def test_subclass_before_meta_init():
"""Test if mention (candidate) subclass can be created before Meta init."""
# Test if mention (candidate) subclass can be created
Part = mention_subclass("Part")
logger.info(f"Create a mention subclass '{Part.__tablename__}'")
# Setup a database
con = psycopg2.connect(
host=os.environ["POSTGRES_HOST"],
port=os.environ["POSTGRES_PORT"],
user=os.environ["PGUSER"],
password=os.environ["PGPASSWORD"],
)
con.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT)
cursor = con.cursor()
cursor.execute(f'create database "{DB}";')
session = Meta.init(CONN_STRING).Session()
# Test if another mention subclass can be created
Temp = mention_subclass("Temp")
logger.info(f"Create a mention subclass '{Temp.__tablename__}'")
# Teardown the database
session.close()
Meta.engine.dispose()
Meta.engine = None
cursor.execute(f'drop database "{DB}";')
cursor.close()
con.close()
def get_subclasses(experiment):
# 1.) Mention subclasses
Data = mention_subclass("Data")
Row = mention_subclass("Row")
Col = mention_subclass("Col")
# 2.) Mention spaces
data_ngrams = MentionSentences() # MentionNgrams(n_max=3)
row_ngrams = MentionSentences() # MentionNgrams(n_min=1, n_max=8)
col_ngrams = MentionSentences() # MentionNgrams(n_min=1, n_max=8)
# 3.) Matchers
data_regex_matcher = RegexMatchSpan(rgx=r"[0-9-,.%$#]+( to | )?[0-9-,.%$#]*|^x$", longest_match_only=True)
data_label_matcher = LambdaFunctionMatcher(func=get_label_matcher("Data", experiment))
data_matcher = Intersect(data_regex_matcher, data_label_matcher)
row_regex_matcher = RegexMatchSpan(rgx=r"^.*$", longest_match_only=True)
row_label_matcher = LambdaFunctionMatcher(func=get_label_matcher("Header", experiment))
row_matcher = Intersect(row_regex_matcher, row_label_matcher)
col_regex_matcher = RegexMatchSpan(rgx=r"^.*$", longest_match_only=True)
col_label_matcher = LambdaFunctionMatcher(func=get_label_matcher("Header", experiment))
col_matcher = Intersect(col_regex_matcher, col_label_matcher)
# 4.) Candidate classes
RowCandidate = candidate_subclass("RowCandidate", [Data, Row])
ColCandidate = candidate_subclass("ColCandidate", [Data, Col])
# 5.) Throttlers
mention_classes = [Data, Row, Col]
mention_spaces = [data_ngrams, row_ngrams, col_ngrams]
matchers = [data_matcher, row_matcher, col_matcher]
candidate_classes = [RowCandidate, ColCandidate]
throttlers = [row_filter, col_filter]
return (mention_classes, mention_spaces, matchers, candidate_classes, throttlers)
def test_subclass_before_meta_init():
"""Test if mention (candidate) subclass can be created before Meta init."""
Part = mention_subclass("Part")
logger.info(f"Create a mention subclass '{Part.__tablename__}'")
Meta.init("postgresql://localhost:5432/" + DB).Session()
Temp = mention_subclass("Temp")
logger.info(f"Create a mention subclass '{Temp.__tablename__}'")
def test_subclass_before_meta_init():
"""Test if it is possible to create a mention (candidate) subclass even before Meta
is initialized.
"""
Part = mention_subclass("Part")
logger.info(f"Create a mention subclass '{Part.__tablename__}'")
Meta.init(CONN_STRING).Session()
Temp = mention_subclass("Temp")
logger.info(f"Create a mention subclass '{Temp.__tablename__}'")
def test_mention_longest_match():
"""Test longest match filtering in mention extraction."""
file_name = "lincoln_short"
docs_path = f"tests/data/pure_html/{file_name}.html"
doc = parse_doc(docs_path, file_name)
# Mention Extraction
name_ngrams = MentionNgramsPart(n_max=3)
place_ngrams = MentionNgramsTemp(n_max=4)
Name = mention_subclass("Name")
Place = mention_subclass("Place")
def is_birthplace_table_row(mention):
if not mention.sentence.is_tabular():
return False
ngrams = get_row_ngrams(mention, lower=True)
if "birth_place" in ngrams:
return True
else:
return False
birthplace_matcher = LambdaFunctionMatcher(
func=is_birthplace_table_row, longest_match_only=False
)
mention_extractor_udf = MentionExtractorUDF(
[Name, Place],
[name_ngrams, place_ngrams],
[PersonMatcher(), birthplace_matcher],
)
doc = mention_extractor_udf.apply(doc)
mentions = doc.places
mention_spans = [x.context.get_span() for x in mentions]
assert "Sinking Spring Farm" in mention_spans
assert "Farm" in mention_spans
assert len(mention_spans) == 23
# Clear manually
for mention in doc.places[:]:
doc.places.remove(mention)
birthplace_matcher = LambdaFunctionMatcher(
func=is_birthplace_table_row, longest_match_only=True
)
mention_extractor_udf = MentionExtractorUDF(
[Name, Place],
[name_ngrams, place_ngrams],
[PersonMatcher(), birthplace_matcher],
)
doc = mention_extractor_udf.apply(doc)
mentions = doc.places
mention_spans = [x.context.get_span() for x in mentions]
assert "Sinking Spring Farm" in mention_spans
assert "Farm" not in mention_spans
assert len(mention_spans) == 4
def test_subclass_before_meta_init(caplog):
"""Test if it is possible to create a mention (candidate) subclass even before Meta
is initialized.
"""
caplog.set_level(logging.INFO)
conn_string = "postgresql://localhost:5432/" + DB
Part = mention_subclass("Part")
logger.info(f"Create a mention subclass '{Part.__tablename__}'")
Meta.init(conn_string).Session()
Temp = mention_subclass("Temp")
logger.info(f"Create a mention subclass '{Temp.__tablename__}'")
def test_multimodal_cand(caplog):
"""Test multimodal candidate generation"""
caplog.set_level(logging.INFO)
PARALLEL = 4
max_docs = 1
session = Meta.init("postgresql://localhost:5432/" + DB).Session()
docs_path = "tests/data/pure_html/radiology.html"
logger.info("Parsing...")
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(session, structural=True, lingual=True)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
assert session.query(Document).count() == max_docs
assert session.query(Sentence).count() == 35
docs = session.query(Document).order_by(Document.name).all()
# Mention Extraction
ms_doc = mention_subclass("m_doc")
ms_sec = mention_subclass("m_sec")
ms_tab = mention_subclass("m_tab")
ms_fig = mention_subclass("m_fig")
ms_cell = mention_subclass("m_cell")
ms_para = mention_subclass("m_para")
ms_cap = mention_subclass("m_cap")
ms_sent = mention_subclass("m_sent")
m_doc = MentionDocuments()
m_sec = MentionSections()
m_tab = MentionTables()
m_fig = MentionFigures()
m_cell = MentionCells()
m_para = MentionParagraphs()
m_cap = MentionCaptions()
m_sent = MentionSentences()
ms = [ms_doc, ms_cap, ms_sec, ms_tab, ms_fig, ms_para, ms_sent, ms_cell]
m = [m_doc, m_cap, m_sec, m_tab, m_fig, m_para, m_sent, m_cell]
matchers = [DoNothingMatcher()] * 8
mention_extractor = MentionExtractor(session,
ms,
m,
matchers,
parallelism=PARALLEL)
mention_extractor.apply(docs)
assert session.query(ms_doc).count() == 1
assert session.query(ms_cap).count() == 2
assert session.query(ms_sec).count() == 5
assert session.query(ms_tab).count() == 2
assert session.query(ms_fig).count() == 2
assert session.query(ms_para).count() == 30
assert session.query(ms_sent).count() == 35
assert session.query(ms_cell).count() == 21
def test_candidate_with_nullable_mentions():
"""Test if mentions can be NULL."""
