def make_tfidf(use_stemming):
# read in
if use_stemming:
with open(get_survey_s(), encoding='latin-1') as s:
survey_hlp = json.load(s)
with open(get_seminal_s(), encoding='latin-1') as s:
seminal_hlp = json.load(s)
with open(get_uninfluential_s(), encoding='latin-1') as s:
uninfluential_hlp = json.load(s)
else:
with open(get_survey_u(), encoding='latin-1') as s:
survey_hlp = json.load(s)
with open(get_seminal_u(), encoding='latin-1') as s:
seminal_hlp = json.load(s)
with open(get_uninfluential_u(), encoding='latin-1') as s:
uninfluential_hlp = json.load(s)
data_set, words = task_tfidf(survey_hlp, seminal_hlp, uninfluential_hlp,
use_stemming)
if use_stemming:
with open(get_file_base() + 'tfidf_data/tfidf_stemmed.sav',
'wb') as output:
joblib.dump(data_set, output)
else:
with open(get_file_base() + 'tfidf_data/tfidf_unstemmed.sav',
'wb') as output:
joblib.dump(data_set, output)
print_words(words, use_stemming)
def main():
with open(get_survey_u(), encoding='latin-1') as s:
survey_hlp = json.load(s)
with open(get_seminal_u(), encoding='latin-1') as s:
seminal_hlp = json.load(s)
with open(get_uninfluential_u(), encoding='latin-1') as s:
uninfluential_hlp = json.load(s)
data_set = task_year(survey_hlp, seminal_hlp, uninfluential_hlp)
# calculate features on which the classification is going to be performed
ds_ct = 0
executor = ThreadPoolExecutor(max_workers=64)
completed_vecs = {}
# data_set[][0] -> P
# data_set[][1] -> X, references
# data_set[][2] -> Y, citations
while ds_ct < len(data_set):
p = 0
while p < len(data_set[ds_ct][0]):
futures = executor.submit(do_cosine, data_set, ds_ct, p)
completed_vecs[data_set[ds_ct][3] + str(p)] = futures.result()
p += 1
ds_ct += 1
write_to_file(
get_file_base() + 'extracted_features/EVAL/d2v_cos_YEAR_' +
less_than_or_more + "_" + str(year) + '_unstemmed.csv', completed_vecs)
def find_equal():
with open(get_seminal_u(), encoding='latin-1') as s:
seminal_hlp = json.load(s)
seminal_hlp = seminal_hlp['seminal']
with open(get_survey_u(), encoding='latin-1') as s:
survey_hlp = json.load(s)
survey_hlp = survey_hlp['survey']
with open(get_uninfluential_u(), encoding='latin-1') as s:
uninfluential_hlp = json.load(s)
uninfluential_hlp = uninfluential_hlp['uninfluential']
lengths_sem = {}
for entry_id in range(0, len(seminal_hlp)):
abs_length = len(seminal_hlp[entry_id]['abs'].split())
if abs_length not in lengths_sem:
lengths_sem[abs_length] = [entry_id]
else:
lengths_sem[abs_length].append(entry_id)
lengths_sur = {}
for entry_id in range(0, len(survey_hlp)):
abs_length = len(survey_hlp[entry_id]['abs'].split())
if abs_length not in lengths_sur:
lengths_sur[abs_length] = [entry_id]
else:
lengths_sur[abs_length].append(entry_id)
lengths_uni = {}
for entry_id in range(0, len(uninfluential_hlp)):
abs_length = len(uninfluential_hlp[entry_id]['abs'].split())
if abs_length not in lengths_uni:
lengths_uni[abs_length] = [entry_id]
else:
lengths_uni[abs_length].append(entry_id)
found = []
for entry in lengths_sem:
if entry in lengths_sur and entry in lengths_uni:
found.append(entry)
sem_ids = []
sur_ids = []
uni_ids = []
for entry in found:
num = min(len(lengths_sem[entry]), len(lengths_sur[entry]), len(lengths_uni[entry]))
for i in range(0, num):
sem_ids.append(lengths_sem[entry][i])
sur_ids.append(lengths_sur[entry][i])
uni_ids.append(lengths_uni[entry][i])
return sem_ids, sur_ids, uni_ids
def make_lda(stem):
ps = PorterStemmer()
if stem:
lda = ldamodel.LdaModel.load(get_file_base() +
'lda_data/lda_model_stemmed')
dictionary = Dictionary.load_from_text(get_file_base() +
'lda_data/dict_stemmed')
sem_raw, sem_in, sem_out = read_in(get_seminal_s(), 'seminal')
sur_raw, sur_in, sur_out = read_in(get_survey_s(), 'survey')
uni_raw, uni_in, uni_out = read_in(get_uninfluential_s(),
'uninfluential')
