def process(specific_info, data, *args):
inputs = get_inputs(data, specific_info)
tfidf_worker_id = inputs.get('tfidf_worker_id')
queue = inputs.get('__read_from_queue')
connection = args[0].get('connection')
all_words = {}
all_data = [] #for bioportal
if queue:
queue_values = connection.zrange(queue, 0, -1)
for queue_raw_data in queue_values:
queue_data = json_loads(queue_raw_data)
all_data.append(queue_data) #for bioportal
tfidf = select_dict_el(queue_data, 'workers_output.%s.tfidf' % tfidf_worker_id)
for word, value in tfidf:
if not all_words.get(word):
all_words[word] = []
all_words[word].append(value)
max_apperance = max([len(values) for (word, values) in all_words.iteritems()])/5
tfidf_results = [(word, 1.0*sum(values)/len(values)*0.65 + 0.35*min(len(values)/max_apperance, 1)) for (word, values) in all_words.iteritems()]
tfidf_results.sort(key=lambda tup: -tup[1])
# Bioportal
bioportal_worker_id = inputs.get('bioportal_worker_id')
bioportal_mesh_names_url = inputs.get('bioporta_mesh_names_url')
mesh_names = json_loads(requests.get(bioportal_mesh_names_url).content)
bioportal_merged = {}
for queue_data in all_data:
bioportal_annotated = select_dict_el(queue_data, 'workers_output.%s.bioportal_annotated' % bioportal_worker_id)
for mesh_data in bioportal_annotated.get('data'):
ontology_id = mesh_data.get('ontology_quote_id')
if not bioportal_merged.get(ontology_id):
if not mesh_names.get(ontology_id):
continue
bioportal_merged[ontology_id] = {
'ontology_quote_id': ontology_id,
'matched_terms': [],
'total_frequency': 0,
'included_in_documents': 0,
'name': mesh_names.get(ontology_id)
}
bioportal_merged[ontology_id]['total_frequency'] += mesh_data.get('frequency')
bioportal_merged[ontology_id]['included_in_documents'] += 1
bioportal_merged[ontology_id]['matched_terms'] = list(set(mesh_data.get('matched_terms')+bioportal_merged[ontology_id]['matched_terms']))
to_return_bioportal = sorted(
bioportal_merged.values(), key=lambda k: k['included_in_documents'], reverse=True
)
return [{'group_tfidf': tfidf_results, 'bioportal_merged': to_return_bioportal},]
def process(specific_info, data, *args):
inputs = get_inputs(data, specific_info)
queue = inputs.get('__read_from_queue')
connection = args[0].get('connection')
to_return_data = {
'phrases': {
'matching': {},
'not_matching': {}
},
'all_phrases': {
'matching': [],
'not_matching': []
}
}
queue_values = connection.zrange(queue, 0, -1)
for queue_raw_data in queue_values:
queue_data = json_loads(queue_raw_data).get('inputs')
match_all = True
for phrase_group, phrase_value in queue_data.get('phrases', {}).iteritems():
if phrase_value:
target = 'matching'
else:
target = 'not_matching'
if not to_return_data['phrases'][target].get(phrase_group):
to_return_data['phrases'][target][phrase_group] = []
if queue_data['doc_id'] not in to_return_data['phrases'][target][phrase_group]:
to_return_data['phrases'][target][phrase_group].append(queue_data['doc_id'])
match_all = match_all and phrase_value
target = 'not_matching'
if match_all:
target = 'matching'
if queue_data['doc_id'] not in to_return_data['all_phrases'][target]:
to_return_data['all_phrases'][target].append(queue_data['doc_id'])
return [to_return_data,]
