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Repository: askerlee/topicvec
Branch: master
Commit: 1c5d2c5ec8a0
Files: 118
Total size: 18.0 MB
Directory structure:
gitextract__1max8y4/
├── .gitignore
├── 20news.bat
├── README.md
├── anatest.py
├── classEval.py
├── corpusLoader.py
├── csv2topic.py
├── file2topic.py
├── psdvec/
│ ├── README.md
│ ├── addheader.py
│ ├── analogy.py
│ ├── bench.sh
│ ├── benchspeed.py
│ ├── catbench.py
│ ├── cleancorpus.py
│ ├── competitors/
│ │ ├── glove/
│ │ │ ├── demo.sh
│ │ │ ├── rcv1.sh
│ │ │ ├── vocab-rcv1.txt
│ │ │ ├── vocab-wiki.txt
│ │ │ └── wiki.sh
│ │ └── hyperwords/
│ │ ├── pmi2.sh
│ │ ├── pmi5-rcv1.sh
│ │ ├── pmi5.sh
│ │ ├── svd-rcv1.sh
│ │ ├── svd.sh
│ │ ├── train-rcv1.sh
│ │ └── train-wiki.sh
│ ├── corpus2liblinear.py
│ ├── eval-logs/
│ │ └── bench.log
│ ├── evaluate-toefl.py
│ ├── evaluate.py
│ ├── extractwiki.py
│ ├── fact-rcv1.bat
│ ├── fact-rcv1.sh
│ ├── fact-wiki.bat
│ ├── fact-wiki.sh
│ ├── factorize.py
│ ├── genSentDict.bat
│ ├── gencatdata.py
│ ├── gram-rcv1.bat
│ ├── gram.bat
│ ├── gramcount.pl
│ ├── patch to gensim.py
│ ├── perlxs.h
│ ├── removeDoubleNewline.pl
│ ├── sent-bench.bat
│ ├── sent-gen.conf
│ ├── sentbench.py
│ ├── tab2list.py
│ ├── testsets/
│ │ ├── analogy/
│ │ │ ├── EN-TOM-ICLR13-SEM.txt
│ │ │ ├── EN-TOM-ICLR13-SYN.txt
│ │ │ ├── google.txt
│ │ │ └── msr.txt
│ │ └── ws/
│ │ ├── EN-RG-65.txt
│ │ ├── EN-TOEFL-80.txt
│ │ ├── bruni_men.txt
│ │ ├── luong_rare.txt
│ │ ├── radinsky_mturk.txt
│ │ ├── simlex_999a.txt
│ │ ├── ws353.txt
│ │ ├── ws353_relatedness.txt
│ │ └── ws353_similarity.txt
│ ├── topwordsInList.py
│ ├── utils.py
│ ├── vecnorms.py
│ └── xml2corpus.pl
├── reuters.bat
├── snippet2topic.py
├── test-docs/
│ ├── Drug Goes From 13.50 a Tablet to 750, Overnight.txt
│ ├── VR-mitrv.txt
│ ├── batman-v-superman.txt
│ ├── batman-v-superman.txt-em100.topic.vec
│ ├── beijing-haze-news.txt
│ ├── brain-scar.txt
│ ├── britain-EU.txt
│ ├── drugstory.log
│ ├── hillary-speech.txt
│ ├── hillary-speech2.txt
│ ├── nips-wiki.txt
│ ├── sanders-speeches.txt
│ ├── spacex-news.txt
│ └── trump-speech.txt
├── topic-competitors/
│ ├── LDA/
│ │ ├── Readme.txt
│ │ ├── classEval.py
│ │ ├── corpusLoader.py
│ │ └── ldaExp.py
│ ├── doc2vec.py
│ ├── kmeans.py
│ ├── labelEval.py
│ ├── lftm2svm.py
│ ├── liu-doc2vec.py
│ └── slda/
│ ├── 20news-test-7532.slda-bow.txt
│ ├── 20news-test-7532.slda-label.txt
│ ├── 20news-train-11314.slda-bow.txt
│ ├── 20news-train-11314.slda-label.txt
│ ├── Makefile
│ ├── corpus.cpp
│ ├── corpus.h
│ ├── images.tgz
│ ├── main.cpp
│ ├── opt.cpp
│ ├── opt.h
│ ├── readme.txt
│ ├── reuters-test-2255.slda-bow.txt
│ ├── reuters-test-2255.slda-label.txt
│ ├── reuters-train-5770.slda-bow.txt
│ ├── reuters-train-5770.slda-label.txt
│ ├── settings.h
│ ├── settings.txt
│ ├── slda
│ ├── slda.cpp
│ ├── slda.h
│ ├── utils.cpp
│ └── utils.h
├── topic-cosine.py
├── topicExp.py
├── topicvecDir.py
└── utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
commit.bat
*.pyc
================================================
FILE: 20news.bat
================================================
python topicExp.py -s 20news train
python topicExp.py -i 20news-train-11314-sep281-em150-best.topic.vec 20news train,test
python classEval.py 20news topicprop
python classEval.py 20news topic-wvavg
================================================
FILE: README.md
================================================
# TopicVec
TopicVec is the source code for "Generative Topic Embedding: a Continuous Representation of Documents" (ACL 2016).
PSDVec (in folder 'psdvec') is the source code for "A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution" (EMNLP 2015).
#### Update v0.7:
The topic inference is now 6 times faster.
#### Update v0.6:
##### Algorithm update:
topicvecDir.py: uses exact inference instead a second-order approximation in the M-step.
#### Update v0.5:
##### Main algorithm:
topicvecDir.py: uses a Dirichlet prior for topic mixting proportions.
####Required files on Dropbox:
https://www.dropbox.com/sh/lqbk3iioobegbp8/AACc8Kfr1KZIkKl9bGaIrOjfa?dl=0
1. Pretrained 180000 embeddings (25000 cores) in 3 archives. For faster loading into Python, 25000-180000-500-BLK-8.0.vec.npy can be used;
2. Unigram files top1grams-wiki.txt & top1grams-reuters.txt;
3. RCV1 cleansed corpus ( before downloading, please apply for permission from NIST according to: http://trec.nist.gov/data/reuters/reuters.html ).
If you are in China, you can also download the above files from baidu netdisk without the hassle of "climbing over the wall":
https://pan.baidu.com/s/1gVmRhK1HA2XwVWZbZHHLZQ#list/path=%2F
================================================
FILE: anatest.py
================================================
import numpy as np
from utils import *
embedding_arrays = np.load("25000-180000-500-BLK-8.0.vec.npy")
V, vocab, word2ID, skippedWords_whatever = embedding_arrays
model = VecModel(V, vocab, word2ID, vecNormalize=True)
w1, w2 = predict_ana(model, "fish", "water", "plant", "soil")
print w1, w2
w1, w2 = predict_ana(model, "player", "team", "student", "classroom")
print w1, w2
================================================
FILE: classEval.py
================================================
from sklearn import svm, metrics
from sklearn.datasets import load_svmlight_file
import sys
def getScores( true_classes, pred_classes, average):
precision = metrics.precision_score( true_classes, pred_classes, average=average )
recall = metrics.recall_score( true_classes, pred_classes, average=average )
f1 = metrics.f1_score( true_classes, pred_classes, average=average )
accuracy = metrics.accuracy_score( true_classes, pred_classes )
return precision, recall, f1, accuracy
"""
TopicProp_ITER = int(sys.argv[1])
topicNum = int(sys.argv[2])
train_file = "20news-train-11314-sep%d-em40-i%d.topic.prop" %(topicNum, TopicProp_ITER)
test_file = "20news-test-7532-sep%d-em40-i%d.topic.prop" %(topicNum, TopicProp_ITER)
train_features, train_docs_cat_name = load_matrix_from_text( train_file, "training proportion", "\t" )
test_features, test_docs_cat_name = load_matrix_from_text( test_file, "test proportion", "\t" )
true_train_classes = []
true_test_classes = []
for train_cat_name in train_docs_cat_name[0]:
true_train_classes.append( int(train_cat_name) )
for test_cat_name in test_docs_cat_name[0]:
true_test_classes.append( int(test_cat_name) )
"""
corpus = sys.argv[1]
filetype = sys.argv[2]
# selected feature dimensions can be specified in the last argument as:
# 1-400 (starting from 1)
if len(sys.argv) > 3:
dims = sys.argv[3].split("-")
dims[0] = int(dims[0]) - 1
dims[1] = int(dims[1])
else:
dims = None
if corpus == '20news':
train_file = "20news-train-11314.svm-%s.txt" %filetype
test_file = "20news-test-7532.svm-%s.txt" %filetype
else:
train_file = "reuters-train-5770.svm-%s.txt" %filetype
test_file = "reuters-test-2255.svm-%s.txt" %filetype
train_features_sparse, true_train_classes = load_svmlight_file(train_file)
test_features_sparse, true_test_classes = load_svmlight_file(test_file)
#nonzeroColIDs = np.union1d( train_features_sparse.nonzero()[1], test_features_sparse.nonzero()[1] )
#train_features = train_features_sparse[:, nonzeroColIDs].toarray()
#test_features = test_features_sparse[:, nonzeroColIDs].toarray()
#pdb.set_trace()
#print "%dx%d sparse feature matrices reduced to %dx%d" %( tuple(train_features_sparse.shape) +
# tuple(train_features.shape) )
train_features = train_features_sparse.toarray()
test_features = test_features_sparse.toarray()
print "Train: %dx%d. Test: %dx%d" %( tuple( train_features.shape + test_features.shape ) )
if dims:
train_features = train_features[ :, dims[0]:dims[1] ]
test_features = test_features[ :, dims[0]:dims[1] ]
print "Choose only features %d-%d" %( dims[0]+1, dims[1] )
else:
train_features = train_features[ :, : ]
test_features = test_features[ :, : ]
model = svm.LinearSVC(penalty='l1', dual=False)
print "Training...",
model.fit( train_features, true_train_classes )
print "Done."
