def buildMscPureMatrix(lang):
if os.path.exists(ARTS_FILE):
logging.warning(
"SKIPPING creating MSC database from meta.xml files (using old %s); is this what you want?"
% ARTS_FILE)
else:
logging.info("creating MSC database from meta.xml files")
createMscsDb(
) # only run this when collection changes (test for file existence / delete file explicitly?)
arts = [
art for art in docsim.getArts(
ARTS_FILE, acceptNoBody=True, acceptNoMsc=False)
if art.language == lang or lang == 'any'
]
cats = dict(enumerate(mscs.getFreqMSCs(arts, minFreq=1, useMains=False)))
rcats = utils_dml.reverseMap(cats)
saveMsc2Id(cats, lang)
logging.info("building MSC binary matrix")
resultBin = numpy.zeros(
(len(cats), len(cats)), dtype=numpy.float32
) # binary msc similarity (with fixed msc hierarchy = identity)
for idi, cati in cats.iteritems():
for idj, catj in cats.iteritems():
# print idi, cati, idj, catj
if idi == idj:
resultBin[idi, idj] = 1.0
else:
resultBin[idi, idj] = 0.0
matutils.saveMatrix(resultBin,
common.matrixFile("mscs_bin_%s.mm" % lang),
sparse=False)
return resultBin
def getBOWMatrix(self): # FIXME not sparse atm; scipy.sparse is crap
"""return collection as sparse term-by-document matrix (index order is array[term,document]=frequency)"""
dictionary = self.getDictionary()
docs = self.getDocs()
numterms = max(dictionary.values()) + 1
logging.info("constructing %ix%i BOW matrix" % (numterms, len(docs)))
# build the matrix
result = numpy.empty((numterms, len(docs)), dtype = int)
for i in xrange(len(docs)):
result[:, i] = docs[i].getFull(length = numterms)
## result = numpy.column_stack([doc.getFull() for doc in self.getDocs()])
# print some stats
reverseDictionary = utils_dml.reverseMap(dictionary)
docids = self.getDocIds()
marginal = [numpy.sum(result, axis = ax) for ax in range(2)] # marginal sums along either axis
empty = [numpy.compress(marginal[ax] == 0, range(len(marginal[ax]))) for ax in range(2)] # indices of empty columns/rows
logging.info("%i empty BOW document vectors: " % len(empty[0]) + str(zip(empty[0], map(docids.__getitem__, empty[0]))))
logging.info("%i empty BOW term vectors:" % len(empty[1]) + str(zip(empty[1], map(reverseDictionary.get, empty[1]))))
zeroElems = len(result.nonzero()[0])
logging.info("BOW sparsity: %i/%i = %.3f%%" % (zeroElems, result.size, 100.0 * zeroElems / result.size))
return result
def getBOWMatrix(self): # FIXME not sparse atm; scipy.sparse is crap
"""return collection as sparse term-by-document matrix (index order is array[term,document]=frequency)"""
dictionary = self.getDictionary()
docs = self.getDocs()
numterms = max(dictionary.values()) + 1
logging.info("constructing %ix%i BOW matrix" % (numterms, len(docs)))
# build the matrix
result = numpy.empty((numterms, len(docs)), dtype=int)
for i in xrange(len(docs)):
result[:, i] = docs[i].getFull(length=numterms)
## result = numpy.column_stack([doc.getFull() for doc in self.getDocs()])
# print some stats
reverseDictionary = utils_dml.reverseMap(dictionary)
docids = self.getDocIds()
marginal = [numpy.sum(result, axis=ax)
for ax in range(2)] # marginal sums along either axis
empty = [
numpy.compress(marginal[ax] == 0, range(len(marginal[ax])))
for ax in range(2)
] # indices of empty columns/rows
logging.info("%i empty BOW document vectors: " % len(empty[0]) +
str(zip(empty[0], map(docids.__getitem__, empty[0]))))
logging.info("%i empty BOW term vectors:" % len(empty[1]) +
str(zip(empty[1], map(reverseDictionary.get, empty[1]))))
zeroElems = len(result.nonzero()[0])
logging.info("BOW sparsity: %i/%i = %.3f%%" %
(zeroElems, result.size, 100.0 * zeroElems / result.size))
return result
def buildMscPureMatrix(lang):
if os.path.exists(ARTS_FILE):
logging.warning(
"SKIPPING creating MSC database from meta.xml files (using old %s); is this what you want?" % ARTS_FILE
)
else:
logging.info("creating MSC database from meta.xml files")
createMscsDb() # only run this when collection changes (test for file existence / delete file explicitly?)
