def __init__(self, stops, minsize=3):
"""initialize index variables"""
self.ix = None
self.tokenizer = StandardAnalyzer(stoplist=stops, minsize=minsize)
self.umls = umls.UMLSLookup()
self.term_dict = {}
self.token2cuis = {}
self.concept_dict = {"__NULL__": 0}
self.synsets = {}
def disambiguate_query(self, ix, term_dict, concept_dict, token2cuis, query, table_name):
"""shallow word-sense disambiguation: disambiguate polysemous terms based on shallow word-concept connectivity within UMLS"""
qcuis = {}
umls_lookup = umls.UMLSLookup()
# tokenize query
q = self.tokenize_query(query)
# convert query into gensim doc2idx format
q2idx = ix.doc2idx(q)
# get cuis from query tokens
for idx in q2idx:
if idx in token2cuis and token2cuis[idx] != ["__NULL__"]:
for cui in token2cuis[idx]:
if cui in qcuis: # increase cui count
qcuis[cui] += 1
else: # initialize cui count
qcuis[cui] = 1
# perform shallow word-sense disambiguation
enc_query = []
for idx in q2idx:
if idx in term_dict: # disambiguate only for terms contained within term_dict
max_edges = 0 # relative maximum connections (edges)
if len(token2cuis[idx]) == 1: # monosemous term
ref_cui = token2cuis[idx][0]
# encode (term, cui) pair
enc_query.append([term_dict[idx], concept_dict[ref_cui]])
else: # polysemous term
candidates = []
# loop over cadidate concepts
for subj_cui in token2cuis[idx]:
num_edges = 0 # number of edges
if qcuis[subj_cui] == 1: # subj_cui is only associated with current term (idx)
obj_cuis = list(set(qcuis.keys()).difference({subj_cui}))
else: # subj_cui is associated with other terms in the query too
obj_cuis = list(qcuis.keys())
num_edges += umls_lookup.compute_num_edges(obj_cuis, subj_cui, table_name) # remember that subj and obj are inverted within UMLS <s, p, o> triples
# verify connectivity
if num_edges > max_edges:
# set candidates to subj_cui
candidates = [subj_cui]
# update max_edges
max_edges = num_edges
else:
# append subj_cui to candidates
candidates.append(subj_cui)
# keep head candidate - when disambiguation is not complete, it allows to get the most likely concept based on QuickUMLS ordering
ref_cui = candidates[0]
# encode (term, cui) pair
enc_query.append([term_dict[idx], concept_dict[ref_cui]])
else: # term oov
continue
return enc_query
def get_syns(self, term2cui, term_dict):
"""get synonymic relations between words within corpus (derived from a semantic lexicon)"""
syns = {}
umls_lookup = umls.UMLSLookup()
analyzer = SimpleAnalyzer()
for term, cui in term2cui.items():
if term in term_dict:
if cui != '__NULL__':
# get synset composed of single-word terms (reference term excluded)
synset = {syn[0].lower() for syn in umls_lookup.lookup_synonyms(cui, preferred=False) if len(list(analyzer(syn[0]))) == 1 and syn[0].lower() in term_dict and syn[0].lower() != term}
if len(synset) > 0:
syns[term] = list(synset)
else:
syns[term] = list()
else:
syns[term] = list()
return syns
def cui2source(self, term2cui, source='MSH'):
"""keep only CUIs presenting an entry in the given 'source' lexicon"""
cui2source = {}
umls_lookup = umls.UMLSLookup()
for term, cui in tqdm(term2cui.items()):
if cui == '__NULL__': # skip __NULL__ concepts
cui2source[term] = '__NULL__'
else:
# lookup codes and sources from UMLS
codes_and_sources = umls_lookup.lookup_code(cui=cui, preferred=False)
source_code = [code for code, src, _ in codes_and_sources if src == source]
if source_code: # CUI in source - keep it