pred_train_classes = model.predict( train_features )
pred_test_classes = model.predict( test_features )
print metrics.classification_report(true_train_classes, pred_train_classes, digits=3)
print metrics.classification_report(true_test_classes, pred_test_classes, digits=3)
for average in ['micro', 'macro']:
train_precision, train_recall, train_f1, train_acc = getScores( true_train_classes, pred_train_classes, average )
print "Train Prec (%s average): %.3f, recall: %.3f, F1: %.3f, Acc: %.3f" %( average,
train_precision, train_recall, train_f1, train_acc )
test_precision, test_recall, test_f1, test_acc = getScores( true_test_classes, pred_test_classes, average )
print "Test Prec (%s average): %.3f, recall: %.3f, F1: %.3f, Acc: %.3f" %( average,
test_precision, test_recall, test_f1, test_acc )
================================================
FILE: corpusLoader.py
================================================
# -*- coding=GBK -*-
from sklearn.datasets import fetch_20newsgroups
from nltk.corpus import reuters
import HTMLParser
import os
import pdb
from utils import *
def extractSentenceWords(doc, remove_url=True, remove_punc="utf-8", min_length=1):
if remove_punc:
# ensure doc_u is in unicode
if not isinstance(doc, unicode):
encoding = remove_punc
doc_u = doc.decode(encoding)
else:
doc_u = doc
# remove unicode punctuation marks, keep ascii punctuation marks
doc_u = doc_u.translate(unicode_punc_tbl)
if not isinstance(doc, unicode):
doc = doc_u.encode(encoding)
else:
doc = doc_u
if remove_url:
re_url = r"(https?:\/\/)?(www\.)?[-a-zA-Z0-9@:%._\+~#=]{2,256}\.[a-z]{2,6}\b([-a-zA-Z0-9@:%_\+.~#?&//=]*)"
doc = re.sub( re_url, "", doc )
sentences = re.split( r"\s*[,;:`\"()?!{}]\s*|--+|\s*-\s+|''|\.\s|\.$|\.\.+||", doc ) #"
wc = 0
wordsInSentences = []
for sentence in sentences:
if sentence == "":
continue
if not re.search( "[A-Za-z0-9]", sentence ):
continue
words = re.split( r"\s+\+|^\+|\+?[\-*\/&%=<>\[\]~\|\@\$]+\+?|\'\s+|\'s\s+|\'s$|\s+\'|^\'|\'$|\$|\\|\s+", sentence )
words = filter( lambda w: w, words )
if len(words) >= min_length:
wordsInSentences.append(words)
wc += len(words)
#print "%d words extracted" %wc
return wordsInSentences, wc
def load_20news(setName):
newsgroups_subset = fetch_20newsgroups(subset=setName, remove=('headers', 'footers')) #, 'quotes'
totalLineNum = 0
readDocNum = 0
print "Loading 20 newsgroup %s data..." %setName
setDocNum = len(newsgroups_subset.data)
orig_docs_name = []
orig_docs_cat = []
orig_docs_words = []
catNum = len(newsgroups_subset.target_names)
cats_docsWords = [ [] for i in xrange(catNum) ]
cats_docNames = [ [] for i in xrange(catNum) ]
emptyFileNum = 0
for d, text in enumerate(newsgroups_subset.data):
if d % 50 == 49 or d == setDocNum - 1:
print "\r%d %d\r" %( d + 1, totalLineNum ),
text = text.encode("utf-8")
lines = text.split("\n")
if len(text) == 0 or len(lines) == 0:
emptyFileNum += 1
continue
readDocNum += 1
totalLineNum += len(lines)
catID = newsgroups_subset.target[d]
category = newsgroups_subset.target_names[catID]
text = " ".join(lines)
wordsInSentences, wc = extractSentenceWords(text)
filename = newsgroups_subset.filenames[d]
filename = os.path.basename(filename)
orig_docs_words.append( wordsInSentences )
orig_docs_name.append(filename)
orig_docs_cat.append(catID)
cats_docsWords[catID].append(wordsInSentences)
cats_docNames[catID].append(filename)
print "Done. %d docs read, %d empty docs skipped. Totally %d lines" %(readDocNum, emptyFileNum, totalLineNum)
return setDocNum, orig_docs_words, orig_docs_name, orig_docs_cat, \
cats_docsWords, cats_docNames, newsgroups_subset.target_names
def load_reuters(setName):
html = HTMLParser.HTMLParser()
doc_ids = reuters.fileids()
cat2all_ids = {}
cat2train_ids = {}
cat2test_ids = {}
cat2all_num = {}
cand_docNum = 0
for doc_id in doc_ids:
# only choose docs belonging in one category
if len( reuters.categories(doc_id) ) == 1:
cat = reuters.categories(doc_id)[0]
cand_docNum += 1
if doc_id.startswith("train"):
cat2set_ids = cat2train_ids
else:
cat2set_ids = cat2test_ids
if cat in cat2set_ids:
cat2set_ids[cat].append(doc_id)
else:
cat2set_ids[cat] = [ doc_id ]
# both train and test doc_ids are put in cat2all_ids
if cat in cat2all_ids:
cat2all_ids[cat].append(doc_id)
else:
cat2all_ids[cat] = [ doc_id ]
if cat in cat2all_num:
cat2all_num[cat] += 1
else:
cat2all_num[cat] = 1
print "Totally %d docs, %d single-category docs in %d categories" %( len(doc_ids),
cand_docNum, len(cat2train_ids) )
sorted_cats = sorted( cat2all_num.keys(), key=lambda cat: cat2all_num[cat],
reverse=True )
catNum = 10
cats_docsWords = [ [] for i in xrange(catNum) ]
cats_docNames = [ [] for i in xrange(catNum) ]
topN_cats = sorted_cats[:catNum]
print "Top 10 categories:"
keptAllDocNum = 0
keptTrainDocNum = 0
keptTestDocNum = 0
for cat in topN_cats:
print "%s: %d/%d" %( cat, len(cat2train_ids[cat]), len(cat2test_ids[cat]) )
keptTrainDocNum += len(cat2train_ids[cat])
keptTestDocNum += len(cat2test_ids[cat])
keptAllDocNum += len(cat2train_ids[cat]) + len(cat2test_ids[cat])
print "Totally %d docs kept, %d in train, %d in test" %( keptAllDocNum,
keptTrainDocNum, keptTestDocNum )
if setName == "train":
cat2set_ids = cat2train_ids
setDocNum = keptTrainDocNum
elif setName == "test":
cat2set_ids = cat2test_ids
setDocNum = keptTestDocNum
elif setName == "all":
cat2set_ids = cat2all_ids
setDocNum = keptAllDocNum
else:
raise Exception("Unknown set name %s" %setName)
orig_docs_name = []
orig_docs_cat = []
orig_docs_words = []
readDocNum = 0
totalLineNum = 0
emptyFileNum = 0
for cat_id, cat in enumerate(topN_cats):
for doc_id in cat2set_ids[cat]:
if readDocNum % 50 == 49 or readDocNum == setDocNum - 1:
print "\r%d %d\r" %( readDocNum + 1, totalLineNum ),
text = html.unescape( reuters.raw(doc_id) )
text = text.encode("utf-8")
lines = text.split("\n")
if len(text) == 0 or len(lines) == 0:
emptyFileNum += 1
continue
readDocNum += 1
totalLineNum += len(lines)
text = " ".join(lines)
wordsInSentences, wc = extractSentenceWords(text)
filename = doc_id
orig_docs_words.append( wordsInSentences )
orig_docs_name.append(filename)
orig_docs_cat.append(cat_id)
cats_docsWords[cat_id].append(wordsInSentences)
cats_docNames[cat_id].append(filename)
print "Done. %d docs read, %d empty docs skipped. Totally %d lines" %(readDocNum, emptyFileNum, totalLineNum)
return setDocNum, orig_docs_words, orig_docs_name, orig_docs_cat, \
cats_docsWords, cats_docNames, topN_cats
================================================
FILE: csv2topic.py
================================================
import numpy as np
import getopt
import sys
import pdb
import os
import csv
from topicvecDir import topicvecDir
from utils import *
customStopwords = "based via using approach learning multi algorithm algorithms"
config = dict( csv_filenames = None,
short_name = None,
unigramFilename = "top1grams-wiki.txt",
word_vec_file = "25000-180000-500-BLK-8.0.vec",
K = 20,
N0 = 500,
max_l = 5,
init_l = 1,
max_grad_norm = 0,
# cap the sum of Em when updating topic embeddings
# to avoid too big gradients
grad_scale_Em_base = 2500,
topW = 30,
topTopicMassFracPrintThres = 0.1,
alpha0 = 0.1,
alpha1 = 0.1,
iniDelta = 0.1,
MAX_EM_ITERS = 100,
topicDiff_tolerance = 2e-3,
printTopics_iterNum = 10,
zero_topic0 = True,
useDrdtApprox = False,
customStopwords = customStopwords,
remove_stop = True,
normalize_vecs = False,
# shift all embeddings in a document, so that their average is 0
rebase_vecs = True,
rebase_norm_thres = 0.2,
evalKmeans = False,
verbose = 1,
seed = 0
)
def usage():
print """topicvecDir.py [ -v vec_file -a alpha ... ] csv_file
Options:
-k: Number of topic embeddings to extract. Default: 20
-v: Existing embedding file of all words.
-r: Existing residual file of core words.
-a: Hyperparameter alpha. Default: 0.1.
-i: Number of iterations of the EM procedure. Default: 100
-u: Unigram file, to obtain unigram probs.
-l: Magnitude of topic embeddings.
-A: Append to the old log file.
-s: Seed the random number generator to x. Used to repeat experiments
-n: Nickname (short name) for the csv_file
"""
def getOptions():
global config
try:
opts, args = getopt.getopt(sys.argv[1:],"k:v:i:u:l:s:n:Ah")
if len(args) < 1:
raise getopt.GetoptError("")
config['csv_filenames'] = args
for opt, arg in opts:
if opt == '-k':
config['K'] = int(arg)
if opt == '-v':
config['vec_file'] = arg
if opt == '-a':
config['alpha1'] = float(opt)
if opt == '-i':
config['MAX_EM_ITERS'] = int(arg)
if opt == '-u':
config['unigramFilename'] = arg
if opt == '-l':
config['max_l'] = int(arg)
if opt == '-s':
config['seed'] = int(arg)
if opt == '-A':
config['appendLogfile'] = True
if opt == '-n':
config['short_name'] = arg
if opt == '-r':
config['useDrdtApprox'] = True
if opt == '-h':
usage()
sys.exit(0)
basename = os.path.basename(args[0])
if config['short_name']:
config['logfilename'] = config['short_name']
elif len(args) > 1:
config['logfilename'] = "(%d)%s" %( len(args), basename )
else:
config['logfilename'] = basename
except getopt.GetoptError:
usage()
sys.exit(2)
return config
def main():
config = getOptions()
docwords = []
csvfiles_filecount = 0
csvfiles_wc = 0
csvfiles_rowcount = 0
file_rownames = []
for csv_filename in config['csv_filenames']:
csvfile_wc = 0
csvfile_rowcount = 0
with open(csv_filename) as DOC:
docreader = csv.reader(DOC)
for row in docreader:
doc = row[0]
wordsInSentences, wc = extractSentenceWords(doc, min_length=2)
csvfile_wc += wc
csvfile_rowcount += 1
docwords.append(wordsInSentences)
file_rownames.append( "%s-row%d" %(csv_filename, csvfile_rowcount) )
csvfile_avgwc = csvfile_wc * 1.0 / csvfile_rowcount
print "%d words extracted from %d rows in '%s'. Avg %.1f words each row" %( csvfile_wc,
csvfile_rowcount, csv_filename, csvfile_avgwc )
csvfiles_wc += csvfile_wc
csvfiles_rowcount += csvfile_rowcount
csvfiles_filecount += 1
csvfiles_avgwc = csvfiles_wc * 1.0 / csvfiles_rowcount
if csvfiles_filecount > 1:
print "%d words extracted from %d rows in %d csv files. Avg %.1f words each row" %(csvfiles_wc,
csvfiles_rowcount, csvfiles_filecount, csvfiles_avgwc)
topicvec = topicvecDir(**config)
topicvec.setDocs( docwords, file_rownames )
if 'evalKmeans' in config and config['evalKmeans']:
topicvec.kmeans()
topicvec.printTopWordsInTopic(None, True)
exit(0)
best_last_Ts, Em, docs_Em, Pi = topicvec.inference()
basename = os.path.basename(config['logfilename'])
basetrunk = os.path.splitext(basename)[0]
best_it, best_T, best_loglike = best_last_Ts[0]
save_matrix_as_text( basetrunk + "-em%d-best.topic.vec" %best_it, "topic", best_T )
if best_last_Ts[1]:
last_it, last_T, last_loglike = best_last_Ts[1]
save_matrix_as_text( basetrunk + "-em%d-last.topic.vec" %last_it, "topic", last_T )
if __name__ == '__main__':
main()
================================================
FILE: file2topic.py
================================================
import numpy as np
import getopt
import sys
import pdb
import os
from topicvecDir import topicvecDir
from utils import *
customStopwords = "based via using approach learning multi algorithm algorithms"
config = dict( doc_filenames = None,
short_name = None,
unigramFilename = "top1grams-wiki.txt",
word_vec_file = "25000-180000-500-BLK-8.0.vec",
K = 20,
N0 = 500,
max_l = 5,
init_l = 1,
max_grad_norm = 0,
# cap the sum of Em when updating topic embeddings
# to avoid too big gradients
grad_scale_Em_base = 2500,
topW = 30,
topTopicMassFracPrintThres = 0.1,
alpha0 = 0.1,
alpha1 = 0.1,
iniDelta = 0.1,
MAX_EM_ITERS = 100,
topicDiff_tolerance = 2e-3,
printTopics_iterNum = 10,
zero_topic0 = True,
useDrdtApprox = False,
customStopwords = customStopwords,
remove_stop = True,
normalize_vecs = False,
# shift all embeddings in a document, so that their average is 0
rebase_vecs = True,
rebase_norm_thres = 0.2,
evalKmeans = False,
verbose = 1,
seed = 0
)
def usage():
print """topicvecDir.py [ -v vec_file -a alpha ... ] doc_file
Options:
-k: Number of topic embeddings to extract. Default: 20
-v: Existing embedding file of all words.
-r: Existing residual file of core words.
-a: Hyperparameter alpha. Default: 0.1.
-i: Number of iterations of the EM procedure. Default: 100
-u: Unigram file, to obtain unigram probs.
-l: Magnitude of topic embeddings.
