def close_words_of_entities():
word_dict_file_name = 'e:/dc/20ng_bydate/words_dict.txt'
word_vec_file_name = 'e:/dc/20ng_bydate/vecs/word_vecs_joint_oml_100.bin'
entity_dict_file_name = 'e:/dc/20ng_bydate/entity_names.txt'
entity_vecs_file_name = 'e:/dc/20ng_bydate/vecs/entity_vecs_joint_oml_100.bin'
word_vecs = ioutils.load_vec_list_file(word_vec_file_name)
words = ioutils.load_words_dict_to_list(word_dict_file_name)
entity_vecs = ioutils.load_vec_list_file(entity_vecs_file_name)
entities = ioutils.load_entity_dict(entity_dict_file_name)
print len(entity_vecs)
print len(entities)
def show_close_words(entity_idx):
print 'entity: ', entities[entity_idx]
entity_vec = entity_vecs[entity_idx]
dist_list = list()
for word_idx in xrange(len(word_vecs)):
# dist = np.dot(entity_vec, word_vecs[word_idx])
dist = scipy.spatial.distance.cosine(entity_vec, word_vecs[word_idx])
dist_list.append((dist, word_idx))
# dist_list.sort(key=lambda tup: tup[0])
closest_words = heapq.nsmallest(100, dist_list, key=lambda tup: tup[0])
for dist, idx in closest_words:
print dist, words[idx], idx
show_close_words(25304)
def close_words_of_entities():
word_dict_file_name = 'e:/dc/20ng_bydate/words_dict.txt'
word_vec_file_name = 'e:/dc/20ng_bydate/vecs/word_vecs_joint_oml_100.bin'
entity_dict_file_name = 'e:/dc/20ng_bydate/entity_names.txt'
entity_vecs_file_name = 'e:/dc/20ng_bydate/vecs/entity_vecs_joint_oml_100.bin'
word_vecs = ioutils.load_vec_list_file(word_vec_file_name)
words = ioutils.load_words_dict_to_list(word_dict_file_name)
entity_vecs = ioutils.load_vec_list_file(entity_vecs_file_name)
entities = ioutils.load_entity_dict(entity_dict_file_name)
print len(entity_vecs)
print len(entities)
def show_close_words(entity_idx):
print 'entity: ', entities[entity_idx]
entity_vec = entity_vecs[entity_idx]
dist_list = list()
for word_idx in xrange(len(word_vecs)):
# dist = np.dot(entity_vec, word_vecs[word_idx])
dist = scipy.spatial.distance.cosine(entity_vec,
word_vecs[word_idx])
dist_list.append((dist, word_idx))
# dist_list.sort(key=lambda tup: tup[0])
closest_words = heapq.nsmallest(100, dist_list, key=lambda tup: tup[0])
for dist, idx in closest_words:
print dist, words[idx], idx
show_close_words(25304)
def merge_vecs():
year = 2010
part = 'train'
method = 3
# vecs_file0 = 'e:/dc/el/vecs/tac_' + file_tag + '_entity_vecs.bin'
# vecs_file1 = 'e:/dc/el/vecs/tac_' + file_tag + '_dw_vecs.bin'
# dst_file = 'e:/dc/el/vecs/tac_' + file_tag + '_vecs.bin'
vecs_file0 = 'e:/dc/el/vecs/%d/%s_%d_de_vecs.bin' % (year, part, method)
vecs_file1 = 'e:/dc/el/vecs/%d/%s_%d_dw_vecs.bin' % (year, part, method)
dst_file = 'e:/dc/el/vecs/%d/%s_%d_vecs.bin' % (year, part, method)
vecs0 = ioutils.load_vec_list_file(vecs_file0)
vecs1 = ioutils.load_vec_list_file(vecs_file1)
if len(vecs0) != len(vecs1):
print 'number of vectors not equal!'
