def repo2bow(repository: str,
repository_format: str,
docfreq_threshold: int,
docfreq: DocumentFrequencies,
languages: List[str] = None,
blacklist_languages=False,
engine_kwargs: Dict[str, Any] = None) -> Dict[str, float]:
log = logging.getLogger("repo2bow")
token_index = {"i." + key: int(val) for (key, val) in docfreq}
session_name = "repo2bow-%s" % uuid4()
engine_args = {
"repositories": repository,
"repository_format": repository_format,
}
if engine_kwargs is not None:
engine_args.update(engine_kwargs)
engine = create_engine(session_name, **engine_args)
root = Ignition(engine) >> RepositoriesFilter(r"^file://.*") >> HeadFiles()
if languages is not None:
file_source = root >> \
LanguageExtractor() >> \
LanguageSelector(languages=languages, blacklist=blacklist_languages)
else:
file_source = root
bag = (file_source >> UastExtractor() >> Moder("repo") >>
UastDeserializer() >> UastRow2Document() >> Uast2BagFeatures(
IdentifiersBagExtractor(docfreq_threshold)) >>
BagFeatures2TermFreq() >> TFIDF(
token_index, docfreq.docs,
engine.session.sparkContext) >> Collector()).execute()
log.info("extracted %d identifiers", len(bag))
return {r.token[2:]: r.value for r in bag}
def create_uast_source(args,
session_name,
select=HeadFiles,
language_selector=None,
extract_uast=True):
if args.parquet:
parquet_loader_args = filter_kwargs(args.__dict__,
create_parquet_loader)
start_point = create_parquet_loader(session_name,
**parquet_loader_args)
root = start_point
if extract_uast and "uast" not in [
col.name for col in start_point.execute().schema
]:
raise ValueError("The parquet files do not contain UASTs.")
else:
engine_args = filter_kwargs(args.__dict__, create_engine)
root = create_engine(session_name, **engine_args)
if language_selector is None:
language_selector = LanguageSelector(languages=args.languages)
start_point = Ignition(root, explain=args.explain) \
.link(select()) \
.link(language_selector)
if extract_uast:
start_point = start_point.link(UastExtractor())
return root, start_point
def preprocess_source(args):
log = logging.getLogger("preprocess_source")
if os.path.exists(args.batches):
log.critical("%s must not exist", args.batches)
return 1
if not args.config:
args.config = []
engine = create_engine("source2bags-%s" % uuid4(), args.repositories, args)
pipeline = Engine(engine, explain=args.explain).link(
DzhigurdaFiles(args.dzhigurda))
uasts = pipeline.link(UastExtractor(languages=[args.language]))
fields = uasts.link(FieldsSelector(fields=args.fields))
saver = fields.link(ParquetSaver(save_loc=args.batches))
saver.explode()
def preprocess_source(args):
log = logging.getLogger("preprocess_source")
if os.path.exists(args.output):
log.critical("%s must not exist", args.output)
return 1
if not args.config:
args.config = []
engine = create_engine("source2bags-%s" % uuid4(), **args.__dict__)
ignition = Ignition(engine, explain=args.explain)
ignition \
.link(DzhigurdaFiles(args.dzhigurda)) \
.link(UastExtractor(languages=args.languages)) \
.link(FieldsSelector(fields=args.fields)) \
.link(ParquetSaver(save_loc=args.output)) \
.execute()
pipeline_graph(args, log, ignition)
def create_file_source(args: argparse.Namespace, session_name: str):
if args.parquet:
parquet_loader_args = filter_kwargs(args.__dict__,
create_parquet_loader)
root = create_parquet_loader(session_name, **parquet_loader_args)
file_source = root.link(
LanguageSelector.maybe(languages=args.languages,
blacklist=args.blacklist))
else:
engine_args = filter_kwargs(args.__dict__, create_engine)
root = Ignition(create_engine(session_name, **engine_args),
explain=args.explain)
file_source = root.link(DzhigurdaFiles(args.dzhigurda))
if args.languages is not None:
file_source = file_source \
.link(LanguageExtractor()) \
.link(LanguageSelector(languages=args.languages, blacklist=args.blacklist))
return root, file_source
def source2bags(args):
log = logging.getLogger("bags")
if os.path.exists(args.batches):
log.critical("%s must not exist", args.batches)
return 1
if not args.config:
args.config = []
try:
cassandra_utils.configure(args)
engine = create_engine("source2bags-%s" % uuid4(), **args.__dict__)
extractors = [
__extractors__[s](args.min_docfreq,
**__extractors__[s].get_kwargs_fromcmdline(args))
for s in args.feature
]
pipeline = Engine(engine, explain=args.explain).link(
DzhigurdaFiles(args.dzhigurda))
uasts = pipeline.link(UastExtractor(languages=[args.language]))
if args.persist is not None:
uasts = uasts.link(Cacher(args.persist))
uasts.link(MetadataSaver(args.keyspace, args.tables["meta"]))
uasts = uasts.link(UastDeserializer())
uasts.link(Repo2Quant(extractors, args.nb_partitions))
uasts.link(Repo2DocFreq(extractors))
pipeline.explode()
bags = uasts.link(Repo2WeightedSet(extractors))
if args.persist is not None:
bags = bags.link(Cacher(args.persist))
batcher = bags.link(BagsBatcher(extractors))
batcher.link(BagsBatchSaver(args.batches, batcher))
bags.link(BagsSaver(args.keyspace, args.tables["bags"]))
bags.explode()
log.info("Writing %s", args.docfreq)
batcher.model.save(args.docfreq)
if args.graph:
log.info("Dumping the graph to %s", args.graph)
with open(args.graph, "w") as f:
pipeline.graph(stream=f)
finally:
if args.pause:
input("Press Enter to exit...")
def setUpClass(cls):
cls.engine = create_engine("test_with_engine", SIVA_DIR, "siva")
cls.spark = cls.engine.session
cls.data = ParquetLoader(session=cls.spark,
paths=PARQUET_DIR).execute().rdd.coalesce(1)
def setUpClass(cls):
cls.engine = create_engine("test_with_engine", SIVA_DIR, "siva")
cls.spark = cls.engine.session
def warmup(args):
create_engine("warmup", "/tmp", **args.__dict__)
def warmup(args):
engine_args = filter_kwargs(args.__dict__, create_engine)
create_engine("warmup", "/tmp", **engine_args)