-A: Append to the old log file.
-s: Seed the random number generator to x. Used to repeat experiments
-n: Nickname (short name) for the doc_file
"""
def getOptions():
global config
try:
opts, args = getopt.getopt(sys.argv[1:],"k:v:i:u:l:s:n:Ah")
if len(args) < 1:
raise getopt.GetoptError("")
config['doc_filenames'] = args
for opt, arg in opts:
if opt == '-k':
config['K'] = int(arg)
if opt == '-v':
config['vec_file'] = arg
if opt == '-a':
config['alpha1'] = float(opt)
if opt == '-i':
config['MAX_EM_ITERS'] = int(arg)
if opt == '-u':
config['unigramFilename'] = arg
if opt == '-l':
config['max_l'] = int(arg)
if opt == '-s':
config['seed'] = int(arg)
if opt == '-A':
config['appendLogfile'] = True
if opt == '-n':
config['short_name'] = arg
if opt == '-r':
config['useDrdtApprox'] = True
if opt == '-h':
usage()
sys.exit(0)
if config['short_name']:
config['logfilename'] = config['short_name']
elif len(args) > 1:
config['logfilename'] = "(%d)%s" %( len(args), args[0] )
else:
config['logfilename'] = args[0]
except getopt.GetoptError:
usage()
sys.exit(2)
return config
def main():
config = getOptions()
docwords = []
for doc_filename in config['doc_filenames']:
with open(doc_filename) as DOC:
doc = DOC.readlines()
doc = "".join(doc)
wordsInSentences, wc = extractSentenceWords(doc, 2)
print "%d words extracted from '%s'" %(wc, doc_filename)
docwords.append(wordsInSentences)
topicvec = topicvecDir(**config)
topicvec.setDocs( docwords, config['doc_filenames'] )
if 'evalKmeans' in config and config['evalKmeans']:
topicvec.kmeans()
topicvec.printTopWordsInTopic(None, True)
exit(0)
best_last_Ts, Em, docs_Em, Pi = topicvec.inference()
basename = os.path.basename(config['logfilename'])
basetrunk = os.path.splitext(basename)[0]
best_it, best_T, best_loglike = best_last_Ts[0]
save_matrix_as_text( basetrunk + "-em%d-best.topic.vec" %best_it, "topic", best_T )
if best_last_Ts[1]:
last_it, last_T, last_loglike = best_last_Ts[1]
save_matrix_as_text( basetrunk + "-em%d-last.topic.vec" %last_it, "topic", last_T )
if __name__ == '__main__':
main()
================================================
FILE: psdvec/README.md
================================================
# PSDVec
PSDVec is the source code for "A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution" (EMNLP 2015).
See "PSDVec.pdf" for a manual (```PSDVec: a Toolbox for Incremental and Scalable Word Embedding```, accepted by Neurocomputing, 2016).
#### Update v0.42: Tikhonov Regularization (=Spherical Gaussian Prior) to embeddings in block-wise factorization:
1. Obtain 25000 core embeddings using Weighted PSD Approximation, into _25000-500-EM.vec_:
* ```python factorize.py -w 25000 top2grams-wiki.txt```
2. Obtain 45000 noncore embeddings using Weighted Least Squares, totaling 80000 (25000 cores + 55000 noncores), into _25000-80000-500-BLK-2.0.vec_:
* ```python factorize.py -v 25000-500-EM.vec -o 55000 -t2 top2grams-wiki.txt```
3. Incrementally learn other 50000 noncore embeddings (based on 25000 cores), into _25000-130000-500-BLK-4.0.vec_:
* ```python factorize.py -v 25000-80000-500-BLK-2.0.vec -b 25000 -o 50000 -t4 top2grams-wiki.txt```
4. Repeat 3 again, with Tikhonov coeff = 8 to get more embeddings of rarer words, into _25000-180000-500-BLK-8.0.vec_:
* ```python factorize.py -v 25000-130000-500-BLK-4.0.vec -b 25000 -o 50000 -t8 top2grams-wiki.txt```
Pretrained 180,000 embeddings and evaluation results are uploaded. Now the performance is systematically better than other methods.
#### Update v0.41: Gradient Descent (GD) solution:
* ```python factorize.py -G 500 -w 120000 top2grams-wiki.txt```
* GD is fast and scalable, but the performance is much worse (~10% lower on the testsets). It's not recommended, unless initialized using unweighted Eigendecomposition (which is still not scalable).
#### Update v0.4: Online Block-wise Factorization
Testsets are by courtesy of Omer Levy (https://bitbucket.org/omerlevy/hyperwords/src).
The Gradient Descent algorithm was based on the suggestion of Peilin Zhao (not included as a part of the papers).
================================================
FILE: psdvec/addheader.py
================================================
#!/usr/bin/python
import os
import sys
import re
oldVecFilename = sys.argv[1]
newVecFilename = sys.argv[2]
stream = os.popen( "wc %s" %oldVecFilename )
output = stream.read()
output = output.strip()
linecount, wordcount, charcount, filename = re.split(" +", output)
linecount = int(linecount)
wordcount = int(wordcount)
if wordcount % linecount != 0:
print "Error: line count %d does not divide word count %d" %(linecount, wordcount)
sys.exit(1)
veclen = wordcount / linecount - 1
print "%d %d" %(linecount, veclen)
VEC = open(newVecFilename, "w")
VEC.write( "%d %d\n" %(linecount, veclen) )
VEC.close()
os.popen( "cat %s >> %s" %(oldVecFilename, newVecFilename) )
stream = os.popen( "ls -l %s" %oldVecFilename )
print stream.read().strip()
stream = os.popen( "ls -l %s" %newVecFilename )
print stream.read().strip()
================================================
FILE: psdvec/analogy.py
================================================
import sys
import re
from utils import *
def pred_ana( model, a, a2, b, maxcands = 10 ):
questWordIndices = [ model.word2id[x] for x in (a,a2,b) ]
# b2 is effectively iterating through the vocab. The row is all the cosine values
b2a2 = model.sim_row(a2)
b2a = model.sim_row(a)
b2b = model.sim_row(b)
mulsims = ( b2a2 + 1 ) * ( b2b + 1 ) / ( b2a + 1.001 )
mulsims[questWordIndices] = -10000
b2s = []
for i in xrange(maxcands):
imul = np.nanargmax(mulsims)
b2mul = model.vocab[imul]
b2s.append( [ b2mul, mulsims[imul] ] )
mulsims[imul] = -10000
return b2s
embedding_npyfile = "25000-180000-500-BLK-8.0.vec.npy"
embedding_arrays = np.load(embedding_npyfile)
V, vocab, word2ID, skippedWords_whatever = embedding_arrays
print "%d words loaded from '%s'" %(len(vocab), embedding_npyfile)
model = VecModel(V, vocab, word2ID, vecNormalize=True)
print "Model initialized. Ready for input:"
while True:
line = raw_input()
line = line.strip()
words = re.split("\s+", line)
if len(words) != 3:
print "Only 3 words are allowed"
continue
oov = 0
for w in words:
if w not in model:
print "'%s' not in vocab" %w
oov += 1
if oov > 0:
continue
a, a2, b = words
b2s = pred_ana( model, a, a2, b )
for word, sim in b2s:
print word, sim
print
================================================
FILE: psdvec/bench.sh
================================================
#!/bin/sh
export ROOT=/home/shaohua/D
#export CORPUS=$ROOT/corpus/cleanwiki.txt
#export DIM=500
#export MINCOUNT=100
#export SUFFIX=wiki
export CORPUS=$ROOT/corpus/rcv1clean.txt
export DIM=50
export MINCOUNT=50
export SUFFIX=rcv1
cd $ROOT/corpus/
echo PSD:
./fact-$SUFFIX.sh
cd $ROOT/word2vec
echo word2vec:
time ./word2vec -train $CORPUS -output $ROOT/corpus/word2vec-$SUFFIX.vec -size $DIM -window 5 -sample 1e-4 -negative 15 -min-count $MINCOUNT
cd $ROOT/corpus/glove/
echo glove:
time ./$SUFFIX.sh
cd $ROOT/corpus/singular/
echo singular:
time ./singular --corpus $CORPUS --output ./$SUFFIX --rare $MINCOUNT --window 3 --dim $DIM
echo PPM and SVD:
cd $ROOT/corpus/hyperwords
./train-$SUFFIX.sh
echo Sparse:
tail -n+2 $ROOT/corpus/word2vec-$SUFFIX.vec > $ROOT/corpus/word2vec-$SUFFIX-headless.vec
cd $ROOT/corpus/sparse/
time ./sparse ../word2vec-$SUFFIX-headless.vec 5 0.5 1e-5 4 sparse-$SUFFIX.vec
================================================
FILE: psdvec/benchspeed.py
================================================
import numpy as np
import time
class Timer(object):
def __init__(self, name=None):
self.name = name
self.tstart = time.time()
self.tlast = self.tstart
self.firstCall = True
def getElapseTime(self, isStr=True):
totalElapsed = time.time() - self.tstart
# elapsed time since last call
interElapsed = time.time() - self.tlast
self.tlast = time.time()
firstCall = self.firstCall
self.firstCall = False
if isStr:
if self.name:
if firstCall:
return '%s elapsed: %.2f' % ( self.name, totalElapsed )
return '%s elapsed: %.2f/%.2f' % ( self.name, totalElapsed, interElapsed )
else:
if firstCall:
return 'Elapsed: %.2f' % ( totalElapsed )
return 'Elapsed: %.2f/%.2f' % ( totalElapsed, interElapsed )
else:
return totalElapsed, interElapsed
def printElapseTime(self):
print self.getElapseTime()
def timeToStr(timeNum, fmt="%H:%M:%S"):
timeStr = time.strftime(fmt, time.localtime(timeNum))
return timeStr
def block_factorize( core_size, noncore_size, N0, tikhonovCoeff ):
# new WGsum: noncore_size * core_size
WGsum = np.random.random((noncore_size,core_size))
Wsum = np.random.random((noncore_size,core_size))
Wsum[ np.isclose(Wsum,0) ] = 0.001
Gwmean = WGsum
V1 = np.random.random((core_size,N0))
# embeddings of noncore words
# new V2: noncore_size * N0
V2 = np.zeros( ( noncore_size, N0 ), dtype=np.float32 )
Tikhonov = np.identity(N0) * tikhonovCoeff
timer = Timer()
print "Begin finding embeddings of non-core words"
# Find each noncore word's embedding
for i in xrange(noncore_size):
# core_size
wi = Wsum[i]
# new VW: N0 * core_size
VW = V1.T * wi
# new VWV: N0 * N0
VWV = VW.dot(V1)
if False:
VWV_Tik = VWV + Tikhonov
V2[i] = np.linalg.inv(VWV_Tik).dot( VW.dot(Gwmean[i]) )
if i >= 0 and i % 100 == 99:
print "\r%d / %d." %(i+1,noncore_size),
print timer.getElapseTime(), "\r",
print
block_factorize(15000, 1000, 500, 2)
#block_factorize(15000, 10000, 50, 2)
================================================
FILE: psdvec/catbench.py
================================================
import os
testsetNames = [ "ap", "battig", "esslli" ]
testsetCatNums = [ 13, 10, 6 ]
algNames = [ "PSDVec", "word2vec", "CCA" ]
CLmethods = [ "rbr", "direct", "graph" ]
vclusterPath = "D:\\cluto-2.1.2\\MSWIN-x86_64-openmp\\vcluster.exe"
testsetDir = "./concept categorization"
for CLmethod in CLmethods:
for i, testsetName in enumerate(testsetNames):
for algName in algNames:
vecFilename = testsetDir + "/" + testsetName + "-" + algName + ".vec"
labelFilename = testsetDir + "/" + testsetName + "-" + algName + ".label"
catNum = testsetCatNums[i]
print "%s on %s using %s:" %( algName, testsetName, CLmethod )
stream = os.popen( '%s -rclassfile="%s" -clmethod=%s "%s" %d' %( vclusterPath,
labelFilename, CLmethod, vecFilename, catNum ) )
output = stream.read()
lines = output.split("\n")
for line in lines:
if line.find("way clustering") >= 0:
print line
print
================================================
FILE: psdvec/cleancorpus.py
================================================
import sys
import os
import gensim.corpora.wikicorpus
# check and process input arguments
if len(sys.argv) < 3:
print "Usage: cleancorpus.py infile_name outfile_name"
sys.exit(1)
infilename, outfilename = sys.argv[1:3]
if os.path.isfile(outfilename):
print "Output file %s exists. Change the file name and try again." %outfilename
sys.exit(1)
linecount = 0
bytecount = 0
wordcount = 0
output = open(outfilename, 'w')
IN = open(infilename)
for line in IN:
tokens = gensim.corpora.wikicorpus.tokenize(line)
output.write( "%s\n" %(" ".join(tokens)) )
linecount += 1
bytecount += len(line)
wordcount += len(tokens)
if linecount % 500 == 0:
print "\r%d %d %d \r" %(linecount, bytecount/1024/1024, wordcount),
================================================
FILE: psdvec/competitors/glove/demo.sh
================================================
#!/bin/bash
# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.