cui2source[term] = cui
else: # CUI not in source - discard it
cui2source[term] = '__NULL__'
# return cui2source
return cui2source
def main(_):
os.chdir(os.path.dirname(os.path.realpath('__file__')))
# load options
opts = Options()
# set folders
corpus_folder = 'corpus/' + opts.corpus_name + '/' + opts.corpus_name
index_folder = 'corpus/' + opts.corpus_name + '/index'
model_folder = 'corpus/' + opts.corpus_name + '/models/' + opts.model_name
data_folder = 'corpus/' + opts.corpus_name + '/data'
query_folder = 'corpus/' + opts.corpus_name + '/queries'
qrels_folder = 'corpus/' + opts.corpus_name + '/qrels'
rankings_folder = 'corpus/' + opts.corpus_name + '/rankings/' + opts.model_name
# create folders
if not os.path.exists(data_folder):
os.makedirs(data_folder)
if not os.path.exists(index_folder):
os.makedirs(index_folder)
if not os.path.exists(rankings_folder):
os.makedirs(rankings_folder)
if not os.path.exists(model_folder):
os.makedirs(model_folder)
if not os.path.exists(query_folder) or not os.path.exists(qrels_folder):
print(
'folders containing queries and qrels are required - please add them'
)
return False
# establish connection with UMLS db
umls_lookup = umls.UMLSLookup()
# load queries
q = tf_utils.read_ohsu_queries(query_folder + '/' + opts.query_fname)
"""
PRE PROCESSING
"""
# pre process distributional data
if not os.path.exists(data_folder + '/words.json'):
# compute required data
words = tf_utils.process_corpus(corpus_folder, data_folder)
# build dataset to train CBOW + RMC model
data, cfs, word_dict, reverse_word_dict = tf_utils.build_dataset(
words, opts.min_cut_freq, data_folder)
del words # free memory from unnecessary data
print('Most common words (+ UNK)', count[:10])
print('Total number of words (+ UNK) within {}: {}'.format(
opts.corpus_name, len(data)))
print('Number of unique words (+ UNK) for {}: {}'.format(
opts.corpus_name, len(count)))
else:
# load required data
print('load processed data required to train CBOW + RMC model')
with open(data_folder + '/data.json', 'r') as df:
data = json.load(df)
with open(data_folder + '/docs.json', 'r') as cf:
corpus = json.load(cf)
with open(data_folder + '/idfs.json', 'r') as wf:
idfs = json.load(wf)
with open(data_folder + '/cfs.json', 'r') as cff:
cfs = json.load(cff)
with open(data_folder + '/word_dict.json', 'r') as wdf:
word_dict = json.load(wdf)
# compute reverse word dictionary
reverse_word_dict = dict(zip(word_dict.values(), word_dict.keys()))
# pre process relational data
if not os.path.exists(data_folder + '/term2cui.json'):
# map terms to cuis using QuickUMLS
term2cui = tf_utils.get_term2cui(word_dict,
data_folder,
threshold=opts.threshold,
stypes_fname=opts.stypes_fname)
else:
# load (term, cui) pairs
print('load (term, cui) pairs')
with open(data_folder + '/term2cui.json', 'r') as tcf:
term2cui = json.load(tcf)
# get synonyms for each word within vocabulary given semantic lexicon
print(
'get synonyms for each word within vocabulary given semantic lexicon')
syns = tf_utils.get_syns(term2cui, word_dict, umls_lookup)
# get synonyms as an array of synonym pairs
syns = [
list(itertools.product([word], synset))
for word, synset in syns.items()
]
syns = [pairs for pairs in syns if pairs]
syns = np.array([pair for pairs in syns for pair in pairs])
print('Total number of synonymy relations within {}: {}'.format(
opts.corpus_name, syns.shape[0]))