else:
lda = ldamodel.LdaModel.load(get_file_base() +
'lda_data/lda_model_unstemmed')
dictionary = Dictionary.load_from_text(get_file_base() +
'lda_data/dict_unstemmed')
sem_raw, sem_in, sem_out = read_in(get_seminal_u(), 'seminal')
sur_raw, sur_in, sur_out = read_in(get_survey_u(), 'survey')
uni_raw, uni_in, uni_out = read_in(get_uninfluential_u(),
'uninfluential')
# write lda information to file
if stem:
write_to_file(
get_file_base() + 'lda_data/sem_lda_stemmed.json',
get_file_base() + 'lda_data/sem_lda_stemmed_one_doc_rep.json',
sem_raw, sem_in, sem_out, 'seminal', '0', lda, dictionary)
write_to_file(
get_file_base() + 'lda_data/sur_lda_stemmed.json',
get_file_base() + 'lda_data/sur_lda_stemmed_one_doc_rep.json',
sur_raw, sur_in, sur_out, 'survey', '1', lda, dictionary)
write_to_file(
get_file_base() + 'lda_data/uni_lda_stemmed.json',
get_file_base() + 'lda_data/uni_lda_stemmed_one_doc_rep.json',
uni_raw, uni_in, uni_out, 'uninfluential', '2', lda, dictionary)
else:
write_to_file(
get_file_base() + 'lda_data/sem_lda_unstemmed.json',
get_file_base() + 'lda_data/sem_lda_unstemmed_one_doc_rep.json',
sem_raw, sem_in, sem_out, 'seminal', '0', lda, dictionary)
write_to_file(
get_file_base() + 'lda_data/sur_lda_unstemmed.json',
get_file_base() + 'lda_data/sur_lda_unstemmed_one_doc_rep.json',
sur_raw, sur_in, sur_out, 'survey', '1', lda, dictionary)
write_to_file(
get_file_base() + 'lda_data/uni_lda_unstemmed.json',
get_file_base() + 'lda_data/uni_lda_unstemmed_one_doc_rep.json',
uni_raw, uni_in, uni_out, 'uninfluential', '2', lda, dictionary)
def make_d2v():
# read in
with open(get_survey_u(), encoding='latin-1') as s:
survey_hlp = json.load(s)
with open(get_seminal_u(), encoding='latin-1') as s:
seminal_hlp = json.load(s)
with open(get_uninfluential_u(), encoding='latin-1') as s:
uninfluential_hlp = json.load(s)
data_set = task_d2v(survey_hlp, seminal_hlp, uninfluential_hlp)
with open(get_file_base() + 'd2v_data/d2v_unstemmed.pickle',
'wb') as output:
pickle.dump(data_set, output)
def task_lda(use_stemming):
with open(get_survey_u(), encoding='latin-1') as s:
survey_hlp = json.load(s)
with open(get_seminal_u(), encoding='latin-1') as s:
seminal_hlp = json.load(s)
with open(get_uninfluential_u(), encoding='latin-1') as s:
uninfluential_hlp = json.load(s)
with open(get_file_base() + 'lda_data/sem_lda_' + ('un' if not use_stemming else '') + 'stemmed.json', 'r') as \
sem_file:
sem = json.load(sem_file)
with open(get_file_base() + 'lda_data/sur_lda_' + ('un' if not use_stemming else '') + 'stemmed.json', 'r') as \
sur_file:
sur = json.load(sur_file)
with open(get_file_base() + 'lda_data/uni_lda_' + ('un' if not use_stemming else '') + 'stemmed.json', 'r') as \
sur_file:
uni = json.load(sur_file)
# seminal
unordered_seminal_p, unordered_seminal_x, unordered_seminal_y = read_in_json_lda_data(
'seminal', sem)
# survey
unordered_survey_p, unordered_survey_x, unordered_survey_y = read_in_json_lda_data(
'survey', sur)
# uninfluential
unordered_uninfluential_p, unordered_uninfluential_x, unordered_uninfluential_y = \
read_in_json_lda_data('uninfluential', uni)
seminal_hlp = seminal_hlp['seminal']
survey_hlp = survey_hlp['survey']
uninfluential_hlp = uninfluential_hlp['uninfluential']
# matching of ordering of publication with sur/sem/uni_stemmed/unstemmed-data
seminal_p, seminal_x, seminal_y = order_publications(
unordered_seminal_p, unordered_seminal_x, unordered_seminal_y,
seminal_hlp)
survey_p, survey_x, survey_y = order_publications(unordered_survey_p,
unordered_survey_x,
unordered_survey_y,
survey_hlp)
uninfluential_p, uninfluential_x, uninfluential_y = order_publications(
unordered_uninfluential_p, unordered_uninfluential_x,
unordered_uninfluential_y, uninfluential_hlp)
return [[seminal_p, seminal_x, seminal_y, 'sem '],