# One optional argument can specify the language used for eval script: matlab, octave or [default] python
CORPUS=../rcv1clean.txt
VOCAB_FILE=vocab.txt
COOCCURRENCE_FILE=cooccurrence.bin
COOCCURRENCE_SHUF_FILE=cooccurrence.shuf.bin
BUILDDIR=build
SAVE_FILE=glove-rcv1.vec
VERBOSE=2
MEMORY=16.0
VOCAB_MIN_COUNT=50
VECTOR_SIZE=50
MAX_ITER=15
WINDOW_SIZE=3
BINARY=0
NUM_THREADS=8
X_MAX=10
$BUILDDIR/vocab_count -min-count $VOCAB_MIN_COUNT -verbose $VERBOSE < $CORPUS > $VOCAB_FILE
if [[ $? -eq 0 ]]
then
$BUILDDIR/cooccur -memory $MEMORY -vocab-file $VOCAB_FILE -verbose $VERBOSE -window-size $WINDOW_SIZE < $CORPUS > $COOCCURRENCE_FILE
if [[ $? -eq 0 ]]
then
$BUILDDIR/shuffle -memory $MEMORY -verbose $VERBOSE < $COOCCURRENCE_FILE > $COOCCURRENCE_SHUF_FILE
if [[ $? -eq 0 ]]
then
$BUILDDIR/glove -save-file $SAVE_FILE -threads $NUM_THREADS -input-file $COOCCURRENCE_SHUF_FILE -x-max $X_MAX -iter $MAX_ITER -vector-size $VECTOR_SIZE -binary $BINARY -vocab-file $VOCAB_FILE -verbose $VERBOSE
if [[ $? -eq 0 ]]
then
if [ "$1" = 'matlab' ]; then
matlab -nodisplay -nodesktop -nojvm -nosplash < ./eval/matlab/read_and_evaluate.m 1>&2
elif [ "$1" = 'octave' ]; then
octave < ./eval/octave/read_and_evaluate_octave.m 1>&2
else
python eval/python/evaluate.py
fi
fi
fi
fi
fi
================================================
FILE: psdvec/competitors/glove/rcv1.sh
================================================
#!/bin/bash
# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.
# One optional argument can specify the language used for eval script: matlab, octave or [default] python
CORPUS=../rcv1clean.txt
VOCAB_FILE=vocab-rcv1.txt
COOCCURRENCE_FILE=cooccurrence-rcv1.bin
COOCCURRENCE_SHUF_FILE=cooccurrence.shuf-rcv1.bin
BUILDDIR=build
SAVE_FILE=glove-rcv1.vec
VERBOSE=2
MEMORY=16.0
VOCAB_MIN_COUNT=50
VECTOR_SIZE=50
MAX_ITER=15
WINDOW_SIZE=3
BINARY=0
NUM_THREADS=8
X_MAX=10
$BUILDDIR/vocab_count -min-count $VOCAB_MIN_COUNT -verbose $VERBOSE < $CORPUS > $VOCAB_FILE
if [[ $? -eq 0 ]]
then
$BUILDDIR/cooccur -memory $MEMORY -vocab-file $VOCAB_FILE -verbose $VERBOSE -window-size $WINDOW_SIZE < $CORPUS > $COOCCURRENCE_FILE
if [[ $? -eq 0 ]]
then
$BUILDDIR/shuffle -memory $MEMORY -verbose $VERBOSE < $COOCCURRENCE_FILE > $COOCCURRENCE_SHUF_FILE
if [[ $? -eq 0 ]]
then
$BUILDDIR/glove -save-file $SAVE_FILE -threads $NUM_THREADS -input-file $COOCCURRENCE_SHUF_FILE -x-max $X_MAX -iter $MAX_ITER -vector-size $VECTOR_SIZE -binary $BINARY -vocab-file $VOCAB_FILE -verbose $VERBOSE
if [[ $? -eq 0 ]]
then
if [ "$1" = 'matlab' ]; then
matlab -nodisplay -nodesktop -nojvm -nosplash < ./eval/matlab/read_and_evaluate.m 1>&2
elif [ "$1" = 'octave' ]; then
octave < ./eval/octave/read_and_evaluate_octave.m 1>&2
else
python eval/python/evaluate.py
fi
fi
fi
fi
fi
================================================
FILE: psdvec/competitors/glove/vocab-rcv1.txt
================================================
the 10027055
of 4587086
to 4566012
in 3906588
and 3414395
said 2511393
on 2155635
for 1664451
at 1281038
was 1160891
that 1143953
is 1086192
it 1066127
by 982213
with 956024
from 934724
percent 857975
be 806244
as 762359
million 726400
he 710222
year 673622
its 668590
will 653452
has 598752
but 596463
were 578481
an 569515
would 560721
not 553982
are 549324
have 537104
which 517309
had 503678
market 456507
this 452646
up 439784
new 435347
we 408281
after 401950
bank 375841
one 370881
they 365045
company 357352
last 332567
government 331941
been 328076
also 307456
two 306608
billion 306571
his 300674
their 297054
first 292165
or 291005
shares 281766
more 275569
over 274325
about 273657
newsroom 260182
there 252838
week 250915
share 246184
who 245482
than 241103
some 241006
may 235280
per 228328
no 226528
tuesday 222208
three 221393
pct 219909
all 217783
expected 215475
wednesday 215426
net 214706
thursday 214393
other 212406
group 208866
monday 208049
friday 207227
prices 203402
state 200172
could 198120
sales 196189
told 192334
out 191802
trade 191650
if 188392
price 187255
down 186904
against 185665
day 182673
into 180960
month 177786
president 176637
minister 174169
stock 170782
when 170491
rate 166959
vs 163452
interest 160771
time 160459
oil 157649
months 156241
foreign 153462
years 153373
income 153233
next 153170
june 150248
between 150137
points 149863
before 147982
end 147897
yen 147802
people 147643
high 145524
european 145339
while 145013
index 144768
inc 144077
quarter 141119
under 140046
total 139395
march 138258
international 137839
only 135913
since 135001
dollar 133575
tax 131527
most 131064
rates 130714
central 130426
corp 130013
reuters 129633
due 129092
off 128698
added 128229
now 127957
july 127750
business 126807
world 126123
profit 125646
can 125265
five 125155
trading 124644
exchange 124567
ended 124283
economic 124123
higher 123456
second 123261
rose 122572
tonnes 121981
traders 121760
official 121390
any 121295
growth 120682
co 120415
statement 120158
should 118808
national 118607
news 118473
ltd 118310
cents 118238
loss 118100
because 118068
financial 117506
united 116327
general 114938
companies 114250
analysts 114165
still 112006
london 111928
south 111620
four 111392
close 111126
lower 111022
around 110953
our 109070
states 108820
party 108542
six 107935
so 106891
made 106522
officials 106174
capital 106138
based 105401
current 104282
long 104135
earlier 103697
through 102573
markets 102479
country 102449
york 102146
average 102143
early 101571
investment 101018
report 100767
industry 100347
china 100257
union 100199
issue 100063
set 99510
earnings 99476
during 98041
april 97514
meeting 97065
very 96940
december 96323
us 95675
back 95526
following 93922
september 93869
term 93313
closed 93161
investors 93035
rise 92777
major 92179
securities 92119
take 91625
them 90315
public 89989
fell 89916
half 89869
ago 89746
data 89592
bonds 89466
stocks 89127
bond 88866
what 88829
well 88477
finance 87826
deal 87591
these 87124
further 87046
demand 86800
chief 86600
local 86112
power 85992
results 85792
being 85124
low 84592
just 84571
note 84345
strong 84271
january 84108
city 83474
reported 83288
prime 83256
dealers 82844
both 82558
did 82526
banks 81738
another 81347
increase 81235
firm 81207
days 81129
such 80718
operating 80709
budget 80625
british 80147
late 79964
money 79642
make 79182
commission 78664
many 78458
currency 77793
where 77710
she 77684
including 77016
talks 76786
futures 76741
do 76630
police 76588
part 76389
production 76368
period 76073
german 75967
say 75731
former 75476
europe 75369
spokesman 75250
plans 74983
level 74468
support 74305
much 74161
countries 73363
good 73208
recent 72613
see 72330
japan 72115
agreement 72014
kong 71979
hong 71934
compared 71879
political 71830
same 71753
board 71474
october 71334
number 71243
third 71235
full 70361
gmt 70236
august 70134
services 70098
sector 69988
gold 69193
cut 69177
previous 69046
ministry 69043
likely 68969
change 68900
economy 68733
system 68574
uk 68565
french 68125
gas 68075
november 67942
pay 67939
forecast 67869
products 67768
court 67543
you 67462
inflation 67326
debt 67042
think 66890
chairman 66358
cash 66300
policy 66156
france 66003
basis 65746
sale 65545
seen 65503
north 65482
fund 65371
held 65231
short 65231
operations 65077
plan 64977
announced 64691
saying 64384
buy 64366
funds 64291
unit 64257
volume 63938
development 63932
going 63844
domestic 63585
eu 63464
offer 63313
re 63299
conference 63194
date 63102
those 62987
germany 62962
federal 62929
way 62629
open 62561
service 62534
until 62498
trader 62428
record 62295
sunday 62186
ahead 61914
bid 61873
go 61664
house 61453
february 61177
called 60989
start 60871
top 60648
decision 60629
move 60262
even 60149
already 60021
today 59959
figures 59824
bln 59657
american 59577
then 59530
later 59415
india 59213
won 58987
seven 58958
like 58950
cost 58882
industrial 58479
contract 58090
leading 57928
common 57852
east 57829
mark 57679
put 57651
exports 57575
key 57458
latest 57299
security 56935
wheat 56843
above 56839
parliament 56696
executive 56454
election 56341
continue 55867
get 55820
dec 55759
buying 55642
de 55629
analyst 55569
costs 55485
sell 55376
bill 55352
him 55323
home 54948
britain 54682
traded 54671
sources 54412
however 54382
saturday 54147
leader 54034
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swedes 622
authors
gitextract__1max8y4/ ├── .gitignore ├── 20news.bat ├── README.md ├── anatest.py ├── classEval.py ├── corpusLoader.py ├── csv2topic.py ├── file2topic.py ├── psdvec/ │ ├── README.md │ ├── addheader.py │ ├── analogy.py │ ├── bench.sh │ ├── benchspeed.py │ ├── catbench.py │ ├── cleancorpus.py │ ├── competitors/ │ │ ├── glove/ │ │ │ ├── demo.sh │ │ │ ├── rcv1.sh │ │ │ ├── vocab-rcv1.txt │ │ │ ├── vocab-wiki.txt │ │ │ └── wiki.sh │ │ └── hyperwords/ │ │ ├── pmi2.sh │ │ ├── pmi5-rcv1.sh │ │ ├── pmi5.sh │ │ ├── svd-rcv1.sh │ │ ├── svd.sh │ │ ├── train-rcv1.sh │ │ └── train-wiki.sh │ ├── corpus2liblinear.py │ ├── eval-logs/ │ │ └── bench.log │ ├── evaluate-toefl.py │ ├── evaluate.py │ ├── extractwiki.py │ ├── fact-rcv1.bat │ ├── fact-rcv1.sh │ ├── fact-wiki.bat │ ├── fact-wiki.sh │ ├── factorize.py │ ├── genSentDict.bat │ ├── gencatdata.py │ ├── gram-rcv1.bat │ ├── gram.bat │ ├── gramcount.pl │ ├── patch to gensim.py │ ├── perlxs.h │ ├── removeDoubleNewline.pl │ ├── sent-bench.bat │ ├── sent-gen.conf │ ├── sentbench.py │ ├── tab2list.py │ ├── testsets/ │ │ ├── analogy/ │ │ │ ├── EN-TOM-ICLR13-SEM.txt │ │ │ ├── EN-TOM-ICLR13-SYN.txt │ │ │ ├── google.txt │ │ │ └── msr.txt │ │ └── ws/ │ │ ├── EN-RG-65.txt │ │ ├── EN-TOEFL-80.txt │ │ ├── bruni_men.txt │ │ ├── luong_rare.txt │ │ ├── radinsky_mturk.txt │ │ ├── simlex_999a.txt │ │ ├── ws353.txt │ │ ├── ws353_relatedness.txt │ │ └── ws353_similarity.txt │ ├── topwordsInList.py │ ├── utils.py │ ├── vecnorms.py │ └── xml2corpus.pl ├── reuters.bat ├── snippet2topic.py ├── test-docs/ │ ├── Drug Goes From 13.50 a Tablet to 750, Overnight.txt │ ├── VR-mitrv.txt │ ├── batman-v-superman.txt │ ├── batman-v-superman.txt-em100.topic.vec │ ├── beijing-haze-news.txt │ ├── brain-scar.txt │ ├── britain-EU.txt │ ├── drugstory.log │ ├── hillary-speech.txt │ ├── hillary-speech2.txt │ ├── nips-wiki.txt │ ├── sanders-speeches.txt │ ├── spacex-news.txt │ └── trump-speech.txt ├── topic-competitors/ │ ├── LDA/ │ │ ├── Readme.txt │ │ ├── classEval.py │ │ ├── corpusLoader.py │ │ └── ldaExp.py │ ├── doc2vec.py │ ├── kmeans.py │ ├── labelEval.py │ ├── lftm2svm.py │ ├── liu-doc2vec.py │ └── slda/ │ ├── 20news-test-7532.slda-bow.txt │ ├── 20news-test-7532.slda-label.txt │ ├── 20news-train-11314.slda-bow.txt │ ├── 20news-train-11314.slda-label.txt │ ├── Makefile │ ├── corpus.cpp │ ├── corpus.h │ ├── images.tgz │ ├── main.cpp │ ├── opt.cpp │ ├── opt.h │ ├── readme.txt │ ├── reuters-test-2255.slda-bow.txt │ ├── reuters-test-2255.slda-label.txt │ ├── reuters-train-5770.slda-bow.txt │ ├── reuters-train-5770.slda-label.txt │ ├── settings.h │ ├── settings.txt │ ├── slda │ ├── slda.cpp │ ├── slda.h │ ├── utils.cpp │ └── utils.h ├── topic-cosine.py ├── topicExp.py ├── topicvecDir.py └── utils.py
SYMBOL INDEX (207 symbols across 32 files)
FILE: classEval.py
function getScores (line 5) | def getScores( true_classes, pred_classes, average):
FILE: corpusLoader.py
function extractSentenceWords (line 10) | def extractSentenceWords(doc, remove_url=True, remove_punc="utf-8", min_...
