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 buildMscOverlapMatrix(lang):
logging.info("building MSC overlap matrix")
arts = [
art for art in docsim.getArts(
ARTS_FILE, acceptNoBody=True, acceptNoMsc=False)
if art.language == lang or lang == 'any'
]
cats, rcats = loadMsc2Id(lang) # from buildPure
logging.info("computing MSC matrices")
overlap = numpy.zeros(
(len(cats), len(cats)), dtype=int
) # binary msc similarity (with fixed msc hierarchy = identity)
for art in arts:
for msc1 in art.msc:
for msc2 in art.msc:
overlap[rcats[mscs.niceMSC(msc1)[0]],
rcats[mscs.niceMSC(msc2)[0]]] += 1
resultOverlap = numpy.zeros((len(cats), len(cats)), dtype=numpy.float32)
for i in xrange(resultOverlap.shape[0]):
max = numpy.max(overlap[i])
for j in xrange(resultOverlap.shape[1]):
resultOverlap[i, j] = math.log(1.0 + 100.0 * overlap[i, j] /
max) / math.log(101)
matutils.saveMatrix(resultOverlap,
common.matrixFile("mscs_overlap_%s.mm" % lang),
sparse=False)
return resultOverlap
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 buildDocDoc(arts, type, language):
ipyutils.loadDicts(prefix="gensim_" + language)
arts = [art for art in arts if art.id_int in ipyutils.rdocids]
assert len(arts) == len(ipyutils.rdocids)
logging.info("loading msc<->id mapping")
cats, rcats = loadMsc2Id(language)
mscsFile = common.matrixFile("mscs_%s.mm" % type)
matMsc = matutils.loadMatrix(mscsFile)
mscDict = {}
for art in arts:
artId = ipyutils.rdocids[art.id_int]
mscIds = [rcats[mscs.niceMSC(msc)[0]] for msc in art.msc]
mscDict[artId] = mscIds
logging.info("computing doc*doc similarity matrix based on %s" % mscsFile)
docdoc = numpy.zeros((len(arts), len(arts)), numpy.float32)
for i in xrange(len(arts)):
if i % 100 == 0:
logging.info("PROGRESS: %i/%i" % (i, len(arts)))
art1Id = ipyutils.rdocids[arts[i].id_int]
for j in xrange(i, len(arts)):
art2Id = ipyutils.rdocids[arts[j].id_int]
bestScore = 0.0
for msc1Id in mscDict[art1Id]:
for msc2Id in mscDict[art2Id]:
bestScore = max(bestScore, matMsc[msc1Id, msc2Id])
docdoc[art1Id, art2Id] = docdoc[art2Id, art1Id] = bestScore
matutils.saveMatrix(docdoc, common.matrixFile("docdoc_" + language + "_%s.mm" % type), sparse=False)
return docdoc
def buildMscOverlapMatrix(lang):
logging.info("building MSC overlap matrix")
arts = [
art
for art in docsim.getArts(ARTS_FILE, acceptNoBody=True, acceptNoMsc=False)
if art.language == lang or lang == "any"
]
cats, rcats = loadMsc2Id(lang) # from buildPure
logging.info("computing MSC matrices")
overlap = numpy.zeros(
(len(cats), len(cats)), dtype=int
) # binary msc similarity (with fixed msc hierarchy = identity)
for art in arts:
for msc1 in art.msc:
for msc2 in art.msc:
overlap[rcats[mscs.niceMSC(msc1)[0]], rcats[mscs.niceMSC(msc2)[0]]] += 1
resultOverlap = numpy.zeros((len(cats), len(cats)), dtype=numpy.float32)
for i in xrange(resultOverlap.shape[0]):
max = numpy.max(overlap[i])
for j in xrange(resultOverlap.shape[1]):
resultOverlap[i, j] = math.log(1.0 + 100.0 * overlap[i, j] / max) / math.log(101)
matutils.saveMatrix(resultOverlap, common.matrixFile("mscs_overlap_%s.mm" % lang), sparse=False)
return resultOverlap
def buildDocDoc(arts, type, language):
ipyutils.loadDicts(prefix='gensim_' + language)
arts = [art for art in arts if art.id_int in ipyutils.rdocids]
assert (len(arts) == len(ipyutils.rdocids))
logging.info("loading msc<->id mapping")