return
fout = open(dst_file, 'wb')
np.asarray([len(vecs0), len(vecs0[0]) + len(vecs1[0])],
np.int32).tofile(fout)
for i in xrange(len(vecs0)):
vecs0[i].tofile(fout)
vecs1[i].tofile(fout)
fout.close()
def merge_vecs():
year = 2010
part = 'train'
method = 3
# vecs_file0 = 'e:/dc/el/vecs/tac_' + file_tag + '_entity_vecs.bin'
# vecs_file1 = 'e:/dc/el/vecs/tac_' + file_tag + '_dw_vecs.bin'
# dst_file = 'e:/dc/el/vecs/tac_' + file_tag + '_vecs.bin'
vecs_file0 = 'e:/dc/el/vecs/%d/%s_%d_de_vecs.bin' % (year, part, method)
vecs_file1 = 'e:/dc/el/vecs/%d/%s_%d_dw_vecs.bin' % (year, part, method)
dst_file = 'e:/dc/el/vecs/%d/%s_%d_vecs.bin' % (year, part, method)
vecs0 = ioutils.load_vec_list_file(vecs_file0)
vecs1 = ioutils.load_vec_list_file(vecs_file1)
if len(vecs0) != len(vecs1):
print 'number of vectors not equal!'
return
fout = open(dst_file, 'wb')
np.asarray([len(vecs0), len(vecs0[0]) + len(vecs1[0])], np.int32).tofile(fout)
for i in xrange(len(vecs0)):
vecs0[i].tofile(fout)
vecs1[i].tofile(fout)
fout.close()
def close_wiki_pages():
dst_wid = 534366
wid_title_file = 'e:/el/tmpres/wiki/enwiki-20150403-id-title-list.txt'
wid_title_dict = __load_id_title_dict(wid_title_file)
wiki_page_id_file = 'e:/dc/el/wiki/wiki_page_ids.bin'
fin = open(wiki_page_id_file, 'rb')
num_pages = np.fromfile(fin, np.int32, 1)
page_ids = np.fromfile(fin, np.int32, num_pages)
fin.close()
pos = 0
for i, wid in enumerate(page_ids):
if wid == dst_wid:
pos = i
break
wiki_vec_file = 'e:/dc/el/vecs/wiki_dedw_vecs_1.bin'
# fin = open(wiki_vec_file, 'rb')
# num_vecs, dim = np.fromfile(fin, np.int32, 2)
# wiki_vecs = np.zeros((num_vecs, 50), np.float32)
# for i in xrange(num_vecs):
# vec = np.fromfile(fin, np.float32, dim)
# wiki_vecs[i][:] = vec[50:]
# fin.close()
wiki_vecs = ioutils.load_vec_list_file(wiki_vec_file)
top_list = close_vecs(wiki_vecs, wiki_vecs[pos])
for idx, dist in top_list:
print wid_title_dict[page_ids[idx]], dist
def close_wiki_pages_el_doc():
wiki_page_id_file = 'e:/dc/el/wiki/wiki_page_ids.bin'
fin = open(wiki_page_id_file, 'rb')
num_pages = np.fromfile(fin, np.int32, 1)
page_ids = np.fromfile(fin, np.int32, num_pages)
fin.close()
wiki_vec_file = 'e:/dc/el/vecs/wiki_dedw_vecs.bin'
fin = open(wiki_vec_file, 'rb')
num_vecs, dim = np.fromfile(fin, np.int32, 2)
# wiki_vecs = np.zeros((num_vecs, 50), np.float32)
wiki_vecs = np.zeros((num_vecs, 100), np.float32)
for i in xrange(num_vecs):
vec = np.fromfile(fin, np.float32, dim)
# wiki_vecs[i][:] = vec[50:]
wiki_vecs[i][:] = vec
fin.close()
# el_doc_vec_file = 'e:/dc/el/vecs/tac_2014_train_doc_vecs_av.bin'
# el_doc_vec_file = 'e:/dc/el/vecs/wiki_vecs_av.bin'
el_doc_vec_file = 'e:/dc/el/vecs/tac_2014_eval_vecs.bin'
# el_doc_vec_file = 'e:/dc/el/vecs/tac_2014_train_dw_vecs.bin'
el_doc_vecs = ioutils.load_vec_list_file(el_doc_vec_file)
top_list = close_vecs(wiki_vecs, el_doc_vecs[0])
for idx, dist in top_list:
print page_ids[idx], dist
def close_wiki_pages():
dst_wid = 534366
wid_title_file = 'e:/el/tmpres/wiki/enwiki-20150403-id-title-list.txt'
wid_title_dict = __load_id_title_dict(wid_title_file)
wiki_page_id_file = 'e:/dc/el/wiki/wiki_page_ids.bin'
