From 420e42662f95928721cca7b681ad0df503d5c098 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Fri, 30 Dec 2016 13:10:24 +0530 Subject: [PATCH 01/18] added wordrank wrapper --- gensim/models/wrappers/__init__.py | 1 + gensim/models/wrappers/wordrank.py | 208 +++++++++++++++++++++++++++ gensim/test/test_wordrank_wrapper.py | 84 +++++++++++ gensim/utils.py | 2 +- 4 files changed, 294 insertions(+), 1 deletion(-) create mode 100644 gensim/models/wrappers/wordrank.py create mode 100644 gensim/test/test_wordrank_wrapper.py diff --git a/gensim/models/wrappers/__init__.py b/gensim/models/wrappers/__init__.py index d222c0dd98..a9027170e7 100644 --- a/gensim/models/wrappers/__init__.py +++ b/gensim/models/wrappers/__init__.py @@ -5,3 +5,4 @@ from .ldamallet import LdaMallet from .dtmmodel import DtmModel from .ldavowpalwabbit import LdaVowpalWabbit +from .wordrank import Wordrank diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py new file mode 100644 index 0000000000..316c4901d6 --- /dev/null +++ b/gensim/models/wrappers/wordrank.py @@ -0,0 +1,208 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Python wrapper around word representation learning from Wordrank. +The wrapped model can NOT be updated with new documents for online training -- use gensim's +`Word2Vec` for that. + +Example: +>>> model = gensim.models.wrappers.Wordrank('/Users/dummy/wordrank', corpus_file='text8') +>>> print model[word] # prints vector for given words + +.. [1] https://bitbucket.org/shihaoji/wordrank/ +.. [2] https://arxiv.org/pdf/1506.02761v3.pdf +""" + +from __future__ import division + +import logging +import os +import sys +import copy +import multiprocessing + +import numpy as np + +from gensim import utils +from gensim.models.keyedvectors import KeyedVectors +from gensim.models.word2vec import Word2Vec +from gensim.scripts.glove2word2vec import glove2word2vec + +from six import string_types +from smart_open import smart_open +from shutil import copyfile, rmtree + +if sys.version_info[:2] == (2, 6): + from backport_collections import Counter +else: + from collections import Counter + +logger = logging.getLogger(__name__) + + +class Wordrank(Word2Vec): + """ + Class for word vector training using Wordrank. Communication between Wordrank and Python + takes place by working with data files on disk and calling the Wordrank binary and glove's + helper binaries (for preparing training data) with subprocess module. + """ + + @classmethod + def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, min_count=5, max_vocab_size=0, + sgd_num=100, lrate=0.001, period=10, iter=90, epsilon=0.75, dump_period=10, reg=0, alpha=100, + beta=99, loss='hinge', memory=4.0, cleanup_files=True, sorted_vocab=1, ensemble=1): + """ + `wr_path` is the path to the Wordrank directory. + `corpus_file` is the filename of the text file to be used for training the Wordrank model. + Expects file to contain space-separated tokens in a single line + `out_path` is the path to directory which will be created to save embeddings and training data. + `size` is the dimensionality of the feature vectors. + `window` is the number of context words to the left (and to the right, if symmetric = 1). + symmetric` if 0, only use left context words, else use left and right both. + `min_count` = ignore all words with total frequency lower than this. + `max_vocab_size` upper bound on vocabulary size, i.e. keep the most frequent words. Default is 0 for no limit. + `sgd_num` number of SGD taken for each data point. + `lrate` is the learning rate (too high diverges, give Nan). + `period` is the period of xi variable updates + `iter` = number of iterations (epochs) over the corpus. + `epsilon` is the power scaling value for weighting function. + `dump_period` is the period after which parameters should be dumped. + `reg` is the value of regularization parameter. + `alpha` is the alpha parameter of gamma distribution. + `beta` is the beta parameter of gamma distribution. + `loss` = name of the loss (logistic, hinge). + `memory` = soft limit for memory consumption, in GB. + `cleanup_files` if to delete directory and files used by this wrapper, setting to False can be useful for debugging + `sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before assigning word indexes. + `ensemble` = 0 (default), use ensemble of word and context vectors + """ + + meta_data_path = 'matrix.meta' + vocab_file = 'vocab.txt' + temp_vocab_file = 'tempvocab.txt' + cooccurrence_file = 'cooccurrence' + cooccurrence_shuf_file = 'wiki.toy' + meta_file = 'meta' + + # prepare training data (cooccurrence matrix and vocab) + model_dir = os.path.join(wr_path, out_path) + meta_dir = os.path.join(model_dir, 'meta') + os.makedirs(meta_dir) + logger.info("Dumped data will be stored in '%s'", model_dir) + copyfile(corpus_file, os.path.join(meta_dir, corpus_file.split('/')[-1])) + os.chdir(meta_dir) + + cmd0 = ['../../glove/vocab_count', '-min-count', str(min_count), '-max-vocab', str(max_vocab_size)] + cmd1 = ['../../glove/cooccur', '-memory', str(memory), '-vocab-file', temp_vocab_file, '-window-size', str(window), '-symmetric', str(symmetric)] + cmd2 = ['../../glove/shuffle', '-memory', str(memory)] + cmd3 = ['cut', '-d', " ", '-f', '1', temp_vocab_file] + cmds = [cmd0, cmd1, cmd2, cmd3] + logger.info("Preparing training data using glove code '%s'", cmds) + o0 = smart_open(temp_vocab_file, 'w') + o1 = smart_open(cooccurrence_file, 'w') + o2 = smart_open(cooccurrence_shuf_file, 'w') + o3 = smart_open(vocab_file, 'w') + i0 = smart_open(corpus_file.split('/')[-1]) + i1 = smart_open(corpus_file.split('/')[-1]) + i2 = smart_open(cooccurrence_file) + i3 = None + outputs = [o0, o1, o2, o3] + inputs = [i0, i1, i2, i3] + prepare_train_data = [utils.check_output(cmd, stdin=inp, stdout=out) for cmd, inp, out in zip(cmds, inputs, outputs)] + o0.close() + o1.close() + o2.close() + o3.close() + i0.close() + i1.close() + i2.close() + + with smart_open(vocab_file) as f: + numwords = sum(1 for line in f) + with smart_open(cooccurrence_shuf_file) as f: + numlines = sum(1 for line in f) + with smart_open(meta_file, 'w') as f: + f.write("{0} {1}\n{2} {3}\n{4} {5}".format(numwords, numwords, numlines, cooccurrence_shuf_file, numwords, vocab_file)) + + wr_args = { + 'path': 'meta', + 'nthread': multiprocessing.cpu_count(), + 'sgd_num': sgd_num, + 'lrate': lrate, + 'period': period, + 'iter': iter, + 'epsilon': epsilon, + 'dump_prefix': 'model', + 'dump_period': dump_period, + 'dim': size, + 'reg': reg, + 'alpha': alpha, + 'beta': beta, + 'loss': loss + } + + os.chdir('..') + # run wordrank executable with wr_args + cmd = ['mpirun', '-np', '1', '../wordrank'] + for option, value in wr_args.items(): + cmd.append("--%s" % option) + cmd.append(str(value)) + logger.info("Running wordrank binary '%s'", cmd) + output = utils.check_output(args=cmd) + + max_iter_dump = iter / dump_period * dump_period - 1 + copyfile('model_word_%d.txt' % max_iter_dump, 'wordrank.words') + copyfile('model_context_%d.txt' % max_iter_dump, 'wordrank.contexts') + model = cls.load_wordrank_model('wordrank.words', os.path.join('meta', vocab_file), 'wordrank.contexts', sorted_vocab, ensemble) + os.chdir('../..') + + if cleanup_files: + rmtree(model_dir) + return model + + @classmethod + def load_wordrank_model(cls, model_file, vocab_file=None, context_file=None, sorted_vocab=1, ensemble=1): + glove2word2vec(model_file, model_file+'.w2vformat') + model = cls.load_word2vec_format('%s.w2vformat' % model_file) + if ensemble and context_file: + model.ensemble_embedding(model_file, context_file) + if sorted_vocab and vocab_file: + model.sort_embeddings(vocab_file) + return model + + def sort_embeddings(self, vocab_file): + """Sort embeddings according to word frequency.""" + counts = {} + vocab_size = len(self.wv.vocab) + prev_syn0 = copy.deepcopy(self.wv.syn0) + prev_vocab = copy.deepcopy(self.wv.vocab) + self.wv.index2word = [] + + with utils.smart_open(vocab_file) as fin: + for index, line in enumerate(fin): + word, count = utils.to_unicode(line).strip(), vocab_size - index + counts[word] = int(count) + self.wv.index2word.append(word) + assert len(self.wv.index2word) == vocab_size, 'mismatch between vocab sizes' + + for word_id, word in enumerate(self.wv.index2word): + self.wv.syn0[word_id] = prev_syn0[prev_vocab[word].index] + self.wv.vocab[word].index = word_id + self.wv.vocab[word].count = counts[word] + + def ensemble_embedding(self, word_embedding, context_embedding): + """Addition of two embeddings.""" + glove2word2vec(word_embedding, word_embedding+'.w2vformat') + glove2word2vec(context_embedding, context_embedding+'.w2vformat') + w_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % word_embedding) + c_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % context_embedding) + assert Counter(w_emb.index2word) == Counter(c_emb.index2word), 'Vocabs are not same for both embeddings' + + prev_c_emb = copy.deepcopy(c_emb.wv.syn0) + for word_id, word in enumerate(w_emb.wv.index2word): + c_emb.wv.syn0[word_id] = prev_c_emb[c_emb.wv.vocab[word].index] + new_emb = w_emb.wv.syn0 + c_emb.wv.syn0 + self.wv.syn0 = new_emb + return new_emb + diff --git a/gensim/test/test_wordrank_wrapper.py b/gensim/test/test_wordrank_wrapper.py new file mode 100644 index 0000000000..416b2ff5e3 --- /dev/null +++ b/gensim/test/test_wordrank_wrapper.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (C) 2010 Radim Rehurek +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html + +""" +Automated tests for checking transformation algorithms (the models package). +""" + + +import logging +import unittest +import os +import tempfile + +import numpy + +from gensim.models.wrappers import wordrank + +module_path = os.path.dirname(__file__) # needed because sample data files are located in the same folder +datapath = lambda fname: os.path.join(module_path, 'test_data', fname) + +def testfile(): + # temporary model will be stored to this file + return os.path.join(tempfile.gettempdir(), 'gensim_wordrank.test') + +class TestWordrank(unittest.TestCase): + def setUp(self): + wr_home = os.environ.get('WR_HOME', None) + self.wr_path = wr_home if wr_home else None + self.corpus_file = datapath('lee.cor') + self.out_path = 'testmodel' + self.wr_file = datapath('test_glove.txt') + if not self.wr_path: + return + self.test_model = wordrank.Wordrank.train(self.wr_path, self.corpus_file, self.out_path, iter=6, dump_period=5,period=5) + + def testLoadWordrankFormat(self): + """Test model successfully loaded from Wordrank format file""" + model = wordrank.Wordrank.load_wordrank_model(self.wr_file) + vocab_size, dim = 76, 50 + self.assertEqual(model.wv.syn0.shape, (vocab_size, dim)) + self.assertEqual(len(model.wv.vocab), vocab_size) + os.remove(self.wr_file+'.w2vformat') + + def testEnsemble(self): + """Test ensemble of two embeddings""" + if not self.wr_path: + return + new_emb = self.test_model.ensemble_embedding(self.wr_file, self.wr_file) + self.assertEqual(new_emb.shape, (76, 50)) + os.remove(self.wr_file+'.w2vformat') + + def testPersistence(self): + """Test storing/loading the entire model""" + if not self.wr_path: + return + self.test_model.save(testfile()) + loaded = wordrank.Wordrank.load(testfile()) + self.models_equal(self.test_model, loaded) + + def testSimilarity(self): + """Test n_similarity for vocab words""" + if not self.wr_path: + return + self.assertTrue(numpy.allclose(self.test_model.n_similarity(['the', 'and'], ['and', 'the']), 1.0)) + self.assertEqual(self.test_model.similarity('the', 'and'), self.test_model.similarity('the', 'and')) + + def testLookup(self): + if not self.wr_path: + return + self.assertTrue(numpy.allclose(self.test_model['night'], self.test_model[['night']])) + + def models_equal(self, model, model2): + self.assertEqual(len(model.vocab), len(model2.vocab)) + self.assertEqual(set(model.vocab.keys()), set(model2.vocab.keys())) + self.assertTrue(numpy.allclose(model.wv.syn0, model2.wv.syn0)) + +if __name__ == '__main__': + logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) + unittest.main() + + \ No newline at end of file diff --git a/gensim/utils.py b/gensim/utils.py index 133650bc42..8ab1903a49 100644 --- a/gensim/utils.py +++ b/gensim/utils.py @@ -1157,7 +1157,7 @@ def check_output(*popenargs, **kwargs): Added extra KeyboardInterrupt handling """ try: - process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs) + process = subprocess.Popen(*popenargs, **kwargs) output, unused_err = process.communicate() retcode = process.poll() if retcode: From d2f56074d02bbae77ac2454f333dacfb51bc5b6e Mon Sep 17 00:00:00 2001 From: parulsethi Date: Fri, 30 Dec 2016 16:19:03 +0530 Subject: [PATCH 02/18] update example --- gensim/models/wrappers/wordrank.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 316c4901d6..65aff7cc55 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -7,7 +7,7 @@ `Word2Vec` for that. Example: ->>> model = gensim.models.wrappers.Wordrank('/Users/dummy/wordrank', corpus_file='text8') +>>> model = gensim.models.wrappers.Wordrank('/Users/dummy/wordrank', corpus_file='text8', out_path='wr_model') >>> print model[word] # prints vector for given words .. [1] https://bitbucket.org/shihaoji/wordrank/ From c175851305b7ced01e6f855488f2e57aa260798b Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sat, 31 Dec 2016 06:15:37 +0530 Subject: [PATCH 03/18] add comparison ipynb --- docs/notebooks/Wordrank_comparison.ipynb | 927 +++++++++++++++++++++++ 1 file changed, 927 insertions(+) create mode 100644 docs/notebooks/Wordrank_comparison.ipynb diff --git a/docs/notebooks/Wordrank_comparison.ipynb b/docs/notebooks/Wordrank_comparison.ipynb new file mode 100644 index 0000000000..2bb3062696 --- /dev/null +++ b/docs/notebooks/Wordrank_comparison.ipynb @@ -0,0 +1,927 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparison of Wordrank and Word2Vec\n", + "\n", + "Wordrank is a fresh new approach to the word embeddings, which formulates it as a ranking problem. That is, given a word w, it aims to output an ordered list (c1, c2, · · ·) of context words such that words that co-occur with w appear at the top of the list. This formulation fits naturally to popular word embedding tasks such as word similarity/analogy since instead of the likelihood of each word, we are interested in finding the most relevant words\n", + "in a given context.\n", + "\n", + "Gensim is used to train the word2vec models, and analogical reasoning task is used for comparing the models. Word2vec and FastText embeddings are trained using the skipgram architecture here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download and preprocess data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package brown to /Users/parul/nltk_data...\n", + "[nltk_data] Package brown is already up-to-date!\n" + ] + } + ], + "source": [ + "import nltk\n", + "from gensim.parsing.preprocessing import strip_punctuation, strip_multiple_whitespaces\n", + "\n", + "# Only the brown corpus is needed in case you don't have it.\n", + "nltk.download('brown') \n", + "\n", + "# Generate brown corpus text file\n", + "with open('brown_corp.txt', 'w+') as f:\n", + " for word in nltk.corpus.brown.words():\n", + " f.write('{word} '.format(word=word))\n", + " f.seek(0)\n", + " brown = f.read()\n", + "\n", + "# Preprocess brown corpus\n", + "with open('proc_brown_corp.txt', 'w') as f:\n", + " proc_brown = strip_punctuation(brown)\n", + " proc_brown = strip_multiple_whitespaces(proc_brown).lower()\n", + " f.write(proc_brown)\n", + "\n", + "# Set WR_HOME and FT_HOME to respective directory root\n", + "WR_HOME = 'wordrank/'\n", + "FT_HOME = 'fastText/'\n", + "\n", + "# download the text8 corpus (a 100 MB sample of preprocessed wikipedia text)\n", + "import os.path\n", + "if not os.path.isfile('text8'):\n", + " !wget -c http://mattmahoney.net/dc/text8.zip\n", + " !unzip text8.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Train Models\n", + "For training the models yourself, you'll need to have Gensim, FastText and Wordrank set up on your machine." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training word2vec on proc_brown_corp.txt corpus..\n", + "CPU times: user 1min 7s, sys: 527 ms, total: 1min 8s\n", + "Wall time: 46.8 s\n", + "\n", + "Saved gensim model as brown_gs.vec\n", + "Training fasttext on proc_brown_corp.txt corpus..\n", + "Read 1M words\n", + "Number of words: 14042\n", + "Number of labels: 0\n", + "Progress: 99.6% words/sec/thread: 58810 lr: 0.000179 loss: 2.348125 eta: 0h0m Progress: 20.1% words/sec/thread: 30702 lr: 0.039934 loss: 2.296231 eta: 0h0m Progress: 100.0% words/sec/thread: 58810 lr: 0.000000 loss: 2.348125 eta: 0h0m \n", + "CPU times: user 842 ms, sys: 284 ms, total: 1.13 s\n", + "Wall time: 41.3 s\n", + "\n", + "Training wordrank on proc_brown_corp.txt corpus..\n", + "CPU times: user 10.8 s, sys: 1.02 s, total: 11.8 s\n", + "Wall time: 8h 24min 25s\n", + "\n", + "Saved wordrank model as brown_wr.vec\n", + "\n", + "Loading ensemble embeddings (vector combination of word and context embeddings)..\n", + "CPU times: user 8.97 s, sys: 279 ms, total: 9.25 s\n", + "Wall time: 13.8 s\n", + "\n", + "Saved wordrank (ensemble) model as brown_wr_ensemble.vec\n" + ] + } + ], + "source": [ + "MODELS_DIR = 'models/'\n", + "!mkdir -p {MODELS_DIR}\n", + "\n", + "from gensim.models import Word2Vec\n", + "from gensim.models.wrappers import Wordrank\n", + "from gensim.models.word2vec import Text8Corpus\n", + "\n", + "# fasttext params\n", + "lr = 0.05\n", + "dim = 100\n", + "ws = 5\n", + "epoch = 5\n", + "minCount = 5\n", + "neg = 5\n", + "loss = 'ns'\n", + "t = 1e-4\n", + "\n", + "w2v_params = {\n", + " 'alpha': 0.025,\n", + " 'size': 100,\n", + " 'window': 15,\n", + " 'iter': 5,\n", + " 'min_count': 5,\n", + " 'sample': t,\n", + " 'sg': 1,\n", + " 'hs': 0,\n", + " 'negative': 5\n", + "}\n", + "\n", + "wr_params = {\n", + " 'size': 100,\n", + " 'window': 15,\n", + " 'iter': 91,\n", + " 'min_count': 5\n", + "}\n", + "\n", + "def train_models(corpus_file, output_name):\n", + " # Train using word2vec\n", + " output_file = '{:s}_gs'.format(output_name)\n", + " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", + " print('\\nTraining word2vec on {:s} corpus..'.format(corpus_file))\n", + " # Text8Corpus class for reading space-separated words file\n", + " %time gs_model = Word2Vec(Text8Corpus(corpus_file), **w2v_params); gs_model\n", + " locals()['gs_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", + " print('\\nSaved gensim model as {:s}.vec'.format(output_file))\n", + " else:\n", + " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", + "\n", + " # Train using fasttext\n", + " output_file = '{:s}_ft'.format(output_name)\n", + " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", + " print('Training fasttext on {:s} corpus..'.format(corpus_file))\n", + " %time !{FT_HOME}fasttext skipgram -input {corpus_file} -output {MODELS_DIR+output_file} -lr {lr} -dim {dim} -ws {ws} -epoch {epoch} -minCount {minCount} -neg {neg} -loss {loss} -t {t}\n", + " else:\n", + " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", + " \n", + " # Train using wordrank\n", + " output_file = '{:s}_wr'.format(output_name)\n", + " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", + " print('\\nTraining wordrank on {:s} corpus..'.format(corpus_file))\n", + " %time wr_model = Wordrank.train(WR_HOME, corpus_file, **wr_params); wr_model\n", + " locals()['wr_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", + " print('\\nSaved wordrank model as {:s}.vec'.format(output_file))\n", + " else:\n", + " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", + " \n", + " # Loading ensemble embeddings\n", + " output_file = '{:s}_wr_ensemble'.format(output_name)\n", + " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", + " print('\\nLoading ensemble embeddings (vector combination of word and context embeddings)..')\n", + " %time wr_model = Wordrank.load_wordrank_model(os.path.join(WR_HOME, 'model/wordrank.words'), os.path.join(WR_HOME, 'model/meta/vocab.txt'), os.path.join(WR_HOME, 'model/wordrank.contexts'), sorted_vocab=1, ensemble=1); wr_model\n", + " locals()['wr_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", + " print('\\nSaved wordrank (ensemble) model as {:s}.vec'.format(output_file))\n", + " else:\n", + " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", + " \n", + "train_models(corpus_file='proc_brown_corp.txt', output_name='brown')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training word2vec on text8 corpus..\n", + "CPU times: user 24min 21s, sys: 8.64 s, total: 24min 29s\n", + "Wall time: 18min 33s\n", + "\n", + "Saved gensim model as text8_gs.vec\n", + "\n", + "Using existing model file text8_ft.vec\n", + "\n", + "Using existing model file text8_wr.vec\n", + "\n", + "Using existing model file text8_wr_ensemble.vec\n" + ] + } + ], + "source": [ + "train_models(corpus_file='text8', output_name='text8')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we train wordrank model using ensemble in second case as it is known to give a small performance boost in some cases. So we'll test accuracy for both the cases." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparisons" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import logging\n", + "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", + "\n", + "def print_accuracy(model, questions_file):\n", + " print('Evaluating...\\n')\n", + " acc = model.accuracy(questions_file)\n", + "\n", + " sem_correct = sum((len(acc[i]['correct']) for i in range(5)))\n", + " sem_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5))\n", + " sem_acc = 100*float(sem_correct)/sem_total\n", + " print('\\nSemantic: {:d}/{:d}, Accuracy: {:.2f}%'.format(sem_correct, sem_total, sem_acc))\n", + " \n", + " syn_correct = sum((len(acc[i]['correct']) for i in range(5, len(acc)-1)))\n", + " syn_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5,len(acc)-1))\n", + " syn_acc = 100*float(syn_correct)/syn_total\n", + " print('Syntactic: {:d}/{:d}, Accuracy: {:.2f}%\\n'.format(syn_correct, syn_total, syn_acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 13:58:37,691 : INFO : loading projection weights from models/brown_gs.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Loading Gensim embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 13:58:45,351 : INFO : loaded (14042, 100) matrix from models/brown_gs.vec\n", + "2016-12-27 13:58:45,418 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Word2Vec:\n", + "Evaluating...\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 13:58:45,823 : INFO : capital-common-countries: 0.0% (0/90)\n", + "2016-12-27 13:58:46,182 : INFO : capital-world: 0.0% (0/53)\n", + "2016-12-27 13:58:46,300 : INFO : currency: 0.0% (0/12)\n", + "2016-12-27 13:58:48,230 : INFO : city-in-state: 0.9% (4/457)\n", + "2016-12-27 13:58:49,250 : INFO : family: 20.0% (48/240)\n", + "2016-12-27 13:58:53,225 : INFO : gram1-adjective-to-adverb: 0.1% (1/812)\n", + "2016-12-27 13:58:54,105 : INFO : gram2-opposite: 0.0% (0/132)\n", + "2016-12-27 13:58:59,197 : INFO : gram3-comparative: 1.8% (19/1056)\n", + "2016-12-27 13:59:00,302 : INFO : gram4-superlative: 0.5% (1/210)\n", + "2016-12-27 13:59:03,744 : INFO : gram5-present-participle: 2.6% (17/650)\n", + "2016-12-27 13:59:05,319 : INFO : gram6-nationality-adjective: 11.4% (34/297)\n", + "2016-12-27 13:59:11,041 : INFO : gram7-past-tense: 3.3% (42/1260)\n", + "2016-12-27 13:59:14,074 : INFO : gram8-plural: 6.6% (46/702)\n", + "2016-12-27 13:59:15,578 : INFO : gram9-plural-verbs: 2.0% (7/342)\n", + "2016-12-27 13:59:15,579 : INFO : total: 3.5% (219/6313)\n", + "2016-12-27 13:59:15,586 : INFO : loading projection weights from models/brown_ft.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 52/852, Accuracy: 6.10%\n", + "Syntactic: 167/5461, Accuracy: 3.06%\n", + "\n", + "\n", + "Loading FastText embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 13:59:21,713 : INFO : loaded (14042, 100) matrix from models/brown_ft.vec\n", + "2016-12-27 13:59:21,775 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for FastText:\n", + "Evaluating...\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 13:59:22,205 : INFO : capital-common-countries: 1.1% (1/90)\n", + "2016-12-27 13:59:22,546 : INFO : capital-world: 0.0% (0/53)\n", + "2016-12-27 13:59:22,651 : INFO : currency: 0.0% (0/12)\n", + "2016-12-27 13:59:24,741 : INFO : city-in-state: 2.4% (11/457)\n", + "2016-12-27 13:59:25,821 : INFO : family: 11.7% (28/240)\n", + "2016-12-27 13:59:29,840 : INFO : gram1-adjective-to-adverb: 79.9% (649/812)\n", + "2016-12-27 13:59:30,628 : INFO : gram2-opposite: 79.5% (105/132)\n", + "2016-12-27 13:59:35,972 : INFO : gram3-comparative: 56.3% (595/1056)\n", + "2016-12-27 13:59:37,289 : INFO : gram4-superlative: 71.4% (150/210)\n", + "2016-12-27 13:59:40,495 : INFO : gram5-present-participle: 65.7% (427/650)\n", + "2016-12-27 13:59:41,863 : INFO : gram6-nationality-adjective: 35.0% (104/297)\n", + "2016-12-27 13:59:47,878 : INFO : gram7-past-tense: 12.1% (153/1260)\n", + "2016-12-27 13:59:51,524 : INFO : gram8-plural: 53.1% (373/702)\n", + "2016-12-27 13:59:53,380 : INFO : gram9-plural-verbs: 69.0% (236/342)\n", + "2016-12-27 13:59:53,382 : INFO : total: 44.9% (2832/6313)\n", + "2016-12-27 13:59:53,389 : INFO : loading projection weights from models/brown_wr.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 40/852, Accuracy: 4.69%\n", + "Syntactic: 2792/5461, Accuracy: 51.13%\n", + "\n", + "\n", + "Loading Wordrank embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 14:00:00,114 : INFO : loaded (14042, 100) matrix from models/brown_wr.vec\n", + "2016-12-27 14:00:00,173 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Wordrank:\n", + "Evaluating...\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 14:00:00,438 : INFO : capital-common-countries: 10.0% (9/90)\n", + "2016-12-27 14:00:00,694 : INFO : capital-world: 15.1% (8/53)\n", + "2016-12-27 14:00:00,762 : INFO : currency: 0.0% (0/12)\n", + "2016-12-27 14:00:02,165 : INFO : city-in-state: 8.1% (37/457)\n", + "2016-12-27 14:00:02,909 : INFO : family: 23.8% (57/240)\n", + "2016-12-27 14:00:05,119 : INFO : gram1-adjective-to-adverb: 0.6% (5/812)\n", + "2016-12-27 14:00:05,616 : INFO : gram2-opposite: 0.0% (0/132)\n", + "2016-12-27 14:00:09,606 : INFO : gram3-comparative: 2.0% (21/1056)\n", + "2016-12-27 14:00:10,392 : INFO : gram4-superlative: 1.0% (2/210)\n", + "2016-12-27 14:00:12,894 : INFO : gram5-present-participle: 0.5% (3/650)\n", + "2016-12-27 14:00:14,405 : INFO : gram6-nationality-adjective: 10.8% (32/297)\n", + "2016-12-27 14:00:18,084 : INFO : gram7-past-tense: 1.6% (20/1260)\n", + "2016-12-27 14:00:20,194 : INFO : gram8-plural: 8.3% (58/702)\n", + "2016-12-27 14:00:21,221 : INFO : gram9-plural-verbs: 0.3% (1/342)\n", + "2016-12-27 14:00:21,222 : INFO : total: 4.0% (253/6313)\n", + "2016-12-27 14:00:21,229 : INFO : loading projection weights from models/brown_wr_ensemble.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 111/852, Accuracy: 13.03%\n", + "Syntactic: 142/5461, Accuracy: 2.60%\n", + "\n", + "\n", + "Loading Wordrank ensemble embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 14:00:25,328 : INFO : loaded (14042, 100) matrix from models/brown_wr_ensemble.vec\n", + "2016-12-27 14:00:25,413 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Wordrank:\n", + "Evaluating...\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 14:00:25,898 : INFO : capital-common-countries: 14.4% (13/90)\n", + "2016-12-27 14:00:26,340 : INFO : capital-world: 18.9% (10/53)\n", + "2016-12-27 14:00:26,469 : INFO : currency: 0.0% (0/12)\n", + "2016-12-27 14:00:28,738 : INFO : city-in-state: 8.3% (38/457)\n", + "2016-12-27 14:00:29,890 : INFO : family: 28.8% (69/240)\n", + "2016-12-27 14:00:33,588 : INFO : gram1-adjective-to-adverb: 0.6% (5/812)\n", + "2016-12-27 14:00:34,111 : INFO : gram2-opposite: 0.0% (0/132)\n", + "2016-12-27 14:00:38,860 : INFO : gram3-comparative: 3.4% (36/1056)\n", + "2016-12-27 14:00:40,451 : INFO : gram4-superlative: 0.0% (0/210)\n", + "2016-12-27 14:00:44,070 : INFO : gram5-present-participle: 1.7% (11/650)\n", + "2016-12-27 14:00:45,958 : INFO : gram6-nationality-adjective: 16.8% (50/297)\n", + "2016-12-27 14:00:52,447 : INFO : gram7-past-tense: 3.8% (48/1260)\n", + "2016-12-27 14:00:56,598 : INFO : gram8-plural: 11.1% (78/702)\n", + "2016-12-27 14:00:58,343 : INFO : gram9-plural-verbs: 0.9% (3/342)\n", + "2016-12-27 14:00:58,344 : INFO : total: 5.7% (361/6313)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 130/852, Accuracy: 15.26%\n", + "Syntactic: 231/5461, Accuracy: 4.23%\n", + "\n" + ] + } + ], + "source": [ + "word_analogies_file = './datasets/questions-words.txt'\n", + "\n", + "print('\\nLoading Gensim embeddings')\n", + "brown_gs = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_gs.vec')\n", + "print('Accuracy for Word2Vec:')\n", + "print_accuracy(brown_gs, word_analogies_file)\n", + "\n", + "print('\\nLoading FastText embeddings')\n", + "brown_ft = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_ft.vec')\n", + "print('Accuracy for FastText:')\n", + "print_accuracy(brown_ft, word_analogies_file)\n", + "\n", + "print('\\nLoading Wordrank embeddings')\n", + "brown_wr = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_wr.vec')\n", + "print('Accuracy for Wordrank:')\n", + "print_accuracy(brown_wr, word_analogies_file)\n", + "\n", + "print('\\nLoading Wordrank ensemble embeddings')\n", + "brown_wr_ensemble = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_wr_ensemble.vec')\n", + "print('Accuracy for Wordrank:')\n", + "print_accuracy(brown_wr_ensemble, word_analogies_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As evident from the above outputs, wordrank performs better in semantic analogies, whereas, fasttext performs significantly better in syntactic analogies. Also ensemble embeddings gives a small performance boost in wordrank's case.\n", + "\n", + "Wordrank's effectiveness in Semantic analogies is possibly due to it's focused attention on getting most relevant words right at the top using the ranking approach.\n", + "And as fasttext is designed to incorporate morphological information about words, it results in it's performance boost in syntactic analogies, as most of the syntactic analogies are morphology based.\n", + "\n", + "Now lets evaluate on a larger corpus, text8, and see how it effects the performance of different embedding models. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:10:28,984 : INFO : loading projection weights from models/text8_gs.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Gensim embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:10:36,842 : INFO : loaded (71290, 100) matrix from models/text8_gs.vec\n", + "2016-12-27 18:10:36,908 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for word2vec:\n", + "Evaluating...\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:10:38,821 : INFO : capital-common-countries: 68.6% (347/506)\n", + "2016-12-27 18:10:44,002 : INFO : capital-world: 52.3% (760/1452)\n", + "2016-12-27 18:10:44,976 : INFO : currency: 19.8% (53/268)\n", + "2016-12-27 18:10:50,552 : INFO : city-in-state: 24.8% (389/1571)\n", + "2016-12-27 18:10:51,692 : INFO : family: 47.7% (146/306)\n", + "2016-12-27 18:10:55,175 : INFO : gram1-adjective-to-adverb: 18.0% (136/756)\n", + "2016-12-27 18:10:56,535 : INFO : gram2-opposite: 13.4% (41/306)\n", + "2016-12-27 18:11:01,901 : INFO : gram3-comparative: 37.8% (476/1260)\n", + "2016-12-27 18:11:03,798 : INFO : gram4-superlative: 22.3% (113/506)\n", + "2016-12-27 18:11:07,566 : INFO : gram5-present-participle: 22.9% (227/992)\n", + "2016-12-27 18:11:12,830 : INFO : gram6-nationality-adjective: 86.7% (1188/1371)\n", + "2016-12-27 18:11:18,266 : INFO : gram7-past-tense: 27.0% (359/1332)\n", + "2016-12-27 18:11:21,913 : INFO : gram8-plural: 54.4% (540/992)\n", + "2016-12-27 18:11:24,023 : INFO : gram9-plural-verbs: 25.2% (164/650)\n", + "2016-12-27 18:11:24,025 : INFO : total: 40.3% (4939/12268)\n", + "2016-12-27 18:11:24,031 : INFO : loading projection weights from models/text8_ft.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 1695/4103, Accuracy: 41.31%\n", + "Syntactic: 3244/8165, Accuracy: 39.73%\n", + "\n", + "Loading FastText embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:11:31,749 : INFO : loaded (71290, 100) matrix from models/text8_ft.vec\n", + "2016-12-27 18:11:31,977 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for FastText (with n-grams):\n", + "Evaluating...\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:11:33,667 : INFO : capital-common-countries: 62.5% (316/506)\n", + "2016-12-27 18:11:38,357 : INFO : capital-world: 43.0% (625/1452)\n", + "2016-12-27 18:11:39,239 : INFO : currency: 12.7% (34/268)\n", + "2016-12-27 18:11:44,264 : INFO : city-in-state: 18.3% (287/1571)\n", + "2016-12-27 18:11:45,264 : INFO : family: 43.5% (133/306)\n", + "2016-12-27 18:11:47,685 : INFO : gram1-adjective-to-adverb: 73.7% (557/756)\n", + "2016-12-27 18:11:48,692 : INFO : gram2-opposite: 53.9% (165/306)\n", + "2016-12-27 18:11:52,716 : INFO : gram3-comparative: 64.8% (816/1260)\n", + "2016-12-27 18:11:54,355 : INFO : gram4-superlative: 53.4% (270/506)\n", + "2016-12-27 18:11:57,536 : INFO : gram5-present-participle: 54.4% (540/992)\n", + "2016-12-27 18:12:01,932 : INFO : gram6-nationality-adjective: 93.9% (1288/1371)\n", + "2016-12-27 18:12:06,220 : INFO : gram7-past-tense: 35.6% (474/1332)\n", + "2016-12-27 18:12:09,390 : INFO : gram8-plural: 90.1% (894/992)\n", + "2016-12-27 18:12:11,479 : INFO : gram9-plural-verbs: 59.4% (386/650)\n", + "2016-12-27 18:12:11,481 : INFO : total: 55.3% (6785/12268)\n", + "2016-12-27 18:12:11,488 : INFO : loading projection weights from models/text8_wr.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 1395/4103, Accuracy: 34.00%\n", + "Syntactic: 5390/8165, Accuracy: 66.01%\n", + "\n", + "\n", + "Loading Wordrank embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:12:18,908 : INFO : loaded (71290, 100) matrix from models/text8_wr.vec\n", + "2016-12-27 18:12:18,987 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Wordrank:\n", + "Evaluating...\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:12:20,718 : INFO : capital-common-countries: 84.6% (428/506)\n", + "2016-12-27 18:12:25,378 : INFO : capital-world: 70.0% (1016/1452)\n", + "2016-12-27 18:12:26,259 : INFO : currency: 19.0% (51/268)\n", + "2016-12-27 18:12:31,261 : INFO : city-in-state: 36.0% (565/1571)\n", + "2016-12-27 18:12:32,263 : INFO : family: 57.8% (177/306)\n", + "2016-12-27 18:12:34,677 : INFO : gram1-adjective-to-adverb: 15.3% (116/756)\n", + "2016-12-27 18:12:35,679 : INFO : gram2-opposite: 15.4% (47/306)\n", + "2016-12-27 18:12:39,683 : INFO : gram3-comparative: 33.8% (426/1260)\n", + "2016-12-27 18:12:41,314 : INFO : gram4-superlative: 21.1% (107/506)\n", + "2016-12-27 18:12:44,488 : INFO : gram5-present-participle: 23.8% (236/992)\n", + "2016-12-27 18:12:48,855 : INFO : gram6-nationality-adjective: 90.2% (1237/1371)\n", + "2016-12-27 18:12:53,089 : INFO : gram7-past-tense: 26.4% (351/1332)\n", + "2016-12-27 18:12:56,261 : INFO : gram8-plural: 60.9% (604/992)\n", + "2016-12-27 18:12:58,352 : INFO : gram9-plural-verbs: 19.7% (128/650)\n", + "2016-12-27 18:12:58,354 : INFO : total: 44.7% (5489/12268)\n", + "2016-12-27 18:12:58,361 : INFO : loading projection weights from models/text8_wr_ensemble.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 2237/4103, Accuracy: 54.52%\n", + "Syntactic: 3252/8165, Accuracy: 39.83%\n", + "\n", + "\n", + "Loading Wordrank ensemble embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:13:05,876 : INFO : loaded (71290, 100) matrix from models/text8_wr_ensemble.vec\n", + "2016-12-27 18:13:05,973 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Wordrank:\n", + "Evaluating...\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2016-12-27 18:13:07,668 : INFO : capital-common-countries: 67.0% (339/506)\n", + "2016-12-27 18:13:12,303 : INFO : capital-world: 59.0% (856/1452)\n", + "2016-12-27 18:13:13,193 : INFO : currency: 17.2% (46/268)\n", + "2016-12-27 18:13:18,175 : INFO : city-in-state: 33.0% (519/1571)\n", + "2016-12-27 18:13:19,222 : INFO : family: 32.0% (98/306)\n", + "2016-12-27 18:13:21,637 : INFO : gram1-adjective-to-adverb: 10.3% (78/756)\n", + "2016-12-27 18:13:22,645 : INFO : gram2-opposite: 10.5% (32/306)\n", + "2016-12-27 18:13:26,626 : INFO : gram3-comparative: 24.4% (308/1260)\n", + "2016-12-27 18:13:28,253 : INFO : gram4-superlative: 11.5% (58/506)\n", + "2016-12-27 18:13:31,412 : INFO : gram5-present-participle: 11.7% (116/992)\n", + "2016-12-27 18:13:35,744 : INFO : gram6-nationality-adjective: 71.8% (985/1371)\n", + "2016-12-27 18:13:39,971 : INFO : gram7-past-tense: 17.0% (226/1332)\n", + "2016-12-27 18:13:43,150 : INFO : gram8-plural: 47.8% (474/992)\n", + "2016-12-27 18:13:45,243 : INFO : gram9-plural-verbs: 11.7% (76/650)\n", + "2016-12-27 18:13:45,245 : INFO : total: 34.3% (4211/12268)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 1858/4103, Accuracy: 45.28%\n", + "Syntactic: 2353/8165, Accuracy: 28.82%\n", + "\n" + ] + } + ], + "source": [ + "print('Loading Gensim embeddings')\n", + "text8_gs = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_gs.vec')\n", + "print('Accuracy for word2vec:')\n", + "print_accuracy(text8_gs, word_analogies_file)\n", + "\n", + "print('Loading FastText embeddings')\n", + "text8_ft = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_ft.vec')\n", + "print('Accuracy for FastText (with n-grams):')\n", + "print_accuracy(text8_ft, word_analogies_file)\n", + "\n", + "print('\\nLoading Wordrank embeddings')\n", + "text8_wr = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_wr.vec')\n", + "print('Accuracy for Wordrank:')\n", + "print_accuracy(text8_wr, word_analogies_file)\n", + "\n", + "print('\\nLoading Wordrank ensemble embeddings')\n", + "text8_wr_ensemble = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_wr_ensemble.vec')\n", + "print('Accuracy for Wordrank:')\n", + "print_accuracy(text8_wr_ensemble, word_analogies_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "With a larger corpus, we observe similar pattern in the accuracies. Here also, wordrank dominates the semantic analogies and fasttext syntactic ones.\n", + "Though we observe a little performance decrease in Wordrank in case of ensemble embeddings here, so it's good to always try both the cases for evaluations.