# load required data to perform retrieval
print('load required data to perform retrieval')
with open(data_folder + '/docs.json', 'r') as cf:
corpus = json.load(cf)
with open(data_folder + '/idfs.json', 'r') as wf:
idfs = json.load(wf)
# get docs and docnos from corpus
docnos = list(corpus.keys())
docs = list(corpus.values())
del corpus # free memory space
"""
NETWORK TRAINING
"""
# begin training
with tf.Graph().as_default(), tf.Session() as sess:
# set graph-level random seed
tf.set_random_seed(opts.seed)
# start data index
tf_globals.initialize()
# setup the model
model = JointRCM(len(word_dict), syns, opts)
# create model saving operation - keeps as many saved models as number of epochs
saver = tf.train.Saver(max_to_keep=opts.epochs)
# initialize the variables using global_variables_initializer()
sess.run(tf.global_variables_initializer())
print('start training')
print('number of batches per epoch: {}'.format(
len(data) // opts.batch_size))
best_score_per_epoch = []
for epoch in range(opts.epochs):
# train CBOW
print('training epoch {}'.format(epoch + 1))
# loop over (len(data) // opts.batch_size) batches
for i in tqdm(range(len(data) // opts.batch_size)):
batch_inputs, batch_labels = tf_utils.generate_batch(
data, opts.batch_size, opts.context_window)
feed_dict = {
model.inputs: batch_inputs,
model.labels: batch_labels
}
# run cbow train_op
sess.run(model.cbow_train_op, feed_dict=feed_dict)
if (i + 1) % opts.minimize_rcm_every == 0:
# run rcm train_op
sess.run(model.rcm_train_op)
# store trained CBOW
print('storing model at epoch {}'.format(epoch + 1))
model_checkpoint_path = os.path.join(
os.getcwd(), model_folder,
opts.model_name + str(epoch + 1) + '.ckpt')
save_path = saver.save(sess, model_checkpoint_path)
print("model saved in file: {}".format(save_path))
"""
DOCUMENT RETRIEVAL
"""
# get embs after training epoch
word_embs = sess.run(model.word_embs)
# evaluate CBOW for IR tasks
print('evaluating at epoch {}'.format(epoch + 1))
# compute doc embeddings and return list of filtered doc ids
doc_embs, filt_ids = tf_utils.compute_doc_embs(
docs, word_dict, word_embs, idfs)
# set query embs and ids
q_embs = []
q_ids = []
# loop over queries and generate rankings
for qid, qtext in q.items():
# prepare queries for semantic matching
q_proj = tf_utils.prepare_query(qtext[opts.field], word_dict,
word_embs)
if q_proj is None:
print('query {} does not contain known terms'.format(qid))
else:
q_embs.append(q_proj)
q_ids.append(qid)
q_embs = np.array(q_embs)
# perform search and evaluate model effectiveness
tf_utils.semantic_search(docnos, doc_embs, q_ids, q_embs,
rankings_folder,
opts.model_name + '_' + str(epoch + 1),
filt_ids)
scores = tf_utils.evaluate(
['Rprec', 'P_5', 'P_10', 'P_20', 'ndcg', 'map'],
rankings_folder, opts.model_name + '_' + str(epoch + 1),
qrels_folder, opts.qrels_fname)
best_score_per_epoch.append(scores[opts.ref_measure])
print('best model (in terms of {}) found at epoch: {}'.format(
opts.ref_measure,
np.argsort(best_score_per_epoch)[-1] + 1))
def main():
os.chdir(os.path.dirname(os.path.realpath('__file__')))
# set folders
corpus_folder = 'corpus/' + FLAGS.corpus_name + '/' + FLAGS.corpus_name
index_folder = 'corpus/' + FLAGS.corpus_name + '/index'
# model_folder = 'corpus/' + FLAGS.corpus_name + '/models/' + FLAGS.model_name
data_folder = 'corpus/' + FLAGS.corpus_name + '/data'
query_folder = 'corpus/' + FLAGS.corpus_name + '/queries'