fin = open(wiki_page_id_file, 'rb')
num_pages = np.fromfile(fin, np.int32, 1)
page_ids = np.fromfile(fin, np.int32, num_pages)
fin.close()
pos = 0
for i, wid in enumerate(page_ids):
if wid == dst_wid:
pos = i
break
wiki_vec_file = 'e:/dc/el/vecs/wiki_dedw_vecs_1.bin'
# fin = open(wiki_vec_file, 'rb')
# num_vecs, dim = np.fromfile(fin, np.int32, 2)
# wiki_vecs = np.zeros((num_vecs, 50), np.float32)
# for i in xrange(num_vecs):
# vec = np.fromfile(fin, np.float32, dim)
# wiki_vecs[i][:] = vec[50:]
# fin.close()
wiki_vecs = ioutils.load_vec_list_file(wiki_vec_file)
top_list = close_vecs(wiki_vecs, wiki_vecs[pos])
for idx, dist in top_list:
print wid_title_dict[page_ids[idx]], dist
def __split_vecs(all_vecs_file_name, split_labels_file_name,
dst_train_vecs_file_name, dst_test_vecs_file_name):
all_vec_list = ioutils.load_vec_list_file(all_vecs_file_name)
split_labels = ioutils.load_labels_file(split_labels_file_name)
train_vec_list = list()
test_vec_list = list()
for vec, split_label in zip(all_vec_list, split_labels):
# vec = np.random.uniform(0, 1, len(vec)).astype(np.float32)
# print split_label
if split_label == 1:
test_vec_list.append(vec)
else:
train_vec_list.append(vec)
print len(train_vec_list), 'training samples'
print len(test_vec_list), 'testing samples'
def save_vecs(vec_list, dst_file_name):
fout = open(dst_file_name, 'wb')
np.asarray([len(vec_list), len(vec_list[0])], np.int32).tofile(fout)
for cur_vec in vec_list:
cur_vec.tofile(fout)
fout.close()
save_vecs(train_vec_list, dst_train_vecs_file_name)
save_vecs(test_vec_list, dst_test_vecs_file_name)
def averaging_vecs(doc_word_file, word_vecs_file, dst_file):
word_vecs = ioutils.load_vec_list_file(word_vecs_file)
dim = len(word_vecs[0])
fin = open(doc_word_file, 'rb')
num_left, num_right = np.fromfile(fin, np.int32, 2)
print num_left, num_right
fout = open(dst_file, 'wb')
np.asarray([num_left, dim], np.int32).tofile(fout)
for i in xrange(num_left):
num_vertices = np.fromfile(fin, np.int32, 1)
indices = np.fromfile(fin, np.int32, num_vertices)
weights = np.fromfile(fin, np.uint16, num_vertices)
vec = np.zeros(dim, np.float32)
sum_weights = np.sum(weights)
for idx, w in zip(indices, weights):
vec += word_vecs[idx] * w
vec /= sum_weights
vec.tofile(fout)
if i % 10000 == 10000 - 1:
print i + 1
# break # TODO
fout.close()
fin.close()
def cluster_nyt():
num_clusters_list = [5, 10, 15, 20]
method = 'RSM'
result_file = 'd:/documents/lab/paper-data/plot/%s-results-ri.csv' % method.lower()
labels_file_name = 'e:/dc/nyt-world-full/processed/test/doc-labels.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/de-vecs.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/de-vecs.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/glove-vecs.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/dedw-vecs.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/dedw2-vecs-ner.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/dedw2-vecs-ner-200.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/dedw4-vecs-015.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/dedw5-vecs-ner.bin'
doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/rsm-vecs-20.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/drbm-vecs-30.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/pvdm-vecs.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/pvdbow-vecs.bin'
# doc_vec_file_name = 'e:/dc/nyt-world-full/processed/vecs/nvdm-nyt.bin'
# doc_vec_file_name = 'e:/dc/20ng_bydate/vecs/test-dedw-vecs.bin'
# labels_file_name = 'e:/dc/20ng_bydate/test_labels.bin'
perf_list = list()
# for num_clusters in [5, 10, 15, 20]:
vec_list = ioutils.load_vec_list_file(doc_vec_file_name)
labels = ioutils.load_labels_file(labels_file_name)
for num_clusters in num_clusters_list:
print '%d clusters' % num_clusters
# nmi_score, purity_score, ri_score = clustering(doc_vec_file_name, labels_file_name, num_clusters)
nmi_score, purity_score, ri_score = cluster_and_eval(vec_list, labels, num_clusters)
perf_list.append((num_clusters, nmi_score, purity_score, ri_score))
# break
write_clustering_perf_to_csv(method, perf_list, result_file)
def split_vecs(all_vecs_file_name,
split_labels_file_name,
dst_train_vecs_file_name,
dst_test_vecs_file_name,
train_label=0,
test_label=1):
all_vec_list = ioutils.load_vec_list_file(all_vecs_file_name)
split_labels = ioutils.load_labels_file(split_labels_file_name)
train_vec_list = list()
test_vec_list = list()
for vec, split_label in zip(all_vec_list, split_labels):
# vec = np.random.uniform(0, 1, len(vec)).astype(np.float32)
# print split_label
if split_label == test_label:
test_vec_list.append(vec)
elif split_label == train_label:
train_vec_list.append(vec)
print len(train_vec_list), 'training samples'
print len(test_vec_list), 'testing samples'
def save_vecs(vec_list, dst_file_name):
fout = open(dst_file_name, 'wb')
np.asarray([len(vec_list), len(vec_list[0])], np.int32).tofile(fout)
for cur_vec in vec_list:
cur_vec.tofile(fout)
fout.close()
save_vecs(train_vec_list, dst_train_vecs_file_name)
save_vecs(test_vec_list, dst_test_vecs_file_name)
def averaging_vecs(doc_word_file, word_vecs_file, dst_file):
word_vecs = ioutils.load_vec_list_file(word_vecs_file)
dim = len(word_vecs[0])
fin = open(doc_word_file, 'rb')
num_left, num_right = np.fromfile(fin, np.int32, 2)
print num_left, num_right
fout = open(dst_file, 'wb')
np.asarray([num_left, dim], np.int32).tofile(fout)
for i in xrange(num_left):
num_vertices = np.fromfile(fin, np.int32, 1)
indices = np.fromfile(fin, np.int32, num_vertices)
weights = np.fromfile(fin, np.uint16, num_vertices)
vec = np.zeros(dim, np.float32)
sum_weights = np.sum(weights)
for idx, w in zip(indices, weights):
vec += word_vecs[idx] * w
vec /= sum_weights
vec.tofile(fout)
if i % 10000 == 10000 - 1:
print i + 1
# break # TODO
fout.close()
fin.close()
def close_wiki_pages_el_doc():
wiki_page_id_file = 'e:/dc/el/wiki/wiki_page_ids.bin'
fin = open(wiki_page_id_file, 'rb')
num_pages = np.fromfile(fin, np.int32, 1)
page_ids = np.fromfile(fin, np.int32, num_pages)
fin.close()
wiki_vec_file = 'e:/dc/el/vecs/wiki_dedw_vecs.bin'
fin = open(wiki_vec_file, 'rb')
num_vecs, dim = np.fromfile(fin, np.int32, 2)