arts = [
art
for art in docsim.getArts(ARTS_FILE, acceptNoBody=True, acceptNoMsc=False)
if art.language == lang or lang == "any"
]
cats = dict(enumerate(mscs.getFreqMSCs(arts, minFreq=1, useMains=False)))
rcats = utils_dml.reverseMap(cats)
saveMsc2Id(cats, lang)
logging.info("building MSC binary matrix")
resultBin = numpy.zeros(
(len(cats), len(cats)), dtype=numpy.float32
) # binary msc similarity (with fixed msc hierarchy = identity)
for idi, cati in cats.iteritems():
for idj, catj in cats.iteritems():
# print idi, cati, idj, catj
if idi == idj:
resultBin[idi, idj] = 1.0
else:
resultBin[idi, idj] = 0.0
matutils.saveMatrix(resultBin, common.matrixFile("mscs_bin_%s.mm" % lang), sparse=False)
return resultBin
def rebuildDictionary(self):
"""remove entries which have no corresponding ids in the database, shrink resulting gaps"""
logging.debug("rebuilding dictionary, shrinking gaps")
uids = sorted(self.getUniqueTokens()) # get unique ids present in the database
newids = range(len(uids)) # new ids
revold = utils_dml.reverseMap(self.getDictionary())
idmap = dict(zip(uids, newids))
newdict = {}
for uid in uids: # generate new dictionary
newdict[revold[uid]] = idmap[uid]
for doc in self.getDocs(): # update ids in documents to reflect the new dictionary
doc.setTokenIds([idmap[i] for i in doc.getTokenIds()])
self.dictionary = newdict
def rebuildDictionary(self):
"""remove entries which have no corresponding ids in the database, shrink resulting gaps"""
logging.debug("rebuilding dictionary, shrinking gaps")
uids = sorted(
self.getUniqueTokens()) # get unique ids present in the database
newids = range(len(uids)) # new ids
revold = utils_dml.reverseMap(self.getDictionary())
idmap = dict(zip(uids, newids))
newdict = {}
for uid in uids: # generate new dictionary
newdict[revold[uid]] = idmap[uid]
for doc in self.getDocs(
): # update ids in documents to reflect the new dictionary
doc.setTokenIds([idmap[i] for i in doc.getTokenIds()])
self.dictionary = newdict
def getTFIDFMatrix(self, sparse = False):
"""construct tf*idf collection matrix. """
docs = self.getDocs()
dictionary = self.getDictionary()
if not dictionary:
raise Exception("cannot build matrix from an empty collection; chances are something went wrong!")
numterms = max(dictionary.values()) + 1
logging.info("constructing %ix%i TFIDF matrix, sparse = %s" % (numterms, len(docs), sparse))
if sparse:
result = scipy.sparse.lil_matrix((numterms, len(docs)), dtype = numpy.float32)
for i in xrange(len(docs)):
if (i + 1) % 1000 == 0:
logging.info(" progress: at vector #%i/%i" % (i, len(docs)))
vec, total = utils_dml.vect2bow_sorted(docs[i].getTokenIds())
if total > 0:
itotal = 1.0 / total
for id, idfreq in vec:
result.rows[id].append(i) # vec must be sorted by id in order for this to work!
result.data[id].append(itotal * idfreq)
nnz = numpy.array([len(result.data[j]) for j in xrange(result.shape[0])])
idfs = numpy.log((1.0 * result.shape[1]) / nnz) / numpy.log(2)
idfs[numpy.isinf(idfs)] = 0.0 # HAX replace INFs with 0.0 - is this ok?