cats, rcats = loadMsc2Id(language)
mscsFile = common.matrixFile("mscs_%s.mm" % type)
matMsc = matutils.loadMatrix(mscsFile)
mscDict = {}
for art in arts:
artId = ipyutils.rdocids[art.id_int]
mscIds = [rcats[mscs.niceMSC(msc)[0]] for msc in art.msc]
mscDict[artId] = mscIds
logging.info("computing doc*doc similarity matrix based on %s" % mscsFile)
docdoc = numpy.zeros((len(arts), len(arts)), numpy.float32)
for i in xrange(len(arts)):
if i % 100 == 0:
logging.info("PROGRESS: %i/%i" % (i, len(arts)))
art1Id = ipyutils.rdocids[arts[i].id_int]
for j in xrange(i, len(arts)):
art2Id = ipyutils.rdocids[arts[j].id_int]
bestScore = 0.0
for msc1Id in mscDict[art1Id]:
for msc2Id in mscDict[art2Id]:
bestScore = max(bestScore, matMsc[msc1Id, msc2Id])
docdoc[art1Id, art2Id] = docdoc[art2Id, art1Id] = bestScore
matutils.saveMatrix(docdoc,
common.matrixFile("docdoc_" + language +
"_%s.mm" % type),
sparse=False)
return docdoc
def buildMscCentroidMatrix(language):
logging.info("building MSC centroid matrix from %s" % ARTS_FILE)
arts = [
art for art in docsim.getArts(
ARTS_FILE, acceptNoBody=False, acceptNoMsc=False)
if art.language == language or language == 'any'
]
prefix = 'mscs_serial_%s_' % language
matFile = common.matrixFile(prefix + 'TFIDF_T.mm')
if os.path.exists(matFile):
logging.warning(
'SKIPPING creating TFIDF matrix for %s (file %s present). Is this what you wanted?'
% (language, matFile))
tfidf = matutils.loadMatrix(matFile).tocsr()
else:
logging.info('creating TFIDF matrix for %s to %s' %
(language, matFile))
tfidf = docsim.buildTFIDFMatrices(arts,
prefix=prefix,
saveMatrices=False).tocsr()
ipyutils.loadDicts(prefix=prefix)
arts = [
art for art in arts if art.id_int in ipyutils.rdocids
] # remove articles that had empty body (according to their tfidf vector)
if len(ipyutils.rdocids) != len(arts):
logging.error(
"no. of TFIDF document = %i, but there are %i documents in the database (mismatch)"
% (len(ipyutils.rdocids), len(arts)))
raise Exception(
"different size of database/dictionary; version mismatch?")
cats, rcats = loadMsc2Id(language) # from buildPure
# print "mscs:", cats
logging.info("loading tfidf collection matrix (for centroids)")
tfidf = matutils.loadMatrix(
common.matrixFile('gensim_' + language + 'TFIDF_T.mm')).tocsr()
logging.debug("loaded %ix%i matrix" % tfidf.shape)
logging.info("computing centroids")
centroids = numpy.zeros((len(cats), tfidf.shape[1]), numpy.float)
# print "centroids.shape =", centroids.shape
num = numpy.zeros((len(cats), ), numpy.int)
artCnt = 0
for art in arts:
if not art.id_int in ipyutils.rdocids:
logging.warning("article not found among docids: %s" % art)
continue
artCnt += 1
artId = ipyutils.rdocids[art.id_int]
tops = [mscs.niceMSC(msc)[0] for msc in art.msc]
tops = set(
tops
) # only count each top-level once (comment out this line to count e.g. 30H55 and 30.13 twice for this article, as cat. 30)
for top in tops:
mscId = rcats[top]
vec = tfidf[artId].toarray()
vec.shape = (vec.size, )
# print "vec.shape = ", vec.shape
centroids[mscId] += vec
num[mscId] += 1
if artCnt < 10 or artCnt % 1000 == 0:
logging.debug(
"sanity check - article %s has id %i and has mscs=%s, mscsIds=%s"
% (art.id_int, artId, art.msc,
[rcats[mscs.niceMSC(msc)[0]] for msc in art.msc]))
if not artCnt == tfidf.shape[0]:
raise Exception("not all articles used; database/matrix mismatch?")