# wiki_vecs = np.zeros((num_vecs, 50), np.float32)
wiki_vecs = np.zeros((num_vecs, 100), np.float32)
for i in xrange(num_vecs):
vec = np.fromfile(fin, np.float32, dim)
# wiki_vecs[i][:] = vec[50:]
wiki_vecs[i][:] = vec
fin.close()
# el_doc_vec_file = 'e:/dc/el/vecs/tac_2014_train_doc_vecs_av.bin'
# el_doc_vec_file = 'e:/dc/el/vecs/wiki_vecs_av.bin'
el_doc_vec_file = 'e:/dc/el/vecs/tac_2014_eval_vecs.bin'
# el_doc_vec_file = 'e:/dc/el/vecs/tac_2014_train_dw_vecs.bin'
el_doc_vecs = ioutils.load_vec_list_file(el_doc_vec_file)
top_list = close_vecs(wiki_vecs, el_doc_vecs[0])
for idx, dist in top_list:
print page_ids[idx], dist
def __cluster_nyt():
# num_clusters_list = [5, 10, 15, 20]
num_clusters_list = [10, 15, 20]
# num_clusters_list = [5]
method = 'RSM'
datadir = 'e:/data/emadr/nyt-less-docs/world'
result_file = 'd:/documents/lab/paper-data/plot/%s-results-ri.csv' % method.lower(
)
labels_file_name = os.path.join(datadir, 'bindata/test-labels.bin')
# doc_vec_file_name = os.path.join(datadir, 'vecs/test-dedw-vecs.bin')
doc_vec_file_name = os.path.join(datadir, 'bindata/test-pvdbow-vecs.bin')
# doc_vec_file_name = os.path.join(datadir, 'rsm/test-rsm-vecs.bin')
perf_list = list()
# for num_clusters in [5, 10, 15, 20]:
vec_list = ioutils.load_vec_list_file(doc_vec_file_name)
labels = ioutils.load_labels_file(labels_file_name)
for num_clusters in num_clusters_list:
print '%d clusters' % num_clusters
# nmi_score, purity_score, ri_score = clustering(doc_vec_file_name, labels_file_name, num_clusters)
nmi_score, purity_score, ri_score = cluster_and_eval(
vec_list, labels, num_clusters)
perf_list.append((num_clusters, nmi_score, purity_score, ri_score))
def close_docs(doc_vec_file, dst_idx=0, k=10):
# doc_vec_file = 'e:/dc/20ng_bydate/vecs/dedw_vecs_0.bin'
print 'loading', doc_vec_file
doc_vecs = ioutils.load_vec_list_file(doc_vec_file)
print 'done'
dst_vec = doc_vecs[dst_idx]
return close_vecs(doc_vecs, dst_vec)
def close_docs(doc_vec_file, dst_idx=0, k=10):
# doc_vec_file = 'e:/dc/20ng_bydate/vecs/dedw_vecs_0.bin'
print 'loading', doc_vec_file
doc_vecs = ioutils.load_vec_list_file(doc_vec_file)
print 'done'
dst_vec = doc_vecs[dst_idx]
return close_vecs(doc_vecs, dst_vec)
def close_words():
words_dict_file = 'e:/dc/el/words_dict_proper.txt'
word_vecs_file = 'e:/dc/el/vecs/word_vecs.bin'
words = ioutils.load_words_dict_to_list(words_dict_file, False)
print len(words)
idx = 774318
print words[idx]
word_vecs = ioutils.load_vec_list_file(word_vecs_file)
print len(word_vecs)
close_list = close_vecs(word_vecs, word_vecs[idx])
for idx, dist in close_list:
print words[idx], dist
def close_words():
words_dict_file = 'e:/dc/el/words_dict_proper.txt'
word_vecs_file = 'e:/dc/el/vecs/word_vecs.bin'
words = ioutils.load_words_dict_to_list(words_dict_file, False)
print len(words)
idx = 774318
print words[idx]
word_vecs = ioutils.load_vec_list_file(word_vecs_file)
print len(word_vecs)
close_list = close_vecs(word_vecs, word_vecs[idx])
for idx, dist in close_list:
print words[idx], dist
def close_words_of_docs():
word_dict_file_name = 'e:/dc/20ng_bydate/words_dict.txt'
words = ioutils.load_words_dict_to_list(word_dict_file_name)