\n", + "\n", + "Now, following graph shows the word frequency effect on analogy task accuracy. For each analogy, the\n", + "mean frequency of the four words involved is computed, and then bucketed with other analogies having similar mean frequencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from __future__ import division\n", + "import matplotlib.pyplot as plt\n", + "import copy\n", + "\n", + "\n", + "def calc_parm(model, vocab):\n", + " freq = {}\n", + " with open(vocab, 'r') as r:\n", + " for line in r:\n", + " key, val = line.split()\n", + " freq[key] = val\n", + " mean_freq = {}\n", + " with open(word_analogies_file, 'r') as r:\n", + " for i, line in enumerate(r):\n", + " if ':' not in line:\n", + " analogy = tuple(line.split())\n", + " else:\n", + " continue\n", + " try:\n", + " mfreq = sum([int(freq[x.lower()]) for x in analogy])/4\n", + " mean_freq['a%d'%i] = [analogy, mfreq]\n", + " except KeyError:\n", + " continue\n", + "\n", + " model = Word2Vec.load_word2vec_format(model)\n", + " acc = model.accuracy(word_analogies_file)\n", + " sem_correct = [acc[i]['correct'] for i in range(5)]\n", + " syn_correct = [acc[i]['correct'] for i in range(5, len(acc)-1)]\n", + " tot_correct = [acc[i]['correct'] for i in range(len(acc)-1)]\n", + "\n", + " sem_x, sem_y = calc_axis(sem_correct, mean_freq)\n", + " syn_x, syn_y = calc_axis(syn_correct, mean_freq)\n", + " total_x, total_y = calc_axis(tot_correct, mean_freq)\n", + " return ((sem_x, sem_y), (syn_x, syn_y), (total_x, total_y))\n", + "\n", + "def calc_axis(correct, mean_freq):\n", + " total_correct = []\n", + " for i in range(len(correct)):\n", + " for analogy in correct[i]:\n", + " total_correct.append(analogy)\n", + "\n", + " dup_mean_freq = copy.deepcopy(mean_freq)\n", + " for key, value in dup_mean_freq.iteritems():\n", + " value[0] = tuple(x.upper() for x in value[0])\n", + " if value[0] in total_correct:\n", + " dup_mean_freq[key].append(1)\n", + " else:\n", + " dup_mean_freq[key].append(0)\n", + "\n", + " x = []\n", + " y = []\n", + " dup_mean_freq = sorted(dup_mean_freq.items(), key=lambda x: x[1][1])\n", + " for centre_p in xrange(50, len(dup_mean_freq), 100):\n", + " bucket = dup_mean_freq[centre_p-50:centre_p+50]\n", + " b_acc = 0\n", + " for analogy in bucket:\n", + " if analogy[1][2]==1:\n", + " b_acc+=1\n", + " y.append(b_acc/100)\n", + " x.append(np.log(dup_mean_freq[centre_p][1][1]))\n", + " return x, y\n", + "\n", + "\n", + "plot_data0 = calc_parm('text8_gs.vec', 'text8.vocab')\n", + "plot_data1 = calc_parm('text8_wr.vec', 'text8.vocab')\n", + "plot_data2 = calc_parm('text8_ft.vec', 'text8.vocab')\n", + "\n", + "fig = plt.figure(figsize=(7,15))\n", + "\n", + "ax = fig.add_subplot('311')\n", + "ax.plot(plot_data0[0][0], plot_data0[0][1], 'r-', label='Word2Vec')\n", + "ax.plot(plot_data1[0][0], plot_data1[0][1], 'g--', label='Wordrank')\n", + "ax.plot(plot_data2[0][0], plot_data2[0][1], 'b:', label='FastText')\n", + "ax.set_ylabel('Average accuracy')\n", + "ax.set_xlabel('Log mean frequency')\n", + "ax.set_title('Semantic Analogies')\n", + "ax.legend()\n", + "\n", + "ax = fig.add_subplot('312')\n", + "ax.plot(plot_data0[1][0], plot_data0[1][1], 'r-', label='Word2Vec')\n", + "ax.plot(plot_data1[1][0], plot_data1[1][1], 'g--', label='Wordrank')\n", + "ax.plot(plot_data2[1][0], plot_data2[1][1], 'b:', label='FastText')\n", + "ax.set_ylabel('Average accuracy')\n", + "ax.set_xlabel('Log mean frequency')\n", + "ax.set_title('Syntactic Analogies')\n", + "ax.legend()\n", + "\n", + "ax = fig.add_subplot('313')\n", + "ax.plot(plot_data0[2][0], plot_data0[2][1], 'r-', label='Word2Vec')\n", + "ax.plot(plot_data1[2][0], plot_data1[2][1], 'g--', label='Wordrank')\n", + "ax.plot(plot_data2[2][0], plot_data2[2][1], 'b:', label='FastText')\n", + "ax.set_ylabel('Average accuracy')\n", + "ax.set_xlabel('Log mean frequency')\n", + "ax.set_title('Total Analogy')\n", + "ax.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusions\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Note: Wordrank can sometimes produce Nan values while model evaluation, when the embedding vector values get too diverged at some iterations, but it dumps embedding vectors after every few iterations, so you could just load embeddings from a different iteration’s text file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 227546c211ecc71e6fd9ee04ffa1cb9a74312fc2 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sat, 31 Dec 2016 06:26:43 +0530 Subject: [PATCH 04/18] update graph output --- docs/notebooks/Wordrank_comparison.ipynb | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/docs/notebooks/Wordrank_comparison.ipynb b/docs/notebooks/Wordrank_comparison.ipynb index 2bb3062696..678c5db518 100644 --- a/docs/notebooks/Wordrank_comparison.ipynb +++ b/docs/notebooks/Wordrank_comparison.ipynb @@ -784,17 +784,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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tbVOYySOgM1vNgAy55qUQFRKUvXqDueevvwY1H8Q99e7xedyeCc7MNz2/8Zmh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\nYUN6q3VYd7Y2x2xxXgtdANWq+cZzOpGMB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uCIfYNoe/CGdAYVfwN2Hs6kcWP4eWmxTjcO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htx82oVfZ+uVcPdWWL6e8RNyFHIfp1nG/juNT/yglQlcRwsKE95CHR21fyX8KRdApKNRxMvIy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IIuI0MiptHOzTUcGsRK0E0FDvc+fxy4XilVCUxC6uuahdb6ea11Z611otZ6uNZ6YcC+E7XW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\nu0zO4dFHxRvvs89i1+ndW1K0iQJTTq8TixLyGdmzy88VVyQWgOH87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P1hqR9D+l8Y/X7w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2+Hh+6AHL4ytaXxtOXBw3LLHyp8XWmEw+cthMcV0f9NDV1Ndgt9gbCeUDnpwcBq8pxlfn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\n+8ImqZYNDbkCbEvwM6P32fx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SyK/IJ8OVgTvOvVfH0mg0Gk3nRveh0+w9tbWwfLlMYVi0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\nUkskEXlYRNJd1/BLEelbl3O0eEEHbAH+jm0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from __future__ import division\n", "import matplotlib.pyplot as plt\n", "import copy\n", + "import numpy as np\n", + "from gensim.models import Word2Vec\n", + "import os\n", "\n", - "\n", + "os.chdir('models')\n", + "word_analogies_file = '../datasets/questions-words.txt'\n", "def calc_parm(model, vocab):\n", " freq = {}\n", " with open(vocab, 'r') as r:\n", From 9b100e4c025b54d6c9f862d68e98bfdb6a8de28d Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 1 Jan 2017 15:49:02 +0530 Subject: [PATCH 05/18] try diff interpreter --- gensim/models/wrappers/wordrank.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 65aff7cc55..39c9e88c48 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -1,6 +1,3 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - """ Python wrapper around word representation learning from Wordrank. The wrapped model can NOT be updated with new documents for online training -- use gensim's From 8e9a6cb7a6fe2d6480a0fb2ff849f31b02f7e3d7 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 1 Jan 2017 19:05:26 +0530 Subject: [PATCH 06/18] use unittest2 --- gensim/test/test_wordrank_wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/test/test_wordrank_wrapper.py b/gensim/test/test_wordrank_wrapper.py index 416b2ff5e3..3034660ff0 100644 --- a/gensim/test/test_wordrank_wrapper.py +++ b/gensim/test/test_wordrank_wrapper.py @@ -10,7 +10,7 @@ import logging -import unittest +import unittest2 import os import tempfile From 5c42762be5a2e846c1c092f4cfd6e5010d70ed74 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 1 Jan 2017 19:06:17 +0530 Subject: [PATCH 07/18] use unittest2 --- gensim/test/test_wordrank_wrapper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/test/test_wordrank_wrapper.py b/gensim/test/test_wordrank_wrapper.py index 3034660ff0..a576a86ffb 100644 --- a/gensim/test/test_wordrank_wrapper.py +++ b/gensim/test/test_wordrank_wrapper.py @@ -10,7 +10,7 @@ import logging -import unittest2 +import unittest2 as unittest import os import tempfile From 17b2b75025428844cdefdf5893e1f63b968678cc Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 1 Jan 2017 20:57:50 +0530 Subject: [PATCH 08/18] add backport_collections for py2.6 --- gensim/test/test_wordrank_wrapper.py | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/gensim/test/test_wordrank_wrapper.py b/gensim/test/test_wordrank_wrapper.py index a576a86ffb..416b2ff5e3 100644 --- a/gensim/test/test_wordrank_wrapper.py +++ b/gensim/test/test_wordrank_wrapper.py @@ -10,7 +10,7 @@ import logging -import unittest2 as unittest +import unittest import os import tempfile diff --git a/setup.py b/setup.py index 3c9a6312b8..a28f67e4cb 100644 --- a/setup.py +++ b/setup.py @@ -118,7 +118,7 @@ def readfile(fname): python_2_6_backports = '' if sys.version_info[:2] < (2, 7): - python_2_6_backports = ['argparse', 'subprocess32'] + python_2_6_backports = ['argparse', 'subprocess32', 'backport_collections'] setup( From f569120f0123f971ea832f668d580d97ff2cf05d Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 11 Jan 2017 14:35:54 +0530 Subject: [PATCH 09/18] added tutorial ipynb --- docs/notebooks/Wordrank_comparison.ipynb | 942 ---------------- docs/notebooks/Wordrank_comparisons.ipynb | 1223 +++++++++++++++++++++ docs/notebooks/Wordrank_wrapper.ipynb | 286 +++++ docs/notebooks/datasets/simlex-999.txt | 999 +++++++++++++++++ docs/notebooks/datasets/ws-353.txt | 353 ++++++ docs/src/apiref.rst | 1 + docs/src/models/wrappers/wordrank.rst | 9 + gensim/models/wrappers/ldamallet.py | 3 +- gensim/models/wrappers/wordrank.py | 7 +- 9 files changed, 2876 insertions(+), 947 deletions(-) delete mode 100644 docs/notebooks/Wordrank_comparison.ipynb create mode 100644 docs/notebooks/Wordrank_comparisons.ipynb create mode 100644 docs/notebooks/Wordrank_wrapper.ipynb create mode 100644 docs/notebooks/datasets/simlex-999.txt create mode 100644 docs/notebooks/datasets/ws-353.txt create mode 100644 docs/src/models/wrappers/wordrank.rst diff --git a/docs/notebooks/Wordrank_comparison.ipynb b/docs/notebooks/Wordrank_comparison.ipynb deleted file mode 100644 index 678c5db518..0000000000 --- a/docs/notebooks/Wordrank_comparison.ipynb +++ /dev/null @@ -1,942 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Comparison of Wordrank and Word2Vec\n", - "\n", - "Wordrank is a fresh new approach to the word embeddings, which formulates it as a ranking problem. That is, given a word w, it aims to output an ordered list (c1, c2, · · ·) of context words such that words that co-occur with w appear at the top of the list. This formulation fits naturally to popular word embedding tasks such as word similarity/analogy since instead of the likelihood of each word, we are interested in finding the most relevant words\n", - "in a given context.\n", - "\n", - "Gensim is used to train the word2vec models, and analogical reasoning task is used for comparing the models. Word2vec and FastText embeddings are trained using the skipgram architecture here." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download and preprocess data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[nltk_data] Downloading package brown to /Users/parul/nltk_data...\n", - "[nltk_data] Package brown is already up-to-date!\n" - ] - } - ], - "source": [ - "import nltk\n", - "from gensim.parsing.preprocessing import strip_punctuation, strip_multiple_whitespaces\n", - "\n", - "# Only the brown corpus is needed in case you don't have it.\n", - "nltk.download('brown') \n", - "\n", - "# Generate brown corpus text file\n", - "with open('brown_corp.txt', 'w+') as f:\n", - " for word in nltk.corpus.brown.words():\n", - " f.write('{word} '.format(word=word))\n", - " f.seek(0)\n", - " brown = f.read()\n", - "\n", - "# Preprocess brown corpus\n", - "with open('proc_brown_corp.txt', 'w') as f:\n", - " proc_brown = strip_punctuation(brown)\n", - " proc_brown = strip_multiple_whitespaces(proc_brown).lower()\n", - " f.write(proc_brown)\n", - "\n", - "# Set WR_HOME and FT_HOME to respective directory root\n", - "WR_HOME = 'wordrank/'\n", - "FT_HOME = 'fastText/'\n", - "\n", - "# download the text8 corpus (a 100 MB sample of preprocessed wikipedia text)\n", - "import os.path\n", - "if not os.path.isfile('text8'):\n", - " !wget -c http://mattmahoney.net/dc/text8.zip\n", - " !unzip text8.zip" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# Train Models\n", - "For training the models yourself, you'll need to have Gensim, FastText and Wordrank set up on your machine." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Training word2vec on proc_brown_corp.txt corpus..\n", - "CPU times: user 1min 7s, sys: 527 ms, total: 1min 8s\n", - "Wall time: 46.8 s\n", - "\n", - "Saved gensim model as brown_gs.vec\n", - "Training fasttext on proc_brown_corp.txt corpus..\n", - "Read 1M words\n", - "Number of words: 14042\n", - "Number of labels: 0\n", - "Progress: 99.6% words/sec/thread: 58810 lr: 0.000179 loss: 2.348125 eta: 0h0m Progress: 20.1% words/sec/thread: 30702 lr: 0.039934 loss: 2.296231 eta: 0h0m Progress: 100.0% words/sec/thread: 58810 lr: 0.000000 loss: 2.348125 eta: 0h0m \n", - "CPU times: user 842 ms, sys: 284 ms, total: 1.13 s\n", - "Wall time: 41.3 s\n", - "\n", - "Training wordrank on proc_brown_corp.txt corpus..\n", - "CPU times: user 10.8 s, sys: 1.02 s, total: 11.8 s\n", - "Wall time: 8h 24min 25s\n", - "\n", - "Saved wordrank model as brown_wr.vec\n", - "\n", - "Loading ensemble embeddings (vector combination of word and context embeddings)..\n", - "CPU times: user 8.97 s, sys: 279 ms, total: 9.25 s\n", - "Wall time: 13.8 s\n", - "\n", - "Saved wordrank (ensemble) model as brown_wr_ensemble.vec\n" - ] - } - ], - "source": [ - "MODELS_DIR = 'models/'\n", - "!mkdir -p {MODELS_DIR}\n", - "\n", - "from gensim.models import Word2Vec\n", - "from gensim.models.wrappers import Wordrank\n", - "from gensim.models.word2vec import Text8Corpus\n", - "\n", - "# fasttext params\n", - "lr = 0.05\n", - "dim = 100\n", - "ws = 5\n", - "epoch = 5\n", - "minCount = 5\n", - "neg = 5\n", - "loss = 'ns'\n", - "t = 1e-4\n", - "\n", - "w2v_params = {\n", - " 'alpha': 0.025,\n", - " 'size': 100,\n", - " 'window': 15,\n", - " 'iter': 5,\n", - " 'min_count': 5,\n", - " 'sample': t,\n", - " 'sg': 1,\n", - " 'hs': 0,\n", - " 'negative': 5\n", - "}\n", - "\n", - "wr_params = {\n", - " 'size': 100,\n", - " 'window': 15,\n", - " 'iter': 91,\n", - " 'min_count': 5\n", - "}\n", - "\n", - "def train_models(corpus_file, output_name):\n", - " # Train using word2vec\n", - " output_file = '{:s}_gs'.format(output_name)\n", - " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", - " print('\\nTraining word2vec on {:s} corpus..'.format(corpus_file))\n", - " # Text8Corpus class for reading space-separated words file\n", - " %time gs_model = Word2Vec(Text8Corpus(corpus_file), **w2v_params); gs_model\n", - " locals()['gs_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", - " print('\\nSaved gensim model as {:s}.vec'.format(output_file))\n", - " else:\n", - " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", - "\n", - " # Train using fasttext\n", - " output_file = '{:s}_ft'.format(output_name)\n", - " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", - " print('Training fasttext on {:s} corpus..'.format(corpus_file))\n", - " %time !{FT_HOME}fasttext skipgram -input {corpus_file} -output {MODELS_DIR+output_file} -lr {lr} -dim {dim} -ws {ws} -epoch {epoch} -minCount {minCount} -neg {neg} -loss {loss} -t {t}\n", - " else:\n", - " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", - " \n", - " # Train using wordrank\n", - " output_file = '{:s}_wr'.format(output_name)\n", - " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", - " print('\\nTraining wordrank on {:s} corpus..'.format(corpus_file))\n", - " %time wr_model = Wordrank.train(WR_HOME, corpus_file, **wr_params); wr_model\n", - " locals()['wr_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", - " print('\\nSaved wordrank model as {:s}.vec'.format(output_file))\n", - " else:\n", - " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", - " \n", - " # Loading ensemble embeddings\n", - " output_file = '{:s}_wr_ensemble'.format(output_name)\n", - " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", - " print('\\nLoading ensemble embeddings (vector combination of word and context embeddings)..')\n", - " %time wr_model = Wordrank.load_wordrank_model(os.path.join(WR_HOME, 'model/wordrank.words'), os.path.join(WR_HOME, 'model/meta/vocab.txt'), os.path.join(WR_HOME, 'model/wordrank.contexts'), sorted_vocab=1, ensemble=1); wr_model\n", - " locals()['wr_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", - " print('\\nSaved wordrank (ensemble) model as {:s}.vec'.format(output_file))\n", - " else:\n", - " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", - " \n", - "train_models(corpus_file='proc_brown_corp.txt', output_name='brown')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Training word2vec on text8 corpus..\n", - "CPU times: user 24min 21s, sys: 8.64 s, total: 24min 29s\n", - "Wall time: 18min 33s\n", - "\n", - "Saved gensim model as text8_gs.vec\n", - "\n", - "Using existing model file text8_ft.vec\n", - "\n", - "Using existing model file text8_wr.vec\n", - "\n", - "Using existing model file text8_wr_ensemble.vec\n" - ] - } - ], - "source": [ - "train_models(corpus_file='text8', output_name='text8')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we train wordrank model using ensemble in second case as it is known to give a small performance boost in some cases. So we'll test accuracy for both the cases." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Comparisons" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import logging\n", - "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", - "\n", - "def print_accuracy(model, questions_file):\n", - " print('Evaluating...\\n')\n", - " acc = model.accuracy(questions_file)\n", - "\n", - " sem_correct = sum((len(acc[i]['correct']) for i in range(5)))\n", - " sem_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5))\n", - " sem_acc = 100*float(sem_correct)/sem_total\n", - " print('\\nSemantic: {:d}/{:d}, Accuracy: {:.2f}%'.format(sem_correct, sem_total, sem_acc))\n", - " \n", - " syn_correct = sum((len(acc[i]['correct']) for i in range(5, len(acc)-1)))\n", - " syn_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5,len(acc)-1))\n", - " syn_acc = 100*float(syn_correct)/syn_total\n", - " print('Syntactic: {:d}/{:d}, Accuracy: {:.2f}%\\n'.format(syn_correct, syn_total, syn_acc))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 13:58:37,691 : INFO : loading projection weights from models/brown_gs.vec\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Loading Gensim embeddings\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 13:58:45,351 : INFO : loaded (14042, 100) matrix from models/brown_gs.vec\n", - "2016-12-27 13:58:45,418 : INFO : precomputing L2-norms of word weight vectors\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for Word2Vec:\n", - "Evaluating...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 13:58:45,823 : INFO : capital-common-countries: 0.0% (0/90)\n", - "2016-12-27 13:58:46,182 : INFO : capital-world: 0.0% (0/53)\n", - "2016-12-27 13:58:46,300 : INFO : currency: 0.0% (0/12)\n", - "2016-12-27 13:58:48,230 : INFO : city-in-state: 0.9% (4/457)\n", - "2016-12-27 13:58:49,250 : INFO : family: 20.0% (48/240)\n", - "2016-12-27 13:58:53,225 : INFO : gram1-adjective-to-adverb: 0.1% (1/812)\n", - "2016-12-27 13:58:54,105 : INFO : gram2-opposite: 0.0% (0/132)\n", - "2016-12-27 13:58:59,197 : INFO : gram3-comparative: 1.8% (19/1056)\n", - "2016-12-27 13:59:00,302 : INFO : gram4-superlative: 0.5% (1/210)\n", - "2016-12-27 13:59:03,744 : INFO : gram5-present-participle: 2.6% (17/650)\n", - "2016-12-27 13:59:05,319 : INFO : gram6-nationality-adjective: 11.4% (34/297)\n", - "2016-12-27 13:59:11,041 : INFO : gram7-past-tense: 3.3% (42/1260)\n", - "2016-12-27 13:59:14,074 : INFO : gram8-plural: 6.6% (46/702)\n", - "2016-12-27 13:59:15,578 : INFO : gram9-plural-verbs: 2.0% (7/342)\n", - "2016-12-27 13:59:15,579 : INFO : total: 3.5% (219/6313)\n", - "2016-12-27 13:59:15,586 : INFO : loading projection weights from models/brown_ft.vec\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Semantic: 52/852, Accuracy: 6.10%\n", - "Syntactic: 167/5461, Accuracy: 3.06%\n", - "\n", - "\n", - "Loading FastText embeddings\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 13:59:21,713 : INFO : loaded (14042, 100) matrix from models/brown_ft.vec\n", - "2016-12-27 13:59:21,775 : INFO : precomputing L2-norms of word weight vectors\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for FastText:\n", - "Evaluating...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 13:59:22,205 : INFO : capital-common-countries: 1.1% (1/90)\n", - "2016-12-27 13:59:22,546 : INFO : capital-world: 0.0% (0/53)\n", - "2016-12-27 13:59:22,651 : INFO : currency: 0.0% (0/12)\n", - "2016-12-27 13:59:24,741 : INFO : city-in-state: 2.4% (11/457)\n", - "2016-12-27 13:59:25,821 : INFO : family: 11.7% (28/240)\n", - "2016-12-27 13:59:29,840 : INFO : gram1-adjective-to-adverb: 79.9% (649/812)\n", - "2016-12-27 13:59:30,628 : INFO : gram2-opposite: 79.5% (105/132)\n", - "2016-12-27 13:59:35,972 : INFO : gram3-comparative: 56.3% (595/1056)\n", - "2016-12-27 13:59:37,289 : INFO : gram4-superlative: 71.4% (150/210)\n", - "2016-12-27 13:59:40,495 : INFO : gram5-present-participle: 65.7% (427/650)\n", - "2016-12-27 13:59:41,863 : INFO : gram6-nationality-adjective: 35.0% (104/297)\n", - "2016-12-27 13:59:47,878 : INFO : gram7-past-tense: 12.1% (153/1260)\n", - "2016-12-27 13:59:51,524 : INFO : gram8-plural: 53.1% (373/702)\n", - "2016-12-27 13:59:53,380 : INFO : gram9-plural-verbs: 69.0% (236/342)\n", - "2016-12-27 13:59:53,382 : INFO : total: 44.9% (2832/6313)\n", - "2016-12-27 13:59:53,389 : INFO : loading projection weights from models/brown_wr.vec\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Semantic: 40/852, Accuracy: 4.69%\n", - "Syntactic: 2792/5461, Accuracy: 51.13%\n", - "\n", - "\n", - "Loading Wordrank embeddings\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 14:00:00,114 : INFO : loaded (14042, 100) matrix from models/brown_wr.vec\n", - "2016-12-27 14:00:00,173 : INFO : precomputing L2-norms of word weight vectors\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for Wordrank:\n", - "Evaluating...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 14:00:00,438 : INFO : capital-common-countries: 10.0% (9/90)\n", - "2016-12-27 14:00:00,694 : INFO : capital-world: 15.1% (8/53)\n", - "2016-12-27 14:00:00,762 : INFO : currency: 0.0% (0/12)\n", - "2016-12-27 14:00:02,165 : INFO : city-in-state: 8.1% (37/457)\n", - "2016-12-27 14:00:02,909 : INFO : family: 23.8% (57/240)\n", - "2016-12-27 14:00:05,119 : INFO : gram1-adjective-to-adverb: 0.6% (5/812)\n", - "2016-12-27 14:00:05,616 : INFO : gram2-opposite: 0.0% (0/132)\n", - "2016-12-27 14:00:09,606 : INFO : gram3-comparative: 2.0% (21/1056)\n", - "2016-12-27 14:00:10,392 : INFO : gram4-superlative: 1.0% (2/210)\n", - "2016-12-27 14:00:12,894 : INFO : gram5-present-participle: 0.5% (3/650)\n", - "2016-12-27 14:00:14,405 : INFO : gram6-nationality-adjective: 10.8% (32/297)\n", - "2016-12-27 14:00:18,084 : INFO : gram7-past-tense: 1.6% (20/1260)\n", - "2016-12-27 14:00:20,194 : INFO : gram8-plural: 8.3% (58/702)\n", - "2016-12-27 14:00:21,221 : INFO : gram9-plural-verbs: 0.3% (1/342)\n", - "2016-12-27 14:00:21,222 : INFO : total: 4.0% (253/6313)\n", - "2016-12-27 14:00:21,229 : INFO : loading projection weights from models/brown_wr_ensemble.vec\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Semantic: 111/852, Accuracy: 13.03%\n", - "Syntactic: 142/5461, Accuracy: 2.60%\n", - "\n", - "\n", - "Loading Wordrank ensemble embeddings\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 14:00:25,328 : INFO : loaded (14042, 100) matrix from models/brown_wr_ensemble.vec\n", - "2016-12-27 14:00:25,413 : INFO : precomputing L2-norms of word weight vectors\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for Wordrank:\n", - "Evaluating...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 14:00:25,898 : INFO : capital-common-countries: 14.4% (13/90)\n", - "2016-12-27 14:00:26,340 : INFO : capital-world: 18.9% (10/53)\n", - "2016-12-27 14:00:26,469 : INFO : currency: 0.0% (0/12)\n", - "2016-12-27 14:00:28,738 : INFO : city-in-state: 8.3% (38/457)\n", - "2016-12-27 14:00:29,890 : INFO : family: 28.8% (69/240)\n", - "2016-12-27 14:00:33,588 : INFO : gram1-adjective-to-adverb: 0.6% (5/812)\n", - "2016-12-27 14:00:34,111 : INFO : gram2-opposite: 0.0% (0/132)\n", - "2016-12-27 14:00:38,860 : INFO : gram3-comparative: 3.4% (36/1056)\n", - "2016-12-27 14:00:40,451 : INFO : gram4-superlative: 0.0% (0/210)\n", - "2016-12-27 14:00:44,070 : INFO : gram5-present-participle: 1.7% (11/650)\n", - "2016-12-27 14:00:45,958 : INFO : gram6-nationality-adjective: 16.8% (50/297)\n", - "2016-12-27 14:00:52,447 : INFO : gram7-past-tense: 3.8% (48/1260)\n", - "2016-12-27 14:00:56,598 : INFO : gram8-plural: 11.1% (78/702)\n", - "2016-12-27 14:00:58,343 : INFO : gram9-plural-verbs: 0.9% (3/342)\n", - "2016-12-27 14:00:58,344 : INFO : total: 5.7% (361/6313)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Semantic: 130/852, Accuracy: 15.26%\n", - "Syntactic: 231/5461, Accuracy: 4.23%\n", - "\n" - ] - } - ], - "source": [ - "word_analogies_file = './datasets/questions-words.txt'\n", - "\n", - "print('\\nLoading Gensim embeddings')\n", - "brown_gs = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_gs.vec')\n", - "print('Accuracy for Word2Vec:')\n", - "print_accuracy(brown_gs, word_analogies_file)\n", - "\n", - "print('\\nLoading FastText embeddings')\n", - "brown_ft = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_ft.vec')\n", - "print('Accuracy for FastText:')\n", - "print_accuracy(brown_ft, word_analogies_file)\n", - "\n", - "print('\\nLoading Wordrank embeddings')\n", - "brown_wr = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_wr.vec')\n", - "print('Accuracy for Wordrank:')\n", - "print_accuracy(brown_wr, word_analogies_file)\n", - "\n", - "print('\\nLoading Wordrank ensemble embeddings')\n", - "brown_wr_ensemble = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_wr_ensemble.vec')\n", - "print('Accuracy for Wordrank:')\n", - "print_accuracy(brown_wr_ensemble, word_analogies_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As evident from the above outputs, wordrank performs better in semantic analogies, whereas, fasttext performs significantly better in syntactic analogies. Also ensemble embeddings gives a small performance boost in wordrank's case.\n", - "\n", - "Wordrank's effectiveness in Semantic analogies is possibly due to it's focused attention on getting most relevant words right at the top using the ranking approach.\n", - "And as fasttext is designed to incorporate morphological information about words, it results in it's performance boost in syntactic analogies, as most of the syntactic analogies are morphology based.\n", - "\n", - "Now lets evaluate on a larger corpus, text8, and see how it effects the performance of different embedding models. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:10:28,984 : INFO : loading projection weights from models/text8_gs.vec\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading Gensim embeddings\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:10:36,842 : INFO : loaded (71290, 100) matrix from models/text8_gs.vec\n", - "2016-12-27 18:10:36,908 : INFO : precomputing L2-norms of word weight vectors\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for word2vec:\n", - "Evaluating...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:10:38,821 : INFO : capital-common-countries: 68.6% (347/506)\n", - "2016-12-27 18:10:44,002 : INFO : capital-world: 52.3% (760/1452)\n", - "2016-12-27 18:10:44,976 : INFO : currency: 19.8% (53/268)\n", - "2016-12-27 18:10:50,552 : INFO : city-in-state: 24.8% (389/1571)\n", - "2016-12-27 18:10:51,692 : INFO : family: 47.7% (146/306)\n", - "2016-12-27 18:10:55,175 : INFO : gram1-adjective-to-adverb: 18.0% (136/756)\n", - "2016-12-27 18:10:56,535 : INFO : gram2-opposite: 13.4% (41/306)\n", - "2016-12-27 18:11:01,901 : INFO : gram3-comparative: 37.8% (476/1260)\n", - "2016-12-27 18:11:03,798 : INFO : gram4-superlative: 22.3% (113/506)\n", - "2016-12-27 18:11:07,566 : INFO : gram5-present-participle: 22.9% (227/992)\n", - "2016-12-27 18:11:12,830 : INFO : gram6-nationality-adjective: 86.7% (1188/1371)\n", - "2016-12-27 18:11:18,266 : INFO : gram7-past-tense: 27.0% (359/1332)\n", - "2016-12-27 18:11:21,913 : INFO : gram8-plural: 54.4% (540/992)\n", - "2016-12-27 18:11:24,023 : INFO : gram9-plural-verbs: 25.2% (164/650)\n", - "2016-12-27 18:11:24,025 : INFO : total: 40.3% (4939/12268)\n", - "2016-12-27 18:11:24,031 : INFO : loading projection weights from models/text8_ft.vec\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Semantic: 1695/4103, Accuracy: 41.31%\n", - "Syntactic: 3244/8165, Accuracy: 39.73%\n", - "\n", - "Loading FastText embeddings\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:11:31,749 : INFO : loaded (71290, 100) matrix from models/text8_ft.vec\n", - "2016-12-27 18:11:31,977 : INFO : precomputing L2-norms of word weight vectors\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for FastText (with n-grams):\n", - "Evaluating...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:11:33,667 : INFO : capital-common-countries: 62.5% (316/506)\n", - "2016-12-27 18:11:38,357 : INFO : capital-world: 43.0% (625/1452)\n", - "2016-12-27 18:11:39,239 : INFO : currency: 12.7% (34/268)\n", - "2016-12-27 18:11:44,264 : INFO : city-in-state: 18.3% (287/1571)\n", - "2016-12-27 18:11:45,264 : INFO : family: 43.5% (133/306)\n", - "2016-12-27 18:11:47,685 : INFO : gram1-adjective-to-adverb: 73.7% (557/756)\n", - "2016-12-27 18:11:48,692 : INFO : gram2-opposite: 53.9% (165/306)\n", - "2016-12-27 18:11:52,716 : INFO : gram3-comparative: 64.8% (816/1260)\n", - "2016-12-27 18:11:54,355 : INFO : gram4-superlative: 53.4% (270/506)\n", - "2016-12-27 18:11:57,536 : INFO : gram5-present-participle: 54.4% (540/992)\n", - "2016-12-27 18:12:01,932 : INFO : gram6-nationality-adjective: 93.9% (1288/1371)\n", - "2016-12-27 18:12:06,220 : INFO : gram7-past-tense: 35.6% (474/1332)\n", - "2016-12-27 18:12:09,390 : INFO : gram8-plural: 90.1% (894/992)\n", - "2016-12-27 18:12:11,479 : INFO : gram9-plural-verbs: 59.4% (386/650)\n", - "2016-12-27 18:12:11,481 : INFO : total: 55.3% (6785/12268)\n", - "2016-12-27 18:12:11,488 : INFO : loading projection weights from models/text8_wr.vec\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Semantic: 1395/4103, Accuracy: 34.00%\n", - "Syntactic: 5390/8165, Accuracy: 66.01%\n", - "\n", - "\n", - "Loading Wordrank embeddings\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:12:18,908 : INFO : loaded (71290, 100) matrix from models/text8_wr.vec\n", - "2016-12-27 18:12:18,987 : INFO : precomputing L2-norms of word weight vectors\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for Wordrank:\n", - "Evaluating...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:12:20,718 : INFO : capital-common-countries: 84.6% (428/506)\n", - "2016-12-27 18:12:25,378 : INFO : capital-world: 70.0% (1016/1452)\n", - "2016-12-27 18:12:26,259 : INFO : currency: 19.0% (51/268)\n", - "2016-12-27 18:12:31,261 : INFO : city-in-state: 36.0% (565/1571)\n", - "2016-12-27 18:12:32,263 : INFO : family: 57.8% (177/306)\n", - "2016-12-27 18:12:34,677 : INFO : gram1-adjective-to-adverb: 15.3% (116/756)\n", - "2016-12-27 18:12:35,679 : INFO : gram2-opposite: 15.4% (47/306)\n", - "2016-12-27 18:12:39,683 : INFO : gram3-comparative: 33.8% (426/1260)\n", - "2016-12-27 18:12:41,314 : INFO : gram4-superlative: 21.1% (107/506)\n", - "2016-12-27 18:12:44,488 : INFO : gram5-present-participle: 23.8% (236/992)\n", - "2016-12-27 18:12:48,855 : INFO : gram6-nationality-adjective: 90.2% (1237/1371)\n", - "2016-12-27 18:12:53,089 : INFO : gram7-past-tense: 26.4% (351/1332)\n", - "2016-12-27 18:12:56,261 : INFO : gram8-plural: 60.9% (604/992)\n", - "2016-12-27 18:12:58,352 : INFO : gram9-plural-verbs: 19.7% (128/650)\n", - "2016-12-27 18:12:58,354 : INFO : total: 44.7% (5489/12268)\n", - "2016-12-27 18:12:58,361 : INFO : loading projection weights from models/text8_wr_ensemble.vec\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Semantic: 2237/4103, Accuracy: 54.52%\n", - "Syntactic: 3252/8165, Accuracy: 39.83%\n", - "\n", - "\n", - "Loading Wordrank ensemble embeddings\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:13:05,876 : INFO : loaded (71290, 100) matrix from models/text8_wr_ensemble.vec\n", - "2016-12-27 18:13:05,973 : INFO : precomputing L2-norms of word weight vectors\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for Wordrank:\n", - "Evaluating...\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2016-12-27 18:13:07,668 : INFO : capital-common-countries: 67.0% (339/506)\n", - "2016-12-27 18:13:12,303 : INFO : capital-world: 59.0% (856/1452)\n", - "2016-12-27 18:13:13,193 : INFO : currency: 17.2% (46/268)\n", - "2016-12-27 18:13:18,175 : INFO : city-in-state: 33.0% (519/1571)\n", - "2016-12-27 18:13:19,222 : INFO : family: 32.0% (98/306)\n", - "2016-12-27 18:13:21,637 : INFO : gram1-adjective-to-adverb: 10.3% (78/756)\n", - "2016-12-27 18:13:22,645 : INFO : gram2-opposite: 10.5% (32/306)\n", - "2016-12-27 18:13:26,626 : INFO : gram3-comparative: 24.4% (308/1260)\n", - "2016-12-27 18:13:28,253 : INFO : gram4-superlative: 11.5% (58/506)\n", - "2016-12-27 18:13:31,412 : INFO : gram5-present-participle: 11.7% (116/992)\n", - "2016-12-27 18:13:35,744 : INFO : gram6-nationality-adjective: 71.8% (985/1371)\n", - "2016-12-27 18:13:39,971 : INFO : gram7-past-tense: 17.0% (226/1332)\n", - "2016-12-27 18:13:43,150 : INFO : gram8-plural: 47.8% (474/992)\n", - "2016-12-27 18:13:45,243 : INFO : gram9-plural-verbs: 11.7% (76/650)\n", - "2016-12-27 18:13:45,245 : INFO : total: 34.3% (4211/12268)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Semantic: 1858/4103, Accuracy: 45.28%\n", - "Syntactic: 2353/8165, Accuracy: 28.82%\n", - "\n" - ] - } - ], - "source": [ - "print('Loading Gensim embeddings')\n", - "text8_gs = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_gs.vec')\n", - "print('Accuracy for word2vec:')\n", - "print_accuracy(text8_gs, word_analogies_file)\n", - "\n", - "print('Loading FastText embeddings')\n", - "text8_ft = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_ft.vec')\n", - "print('Accuracy for FastText (with n-grams):')\n", - "print_accuracy(text8_ft, word_analogies_file)\n", - "\n", - "print('\\nLoading Wordrank embeddings')\n", - "text8_wr = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_wr.vec')\n", - "print('Accuracy for Wordrank:')\n", - "print_accuracy(text8_wr, word_analogies_file)\n", - "\n", - "print('\\nLoading Wordrank ensemble embeddings')\n", - "text8_wr_ensemble = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_wr_ensemble.vec')\n", - "print('Accuracy for Wordrank:')\n", - "print_accuracy(text8_wr_ensemble, word_analogies_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "With a larger corpus, we observe similar pattern in the accuracies. Here also, wordrank dominates the semantic analogies and fasttext syntactic ones.\n", - "Though we observe a little performance decrease in Wordrank in case of ensemble embeddings here, so it's good to always try both the cases for evaluations.\n", - "\n", - "Now, following graph shows the word frequency effect on analogy task accuracy. For each analogy, the\n", - "mean frequency of the four words involved is computed, and then bucketed with other analogies having similar mean frequencies." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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tbVOYySOgM1vNgAy55qUQFRKUvXqDueevvwY1H8Q99e7xedyeCc7MNz2/8Zmh\nC7bY2FhWr14NGMOtFStW5Oqrr3Ycs2ffEhISXJ6fO3ToEGFhYdSuXdulvapVq1KiRAkOHnR9dKFW\nrVoe1z548CBhYWGO69nVq1fPZ3/d6wIkJSUxYsQI5s2bx6lTpxzlSinOnz/vUb969eouX5cpUwaA\ns2fPUqXKf/f4888/5/Lly0ydOpVu3br57FNBIj+lhBCFhn3INVi2n9zOxHUTebf9u14DOnuGJzRE\nMnR5pTBn6KqUyPmwaFZDssEUFxfH0qVL2bZtG2vWrHFk58AI6F5++WUSExNJSEggJiaGGjVqAGS6\njI+3Y1FRUX7Vy6ptb+10796djRs3MnToUJo0aUKxYsXIyMigU6dOWK2e30ehod7/L7tf9+abb2bD\nhg1MmjSJ7t27U6pUKZ/9KigKzJCrUuoppdR+pVSKUuoPpVTLTOo+qpT6XSl1xvZa6V5fKfWFUsrq\n9lqW++9ECJFb7EOuwXLo/CE+3fQpKRkpkqErIIIV0D39w9O5mtG9EsTFxQGwevVqj+femjdvTmRk\nJL/99hvr1q1z1AWoWbMmZrOZffv2ubSXmJjIpUuXHIFfZuxt7N/vuqbk7t27/e5/UlISv//+O8OG\nDWPYsGHcc889tGvXjpo1a/rdhi9169Zl+fLlHDhwgE6dOhWKZVIKRECnlLoPGAu8ATQFtgDLlVLl\nfZxyCzAbuBW4ETgMrFBKuf9q9ANQCahsexX8QXAhhE+1ytTipdiXqFXGcwgnEM5bf3kL6CJCIxhx\ny4g8zZoUdUkpSVxIuxC09mRRaN9atmxJZGQks2bNIjEx0SVDFxERQdOmTZk8eTLJyckuAV2nTp3Q\nWjNhwgSX9saOHYtSirvuuivLa9vbmDhxokv5hAkTPGa5+mLPtrln4saPH+93G5m57rrrWLZsGVu2\nbKFLly4eS7IUNAXl184hwCda6y8BlFKPA3cBDwPvuVfWWj/g/LVS6lGgO9AOmOl0KE1rfQohxBUj\nPCQ8KBkcAI3xYR+iQuhavyuNKzZ2OR4dHk1yRjLrjqyToC6PXEy7SNxVcVlX9JNVW2VSiw/h4eG0\naNGC+Ph4TCYTzZs3dzkeGxvrCNKcA7pmzZrRt29fpkyZQlJSEm3atGHt2rXMnDmTXr16+bX4cLNm\nzejZsycTJ07kzJkz3HjjjaxcuZL9+/f7HYSXLl2a2NhY3n33XVJSUoiJieHHH3/k0KFDQQvkW7du\nzaJFi+i29gLVAAAgAElEQVTcuTM9e/ZkwYIFPodt81u+Z+iUUuFAc+Bne5k2/iV+Alr72UwxIBw4\n41Z+q1LqhFJql1JqilKqbDD6LITIP8F82N05Q/f+He/Tt0lfjzor/l3BxmMbg3I9kTWrthKigvfR\nFKzg/0rVpk0blFK0aNGC8HDX9R1vuukmlFKULFnSsXac3fTp03njjTdYt24dQ4YMYfXq1bz++uvM\nnDnTpZ59/TdvvvzyS55++mmWLVvGK6+8glKK7777LtNz3M2dO5fbb7+dDz/8kP/9738UK1aMpUuX\nZqsNd+7ntm/fnjlz5rBs2TIeeuihgNrMCwUhQ1ceCAVOuJWfAHxPd3E1BjiKEQTa/QAsAPYDtYF3\ngWVKqdZacvBCFFrBfNjdOaDzJSwkzPEsnch9Vm0NynBZ3XJ1uafuPUFfhPpK88477/DOO+94Pda1\na1csFu//10JDQxk+fDjDhw/PtP1Dhw75PBYZGckHH3zABx984FLufs233nrL5zpxVatWZcGCBR7l\n/rbxyCOPOBYpBuN9eXvPXbt2JT29YD+TWRACOl8UkGXgpZR6BegF3KK1dtxtrfU8p2rblVLbgH0Y\nz9396qu9IUOGeMxm6d27d6FYg0aIouC1Nq/xaptXg9KWBHQFT7AydPaJLkIUFHPmzGHOnDkuZd6W\nVglUQQjoTgMWjMkLzirimbVzoZR6EXgZaKe13p5ZXa31fqXUaaAOmQR048ePp1mzZv70WwiRD4KZ\ncfE3oAv27Frhm0YHJaBLNacSFe65zIUQ+cVbcmjTpk0ezy4GKt+fodNaZwAbMSY0AKCMfHs7YI2v\n85RSLwH/Azporf/K6jpKqWpAOcD3vixCiCKlQnQF2l7dNtMgUTJ0eStoGTqzZOhE0VIQMnQA44AZ\nSqmNwHqMWa/RwHQApdSXwBGt9Wu2r18G3sRYhuSQUsqe3buktb6slCqGsQTKAuA4RlZuDLAHWJ5X\nb0oIUbDdUvMWfq75c6Z1JKDLW8EK6L7o8gWNKjYKQo+EKBzyPUMHjufdXsAI0v4CmmBk3uxLjlTD\nWEfO7gmMWa3zgUSn1wu24xZbG4uB3cBU4E/gZltGUAhRCK07so7759/P5fTLAZ1/LvUcaqSi85zO\nftVPyUhh1+ldXEy7GND1RPYFK6Dr0bAH9cvXD0KPhCgcCkqGDq31FGCKj2Nt3b723NDN9Xgq0DF4\nvRNCFAQHzh1g7va5TO08NaDz7VuH/Xv2X49jZqsZq7YSERrhKNt+ajtHLhwhrnrw1kUTmWtfqz17\nkvbkdzeEKHQKRIZOCCH8YZ+cEOjECPtCwt62hOqzoA93z77bpcw+1Ppa3GsBXU9kn0Jx4NyB/O6G\nEIWOBHRCiELDHoiFhwQW0NlntaaZ0zyOhYZ4rm9nX8BY9nLNO8Hay3Xt4bXU+7Aeh88fDkKvhCj4\nJKATQhQa9iHTAYsGBLS1j/0cbxk6b5Mf7F+HhhTMrX6uRCEqJCgLR6eaU9mTtIc0i2fwLsSVSAI6\nIUShYR9ynbVtVkAf+vbMj7eALlR5bilmD+gkQ5d3gpWhswfhwdomToiCTgI6IUSe2nlqp9eAyh/2\nDB0E9kHtGHJ1y9ocPAjmyyU8MnT2oLGwBnQpGSkFdoLBmZQzXodDQ1VocAI6ZQvogrRNnBAFnQR0\nQog8Y9VWGk5pyJAfhwR0vnMgGMgHtfukiGl/TaPS+5WoWROWvvqqR5uOIVdVOIdcH1z8IPU+9HdL\n7Lx1zaRrqD6hukd5MDJ0Jy6dYNGuRYBk6ETRUTh/7RRCFEr2DNuWE1sCOv+actcQFRZFijkloA/q\nkpElaVKpCX0a9QGM56zOpZ7jzz9h3KZP2GW9siZF/Hn0z/zugk9nUs54LQ9GQLf5+GbeX/s+IBk6\nUXRIhk4IkWfsz8AFunBstwbdmNF1BhDYB3VpU2m2PL6FoXFDgf8WsW3RAspVO+Mx5HprzVvZ/uR2\nykWXy7Td+EPxTPnT6zKa+eqBJg8QHR4d0ASS/JJwOIGYEjE5aiPVnOr4u2TovJsxYwYhISFeX6+9\nFtxlei5fvszIkSOJj493lO3bt8/n9Z1foaGhJCYmBrU/AAcPHmTkyJHs2rUr6G3nl8L5a6cQolCy\nB0zGds2BCebD7harxRFcDmw+kHsb3OtyvERkCT7b9Bkr9q3g7yf/9tnOz//+zNRNU3my5ZM57lMw\nKaUobSqdo/udW+6tfy/f7vrWo3zHqR2MaT8mR22nmFMcf5cMnW9KKd566y1q1qzpUt6oUXC3TLt0\n6RIjR44kPDycuDhjke7KlSszc+ZMl3rvvfceJ0+eZOzYsS6/hJQtWzao/QE4cOAAI0eOpHHjxtSv\nf2XsKCIBnRAiz0SHRwPQpV6XgNsI5sPuzttMNanUxOf1strLNVgzM4Mtw5IR8Jp9ua1RxUZsSNzg\nUR6Mrb/sGbrhNw+naomqOWrrStexY0eaNWuWq9fwliEuVqwYffr0cSn76quvSE5Opnfv3rnaH3uf\nCuIvOjkhQ65CiDwTERqBfkPzfOvnA24jpkQMnet2DkqgYg8elAJfSQlv69O5U0oVzIDOmhHwrhq5\nrWKxilxV6iqP8mAFdGEhYYy8bSRVS0pAF6jPP/+cdu3aUalSJaKiomjUqBFTp3puu7d+/Xpuv/12\nypcvT3R0NLVq1eKxxx4DjKHVmJgYlFIMGzbMMZQ6atSobPcnKSmJJ598kmrVqmEymahXrx4ffPCB\nS50XXniB8PBw1q9f71Leu3dvihcvzt69e1m6dClt2xo7ivbo0cMxtLtw4cJs96kgkQydEKJQaVm1\nJUt6LwlKW87BQ+XK3uv4E9D9duA3Tlw+EZQ+BZPZai6wEzqevuFpnr7haY/yYAR0KRkpmMJMOWqj\nqDh//jxJSUkuZeXKGc+MfvTRRzRt2pQuXboQFhbG4sWLGTRoEAADBw4E4MSJE3To0IGYmBj+97//\nUbJkSQ4cOMCSJcb/0cqVKzN58mSeeuopevbsSZcuRnb++uuvz1Y/L168yE033cT58+d5/PHHiYmJ\n4bfffmPIkCEkJSXx5ptvAjBq1Ch++OEHBgwYwObNm4mMjGTBggXMnTuXDz74gDp16hAdHc1rr73G\nu+++y+DBg2nZsiUAN9xwQ4B3sYDQWsvLSAc3A/TGjRu1EKJoGL16tC47pmymdUb8OkJXHVs10zqM\nQDOCYHYtKNLMafpS2qX87ka2qBFKf7rh0xy1Mer3Ubr8e+Vz1MbGjRt1dj8TEhO13rrVs/yvv7Q+\nfty17NQprb01vX271ocPu5adP2+0HUzTp0/XSimPV0hIiKNOamqqx3nt27fX9evXd3w9f/58HRIS\nord6e+M2x48f10op/c4772Tap44dO+prrrnG67GhQ4fqMmXK6CNHjriUP/PMM9pkMunTp087ytav\nX6/DwsL0888/r0+dOqUrVKigb7vtNpfzfvvtN62U0gsWLMi0Tznhz/eQvQ7QTOcwjpEhVyFEkXVH\n7TsYe8fYTOv4k6ErqCJCIygWUSy/u+E3rTUaneMMXZoljaiwqCD1yn+ffAJ33ulZfvPNMGuWa9mi\nRdC8uWfdnj1h3DjXsrVrjbaDTSnFRx99xE8//eR4rVy50nE8MjLS8fcLFy6QlJTELbfcwp49e0hJ\nMSaelC5dGq01S5YswWLJvQko8+fPp3379phMJpKSkhyv9u3bk5aWxpo1axx1W7ZsySuvvMKECRPo\n1KkTqampfPHFF7nWt4KiYObihRDCC53DB5lTzamsP7oes9XMbTVvo2mVpjSt0jTTcwpzQFfY2Bd+\nzmlAN+LWEbx+8+vB6FK2DBoE3bt7lv/+O1Sp4lrWtSt4m4vwzTdQsqRrWevW0MT7nJ0ca9mypc9J\nEatXr+aNN95g/fr1JCcnO8qVUpw/f56oqCjatm3Lvffey/Dhw3n//fe59dZb6dq1K7179yYiIiIo\nfbRarezfv5/9+/czf/58j+NKKU6ePOlSNnz4cObNm8fGjRuZOHEiNWrUCEpfCjIJ6IQQhUbPb3qS\nnJHMsr7LAjp/T9Iebpl+CwDpw9IdEwamTjU+NN0nRqw9vJbRCaML1Tpuzk4nn+allS/xYusXubbi\ntfndnSxZtZWqJaoG5bk/+/I2ealKFc/ADcDb42Llyxsvdw0bepaVLOkZ5OW2f/75h9tvv51GjRox\nfvx4rrrqKiIiIliyZAmTJk3CajUmASmlWLBgAX/88Qfff/89y5cv56GHHmLChAmsWbOGqKicZ0rt\nQ4pdunThmWee8VqnQYMGLl/v2rWLgwcPArBt27Yc96EwkCFXIUShkWHNIHTPP/DhhwGd7xyY2Ze2\n0BoeewwaN4av//7apf4fR/7gXOo5Pr/n80zbHXDdAOKqxwXUp9yUZk5j+ubpHL7guWcq06fD4MF5\n3ie7BTsWsGLfCpeysJAwnmz5JC+ufDGfeiXslixZQkZGBkuXLmXgwIF07NiRtm3bugzDOrvxxht5\n++23+fPPP5kxYwZbtmzhm2++AXK27iRAaGgo1atXJyUlhbZt23p9VXGKpM1mMwMGDCAmJoYXXniB\nTz/9lJ9++smlzSttyRKQgE4IkYdOXDrBV1u+4kLahYDOz7BkEH7qDPzxR0DnOy8tYl98Vin46Sd4\nctJ8HlnyiEt9s9VMqchSdG/oZRzNiUVbcjxMmBsiw4wPX+edExzWrYOff87jHv1n4vqJfLX1K4/y\nYK3pZ7Fa2HpiK+dSz+W4raIoNNTIcNozcQBnz57lyy+/dKl37pzn/b3uuusASEtLA4w153zV9Vev\nXr346aefSEhI8Dh25ozrNnJvv/02W7du5YsvvmDUqFE0bdqURx99lIsXLzrqBKNPBY0MuQoh8syu\n07vov6g/fRv3ZWa3mT7rrdi3grCQMNpe3dalPMOaQQkrEODD1/ZntMA1yGnXDnaWOI5lhWu7/i77\nYdVWx4LHBYl96Y40c5rnQYsFMjLyuEdOl7davN6zYAV0F9Mvct3H1zGvxzx6Xtszx+1diTJ7lKBD\nhw4MHTqUTp06MXDgQC5cuMDUqVOpUqWKy/Nqn3/+OZ999hldu3alVq1ajnplypShY8eOgBE81a1b\nlzlz5lCrVi3KlClDkyZNPIZJMzNs2DCWLVtGu3bteOSRR7juuuu4cOECmzdv5ttvv+Xs2bNERESw\nadMmRo0axdNPP80ttxiPV8yYMYMWLVrw3HPP8fnnRra9QYMGREVFMXHiRJRSREdHExcXR9WqhXfd\nwoL3K6UQ4opl38v1fNr5TOuN/2O8171R0y3p7CyWQkS9eWxM3Jjt6zsHCu5Zq1AV6rH7hEVb/Aro\nSkWWokKxCtnuD8A/Sf+w/eT2gM7NSmRoJhk6iwXS03Pluv5IOJzAjC0zOHbxmEt5iAoJyrZuwdxR\n5EqV2bBjgwYNmD9/PlarlRdffJHPPvuMZ555hiefdN3e7rbbbqNZs2bMmTOHwYMHM3bsWBo2bMgv\nv/zCVVf9t3D0tGnTqFy5MkOGDKFPnz58+63ntm+Z9alkyZKsWbOGwYMHs3z5cp599lnGjh3LkSNH\nGDNmDBEREWRkZPDggw9Sq1YtRo8e7Ti3UaNGvPHGG0yfPp0ff/wRgOjoaGbMmEF6ejqDBg2iT58+\nrFu3zu97VxBJhk4IkWcce7mS+fMrznusOsuwZBBpgQxlDWjmaWYBnbfZrGar2a+H66fc5Rl8+mvk\nqpEcvnCYVQ+uCrgNX8b/MR7wEdBZrfmaobNLzkh2+TpYGbpg7vl7JRowYAADBgzItE7nzp3p3Lmz\nR/mjjz7q+HuzZs2Y5b4mixexsbH8+eefmdb54YcfMj1esmRJxowZw5gx3vf6DQ8PZ+vWrV6Pvfrq\nq7z66qsuZT169KBHjx6ZXrMwkQydECLPZFiMACKr580s2uI1kMqwZmCyKEed7HKfFPFP0j/8sHU9\nSsFX79wMuAZ9Fqt/GbqcCMa6a778uNfIRqRZCt6Qq126xTVLGLSATjJ0ooiRDJ0QIs/Yh1yzmmHm\n6/mqdEs6pc3/1cku9wzd1E1TWbj5J2ATuzdUgeuMrFxEqLF+ltlqzvVn42Zvm51rbdvvd0EccrVz\nD+i8DX1n1wvLX6Be+XqAZOhE0SEZOiFEnvF3yHXVwVXM2uY5jPNe+/d49J8SQGCZl+sqX8dfg/5i\n99O7aVW1FVZtJTw6Ba1h3PffGe06BQAVi1WkQQX/H9wuaOwZ0Zqla3oevIIzdMv2LmPnqZ2AZOhE\n0SEBnRAiz/g75OpLhzodaHnCGFgIJPMSHR7N9ZWvp265ukSGRbpsBB8eGk54SLhLADCk9RA+6/wZ\n32z/xnuWy2bRrkU0/9TLPk75zGw1M6j5IHpd28vzYD5n6MpHG6vqOgd0Z1LOMGLVCIbcOCRHbaea\nU4kOj0ahJEMnigwJ6IQQeca+bEhOFvUMNRsf0MHIvDgHdL2u7UX66+kUjyjuUmfTsU30mt8r0/XM\nkpKT2HRsU4HbUSLDmkF4SLj3g/YMXT71+Y9HjLUEnQO6dEs6p5NP53iR5pSMFExhJkJDcj58K0Rh\nIc/QCSHyTL8m/YgIjcjRxumhZmM4LhiZF6u2otOKs3Il3HADlCrlWcc+KSKzWbX2ALWgrUeXYcnw\nPanDvmCs2QzhPoK+XGR/TtE5oLMPteZ0kkiqOZWo8Cj+fuJvKhWvlKO2hCgsJEMnhAjY2sNr2XJ8\nS7bO6XVtLzrX81wKwdkrN71C7TK1vR4re9nKV7uvpUmlnO9WbtVWMvaX5Y47wGnJLLYc38Law2sB\n/wI6ewDivHBxQWC2mh371XqwL86cy8/Rzf17LmdTznqUlz5wnNeq93N5vi+YAZ0pzES98vUobSqd\no7aEKCwkQyeECFjstFgA9BvBDWRCQ0J9BlDRqRb6nagEJXO+ortVW4lK+YlPe/2E5bb2jvLrPzF2\nU9dvaL8Cumd/eNbRXnY9sw6mtMz2aX65ucbNNKzgZbd3yJOAzqqt3L/gft645Q1G3DrC5ViJyVN5\nZ/NueKieS33IWUBn1VbSLGk5ygI727lzZ1DaEUVPXn/vSEAnhChwvC3y62A2/zdcmENWbSUsJIOB\n1/8Jj7f3WsefgO5i+kVHe9lV8TKUS866XiA+u+cz3wftAV0uTowIUSFEhEZQsVhFz4MZGR7XDkZA\nZ9/mzL7tWaDKly9PdHQ0/fr1y1E7omiLjo6mfPnyeXItCeiEEAVOpgGdxRLwXq7uxt4xFnOvT+HF\nlEz7ApkHdHaBTIq4Yx9UuJzt03Iuj4ZcTWEmUjK83F8vy6YEI6DTaO679j5qlakVcBsA1atXZ+fO\nnZw+fTpH7YiirXz58lSvXj1PriUBnRCiwGlWpRl9G/d1KTNbzSzcuZDYqAyqBZih23dmHzO3ziTV\nnErrq1pzT93OkKIhxXdAZ9+xwp+ALpAM3Q1HjVeey4MMHRgBnc+Fjc2u99R+/7JapzAz0eHRfN3j\n64DPd1a9evU8+zAWuWfFCrj+eqjoJVF8JZGATggRsDdueYMSESWC3m6nazrR6ZpOLmWX0i9x3/z7\nmF9JBRzQ7T2zlxGrRlAysiRpljTuueZuNtGUu6YMY0QdGDTIqDfy1pGYwkwM/mEwCYcTgP/W0PMm\nMjSSNEtarm8TFojHvnuMfWf38XP/n10P2O9hHmTofAZ0btcubSpNz4Y9+fXAr8RVj/M9oUMIP525\nmMydnSIZPjqJN168siO6gvfTRwhRaLg/6J6b7EGBKUMH/AydPQMUFRZltGexEIKV48mlePElC8tK\ndWNix4kMv2U4APfOvZcKxSpgGW7JdBiwcaXGNKvcjMiwyID6lZu01lxMu+h5IJ8zdO2v+pVBiRZ6\nOpWVjy5Pj4Y9uG/+fbwY+6IEdCLH/j6zAetzvWh790/AlR3QybIlQog8M+2vaXSc2ZG/T/6d7XPt\nQUGkhYCfobMvKxIdHk2KOQUsFq5nC+m9B/Djlk0s2b3EZQFhi9VCeEh4ls90WayZB3z5yRRmIs2S\n5nkgj56hcwTPbuKjT3M80jOYtK/jF4x1BsevHc+KfSty3I4ovNYeXkvxcpeJrVt4t/DzV8H8CSSE\nuCJtO7GN5fuW88iSRzKt129hP2ZunelS5sjQmWFJhTMcOHcg29e3Z+iiw6ON9myZvvC0S0SZbIGE\n084CZqvZ8QxdVu36Uy8/ZDrkCfmWobMozaHoDE5dPuVSbg+Mc7qfK8DkPyfzy/5fctyOKLzWHlnL\nDVVvKLD/P4NJAjohRJ6xTyzIaoLBz/t/5t+z/7qUOQd0XW/4l+V7l2f7+o4h1/D/hlwBSElxZIac\n+2bRFr+ei+vTuA+31bwt2/0B+LgFfHldQKf6JTIsMvOALhczdOmWdIbeNJQXY1/0vDya95ulMmn9\nJJfyYAZ0oSGhspdrEaa1Zs3hNcRWi83vruQJCeiEEHkmw2oED1l9yFqsFo8ttJwDulAd2F6u9mVF\nHBk6i4VzlGJXUgVCbI8UO/fNbDX7tZXXyze9TPeG3bPdH4D5DWHZNQGdmqnkjGSi3oniuz3fOdZm\nc5EHAd2xi8foNq8bB88fdCnXWqNtE1mdt/6CIAd0SvZyLcp+++sQp0b9QcWLd+R3V/KEBHRCiFxn\n1VYyLBmOmaLOWTBvwYZFWzyGSFwDOhVQ5sV9UsQnW6fxdK0uNFg/gwe7XO24tp3Zag7KzFWz1ewz\nQDlWTJFQNdT/NezMZr+eIcywZDi2wPKaobNPLMnFIVf7vXQPip3vsXtAZ/93DzSgs1gtju8NydAV\nbVtOboL6i+nUopH/J/37b64/hpBbJKATQuS6Bxc9SMTbEZi1EcjZP9AvpV+i9JjSrDuyzqV+qjmV\n86nnXYIce+AXmYMMXfGQSOqfCaFJainqlq3LioO/kNj0B4ZX+oRmrYz2XYZcrf4NuWYl/K1wen3T\ny+uxHRsmceTbX/xa5w6Anj3h9dezrGbPhpaIKJFvkyLsQZl7cO78XoOdoZu9bTZhb4WRZk4jVPne\nQk5c+fZYVlKv7yfUrlrG/5PuuQeefjr3OpWLJKATQgTkbMpZnv3hWX7c+2OWdXec2gHgkaE7m3KW\nVHMqZ1NdN29PzkhmdMJoLqVfcpR1qNMByyOHqHYh8Azd7TFt2DnRynuWtnzS+ROsVgtRkacYWXo8\nr400ZrdarBb+PPonD3z7AF3qdaFbg25Ztpuckcy+M/syDR4W7Fzg/UDkBSh+3P8A5uhROHYsy2r2\ne31nnTuZ2nmqZ4U8mBThyJS5Z+ic/u3SnTK0qeZUDp8/TIgKCTigSzGnoFBEhEYYGToZci2yrq1w\nLQ83fdj/E44cge3boV273OtULpJ16IQQATl26RiT1k9i0vpJ6DcyHy68sdqNZFgzPJ6hSzEbOzRE\nh0d7Pc/9wzjEYsv4WL1n6LTWpJpTHR/mHuzDjOfOOeqHaCAlhRKRJejTuA8Vi1WkycdNANj99G5q\nlKpBr2968WyrZ4mrHue1n6sOrKLT7E4cHnKYaiWrZXInvGj/mu29+rf/16zKpyhuOkCXLOrZg8tG\nFRvRoU4Hzwp5kKGz/xu5L+kSGhLKM4dj+KxyIukZ/w0Hbzm+hce+f4ytj2/lqlJXBXRN+zCzUsp4\nhk6GXIusp254KnsnrFgBSkF77/s6F3SSoRNCBMTrc1mZ1DWFmWhTvQ2mMJMj2EjOMHaljwqL8nre\nsYvHmLpxKmdSzhgFtq2iQrX3iRV7z+wlelS0Y3cHD24BndVqMQK61FTKR5dnVrdZNK7U2OM9frPj\nm0yXSbEHLIHs5Wrnb+DxefXTzC3muy929uDZ5+K8eZmhCwnFqq38uv9Xjl08hinMxMSdNbnxiGuG\nLhh7uaZkpBAVbnw/1S1Xl5gSMTl4B6Kw2rEDpk6FNC9PG/i0fDm0bAnlyuVav3KTBHRCiIBkJ6BL\ns6QRGRrJs62eJfH5RNY+shb4L6Bzz9DZP4T3nd3HY98/xqHzh4wDtiDE1zN09ufdfG7TZQ/ozhpD\nvFZt5VRiB3qd+YjffvP+Hu1tZjac6s9zX13q+cipXYiBc1dh9TOgC7VqLGQ9HGm/B+EhWQR0efEM\nnTICurZftmX5vuWO60dYcMnQOfZyVYHv5Wr/5QFgZreZjLxtZMBticIrPh5eeQXC/d1sxGKBlSuh\ng5dsdiEhAZ0QIiDZCujMaY4P2TJRZahasipgZFPAM6D7/J7PARxbVjkCNFuG7tDcqrwa96rHdezZ\nKJ/Blz2D5pShy0itwDfmbtx+u2f1VHOqI1jLLKA7fum40TzeM3R/DvyT9+943+uxiB/eg0UzsJj9\ny5SFWo013LKSZYYuD/ZytQfdE9ZNcCwgbA/isViYvBTGxo74r0vByNCZUxzfa6LoeuwxSEyEEH+/\nlTZsMH7Rk4BOCFHUZHfI1ds+p44h13DXIVd7Vuzr7V8DTsGULaCLNGuvz8h9t/s74L9gxoP7kKu2\nUrPGTM5Rit07jODDedg01ZyKUoqwkLBMA7r+i/o72vOmRUwL6pSt4/VYo0Nl4HBrLBn+jQ2lhFhJ\nJeuZm1VLVGV6l+nULlPbe4U8GHJtUL4B/Zr0Y9GuRaw6uArgv4kuFgu1z0INU2VHfXtAnJOALtWc\n6nMIXxQtkdnZWnn5cihVClq1yrX+5LYCE9AppZ5SSu1XSqUopf5QSrXMpO6jSqnflVJnbK+V3uor\npd5USiUqpZJtdbz/RBVCZJs9oPPnw9dsNRMZ6vnT1dekCPehU0eAZgvofK3DtnDXQpfzPLgFdNeX\nqkfjk1CKC9SqYvTFeSjXvlRKVgGdo/kAZmb2LPMu3Nsfq5+Zst8rpbC0RNazXMtElWHA9QOoUKyC\n9wp5MOQaFR5F8yrNUSgupxuTPiJCI3xeP1jP0EmGTmTb8uXG7NawwjtXtED0XCl1HzAWeAxYDwwB\nliul6mqtT3s55RZgNrAGSAVeAVYopRpqrY/Z2hwKPA0MAPYDb9vabKC1LpyrBgpRgNiHS/1Zp21Z\n3yehuW8AACAASURBVGVeJwyYwkzULVeXy+mXUShKRJYA/lvmwh7IuQ+5OgIzN/bsj8/gyy2gG914\nCKz6zChLTYXixV2CQXvQ6m9AF8ikiMHH43lgOZQP/yjb5+ZIHu/lejnDCOgcQa/9+ub/7mswAron\nWj7B2ZSzWVcUV6zLlyE62piw6pdz52DdOpgyJVf7lduy/b9GKTVdKXVzkPsxBPhEa/2l1noX8DiQ\nDHhdQEZr/YDW+mOt9Vat9R7gUYz34rx4zGDgLa31d1rrv4H+QAzQNch9F6JIsmda/F149++Tf3P4\n/GGXsnvq3cPWx7cSMy6G+TvmO8obV2rMygdWUrWE8ayd2Wpm2l/TeGHHeKNCFgGdryHXRf8u49on\nYVz1ozT9pClYLKQSSTJRkJLice6p5FOcvHwyVzN0UWaoetGY7JAbLqdfZsnuJY5n2BzyIEMHRkBn\n0RbOp54HnILtXMrQXV/5em67OrB9dcWVoe9jx7khNsX/E37+2fh+LMTPz0FgQ65lgJVKqX+UUq8p\nparmpANKqXCgOfCzvUwbv+b+BLT2s5liQDhwxtbm1UBltzYvAOuy0aYQIhM9r+3J2aFn2f7kdr/q\n3zX7Lj7d+KlHeWRYJJWLV3bZ77O0qTTta7V3DMVmWDPYkLiBX5M2GhUCzNCdSTvHjoqQbE7h6IWj\nYLHwGqMoRjKdB5ThYnI6EaERLOuzjMTnE3lz1Zt8uvFTutbv6vMZOLu+jftSo3SNLO+Du48ZxESe\ncclUBdPJyyfp8nUXtpzY4nogDzN0AKeTjcEW+xZwiRFppIfi8r5bxrRkTvc5DPxuIPvO7MvVfokr\n17aYFynT7jP/T1i+HOrVgxrZ//9bkGQ7oNNadwGqAR8B9wEHlFI/KKV62IKz7CoPhAIn3MpPYARl\n/hgDHMUIArGdp3PYphAiC6VNpalZuqZfdctEleHYpWNeg60apWow7a9pTN3ouqOB+wQFk30JDh/P\n0F1Mu0jraq1pe3Vbr8e1LRAMT7ft92m10p8veYV3CdEWSo4qxZdbvuTOa+6kSokqmK1mQn/+lS/q\nvEjX+r6T+1FhUbSMacmGxA2Mjh+d6X1wN4u+vMlwv/ZnBYjOgAlH/N+b0h5QeeyZmwezXJ2vfzrF\nCOjMVjN7kvZQtdt+NlZxvX4pUynql6/PT//+xLnUc7naL3FlOp96nv3FZ9O7e3H/TtDaCOgKeXYO\nAnyGTmt9ChgHjFNKNQMeAr4CLimlZgJTtNb/5LBvCrKem6+UegXoBdzix7NxWbY5ZMgQSpUq5VLW\nu3dvevfunVVXhBCZKG0qzed/fc7eM3tpXqU579/xvmO9sedufI6hPw1l9t+zebTZo1xKv0RUeBQl\nI0tSp2wd7q57N7O3zcaELaDzkqHTWnMu9RwPNHnA524N9rXewqxgtmSAxcL1bOF6tsDotoSvMLss\n8GvRFsJ+XQVlf4Jrr/X53qzaSmhIKG2+aAPAK3GvuBz/6M+PaFWtFc2qNPM4N44EzlMKzKU8jnlj\nDjH67y97QOUxKzmPhlztM06TkpOoVaYWL930EnvP7IWDcaxNKUlrt+vbh1uDsWXXcz8+x4W0C0zr\nMi3HbYnCYf3R9Wg0ra/yczBu9244dChPAro5c+YwZ84cl7Lz588Hrf0cTYpQSlUBbgfuACzAMqAx\nsEMp9bLWerwfzZy2nVvJrbwinhk29+u/CLwMtNNaO4/7HMcI3iq5tVER+CuzNsePH0+zZp4/dIUQ\nOVPGZGyQvSdpD6sOruK9299zTH64v9H9LPtnGQfOHSApJYkK/1eBb+/7lnRLumNh3FRzKibbIMDr\nrVO4+q9pLvs0Xkq/hEVbKBPleyNura2EWO0LE5tds2KpqR7PypmtZkIt1iyDHou2ZPrc15PLnqRD\n7Q782M9z39t3w17nXfOrYN6b6TUcfVIQbvV/4V37cjE+A7pcHnItbSpNg/INuJR+iRqlahAdHm0E\nzZse4cP0Oqh9Mxlyww2O+v4s0uyv45eOO4Z6RdGw9shaypjKULdcXf9OWL4cIiLglltyt2N4Tw5t\n2rSJ5s2bB6X9QCZFhCuluiulvgcOAj2B8UAVrfUArXV7jIzZcH/a01pnABtxmtCgjF/b22HMYvXV\nj5eA/wEdtNYuQZrWej9GUOfcZkmgVWZtCiFyT2lTaeC/Nefch17twZTzg/EvxL7AvJ7zANeA7sea\nFv448ofL+SnmFK6rdF2mWz3Zt/oKtYLZNuT6XwMpxt6fTpkhs9VMmEVn+XzbqgdXcW/9ezOt49gh\nwc3tfS2MuBW/nqGzaitWPzN0Ry8c5fs93zsmraRZ3IZc8yBDt/v0bpb9s4z1A9fzc/+fWXz/YuD/\n2Tvv8CjKvQ3fsy290nsvUqVKERGkWdADKIq9F/RYjljwqKhHsfdGsR0sqIh4wBaUItKULkU6hFBD\nerLZ7E55vz9mZ7Kb7CZhCXwQ5r6uvSCz7868O4Hsk+fX/N/7f9zE2cMH8tLez4NeU52Czm6zV4vT\nZ3H68OnbzWlx+MGqF9akpcGAARAXd2I3dhKIpCjiEDAdXcz1FkL09FecFgasWQQcSwLEa8DtkiRd\nL0lSe2AKEAt8AiBJ0gxJkiYbiyVJehj4D3oV7D5Jkur5H4HfkTeAxyVJGilJUmdgBrAf+N8xvl8L\nC4tqwHDojH50ZT9oDUFnzv+U7DRObEynunq+WIlSQhS6o2fXRLnZp3Xj6rL+zvWc1yx8Eb4mVCR0\nQaQKFVSV2YxmCnewY7cdm3AFCU1VU3XxVIno6dekHw0SGtCxTviwbDj2x0ST5YyqkqCTkPhxVhRD\n81IrXbto7yJGzhyJJjScNmewQydEqZg9gQ7d1qytPLPkGTyyhyhHlNmWRhUq7D2fPX9PwFemItlw\nbas627Yi7JK9Ws5jcXqgCY29W1Oo6+td+WLQWxUtXlwj8ucgspDrA8AsIUTYNvFCiDygRVVPKIT4\nWpKk2sAz6GHS9ejOm1Fn3xiCWqPfhV7V+g3BPO0/B0KIlyRJigWmAsnA78CFVg86C4uTz+ivRnO0\nWP/vbOR0lXXonhn0DF7Fawq9spMgSpQSotHFoF2LLMdKaJru0AlQhMqgNfeS2+RWdmTcSPHdcST/\nu36QAFA0BXsVBJ3Bv/r+i3WHKszqKMeOX2ax1enj0cIDNKZiQShJEhfuBBpV3gI/cJZrlCMquCgi\n0Jk8CaO/yn4vVU2FI505cGAMPu3toOeO16GbtXkWHep0oGPdjpZDd4axNWsryhWXMuG6XytfDPrA\nV4+nxgi6SBy6uejuWRCSJKX6w5oRIYR4TwjRXAgRI4ToK4RYHfDcYCHEzQFftxBC2EM8nilzzqeE\nEA2FELFCiOFCiKolqVhYWFQbiqYwZ+scs6WIEXJVNZXb5t7G/T/fD0D9+Po0S25mCqoP1n5Ael5p\nK5PBLQbTJ0ofY2XXREQf1L1SOvLvJdA3A16Pv5w8pYh+Xe5hH01Z/PCPOOIKTKGpCQ2B0B26KrYU\nubnbzbx90duVLwxAdfpg6yiOFB+tfDHoodIwbVsCMd6HTbKZzX1NAl9/Ah0643tZNvylChX6vM24\nPufgE6GLIiIVdHf+cCffb/8esBy6M430vHRSY1Lp3aiKDl1aGjRoAJ07n9iNnSQiEXRfAleFOD7W\n/5yFhcUZwIT5Exj26TAe/uXh0vmcITCcodu73w6UhlwVTWFHzg7TuTMwhNrsv2fz15G/zOPPDn6W\nOxL0hrF2LbKQXO/kTjyxBDpnwv2+7nqRhIBa5DCw2V4cLpWDhQf5aN1HDPxkIH3q92TM34AsVzgF\n4qj7KC8sfYGDhQePeU/0eg+GPIKqVFFYqaoeMq0EWZNx2pxIksS++/fxyLmPBJ/DXHjiHDpDlBlh\nVANFU+CHd1i2+Q18QjHv7dasrUxZPYUrO15JvfiydXJVwyN7zF8ayuZEWtRsLmxzIVkPZZmh/UpJ\nS4Nhw45hpMSpTSSC7hz0HLmyLPY/Z2FhcQawLXsbC/cs5OXlL1PoLQy7zkjGrxdfD6fNaYZcVaHi\nUTzlBqmXDXkG4XfK7CK8oJvz9xxWH1wd8rkgZyo3F9Uv6ADwePjksk/oWr8rt8y9hUOFh2gUW5/k\nEugX/Tm3zr017Hs84j7CxAUTy03CqBKtfoVzX0JTqiCshODVPoLR7TdUulRWZZx2vYgkxhkT7JKd\nJEEXLuTavUF3HkrfQgebLtiN7/OGwxt4ZcUrTB85nQ51Ohzz9YQQemje/2/MbrMcujOJ9etBlqso\nzg4ehI0ba0y4FSITdFGEzr1zAjEhjltYWNRASpQS4l16886KXBAj1Bdlj+K7q77jui7X0btRb2yS\njWK52JwGYRB4LlWoXPnNlXyx8Qv9gCHoNFDDTIOYuGAiX24KEywwnC1Jgrw8vX+cofFKShjeejgt\nkvX034SoBEr882ptouL3KKF/iBxPZaZaFUGnqhxKgC2x7kqXGg5duPOYnISQq+HQvbnyTaavmU5y\ndDIv5X/IhQl6fzifqu/heEd/+VQfAmEKukvbXcr4XuOP6z1YnB7k5EC3bvBlVeOE8+frPweGDDmh\n+zqZRFIU8SdwO/DPMsfvRG8/YmFhcQZgCLp8b36Fc06NkGu0I5oLWuqdhMZ11nsxhRJ0jRMb8/Xl\nXzP2m7EomsLv6b+XVo/6BV3/DHDVD90v0ml3ht+P4dAlJ5uCbu6auayXEmn/bTRP3Vo6yzXeFU+J\nrItRhxZ+nBhUnvd1XZfrEOF6mm+4Fpxu1C5VE3R2DVSpCiHXAIcu1HkAcDhOikNn3J8fd/5IYlQi\nt/W4jW+Uf3DD5k9oO7qTue54BZ3xy4Ph+o5oPeK49m9x+pCYCH/8Aa1aVfEFaWnQvTvUqXNC93Uy\niUTQPQ78KklSV0pnpV4A9EJvMGxhYXEGEOjQVSjo/CFXo8FtIB65fMg13hXPyHYjAfh4/cccKjpk\nVmwaQuTJ34A+E8zXLNu3DLfsZlirYThsjtL1ZTEEXWqqLujQ6NDgU/q4u/Lt/uvxeEqrQxOjEsn3\n6H3JHSL8e/TIHt5Y+QZAWNE2Y9SM0PsB6i54iExbAurls8wcvLC99FRVDzcj+Pvo3zRPbm7mi5VF\n0RQckh22bIEOHcqd52gs/NbeAckH6Z67m5YpLcPuMVKSopLoUKcD3aZ2Y/PRzdSNq0vfxnoH/67a\nOl4TE7it/mNERen1dMcr6DyK7qgaDp3FmYPDAb2rWAuBqsIvv8Add5zQPZ1sIpnlugx9wH0GeiHE\nSGAn0EUI8Xv1bs/CwuJUJSjkWkGeUmDItSyhHDooDdHN3zUfKHXNgqpN/eLO7XNz3ifncckXl/DM\nb8/gtFXBoUtKgoICNKHRod4s/l1rCmsuf4HmzUuvleBKoEQtYR9NEJ7EsOd0y26mrZ1mvtdMd+Yx\nhV6vi/4RfPFoqkKj1xrR6LVGYdfmubOZVrcPB3LPosN7HbhuznVh104cMJFdcY/BOeeUnxOraUwe\nAFdcWsIVXbZy1w93VXm/x8Kos0axefxmNh/VB/lkujP1+ygE2SKFwSwkSioN+R6voDPc4FC/PFhY\nmKxdC9nZNSp/DiLLoUMIsV4IcY0QoqO/sfDN1TC71cLC4jSiqg5dYlQiV3W6ijpx5UMbHsVjCrrf\n039n8u96//CySfTm+QMFnV+c5ZXoodMvxnzB4+c9rjt0WiUOncsFisIzdcYy+m8gOto8t+HQJbgS\nKFG8dGALhzZcFfY9BorZH3f8SL1X6h3TuKnxKR/Rc3Q7EqhchOzPzyB76cv4/rwPgI2ZG8Ouddgc\nxLp9UFSkz6oEXlvxGrO3zAZVxe2CbllOrs5IqbBKuTpJjk7W76Om8U/e5k3uCwr5Hq+gU4WKy+4K\nnztoYQF6uDUhAfpWcd7raUJk/2v8SJIUI0lSYuCjujZmYWFxalOilBDn0oezVCToWqa0ZOaYmTRP\nbh50XAjB4wMe55zGenH8yv0reWX5K4D+gZ4aUzoNQVZlFE1BBOZ7+cVZgbcA0PvY2SQbTrszrKDL\n8uayvRbkxUgsS8pnVGwPzjmALuj85zYduqgESlQvc7mUxq1/DPseA92411e+bu63qrRUBas+y6V3\nVOUhT0XxwVWjYMT9tE5tXem4MdOZ274dgM83fs6vu38FVUWxQbSw0/9oNOc1DT9dozpJjk7W76+q\nMo+RTOaxahV0LVNa4n3cy6AWg6plvxanB2vWezl/kEZ6euVrAV3QDR4Mzpol/COZ5RorSdI7kiRl\nAkVAbpmHhYXFGcBdPe/i4jYX06NBj4hCXJIk8cTAJ+jZsCdQOvrLIPvhbNrXbg9ATkkOzv84mSWv\nLz2BX9AV+vSWKYn+PKyKQq5T981hwE2wJsXDueft5LCcy2xGs1Lugcej6x8JiThnHCNaj8Ap2Wme\nsojE+APhHboQ1a8VCdxy2Pw/hhWFIS2HMLbj2LBLFdkLcVkQk0+8K77CdjH65oIFncPmMEeeyTZw\nYGP8tkSeH/J81fd7HJgOnariQOFrxnI0r/RDNc4VR7OkZngUz7HdQ4szmu+2zGXZ0R9ITKlCxXZ+\nPqxYUePCrRCZQ/cyMBh9/JYXuBWYBBwErq++rVlYWJzKPDbgMcb3Gs/q21ebwut4sNvs5T7EjXYW\nbp/epsOlBfSY8osVQ9QkuPRmorHO2HKNbA2MRsJ2f/Wnoso8zrM8cfhuYr/8iHf/t4JWqa0oeqyI\nFskt2Fqwjza5Mu2W38VjAx4LeU7DVUq7No368fUBwod8QzD8wIfczTug6HNsw+0d/A7dl9/S7cfH\niXfFUyRXEioNIegMQXXVJrhrf4MT2rakLElRSSiawpbMzbzUqRH38hbpWaUjuK/qdBUb79pI3OQ4\nPTRsYVEF9sV+T9d7nyYl0VX54oUL9f8XNVDQRVLlOhK4XgixWJKkj4HfhRA7JUlKB64BPq/WHVpY\nWJwRlHXoQA9dOmwOejXsxZytc3AFmmFlQq5Gd/i54+aGvYamqUiAwy/oVFVmCx3I7XIZP5YM4u2M\n+ay11aFfk37+vQj+lXIP1zkV2jd7POQ5A8dbGaHCY3GXYm1e3uNu3lU+1WfH2sILOlnxQoP13HS4\nkB9d8ZXnvpURdHbJbgq6i3cADRqBvLvKez1eejfqjdvn5o8Df/Dy5X+hbHZhrzspaM3xjv4KJNOd\nSaY7k051Ox33uSxOXVZkrGB4qyoKtLQ0aN0aWlZ/Vff/N5E4dKnAHv/fC/xfAywFTk4ihoWFxWnL\nzpydNH29KasOrAo6boxpOlh4kA/XfkiBtwCf6mPSwElm37pQgs4IuRoOXUUIIfwOnf67rKIpSEBq\nvI9rGywkJslthlBVoYJNkKM0YE9R+F5VgeOtDHctMIeuyFdE/4/6s2hPqAE7cEft2dzKdL1IY9Az\n3NPrnrDXUhQZlBh2FLbVHbpjFHRmyNUoDomOPqkO3ZMDn+Tdi99FVRWY8zGfiuvLzcmtTkFnjHCz\nqLlkFWexI2cHfZtUocBBCF3Q1UB3DiITdLuB5v6/b0VvXQK6c5dXDXuysLCowWhCI6Mgw+wZZuCw\nOdCExubMzdw671ayi7NpktSE2rG1zdCrIejGXwx9v70YgCs6XEH6/elVyuPT/CFXh10PzaiqX0xE\nRYEsB4V9jT83efuxx1037Dltko2GCQ2JdkSHdOhkVWZ5xnKumh1qBDbUbraApxJuB1Xl/Obnm0Ui\noVBUH0QVkGQr5PkLnue9i96r+A0bgi49HUpKgkKuQFAxyInkhq43AJgVzaoqIzk8FEfJdLZPZem+\npeba6hR0dska/VXTmfXbRtg5lD6N+lW+eOdO2Lu3xgq6SEKuHwNdgd+AF4B5kiT903+uf1Xj3iws\nLGoghotV9oPWYdN/HBn5ZzbJxqrbdBdvU+YmoFTQCUrz62KcMTRNalqla2tCRQp06FRZL0pwOqGo\nSHew/Psy/pySOoxudfoCD4Q8Z4uUFhz41wFzzxAs6AxhkunODPn6EX138aACE5UqhGlVjbjeL3Fn\nXjsapT5a4dIP1n7AQWkJT4LuTOzaVV7QxcScUIduyuopfLHxC5bctIRP/vGJeVxRZJwXjmf0Nrjb\npreeMTBCztUi6Gz2Cke2WZz+fDFTwzZ3Bs3/W6/yxWlp+v/1QTWzCvqYBZ0Q4vWAv/8qSVJ7oAew\nUwjxV3VuzsLC4vRHD2tK5ge1IdzK5pnViq1Fl3pdzHBlYC5ZWYfOXsls1XAI06HTc+gWeP/mNttS\nrt21k+JcL96jy1Aa5pfuT47i66LbSCzMoCoThZonNyfbk0272u1Krxlu5Jcf745LyMjPLBd6DMXQ\nun34afK5KG0qn4SwdN9Sdtj38KR/zBnbt9OhTgddUJ8kh+5I0RF25e4qd1xVZWy+GM51r4dtD5rf\nXygVxdUhxAIFukXNxD74WYb3a4nN9mHli9PSoH9/iI8/8Rv7f+CYQq6SJDklSVogSVIb45gQIl0I\n8a0l5iwsLELx0rKXaPBqA/NrQ6hlFWex8chG8wP3kraXsOHODebzgdWepqBTdHFk1yqeThEOTdOQ\nALtDD7k+7P6OpAa/USvGw5v7x+DLrR+cQ+eL5/Wil9jsbl6l808aOIm5V801W6hA5U6TO+1t3s95\nna+LV1W4Tt+UyijmMM09jG5Tu/HH/j/CLxUqdiFBgwb6ZIzt23ll2Cu8OPTFcoJOaMfvhoXdQ4iq\nXVVTsEs+LpG+g8T9QYJOQq9kjsShW394PZfOvJQjRfrINiMv06JmomgKqw7+yaDOVaiy9/lg0aIa\nG26FYxR0QggZ6HKC9mJhYXEacNvc20h9MZXpa6ZXab1X8Qbltxkf8D/s+IEuU7pQcl5ffd6oH+OD\n/Ja5tzBtjT5Sq01qG2aPnU1Tnz631KXClvwdjJw58pj2fl+9S/n5M2gr1eLAx6nE4OTilk9yffs/\nyew2gnodt5rOoSY0iMvmr5Q4LolZUMmZdQY2H8jA5gPxKl7qvFyHFRkrKhUmrn4vwIFzKFI9vLbi\nNdq+3Tb8YlVlDqPonfg76w+vp1guDr9U0+e+YrdDmzZmYYRxHgBiYpjaXeB67sSMylIUjf2PbGfe\nvDJ7UxVs2a15lBegwfogQXdT5zVIvz0ekaA7UnSEedvnmeez26wcupqMXbKzZfwWbjj7hsoXL1sG\nbrcl6MrwGXBLdW/EwsLi9ODnXT/Ts2FPejTsQaG3kJZvtuSH7T+EXV+ilATNcTVCrkZ1aszyVbCx\ndISV8QG86uAqtmfrIqRWbC1GnzWaREUXg+NXwajGQ5m/az5CVBzSDKSeI5m22eB0RtOwUG+sa8Of\nQyfLRDui+XHHj9z1/V30bdyXbxo+wJjcDdyQ/QDfb/8+7HkLvAV0n9qdBbt14eeW3WQVZ3Go6FCl\nwsTRYRbc0Q1VU5BVmWxPdvjFqsqbtvE8efhh/bW28FkzpkNnt0Pt2pAb0Pdd09hUF9KjvTg03emo\njpy1cnvQBJKrmOIyujNGclH0yRq+1PRZtIYA++/6/zInW2PIoX5Vb0MRgCHGA11ey6GryUg0S25G\n3bjwRUsmaWlQty507Xrit/X/RCRFEQ7gZkmShgKrAXfgk0IIqzDCwqKG4lN9HCg4wKSBk+jeoDtF\nviL25O0xe8GFwqt6iXaU5nwZH7aF3kKi7FHYhFf/zdmP8QEcZY9C0RQ8sgdZk/Uwpj/PrEUeXNts\nJLMz0sgqzgqaE/vB2g9YdWAVU0dOLb8ZI7TocICqoiH0nC2nExSFmWNmkhqdyvL9y7Hb7IyJ7YGd\nJ5jXvojxP2znkraXhHyPmtBYd3gduSW6aDLyAI3K3YqQYrMh6Sia1lQfW1bR2DBVpU+bJ5g9xGGe\nPxyKpuDQ0AVdXBwUBkyVUFWuHwV9olfR1693ZFWu9qH2wibT6vk+XHnl9qDjd7ceRw/lAppK+3lY\n2ExBd6joEI7rxzMvYyxRyR8f8/WMfzvGfQkssIh0nJjFqcuNN4IkwSefVGFxWhoMG1Y6maUGEsk7\n6wSsRe9B1xboFvA4u/q2ZmFhcaqRkZ+BQJhzWY0PzopckBKlJEgoxDnjeHnoy7RKaUWM4dwFWDh2\nyU68K54ohy7o/vnTPxn26TD9SUXRBQrQL7UrP179I/Gu4ATn7dnbWZy+OPRmDEHndIKqogrBtpxh\nHCipFTTL1Rzurij8g//RInF1UFiwLGXzvoxKXafNSZxTn4QQTnwZRROqppC2K810LkOiqjR07YI6\n2wAqbEKsh1ylUkEXIJqNWa5Ouwun/5Ycy3SLqqJqatAep6yeQrM3munClD/Y4uyK4+hZ5r3VhIbd\n7iMKb0TXMxw6416POWsM2+7ZZn5/LGoWl1xSxQjqkSOwfn2NDrdCBIJOCDGogsfgE7FJCwuLU4P0\nfH36dVlBV9FkBK/qDQq5RjmimNBvAo0TGxNr9zt3AYJu1FmjKJxYSEp0CqqmBk+QUBRw6QUNdV0p\nXNjmQl5c9iLztpUmaTlsjvAuVxlBpwmJ9//6H1/t7EHt7cuYN88v6PxVsOmHo2jBbg5m9qxQ8Bju\njxH+Na7vtDtJiUlh1hWzePr8p0O+1jPrW1j6EKqqMn/X/LDXABCKyit7voedQwF4YekLHHUfDblW\nD7lSKugC457GLFeHy6wcrkiwRkrZoghZlfX9+nP4bpKn0Wfl3fRu1Bso7RNIhEUaRrje+HeZFJ1E\n21ptkSRL0NVErrgCxo2rwsL5/v9Xw4ad0P38f1NzvUcLC4tqZ2/eXiQkmiQ2AUoLHCoUdEpwyNXA\no3hCCjqDw0WHmbJmCusOrysVU4qiNwEGUxRMXzudNYfWmK9z2pzh92Pk2xmCTlJ5tt9Z3HD2BiYk\nTKNtW110GA5dvN3D1XxBqv1wWMGz4fAG+n/UHwjt0AFc3uHysLNgBx5KhsVPIyulIiZcXqBQVByS\nDJK+ds7WOewv2B9yba+GvehdUksXdLGxoR06R5Qp6CoM9UbI0OYXEvf9bKbptS262NZk83u3saEx\nBAAAIABJREFUts5w5tVbSL8melNYTWjkf/sV45aHn5ZREWYOXQXzcC3OQNLSoFs3PYeuBnPMgk6S\npEWSJC0M9zgRm7SwsDg12Ju3l4YJDc0QqiRJ2CRbhYKubMjVoFguJsZWPuRq4FX1sJviLxYAdCHg\n9IdDjdFf3sKgNiGmaAhFGYdOoFEr5hC1khQejX2Ldu2CHbpa0W46sYnsgjZhBU++N5+NmRvNvb77\n57tsPKJ/bZynIh5JmIG94UpUtVTQhdv/d5mLWDX4O8jobx4LN/7ryYFP8nh2x7AhV9muO3TOE+jQ\n9UwZzp8/tWO6vyDacFu9HpVbmc5uexviROn+NaFRsnMkmSWJYc5YMWVDrhYWaJru0NXwcCtE5tCt\nBzYEPLYALqA7sLGC11lYWJzmnNPoHO47576gY5U1b3303Ed54rwnyh0vlouJDRB06w6to+3bbdmZ\nsxModbvinHEhQ65oGprQKPQVBs1xrbCwoExRxFHtQW7clWAWRUCwQ4ei8CnX8ffREWFFVuB7/y39\nN+756R6WpC/R92KrXNANj1nCW/UGM1gtdQ/C7X938QEorgP5Tcwq0ArnuapqUMj1qcVPccGMC0DT\ndIfOGeDQnYAcutRUWLUKfvlF/9oQuLJPYROdyHekBjU21oRGYp/J3NX8p4iu1yKlBdd0vsYSdGcA\nb74JK1ZUYeH69XD06Bkh6CKZFBFy/o0kSU8BNbP9soWFBQAXt72Yi9teHHQsKMctBEZ+VFkmXzCZ\n4j+WAmPA7can+tiRswOPrM94vbD1hXy+8XNinbHsyNnBrM2zuKKMoHP7dNcpIapU0FW0nx8L1pDR\nA2502nh4BFyvZtFD2HWBF1gUYS8VdD9yMR83cfCb0Mol+UNwA9wP1+nd6n2qj6SopKpVjQrB+FXA\nObUgQc9PDOfsKZoC/V8B4I0Rf3PWu2dVTdD5Q64F3gIOFR6CVH8OnTOKjkfhq14vUie2TvjzRIjT\nCT17ln5tCC2Ho4SVnA+pXUFJNp/XhEZyv+e5fOsI4JFjvt65Tc/l3KbnHueuLU51vIqPiS8d4OZb\nVPr2bV3x4rQ0fTJEvyrMej3Nqc4cus+Am6vxfBYWFqcBzw56lv5N+1e+sAx14+rS3OkXEcXF5Spm\n/9n7n0DpQPdb591aLofOqAgNDLk6bc6wbtMPhWuZ2hM0p523+sDa4kKGZ85g2f5mzCi+nJ07YUiL\nIQxtqRcd5BXamcM/8JXov6uGOq+x33Gdxpm5Wx3rdmRku5Gs3L+y0vuw1NODnbQCVSU5Opk7e9yJ\ny+4KudbrUyGrLXjjiHHoTZar7ND5fDiwmbNcFRs4nNHUdcPYOoOCRPGJwnAsFUUP7z545GGe2X2t\n+fyApgO4c1sCXycf4K8j1vAhi9BsOLIez+0tuebWCno2GqSl6bNbXaH/T9UkqlPQ9QVKqvF8FhYW\npwEP9H2Ang17Vr4wFEa4LUDQGe6aIZ4MQeeyu8rl0Bn97wJDrh3rduTqTleHvJyGhiTA7tRFoarK\nJNrdOFw2bvBNZ/KnS9matZV7eutJ+TszExnNHHptao72uBKyuMNw6F4e+jJn1z/bfA+rDpQ2Rq6I\nKzPfYjKPoclqSAcwkKLCaHhnG69/eikNEhoQ7YiuuqAD7IqmC1BVZf0UuKPZaH3dCZznKgR4/V1I\njO/xC3s+5abLoHZ0EbWkHHPt8NbD6fPHMO7wNmPutrknbE8WpzcrMlYQZY+iR6NuFS8sLNQnRJwB\n4VaIrCji2zKPOZIkrQQ+BkJ08rSwsLAIZvXB1RwsPBhe0K1ahW/TBgBuPPtGzm9+vi7oFIXfvb3Z\nQBfQNLZmbS137iEthzD90tBjyYQQ2IQeagRwOQqY1egBzmmTQ4kUQ61+8/h1z6/m+rPrHCCLWnRh\nI5IaOk/QyKGzLVxUWuWqyrjsLmRVRlZlDhYeDFt08FDiND7mZoo8dgQidIVmTg58/jn26Hzq/WM4\nI907cNldxLviTUHnU31MXT01uEK2jKBzqMJ06JoUQHKiP2/PV/0FEQAHD+p9XBMTNPjlF7o36M6U\ni6eQUXyUdSnxPNxmDnfX+jLoNbfnPE/hL69Wy+SKnTk7+feCf5NXknfc57I4dVi+fzk9G/YM62Sb\nLFqku/qWoAtLfplHDrAYuEgIEbrRkoWFhUUAQz8dyhcbvwgSdIYzNWvzLEZ9cSnyZ/8FoF2tdvRt\n3NcUdOdt/4DRfAuaRtd6XelQpwOd63Wu0nU1oSIBNqf+QaAqsi54HA6iRAlOuy2oyMGh+fgXr3Er\nH5hFE+XPqQsP+8THzL8rmoLL7sKn+vg7628avdaIX3b9EvL1Y2PmMZeRxOChcGIh9/e5v/yiefPg\n2msRFONNH8JF+TMB6FS3E3EuXaxNXT2VO3+4k192B1wnMIcOcCiaKegAiPY7jifIofP59Es8efY8\nuPdeWqS04I6ed+DNrceGTwpZWNiLdKc7qPWKW4shrvNn1SLo9ubtZfLSyeR6citfbHHasHTLdvo2\n7lv5wrQ0aNkSWleSZ1dDiKQo4qYTsRELC4szB7tk14VFCIduR84OFidk8cKuFkw8dyJJ0Un4VJ8p\n6Lb3vpaoP5eAOoMWKS3YPH5zla+rCT3kitOJQwVF9Qs6fxjXLsr01JNlLuQnJATIo0Kes0VKC+7I\nH0yuuwBNlLZacdqd+FSf6ZhdMvMSxKTg/nKa0Hil71bGrViNk2sAQjfB9YtJRfGR3G4Gb+9YCcxm\n0Q2LzCUhmzyXC7nqYV2z2vcECzpbyj62HhI0u+ML+K1UVDljc2gzaCxd4pO4uvF+6v7yEDPH6CJ1\nWcpIBnQ/gCZuPe7rG26nNc+15rDq70McnLQO1/vLK1+clnbGuHMQWci1lyRJ54Q4fo4kSREm0lhY\nWNQEdufu5qH5D5FVnFXhOrvNrgsLw/Vyu01B4lW92AW0K4xi8gWTSYxKLBV0qkqbpEyakhHRNAEj\n5IrTiV1AiQ82+9rgVnVh4xC24A9/ReEqvuJKvg4rejrV7cTO2e/TPmOV6SpFOaL0kKsmV+g0aULj\n9c5FbKlDxe/H76j1sjXhmsxNDLMvKLekRUoLALrU6wJAj2k9mNB0a3DIVVZDO3QnKOT6QNoD3PH9\nHZCXB7m5ZmNnm7OYes1n4XPG4cluG+SKtmAPzuicClvhVBXD9a2Oc1mcGmwtWgGXj+Xai1pVvHDX\nLv1hCboKeRdoEuJ4I/9zFhYWNZDs4mw2Z26uUKAcLDzIKyteMcdRbc3ayifrPynXV81sLRLg0KVE\np/CfQf+hWVIzfQZpQIgz0KELbFtyrGhC06d6Op347PCv6Fw6bZvNmoy69GcpGxecHfThv+ZgfQaw\nhIM0CBtyBWgYm2ee/199/sXD/R/GadMdusoEHbM/5UvtavbYC8Kue7doIW3+CQOL+pK18H3W25qx\nM2dnUL5c2aa6xXKxfv4AQXdBbCeeGfTMSQu5mkUeeXm6aPR4/McV7AKe//syNi34KPgeqSo2QUQh\nV1mVg3IVLYeu5rEuZynNz13FWU3rVbwwLU1vRzRo0MnZ2ClAJIKuA7A2xPF1/ucsLCxqIHO2zqHT\n+50qXGPMbDWmPMzfNZ87v7+zXOWmXbLz1G9PsaLwb/1AcTFJ0Uk8ft7jNE1qil2AWyshvyQf0D/c\no+xRoCj8kteLLZwVkaBrYEuieR5gt9PjIJCUwbP9r+DsNm76sJKU1MKgkGU0Xt0xQq5Q9HwyaAZq\nnfr8dddfvDj0RQAzh64iYSKEgLwW/JzxH16LPUCRr4hfdv1SLom/QC0mNwaKizRW05P/NXHQ5u02\nCMoLOlPEaCp2TQTl0PWyNWZ8r/Glgi4mBtkGs3J+Z1/+virfx6oiy5C/vRP/2XU1Xly6sAMUoWLX\n4NFeCzn7/OuDBNdzxQ9w5NtZEQm6Z5c8S6u3Sp0by6GreYxqP4rJgydXvjAtTe89lxjZ1JHTkUgE\nnRcIJY0bAOF/hbWwsDitKZaLiXZEm4PoDbYc3UJ6XjqA2UjXq+iCbnfublqktCj3GuODdkPJHv/J\nS0d/GQPa72u3h+Gf6eGSaSOnsfyW5aAoDFs2iY5sKRUlx8Dk+Mv4Yo4N7HZ+/gxwltCh1gYSE+FV\nJtC2yx5yS3LN/XdMzGA87/EXXSp06GyqTHq8D7dPDx2n7Uzjp50/cUGLCyp36AY9ATYVn+IgIz+D\nYZ8NY3NmcF5guppDdiykJKTzChPYkDccm2QLuq9lHTrVL5oCHTpz/Jdx71wufHYYe/htlu1bdgx3\nsmpk72jNsqdf5Mns+3ATp4ddgaLcZNLXP4kr1kFS/HZTcGUVZ/G071EoSaywfUs4VKEGTYmwHLqa\nx4BmAxjXeVzFi3w+WLjwjAq3QmSCbj7wvCRJScYBSZKSgclA6DIuCwuL055iuZg4Z1y549d8ew0v\nL38ZgOeXPg+UOnS7cnfRMqVludcYocIYzf+hLZc6YLqzBA6N8hMfFIX72//MeN4N69CpmkqRryi0\nkNI0vY+G3Y7w1x7YpNKiiI6JrdCExovLXjSv9y5385h9EpcvuitkmxT9oiodr87lvxv0ytyMggwA\nxvcaH+SilbsPCGi5CO5tiysmu3Q0VpkGxjuUTAAO+3JY7urMPO+ocu8vxhETNGVC1VQcqt+hi9Gb\nEBvCuUh2M2G4xOacbTj9pzkRs1xjG6TT58EXKXCkkkquKeh6qX3I+vt2stVkbJow38sTC58gruVc\nZrrmMPmCKrgwZVA0Jajti+XQ1SzWrYObbtK7+FTIihVQVATDhp2UfZ0qRCLoJqDn0KVLkrRIkqRF\nwB6gPvBgdW7OwsLi1KFYLjab/AYSOGorI18XMoEOXauU8snLhuCL1QJcGL/YUIWKXegtNlShsjdv\nL1NXT6VEKQFV5fX+3/Au94QVdEvSl5DwfAJ78/aWfzJA0GmGoLPZTEF3Uf0B1I6tHTTLdZp9PNOT\nhjM7I41Md2bIa36xvSfqigdNoSqrMg6bA0mSKg+5GltDmO5S+Vmu+jqfz8NlSe+g3FV+nNrFbS9m\nz317iHfpUy0UTSkNudps5vgvgELNw6t9BXvz03Ea1zwBs1zt8Xk06fknCYou5NxZh/h196/c164e\neXJjOjYpwK5qpoOmCY3WQ8YyMvG3iK6nasEOXZwzji71ulRtBJvFKU9ODmzdCgmVDTVJS4PataF7\n95Oyr1OFYxZ0QogDQBfgYWALsAa4D+gshMio3u1ZWFicKrh97pCCzmxBQqmjVqKUIIRgd+7ukA7d\nD1f/AECMGvAjyC/oujfozpUZSdg1vQnuhsMbuPOHO/WpEFUoiggvitCrLCWpVNDltOCdtc+wPzeO\n5fRlzx79dYbLleOOYpOrO7H+c4VzsZ5YNwrfwleC+tAZorBT3U6MOWsMEuXbkWhCg6w2UJxKoSQz\nbrYeSiorriS/7iv2liBr0VRg+pmoQsVuOHQQJOhk//tw2BxIrigc2E6IQ6dqKnal1B3bn72HoZ8O\nZaNHD9H/nN6edSummfdNbysjIgqng37fAwVdu9rt2HDnBjrVrTj30+L04IILdPPNGXrUcSlpaTB0\nqP6LzBlERO9WCOEWQkwTQtwthJgghJghhDhxs2MsLCz+3ymWi80mtoEEOnTGn17Vy+Giw5QoJSEd\nuuRofSB7rBIgcvyCbvRZo3lpXW3yHAo7sneY4krRlHKzXEMRtL4sZR06OZb0wjZ4NSdXMIv/zo5H\n1mRTjC082J7ent/wqrolEEokFvmKeP6ct3D1ebF0UoQmm8IiMSqRN0a8wZKblpR7rU2yYZ++Etbe\nSrFNMWe/ltu738lbnd6KbtkeyNEbpb6x8g0u/PzCkPchqCgC9Dw6v6BTVKX0XjmduLCfGEEnVOxy\nQKPmAn2qhSEo3VoszTIaMu2SaYDe+NmX15qFBZF1wFI0JaLcO4saRGYmrF17xuXPQWR96CZKknRz\niOM3S5L0SPVsy8LC4lSjWKk85GqEzhKjEtmVuwsgpENXLOviLUYr79ABoCjMaFWEV/Wa4kpWZVAU\nen35L6Zza3mHzucDuVRIhQwhlhV09Tbz5gU30qqFxnL6cd+Vh3WHDhvIMkNS17Ku3ghqqQVhz/nF\nxi8Yd+5kYs9/1MyXC3T5ABonNubcpueWe22cK45nbO/Bry+SV9DQPF5WOBqyNzlpGy8mXA3xhwHI\n8eQEFVBM/PkhhswYAsA7F73DpfvjgwWd/x4HOnS4XDixh3Y0j5MnzvoCbdbbxOImjWE4CgoBUPz3\ncUyfAyxWLqRZUlMANE1wYO0kxmS8HtH1VKFiU8r/0mFxBvGLP5X/DMufg8gcujuAUJnBm4E7j287\nFhYWpypVyaFTNZV7et3DsFbDyPXk0jSpKc2Tm5d7jUfW+5GVdeg2HN6gNyVWFAYedJnnB7+YUhRW\nH27C7UwvL+huvx3uuCNYAJYlQNC5VLjgUAy11ChwOmnGPpJjSnSH7ps5cN11JNsK+FUdxD3eT4HQ\nIVdVU7Ehmb3THpr/EBMXTCzNw6uE611f0rLvXYjo0kzvssLxBs7W/2w6i3e7JsEbenVwjCPGFMds\n3oz65uuk5+wGYGzHsXTOdQYJumxPNsszllPiz2F02vwOnTgxIVdR0IhFK+sznvdomZCFM08XdH9u\nak0SeRzyJPvfsP5+FR9k7bqWianTIrpeQXYM6/+5nGXVX7Br8f/M7t2wbVsVFqalQZcu0KDBCd/T\nqUYkgq4+cCjE8aPorUssLCxqIG+NeIvpI8sPvbfb7KYz55bdZlh2ZLuRpN+fHjZM261+N5Lk4KKI\nHtN68M2Wb0CW+el/CRx68FBwCFWW+eueafzGeeUF3fr1sH9/6BFYBoagczio64Zff6xNz5JUMylH\n9ZUA4DxyFNLTQVFoHJtDO/RPklAiURMaNsUJqp1Hfn2EV1a8gkAEOXQV0Zj9rPhrCm8dqG8eK3ud\nEaLU5UyptQIGTeLlIS8R64zFo+jimMOHSXarwT3sjNFfALGxLBZ76P9Rf3JUPfTqsDnA6cSJLfT9\nOk6GDoVDb8/mFR6iTWuBI18XdHVSD/Gk9B8SE/zJgEZLGEmm84hBXBf3bUTXu7HrzXTvl2sW9VrU\nHMY/sZ1Bw4oqXqRpMH/+GRluhcgEXQbQP8Tx/sDB49uOhYXFqUqDhAY09YfGAgl06Nw+d8jWJmXp\n1qAba+9YS3M5LqhHmnkuRSHGq1I/vr7pdE1dPRV8Pjo3L+Q8fi+fQ7dvH3g8YVt/ANzn/Y67LvCA\n3c7Q6+DRLgUUaPFoxmsMQVdYrOebKQpXNVzCUzwDhHHohIr8yW/kvOYJOj7mrDGVjkADQAjquiHB\np7uVX475kis7XRm0JEqVaKBrIVKTN0Dv9zirdntinLpDJ4QARSHFA3m+wtLq2UBBFxeHo0S/JyVC\nfx9OuxNcLjKKbmfS+ZMq32sk5OXphSwNGuDM00PXtVMP8aDjLZzRdg5TD9W/L4FCav3FNJAOR3Sp\nIV07smZZyplW3HhGcKT37fR44LmKF/31Fxw5Ygm6Y2A68IYkSTdJktTM/7gZeN3/nIWFxRnEByM/\nYMrFUwC475z7OK/ZeVV/saJAsj/sVlwcPONVCW6Uuylzkx6aM8ZVBTp0hYV6jzOPxxSAoRyndC2X\nfQl6oUB2LGzMPY+kn7/iUE4U43mXGfMa8r+r/seg3RoUF7PoaCeu3v0sNk3iipRzQwpaTWjYk/eA\nVurIjWg9gtSYVLq83yXsW9+evZ3/bf0f93heZgGDTZezVmwtfcxZAAmak4OvQre953Bg3WO0OhJN\njD2aGIduRXlVLygKySWgCKU0DFtG0NlL9FCrQ4NemU69xYnTiU0+gT3h8/L073FKCo5cffLHQSWP\nvFgbv25pSAMOc+Sg/t6f6TORKd/DQz2yeW3FayduTxanFcVyMZsKlnHxuc0qXpiWpldzn1s+X/VM\nIBJB9zLwIfAesNv/eBt4SwjxfDXuzcLC4jSgQUID6sXrw2MmDpjIwOYDq/5iWS4dzVNcHOTQGXlV\nRg8xp2RHxsGqQ43JIylY0O3zj60qKaF+fH1+uPoHc0h9IEII/Yee3Y5dg9SkdXzV+1VSa9uwo7LB\nvZPLvryM6Kw8cLvxyA4OKbWxIfi62QQGtSg/F1LVVGIuuot0mpIUpfdbl1XZHP0Vjnnb5nH9d9eT\npgxmEk+T7dP7xwU2xi29iD+knduMfVvvZe37TgY3HUiMUxd0HtnDJ5nzufpyfbkZdi0TcnV4dEHX\nXk3hz7n1aJ3aWnfPTtAsV30zeXznGMMauQvOHF3Q3euZzSODVXq2K2QuI0mJ0+9Ty4SmbMkaxYer\nZ7LhyIaILldQoD8sag6rD65G0RT6Nu5b8cK0NDj//NJK+DOMSPrQCSHEI0AdoA/QFUgVQjxT3Zuz\nsLComUxfM51P1n+iC4mYGD2HzS/oCn2FFCObDl2nup1omNCQPvV7kk0tej93GSP4ObSg83iIccZw\nUZuLqB1bu9x1NTS9H5zdjkMDV9RBxjb7g5hEJ29zL8MHrAZALciD4mIuSlzKJ73fZxW9wo7+0oSG\nw+WmKRmmy6ZoCi67C1mT2Z27mzdWvoHb5w563dHioxR4C/gg5nZW05NidypXdLjCFMdB+AVdfKsf\n+Uff85jJODRFM4tUiuViDniPmstDCrq4OFPQqapSetzp1CuETwAzZkDM688xIXsiM/f1Jyonn+yH\ns2md04fte26iVqpgJN8T49AFZVG+ygRewVfUKKJZrj4fNG8O//63/nVeSR5t3m7D/F3zq/FdWZxs\nVmSsIN4VX3E/Qbcbli49Y8OtEGEfOgAhRJEQYpUQYpMQwludm7KwsKjZfLftO+Zum6sLOqdTD5MU\nF2OX7Dz929MMu9yjixF/LphP9eESNmqRzS0X7KGY2OAcunS9US0eT4irlaI7dLqgswtQ8RdJ+Isi\nHJp+PUVC/4CQZT7IGM7lfBPWxVKFis2fshbYh85pc+JTfWzO3MwDaQ/w/NLng0ZQ/ZauT0NoF7uC\nEmI4z7afr6/4mg51OpS/iF+8KqgczenD3byLqgja1WrHpIGTiHXGogQUUuSW5Po3F1rQKVqAoDuB\nDl3PnvBip8/YNGA8L1/xJ1JuHqkxqZRk9GLx+mkIp4ufWsP7mz8BwOcVJFJAm3MmRDSua+1aPfLe\nPyDLe2fOTr0ptcVpy3tPdqfOn+9W3GNw8WL937El6I4NSZJ6SZL0kiRJX0qS9G3gI9KNSJJ0tyRJ\neyRJ8kiStFKSpF4VrO0gSdI3/vWaJEn3hlgzyf9c4GNLpPuzsLCoPszpEooSJOiMfDmbFlz96FN9\nuLDhROGD+zbxl3R2WIeuIjSCQ66KTf87Dv26dlU/p2pDF0PFxdzb9TcWMSis6Lmuy3XcN/c+5nFJ\n0KQII+RqHHvu9+eC8vqMwoUG95SwuDlhJ1/oG/I7fwg6N5yBghOnXaNVaiueOv8pUmJSUDS9KOLt\nZnfRNKkpc7fNZX+0LziHzqMXfSiqfFIcul8K3uSs1m8TXSsOKTXFFMmJZ3/FvZfFYnfZ+bk1vLf1\nMwBSExXW041aDZZE5NC1awfz5sHFF+tfG+Fra5br6YsQguyY5XRqE1/xwrQ0aNYM2rY9ORs7BYmk\nsfBVwDLgLGAU4AQ6AIOB/Eg2IUnSlcCrwCSgG7ABSJMkqXzMRCcW2AU8QugWKgabgHrorVbqA2dm\npqSFxUlk1FejeGj+QxWuMVudBDp0/ipXALvxWR4o6ESAo2Szhc2hqwhNBIdcsws689Smy5FxkkFj\n8nP0ilvF/5OxKFdGi4qhFbvDhlwbJTbitb+f5FLmocoOBjQdwJizxuCyu9CEFlRtGyhSzL9743Db\nHBUKuj+lg+xIhamr6nH7GuMEwesNQXdPynBqx9bmsi8vY0kDX3AOXbF+f1ShBgu6E+TQvfXnWyyM\nPqwXRRjFL3l5qELDIUlkF8ewbOsLeDL9TZX9wtWmiYgEXUoKXHJJ6axPw9ExQuEWpx+7c3fj7v0U\nd94WXfHCtDTdnZPKj9g7U4jEoXsMeEAIMRLwoc9xPQv4GtgX4T4eAKb6R4htRW9QXAyUm0gBIIRY\nLYR4RAjxtX8P4VCEEEeFEJn+R04Fay0sLMLgU31M/HUiG49srHRtdnE2R9xH+PeCf3Pn96F7jZvF\nD7Ksu2N+h27xjYsZ2mIIdmNWaZCg8/+4crl0MRIoaIyQa0mJGaYNhaA05LqlDvxcqxXv7xqAanMy\nlq/57xw9XKP6PxOmZF9B28+f1D8kKhA9c5vfyxi+4eKWI5h47kQe7v+w2T7Fq5RmpASKFGOqBM8X\n8HzczbzaIvzvprfE/co7vWH71qsYlHWI79rD51u+Clqjqop+32TZdALtqhbk0HXfU0LGAxm0lRNL\n51y6XCfMoVM1FXtJiVnlCkBuLioaDmx4VBe7MkcjF6YYb4JcknFnd0KtyLEMw/xd8/k9/Xfza8Oh\nOxE99ixODsVyMSNaj6BP4z7hF+3dC9u3n9HhVohM0LUCfvD/3QfECT128Dpw+7GeTJIkJ9ADWGAc\n85/vV6CSkpZKaSNJ0gFJknZJkvSZJElNjvN8FhZnJEW+Il5Y9gI7c3ZWujbKEYVX9bI9Zzt78vaE\nXPPNlm/0RPUyOXQtU1qS5EoodehkGSEE4zqNo21MY3bTgnHPdWIvzYNz6Pbtg6b+liIVuHTXqB25\nYm8c2O3cuhY46zt+uvYWouPsvMfdXD14EVDq0I3yfclXl36h7zGMQwcwIGoV33AFc0Z9xIVtLqTA\nW2BWuHrVUkEX6BSZveLOe45lG6fyQbTeWPiJhU8wa/OsoPP7UHFq0E1azwRe4ctO8PHmz4LWKJqC\nQ9PvmRFitCvBgi7Kq9I4ph5OlSCH7qXa23j+9+pvUuDZ346/to3lyVUjuf4Vf9Vxbi6q0LAj0aSx\n4Lb+bXE11YtRNFnlGy5n+dx1qMqxfzw9v/R5nvzwd573vxXTobNCrqctnet15qdrfiJzKEU0AAAg\nAElEQVQ1JjX8orQ0/d/zBRecvI2dgkQi6HIAv6HNAcAoO0lGD4UeK7UBO3CkzPEj6GHSSFkJ3AgM\nR3f8WgBLJEmyBv1Z1FgW713MM79FXnC+7tC6kOFSo0Iz1Oivudvm8uryV8kvyWdr1lacNidexYtX\n8RLtqCRMYuTQBQyOV1XZLDJAUZAkic9Gf8b5yWfjw8WXC+rQwret1KFTFDhwgP8m3MNnXFOhoLte\n7cTYfQlgt3OZf4yQza6Hebs5N5GUqgvQr8VYpnEbrdiNlyiuUWcgfD5UTeXzvz7n43Ufl3sfU3vA\novTFAFw35zqmrZnGohsWEWUvbaEQMuTacwq1u0+G6Fxm3LWCD94rYuX+lUGnl9F4tR/81nkpXfiL\nH9K2Ijz+di/ffQcffFAq6Hw+UziWdegA/T4HFku4XPwRm8uSfUvC3rdIKdnRjwXbnqVdsxK6Gc1+\nc3PJWzeOGb8tAKcTu1Z6L95d/ju3M51LWjzCwBah+tdXTEFGE5a8MIFZfj1s5tBZIdfTlk8/hZ2V\n/R6ZlgZ9+kBS0knZ06mKI4LX/A4MBTYCs4A3JUka7D+2oKIXHiMSED52UglCiLSALzdJkvQnkA6M\nBT4O/Sp44IEHSCrzj2LcuHGMGzcu0q1YWJw0Bv1X75P25MAnI3r9qK9GkZ6fzsvDXg46bjSqDSXo\nFu1ZxPzd82mU2Ihxs8cxrNUwvKoXVVNJjk4OeZ2F1y/UKzGXT9XblkRHg7e0pYZdgIaELdAV8/lo\nzzY2frON5de8C1oL/+aKQVV5dPdtaHi51uPhs4wf6Fa/Gx3rdgy+sBAgScxNi+J3913A+9gk/++1\nTidDpJY8ZhvIFzmT2ENLbmc6qs2JV4qmW+HL9P9pB++tfg+Am7rdVHpeVeXVfvCPPb8wqNNIZFWm\nYUJDzm9+Pl9u+tJcFijo3rv4Pfp+2BcSD9Kk9+N4Yp08+X5LsmyPIN8yOWjbPnRBsivexxAOUtR5\nLgsP+n/cfv45HDrEBaM70GLdYuhe6tA5Ah26WP/3zu1mlnMHE4ZtYZem4HC5cKrihMxyjRnwPvfk\nPcU1gz+Fi4Fn9OtPjmvCRscf4BiCTZQKrrk5M+nZewEfrF9MvX7HnsHjyUkhvk4OCxfqXoAkSUhI\nlkN3mlJSAnfdBW+8Aa1bh1kky7BgAUyYcFL3FgkzZ85k5syZQcfy8yMqPQhJJILuHsD4tfs5QAb6\nAbOBZyM4XxagohcvBFKX8q5dxAgh8iVJ2g6E+2cBwOuvv053a26MxRlK69TWpOenlztuCLpwc1lV\nTTVdvJToFDLdmSiaEtahMxv0yu/oOXQBeVyqprBu6UyGU4tfAvPW/M936qDRKXoGaE/4z6GvOXTL\nE/DOO+DZyd0/3s2kgZPKCzr/LNc/1zlZ6R0BvI/dX4iB00mMauO5gl48F9XdFJijO+9g9II36CWi\nkTWZYa2GkeBKCDrtORnfcHDePpTzf9G3pMnE2/SqvKiAH7Oa0NiRvYPVB1fTplYb87hTSBRIgm0D\nbqZvh6XI6tVB5/dJuhD0Shqd2AzDHoZNYxk1Ckbv7MV18XO4zN5Rj0tcH5BDp4Rw6IqLyaeEfasn\ncM94O1OionApwpwf++mn0L499ArbZ6DqqJqih89dLv0BPH7wM3o0FdyZ8jc4L9TFu1/o2qILadEs\njXprI/logvizVjD2/SdITi4dWvTs4Gfp1aga3ozFSSc6GnJyyk/5C+KPP/RO0qdB/lwoc2jt2rX0\n6NGjWs4fSWPhHCHEQf/fNSHEC0KIS4UQDwohciM4nwysAczgtyRJkv/r5cd6vnBIkhSPnv9XUVWs\nhcUZzfnNz6d+fPlMB7ccPuRqFDi4ZTfRjmhinDF4VS8lSknlIVcjhy5A0D3d7UGeLJrKv3gtOG/N\nSNw3qlyNn/LGGmPihH/8V0jHyS/onn1a5YU6l8Hq2znvA31s2XT1Jt76rav+CdK4MQBpDOP5peeC\n04lL2JE1Ga/iNadXGNSzZeLeeynFhfpxWZXNoohRqf346TOoq+lTHRbsWcB1c66jS70u/HrtIlj8\nBL6jXdmVIBPd70f2xxSXm0Mrowuew/kdmcXloEnwzVd89x3slr1k2jyl9yEw5KoRMuSqCBVbTBa5\nuRKH1To4Zc28X9dfD7feWtE3reqomr9Hn8uFcEVxPov48I8E/pAO6PtyOnlhcTExa+8GdGFnE1Ty\nCV7B9YRqVkobPDbgMbo3sH5JP11xuXQTPyxpaZCaCtUkik5nIm4sXM28BtwuSdL1kiS1B6ag5+N9\nAiBJ0gxJkswYhCRJTkmSukqSdDbgAhr5v24VsOZlSZLO88+a7QfMARQg2O+0sKhBPNr/0eN6vazK\n5T4QoeKQq92m95Rz+9zEu+KJskfpOXTqMeTQBQi6Hikd+DnnXr7jH8GVpT4fHqI5nBeNJgVUuRpr\njF4VJSXhx275Bd3/fnSyyjsYGv3JnefMA2CXaMX2zKQgQfc3Z5G2oxU4HDiFhE/14VN9QXlxi/cu\nZsTZt9N8VH9c0fp9MhoLA3DoECN2wpHMG6gdW5tYZ6wpuDrX6UrUkocoWPUIFOtJ31Eq5QSdT9LX\n7zn8D+7nDZAEPJpISQm8NfppPmqcaQq6v7zprDu0Tv/eCEIKOlkoOLt+wnffwTfpvXAppe1V/vEP\nuPDCir9tVaV9UivqugGXC8lhpxnpOF3FyELVRbnDwVviAb49Xx8Xp2ka+blduVF8REH+sWfcKJqC\n3WZHiAqLnS1qEmlpMHRo6b/zM5hTQtD52488iJ5hsQ7oAgwXQhizbBoTXCDR0L9ujf/4BGAtMD1g\nTWPgC2Ar8CVwFOgjhMg+ce/EwuL/lzpxdcqFA48FRVNKhUgAZsjVGTrkajh0cc44Lu9wOfedcx8l\nSkmQ8AlJCIcOWWYsX3MxP5Rz6H7iQhr0bMQzJQ+Xdtrwr/l6RzfW0g08nkoF3TtT7PzkvgEarOfG\nngsBeCH1Jd4ZNg9yc5npG8NCBnE/b/L97XPZS3OcQkJWZbyqF5fdZZ5yzt9zmNJzP/H1lyM5fPoY\nroIDQYIOMF2nwHFddROSWaudw+6MKyGrPfxxD/LGq8zwp3mbJF2dbLriP8xM7Qk7LgKbQlQUxMpQ\nLKnm+f9d8hMfrvsQz4Q8Bu0hZA6dIlScNpWVK+GGbn8FOXRz5sALL1T8basqozPm89n6X/XvryTx\n3+g7SWq5nKNZjfm9pCc4ndzJVDo21IM7nrxk/tr8LMvoj+I79rYlqqaSs7sZDgesX18978Hi/wch\nKiws18nKgtWrT4tw68kgskSFE4AQ4j3gvTDPDS7zdTqViFEhhFXFYHHGcUu3W7i8w+URv17WQjt0\nMY4YutbrWnHI1ecmzhXHkJZDADhcdLji2YtQ2odOksDn44O1H5CQXcg4/IUESkDFrs9HP5Zz8zVe\nJn9+H/eXvIHLfw4vLq78aDgwHOGZH17Q+YsifvlF4kDiDfwwD+oO9gtgozVJTg5Xb/wn8E8EEl//\n1Z5b9v/OhWprfKpPD7kGCFV99JdAAzRN5YO1H5Cen26GXE1B5/90Mu6hR/aQ7EygLdt5t18r7m60\nDz5axhFAvumKoG0f3HEpVzm+Y0FLwZzUjvDFD3CfXhQSIws8kmqeP0pIuDWZaMmpl5UFOHSFLnhy\nxztku3JwColu3YDvbbh2qeVEZHXQsUUxGmngukQ/EBWFQ0hs/GsUt+4fxDb/hA5j7978VLJz+jCf\ngaQmr0dvgFB18paNZeYXDzFlCjRqVI1vxOKk88eGXAb/H3vnHR5F9f7te7anNyCBxIQuvUsLXRQE\nwYIFK0qxIRZEURCsiBUbiA2xIigiipQASu8dQg29hiSkbzZbZub942xNgwT0q7937uvKlc20c2Zm\ns/uZpyaH89df0LFDOe+D5cvF//T11/+zk/uX8q8RdBoaGpdPhCWCCEvVU/c7xHcoU7T1qd+HPvXL\nfgr2CLpCR2GABe/Z5Iq7RQA+C51eDw4Hs/bMIk4OIpbunCCJIf4uV6eTOM4z41MnM1ZcDWZ32UuX\nCyNO/hi/gaBJ46H4qXIF3TFysATLfPm6xIyiQ/x6piEROneAjqdjQk4Oq4Z8xaFf9kAh9G1+huWJ\nQ/lIVXEqTuFyNQSWIsk/chPF0UeZuu9rrrWd8l4XANLTvfMEvI3ii5xFoKoYkHnslJOEPyO46bY7\naHegAc2qBwrhag4DoW6DVeOQtUQ/WpOnegzhQqaCMTcKm84n6EyKjmyX3ReH5ifo7Ab4IGsB7UxR\nkNmYI0egntlM63PgqHflvxRv6JTLDbwD5kFigcmEUZVo0ukT3rD/BEZ3fRH3fQ6O38/gftVpOh8x\nf2Npa3FFLB0/kbXd4aFKV0TV+LexKWsptuRt1Ko9HijnMy0lBZo109S7myq7XCVJqi9JUh9JkoLc\nf///229DQ+P/CIOaDOLlHi9Xap9aYbVoEdtCuFzLyIKtkBIxdAadAZfsJIU+fMojFSdF+NWh06HS\nv2cRvVhRoct1cOgSJjTN4LrroE7zN7mnc2t+PNBaHMZgoagIyM6mW6t8hsf8Ks6vupNro7ZjVnU4\nZSfNajSjdmRt7zFlRebYknmcm7UTrDFey9x9Le5j2uZpnDqf5t5QCKxPtghHhEfQAWAwIKNAs59Z\nfOItXurxUuDEFYUb3IcJ1svokfnsuUep10AifdV0inQ+l6tZFrF+pQSdxSLq1AHFyFiXfMzEiYDZ\nzD27VT664SMAmjaF7wNrFlcd/3sG7NM1Q85OQGfKo1ZQDhiNfM89rE4V8YNxpiiqW8Ghh2JHUaWH\na9JEE3P/V9hr+5NmtywmMbYcMaeqsHSp5m71oyq9XGMkSVoOHAIWATXdq2ZIkvTelZychobGv5/7\nW97PX0P+4t3r32XGwBmV27lEDJ3H2teJDXzIk2ULOo9Fr7ykCJuNcHO4t6isPyqgQ6JjR2ia+A3Z\nJwcw5i+RATAxcxTNvhsr6tpFR0NICE/yAW1euxkMBkYXNGNCtwnMvGkmreJaeQWjoio0HtCF5tIO\nkE3e+LqY4BgeX/w4+/OPiMHd5+J59h27fCzvrPyMSHKYV9wP2V12U++US7cZk2Xu2QOsGcvUYz/y\nyDaZlo2cfPhyLnWbT8KmUzjnyuV8CJgUd3eKkoJOkjBYhPW1Z2E1JrR7g+eeg76f38paW1uvuNy3\nD5566pLvYMWUEHR35Uwjff3DzAxNIzWiGAwGpjCahdvF18jPTV9h8jI9D1wfybU/DrhCk9D4L7Lh\n9AY6JVTQLCo1Fc6e1QSdH1Vxub6PyBZNBPb7LZ+DyFZ95grMS0ND43+AqqreTEFvwd1LpFZYrcoP\n6Imh0+nA4UCv0yPLLsYziV78RfsSWa5r9d1ZMkHi9bLKlgQFCfFSXMzKR1aWOZyiKkhItG8PocUP\nUb3Lq+zqpwNe4s7qf9Ex+BxsQQi64GBu52e6DGwDW4x0tMVAUleWHllKn+/7cPzJ4yRFJiGrMpE1\n1vFbUBtqhOONr/NcP2fuhYB5SghBt/jwYqJ01RlOMY9mvMxT1feAulpkphYVBVa9VxTRjiz6CPWC\nTby6ORsOS+BI56ctuyjSWxhmXERQf6illGOhA/RBIUAR7QsjuNdhQm4GwRYFCcVrLV24sMK2tZVi\n114D+XShq1vQzUkYw/sdXHwObIosoplez3apHdw8HWgBssxGOvLj4rW06nh7hceuiK++goYNoUsX\n2J+5n0hLJDXDal58R41/BXnFeezN2MvojqPL3yglRfzPd+36z03sX05VXK7XA2NVVT1dYnkakHT5\nU9LQ0PhfcejCIUyvm1h7cm3VDuBywbZtl759GRa63zPXMC2hNe/xTCkL3RF9QyZNgtqnVnM8J8J7\njNV0RWrRnOd074LNVu5wHgvdjTdCtZA0XDrQGcTHYMuI4wzM+w4AacCNdD00gy6so2FCEU+ceIYi\nmxBinjhBT20+xV1rTXEHnXgsdB7hNjviND0ewCuwaoXV4oUuL9C8RnNmHfqWAdXfpE/oeq7JdGB+\n3Uov++bS5yDLQtA1nUty9EKuZTlHjushP59gJ9h0Ci4U9CqYXWAvK4YOMASJucuqaP2l18O8MetJ\nZr23kHK/fnDTTRe/dZfCtF/ieIb3vBa6RuFnmabUhfWj+WnXOLGRX5/c1TvC6M4qOjZ9Cn1YRqXH\n27RJiLn33xfNAwD6zerH1M1Tr8j5aPwzfJ+yD3XlBNpU71z+Rikp0L27qD6sAVRN0IlHvNJEA/Yy\nlmtoaPxDfLn9y9J9RiuBJ9jf7qriv/KSJdC+PVxqO5syYugA5rSQMSCXEnRDQuZy9CjcG/Y7oXqb\n9xiqWzyl0qxCQaegopMkbr0V8qwNkHX4BE/t2nDoEJjN1IyVWZsvmslnWYNYmd8ah124JD1xgp7O\nGJGmcGpYyxB0btfqcXMRq2pDt/prOJF7ApvLhsVg8QrCP1pdYHrd8fQuPkhT4zG2cY2w0Pkjyzjd\nn9YmHdQgA72kQH4+U1Lgqz9DRVFdBUwyFVjohMvVpcoBsXVAhT1wq8q7D+5lMTd4BR0mE7LDDoZi\nLHrxHvupKTTPngTAVTFFvMYEGiXMRDLnV3q85cvhjTdgzx54yR2GqJf0Wi/X/xh/bTmLLvU+mtVq\nUPYGRUWwZo3mbi1BVQTdGuB+v79VSZJ0wHPAiisyKw0NjSrxy/5f+CPtjyrv73EX2uUqCrq8PLLN\nCuuPrPS2n6oQj8vVLeg8cW+pxx+jGXtKFRbGZKJOHXg99mOqWQq9x+jOatRjx1kUN/Qigk64XFev\nhhX73hZWL537Y/C776CgAPLyOH1WjzJIuPyubZPD7s6PEqkTAqOkhe6Dnm+ROWc1X7keBUVXqvae\np4bcmrBsipxF2Jw2ggxBqO6YtXeTIS3CBYrC8oQHOUFiaUGnKCgShNmhdtBpPuchzp7X8+3vkczN\nf4qEfHCpCgYFxmc1YfZts7lvxROkhxIg6HRmCzpVwqXKjNo9nB9/BMzu+drtOJ2wYIGv0srlYHPa\naH9kGBsbZPsEndmMy2GD9p8wuNU0APKC9aQqYsA61QsZx2QshvyAvreXyvjxpRu563V6rZfrfwxr\noy/o++GT6HXlSJRVq4RFWRN0AVRF0D2H6OqwGNGl4W0gFegGjL2Cc9PQ0KgkezP2Mm//PPKKq9bw\n+bItdA4Hq5IgecHNbD27FZuzfHEFlHK51okUtdVqhO3ifr4tnRThEQZlxdAZjSKmpgJLk8fl+thj\n8OCtNTiWOpYB34mSGrtTdbz2fiiK0YxOB1JoCEu5jl82JfhKmlDaQocsoyLxYvEnkNmYbkndWP3A\namKCYsS0/XIzFFXB5rIRZAxCRQWnGbY/yHlnAigKUeSQyKkAUXo6/zSP1tmHSwfrJzcl5kxH9tCc\n5LuTWLipGhvoJFyyCAtdiF2h0FHI90d/xWoksIK+yUSyvQbV7QZynaHYbHA8J4I9NAO7ncJCGDgQ\nnnyy4tt2KTgVJwftZyg04RWNUzPuYPL2gQBe8T4z7Qv4Y5oQcO5EF50KyhWyqmkWuv8WqqpyMu9k\nxQkRKSlw1VWi6bCGl6r0ck0FGgJrgd8QLth5QGtVVY9c2elpaGhUhlP5ogZavr3y7iqAYpcQQyUt\ndE8sfoL7fr3v4gew2zG7vzs7zejElrNbKt7eX9ApCpN7vs7V5ng2p71EJLmlBZ2nLpl/2RKPFc9g\nEILuIi5XWTHz22+wOjwKqh2kXZ2TABw4AJ984mcUDA5mDnfyeUpSQJxXSQsdLhe/cRNf6+/k8+7D\n6FG7B12Tunrr+dn99JRLcRFpiSTCHCEsdPZw+P0r3su9kdghOjIN7qxQPwvd+cLzfFrrDNlB8Dov\n8lz2WFqxk9Rf0/j2zoX8xJ3gcokYOkVcD49FKqD1F4DZzOpTvbk1I4bvunzG0KHw9ty6DOEbZJuV\n8HCVu++GXbsqvm2XgsfCpnf3cgXIU8PJLhYuXo+gaxK1FpLWiO1lmQyqs+Xgq9iyalR6zFvm3MLC\nQwsDlmkWuv8WkiSx97G9jE2uwD6UkiKKCWvV0gKoUh06VVXzVFWdpKrqHaqq9lNV9UVVVbWm9xoa\n/xJK9gK9VB6Y/wBQ2kJ3tuAsmdbMMvYogd2OxU+DVdjLVVV9xWM9ljeHA0VVqBWxmUROlnK5Pps7\njqQkmFdwHRes7mO7XJwigdMZJuymsAoF3a/p3Rl2pCk33wx9tiRD4/k8de1mAO64Q7gaVRV+/hnO\nqXHMYDhL3t6NS2dCdZRjoXO5iCGbIfJPjLi6JzXDajJ9y3QOZB0gTh8hBI0f58ec576W9wkLXUgm\njK7Fsr1PknGhCzNy7mJ4m/a02jzUd9ru8igOPTwfOoovEl4ljQYk1XRgLhIts5BlZLfLFYfDa5HS\nK5QSdNjdCRNud9a44Rk8UO9ODHNbUODM44cf4ODB8m/bpSIrMqx4iR/S3/QK8fFN5/Nq42mQm0iO\nTWSddqv1EzSfjazInDhnYhoj2Xt6OM7CyEqPueDgAk7ln+K116BbN7FMs9D9t0hPB6tV8nVaKcnJ\nk+LpS3O3lqIqdehalPPTXJKkBpIkXaR5o4aGxt/NJcWvlYHqroVWsiivU3GW/wHrj8OB2W/oCnu5\neqxvnhg69/6yqtCj7ov0JSXQQud0cmvUSh58EAadmMKB7Bre5U/xAVe1iCJ2++IKXa4N7KG00hWR\nkQHtIjYAoNMHVm/KyhLirtanE8kmihV7qmH89Sf+dAUzdfNUan9QGwi00AE8fCOM2/sRsiLz2KLH\n2HB6A+csL3LfwcCuEh761e8HEhCSyZgbBkLSGl44/yYztm8ix271nbZbnH/XEpKfyMKuD6YVu9ix\nzwz5+d45uHALuoosdG7X9iFzIQcsBQAkJOpINIrAsyvZ/ktWZQjKIdyQ67OkmM2oTgfmBZ8yc7Po\nJOKx1CmqwuANa3mVl/gztBFfDapc2RJVVZGXv8z00TfQoQPceadYrlno/luMH+8T42WSkiIeRnr3\n/sfm9F+hKha6ncAO989Ov793AgeAPEmSvpEkScsl1tD4H1FVQef5Qi/pcnXKTl+z+Yrwc7nCRSx0\nHutbCQudrMoUFCUxhzu8VjHPuk5RB3jxRbjQshcdqvkK9k5iPLO/dfBps6lgs/Hp1k95eMHDpcdU\nFHR6ia1b4dmtq8ARXErQ1aoFa9fCyE7bMeCicV07Mzt9zsZqaYxaPIozBWf4+qavGdrabUWTZVbS\nnb2WOI5Zz3gFmFFnhHPnUDwFjwkUdNNvnC5e6F3UjTkElny+S7iZRjG/4pR923nEdYgD7AZICsli\nM9fQqoEV8vOFBJdlZp3uwLPrxXX13H9DORa68fVP8JDppPDsWiyY3PesqpbdspAVGTp+xO2x7/sW\nmkxUL1TZUWMuM5rMA0Dnnp+symQ3/JanelnocCGf7tWvqdR4iqpA/GZaJJ/l+uth5EixXLPQ/bcY\nOxY++qiCDVJSRCZ9VNQ/Nqf/ClURdLcgas49BLQEWrlfHwTuBoYBvYDXr9AcNTQ0KklVLS0uxUV0\nUDT9G/QPPJ7i9PUmrQi7PcBCVxVBFyyZOHehO4OZg8vhl+noToowGCDaVIgBl/c4jTjInYMlBtcV\nNdwOZx9m9cnVpcdUFNDpiI+HdrGLIKMp204GFkTW6SA5GabevZ5wCqgZp/JAo42E6Qq929SPru+N\nkVOdLnqykm2LN2LNjvZee6NeCLq2Ui36FSeK4d2CbszSMXy86WNv8WGTuwn9yWbLuK/Jrbh0Piuj\n53ihDpB1YDTJuDDw4MuJvLi2D7GcB1WlSWEQiXlcksvVhcqaTzaLFl9ms1fQHT8h07Mn7N5d/m27\nVLwxdH6WXasujJFxeTzU8RcaRZ0H4FhBWziRjKIqqJILk84uitDIlRNhLsUFjRZw/eDANNefb/+Z\n13q+dlnnovHP4SkIXSYul6hNo7lby6Qqgm488KSqqjNUVd2jqupuVVVnAE8Dz6iq+gMwCiH8NDQ0\n/kGaVm8KXIaFTnFya6NbqRddL3C5XAmXq9/3sH8T+9KDlS3o9sW9xkOH0jlgbI5B8XP9lsxy9evl\nCgQkRRh1xjJ7uaKqIEm0aAF3NpoMm0cxbp6oNL9li3joP3DAvW1ICMP4khGT64LBgNHlC4YLOC+X\niz/oj9Megy0vwnvtPRa63pYmfF7Ui8ePxFA9pDoAm85sYuu5rcxNXgzT9nAisx0A4zsWYVDcdeI8\np+0+j1AHsHwSb6bfixMjBVYd/cPX8nDMaCZ3wVsY+FJcrk4UOt4+lL594de/wvko41MAFJysXAmd\nKkgwvFS8otLge998uP96Zi74k1y9yzuvtYcfpc6f72PSm1BUBZ3nMiuVK1viGa/kg0dSZBKxobFV\nPAuNfxWbN4sal5qgK5OqCLrmwIkylp9wrwPhftX6rGho/MP8OOhHoOqCzqW4yrTEOZVLd7leclJE\nOTF0uFz0ZyErdL2QXIEu10V5ycyfT+lernq9iNNyly0x6U1lCzpFYWvB1fToAdmOWOj7JD8+KloK\nxMfDuHEQE+PeNjiYHqyk+zVFYDRicvoEhqd4MMBTW15lfa9F9B4cRmTiQa/b0qAziAjvuDjidZF8\nvC2W2pG1AQgyBGFz2uiU0IzBuRv4afM4ON0eAKMMTtV3EQMEnd6JZFDpxhpSPjxIJ/1mwhrP4p1k\nvLGDqZZ8Jq6cSJeolsLyVpaFTqcQX28diYlgc5kokMVJR9WwsWoVjBlT/m27VMLN4YzKvAtLnq84\n7B1NUunf6WGckuKd15yr3mZXr++wGCwoqBQVxxJCIUtWBVVqPM97Xq/Tk5kJs2b5Qgw1/o+QkgKR\nkXBN5dzx/79QFUF3AHhekiTvJ5okSUbgefc6gHjg/OVPT0NDozKEmkK5ptY1BBkr92XowSmX7Vqt\nTAxdrQKYHCwaq1fF5YrLRZqhCXcHzS9VtuTHjGuZMgW6pn7CmrNuK6LLxXC+YNrTy7UAACAASURB\nVPp0mLx3IEpRxYLOZJBJSACd0QXBOdSMEtvVqgXPPgunTkGzZjBjXSNu4jeu62Lj7V3XYy2M8R7G\nP9ljb14ah6NBUoWbMasoC4AMa4ZIm61ZU4gXv3MJNgZT5CwiLtrIl86nCDPawBkEb2eQcvI1HC5f\nOYaY4Bi6Z4ZwOBro+TI9GixgCF+z84AF8vMxmoNEFwm3he6cwcaBrAN833QiwU7Kdbka3B//d9+r\n450okYDgVJx06wavvFL+bbtUIi2R7FnwGp+dG+9dVj+ukHrV/iJzw3N8c1RYRsPMDsIk4c4u3NuH\nH3asYBLjuTrxIjUMS+BSXHCqI4d31eDIEbjnHjhRlulB41/LqCdkvvqqgg1SUkQyhKEqbej/71MV\nQTcSuBE4LUnSckmSlgGn3csedW9TF/jkykxRQ0PjUqkTVYfNIzbTIrZFlfZ3Ka4yXasjrxnJoCaD\nSu/w3HPwzju+v+12DAo8TDu2PbQtMMvV6YTGjUXj+xUryhR0Hx38lm45U6hjPE240SZEkMMhPsAX\nLOC7DlNZtAjqWs4R7I4zcxbL7FWbsmEDTNnVC5fNWaGgaxFxkg8/hFpHe3FqcnBAjBcII5/JBMM/\nbskhGnK+MIQ3t11HkbWadxvzR9O8r2XFhV6FfDPMy1zN2XGjwGXioXYjWJZ3DcTFifn7xYQFG4Mp\nupAOXboQQhGLe04kJnYVVNtPytkXcRTEeTtJ9KrTi5UraxPh9qgqehNHqUtRsQR5eRgsIaLjhVvQ\nmd1xhw53TcGSLtceXY/wZ6wVozu71D+Gzik7aTU+mjc+qlyGaXl81nMOr8X5rhUmE0aHgj2vNufs\n7qB2v6LNUnAWdSJX8BQfUqdm5VqRGXVGEvZMZfFXrWjXDgoLhTDX+G8gKzJfbPmWNcfL6SOdnS3i\nIjR3a7lUpbDweqA2MBHYjegSMRGoo6rqRvc236mq+k65B9HQ0PhXMvOmmTza7tFSy4e0GkLf+n1L\n77Bypeip6MEhRFRUsUSbmm28/UwByMwUAWo5ObB+fZmCLqc4hyNyJhulTnTIXUJGjhGsViGGVBVM\nJkJD4Zsmb9E26qjYXXWwIbwP334LmWPexmQvKFfQvV8tjRUR2ezZA4PSPqDYXhOpxNN+48awcSOc\nOCrT4qcJtLghnuzRk2hg9lXbNW/zvXbazKxZP5+DRSLwrNqWvfx8+y/UjJWxESRcRAYDuFysPL6S\nHl/3QFZlbIU5oncsUE0KYeRmiLvxJr6L6MrnytWBra8UhWfXwe+zoIvByEL6k1jNxv7z0aw8MxqH\npPcKOpNDWAJ3HwrmD/qXstBlmVxgi2DHn2NFvKAkkVRk5OfQocSa6rHrQhLjMxaUvtdVoGHIGeqG\nZgSMb3DKRPR7iOfbLBPL/MSuMWkj/Ru601MrmRQRZg5j/59tWTgvEoMBQkK0urP/JfZm7sXedygP\nDC3nvi9fLsIsNEFXLlUtLFyoquqnqqqOVlX1aVVVP1NVteBKT05DQ6M0Velxeal0SOhAg5hyGmKX\nRU6OCFL24AnML6u4b26u7/XJk2XG0BkUkFWFMIONlqYD6BVnoNvV0ynCP4bO5fItdydFlCfoptQ4\nwsqwbDp1gvS2/fmKobw8T1gzi4vh66+Fm85kgsQ6ejbG3sS27RKYzRjtYh5jz9QhotiXIOGSZazW\n2lyYvR6O9UDndHFbixs5u/4E825ewEd5S70u17MFZ1l1YhWhxlCsip0iglhGb3LkcJHBas7l3ry1\njMiviV7nJ8RkGaMCzQ7VJkOO4UuG0/jeNqTa67N43+MoipnMoiBeYSLZ+XEALFiZxFN8UErQ6WUV\nnMGcOdKVCxfErSpU6jBIbcLRfZHw2Q7YMrKyeqps/BNZgL05tVi2dxIOZ7C3sPGU03cwdMMIAB4z\ndyH5JOyKhdmnl1R6uNBQCA+/AvPW+MdZf2o9eknPNfHlxMelpIinrauu+mcn9h+iSoIOQJKkJpIk\n9ZUkaaD/z5WcnIaGRiDf7foO/av6K1oA9rLIzb10QZfj7mrQsKFQTWVY6PSyitNh4bniVxgSMZ8Y\nU4HX6geU3cvV6fTF1FgsYLPRpHoThrQc4nVbelBQsdqqs38/xAblE04+wRbFO+UHH4StW33bjxvn\nrollsTDwoIr6ksqbR+qQEp7B5DWTxVTMhfTr0pVREc9A1FF0Lve8rFZ2xEGa67zXCuXpbWvUG9nj\nOsNn9RO4nmUsz2hBsWIRZUYgoPWXmLhYcRO/8fr+QQziF+Y/upTbmcuno+8AUxGPhbXlZV7hSEET\nAIbevobttEHVBbpcDYrKLTl5HH5oKsnJMHs2JFgPItsc1GmcC53fgaXvlZpClXA4vH1cATKKw0nN\n6UOWzkSaUdgAqlkKSbCIuMNxluvoeVTPS2EjGLlm/hWYgMZ/hQ2nN9AqrpW3HFAAqgpLl2rWuYtQ\n6chCSZLqAr8iMlpVRK1z3K8B9GXtp6Ghcfl4kh2sTiuR+sq3RroUVh1fhaIq9KzTs+INVVUIutBQ\n3zKP+KrIQteyJezZU6agEyU7VEw6F5JBL7bxa/91y1+P03As3FbQiCRTITUAxSlToI8mxAUGt4Wu\ne1I3utfuXnrKQOrhAXTvDnmt9TzPW3Bre6AFkZFiqKws2LQJOnSARYvcbruvzEh2h/cc14Xn8vvO\nmbzQ9QVkRcZiKGBw+BQ+jgS90309CgsxKHBBtZKhL6aGy4XNZcOsN9O2ZlsAahmOcYgGNFyVxg2N\nt2E7YOUU67mqpJpyi9dvGEJY876EHiykS8gOcfmiq0ERzE1eRc/z79JFtwKAlZsT6U4+uUVniPAc\nx2zG4FKJKZaI0YuCx9ddB4ti7kPnqEt8dCg1206kUYtvCAlJLe/OXzLjt95MIw7i6QLcs3kWbzVs\nwiOGMPLdTW7vT1zptiLezqmsINLoxu+HPyXk1COXNfb114uOH8EdZuGQHTzQ6oHLOp7G38vyFAP9\nelxb9sr9++H0aU3QXYSqWOg+BI4BsUAR0BToBmwFelyxmWloaJTC0xi+0FF4kS2rzvsb3+fdDe9e\nfMOiIuHurKyFrlWrci10Blkl33yB4W2G0Dlsjy8pwk3fOofo2BHab/yI386I2m2nLwQReTqVceOg\n+nMPcF6pFuim9UNBpVXDP1i1Cp8r0v1bkoQh7ddfoWNH6NsXUlNFLHbcxBGssYnxcDoJtftafymq\ngl4Bxf1oq3PK5OfD4HF1yN77ED+eWkwn16ekmxzkFucSZAxi4NXCmTF4sIsfuh3h9z7TeHv/Zs6n\nzKEVOzlyoYRYd1voWrOTWuGFVOMCv6wTrtXo6kk0Pw/U2M+gq54lximuc+2kE3zJMEwWv495sxm9\nrCCjeF2eCQlwQ+QGdI5iDA4Xo3YUM6BY9qy+LM5Zw8lW/Sr6m0w0ywA+3cXnm0WSTZER9hpzsbvs\nzNnegFuZx5vJeowtZ1d6vEcegTffFK9btxalaObtn8ePqT9e/slo/G0cOJHF2U9nYDl+U9kbpKQI\nS2+FPcE0qvIv2wmYqKpqJqAAiqqqa4EXgIoadmhoaFwmZwrOAH6N4UvQ8OOGzNwx87LGMBvM2F32\ni2/osbjl5wtrHVw8hs5kgquvFuvT08Vyvxg6vSyOczQoiHX2dhQU6QMsdA+33cott8DObqMYVHM9\nADHGfH6q9SQ33QSj+x8imKKyx0cIurDgXCIjoeeO99hLE0oql8GDRVJEaKiYWkQEjLz2IAmqO+7P\n6STEoXrvwR0RPWl2oC42VZRo0blcFBTAnJVxHF/3GQDn1UJqPpTPwrSFWAyWgDqBr/RS6V5vB03Z\ny26akyCdodBaIprfL6DNbIZfuJWu+vVgsdAnqRe7p4uuEHpJR2iRi2vrXMvV1R0M4yuCQku6XMGp\nqoGuWItF3LuiIl5YC08fu/xCvFaHlaeveYWHr/7Dt9BsRtYB/R7nxibCt709tIBmzVdzLPcYw9vt\nZDtt0KlVixVNSIAa7ha/b70FN9yg9XL9L5BWvAGeTuDhu+PL3iAlRYi54DLcsRpeqiLo9IDHPJAF\nePrmnACuvhKT0tDQKJuRi0QGoLcxfAlO5J3A5qpc/a6SmPXmUr1cy8Qj6GRZZKKC15r2RsRufj/4\ne+D2OTmiFUNSkvj7iLsXa0AMnfgSv1Bcjy6Hv+ZAZkxgDJ07+aFl5Emi9cIyGCIVcXvUnyQnwwt3\nHSeMwnIFnSqBDgmjEWpZcjhNAmdyAr8koqKEu3XuXNEyMjISJtxxkDocF5kTDgchdtV7DwZJd/DE\n4SO8nf0F5Caic7qIj4fcL36myz3C/WpwlwixOqwEGYJK9Uy16VUkoDmp7IrqSUupRO8tv64JerMB\nHQrf72vDlqjrib+jM2nUQ9aBwWAkusDFDfX7cu1MO0OZUSopwqBAekYHdC9N8HXFMJuhuJjN65zc\nzzdYCwNjD6vCzvSdtGi2hqOhfudqNouGbQ0W0SROlCq9YIuD9BbIikykqYi6HBOCjsrP4cUXYejQ\nwGVaL9d/PxtObyAuXqZpQmLplTYbrFqluVsvgaoIulTAU+RqE/CcJEnJiNIlR6/UxDQ0NMqnPJer\nQ3YwctFI5qTOqfKxzfrSFrqtZ7eSac0M3NDjQgWfuHNb6MYnHGTEghGB2+fmCnXkEXSH3T03jUav\nULtGfxW4jOglHduaD6FZ+MlAQecRjiVbf/lnuYK3a0JJFFQkSSI+Hn5o8x6v8BITfvA9h3btCjNm\n+La//3748EOEBctzXKeTELuCS3HhkB3UjCjiJ25nufVeONcanayCohCh5GDWi/l6ivgWOYsIMgb5\nklpyasP3izicX4u1iZAeCoSFBQjSF/96keQB4to/x1ssPNmcQzRkQ1YD4uJgxMAMQqR8yItnxPpi\n3uZZHM5ipPBTNCCtlKAbuRkeOH6Iz29dQs2aonLKM+fGkJOvpyjXwQIG0HPfVE6eLPMSXjK+1l++\nLNdj2RH0PiHDsZ7o3QWs56fdBD/+Jixysq8HbWUtdLIikk5K7mfQGTQL3b+c0Z1G8/vg3wPLHHlY\ns0b832mC7qJURdC97rffRKAOsAboBzxxhealoaFRBpI7B6ksl6v/F1mePa/U+kth2uZpHM09WspC\n1/HLjszdNzdwY/8yJJ44OrtvvwxrRuD2HgtdtWpCePkLOp0ODAZaqDUgL4mJa5dTKIUTpBYFuFwX\n7Ulg/35Kt/7yZLl6BF05FrqGRcHsTb1LdELQ6/mchxh313Hv+q5dfXoTRIWEatXwZWra7ULQ2YRA\nsDqsBOkd3MZcZCSci5ZTOxchMq1WjO4cMY+FrtNVnbi3+b0+l6ukgLGINzfcQtfr27OwARAWxgFj\nHovSFolLW5xHoVGc6x6ak14cyXO8w+/2PlyVpOPlh88Ro8uEo70BSGYdsstJULP5mLGTdswv981k\nYtB+uOfoOUZ0SiUiQiSBLMrrzBzOEl97O5voQEP90QAdWBVkRYaiaBySL2mmeg2Jx6s/AjGH0OvF\nvO5suhDuvUEIQPc9fWfPDhw776rUeNvObSP4jWBSM0QyR1qaaP2p12kWun871YKrVVyuJD4emjb9\nZyf1H6QqhYVTVFWd5359WFXVRkA1oIaqqn9d6QlqaGj48DzBlmWh87dCVKWXq6zIPL74cXac2xFg\noVNVFVmVS3eQKEvQOcrozuC/fWSkyD5ISvIJOo8YM5mQHXYIP82E5DtpGXG8VFLE8HUP8vPP8MiO\nh5mf3gGAs/mhvJz+CKmp8P2KeHKILFfQbdjWimYFtcnKAvR6mrGX+om+47/xhkimGz1a1EAePRpu\nuw1mrUvkFAkcy0pjZ5iVkGIhPKxOK7hcSEBWCJw3OYXkdgu6gaeCCDIEoXd/1A5qdCsvdH3B53KN\nPAl33saBrHikE8m8fnAjq9WuzEooYvjvwwHRjsvTyWEx/RjWbhcTeYXN+LpQuHRAszlM696GzqxH\ndjrQKSZe4hX2HvK7b34lRDyKrXNn2N/tEZ5u8DspZ1bRkDS+jxhJfDnhTJeKrMowfRdfpvo6jIRG\nGugT+QXsGsKuM6J1W42wAqh+AFmReX9nbe7Xf0nz8EWYo09VbjxFhvQWZKULa+oHH8DDD7tdrpqF\n7l/L9u0irNYTgVGKlBSRsqxVib4olRJ0kiQZJElySZIU0FBFVdVstWTBJw0NjSuOhET7+PZ0TOhY\nap2/iCtX0C1c6O1OUN7+IaaQAAudR3wYf/pFNPn0WMwuYqELGHP/fp+FDoSgO+X+wva4S00mZEcx\nGItpUn07ERa7NwnBw/4WgxkzBi44wihyCldeZoGFLzMGsHUr3DexDidJRC4qJK84z3tOdpedT7Z8\ngqrIjG2+mClTYGdBPfIIp6QpqrhYtI1q3Rp++EF4ee+Z1JQ/Te2pO78nrW85T1yeTNfEru4LJ8Z4\n5nq4e6AQh/tTZaQJL/LEimz6Gt/wWugUWWybGJHIHOVW75ibHnkLY7PviLKcoPveT0g/2p8Maway\nIuOQHV5B57leP3APaTQQgk6vR5WgbkExSUF5SIAsOzCZiyggnJtv9tvXLejGXQurlWMBy42qDofd\nXS6lHEFcGRRVgZuGMbCxX2E/k4nauRC5cQgnLogCsZ4CyoqqMFb6i7QYPYvTx1MwsHIOH5figtnz\n+enJEzBmDC+pL7Nw6C9aDN2/nJAQUTqnzAeI06dh717N3XqJVErQqarqAk6i1ZrT0PjHUVWVYlcx\nQ1sN5aqI0tXS/UVcuYWHn3oKvviizFUe4RZuDve6dv2PZVy0BF5+GXa7A/Zzc31ZZ/6CzmBgTGo4\nb/R6Qyx74glhLvFY6ABuvlk8lvfu7atj57HQAcWuKG7bM5GtGYk+C12jRkS8O4HgYPi560fcXWM5\nAC3Dj3G6y13cdx/Y9h2jBbvZl32QyLci2X5uOwBTN09l5KKRrIjMBUkiNxdaL3+HlfQoleX6yCPw\n+eei3ezttwsNWrh2J72NvkK3rc8qrH5wNQnhCZzKMDOSqdisCd4w/moRTl5pv5DrwzagN9l9gs4l\nrmWkJZI7XI28x9PpDRiDsrinwd380HYK7ZX1yKrMBdsFHLIDYwlBd4iG3M0sikJr8NOyKAoccRz5\nCPoX1ATA5XQgZzRiH40DBOvyXdXZSlver9eI91e28WlliwWTIvHG8d+YXTf6igg6WZGh/lKujvWL\ntTSbaZoJOUVXM7KDyMjw9NKVVRmpxQ/cd7W79VwlW1W4FBfccwNDdt4PP/1EjbmfUOvVR0iMSKR+\ndP3LPh+Nv4err4apU31hqgEsXSosc717/+Pz+i9SlRi6ScAbkiRFX+nJaGholI9dtqOiEmIKKXP9\nJVno7PZy3aKefV7p8QpHn/TlN3ktdOFuMeaxzOXmisdqnS7Q5RoZyTtrgnih6wtiWXa2sMb5W+ge\neURY7ZYt8wkOkwmjU2ZoXl2SXEFYlSBh/PKojnXrfHWoyoih0+vBEmlBAm+bLk/7L09GarEkg05H\nVBRs6fMiy7iOOStqeM91yxZRew5ELbrz5+HsWQiJNBLkl2nqfw0LrRIr6Mm8ZcfIOtUfgOoRDiY2\n/YWFTcfy5v23MbOmKJLrsdABIMvcutsCmY2wKxasJpjQW+buepto5hDWy/TCdOFydQmpWEgILsnI\nXfxIH1LIcYVx57OJ7KQVAMuKkllCH2TZSe6yFxnLWwGCbtwXdfich3BmtmTR0sG+5FmzGYMMF7be\nxl1HL7DZfjX5eZfndPEmRRj93LwmE1MZyQ5aeee15kQ7mD8DRVVE0opnvlURdNUPUot0mDULJkyA\nwkLGdxvPj4O0OnT/SVJS4JprICbmfz2T/wRVEXSPIwoJn5Uk6aAkSdv9f67w/DQ0NNx4EiE8xYVL\nYjFYeK9IuAHLFXROZ7mCzmuJ0xnLXh5WQtDl5EB0tGie6W+hi4jwWXgURaw7eVLsFxVFuZhMhDhg\n/MF+zNnzLDPaTqdj2F7ffP16gpab5ep+zDc5hBjwCLr+DYTQMigqmfZwbDZoV+MkWVTjfK5PcDz5\nJLz3nm+YG28U1josFoz+yZOyL4C/cY0L7KIljetMxRCc7puT1QohIdSLrkdyaBNWzYSetZIDjtFz\nY3OYtp/jecKyZjOK84xzh0ieLzzvs9DpdESSyxdbW/Mo03mG96hZQ2bn6pU8+dgy3oy6jet3vMXH\njEJ2OYm9cRyfMDIg9ujPGcf5mFEoLebw8VvDMZth3z7QfTsTTnWC+ouJ7j6KDsoONq2pIB7yEvDE\nren975vZzOu8yAY6eQVdpBxN9xO1aRXXCgUVnd7An/Ti0OnK1RzzCkgV8V4JCRH+8yvSlFbjH0eW\nxQOf5m69ZKoi6OYD7wKTgVnAbyV+NDQ0/gYiLZEcHnWY3nXLdj8EGYMYXdic+AKpVJ0zLyVaafnj\nEYEGdzmJPw79geV1C+cKzwFgDHU3kPK30EVFCQGXlycEjssl3KoeQZeXJ4oOnzghChB7XK5lYTKB\nw0FBsZGtBQ2x64IC5lssG+ncGZYvh0xHBHl2i++cSmS5lhR0SZEidbVIkum39CnGjAH0emZzF0/c\n5SvH8ssvMHYsXLgg/l66FB59FDCbA+PYPOMCuFwYcdGp2dOEVN/mW1dYKEQFoDea6HYCqpv8zl+W\nGXJ2P2tJpuv0h2D1C3CyE1YplFi3oEsvTHfH0KlgMvE999K76Tl6spI+LEUXZCYkXCWthoxqtDGk\n9koWciPvHfiKiMgshktf8vzz4lgHDsD+U6EYJAeq5BPutWrBpz1/Qo08AtHHaJT0GfrhbenQrOxa\nh5dKrzq9ePSjtzhw1i870WwmnZo8xnSvoLur0T5Wxj1OsDEYxRpDruMq7udbZq2pXBN27/tXEeOs\nP5NEPxZSeP7yzkPj72P7dpg5M6DMoo+tW8VDoyboLpmqZLm+UtHP3zFJDQ0NETxeL7oeYeaw8jdy\nuZiwRuK6uteVvf4SBJ0nm9WgM2CX7RTYRRN1o8kirHH+gi4y0ifoPAkRnqaosuyrVVdYKITdRSx0\nBc5ClOrbWd/9aepE5gRkuSp6I40aiTJtPZaM5aWTwwBYlt6c8IWz2LIF2nQ2s5n2mIrFOXoEXZhJ\nXLMCvYspnX5m5Eh8ItDPJVmzJjz9NIwYAePHi3jsGjXgmgGxLHYNLH0tQZynXo+k+tp/ZZxXmXmk\nK0vyO4uGGJ6xXIEu1zAKSWY9n9yyHELPw1frGbFhKLtd7YkwR5BemM7Ia0byyFYJTCYGM4cG8UXE\nc5opPC3c1G6XZrvQhXzd/WsAGoXWoZehAbfp53u91NOmwQPjanKwGu77a/TerofabsMaeRaARKsT\nOWE7IfrLay8XZg5jZe5AjuXH+RaaTNiwEEY+P+9uKJbp9eByoaoqrHqJqTtns422jOm3r1LjuRQX\n/Po1q5x9RIuzEAsWinHlF118Z43/Ca/MXMNTz+eW3WYuJUV8tnTo8I/P679Klbr1SZIUKUnScEmS\nJnti6SRJaiNJ0mUmumtoaFwWLhcPb1ZITuhU9voKXK4qKtWCq2ExWNibsZcPN30IQHRQNEe3d6WL\nPVZ8+5cn6DzHjXBb8mw2ET/nz0UsdJs4Q5vGqzhrkTlpj6Wg2CjmrNcTHKrjq6/E5/v0LrN4qPqv\nANQ3neTlZr8QGwsdO0qEm+2lBJ3ZYMaoM5Kvl+kaf5TmzeH+VcNYSfdSWa6TJonwq8OHRXcySYLW\nraA6FwLn6z5f2SFjN4ayMR42J4DNAGlHdAw9+Dw3rBzLr7/iHSOj8LxXIPuLu+HJ+2kfNYu2Nzdh\ndXoDdtGSuOAa5Bbn0q9BP/qkKT6Xs8HAS7xCd1aJ2De3oHPq8bqczZIBu+JkhOkb+vUTu739Niz6\nIZdJ7uRcg3/rL7MZq1HEzCXki0V2a9VqGfqzz9SaIcmHA8Yx4OJVJtI8Idd7PsgyKip0/JAHmz9L\nHOcJNV5CtxI/OiZ0pJ2uMzpnEJhMdGjrYh6DiNQXXPZ5aPw9ZLcZx3UfjCx7ZUoKXHut72FI46JU\nWtBJktQCOASMBcYAnk/oWxFuWA0Njf8VHqtRefXgKrDQJUYkkvlsJt2SurHy+EqWHF4CCMtHnQID\nwcbgigWdx0LnL+j8u0nARS10eqfwa7oMOpLmvsfs8z3FufjHYQHd4o/QxCwKV9UxnmZ082UkJsIn\nn0Cj4JOlBB3A9P7TebLZKX4KOookwQlrDGeIx+4KFHRt2oiSJXPmwKhRQtB9/jl0YV3pawmsPFgT\nS3EuR46PgLwEFAk6N8vH1agZW+57jd4DsrxfSr3+uJ2JKyaK/f1ju/R6ajiKqRW0n9OPvsFDfEHq\nXWuYdO0knz/KU0POZCKUQjKpDmYzNw9uDPtvxqnDm3VskgysWzKMj+XHvEMEBcEPv4WyadsXsHEU\n498c5xvfYmHxH5FwpDeHDjwLQPEVEHSl7p3ZjBEXT/MBjeKF0LIpZs4WRyMrCsQcpl5MaunrcwnE\nhcax5fU0blfmi2vlyZ4uvDxLo8bfg0N2sOXMFrrWaV96ZW4ubNqkuVsrSVUsdFOAr1VVbQD499dZ\nhEiW0NDQ+BvZfGYzy48uL3ulx+pTlqBT1VJ13crj4IWD3tc2l03sYzKVL+hycwNdrlC2oLuIhc7g\nlEHWUygFkdL/I24IXSPGNpYoaqzT+b7wXa7Ap/igIEz20oJuWJthqBLk6MWyVYM+ZjRTePdbX5br\nlCnwzDO+Qz34ICxahDi+TscAXWO+/8W90n2Nm8Sc59uQR7Et+RzS+qFTQZJd6K35zK69kIG/dfXO\nzyE7fAWaZZk06jOCz0kvDEXWiZZXHuFm8MQVuQVdsSGUV5nA/vPRfMMQFtJflAFp7ARLDhlKHM9v\nvIl2bMGMAYesxyUFXrf4q3SEBx+FuF1c31G4NO12+GBzZ3Y7r4LzzTlw4RaY9y0r119mdSqXS8zd\nX9CZTAzkN57zy76dfaAV8We3gKJnS/pA+l+IYU0i9D8/BZuzkuVTPO97W77e0wAAIABJREFUs9kb\nv+htF6fxr2Jn+k7ssp1OV5XhTfjzT/H/rQm6SlEVQXcN8FkZy88AcWUs19DQuIJM3zrdZ+UpiUes\nlVXgtyKxV4IDWQdoWl0Es9ucNp+lxSPoFKV8l2tJQafTQUKCWHZRC50L0vrTetYvtIpLJ0F31jt2\nYaF4aC8qIjDLtaTgCwpCb7Oz+J7FpWIJVeC3NSNYvx7Q6/mS4dzax/eFbzIF1sPKzHSPJ0lgNvN7\n8S3cswffuEDNkHzuC/6F95vWgeY/oFPd66xWDCaLyBL2CDrFiUnvFjiyTCGh7KIln6xrQXZ2W1+G\npnu9/+9iYxif8ghHsiJIoS8f84QoAzKlAOqs4qEaT/LWhu70IQUTelr0eZee5vUsWOA7nweG6emR\nOJnGIav59HbhW3U44MWULmTlNiK59fdMazIA8hLJy6mcy7MU5WQnD9AtogtrvYLuugbHWRh5D3q9\nRLviaKqpQbyc/wWLVnctP7mnPDzve7MZOSiUA1xN/vnLr6mnceVZcWgzZr2FVnGtSq9MSREF6vz7\n8GlclKoIOjsQXsbyhkBmGcs1NDSuIKHGUG9dtVJUJNo8Yu8iFrp9mftYdnQZ1YJF9Hyxq7i0oCss\nFIKqIpdrcbGv9lxSkhBFnnVlYTIhOV1Qcxuv95xCeLDL5yI2mUhNFbXhjh6Fz/Z1ZVZ2XwDSCmvy\n5aFu2O2wciWcN8Qj2YrpW78v8eGBYb2qBLuPdOLYMUCvZwB/0LiBL5bt8ceFoFu4UAi5OXNE66/1\n6+G0qS4U+MVjea6x20IYFXQazFYh6NxlSwxGM0dyjvBL1hpx6UsIutDonXwZ24Gv1jfCtvVp0pb/\nTJYzwndc8ArXyCA7Z4nnxk4X+IG7Resvsxmjyd2/tv00Zj/5LpN4ETN67IqTH52DGD0avv1W1GZV\n9QYMCsgSXkEVFgaFH3/N2yfnsvZAZ4JCI+HBHnRs5rPSVgVbngMzxczeUjdg+V3meZwnljN5wiWa\nEGOjn2EpOh18c6gT76ffRag+F0yFlWrZpShwNl1HMWYwmchXQmnMAcYsO0L7L8pw62n8T5k2vg3B\nc5f7/h88qKoQdJp1rtJURdD9DkyUJK8tX5UkKRF4C/il/N00NDSuBCGmkDJ7uRbYC9hgzqTISNkW\nunIEnVN2CtEGoKqcOyhKb3hKfdhcZVjoPG7XyEjxUzLLFXwWuqgoSEwUGbJlprO5MZlIM+RBxBmi\nWs7DEiT5slyNRlq0gD17oH59WJ9el+1FotPChvymjFh5D0VF0LMnrHAmCzEJuJx2ivIv8PXmufxx\n6A8AXh36MPfcA9mOUKwEl0qKWLtWJEQMGADDRCItN9wAc5TbAgWd0ynO0d0dQ9GJFFe9Cmu2WGhu\n38Lc716Fvbcx/ex8pl0DZ20ZmCSD2E+WeaMrPNYfTr8/lxmZ71MzO5QOn9zPBF4ViQIqWPNl7/UB\nwGjkJV7hD250CzoL/Q8BEadpUle4uE3oceDilfAppKaKTN2WLUHSSRgkvej9qg9MikBVIS+PxqZa\nzJsN8XLZ9Q4vFYPiYAqjaXN1YJZplrEmD/EF+88La61Np/BSuwL2Ze7jcH4N9trq8kDN56HVt6J9\n2CVSWAjxT93O7wwEk4nwuGBW0Y3YqzZzJKe8RqEa/ytsrT7g2jv2l15x8KCoW6kJukpTFUH3DBAK\nZABBwCrgMFAAjL9yU9PQ0PDnr2N/8dqq1wgxhniLDPuzL3MfnRuv42gU5Vro8s1QKAe6oHp/15ug\nSW4rz8yZhN95PwC3hnfg65u+pk3NNqUFnadQm6cOXX6+V0QFCLrsbLFNo0Y+t2t5mM3UzxeuyYZq\nNBPX9yUlr6PXQhccDM2aCQvaN/1/4t1o0Vrs/rBfUUaPISJCtKntH7vNWwfvgXeSCRkxggc73MaM\nlUsB0EniY6/RjDF8yJOlBF1KiigwPHGi6JQGoiTWg5HzS1voevTgwNu/M7lwFHaEr1ZSIUbKpicr\nQDKAbEIn6XlaGBQxbdwCnTqBLPNTU1iXCKpORzu2sYQbGNd3B9ciYogmT4bQuFAUpABBd5gGvMpL\nYDazM9XEAyuE+DZYRFLEyIjrGJxzPTZ9KGYz9O0rCiafOwfn8jpjzWzLn3t8sYPehIucHKIja3LL\nAQh3XF4z9B0Zm8jq/gkN6wQ+QNQOzkBB4toWWQDYDfBqRzv7MvfxWrM5fNlkCjp367nKCLqgIFj0\n6AK6GjeBJKEPMtHNuJFwc06lLH0afz+n8k6RFfszd99SRgeIlBTxXu/e/Z+f2H+cqtShy1NV9Tpg\nAPAEMBXop6pqd1VVtehTDY2/iTUn1vDptk8JMYWU6XL11JE7HA2HLhwqfQCnk87DYHy9EwGLV59Y\n7fsjPZ1rzsLx9+EmSyuGtBpCYkRiaUF31N0arE4d0S1CUSBLfEGXynKNioJnnxWBzhUREUHnU3Bi\nYUN6q3VYd7Y2x2xxXgtdANWq+cZzOpGMB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uCIfYNoe/CGdAYVfwN2Hs6kcWP4eWmxTjcO\nh0iQVcOtN4UbIXQVZVkeKcvyvuI7JEmqH+iAa0FpIdYUcLuOyiLZYR3Q6gbW97fMqULFfwI2G5PW\nwbLwp/x2mZWq2HYpgUNZdqcdnVNCMprEQ7GoiGCdeBg3P4/nQRkZ6bEsccFVGOEiI94KXWmrXBVD\n4pZs5/kuR8QcgCE3i+Wdp3P4sPCJA6GcfZxxH5w/zzcM5fG57QBhBjxjewsoLGTjuY2siMzEip61\na2SsuZFQaTNrxz5PtWoErHLNyICJE8XSJ06EOnXE9h49YNz+e33X651Pp9OhQYNLC1qZUode/Mon\nC2KYM8frXGazx+rEq+hg+9cHeYxZ/E5nRn+awGBmg8NBWUcjBga9zp2nQAoyoMWJpPMn5HpZIqbM\nNlo3KRQk0mIRPwEI3bYIsW7Z67rDY4MIRllXSAjB6ChylPRduXQI15m5j4VEl/MNEe/Iq0MPVnPF\nrPyuKPfG6bDB4QHsTGvBpDJTmNxpVfEpr4nzh6py6t0cTtg9vWOHNDnA1MgJN3UdKm4djlvXw2Nt\naNG60HfHnj3C1VsldDeFG6ly9dGvJUkKU8KwOxBhzetFDKAFMoptzwDK38B8f9ecKlT867D46GJm\nbp/p2WC3U8YC5eziYbnm9BrGbxgPeAidqcjpzk+yO+3uZPF6sfV4+rRiAqwodEadkZ6JndnwLf6E\nzhuBCJ3Ll6C0Cp1O5yEgOh2L/ijDEvqAw0FQsETdutBO8DamTYOtj34BqamMYSIb3hDe5j/9BIM7\npPjkuo3ifbrdY+LoHrGObEk8THp8fBePMMenyKBePdEIo1kz0Rf2lVfE9i+/hOebbfWsNSjITegO\nU5cFF9szo04u6UpEWmu1EEwhe46aRA6eTkdmCHx0YTGXTMocBR7bmagoGMZX7KYZdWvZacIesNuJ\n3TCTH15/l72OFoGLIhBFwXJ+HDatcg8NBuTsy9jRIof6EjqHA/bbouFsW85lelmCFKtyDUZHoTNA\nP+BSIDUnlaFLhxJrymYSr5NQxZfQdal4klQSKBvj9Lkep80GA3vzUvvPrl0UsXAhdOiAo0N7hj2d\nwOldovR5ygNP8kX9TygX5iHKjavn0c0ZyPBMxT+BHRc3Ub1RJlXjimU+JSWJv/1Wqt5yM9D99ZDA\nkCSpPSJ8eR+QDvwCPHuL1gUgAbc6+eEv53zppZeIcLUuUvDggw/y4IMP3uKlqFBx8+i7oC8Az7d4\nXmywKblzCqHpPk984x3bYayH0NmUcUFBTPhzAnP2zyHlxRQ6VO5Ah21RcJ9C6BSF7pJNSVxzEYo3\n3vAY+7rgInSuMc89BxUrit5ZodcROouMFERJp+OnZWEYuI97WOqXhwWINg7p6UTgJKKsePi3agWk\niFDlb/2Xkz/mVcxs55nBZh68pwwX+wbTvrNQcB4aZmLJ1y1JS9cEbDPburX4AWjRAlimKHIajU/X\niBX0ZNKR5+mfGc6eolje4BQTQtbQnfc49/ZWluTuIM8xkDORMLxgEZ3CINaMb9K/F0l7qL8NRswE\nR1cW5H3OG2RSjgwhPxoMUKGCzzpffhlOJ82nQcs2oNNxMqg+2hM2qmHnm60Heay/Z2xODqz8WhDT\nBVyieW9lh3J/72cB1T9NJChGR2GAPMzSYOqWqczaN4vnY+JppNH4FqwAwSYNCZwHg7jmbLORebxA\nqww9lc0GjBoDTo2ExVlIsNOBVlNMkZw3D1JSWNGyM1+dr0nDPxbyXLOuxMXBsHJLIdSLLISE+BBn\nFf8stqZtpXVia/8da9ZA587+Lf7+j2H+/PnMnz/fZ1tOTs4tm/+6CJ0kSXGIQoihQDiiICIIuEeW\n5SM3uIYswAEU7wtUFn+F7W+fc/r06TRp0uQGT6tCxf8GQ5cO5Y6qd/jvcOVdFfmrK2abUC5MNgTh\nCwoiy5wlcstcsFiEuqaEXI16I4U2oWgVEcTVDIjpe39xkcijwrmUnvvvFz/Xi6godqaWI21/VRZ8\nb4ewh9lAeyasHM7i/GLcMCHBQ4qKV7kCncu2hAvhwE88MOEDCDHxxo5guENUCrftXYaHXijDsKME\nJHSnT0PNmrBunVDrXP1O0evF/VEI3ctM44Um23n4yOtcza/KcbRgWQrAiYJzvLj6RRa+NpSqeeOB\nsRi8hKcl9GEw33KuMBX3u+DV+qtu1AV+oR8A3/xZg29+78aWsb7rHDECthaOZGPaE/yyLpypZ8ZR\ny7yP2QymjYvoKwgPhzV3TiNs9QIqD/kOEAnoL7woEaWdQCfHeso17cyXH87m13wdH5f8TpWI28oL\nO5R6zpjARDzIN9SakWtkNO/y++VcUn5rAM3i2Bx1kra6b9md8ShN4op9HicnQ69eLIm/F95uy8D2\nX3r2FRX5tf7CbHb3iFXxz8FsM7M/Yz+PN3ncd0duLmzdCjNm/DML+x8ikDi0Z88emjZtekvmL3XI\nVZKkZcAxoCEwHIiXZfn5ax/115Bl2QbsBrp4nUtSXm/5t8ypQsW/CevPrGdr2lb/HS5CZ/XNf5Jl\nGbPdgiRDkB23kpdtycbqsGKaaCLpVJKH0HmFXF0txn7eWJ7y5YVA4ofiIdcbRWQk83mQ0YuaeHLo\nsLIpvRphYaKDgxuJiZ7/F69yBXEtVk9RBMCiol5Ufm8YVqsQD51Or9ZexRATA59+CrVchcWuazMY\nxPkUQqfDQbA1l58TXuIEtfiZ/p5ergZBApu1slA5WKQdexO6OhzlDSbwzHuJTOMlET5wERK73ed+\nVk6w07mz/zrr14cTdbaQcrk7W/eb+KbnL4yzvMpg5lCjrq/iodNB12rJtGS7j8hasSLE6y/xDJ/R\nr7+W+EY/U7XG7MA35i9QZC9CK2nJuaJnmdTHXyArRujqViuigFBaNLS4idf7ye/A9yv9W3/JMqSk\nQJUq2OrNpemQskR7d0xRvqi4cPRqHK9qx7P12HasN5kTqOLmMGf5cezfrqBWcFvfHb//Ln7X1fy5\nm8b15NDdBXwDvCXL8gpZvqXW29OAYZIkPSJJUm3gc4Q1ybcAkiTNlSTpXddgSZL0kiTdJklSI8AA\nVFBeVyvtnCpU/JcRaghl9wXRXP3V1q96drhCropCN+9ewb7MNjNmmxmTTeQduMZlmbOID4vHYrcI\nBa+YQhesC6bQIeZq17yQevVK6MpzqwhdVBRTGcGBKWvEA1+vpxXb+GOgUGH27BHDfv4ZEvs1x4GG\nD3iZl2aJeqz33oMft1QUgywWz/1Q/q1KMg81PeYW9CSpZFP6CxdEdDU+XogHcw80EjuKKXSAuN9a\nLcepyU/0JzvPwEmqo9UFg1PDp58YOOsQzFDv9clZixOM4AMqJ9j5mOdIJJVTZ/Vsoo0gN173s0vL\nAiZcK79/wH1MGZtHnfo6KqF0zgjU+qsYoQKh8j0Z9oN4YTKRUHMrFeJWXONkJcNit2DSmzhwLpI+\nlh/JKB4TKZ4L6PrX4aDjyS8Zvb0PAyvMgy6j/Qlddrbw/6talS2pW9BveY1FO8X7ffIkfJTWjyKd\nx6sxtSCaOdIjtJ7Th4z8Gw34qLgVOJBxEK3RQptadXx3JCWJnNzL0DguAAAgAElEQVSqVQMfqKLU\nuB5C1w4IA3ZJkrRdkqTnJEm6JYYxiv3IK8B4YC9CBewuy/IlZUgCvsUM8cq43cr2EcAeRAuy0s6p\nQsV/FiGGEDILMqkbW5cx7YXdxLrkdbzZzjeHLjJYeLvlFuUSSTDNlMJT1/5sczYJ4SLeaLGZhUut\nn0InQq6VKkscOgSNGwdYUPEcuhtFZCQaZPTBykNesS5pUTkDWfZEZWrUgKGPa3BKOkyYCVEKDQ4e\nhORLXr1kbTZ204QqnSqxYgU0lvYxodc2NBqRtrNsWclL2bQJhg0TKt7+/XD8svJxp9f7KHSAuG8a\nDUl0Zwiz+ebyvbRgO+NGNoJ5q0l8ZCw/91skblGAr8ITRlxlIfcxVTOKb74P4lHmgN3OdxfuoArJ\nnKHSNUOGL5xW1qbVQlycZ0cpe7kCIqSskOhVxieYt6r0fVS9YbFZMOqNtI07TUa5hlSqVGxAcUKp\n09FxMHx67DseDl9Gp8RT1A8/BpX/9Cd0yckAZMSHk3wlGWdWY85cEgTu4EEYc+FZrFrPuru1s/B9\nxcoQkqW2//qHkR65iI6vzsSg9/q9k2VB6FR17pag1IROluWtSpuvOOAL4AHgvDJHV0mSAnxylB6y\nLH8qy3JlWZaNsiy3kmV5l9e+zrIsP+b1+qwsyxpZlrXFfjqXdk4VKv5LOJZ1jMTpiey9sBeAEH0I\nTeOacviZw+4cuO2pW/nSlW6kKHRtK7Zl35P7KGMqw2DD7Xz5K3zaHBxFgqRlmbNICBOErtCiEBSj\npyhiXMdxnGw+V2y/VsLyLVToANDpePlluLtwQcBzN2oE48Zr0MfH8jSfM+GJswD88AOMfvqKGFRY\nCDYbI5nCmfMGoco5nW5Jbu5cEVItCUOHiuEaDXzzDUwccMCzFi+FbibPcXf6F5gxMYCfOE8FBvID\nK7mL/o9mQ+uphDfYSEH8CSAwoUOrpQl7eUC/iJGvyPxJe3A4uCJHcIYqbKCDj72KN1asgMN7RhFT\nIO4b8fGYMTKZkZzK9P9YHrvlTiK5wuo/Tb47goJYF9STo8ekm2r9ZbFbMOqMTJTW8GyP0/48NIBC\ndyoaMi1ZDI1YSNeqp9FK4lr9CF1KCgB/OLJAhoV1x/JKdZGv2Lcv5Fdp6MthQ0PdBs0Op0ro/kmU\nDylPzxo9fTeeOiXeU5XQ3RLciG2JWZblWbIstwUaILoxvAZkKnl2KlSouMXIKcwhLTfNXfEXYggh\n3+przmm3W9E5YUMlGG0UqaIRwRHcVv42DFoD2GzsjIdne0JhkTg225JN2ZCyGLQGLBbF8NYr5KrX\n6tHZlAfhtdS34kURNwovQtexI/QNW0cmsRy5Ehd4vKuaoaQcOpuN+TxIxpr94pkhy25i9MYbcPas\nV2uvYrh0SSiC7u4UrqKIYjl0FTlHY2kfbU/NpjwZ9GcBCUWnacl2Wra3QvU15J5sAOeEJYM+yOgm\nlaeoxiyG4MBDbqJjtVQgXYQgmy5lcPcI7gr6gWUbIjh3zn+dyclw9nJTdE5x3z7/sy6v8x6TeI3T\nqf7vx7b0iuQQic3h+fg/fRoOyfV4omgm8+bBxrQq/GjpLe7XdcKl0J135nAuNIDnYfGiiJxgLn51\nhtMHqrpz6DTK77kfCUtOhshIps2MQ//VIRINMf6hb+/fwdBQtMolqArdP4sv7v6Cl1q95LsxKUn8\nLXXq9M8s6v8YbriXK4Asy8dlWX4VERJVfT1UqPibUGATid+uXq6hhlD3Nhccdhs6J+yNgxmhh/0n\nsdkIdhXBFhZgsYm8uTKmMgTrgsnITedKMIK4KCFXgFeSP6P//bDtYAjNmpVAgG5hyHUMExjy4W30\n7g1Dyq1kLo/QeMZg1q4VKVQ+cBE67ypXF/FSCF05MikbLhTJI45arDxSGRDRyLvucvsZ+yEtDV5/\nPUDrL5dCZ7OBRkMfljFeM45JCR8zk+cYw0R3f1mdVhDNjNVDYLPIdTSYwtxzbaE1Q5nF4ZMGzlBZ\nkByXEme3c8JUwLetcinSQp9nE1i71n+dzz8PA+94hOw5+xgz0US2IxInGi5H1wgofKx56hdkJO7u\n4elEMX48PJ3xFrsS7uW112DxwepMZUTAaum/wnO3P8fce+bidDrcSpsPihE6U4hEaO3vCIm47CZ0\nGy+3g20vBFboqlThvn4aBj2TKqpYlZ66E/+cyNsNr/j1cnUpdAH7G6v4n+DgQdHdxQ9JSdCmzfVZ\nG6koETdF6FyQZdkhy/ISWZZ7//VoFSpUXC8KXB5yehEmC9GHuLe5YLdb0cqgc4KdAMqIzUaQIlIU\nFRWg1WhZMmAJHSp1wKgz8s7ej7jjEXwUOoCLRdlcMkHy+SB274bvvguwwFuo0NXmGLfVUK7NaOQh\n5vHTkCS6dYPeyidMQYEojLgYXZczVOJcltF1iZ4cKiXk6t4BLHD0Y9j8joCwc/vgA99iWW80aSIc\nL267TfAMu16Z15VDB54ctaIiukXu5Dk+oSMb3OOCdcHglAiOPU/V+h/w/HYwhEa479PDfIcDDcNe\ni6YzvzHXMVCod4qx7jjTDgBMDjtZO1MYMCDwWh1aifAmH9DjTpkx44OYGfJ64Pw5CFgUMXEizK05\nkTLhNsLCYNrjR9hFc59uFqVFlagqNK/QnPNnGnP4l9U+AhrgF3INC5eIafUmkeXS+CXvDvZnVeBI\nXn040atEQjeyf3tmj7mTS2FB7LIJH68d6TvYHeNrW3LFHkbXtDw4cq8acv0H8fXX8PTTxTZarbB+\nvRpuvYW4JYROhQoVfy/cCp1BKHRtEtvQrVo3nzF2h1DodE6wSwFCZTabsCwBiorMGLQG+tTuQ2JE\norvFl86JT1EEgM1hQ++EgQOFQjZuXIAF6nTC5OwW2JY8zDyGP6BUJBqNlCeDXo3PM3EiTJ0qNl++\nDP37wz75Np7jY174QGTe9+wJg54VhSDetiWnzujIzITX5Pc49NbCUi1l0yaoXRsuXhT5WfdOULyi\nDAYPaXCpgQ4HaDT8Tieasou5PMxoxzuk7KlKwulxFB7phsNcHqeEIFrKcRKgQWbuR1doodnJl9bB\nLFsGfeTF4HCgKYoASyRBTihTpmQhw66ByNrf0ba9RhDC+PjrInQJCVAl8oq7CMUnbH2DWBNygtzo\ns/45dAGKIjQyHMg9yQOWMXx9vCoTEuex/I53aVCuge+xyck+1ZB9D9xDi0OiFu7M+o78vuSIz+9g\naFkTT4a9CWUPqSHXfxDTpwt3Eh9s3iy+mamE7pZBJXQqVPwH4DIFdoVcH230KBM6+3pYuHLo9Ghx\nSLK7pZcbPgqdr/Iyrfs0GoZVR+9F6ArMEvv2gc1pE1YbBgPR0Z5nPcCJE3DsmPIiOvqGQq7btnn1\noffKoTtwAJblizonnVHP6NGezkAVKogWXV07O5jKCCa9LIrXR42C519QfEiUkOsjzKHGwy35eFQq\nwRQSGSLUuvHjBQEsCbGx0KuX4AcvvgjDByok01uhCw7mBDXYTRPQaIjmMrezgwzKcUSuw+rVcHlb\nLwbPHk90taUeQleM+Nas7mS+YTCbovug14NRKgS7nQtr3ob3r/C9fUiJVa6yDE9frMaSH/GMuQah\ns2mCOEU1LNZi8wUFlZ7QXbjgLlDwQ1GRaM+VsBP6Peye0uc84FMUoXVCjj0f2/DqPN5xFSbJQM/8\neGJMXl0fHA44dw6qVHFv6th4E86ur+FwOjDGnaVSxXk+91YfYuDh8JkQc1JV6P5BaDS+xdeACLeW\nLSskcBW3BCqhU6HiP4ACawFB2iD/NkjAsuPLOHrpKHaHIHQ6vXig2Z12Rv82mm/2fCMGeufQ2XwJ\nXd86fWlgrOxR6AwG5qR3pXFjodDpnASscn36aZg0SXnRtKmQtICNG30tQbKzA3ODy5cFSfvKZThU\nrRqUKwcJCfz8Mzx/7Bmxvdi5NRqR+6Zt2oja5XOo3UoQwS5doH0nrSCWCqELoohXWm7i8W/beA5G\nmOk6HCV3hqpVSyiCUVGiK1GXNkWetbiIa1AQ06VXGBa7hG8v9eRN3qEHqxjJVJZEP8aUKfDWnHV0\nrNwRDZIwDo6I8FcyJUmsS6ulRw/40TQUHA6eabgB4ndQXr7kS+h0OtFeDfjsM6ixKon6meLazGbY\nXKE/cftXsXu3/3Xd/n4/anCK37cVY1p163Ln6U9YuJC/JnRvvAFPPhl435o1cP/9dE6GlkUBnK2q\nVxc3XyGcDklHwal7KciOBZ0Ng94TcvZBWhrY7Xx1vL3bYPq2Kpeg5kpyi3IJrXKABnXe8b23kkRd\ncwgngkdSr2y9wOtV8c8gKQm6dSuxelvF9UO9kypU/AdQYCtwh1uL4/Flj7P42GIqGeNpdNGX0K08\nuZI9FxQ3XpuNIK3YV1yhA7DZregdIAcbkQ1B1NGdZPBgsDps6OXADrzTpoleooBQZZQXc+fC5Mme\ncc8846uGnTwpuv1ERMDs2dCjh7IjLo69qy5y8GoiY8bAsV4jmccg3lxcQju+mjWFWlSmjO92k0nk\nf9ntfMUwpjaZT0VSxT4vQpeUhL/xbUkoXuWqbBuf8SRLdiey7Eo7lnO3sBwB95hX27zKqV/7olk6\niw5nEYRVIR0L6UdD9vsQOkAQNrudt0P0yOktsGCi20Avtcpmg5kzAUE2v7rtE2ZrhrJvv8SUKdBx\nwTM8+3qEX8tdgAnTQ3j9dbi9taeQZP58eL5oKvFd6hAWBs/8kE905K6SCV1enmjZFACHD8NXPM66\n6ZfZOjHAzW3cWJQXK6TRJutITfqFShfuA0Cj1Yl74SyWP6d40O26mOA2mI40imKcnKIcHA67+OJR\nTCUONoZRw2JypxWo+N8iYKF0Rgbs26eGW28xrquXqwoVKv4ZdKrciYigwOWY4UHh5BblMqnmY7D4\nXRZ08RA6m9PGj4d/pEvVLnwetIAKraCMGWS7f8Wf3W7l1K4paColYnnRRCd9EhU/OE31mcdoE6bh\n1VdhyhRBzj75RBxTUrTk4499n6ujRgluMHWqKELYvh1Wr4YjR2DwYN9jx4wRz/pFi4BwPVlEkXol\nMJktESaTL+FQkvsnMprUua35fIgorktLE/yqVChe5QoQHEysIkL90uAtUcqn04EdH1WxfHnoZc7m\ngVTgnnJuchhPOp35nfmLh7DVOpUZ2vfEAS6FSnmfIsihWpXAIcPataF25Q3E7vuSUevggQeE6tmt\nW8Dh9OzpH2ouLBS3a84c8XrF3jPkNdwAlhL6otlsPhWwO8/vJD4sngrhFdh4KIqXmME99mDS9kmB\njai9EGTSkk00ezuPYdlJ0Gh0TDr7AF9u7kmy90AlxPvFnGBQuFlkaAzkwdW8S9iVKu/i6udCZ1/K\nHivrotkq/se4o2cutauZ+GSmF91Ys0b8W9IvqYobgkroVKj4D6BFQgtaJLQIuM9F6FwP/1gphJaX\nTcjIWB1WLlsu89PhnziluUozaxBZk4tgURW/eWwOK2eOjiA2RkYTLKpcc4sEKboaDPfeC7t3i/w1\nb6SliQb2AwZ4InXFI4pNFIHt449F7tv06W6ByQ+zvVuIGo0M5yN4pSvg23KgXz8RYr18WST1Dx4s\nwrx5eTDIaIScHM9gJa5anotIMWb3GotfS0lYuRKunI5iEPjm0HlfqEZDHqHs0behvn0nJm0oRkTa\n15NPwopy+yAbH4WuNVtpzVZmFQ1lpvVJLmYlMLcQkqlL1UIIVt7TO0nizqlmRPfCANDpuBRSBUYI\nCw93/9lSYsgQ8eNC/WbZOAonIJtbE7AzmtXqQ+gGLBzA/XXv5/2u7/Ngg0PM02h4/oXW/Pyzf+S0\nOCSdlmiuoJWsMOtPlra6QOeYrcSYLMCznoEpKeINC/YobVfzq8EfY7nQPZPc1ETSL0T6/fJNynmK\n1oczVEL3D0CWZbaHvU5s2faAV4l2UpJQasuW/cfW9n8RashVhYr/ONyETrHm6GQpz9a1lQgPCsfm\nENtyCnMwY8OkUR52Vv9G5XaHla53VWfvHhmDSUdSXit2bBChzD5njbRqBb/9BqNH+x538KAgAy6P\nuOXLRTpdoNy0+fOFt9uUKdC1qxhz6pTHXQQE33GrZkYvq5BiqFVLjDtzxuMVt3q1sDPBZIKcHJxI\nHKEOo/fcxxweYSizGN1HePSdOCGeK6XBihXw/TKlxDRQlSuARsNxatHRspp7WEKrtAV88IEgnZMn\nQw3bETGubFk/0vHYQ1YWhT5Kr9ANHDoE9bI2cCwjih9ONmc40zlFtWu2/kKr9fXiu0kEBYciS2C3\n5Ace4KXQ2Z12zuWco2qUqD79oeg3wsPO8sgjsGNHKU6mrNvpsEP8TspH5nN7mdM8nljszUlOJrtm\nAstPLHdb9uQXJcLuYaSfv0LE0SfYu/cTv5DrzqZPM6PRrNJfvIpbhhPZJyio+ymPDYzybHQ6hUKn\nhltvOVRCp0LFfxzhQeHkFOV4SkVDQjyWI07BlHKLcjFLdg+hs9nYnb6bpceWuud5P+hupu0/S4VE\nDRgMTMt9gjHPVwC7gU6Z/spQXp7odRofL57vLo/f2FgR8vOrbsSzvBEjBCHbskX0ZXWFcL2xfj00\nnTecPEIDVs+++65Q6b7+WpBEEK28lizBTegKCaYeR5ie0oc0lAUqOXQ//wyPPHKNG+uFTz6BlYsV\nElxMoZsyBd58U8xbn0McL9uO9xnFpLgZNGsmQtTPPw8Lauzl0XvwUejckCT6GlfxSNSv1K4Nm8v1\npUZEJtszq/ARw5nEayUmj586BZOP98Gi8YSlMzIEefbzgEP4CBqNXobJCpxO2LMHrlyBYJMoWCgs\nyPGfABTDP3E/UnNSccgOqkQJ1fct50ratn2Ku+4SxP4voRBVp9MOd75C21pnxbUWl/ZSUvizSjR3\nf9ePy5bLAPRsr4VXEtAGnWHN2Eqcsjb2u7dSaIjbfFjF/xZbUrcgIdGigld0Yd8+0YZFJXS3HCqh\nU6HiP47iIVdCQtzqiVuhK8oRhE6rKEo2G/MPzWfUulHueRrYoqifr5CCoCBe1U0jO0sLefEUBvmr\nQ/n5sHevSE/zFodatBAts+rWhW+/FXlyL78siEJxNGsGMTG4k9y9ERUFt1fMII8w8m3X6W9nMsHV\nqwRRxGZac+623ozhXbFPIUZNm4oIXiDSExAuNa5YDp0L9+1/EyOFLLbfTWu2cmeZnXToAEuiOjBr\nQxJbHA04XBZB6JTjzpHIb3T2mAlrtYSGQuuQ/YRoLHx02yxOU5WHmMfJlMAK3MmTMOnI3eRrRY7l\nvn3CgHngQK+2ZV5YulTkzHkrqAUFcPy4uCdJSZB3NRaO3kNBSUTIK+SafEVkuvX/uT8HMg5gwYZR\ncx32NVotXVjH0V238+oWDRG6UKw6iW/LnCPlipc1SkoK8y/0gkk5lA+JB0AfEcXhT6BfREuCKSSC\nXH+yHBpacimzir8VW9O2Uq9sPSKCvfJ/k5LEe9K69T+3sP+jUAmdChX/cRQPuXordFaHFaPOSJY5\nC4ckY9IYBKGxWt09NwGOXDrCOsthLgfFMWMGpJsjud2+hY07ciEsHUuQlhUrRJP6bdvEaeLiYOdO\njzecNzQakZzvKkJduVLwIFkWBGr9elHZGhIivqzPnes5dvhwkWvXqBF8NnAjT/MZD46vc303RSmK\n0OKkNVuJtZ4X10kdTisN6yMjhYNGqV0TdEr1ZbEq15Ej4Z134MH4DdzFCtqH7xP7lDFbU7ey5MdI\nVu5cgcGBj0K3mHu5m19xyhKpcgJ5KN5xXkURVUlhGi/zypjApLZHD8ge9CJtrixn0SJITRX39NIl\nH8s2NxYuFO9D9eqebbNnQ4MGglh36wYn98XDT4vJyy+hU4RXyDXlqiBdOUU5ZJmzKFg5nYX7Z5Ty\npor7Wo3T1Ak18v5vElH6MPbmV2TI1ZbsOK/EbM1muHiRsEa7KffQq+h1yheM0FDqXoKwQtmT01ec\n0IWoCt0/hWWza1GjqL/vxqQk0bv1ZtsEqvCDSuhUqPiPIzwoXBgPB1DoetbsSfMKzcksEFKNSVLI\niM2GxW7BqBOEbs6+OQzYWImnCqYyahQk58YQZr9C89v0oLNSGKRh3jx4/HHxxbo0PdvfekuM7dJF\nmA+Hhor8uvBw+PFHscwAxbYYDF6Kn8nEa0zi9aH+UtPp00KNyskRipMPlJCrG0qV6zN8yls/1weg\nZUtYsECso9QIDvbzoXOhX4VtrKAXraKPiw0KobPlRFNQVEiHFvcIg+ZIT+L+EGZzjNqYCzVUzNjJ\n6xnDxbHFqlw/5jk++qDk6gKnRsc9pjVUrAh33y0cPmJiSk9We/eGVatEnnp0NHTpmQMjY8B+NfAB\nXoQu+UoyBq24H1cLryInbCM6+AoTJ4pw819Cq+VLnuSOuumsd7TjQl4oW7Lqw6qPPK2/zpwR8yec\nomHnY55jXebJeXkeQleMKIw71odqZ97hyKUjpbkVKm4RMnNyyPi9H9G5nTwb8/JEhwg13Pq3QCV0\nKlT8x/Ful3c5/tzxgArd932/56EGD7nHmqQg8cCzWgWhUxQ6o96IXSqijvEsFgu0rS8e5AanhCSD\nxaBh/nzIyhJFEN5ITRX2JTt2iGfqggVCHQqEZs3Ev+fPC6UoUJ7d5Mnw1FPKC6ORVmyj9e3+zG/S\nJJHD17ix6PoAIpetXTv8CF1hgYMCTMxmCO/22FjyzSwBb74JHTogiFixThFuaLVcJop7UqbzBu8w\nJ+suTp4ENo/i9zltyI28SoEBEV5VCF04eVQkldAwUUt6sLAGAM9mvsXa5GpuQleRVKpUK/njWqvX\nMDlmMs2bX/elifkriiIVFyrHlOfh82ZM1hJIpCvkKsukXE2hQVnRoivLnAW3zeOypTJvvAHp6aU4\nuU7HmUg4U3CRzqxn5ZFKPF37N3gjxEPoFMuSZEe2u/gCwBYcRhSXmZcUy3vfxjGML/y7cFTIJLnh\nco5nHS/1/VBx89iXtR1eqsIrT3mZS69fL36nVbuSvwWqbYkKFf8B7Dy/k/Kh5UmM8O8kr5HEg37g\nsYmE94LPvRQ6gLiwOCpGVGTIIT117FFuhc5sM7sVumBdMLpWk3j7cn3gGTAYaMABRs6Rua+gEol2\nIWOVKePx8HV5zppMgkRFRIjn7oABsGEDbn82b8TFCXXPpcytXi0qZ6dM8R9bWAg7khOoTxTRAapc\n335bzHP4sKcgo0MHqFQJ2CMIXSoJjGcsu7OaE8d5VtALrKcA+PVXYYy8fv1f3HzE9SUkAG8H+1a5\nBgWRni6UsPIaDRqc2DV61nEHR7Ic5K8Bdj5DyDuRbLV55XEFFzO5lSTMleooNg4bOG6tTFaBhd57\n3+RXVvI2Yxl7rSpXna7UVa4uv95rqXc1y9Rk7vZ4SPS/74Dny4PNxpmrZ6hftj67L+wWhA4Y2vgT\nftpyl/t9uSa0Wp64G6Iu/8gpPiOm+Xg0W8Xi3IQuORnZoCe5IJUHIj1xZH2IgTGaN2kU0wibOQIH\n5/wI3V23nQXnhzjkNqVYjIpbhSOXjlDGWIbasTU9G5OSRB6Ad7xfxS2DqtCpUPEfwL0/3cvXe76+\n5pgM2xVyghAKndPprhLsVbMXZ4efZVxKJaoQSf+eZr617vDJoTPqjFiw+xjJtWEzb7xj4I1DA+iR\n5+++++mnIh2sTBmR81arljC5zc4WhRHp6aI71RNP+K/VxT3S04XZcJkyMHYsjBzpGXPpEnQY24Gd\nNA8o5cXHC2WpRw+R/wVwxx0iLIzJBHY7ZkwcoCHPyR8zivfd14ZymxISShc+7tZN6XQVGirukUIw\nzxaVp2tXcZ2/Zd3GE3zF9Lpfs41W/HL7JHHtr5ahenSxB1iAKlejzoZRL5juulrP8WDV7QyL+5X2\nbMCCsUQGtn8/lJ/1LofxtLb67Tdo3jxwwcdrr4mIbvEilcxMeOghUcQCiHtYQjHBE80v0GMQYLWy\ncY6WGYN/Itih4VKBkGYrReaVjswp116Q3Yj8q9FUI5mIMKfoFgE4ZEUhPHOGs1XrkLvsTYx5DXwO\nHxH5NfXDzjKkQzJvMNEv5KoNEXYzai/X/y2GtxxOyospSN4dZpKSRLg1QNcZFTcPldCpUPEfQIGt\ngFBDKNu3+7bU+uknjxGv3SlaH50IyifhZdh9bpvvJDYbTp2B7WXglCPTJ4fOqDdi0TiQjZ72VhN4\ngxqVbULsC5DA3KsX7p6a3oiOFnwlNBTq1YP77/cfY7WKvLoBAwQhGTNGWJikpIjcuKwsoeYdP2zn\nwJAP+XVPKR2AXVCIaS1OsJ2WPCZ/Q/sqaaI645VXANEy67vvrvPZ8v338Oyz7vvRb/EgwsNh3DiY\nefouFnI/WwqU9hl6PQYDbHl6LW9X/oPuf6ZztIfiraYQuq94nCf40r/1l5JD1ytyExvu+ZD0boP5\n9dfASypbFp54QmbrgOlkZIhClTvu8K3d8Mbbb4vnapSXNdjBg4KUnjghxN19+6Bd2nzSDge2Lbmq\ns+OUgKIidLv2EG6MJKTISU5RDtGHe5NjuT4FZs/65aTs7OO+dhehcyt0OTkcjkqEE32IknwNpgkL\ng7w8zIV52DX4kWVtiNIz1mHjWhi+ejhrTq+5rnWrCAynU3yxCwsK82w8fVr8qPlzfxtUQqdCxb8c\nsiyTb80n1BDK/v3wxReefRs3ii4N4CF0stHI+XAwm4s9jG022iSN5eraTyly2JCQCNGLUKpeNsHF\nhqyOlKhXD77+rQoxZPPb3HSahx4Fg4EePYSBcP/+wiqjVi3o27fkdYeHw9NPB06XOXcO6tQR5KNh\nQ2FrkpQkCGKXLvDhh0LFq1lXx/qLddi58/ru2YRtd5BEsRMbDDy7vAc//nIT1XXNmwumqTClr+9f\nw48/Qv368EvbaTiReLj6VjFWGdMqsRUmbSSyLo5qjZU1KaTDgBUTZh/bEsC3KCIqihxjXCAvaEAs\n59nXwnliQmV27BAh508+EfYkxSO7ILhu8fckM1OErlevFt5K9dMAACAASURBVDmJRiNUiStEe/KY\n/wTAhcuNOLppNvaCIsEAq1QhxApxprJYF/3A2dQ7ycsTXTxKg5ZdulOx2Xz3ta9IawQzjlNoUdi2\n2UzPYDOOS9UY1L1YxXNoKOTlUenks0xpjR+hu2QvC0f7UFRQxLXw5e4vOXrpaOkWrOKaOHpUFOVs\n3uy1MSlJ/FF37vyPrev/OtQcOhUq/uWwOqzYnXZCDaE8PEwUArjw8cee/9udDnRO0JtEiMluLVb6\nabNROyqD1PhtFDmt/Dlku2dXfiR8vp/P7h7GAw9AnTIKe7BaxU9oKL26CxFp8WL/RhO//y4MghP9\nU/wCIiFBkNEGXtEzrVY8m3/+2Tf/buXKwHNs3QrvvSfm6NVL2KccPAhr18Kb6zrwMGfpzhp3o3sM\nBnJySu43fy2kp4t5BwxQSJKi0DWqmuvuSKbRCvJxxl6BaMKJUAjd6tXwwguwa5eXYqaQjkeZy6PM\nBc1TPgqdRTJhM+sIt9tBpxNmyddAbKywh4mMFOsrVXWpF7p0EYTOhVq1YO6IAzB0lwi7FisFPrjt\nI3KzbufqxWRinE6Ijua5HVD/0aa8XKMbxxPvIzxcqIClIXURkce4rKtBF/0y3j5pIiHsFPpaS9Fq\nlV+EggIwmdz5ot74ILgtu9MtWOVqXCko8FOT96Qnwk9LyHvgyxLPb3fasdgtvoqSihtGhQowb16x\nXs9JSeKPNDz8H1vX/3WoCp0KFf9y5FuFh1aoIZThw31DmF995akctctCodMpOUP2omLMxWZjYUoT\nNIXhFMq+jCw2RoInmhGbuJc334TaNRwspTf5V5SOAAYDzz4rFLc1a0Qo1Rvdu4s+qvPmla6ALTgY\n2rYVvMaVXy/Lwl2kWTOoXLl090aWYdYsDxn59VcRUR3e5SDjGIcVPfnGWFbSgy9yHmDePN+epaXF\n4cOiV+zFi8oGvZ7zxLMjPcFdZODKcau5+H0iyeHBLc+xdi306SPuiQ/PCFAU4U3o+h8fz0ObnmRR\nVnt+OdfsL6tFtVooXz6wInfDqFtX3ODj/tWhZWrO5fb6LxCjV1TgqChGboEesa0pY7tIRJhMlSqi\nZ29poJUlNpHG71VkCiUHjcuex1o4nydbDBYDzOYS/WW+SX+Y+ce6UJD0LqvOTvaLod/ZthBGxmAM\nKplZuv7GwgweQpdZkMlLq19yGyerKD0iI2HQIPEFDRCfIb//roZb/2aohE6Fin85vAld+/aiCMCF\nmjWF7xiAXRYKnc4oPkVt1kJk74x/m43Mp94iocHXFDl9bUB6172TiLL7qG4UHwn7z0RwD0vZf1hH\nvkUbMIfus8+ErxyIZ/4jj4jihtq1S39tgwYJu4xZs0RuYEiIbx7+o48qvVkDoFUrQeAuXFAKIYBX\nXxURwOlDDlCVFBZzL2F56azmThbldCn9woqhUydRdesmmgYDi+hH+w/6MHWqSK1zEbo193zKTJ7j\n/qp7iIoS1zBlSjGyFaAowpvQvVZlAa/VXsrHmf3plzSMTp0oEWazaPPlTfrS02HTpuu7RqtVhF7d\n3oB1lNDmEX//toiGn9C00kzIzRUboqPFvwUFYLFQr1I+ycni2ksDLRoIyoeBfWhd96on5OxCQQF5\n+uiABSxju42A+x/EcdfT9K32qt9+Y7SJxrnZlKHkUHtekage8VboDFoDH27/kM3nNpd0mIrSYutW\nYe6sErq/FSqhU6HiXw5vQte3Lzz2mGdfhw4w5k07fX/qywFbGjpZQh8sKkKPZB9DO17LxrOK75rN\nhtEIRq2TItk3QVyr0YIso9cJotG+pZUMytLhqdp8m15cXhJYtkx8TgNUrSpy03v0EG2/Sovhw+Gl\nl2DoUFF9+dZbvu4bTmfpqlBd0OmUpSpVsa3YyvzYF5jGy6y5zf9hfz3z+nAwvZ4hzOarYTsZNQoO\nHYLBW4YhIWMM0fAcn9C35iGaNYMvvxRcLTXViywpk+2nIVeI9Muha1PmGK0jj7C+4mAOPjyZt97y\nD3O7kJMj2ny52qdZLKIool2767vGnTtF1fKJE4K8bj4SRFaFmiIhyhuyjEUHRhueMlpXhUV+vmCY\nrmrpUuLg3ndhn2isq9Hq/Hu5ms00WzGO117zPzY6WJGBos5SLiSAlBkayp4voG9EyxLPn2dVCJ2X\nQhcZHEn16OrsSt91XdeiIgCSkkRSXZMm//RK/k9DJXQqVPzL4U3oNm8W4YxkJQrUpAm8MVrL8hPL\naaGrzD2ndOgUQmexFiAjo9PoSMtNY19YAevP1yTj4GMUyf5GvTZJRq8PYuNG2H0ynLJcYsWEfdwd\n8jsYDCxdKooh0tMFiVi1CiZOvPHrGj5cCDq9e4uK1+eeEwWkr74qbDdAVKG+9FLpQ3dumEw4kahI\nKg+U/R0dDjAYSE11N424ORgMhJFP9cQiOnYURR1NY8+ixU5MpHJvvUpMN20SFiupqcqGoCBkoDk7\n+YGBJVa5Yrdjl/QMGgQHDgReSrlyIiQ8daoQ0woLBQfzrob+K+zcKULg48eLPMjz56FtqyDG1m3p\nr9DZ7RTqQFcUxIVzyhcDRaE7ftRJh9xlnMwrX/qTA03SytEzty4AklZHZlEEy3PaeTqAmM1M7riK\nAQP8j40MjnT/X6sNkBbuCtVeo59rIIUOoFl8M3Zf2F36C1HBxo0yw4YV+ztLShJSfKn77Km4Eah3\nV4WKfzmaxDUh/eV0asfUplIlYfERofS6fvll6NlTIjwonHu09ehwMQidQagj5iLxADNoDSROT6Rx\nnwtsSKtGwcHBdM8r63OOU6eg8NfZ5JkrMG4cfDhXKC7db7tIJc6CwcCgQSLEWqeOyN27WRw5Irzm\nJEkk4c+cKZL79+3z7TQxdiy0b+9/vNMpbE5yihXzpqRA9P2d6YUSD1bUOllvoGJF+OGHm1+7u4K1\nkYX168W6n6//B3b0VI8r8BmTnCzCwqtXC/IFuOOv22hJPxb5EzpXIYfdTtWYXJKSRHg9EDQaoY7G\nx4tTRkUJVdPb0++vULmyeE+feUbMlZAAES90olzddH+Fzmaj/KkGLN6dRI/3W/LwvXA8TFSQ6grz\nqSifxRimY/p0YedSGvwQMZp7a66E7OoU2gzsyarI3WmfkZWlDCgooE+jswEFnkhTtPv/Oo0/ocuV\nw+jIetZvLTnBMJBCB9A0ril7L+5VPeyuA59smM+CP/d7RNrMTCEfq+HWvx1qlasKFf9y6LV64sLi\nABHa69bN063hzjuFNUCoVJZchxl0OoJNYXyQBLHDE93HA2CO4teTtZhTbRrtMyv6nMNiAefVqmh0\n21m8GPRXC2ApIiFNKYpISxORwoYNoVo13zU+84yoaOvUSfCQunX/+rrWlGD59eefvq+ffDLwOIdD\nhHpB9IYdMAAWLYL77gPQ8zwzxU7Xk0WvZ/Xq0q0tEHr0ED8vvEDAXq4u9WHchk5k8CkPXtDQ8KpQ\nHJcsKda3NigICWjCXvG6GKHbcrUu69LrMtY+hvAQx18WmiQm3hxRjY315CG6Liuk0gmQmwi2X1Tk\nuVabDfOaz8gIMTKt5488Xg6eCRHXXk1/ju94FBJ+4OWBYnipSJ1OR3ZONMzfwPaEXXSstJELlVsR\nG6fE9M3mwH3igOVHusGux6DK72y6ksXQYvuDokwkkIbRGVPi6cMMYdxR9Q4ftQ8EoTPbzBzLOka9\nsvVKOFqFN7IqzqLDuBAkaanYsHat+Fdt9/W3Q1XoVKj4D+Hll2HOHM/rgwfhnnvAaKlOjtMMej36\n4BBe3gpV9cLyQa9RCJ3Gwe0VM4gyFnpKSxXUq++kbP/2VCpnJjwcvl0YykC+9yF0kZGCG3Xp4l+F\najAIUentt0XYtLSYMcND4GRZ2JDs8kpZysgouR+oXi9Ur9de89gjlC8vqljPrTtBD1azgQ58cHEQ\nU3mFGr99TvfupbdWKY5WrTwEslAOogvr2HwilqIihawphG53enk+52m+OtCCPXsEySxOUgMWRXjl\n0B03JzD/QgfuypxNtx8eZcSIG1vzzSBYF0xhdIRgzidPenZYrTybMIjcIXeTGCPeLEOE+Ibxdfpy\n+jwAGI1s2+ZuwfrX0GoJC8qARzvSrLaF4CCZ8lKGW7BcnJBHJ9tXAZWy+gl2qLaGauZEKuWX8dsf\nFBHMPM2jtCx/psTTt0howdqH1xJljPLZ3iROSIJq2LV0cDgdbD+/ndYJrT0b16wR3wLj4v65hf1/\nApXQqVDxH8LO+aeY0Gih+3Vr414yf/ydmAq55DotXlUBYC0yw6XabJx6VgwOzuWzAX/QIOYCmXI+\nd33blX0zRoMso3E4yZgKg6I6ABAdqyWBND5YWo3FuV38iiJOnRJRQ5dx6IcfisKGyZNFxWppMWcO\n7NghVLPu3UXl7MGDnv1DhojcupLQvbsgga7K2jZtxPkTq4n17tE254eLnWnHRl6pu6r0CwuAsWOF\n3x1AgSOYMmQTHKolIQGeegq2ZFTjLcbx49N/4kDDt/1+pUULEbF0WcuA4EY/b473P4GXQjek+iaO\nthhCd93vlA21uHMKA0GW4fbb4ZdfPNsWLBDvx/Xg8GF4/XVPqlmQNoiiKCUE6R12tdmopDkL4enk\nWK6KsaER5IRoWW0+wKGygNFIixalt5/JlmIoKpKgygaiI2VxL1x+MDYbC+jB7q29RfFOMXRraqZF\nzbG832oW42IX+08uScI/Y/58vuk9kzkfLy/doj78kIjX3qKGI5JdP04TSZ+fflq6Y/8/xaHMQ+Rb\n82mV2EpskGVB6NRw6/8EKqFToeI/BP2nH/HTU+tJThafld+8sJ/Lb31EpCmMXLnQpxzTZiuCNVN4\ncvKdfHTnR4zaohGyll7PVdnCqrPryJ32Hpw5gztZSalWHDBIx2RG8eeRWI5YKvtVLVatKmxKatTw\nXV+FCqL3dmlQWCiUqxEjRJVm165w9qzw2XOFJ995R3yxP3XqOm+UEp57KfgzdnceSQt28HT9j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htx82oVfZ+uVcPdWWL6e8RNyFHIfp1nG/juNT/yglQlcRwsKE95CHR21fyX8KRdApKNRxMvIy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IIuI0MiptHOzTUcGsRK0E0FDvc+fxy4XilVCUxC6uuahdb6ea11Z611otZ6uNZ6YcC+E7XW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\nu0zO4dFHxRvvs89i1+ndW1K0iQJTTq8TixLyGdmzy88VVyQWgOH87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P1hqR9D+l8Y/X7w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2+Hh+6AHL4ytaXxtOXBw3LLHyp8XWmEw+cthMcV0f9NDV1Ndgt9gbCeUDnpwcBq8pxlfn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\n+8ImqZYNDbkCbEvwM6P32fx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SyK/IJ8OVgTvOvVfH0mg0Gk3nRveh0+w9tbWwfLlMYVi0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\nUkskEXlYRNJd1/BLEelbl3O0eEEHbAH+jm0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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from __future__ import division\n", - "import matplotlib.pyplot as plt\n", - "import copy\n", - "import numpy as np\n", - "from gensim.models import Word2Vec\n", - "import os\n", - "\n", - "os.chdir('models')\n", - "word_analogies_file = '../datasets/questions-words.txt'\n", - "def calc_parm(model, vocab):\n", - " freq = {}\n", - " with open(vocab, 'r') as r:\n", - " for line in r:\n", - " key, val = line.split()\n", - " freq[key] = val\n", - " mean_freq = {}\n", - " with open(word_analogies_file, 'r') as r:\n", - " for i, line in enumerate(r):\n", - " if ':' not in line:\n", - " analogy = tuple(line.split())\n", - " else:\n", - " continue\n", - " try:\n", - " mfreq = sum([int(freq[x.lower()]) for x in analogy])/4\n", - " mean_freq['a%d'%i] = [analogy, mfreq]\n", - " except KeyError:\n", - " continue\n", - "\n", - " model = Word2Vec.load_word2vec_format(model)\n", - " acc = model.accuracy(word_analogies_file)\n", - " sem_correct = [acc[i]['correct'] for i in range(5)]\n", - " syn_correct = [acc[i]['correct'] for i in range(5, len(acc)-1)]\n", - " tot_correct = [acc[i]['correct'] for i in range(len(acc)-1)]\n", - "\n", - " sem_x, sem_y = calc_axis(sem_correct, mean_freq)\n", - " syn_x, syn_y = calc_axis(syn_correct, mean_freq)\n", - " total_x, total_y = calc_axis(tot_correct, mean_freq)\n", - " return ((sem_x, sem_y), (syn_x, syn_y), (total_x, total_y))\n", - "\n", - "def calc_axis(correct, mean_freq):\n", - " total_correct = []\n", - " for i in range(len(correct)):\n", - " for analogy in correct[i]:\n", - " total_correct.append(analogy)\n", - "\n", - " dup_mean_freq = copy.deepcopy(mean_freq)\n", - " for key, value in dup_mean_freq.iteritems():\n", - " value[0] = tuple(x.upper() for x in value[0])\n", - " if value[0] in total_correct:\n", - " dup_mean_freq[key].append(1)\n", - " else:\n", - " dup_mean_freq[key].append(0)\n", - "\n", - " x = []\n", - " y = []\n", - " dup_mean_freq = sorted(dup_mean_freq.items(), key=lambda x: x[1][1])\n", - " for centre_p in xrange(50, len(dup_mean_freq), 100):\n", - " bucket = dup_mean_freq[centre_p-50:centre_p+50]\n", - " b_acc = 0\n", - " for analogy in bucket:\n", - " if analogy[1][2]==1:\n", - " b_acc+=1\n", - " y.append(b_acc/100)\n", - " x.append(np.log(dup_mean_freq[centre_p][1][1]))\n", - " return x, y\n", - "\n", - "\n", - "plot_data0 = calc_parm('text8_gs.vec', 'text8.vocab')\n", - "plot_data1 = calc_parm('text8_wr.vec', 'text8.vocab')\n", - "plot_data2 = calc_parm('text8_ft.vec', 'text8.vocab')\n", - "\n", - "fig = plt.figure(figsize=(7,15))\n", - "\n", - "ax = fig.add_subplot('311')\n", - "ax.plot(plot_data0[0][0], plot_data0[0][1], 'r-', label='Word2Vec')\n", - "ax.plot(plot_data1[0][0], plot_data1[0][1], 'g--', label='Wordrank')\n", - "ax.plot(plot_data2[0][0], plot_data2[0][1], 'b:', label='FastText')\n", - "ax.set_ylabel('Average accuracy')\n", - "ax.set_xlabel('Log mean frequency')\n", - "ax.set_title('Semantic Analogies')\n", - "ax.legend()\n", - "\n", - "ax = fig.add_subplot('312')\n", - "ax.plot(plot_data0[1][0], plot_data0[1][1], 'r-', label='Word2Vec')\n", - "ax.plot(plot_data1[1][0], plot_data1[1][1], 'g--', label='Wordrank')\n", - "ax.plot(plot_data2[1][0], plot_data2[1][1], 'b:', label='FastText')\n", - "ax.set_ylabel('Average accuracy')\n", - "ax.set_xlabel('Log mean frequency')\n", - "ax.set_title('Syntactic Analogies')\n", - "ax.legend()\n", - "\n", - "ax = fig.add_subplot('313')\n", - "ax.plot(plot_data0[2][0], plot_data0[2][1], 'r-', label='Word2Vec')\n", - "ax.plot(plot_data1[2][0], plot_data1[2][1], 'g--', label='Wordrank')\n", - "ax.plot(plot_data2[2][0], plot_data2[2][1], 'b:', label='FastText')\n", - "ax.set_ylabel('Average accuracy')\n", - "ax.set_xlabel('Log mean frequency')\n", - "ax.set_title('Total Analogy')\n", - "ax.