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 repos2coocc(args):
log = logging.getLogger("repos2coocc")
id_extractor = IdentifiersBagExtractor(docfreq_threshold=args.min_docfreq,
split_stem=args.split)
session_name = "repos2coocc-%s" % uuid4()
root, start_point = create_uast_source(args, session_name)
uast_extractor = start_point \
.link(Moder("file")) \
.link(UastRow2Document()) \
.link(Repartitioner.maybe(args.partitions, args.shuffle)) \
.link(Cacher.maybe(args.persist))
log.info("Extracting UASTs...")
ndocs = uast_extractor.link(Counter()).execute()
log.info("Number of documents: %d", ndocs)
uast_extractor = uast_extractor.link(UastDeserializer())
df_model = create_or_load_ordered_df(
args, ndocs, uast_extractor.link(Uast2BagFeatures(id_extractor)))
token2index = root.session.sparkContext.broadcast(df_model.order)
uast_extractor \
.link(CooccConstructor(token2index=token2index,
token_parser=id_extractor.id2bag.token_parser,
namespace=id_extractor.NAMESPACE)) \
.link(CooccModelSaver(args.output, df_model)) \
.execute()
pipeline_graph(args, log, root)
def test_extract_functions_from_uast(self):
client = bblfsh.BblfshClient("localhost:9432")
uast = client.parse(MODER_FUNC).uast
functions = list(Moder(mode="func").extract_functions_from_uast(uast))
self.assertEqual(len(functions), 3)
function_names = ["func_a", "func_b", "func_c"]
for f in functions:
self.assertIn(f[0].token, function_names)
def test_error(self):
with self.assertRaises(ValueError):
create_or_load_ordered_df(argparse.Namespace(docfreq_in=None), 10, None)
with self.assertRaises(ValueError):
session = create_spark("test_df_util")
uast_extractor = ParquetLoader(session, paths.PARQUET_DIR) \
.link(Moder("file")) \
.link(UastRow2Document()) \
.link(UastDeserializer()) \
.link(Uast2BagFeatures(IdentifiersBagExtractor()))
create_or_load_ordered_df(argparse.Namespace(docfreq_in=None), None, uast_extractor)
def repos2bow_entry_template(args,
select=HeadFiles,
cache_hook=None,
save_hook=None):
log = logging.getLogger("repos2bow")
extractors = create_extractors_from_args(args)
session_name = "repos2bow-%s" % uuid4()
root, start_point = create_uast_source(args, session_name, select=select)
uast_extractor = start_point.link(Moder(args.mode)) \
.link(Repartitioner.maybe(args.partitions, args.shuffle)) \
.link(Cacher.maybe(args.persist))
if cache_hook is not None:
uast_extractor.link(cache_hook()).execute()
# We link UastRow2Document after Cacher here because cache_hook() may want to have all possible
# Row items.
uast_extractor = uast_extractor.link(UastRow2Document())
log.info("Extracting UASTs and indexing documents...")
document_indexer = Indexer(Uast2BagFeatures.Columns.document)
uast_extractor.link(document_indexer).execute()
ndocs = len(document_indexer)
log.info("Number of documents: %d", ndocs)
uast_extractor = uast_extractor.link(UastDeserializer())
quant = Uast2Quant(extractors)
uast_extractor.link(quant).execute()
if quant.levels:
log.info("Writing quantization levels to %s", args.quant)
QuantizationLevels().construct(quant.levels).save(args.quant)
uast_extractor = uast_extractor \
.link(Uast2BagFeatures(extractors))
log.info("Calculating the document frequencies...")
