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()
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 get_all_evidence_for_datatype(
self,
datatype,
fields=None,
):
# https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-multi-get.html
index_name = Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_INDEX_NAME, True)
res = helpers.scan(
client=self.handler,
query={
"query": {
"match": {
"type": datatype
}
},
'_source': self._get_source_from_fields(fields),
'size': 1000,
},
scroll='12h',
index=index_name,
timeout="10m",
)
# res = list(res)
for hit in res:
yield hit['_source']
def get_validated_evidence_strings(self,
size=1000,
datasources=[],
is_valid=True):
# https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-multi-get.html
index_name = Loader.get_versioned_index(
Const.ELASTICSEARCH_VALIDATED_DATA_INDEX_NAME + '*', True)
doc_type = None
if datasources:
doc_type = datasources
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_phrase": {
"is_valid": is_valid
}
},
'_source': True,
'size': size,
},
scroll='12h',
doc_type=doc_type,
index=index_name,
timeout="20m",
)
for hit in res:
yield hit['_source']
def get_associations_for_disease(self,
disease,
fields=None,
size=100,
get_top_hits=True):
source = self._get_source_from_fields(fields)
aggs = addict.Dict()
if get_top_hits:
aggs.direct_associations.filter.term.is_direct = True
aggs.direct_associations.aggs.top_direct_ass.top_hits.sort[
'harmonic-sum.overall'].order = 'desc'
aggs.direct_associations.aggs.top_direct_ass.top_hits._source = source
aggs.direct_associations.aggs.top_direct_ass.top_hits.size = size
q = addict.Dict()
q.query.constant_score.filter.terms['disease.id'] = [disease]
q.sort['harmonic-sum.overall'].order = 'desc'
q._source = source
q.aggs = aggs
q.size = size
res = self.handler.search(
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_ASSOCIATION_INDEX_NAME, True),
doc_type=Const.ELASTICSEARCH_DATA_ASSOCIATION_DOC_NAME,
body=q.to_dict())
return AssociationSummary(res)
def setup_writers(dry_run, es_hosts, output_folder):
global_init = None
local_init = None
main = None
local_shutdown = None
global_shutdown = None
if dry_run:
main = dry_run_main
elif es_hosts:
#have to bake the loader object in so that the prepare for bulk indexing works
es_loader = Loader(new_es_client(es_hosts))
#use partial to "bake" arguments into the function we return
global_init = functools.partial(elasticsearch_global_init, es_loader)
local_init = functools.partial(elasticsearch_local_init, es_hosts)
main = elasticsearch_main
local_shutdown = elasticsearch_local_shutdown
global_shutdown = functools.partial(elasticsearch_global_shutdown,
es_loader)
elif output_folder:
#use partial to "bake" arguments into the function we return
global_init = functools.partial(file_global_init, output_folder)
local_init = functools.partial(file_local_init, output_folder)
main = file_main
local_shutdown = file_local_shutdown
else:
raise ValueError(
"Must specify one of dry_run, es_hosts, output_folder")
return global_init, local_init, main, local_shutdown, global_shutdown
def get_disease_to_targets_vectors(self, treshold=0.1, evidence_count=3):
'''
Get all the association objects that are:
- direct -> to avoid ontology inflation
- > 3 evidence count -> remove noise
- overall score > threshold -> remove very lo quality noise
:param treshold: minimum overall score threshold to consider for fetching association data
:param evidence_count: minimum number of evidence consider for fetching association data
:return: two dictionaries mapping target to disease and the reverse
'''
self.logger.debug('scan es to get all diseases and targets')
res = helpers.scan(
client=self.handler,
query={
"query": {
"term": {
"is_direct": True,
}
},
'_source': {
'includes': [
"target.id", 'disease.id', 'harmonic-sum',
'evidence_count'