qrels_folder = 'corpus/' + FLAGS.corpus_name + '/qrels'
rankings_folder = 'corpus/' + FLAGS.corpus_name + '/rankings/' + FLAGS.model_name
# create folders
if not os.path.exists(rankings_folder):
os.makedirs(rankings_folder)
# if not os.path.exists(model_folder):
# os.makedirs(model_folder)
if not os.path.exists(query_folder) or not os.path.exists(qrels_folder):
print(
'folders containing queries and qrels are required - please add them'
)
return False
# set random seed - enable reproducibility
np.random.seed(FLAGS.seed)
# establish connection with UMLS db
umls_lookup = umls.UMLSLookup()
# load required data
print(
'load processed data required to retrofit word vectors and perform retrieval tasks'
)
with open(data_folder + '/docs.json', 'r') as df:
corpus = json.load(df)
with open(data_folder + '/idfs.json', 'r') as wf:
idfs = json.load(wf)
with open(data_folder + '/cfs.json', 'r') as cff:
cfs = json.load(cff)
with open(data_folder + '/word_dict.json', 'r') as wdf:
word_dict = json.load(wdf)
# compute reverse word dict
reverse_word_dict = dict(zip(word_dict.values(), word_dict.keys()))
# store docnos and docs as separate lists
docnos = list(corpus.keys())
docs = list(corpus.values())
del corpus # free memory space
# pre process relational data
if not os.path.exists(data_folder + '/term2cui.json'):
# map terms to cuis using QuickUMLS
term2cui = tf_utils.get_term2cui(word_dict,
data_folder,
threshold=FLAGS.threshold,
stypes_fname=FLAGS.stypes_fname)
else:
# laod (term, cui) pairs
print('load (term, cui) pairs')
with open(data_folder + '/term2cui.json', 'r') as tcf:
term2cui = json.load(tcf)
"""
SEMANTIC PROCESSING
"""
# load semantic model
print('load semantic model')
with tf.Session() as sess:
# restore model and get required tensors
saver = tf.train.import_meta_graph(FLAGS.semantic_model + '.ckpt.meta')
saver.restore(sess, FLAGS.semantic_model + '.ckpt')
word_embs = sess.run(tf.get_default_graph().get_tensor_by_name(
'embeddings/word_embs:0'))
"""
RETROFITTING
"""
if FLAGS.retrofit:
# get synonyms for each word within vocabulary
print('get synonyms')
syns = tf_utils.get_syns(term2cui, word_dict, umls_lookup)
if FLAGS.syn_weights:
# convert collection frequencies from list to dict
cfs = dict(cfs)
else:
cfs = None
# retrofit word vectors
print('retrofit word vectors for {} iterations'.format(
FLAGS.iterations))
word_embs = retrofit(word_embs,
syns,
reverse_word_dict,
FLAGS.iterations,
alpha=1.0,
beta=FLAGS.beta,
cfs=cfs)
# compute doc embeddings
print('compute document vectors w/ retrofitted word vectors')
doc_embs, filt_ids = tf_utils.compute_doc_embs(docs, word_dict, word_embs,
idfs)
if not FLAGS.reranking:
"""
RETRIEVAL
"""
print('perform retrieval over the entire collection')
# load queries
q = tf_utils.read_ohsu_queries(query_folder + '/' + FLAGS.query_fname)
# set query embs and ids
q_embs = []
q_ids = []
# loop over queries and generate rankings
for qid, qtext in q.items():
# prepare queries for semantic matching
q_proj = tf_utils.prepare_query(qtext[FLAGS.qfield], word_dict,
word_embs)
if q_proj is None:
print('query {} does not contain known terms'.format(qid))
else:
q_embs.append(q_proj)
q_ids.append(qid)
q_embs = np.array(q_embs)
# perform search and evaluate model effectiveness
tf_utils.semantic_search(docnos, doc_embs, q_ids, q_embs,
rankings_folder, FLAGS.model_name)
scores = tf_utils.evaluate(
['Rprec', 'P_5', 'P_10', 'P_20', 'ndcg', 'map'], rankings_folder,
