def test_incremental():
"""Run an end-to-end test on incremental additions."""
# GitHub Actions gives 2 cores
# help.github.com/en/actions/reference/virtual-environments-for-github-hosted-runners
PARALLEL = 2
max_docs = 1
session = Meta.init(CONN_STRING).Session()
docs_path = "tests/data/html/dtc114w.html"
pdf_path = "tests/data/pdf/dtc114w.pdf"
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(
session,
parallelism=PARALLEL,
structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path,
)
corpus_parser.apply(doc_preprocessor)
num_docs = session.query(Document).count()
logger.info(f"Docs: {num_docs}")
assert num_docs == max_docs
docs = corpus_parser.get_documents()
last_docs = corpus_parser.get_documents()
assert len(docs[0].sentences) == len(last_docs[0].sentences)
# Mention Extraction
part_ngrams = MentionNgramsPart(parts_by_doc=None, n_max=3)
temp_ngrams = MentionNgramsTemp(n_max=2)
Part = mention_subclass("Part")
Temp = mention_subclass("Temp")
mention_extractor = MentionExtractor(session, [Part, Temp],
[part_ngrams, temp_ngrams],
[part_matcher, temp_matcher])
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Part).count() == 11
assert session.query(Temp).count() == 8
# Candidate Extraction
PartTemp = candidate_subclass("PartTemp", [Part, Temp])
candidate_extractor = CandidateExtractor(session, [PartTemp],
throttlers=[temp_throttler])
candidate_extractor.apply(docs, split=0, parallelism=PARALLEL)
assert session.query(PartTemp).filter(PartTemp.split == 0).count() == 70
assert session.query(Candidate).count() == 70
# Grab candidate lists
train_cands = candidate_extractor.get_candidates(split=0)
assert len(train_cands) == 1
assert len(train_cands[0]) == 70
# Featurization
featurizer = Featurizer(session, [PartTemp])
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
assert session.query(Feature).count() == 70
assert session.query(FeatureKey).count() == 512
F_train = featurizer.get_feature_matrices(train_cands)
assert F_train[0].shape == (70, 512)
assert len(featurizer.get_keys()) == 512
# Test Dropping FeatureKey
featurizer.drop_keys(["CORE_e1_LENGTH_1"])
assert session.query(FeatureKey).count() == 512
stg_temp_lfs = [
LF_storage_row,
LF_operating_row,
LF_temperature_row,
LF_tstg_row,
LF_to_left,
LF_negative_number_left,
]
labeler = Labeler(session, [PartTemp])
labeler.apply(split=0,
lfs=[stg_temp_lfs],
train=True,
parallelism=PARALLEL)
assert session.query(Label).count() == 70
# Only 5 because LF_operating_row doesn't apply to the first test doc
assert session.query(LabelKey).count() == 5
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (70, 5)
assert len(labeler.get_keys()) == 5
docs_path = "tests/data/html/112823.html"
pdf_path = "tests/data/pdf/112823.pdf"
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser.apply(doc_preprocessor, pdf_path=pdf_path, clear=False)
assert len(corpus_parser.get_documents()) == 2
new_docs = corpus_parser.get_last_documents()
assert len(new_docs) == 1
assert new_docs[0].name == "112823"
# Get mentions from just the new docs
mention_extractor.apply(new_docs, parallelism=PARALLEL, clear=False)
assert session.query(Part).count() == 81
assert session.query(Temp).count() == 31
# Test if existing mentions are skipped.
mention_extractor.apply(new_docs, parallelism=PARALLEL, clear=False)
assert session.query(Part).count() == 81
assert session.query(Temp).count() == 31
# Just run candidate extraction and assign to split 0
candidate_extractor.apply(new_docs,
split=0,
parallelism=PARALLEL,
clear=False)
# Grab candidate lists
train_cands = candidate_extractor.get_candidates(split=0)
assert len(train_cands) == 1
assert len(train_cands[0]) == 1502
# Test if existing candidates are skipped.
candidate_extractor.apply(new_docs,
split=0,
parallelism=PARALLEL,
clear=False)
train_cands = candidate_extractor.get_candidates(split=0)
assert len(train_cands) == 1
assert len(train_cands[0]) == 1502
# Update features
featurizer.update(new_docs, parallelism=PARALLEL)
assert session.query(Feature).count() == 1502
assert session.query(FeatureKey).count() == 2573
F_train = featurizer.get_feature_matrices(train_cands)
assert F_train[0].shape == (1502, 2573)
