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)
mongo_connection = args[0].get('mongo_connection')
data_filter = inputs.get('data_filter', {})
fields = inputs.get('fields', [])
names = inputs.get('names', [])
collection = inputs.get('collection', [])
target_file = inputs.get('target_file')
data_selector = dict([(field, 1) for field in fields])
matrix_id = inputs.get('matrix_id')
if matrix_id:
matrix_documents = set()
matrix = mongo_connection.matrix.find_one({'_id': ObjectId(inputs.get('matrix_id'))})
matrix_documents |= set([
matrix_el.get('id') for matrix_el in select_dict_el(matrix, 'matrix_dict.matrix', [])
])
matrix_documents |= set([
matrix_el.get('id')
for matrix_el in select_dict_el(matrix, 'matrix_dict.studies_order', [])
])
data_filter.update({"id": {"$in": list(matrix_documents)}})
if target_file:
target_file = open(target_file, 'w')
target_file.write("\t".join(names)+"\n")
for episte_data in mongo_connection[collection].find(data_filter, data_selector):
if target_file:
text = u"\t".join([
to_unicode(
select_dict_el(episte_data, field) or ''
).replace('\r\n', ' ').replace('\n', '').replace('\t', ' ') for field in fields
]).encode('utf-8')
target_file.write(text+"\n")
else:
yield dict(
[
(names[i], (select_dict_el(episte_data, field) or ''))
for i, field in enumerate(fields)
]
)
if target_file:
yield {'episte_data_target_file': inputs.get('target_file')}