FLAGS.model_name, qrels_folder, FLAGS.qrels_fname)
else:
"""
RE-RANKING
"""
print('perform re-ranking over top 1000 documents from a baseline run')
# parse and store qrels
with open(qrels_folder + '/' + FLAGS.qrels_fname + '.txt',
'r') as qrelf:
qrels = pytrec_eval.parse_qrel(qrelf)
# initialize evaluator over qrels
evaluator = pytrec_eval.RelevanceEvaluator(
qrels, {'P'}) # evaluate on Precision
# parse input run
print('parse input run')
with open(FLAGS.run_path, 'r') as runf:
run = pytrec_eval.parse_run(runf)
# load queries
q = tf_utils.read_ohsu_queries(query_folder + '/' + FLAGS.query_fname)
# get query ids
qids = list(q.keys())
# shuffle query ids
np.random.shuffle(qids)
if FLAGS.fixed_gamma:
# perform re-ranking based on a fixed value of gamma
print('perform re-ranking w/ gamma=%.2f' % (FLAGS.fixed_gamma))
# initialize combined (output) run
crun = trec_utils.OnlineTRECRun(FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma))
# combine rankings using fixed gamma
comb_run = tf_utils.compute_combined_run(
run, FLAGS.qfield, q, docnos, doc_embs, word_dict, word_embs,
SCORE_NORMALIZERS[FLAGS.normalizer], FLAGS.fixed_gamma)
# store test ranking in combined run
for qid, doc_ids_and_scores in comb_run.items():
crun.add_ranking(
qid, [(score, docno)
for docno, score in doc_ids_and_scores.items()])
# close and store run
crun.close_and_write(out_path=rankings_folder + '/' +
FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma) + '.txt',
overwrite=True)
print('combined run stored in {}'.format(rankings_folder))
# evalaute combined run
print('evaluate run combined w/ gamma=%.2f' % (FLAGS.fixed_gamma))
tf_utils.evaluate(['map', 'P_10', 'ndcg'], rankings_folder,
FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma), qrels_folder,
FLAGS.qrels_fname)
else:
# learn optimal weight to combine runs
print("learn optimal weight to combine runs with sweep: {}".format(
FLAGS.sweep))
# set variable to store scores and weights
scores_and_weights = []
# initialize kfold with FLAGS.num_folds
kfold = sklearn.model_selection.KFold(n_splits=FLAGS.num_folds)
for fold, (train_qids, test_qids) in enumerate(kfold.split(qids)):
print('fold n. {}'.format(fold))
# restrict queries to train_qids and test_qids
qtrain = {qids[ix]: q[qids[ix]] for ix in train_qids}
qtest = {qids[ix]: q[qids[ix]] for ix in test_qids}
# obtain best combination on training queries
train_score, best_train_weight = max(
tf_utils.perform_reranking(
run, FLAGS.qfield, qtrain, docnos, doc_embs, word_dict,
word_embs, FLAGS.sweep,
SCORE_NORMALIZERS[FLAGS.normalizer], FLAGS.ref_measure,
evaluator))
print(
'fold %d: best_train_weight=%.2f, %s =%.4f' %
(fold, best_train_weight, FLAGS.ref_measure, train_score))
# compute combined run with best combination on test queries
test_crun = tf_utils.compute_combined_run(
run, FLAGS.qfield, qtest, docnos, doc_embs, word_dict,
word_embs, SCORE_NORMALIZERS[FLAGS.normalizer],
best_train_weight)
# evaluate test run
test_res = evaluator.evaluate(test_crun)
# compute aggregated measure score for test queries
test_score = pytrec_eval.compute_aggregated_measure(
FLAGS.ref_measure, [
qscore[FLAGS.ref_measure]
for qscore in test_res.values()
])
# store averaged scores w/ best weights
scores_and_weights.append(
(np.mean([train_score, test_score]), best_train_weight))
# get (best) weight that produces the highest averaged score
best_score, best_weight = max(scores_and_weights)