self.setIDFs(idfs)
for iterm in xrange(result.shape[0]): # multiply by IDF
if (iterm + 1) % 10000 == 0:
logging.info(" progress: idf#%i" % iterm)
result.data[iterm] = [val * idfs[iterm] for val in result.data[iterm]]
result = result.tocsc()
logging.info("TFIDF sparsity: %i/%i = %.3f%%" % (result.getnnz(), result.shape[0] * result.shape[1], 100.0 * result.getnnz() / (result.shape[0] * result.shape[1])))
else:
result = numpy.empty((numterms, len(docs)), dtype = numpy.float32)
for i in xrange(len(docs)):
if (i + 1) % 10000 == 0:
logging.debug(" progress: vec#%i" % i)
vec = docs[i].getFull(numterms)
sm = 1.0 * numpy.sum(vec)
if sm == 0:
result[:, i] = 0.0 # HACK: don't scale zero vectors (INF bad?)
else:
result[:, i] = vec / sm
idfs = self.getIDFs(result)
self.setIDFs(idfs)
for iterm in xrange(result.shape[0]): # multiply by IDF
if (iterm + 1) % 10000 == 0:
logging.debug(" progress: idf#%i" % iterm)
result[iterm, :] *= idfs[iterm]
# print some stats
reverseDictionary = utils_dml.reverseMap(self.getDictionary())
docids = self.getDocIds()
marginal = [numpy.sum(result, axis = ax) for ax in range(2)] # marginal sums along both axes
empty = [numpy.compress(marginal[ax] == 0, range(len(marginal[ax]))) for ax in range(2)] # indices of empty columns/rows
logging.debug("%i empty TFIDF document vectors: " % len(empty[0]) + str(zip(empty[0], map(docids.__getitem__, empty[0]))))
logging.debug("%i empty TFIDF term vectors: " % len(empty[1]) + str(zip(empty[1], map(reverseDictionary.get, empty[1]))))
zeroElems = len(result.nonzero()[0])
nearZeroElems = len(result.compress(result.flat >= 1e-3))
logging.info("TFIDF sparsity: %i/%i = %.3f%% (%.3f%% < 1e-3)" % (zeroElems, result.size, 100.0 * zeroElems / result.size, 100.0 * nearZeroElems / result.size))
return result
coll.addDocument(df.createDocument("minors graph eps trees system computer survey user human time interface response.", "full_doc"))
if not coll.docExists('m1'):
coll.addDocument(df.createDocument(texts['m1'], 'brekeke'))
coll.createDictionary()
for doc in coll.getDocs():
print "dc1: %s (%i):" % (doc.getId(), len(doc.getTokenIds())), doc.getTokenIds()
print coll.getDictionary()
mat = coll.getBOWMatrix()
dfm = coll.getDocFreqMap()
stopList = ['a','and','of','the',':', 'totallyirrelevant'] # fixed stoplist
stopIds = utils_dml.text2vect(stopList, coll.getDictionary())
stopIds.extend(coll.freqRange(dfm, 0, 2)) # extend stopIds with ids that have 0 <= document frequency < 2
print 'stoplist = ', map(utils_dml.reverseMap(coll.getDictionary()).__getitem__, stopIds)
for doc in coll.getDocs():
print "before filter: %s (%i):" % (doc.getId(), len(doc.getTokenIds())), zip(doc.getTokenIds(), doc.getTokens())
coll.filterIds(stopIds) # remove unwanted tokens
for doc in coll.getDocs():
print "after filter, before rebuild: %s (%i):" % (doc.getId(), len(doc.getTokenIds())), zip(doc.getTokenIds(), doc.getTokens())
coll.rebuildDictionary()
for doc in coll.getDocs():
print "after rebuild: %s (%i):" % (doc.getId(), len(doc.getTokenIds())), zip(doc.getTokenIds(), doc.getTokens())
coll.filterExtremes(1)
def getTFIDFMatrix(self, sparse=False):
"""construct tf*idf collection matrix. """
docs = self.getDocs()
dictionary = self.getDictionary()
if not dictionary:
raise Exception(
"cannot build matrix from an empty collection; chances are something went wrong!"