function load_20news (line 51) | def load_20news(setName):
function load_reuters (line 98) | def load_reuters(setName):
FILE: csv2topic.py
function usage (line 45) | def usage():
function getOptions (line 60) | def getOptions():
function main (line 108) | def main():
FILE: file2topic.py
function usage (line 44) | def usage():
function getOptions (line 59) | def getOptions():
function main (line 106) | def main():
FILE: psdvec/analogy.py
function pred_ana (line 5) | def pred_ana( model, a, a2, b, maxcands = 10 ):
FILE: psdvec/benchspeed.py
class Timer (line 4) | class Timer(object):
method __init__ (line 5) | def __init__(self, name=None):
method getElapseTime (line 11) | def getElapseTime(self, isStr=True):
method printElapseTime (line 32) | def printElapseTime(self):
function timeToStr (line 35) | def timeToStr(timeNum, fmt="%H:%M:%S"):
function block_factorize (line 39) | def block_factorize( core_size, noncore_size, N0, tikhonovCoeff ):
FILE: psdvec/corpus2liblinear.py
function usage (line 11) | def usage():
function parseConfigFile (line 25) | def parseConfigFile(configFilename):
function getFileFeatures (line 34) | def getFileFeatures(filename, V, word2id, sentword2id, remove_stop=False):
function processDir (line 86) | def processDir( outFilename, docDir, label, appendToOutput, V, word2ID, ...
function main (line 123) | def main():
FILE: psdvec/evaluate-toefl.py
function loadToeflTestset (line 12) | def loadToeflTestset(toeflTestsetFilename):
function usage (line 31) | def usage():
FILE: psdvec/evaluate.py
function usage (line 45) | def usage():
FILE: psdvec/factorize.py
function uniwe_factorize (line 16) | def uniwe_factorize(G, u, N0, MAXITERS=0, tikhonovCoeff=0, testenv=None):
function nowe_factorize (line 86) | def nowe_factorize(G, N, tikhonovCoeff=0):
function we_factorize_GD (line 144) | def we_factorize_GD(G, Weight, N0, MAXITERS=5000, tikhonovCoeff=0, teste...
function we_factorize_EM (line 239) | def we_factorize_EM(G, Weight, N0, MAXITERS=5, tikhonovCoeff=0, testenv=...
function we_factorize_FW (line 303) | def we_factorize_FW(G, Weight, N0, MAXITERS=6, tikhonovCoeff=0, testenv=...
function normalizeWeight (line 490) | def normalizeWeight( RawCounts, do_weight_cutoff, cutQuantile=0.0002, ze...
function block_factorize (line 529) | def block_factorize( G, F, V1, N0, tikhonovCoeff, do_weight_cutoff ):
function factorize (line 591) | def factorize( alg, algName, G, Weight, N0, MAX_ITERS, tikhonovCoeff, vo...
function usage (line 606) | def usage():
function main (line 629) | def main():
FILE: psdvec/patch to gensim.py
function get_texts (line 5) | def get_texts(self):
function tokenize (line 15) | def tokenize(content):
function get_texts (line 19) | def get_texts(self):
function tokenize (line 31) | def tokenize(text, lowercase=False, deacc=False, errors="strict", to_low...
FILE: psdvec/perlxs.h
function XS_pack_charPtrPtr (line 58) | void XS_pack_charPtrPtr( SV * st, char ** s )
function XS_release_charPtrPtr (line 76) | void XS_release_charPtrPtr(char** s)
FILE: psdvec/topwordsInList.py
function usage (line 10) | def usage():
function parseConfigFile (line 21) | def parseConfigFile(configFilename):
function getListWordCount (line 30) | def getListWordCount( docPath, word2freq ):
function processDir (line 45) | def processDir( docDir, word2freq ):
function main (line 65) | def main():
FILE: psdvec/utils.py
function str2dict (line 26) | def str2dict(s):
function initConsoleLogger (line 53) | def initConsoleLogger(loggerName):
function initFileLogger (line 59) | def initFileLogger(loggerName, isAppending=False):
function warning (line 78) | def warning(*objs):
class Timer (line 81) | class Timer(object):
method __init__ (line 82) | def __init__(self, name=None):
method getElapseTime (line 88) | def getElapseTime(self, isStr=True):
method printElapseTime (line 109) | def printElapseTime(self):
function timeToStr (line 112) | def timeToStr(timeNum, fmt="%H:%M:%S"):
function norm1 (line 117) | def norm1(M, Weight=None):
function normF (line 140) | def normF(M, Weight=None):
function normalize (line 167) | def normalize(data, axis=1):
function normalizeF (line 182) | def normalizeF(data, axis=1):
function cosine (line 203) | def cosine(x, y):
function matSizes (line 209) | def matSizes( norm, Ms, Weight=None ):
function sym (line 216) | def sym(M):
function skew (line 219) | def skew(M):
function getQuantileCut (line 227) | def getQuantileCut(A, fraction):
function power_iter (line 247) | def power_iter(M):
function lowrank_fact (line 277) | def lowrank_fact(VV, N0):
function save_embeddings (line 289) | def save_embeddings( filename, vocab, V, matrixName ):
function save_matrix_as_text (line 307) | def save_matrix_as_text( filename, rowTypeName, T, *extraCols, **kwargs ):
function load_matrix_from_text (line 333) | def load_matrix_from_text( filename, rowTypeName, colSep=" " ):
function load_embeddings (line 401) | def load_embeddings( filename, maxWordCount=-1, extraWords={}, record_sk...
function load_embeddings_bin (line 498) | def load_embeddings_bin( filename, maxWordCount=-1, extraWords={}, recor...
function load_embeddings_hyper (line 587) | def load_embeddings_hyper(modelPath, vecType):
function load_residuals (line 609) | def load_residuals( filename, word2id={}, maxRowCount=-1, maxColCount=-1 ):
function loadBigramFile (line 679) | def loadBigramFile( bigram_filename, topWordNum, extraWords, kappa=0.01 ):
function loadBigramFileInBlock (line 894) | def loadBigramFileInBlock( bigram_filename, core_size, noncore_size=-1, ...
function loadUnigramFile (line 1368) | def loadUnigramFile(filename):
function loadExtraWordFile (line 1384) | def loadExtraWordFile(filename):
function loadSimTestset (line 1396) | def loadSimTestset(path, extraArgs=None):
function loadAnaTestset (line 1405) | def loadAnaTestset(path, extraArgs=None):
function loadTestsets (line 1430) | def loadTestsets(loader, testsetDir, testsetNames, extraArgs=None):
function predict_ana (line 1458) | def predict_ana( model, a, a2, b, realb2 ):
function evaluate_sim (line 1522) | def evaluate_sim(model, testsets, testsetNames, getAbsentWords=False, vo...
function evaluate_ana (line 1572) | def evaluate_ana(model, testsets, testsetNames, getAbsentWords=False, vo...
function bench (line 1648) | def bench(func, N, topEigenNum=0):
function isMemEnoughGramian (line 1665) | def isMemEnoughGramian(D, extraVarsRatio=0):
function isMemEnoughEigen (line 1692) | def isMemEnoughEigen(D, extraVarsRatio=5):
function extractSentenceWords (line 1711) | def extractSentenceWords(doc, remove_url=True, remove_punc="utf-8", min_...
function randomsample (line 1752) | def randomsample( X, n ):
function relu (line 1759) | def relu(v, bias):
function maxpool (line 1764) | def maxpool(vs):
function avgpool (line 1771) | def avgpool(vs):
class VecModel (line 1777) | class VecModel:
method __init__ (line 1778) | def __init__(self, V, vocab, word2id, vecNormalize=True, precompute_gr...
method __contains__ (line 1799) | def __contains__(self, w):
method __getitem__ (line 1802) | def __getitem__(self, w):
method orig (line 1811) | def orig(self, w):
method precomputeGramian (line 1817) | def precomputeGramian(self):
method similarity (line 1822) | def similarity(self, x, y):
method sim_row (line 1842) | def sim_row(self, x):
method most_similar (line 1859) | def most_similar(self, vx, top_num=1):
FILE: psdvec/vecnorms.py
function usage (line 12) | def usage():
function expectation (line 15) | def expectation(value_probs):
function var_div (line 24) | def var_div(value_probs):
FILE: snippet2topic.py
function usage (line 44) | def usage():
function getOptions (line 59) | def getOptions():
function main (line 107) | def main():
FILE: topic-competitors/LDA/classEval.py
function getScores (line 6) | def getScores( true_classes, pred_classes, average):
FILE: topic-competitors/LDA/corpusLoader.py
function extractSentenceWords (line 17) | def extractSentenceWords(doc, remove_url=True, remove_punc="utf-8", min_...
function load_20news (line 62) | def load_20news(setName):
function load_reuters (line 109) | def load_reuters(setName):
FILE: topic-competitors/LDA/ldaExp.py
function usage (line 14) | def usage():
FILE: topic-competitors/kmeans.py
function kmeans (line 18) | def kmeans( X, centres, delta=.001, maxiter=10, metric="euclidean", p=2,...
function randomsample (line 72) | def randomsample( X, n ):
FILE: topic-competitors/labelEval.py
function getScores (line 4) | def getScores( true_classes, pred_classes, average):
FILE: topic-competitors/liu-doc2vec.py
function genDocEmbedding (line 4) | def genDocEmbedding( setName, words_file, topics_file, label_file, V, wo...