print('found best weight=%.2f' % (best_weight))
# initialize combined (output) run
crun = trec_utils.OnlineTRECRun(FLAGS.model_name +
'_best_weight_' +
str(FLAGS.best_weight))
# compute combined run based on test weight
comb_run = tf_utils.compute_combined_run(
run, FLAGS.qfield, q, docnos, doc_embs, word_dict, word_embs,
SCORE_NORMALIZERS[FLAGS.normalizer], best_weight)
# store ranking in crun
for qid, doc_ids_and_scores in comb_run.items():
crun.add_ranking(
qid, [(score, doc_id)
for doc_id, score in doc_ids_and_scores.items()])
# close and store run
crun.close_and_write(out_path=rankings_folder + '/' +
FLAGS.model_name + '_best_weight_' +
str(FLAGS.best_weight) + '.txt',
overwrite=True)
print('combined run stored in {}'.format(rankings_folder))
# evalaute combined run
print(
'evaluate run combined w/ {}-fold cross validation and best weight={}'
.format(FLAGS.num_folds, FLAGS.best_weight))
tf_utils.evaluate(['map', 'P_10', 'ndcg'], rankings_folder,
FLAGS.model_name + '_best_weight_' +
str(FLAGS.best_weight), qrels_folder,
FLAGS.qrels_fname)
def main():
os.chdir(os.path.dirname(os.path.realpath('__file__')))
# load options
opts = Options()
# set folders
query_folder = 'corpus/' + opts.corpus_name + '/queries'
qrels_folder = 'corpus/' + opts.corpus_name + '/qrels'
rankings_folder = 'corpus/' + opts.corpus_name + '/rankings/' + opts.model_name
# create folders
if not os.path.exists(rankings_folder):
os.makedirs(rankings_folder)
if not os.path.exists(query_folder) or not os.path.exists(qrels_folder):
print(
'folders containing queries and qrels are required - please add them'
)
return False
# load utils functions - set random seed
utils = Utils(opts.seed)
# load UMLS lookup functions
umls_lookup = umls.UMLSLookup()
# load queries
print('load {} queries'.format(opts.corpus_name))
queries = utils.read_queries(query_folder + '/' + opts.qfname)
# load BoW run
bow_model = read_ranking(opts.bow_model_path)
# load models
print('load models')
txt_d2v_model = gensim.models.Doc2Vec.load(opts.txt_d2v_model_path)
concept_d2v_model = gensim.models.Doc2Vec.load(opts.concept_d2v_model_path)
retro_model = np.load(opts.retro_model_path, allow_pickle=True).item()
##### QUERY EXPANSION #####
N_top_docs = opts.num_top_docs
N_top_words_doc = opts.num_top_words_per_doc
N_top_words_query = opts.num_top_words
queries_t = {}
queries_c = {}
queries_r = {}
print('perform query expansion for each model')
for qid, qtext in tqdm(queries.items()):
# get N_top_docs for given query
print('get top {} docs for query {}'.format(N_top_docs, qid))
query_top_docs = get_query_top_docs(bow_model, qid, N_top_docs)
'''
for each doc in query_top_docs, pick N_top_words_doc, add to pool
sort the pool, get N_top_words_query to add in given query
'''
top_concept_t = {}
top_concept_c = {}
top_concept_r = {}
for top_doc_id in query_top_docs:
top_t = top_words_of_doc(txt_d2v_model, top_doc_id,
N_top_words_doc)
if top_doc_id in concept_d2v_model.docvecs:
top_c = top_words_of_doc(concept_d2v_model, top_doc_id,
N_top_words_doc)
if top_doc_id in retro_model:
top_retro_doc = retro_model[top_doc_id]
if opts.beta < 0.5: # prioritize concepts
top_r = top_words_of_vector(concept_d2v_model,
top_retro_doc, N_top_words_doc)
else: # prioritize words
top_r = top_words_of_vector(txt_d2v_model, top_retro_doc,
N_top_words_doc)
for i in range(N_top_words_doc):
if len(top_t