)
numterms = max(dictionary.values()) + 1
logging.info("constructing %ix%i TFIDF matrix, sparse = %s" %
(numterms, len(docs), sparse))
if sparse:
result = scipy.sparse.lil_matrix((numterms, len(docs)),
dtype=numpy.float32)
for i in xrange(len(docs)):
if (i + 1) % 1000 == 0:
logging.info(" progress: at vector #%i/%i" %
(i, len(docs)))
vec, total = utils_dml.vect2bow_sorted(docs[i].getTokenIds())
if total > 0:
itotal = 1.0 / total
for id, idfreq in vec:
result.rows[id].append(
i
) # vec must be sorted by id in order for this to work!
result.data[id].append(itotal * idfreq)
nnz = numpy.array(
[len(result.data[j]) for j in xrange(result.shape[0])])
idfs = numpy.log((1.0 * result.shape[1]) / nnz) / numpy.log(2)
idfs[numpy.isinf(
idfs)] = 0.0 # HAX replace INFs with 0.0 - is this ok?
self.setIDFs(idfs)
for iterm in xrange(result.shape[0]): # multiply by IDF
if (iterm + 1) % 10000 == 0:
logging.info(" progress: idf#%i" % iterm)
result.data[iterm] = [
val * idfs[iterm] for val in result.data[iterm]
]
result = result.tocsc()
logging.info("TFIDF sparsity: %i/%i = %.3f%%" %
(result.getnnz(), result.shape[0] * result.shape[1],
100.0 * result.getnnz() /
(result.shape[0] * result.shape[1])))
else:
result = numpy.empty((numterms, len(docs)), dtype=numpy.float32)
for i in xrange(len(docs)):
if (i + 1) % 10000 == 0:
logging.debug(" progress: vec#%i" % i)
vec = docs[i].getFull(numterms)
sm = 1.0 * numpy.sum(vec)
if sm == 0:
result[:,
i] = 0.0 # HACK: don't scale zero vectors (INF bad?)
else:
result[:, i] = vec / sm
idfs = self.getIDFs(result)
self.setIDFs(idfs)
for iterm in xrange(result.shape[0]): # multiply by IDF
if (iterm + 1) % 10000 == 0:
logging.debug(" progress: idf#%i" % iterm)
result[iterm, :] *= idfs[iterm]
# print some stats
reverseDictionary = utils_dml.reverseMap(self.getDictionary())
docids = self.getDocIds()
marginal = [numpy.sum(result, axis=ax)
for ax in range(2)] # marginal sums along both axes
empty = [
numpy.compress(marginal[ax] == 0, range(len(marginal[ax])))
for ax in range(2)
] # indices of empty columns/rows
logging.debug(
"%i empty TFIDF document vectors: " % len(empty[0]) +
str(zip(empty[0], map(docids.__getitem__, empty[0]))))
logging.debug(
"%i empty TFIDF term vectors: " % len(empty[1]) +
str(zip(empty[1], map(reverseDictionary.get, empty[1]))))
zeroElems = len(result.nonzero()[0])
nearZeroElems = len(result.compress(result.flat >= 1e-3))
logging.info("TFIDF sparsity: %i/%i = %.3f%% (%.3f%% < 1e-3)" %
(zeroElems, result.size, 100.0 * zeroElems /
result.size, 100.0 * nearZeroElems / result.size))
return result
for doc in coll.getDocs():
print "dc1: %s (%i):" % (doc.getId(), len(
doc.getTokenIds())), doc.getTokenIds()
print coll.getDictionary()
mat = coll.getBOWMatrix()
dfm = coll.getDocFreqMap()
stopList = ['a', 'and', 'of', 'the', ':',
'totallyirrelevant'] # fixed stoplist
stopIds = utils_dml.text2vect(stopList, coll.getDictionary())
stopIds.extend(coll.freqRange(
dfm, 0,
2)) # extend stopIds with ids that have 0 <= document frequency < 2
print 'stoplist = ', map(
utils_dml.reverseMap(coll.getDictionary()).__getitem__, stopIds)
for doc in coll.getDocs():
print "before filter: %s (%i):" % (doc.getId(), len(
doc.getTokenIds())), zip(doc.getTokenIds(), doc.getTokens())
coll.filterIds(stopIds) # remove unwanted tokens
for doc in coll.getDocs():
print "after filter, before rebuild: %s (%i):" % (
doc.getId(), len(doc.getTokenIds())), zip(doc.getTokenIds(),
doc.getTokens())
coll.rebuildDictionary()
for doc in coll.getDocs():
print "after rebuild: %s (%i):" % (doc.getId(), len(