docs_path = "tests/data/html/112823.html"
pdf_path = "tests/data/pdf/"
doc = parse_doc(docs_path, "112823", pdf_path)
# Mention Extraction
MentionTemp = mention_subclass("MentionTemp")
temp_ngrams = MentionNgramsTemp(n_max=2)
mention_extractor_udf = MentionExtractorUDF(
[MentionTemp],
[temp_ngrams],
[temp_matcher],
)
doc = mention_extractor_udf.apply(doc)
assert len(doc.mention_temps) == 23
# Candidate Extraction
CandidateTemp = candidate_subclass("CandidateTemp", [MentionTemp],
nullables=[True])
candidate_extractor_udf = CandidateExtractorUDF([CandidateTemp], [None],
False, False, True)
doc = candidate_extractor_udf.apply(doc, split=0)
# The number of extracted candidates should be that of mentions + 1 (NULL)
assert len(doc.candidate_temps) == len(doc.mention_temps) + 1
# Extracted candidates should include one with NULL mention.
assert None in [c[0] for c in doc.candidate_temps]
def test_row_col_ngram_extraction():
"""Test whether row/column ngrams list is empty, if mention is not in a table."""
file_name = "lincoln_short"
docs_path = f"tests/data/pure_html/{file_name}.html"
doc = parse_doc(docs_path, file_name)
# Mention Extraction
place_ngrams = MentionNgramsTemp(n_max=4)
Place = mention_subclass("Place")
def get_row_and_column_ngrams(mention):
row_ngrams = list(get_row_ngrams(mention))
col_ngrams = list(get_col_ngrams(mention))
if not mention.sentence.is_tabular():
assert len(row_ngrams) == 1 and row_ngrams[0] is None
assert len(col_ngrams) == 1 and col_ngrams[0] is None
else:
assert not any(x is None for x in row_ngrams)
assert not any(x is None for x in col_ngrams)
if "birth_place" in row_ngrams:
return True
else:
return False
birthplace_matcher = LambdaFunctionMatcher(func=get_row_and_column_ngrams)
mention_extractor_udf = MentionExtractorUDF(
[Place], [place_ngrams], [birthplace_matcher]
)
doc = mention_extractor_udf.apply(doc)
def test_ngrams():
"""Test ngram limits in mention extraction"""
file_name = "lincoln_short"
docs_path = f"tests/data/pure_html/{file_name}.html"
doc = parse_doc(docs_path, file_name)
# Mention Extraction
Person = mention_subclass("Person")
person_ngrams = MentionNgrams(n_max=3)
person_matcher = PersonMatcher()
mention_extractor_udf = MentionExtractorUDF(
[Person], [person_ngrams], [person_matcher]
)
doc = mention_extractor_udf.apply(doc)
assert len(doc.persons) == 118
mentions = doc.persons
assert len([x for x in mentions if x.context.get_num_words() == 1]) == 49
assert len([x for x in mentions if x.context.get_num_words() > 3]) == 0
# Test for unigram exclusion
for mention in doc.persons[:]:
doc.persons.remove(mention)
assert len(doc.persons) == 0
person_ngrams = MentionNgrams(n_min=2, n_max=3)
mention_extractor_udf = MentionExtractorUDF(
[Person], [person_ngrams], [person_matcher]
)
doc = mention_extractor_udf.apply(doc)
assert len(doc.persons) == 69
mentions = doc.persons
assert len([x for x in mentions if x.context.get_num_words() == 1]) == 0
assert len([x for x in mentions if x.context.get_num_words() > 3]) == 0
def test_visualizer():
from fonduer.utils.visualizer import Visualizer # noqa
"""Unit test of visualizer using the md document.
"""
docs_path = "tests/data/html_simple/md.html"
pdf_path = "tests/data/pdf_simple/md.pdf"
# Grab the md document
doc = parse_doc(docs_path, "md", pdf_path)
assert doc.name == "md"
organization_ngrams = MentionNgrams(n_max=1)
Org = mention_subclass("Org")
organization_matcher = OrganizationMatcher()
mention_extractor_udf = MentionExtractorUDF([Org], [organization_ngrams],
[organization_matcher])
doc = mention_extractor_udf.apply(doc)
Organization = candidate_subclass("Organization", [Org])
candidate_extractor_udf = CandidateExtractorUDF([Organization], None,
False, False, True)
doc = candidate_extractor_udf.apply(doc, split=0)
cands = doc.organizations
# Test visualizer
pdf_path = "tests/data/pdf_simple"
vis = Visualizer(pdf_path)
vis.display_candidates([cands[0]])
def test_save_subclasses():
"""Test if subclasses can be saved."""
mention_class = mention_subclass("test_mention_class")
_save_mention_classes([mention_class], "./")
assert os.path.exists("./mention_classes.pkl")
candidate_class = candidate_subclass("test_candidate_class", [mention_class])
_save_candidate_classes([candidate_class], "./")
assert os.path.exists("./candidate_classes.pkl")
def test_pickle_subclasses():
"""Test if mention/candidate subclasses and their objects can be pickled."""
Part = mention_subclass("Part")
Temp = mention_subclass("Temp")
PartTemp = candidate_subclass("PartTemp", [Part, Temp])
logger.info("Test if mention/candidate subclasses are picklable")
pickle.loads(pickle.dumps(Part))
pickle.loads(pickle.dumps(Temp))
pickle.loads(pickle.dumps(PartTemp))
logger.info("Test if their objects are pickable")
part = Part()
temp = Temp()
parttemp = PartTemp()
pickle.loads(pickle.dumps(part))
pickle.loads(pickle.dumps(temp))
pickle.loads(pickle.dumps(parttemp))
def test_visualizer(caplog):
from fonduer.utils.visualizer import Visualizer # noqa
"""Unit test of visualizer using the md document.
"""
caplog.set_level(logging.INFO)
session = Meta.init("postgresql://localhost:5432/" + ATTRIBUTE).Session()
PARALLEL = 1
max_docs = 1
docs_path = "tests/data/html_simple/md.html"
pdf_path = "tests/data/pdf_simple/md.pdf"
# Preprocessor for the Docs
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
# Create an Parser and parse the md document
corpus_parser = Parser(session,
structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
# Grab the md document
doc = session.query(Document).order_by(Document.name).all()[0]
assert doc.name == "md"
organization_ngrams = MentionNgrams(n_max=1)
Org = mention_subclass("Org")
organization_matcher = OrganizationMatcher()
mention_extractor = MentionExtractor(session, [Org], [organization_ngrams],
[organization_matcher])
mention_extractor.apply([doc], parallelism=PARALLEL)
Organization = candidate_subclass("Organization", [Org])
candidate_extractor = CandidateExtractor(session, [Organization])
candidate_extractor.apply([doc], split=0, parallelism=PARALLEL)
cands = session.query(Organization).filter(Organization.split == 0).all()
# Test visualizer
pdf_path = "tests/data/pdf_simple"
vis = Visualizer(pdf_path)
vis.display_candidates([cands[0]])
def test_multimodal_cand():
"""Test multimodal candidate generation"""
file_name = "radiology"
docs_path = f"tests/data/pure_html/{file_name}.html"
doc = parse_doc(docs_path, file_name)
assert len(doc.sentences) == 35
# Mention Extraction
ms_doc = mention_subclass("m_doc")
ms_sec = mention_subclass("m_sec")
ms_tab = mention_subclass("m_tab")
ms_fig = mention_subclass("m_fig")
ms_cell = mention_subclass("m_cell")
ms_para = mention_subclass("m_para")
ms_cap = mention_subclass("m_cap")
ms_sent = mention_subclass("m_sent")
m_doc = MentionDocuments()
m_sec = MentionSections()
m_tab = MentionTables()
m_fig = MentionFigures()
m_cell = MentionCells()
m_para = MentionParagraphs()
m_cap = MentionCaptions()
m_sent = MentionSentences()
ms = [ms_doc, ms_cap, ms_sec, ms_tab, ms_fig, ms_para, ms_sent, ms_cell]
m = [m_doc, m_cap, m_sec, m_tab, m_fig, m_para, m_sent, m_cell]
matchers = [DoNothingMatcher()] * 8
mention_extractor_udf = MentionExtractorUDF(ms, m, matchers)
doc = mention_extractor_udf.apply(doc)
assert len(doc.m_docs) == 1
assert len(doc.m_caps) == 2
assert len(doc.m_secs) == 5
assert len(doc.m_tabs) == 2
assert len(doc.m_figs) == 2
assert len(doc.m_paras) == 30
assert len(doc.m_sents) == 35
assert len(doc.m_cells) == 21
def test_visualizer():
"""Unit test of visualizer using the md document."""