assert len(featurizer.get_keys()) == 2573
# Update LF_storage_row. Now it always returns ABSTAIN.
@labeling_function(name="LF_storage_row")
def LF_storage_row_updated(c):
return ABSTAIN
stg_temp_lfs = [
LF_storage_row_updated,
LF_operating_row,
LF_temperature_row,
LF_tstg_row,
LF_to_left,
LF_negative_number_left,
]
# Update Labels
labeler.update(docs, lfs=[stg_temp_lfs], parallelism=PARALLEL)
labeler.update(new_docs, lfs=[stg_temp_lfs], parallelism=PARALLEL)
assert session.query(Label).count() == 1502
# Only 5 because LF_storage_row doesn't apply to any doc (always ABSTAIN)
assert session.query(LabelKey).count() == 5
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (1502, 5)
# Test clear
featurizer.clear(train=True)
assert session.query(FeatureKey).count() == 0
def test_incremental(caplog):
"""Run an end-to-end test on incremental additions."""
caplog.set_level(logging.INFO)
PARALLEL = 1
max_docs = 1
session = Meta.init("postgresql://localhost:5432/" + DB).Session()
docs_path = "tests/data/html/dtc114w.html"
pdf_path = "tests/data/pdf/dtc114w.pdf"
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(
session,
parallelism=PARALLEL,
structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path,
)
corpus_parser.apply(doc_preprocessor)
num_docs = session.query(Document).count()
logger.info(f"Docs: {num_docs}")
assert num_docs == max_docs
docs = corpus_parser.get_documents()
last_docs = corpus_parser.get_documents()
assert len(docs[0].sentences) == len(last_docs[0].sentences)
# Mention Extraction
part_ngrams = MentionNgramsPart(parts_by_doc=None, n_max=3)
temp_ngrams = MentionNgramsTemp(n_max=2)
Part = mention_subclass("Part")
Temp = mention_subclass("Temp")
mention_extractor = MentionExtractor(session, [Part, Temp],
[part_ngrams, temp_ngrams],
[part_matcher, temp_matcher])
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Part).count() == 11
assert session.query(Temp).count() == 8
# Candidate Extraction
PartTemp = candidate_subclass("PartTemp", [Part, Temp])
candidate_extractor = CandidateExtractor(session, [PartTemp],
throttlers=[temp_throttler])
candidate_extractor.apply(docs, split=0, parallelism=PARALLEL)
assert session.query(PartTemp).filter(PartTemp.split == 0).count() == 70
assert session.query(Candidate).count() == 70
# Grab candidate lists
train_cands = candidate_extractor.get_candidates(split=0)
assert len(train_cands) == 1
assert len(train_cands[0]) == 70
# Featurization
featurizer = Featurizer(session, [PartTemp])
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
assert session.query(Feature).count() == 70
assert session.query(FeatureKey).count() == 512
F_train = featurizer.get_feature_matrices(train_cands)
assert F_train[0].shape == (70, 512)
assert len(featurizer.get_keys()) == 512
# Test Dropping FeatureKey
featurizer.drop_keys(["CORE_e1_LENGTH_1"])
assert session.query(FeatureKey).count() == 512
stg_temp_lfs = [
LF_storage_row,
LF_operating_row,
LF_temperature_row,
LF_tstg_row,
LF_to_left,
LF_negative_number_left,
]
labeler = Labeler(session, [PartTemp])
labeler.apply(split=0,
lfs=[stg_temp_lfs],
train=True,
parallelism=PARALLEL)
assert session.query(Label).count() == 70
# Only 5 because LF_operating_row doesn't apply to the first test doc
assert session.query(LabelKey).count() == 5
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (70, 5)
assert len(labeler.get_keys()) == 5
docs_path = "tests/data/html/112823.html"
pdf_path = "tests/data/pdf/112823.pdf"
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser.apply(doc_preprocessor, pdf_path=pdf_path, clear=False)
assert len(corpus_parser.get_documents()) == 2
new_docs = corpus_parser.get_last_documents()
assert len(new_docs) == 1
assert new_docs[0].name == "112823"
# Get mentions from just the new docs
mention_extractor.apply(new_docs, parallelism=PARALLEL, clear=False)
assert session.query(Part).count() == 81
assert session.query(Temp).count() == 31
# Just run candidate extraction and assign to split 0
candidate_extractor.apply(new_docs,
split=0,
parallelism=PARALLEL,
clear=False)
# Grab candidate lists
train_cands = candidate_extractor.get_candidates(split=0)
assert len(train_cands) == 1
assert len(train_cands[0]) == 1502
# Update features
featurizer.update(new_docs, parallelism=PARALLEL)
assert session.query(Feature).count() == 1502
assert session.query(FeatureKey).count() == 2573
F_train = featurizer.get_feature_matrices(train_cands)
assert F_train[0].shape == (1502, 2573)
assert len(featurizer.get_keys()) == 2573
# Update Labels
labeler.update(new_docs, lfs=[stg_temp_lfs], parallelism=PARALLEL)
assert session.query(Label).count() == 1502
assert session.query(LabelKey).count() == 6
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (1502, 6)
# Test clear
featurizer.clear(train=True)
assert session.query(FeatureKey).count() == 0
def test_incremental(caplog):
"""Run an end-to-end test on incremental additions."""