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conclusions\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Note: Wordrank can sometimes produce Nan values while model evaluation, when the embedding vector values get too diverged at some iterations, but it dumps embedding vectors after every few iterations, so you could just load embeddings from a different iteration’s text file." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/Wordrank_comparisons.ipynb b/docs/notebooks/Wordrank_comparisons.ipynb new file mode 100644 index 0000000000..db008f45cd --- /dev/null +++ b/docs/notebooks/Wordrank_comparisons.ipynb @@ -0,0 +1,1223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparison of WordRank, Word2Vec and FastText\n", + "\n", + "Wordrank is a fresh new approach to the word embeddings, which formulates it as a ranking problem. That is, given a word w, it aims to output an ordered list (c1, c2, · · ·) of context words such that words that co-occur with w appear at the top of the list. This formulation fits naturally to popular word embedding tasks such as word similarity/analogy since instead of the likelihood of each word, we are interested in finding the most relevant words\n", + "in a given context[1].\n", + "\n", + "Gensim is used to train and evaluate the word2vec models. Analogical reasoning and Word Similarity tasks are used for comparing the models. Word2vec and FastText embeddings are trained using the skipgram architecture here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download and preprocess data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package brown to /Users/parul/nltk_data...\n", + "[nltk_data] Package brown is already up-to-date!\n" + ] + } + ], + "source": [ + "import nltk\n", + "from gensim.parsing.preprocessing import strip_punctuation, strip_multiple_whitespaces\n", + "\n", + "# Only the brown corpus is needed in case you don't have it.\n", + "nltk.download('brown') \n", + "\n", + "# Generate brown corpus text file\n", + "with open('brown_corp.txt', 'w+') as f:\n", + " for word in nltk.corpus.brown.words():\n", + " f.write('{word} '.format(word=word))\n", + " f.seek(0)\n", + " brown = f.read()\n", + "\n", + "# Preprocess brown corpus\n", + "with open('proc_brown_corp.txt', 'w') as f:\n", + " proc_brown = strip_punctuation(brown)\n", + " proc_brown = strip_multiple_whitespaces(proc_brown).lower()\n", + " f.write(proc_brown)\n", + "\n", + "# Set WR_HOME and FT_HOME to respective directory root\n", + "WR_HOME = 'wordrank/'\n", + "FT_HOME = 'fastText/'\n", + "\n", + "# download the text8 corpus (a 100 MB sample of preprocessed wikipedia text)\n", + "import os.path\n", + "if not os.path.isfile('text8'):\n", + " !wget -c http://mattmahoney.net/dc/text8.zip\n", + " !unzip text8.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Train Models\n", + "For training the models yourself, you'll need to have Gensim, FastText and Wordrank set up on your machine." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training word2vec on proc_brown_corp.txt corpus..\n", + "CPU times: user 1min 7s, sys: 527 ms, total: 1min 8s\n", + "Wall time: 46.8 s\n", + "\n", + "Saved gensim model as brown_gs.vec\n", + "Training fasttext on proc_brown_corp.txt corpus..\n", + "Read 1M words\n", + "Number of words: 14042\n", + "Number of labels: 0\n", + "Progress: 99.6% words/sec/thread: 58810 lr: 0.000179 loss: 2.348125 eta: 0h0m Progress: 20.1% words/sec/thread: 30702 lr: 0.039934 loss: 2.296231 eta: 0h0m Progress: 100.0% words/sec/thread: 58810 lr: 0.000000 loss: 2.348125 eta: 0h0m \n", + "CPU times: user 842 ms, sys: 284 ms, total: 1.13 s\n", + "Wall time: 41.3 s\n", + "\n", + "Training wordrank on proc_brown_corp.txt corpus..\n", + "CPU times: user 10.8 s, sys: 1.02 s, total: 11.8 s\n", + "Wall time: 8h 24min 25s\n", + "\n", + "Saved wordrank model as brown_wr.vec\n", + "\n", + "Loading ensemble embeddings (vector combination of word and context embeddings)..\n", + "CPU times: user 8.97 s, sys: 279 ms, total: 9.25 s\n", + "Wall time: 13.8 s\n", + "\n", + "Saved wordrank (ensemble) model as brown_wr_ensemble.vec\n" + ] + } + ], + "source": [ + "MODELS_DIR = 'models/'\n", + "!mkdir -p {MODELS_DIR}\n", + "\n", + "from gensim.models import Word2Vec\n", + "from gensim.models.wrappers import Wordrank\n", + "from gensim.models.word2vec import Text8Corpus\n", + "\n", + "# fasttext params\n", + "lr = 0.05\n", + "dim = 100\n", + "ws = 5\n", + "epoch = 5\n", + "minCount = 5\n", + "neg = 5\n", + "loss = 'ns'\n", + "t = 1e-4\n", + "\n", + "w2v_params = {\n", + " 'alpha': 0.025,\n", + " 'size': 100,\n", + " 'window': 15,\n", + " 'iter': 5,\n", + " 'min_count': 5,\n", + " 'sample': t,\n", + " 'sg': 1,\n", + " 'hs': 0,\n", + " 'negative': 5\n", + "}\n", + "\n", + "wr_params = {\n", + " 'size': 100,\n", + " 'window': 15,\n", + " 'iter': 91,\n", + " 'min_count': 5\n", + "}\n", + "\n", + "def train_models(corpus_file, output_name):\n", + " # Train using word2vec\n", + " output_file = '{:s}_gs'.format(output_name)\n", + " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", + " print('\\nTraining word2vec on {:s} corpus..'.format(corpus_file))\n", + " # Text8Corpus class for reading space-separated words file\n", + " %time gs_model = Word2Vec(Text8Corpus(corpus_file), **w2v_params); gs_model\n", + " locals()['gs_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", + " print('\\nSaved gensim model as {:s}.vec'.format(output_file))\n", + " else:\n", + " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", + "\n", + " # Train using fasttext\n", + " output_file = '{:s}_ft'.format(output_name)\n", + " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", + " print('Training fasttext on {:s} corpus..'.format(corpus_file))\n", + " %time !{FT_HOME}fasttext skipgram -input {corpus_file} -output {MODELS_DIR+output_file} -lr {lr} -dim {dim} -ws {ws} -epoch {epoch} -minCount {minCount} -neg {neg} -loss {loss} -t {t}\n", + " else:\n", + " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", + " \n", + " # Train using wordrank\n", + " output_file = '{:s}_wr'.format(output_name)\n", + " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", + " print('\\nTraining wordrank on {:s} corpus..'.format(corpus_file))\n", + " %time wr_model = Wordrank.train(WR_HOME, corpus_file, **wr_params); wr_model\n", + " locals()['wr_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", + " print('\\nSaved wordrank model as {:s}.vec'.format(output_file))\n", + " else:\n", + " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", + " \n", + " # Loading ensemble embeddings\n", + " output_file = '{:s}_wr_ensemble'.format(output_name)\n", + " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", + " print('\\nLoading ensemble embeddings (vector combination of word and context embeddings)..')\n", + " %time wr_model = Wordrank.load_wordrank_model(os.path.join(WR_HOME, 'model/wordrank.words'), os.path.join(WR_HOME, 'model/meta/vocab.txt'), os.path.join(WR_HOME, 'model/wordrank.contexts'), sorted_vocab=1, ensemble=1); wr_model\n", + " locals()['wr_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", + " print('\\nSaved wordrank (ensemble) model as {:s}.vec'.format(output_file))\n", + " else:\n", + " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", + " \n", + "train_models(corpus_file='proc_brown_corp.txt', output_name='brown')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training word2vec on text8 corpus..\n", + "CPU times: user 24min 21s, sys: 8.64 s, total: 24min 29s\n", + "Wall time: 18min 33s\n", + "\n", + "Saved gensim model as text8_gs.vec\n", + "\n", + "Using existing model file text8_ft.vec\n", + "\n", + "Using existing model file text8_wr.vec\n", + "\n", + "Using existing model file text8_wr_ensemble.vec\n" + ] + } + ], + "source": [ + "train_models(corpus_file='text8', output_name='text8')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we train wordrank model using ensemble in second case as it is known to give a small performance boost in some cases. So we'll test accuracy for both the cases." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparisons" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import logging\n", + "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", + "\n", + "def print_analogy_accuracy(model, questions_file):\n", + " acc = model.wv.accuracy(questions_file)\n", + "\n", + " sem_correct = sum((len(acc[i]['correct']) for i in range(5)))\n", + " sem_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5))\n", + " sem_acc = 100*float(sem_correct)/sem_total\n", + " print('\\nSemantic: {:d}/{:d}, Accuracy: {:.2f}%'.format(sem_correct, sem_total, sem_acc))\n", + " \n", + " syn_correct = sum((len(acc[i]['correct']) for i in range(5, len(acc)-1)))\n", + " syn_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5,len(acc)-1))\n", + " syn_acc = 100*float(syn_correct)/syn_total\n", + " print('Syntactic: {:d}/{:d}, Accuracy: {:.2f}%\\n'.format(syn_correct, syn_total, syn_acc))\n", + " \n", + "def print_similarity_accuracy(model, similarity_file):\n", + " acc = model.wv.evaluate_word_pairs(similarity_file)\n", + " print('Pearson correlation coefficient: {:.2f}'.format(acc[0][0]))\n", + " print('Spearman rank correlation coefficient: {:.2f}'.format(acc[1][0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:09,062 : INFO : 'pattern' package found; tag filters are available for English\n", + "2017-01-10 14:53:09,067 : INFO : loading projection weights from models/brown_gs.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Loading Gensim embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:10,730 : INFO : loaded (14042, 100) matrix from models/brown_gs.vec\n", + "2017-01-10 14:53:10,823 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Word2Vec:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:11,052 : INFO : capital-common-countries: 0.0% (0/90)\n", + "2017-01-10 14:53:11,259 : INFO : capital-world: 0.0% (0/53)\n", + "2017-01-10 14:53:11,284 : INFO : currency: 0.0% (0/12)\n", + "2017-01-10 14:53:12,010 : INFO : city-in-state: 0.9% (4/457)\n", + "2017-01-10 14:53:12,380 : INFO : family: 20.0% (48/240)\n", + "2017-01-10 14:53:13,614 : INFO : gram1-adjective-to-adverb: 0.1% (1/812)\n", + "2017-01-10 14:53:13,839 : INFO : gram2-opposite: 0.0% (0/132)\n", + "2017-01-10 14:53:15,703 : INFO : gram3-comparative: 1.8% (19/1056)\n", + "2017-01-10 14:53:16,104 : INFO : gram4-superlative: 0.5% (1/210)\n", + "2017-01-10 14:53:17,184 : INFO : gram5-present-participle: 2.6% (17/650)\n", + "2017-01-10 14:53:17,653 : INFO : gram6-nationality-adjective: 11.4% (34/297)\n", + "2017-01-10 14:53:20,023 : INFO : gram7-past-tense: 3.3% (42/1260)\n", + "2017-01-10 14:53:21,215 : INFO : gram8-plural: 6.6% (46/702)\n", + "2017-01-10 14:53:21,984 : INFO : gram9-plural-verbs: 2.0% (7/342)\n", + "2017-01-10 14:53:21,987 : INFO : total: 3.5% (219/6313)\n", + "2017-01-10 14:53:22,044 : INFO : Pearson correlation coefficient against ./datasets/simlex-999.txt: 0.1538\n", + "2017-01-10 14:53:22,046 : INFO : Spearman rank-order correlation coefficient against ./datasets/simlex-999.txt: 0.1294\n", + "2017-01-10 14:53:22,047 : INFO : Pairs with unknown words ratio: 0.2%\n", + "2017-01-10 14:53:22,080 : INFO : Pearson correlation coefficient against ./datasets/ws-353.txt: 0.3242\n", + "2017-01-10 14:53:22,081 : INFO : Spearman rank-order correlation coefficient against ./datasets/ws-353.txt: 0.3164\n", + "2017-01-10 14:53:22,082 : INFO : Pairs with unknown words ratio: 21.8%\n", + "2017-01-10 14:53:22,087 : INFO : loading projection weights from models/brown_ft.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 52/852, Accuracy: 6.10%\n", + "Syntactic: 167/5461, Accuracy: 3.06%\n", + "\n", + "SimLex-999 similarity\n", + "Pearson correlation coefficient: 0.15\n", + "Spearman rank correlation coefficient: 0.13\n", + "\n", + "WordSim-353 similarity\n", + "Pearson correlation coefficient: 0.32\n", + "Spearman rank correlation coefficient: 0.32\n", + "\n", + "Loading FastText embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:23,984 : INFO : loaded (14042, 100) matrix from models/brown_ft.vec\n", + "2017-01-10 14:53:24,006 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for FastText:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:24,275 : INFO : capital-common-countries: 1.1% (1/90)\n", + "2017-01-10 14:53:24,446 : INFO : capital-world: 0.0% (0/53)\n", + "2017-01-10 14:53:24,487 : INFO : currency: 0.0% (0/12)\n", + "2017-01-10 14:53:25,355 : INFO : city-in-state: 2.4% (11/457)\n", + "2017-01-10 14:53:25,867 : INFO : family: 11.7% (28/240)\n", + "2017-01-10 14:53:27,239 : INFO : gram1-adjective-to-adverb: 79.9% (649/812)\n", + "2017-01-10 14:53:27,477 : INFO : gram2-opposite: 79.5% (105/132)\n", + "2017-01-10 14:53:29,220 : INFO : gram3-comparative: 56.3% (595/1056)\n", + "2017-01-10 14:53:29,618 : INFO : gram4-superlative: 71.4% (150/210)\n", + "2017-01-10 14:53:31,081 : INFO : gram5-present-participle: 65.7% (427/650)\n", + "2017-01-10 14:53:31,749 : INFO : gram6-nationality-adjective: 35.0% (104/297)\n", + "2017-01-10 14:53:34,210 : INFO : gram7-past-tense: 12.1% (153/1260)\n", + "2017-01-10 14:53:35,299 : INFO : gram8-plural: 53.1% (373/702)\n", + "2017-01-10 14:53:35,841 : INFO : gram9-plural-verbs: 69.0% (236/342)\n", + "2017-01-10 14:53:35,842 : INFO : total: 44.9% (2832/6313)\n", + "2017-01-10 14:53:35,928 : INFO : Pearson correlation coefficient against ./datasets/simlex-999.txt: 0.1208\n", + "2017-01-10 14:53:35,929 : INFO : Spearman rank-order correlation coefficient against ./datasets/simlex-999.txt: 0.1039\n", + "2017-01-10 14:53:35,930 : INFO : Pairs with unknown words ratio: 0.2%\n", + "2017-01-10 14:53:35,958 : INFO : Pearson correlation coefficient against ./datasets/ws-353.txt: 0.3789\n", + "2017-01-10 14:53:35,960 : INFO : Spearman rank-order correlation coefficient against ./datasets/ws-353.txt: 0.3791\n", + "2017-01-10 14:53:35,963 : INFO : Pairs with unknown words ratio: 21.8%\n", + "2017-01-10 14:53:35,977 : INFO : loading projection weights from models/brown_wr.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 40/852, Accuracy: 4.69%\n", + "Syntactic: 2792/5461, Accuracy: 51.13%\n", + "\n", + "SimLex-999 similarity\n", + "Pearson correlation coefficient: 0.12\n", + "Spearman rank correlation coefficient: 0.10\n", + "\n", + "WordSim-353 similarity\n", + "Pearson correlation coefficient: 0.38\n", + "Spearman rank correlation coefficient: 0.38\n", + "\n", + "Loading Wordrank embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:37,423 : INFO : loaded (14042, 100) matrix from models/brown_wr.vec\n", + "2017-01-10 14:53:37,437 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Wordrank:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:37,666 : INFO : capital-common-countries: 10.0% (9/90)\n", + "2017-01-10 14:53:37,799 : INFO : capital-world: 15.1% (8/53)\n", + "2017-01-10 14:53:37,832 : INFO : currency: 0.0% (0/12)\n", + "2017-01-10 14:53:38,543 : INFO : city-in-state: 8.1% (37/457)\n", + "2017-01-10 14:53:38,921 : INFO : family: 23.8% (57/240)\n", + "2017-01-10 14:53:40,150 : INFO : gram1-adjective-to-adverb: 0.6% (5/812)\n", + "2017-01-10 14:53:40,381 : INFO : gram2-opposite: 0.0% (0/132)\n", + "2017-01-10 14:53:41,979 : INFO : gram3-comparative: 2.0% (21/1056)\n", + "2017-01-10 14:53:42,377 : INFO : gram4-superlative: 1.0% (2/210)\n", + "2017-01-10 14:53:43,498 : INFO : gram5-present-participle: 0.5% (3/650)\n", + "2017-01-10 14:53:44,027 : INFO : gram6-nationality-adjective: 10.8% (32/297)\n", + "2017-01-10 14:53:46,105 : INFO : gram7-past-tense: 1.6% (20/1260)\n", + "2017-01-10 14:53:47,194 : INFO : gram8-plural: 8.3% (58/702)\n", + "2017-01-10 14:53:47,732 : INFO : gram9-plural-verbs: 0.3% (1/342)\n", + "2017-01-10 14:53:47,733 : INFO : total: 4.0% (253/6313)\n", + "2017-01-10 14:53:47,774 : INFO : Pearson correlation coefficient against ./datasets/simlex-999.txt: 0.0901\n", + "2017-01-10 14:53:47,776 : INFO : Spearman rank-order correlation coefficient against ./datasets/simlex-999.txt: 0.0974\n", + "2017-01-10 14:53:47,777 : INFO : Pairs with unknown words ratio: 0.2%\n", + "2017-01-10 14:53:47,816 : INFO : Pearson correlation coefficient against ./datasets/ws-353.txt: 0.3864\n", + "2017-01-10 14:53:47,817 : INFO : Spearman rank-order correlation coefficient against ./datasets/ws-353.txt: 0.3831\n", + "2017-01-10 14:53:47,821 : INFO : Pairs with unknown words ratio: 21.8%\n", + "2017-01-10 14:53:47,832 : INFO : loading projection weights from models/brown_wr_ensemble.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 111/852, Accuracy: 13.03%\n", + "Syntactic: 142/5461, Accuracy: 2.60%\n", + "\n", + "SimLex-999 similarity\n", + "Pearson correlation coefficient: 0.09\n", + "Spearman rank correlation coefficient: 0.10\n", + "\n", + "WordSim-353 similarity\n", + "Pearson correlation coefficient: 0.39\n", + "Spearman rank correlation coefficient: 0.38\n", + "\n", + "Loading Wordrank ensemble embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:49,306 : INFO : loaded (14042, 100) matrix from models/brown_wr_ensemble.vec\n", + "2017-01-10 14:53:49,327 : INFO : precomputing L2-norms of word weight vectors\n", + "2017-01-10 14:53:49,495 : INFO : capital-common-countries: 14.4% (13/90)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Wordrank:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:53:49,620 : INFO : capital-world: 18.9% (10/53)\n", + "2017-01-10 14:53:49,649 : INFO : currency: 0.0% (0/12)\n", + "2017-01-10 14:53:50,352 : INFO : city-in-state: 8.3% (38/457)\n", + "2017-01-10 14:53:50,717 : INFO : family: 28.8% (69/240)\n", + "2017-01-10 14:53:51,915 : INFO : gram1-adjective-to-adverb: 0.6% (5/812)\n", + "2017-01-10 14:53:52,122 : INFO : gram2-opposite: 0.0% (0/132)\n", + "2017-01-10 14:53:53,669 : INFO : gram3-comparative: 3.4% (36/1056)\n", + "2017-01-10 14:53:53,992 : INFO : gram4-superlative: 0.0% (0/210)\n", + "2017-01-10 14:53:54,948 : INFO : gram5-present-participle: 1.7% (11/650)\n", + "2017-01-10 14:53:55,404 : INFO : gram6-nationality-adjective: 16.8% (50/297)\n", + "2017-01-10 14:53:57,244 : INFO : gram7-past-tense: 3.8% (48/1260)\n", + "2017-01-10 14:53:58,294 : INFO : gram8-plural: 11.1% (78/702)\n", + "2017-01-10 14:53:58,813 : INFO : gram9-plural-verbs: 0.9% (3/342)\n", + "2017-01-10 14:53:58,814 : INFO : total: 5.7% (361/6313)\n", + "2017-01-10 14:53:58,852 : INFO : Pearson correlation coefficient against ./datasets/simlex-999.txt: -0.0007\n", + "2017-01-10 14:53:58,853 : INFO : Spearman rank-order correlation coefficient against ./datasets/simlex-999.txt: 0.0081\n", + "2017-01-10 14:53:58,854 : INFO : Pairs with unknown words ratio: 0.2%\n", + "2017-01-10 14:53:58,880 : INFO : Pearson correlation coefficient against ./datasets/ws-353.txt: 0.2464\n", + "2017-01-10 14:53:58,881 : INFO : Spearman rank-order correlation coefficient against ./datasets/ws-353.txt: 0.2148\n", + "2017-01-10 14:53:58,882 : INFO : Pairs with unknown words ratio: 21.8%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 130/852, Accuracy: 15.26%\n", + "Syntactic: 231/5461, Accuracy: 4.23%\n", + "\n", + "SimLex-999 similarity\n", + "Pearson correlation coefficient: -0.00\n", + "Spearman rank correlation coefficient: 0.01\n", + "\n", + "WordSim-353 similarity\n", + "Pearson correlation coefficient: 0.25\n", + "Spearman rank correlation coefficient: 0.21\n" + ] + } + ], + "source": [ + "# from gensim.models import Word2Vec\n", + "MODELS_DIR = 'models/'\n", + "word_analogies_file = './datasets/questions-words.txt'\n", + "simlex_file = './datasets/simlex-999.txt'\n", + "wordsim_file = './datasets/ws-353.txt'\n", + "\n", + "print('\\nLoading Gensim embeddings')\n", + "brown_gs = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_gs.vec')\n", + "print('Accuracy for Word2Vec:')\n", + "print_analogy_accuracy(brown_gs, word_analogies_file)\n", + "print('SimLex-999 similarity')\n", + "print_similarity_accuracy(brown_gs, simlex_file)\n", + "print('\\nWordSim-353 similarity')\n", + "print_similarity_accuracy(brown_gs, wordsim_file)\n", + "\n", + "print('\\nLoading FastText embeddings')\n", + "brown_ft = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_ft.vec')\n", + "print('Accuracy for FastText:')\n", + "print_analogy_accuracy(brown_ft, word_analogies_file)\n", + "print('SimLex-999 similarity')\n", + "print_similarity_accuracy(brown_ft, simlex_file)\n", + "print('\\nWordSim-353 similarity')\n", + "print_similarity_accuracy(brown_ft, wordsim_file)\n", + "\n", + "print('\\nLoading Wordrank embeddings')\n", + "brown_wr = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_wr.vec')\n", + "print('Accuracy for Wordrank:')\n", + "print_analogy_accuracy(brown_wr, word_analogies_file)\n", + "print('SimLex-999 similarity')\n", + "print_similarity_accuracy(brown_wr, simlex_file)\n", + "print('\\nWordSim-353 similarity')\n", + "print_similarity_accuracy(brown_wr, wordsim_file)\n", + "\n", + "print('\\nLoading Wordrank ensemble embeddings')\n", + "brown_wr_ensemble = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_wr_ensemble.vec')\n", + "print('Accuracy for Wordrank:')\n", + "print_analogy_accuracy(brown_wr_ensemble, word_analogies_file)\n", + "print('SimLex-999 similarity')\n", + "print_similarity_accuracy(brown_wr_ensemble, simlex_file)\n", + "print('\\nWordSim-353 similarity')\n", + "print_similarity_accuracy(brown_wr_ensemble, wordsim_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As evident from the above outputs, WordRank performs significantly better in Semantic analogies, whereas, FastText on Syntactic analogies. Also ensemble embeddings gives a small performance boost in WordRank's case.\n", + "\n", + "Wordrank's effectiveness in Semantic analogies is possibly due to it's focused attention on getting most relevant words right at the top using the ranking approach.\n", + "And as fasttext is designed to incorporate morphological information about words, it results in it's performance boost in Syntactic analogies, as most of the Syntactic analogies are morphology based[2].\n", + "\n", + "And for the Word Similarity, Word2Vec performed better on SimLex-999 test data, whereas, WordRank on WS-353. This is probably due to the different types of similarities these datasets address. SimLex-999 provides a measure of how well the two words are interchangeable in similar contexts, and WS-353 tries to estimate the relatedness or co-occurrence of two words. Also, ensemble embeddings doesn't help in the Word Similarity task[1], which is evident from the results above so we'll use just the Word Embeddings for it. \n", + "\n", + "Now lets evaluate on a larger corpus, text8, and see how it effects the performance of different embedding models. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:54:59,108 : INFO : loading projection weights from models/text8_gs.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading Gensim embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:55:06,748 : INFO : loaded (71290, 100) matrix from models/text8_gs.vec\n", + "2017-01-10 14:55:06,788 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for word2vec:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:55:08,624 : INFO : capital-common-countries: 68.6% (347/506)\n", + "2017-01-10 14:55:13,392 : INFO : capital-world: 52.3% (760/1452)\n", + "2017-01-10 14:55:14,287 : INFO : currency: 19.8% (53/268)\n", + "2017-01-10 14:55:19,439 : INFO : city-in-state: 24.8% (389/1571)\n", + "2017-01-10 14:55:20,668 : INFO : family: 47.7% (146/306)\n", + "2017-01-10 14:55:23,721 : INFO : gram1-adjective-to-adverb: 18.0% (136/756)\n", + "2017-01-10 14:55:24,737 : INFO : gram2-opposite: 13.4% (41/306)\n", + "2017-01-10 14:55:28,860 : INFO : gram3-comparative: 37.8% (476/1260)\n", + "2017-01-10 14:55:30,518 : INFO : gram4-superlative: 22.3% (113/506)\n", + "2017-01-10 14:55:33,766 : INFO : gram5-present-participle: 22.9% (227/992)\n", + "2017-01-10 14:55:38,413 : INFO : gram6-nationality-adjective: 86.7% (1188/1371)\n", + "2017-01-10 14:55:42,759 : INFO : gram7-past-tense: 27.0% (359/1332)\n", + "2017-01-10 14:55:45,924 : INFO : gram8-plural: 54.4% (540/992)\n", + "2017-01-10 14:55:48,088 : INFO : gram9-plural-verbs: 25.2% (164/650)\n", + "2017-01-10 14:55:48,091 : INFO : total: 40.3% (4939/12268)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 1695/4103, Accuracy: 41.31%\n", + "Syntactic: 3244/8165, Accuracy: 39.73%\n", + "\n", + "SimLex-999 similarity\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:55:48,307 : INFO : Pearson correlation coefficient against ./datasets/simlex-999.txt: 0.3094\n", + "2017-01-10 14:55:48,308 : INFO : Spearman rank-order correlation coefficient against ./datasets/simlex-999.txt: 0.2937\n", + "2017-01-10 14:55:48,309 : INFO : Pairs with unknown words ratio: 0.7%\n", + "2017-01-10 14:55:48,523 : INFO : Pearson correlation coefficient against ./datasets/ws-353.txt: 0.6865\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson correlation coefficient: 0.31\n", + "Spearman rank correlation coefficient: 0.29\n", + "\n", + "WordSim-353 similarity\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:55:48,524 : INFO : Spearman rank-order correlation coefficient against ./datasets/ws-353.txt: 0.6947\n", + "2017-01-10 14:55:48,525 : INFO : Pairs with unknown words ratio: 0.6%\n", + "2017-01-10 14:55:48,537 : INFO : loading projection weights from models/text8_ft.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson correlation coefficient: 0.69\n", + "Spearman rank correlation coefficient: 0.69\n", + "Loading FastText embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:55:56,320 : INFO : loaded (71290, 100) matrix from models/text8_ft.vec\n", + "2017-01-10 14:55:56,373 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for FastText (with n-grams):\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:55:58,389 : INFO : capital-common-countries: 62.5% (316/506)\n", + "2017-01-10 14:56:03,138 : INFO : capital-world: 43.0% (625/1452)\n", + "2017-01-10 14:56:04,165 : INFO : currency: 12.7% (34/268)\n", + "2017-01-10 14:56:09,344 : INFO : city-in-state: 18.3% (287/1571)\n", + "2017-01-10 14:56:10,342 : INFO : family: 43.5% (133/306)\n", + "2017-01-10 14:56:13,360 : INFO : gram1-adjective-to-adverb: 73.7% (557/756)\n", + "2017-01-10 14:56:14,469 : INFO : gram2-opposite: 53.9% (165/306)\n", + "2017-01-10 14:56:19,780 : INFO : gram3-comparative: 64.8% (816/1260)\n", + "2017-01-10 14:56:21,954 : INFO : gram4-superlative: 53.4% (270/506)\n", + "2017-01-10 14:56:25,950 : INFO : gram5-present-participle: 54.4% (540/992)\n", + "2017-01-10 14:56:31,082 : INFO : gram6-nationality-adjective: 93.9% (1288/1371)\n", + "2017-01-10 14:56:36,499 : INFO : gram7-past-tense: 35.6% (474/1332)\n", + "2017-01-10 14:56:40,886 : INFO : gram8-plural: 90.1% (894/992)\n", + "2017-01-10 14:56:43,304 : INFO : gram9-plural-verbs: 59.4% (386/650)\n", + "2017-01-10 14:56:43,305 : INFO : total: 55.3% (6785/12268)\n", + "2017-01-10 14:56:43,495 : INFO : Pearson correlation coefficient against ./datasets/simlex-999.txt: 0.3005\n", + "2017-01-10 14:56:43,496 : INFO : Spearman rank-order correlation coefficient against ./datasets/simlex-999.txt: 0.2872\n", + "2017-01-10 14:56:43,497 : INFO : Pairs with unknown words ratio: 0.7%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 1395/4103, Accuracy: 34.00%\n", + "Syntactic: 5390/8165, Accuracy: 66.01%\n", + "\n", + "SimLex-999 similarity\n", + "Pearson correlation coefficient: 0.30\n", + "Spearman rank correlation coefficient: 0.29\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:56:43,735 : INFO : Pearson correlation coefficient against ./datasets/ws-353.txt: 0.6418\n", + "2017-01-10 14:56:43,735 : INFO : Spearman rank-order correlation coefficient against ./datasets/ws-353.txt: 0.6475\n", + "2017-01-10 14:56:43,736 : INFO : Pairs with unknown words ratio: 0.6%\n", + "2017-01-10 14:56:43,748 : INFO : loading projection weights from models/text8_wr.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "WordSim-353 similarity\n", + "Pearson correlation coefficient: 0.64\n", + "Spearman rank correlation coefficient: 0.65\n", + "\n", + "Loading Wordrank embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:56:52,829 : INFO : loaded (71290, 100) matrix from models/text8_wr.vec\n", + "2017-01-10 14:56:52,892 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Wordrank:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:56:55,183 : INFO : capital-common-countries: 84.6% (428/506)\n", + "2017-01-10 14:57:00,832 : INFO : capital-world: 70.0% (1016/1452)\n", + "2017-01-10 14:57:02,166 : INFO : currency: 19.0% (51/268)\n", + "2017-01-10 14:57:08,742 : INFO : city-in-state: 36.0% (565/1571)\n", + "2017-01-10 14:57:09,847 : INFO : family: 57.8% (177/306)\n", + "2017-01-10 14:57:12,342 : INFO : gram1-adjective-to-adverb: 15.3% (116/756)\n", + "2017-01-10 14:57:13,343 : INFO : gram2-opposite: 15.4% (47/306)\n", + "2017-01-10 14:57:18,192 : INFO : gram3-comparative: 33.8% (426/1260)\n", + "2017-01-10 14:57:20,238 : INFO : gram4-superlative: 21.1% (107/506)\n", + "2017-01-10 14:57:23,527 : INFO : gram5-present-participle: 23.8% (236/992)\n", + "2017-01-10 14:57:28,243 : INFO : gram6-nationality-adjective: 90.2% (1237/1371)\n", + "2017-01-10 14:57:32,737 : INFO : gram7-past-tense: 26.4% (351/1332)\n", + "2017-01-10 14:57:36,066 : INFO : gram8-plural: 60.9% (604/992)\n", + "2017-01-10 14:57:38,646 : INFO : gram9-plural-verbs: 19.7% (128/650)\n", + "2017-01-10 14:57:38,647 : INFO : total: 44.7% (5489/12268)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 2237/4103, Accuracy: 54.52%\n", + "Syntactic: 3252/8165, Accuracy: 39.83%\n", + "\n", + "SimLex-999 similarity\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:57:38,942 : INFO : Pearson correlation coefficient against ./datasets/simlex-999.txt: 0.2829\n", + "2017-01-10 14:57:38,943 : INFO : Spearman rank-order correlation coefficient against ./datasets/simlex-999.txt: 0.2770\n", + "2017-01-10 14:57:38,944 : INFO : Pairs with unknown words ratio: 0.7%\n", + "2017-01-10 14:57:39,047 : INFO : Pearson correlation coefficient against ./datasets/ws-353.txt: 0.7028\n", + "2017-01-10 14:57:39,048 : INFO : Spearman rank-order correlation coefficient against ./datasets/ws-353.txt: 0.7105\n", + "2017-01-10 14:57:39,049 : INFO : Pairs with unknown words ratio: 0.6%\n", + "2017-01-10 14:57:39,063 : INFO : loading projection weights from models/text8_wr_ensemble.vec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson correlation coefficient: 0.28\n", + "Spearman rank correlation coefficient: 0.28\n", + "\n", + "WordSim-353 similarity\n", + "Pearson correlation coefficient: 0.70\n", + "Spearman rank correlation coefficient: 0.71\n", + "\n", + "Loading Wordrank ensemble embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:57:48,289 : INFO : loaded (71290, 100) matrix from models/text8_wr_ensemble.vec\n", + "2017-01-10 14:57:48,355 : INFO : precomputing L2-norms of word weight vectors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for Wordrank:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:57:50,393 : INFO : capital-common-countries: 67.0% (339/506)\n", + "2017-01-10 14:57:55,893 : INFO : capital-world: 59.0% (856/1452)\n", + "2017-01-10 14:57:57,069 : INFO : currency: 17.2% (46/268)\n", + "2017-01-10 14:58:03,097 : INFO : city-in-state: 33.0% (519/1571)\n", + "2017-01-10 14:58:04,262 : INFO : family: 32.0% (98/306)\n", + "2017-01-10 14:58:07,506 : INFO : gram1-adjective-to-adverb: 10.3% (78/756)\n", + "2017-01-10 14:58:08,548 : INFO : gram2-opposite: 10.5% (32/306)\n", + "2017-01-10 14:58:12,550 : INFO : gram3-comparative: 24.4% (308/1260)\n", + "2017-01-10 14:58:14,443 : INFO : gram4-superlative: 11.5% (58/506)\n", + "2017-01-10 14:58:18,236 : INFO : gram5-present-participle: 11.7% (116/992)\n", + "2017-01-10 14:58:23,111 : INFO : gram6-nationality-adjective: 71.8% (985/1371)\n", + "2017-01-10 14:58:28,082 : INFO : gram7-past-tense: 17.0% (226/1332)\n", + "2017-01-10 14:58:32,411 : INFO : gram8-plural: 47.8% (474/992)\n", + "2017-01-10 14:58:35,146 : INFO : gram9-plural-verbs: 11.7% (76/650)\n", + "2017-01-10 14:58:35,148 : INFO : total: 34.3% (4211/12268)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semantic: 1858/4103, Accuracy: 45.28%\n", + "Syntactic: 2353/8165, Accuracy: 28.82%\n", + "\n", + "SimLex-999 similarity\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:58:35,422 : INFO : Pearson correlation coefficient against ./datasets/simlex-999.txt: 0.1945\n", + "2017-01-10 14:58:35,424 : INFO : Spearman rank-order correlation coefficient against ./datasets/simlex-999.txt: 0.1872\n", + "2017-01-10 14:58:35,425 : INFO : Pairs with unknown words ratio: 0.7%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson correlation coefficient: 0.19\n", + "Spearman rank correlation coefficient: 0.19\n", + "\n", + "WordSim-353 similarity\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2017-01-10 14:58:35,674 : INFO : Pearson correlation coefficient against ./datasets/ws-353.txt: 0.5338\n", + "2017-01-10 14:58:35,675 : INFO : Spearman rank-order correlation coefficient against ./datasets/ws-353.txt: 0.5107\n", + "2017-01-10 14:58:35,676 : INFO : Pairs with unknown words ratio: 0.6%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson correlation coefficient: 0.53\n", + "Spearman rank correlation coefficient: 0.51\n" + ] + } + ], + "source": [ + "print('Loading Gensim embeddings')\n", + "text8_gs = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_gs.vec')\n", + "print('Accuracy for word2vec:')\n", + "print_analogy_accuracy(text8_gs, word_analogies_file)\n", + "print('SimLex-999 similarity')\n", + "print_similarity_accuracy(text8_gs, simlex_file)\n", + "print('\\nWordSim-353 similarity')\n", + "print_similarity_accuracy(text8_gs, wordsim_file)\n", + "\n", + "print('Loading FastText embeddings')\n", + "text8_ft = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_ft.vec')\n", + "print('Accuracy for FastText (with n-grams):')\n", + "print_analogy_accuracy(text8_ft, word_analogies_file)\n", + "print('SimLex-999 similarity')\n", + "print_similarity_accuracy(text8_ft, simlex_file)\n", + "print('\\nWordSim-353 similarity')\n", + "print_similarity_accuracy(text8_ft, wordsim_file)\n", + "\n", + "print('\\nLoading Wordrank embeddings')\n", + "text8_wr = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_wr.vec')\n", + "print('Accuracy for Wordrank:')\n", + "print_analogy_accuracy(text8_wr, word_analogies_file)\n", + "print('SimLex-999 similarity')\n", + "print_similarity_accuracy(text8_wr, simlex_file)\n", + "print('\\nWordSim-353 similarity')\n", + "print_similarity_accuracy(text8_wr, wordsim_file)\n", + "\n", + "print('\\nLoading Wordrank ensemble embeddings')\n", + "text8_wr_ensemble = Word2Vec.load_word2vec_format(MODELS_DIR + 'text8_wr_ensemble.vec')\n", + "print('Accuracy for Wordrank:')\n", + "print_analogy_accuracy(text8_wr_ensemble, word_analogies_file)\n", + "print('SimLex-999 similarity')\n", + "print_similarity_accuracy(text8_wr_ensemble, simlex_file)\n", + "print('\\nWordSim-353 similarity')\n", + "print_similarity_accuracy(text8_wr_ensemble, wordsim_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "With a larger corpus, we observe similar patterns in the accuracies. Here also, WordRank dominates the Semantic analogies and FastText Syntactic ones. Word2Vec again performs better on SimLex-999 dataset and WordRank on WS-353.