from fonduer.utils.visualizer import Visualizer, get_box # noqa
docs_path = "tests/data/html_simple/md.html"
pdf_path = "tests/data/pdf_simple/"
# Grab the md document
doc = parse_doc(docs_path, "md", pdf_path)
assert doc.name == "md"
organization_ngrams = MentionNgrams(n_max=1)
Org = mention_subclass("Org")
organization_matcher = OrganizationMatcher()
mention_extractor_udf = MentionExtractorUDF([Org], [organization_ngrams],
[organization_matcher])
doc = mention_extractor_udf.apply(doc)
Organization = candidate_subclass("Organization", [Org])
candidate_extractor_udf = CandidateExtractorUDF([Organization], None,
False, False, True)
doc = candidate_extractor_udf.apply(doc, split=0)
# Take one candidate
cand = doc.organizations[0]
pdf_path = "tests/data/pdf_simple"
vis = Visualizer(pdf_path)
# Test bounding boxes
boxes = [get_box(mention.context) for mention in cand.get_mentions()]
for box in boxes:
assert box.top <= box.bottom
assert box.left <= box.right
assert boxes == [
mention.context.get_bbox() for mention in cand.get_mentions()
]
# Test visualizer
vis.display_candidates([cand])
def test_ngrams(caplog):
"""Test ngram limits in mention extraction"""
caplog.set_level(logging.INFO)
PARALLEL = 4
max_docs = 1
session = Meta.init("postgresql://localhost:5432/" + DB).Session()
docs_path = "tests/data/pure_html/lincoln_short.html"
logger.info("Parsing...")
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(session, structural=True, lingual=True)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
assert session.query(Document).count() == max_docs
assert session.query(Sentence).count() == 503
docs = session.query(Document).order_by(Document.name).all()
# Mention Extraction
Person = mention_subclass("Person")
person_ngrams = MentionNgrams(n_max=3)
person_matcher = PersonMatcher()
mention_extractor = MentionExtractor(
session, [Person], [person_ngrams], [person_matcher]
)
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Person).count() == 118
mentions = session.query(Person).all()
assert len([x for x in mentions if x.context.get_num_words() == 1]) == 49
assert len([x for x in mentions if x.context.get_num_words() > 3]) == 0
# Test for unigram exclusion
person_ngrams = MentionNgrams(n_min=2, n_max=3)
mention_extractor = MentionExtractor(
session, [Person], [person_ngrams], [person_matcher]
)
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Person).count() == 69
mentions = session.query(Person).all()
assert len([x for x in mentions if x.context.get_num_words() == 1]) == 0
assert len([x for x in mentions if x.context.get_num_words() > 3]) == 0
def test_row_col_ngram_extraction(caplog):
"""Test whether row/column ngrams list is empty, if mention is not in a table."""
caplog.set_level(logging.INFO)
PARALLEL = 1
max_docs = 1
session = Meta.init("postgresql://localhost:5432/" + DB).Session()
docs_path = "tests/data/pure_html/lincoln_short.html"
# Parsing
logger.info("Parsing...")
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(session, structural=True, lingual=True)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
docs = session.query(Document).order_by(Document.name).all()
# Mention Extraction
place_ngrams = MentionNgramsTemp(n_max=4)
Place = mention_subclass("Place")
def get_row_and_column_ngrams(mention):
row_ngrams = list(get_row_ngrams(mention))
col_ngrams = list(get_col_ngrams(mention))
if not mention.sentence.is_tabular():
assert len(row_ngrams) == 1 and row_ngrams[0] is None
assert len(col_ngrams) == 1 and col_ngrams[0] is None
else:
assert not any(x is None for x in row_ngrams)
assert not any(x is None for x in col_ngrams)
if "birth_place" in row_ngrams:
return True
else:
return False
birthplace_matcher = LambdaFunctionMatcher(func=get_row_and_column_ngrams)
mention_extractor = MentionExtractor(
session, [Place], [place_ngrams], [birthplace_matcher]
)
mention_extractor.apply(docs, parallelism=PARALLEL)
def test_multinary_relation_feature_extraction():
"""Test extracting candidates from mentions from documents."""
docs_path = "tests/data/html/112823.html"
pdf_path = "tests/data/pdf/112823.pdf"
# Parsing
doc = parse_doc(docs_path, "112823", pdf_path)
assert len(doc.sentences) == 799
# Mention Extraction
part_ngrams = MentionNgrams(n_max=1)
temp_ngrams = MentionNgrams(n_max=1)
volt_ngrams = MentionNgrams(n_max=1)
Part = mention_subclass("Part")
Temp = mention_subclass("Temp")
Volt = mention_subclass("Volt")
mention_extractor_udf = MentionExtractorUDF(
[Part, Temp, Volt],
[part_ngrams, temp_ngrams, volt_ngrams],
[part_matcher, temp_matcher, volt_matcher],
)
doc = mention_extractor_udf.apply(doc)
assert len(doc.parts) == 62
assert len(doc.temps) == 16
assert len(doc.volts) == 33
part = doc.parts[0]
temp = doc.temps[0]
volt = doc.volts[0]
logger.info(f"Part: {part.context}")
logger.info(f"Temp: {temp.context}")
logger.info(f"Volt: {volt.context}")
# Candidate Extraction
PartTempVolt = candidate_subclass("PartTempVolt", [Part, Temp, Volt])
candidate_extractor_udf = CandidateExtractorUDF([PartTempVolt], None,
False, False, True)
doc = candidate_extractor_udf.apply(doc, split=0)
# Manually set id as it is not set automatically b/c a database is not used.
i = 0
for cand in doc.part_temp_volts:
cand.id = i
i = i + 1
n_cands = len(doc.part_temp_volts)
# Featurization based on default feature library
featurizer_udf = FeaturizerUDF([PartTempVolt], FeatureExtractor())
# Test that featurization default feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_default_feats = len(key_set)
# Example feature extractor
def feat_ext(candidates):
candidates = candidates if isinstance(candidates,
list) else [candidates]
for candidate in candidates:
yield candidate.id, f"cand_id_{candidate.id}", 1
# Featurization with one extra feature extractor
feature_extractors = FeatureExtractor(customize_feature_funcs=[feat_ext])
featurizer_udf = FeaturizerUDF([PartTempVolt],
feature_extractors=feature_extractors)
# Test that featurization default feature library with one extra feature extractor
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_default_w_customized_features = len(key_set)
# Example spurious feature extractor
def bad_feat_ext(candidates):
raise RuntimeError()
# Featurization with a spurious feature extractor
feature_extractors = FeatureExtractor(
customize_feature_funcs=[bad_feat_ext])
featurizer_udf = FeaturizerUDF([PartTempVolt],
feature_extractors=feature_extractors)
# Test that featurization default feature library with one extra feature extractor
logger.info("Featurizing with a spurious feature extractor...")