df = uast_extractor.link(BagFeatures2DocFreq()).execute()
log.info("Writing docfreq to %s", args.docfreq)
df_model = OrderedDocumentFrequencies() \
.construct(ndocs, df) \
.prune(args.min_docfreq) \
.greatest(args.vocabulary_size) \
.save(args.docfreq)
bags_writer = uast_extractor \
.link(BagFeatures2TermFreq()) \
.link(TFIDF(df_model)) \
.link(document_indexer) \
.link(Indexer(Uast2BagFeatures.Columns.token, df_model.order))
if save_hook is not None:
bags_writer = bags_writer \
.link(Repartitioner.maybe(args.partitions * 10, args.shuffle)) \
.link(save_hook())
bags_writer.link(BOWWriter(document_indexer, df_model, args.bow, args.batch)) \
.execute()
pipeline_graph(args, log, root)
def test_create(self):
session = create_spark("test_df_util")
uast_extractor = ParquetLoader(session, paths.PARQUET_DIR) \
.link(Moder("file")) \
.link(UastRow2Document())
ndocs = uast_extractor.link(Counter()).execute()
uast_extractor = uast_extractor.link(UastDeserializer()) \
.link(Uast2BagFeatures(IdentifiersBagExtractor()))
with tempfile.TemporaryDirectory() as tmpdir:
tmp_path = os.path.join(tmpdir, "df.asdf")
args = argparse.Namespace(docfreq_in=None, docfreq_out=tmp_path, min_docfreq=1,
vocabulary_size=1000)
df_model = create_or_load_ordered_df(args, ndocs, uast_extractor)
self.assertEqual(df_model.docs, ndocs)
self.assertTrue(os.path.exists(tmp_path))
def repos2roles_and_ids(args):
log = logging.getLogger("repos2roles_and_ids")
session_name = "repos2roles_and_ids-%s" % uuid4()
extractor = RoleIdsExtractor()
root, start_point = create_uast_source(args, session_name)
start_point \
.link(Moder("file")) \
.link(UastRow2Document()) \
.link(UastDeserializer()) \
.link(Uast2Features(extractor)) \
.link(Rower(lambda x: {"identifier": x["roleids"][0], "role": x["roleids"][1]})) \
.link(CsvSaver(args.output)) \
.execute()
pipeline_graph(args, log, root)
def test_create(self):
session = create_spark("test_quant_util")
extractor = ChildrenBagExtractor()
with tempfile.NamedTemporaryFile(mode="r+b", suffix="-quant.asdf") as tmp:
path = tmp.name
uast_extractor = ParquetLoader(session, paths.PARQUET_DIR) \
.link(Moder("file")) \
.link(UastRow2Document()) \
.link(UastDeserializer())
create_or_apply_quant(path, [extractor], uast_extractor)
self.assertIsNotNone(extractor.levels)
self.assertTrue(os.path.exists(path))
model_levels = QuantizationLevels().load(source=path)._levels["children"]
for key in model_levels:
self.assertListEqual(list(model_levels[key]), list(extractor.levels[key]))
def preprocess_repos(args):
log = logging.getLogger("preprocess_repos")
session_name = "preprocess_repos-%s" % uuid4()
if os.path.exists(args.output):
log.critical("%s must not exist", args.output)
return 1
if not args.config:
args.config = []
root, start_point = create_uast_source(args, session_name)
start_point \
.link(Moder(args.mode)) \
.link(FieldsSelector(fields=args.fields)) \
.link(ParquetSaver(save_loc=args.output)) \
.execute()
pipeline_graph(args, log, root)
def repos2id_distance(args):
log = logging.getLogger("repos2roles_and_ids")
extractor = IdentifierDistance(args.split, args.type, args.max_distance)
session_name = "repos2roles_and_ids-%s" % uuid4()
root, start_point = create_uast_source(args, session_name)
start_point \
.link(Moder(args.mode)) \
.link(UastRow2Document()) \
.link(UastDeserializer()) \
.link(Uast2BagFeatures(extractor)) \
.link(Rower(lambda x: {"identifier1": x[0][0][0],
"identifier2": x[0][0][1],
"distance": x[1]})) \
.link(CsvSaver(args.output)) \
.execute()
pipeline_graph(args, log, root)
def repos2id_sequence(args):
log = logging.getLogger("repos2id_distance")
extractor = IdSequenceExtractor(args.split)
session_name = "repos2roles_and_ids-%s" % uuid4()
root, start_point = create_uast_source(args, session_name)
if not args.skip_docname:
mapper = Rower(lambda x: {"document": x[0][1], "identifiers": x[0][0]})
else:
mapper = Rower(lambda x: {"identifiers": x[0][0]})
start_point \
.link(Moder(args.mode)) \
.link(UastRow2Document()) \
.link(UastDeserializer()) \
.link(Uast2BagFeatures(extractor)) \
.link(mapper) \
.link(CsvSaver(args.output)) \
.execute()
pipeline_graph(args, log, root)
def code2vec_extract_features(args):
log = logging.getLogger("code2vec")
session_name = "code2vec-%s" % uuid4()
root, start_point = create_uast_source(args, session_name)
res = start_point \
.link(Moder("func")) \
.link(UastRow2Document()) \
.link(UastDeserializer()) \
.link(Uast2BagFeatures([UastPathsBagExtractor(args.max_length, args.max_width)])) \
.link(Vocabulary2Id(root.session.sparkContext, args.output)) \
.execute()
# TODO: Add rest of data pipeline: extract distinct paths and terminal nodes for embedding mapping
# TODO: Add transformer to write bags and vocabs to a model
# TODO: Add ML pipeline
pipeline_graph(args, log, root)
def repos2bow_index_template(args):
log = logging.getLogger("repos2bow_index")
extractors = create_extractors_from_args(args)
session_name = "repos2bow_index_features-%s" % uuid4()
root, start_point = create_uast_source(args, session_name)
uast_extractor = start_point.link(Moder(args.mode)) \
.link(Repartitioner.maybe(args.partitions, args.shuffle)) \
.link(UastRow2Document()) \
.link(Cacher.maybe(args.persist))
log.info("Extracting UASTs and indexing documents ...")