\n", + "Though we observe a little performance decrease in WordRank in case of ensemble embeddings here, so it's good to try both the cases for evaluations.\n", + "\n", + "Now, following graph shows the word frequency effect on Analogy task accuracy. For each analogy, the\n", + "mean frequency of the four words involved is computed, and then bucketed with other analogies having similar mean frequencies. Each bucket has 200 analogies." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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x8e6J9Lu1H4MXDqblly15vMnjvNP+HUoXK33tCpRycSEhIQwdOpRLly6ltsCB\nlcANGzaMpKSk1CSrffv2NGzYkEceeYTx48eTlJTE0KFDCQkJuaILM63ixYszcOBAnn/+eUqXLk3Z\nsmV59dVXcXd3v2Z8gwYN4s0332TevHncf//91K5dm7CwMJYsWUL16tUJCwtj/fr11KhRI0efv1mz\nZixatIjOnTvj4eHBs88+e9XzIyIiGDNmTLqy06dP5+je4BoJXDxwGSifobwcV7bKpXK0ou11vN0q\nIvWBl4CM4+NSzt8nIvFALa6SwI0fP54mTZpcf/RKqVxx8vxJTl44SQ2fnP1ydRV3+N/B2sfW8umG\nT3llxSvM3zmfDzt8yH8a/cflZtYqlR0hISFcuHCBevXqUbZs2dTyoKAgzp49S0BAABUqVEgt//77\n73nqqacICgrCzc2Nzp07X9eOBh988AHnzp2ja9eueHl58dxzz/HPP/+kOyezv0s+Pj707duXMWPG\ncP/99zNo0CA2b95Mr169EBF69+7N0KFDWbRoUbY+d9p7tWzZkh9//JEuXbrg4eHBsGHDsryuU6dO\nvPzyy+nKNm7cmDr+L7vEFXoTRWQNsNYY84zjvQAHgAnGmA+us44vgerGmLZZHK8E/AF0M8b8mMnx\nJkB0dHS0JnBKuYDPoz9nyMIhHH/hON5Fva99QR5w5MwRRiwZQczfMWx4YoOOiyvgUv7x1n938rer\nfZ/TJHCBxpiN2anXVX57jAOmi0g0/y4j4glMAxCRGcAhY8zLjvejgA1YXaJFsGas9gGedBwvDozG\nGgP3F1ar23vAbmDxzfpQSqmci4iNoHml5vkmeQNrHNOsHrM4m3hWkzel1A1xid8gxphwEfEF3sDq\nSt0MdEyzBEglrIkKKYoDkxzl57HWg3vEGJOyGt9loBHQFygFHMZK3F4zxiTl8sdRSt2gpMtJLNu7\njBfvtGd2V24rUbiE3SEopfI4l0jgAIwxnwCfZHGsbYb3/wX+e5W6LgCdnBqgUuqm+e3gb5xJPEOn\nWvrXWCmlMuMyW2kppVSKiNgIyhUvR2O/rGen5VeJlxOZHD2ZpMvaWaCUypomcEoplxMRF0HHmh1x\nk4L3K+rnP37myYVPEvh5IL8d/M3ucJRSLqrg/XZUSrm0I2eOsPmvzQW2+7R9jfase2wdRT2KcueU\nO3n8h8c5nnDc7rCUUi5GEzillEvZ8fcOvIt406FmB7tDsU1gxUBWD1zNpLsnMWfHHAImBTB101Rc\nYdknpZSxx+ZjAAAgAElEQVRr0AROKeVS2tVoR/wL8fh6+todiq3c3dwZcscQdg7bSceaHRnwwwCC\npgVx5MwRu0NTSrkATeCUUi5H10j7V4USFZh5/0yW/WcZPsV8KONZxu6QlFIuQH9LKqVUHtCuRjva\n1WhndxhKKRehLXBKKaWUypb+/fvj5uaGu7s7bm5uqX/eu3fvtS++isuXL+Pm5sZPP/2UWta6devU\ne2T26tDBOeNlFy5ciJubG8nJyU6pL7dpC5xSSimlsq1z585MmzYt3eSatJva50RmE3UWLFhAYmIi\nAPv27aNly5asXLmSOnXqAFCkSJEbumfae4tInpkspC1wSimVD1xKvsQj8x5h1R+r7A5FFRBFihSh\nbNmylCtXLvUlIvz000+0atUKHx8ffH196dq1K/v27Uu9LjExkcGDB1OxYkWKFStGjRo1+PDDDwGo\nXr06IsI999yDm5sbderUoVSpUqn1+/r6YoyhdOnSqWXe3tZ+yfHx8fTr1w9fX198fHzo2LEjO3fu\nBCA5OZk777yTBx54IDWOo0ePUr58ecaOHcv27dvp2rUrAIUKFcLd3Z2nn376Zj3KHNEETiml8oHj\nCceJOxFHm2ltGPD9AOIT4u0OSRVQ58+f5/nnn2fjxo0sX74cYww9evRIPT5u3DgWL17Mt99+y+7d\nuwkLC6NKlSoArF+/HmMMX331FX/99Rdr1qy57vt269aNxMREVqxYwbp166hduzZ33XUX586dw83N\njZkzZ7J06VKmTp0KwIABA2jYsCHPPfccAQEBhIWFAXD48GGOHDnCO++848Sn4nzahaqUcglxJ+Ko\nVqoa7m7udoeSJ5UvUZ7fBv7G5OjJjFo+iu93fc/77d+nf+P+BXJHi/zmyBGIj4eGDdOXb94Mfn5Q\nvvy/ZfHxcOAANGmS/twdO6BkSahUyTkxLViwAC8vr9T3d999N7Nnz06XrAFMnjyZihUrsnv3burU\nqcPBgwepU6cOLVq0AKBy5cqp56Z0wXp7e1OuXLnrjmXx4sXs27ePVatW4eZm/bxPmDCB+fPns2DB\nAnr16kX16tX5+OOPeeqpp4iJiWH16tVs27YNAHd3d0qVKgVAuXLlUutwZa4foVIq30s2ybT4sgVj\nosbYHUqe5iZuDLp9ELuG7eKeOvfw2ILHaD21NduObrM7NHWDQkOhc+cry9u0ga++Sl/23XcQGHjl\nuQ8+COPGOS+mtm3bsnXrVrZs2cKWLVuYMGECAHv27KFXr17UqFGDkiVLUrt2bUSEAwcOANYEiHXr\n1hEQEMCzzz7L8uXLbziWLVu2cOzYMby9vfHy8sLLywtvb2+OHTtGXFxc6nmPPvoobdu25cMPP2TS\npEn4+/vf8L3toi1wSinbbTqyib8T/qZ9jfZ2h5IvlCtejundp9P/tv4MXjiYxqGNWfjwQjrW6mh3\naCqHBg2CDA1bAPz8s9UCl1b37le2vgHMmWO1wDlL8eLFqV69+hXlXbp0oU6dOkyZMgU/Pz8SExO5\n9dZbUyci3H777fzxxx8sWrSIZcuW0aNHDzp37sysWbNyHMvZs2epVasWixYtumISQunSpVP//M8/\n/7B161Y8PDzYvXt3ju/nCjSBU0rZblHsIrwKe9Gycku7Q8lXgqsFs+XJLXy24TPaVG1jdzjqBvj5\nXZmoAdx225Vlvr7WK6P69Z0fV0bHjh0jNjaWsLAwmjVrBkBUVBQiku48Ly8vHnroIR566CG6d+/O\nPffcw+TJkylRogTu7u5cvnw5y3tkrAugSZMmfPjhhxQvXvyqXa9Dhw6lTJkyTJo0ie7du9O5c2ea\nNm0KQOHChYF/lzJxda4foVIq34uIjaB9jfYUci9kdyj5TmH3wjzd7GmKFSpmdyiqAChTpgw+Pj6E\nhoayd+9eli9fzvPPP5/unLFjxxIeHs7u3bvZvXs3c+bMoVKlSpQoUQKAKlWqsGzZMo4ePcqpU6eu\nuEdmy3zce++93HLLLXTt2pUVK1awf/9+fvnlF1588UViYmIACA8PZ968eXz11VfcfffdDB48mEce\neYSEhAQAqlWrBsAPP/xAfHx8armr0gROKWWrk+dPsvrQajrV6mR3KEqpG+Tu7s7s2bNZu3Ytt9xy\nC88//3zqEiEpSpQowdtvv83tt99Os2bNOHz4MAsXLkw9Pn78eCIiIqhSpUpq61hambXAubu7s3Tp\nUpo0acJ//vMf6tWrR9++ffn777/x9fXl8OHDDBkyhA8++IC6desC8P7771O0aFGeeeYZAGrXrs2o\nUaMYOnQoFSpUYNSoUc58NE4neWXButwmIk2A6OjoaJpkNnhAKZUr5u6Yy4NzHuSPZ/+gincVu8Mp\nkM4nndcWOhts3LiRwMBA9N+d/O1q3+eUY0CgMWZjdurVFjillK0W7VlE/bL1NXmzSbJJJmR6CH3n\n9+XYuWN2h6OUuk6awCmlbLXz+E461dTuUzs93uRxFu5ZSN2JdQndEEqyyRt7QSpVkGkCp5Sy1S/9\nf+Htdm/bHUaB5SZuDGwykF3DdnFfwH08ufBJWn7Zks1/bbY7NKXUVWgCp5SylYhQxMM5m1GrnPP1\n9GVKtyms6r+Ks4lnCfw8kOERwzlz8YzdoSmlMqEJnFJKqVStqrRi06BNvNPuHT7f+DnjVjtx6X6l\nlNPoQr5KKaXSKeReiBfufIGeDXri65nJirBKKdtpAqeUUipTVUtVtTuEAiFloVmVP+XW91cTOKWU\nUsoGvr6+eHp60qdPH7tDUbnM09MT38z2N7sBmsAppZTKkX0n91GsUDEqlKhgdyh5UpUqVYiJiSE+\nPt7uUFQu8/X1pUoV5651qQmcUuqmO33hNCJCySIl7Q5F3YARS0YQuS+St9u9zaDAQbi7udsdUp5T\npUoVp//DrgoGnYWqlLrppmyaQqVxlUi8nGh3KOoGfHHvFzxY/0GG/jSUFl+2IPpwtN0hKVVgaAKn\nlLrpIuIiaFm5JYXdC9sdiroBZTzLMLnrZH4d8CsXLl2g6RdNeXrR05y+cNru0JTK9zSBU0rdVAlJ\nCazcv5JOtXT7rPyiZeWWRD8Rzfvt32fKpinUm1SP8O3hdoelVL7mMgmciAwVkX0icl5E1ojIHVc5\n9z4RWS8iJ0XkrIhsEpErpvGIyBsiclhEEkRkqYjUyt1PoZS6lqj9UVy8fFETuHymkHshnmv5HDFD\nY2hRuQW/HPjF7pCUytdcYhKDiPQExgJPAOuA4cBiEaljjMlses5x4P+AnUAicC8wVUSOGmOWOup8\nERgG9AP2Oc5fLCL1jDE68EYpm0TERlCtVDXqlqlrdygqF1T2rsy3D33LpeRLdoeiVL7mKi1ww4FQ\nY8wMY8xO4EkgARiQ2cnGmJ+NMd8bY3YZY/YZYyYAW4FWaU57BnjTGLPAGPM70BeoCHTP1U+ilLqq\nRbGL6FSzEyJidygqF3m4uUT7gFL5lu0JnIgUAgKB5SllxhgDLANaXGcd7YA6wErH++pAhQx1/gOs\nvd46lVLOF3siltgTsdp9qpRSN8j2BA7wBdyBoxnKj2IlYZkSkZIickZEEoEFwFPGmBWOwxUAk906\nlVK5K/pwNEU9itK2elu7Q1E22nN8DxPXTeRy8mW7Q1Eqz3LlNm7BSsKycga4FSgBtAPGi8heY8zP\nN1Anw4cPx9vbO11Z79696d2793UFrZTKWs9benJ37bvxKuJldyjKRkv3LuXpRU8zbfM0PrvnM26v\neLvdISmV62bNmsWsWbPSlZ0+nfMld8TqrbSPows1AehhjPkhTfk0wNsYc9911jMZqGSM6ezoQo0D\nbjPGbE1zThSwyRgzPJPrmwDR0dHRNGnS5EY+klJKqWtYc2gNgxcOZstfWxh8+2DeavcWpYqWsjss\npW6qjRs3EhgYCBBojNmYnWtt70I1xiQB0VitaACINbq5HfBbNqpyA4o46twH/JWhzpJAs2zWqZRS\nKhc0r9Sc9Y+vZ1zHcczYOoOAiQF8ve1r7G5UUCqvsD2BcxgHPCEifUUkAPgM8ASmAYjIDBF5O+Vk\nERklIu1FpLqIBIjIc0AfICxNnR8Br4rIvSLSEJgBHAK+vzkfSSml1NV4uHnwbPNn2Tl0J62rtuaR\neY/QYWYHki4n2R2aUi7PJcbAGWPCRcQXeAMoD2wGOhpj/nacUglIu6hQcWCSo/w81npwjxhj5qap\n830R8QRCgVLAKqCzrgGnlFKuxb+kP3MenENEbARrD62lkHshu0NSyuXZPgbOVegYOKWUUkrdTHl6\nDJxSSimllMoeTeCUUrkuPiGzHfGUUkrllCZwSqlclXQ5iRof1+B/a/9ndygqj/vj1B+0mtKKtYfW\n2h2KUrbTBE4plat+O/gbZxLP0LJyS7tDUXnc2cSznL90nhZftmDwj4M5ef6k3SEpZRtN4JRSuSoi\nNoJyxcvR2K+x3aGoPK5BuQase2wdH3f6mK+2fUXApADCtoTp2nGqQNIETimVqyLiIuhYsyNuor9u\n1I1zd3PnqWZPsXPYTkKqhdD3u760ndGWnfE77Q5NqZtKf6MqpXLNkTNH2PzXZjrV6mR3KCqfqehV\nkW8e+IbFfRZz6J9D3PrZrRw4fcDusJS6aVxiIV+lVP60OG4xgtChZge7Q1H5VIeaHdg2eBs/7fmJ\nKt5V7A5HqZtGW+CUUrkmIjaCO/zvwNfT1+5QVD5W1KMo99e73+4wlLqpNIFTSuUKYwxrDq2hU03t\nPlVKKWfTLlSlVK4QEXY/tZsLly7k3k3On4dixXKvfpVvxCfEa0uwyle0BU4plWsKuxemZJGSuVP5\n/Png5QXPPAMndT0wlbW/zv5FzQk1efyHxzmecNzucJRyCk3glFJ50/TpUKECTJkCderA55/D5ct2\nR6VcUFnPsrzT7h3m7JhDwKQApm2epmvHqTxPEzilVN5z+jQsWgQjR8Lu3XD33TBoENxxB/zyi93R\nKRfj7ubOkDuGsHPYTjrW7Ej/7/sTNC2I7ce22x2aUjmmCZxSKu/54QdITIQHHgA/P6s1bvVqcHeH\n1q3h4Yfh0CG7o1QupkKJCsy8fybL/rOMo+eOclvobby49EXOJZ6zOzSlsk0TOKVU3hMeDnfeCZUq\n/VvWvDmsXWt1qS5fDnXrwltvwYVcnESh8qR2Ndqx9cmtvNbmNSasm8DqQ6vtDkmpbNMETimVt5w6\nBYsXw0MPXXnMzQ3697e6VQcPhjFjoH59+O470DFPKo0iHkX4b9B/2f/MftrXaG93OEplmyZwSimn\nSkhK4HJyLk4m+P57uHQJevTI+hxvb/jwQ9i2zWqJu+8+6NABduzIvbhUnlS+RHm7Q1AqRzSBU0o5\n1bjV46j1v1q5N8svPBxatQJ//2ufGxAAP/0ECxbAvn3QqBE8+6zViqeUUnmYJnBKKaeKiI2gcYXG\niIjzKz95EpYsgZ49r/8aEbjnHti+3RoT98UXULs2TJ6sy46oa4raH0V8QrzdYSh1hWwncCIyTUTa\n5EYwSqm87eT5k6w+tJrOtTrnzg3mz7eSrqt1n2alSBF48UVrfFznzvDEE9C0Kfz6q/PjVPnC5eTL\nDPxhIHUn1uWLjV+QbJLtDkmpVDlpgfMBlorIHhF5WUSuox9DKVUQLNu7jGSTTKdaubT/aXg4BAVZ\nC/jmVMWKMGMG/Pab1TrXqhX06QN//um8OFW+4O7mzuqBq7mnzj08vuBxWk9tzbaj2+wOSykgBwmc\nMaYbUAn4FOgJ7BeRRSLygIgUcnaASqm8IyI2ggZlG1DZu7LzKz9+HJYty3z2aU60aAHr1sGXX8LS\npdZkh7ff1mVHVDrlipdjevfpRPaL5MT5EzQObczIJSM5m3jW7tBUAZejMXDGmL+NMeOMMbcCzYBY\nIAw4LCLjRaS2M4NUSrk+YwwRcRG51/o2f761FMj99zuvTjc3GDDA6lYdNAhGj4YGDayFgnXZEZVG\ncLVgtjy5hTdD3mTS+knUm1SPRXsW2R2WKsBuaBKDiPgBdwEdgMvAT0BDYIeIDL/x8JRSecW2Y9s4\nfOZw7o1/Cw+H4GAonwvLPnh7w9ixsHWrNcGhWzfo1AliYpx/L5VnFXYvzEutX2LHkB00Kt+IxMuJ\ndoekCrCcTGIoJCI9RORH4A/gQWA84GeM6WeMaQ88BLzm3FCVUq5s5f6VFC9UnFZVWjm/8r//hhUr\nnNd9mpV69aw9Vn/4AWJjrWVHhg/XZUdUOtV9qvNj7x/pFtDN7lBUAZaTFrgjwGSs5K2pMeZ2Y8xn\nxpgzac6JBPQ3nlIFyLCmw9g1bBdFPIo4v/L5862vzuw+zYoI3HuvtezIG29Yy43UqWONlUvWWYjK\nkivL5CiVDTlJ4IYDFY0xQ40xmzM7wRhzyhhT/cZCU0rlJSKCf8lcmpQ+eza0bQtly+ZO/ZkpWhRe\negl27YKOHeGxx6xlR3777ebFoJRSWchJAvcD4JmxUERKi0jJGw9JKaXSOHoUoqJyv/s0K/7+EBb2\n73pxd94J//kPHD5sTzwqT5j9+2w+j/5c145TuSYnCdw3QK9Myh9yHFNKKeeZN8/q1rzvPnvjaNkS\n1q61ulQXL7a6Vd99Fy5etDcu5ZI2HN7AoB8HceeUO9n8V6adVUrdkJwkcM2wxrhlFOU4ppRSzhMe\nDu3bQ5kydkcC7u5WV+ru3fD44/Df/1rLjixYoMuOqHQ+6PABPz/6M2cuniHw80BGLB7BmYtnrn2h\nUtcpJwlcEcAjk/JCQLEbC0cppdL46y9YudK+7tOslCoF48fDli1QowZ07Wptz7Vzp92RKRfSumpr\nNg3axDvt3iE0OpR6k+oxd8dcjCb7yglyksCtA57IpPxJIDqngYjIUBHZJyLnRWSNiNxxlXMfE5Gf\nReSE47U04/kiMlVEkjO8fsppfEopG3z7rdXq1b273ZFkrn59qzv1u+9gzx5o2BCeew5On7Y7MuUi\nCrkX4oU7X2DHkB0EVgzkwTkPMvCHgXaHpfKBzFrSruVVYJmI3Aosd5S1A+7AWtA320SkJzAWKzFc\nhzXTdbGI1DHGxGdySRDwNfAbcAEYBSwRkfrGmCNpzlsEPAqkzPfWwSpKOdmFSxco6lE0dyoPD4cO\nHaB06dyp3xlErIV/O3aEcePgrbdg5kx45x149FFrtwdV4FUtVZXve33PD7t+QNAlSNSNy8leqL8C\nLYCDWBMX7sXaSquRMWZVDuMYDoQaY2YYY3ZiteYlAAOyiOE/jrXnthpjdgOPYX2WdhlOvejY9uuY\n46X/LVbKic4lnqPsB2WZ/fts51d++DCsWuV63adZKVoUXn7ZWnakfXsYOBCaNYPVq+2OTLmQrnW7\ncm/de+0OQ+UDOd0LdbMx5hFjTAPHQr4DjDF7clKXiBQCAvm3NQ9jDRBYhpUoXo/iWGPwTmQoDxaR\noyKyU0Q+EREX/m+8UnnPyj9WcjbxLLdWuNX5lc+dCx4eVutWXlKpEnz1Ffzyi7Xwb8uW0LevLjui\nlHKqG90LtZiIlEz7ykE1voA7cDRD+VGgwnXW8R7wJ1bSl2IR0BdoC7yA1e36k+jy2Uo5zaI9i6hW\nqhp1y9R1fuXh4Va3ZKlSzq/7ZrjzTli3Dj7/3Nqeq25deO89XXZEXZUxRteOU9cl22PgRMQTeB+r\n+zSzef3uNxpUyq2Aa07VEZFRjliCjDGpOwsbY8LTnLZdRLYBcUAwmS+DAsDw4cPx9vZOV9a7d296\n9+6dreCVKggi4iLoVLOT87cVOnTIWjh3xgzn1nuzubtby4088AC8/jq88gp88YU1g7VLF2v8nFJp\nhG8PZ9yacXza5VOa+DWxOxzlRLNmzWLWrFnpyk7fwISnnExi+AAIAQYDYcBQwB8YhDWZILvigctA\n+Qzl5biyVS4dERmJ1brWzhiz/WrnGmP2iUg8UIurJHDjx4+nSRP9S6PUtcSeiCX2RCwf3vWh8yuf\nOxcKF7aW58gPfHzgo4/giSfgmWesvVY7d7YSubq50Hqp8qyqpapyPuk8d0y+g2F3DOPNtm9Ssohu\ncpQfZNYYtHHjRgIDA3NUX066UO8FhhhjvgUuAauMMf8HvAw8kt3KjDFJWMuPpE5AcHRztsOaZZop\nEXkeeAXoaIzZdK37iEglrBbDI9c6Vyl1bYtjF1PIrRBtq7d1fuXh4dCpE2RoDc/z6teHJUtg/nxr\nzbhbboGRI+Gff+yOTLmI5pWaE/1ENO+1f48vN31JwMQAwreH69px6go5SeBKA/scf/7H8R7gF6BN\nDuMYBzwhIn1FJAD4DGu/1WkAIjJDRN5OOVlEXgDexJqlekBEyjtexR3Hi4vI+yLSTESqikg74Dtg\nN7A4hzEqpdJYFLuIVlVa4VXEy7kVHzhgzdzMK7NPs0vEWtduxw4YMwY+/dTalmvqVGvSgyrwCrkX\nYmTLkcQMjaF5peb0nNuTTl91IvZErN2hKReSkwRuL1DN8eedWOPPwGqZO5WTIBzj1Z4D3gA2AY2w\nWtb+dpxSifQTGgZjzTqdCxxO83rOcfyyo47vgV3AZGA90MbR4qeUugFJl5P4+Y+f6VSrk/MrnzsX\nihTJP92nWSla1BoTt2sXtG0LAwZA8+bWfqtKAZW9KzOv5zwW9F7ArvhdhEwPIemy/hOmLJLdZlkR\nGQ5cNsZMEJH2wAKsRNADGGGM+dj5YeY+EWkCREdHR+sYOKWuw/GE44gIpYs5eXWeZs3A39/axL4g\nWbUKnn4aNm+2lh15913w87M7KuUiEpIS2PH3Dm6veLvdoSgnSjMGLtAYszE71+ZkId/xxpgJjj8v\nAwKA3kDjvJq8KaWyr4xnGecnb/v3W0tv5Nfu06tp3Ro2bIDQUFi40OpWff99XXZEAeBZyFOTN5VO\nthI4ESkkIstFpHZKmTHmD2PMPGPMVueHp5QqUObMsboW77nH7kjs4e5uzVTds8fqUn35ZWt/1YUL\n7Y5MKeVispXAOcaPNcqlWJRSBV14uLU+WokSdkdiLx8f+Phjqzu1ShUroe3SBXbvtjsy5cJ2H9ef\nj4IkJ5MYZgIDnR2IUqqA27vX6kIsiN2nWbnlFli61BoPuGOH9f6FF3TZEXWFJXFLCJgYwLCfhnHq\nQo7mE6o8JicJnAcwWESiRSRURMalfTk7QKVUATFnDnh6Wi1N6l8icN99VgL32mswcaI1Pm76dF12\nRKVqW70tYzuMZfqW6QRMDGDWtlm6dlw+l5ME7hZgI9YacHWAxmletzkvNKVUgRIebnUVFi9udySu\nqVgxePVVa9mRkBB49FFo0cKa9KEKPA83D4a3GE7M0BhaVWnFw/Me5q6wu7RbNR/LySzUkKu8cmFJ\ndqWUq7iUfCl3Ko6NhY0btfv0elSuDLNmwcqV1gzVZs2gf3/46y+7I1MuoFLJSsx9aC4/PfwTe0/u\npeGnDRkdOZoLly7YHZpyspy0wCmlCqj/rvgvbabmdMOVqwgPt1reOnd2ft35VZs2EB1t7eSwYIHV\nrfrhh5CYaHdkygV0rt2Z7UO280LLFwiNDuX0hZxvmq5cU7YTOBGJFJEVWb1yI0illGtYFLuIaqWq\nOb/i8HBrg3dPT+fXnZ+5u8OTT1qzUx99FEaNspYdWbTI7siUCyhWqBhvtn2TuKfjKF+ivN3hKCfL\nSQvcZmBLmtcOoDDQBNjmvNCUUq7k8JnDbDm6xfnbZ+3aBVu2aPfpjShdGiZMsJYdqVQJ7r7bGk+4\nZ4/dkSkXULywjivNjzyye4ExZnhm5SIyBijgizcplX8tjl2MIHSo2cG5Fc+ZY6371ikX9lUtaG65\nBZYts5Ydee45aNAAhg+3Jj94edkdnVLKiZw5Bm4mMMCJ9SmlXEhEXAR3+N+Br6evcysOD7c2ri9W\nzLn1FlQi0KMHxMRYidv//meNj5sxQ5cdUZn6YuMXnDx/0u4wVDY5M4FrAeg0F6XyoUvJl1gat5RO\nNZ3cShYTA9u2Qc+ezq1XWQnxa6/Bzp3WhId+/aBlS1i/3u7IlAv5858/eW7Jc9SdWJewLWG6dlwe\nkpNJDPMyvOaLyBpgKhDq/BCVUnZb9+c6Tl446fzxb3PmQMmS0MHJ3bLqX1WqwOzZEBUF589D06bW\nPqtHj9odmXIB/iX9iRkaQ9vqben7XV/azmhLzN8xdoelrkNOWuBOZ3idAKKAu40xrzsvNKWUq1gS\ntwSfoj409W/q3Ipnz4Zu3awN7FXuCgqylh355BP4/nurW3XsWF12RFHRqyLfPPANS/os4dA/h7j1\ns1t5ZfkrJCQl2B2augrR5lKLiDQBoqOjo2nSpInd4SjlUhIvJxJ7Ipb6Zes7r9Lt261B9wsWWDMm\n1c1z4oTVvfrpp1C7Nnz0kU4iUQBcuHSBd395l3d+eYeKXhUJfyCcO/zvsDusfGvjxo0EBgYCBBpj\nNmbn2px0od4hIs0yKW8mIrdntz6llOsr7F7YuckbWJMXvL3hrrucW6+6ttKlrT1VN20CPz9rAeV7\n77V2xFAFWlGPoowJHsPvg3/ntgq3UdGrot0hqSzkpAt1ElA5k3J/xzGllLo6Y6wErnt3KFLE7mgK\nrkaNYMUKayzi1q3WsiOjRsGZM3ZHpmxWu0xt5vecj39Jf7tDUVnISQJXH2sz+4w2OY4ppdTV/f67\nNTtSF++1nwg88IA1I/jll+Hjj6FuXQgL02VHlHJhOUngLgKZ7cnhB+TSTtdKqXwlPBx8fKB9e7sj\nUSk8PWH0aCuxbtUK+va1vm7YYHdkSqlM5CSBWwK8IyLeKQUiUgp4G1jqrMCUUvlUSvfpffdB4cJ2\nR6MyqlrV+v5ERsLZs9ayIwMH6rIj6gojFo9g2uZpunacTXKSwI3EGgP3h2Nj+0hgH1ABeM6ZwSml\n8qEtW6zN17X71LUFB8PGjdZkh/nzrWVHxo+HpCS7I1Mu4FLyJY6dO0b/7/sTNC2I7ce22x1SgZPt\nBM4Y8yfQCHgBayP7aOAZoKEx5qBzw1NK2SnZ5MIYqPBwaxZk27bOr1s5l4cHDBkCe/ZAnz4wcqQ1\n8YxJCjQAACAASURBVGHxYrsjUzbzcPNg5v0zWfafZRw9d5TbQm9j1LJRnEs8Z3doBUaOttIyxpwz\nxnxujBlqjBlpjJlhjNH/limVj5w8f5IKH1Zg+d7lzqs0pfv0/vuhUCHn1atyV5kyMGmStexI+fLW\nmnHdukFcnN2RKZu1q9GOrU9u5bU2r/HRmo9o8EkDftj1g91hFQg5WQfuJRG5YtN6Efl/9u48Tub6\nD+D4621duZXkSgghV4RECh3oECpUVCq/UikK6ZREBx1SkZIksihR7kgkd5H7Ts4c5b7Wvn9/fEbt\n7C52Z2f2O7Pzfj4e87D7me985j2Dnfd+jvennYh0C05YxhivTds4jd1HdlP2grLB6/TXX92Hvk2f\nRqbKld3auFGj3N9lhQpu5+qhQ15HZjyULXM2Xrz2RZZ3WE65AuVo+lVTXp/zutdhZXiBjMD9D1id\nTPsK4JG0hWOMCReT10/m8gsv5+K8yZV9DFBsrBvNqV8/eH2a9CXiEvDVq6F7d7cu7rLLYPhwN8Jq\nolbp80sz6Z5JxN4RS6uKrbwOJ8MLJIErBOxIpn03rpSIMSbCqSqT108O7uH1p6dPW7Rwa6tMZMuR\nA3r0cPXjrr4a2rRxZUcWL/Y6MuMhEeHOy++kRL4SXoeS4QWSwP0J1EmmvQ6wPW3hGGPCwbJdy9hx\naAeNSzcOXqeLF8OmTdCyZfD6NN4rUcKd5DBjBhw4ADVqwMMPw19/eR2ZMRlaIAncYOBdEXlARC7x\n3doB7/juM8ZEuMnrJ5MjSw7qFq8bvE5HjYKCBaFeveD1acJH/fpuXVz//jB2rCs78u67VnbEJHH0\n5NHQ7HCPMoEkcG8BnwIfAht9t/eB/qraJ4ixGWM8MnnDZBqUbEC2zEE6p9SmT6ND5szw+OOuzt/d\nd8PTT0OVKjB1qteRmTDy1OSnqPdZPX7f9bvXoUS0QOrAqap2Ay4ErgKqAOeras9gB2eMSX8Hjx9k\nzpY5wZ0+XbAAtmyx3afRokAB+PBDN21+4YVw001w++2wcaPXkZkw0KpiK/Ye3csVg66gy9QuHDph\nu5gDEVAdOABVPaSqC1V1uaoeD2ZQxhjv5Mqai2WPLOOuy4OYbMXGuvph11wTvD5N+KtaFX78Eb76\nyp3qUL48PP+8lR2JcvVL1mfpI0vpWb8nAxYOoMIHFRi3epwdyZVKASVwIlJDRN4Uka9E5OuEt0AD\nEZHHRGSTiBwVkXkiUuMs1z4kIj+JyD7fbVpy14tITxHZLiJHfNeUDjQ+Y6KFiFD+wvIUyFEgOB3G\nx7tF7nfcATExwenTRA4Rt3Fl9Wp49lno18+VHRkxwsqORLGsMVl57prnWNFhBZUuqkSzUc247avb\n2PzPZq9DixiBFPJtBfwMlAeaAVmACkADYH8gQYhIS6Af8DJwBbAUmCIiZ/oEuRYYAVyHm8b9E5gq\nIv+WMfEVFX4cV7euJnDY16ednm1Mepo/H/7806ZPo12OHPDKKy6Rq10b7rnHjcguWeJ1ZMZDpfKX\n4rvW3zH2rrH8tvM3Ok7q6HVIESOQEbjngE6qeitwAncOankgFtgSYBydgEG+I7lW4woCHwGSnPgA\noKptVHWgqi5T1bXAQ7jX0jDBZU8Cr6rqBFVdDrQFigC3BxijMSYQsbFQuDDUSa76kIk6JUrAmDHw\nww/wzz9w5ZXQvj3s3u11ZMYjIkLz8s1Z2WElA28Z6HU4ESOQBO5S4Hvf1yeAnOomrt8B2qe2MxHJ\nAlQH/j1w0dffdKB2CrvJiRsJ3OfrsySu4HDCPg8A81PRpzEmrU5Pn955p02fGn8NGsBvv8F777l/\nI2XKuK+t7EjUyp0tN0VyF/E6jIgRSAK3D8jt+3obUNH3dT4gRwD9FQBigF2J2nfhkrCUeMMXy3Tf\n94UATWOfxpi0mjsXtm2z6VOTvMyZ4YknYN06aNUKOnVyGx+mTz/3Y42JcoEUZJoN3AD8DowG3hOR\nBr62H872wFQSXBJ29otEngXuAq5V1RNp7bNTp07kzZvXr61169a0bt36XKEYYxKLjYWiRd2aJ2PO\npEABGDgQ/vc/6NgRbrjBlR3p1w9KlfI6OhMmDhw/wKa/N1GlUBWvQwnIyJEjGTlypF/b/v0BbR0A\nQFK7bVdEzgeyq+p2EckEdAWuBtYBvVT171T2lwW33q2Fqo5P0D4UyKuqzc7y2Gdwa/IaquqvCdpL\nAhuAqqq6LEH7j8Cvqtopmb6qAYsXL15MtWrVUvMSjMkQVBURCV6Hp07BxRe7HYjvvBO8fk3GpupO\n7XjmGdizx/3ZvTvkzOl1ZMZjvX7qRY8fe9CxVkdeue4VcmfLfe4HhbklS5ZQvXp1gOqqmqodPYEU\n8t2nqtt9X8er6uuqepuqPp3a5M3Xx0lgMQk2IIj7FGkIzD3T40SkC/A8cFPC5M3X5yZgZ6I+8wC1\nztanMdHsmanP0OabNsHr8OefYccOmz41qSPiplPXrIEuXaBvXyhXDkaOtLIjUa5rna70atCLgYsG\nUv6D8oxdOTaqa8cFXMg3yN4G2otIWxEpBwzEracbCiAiw0Sk9+mLRaQr8Cpul+oWEbnId0v4K9q7\nwAsicquIVAKGAVuBb9PlFRkTYcavHU+uLLmC12FsrBuBq1UreH2a6JEzJ7z6KqxaBTVquKO56tVz\n562aqJQ1JivP1n2WlY+tpFrhatwx+g5uGXkLG/+OzhM+wiKBU9VY4GmgJ/ArUBk3snZ6X3kx/Dcf\nPIrbdToG2J7g9nSCPt/EndE6CLf79DygcQrWyRkTddbvW8/6fetpVLpRcDo8dcqVirjzTsgUFj9m\nTKQqWRK+/hqmTYN9+6B6dbdWzsqORK0S+UowvvV4xrUcx++7fufyDy/ntZ9eIy4+zuvQ0lXY/GRV\n1Q9VtYSqnqeqtVV1UYL7GqhquwTfl1TVmGRuPRP12UNVi6hqDlW9SVXXp+drMiZSTFk/hSyZstCg\nZIPgdDh7Nuza5da/GRMM11/vyo68+65bI1e2LPTvb2VHoljTck1Z+dhKnqj5BDM2zyBGoqtUUdgk\ncMYY70xaP4m6xesGb1HwqFFwySVu6suYYMmSxe1SXbfOra186im44gpXFNhEpVxZc/HmDW8y9d6p\nwd2EFQECTuBEpLSI3CQi5/m+j653zpgM4ljcMWZunhm86dO4OBg71n3A2o8FEwoXXgiDBsGiRZAv\nnxuda9ECNm3yOjLjkZhM0TX6BoGdhXqBiEwH1gITgdPnj34qIv2CGZwxJvTmbJnDkZNHaFy6cXA6\nnDXLrU+y3acm1KpVc9P1I0a4M3fLl4eXXoLDh72OzJiQC2QE7h0gDiiOq9922iggSL/CG2PSy6R1\nkyiSuwgVC1Y898UpERvrFp672kbGhJYItG4Nq1e7mnFvvunKjnz1lZUdMQAcPnGYl2a+xIHjB7wO\nJagCSeBuBLqp6tZE7euAS9IekjEmPXW/pjtf3/V1cNaP2PSp8UquXNCrF6xcCVde6ZK6a691Gx9M\nVFu0fRH9fulHuQHliF0Rm2FqxwWSwOXEf+TttPOB42kLxxiT3grkKECtYkGq1TZzJuzda9Onxjul\nSsE338DUqe4kh+rV4dFH3dcmKl1b4lpWPbaKWsVq0XJMSxp92Yj1+yK/KEUgCdxsoG2C7zXBkVoz\ngxKVMSYyxcZC6dJuZ6AxXrrhBli6FN5+253iUKYMDBjgRolN1CmetzjftPyGCa0nsGbPGip+WJGe\ns3pyPC5yx50CSeC64k5NmARkBd4ElgP1gG5BjM0YE0lOnnQFV2361ISLLFngySdh7VpXVLpjR/fL\nxYwZXkdmPHJL2VtY+dhKOtfuzKs/vUqljyrx5/4/vQ4rIIGchbocKAvMwR1LlRP4GrhCVTcENzxj\nTMT44QdXKd+mT024KVgQPv4YFi6EPHmgYUO44w7YvNnryIwHcmTJQe+GvVn6yFIalW5E0TxFvQ4p\nIAHVgVPV/ar6mqrepapNVPUFVd0R7OCMMREkNtZVx69c2etIjEle9eowZw4MHw6//OLKjrz8MhxJ\nblm3yegqXFiB/o37k0ki80yDQOrAVT7DrZKIlBGRbKEI1BgTxk6ccAvHbfrUhDsRuOceWLMGOnWC\n1193ZUdGjbKyIyaiBJJ2/oY7cP5X39e/Jfh6NbBfRD4XkexBi9IYE96mT4d//rHpUxM5cuWC3r1d\n2ZErroBWreC669zGB2MiQCAJXDNczbf2QBWgqu/rNcDdwINAA6BXkGI0xgTZ9oPbKf9BeZbsWBKc\nDmNj3ShGxSAVAzYmvVx6KXz7LUyZAn/95U536NDBlcMxJowFksA9Dzypqp+q6u+qukxVPwU6AU+r\n6pfAE7hEzxgThqasn8KaPWsonrd42js7fhzGjbPpUxPZbrwRli2Dvn3hyy9d2ZEPPrCyIyZsBZLA\nVQL+SKb9D9994KZTCydzjTEmDEzeMJkaRWtQIEeBtHc2bRrs32/TpybyZcni1sWtWwfNm8MTT7gR\nuZlW4tSEn0ASuNXAsyKS9XSDiGQBnvXdB1AU2JX28IwxwRYXH8fUDVNpdGmQji4eNQouv9zdjMkI\nChaETz5xZUdy5YIGDVwduT+SG7swxhuBJHCPAbcAW0VkuohMA7b62h71XVMK+DA4IRpjgmnBtgX8\nc+wfGpdpnPbOjh1z64ds9M1kRNWrw88/wxdfuD/LlYMePazsiAkLgRTynQuUAF4CluFOYXgJKKmq\n83zXfKGqbwUxTmNMkExeP5nzzzufGkVqpL2zKVPg4EE3OmFMRiQC997ryo489RT06ePqx40ebWVH\njKcCLeR7SFUHqmpnVe2kqoNU9WCwgzPGBN+k9ZO48dIbickUk/bOYmOhUiX3gWZMRpY7t0veVqyA\nKlXcqHODBm7jgzEeCLj8sIhUEJFGInJbwlswgzPGBNdfh/9i0fZFwVn/dvQojB9v06cmupQu7f7d\nT5oEO3a4GnKPPWZlR0y6y5zaB4hIKeAb3I5TBU7XDTg9lhyEX+uNMaGQP3t+ZrSdQZVCVdLe2eTJ\ncOiQJXAmOjVq5EbfBgyAV16Br76CV1+F9u0hc6o/Wo1JtUBG4N4DNgEXAUeAy4F6wCLguqBFZowJ\nuiwxWahfsj7nn3d+2juLjYWqVd35p8ZEo6xZoXNnWLsWbr/djcRVrw6zZnkdmYkCgSRwtYGXVHU3\nEA/Eq+ocoDvQP5jBGWPC1JEjMGGCjb4ZA3DRRfDpp7BgAeTI4Y7katkStmzxOjKTgQWSwMUAh3xf\n7wGK+L7+A7gsGEEZY8LcxIlw+LDtPjUmoRo1XLmRYcPgp59c2ZGePd16UWOCLJAEbjlQ2ff1fKCr\niNTBlRLZGKzAjDFhLDbWVagvXdrrSIwJL5kyQZs2blq1Y0fo1cvt0h4zxsqOmKAKJIHrleBxLwEl\ngdlAE6BjkOIyxoSrw4fhu+9s+tSYs8mdG15/3ZUdqVTJjVY3bAi//+51ZCaDCKSQ7xRV/dr39XpV\nLQcUAAqq6oxgB2iMCTPff++mhGz61JhzK1PGrRedOBG2bXMbf554Avbt8zoyE+FSlcCJSGYRiROR\nignbVXWfqo0NGxMVYmPhyiuhVCmvIzEmcjRu7Ebf3nwTPv/c7d7+6CM4dcrryEyESlUCp6pxwBas\n1psxEeXpKU/TfXr3tHd06JAbgbPpU2NSL2tWePpptz7uttugQwdXduSnn7yOzESgQNbAvQb0FpEg\nFJIyxoSaqjJy+Uji4uPS3tl337kD7C2BMyZwhQrBkCEwfz5kzw7XXgutWsGff3odmYkggSRwj+MK\n924XkTUisiThLcjxGWPS6Pe/fmfHoR00Kh2E47NGjYJateCSS9LelzHRrmZNmDvXTanOmgWXXeZO\nc7CyIyYFAjnvY1zQozDGhMykdZPImSUndYvXTVtHBw648x979w5OYMYYV3akbVt3ksNrr7kEbsgQ\n6NcPmjUDkXP3YaJSILtQXznbLdBAROQxEdkkIkdFZJ6I1DjLtRVEZIzv+ngRSVK+RERe9t2X8LYy\n0PiMiVSTN0ymQckGZMucLW0dTZgAx4/DHXcEJzBjzH/y5IE33oDly+Hyy6FFC7j+eve9MckIZAoV\nEcknIg+JSJ/Ta+FEpJqIFA2wv5ZAP+Bl4ApgKTBFRAqc4SE5gA1AN2DHWbpejjuztZDvlsYhCGMi\ny8HjB5mzZU5wpk9jY6F2bShePO19GWOSV7asW2v6/fduTVzVqq4g8N9/ex2ZCTOpTuBEpDKwFpc8\nPQPk893VHOgTYBydgEGqOkxVVwOPAEeAdsldrKqLVLWbqsYCJ87Sb5yq7lbVv3w3K7xjosoPm34g\nLj4u7Qnc/v0webJtXjAmvTRp4kbf+vSBzz5z9eQGDbKyI+ZfgYzAvQ0MVdUywLEE7RNxmxtSRUSy\nANWBH063+WrKTQdqBxBfQmVEZJuIbBCR4SJycRr7MyaiTF4/mbIXlKVU/jTWbBs/Hk6csOlTY9JT\n1qzQpYsrO3LLLfDII64G4+zZXkdmwkAgCVwNYFAy7dtw05SpVQBXV25XovZdAfZ32jzgfuAm3Ihe\nSeAnEcmZhj6NiSida3dm4M0D095RbCzUrQvFiqW9L2NM6hQuDEOHwrx5kCUL1KsHrVtb2ZEoF8gu\n1ONAnmTaywK70xaOHwECPt1BVack+Ha5iCwA/gDuAj470+M6depE3rx5/dpat25N69atAw3FGM+U\nvaAsZS8om7ZO/v4bpkxxu+KMMd6pVcslccOGwbPPQrly0L07PPOMqydnwtrIkSMZOXKkX9v+/fsD\n7k9SewKWiHwCXIBLhPYBlYFTuPIiP6nqU6nsLwtuvVsLVR2foH0okFdVm53j8ZuAd1S1fwqeawEw\nTVWfT+a+asDixYsXU61atdS8BGMytqFDoV072LoVihTxOhpjDLiyPq++Cu+950bG+/VzpUis7EhE\nWbJkCdWrVweorqqpqqUbyBTq00Au4C/gPGAWsB44CCRJjM5FVU8Ci4GGp9tERHzfzw0gvmSJSC7g\nUs6+a9UYk1hsLFxzjSVvxoSTPHngrbfc+arlykHz5nDjjbDSqmVFi0DqwO1X1RuAW4GOwACgiape\nq6qHA4zjbaC9iLQVkXLAQFypkKEAIjJMRP6tHioiWUSkiohUBbICRX3fX5rgmrdEpJ6IXCIiVwPf\nAHGA//ilMebM9u2DadNs96kx4eqyy2DiRFd65I8/oHJlePJJKzsSBQIpI3IxgKrOUdUPVfVNVZ2e\nliB85UCeBnoCv+KmZW9S1dNr6orhv6GhiO+6xb72Z4AlwOAE1xQDRgCrga9w6/OuUtW9aYnVmKgy\nbpwrW9CihdeRGGPO5uab3Whc797uJIeyZeHjj63sSAYWyBq4U8BsYDgwRlX/CUVg6c3WwBmTjEaN\n3OkLM2d6HYkxJqV27HCbHIYNgyuugP793S5yE3bSew1cDWAh7tSEnSLyjYi0EJE0ntNjjAkre/fC\n9OnQsqXXkRhjUqNwYfj8c/jlF4iJcWtY777bbUQyGUYga+CWqGoXoDjQGNiDm7rcJSJDghyfMSaV\nNuzbQP3P67N+3/q0dfT116DqFkcbYyLPVVfB/PluSvWHH9x6uddeg2PHzv1YE/YCOgsV3GkJqjpT\nVR8Grgc2AfcFLTJjTEAmrZ/Ez1t+5qKcF6Wto9hYqF8fChYMTmDGmPSXKRM88IA7zeHRR6FHD6hQ\nwa1vTeUSKhNeAk7gRORiEekqIr/hplQPA48HLTJjTEAmr59M3eJ1yZ0td+Cd7N4NM2bY7lNjMoq8\neaFvX7fR4bLLoFkzuOkmKzsSwQLZhdpeRGbx34hbLHCpqtZV1Y+CHaAxJuWOxR1jxqYZaT+8/uuv\nXUHQZmeto22MiTTlyrmyIxMmwMaNruxIp07wT4bYjxhVAhmBexFYAFypqperam9V3RzcsIwxgZj9\nx2yOxh2lcenGaesoNhYaNIALLwxOYMaY8CECt9wCK1a4NXGDB7uyI4MHW9mRCBJIAldcVbuo6m+J\n7xCRikGIyRgToMnrJ1MkdxEqFkzDf8Vdu+DHH2361JiMLls26NbNrY9r1Ajat4eaNeHnn72OzKRA\nILtQ/VY9ikhu37TqAmBp0CIzxqTapPWTaHRpIyQt5yF+/bVb+GzTp8ZEhyJFXM24uXPd6FzdunDv\nvbBtm9eRmbNIyyaGer4D53fgTkKYAVwVpLiMMan0xz9/sGrPKhqXCcL06fXXwwUXBCcwY0xkqF0b\nFiyATz+FqVPdZoc+fazsSJhKVQInIoVF5FkRWQeMxh1gnw24XVWfVdWFoQjSGHNuF+S4gBHNR3B9\nqesD72THDpg1y6ZPjYlWmTJBu3ZuWrV9e3jpJbj8chg/3sqOhJkUJ3AiMh53rmhl4CmgiKo+EarA\njEmpeI2n/YT23Pv1vRw9edTrcDyTK2suWldqTb7s+QLvZOxYyJwZbr89eIEZYyJPvnzw9tuwbBmU\nKQNNm7p1cqtWeR2Z8UnNCFwT4FPgZVX9XlVtq4rxnKrSeUpnPv31U8auGkujLxux/9h+r8OKXLGx\ncMMNkD+/15EYY8JB+fIwaZIbgVu/3pUd6dwZ9tvPWa+lJoG7BsgNLBKR+SLyuIhYjQHjqb5z+/Le\n/PcY0HgA09tM58/9f7LtoC28Dci2bTBnjk2fGmP8icCtt7qyIz17wscfu1G5Tz+F+Hivo4taKU7g\nVPUX37FZhYFBQCtgm6+PG0QkDWXfjUm94cuG03V6V56/5nkerfEodYrXYc3ja6hwYQWvQwsZVWXR\n9kWh6fz09GnTpqHp3xgT2bJnh+7dYc0ad4rDQw+5siNz53odWVQKpIzIEVUdoqp1gUpAP+BZ4C/f\nOjljQm7f0X10+L4DD1R9gFfrv/pve5aYLB5GFVord6+k8ZeNqTG4Bou3Lw7+E8TGuh/K+dKwhs4Y\nk/EVLQpffPFfvbg6daBNG9i+3du4okzAZUQAVHWNqnYFigGtgxOSMed2/nnn89MDPzHolkFpq3kW\nAfYd3UfHSR2p/FFl1u9bz7iW46hWuFpwn2TrVvfDuGXL4PZrjMm4rr4a5s93JzhMmeJOc3j9dTh+\n3OvIokKaErjTVPWUqo5T1duC0Z8xKVG1UNUMPeIWFx/HBws+oMz7ZRj621B6N+zNig4raFquafCT\n1tGjXVX22+y/sDEmFWJi3FTq2rXw8MPw4ouu7MiECVZ2JMSCksAZEwn2Hd3ndQgp9tvO36g6sCpP\nTHqCZuWasfaJtXSt05VsmbOF5gljY12JgDx5QtO/MSZjy5cP3nkHli6FUqXcL4ONG8Pq1V5HlmFZ\nAmeiwrYD2yj7fln6zu3rdSgpUiBHAYrmKcqi9ov45LZPKJSrUOie7I8/YN48231qjEm7ChXcdOq4\ncbBuHVSqBE8/bWVHQsASOBMViuQuwv+q/48u07rQbVo3NMyH9ovlKcaUe6cEf61bcsaMcdOnt94a\n+ucyxmR8Im43+4oV8MorMHCgWx83ZIiVHQkiS+BMWFPVoEx9igivNXyNd256hzfnvslD4x8iLj4u\nCBFmALGx0KQJ5LZKQMaYIMqeHZ57zpUduf56ePBBuOoqN+Jv0swSOBPWes7qSbVB1Th4/GBQ+nvq\nqqcYdvswPl/6OXeOvpNjcd4d0hwWCeTmze7waps+NcaESrFi8OWXMHs2xMVB7dpw333u7GUTMEvg\nTNgavHgwPWb1oH319uTOFrzRoTZV2vBtq2+Zsn4KjYan/9Fbm//ZzF2j7+LhCQ+n6/Mma/RoOO88\nuOUWryMxxmR0devCwoXuJIeJE9206htvWNmRAFkCZ8LShDUTeOT7R+hwZQe61+0e9P5vLnsz09pM\nY+mupbwz752g95+cwycO8+KMFyk3oBxztsyhQYkG6fK8ZzVqFNx8M+TK5XUkxphoEBPjyo2sXeum\nVJ9/HipWhO+/9zqyiGMJnAk787bOo+WYltxe7nb6N+4fskK9dYrXYeHDC3n+mudD0v9p8RrP8GXD\nuWzAZbw19y2eufoZ1j6xljZV2oT0ec9pwwZYvNimT40x6S9/fnj3XVi2DEqUcLMATZq49XImRSyB\nM2FlzZ413DLiFqoXqc7wZsOJyRQT0ucrfX7pkBYDXrBtAXWG1KHNN224qthVrHpsFb0a9CJX1jAY\n8Ro9GnLkcD80jTHGCxUqwNSp8M03rmZcxYrwzDNw4IDXkYU9S+BM2Nh5aCc3Db+Ji3JdxPhW4zkv\ny3leh5RmvWf35ujJo8y8byZj7hpDyfwlvQ7pP7Gx7rfenDm9jsQYE81E4PbbYeVK6NEDPvrIrY/7\n7DMrO3IWmb0OwJjTcmXNRcOSDelxXQ/yn5ff63AAuOazazh04tAZ73+x3os0L9/8jPd/1vQz8mTL\nE/KRxFRbtw5+/dWtPzHGmHCQPbv7mXTffdC1K7Rr52rI9e8PtWp5HV3YsQTOhI1cWXPxadNPvQ7D\nT62itTh68ugZ778o50VnfXy4JKJJjB7tRt4aN/Y6EmOM8VesGIwYAY8+Ch07utpx990HffpA4cJe\nRxc2LIEz5iz63hgZR2+lWmysO3khRw6vIzHGmORdcw0sWgSffuoKAo8dCy+9BE8+CVmzeh2d52wN\nnDHRZs0ad+B0y5ZeR2KMMWcXEwPt27tlH+3aQffubqPDxIleR+a5sEngROQxEdkkIkdFZJ6I1DjL\ntRVEZIzv+ngR6ZjWPo2JGrGxru5bo0ZeR2KMMSmTPz+89x789hsUL+7qV958s6snF6XCIoETkZZA\nP+Bl4ApgKTBFRAqc4SE5gA1ANyDZszgC6NOY6BAb6w6azp7d60iMMSZ1KlaEadPcdOrKle77rl2j\nsuxIWCRwQCdgkKoOU9XVwCPAEaBdcher6iJV7aaqscCJYPRp0s/MTTPpO7cvqup1KNFn5UpYvtyK\n9xpjIpcING/ufp69+CIMGACXXQaffx5VZUc8T+BEJAtQHfjhdJu6T/bpQO1w6dMEx7Jdy7h9W/rG\nlQAAIABJREFU1O1M3TCVU3rK63Ciz+jRkCcP3Hij15EYY0zanHeeS+DWrIHrroP774fatWHBAq8j\nSxeeJ3BAASAG2JWofRdQKIz6NGm06e9NNP6yMZfmv5Sxd40lcybbBJ3uMvD06fr1sGePf5sN8hoT\nBS6+GEaOhFmz4PhxVzPugQdg506vIwupcP4EFSDYP37P2WenTp3ImzevX1vr1q1p3bp1kEOJHqt2\nr6LfL/34YtkXFM1dlIn3TCR3ttxehxV9VqxwUw5vvOF1JGkWHw+ZEv36ef31cO+90KvXf23jxrnZ\n4p074YIL/mu//363Drpnz//aNmxwFQr69HH3nfbNN+4zoVWr/9qOHYMpU9wv+wUL/te+Z4+7tmjR\noLxMY0xq1KvnzncePBheeOG/siMdO4ZF2ZGRI0cycuRIv7b9+/cH3qGqenoDsgAngdsStQ8FvknB\n4zcBHdPaJ1AN0MWLF6sJjj/++UNvGXGL0gMt3Lewvj77df376N9ehxW9XnpJNW9e1ePHvY4kTU6e\nVG3aVLVfP//2hQtVN23yb9uwQfWjj5K+5H79VL/4wr9txQrV665T3bjRv/3BB1XvuMO/bcsWVVCd\nMsW//amnVMuX92+Lj1fNnl116FD/9uHDVevXT/r6nnpKddw4/7ZVq1zMx475t8+d6+JO6OhR1T//\ndO+TMVFr717Vxx9XjYlRLVtWdeJEryNK1uLFixU3sFRNU5k/eT6FqqongcVAw9NtIiK+7+eGS58m\n9fJlz8e+o/v4rOlnbH5qM93qdiNf9nxehxWdVGHUKGjWLCx+E02LmBioWhXKlfNvv/JKKFHCv61U\nKXjkkaQvuXNnN1qXUIUKMHMmlEx0XO0nn7ilgwkVKQK7dsG11/q3d+gAQ4b4t6nCW29BjURFjAoW\nhMqVk76+DRtg3z7/thUr4JVX4FSiZaNPPOFOGUpo4UI3o7Rhg3/7PfckLf23YwfUqeMGLRIaOdJ/\nJBPcqOd777nzxhPavBl+/jnp6/jrLzh65kNMjAmt88+H9993ZUeKFYMmTdzZz+vWeR1Z8KQ24wvF\nDbgLOAq0BcoBg4C9wIW++4cBvRNcnwWoAlQFtgFv+L6/NKV9JhODjcCZjGvpUjdkFKa/hZrA7Nql\numePf9veve6v+dAh//Zx41THjvVv27lT9f77VVeu9G9/4w3Vu+/2bzt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QPu9pEybAtGlu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CNIuYxJ1tpAi0rmdN1NSHxYV4dSCYAxZqjbvC2IEtEEh6Lg5z7ubEEUn4vwv/THACT+bQBiFXSl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\nKBy0hjNn4NQpuyMRGVm1iaEN0Bd4E/hUKbUA+Bb4TWt90aI+C7U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N4LSGjz6C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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from __future__ import division\n", + "import matplotlib.pyplot as plt\n", + "import copy\n", + "import multiprocessing\n", + "import numpy as np\n", + "# from gensim.models import Word2Vec\n", + "# import os\n", + "# from gensim.models.word2vec import Text8Corpus\n", + "\n", + "# word_analogies_file = '../datasets/questions-words.txt'\n", + "\n", + "def calc_parm(model, freq, bucket_size=100):\n", + " # mean_freq will contain analogies with their mean frequency of 4 words \n", + " mean_freq = {}\n", + " with open(word_analogies_file, 'r') as r:\n", + " for i, line in enumerate(r):\n", + " if ':' not in line:\n", + " analogy = tuple(line.split())\n", + " else:\n", + " continue\n", + " try:\n", + " mfreq = sum([int(freq[x.lower()]) for x in analogy])/4\n", + " mean_freq['a%d'%i] = [analogy, mfreq]\n", + " except KeyError:\n", + " continue\n", + " \n", + " # compute model's accuracy\n", + " model = Word2Vec.load_word2vec_format(model)\n", + " acc = model.accuracy(word_analogies_file)\n", + " \n", + " sem_correct = [acc[i]['correct'] for i in range(5)]\n", + " sem_total = [acc[i]['correct'] + acc[i]['incorrect'] for i in range(5)]\n", + " syn_correct = [acc[i]['correct'] for i in range(5, len(acc)-1)]\n", + " syn_total = [acc[i]['correct'] + acc[i]['incorrect'] for i in range(5, len(acc)-1)]\n", + " total_correct = sem_correct + syn_correct\n", + " total_total = sem_total + syn_total\n", + "\n", + " sem_x, sem_y = calc_axis(sem_correct, sem_total, mean_freq, bucket_size)\n", + " syn_x, syn_y = calc_axis(syn_correct, syn_total, mean_freq, bucket_size)\n", + " total_x, total_y = calc_axis(total_correct, total_total, mean_freq, bucket_size)\n", + " return ((sem_x, sem_y), (syn_x, syn_y), (total_x, total_y))\n", + "\n", + "def calc_axis(correct, total, mean_freq, bucket_size=100):\n", + " # make flat lists\n", + " correct_analogies = []\n", + " for i in range(len(correct)):\n", + " for analogy in correct[i]:\n", + " correct_analogies.append(analogy) \n", + " total_analogies = []\n", + " for i in range(len(total)):\n", + " for analogy in total[i]:\n", + " total_analogies.append(analogy)\n", + "\n", + " copy_mean_freq = copy.deepcopy(mean_freq)\n", + " # delete other case's analogy from total analogies \n", + " for key, value in copy_mean_freq.items():\n", + " value[0] = tuple(x.upper() for x in value[0])\n", + " if value[0] not in total_analogies:\n", + " del copy_mean_freq[key]\n", + "\n", + " # append 0 or 1 for incorrect or correct analogy\n", + " for key, value in copy_mean_freq.iteritems():\n", + " value[0] = tuple(x.upper() for x in value[0])\n", + " if value[0] in correct_analogies:\n", + " copy_mean_freq[key].append(1)\n", + " else:\n", + " copy_mean_freq[key].append(0)\n", + "\n", + " x = []\n", + " y = []\n", + " # sort analogies according to their mean frequences \n", + " copy_mean_freq = sorted(copy_mean_freq.items(), key=lambda x: x[1][1])\n", + " # prepare analogies buckets according to given size\n", + " for centre_p in xrange(bucket_size//2, len(copy_mean_freq), bucket_size):\n", + " bucket = copy_mean_freq[centre_p-bucket_size//2:centre_p+bucket_size//2]\n", + " b_acc = 0\n", + " # calculate current bucket accuracy with b_acc count\n", + " for analogy in bucket:\n", + " if analogy[1][2]==1:\n", + " b_acc+=1\n", + " y.append(b_acc/bucket_size)\n", + " x.append(np.log(copy_mean_freq[centre_p][1][1]))\n", + " return x, y\n", + "\n", + "# a sample model using gensim's Word2Vec for getting vocab counts\n", + "corpus = Text8Corpus('proc_brown_corp.txt')\n", + "model = Word2Vec(min_count=5)\n", + "model.build_vocab(corpus)\n", + "freq = {}\n", + "for word in model.wv.index2word:\n", + " freq[word] = model.wv.vocab[word].count\n", + "\n", + "# plot results\n", + "word2vec = calc_parm('brown_gs.vec', freq, bucket_size=100)\n", + "wordrank = calc_parm('brown_wr_ensemble.vec', freq, bucket_size=100)\n", + "fasttext = calc_parm('brown_ft.vec', freq, bucket_size=100)\n", + "\n", + "fig = plt.figure(figsize=(7,15))\n", + "\n", + "for i, subplot, title in zip([0, 1, 2], ['311', '312', '313'], ['Semantic Analogies', 'Syntactic Analogies', 'Total Analogy']):\n", + " ax = fig.add_subplot(subplot)\n", + " ax.plot(word2vec[i][0], word2vec[i][1], 'r-', label='Word2Vec')\n", + " ax.plot(wordrank[i][0], wordrank[i][1], 'g--', label='WordRank')\n", + " ax.plot(fasttext[i][0], fasttext[i][1], 'b:', label='FastText')\n", + " ax.set_ylabel('Average accuracy')\n", + " ax.set_xlabel('Log mean frequency')\n", + " ax.set_title(title)\n", + " ax.legend(loc='upper right', prop={'size':10})\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CQkIIDg42lycmJrJjxw46dTKOEu/RowdXr15l8+bNbNiwgePHj9OvX79M7R47dowVK1aw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\nRHh2aNGiF+XuPGL3bukeXLDAN1OVLErevOJ1TZ8uTtzHH8touXbtpDW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pv/EHTLVomdry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\nyNrGExtpXb01ZfwsTEOwdsC8Ke64o+DQ+a1bJXpn6c2jlAybP3bMpu3at4d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y943Lp1forHZmbKVFwhrNm3Juh2UWv2rQHgjP1xstTWpQvs3RtCy4HiN37leHYd28XgLoMD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\ncY6zMIwxw/KN2/n6w899+hljVhpjjhtjlhpjLiuu8RZWs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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# a sample model using gensim's Word2Vec for getting vocab counts\n", + "corpus = Text8Corpus('text8')\n", + "model = Word2Vec(min_count=5)\n", + "model.build_vocab(corpus)\n", + "freq = {}\n", + "for word in model.wv.index2word:\n", + " freq[word] = model.wv.vocab[word].count\n", + " \n", + "word2vec = calc_parm('text8_gs.vec', freq, bucket_size=200)\n", + "wordrank = calc_parm('text8_wr.vec', freq, bucket_size=200)\n", + "fasttext = calc_parm('text8_ft.vec', freq, bucket_size=200)\n", + "\n", + "fig = plt.figure(figsize=(7,15))\n", + "\n", + "for i, subplot, title in zip([0, 1, 2], ['311', '312', '313'], ['Semantic Analogies', 'Syntactic Analogies', 'Total Analogy']):\n", + " ax = fig.add_subplot(subplot)\n", + " ax.plot(word2vec[i][0], word2vec[i][1], 'r-', label='Word2Vec')\n", + " ax.plot(wordrank[i][0], wordrank[i][1], 'g--', label='WordRank')\n", + " ax.plot(fasttext[i][0], fasttext[i][1], 'b:', label='FastText')\n", + " ax.set_ylabel('Average accuracy')\n", + " ax.set_xlabel('Log mean frequency')\n", + " ax.set_title(title)\n", + " ax.legend(loc='upper right', prop={'size':10})\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusions\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Note: Wordrank can sometimes produce Nan values while model evaluation, when the embedding vector values get too diverged at some iterations, but it dumps embedding vectors after every few iterations, so you could just load embeddings from a different iteration’s text file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "1. [WordRank: Learning Word Embeddings via Robust Ranking](https://arxiv.org/pdf/1506.02761v3.pdf)\n", + "2. [Word2Vec and FastText comparison notebook](https://github.com/jayantj/gensim/blob/9f3e275ddad22afd54b7986654f3033f9baf8983/docs/notebooks/Word2Vec_FastText_Comparison.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/Wordrank_wrapper.ipynb b/docs/notebooks/Wordrank_wrapper.ipynb new file mode 100644 index 0000000000..180d76019a --- /dev/null +++ b/docs/notebooks/Wordrank_wrapper.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# WordRank wrapper tutorial on Lee Corpus\n", + "\n", + "WordRank is a new word embedding algorithm which captures the semantic similarities in a text data well. See this [notebook](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Wordrank_comparisons.ipynb) for it's comparisons to other popular embedding models. This tutorial will serve as a guide to use the WordRank wrapper in gensim. You need to install [WordRank](https://bitbucket.org/shihaoji/wordrank) before proceeding with this tutorial.\n", + "\n", + "\n", + "# Train model\n", + "\n", + "We'll use [Lee corpus](https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/test/test_data/lee_background.cor) for training which is already available in gensim. Now for Wordrank, two parameters `dump_period` and `iter` needs to be in sync as it dumps the embedding file with the start of next iteration. For example, if you want results after 10 iterations, you need to use `iter=11` and `dump_period` can be anything that gives mod 0 with resulting iteration, in this case 2 or 5.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from gensim.models.wrappers import Wordrank\n", + "\n", + "wr_path = 'wordrank' # path to Wordrank directory\n", + "out_dir = 'model' # name of output directory to save data to\n", + "data = '../../gensim/test/test_data/lee.cor' # sample corpus\n", + "\n", + "model = Wordrank.train(wr_path, data, out_dir, iter=11, dump_period=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, you can use any of the Keyed Vector function in gensim, on this model for further tasks. For example," + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u'Bush', 0.7258214950561523),\n", + " (u'world', 0.5512409210205078),\n", + " (u'Iraq,', 0.5380253195762634),\n", + " (u'has', 0.5292117595672607),\n", + " (u'But', 0.5288761854171753),\n", + " (u'Iraq', 0.500893771648407),\n", + " (u'Iraqi', 0.4988182783126831),\n", + " (u'new', 0.47176095843315125),\n", + " (u'U.S.', 0.4699680209159851),\n", + " (u'with', 0.46098268032073975)]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.most_similar('President')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15981575765235229" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.similarity('President', 'military')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "As Wordrank provides two sets of embeddings, the word and context embedding, you can obtain their addition by setting ensemble parameter to 1 in the train method.\n", + "\n", + "# Save and Load models\n", + "In case, you have trained the model yourself using demo scripts in Wordrank, you can then simply load the embedding files in gensim. \n", + "\n", + "Also, Wordrank doesn't return the embeddings sorted according to the word frequency in corpus, so you can use the sorted_vocab parameter in the load method. But for that, you need to provide the vocabulary file generated in the 'matrix.toy' directory(if you used default names in demo) where all the metadata is stored." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "wr_word_embedding = 'wordrank.words'\n", + "vocab_file = 'vocab.txt'\n", + "\n", + "model = Wordrank.load_wordrank_model(wr_word_embedding, vocab_file, sorted_vocab=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "If you want to load the ensemble embedding, you similarly need to provide the context embedding file and set ensemble to 1 in `load_wordrank_model` method." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "wr_context_file = 'wordrank.contexts'\n", + "model = Wordrank.load_wordrank_model(wr_word_embedding, vocab_file, wr_context_file, sorted_vocab=1, ensemble=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can save these sorted embeddings using the standard gensim methods." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from tempfile import mkstemp\n", + "\n", + "fs, temp_path = mkstemp(\"gensim_temp\") # creates a temp file\n", + "model.wv.save(temp_path) # save the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Evaluating models\n", + "Now that the embeddings are loaded in Word2Vec format and sorted according to the word frequencies in corpus, you can use the evaluations provided by gensim on this model.\n", + "\n", + "For example, it can be evaluated on following Word Analogies and Word Similarity benchmarks. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'correct': [], 'incorrect': [], 'section': u'capital-common-countries'},\n", + " {'correct': [], 'incorrect': [], 'section': u'capital-world'},\n", + " {'correct': [], 'incorrect': [], 'section': u'currency'},\n", + " {'correct': [], 'incorrect': [], 'section': u'city-in-state'},\n", + " {'correct': [], 'incorrect': [], 'section': u'family'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram1-adjective-to-adverb'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram2-opposite'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram3-comparative'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram4-superlative'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram5-present-participle'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram6-nationality-adjective'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram7-past-tense'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram8-plural'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram9-plural-verbs'},\n", + " {'correct': [], 'incorrect': [], 'section': 'total'}]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "word_analogies_file = 'datasets/questions-words.txt'\n", + "model.wv.accuracy(word_analogies_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n", + " warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n", + "/usr/local/lib/python2.7/site-packages/numpy/core/_methods.py:70: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/usr/local/lib/python2.7/site-packages/scipy/stats/stats.py:3029: RuntimeWarning: invalid value encountered in double_scalars\n", + " r = r_num / r_den\n" + ] + }, + { + "data": { + "text/plain": [ + "((nan, nan), SpearmanrResult(correlation=nan, pvalue=nan), 100.0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "word_similarity_file = 'datasets/ws-353.txt'\n", + "model.wv.evaluate_word_pairs(word_similarity_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These methods take an [optional parameter](http://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec.accuracy) restrict_vocab which limits which test examples are to be considered.\n", + "\n", + "The results here don't look good because the training corpus is very small. To get meaningful results one needs to train on 500k+ words.\n", + "\n", + "# Conclusion\n", + "We learned to use Wordrank wrapper on a sample corpus and also how to directly load the Wordrank embedding files in gensim. Once loaded, you can use the standard gensim methods on this embedding." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/datasets/simlex-999.txt b/docs/notebooks/datasets/simlex-999.txt new file mode 100644 index 0000000000..c16a182e5d --- /dev/null +++ b/docs/notebooks/datasets/simlex-999.txt @@ -0,0 +1,999 @@ +old new 1.58 +smart intelligent 9.2 +hard difficult 8.77 +happy cheerful 9.55 +hard easy 0.95 +fast rapid 8.75 +happy glad 9.17 +short long 1.23 +stupid dumb 9.58 +weird strange 8.93 +wide narrow 1.03 +bad awful 8.42 +easy difficult 0.58 +bad terrible 7.78 +hard simple 1.38 +smart dumb 0.55 +insane crazy 9.57 +happy mad 0.95 +large huge 9.47 +hard tough 8.05 +new fresh 6.83 +sharp dull 0.6 +quick rapid 9.7 +dumb foolish 6.67 +wonderful terrific 8.63 +strange odd 9.02 +happy angry 1.28 +narrow broad 1.18 +simple easy 9.4 +old fresh 0.87 +apparent obvious 8.47 +inexpensive cheap 8.72 +nice generous 5 +weird normal 0.72 +weird odd 9.2 +bad immoral 7.62 +sad funny 0.95 +wonderful great 8.05 +guilty ashamed 6.38 +beautiful wonderful 6.5 +confident sure 8.27 +dumb dense 7.27 +large big 9.55 +nice cruel 0.67 +impatient anxious 6.03 +big broad 6.73 +strong proud 3.17 +unnecessary necessary 0.63 +restless young 1.6 +dumb intelligent 0.75 +bad great 0.35 +difficult simple 0.87 +necessary important 7.37 +bad terrific 0.65 +mad glad 1.45 +honest guilty 1.18 +easy tough 0.52 +easy flexible 4.1 +certain sure 8.42 +essential necessary 8.97 +different normal 1.08 +sly clever 7.25 +crucial important 8.82 +harsh cruel 8.18 +childish foolish 5.5 +scarce rare 9.17 +friendly generous 5.9 +fragile frigid 2.38 +long narrow 3.57 +big heavy 6.18 +rough frigid 2.47 +bizarre strange 9.37 +illegal immoral 4.28 +bad guilty 4.2 +modern ancient 0.73 +new ancient 0.23 +dull funny 0.55 +happy young 2 +easy big 1.12 +great awful 1.17 +tiny huge 0.6 +polite proper 7.63 +modest ashamed 2.65 +exotic rare 8.05 +dumb clever 1.17 +delightful wonderful 8.65 +noticeable obvious 8.48 +afraid anxious 5.07 +formal proper 8.02 +dreary dull 8.25 +delightful cheerful 6.58 +unhappy mad 5.95 +sad terrible 5.4 +sick crazy 3.57 +violent angry 6.98 +laden heavy 5.9 +dirty cheap 1.6 +elastic flexible 7.78 +hard dense 5.9 +recent new 7.05 +bold proud 3.97 +sly strange 1.97 +strange sly 2.07 +dumb rare 0.48 +sly tough 0.58 +terrific mad 0.4 +modest flexible 0.98 +fresh wide 0.4 +huge dumb 0.48 +large flexible 0.48 +dirty narrow 0.3 +wife husband 2.3 +book text 6.35 +groom bride 3.17 +night day 1.88 +south north 2.2 +plane airport 3.65 +uncle aunt 5.5 +horse mare 8.33 +bottom top 0.7 +friend buddy 8.78 +student pupil 9.35 +world globe 6.67 +leg arm 2.88 +plane jet 8.1 +woman man 3.33 +horse colt 7.07 +actress actor 7.12 +teacher instructor 9.25 +movie film 8.87 +bird hawk 7.85 +word dictionary 3.68 +money salary 7.88 +dog cat 1.75 +area region 9.47 +navy army 6.43 +book literature 7.53 +clothes closet 3.27 +sunset sunrise 2.47 +child adult 2.98 +cow cattle 9.52 +book story 5.63 +winter summer 2.38 +taxi cab 9.2 +tree maple 5.53 +bed bedroom 3.4 +roof ceiling 7.58 +disease infection 7.15 +arm shoulder 4.85 +sheep lamb 8.42 +lady gentleman 3.42 +boat anchor 2.25 +priest monk 6.28 +toe finger 4.68 +river stream 7.3 +anger fury 8.73 +date calendar 4.42 +sea ocean 8.27 +second minute 4.62 +hand thumb 3.88 +wood log 7.3 +mud dirt 7.32 +hallway corridor 9.28 +way manner 7.62 +mouse cat 1.12 +cop sheriff 9.05 +death burial 4.93 +music melody 6.98 +beer alcohol 7.5 +mouth lip 7.1 +storm hurricane 6.38 +tax income 2.38 +flower violet 6.95 +paper cardboard 5.38 +floor ceiling 1.73 +beach seashore 8.33 +rod curtain 3.03 +hound fox 2.38 +street alley 5.48 +boat deck 4.28 +car horn 2.57 +friend guest 4.25 +employer employee 3.65 +hand wrist 3.97 +ball cannon 2.58 +alcohol brandy 6.98 +victory triumph 8.98 +telephone booth 3.63 +door doorway 5.4 +motel inn 8.17 +clothes cloth 5.47 +steak meat 7.47 +nail thumb 3.55 +band orchestra 7.08 +book bible 5 +business industry 7.02 +winter season 6.27 +decade century 3.48 +alcohol gin 8.65 +hat coat 2.67 +window door 3.33 +arm wrist 3.57 +house apartment 5.8 +glass crystal 6.27 +wine brandy 5.15 +creator maker 9.62 +dinner breakfast 3.33 +arm muscle 3.72 +bubble suds 8.57 +bread flour 3.33 +death tragedy 5.8 +absence presence 0.4 +gun cannon 5.68 +grass blade 4.57 +ball basket 1.67 +hose garden 1.67 +boy kid 7.5 +church choir 2.95 +clothes drawer 3.02 +tower bell 1.9 +father parent 7.07 +school grade 4.42 +parent adult 5.37 +bar jail 1.9 +car highway 3.4 +dictionary definition 6.25 +door cellar 1.97 +army legion 5.95 +metal aluminum 7.25 +chair bench 6.67 +cloud fog 6 +boy son 6.75 +water ice 6.47 +bed blanket 3.02 +attorney lawyer 9.35 +area zone 8.33 +business company 9.02 +clothes fabric 5.87 +sweater jacket 7.15 +money capital 6.67 +hand foot 4.17 +alcohol cocktail 6.73 +yard inch 3.78 +molecule atom 6.45 +lens camera 4.28 +meal dinner 7.15 +eye tear 3.55 +god devil 1.8 +loop belt 3.1 +rat mouse 7.78 +motor engine 8.65 +car cab 7.42 +cat lion 6.75 +size magnitude 6.33 +reality fantasy 1.03 +door gate 5.25 +cat pet 5.95 +tin aluminum 6.42 +bone jaw 4.17 +cereal wheat 3.75 +house key 1.9 +blood flesh 4.28 +door corridor 3.73 +god spirit 7.3 +capability competence 7.62 +abundance plenty 8.97 +sofa chair 6.67 +wall brick 4.68 +horn drum 2.68 +organ liver 6.15 +strength might 7.07 +phrase word 5.48 +band parade 3.92 +stomach waist 5.9 +cloud storm 5.6 +joy pride 5 +noise rattle 6.17 +rain mist 5.97 +beer beverage 5.42 +man uncle 3.92 +apple juice 2.88 +intelligence logic 6.5 +communication language 7.47 +mink fur 6.83 +mob crowd 7.85 +shore coast 8.83 +wire cord 7.62 +bird turkey 6.58 +bed crib 7.3 +competence ability 7.5 +cloud haze 7.32 +supper meal 7.53 +bar cage 2.8 +water salt 1.3 +sense intuition 7.68 +situation condition 6.58 +crime theft 7.53 +style fashion 8.5 +boundary border 9.08 +arm body 4.05 +boat car 2.37 +sandwich lunch 6.3 +bride princess 2.8 +heroine hero 8.78 +car gauge 1.13 +insect bee 6.07 +crib cradle 8.55 +animal person 3.05 +marijuana herb 6.5 +bed hospital 0.92 +cheek tongue 4.52 +disc computer 3.2 +curve angle 3.33 +grass moss 5 +school law 1.13 +foot head 2.3 +mother guardian 6.5 +orthodontist dentist 8.27 +alcohol whiskey 7.27 +mouth tooth 6.3 +breakfast bacon 4.37 +bathroom bedroom 3.4 +plate bowl 5.23 +meat bacon 5.8 +air helium 3.63 +worker employer 5.37 +body chest 4.45 +son father 3.82 +heart surgery 1.08 +woman secretary 1.98 +man father 4.83 +beach island 5.6 +story topic 5 +game fun 3.42 +weekend week 4 +couple pair 8.33 +woman wife 5.72 +sheep cattle 4.77 +purse bag 8.33 +ceiling cathedral 2.42 +bean coffee 5.15 +wood paper 2.88 +top side 1.9 +crime fraud 5.65 +pain harm 5.38 +lover companion 5.97 +evening dusk 7.78 +father daughter 2.62 +wine liquor 7.85 +cow goat 2.93 +belief opinion 7.7 +reality illusion 1.42 +pact agreement 9.02 +wealth poverty 1.27 +accident emergency 4.93 +battle conquest 7.22 +friend teacher 2.62 +illness infection 6.9 +game trick 2.32 +brother son 3.48 +aunt nephew 3.1 +worker mechanic 4.92 +doctor orthodontist 5.58 +oak maple 6.03 +bee queen 3.27 +car bicycle 3.47 +goal quest 5.83 +august month 5.53 +army squad 5.08 +cloud weather 4.87 +physician doctor 8.88 +canyon valley 6.75 +river valley 1.67 +sun sky 2.27 +target arrow 3.25 +chocolate pie 2.27 +circumstance situation 7.85 +opinion choice 5.43 +rhythm melody 6.12 +gut nerve 4.93 +day dawn 5.47 +cattle beef 7.03 +doctor professor 4.65 +arm vein 3.65 +room bath 3.33 +corporation business 9.02 +fun football 1.97 +hill cliff 4.28 +bone ankle 3.82 +apple candy 2.08 +helper maid 5.58 +leader manager 7.27 +lemon tea 1.6 +bee ant 2.78 +basketball baseball 4.92 +rice bean 2.72 +bed furniture 6.08 +emotion passion 7.72 +anarchy chaos 7.93 +crime violation 7.12 +machine engine 5.58 +beach sea 4.68 +alley bowl 1.53 +jar bottle 7.83 +strength capability 5.28 +seed mustard 3.48 +guitar drum 3.78 +opinion idea 5.7 +north west 3.63 +diet salad 2.98 +mother wife 3.02 +dad mother 3.55 +captain sailor 5 +meter yard 5.6 +beer champagne 4.45 +motor boat 2.57 +card bridge 1.97 +science psychology 4.92 +sinner saint 1.6 +destruction construction 0.98 +crowd bunch 7.42 +beach reef 3.77 +man child 4.13 +bread cheese 1.95 +champion winner 8.73 +celebration ceremony 7.72 +menu order 3.62 +king princess 3.27 +wealth prestige 6.07 +endurance strength 6.58 +danger threat 8.78 +god priest 4.5 +men fraternity 3.13 +buddy companion 8.65 +teacher helper 4.28 +body stomach 3.93 +tongue throat 3.1 +house carpet 1.38 +intelligence skill 5.35 +journey conquest 4.72 +god prey 1.23 +brother soul 0.97 +adversary opponent 9.05 +death catastrophe 4.13 +monster demon 6.95 +day morning 4.87 +man victor 1.9 +friend guy 3.88 +song story 3.97 +ray sunshine 6.83 +guy stud 5.83 +chicken rice 1.43 +box elevator 1.32 +butter potato 1.22 +apartment furniture 1.28 +lake swamp 4.92 +salad vinegar 1.13 +flower bulb 4.48 +cloud mist 6.67 +driver pilot 6.28 +sugar honey 5.13 +body shoulder 2.88 +idea image 3.55 +father brother 4.2 +moon planet 5.87 +ball costume 2.32 +rail fence 5.22 +room bed 2.35 +flower bush 4.25 +bone knee 4.17 +arm knee 2.75 +bottom side 2.63 +vessel vein 5.15 +cat rabbit 2.37 +meat sandwich 2.35 +belief concept 5.08 +intelligence insight 5.9 +attention interest 7.22 +attitude confidence 4.35 +right justice 7.05 +argument agreement 1.45 +depth magnitude 6.12 +medium news 3.65 +winner candidate 2.78 +birthday date 5.08 +fee payment 7.15 +bible hymn 5.15 +exit doorway 5.5 +man sentry 3.25 +aisle hall 6.35 +whiskey gin 6.28 +blood marrow 3.4 +oil mink 1.23 +floor deck 5.55 +roof floor 2.62 +door floor 1.67 +shoulder head 3.42 +wagon carriage 7.7 +car carriage 5.13 +elbow ankle 3.13 +wealth fame 4.02 +sorrow shame 4.77 +administration management 7.25 +communication conversation 8.02 +pollution atmosphere 4.25 +anatomy biology 5.33 +college profession 3.12 +book topic 2.07 +formula equation 7.95 +book information 5 +boy partner 1.9 +sky universe 4.68 +population people 7.68 +college class 4.13 +chief mayor 4.85 +rabbi minister 7.62 +meter inch 5.08 +polyester cotton 5.63 +lawyer banker 1.88 +violin instrument 6.58 +camp cabin 4.2 +pot appliance 2.53 +linen fabric 7.47 +whiskey champagne 5.33 +girl child 5.38 +cottage cabin 7.72 +bird hen 7.03 +racket noise 8.1 +sunset evening 5.98 +drizzle rain 9.17 +adult baby 2.22 +charcoal coal 7.63 +body spine 4.78 +head nail 2.47 +log timber 8.05 +spoon cup 2.02 +body nerve 3.13 +man husband 5.32 +bone neck 2.53 +frustration anger 6.5 +river sea 5.72 +task job 8.87 +club society 5.23 +reflection image 7.27 +prince king 5.92 +snow weather 5.48 +people party 2.2 +boy brother 6.67 +root grass 3.55 +brow eye 3.82 +money pearl 2.1 +money diamond 3.42 +vehicle bus 6.47 +cab bus 5.6 +house barn 4.33 +finger palm 3.33 +car bridge 0.95 +effort difficulty 4.45 +fact insight 4.77 +job management 3.97 +cancer sickness 7.93 +word newspaper 2.47 +composer writer 6.58 +actor singer 4.52 +shelter hut 6.47 +bathroom kitchen 3.1 +cabin hut 6.53 +door kitchen 1.67 +value belief 7.07 +wisdom intelligence 7.47 +ignorance intelligence 1.5 +happiness luck 2.38 +idea scheme 6.75 +mood emotion 8.12 +happiness peace 6.03 +despair misery 7.22 +logic arithmetic 3.97 +denial confession 1.03 +argument criticism 5.08 +aggression hostility 8.48 +hysteria confusion 6.33 +chemistry theory 3.17 +trial verdict 3.33 +comfort safety 5.8 +confidence self 3.12 +vision perception 6.88 +era decade 5.4 +biography fiction 1.38 +discussion argument 5.48 +code symbol 6.03 +danger disease 3 +accident catastrophe 5.9 +journey trip 8.88 +activity movement 7.15 +gossip news 5.22 +father god 3.57 +action course 5.45 +fever illness 7.65 +aviation flight 8.18 +game action 4.85 +molecule air 3.05 +home state 2.58 +word literature 4.77 +adult guardian 6.9 +newspaper information 5.65 +communication television 5.6 +cousin uncle 4.63 +author reader 1.6 +guy partner 3.57 +area corner 2.07 +ballad song 7.53 +wall decoration 2.62 +word page 2.92 +nurse scientist 2.08 +politician president 7.38 +president mayor 5.68 +book essay 4.72 +man warrior 4.72 +article journal 6.18 +breakfast supper 4.4 +crowd parade 3.93 +aisle hallway 6.75 +teacher rabbi 4.37 +hip lip 1.43 +book article 5.43 +room cell 4.58 +box booth 3.8 +daughter kid 4.17 +limb leg 6.9 +liver lung 2.7 +classroom hallway 2 +mountain ledge 3.73 +car elevator 1.03 +bed couch 3.42 +clothes button 2.3 +clothes coat 5.35 +kidney organ 6.17 +apple sauce 1.43 +chicken steak 3.73 +car hose 0.87 +tobacco cigarette 7.5 +student professor 1.95 +baby daughter 5 +pipe cigar 6.03 +milk juice 4.05 +box cigar 1.25 +apartment hotel 3.33 +cup cone 3.17 +horse ox 3.02 +throat nose 2.8 +bone teeth 4.17 +bone elbow 3.78 +bacon bean 1.22 +cup jar 5.13 +proof fact 7.3 +appointment engagement 6.75 +birthday year 1.67 +word clue 2.53 +author creator 8.02 +atom carbon 3.1 +archbishop bishop 7.05 +letter paragraph 4 +page paragraph 3.03 +steeple chapel 7.08 +muscle bone 3.65 +muscle tongue 5 +boy soldier 2.15 +belly abdomen 8.13 +guy girl 3.33 +bed chair 3.5 +clothes jacket 5.15 +gun knife 3.65 +tin metal 5.63 +bottle container 7.93 +hen turkey 6.13 +meat bread 1.67 +arm bone 3.83 +neck spine 5.32 +apple lemon 4.05 +agony grief 7.63 +assignment task 8.7 +night dawn 2.95 +dinner soup 3.72 +calf bull 4.93 +snow storm 4.8 +nail hand 3.42 +dog horse 2.38 +arm neck 1.58 +ball glove 1.75 +flu fever 6.08 +fee salary 3.72 +nerve brain 3.88 +beast animal 7.83 +dinner chicken 2.85 +girl maid 2.93 +child boy 5.75 +alcohol wine 7.42 +nose mouth 3.73 +street car 2.38 +bell door 2.2 +box hat 1.3 +belief impression 5.95 +bias opinion 5.6 +attention awareness 8.73 +anger mood 4.1 +elegance style 5.72 +beauty age 1.58 +book theme 2.58 +friend mother 2.53 +vitamin iron 5.55 +car factory 2.75 +pact condition 2.45 +chapter choice 0.48 +arithmetic rhythm 2.35 +winner presence 1.08 +belief flower 0.4 +winner goal 3.23 +trick size 0.48 +choice vein 0.98 +hymn conquest 0.68 +endurance band 0.4 +jail choice 1.08 +condition boy 0.48 +flower endurance 0.4 +hole agreement 0.3 +doctor temper 0.48 +fraternity door 0.68 +task woman 0.68 +fraternity baseball 0.88 +cent size 0.4 +presence door 0.48 +mouse management 0.48 +task highway 0.48 +liquor century 0.4 +task straw 0.68 +island task 0.3 +night chapter 0.48 +pollution president 0.68 +gun trick 0.48 +bath trick 0.58 +diet apple 1.18 +cent wife 0.58 +chapter tail 0.3 +course stomach 0.58 +hymn straw 0.4 +dentist colonel 0.4 +wife straw 0.4 +hole wife 0.68 +pupil president 0.78 +bath wife 0.48 +people cent 0.48 +formula log 1.77 +woman fur 0.58 +apple sunshine 0.58 +gun dawn 1.18 +meal waist 0.98 +camera president 0.48 +liquor band 0.68 +stomach vein 2.35 +gun fur 0.3 +couch baseball 0.88 +worker camera 0.68 +deck mouse 0.48 +rice boy 0.4 +people gun 0.68 +cliff tail 0.3 +ankle window 0.3 +princess island 0.3 +container mouse 0.3 +wagon container 2.65 +people balloon 0.48 +dollar people 0.4 +bath balloon 0.4 +stomach bedroom 0.4 +bicycle bedroom 0.4 +log bath 0.4 +bowl tail 0.48 +go come 2.42 +take steal 6.18 +listen hear 8.17 +think rationalize 8.25 +occur happen 9.32 +vanish disappear 9.8 +multiply divide 1.75 +plead beg 9.08 +begin originate 8.2 +protect defend 9.13 +kill destroy 5.9 +create make 8.72 +accept reject 0.83 +ignore avoid 6.87 +carry bring 5.8 +leave enter 0.95 +choose elect 7.62 +lose fail 7.33 +encourage discourage 1.58 +achieve accomplish 8.57 +make construct 8.33 +listen obey 4.93 +inform notify 9.25 +receive give 1.47 +borrow beg 2.62 +take obtain 7.1 +advise recommend 8.1 +imitate portray 6.75 +win succeed 7.9 +think decide 5.13 +greet meet 6.17 +agree argue 0.77 +enjoy entertain 5.92 +destroy make 1.6 +save protect 6.58 +give lend 7.22 +understand know 7.47 +take receive 5.08 +accept acknowledge 6.88 +decide choose 8.87 +accept believe 6.75 +keep possess 8.27 +roam wander 8.83 +succeed fail 0.83 +spend save 0.55 +leave go 7.63 +come attend 8.1 +know believe 5.5 +gather meet 7.3 +make earn 7.62 +forget ignore 3.07 +multiply add 2.7 +shrink grow 0.23 +arrive leave 1.33 +succeed try 3.98 +accept deny 1.75 +arrive come 7.05 +agree differ 1.05 +send receive 1.08 +win dominate 5.68 +add divide 2.3 +kill choke 4.92 +acquire get 8.82 +participate join 7.7 +leave remain 2.53 +go enter 4 +take carry 5.23 +forget learn 1.18 +appoint elect 8.17 +engage marry 5.43 +ask pray 3.72 +go send 3.75 +take deliver 4.37 +speak hear 3.02 +analyze evaluate 8.03 +argue rationalize 4.2 +lose keep 1.05 +compare analyze 8.1 +disorganize organize 1.45 +go allow 3.62 +take possess 7.2 +learn listen 3.88 +destroy construct 0.92 +create build 8.48 +steal buy 1.13 +kill hang 4.45 +forget know 0.92 +create imagine 5.13 +do happen 4.23 +win accomplish 7.85 +give deny 1.43 +deserve earn 5.8 +get put 1.98 +locate find 8.73 +appear attend 6.28 +know comprehend 7.63 +pretend imagine 8.47 +satisfy please 7.67 +cherish keep 4.85 +argue differ 5.15 +overcome dominate 6.25 +behave obey 7.3 +cooperate participate 6.43 +achieve try 4.42 +fail discourage 3.33 +begin quit 1.28 +say participate 3.82 +come bring 2.42 +declare announce 9.08 +read comprehend 4.7 +take leave 2.47 +proclaim announce 8.18 +acquire obtain 8.57 +conclude decide 7.75 +please plead 2.98 +argue prove 4.83 +ask plead 6.47 +find disappear 0.77 +inspect examine 8.75 +verify justify 4.08 +assume predict 4.85 +learn evaluate 4.17 +argue justify 5 +make become 4.77 +discover originate 4.83 +achieve succeed 7.5 +give put 3.65 +understand listen 4.68 +expand grow 8.27 +borrow sell 1.73 +keep protect 5.4 +explain prove 4.1 +assume pretend 3.72 +agree please 4.13 +forgive forget 3.92 +clarify explain 8.33 +understand forgive 4.87 +remind forget 0.87 +get remain 1.6 +realize discover 7.47 +require inquire 1.82 +ignore ask 1.07 +think inquire 4.77 +reject avoid 4.78 +argue persuade 6.23 +pursue persuade 3.17 +accept forgive 3.73 +do quit 1.17 +investigate examine 8.1 +discuss explain 6.67 +owe lend 2.32 +explore discover 8.48 +complain argue 4.8 +withdraw reject 6.38 +keep borrow 2.25 +beg ask 6 +arrange organize 8.27 +reduce shrink 8.02 +speak acknowledge 4.67 +give borrow 2.22 +kill defend 2.63 +disappear shrink 5.8 +deliver carry 3.88 +breathe choke 1.37 +acknowledge notify 5.3 +become seem 2.63 +pretend seem 4.68 +accomplish become 4 +contemplate think 8.82 +determine predict 5.8 +please entertain 5 +remain retain 5.75 +pretend portray 7.03 +forget retain 0.63 +want choose 4.78 +lose get 0.77 +try think 2.62 +become appear 4.77 +leave ignore 4.42 +accept recommend 2.75 +leave wander 3.57 +keep give 1.05 +give allow 5.15 +bring send 2.97 +absorb learn 5.48 +acquire find 6.38 +leave appear 0.97 +create destroy 0.63 +begin go 7.42 +get buy 5.08 +collect save 6.67 +replace restore 5.73 +join add 8.1 +join marry 5.35 +accept deliver 1.58 +attach join 7.75 +put hang 3 +go sell 0.97 +communicate pray 3.55 +give steal 0.5 +add build 4.92 +bring restore 2.62 +comprehend satisfy 2.55 +portray decide 1.18 +organize become 1.77 +give know 0.88 +say verify 4.9 +cooperate join 5.18 +arrange require 0.98 +borrow want 1.77 +investigate pursue 7.15 +ignore explore 0.4 +bring complain 0.98 +enter owe 0.68 +portray notify 0.78 +remind sell 0.4 +absorb possess 5 +join acquire 2.85 +send attend 1.67 +gather attend 4.8 +absorb withdraw 2.97 +attend arrive 6.08 diff --git a/docs/notebooks/datasets/ws-353.txt b/docs/notebooks/datasets/ws-353.txt new file mode 100644 index 0000000000..bad1738106 --- /dev/null +++ b/docs/notebooks/datasets/ws-353.txt @@ -0,0 +1,353 @@ +love sex 6.77 +tiger cat 7.35 +tiger tiger 10.00 +book paper 7.46 +computer keyboard 7.62 +computer internet 7.58 +plane car 5.77 +train car 6.31 +telephone communication 7.50 +television radio 6.77 +media radio 7.42 +drug abuse 6.85 +bread butter 6.19 +cucumber potato 5.92 +doctor nurse 7.00 +professor doctor 6.62 +student professor 6.81 +smart student 4.62 +smart stupid 5.81 +company stock 7.08 +stock market 8.08 +stock phone 1.62 +stock CD 1.31 +stock jaguar 0.92 +stock egg 1.81 +fertility egg 6.69 +stock live 3.73 +stock life 0.92 +book library 7.46 +bank money 8.12 +wood forest 7.73 +money cash 9.15 +professor cucumber 0.31 +king cabbage 0.23 +king queen 8.58 +king rook 5.92 +bishop rabbi 6.69 +Jerusalem Israel 8.46 +Jerusalem Palestinian 7.65 +holy sex 1.62 +fuck sex 9.44 +Maradona football 8.62 +football soccer 9.03 +football basketball 6.81 +football tennis 6.63 +tennis racket 7.56 +Arafat peace 6.73 +Arafat terror 7.65 +Arafat Jackson 2.50 +law lawyer 8.38 +movie star 7.38 +movie popcorn 6.19 +movie critic 6.73 +movie theater 7.92 +physics proton 8.12 +physics chemistry 7.35 +space chemistry 4.88 +alcohol chemistry 5.54 +vodka gin 8.46 +vodka brandy 8.13 +drink car 3.04 +drink ear 1.31 +drink mouth 5.96 +drink eat 6.87 +baby mother 7.85 +drink mother 2.65 +car automobile 8.94 +gem jewel 8.96 +journey voyage 9.29 +boy lad 8.83 +coast shore 9.10 +asylum madhouse 8.87 +magician wizard 9.02 +midday noon 9.29 +furnace stove 8.79 +food fruit 7.52 +bird cock 7.10 +bird crane 7.38 +tool implement 6.46 +brother monk 6.27 +crane implement 2.69 +lad brother 4.46 +journey car 5.85 +monk oracle 5.00 +cemetery woodland 2.08 +food rooster 4.42 +coast hill 4.38 +forest graveyard 1.85 +shore woodland 3.08 +monk slave 0.92 +coast forest 3.15 +lad wizard 0.92 +chord smile 0.54 +glass magician 2.08 +noon string 0.54 +rooster voyage 0.62 +money dollar 8.42 +money cash 9.08 +money currency 9.04 +money wealth 8.27 +money property 7.57 +money possession 7.29 +money bank 8.50 +money deposit 7.73 +money withdrawal 6.88 +money laundering 5.65 +money operation 3.31 +tiger jaguar 8.00 +tiger feline 8.00 +tiger carnivore 7.08 +tiger mammal 6.85 +tiger animal 7.00 +tiger organism 4.77 +tiger fauna 5.62 +tiger zoo 5.87 +psychology psychiatry 8.08 +psychology anxiety 7.00 +psychology fear 6.85 +psychology depression 7.42 +psychology clinic 6.58 +psychology doctor 6.42 +psychology Freud 8.21 +psychology mind 7.69 +psychology health 7.23 +psychology science 6.71 +psychology discipline 5.58 +psychology cognition 7.48 +planet star 8.45 +planet constellation 8.06 +planet moon 8.08 +planet sun 8.02 +planet galaxy 8.11 +planet space 7.92 +planet astronomer 7.94 +precedent example 5.85 +precedent information 3.85 +precedent cognition 2.81 +precedent law 6.65 +precedent collection 2.50 +precedent group 1.77 +precedent antecedent 6.04 +cup coffee 6.58 +cup tableware 6.85 +cup article 2.40 +cup artifact 2.92 +cup object 3.69 +cup entity 2.15 +cup drink 7.25 +cup food 5.00 +cup substance 1.92 +cup liquid 5.90 +jaguar cat 7.42 +jaguar car 7.27 +energy secretary 1.81 +secretary senate 5.06 +energy laboratory 5.09 +computer laboratory 6.78 +weapon secret 6.06 +FBI fingerprint 6.94 +FBI investigation 8.31 +investigation effort 4.59 +Mars water 2.94 +Mars scientist 5.63 +news report 8.16 +canyon landscape 7.53 +image surface 4.56 +discovery space 6.34 +water seepage 6.56 +sign recess 2.38 +Wednesday news 2.22 +mile kilometer 8.66 +computer news 4.47 +territory surface 5.34 +atmosphere landscape 3.69 +president medal 3.00 +war troops 8.13 +record number 6.31 +skin eye 6.22 +Japanese American 6.50 +theater history 3.91 +volunteer motto 2.56 +prejudice recognition 3.00 +decoration valor 5.63 +century year 7.59 +century nation 3.16 +delay racism 1.19 +delay news 3.31 +minister party 6.63 +peace plan 4.75 +minority peace 3.69 +attempt peace 4.25 +government crisis 6.56 +deployment departure 4.25 +deployment withdrawal 5.88 +energy crisis 5.94 +announcement news 7.56 +announcement effort 2.75 +stroke hospital 7.03 +disability death 5.47 +victim emergency 6.47 +treatment recovery 7.91 +journal association 4.97 +doctor personnel 5.00 +doctor liability 5.19 +liability insurance 7.03 +school center 3.44 +reason hypertension 2.31 +reason criterion 5.91 +hundred percent 7.38 +Harvard Yale 8.13 +hospital infrastructure 4.63 +death row 5.25 +death inmate 5.03 +lawyer evidence 6.69 +life death 7.88 +life term 4.50 +word similarity 4.75 +board recommendation 4.47 +governor interview 3.25 +OPEC country 5.63 +peace atmosphere 3.69 +peace insurance 2.94 +territory kilometer 5.28 +travel activity 5.00 +competition price 6.44 +consumer confidence 4.13 +consumer energy 4.75 +problem airport 2.38 +car flight 4.94 +credit card 8.06 +credit information 5.31 +hotel reservation 8.03 +grocery money 5.94 +registration arrangement 6.00 +arrangement accommodation 5.41 +month hotel 1.81 +type kind 8.97 +arrival hotel 6.00 +bed closet 6.72 +closet clothes 8.00 +situation conclusion 4.81 +situation isolation 3.88 +impartiality interest 5.16 +direction combination 2.25 +street place 6.44 +street avenue 8.88 +street block 6.88 +street children 4.94 +listing proximity 2.56 +listing category 6.38 +cell phone 7.81 +production hike 1.75 +benchmark index 4.25 +media trading 3.88 +media gain 2.88 +dividend payment 7.63 +dividend calculation 6.48 +calculation computation 8.44 +currency market 7.50 +OPEC oil 8.59 +oil stock 6.34 +announcement production 3.38 +announcement warning 6.00 +profit warning 3.88 +profit loss 7.63 +dollar yen 7.78 +dollar buck 9.22 +dollar profit 7.38 +dollar loss 6.09 +computer software 8.50 +network hardware 8.31 +phone equipment 7.13 +equipment maker 5.91 +luxury car 6.47 +five month 3.38 +report gain 3.63 +investor earning 7.13 +liquid water 7.89 +baseball season 5.97 +game victory 7.03 +game team 7.69 +marathon sprint 7.47 +game series 6.19 +game defeat 6.97 +seven series 3.56 +seafood sea 7.47 +seafood food 8.34 +seafood lobster 8.70 +lobster food 7.81 +lobster wine 5.70 +food preparation 6.22 +video archive 6.34 +start year 4.06 +start match 4.47 +game round 5.97 +boxing round 7.61 +championship tournament 8.36 +fighting defeating 7.41 +line insurance 2.69 +day summer 3.94 +summer drought 7.16 +summer nature 5.63 +day dawn 7.53 +nature environment 8.31 +environment ecology 8.81 +nature man 6.25 +man woman 8.30 +man governor 5.25 +murder manslaughter 8.53 +soap opera 7.94 +opera performance 6.88 +life lesson 5.94 +focus life 4.06 +production crew 6.25 +television film 7.72 +lover quarrel 6.19 +viewer serial 2.97 +possibility girl 1.94 +population development 3.75 +morality importance 3.31 +morality marriage 3.69 +Mexico Brazil 7.44 +gender equality 6.41 +change attitude 5.44 +family planning 6.25 +opera industry 2.63 +sugar approach 0.88 +practice institution 3.19 +ministry culture 4.69 +problem challenge 6.75 +size prominence 5.31 +country citizen 7.31 +planet people 5.75 +development issue 3.97 +experience music 3.47 +music project 3.63 +glass metal 5.56 +aluminum metal 7.83 +chance credibility 3.88 +exhibit memorabilia 5.31 +concert virtuoso 6.81 +rock jazz 7.59 +museum theater 7.19 +observation architecture 4.38 +space world 6.53 +preservation world 6.19 +admission ticket 7.69 +shower thunderstorm 6.31 +shower flood 6.03 +weather forecast 8.34 +disaster area 6.25 +governor office 6.34 +architecture century 3.78 diff --git a/docs/src/apiref.rst b/docs/src/apiref.rst index c1f2ee183f..e990745c0f 100644 --- a/docs/src/apiref.rst +++ b/docs/src/apiref.rst @@ -43,6 +43,7 @@ Modules: models/wrappers/ldamallet models/wrappers/dtmmodel models/wrappers/ldavowpalwabbit.rst + models/wrappers/wordrank similarities/docsim similarities/index topic_coherence/aggregation diff --git a/docs/src/models/wrappers/wordrank.rst b/docs/src/models/wrappers/wordrank.rst new file mode 100644 index 0000000000..25f791ab88 --- /dev/null +++ b/docs/src/models/wrappers/wordrank.rst @@ -0,0 +1,9 @@ +:mod:`models.wrappers.wordrank` -- Word Embeddings from WordRank +================================================================================================ + +.. automodule:: gensim.models.wrappers.wordrank + :synopsis: Wordrank Embeddings + :members: + :inherited-members: + :undoc-members: + :show-inheritance: diff --git a/gensim/models/wrappers/ldamallet.py b/gensim/models/wrappers/ldamallet.py index 60ba004353..c4957d0f89 100644 --- a/gensim/models/wrappers/ldamallet.py +++ b/gensim/models/wrappers/ldamallet.py @@ -33,6 +33,7 @@ import random import tempfile import os +import subprocess import numpy @@ -234,7 +235,7 @@ def show_topics(self, num_topics=10, num_words=10, log=False, formatted=True): if formatted: topic = self.print_topic(i, topn=num_words) else: - topic = self.show_topic(i, topn=num_words) + topic = self.show_topic(i, num_words=num_words) shown.append((i, topic)) if log: logger.info("topic #%i (%.3f): %s", i, self.alpha[i], topic) diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 39c9e88c48..73a617a349 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -47,7 +47,7 @@ class Wordrank(Word2Vec): @classmethod def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, min_count=5, max_vocab_size=0, - sgd_num=100, lrate=0.001, period=10, iter=90, epsilon=0.75, dump_period=10, reg=0, alpha=100, + sgd_num=100, lrate=0.001, period=10, iter=91, epsilon=0.75, dump_period=10, reg=0, alpha=100, beta=99, loss='hinge', memory=4.0, cleanup_files=True, sorted_vocab=1, ensemble=1): """ `wr_path` is the path to the Wordrank directory. @@ -64,7 +64,7 @@ def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, `period` is the period of xi variable updates `iter` = number of iterations (epochs) over the corpus. `epsilon` is the power scaling value for weighting function. - `dump_period` is the period after which parameters should be dumped. + `dump_period` is the period after which embeddings should be dumped. `reg` is the value of regularization parameter. `alpha` is the alpha parameter of gamma distribution. `beta` is the beta parameter of gamma distribution. @@ -190,11 +190,10 @@ def sort_embeddings(self, vocab_file): def ensemble_embedding(self, word_embedding, context_embedding): """Addition of two embeddings.""" - glove2word2vec(word_embedding, word_embedding+'.w2vformat') glove2word2vec(context_embedding, context_embedding+'.w2vformat') w_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % word_embedding) c_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % context_embedding) - assert Counter(w_emb.index2word) == Counter(c_emb.index2word), 'Vocabs are not same for both embeddings' + assert Counter(w_emb.wv.index2word) == Counter(c_emb.wv.index2word), 'Vocabs are not same for both embeddings' prev_c_emb = copy.deepcopy(c_emb.wv.syn0) for word_id, word in enumerate(w_emb.wv.index2word): From 11ac3e86a6bb4bf80e6a53077b3ba83373dd1ac5 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 11 Jan 2017 14:38:57 +0530 Subject: [PATCH 10/18] remove subprocess32 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index a28f67e4cb..17676945db 100644 --- a/setup.py +++ b/setup.py @@ -118,7 +118,7 @@ def readfile(fname): python_2_6_backports = '' if sys.version_info[:2] < (2, 7): - python_2_6_backports = ['argparse', 'subprocess32', 'backport_collections'] + python_2_6_backports = ['argparse', 'backport_collections'] setup( From 7f541a29351ec09d49e945263fd68189224c1d27 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Fri, 13 Jan 2017 02:55:15 +0530 Subject: [PATCH 11/18] made requested changes --- ...pynb => WordRank_wrapper_quickstart.ipynb} | 22 ++---- docs/notebooks/Wordrank_comparisons.ipynb | 50 +++++++------- gensim/models/wrappers/ldamallet.py | 1 - gensim/models/wrappers/wordrank.py | 67 +++++++++---------- setup.py | 2 +- 5 files changed, 62 insertions(+), 80 deletions(-) rename docs/notebooks/{Wordrank_wrapper.ipynb => WordRank_wrapper_quickstart.ipynb} (91%) diff --git a/docs/notebooks/Wordrank_wrapper.ipynb b/docs/notebooks/WordRank_wrapper_quickstart.ipynb similarity index 91% rename from docs/notebooks/Wordrank_wrapper.ipynb rename to docs/notebooks/WordRank_wrapper_quickstart.ipynb index 180d76019a..8d89e25152 100644 --- a/docs/notebooks/Wordrank_wrapper.ipynb +++ b/docs/notebooks/WordRank_wrapper_quickstart.ipynb @@ -161,7 +161,7 @@ "from tempfile import mkstemp\n", "\n", "fs, temp_path = mkstemp(\"gensim_temp\") # creates a temp file\n", - "model.wv.save(temp_path) # save the model" + "model.save(temp_path) # save the model" ] }, { @@ -211,42 +211,30 @@ ], "source": [ "word_analogies_file = 'datasets/questions-words.txt'\n", - "model.wv.accuracy(word_analogies_file)" + "model.accuracy(word_analogies_file)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n", - " warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n", - "/usr/local/lib/python2.7/site-packages/numpy/core/_methods.py:70: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/usr/local/lib/python2.7/site-packages/scipy/stats/stats.py:3029: RuntimeWarning: invalid value encountered in double_scalars\n", - " r = r_num / r_den\n" - ] - }, { "data": { "text/plain": [ "((nan, nan), SpearmanrResult(correlation=nan, pvalue=nan), 100.0)" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word_similarity_file = 'datasets/ws-353.txt'\n", - "model.wv.evaluate_word_pairs(word_similarity_file)" + "model.evaluate_word_pairs(word_similarity_file)" ] }, { diff --git a/docs/notebooks/Wordrank_comparisons.ipynb b/docs/notebooks/Wordrank_comparisons.ipynb index db008f45cd..eba17b6e25 100644 --- a/docs/notebooks/Wordrank_comparisons.ipynb +++ b/docs/notebooks/Wordrank_comparisons.ipynb @@ -6,8 +6,9 @@ "source": [ "# Comparison of WordRank, Word2Vec and FastText\n", "\n", - "Wordrank is a fresh new approach to the word embeddings, which formulates it as a ranking problem. That is, given a word w, it aims to output an ordered list (c1, c2, · · ·) of context words such that words that co-occur with w appear at the top of the list. This formulation fits naturally to popular word embedding tasks such as word similarity/analogy since instead of the likelihood of each word, we are interested in finding the most relevant words\n", - "in a given context[1].\n", + "[Wordrank](https://arxiv.org/pdf/1506.02761v3.pdf) is a fresh new approach to the word embeddings, which formulates it as a ranking problem. That is, given a word w, it aims to output an ordered list (c1, c2, · · ·) of context words such that words that co-occur with w appear at the top of the list. This formulation fits naturally to popular word embedding tasks such as word similarity/analogy since instead of the likelihood of each word, we are interested in finding the most relevant words in a given context[1].\n", + "\n", + "This notebook accompanies a more theoretical blog post [here](https://rare-technologies.com/wordrank-word2vec-fasttext-comparison).