def vector_maker(datum):
try:
dclass, dname, dpath, df, u = datum
doc = WCDocument(dpath)
v = sp.sparse.lil_matrix((1, df.vocab_size))
norm = 0
for word, count in doc.wc_dict().items():
idx = df.word_index(word)
if idx is not None:
tfidf = (1 + log(count, 2)) * log(float(df.ndocs) / df[word])
if u is not None:
tfidf -= u[0, idx]
v[0, idx] = tfidf
norm += tfidf ** 2
norm = sqrt(norm)
if norm != 0:
v /= norm
return {'class': dclass,
'disaster': dname,
'file': dpath,
'vector': v}
except KeyboardInterrupt, e:
pass
def mean_vec_worker(datum):
try:
dclass, dname, dpath, df = datum
doc = WCDocument(dpath)
results = []
for word, count in doc.wc_dict().items():
idx = df.word_index(word)
if idx is not None:
tfidf = (1 + log(count, 2)) * log(float(df.ndocs) / df[word])
results.append((0, idx, tfidf))
return results
except KeyboardInterrupt, e:
pass
def file2vector(filename, df):
doc = WCDocument(filename)
vsize = df.vocab_size
v = sp.sparse.lil_matrix((vsize, 1))
norm = 0
for word, count in doc.wc_dict().items():
idx = df.word_index(word)
if idx is not None:
v[idx] = 1
norm += 1
norm = sqrt(norm)
if norm != 0:
v /= norm
return v.tocsr()
def file2vector(filename, df):
doc = WCDocument(filename)
vsize = df.vocab_size
v = sp.sparse.lil_matrix((1, vsize))
norm = 0
for word, count in doc.wc_dict().items():
idx = df.word_index(word)
if idx is not None:
tfidf = (1 + log(count, 2)) * log(float(df.ndocs) / df[word])
v[0, idx] = tfidf
norm += tfidf ** 2
norm = sqrt(norm)
if norm != 0:
v /= norm
return v