bow_docs_file_name = 'e:/dc/20ng_bydate/all_docs_dw_net.bin'
word_indices_list, word_cnts_list, num_words = ioutils.load_bow_file(bow_docs_file_name)
print num_words, 'words'
doc_vec_file_name = 'e:/dc/20ng_bydate/vecs/doc_vec_cpp_100.bin'
word_vec_file_name = 'e:/dc/20ng_bydate/vecs/word_vecs_cpp_100.bin'
doc_vecs = ioutils.load_vec_list_file(doc_vec_file_name)
word_vecs = ioutils.load_vec_list_file(word_vec_file_name)
def show_close_words(doc_vec, word_indices):
dist_list = list()
for word_idx in word_indices:
dist_list.append((np.dot(doc_vec, word_vecs[word_idx]), word_idx))
dist_list.sort(key=lambda tup: tup[0])
# closest_words = heapq.nlargest(10, dist_list, key=lambda tup: tup[0])
for dist, idx in dist_list:
print dist, words[idx]
show_close_words(doc_vecs[0], word_indices_list[0])
def close_words_of_docs():
word_dict_file_name = 'e:/dc/20ng_bydate/words_dict.txt'
words = ioutils.load_words_dict_to_list(word_dict_file_name)
bow_docs_file_name = 'e:/dc/20ng_bydate/all_docs_dw_net.bin'
word_indices_list, word_cnts_list, num_words = ioutils.load_bow_file(
bow_docs_file_name)
print num_words, 'words'
doc_vec_file_name = 'e:/dc/20ng_bydate/vecs/doc_vec_cpp_100.bin'
word_vec_file_name = 'e:/dc/20ng_bydate/vecs/word_vecs_cpp_100.bin'
doc_vecs = ioutils.load_vec_list_file(doc_vec_file_name)
word_vecs = ioutils.load_vec_list_file(word_vec_file_name)
def show_close_words(doc_vec, word_indices):
dist_list = list()
for word_idx in word_indices:
dist_list.append((np.dot(doc_vec, word_vecs[word_idx]), word_idx))
dist_list.sort(key=lambda tup: tup[0])
# closest_words = heapq.nlargest(10, dist_list, key=lambda tup: tup[0])
for dist, idx in dist_list:
print dist, words[idx]
show_close_words(doc_vecs[0], word_indices_list[0])
def __cluster_20ng():
num_clusters = 20
labels_file = 'e:/data/emadr/20ng_bydate/bindata/test-labels.bin'
# doc_vec_file = 'e:/data/emadr/20ng_bydate/bindata/test-dedw-vecs.bin'
# doc_vec_file = 'e:/data/emadr/20ng_bydate/vecs/dew-vecs-0_8-50.bin'
# doc_vec_file = 'e:/data/emadr/20ng_bydate/vecs/dew-vecs-cluster-0_15-50.bin'
# doc_vec_file = 'e:/data/emadr/20ng_bydate/bindata/test-pvdbow-vecs.bin'
doc_vec_file = 'e:/data/emadr/20ng_bydate/bindata/test-pvdm-vecs.bin'
# doc_vec_file = 'e:/data/emadr/20ng_bydate/rsm/test-rsm-vecs.bin'
vec_list = ioutils.load_vec_list_file(doc_vec_file)
labels = ioutils.load_labels_file(labels_file)
nmi_score, purity_score, ri_score = cluster_and_eval(
vec_list, labels, num_clusters)
print '%f\t%f\t%f' % (nmi_score, purity_score, ri_score)
def close_20ng_docs():
# doc_vec_file = 'e:/dc/20ng_bydate/vecs/dedw_vecs_0.bin'
doc_vec_file = 'e:/dc/20ng_bydate/vecs/dbow_doc_vecs.bin'
# av_doc_vec_file = 'e:/dc/20ng_bydate/vecs/doc_vecs_av.bin'
print 'loading', doc_vec_file
doc_vecs = ioutils.load_vec_list_file(doc_vec_file)
print len(doc_vecs)
# dw_vecs = np.zeros((len(doc_vecs), 50), np.float32)
# for i in xrange(len(doc_vecs)):
# dw_vecs[i] = doc_vecs[i][50:]
# print 'done'
# print 'loading', av_doc_vec_file
# av_doc_vecs = ioutils.load_vec_list_file(av_doc_vec_file)
# print 'done'
dst_vec = doc_vecs[0]
close_list = close_vecs(doc_vecs, dst_vec)