with pytest.raises(RuntimeError):
features = featurizer_udf.apply(doc)
# Featurization with only textual feature
feature_extractors = FeatureExtractor(features=["textual"])
featurizer_udf = FeaturizerUDF([PartTempVolt],
feature_extractors=feature_extractors)
# Test that featurization textual feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_textual_features = len(key_set)
# Featurization with only tabular feature
feature_extractors = FeatureExtractor(features=["tabular"])
featurizer_udf = FeaturizerUDF([PartTempVolt],
feature_extractors=feature_extractors)
# Test that featurization tabular feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_tabular_features = len(key_set)
# Featurization with only structural feature
feature_extractors = FeatureExtractor(features=["structural"])
featurizer_udf = FeaturizerUDF([PartTempVolt],
feature_extractors=feature_extractors)
# Test that featurization structural feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_structural_features = len(key_set)
# Featurization with only visual feature
feature_extractors = FeatureExtractor(features=["visual"])
featurizer_udf = FeaturizerUDF([PartTempVolt],
feature_extractors=feature_extractors)
# Test that featurization visual feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_visual_features = len(key_set)
assert (n_default_feats == n_textual_features + n_tabular_features +
n_structural_features + n_visual_features)
assert n_default_w_customized_features == n_default_feats + n_cands
def test_unary_relation_feature_extraction():
"""Test extracting unary candidates from mentions from documents."""
docs_path = "tests/data/html/112823.html"
pdf_path = "tests/data/pdf/112823.pdf"
# Parsing
doc = parse_doc(docs_path, "112823", pdf_path)
assert len(doc.sentences) == 799
# Mention Extraction
part_ngrams = MentionNgrams(n_max=1)
Part = mention_subclass("Part")
mention_extractor_udf = MentionExtractorUDF([Part], [part_ngrams],
[part_matcher])
doc = mention_extractor_udf.apply(doc)
assert doc.name == "112823"
assert len(doc.parts) == 62
part = doc.parts[0]
logger.info(f"Part: {part.context}")
# Candidate Extraction
PartRel = candidate_subclass("PartRel", [Part])
candidate_extractor_udf = CandidateExtractorUDF([PartRel], None, False,
False, True)
doc = candidate_extractor_udf.apply(doc, split=0)
# Featurization based on default feature library
featurizer_udf = FeaturizerUDF([PartRel], FeatureExtractor())
# Test that featurization default feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_default_feats = len(key_set)
# Featurization with only textual feature
feature_extractors = FeatureExtractor(features=["textual"])
featurizer_udf = FeaturizerUDF([PartRel],
feature_extractors=feature_extractors)
# Test that featurization textual feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_textual_features = len(key_set)
# Featurization with only tabular feature
feature_extractors = FeatureExtractor(features=["tabular"])
featurizer_udf = FeaturizerUDF([PartRel],
feature_extractors=feature_extractors)
# Test that featurization tabular feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_tabular_features = len(key_set)
# Featurization with only structural feature
feature_extractors = FeatureExtractor(features=["structural"])
featurizer_udf = FeaturizerUDF([PartRel],
feature_extractors=feature_extractors)
# Test that featurization structural feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_structural_features = len(key_set)
# Featurization with only visual feature
feature_extractors = FeatureExtractor(features=["visual"])
featurizer_udf = FeaturizerUDF([PartRel],
feature_extractors=feature_extractors)
# Test that featurization visual feature library
features_list = featurizer_udf.apply(doc)
features = itertools.chain.from_iterable(features_list)
key_set = set([key for feature in features for key in feature["keys"]])
n_visual_features = len(key_set)
assert (n_default_feats == n_textual_features + n_tabular_features +
n_structural_features + n_visual_features)
def test_e2e():
"""Run an end-to-end test on documents of the hardware domain."""
PARALLEL = 4
max_docs = 12
fonduer.init_logging(
log_dir="log_folder",
format="[%(asctime)s][%(levelname)s] %(name)s:%(lineno)s - %(message)s",
level=logging.INFO,
)
session = fonduer.Meta.init(CONN_STRING).Session()
docs_path = "tests/data/html/"
pdf_path = "tests/data/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)
assert session.query(Document).count() == max_docs
num_docs = session.query(Document).count()
logger.info(f"Docs: {num_docs}")
assert num_docs == max_docs
num_sentences = session.query(Sentence).count()
logger.info(f"Sentences: {num_sentences}")
# Divide into test and train
docs = sorted(corpus_parser.get_documents())
last_docs = sorted(corpus_parser.get_last_documents())
ld = len(docs)
assert ld == len(last_docs)
assert len(docs[0].sentences) == len(last_docs[0].sentences)
assert len(docs[0].sentences) == 799
assert len(docs[1].sentences) == 663
assert len(docs[2].sentences) == 784
assert len(docs[3].sentences) == 661
assert len(docs[4].sentences) == 513
assert len(docs[5].sentences) == 700
assert len(docs[6].sentences) == 528
assert len(docs[7].sentences) == 161
assert len(docs[8].sentences) == 228
assert len(docs[9].sentences) == 511
assert len(docs[10].sentences) == 331
assert len(docs[11].sentences) == 528
# Check table numbers
assert len(docs[0].tables) == 9
assert len(docs[1].tables) == 9
assert len(docs[2].tables) == 14
assert len(docs[3].tables) == 11
assert len(docs[4].tables) == 11
assert len(docs[5].tables) == 10
assert len(docs[6].tables) == 10
assert len(docs[7].tables) == 2
assert len(docs[8].tables) == 7
assert len(docs[9].tables) == 10
assert len(docs[10].tables) == 6
assert len(docs[11].tables) == 9
# Check figure numbers
assert len(docs[0].figures) == 32
assert len(docs[1].figures) == 11
assert len(docs[2].figures) == 38
assert len(docs[3].figures) == 31
assert len(docs[4].figures) == 7
assert len(docs[5].figures) == 38
assert len(docs[6].figures) == 10
assert len(docs[7].figures) == 31
assert len(docs[8].figures) == 4
assert len(docs[9].figures) == 27
assert len(docs[10].figures) == 5
assert len(docs[11].figures) == 27
# Check caption numbers
assert len(docs[0].captions) == 0
assert len(docs[1].captions) == 0
assert len(docs[2].captions) == 0
assert len(docs[3].captions) == 0
assert len(docs[4].captions) == 0
assert len(docs[5].captions) == 0
assert len(docs[6].captions) == 0
assert len(docs[7].captions) == 0
assert len(docs[8].captions) == 0
assert len(docs[9].captions) == 0
assert len(docs[10].captions) == 0
assert len(docs[11].captions) == 0
train_docs = set()
dev_docs = set()
test_docs = set()
splits = (0.5, 0.75)
data = [(doc.name, doc) for doc in docs]
data.sort(key=lambda x: x[0])
for i, (doc_name, doc) in enumerate(data):
if i < splits[0] * ld:
train_docs.add(doc)
elif i < splits[1] * ld:
dev_docs.add(doc)
else:
test_docs.add(doc)
logger.info([x.name for x in train_docs])