\n", "\n", "Gensim is used to train and evaluate the word2vec models. Analogical reasoning and Word Similarity tasks are used for comparing the models. Word2vec and FastText embeddings are trained using the skipgram architecture here." ] @@ -541,7 +542,6 @@ } ], "source": [ - "# from gensim.models import Word2Vec\n", "MODELS_DIR = 'models/'\n", "word_analogies_file = './datasets/questions-words.txt'\n", "simlex_file = './datasets/simlex-999.txt'\n", @@ -990,7 +990,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false @@ -998,9 +998,9 @@ "outputs": [ { "data": { - "image/png": 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x8e6J9Lu1H4MXDqblly15vMnjvNP+HUoXK33tCpRycSEhIQwdOpRLly6ltsCB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\numePf9veve6v+dAh//Zx41THjvVv27lT9f77VVeu9G9/4w3Vu+/2bzt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QPu9pEybAtGlu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CNIuYxJ1tpAi0rmdN1NSHxYV4dSCYAxZqjbvC2IEtEEh6Lg5z7ubEEUn4vwv/THACT+bQBiFXSl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\nKBy0hjNn4NQpuyMRGVm1iaEN0Bd4E/hUKbUA+Bb4TWt90aI+C7U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N4LSGjz6C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cOIyDFAO+DFWI+t8EEMsF956wy0UdW18ZbHiZlKQNV4C+GEZSYWYRXMK9gPzBPq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\njuM4ToLhCpzjOI7jOE6C8f+ugMsrzCyGoQAAAABJRU5ErkJggg==\n", 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AO3fu5MSJEzRunHtcoaGheHh4sGHDBnr37g3A8ePH2bNnD5GRkbmeV6lSJcaM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elsSoUdCypb8xVRZZWV7vZzh31Fi8GE48EfLN7ZcCUlNTSUtLY86cOfTt2zevvHfv3rz33nss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\nQScSvV2qu3bl1uPtUu3SxYVbGAFhwi6ImLAz8iUpCd5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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1013,13 +1013,14 @@ "import copy\n", "import multiprocessing\n", "import numpy as np\n", - "# from gensim.models import Word2Vec\n", - "# import os\n", - "# from gensim.models.word2vec import Text8Corpus\n", + "from gensim.models import Word2Vec\n", + "import os\n", + "from gensim.models.word2vec import Text8Corpus\n", "\n", - "# word_analogies_file = '../datasets/questions-words.txt'\n", + "# os.chdir('models')\n", + "word_analogies_file = '../datasets/questions-words.txt'\n", "\n", - "def calc_parm(model, freq, bucket_size=100):\n", + "def calc_parm(model, freq):\n", " # mean_freq will contain analogies with their mean frequency of 4 words \n", " mean_freq = {}\n", " with open(word_analogies_file, 'r') as r:\n", @@ -1045,12 +1046,12 @@ " total_correct = sem_correct + syn_correct\n", " total_total = sem_total + syn_total\n", "\n", - " sem_x, sem_y = calc_axis(sem_correct, sem_total, mean_freq, bucket_size)\n", - " syn_x, syn_y = calc_axis(syn_correct, syn_total, mean_freq, bucket_size)\n", - " total_x, total_y = calc_axis(total_correct, total_total, mean_freq, bucket_size)\n", + " sem_x, sem_y = calc_axis(sem_correct, sem_total, mean_freq)\n", + " syn_x, syn_y = calc_axis(syn_correct, syn_total, mean_freq)\n", + " total_x, total_y = calc_axis(total_correct, total_total, mean_freq)\n", " return ((sem_x, sem_y), (syn_x, syn_y), (total_x, total_y))\n", "\n", - "def calc_axis(correct, total, mean_freq, bucket_size=100):\n", + "def calc_axis(correct, total, mean_freq):\n", " # make flat lists\n", " correct_analogies = []\n", " for i in range(len(correct)):\n", @@ -1078,6 +1079,7 @@ "\n", " x = []\n", " y = []\n", + " bucket_size = int(len(copy_mean_freq) * 0.06)\n", " # sort analogies according to their mean frequences \n", " copy_mean_freq = sorted(copy_mean_freq.items(), key=lambda x: x[1][1])\n", " # prepare analogies buckets according to given size\n", @@ -1101,9 +1103,9 @@ " freq[word] = model.wv.vocab[word].count\n", "\n", "# plot results\n", - "word2vec = calc_parm('brown_gs.vec', freq, bucket_size=100)\n", - "wordrank = calc_parm('brown_wr_ensemble.vec', freq, bucket_size=100)\n", - "fasttext = calc_parm('brown_ft.vec', freq, bucket_size=100)\n", + "word2vec = calc_parm('brown_gs.vec', freq)\n", + "wordrank = calc_parm('brown_wr_ensemble.vec', freq)\n", + "fasttext = calc_parm('brown_ft.vec', freq)\n", "\n", "fig = plt.figure(figsize=(7,15))\n", "\n", @@ -1127,16 +1129,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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CQkIIDg42lycmJrJjxw46dTKOEu/RowdXr15l8+bNbNiwgePHj9OvX79M7R47dowVK1aw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\nRHh2aNGiF+XuPGL3bukeXLDAN1OVLErevOJ1TZ8uTtzHH8touXbtpDW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pv/EHTLVomdry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\nyNrGExtpXb01ZfwsTEOwdsC8Ke64o+DQ+a1bJXpn6c2jlAybP3bMpu3at4d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y943Lp1forHZmbKVFwhrNm3Juh2UWv2rQHgjP1xstTWpQvs3RtCy4HiN37leHYd28XgLoMD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\nAElFTkSuQmCC\n", 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74+fnl1j+4MEDtm/fTpMmxlHibdq04fr162zcuJG1a9dy/PhxOnfunOy+x44dY9GiRSxe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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1152,9 +1154,9 @@ "for word in model.wv.index2word:\n", " freq[word] = model.wv.vocab[word].count\n", " \n", - "word2vec = calc_parm('text8_gs.vec', freq, bucket_size=200)\n", - "wordrank = calc_parm('text8_wr.vec', freq, bucket_size=200)\n", - "fasttext = calc_parm('text8_ft.vec', freq, bucket_size=200)\n", + "word2vec = calc_parm('text8_gs.vec', freq)\n", + "wordrank = calc_parm('text8_wr.vec', freq)\n", + "fasttext = calc_parm('text8_ft.vec', freq)\n", "\n", "fig = plt.figure(figsize=(7,15))\n", "\n", diff --git a/gensim/models/wrappers/ldamallet.py b/gensim/models/wrappers/ldamallet.py index c4957d0f89..5e479a860b 100644 --- a/gensim/models/wrappers/ldamallet.py +++ b/gensim/models/wrappers/ldamallet.py @@ -33,7 +33,6 @@ import random import tempfile import os -import subprocess import numpy diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 73a617a349..1062ee559d 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -1,3 +1,7 @@ +# Copyright (C) 2017 Parul Sethi +# Copyright (C) 2017 Radim Rehurek +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html + """ Python wrapper around word representation learning from Wordrank. The wrapped model can NOT be updated with new documents for online training -- use gensim's @@ -30,10 +34,6 @@ from smart_open import smart_open from shutil import copyfile, rmtree -if sys.version_info[:2] == (2, 6): - from backport_collections import Counter -else: - from collections import Counter logger = logging.getLogger(__name__) @@ -48,7 +48,7 @@ class Wordrank(Word2Vec): @classmethod def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, min_count=5, max_vocab_size=0, sgd_num=100, lrate=0.001, period=10, iter=91, epsilon=0.75, dump_period=10, reg=0, alpha=100, - beta=99, loss='hinge', memory=4.0, cleanup_files=True, sorted_vocab=1, ensemble=1): + beta=99, loss='hinge', memory=4.0, cleanup_files=True, sorted_vocab=1, ensemble=0): """ `wr_path` is the path to the Wordrank directory. `corpus_file` is the filename of the text file to be used for training the Wordrank model. @@ -56,7 +56,7 @@ def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, `out_path` is the path to directory which will be created to save embeddings and training data. `size` is the dimensionality of the feature vectors. `window` is the number of context words to the left (and to the right, if symmetric = 1). - symmetric` if 0, only use left context words, else use left and right both. + `symmetric` if 0, only use left context words, else use left and right both. `min_count` = ignore all words with total frequency lower than this. `max_vocab_size` upper bound on vocabulary size, i.e. keep the most frequent words. Default is 0 for no limit. `sgd_num` number of SGD taken for each data point. @@ -90,37 +90,30 @@ def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, copyfile(corpus_file, os.path.join(meta_dir, corpus_file.split('/')[-1])) os.chdir(meta_dir) - cmd0 = ['../../glove/vocab_count', '-min-count', str(min_count), '-max-vocab', str(max_vocab_size)] - cmd1 = ['../../glove/cooccur', '-memory', str(memory), '-vocab-file', temp_vocab_file, '-window-size', str(window), '-symmetric', str(symmetric)] - cmd2 = ['../../glove/shuffle', '-memory', str(memory)] - cmd3 = ['cut', '-d', " ", '-f', '1', temp_vocab_file] - cmds = [cmd0, cmd1, cmd2, cmd3] - logger.info("Preparing training data using glove code '%s'", cmds) - o0 = smart_open(temp_vocab_file, 'w') - o1 = smart_open(cooccurrence_file, 'w') - o2 = smart_open(cooccurrence_shuf_file, 'w') - o3 = smart_open(vocab_file, 'w') - i0 = smart_open(corpus_file.split('/')[-1]) - i1 = smart_open(corpus_file.split('/')[-1]) - i2 = smart_open(cooccurrence_file) - i3 = None - outputs = [o0, o1, o2, o3] - inputs = [i0, i1, i2, i3] - prepare_train_data = [utils.check_output(cmd, stdin=inp, stdout=out) for cmd, inp, out in zip(cmds, inputs, outputs)] - o0.close() - o1.close() - o2.close() - o3.close() - i0.close() - i1.close() - i2.close() - - with smart_open(vocab_file) as f: + cmd_vocab_count = ['../../glove/vocab_count', '-min-count', str(min_count), '-max-vocab', str(max_vocab_size)] + cmd_cooccurence_count = ['../../glove/cooccur', '-memory', str(memory), '-vocab-file', temp_vocab_file, '-window-size', str(window), '-symmetric', str(symmetric)] + cmd_shuffle_cooccurences = ['../../glove/shuffle', '-memory', str(memory)] + cmd_del_vocab_freq = ['cut', '-d', " ", '-f', '1', temp_vocab_file] + + commands = [cmd_vocab_count, cmd_cooccurence_count, cmd_shuffle_cooccurences] + logger.info("Prepare training data using glove code '%s'", commands) + input_fnames = [corpus_file.split('/')[-1], corpus_file.split('/')[-1], cooccurrence_file] + output_fnames = [temp_vocab_file, cooccurrence_file, cooccurrence_shuf_file] + + for command, input_fname, output_fname in zip(commands, input_fnames, output_fnames): + with smart_open(input_fname, 'rb') as r: + with smart_open(output_fname, 'wb') as w: + utils.check_output(command, stdin=r, stdout=w) + with smart_open(vocab_file, 'wb') as w: + utils.check_output(cmd_del_vocab_freq, stdout=w) + + with smart_open(vocab_file, 'rb') as f: numwords = sum(1 for line in f) - with smart_open(cooccurrence_shuf_file) as f: + with smart_open(cooccurrence_shuf_file, 'rb') as f: numlines = sum(1 for line in f) - with smart_open(meta_file, 'w') as f: - f.write("{0} {1}\n{2} {3}\n{4} {5}".format(numwords, numwords, numlines, cooccurrence_shuf_file, numwords, vocab_file)) + with smart_open(meta_file, 'wb') as f: + meta_info = "{0} {1}\n{2} {3}\n{4} {5}".format(numwords, numwords, numlines, cooccurrence_shuf_file, numwords, vocab_file) + f.write(meta_info.encode('utf-8')) wr_args = { 'path': 'meta', @@ -189,11 +182,11 @@ def sort_embeddings(self, vocab_file): self.wv.vocab[word].count = counts[word] def ensemble_embedding(self, word_embedding, context_embedding): - """Addition of two embeddings.""" + """Replace syn0 with the sum of context and word embeddings.""" glove2word2vec(context_embedding, context_embedding+'.w2vformat') w_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % word_embedding) c_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % context_embedding) - assert Counter(w_emb.wv.index2word) == Counter(c_emb.wv.index2word), 'Vocabs are not same for both embeddings' + assert set(w_emb.wv.index2word) == set(c_emb.wv.index2word), 'Vocabs are not same for both embeddings' prev_c_emb = copy.deepcopy(c_emb.wv.syn0) for word_id, word in enumerate(w_emb.wv.index2word): diff --git a/setup.py b/setup.py index 8d1f3118e7..629a77ff6a 100644 --- a/setup.py +++ b/setup.py @@ -118,7 +118,7 @@ def readfile(fname): python_2_6_backports = '' if sys.version_info[:2] < (2, 7): - python_2_6_backports = ['argparse', 'backport_collections'] + python_2_6_backports = ['argparse'] setup( From fb75890b5f1838ad48d8797b1168291807c9465e Mon Sep 17 00:00:00 2001 From: parulsethi Date: Fri, 13 Jan 2017 17:35:20 +0530 Subject: [PATCH 12/18] replace with vocab for comparison --- gensim/models/wrappers/wordrank.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 1062ee559d..54d8b21ae5 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -186,7 +186,7 @@ def ensemble_embedding(self, word_embedding, context_embedding): glove2word2vec(context_embedding, context_embedding+'.w2vformat') w_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % word_embedding) c_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % context_embedding) - assert set(w_emb.wv.index2word) == set(c_emb.wv.index2word), 'Vocabs are not same for both embeddings' + assert set(w_emb.wv.vocab) == set(c_emb.wv.vocab), 'Vocabs are not same for both embeddings' prev_c_emb = copy.deepcopy(c_emb.wv.syn0) for word_id, word in enumerate(w_emb.wv.index2word): From 14d6f90e09962cf3c6db63b588b2ffe73110484e Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 15 Jan 2017 16:14:33 +0530 Subject: [PATCH 13/18] added conclusions --- docs/notebooks/Wordrank_comparisons.ipynb | 59 +++++++++++++++-------- 1 file changed, 38 insertions(+), 21 deletions(-) diff --git a/docs/notebooks/Wordrank_comparisons.ipynb b/docs/notebooks/Wordrank_comparisons.ipynb index eba17b6e25..c982aefc3a 100644 --- a/docs/notebooks/Wordrank_comparisons.ipynb +++ b/docs/notebooks/Wordrank_comparisons.ipynb @@ -981,11 +981,12 @@ "collapsed": true }, "source": [ - "With a larger corpus, we observe similar patterns in the accuracies. Here also, WordRank dominates the Semantic analogies and FastText Syntactic ones. Word2Vec again performs better on SimLex-999 dataset and WordRank on WS-353.\n", + "With a larger corpus, we observe similar patterns in the accuracies. Here also, WordRank dominates the Semantic analogies and FastText Syntactic ones. Word2Vec again performs better on SimLex-999 dataset and WordRank on WordSim-353.\n", "Though we observe a little performance decrease in WordRank in case of ensemble embeddings here, so it's good to try both the cases for evaluations.\n", "\n", - "Now, following graph shows the word frequency effect on Analogy task accuracy. For each analogy, the\n", - "mean frequency of the four words involved is computed, and then bucketed with other analogies having similar mean frequencies. Each bucket has 200 analogies." + "# Word Frequency and Model Performance\n", + "\n", + "In this section, we'll see if the frequency of a word has any effect on embedding model's performance in Analogy task. Accuracy vs. Frequency graph is used to analyze this effect. The mean frequency of four words involved in each analogy is computed, and then bucketed with other analogies having similar mean frequencies. Each bucket has six percent of the total analogies involved in the particular task. You can go to this [repo](https://github.com/parulsethi/EmbeddingVisData/tree/master/WordAnalogyFreq) if you want to inspect about what analogies(with their sorted frequencies) were used for each of the plot." ] }, { @@ -1013,15 +1014,10 @@ "import copy\n", "import multiprocessing\n", "import numpy as np\n", - "from gensim.models import Word2Vec\n", - "import os\n", - "from gensim.models.word2vec import Text8Corpus\n", "\n", - "# os.chdir('models')\n", - "word_analogies_file = '../datasets/questions-words.txt'\n", "\n", - "def calc_parm(model, freq):\n", - " # mean_freq will contain analogies with their mean frequency of 4 words \n", + "def compute_accuracies(model, freq):\n", + " # mean_freq will contain analogies together with the mean frequency of 4 words involved\n", " mean_freq = {}\n", " with open(word_analogies_file, 'r') as r:\n", " for i, line in enumerate(r):\n", @@ -1103,9 +1099,9 @@ " freq[word] = model.wv.vocab[word].count\n", "\n", "# plot results\n", - "word2vec = calc_parm('brown_gs.vec', freq)\n", - "wordrank = calc_parm('brown_wr_ensemble.vec', freq)\n", - "fasttext = calc_parm('brown_ft.vec', freq)\n", + "word2vec = compute_accuracies('brown_gs.vec', freq)\n", + "wordrank = compute_accuracies('brown_wr_ensemble.vec', freq)\n", + "fasttext = compute_accuracies('brown_ft.vec', freq)\n", "\n", "fig = plt.figure(figsize=(7,15))\n", "\n", @@ -1125,7 +1121,17 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "This graph show the results trained over Brown corpus(1 million tokens).\n", + "\n", + "The main observations that can be drawn here are-\n", + "1. In Semantic Analogies, all the models perform poorly for rare words as compared to their performance at more frequent words.\n", + "2. In Syntactic Analogies, FastText performance is way better than Word2Vec and WordRank.\n", + "3. If we go through the frequency range in Syntactic Analogies plot, FastText performance drops significantly at highly frequent words, whereas, for Word2Vec and WordRank there is no significant difference over the whole frequency range.\n", + "4. End plot shows the results of combined Semantic and Syntactic Analogies. It has more resemblance to the Syntactic Analogy's plot because the total no. of Syntactic Analogies(=5461) is much greater than the total no. of Semantic ones(=852). So it's bound to trace the Syntactic's results as they have more weightage in the total analogies considered.\n", + "\n", + "Now, let’s see if a larger corpus creates any difference in this pattern of model's performance over different frequencies." + ] }, { "cell_type": "code", @@ -1154,9 +1160,9 @@ "for word in model.wv.index2word:\n", " freq[word] = model.wv.vocab[word].count\n", " \n", - "word2vec = calc_parm('text8_gs.vec', freq)\n", - "wordrank = calc_parm('text8_wr.vec', freq)\n", - "fasttext = calc_parm('text8_ft.vec', freq)\n", + "word2vec = compute_accuracies('text8_gs.vec', freq)\n", + "wordrank = compute_accuracies('text8_wr.vec', freq)\n", + "fasttext = compute_accuracies('text8_ft.vec', freq)\n", "\n", "fig = plt.figure(figsize=(7,15))\n", "\n", @@ -1176,7 +1182,16 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "This shows the results for text8(17 million tokens). Following points can be observed in this case-\n", + "\n", + "1. For Semantic analogies, all the models perform comparitively poor on rare words and also when the word frequency is high towards the end.\n", + "2. For Syntactic Analogies, FastText performance is fairly well on rare words but then falls steeply at highly frequent words.\n", + "3. WordRank and Word2Vec perform very similar with low accuracy for rare and highly frequent words in Syntactic Analogies.\n", + "4. FastText is again better in total analogies case due to the same reason described previously. Here the total no. of Semantic analogies is 7416 and Syntactic Analogies is 10411.\n", + "\n", + "These graphs also conclude that WordRank is the best suited method for Semantic Analogies, and FastText for Syntactic Analogies for all the frequency ranges and over different corpus sizes, though all the embedding methods could become very competitive as the corpus size increases largerly[2]. " + ] }, { "cell_type": "markdown", @@ -1185,10 +1200,11 @@ "# Conclusions\n", "\n", "\n", + "The experiments here conclude two main points from comparing Word embeddings. Firstly, there is no single global embedding model we could rely on for different types of NLP applications. For example, in Word Similarity, WordRank performed better than the other two algorithms for WS-353 test data whereas, Word2Vec performed better on SimLex-999. This is probably due to the different type of similarities these datasets address[3]. And in Word Analogy task, WordRank performed better for Semantic Analogies and FastText for Syntactic Analogies. This basically tells us that we need to choose the embedding method carefully according to our final use-case.\n", "\n", + "Secondly, our query words do matter apart from the generalized model performance. As we observed in Accuracy vs. Frequency graphs that models perform differently depending on the frequency of question analogy words in training corpus. For example, we are likely to get poor results if our query words are all highly frequent.\n", "\n", - "\n", - "Note: Wordrank can sometimes produce Nan values while model evaluation, when the embedding vector values get too diverged at some iterations, but it dumps embedding vectors after every few iterations, so you could just load embeddings from a different iteration’s text file." + "*__Note__:* WordRank can sometimes produce NaN values during model evaluation, when the embedding vector values get too diverged at some iterations, but it dumps embedding vectors after every few iterations, so you could just load embeddings from a different iteration’s text file." ] }, { @@ -1197,7 +1213,8 @@ "source": [ "# References\n", "1. [WordRank: Learning Word Embeddings via Robust Ranking](https://arxiv.org/pdf/1506.02761v3.pdf)\n", - "2. [Word2Vec and FastText comparison notebook](https://github.com/jayantj/gensim/blob/9f3e275ddad22afd54b7986654f3033f9baf8983/docs/notebooks/Word2Vec_FastText_Comparison.ipynb)" + "2. [Word2Vec and FastText comparison notebook](https://github.com/jayantj/gensim/blob/9f3e275ddad22afd54b7986654f3033f9baf8983/docs/notebooks/Word2Vec_FastText_Comparison.ipynb)\n", + "3. [Similarity test data](https://www.cl.cam.ac.uk/~fh295/simlex.html)" ] } ], From 4b9271e63737e725eaa5b60596e13d57af5eed17 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 15 Jan 2017 16:22:54 +0530 Subject: [PATCH 14/18] added some comments --- gensim/models/wrappers/wordrank.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 54d8b21ae5..27ed9b4733 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -141,6 +141,7 @@ def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, logger.info("Running wordrank binary '%s'", cmd) output = utils.check_output(args=cmd) + # use embeddings from max. iteration's dump max_iter_dump = iter / dump_period * dump_period - 1 copyfile('model_word_%d.txt' % max_iter_dump, 'wordrank.words') copyfile('model_context_%d.txt' % max_iter_dump, 'wordrank.contexts') @@ -186,6 +187,7 @@ def ensemble_embedding(self, word_embedding, context_embedding): glove2word2vec(context_embedding, context_embedding+'.w2vformat') w_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % word_embedding) c_emb = Word2Vec.load_word2vec_format('%s.w2vformat' % context_embedding) + # compare vocab words using keys of dict wv.vocab assert set(w_emb.wv.vocab) == set(c_emb.wv.vocab), 'Vocabs are not same for both embeddings' prev_c_emb = copy.deepcopy(c_emb.wv.syn0) From 207dd8b2c50c166bdfb3653096a9f1d8d9273b5e Mon Sep 17 00:00:00 2001 From: parulsethi Date: Mon, 16 Jan 2017 23:47:12 +0530 Subject: [PATCH 15/18] changed test data loc and update check_output --- .../WordRank_wrapper_quickstart.ipynb | 2 +- docs/notebooks/Wordrank_comparisons.ipynb | 4 +- docs/notebooks/datasets/ws-353.txt | 353 ------------------ gensim/models/wrappers/wordrank.py | 2 +- .../test/test_data/simlex999.txt | 2 + gensim/utils.py | 5 +- 6 files changed, 9 insertions(+), 359 deletions(-) delete mode 100644 docs/notebooks/datasets/ws-353.txt rename docs/notebooks/datasets/simlex-999.txt => gensim/test/test_data/simlex999.txt (99%) diff --git a/docs/notebooks/WordRank_wrapper_quickstart.ipynb b/docs/notebooks/WordRank_wrapper_quickstart.ipynb index 8d89e25152..5c300783e3 100644 --- a/docs/notebooks/WordRank_wrapper_quickstart.ipynb +++ b/docs/notebooks/WordRank_wrapper_quickstart.ipynb @@ -8,7 +8,7 @@ "source": [ "# WordRank wrapper tutorial on Lee Corpus\n", "\n", - "WordRank is a new word embedding algorithm which captures the semantic similarities in a text data well. See this [notebook](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Wordrank_comparisons.ipynb) for it's comparisons to other popular embedding models. This tutorial will serve as a guide to use the WordRank wrapper in gensim. You need to install [WordRank](https://bitbucket.org/shihaoji/wordrank) before proceeding with this tutorial.\n", + "WordRank is a new word embedding algorithm which captures the semantic similarities in a text data well. See this [notebook](https://github.com/parulsethi/gensim/blob/4b9271e63737e725eaa5b60596e13d57af5eed17/docs/notebooks/Wordrank_comparisons.ipynb) for it's comparisons to other popular embedding models. This tutorial will serve as a guide to use the WordRank wrapper in gensim. You need to install [WordRank](https://bitbucket.org/shihaoji/wordrank) before proceeding with this tutorial.\n", "\n", "\n", "# Train model\n", diff --git a/docs/notebooks/Wordrank_comparisons.ipynb b/docs/notebooks/Wordrank_comparisons.ipynb index c982aefc3a..3ee4624756 100644 --- a/docs/notebooks/Wordrank_comparisons.ipynb +++ b/docs/notebooks/Wordrank_comparisons.ipynb @@ -544,8 +544,8 @@ "source": [ "MODELS_DIR = 'models/'\n", "word_analogies_file = './datasets/questions-words.txt'\n", - "simlex_file = './datasets/simlex-999.txt'\n", - "wordsim_file = './datasets/ws-353.txt'\n", + "simlex_file = '../../gensim/test/test_data/simlex999.txt'\n", + "wordsim_file = '../../gensim/test/test_data/wordsim353.tsv'\n", "\n", "print('\\nLoading Gensim embeddings')\n", "brown_gs = Word2Vec.load_word2vec_format(MODELS_DIR + 'brown_gs.vec')\n", diff --git a/docs/notebooks/datasets/ws-353.txt b/docs/notebooks/datasets/ws-353.txt deleted file mode 100644 index bad1738106..0000000000 --- a/docs/notebooks/datasets/ws-353.txt +++ /dev/null @@ -1,353 +0,0 @@ -love sex 6.77 -tiger cat 7.35 -tiger tiger 10.00 -book paper 7.46 -computer keyboard 7.62 -computer internet 7.58 -plane car 5.77 -train car 6.31 -telephone communication 7.50 -television radio 6.77 -media radio 7.42 -drug abuse 6.85 -bread butter 6.19 -cucumber potato 5.92 -doctor nurse 7.00 -professor doctor 6.62 -student professor 6.81 -smart student 4.62 -smart stupid 5.81 -company stock 7.08 -stock market 8.08 -stock phone 1.62 -stock CD 1.31 -stock jaguar 0.92 -stock egg 1.81 -fertility egg 6.69 -stock live 3.73 -stock life 0.92 -book library 7.46 -bank money 8.12 -wood forest 7.73 -money cash 9.15 -professor cucumber 0.31 -king cabbage 0.23 -king queen 8.58 -king rook 5.92 -bishop rabbi 6.69 -Jerusalem Israel 8.46 -Jerusalem Palestinian 7.65 -holy sex 1.62 -fuck sex 9.44 -Maradona football 8.62 -football soccer 9.03 -football basketball 6.81 -football tennis 6.63 -tennis racket 7.56 -Arafat peace 6.73 -Arafat terror 7.65 -Arafat Jackson 2.50 -law lawyer 8.38 -movie star 7.38 -movie popcorn 6.19 -movie critic 6.73 -movie theater 7.92 -physics proton 8.12 -physics chemistry 7.35 -space chemistry 4.88 -alcohol chemistry 5.54 -vodka gin 8.46 -vodka brandy 8.13 -drink car 3.04 -drink ear 1.31 -drink mouth 5.96 -drink eat 6.87 -baby mother 7.85 -drink mother 2.65 -car automobile 8.94 -gem jewel 8.96 -journey voyage 9.29 -boy lad 8.83 -coast shore 9.10 -asylum madhouse 8.87 -magician wizard 9.02 -midday noon 9.29 -furnace stove 8.79 -food fruit 7.52 -bird cock 7.10 -bird crane 7.38 -tool implement 6.46 -brother monk 6.27 -crane implement 2.69 -lad brother 4.46 -journey car 5.85 -monk oracle 5.00 -cemetery woodland 2.08 -food rooster 4.42 -coast hill 4.38 -forest graveyard 1.85 -shore woodland 3.08 -monk slave 0.92 -coast forest 3.15 -lad wizard 0.92 -chord smile 0.54 -glass magician 2.08 -noon string 0.54 -rooster voyage 0.62 -money dollar 8.42 -money cash 9.08 -money currency 9.04 -money wealth 8.27 -money property 7.57 -money possession 7.29 -money bank 8.50 -money deposit 7.73 -money withdrawal 6.88 -money laundering 5.65 -money operation 3.31 -tiger jaguar 8.00 -tiger feline 8.00 -tiger carnivore 7.08 -tiger mammal 6.85 -tiger animal 7.00 -tiger organism 4.77 -tiger fauna 5.62 -tiger zoo 5.87 -psychology psychiatry 8.08 -psychology anxiety 7.00 -psychology fear 6.85 -psychology depression 7.42 -psychology clinic 6.58 -psychology doctor 6.42 -psychology Freud 8.21 -psychology mind 7.69 -psychology health 7.23 -psychology science 6.71 -psychology discipline 5.58 -psychology cognition 7.48 -planet star 8.45 -planet constellation 8.06 -planet moon 8.08 -planet sun 8.02 -planet galaxy 8.11 -planet space 7.92 -planet astronomer 7.94 -precedent example 5.85 -precedent information 3.85 -precedent cognition 2.81 -precedent law 6.65 -precedent collection 2.50 -precedent group 1.77 -precedent antecedent 6.04 -cup coffee 6.58 -cup tableware 6.85 -cup article 2.40 -cup artifact 2.92 -cup object 3.69 -cup entity 2.15 -cup drink 7.25 -cup food 5.00 -cup substance 1.92 -cup liquid 5.90 -jaguar cat 7.42 -jaguar car 7.27 -energy secretary 1.81 -secretary senate 5.06 -energy laboratory 5.09 -computer laboratory 6.78 -weapon secret 6.06 -FBI fingerprint 6.94 -FBI investigation 8.31 -investigation effort 4.59 -Mars water 2.94 -Mars scientist 5.63 -news report 8.16 -canyon landscape 7.53 -image surface 4.56 -discovery space 6.34 -water seepage 6.56 -sign recess 2.38 -Wednesday news 2.22 -mile kilometer 8.66 -computer news 4.47 -territory surface 5.34 -atmosphere landscape 3.69 -president medal 3.00 -war troops 8.13 -record number 6.31 -skin eye 6.22 -Japanese American 6.50 -theater history 3.91 -volunteer motto 2.56 -prejudice recognition 3.00 -decoration valor 5.63 -century year 7.59 -century nation 3.16 -delay racism 1.19 -delay news 3.31 -minister party 6.63 -peace plan 4.75 -minority peace 3.69 -attempt peace 4.25 -government crisis 6.56 -deployment departure 4.25 -deployment withdrawal 5.88 -energy crisis 5.94 -announcement news 7.56 -announcement effort 2.75 -stroke hospital 7.03 -disability death 5.47 -victim emergency 6.47 -treatment recovery 7.91 -journal association 4.97 -doctor personnel 5.00 -doctor liability 5.19 -liability insurance 7.03 -school center 3.44 -reason hypertension 2.31 -reason criterion 5.91 -hundred percent 7.38 -Harvard Yale 8.13 -hospital infrastructure 4.63 -death row 5.25 -death inmate 5.03 -lawyer evidence 6.69 -life death 7.88 -life term 4.50 -word similarity 4.75 -board recommendation 4.47 -governor interview 3.25 -OPEC country 5.63 -peace atmosphere 3.69 -peace insurance 2.94 -territory kilometer 5.28 -travel activity 5.00 -competition price 6.44 -consumer confidence 4.13 -consumer energy 4.75 -problem airport 2.38 -car flight 4.94 -credit card 8.06 -credit information 5.31 -hotel reservation 8.03 -grocery money 5.94 -registration arrangement 6.00 -arrangement accommodation 5.41 -month hotel 1.81 -type kind 8.97 -arrival hotel 6.00 -bed closet 6.72 -closet clothes 8.00 -situation conclusion 4.81 -situation isolation 3.88 -impartiality interest 5.16 -direction combination 2.25 -street place 6.44 -street avenue 8.88 -street block 6.88 -street children 4.94 -listing proximity 2.56 -listing category 6.38 -cell phone 7.81 -production hike 1.75 -benchmark index 4.25 -media trading 3.88 -media gain 2.88 -dividend payment 7.63 -dividend calculation 6.48 -calculation computation 8.44 -currency market 7.50 -OPEC oil 8.59 -oil stock 6.34 -announcement production 3.38 -announcement warning 6.00 -profit warning 3.88 -profit loss 7.63 -dollar yen 7.78 -dollar buck 9.22 -dollar profit 7.38 -dollar loss 6.09 -computer software 8.50 -network hardware 8.31 -phone equipment 7.13 -equipment maker 5.91 -luxury car 6.47 -five month 3.38 -report gain 3.63 -investor earning 7.13 -liquid water 7.89 -baseball season 5.97 -game victory 7.03 -game team 7.69 -marathon sprint 7.47 -game series 6.19 -game defeat 6.97 -seven series 3.56 -seafood sea 7.47 -seafood food 8.34 -seafood lobster 8.70 -lobster food 7.81 -lobster wine 5.70 -food preparation 6.22 -video archive 6.34 -start year 4.06 -start match 4.47 -game round 5.97 -boxing round 7.61 -championship tournament 8.36 -fighting defeating 7.41 -line insurance 2.69 -day summer 3.94 -summer drought 7.16 -summer nature 5.63 -day dawn 7.53 -nature environment 8.31 -environment ecology 8.81 -nature man 6.25 -man woman 8.30 -man governor 5.25 -murder manslaughter 8.53 -soap opera 7.94 -opera performance 6.88 -life lesson 5.94 -focus life 4.06 -production crew 6.25 -television film 7.72 -lover quarrel 6.19 -viewer serial 2.97 -possibility girl 1.94 -population development 3.75 -morality importance 3.31 -morality marriage 3.69 -Mexico Brazil 7.44 -gender equality 6.41 -change attitude 5.44 -family planning 6.25 -opera industry 2.63 -sugar approach 0.88 -practice institution 3.19 -ministry culture 4.69 -problem challenge 6.75 -size prominence 5.31 -country citizen 7.31 -planet people 5.75 -development issue 3.97 -experience music 3.47 -music project 3.63 -glass metal 5.56 -aluminum metal 7.83 -chance credibility 3.88 -exhibit memorabilia 5.31 -concert virtuoso 6.81 -rock jazz 7.59 -museum theater 7.19 -observation architecture 4.38 -space world 6.53 -preservation world 6.19 -admission ticket 7.69 -shower thunderstorm 6.31 -shower flood 6.03 -weather forecast 8.34 -disaster area 6.25 -governor office 6.34 -architecture century 3.78 diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 27ed9b4733..07467408e5 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -103,7 +103,7 @@ def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, for command, input_fname, output_fname in zip(commands, input_fnames, output_fnames): with smart_open(input_fname, 'rb') as r: with smart_open(output_fname, 'wb') as w: - utils.check_output(command, stdin=r, stdout=w) + utils.check_output(command, stdout=w, stdin=r) with smart_open(vocab_file, 'wb') as w: utils.check_output(cmd_del_vocab_freq, stdout=w) diff --git a/docs/notebooks/datasets/simlex-999.txt b/gensim/test/test_data/simlex999.txt similarity index 99% rename from docs/notebooks/datasets/simlex-999.txt rename to gensim/test/test_data/simlex999.txt index c16a182e5d..d57b2ef344 100644 --- a/docs/notebooks/datasets/simlex-999.txt +++ b/gensim/test/test_data/simlex999.txt @@ -1,3 +1,5 @@ +# The SimLex-999 Test data (https://www.cl.cam.ac.uk/~fh295/simlex.html) +# Word 1 Word 2 Human (mean) old new 1.58 smart intelligent 9.2 hard difficult 8.77 diff --git a/gensim/utils.py b/gensim/utils.py index 60c4a8f028..55c5bcdf38 100644 --- a/gensim/utils.py +++ b/gensim/utils.py @@ -1146,7 +1146,7 @@ def keep_vocab_item(word, count, min_count, trim_rule=None): else: return default_res -def check_output(*popenargs, **kwargs): +def check_output(*popenargs, stdout=subprocess.PIPE, **kwargs): r"""Run command with arguments and return its output as a byte string. Backported from Python 2.7 as it's implemented as pure python on stdlib. >>> check_output(['/usr/bin/python', '--version']) @@ -1154,7 +1154,8 @@ def check_output(*popenargs, **kwargs): Added extra KeyboardInterrupt handling """ try: - process = subprocess.Popen(*popenargs, **kwargs) + # process = subprocess.Popen(*popenargs, stdout=subprocess.PIPE, **kwargs) + process = subprocess.Popen(*popenargs, stdout=stdout, **kwargs) output, unused_err = process.communicate() retcode = process.poll() if retcode: From 09f4617486bd64a79c7fb567ddde3220084b0a47 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Mon, 16 Jan 2017 23:51:44 +0530 Subject: [PATCH 16/18] remove extra comment in check_output --- gensim/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gensim/utils.py b/gensim/utils.py index 55c5bcdf38..7ae43e94de 100644 --- a/gensim/utils.py +++ b/gensim/utils.py @@ -1154,7 +1154,6 @@ def check_output(*popenargs, stdout=subprocess.PIPE, **kwargs): Added extra KeyboardInterrupt handling """ try: - # process = subprocess.Popen(*popenargs, stdout=subprocess.PIPE, **kwargs) process = subprocess.Popen(*popenargs, stdout=stdout, **kwargs) output, unused_err = process.communicate() retcode = process.poll() From b3ecdd457265f4487d7a1040befa7e6eedf0dee1 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 22 Jan 2017 20:51:40 +0530 Subject: [PATCH 17/18] update check_output --- gensim/models/wrappers/dtmmodel.py | 2 +- gensim/models/wrappers/ldamallet.py | 6 +++--- gensim/models/wrappers/wordrank.py | 4 ++-- gensim/utils.py | 6 +++--- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/gensim/models/wrappers/dtmmodel.py b/gensim/models/wrappers/dtmmodel.py index 72dba1a741..a953ce858a 100644 --- a/gensim/models/wrappers/dtmmodel.py +++ b/gensim/models/wrappers/dtmmodel.py @@ -198,7 +198,7 @@ def train(self, corpus, time_slices, mode, model): cmd = [self.dtm_path] + arguments.split() logger.info("Running command %s" % cmd) - check_output(cmd, stderr=PIPE) + check_output(args=cmd, stderr=PIPE) self.em_steps = np.loadtxt(self.fem_steps()) self.init_ss = np.loadtxt(self.flda_ss()) diff --git a/gensim/models/wrappers/ldamallet.py b/gensim/models/wrappers/ldamallet.py index 5e479a860b..9b5cfcaa91 100644 --- a/gensim/models/wrappers/ldamallet.py +++ b/gensim/models/wrappers/ldamallet.py @@ -152,7 +152,7 @@ def convert_input(self, corpus, infer=False, serialize_corpus=True): else: cmd = cmd % (self.fcorpustxt(), self.fcorpusmallet()) logger.info("converting temporary corpus to MALLET format with %s", cmd) - check_output(cmd, shell=True) + check_output(args=cmd, shell=True) def train(self, corpus): self.convert_input(corpus, infer=False) @@ -164,7 +164,7 @@ def train(self, corpus): self.fstate(), self.fdoctopics(), self.ftopickeys(), self.iterations, self.finferencer(), self.topic_threshold) # NOTE "--keep-sequence-bigrams" / "--use-ngrams true" poorer results + runs out of memory logger.info("training MALLET LDA with %s", cmd) - check_output(cmd, shell=True) + check_output(args=cmd, shell=True) self.word_topics = self.load_word_topics() # NOTE - we are still keeping the wordtopics variable to not break backward compatibility. # word_topics has replaced wordtopics throughout the code; wordtopics just stores the values of word_topics when train is called. @@ -180,7 +180,7 @@ def __getitem__(self, bow, iterations=100): cmd = self.mallet_path + " infer-topics --input %s --inferencer %s --output-doc-topics %s --num-iterations %s --doc-topics-threshold %s" cmd = cmd % (self.fcorpusmallet() + '.infer', self.finferencer(), self.fdoctopics() + '.infer', iterations, self.topic_threshold) logger.info("inferring topics with MALLET LDA '%s'", cmd) - check_output(cmd, shell=True) + check_output(args=cmd, shell=True) result = list(self.read_doctopics(self.fdoctopics() + '.infer')) return result if is_corpus else result[0] diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 07467408e5..0091f92ea9 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -103,9 +103,9 @@ def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, for command, input_fname, output_fname in zip(commands, input_fnames, output_fnames): with smart_open(input_fname, 'rb') as r: with smart_open(output_fname, 'wb') as w: - utils.check_output(command, stdout=w, stdin=r) + utils.check_output(w, command, stdin=r) with smart_open(vocab_file, 'wb') as w: - utils.check_output(cmd_del_vocab_freq, stdout=w) + utils.check_output(w, cmd_del_vocab_freq) with smart_open(vocab_file, 'rb') as f: numwords = sum(1 for line in f) diff --git a/gensim/utils.py b/gensim/utils.py index 7ae43e94de..8cc7f9574c 100644 --- a/gensim/utils.py +++ b/gensim/utils.py @@ -1146,15 +1146,15 @@ def keep_vocab_item(word, count, min_count, trim_rule=None): else: return default_res -def check_output(*popenargs, stdout=subprocess.PIPE, **kwargs): +def check_output(stdout=subprocess.PIPE, *popenargs, **kwargs): r"""Run command with arguments and return its output as a byte string. Backported from Python 2.7 as it's implemented as pure python on stdlib. - >>> check_output(['/usr/bin/python', '--version']) + >>> check_output(args=['/usr/bin/python', '--version']) Python 2.6.2 Added extra KeyboardInterrupt handling """ try: - process = subprocess.Popen(*popenargs, stdout=stdout, **kwargs) + process = subprocess.Popen(stdout=stdout, *popenargs, **kwargs) output, unused_err = process.communicate() retcode = process.poll() if retcode: From 4256252733ab76695d923f09df86121eed05f1c6 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 22 Jan 2017 21:06:29 +0530 Subject: [PATCH 18/18] update wordrank_wrapper's check_output call --- gensim/models/wrappers/wordrank.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gensim/models/wrappers/wordrank.py b/gensim/models/wrappers/wordrank.py index 0091f92ea9..81bde09013 100644 --- a/gensim/models/wrappers/wordrank.py +++ b/gensim/models/wrappers/wordrank.py @@ -103,9 +103,9 @@ def train(cls, wr_path, corpus_file, out_path, size=100, window=15, symmetric=1, for command, input_fname, output_fname in zip(commands, input_fnames, output_fnames): with smart_open(input_fname, 'rb') as r: with smart_open(output_fname, 'wb') as w: - utils.check_output(w, command, stdin=r) + utils.check_output(w, args=command, stdin=r) with smart_open(vocab_file, 'wb') as w: - utils.check_output(w, cmd_del_vocab_freq) + utils.check_output(w, args=cmd_del_vocab_freq) with smart_open(vocab_file, 'rb') as f: numwords = sum(1 for line in f)