for idx, dist in close_list:
print idx, dist
def close_20ng_docs():
# doc_vec_file = 'e:/dc/20ng_bydate/vecs/dedw_vecs_0.bin'
doc_vec_file = 'e:/dc/20ng_bydate/vecs/dbow_doc_vecs.bin'
# av_doc_vec_file = 'e:/dc/20ng_bydate/vecs/doc_vecs_av.bin'
print 'loading', doc_vec_file
doc_vecs = ioutils.load_vec_list_file(doc_vec_file)
print len(doc_vecs)
# dw_vecs = np.zeros((len(doc_vecs), 50), np.float32)
# for i in xrange(len(doc_vecs)):
# dw_vecs[i] = doc_vecs[i][50:]
# print 'done'
# print 'loading', av_doc_vec_file
# av_doc_vecs = ioutils.load_vec_list_file(av_doc_vec_file)
# print 'done'
dst_vec = doc_vecs[0]
close_list = close_vecs(doc_vecs, dst_vec)
for idx, dist in close_list:
print idx, dist
def doc_vecs_file_for_tsne():
tag = 'all'
vecs_file_tag = '012'
all_doc_vecs_file = 'e:/dc/nyt-world-full/processed/vecs/dedw4-vecs-%s.bin' % vecs_file_tag
all_doc_labels_file = 'e:/dc/nyt-world-full/processed/test/doc-labels.bin'
doc_indices_file = 'e:/dc/nyt-world-full/processed/test/sne-doc-indices-%s.bin' % tag
dst_doc_vecs_file = 'e:/dc/nyt-world-full/processed/test/sne-emadr-vecs-%s-%s.txt' % (vecs_file_tag, tag)
dst_labels_file = 'e:/dc/nyt-world-full/processed/test/sne-doc-labels-%s.txt' % tag
fin = open(doc_indices_file, 'rb')
num_indices = np.fromfile(fin, np.int32, 1)
doc_indices = np.fromfile(fin, np.int32, num_indices)
fin.close()
doc_vecs = ioutils.load_vec_list_file(all_doc_vecs_file)
fout_vecs = open(dst_doc_vecs_file, 'wb')
for idx in doc_indices:
for v in doc_vecs[idx]:
fout_vecs.write(' %f' % v)
fout_vecs.write('\n')
fout_vecs.close()
def doc_vecs_file_for_tsne():
tag = 'all'
vecs_file_tag = '012'
all_doc_vecs_file = 'e:/dc/nyt-world-full/processed/vecs/dedw4-vecs-%s.bin' % vecs_file_tag
all_doc_labels_file = 'e:/dc/nyt-world-full/processed/test/doc-labels.bin'
doc_indices_file = 'e:/dc/nyt-world-full/processed/test/sne-doc-indices-%s.bin' % tag
dst_doc_vecs_file = 'e:/dc/nyt-world-full/processed/test/sne-emadr-vecs-%s-%s.txt' % (
vecs_file_tag, tag)
dst_labels_file = 'e:/dc/nyt-world-full/processed/test/sne-doc-labels-%s.txt' % tag
fin = open(doc_indices_file, 'rb')
num_indices = np.fromfile(fin, np.int32, 1)
doc_indices = np.fromfile(fin, np.int32, num_indices)
fin.close()
doc_vecs = ioutils.load_vec_list_file(all_doc_vecs_file)
fout_vecs = open(dst_doc_vecs_file, 'wb')
for idx in doc_indices:
for v in doc_vecs[idx]:
fout_vecs.write(' %f' % v)
fout_vecs.write('\n')
fout_vecs.close()
def split_vectors(all_vec_file_name, all_labels_file_name,
dst_train_vec_file_name, dst_train_labels_file_name,
dst_test_vec_file_name, dst_test_labels_file_name):
all_vec_list = ioutils.load_vec_list_file(all_vec_file_name)
all_labels = ioutils.load_labels_file(all_labels_file_name)
train_vec_list = list()
train_labels = list()
test_vec_list = list()
test_labels = list()
for vec, label in zip(all_vec_list, all_labels):
rand_val = random.randint(1, 10)
if rand_val == 1:
test_vec_list.append(vec)
test_labels.append(label)
else:
train_vec_list.append(vec)
train_labels.append(label)
print len(train_labels), 'training samples'
print len(test_labels), 'testing samples'