# NOTE: With multi-relation support, return values of getting candidates,
# mentions, or sparse matrices are formatted as a list of lists. This means
# that with a single relation, we need to index into the list of lists to
# get the candidates/mentions/sparse matrix for a particular relation or
# mention.
# Mention Extraction
part_ngrams = MentionNgramsPart(parts_by_doc=None, n_max=3)
temp_ngrams = MentionNgramsTemp(n_max=2)
volt_ngrams = MentionNgramsVolt(n_max=1)
Part = mention_subclass("Part")
Temp = mention_subclass("Temp")
Volt = mention_subclass("Volt")
mention_extractor = MentionExtractor(
session,
[Part, Temp, Volt],
[part_ngrams, temp_ngrams, volt_ngrams],
[part_matcher, temp_matcher, volt_matcher],
)
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Part).count() == 299
assert session.query(Temp).count() == 138
assert session.query(Volt).count() == 140
assert len(mention_extractor.get_mentions()) == 3
assert len(mention_extractor.get_mentions()[0]) == 299
assert (
len(
mention_extractor.get_mentions(
docs=[session.query(Document).filter(Document.name == "112823").first()]
)[0]
)
== 70
)
# Candidate Extraction
PartTemp = candidate_subclass("PartTemp", [Part, Temp])
PartVolt = candidate_subclass("PartVolt", [Part, Volt])
candidate_extractor = CandidateExtractor(
session, [PartTemp, PartVolt], throttlers=[temp_throttler, volt_throttler]
)
for i, docs in enumerate([train_docs, dev_docs, test_docs]):
candidate_extractor.apply(docs, split=i, parallelism=PARALLEL)
assert session.query(PartTemp).filter(PartTemp.split == 0).count() == 3493
assert session.query(PartTemp).filter(PartTemp.split == 1).count() == 61
assert session.query(PartTemp).filter(PartTemp.split == 2).count() == 416
assert session.query(PartVolt).count() == 4282
# Grab candidate lists
train_cands = candidate_extractor.get_candidates(split=0, sort=True)
dev_cands = candidate_extractor.get_candidates(split=1, sort=True)
test_cands = candidate_extractor.get_candidates(split=2, sort=True)
assert len(train_cands) == 2
assert len(train_cands[0]) == 3493
assert (
len(
candidate_extractor.get_candidates(
docs=[session.query(Document).filter(Document.name == "112823").first()]
)[0]
)
== 1432
)
# Featurization
featurizer = Featurizer(session, [PartTemp, PartVolt])
# Test that FeatureKey is properly reset
featurizer.apply(split=1, train=True, parallelism=PARALLEL)
assert session.query(Feature).count() == 214
assert session.query(FeatureKey).count() == 1260
# Test Dropping FeatureKey
# Should force a row deletion
featurizer.drop_keys(["DDL_e1_W_LEFT_POS_3_[NNP NN IN]"])
assert session.query(FeatureKey).count() == 1259
# Should only remove the part_volt as a relation and leave part_temp
assert set(
session.query(FeatureKey)
.filter(FeatureKey.name == "DDL_e1_LEMMA_SEQ_[bc182]")
.one()
.candidate_classes
) == {"part_temp", "part_volt"}
featurizer.drop_keys(["DDL_e1_LEMMA_SEQ_[bc182]"], candidate_classes=[PartVolt])
assert session.query(FeatureKey).filter(
FeatureKey.name == "DDL_e1_LEMMA_SEQ_[bc182]"
).one().candidate_classes == ["part_temp"]
assert session.query(FeatureKey).count() == 1259
# Inserting the removed key
featurizer.upsert_keys(
["DDL_e1_LEMMA_SEQ_[bc182]"], candidate_classes=[PartTemp, PartVolt]
)
assert set(
session.query(FeatureKey)
.filter(FeatureKey.name == "DDL_e1_LEMMA_SEQ_[bc182]")
.one()
.candidate_classes
) == {"part_temp", "part_volt"}
assert session.query(FeatureKey).count() == 1259
# Removing the key again
featurizer.drop_keys(["DDL_e1_LEMMA_SEQ_[bc182]"], candidate_classes=[PartVolt])
# Removing the last relation from a key should delete the row
featurizer.drop_keys(["DDL_e1_LEMMA_SEQ_[bc182]"], candidate_classes=[PartTemp])
assert session.query(FeatureKey).count() == 1258
session.query(Feature).delete(synchronize_session="fetch")
session.query(FeatureKey).delete(synchronize_session="fetch")
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
assert session.query(Feature).count() == 6478
assert session.query(FeatureKey).count() == 4538
F_train = featurizer.get_feature_matrices(train_cands)
assert F_train[0].shape == (3493, 4538)
assert F_train[1].shape == (2985, 4538)
assert len(featurizer.get_keys()) == 4538
featurizer.apply(split=1, parallelism=PARALLEL)
assert session.query(Feature).count() == 6692
assert session.query(FeatureKey).count() == 4538
F_dev = featurizer.get_feature_matrices(dev_cands)
assert F_dev[0].shape == (61, 4538)
assert F_dev[1].shape == (153, 4538)
featurizer.apply(split=2, parallelism=PARALLEL)
assert session.query(Feature).count() == 8252
assert session.query(FeatureKey).count() == 4538
F_test = featurizer.get_feature_matrices(test_cands)
assert F_test[0].shape == (416, 4538)
assert F_test[1].shape == (1144, 4538)
gold_file = "tests/data/hardware_tutorial_gold.csv"
labeler = Labeler(session, [PartTemp, PartVolt])
labeler.apply(
docs=last_docs,
lfs=[[gold], [gold]],
table=GoldLabel,
train=True,
parallelism=PARALLEL,
)
assert session.query(GoldLabel).count() == 8252
stg_temp_lfs = [
LF_storage_row,
LF_operating_row,
LF_temperature_row,
LF_tstg_row,
LF_to_left,
LF_negative_number_left,
]
ce_v_max_lfs = [
LF_bad_keywords_in_row,
LF_current_in_row,
LF_non_ce_voltages_in_row,
]
with pytest.raises(ValueError):
labeler.apply(split=0, lfs=stg_temp_lfs, train=True, parallelism=PARALLEL)
labeler.apply(
docs=train_docs,
lfs=[stg_temp_lfs, ce_v_max_lfs],
train=True,
parallelism=PARALLEL,
)
assert session.query(Label).count() == 6478
assert session.query(LabelKey).count() == 9
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (3493, 9)
assert L_train[1].shape == (2985, 9)
assert len(labeler.get_keys()) == 9
# Test Dropping LabelerKey
labeler.drop_keys(["LF_storage_row"])
assert len(labeler.get_keys()) == 8
# Test Upserting LabelerKey
labeler.upsert_keys(["LF_storage_row"])
assert "LF_storage_row" in [label.name for label in labeler.get_keys()]
L_train_gold = labeler.get_gold_labels(train_cands)
assert L_train_gold[0].shape == (3493, 1)
L_train_gold = labeler.get_gold_labels(train_cands, annotator="gold")
assert L_train_gold[0].shape == (3493, 1)
gen_model = LabelModel()
gen_model.fit(L_train=L_train[0], n_epochs=500, log_freq=100)
train_marginals = gen_model.predict_proba(L_train[0])
disc_model = LogisticRegression()
disc_model.train(
(train_cands[0], F_train[0]),
train_marginals,
X_dev=(train_cands[0], F_train[0]),
Y_dev=L_train_gold[0].reshape(-1),
b=0.6,
pos_label=TRUE,
n_epochs=5,
lr=0.001,
)
test_score = disc_model.predict((test_cands[0], F_test[0]), b=0.6, pos_label=TRUE)
true_pred = [test_cands[0][_] for _ in np.nditer(np.where(test_score == TRUE))]
pickle_file = "tests/data/parts_by_doc_dict.pkl"
with open(pickle_file, "rb") as f:
parts_by_doc = pickle.load(f)