) == N_top_words_doc: # doc_id found by txt_d2v_model
term = top_t[i][0]
score = top_t[i][1]
if term in top_concept_t: # combsum
top_concept_t[term] += score
else:
top_concept_t[term] = score
if len(
top_c
) == N_top_words_doc: # doc_id found by concept_d2v_model
term = top_c[i][0]
score = top_c[i][1]
if term in top_concept_c: # combsum
top_concept_c[term] += score
else:
top_concept_c[term] = score
if len(top_r
) == N_top_words_doc: # doc_id found by retro_model
term = top_r[i][0]
score = top_r[i][1]
if term in top_concept_r: # combsum
top_concept_r[term] += score
else:
top_concept_r[term] = score
# sorting top_concept lists
sorted_candidates_t = sorted(
top_concept_t.items(), key=operator.itemgetter(
1)) # [(id1,min_sim), ... , (idn, max_sim)]
sorted_candidates_c = sorted(
top_concept_c.items(), key=operator.itemgetter(
1)) # [(id1,min_sim), ... , (idn, max_sim)]
sorted_candidates_r = sorted(
top_concept_r.items(), key=operator.itemgetter(
1)) # [(id1,min_sim), ... , (idn, max_sim)]
top_term_t = sorted_candidates_t[-N_top_words_query:]
top_term_c = sorted_candidates_c[-N_top_words_query:]
top_term_r = sorted_candidates_r[-N_top_words_query:]
query_new_t = qtext[opts.qfield]
query_new_c = qtext[opts.qfield]
query_new_r = qtext[opts.qfield]
# query_new_t = ''
# query_new_c = ''
# query_new_r = ''
count_t = 0
count_c = 0
count_r = 0
for term, _ in top_term_t:
query_new_t += ' ' + term
count_t += 1
for cui, _ in top_term_c:
cui = cui.upper()
term_variants = [
term_and_source for term_and_source in
umls_lookup.lookup_synonyms(cui=cui, preferred=True)
if term_and_source[1] == 'MSH'
]
term = term_variants[0][0] # preferred term
query_new_c += ' ' + term
count_c += 1
if opts.beta < 0.5:
for cui, _ in top_term_r:
cui = cui.upper()
term_variants = [
term_and_source for term_and_source in
umls_lookup.lookup_synonyms(cui=cui, preferred=True)
if term_and_source[1] == 'MSH'
]
term = term_variants[0][0]
query_new_r += ' ' + term
count_r += 1
else:
for term, _ in top_term_r:
query_new_r += ' ' + term
count_r += 1
queries_t[qid] = {opts.qfield: query_new_t}
queries_c[qid] = {opts.qfield: query_new_c}
queries_r[qid] = {opts.qfield: query_new_r}
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
# set Index instance
ix = Index()
print('search and evaluate text-based doc2vec query expansion')
# perform lexical search over given query field w/ chosen model
ix.lexical_search(queries_t, opts.qfield, rankings_folder,
opts.model_name + '_txt_d2v')
# evaluate performed search
scores = utils.evaluate(['recall.20', 'P_20', 'map'], rankings_folder,
opts.model_name + '_txt_d2v', qrels_folder,
opts.qrels_fname)
print('search and evaluate concept-based doc2vec query expansion')
# perform lexical search over given query field w/ chosen model
ix.lexical_search(queries_c, opts.qfield, rankings_folder,
opts.model_name + '_concept_d2v')
# evaluate performed search
scores = utils.evaluate(['recall.20', 'P_20', 'map'], rankings_folder,
opts.model_name + '_concept_d2v', qrels_folder,
opts.qrels_fname)
print('search and evaluate retrofitted doc2vec query expansion')
# perform lexical search over given query field w/ chosen model
ix.lexical_search(queries_r, opts.qfield, rankings_folder,
opts.model_name + '_retro_d2v')
# evaluate performed search
scores = utils.evaluate(['recall.20', 'P_20', 'map'], rankings_folder,
opts.model_name + '_retro_d2v', qrels_folder,
opts.qrels_fname)