caplog.set_level(logging.INFO)
# SpaCy on mac has issue on parallel parsing
if os.name == "posix":
logger.info("Using single core.")
PARALLEL = 1
else:
PARALLEL = 2 # Travis only gives 2 cores
max_docs = 1
session = Meta.init("postgres://localhost:5432/" + DB).Session()
docs_path = "tests/data/html/dtc114w.html"
pdf_path = "tests/data/pdf/dtc114w.pdf"
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(
session,
parallelism=PARALLEL,
structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path,
)
corpus_parser.apply(doc_preprocessor)
num_docs = session.query(Document).count()
logger.info("Docs: {}".format(num_docs))
assert num_docs == max_docs
docs = corpus_parser.get_documents()
# Mention Extraction
part_ngrams = MentionNgramsPart(parts_by_doc=None, n_max=3)
temp_ngrams = MentionNgramsTemp(n_max=2)
Part = mention_subclass("Part")
Temp = mention_subclass("Temp")
mention_extractor = MentionExtractor(
session, [Part, Temp], [part_ngrams, temp_ngrams], [part_matcher, temp_matcher]
)
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Part).count() == 11
assert session.query(Temp).count() == 9
# Candidate Extraction
PartTemp = candidate_subclass("PartTemp", [Part, Temp])
candidate_extractor = CandidateExtractor(
session, [PartTemp], throttlers=[temp_throttler]
)
candidate_extractor.apply(docs, split=0, parallelism=PARALLEL)
assert session.query(PartTemp).filter(PartTemp.split == 0).count() == 78
# Grab candidate lists
train_cands = candidate_extractor.get_candidates(split=0)
assert len(train_cands) == 1
assert len(train_cands[0]) == 78
# Featurization
featurizer = Featurizer(session, [PartTemp])
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
assert session.query(Feature).count() == 78
assert session.query(FeatureKey).count() == 496
F_train = featurizer.get_feature_matrices(train_cands)
assert F_train[0].shape == (78, 496)
assert len(featurizer.get_keys()) == 496
stg_temp_lfs = [
LF_storage_row,
LF_operating_row,
LF_temperature_row,
LF_tstg_row,
LF_to_left,
LF_negative_number_left,
]
labeler = Labeler(session, [PartTemp])
labeler.apply(split=0, lfs=[stg_temp_lfs], train=True, parallelism=PARALLEL)
assert session.query(Label).count() == 78
# Only 5 because LF_operating_row doesn't apply to the first test doc
assert session.query(LabelKey).count() == 5
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (78, 5)
assert len(labeler.get_keys()) == 5
docs_path = "tests/data/html/112823.html"
pdf_path = "tests/data/pdf/112823.pdf"
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser.apply(doc_preprocessor, pdf_path=pdf_path, clear=False)
assert len(corpus_parser.get_documents()) == 2
new_docs = corpus_parser.get_last_documents()
assert len(new_docs) == 1
assert new_docs[0].name == "112823"
# Get mentions from just the new docs
mention_extractor.apply(new_docs, parallelism=PARALLEL, clear=False)
assert session.query(Part).count() == 81
assert session.query(Temp).count() == 33
# Just run candidate extraction and assign to split 0
candidate_extractor.apply(new_docs, split=0, parallelism=PARALLEL, clear=False)
# Grab candidate lists
train_cands = candidate_extractor.get_candidates(split=0)
assert len(train_cands) == 1
assert len(train_cands[0]) == 1574
# Update features
featurizer.update(new_docs, parallelism=PARALLEL)
assert session.query(Feature).count() == 1574
assert session.query(FeatureKey).count() == 2425
F_train = featurizer.get_feature_matrices(train_cands)
assert F_train[0].shape == (1574, 2425)
assert len(featurizer.get_keys()) == 2425
# Update Labels
labeler.update(new_docs, lfs=[stg_temp_lfs], parallelism=PARALLEL)
assert session.query(Label).count() == 1574
assert session.query(LabelKey).count() == 6
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (1574, 6)