]
},
'size': 1000,
},
scroll='12h',
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_ASSOCIATION_INDEX_NAME, True),
timeout="10m",
)
target_results = dict()
disease_results = dict()
self.logger.debug('start getting all targets and diseases from es')
c = 0
for hit in res:
c += 1
hit = hit['_source']
if hit['evidence_count']['total']>=evidence_count and \
hit['harmonic-sum']['overall'] >=treshold:
'''store target associations'''
if hit['target']['id'] not in target_results:
target_results[hit['target']['id']] = SparseFloatDict()
#TODO: return all counts and scores up to datasource level
target_results[hit['target']['id']][
hit['disease']['id']] = hit['harmonic-sum']['overall']
'''store disease associations'''
if hit['disease']['id'] not in disease_results:
disease_results[hit['disease']['id']] = SparseFloatDict()
# TODO: return all counts and scores up to datasource level
disease_results[hit['disease']['id']][
hit['target']['id']] = hit['harmonic-sum']['overall']
if c % 10000 == 0:
self.logger.debug('%d elements retrieved', c)
return target_results, disease_results
def score_producer_local_init(es_hosts, redis_host, redis_port, lookup_data,
datasources_to_datatypes, dry_run):
#set the R server to lookup into
r_server = new_redis_client(redis_host, redis_port)
scorer = Scorer()
loader = Loader(new_es_client(es_hosts))
return scorer, loader, r_server, lookup_data, datasources_to_datatypes, dry_run
def get_evidence_for_target_simple(self, target, expected=None):
query_body = {
"query": {
"constant_score": {
"filter": {
"term": {
"target.id": target
}
}
}
},
'_source': {
"includes": [
"target.id",
"private.efo_codes",
"disease.id",
"scores.association_score",
"sourceID",
"id",
]
},
}
if expected is not None and expected < 10000:
query_body['size'] = 10000
res = self.handler.search(index=Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_INDEX_NAME, True),
body=query_body)
for hit in res['hits']['hits']:
yield hit['_source']
else:
res = helpers.scan(client=self.handler,
query=query_body,
scroll='1h',
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_INDEX_NAME, True),
timeout="1h",
request_timeout=2 * 60 * 60,
size=1000)
for hit in res:
yield hit['_source']
def count_elements_in_index(self, index_name, doc_type=None, query=None):
if query is None:
query = {"match_all": {}}
res = self.handler.search(index=Loader.get_versioned_index(
index_name, True),
doc_type=doc_type,
body={
"query": query,
'_source': False,
'size': 0,
})
return res['hits']['total']
def get_reaction(self, reaction_id):
res = self.handler.search(
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_REACTOME_INDEX_NAME, True),
doc_type=Const.ELASTICSEARCH_REACTOME_REACTION_DOC_NAME,
body={
"query": {
"ids": {
"values": [reaction_id]
}
},
'_source': True,
'size': 1,
})
for hit in res['hits']['hits']:
return hit['_source']
def get_all_associations(self, ):
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_all": {}
},
'_source': True,
'size': 1000,
},
scroll='1h',
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_ASSOCIATION_INDEX_NAME, True),
timeout="10m",
)
for hit in res:
yield hit['_source']
def count_evidence_for_target(self, target):
res = self.handler.search(index=Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_INDEX_NAME, True),
body={
"query": {
"constant_score": {
"filter": {
"term": {
"target.id": target
}
}
}
},
'_source': [],
'size': 0
})
return res['hits']['total']
def get_all_ensembl_genes(self):
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_all": {}
},
'_source': True,
'size': 1000,
},
scroll='1h',
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_ENSEMBL_INDEX_NAME, True),
timeout="10m",
)
for hit in res:
yield hit['_source']
def get_all_uniprot_entries(self):
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_all": {}
},
'_source': True,