for i, vec in enumerate(centroids):
logging.info(
"centroid for msc %s (id %i) is an average of %i vectors" %
(cats[i], i, num[i]))
if numpy.sum(numpy.abs(vec)) == 0:
logging.warning("empty centroid for msc %s (msc int id %i)" %
(cats[i], i))
for mscId in cats.iterkeys():
centroids[mscId] /= num[mscId]
logging.info(
"used %i articles for %i vectors (articles may have more than one msc and so can be counted more than once)"
% (artCnt, sum(num)))
logging.info("computing MSC centroid matrix")
resultCentroid = numpy.zeros((len(cats), len(cats)), dtype=numpy.float32)
for idi, cati in cats.iteritems():
for idj, catj in cats.iteritems():
# print idi, cati, idj, catj
sim = matutils.cossim(centroids[idi], centroids[idj])
if numpy.isfinite(sim):
resultCentroid[idi, idj] = sim
else:
resultCentroid[idi, idj] = 0.0
matutils.saveMatrix(resultCentroid,
common.matrixFile("mscs_centroid_%s.mm" % language),
sparse=False)
def buildMscCentroidMatrix(language):
logging.info("building MSC centroid matrix from %s" % ARTS_FILE)
arts = [
art
for art in docsim.getArts(ARTS_FILE, acceptNoBody=False, acceptNoMsc=False)
if art.language == language or language == "any"
]
prefix = "mscs_serial_%s_" % language
matFile = common.matrixFile(prefix + "TFIDF_T.mm")
if os.path.exists(matFile):
logging.warning(
"SKIPPING creating TFIDF matrix for %s (file %s present). Is this what you wanted?" % (language, matFile)
)
tfidf = matutils.loadMatrix(matFile).tocsr()
else:
logging.info("creating TFIDF matrix for %s to %s" % (language, matFile))
tfidf = docsim.buildTFIDFMatrices(arts, prefix=prefix, saveMatrices=False).tocsr()
ipyutils.loadDicts(prefix=prefix)
arts = [
art for art in arts if art.id_int in ipyutils.rdocids
] # remove articles that had empty body (according to their tfidf vector)
if len(ipyutils.rdocids) != len(arts):
logging.error(
"no. of TFIDF document = %i, but there are %i documents in the database (mismatch)"
% (len(ipyutils.rdocids), len(arts))
)
raise Exception("different size of database/dictionary; version mismatch?")
cats, rcats = loadMsc2Id(language) # from buildPure
# print "mscs:", cats
logging.info("loading tfidf collection matrix (for centroids)")
tfidf = matutils.loadMatrix(common.matrixFile("gensim_" + language + "TFIDF_T.mm")).tocsr()
logging.debug("loaded %ix%i matrix" % tfidf.shape)
logging.info("computing centroids")
centroids = numpy.zeros((len(cats), tfidf.shape[1]), numpy.float)
# print "centroids.shape =", centroids.shape
num = numpy.zeros((len(cats),), numpy.int)
artCnt = 0
for art in arts:
if not art.id_int in ipyutils.rdocids:
logging.warning("article not found among docids: %s" % art)
continue
artCnt += 1
artId = ipyutils.rdocids[art.id_int]
tops = [mscs.niceMSC(msc)[0] for msc in art.msc]
tops = set(
tops
) # only count each top-level once (comment out this line to count e.g. 30H55 and 30.13 twice for this article, as cat. 30)
for top in tops:
mscId = rcats[top]
vec = tfidf[artId].toarray()
vec.shape = (vec.size,)
# print "vec.shape = ", vec.shape
centroids[mscId] += vec
num[mscId] += 1
if artCnt < 10 or artCnt % 1000 == 0:
logging.debug(
"sanity check - article %s has id %i and has mscs=%s, mscsIds=%s"
% (art.id_int, artId, art.msc, [rcats[mscs.niceMSC(msc)[0]] for msc in art.msc])
)
if not artCnt == tfidf.shape[0]:
raise Exception("not all articles used; database/matrix mismatch?")
for i, vec in enumerate(centroids):
logging.info("centroid for msc %s (id %i) is an average of %i vectors" % (cats[i], i, num[i]))
if numpy.sum(numpy.abs(vec)) == 0:
logging.warning("empty centroid for msc %s (msc int id %i)" % (cats[i], i))
for mscId in cats.iterkeys():
centroids[mscId] /= num[mscId]
logging.info(
"used %i articles for %i vectors (articles may have more than one msc and so can be counted more than once)"
% (artCnt, sum(num))
)
logging.info("computing MSC centroid matrix")
resultCentroid = numpy.zeros((len(cats), len(cats)), dtype=numpy.float32)
for idi, cati in cats.iteritems():
for idj, catj in cats.iteritems():
# print idi, cati, idj, catj
sim = matutils.cossim(centroids[idi], centroids[idj])
if numpy.isfinite(sim):
resultCentroid[idi, idj] = sim
else:
resultCentroid[idi, idj] = 0.0
matutils.saveMatrix(resultCentroid, common.matrixFile("mscs_centroid_%s.mm" % language), sparse=False)