(TP, FP, FN) = entity_level_f1(
true_pred, gold_file, ATTRIBUTE, test_docs, parts_by_doc=parts_by_doc
)
tp_len = len(TP)
fp_len = len(FP)
fn_len = len(FN)
prec = tp_len / (tp_len + fp_len) if tp_len + fp_len > 0 else float("nan")
rec = tp_len / (tp_len + fn_len) if tp_len + fn_len > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
logger.info(f"prec: {prec}")
logger.info(f"rec: {rec}")
logger.info(f"f1: {f1}")
assert f1 < 0.7 and f1 > 0.3
stg_temp_lfs_2 = [
LF_to_left,
LF_test_condition_aligned,
LF_collector_aligned,
LF_current_aligned,
LF_voltage_row_temp,
LF_voltage_row_part,
LF_typ_row,
LF_complement_left_row,
LF_too_many_numbers_row,
LF_temp_on_high_page_num,
LF_temp_outside_table,
LF_not_temp_relevant,
]
labeler.update(split=0, lfs=[stg_temp_lfs_2, ce_v_max_lfs], parallelism=PARALLEL)
assert session.query(Label).count() == 6478
assert session.query(LabelKey).count() == 16
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (3493, 16)
gen_model = LabelModel()
gen_model.fit(L_train=L_train[0], n_epochs=500, log_freq=100)
train_marginals = gen_model.predict_proba(L_train[0])
disc_model = LogisticRegression()
disc_model.train(
(train_cands[0], F_train[0]), train_marginals, n_epochs=5, lr=0.001
)
test_score = disc_model.predict((test_cands[0], F_test[0]), b=0.6, pos_label=TRUE)
true_pred = [test_cands[0][_] for _ in np.nditer(np.where(test_score == TRUE))]
(TP, FP, FN) = entity_level_f1(
true_pred, gold_file, ATTRIBUTE, test_docs, parts_by_doc=parts_by_doc
)
tp_len = len(TP)
fp_len = len(FP)
fn_len = len(FN)
prec = tp_len / (tp_len + fp_len) if tp_len + fp_len > 0 else float("nan")
rec = tp_len / (tp_len + fn_len) if tp_len + fn_len > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
logger.info(f"prec: {prec}")
logger.info(f"rec: {rec}")
logger.info(f"f1: {f1}")
assert f1 > 0.7
# Testing LSTM
disc_model = LSTM()
disc_model.train(
(train_cands[0], F_train[0]), train_marginals, n_epochs=5, lr=0.001
)
test_score = disc_model.predict((test_cands[0], F_test[0]), b=0.6, pos_label=TRUE)
true_pred = [test_cands[0][_] for _ in np.nditer(np.where(test_score == TRUE))]
(TP, FP, FN) = entity_level_f1(
true_pred, gold_file, ATTRIBUTE, test_docs, parts_by_doc=parts_by_doc
)
tp_len = len(TP)
fp_len = len(FP)
fn_len = len(FN)
prec = tp_len / (tp_len + fp_len) if tp_len + fp_len > 0 else float("nan")
rec = tp_len / (tp_len + fn_len) if tp_len + fn_len > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
logger.info(f"prec: {prec}")
logger.info(f"rec: {rec}")
logger.info(f"f1: {f1}")
assert f1 > 0.7
# Testing Sparse Logistic Regression
disc_model = SparseLogisticRegression()
disc_model.train(
(train_cands[0], F_train[0]), train_marginals, n_epochs=5, lr=0.001
)
test_score = disc_model.predict((test_cands[0], F_test[0]), b=0.6, pos_label=TRUE)
true_pred = [test_cands[0][_] for _ in np.nditer(np.where(test_score == TRUE))]
(TP, FP, FN) = entity_level_f1(
true_pred, gold_file, ATTRIBUTE, test_docs, parts_by_doc=parts_by_doc
)
tp_len = len(TP)
fp_len = len(FP)
fn_len = len(FN)
prec = tp_len / (tp_len + fp_len) if tp_len + fp_len > 0 else float("nan")
rec = tp_len / (tp_len + fn_len) if tp_len + fn_len > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
logger.info(f"prec: {prec}")
logger.info(f"rec: {rec}")
logger.info(f"f1: {f1}")
assert f1 > 0.7
# Testing Sparse LSTM
disc_model = SparseLSTM()
disc_model.train(
(train_cands[0], F_train[0]), train_marginals, n_epochs=5, lr=0.001
)
test_score = disc_model.predict((test_cands[0], F_test[0]), b=0.6, pos_label=TRUE)
true_pred = [test_cands[0][_] for _ in np.nditer(np.where(test_score == TRUE))]
(TP, FP, FN) = entity_level_f1(
true_pred, gold_file, ATTRIBUTE, test_docs, parts_by_doc=parts_by_doc
)
tp_len = len(TP)
fp_len = len(FP)
fn_len = len(FN)
prec = tp_len / (tp_len + fp_len) if tp_len + fp_len > 0 else float("nan")
rec = tp_len / (tp_len + fn_len) if tp_len + fn_len > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
logger.info(f"prec: {prec}")
logger.info(f"rec: {rec}")
logger.info(f"f1: {f1}")
assert f1 > 0.7
# Evaluate mention level scores
L_test_gold = labeler.get_gold_labels(test_cands, annotator="gold")
Y_test = L_test_gold[0].reshape(-1)
scores = disc_model.score((test_cands[0], F_test[0]), Y_test, b=0.6, pos_label=TRUE)
logger.info(scores)
assert scores["f1"] > 0.6
def test_mention_longest_match(caplog):
"""Test longest match filtering in mention extraction."""
caplog.set_level(logging.INFO)
# SpaCy on mac has issue on parallel parsing
PARALLEL = 1
max_docs = 1
session = Meta.init("postgresql://localhost:5432/" + DB).Session()
docs_path = "tests/data/pure_html/lincoln_short.html"
# Parsing
logger.info("Parsing...")
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(session, structural=True, lingual=True)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
docs = session.query(Document).order_by(Document.name).all()
# Mention Extraction
name_ngrams = MentionNgramsPart(n_max=3)
place_ngrams = MentionNgramsTemp(n_max=4)
Name = mention_subclass("Name")
Place = mention_subclass("Place")
def is_birthplace_table_row(mention):
if not mention.sentence.is_tabular():
return False
ngrams = get_row_ngrams(mention, lower=True)
if "birth_place" in ngrams:
return True
else:
return False
birthplace_matcher = LambdaFunctionMatcher(
func=is_birthplace_table_row, longest_match_only=False
)
mention_extractor = MentionExtractor(
session,
[Name, Place],
[name_ngrams, place_ngrams],
[PersonMatcher(), birthplace_matcher],
)
mention_extractor.apply(docs, parallelism=PARALLEL)
mentions = session.query(Place).all()
mention_spans = [x.context.get_span() for x in mentions]
assert "Sinking Spring Farm" in mention_spans
assert "Farm" in mention_spans
assert len(mention_spans) == 23
birthplace_matcher = LambdaFunctionMatcher(
func=is_birthplace_table_row, longest_match_only=True
)
mention_extractor = MentionExtractor(
session,
[Name, Place],
[name_ngrams, place_ngrams],
[PersonMatcher(), birthplace_matcher],
)
mention_extractor.apply(docs, parallelism=PARALLEL)
mentions = session.query(Place).all()
mention_spans = [x.context.get_span() for x in mentions]
assert "Sinking Spring Farm" in mention_spans
assert "Farm" not in mention_spans
assert len(mention_spans) == 4
def test_cand_gen(caplog):
"""Test extracting candidates from mentions from documents."""
caplog.set_level(logging.INFO)
if platform == "darwin":
logger.info("Using single core.")
PARALLEL = 1
else:
logger.info("Using two cores.")
PARALLEL = 2 # Travis only gives 2 cores
def do_nothing_matcher(fig):
return True
max_docs = 1
session = Meta.init("postgresql://localhost:5432/" + DB).Session()
docs_path = "tests/data/html/"
pdf_path = "tests/data/pdf/"
# Parsing
logger.info("Parsing...")