'size': 100,
},
scroll='12h',
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_UNIPROT_INDEX_NAME, True),
timeout="10m",
)
for hit in res:
yield jsonpickle.decode(base64.b64decode(hit['_source']['entry']))
def get_all_reactions(self):
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_all": {}
},
'_source': True,
'size': 1000,
},
scroll='1h',
doc_type=Const.ELASTICSEARCH_REACTOME_REACTION_DOC_NAME,
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_REACTOME_INDEX_NAME, True),
timeout="10m",
)
for hit in res:
yield hit['_source']
def get_all_diseases(self, fields=None):
source = self._get_source_from_fields(fields)
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_all": {}
},
'_source': source,
'size': 1000,
},
scroll='12h',
doc_type=Const.ELASTICSEARCH_EFO_LABEL_DOC_NAME,
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_EFO_LABEL_INDEX_NAME, True),
timeout="10m",
)
for hit in res:
yield hit['_source']
def get_all_target_ids_with_evidence_data(self):
#TODO: use an aggregation to get those with just data
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_all": {}
},
'_source': False,
'size': 100,
},
scroll='12h',
doc_type=Const.ELASTICSEARCH_GENE_NAME_DOC_NAME,
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_GENE_NAME_INDEX_NAME, True),
timeout="30m",
)
for target in res:
yield target['_id']
def get_disease_labels(self, ids):
res = helpers.scan(
client=self.handler,
query={
"query": {
"ids": {
"values": ids,
}
},
'_source': 'label',
'size': 1,
},
scroll='12h',
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_EFO_LABEL_INDEX_NAME, True),
timeout="10m",
)
return dict((hit['_id'], hit['_source']['label']) for hit in res)
def get_all_target_disease_pair_from_evidence(self, only_direct=False):
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_all": {}
},
'_source':
self._get_source_from_fields([
'target.id', 'disease.id', 'private.efo_codes',
'scores.association_score'
]),
'size':
1000,
},
scroll='6h',
index=Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_INDEX_NAME, True),
timeout="1h",
request_timeout=2 * 60 * 60,
)
yielded_pairs = set()
for hit in res:
if hit['_source']['scores']['association_score'] > 0:
if only_direct:
pair = '-'.join([
hit['_source']['target']['id'],
hit['_source']['disease']['id']
])
if pair not in yielded_pairs:
yield pair
yielded_pairs.add(pair)
else:
for efo_id in hit['_source']['private']['efo_codes']:
pair = '-'.join(
[hit['_source']['target']['id'], efo_id])
if pair not in yielded_pairs:
yield pair
yielded_pairs.add(pair)
def get_all_evidence(self, fields=None):
index_name = Loader.get_versioned_index(
Const.ELASTICSEARCH_DATA_INDEX_NAME, True)
doc_type = None
res = helpers.scan(
client=self.handler,
query={
"query": {
"match_all": {}
},
'_source': self._get_source_from_fields(fields),
'size': 1000,
},
scroll='12h',
index=index_name,
timeout="10m",
)
# res = list(res)
for hit in res:
yield hit['_source']
def elasticsearch_local_init(es_hosts):
return Loader(new_es_client(es_hosts)),
def main():
#parse config file, environment, and command line arguments
mrtarget.cfg.setup_ops_parser()
args = mrtarget.cfg.get_ops_args()
#set up logging
logger = None
if args.log_config:
if os.path.isfile(args.log_config) and os.access(
args.log_config, os.R_OK):
#read a log configuration file
logging.config.fileConfig(args.log_config,
disable_existing_loggers=False)
logger = logging.getLogger(__name__ + ".main()")
else:
#unable to read the logging config file, abort
logging.basicConfig()
logger = logging.getLogger(__name__ + ".main()")
logger.error("unable to read file {}".format(args.log_config))
return 1
else:
#no logging config specified, fall back to default
logging.basicConfig()
logger = logging.getLogger(__name__ + ".main()")
if not args.release_tag:
logger.error('A [release-tag] has to be specified.')
