#!/usr/bin/env python """ LDfeatureselect.py - LD (Lang-Domain) feature extractor Marco Lui November 2011 Based on research by Marco Lui and Tim Baldwin. Copyright 2011 Marco Lui . All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. The views and conclusions contained in the software and documentation are those of the authors and should not be interpreted as representing official policies, either expressed or implied, of the copyright holder. """ ###### # Default values # Can be overriden with command-line options ###### FEATURES_PER_LANG = 300 # number of features to select for each language import os, sys, argparse import csv import marshal import numpy import multiprocessing as mp from collections import defaultdict from common import read_weights, Enumerator, write_features def select_LD_features(ig_lang, ig_domain, feats_per_lang, ignore_domain=False): """ @param ignore_domain boolean to indicate whether to use domain weights """ assert (ig_domain is None) or (len(ig_lang) == len(ig_domain)) num_lang = len(ig_lang.values()[0]) num_term = len(ig_lang) term_index = defaultdict(Enumerator()) ld = numpy.empty((num_lang, num_term), dtype=float) for term in ig_lang: term_id = term_index[term] if ignore_domain: ld[:, term_id] = ig_lang[term] else: ld[:, term_id] = ig_lang[term] - ig_domain[term] terms = sorted(term_index, key=term_index.get) # compile the final feature set selected_features = dict() for lang_id, lang_w in enumerate(ld): term_inds = numpy.argsort(lang_w)[-feats_per_lang:] selected_features[lang_id] = [terms[t] for t in term_inds] return selected_features if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-o","--output", metavar="OUTPUT_PATH", help = "write selected features to OUTPUT_PATH") parser.add_argument("--feats_per_lang", type=int, metavar='N', help="select top N features for each language", default=FEATURES_PER_LANG) parser.add_argument("--per_lang", action="store_true", default=False, help="produce a list of features selecter per-language") parser.add_argument("--no_domain_ig", action="store_true", default=False, help="use only per-langugage IG in LD calculation") parser.add_argument("model", metavar='MODEL_DIR', help="read index and produce output in MODEL_DIR") args = parser.parse_args() lang_w_path = os.path.join(args.model, 'IGweights.lang.bin') domain_w_path = os.path.join(args.model, 'IGweights.domain') feature_path = args.output if args.output else os.path.join(args.model, 'LDfeats') # display paths print "model path:", args.model print "lang weights path:", lang_w_path print "domain weights path:", domain_w_path print "feature output path:", feature_path lang_w = read_weights(lang_w_path) domain_w = read_weights(domain_w_path) if not args.no_domain_ig else None features_per_lang = select_LD_features(lang_w, domain_w, args.feats_per_lang, ignore_domain=args.no_domain_ig) if args.per_lang: with open(feature_path + '.perlang', 'w') as f: writer = csv.writer(f) for i in range(len(features_per_lang)): writer.writerow(map(repr,features_per_lang[i])) final_feature_set = reduce(set.union, map(set, features_per_lang.values())) print 'selected %d features' % len(final_feature_set) write_features(sorted(final_feature_set), feature_path) print 'wrote features to "%s"' % feature_path