class DataDrivenRelationProcess(object):
def __init__(self, es):
self.es = es
self.es_query=ESQuery(self.es)
self.logger = logging.getLogger(__name__)
def process_all(self, dry_run,
ddr_workers_production,
ddr_workers_score,
ddr_queue_production_score,
ddr_queue_score_result):
start_time = time.time()
target_data, disease_data = self.es_query.get_disease_to_targets_vectors()
self.logger.info('Retrieved all the associations data in %i s'%(time.time()-start_time))
self.logger.info('target data length: %s size in memory: %f Kb'%(len(target_data),sys.getsizeof(target_data)/1024.))
self.logger.info('disease data length: %s size in memory: %f Kb' % (len(disease_data),sys.getsizeof(disease_data)/1024.))
'''sort the lists and keep using always the same order in all the steps'''
disease_keys = sorted(disease_data.keys())
target_keys = sorted(target_data.keys())
self.logger.info('getting disese labels')
disease_id_to_label = self.es_query.get_disease_labels(disease_keys)
disease_labels = [disease_id_to_label[hit_id] for hit_id in disease_keys]
self.logger.info('getting target labels')
target_id_to_label = self.es_query.get_target_labels(target_keys)
target_labels = [target_id_to_label[hit_id] for hit_id in target_keys]
#setup elasticsearch
self.loader = Loader(self.es, dry_run=dry_run)
if not dry_run:
#need to directly get the versioned index name for this function
self.loader.create_new_index(Const.ELASTICSEARCH_RELATION_INDEX_NAME)
self.loader.prepare_for_bulk_indexing(self.loader.get_versioned_index(Const.ELASTICSEARCH_RELATION_INDEX_NAME))
#calculate and store disease-to-disease in multiple processess
self.logger.info('handling disease-to-disease')
handle_pairs(RelationType.SHARED_TARGET, disease_labels, disease_data, disease_keys,
target_keys, 0.19, 1024, self.loader, dry_run,
ddr_workers_production, ddr_workers_score,
ddr_queue_production_score, ddr_queue_score_result)
self.logger.info('handled disease-to-disease')
#calculate and store target-to-target in multiple processess
self.logger.info('handling target-to-target')
handle_pairs(RelationType.SHARED_DISEASE, target_labels, target_data, target_keys,
disease_keys, 0.19, 1024, self.loader, dry_run,
ddr_workers_production, ddr_workers_score,
ddr_queue_production_score, ddr_queue_score_result)
self.logger.info('handled target-to-target')
#cleanup elasticsearch
if not dry_run:
self.loader.flush_all_and_wait(Const.ELASTICSEARCH_RELATION_INDEX_NAME)
#restore old pre-load settings
#note this automatically does all prepared indexes
self.loader.restore_after_bulk_indexing()
class ScoringProcess():
def __init__(self, redis_host, redis_port, es_hosts):
self.logger = logging.getLogger(__name__)
self.es_hosts = es_hosts
self.es = new_es_client(self.es_hosts)
self.es_loader = Loader(self.es)
self.es_query = ESQuery(self.es)
self.redis_host = redis_host
self.redis_port = redis_port
self.r_server = new_redis_client(self.redis_host, self.redis_port)
def process_all(self, scoring_weights, is_direct_do_not_propagate,
datasources_to_datatypes, dry_run, num_workers_produce,
num_workers_score, max_queued_produce_to_score):
lookup_data = LookUpDataRetriever(
self.es,
self.r_server,
targets=[],
data_types=(LookUpDataType.DISEASE, LookUpDataType.TARGET,
LookUpDataType.ECO, LookUpDataType.HPA)).lookup
targets = list(self.es_query.get_all_target_ids_with_evidence_data())
#setup elasticsearch
if not dry_run:
self.es_loader.create_new_index(
Const.ELASTICSEARCH_DATA_ASSOCIATION_INDEX_NAME)
self.es_loader.prepare_for_bulk_indexing(
self.es_loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_ASSOCIATION_INDEX_NAME))
self.logger.info('setting up stages')
#bake the arguments for the setup into function objects
produce_evidence_local_init_baked = functools.partial(
produce_evidence_local_init, self.es_hosts, scoring_weights,
is_direct_do_not_propagate, datasources_to_datatypes)
score_producer_local_init_baked = functools.partial(
score_producer_local_init, self.es_hosts, self.redis_host,
self.redis_port, lookup_data, datasources_to_datatypes, dry_run)
#this doesn't need to be in the external config, since its so content light
#as to be meaningless
max_queued_score_out = 10000
#pipeline stage for making the lists of the target/disease pairs and evidence
pipeline_stage = pr.flat_map(
produce_evidence,
targets,
workers=num_workers_produce,
maxsize=max_queued_produce_to_score,
on_start=produce_evidence_local_init_baked,
on_done=produce_evidence_local_shutdown)
#pipeline stage for scoring the evidence sets
#includes writing to elasticsearch
pipeline_stage = pr.each(score_producer,
pipeline_stage,
workers=num_workers_score,
maxsize=max_queued_score_out,
on_start=score_producer_local_init_baked,
on_done=score_producer_local_shutdown)
#loop over the end of the pipeline to make sure everything is finished
self.logger.info('stages created, running scoring and writing')
pr.run(pipeline_stage)
self.logger.info('stages created, ran scoring and writing')
#cleanup elasticsearch
if not dry_run:
self.logger.info('flushing data to index')
self.es_loader.flush_all_and_wait(
Const.ELASTICSEARCH_DATA_ASSOCIATION_INDEX_NAME)
#restore old pre-load settings
#note this automatically does all prepared indexes
self.es_loader.restore_after_bulk_indexing()
self.logger.info('flushed data to index')
self.logger.info("DONE")
"""
Run a series of QC tests on EFO elasticsearch index. Returns a dictionary
of string test names and result objects
"""
def qc(self, esquery):
#number of eco entries
association_count = 0
#Note: try to avoid doing this more than once!
for association in esquery.get_all_associations():
association_count += 1
if association_count % 1000 == 0:
self.logger.debug("checking %d", association_count)
#put the metrics into a single dict
metrics = dict()
metrics["association.count"] = association_count
return metrics