document_indexer = Indexer(Uast2BagFeatures.Columns.document)
uast_extractor.link(document_indexer).execute()
document_indexer.save_index(args.cached_index_path)
ndocs = len(document_indexer)
log.info("Number of documents: %d", ndocs)
uast_extractor = uast_extractor.link(UastDeserializer())
if args.quant:
create_or_apply_quant(args.quant, extractors, uast_extractor)
if args.docfreq_out:
create_or_load_ordered_df(args, ndocs, uast_extractor.link(Uast2BagFeatures(*extractors)))
pipeline_graph(args, log, root)
def repos2df(args):
log = logging.getLogger("repos2df")
extractors = create_extractors_from_args(args)
session_name = "repos2df-%s" % uuid4()
root, start_point = create_uast_source(args, session_name)
uast_extractor = start_point \
.link(Moder(args.mode)) \
.link(UastRow2Document()) \
.link(Cacher.maybe(args.persist))
log.info("Extracting UASTs...")
ndocs = uast_extractor.link(Counter()).execute()
log.info("Number of documents: %d", ndocs)
uast_extractor = uast_extractor.link(UastDeserializer())
if args.quant:
create_or_apply_quant(args.quant, extractors, uast_extractor)
df = uast_extractor \
.link(Uast2BagFeatures(*extractors)) \
.link(BagFeatures2DocFreq()) \
.execute()
log.info("Writing docfreq model to %s", args.docfreq_out)
OrderedDocumentFrequencies().construct(ndocs, df).save(args.docfreq_out)
pipeline_graph(args, log, root)
def repos2bow_template(args, cache_hook: Transformer = None,
save_hook: Transformer = None):
log = logging.getLogger("repos2bow")
extractors = create_extractors_from_args(args)
session_name = "repos2bow-%s" % uuid4()
root, start_point = create_uast_source(args, session_name)
log.info("Loading the document index from %s ...", args.cached_index_path)
docfreq = DocumentFrequencies().load(source=args.cached_index_path)
document_index = {key: int(val) for (key, val) in docfreq}
try:
if args.quant is not None:
create_or_apply_quant(args.quant, extractors, None)
df_model = create_or_load_ordered_df(args, None, None)
except ValueError:
return 1
ec = EngineConstants.Columns
if args.mode == Moder.Options.repo:
def keymap(r):
return r[ec.RepositoryId]
else:
def keymap(r):
return r[ec.RepositoryId] + UastRow2Document.REPO_PATH_SEP + \
r[ec.Path] + UastRow2Document.PATH_BLOB_SEP + r[ec.BlobId]
log.info("Caching UASTs to disk after partitioning by document ...")
start_point = start_point.link(Moder(args.mode)) \
.link(Repartitioner.maybe(args.num_iterations, keymap=keymap)) \
.link(Cacher.maybe("DISK_ONLY"))
for num_part in range(args.num_iterations):
log.info("Running job %s of %s", num_part + 1, args.num_iterations)
selected_part = start_point \
.link(PartitionSelector(num_part)) \
.link(Repartitioner.maybe(args.partitions, args.shuffle)) \
.link(Cacher.maybe(args.persist))
if cache_hook is not None:
selected_part.link(cache_hook()).execute()
uast_extractor = selected_part \
.link(UastRow2Document()) \
.link(Cacher.maybe(args.persist))
log.info("Collecting distinct documents ...")
documents = uast_extractor \
.link(FieldsSelector([Uast2BagFeatures.Columns.document])) \
.link(Distinct()) \
.link(Collector()) \
.execute()
selected_part.unpersist()
documents = {row.document for row in documents}
reduced_doc_index = {
key: document_index[key] for key in document_index if key in documents}
document_indexer = Indexer(Uast2BagFeatures.Columns.document, reduced_doc_index)
log.info("Processing %s distinct documents", len(documents))
bags = uast_extractor \
.link(UastDeserializer()) \
.link(Uast2BagFeatures(*extractors)) \
.link(BagFeatures2TermFreq()) \
.link(Cacher.maybe(args.persist))
log.info("Extracting UASTs and collecting distinct tokens ...")
tokens = bags \
.link(FieldsSelector([Uast2BagFeatures.Columns.token])) \
.link(Distinct()) \
.link(Collector()) \
.execute()
uast_extractor.unpersist()
tokens = {row.token for row in tokens}
reduced_token_freq = {key: df_model[key] for key in df_model.df if key in tokens}
reduced_token_index = {key: df_model.order[key] for key in df_model.df if key in tokens}
log.info("Processing %s distinct tokens", len(reduced_token_freq))
log.info("Indexing by document and token ...")
bags_writer = bags \
.link(TFIDF(reduced_token_freq, df_model.docs, root.session.sparkContext)) \
.link(document_indexer) \
.link(Indexer(Uast2BagFeatures.Columns.token, reduced_token_index))
if save_hook is not None:
bags_writer = bags_writer \
.link(Repartitioner.maybe(args.partitions, args.shuffle)) \
.link(save_hook())
bow = args.bow.split(".asdf")[0] + "_" + str(num_part + 1) + ".asdf"
bags_writer \
.link(Repartitioner.maybe(
args.partitions, keymap=lambda x: x[Uast2BagFeatures.Columns.document])) \
.link(BOWWriter(document_indexer, df_model, bow, args.batch)) \
.execute()
bags.unpersist()
pipeline_graph(args, log, root)