#!/usr/bin/env python
from matplotlib import pyplot as plt
from sklearn.decomposition import PCA, KernelPCA
from sklearn.manifold import Isomap, TSNE
from analogy import Analogy
from vstore import VStore
a = Analogy(VStore("vectors.lmdb", "big-glove"))
buf = ""
linebuf = raw_input("Please enter some words to plot, or empty for a canned list: ")
while linebuf:
buf += linebuf + " "
linebuf = raw_input("... ")
labels = buf.split() \
or "doctor nurse politician senator lawyer barrister defend accuse heal treat cure elect vote".split()
vs = [a.w(x) for x in labels if a.w(x) is not None ]
flatplot = TSNE(2)
ps = flatplot.fit_transform(vs)
plt.title("Reduced vector space model")
plt.xlabel("First Principal Component")
plt.ylabel("Second Principal Component")
plt.scatter(ps[:, 0], ps[:, 1])
for (x, y), label in zip(ps, labels):
print "plotting %f, %f, %s" %(x, y, label)
plt.annotate(label, xy = (x, y), xytext = (0, 0), textcoords = 'offset points')
import sys
import argparse
import numpy
from vstore import VStore
parser = argparse.ArgumentParser(description="Load GloVe vectors into LMDB")
parser.add_argument(
"--name", action="store", type=str, default="glove", help="name of the database into which to load the vectors"
)
parser.add_argument("--dbfile", action="store", type=str, default="vectors.lmdb", help="shared database filename")
parser.add_argument("--merge", action="store_true", help="merge the new dataset, rather than replacing")
parser.add_argument("source", type=file, help="uncompressed GloVe dataset")
args = parser.parse_args()
# Invert control in order to use one transaction
table = VStore(args.dbfile, args.name)
table.drop()
def loader():
for loaded, line in enumerate(args.source):
line = line.split()
name = line.pop(0)
if loaded % 10000 == 0:
print "Loaded {} rows".format(loaded)
yield name, numpy.array(line, dtype=numpy.float32)
table.load(loader())
print "Finished loading. Creating similarity index."
import numpy
from vstore import VStore
parser = argparse.ArgumentParser(description="Load GloVe vectors into LMDB")
parser.add_argument("--name", action="store", type=str, default="glove",
help="name of the database into which to load the vectors")
parser.add_argument("--dbfile", action="store", type=str, default="vectors.lmdb",
help="shared database filename")
parser.add_argument("--merge", action="store_true",
help="merge the new dataset, rather than replacing")
parser.add_argument("source", type=file,
help="uncompressed GloVe dataset")
args = parser.parse_args()
# Invert control in order to use one transaction
table = VStore(args.dbfile, args.name)
table.drop()
def loader():
for loaded, line in enumerate(args.source):
line = line.split()
name = line.pop(0)
## Tokenization errors can cause a word to be too long for lmdb
if len(name) > 100:
continue
if loaded % 10000 == 0:
print "Loaded {} rows".format(loaded)
yield name, numpy.array(line, dtype=numpy.float32)
table.load(loader())
print "Finished loading"
for idnum in unidict)
print "using LDA"
unildavstore = VStore(SOURCE+"vectors.lmdb", "mini-plain-lda")
unilda = models.LdaMulticore(unicorpus, id2word=unidict, num_topics=300, chunksize=25000, passes=10, iterations=50, workers=40, batch=True) # Lda
unilda.save(SOURCE+'.unilda_model')
unilda.print_topics(20)
unildavstore.drop()
unildavstore.load(
(unidict[idnum].encode(), matutils.sparse2full(unilda[[(idnum, 1)]], 300))
for idnum in unidict)
'''
print "using W2V"
uniw2vvstore = VStore(SOURCE+"vectors.lmdb", "w2v-word8-lines")
uniw2v = models.word2vec.Word2Vec([line.split() for line in open('word8-lines.short')], size=300, window=5, min_count=5, workers=8)
uniw2v.save('word8-lines.short.uniw2vmodel')
uniw2vvstore.drop()
uniw2vvstore.load(
(rnidict[idnum].encode(), matutils.sparse2full(uniw2v[[(idnum, 1)]], 300))
for idnum in unidict)
#uniindex = similarities.MatrixSimilarity(unilsi[unicorpus], num_features=300)
#uniindex.save('word8-lines.short.matsim')
## Get a query
import sys
sys.exit()
'''
query = raw_input("Search: ")
unidict.save(SOURCE + '.dict')
print(unidict)
#unidict = corpora.Dictionary.load("word8.dict")
### Preprocessing
unicorpus = digestion.LineCorpus(SOURCE, unidict)
### Creating the index
tfidf = models.TfidfModel(unicorpus)
corpus_tfidf = tfidf[unicorpus]
import code
code.interact(local=vars())
print "using LSI"
unilsivstore = VStore(SOURCE+"vectors.lmdb", "lsi")
unilsi = models.LsiModel(corpus_tfidf, chunksize=1000000, id2word=unidict, num_topics=300) # initialize an LSI transformation
unilsi.save(SOURCE + '.unilsimodel')
unilsi.print_topics(20)
unilsivstore.drop()
unilsivstore.load(
(unidict[idnum].encode(), matutils.sparse2full(unilsi[[(idnum, 1)]], 300))
for idnum in unidict)
print "using LDA"
unildavstore = VStore(SOURCE+"vectors.lmdb", "mini-plain-lda")
unilda = models.LdaMulticore(unicorpus, id2word=unidict, num_topics=300, chunksize=25000, passes=10, iterations=50, workers=40, batch=True) # Lda
unilda.save(SOURCE+'.unildamodel')
unilda.print_topics(20)
unildavstore.drop()
unildavstore.load(
return (np.sum(left * right) /
(np.sqrt(np.sum(left**2)) * np.sqrt(np.sum(right**2))))
return None
if __name__ == '__main__':
np.set_printoptions(threshold=20)
parser = ArgumentParser(description="Perform simple algebra on words")
parser.add_argument("--dbfile",
default="vectors.lmdb",
help="use this database file to get vectors")
parser.add_argument("model",
action="store",
help="compare using this model database (e.g. glove)")
args = parser.parse_args()
model = VStore(args.dbfile, args.model)
analogy = Analogy(model)
w = analogy.w
pcaw = analogy.pcaw
context = analogy.context
sim = analogy.sim
print "What follows is a python prompt."
print "w('elicidate') --> vector for `elicidate`"
print "w('mogrify') + w('frobnicate') --> vector sum"
print " same for -, *, /, **, etc as usual for numpy"
print "sim(w('republican'), w('democrat')) -> society in a 32bit float"
print " (actually a simple cosine similarity)"
code.interact(local=vars())
