unknown selection method {0}".format(method))
                                exit_with_help()
                i = i + 1

        dataset_filename = argv[i]
        n = int(argv[i+1])
        if i+2 < argc:
                subset_filename = argv[i+2]
        if i+3 < argc:
                rest_filename = argv[i+3]

def main():
        class Label:
                def __init__(self, label, index, selected):
                        self.label = label
                        self.index = index
                        self.selected = selected

        process_options()
        
        # get labels
        i = 0
        labels = []
        f = open(dataset_filename, 'r')
        for line in f:
                labels.append(Label(float((line.split())[0]), i, 0))
                i = i + 1
        f.close()
        l = i
        
        # determine where to output
        if subset_filename != "":
                file1 = open(subset_filename, 'w')
        else:
                file1 = stdout
        split = 0
        if rest_filename != "":
                split = 1       
                file2 = open(rest_filename, 'w')
        
        # select the subset
        warning = 0
        if method == 0: # stratified
                labels.sort(key = lambda x: x.label)
                
                label_end = labels[l-1].label + 1
                labels.append(Label(label_end, l, 0))

                begin = 0
                label = labels[begin].label
                for i in range(l+1):
                        new_label = labels[i].label
                        if new_label != label:
                                nr_class = i - begin
                                k = i*n//l - begin*n//l
                                # at least one instance per class
                                if k == 0:
                                        k = 1
                                        warning = warning + 1
                                for j in range(nr_class):
                                        if randint(0, nr_class-j-1) < k:
                                                labels[begin+j].selected = 1
                                                k = k - 1
                                begin = i
                                label = new_label
        elif method == 1: # random
                k = n
                for i in range(l):
                        if randint(0,l-i-1) < k:
                                labels[i].selected = 1
                                k = k - 1
                        i = i + 1

        # output
        i = 0
        if method == 0:
                labels.sort(key = lambda x: int(x.index))
        
        f = open(dataset_filename, 'r')
        for line in f:
                if labels[i].selected == 1:
                        file1.write(line)
                else:
                        if split == 1:
                                file2.write(line)
                i = i + 1

        if warning > 0:
                stderr.write("""\
Warning:
1. You may have regression data. Please use -s 1.
2. Classification data unbalanced or too small. We select at least 1 per class.
   The subset thus contains {0} instances.
""".format(n+warning))

        # cleanup
        f.close()
        
        file1.close()
        
        if split == 1:
                file2.close()

main()