print('A [release-tag] has to be specified.', file=sys.stderr)
return 1
else:
Config.RELEASE_VERSION = args.release_tag
logger.info('setting release version %s' % Config.RELEASE_VERSION)
with RedisManager(args.redis_remote, args.redis_host, args.redis_port):
es = new_es_client(args.elasticseach_nodes)
redis = new_redis_client(args.redis_host, args.redis_port)
#create a single query object for future use
esquery = ESQuery(es)
#read the data configuration
data_config = mrtarget.cfg.get_data_config(args.data_config)
#create something to accumulate qc metrics into over various steps
qc_metrics = QCMetrics()
with Loader(es,
chunk_size=ElasticSearchConfiguration.bulk_load_chunk,
dry_run=args.dry_run) as loader:
if args.rea:
process = ReactomeProcess(
loader, data_config.reactome_pathway_data,
data_config.reactome_pathway_relation)
if not args.qc_only:
process.process_all(args.dry_run)
if not args.skip_qc:
qc_metrics.update(process.qc(esquery))
if args.ens:
process = EnsemblProcess(loader)
if not args.qc_only:
process.process(data_config.ensembl_filename, args.dry_run)
if not args.skip_qc:
qc_metrics.update(process.qc(esquery))
if args.unic:
process = UniprotDownloader(loader)
if not args.qc_only:
process.process(data_config.uniprot_uri, args.dry_run)
if not args.skip_qc:
qc_metrics.update(process.qc(esquery))
if args.hpa:
process = HPAProcess(loader, redis, args.elasticseach_nodes,
data_config.tissue_translation_map,
data_config.tissue_curation_map,
data_config.hpa_normal_tissue,
data_config.hpa_rna_level,
data_config.hpa_rna_value,
data_config.hpa_rna_zscore)
if not args.qc_only:
process.process_all(args.dry_run)
if not args.skip_qc:
qc_metrics.update(process.qc(esquery))
if args.gen:
process = GeneManager(
loader,
redis,
args.gen_plugin_places,
data_config.gene_data_plugin_names,
)
if not args.qc_only:
process.merge_all(data_config, dry_run=args.dry_run)
if not args.skip_qc:
qc_metrics.update(process.qc(esquery))
if args.efo:
process = EfoProcess(loader, data_config.ontology_efo,
data_config.ontology_hpo,
data_config.ontology_mp,
data_config.disease_phenotype)
if not args.qc_only:
process.process_all(args.dry_run)
if not args.skip_qc:
qc_metrics.update(process.qc(esquery))
if args.eco:
process = EcoProcess(loader, data_config.ontology_eco,
data_config.ontology_so)
if not args.qc_only:
process.process_all(args.dry_run)
if not args.skip_qc:
qc_metrics.update(process.qc(esquery))
if args.val:
es_output_folder = None
if "elasticsearch_folder" in vars(
args) and args.elasticsearch_folder is not None:
es_output_folder = args.elasticsearch_folder
process_evidences_pipeline(
filenames=data_config.input_file,
first_n=args.val_first_n,
es_client=es,
redis_client=redis,
dry_run=args.dry_run,
output_folder=es_output_folder,
num_workers=args.val_workers_validator,
num_writers=args.val_workers_writer,
max_queued_events=args.val_queue_validator_writer,
eco_scores_uri=data_config.eco_scores,
schema_uri=data_config.schema,
es_hosts=args.elasticseach_nodes,
excluded_biotypes=data_config.excluded_biotypes,
datasources_to_datatypes=data_config.