FILE: topic-competitors/slda/corpus.h
function class (line 29) | class document
function class (line 67) | class corpus
FILE: topic-competitors/slda/main.cpp
function help (line 28) | void help( void ) {
function main (line 33) | int main(int argc, char* argv[])
FILE: topic-competitors/slda/opt.cpp
function softmax_f (line 30) | double softmax_f(const gsl_vector * x, void * opt_param)
function softmax_df (line 85) | void softmax_df(const gsl_vector * x, void * opt_param, gsl_vector * df)
function softmax_fdf (line 159) | void softmax_fdf(const gsl_vector * x, void * opt_param, double * f, gsl...
FILE: topic-competitors/slda/opt.h
type opt_parameter (line 31) | struct opt_parameter
FILE: topic-competitors/slda/slda.cpp
function suffstats (line 214) | suffstats * slda::new_suffstats(int num_docs)
FILE: topic-competitors/slda/slda.h
type z_stat (line 27) | typedef struct {
type suffstats (line 32) | typedef struct {
function class (line 41) | class slda
FILE: topic-competitors/slda/utils.cpp
function log_sum (line 8) | double log_sum(double log_a, double log_b)
function trigamma (line 29) | double trigamma(double x)
function digamma (line 51) | double digamma(double x)
function make_directory (line 114) | void make_directory(char* name)
function argmax (line 125) | int argmax(double* x, int n)
function map_idx (line 148) | int map_idx(int row, int col, int dim)
FILE: topicExp.py
function usage (line 49) | def usage():
FILE: topicvecDir.py
class topicvecDir (line 23) | class topicvecDir:
method __init__ (line 24) | def __init__(self, **kwargs):
method setK (line 167) | def setK(self, K):
method precompute (line 177) | def precompute(self):
method calcEm (line 192) | def calcEm(self, docs_Pi):
method calcLoglikelihood (line 199) | def calcLoglikelihood(self):
method updateTheta (line 220) | def updateTheta(self):
method updatePi (line 224) | def updatePi(self, docs_theta):
method calcTopicResiduals (line 256) | def calcTopicResiduals(self, T):
method updateTopicEmbeddings (line 269) | def updateTopicEmbeddings(self):
method calcSum_pi_v (line 326) | def calcSum_pi_v(self):
method genOutputter (line 349) | def genOutputter(self, screenVerboseThres=1):
method genProgressor (line 356) | def genProgressor(self):
method printTopWordsInTopics (line 366) | def printTopWordsInTopics( self, docs_theta, outputToScreen=False ):
method docSentences2wids (line 486) | def docSentences2wids( self, docs_wordsInSentences ):
method setDocs (line 537) | def setDocs( self, docs_wordsInSentences, docs_name ):
method kmeans (line 579) | def kmeans( self, maxiter=10 ):
method inferTopicProps (line 647) | def inferTopicProps( self, T, MAX_ITERS=5 ):
method inference (line 683) | def inference(self):
FILE: utils.py
function str2dict (line 26) | def str2dict(s):
function initConsoleLogger (line 53) | def initConsoleLogger(loggerName):
function initFileLogger (line 59) | def initFileLogger(loggerName, isAppending=False):
function warning (line 78) | def warning(*objs):
class Timer (line 81) | class Timer(object):
method __init__ (line 82) | def __init__(self, name=None):
method getElapseTime (line 88) | def getElapseTime(self, isStr=True):
method printElapseTime (line 109) | def printElapseTime(self):
function timeToStr (line 112) | def timeToStr(timeNum, fmt="%H:%M:%S"):
function norm1 (line 117) | def norm1(M, Weight=None):
function normF (line 140) | def normF(M, Weight=None):
function normalize (line 167) | def normalize(data, axis=1):
function normalizeF (line 182) | def normalizeF(data, axis=1):
function cosine (line 203) | def cosine(x, y):
function matSizes (line 209) | def matSizes( norm, Ms, Weight=None ):
function sym (line 216) | def sym(M):
function skew (line 219) | def skew(M):
function getQuantileCut (line 227) | def getQuantileCut(A, fraction):
function power_iter (line 247) | def power_iter(M):
function lowrank_fact (line 277) | def lowrank_fact(VV, N0):
function save_embeddings (line 289) | def save_embeddings( filename, vocab, V, matrixName ):
function save_matrix_as_text (line 307) | def save_matrix_as_text( filename, rowTypeName, T, *extraCols, **kwargs ):
function load_matrix_from_text (line 333) | def load_matrix_from_text( filename, rowTypeName, colSep=" " ):
function load_embeddings (line 401) | def load_embeddings( filename, maxWordCount=-1, extraWords={}, record_sk...
function load_embeddings_bin (line 498) | def load_embeddings_bin( filename, maxWordCount=-1, extraWords={}, recor...
function load_embeddings_hyper (line 587) | def load_embeddings_hyper(modelPath, vecType):
function load_residuals (line 609) | def load_residuals( filename, word2id={}, maxRowCount=-1, maxColCount=-1 ):
function loadBigramFile (line 679) | def loadBigramFile( bigram_filename, topWordNum, extraWords, kappa=0.01 ):
function loadBigramFileInBlock (line 894) | def loadBigramFileInBlock( bigram_filename, core_size, noncore_size=-1, ...
function loadUnigramFile (line 1368) | def loadUnigramFile(filename):
function loadExtraWordFile (line 1384) | def loadExtraWordFile(filename):
function loadSimTestset (line 1396) | def loadSimTestset(path, extraArgs=None):
function loadAnaTestset (line 1405) | def loadAnaTestset(path, extraArgs=None):
function loadTestsets (line 1430) | def loadTestsets(loader, testsetDir, testsetNames, extraArgs=None):
function predict_ana (line 1458) | def predict_ana( model, a, a2, b, realb2 ):
function evaluate_sim (line 1522) | def evaluate_sim(model, testsets, testsetNames, getAbsentWords=False, vo...
function evaluate_ana (line 1572) | def evaluate_ana(model, testsets, testsetNames, getAbsentWords=False, vo...
function bench (line 1648) | def bench(func, N, topEigenNum=0):
function isMemEnoughGramian (line 1665) | def isMemEnoughGramian(D, extraVarsRatio=0):
function isMemEnoughEigen (line 1692) | def isMemEnoughEigen(D, extraVarsRatio=5):
function extractSentenceWords (line 1711) | def extractSentenceWords(doc, remove_url=True, remove_punc="utf-8", min_...
function randomsample (line 1752) | def randomsample( X, n ):
function relu (line 1759) | def relu(v, bias):
function maxpool (line 1764) | def maxpool(vs):
function avgpool (line 1771) | def avgpool(vs):
class VecModel (line 1777) | class VecModel:
method __init__ (line 1778) | def __init__(self, V, vocab, word2id, vecNormalize=True, precompute_gr...
method __contains__ (line 1799) | def __contains__(self, w):
method __getitem__ (line 1802) | def __getitem__(self, w):
method orig (line 1811) | def orig(self, w):
method precomputeGramian (line 1817) | def precomputeGramian(self):
method similarity (line 1822) | def similarity(self, x, y):
method sim_row (line 1842) | def sim_row(self, x):
method most_similar (line 1859) | def most_similar(self, vx, top_num=1):
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Download .json
Condensed preview — 118 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (19,524K chars).
[
{
"path": ".gitignore",
"chars": 19,
"preview": "commit.bat\r\n*.pyc\r\n"
},
{
"path": "20news.bat",
"chars": 202,
"preview": "python topicExp.py -s 20news train\r\npython topicExp.py -i 20news-train-11314-sep281-em150-best.topic.vec 20news train,te"
},
{
"path": "README.md",
"chars": 1228,
"preview": "# TopicVec\nTopicVec is the source code for \"Generative Topic Embedding: a Continuous Representation of Documents\" (ACL 2"
},
{
"path": "anatest.py",
"chars": 386,
"preview": "import numpy as np\r\nfrom utils import *\r\n\r\nembedding_arrays = np.load(\"25000-180000-500-BLK-8.0.vec.npy\")\r\nV, vocab, wor"
},
{
"path": "classEval.py",
"chars": 3893,
"preview": "from sklearn import svm, metrics\r\nfrom sklearn.datasets import load_svmlight_file\r\nimport sys\r\n\r\ndef getScores( true_cla"
},
{
"path": "corpusLoader.py",
"chars": 7327,
"preview": "# -*- coding=GBK -*-\r\n\r\nfrom sklearn.datasets import fetch_20newsgroups\r\nfrom nltk.corpus import reuters\r\nimport HTMLPar"
},
{
"path": "csv2topic.py",
"chars": 5731,
"preview": "import numpy as np\r\nimport getopt\r\nimport sys\r\nimport pdb\r\nimport os\r\nimport csv\r\nfrom topicvecDir import topicvecDir\r\nf"
},
{
"path": "file2topic.py",
"chars": 4730,
"preview": "import numpy as np\r\nimport getopt\r\nimport sys\r\nimport pdb\r\nimport os\r\nfrom topicvecDir import topicvecDir\r\nfrom utils im"
},
{
"path": "psdvec/README.md",
"chars": 1944,
"preview": "# PSDVec\r\nPSDVec is the source code for \"A Generative Word Embedding Model and its Low Rank Positive Semidefinite Soluti"
},
{
"path": "psdvec/addheader.py",
"chars": 830,
"preview": "#!/usr/bin/python\nimport os\nimport sys\nimport re\n\noldVecFilename = sys.argv[1]\nnewVecFilename = sys.argv[2]\n\nstream = os"
},
{
"path": "psdvec/analogy.py",
"chars": 1496,
"preview": "import sys\r\nimport re\r\nfrom utils import *\r\n\r\ndef pred_ana( model, a, a2, b, maxcands = 10 ):\r\n questWordIndices = [ "
},
{
"path": "psdvec/bench.sh",
"chars": 905,
"preview": "#!/bin/sh\nexport ROOT=/home/shaohua/D\n#export CORPUS=$ROOT/corpus/cleanwiki.txt\n#export DIM=500\n#export MINCOUNT=100\n#ex"
},
{
"path": "psdvec/benchspeed.py",
"chars": 2378,
"preview": "import numpy as np\r\nimport time\r\n\r\nclass Timer(object):\r\n def __init__(self, name=None):\r\n self.name = name\r\n "
},
{
"path": "psdvec/catbench.py",
"chars": 1071,
"preview": "import os\r\n\r\ntestsetNames = [ \"ap\", \"battig\", \"esslli\" ]\r\ntestsetCatNums = [ 13, 10, 6 ]\r\nalgNames = [ \"PSDVec\", \"word2v"
},
{
"path": "psdvec/cleancorpus.py",
"chars": 816,
"preview": "import sys\r\nimport os\r\nimport gensim.corpora.wikicorpus\r\n\r\n# check and process input arguments\r\nif len(sys.argv) < 3:\r\n "
},
{
"path": "psdvec/competitors/glove/demo.sh",
"chars": 1510,
"preview": "#!/bin/bash\n\n# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.\n# One optional argume"
},
{
"path": "psdvec/competitors/glove/rcv1.sh",
"chars": 1525,
"preview": "#!/bin/bash\n\n# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.\n# One optional argume"
},
{
"path": "psdvec/competitors/glove/vocab-rcv1.txt",
"chars": 584728,