[survey_p, survey_x, survey_y, 'surv '],
[uninfluential_p, uninfluential_x, uninfluential_y, 'uni ']]
def make_bert():
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
with open(get_seminal_u(), encoding='latin-1') as s:
seminal_hlp = json.load(s)
with open(get_survey_u(), encoding='latin-1') as s:
survey_hlp = json.load(s)
with open(get_uninfluential_u(), encoding='latin-1') as s:
uninfluential_hlp = json.load(s)
print('sem')
seminal_p = {}
seminal_x = {}
seminal_y = {}
ct = 0
for p in seminal_hlp['seminal']:
seminal_p[ct] = do_bert(p['abs'], tokenizer)
seminal_x[ct] = {}
seminal_y[ct] = {}
ct_ref = 0
for ref in p['ref']:
seminal_x[ct][ct_ref] = do_bert(ref['abs'], tokenizer)
ct_ref += 1
ct_cit = 0
for cit in p['cit']:
seminal_y[ct][ct_cit] = do_bert(cit['abs'], tokenizer)
ct_cit += 1
ct += 1
write_to_file(get_file_base() + 'bert_data/sem_bert_unstemmed.json',
seminal_p, seminal_x, seminal_y, 'seminal')
survey_p = {}
survey_x = {}
survey_y = {}
print('sur')
ct = 0
for p in survey_hlp['survey']:
survey_p[ct] = do_bert(p['abs'], tokenizer)
survey_x[ct] = {}
survey_y[ct] = {}
ct_ref = 0
for ref in p['ref']:
survey_x[ct][ct_ref] = do_bert(ref['abs'], tokenizer)
ct_ref += 1
ct_cit = 0
for cit in p['cit']:
survey_y[ct][ct_cit] = do_bert(cit['abs'], tokenizer)
ct_cit += 1
ct += 1
write_to_file(get_file_base() + 'bert_data/sur_bert_unstemmed.json',
survey_p, survey_x, survey_y, 'survey')
print('uni')
uninfluential_p = {}
uninfluential_x = {}
uninfluential_y = {}
ct = 0
for p in uninfluential_hlp['uninfluential']:
uninfluential_p[ct] = do_bert(p['abs'], tokenizer)
uninfluential_x[ct] = {}
uninfluential_y[ct] = {}
ct_ref = 0
for ref in p['ref']:
uninfluential_x[ct][ct_ref] = do_bert(ref['abs'], tokenizer)
ct_ref += 1
ct_cit = 0
for cit in p['cit']:
uninfluential_y[ct][ct_cit] = do_bert(cit['abs'], tokenizer)
ct_cit += 1
ct += 1
write_to_file(get_file_base() + 'bert_data/uni_bert_unstemmed.json',
uninfluential_p, uninfluential_x, uninfluential_y,
'uninfluential')
import json
from gensim.models import ldamodel
from gensim.corpora.dictionary import Dictionary
from general.baseFileExtractor import get_file_base, get_seminal_u, get_survey_u, get_uninfluential_u
# read in
with open(get_survey_u(), encoding='latin-1') as s:
survey_hlp = json.load(s)
survey_hlp = survey_hlp['survey']
with open(get_seminal_u(), encoding='latin-1') as s:
seminal_hlp = json.load(s)
seminal_hlp = seminal_hlp['seminal']
with open(get_uninfluential_u(), encoding='latin-1') as s:
uninfluential_hlp = json.load(s)
uninfluential_hlp = uninfluential_hlp['uninfluential']
lda = ldamodel.LdaModel.load(get_file_base() + 'lda_data/lda_model_unstemmed')
dictionary = Dictionary.load_from_text(get_file_base() +
'lda_data/dict_unstemmed')
sem = []
sur = []
uni = []
for p in seminal_hlp:
sem.append(lda[dictionary.doc2bow(p['abs'].split())])
for p in survey_hlp:
sur.append(lda[dictionary.doc2bow(p['abs'].split())])
for p in uninfluential_hlp:
uni.append(lda[dictionary.doc2bow(p['abs'].split())])
if use_stemming:
with open(get_seminal_s(), 'r', encoding='utf8') as f:
sem = json.load(f)
sem = sem['seminal']
with open(get_survey_s(), 'r', encoding='utf8') as f:
sur = json.load(f)
sur = sur['survey']
with open(get_uninfluential_s(), 'r', encoding='utf8') as f:
uni = json.load(f)
uni = uni['uninfluential']
else:
with open(get_seminal_u(), 'r', encoding='utf8') as f:
sem = json.load(f)
sem = sem['seminal']
with open(get_survey_u(), 'r', encoding='utf8') as f:
sur = json.load(f)
sur = sur['survey']
with open(get_uninfluential_u(), 'r', encoding='utf8') as f:
uni = json.load(f)
uni = uni['uninfluential']
avg_length_abs_sem = 0
ref_sem = []
cit_sem = []
for p in sem:
avg_length_abs_sem += len(p['abs'].split())
ref_sem.append(len(p['ref']))
cit_sem.append(len(p['cit']))