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(
session, structural=True, lingual=True, visual=True, pdf_path=pdf_path
)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
assert session.query(Document).count() == max_docs
assert session.query(Sentence).count() == 799
docs = session.query(Document).order_by(Document.name).all()
# Mention Extraction
part_ngrams = MentionNgramsPart(parts_by_doc=None, n_max=3)
temp_ngrams = MentionNgramsTemp(n_max=2)
volt_ngrams = MentionNgramsVolt(n_max=1)
figs = MentionFigures(types="png")
Part = mention_subclass("Part")
Temp = mention_subclass("Temp")
Volt = mention_subclass("Volt")
Fig = mention_subclass("Fig")
fig_matcher = LambdaFunctionFigureMatcher(func=do_nothing_matcher)
with pytest.raises(ValueError):
mention_extractor = MentionExtractor(
session,
[Part, Temp, Volt],
[part_ngrams, volt_ngrams], # Fail, mismatched arity
[part_matcher, temp_matcher, volt_matcher],
)
with pytest.raises(ValueError):
mention_extractor = MentionExtractor(
session,
[Part, Temp, Volt],
[part_ngrams, temp_matcher, volt_ngrams],
[part_matcher, temp_matcher], # Fail, mismatched arity
)
mention_extractor = MentionExtractor(
session,
[Part, Temp, Volt, Fig],
[part_ngrams, temp_ngrams, volt_ngrams, figs],
[part_matcher, temp_matcher, volt_matcher, fig_matcher],
)
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Part).count() == 70
assert session.query(Volt).count() == 33
assert session.query(Temp).count() == 23
assert session.query(Fig).count() == 31
part = session.query(Part).order_by(Part.id).all()[0]
volt = session.query(Volt).order_by(Volt.id).all()[0]
temp = session.query(Temp).order_by(Temp.id).all()[0]
logger.info(f"Part: {part.context}")
logger.info(f"Volt: {volt.context}")
logger.info(f"Temp: {temp.context}")
# Candidate Extraction
PartTemp = candidate_subclass("PartTemp", [Part, Temp])
PartVolt = candidate_subclass("PartVolt", [Part, Volt])
with pytest.raises(ValueError):
candidate_extractor = CandidateExtractor(
session,
[PartTemp, PartVolt],
throttlers=[
temp_throttler,
volt_throttler,
volt_throttler,
], # Fail, mismatched arity
)
with pytest.raises(ValueError):
candidate_extractor = CandidateExtractor(
session,
[PartTemp], # Fail, mismatched arity
throttlers=[temp_throttler, volt_throttler],
)
# Test that no throttler in candidate extractor
candidate_extractor = CandidateExtractor(
session, [PartTemp, PartVolt]
) # Pass, no throttler
candidate_extractor.apply(docs, split=0, parallelism=PARALLEL)
assert session.query(PartTemp).count() == 1610
assert session.query(PartVolt).count() == 2310
assert session.query(Candidate).count() == 3920
candidate_extractor.clear_all(split=0)
assert session.query(Candidate).count() == 0
assert session.query(PartTemp).count() == 0
assert session.query(PartVolt).count() == 0
# Test with None in throttlers in candidate extractor
candidate_extractor = CandidateExtractor(
session, [PartTemp, PartVolt], throttlers=[temp_throttler, None]
)
candidate_extractor.apply(docs, split=0, parallelism=PARALLEL)
assert session.query(PartTemp).count() == 1432
assert session.query(PartVolt).count() == 2310
assert session.query(Candidate).count() == 3742
candidate_extractor.clear_all(split=0)
assert session.query(Candidate).count() == 0
candidate_extractor = CandidateExtractor(
session, [PartTemp, PartVolt], throttlers=[temp_throttler, volt_throttler]
)
candidate_extractor.apply(docs, split=0, parallelism=PARALLEL)
assert session.query(PartTemp).count() == 1432
assert session.query(PartVolt).count() == 1993
assert session.query(Candidate).count() == 3425
assert docs[0].name == "112823"
assert len(docs[0].parts) == 70
assert len(docs[0].volts) == 33
assert len(docs[0].temps) == 23
# Test that deletion of a Candidate does not delete the Mention
session.query(PartTemp).delete(synchronize_session="fetch")
assert session.query(PartTemp).count() == 0
assert session.query(Temp).count() == 23
assert session.query(Part).count() == 70
# Test deletion of Candidate if Mention is deleted
assert session.query(PartVolt).count() == 1993
assert session.query(Volt).count() == 33
session.query(Volt).delete(synchronize_session="fetch")
assert session.query(Volt).count() == 0
assert session.query(PartVolt).count() == 0
def test_cand_gen_cascading_delete(caplog):
"""Test cascading the deletion of candidates."""
caplog.set_level(logging.INFO)
if platform == "darwin":
logger.info("Using single core.")
PARALLEL = 1
else:
logger.info("Using two cores.")
PARALLEL = 2 # Travis only gives 2 cores
max_docs = 1
session = Meta.init("postgresql://localhost:5432/" + DB).Session()
docs_path = "tests/data/html/"
pdf_path = "tests/data/pdf/"
# Parsing
logger.info("Parsing...")
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(
session, structural=True, lingual=True, visual=True, pdf_path=pdf_path
)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
assert session.query(Document).count() == max_docs
assert session.query(Sentence).count() == 799
docs = session.query(Document).order_by(Document.name).all()
# 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.clear_all()
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Mention).count() == 93
assert session.query(Part).count() == 70
assert session.query(Temp).count() == 23
part = session.query(Part).order_by(Part.id).all()[0]
temp = session.query(Temp).order_by(Temp.id).all()[0]
logger.info(f"Part: {part.context}")
logger.info(f"Temp: {temp.context}")
# 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).count() == 1432
assert session.query(Candidate).count() == 1432
assert docs[0].name == "112823"
assert len(docs[0].parts) == 70
assert len(docs[0].temps) == 23
# Delete from parent class should cascade to child
x = session.query(Candidate).first()
session.query(Candidate).filter_by(id=x.id).delete(synchronize_session="fetch")
assert session.query(Candidate).count() == 1431
assert session.query(PartTemp).count() == 1431
# Clearing Mentions should also delete Candidates
mention_extractor.clear()
assert session.query(Mention).count() == 0
assert session.query(Part).count() == 0
assert session.query(Temp).count() == 0
assert session.query(PartTemp).count() == 0
assert session.query(Candidate).count() == 0
def test_e2e():
"""Run an end-to-end test on documents of the hardware domain."""
# GitHub Actions gives 2 cores
# help.github.com/en/actions/reference/virtual-environments-for-github-hosted-runners
PARALLEL = 2
max_docs = 12
fonduer.init_logging(
format="[%(asctime)s][%(levelname)s] %(name)s:%(lineno)s - %(message)s",
level=logging.INFO,
)
session = fonduer.Meta.init(CONN_STRING).Session()
docs_path = "tests/data/html/"
pdf_path = "tests/data/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)
assert session.query(Document).count() == max_docs
num_docs = session.query(Document).count()
logger.info(f"Docs: {num_docs}")
assert num_docs == max_docs
num_sentences = session.query(Sentence).count()
logger.info(f"Sentences: {num_sentences}")
# Divide into test and train
docs = sorted(corpus_parser.get_documents())
last_docs = sorted(corpus_parser.get_last_documents())
ld = len(docs)
assert ld == len(last_docs)
assert len(docs[0].sentences) == len(last_docs[0].sentences)
assert len(docs[0].sentences) == 799
assert len(docs[1].sentences) == 663
assert len(docs[2].sentences) == 784
assert len(docs[3].sentences) == 661
assert len(docs[4].sentences) == 513
assert len(docs[5].sentences) == 700
assert len(docs[6].sentences) == 528
assert len(docs[7].sentences) == 161
assert len(docs[8].sentences) == 228
assert len(docs[9].sentences) == 511
assert len(docs[10].sentences) == 331
assert len(docs[11].sentences) == 528
# Check table numbers
assert len(docs[0].tables) == 9
assert len(docs[1].tables) == 9
assert len(docs[2].tables) == 14
assert len(docs[3].tables) == 11
assert len(docs[4].tables) == 11
assert len(docs[5].tables) == 10
assert len(docs[6].tables) == 10
assert len(docs[7].tables) == 2
assert len(docs[8].tables) == 7
assert len(docs[9].tables) == 10
assert len(docs[10].tables) == 6
assert len(docs[11].tables) == 9
# Check figure numbers
assert len(docs[0].figures) == 32
assert len(docs[1].figures) == 11
assert len(docs[2].figures) == 38
assert len(docs[3].figures) == 31
assert len(docs[4].figures) == 7
assert len(docs[5].figures) == 38
assert len(docs[6].figures) == 10
assert len(docs[7].figures) == 31
assert len(docs[8].figures) == 4
assert len(docs[9].figures) == 27
assert len(docs[10].figures) == 5
assert len(docs[11].figures) == 27
# Check caption numbers
assert len(docs[0].captions) == 0
assert len(docs[1].captions) == 0
assert len(docs[2].captions) == 0
assert len(docs[3].captions) == 0
assert len(docs[4].captions) == 0
assert len(docs[5].captions) == 0
assert len(docs[6].captions) == 0
assert len(docs[7].captions) == 0
assert len(docs[8].captions) == 0
assert len(docs[9].captions) == 0
assert len(docs[10].captions) == 0
assert len(docs[11].captions) == 0
train_docs = set()
dev_docs = set()
test_docs = set()
splits = (0.5, 0.75)
data = [(doc.name, doc) for doc in docs]
data.sort(key=lambda x: x[0])
for i, (doc_name, doc) in enumerate(data):
if i < splits[0] * ld:
train_docs.add(doc)
elif i < splits[1] * ld:
dev_docs.add(doc)
else:
test_docs.add(doc)
logger.info([x.name for x in train_docs])