"preview": "the 10027055\nof 4587086\nto 4566012\nin 3906588\nand 3414395\nsaid 2511393\non 2155635\nfor 1664451\nat 1281038\nwas 1160891\ntha"
},
{
"path": "psdvec/competitors/glove/vocab-wiki.txt",
"chars": 3634391,
"preview": "the 138261694\nof 70257704\nand 57915186\nin 55094539\nto 39015668\nwas 21523514\nis 18525686\nfor 16915183\non 16065501\nas 1592"
},
{
"path": "psdvec/competitors/glove/wiki.sh",
"chars": 1166,
"preview": "#!/bin/bash\n\n# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.\n# One optional argume"
},
{
"path": "psdvec/competitors/hyperwords/pmi2.sh",
"chars": 409,
"preview": "#!/bin/bash\n# A) Window size 2 with \" subsampling\nCORPUS=/home/shaohua/D/corpus/cleanwiki.txt\n\nmkdir w2.sub\npython hyper"
},
{
"path": "psdvec/competitors/hyperwords/pmi5-rcv1.sh",
"chars": 460,
"preview": "#!/bin/bash\n\n# B) Window size 5 with dynamic contexts and \"dirty\" subsampling\n\nCORPUS=/home/shaohua/D/corpus/rcv1clean.t"
},
{
"path": "psdvec/competitors/hyperwords/pmi5.sh",
"chars": 507,
"preview": "#!/bin/bash\n\n# B) Window size 5 with dynamic contexts and \"dirty\" subsampling\n\nCORPUS=/home/shaohua/D/corpus/cleanwiki.t"
},
{
"path": "psdvec/competitors/hyperwords/svd-rcv1.sh",
"chars": 226,
"preview": "#!/bin/bash\n\n# Create embeddings with SVD\nDIR=w5.rcv1.dyn.sub.del\npython hyperwords/pmi2svd.py --dim 50 --neg 5 $DIR/pmi"
},
{
"path": "psdvec/competitors/hyperwords/svd.sh",
"chars": 482,
"preview": "#!/bin/bash\n\n# Create embeddings with SVD\n\nCORPUS=/home/shaohua/D/corpus/cleanwiki.txt\n\npython hyperwords/pmi2svd.py --d"
},
{
"path": "psdvec/competitors/hyperwords/train-rcv1.sh",
"chars": 106,
"preview": "#!/bin/sh\nrm -rf w5.rcv1.dyn.sub.del\nrm -rf w5.rcv1.dyn.sub.del\ntime ./pmi5-rcv1.sh\ntime ./svd-rcv1.sh\n\n\n\n"
},
{
"path": "psdvec/competitors/hyperwords/train-wiki.sh",
"chars": 115,
"preview": "#!/bin/sh\nrm -rf w2.sub w5.dyn.sub.del\nrm -rf w2.sub w5.dyn.sub.del\ntime ./pmi2.sh\ntime ./pmi5.sh\ntime ./svd.sh\n\n\n\n"
},
{
"path": "psdvec/corpus2liblinear.py",
"chars": 7098,
"preview": "import numpy as np\r\nimport getopt\r\nimport sys\r\nfrom utils import *\r\nimport pdb\r\nimport time\r\nimport os\r\nimport json\r\nimp"
},
{
"path": "psdvec/eval-logs/bench.log",
"chars": 13071,
"preview": "shaohua@shaohua:/media/shaohua/Outerspace/corpus$ ./bench.sh \nPSD:\nRead sim testset ./testsets/ws/ws353_similarity.txt\nR"
},
{
"path": "psdvec/evaluate-toefl.py",
"chars": 2765,
"preview": "#!/usr/bin/python\nimport getopt\nimport glob\nimport sys\nimport os.path\nfrom utils import *\nimport numpy as np\nimport copy"
},
{
"path": "psdvec/evaluate.py",
"chars": 9340,
"preview": "#!/usr/bin/python\n\nimport getopt\nimport glob\nimport sys\nimport os.path\nfrom utils import *\nimport numpy as np\n#import pd"
},
{
"path": "psdvec/extractwiki.py",
"chars": 1299,
"preview": "#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# code based on http://textminingonline.com/training-word2vec-model-on-e"
},
{
"path": "psdvec/fact-rcv1.bat",
"chars": 477,
"preview": "@echo off\r\nset N0=50\r\nrem Old way of exact factorization: \r\nrem python factorize.py -n 50 -t 28000 -e absentwords.txt to"
},
{
"path": "psdvec/fact-rcv1.sh",
"chars": 158,
"preview": "#!/bin/bash\nN0=50\ntime python factorize.py -w 15000 -n $N0 -E5 top2grams-rcv1.txt\ntime python factorize.py -v 15000-$N0-"
},
{
"path": "psdvec/fact-wiki.bat",
"chars": 872,
"preview": "@echo off\r\nset N0=500\r\nrem Old way of exact factorization: \r\nrem python factorize.py -n 500 -t 28000 -e absentwords.txt "
},
{
"path": "psdvec/fact-wiki.sh",
"chars": 856,
"preview": "#!/bin/bash\nN0=500\n# Old way of exact factorization: \n# python factorize.py -n 500 -t 28000 -e absentwords.txt top2grams"
},
{
"path": "psdvec/factorize.py",
"chars": 36801,
"preview": "import sys\r\nimport numpy as np\r\nfrom scipy import linalg\r\nimport getopt\r\nimport timeit\r\nimport pdb\r\nfrom utils import *\r"
},
{
"path": "psdvec/genSentDict.bat",
"chars": 184,
"preview": "@echo off\r\nset N=200\r\npython topwordsInList.py -c sent-gen-config.txt -l d:\\Dropbox\\sentiment\\positive-words.txt,d:\\Drop"
},
{
"path": "psdvec/gencatdata.py",
"chars": 2281,
"preview": "import numpy as np\r\nimport scipy.linalg\r\nfrom scipy.special import *\r\nimport getopt\r\nimport sys\r\nfrom utils import *\r\nim"
},
{
"path": "psdvec/gram-rcv1.bat",
"chars": 258,
"preview": "set CORPUS=rcv1clean.txt\r\nset SUFFIX=rcv1\r\nperl gramcount.pl -i %CORPUS% -m1 --f1 top1grams-%SUFFIX%.txt -c --nofilter -"
},
{
"path": "psdvec/gram.bat",
"chars": 273,
"preview": "set CORPUS=reuters-train-5770.orig.txt\r\nset SUFFIX=reuters\r\nperl gramcount.pl -i %CORPUS% -m1 --f1 top1grams-%SUFFIX%.tx"
},
{
"path": "psdvec/gramcount.pl",
"chars": 32535,
"preview": "use strict;\nuse warnings 'all';\nuse List::Util qw( min max sum );\nuse List::MoreUtils 'all';\nuse Getopt::Long;\nuse Inlin"
},
{
"path": "psdvec/patch to gensim.py",
"chars": 1621,
"preview": "# -*- coding: utf-8 -*-\r\n\r\n### change to \"\\Lib\\site-packages\\gensim\\corpora\\wikicorpus.py\" ###\r\n\r\n def get_texts(self"
},
{
"path": "psdvec/perlxs.h",
"chars": 1996,
"preview": "// http://ftp.ledas.ac.uk/software/lheasoft/lheasoft6.3.1/source/heacore/pil/perl/Av_CharPtrPtr.c\r\n\r\n/* Used by the INPU"
},
{
"path": "psdvec/removeDoubleNewline.pl",
"chars": 466,
"preview": "$wc = 0;\r\n$doubleNewlineCount = 0;\r\nwhile(<>){\r\n $wc++;\r\n if( /^\\r?\\n$/ ){\r\n if( $lastIsNewline ){\r\n "
},
{
"path": "psdvec/sent-bench.bat",
"chars": 495,
"preview": "@echo off\r\nrem cd d:\\corpus\r\nrem python corpus2liblinear.py -d aclImdb\\test\\pos -o sent-test.txt 1\r\nrem python corpus2li"
},
{
"path": "psdvec/sent-gen.conf",
"chars": 472,
"preview": "{ \"outFilenameTrunk\": \"sent-train\", \"docDir\": \"D:/corpus/aclImdb/train/neg\", \"label\": \"-1\", \"appendToOutput\": false }\r\n{"
},
{
"path": "psdvec/sentbench.py",
"chars": 2773,
"preview": "import os\r\nimport re\r\nimport subprocess\r\n\r\nalg2vec = { \"PSD-reg\": \"25000-180000-500-BLK-8.0.vec\", \r\n #\"PSD-un"
},
{
"path": "psdvec/tab2list.py",
"chars": 806,
"preview": "import sys\r\n\r\nCAT = 1\r\nWORDS = 2\r\n\r\nFTAB = open(sys.argv[1])\r\nFLIST = open(sys.argv[2], \"w\")\r\nFLIST.write(\"WORD\\tTRUECLA"
},
{
"path": "psdvec/testsets/analogy/EN-TOM-ICLR13-SEM.txt",
"chars": 280513,
"preview": "Athens Greece Baghdad Iraq\nAthens Greece Bangkok Thailand\nAthens Greece Beijing China\nAthens Greece Berlin Germany\nAthen"
},
{
"path": "psdvec/testsets/analogy/EN-TOM-ICLR13-SYN.txt",
"chars": 323166,
"preview": "amazing amazingly apparent apparently\namazing amazingly calm calmly\namazing amazingly cheerful cheerfully\namazing amazin"
},
{
"path": "psdvec/testsets/analogy/google.txt",
"chars": 623223,
"preview": "athens\tgreece\tbaghdad\tiraq\r\nathens\tgreece\tbangkok\tthailand\r\nathens\tgreece\tbeijing\tchina\r\nathens\tgreece\tberlin\tgermany\r\na"
},
{
"path": "psdvec/testsets/analogy/msr.txt",
"chars": 239628,
"preview": "good\tbetter\trough\trougher\r\nbetter\tgood\trougher\trough\r\ngood\tbest\trough\troughest\r\nbest\tgood\troughest\trough\r\nbest\tbetter\tro"
},
{
"path": "psdvec/testsets/ws/EN-RG-65.txt",
"chars": 1208,
"preview": "gem\tjewel\t3.94\nmidday\tnoon\t3.94\nautomobile\tcar\t3.92\ncemetery\tgraveyard\t3.88\ncushion\tpillow\t3.84\nboy\tlad\t3.82\ncock\trooste"
},
{
"path": "psdvec/testsets/ws/EN-TOEFL-80.txt",
"chars": 4300,
"preview": "enormously | tremendously | appropriately | uniquely | decidedly\nprovisions | stipulations | jurisdictions | interrelati"
},
{
"path": "psdvec/testsets/ws/bruni_men.txt",
"chars": 69858,
"preview": "sun sunlight 50.000000\nautomobile car 50.000000\nriver water 49.000000\nstairs staircase 49.000000\nmorning sunrise 49.0000"
},
{
"path": "psdvec/testsets/ws/luong_rare.txt",
"chars": 50486,
"preview": "squishing\tsquirt\t5.88\r\nundated\tundatable\t5.83\r\ncircumvents\tbeat\t5.33\r\ncircumvents\tebb\t3.25\r\ndispossess\tdeprive\t6.83\r\npro"
},
{
"path": "psdvec/testsets/ws/radinsky_mturk.txt",
"chars": 7507,
"preview": "episcopal\trussia\t2.75\r\nwater\tshortage\t2.714285714\r\nhorse\twedding\t2.266666667\r\nplays\tlosses\t3.2\r\nclassics\tadvertiser\t2.25"
},
{
"path": "psdvec/testsets/ws/simlex_999a.txt",
"chars": 18024,
"preview": "old\tnew\t1.58\nsmart\tintelligent\t9.2\nhard\tdifficult\t8.77\nhappy\tcheerful\t9.55\nhard\teasy\t0.95\nfast\trapid\t8.75\nhappy\tglad\t9.1"
},
{
"path": "psdvec/testsets/ws/ws353.txt",
"chars": 7405,
"preview": "love\tsex\t6.77\r\ntiger\tcat\t7.35\r\ntiger\ttiger\t10.00\r\nbook\tpaper\t7.46\r\ncomputer\tkeyboard\t7.62\r\ncomputer\tinternet\t7.58\r\nplane"
},
{
"path": "psdvec/testsets/ws/ws353_relatedness.txt",
"chars": 5134,
"preview": "computer\tkeyboard\t7.62\nJerusalem\tIsrael\t8.46\nplanet\tgalaxy\t8.11\ncanyon\tlandscape\t7.53\nOPEC\tcountry\t5.63\nday\tsummer\t3.94\n"
},
{
"path": "psdvec/testsets/ws/ws353_similarity.txt",
"chars": 4002,
"preview": "tiger\tcat\t7.35\ntiger\ttiger\t10.00\nplane\tcar\t5.77\ntrain\tcar\t6.31\ntelevision\tradio\t6.77\nmedia\tradio\t7.42\nbread\tbutter\t6.19\n"
},
{
"path": "psdvec/topwordsInList.py",
"chars": 3821,
"preview": "import numpy as np\r\nimport getopt\r\nimport sys\r\nfrom utils import *\r\nimport pdb\r\nimport time\r\nimport os\r\nimport json\r\n\r\nd"
},
{
"path": "psdvec/utils.py",
"chars": 67445,
"preview": "# -*- coding=GBK -*-\r\n\r\nimport numpy as np\r\nimport scipy.linalg\r\nfrom scipy.stats.stats import spearmanr\r\nimport time\r\ni"
},
{
"path": "psdvec/vecnorms.py",
"chars": 3398,
"preview": "#!/usr/bin/python\n\n# this simple script is to find patterns of the norms (L1) of the learned embeddings\nfrom utils impor"
},
{
"path": "psdvec/xml2corpus.pl",
"chars": 767,
"preview": "use strict; \r\nuse warnings;\r\nuse XML::LibXML;\r\nuse File::Find;\r\n\r\nmy $rootdir = \"D:/corpus/rcv1/\";\r\nmy $fc = 0;\r\nmy $tot"
},
{
"path": "reuters.bat",
"chars": 205,
"preview": "python topicExp.py -s reuters train\r\npython topicExp.py -i reuters-train-5770-sep91-em150-best.topic.vec reuters train,t"
},
{