def save_vecs(vec_list, dst_file_name):
fout = open(dst_file_name, 'wb')
np.asarray([len(vec_list), len(vec_list[0])], np.int32).tofile(fout)
for cur_vec in vec_list:
cur_vec.tofile(fout)
fout.close()
def save_labels(labels_list, dst_file_name):
fout = open(dst_file_name, 'wb')
np.asarray([len(labels_list)], np.int32).tofile(fout)
np.asarray(labels_list, np.int32).tofile(fout)
fout.close()
save_vecs(train_vec_list, dst_train_vec_file_name)
save_labels(train_labels, dst_train_labels_file_name)
save_vecs(test_vec_list, dst_test_vec_file_name)
save_labels(test_labels, dst_test_labels_file_name)
def split_vectors(all_vec_file_name, all_labels_file_name, dst_train_vec_file_name,
dst_train_labels_file_name, dst_test_vec_file_name, dst_test_labels_file_name):
all_vec_list = ioutils.load_vec_list_file(all_vec_file_name)
all_labels = ioutils.load_labels_file(all_labels_file_name)
train_vec_list = list()
train_labels = list()
test_vec_list = list()
test_labels = list()
for vec, label in zip(all_vec_list, all_labels):
rand_val = random.randint(1, 10)
if rand_val == 1:
test_vec_list.append(vec)
test_labels.append(label)
else:
train_vec_list.append(vec)
train_labels.append(label)
print len(train_labels), 'training samples'
print len(test_labels), 'testing samples'
def save_vecs(vec_list, dst_file_name):
fout = open(dst_file_name, 'wb')
np.asarray([len(vec_list), len(vec_list[0])], np.int32).tofile(fout)
for cur_vec in vec_list:
cur_vec.tofile(fout)
fout.close()
def save_labels(labels_list, dst_file_name):
fout = open(dst_file_name, 'wb')
np.asarray([len(labels_list)], np.int32).tofile(fout)
np.asarray(labels_list, np.int32).tofile(fout)
fout.close()
save_vecs(train_vec_list, dst_train_vec_file_name)
save_labels(train_labels, dst_train_labels_file_name)
save_vecs(test_vec_list, dst_test_vec_file_name)
save_labels(test_labels, dst_test_labels_file_name)
def __lda_clustering():
num_topics = 20
min_occurrence = 30
# datadir = 'e:/data/emadr/20ng_bydate/'
# labels_file = os.path.join(datadir, 'bindata/test-labels.bin')
# topic_vecs_file = os.path.join(datadir, 'lda/test-vecs-%d-%d.bin' % (num_topics, min_occurrence))
datadir = 'e:/data/emadr/nyt-less-docs/world'
labels_file = os.path.join(datadir, 'bindata/test-labels.bin')
topic_vecs_file = os.path.join(
datadir, 'lda/test-vecs-%d-%d.bin' % (num_topics, min_occurrence))
topic_vecs = ioutils.load_vec_list_file(topic_vecs_file)
gold_labels = ioutils.load_labels_file(labels_file)
sys_labels = list()
for i, topic_vec in enumerate(topic_vecs):
cluster_idx = 0
max_dist = 0
for j, v in enumerate(topic_vec):
if v > max_dist:
cluster_idx = j
max_dist = v
# print cluster_idx, max_dist
sys_labels.append(cluster_idx)
if len(sys_labels) % 5000 == 0:
print len(sys_labels)
nmi_score = normalized_mutual_info_score(gold_labels, sys_labels)
purity_score = purity(gold_labels, sys_labels)
# ri_score = rand_index(gold_labels, sys_labels)
ri_score = 0
print 'NMI: %f Purity: %f Rand index: %f' % (nmi_score, purity_score,
ri_score)
# print 'Accuracy: %f' % cluster_accuracy(labels, model.labels_)
print '%f\t%f\t%f' % (nmi_score, purity_score, ri_score)