datasources_to_datatypes)
#TODO qc
if args.assoc:
process = ScoringProcess(args.redis_host, args.redis_port,
args.elasticseach_nodes)
if not args.qc_only:
process.process_all(data_config.scoring_weights,
data_config.is_direct_do_not_propagate,
data_config.datasources_to_datatypes,
args.dry_run,
args.as_workers_production,
args.as_workers_score,
args.as_queue_production_score)
if not args.skip_qc:
qc_metrics.update(process.qc(esquery))
pass
if args.ddr:
process = DataDrivenRelationProcess(es)
if not args.qc_only:
process.process_all(args.dry_run,
args.ddr_workers_production,
args.ddr_workers_score,
args.ddr_queue_production_score,
args.ddr_queue_score_result)
#TODO qc
if args.sea:
process = SearchObjectProcess(loader, redis)
if not args.qc_only:
process.process_all(
data_config.chembl_target,
data_config.chembl_mechanism,
data_config.chembl_component,
data_config.chembl_protein,
data_config.chembl_molecule_set_uri_pattern,
args.dry_run)
#TODO qc
if args.metric:
process = Metrics(
es, args.metric_file,
data_config.datasources_to_datatypes).generate_metrics()
if args.qc_in:
#handle reading in previous qc from filename provided, and adding comparitive metrics
qc_metrics.compare_with(args.qc_in)
if args.qc_out:
#handle writing out to a tsv file
qc_metrics.write_out(args.qc_out)
logger.info('`' + " ".join(sys.argv) + '` - finished')
return 0
def exists(self, index, doc_type, id, realtime=False):
return self.handler.exists(index=Loader.get_versioned_index(
index, True),
doc_type=doc_type,
id=id,
realtime=realtime)
def delete_data(self,
index,
query,
doc_type='',
chunk_size=1000,
altered_keys=()):
'''
Delete all the documents in an index matching a given query
:param index: index to use
:param query: query matching the elements to remove
:param doc_type: document types, default is to look for all the doc types
:param chunk_size: size of the bulk action sent to delete
:param altered_keys: list of fields to fetch data and return as being altered by the delete query
:return: dict of keys altered by the query
'''
'''count available data'''
res = self.handler.search(
index=Loader.get_versioned_index(index, True),
body={
"query": query,
'_source': False,
'size': 0,
},
doc_type=doc_type,
)
total = res['hits']['total']
'''if data is matching query, delete it with scan and bulk'''
altered = dict()
for key in altered_keys:
altered[key] = set()
if total:
batch = []
for hit in helpers.scan(
client=self.handler,
query={
"query": query,
'_source': self._get_source_from_fields(altered_keys),
'size': chunk_size,
},
scroll='1h',
index=Loader.get_versioned_index(index, True),
doc_type=doc_type,
timeout='1h',
):
action = {
'_op_type': 'delete',
'_index': hit['_index'],
'_type': hit['_type'],
'_id': hit['_id'],
}
batch.append(action)
flat_source = self.flatten(hit['_source'])
for key in altered_keys:
if key in flat_source:
altered[key].add(flat_source[key])
if len(batch) >= chunk_size:
self._flush_bulk(batch)
batch = []
#if len(batch) >= chunk_size:
self._flush_bulk(batch)
'''flush changes'''
self.handler.indices.flush(Loader.get_versioned_index(index, True),
wait_if_ongoing=True)
return altered
def get_objects_by_id(self,
ids,
index,
doc_type,
source=True,
source_exclude=[],
realtime=False):
'''
:param ids: list of idientifiers for documents
:param index: index for all the documents
:param doc_type: doc type for all the documents
:return: generator of documents
'''
if isinstance(ids, (list, tuple)):
res = self.handler.mget(
index=Loader.get_versioned_index(index, True),
doc_type=doc_type,
body=dict(ids=ids),
_source=source,
_source_exclude=source_exclude,
realtime=True,
)
if not res:
time.sleep(0.1)
res = self.handler.mget(
index=Loader.get_versioned_index(index, True),
doc_type=doc_type,
body=dict(ids=ids),
_source=source,
_source_exclude=source_exclude,
realtime=True,
)
for doc in res['docs']:
if doc['found']:
yield doc['_source']
else:
raise KeyError('object with id %s not found' %
(doc['_id']))
else:
try:
res = self.handler.get(
index=Loader.get_versioned_index(index, True),
doc_type=doc_type,
id=ids,
_source=source,
_source_exclude=source_exclude,
realtime=True,
)
try:
yield res['_source']
except Exception as e:
self.logger.exception(
'cannot retrieve single object by id %s ' % ids)
raise KeyError('object with id %s not found' % ids)
except TransportError as te:
if te.status_code == 404:
raise KeyError('object with id %s not found' % ids)
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