"path": "snippet2topic.py",
"chars": 5171,
"preview": "import numpy as np\r\nimport getopt\r\nimport sys\r\nimport pdb\r\nimport os\r\nfrom topicvecDir import topicvecDir\r\nfrom utils im"
},
{
"path": "test-docs/Drug Goes From 13.50 a Tablet to 750, Overnight.txt",
"chars": 8132,
"preview": "Specialists in infectious disease are protesting a gigantic overnight increase in the price of a 62-year-old drug that i"
},
{
"path": "test-docs/VR-mitrv.txt",
"chars": 5331,
"preview": "Though virtual reality is still far from mainstream, 2015 was a big year for the industry as new headsets were introduce"
},
{
"path": "test-docs/batman-v-superman.txt",
"chars": 8899,
"preview": "Batman v Superman: Dawn of Justice begins where its 2013 DC-Comics predecessor, Man of Steel, ended: The titular Krypton"
},
{
"path": "test-docs/batman-v-superman.txt-em100.topic.vec",
"chars": 84940,
"preview": "20 500 0\n0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000"
},
{
"path": "test-docs/beijing-haze-news.txt",
"chars": 8480,
"preview": "BEIJING Wu Yiling, a 15-year-old student with a love of hamburgers and Hello Kitty, celebrated when she heard that her "
},
{
"path": "test-docs/brain-scar.txt",
"chars": 2431,
"preview": "Brain images taken from hundreds of soldiers diagnosed with mild traumatic brain injuries suggest that methods for diagn"
},
{
"path": "test-docs/britain-EU.txt",
"chars": 2629,
"preview": "Would Britain be better off economically outside the European Union? The answer is most likely no, which is why Britons "
},
{
"path": "test-docs/drugstory.log",
"chars": 286254,
"preview": "topicvecDir() init at Thu Mar 17 14:45:54 2016\n1 docs scanned, 1 kept. 654 words kept, 405 unique. 548 stop words, 113 o"
},
{
"path": "test-docs/hillary-speech.txt",
"chars": 23146,
"preview": "Hello, Madison!\r\nWell, thank you all very much. Thank you. Thank you and please please be seated.\r\nI am delighted to be "
},
{
"path": "test-docs/hillary-speech2.txt",
"chars": 9574,
"preview": "Thank you. Today, today you proved once again theres no place like home. You know, in this campaign weve won in every re"
},
{
"path": "test-docs/nips-wiki.txt",
"chars": 3565,
"preview": "Conference on Neural Information Processing Systems\r\nAbbreviation\tNIPS\r\nDiscipline\tMachine learning, statistics, artific"
},
{
"path": "test-docs/sanders-speeches.txt",
"chars": 20762,
"preview": "I am honored to be with you today and was pleased to receive your invitation to speak to this conference of The Pontific"
},
{
"path": "test-docs/spacex-news.txt",
"chars": 5321,
"preview": "Some of you may have been following our recent attempts to vertically land the first stage of our Falcon 9 rocket back o"
},
{
"path": "test-docs/trump-speech.txt",
"chars": 34294,
"preview": "Wow. Whoa. That is some group of people. Thousands.\r\n\r\nSo nice, thank you very much. Thats really nice. Thank you. Its g"
},
{
"path": "topic-competitors/LDA/Readme.txt",
"chars": 440,
"preview": ": sklearnԴ20 newsgroups (~20000ƪĵ)nltkԴreuters(10788ƪĵ)\r\n\r\nldaExp.py: gensimָ(20newsgroup reutersָ֮ͨһ)ѧϰdoc-topicֲΪ '-tr"
},
{
"path": "topic-competitors/LDA/classEval.py",
"chars": 2932,
"preview": "from sklearn import svm, metrics\r\nfrom sklearn.datasets import load_svmlight_file\r\nimport sys\r\n\r\n# precision, recall, f1"
},
{
"path": "topic-competitors/LDA/corpusLoader.py",
"chars": 7646,
"preview": "# -*- coding=GBK -*-\r\n\r\nfrom sklearn.datasets import fetch_20newsgroups\r\nfrom nltk.corpus import reuters\r\nimport HTMLPar"
},
{
"path": "topic-competitors/LDA/ldaExp.py",
"chars": 3621,
"preview": "import sys\r\nimport pdb\r\nimport os\r\nimport getopt\r\nimport time\r\nimport gensim\r\nfrom sklearn.datasets import load_svmlight"
},
{
"path": "topic-competitors/doc2vec.py",
"chars": 2001,
"preview": "import gensim.models.doc2vec as doc2vec\nimport sys\nimport pdb\n\ncorpus = sys.argv[1]\n\nif corpus == '20news':\n all_word"
},
{
"path": "topic-competitors/kmeans.py",
"chars": 4854,
"preview": "#!/usr/bin/env python\r\n# kmeans.py using any of the 20-odd metrics in scipy.spatial.distance\r\n# kmeanssample 2 pass, fir"
},
{
"path": "topic-competitors/labelEval.py",
"chars": 1156,
"preview": "from sklearn import metrics\r\nimport sys\r\n\r\ndef getScores( true_classes, pred_classes, average):\r\n precision = metrics"
},
{
"path": "topic-competitors/lftm2svm.py",
"chars": 1470,
"preview": "import sys\n\nlftm_file = sys.argv[1]\n\ntrain_words_file = \"reuters-train-5770.gibbslda-words.txt\"\ntrain_label_file = \"reut"
},
{
"path": "topic-competitors/liu-doc2vec.py",
"chars": 3983,
"preview": "from utils import *\nimport pdb\n\ndef genDocEmbedding( setName, words_file, topics_file, label_file, V, word2ID, T ):\n "
},
{
"path": "topic-competitors/slda/20news-test-7532.slda-bow.txt",
"chars": 3847987,
"preview": "30 5:2 8:1 55:1 58:1 150:2 328:1 385:1 389:1 459:1 463:1 495:1 497:1 628:1 837:2 843:1 940:1 1058:1 1182:1 1390:1 1859:1"
},
{
"path": "topic-competitors/slda/20news-test-7532.slda-label.txt",
"chars": 26189,
"preview": "7\r\n5\r\n0\r\n17\r\n19\r\n13\r\n15\r\n15\r\n5\r\n1\r\n2\r\n5\r\n17\r\n8\r\n0\r\n2\r\n4\r\n1\r\n6\r\n16\r\n1\r\n6\r\n17\r\n14\r\n3\r\n13\r\n11\r\n7\r\n7\r\n3\r\n5\r\n5\r\n4\r\n3\r\n14\r\n1\r\n"
},
{
"path": "topic-competitors/slda/20news-train-11314.slda-bow.txt",
"chars": 5903907,
"preview": "33 11:1 33:1 39:1 45:1 55:1 77:1 80:1 167:1 210:1 220:1 433:1 450:1 459:1 460:1 462:4 467:1 713:1 724:1 910:1 1109:1 118"
},
{
"path": "topic-competitors/slda/20news-train-11314.slda-label.txt",
"chars": 39374,
"preview": "7\r\n4\r\n4\r\n1\r\n14\r\n16\r\n13\r\n3\r\n2\r\n4\r\n8\r\n19\r\n14\r\n6\r\n0\r\n1\r\n7\r\n12\r\n5\r\n0\r\n10\r\n6\r\n2\r\n4\r\n1\r\n12\r\n9\r\n15\r\n7\r\n6\r\n13\r\n12\r\n17\r\n18\r\n10\r\n8"
},
{
"path": "topic-competitors/slda/Makefile",
"chars": 252,
"preview": "CC = g++\r\nLDFLAGS = -lgsl -lm -lgslcblas\r\n\r\n\r\nLSOURCE = main.cpp corpus.cpp slda.cpp utils.cpp opt.cpp\r\nLHEADER = corpus"
},
{
"path": "topic-competitors/slda/corpus.cpp",
"chars": 2983,
"preview": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This"
},
{
"path": "topic-competitors/slda/corpus.h",
"chars": 1934,
"preview": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\r\n\r\n// written by Chong Wang, chongw@cs.princeton.edu\r\n\r\n// "
},
{
"path": "topic-competitors/slda/main.cpp",
"chars": 2619,
"preview": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This"
},
{
"path": "topic-competitors/slda/opt.cpp",
"chars": 8124,
"preview": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This"
},
{
"path": "topic-competitors/slda/opt.h",
"chars": 1533,
"preview": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This"
},
{
"path": "topic-competitors/slda/readme.txt",
"chars": 3034,
"preview": "**********************************************************\nSUPERVISED LATENT DIRICHLET ALLOCATION FOR CLASSIFICATION\n***"
},
{
"path": "topic-competitors/slda/reuters-test-2255.slda-bow.txt",
"chars": 574882,
"preview": "41 1:1 2:2 5:2 56:1 76:1 150:2 244:1 580:2 660:1 703:3 718:1 901:1 914:1 1044:1 1179:1 1227:1 1503:2 1512:1 1525:1 1799:"
},
{
"path": "topic-competitors/slda/reuters-test-2255.slda-label.txt",
"chars": 6765,
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"path": "topic-competitors/slda/settings.h",
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"preview": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\r\n\r\n// written by Chong Wang, chongw@cs.princeton.edu\r\n\r\n// "
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"path": "topic-competitors/slda/settings.txt",
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"preview": "var max iter 20\nvar convergence 1e-3\nem max iter 50\nem convergence 1e-4\nL2 penalty 0.01\nalpha fixed\n"
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"path": "topic-competitors/slda/slda.cpp",
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"preview": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This"
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"path": "topic-competitors/slda/slda.h",
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"preview": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This"
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"path": "topic-competitors/slda/utils.cpp",
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"preview": "#include \"utils.h\"\r\n\r\n/*\r\n * given log(a) and log(b), return log(a + b)\r\n *\r\n */\r\n\r\ndouble log_sum(double log_a, double "
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"preview": "#ifndef UTILS_H\n#define UTILS_H\n\n#include <stdio.h>\n#include <math.h>\n#include <float.h>\n#include <stdlib.h>\n#include <s"
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"path": "topic-cosine.py",
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"preview": "import numpy as np\r\nimport sys\r\nimport pdb\r\nfrom utils import *\r\n\r\ntopic_vec_file = sys.argv[1]\r\nT = load_matrix_from_te"
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{
"path": "topicExp.py",
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"preview": "import sys\r\nimport pdb\r\nimport os\r\nimport getopt\r\nfrom corpusLoader import *\r\nfrom utils import *\r\nfrom topicvecDir impo"
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"path": "topicvecDir.py",
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"preview": "import numpy as np\r\nimport scipy.linalg\r\n# import for using gammaln, psi\r\nfrom scipy.special import *\r\nimport getopt\r\nim"
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{
"path": "utils.py",
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"preview": "# -*- coding=GBK -*-\r\n\r\nimport numpy as np\r\nimport scipy.linalg\r\nfrom scipy.stats.stats import spearmanr\r\nimport time\r\ni"
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the askerlee/topicvec GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 118 files (18.0 MB), approximately 4.7M tokens, and a symbol index with 207 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.