# NOTE: With multi-relation support, return values of getting candidates,
# mentions, or sparse matrices are formatted as a list of lists. This means
# that with a single relation, we need to index into the list of lists to
# get the candidates/mentions/sparse matrix for a particular relation or
# mention.
# Mention Extraction
part_ngrams = MentionNgramsPart(parts_by_doc=None, n_max=3)
temp_ngrams = MentionNgramsTemp(n_max=2)
volt_ngrams = MentionNgramsVolt(n_max=1)
Part = mention_subclass("Part")
Temp = mention_subclass("Temp")
Volt = mention_subclass("Volt")
mention_extractor = MentionExtractor(
session,
[Part, Temp, Volt],
[part_ngrams, temp_ngrams, volt_ngrams],
[part_matcher, temp_matcher, volt_matcher],
)
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Part).count() == 299
assert session.query(Temp).count() == 138
assert session.query(Volt).count() == 140
assert len(mention_extractor.get_mentions()) == 3
assert len(mention_extractor.get_mentions()[0]) == 299
assert (len(
mention_extractor.get_mentions(docs=[
session.query(Document).filter(Document.name == "112823").first()
])[0]) == 70)
# Candidate Extraction
PartTemp = candidate_subclass("PartTemp", [Part, Temp])
PartVolt = candidate_subclass("PartVolt", [Part, Volt])
candidate_extractor = CandidateExtractor(
session, [PartTemp, PartVolt],
throttlers=[temp_throttler, volt_throttler])
for i, docs in enumerate([train_docs, dev_docs, test_docs]):
candidate_extractor.apply(docs, split=i, parallelism=PARALLEL)
assert session.query(PartTemp).filter(PartTemp.split == 0).count() == 3493
assert session.query(PartTemp).filter(PartTemp.split == 1).count() == 61
assert session.query(PartTemp).filter(PartTemp.split == 2).count() == 416
assert session.query(PartVolt).count() == 4282
# Grab candidate lists
train_cands = candidate_extractor.get_candidates(split=0, sort=True)
dev_cands = candidate_extractor.get_candidates(split=1, sort=True)
test_cands = candidate_extractor.get_candidates(split=2, sort=True)
assert len(train_cands) == 2
assert len(train_cands[0]) == 3493
assert (len(
candidate_extractor.get_candidates(docs=[
session.query(Document).filter(Document.name == "112823").first()
])[0]) == 1432)
# Featurization
featurizer = Featurizer(session, [PartTemp, PartVolt])
# Test that FeatureKey is properly reset
featurizer.apply(split=1, train=True, parallelism=PARALLEL)
assert session.query(Feature).count() == 214
assert session.query(FeatureKey).count() == 1260
# Test Dropping FeatureKey
# Should force a row deletion
featurizer.drop_keys(["DDL_e1_W_LEFT_POS_3_[NNP NN IN]"])
assert session.query(FeatureKey).count() == 1259
# Should only remove the part_volt as a relation and leave part_temp
assert set(
session.query(FeatureKey).filter(
FeatureKey.name ==
"DDL_e1_LEMMA_SEQ_[bc182]").one().candidate_classes) == {
"part_temp", "part_volt"
}
featurizer.drop_keys(["DDL_e1_LEMMA_SEQ_[bc182]"],
candidate_classes=[PartVolt])
assert session.query(FeatureKey).filter(
FeatureKey.name ==
"DDL_e1_LEMMA_SEQ_[bc182]").one().candidate_classes == ["part_temp"]
assert session.query(FeatureKey).count() == 1259
# Inserting the removed key
featurizer.upsert_keys(["DDL_e1_LEMMA_SEQ_[bc182]"],
candidate_classes=[PartTemp, PartVolt])
assert set(
session.query(FeatureKey).filter(
FeatureKey.name ==
"DDL_e1_LEMMA_SEQ_[bc182]").one().candidate_classes) == {
"part_temp", "part_volt"
}
assert session.query(FeatureKey).count() == 1259
# Removing the key again
featurizer.drop_keys(["DDL_e1_LEMMA_SEQ_[bc182]"],
candidate_classes=[PartVolt])
# Removing the last relation from a key should delete the row
featurizer.drop_keys(["DDL_e1_LEMMA_SEQ_[bc182]"],
candidate_classes=[PartTemp])
assert session.query(FeatureKey).count() == 1258
session.query(Feature).delete(synchronize_session="fetch")
session.query(FeatureKey).delete(synchronize_session="fetch")
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
assert session.query(Feature).count() == 6478
assert session.query(FeatureKey).count() == 4538
F_train = featurizer.get_feature_matrices(train_cands)
assert F_train[0].shape == (3493, 4538)
assert F_train[1].shape == (2985, 4538)
assert len(featurizer.get_keys()) == 4538
featurizer.apply(split=1, parallelism=PARALLEL)
assert session.query(Feature).count() == 6692
assert session.query(FeatureKey).count() == 4538
F_dev = featurizer.get_feature_matrices(dev_cands)
assert F_dev[0].shape == (61, 4538)
assert F_dev[1].shape == (153, 4538)
featurizer.apply(split=2, parallelism=PARALLEL)
assert session.query(Feature).count() == 8252
assert session.query(FeatureKey).count() == 4538
F_test = featurizer.get_feature_matrices(test_cands)
assert F_test[0].shape == (416, 4538)
assert F_test[1].shape == (1144, 4538)
gold_file = "tests/data/hardware_tutorial_gold.csv"
labeler = Labeler(session, [PartTemp, PartVolt])
labeler.apply(
docs=last_docs,
lfs=[[gold], [gold]],
table=GoldLabel,
train=True,
parallelism=PARALLEL,
)
assert session.query(GoldLabel).count() == 8252
stg_temp_lfs = [
LF_storage_row,
LF_operating_row,
LF_temperature_row,
LF_tstg_row,
LF_to_left,
LF_negative_number_left,
]
ce_v_max_lfs = [
LF_bad_keywords_in_row,
LF_current_in_row,
LF_non_ce_voltages_in_row,
]
with pytest.raises(ValueError):
labeler.apply(split=0,
lfs=stg_temp_lfs,
train=True,
parallelism=PARALLEL)
labeler.apply(
docs=train_docs,
lfs=[stg_temp_lfs, ce_v_max_lfs],
train=True,
parallelism=PARALLEL,
)
assert session.query(Label).count() == 6478
assert session.query(LabelKey).count() == 9
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (3493, 9)
assert L_train[1].shape == (2985, 9)
assert len(labeler.get_keys()) == 9
# Test Dropping LabelerKey
labeler.drop_keys(["LF_storage_row"])
assert len(labeler.get_keys()) == 8
# Test Upserting LabelerKey
labeler.upsert_keys(["LF_storage_row"])
assert "LF_storage_row" in [label.name for label in labeler.get_keys()]
L_train_gold = labeler.get_gold_labels(train_cands)
assert L_train_gold[0].shape == (3493, 1)
L_train_gold = labeler.get_gold_labels(train_cands, annotator="gold")
assert L_train_gold[0].shape == (3493, 1)
label_model = LabelModel()
label_model.fit(L_train=L_train[0], n_epochs=500, log_freq=100)
train_marginals = label_model.predict_proba(L_train[0])
# Collect word counter
word_counter = collect_word_counter(train_cands)
emmental.init(fonduer.Meta.log_path)
# Training config
config = {
"meta_config": {
"verbose": False
},
"model_config": {
"model_path": None,
"device": 0,
"dataparallel": False
},
"learner_config": {
"n_epochs": 5,
"optimizer_config": {
"lr": 0.001,
"l2": 0.0
},
"task_scheduler": "round_robin",
},
"logging_config": {
"evaluation_freq": 1,
"counter_unit": "epoch",
"checkpointing": False,
"checkpointer_config": {
"checkpoint_metric": {
f"{ATTRIBUTE}/{ATTRIBUTE}/train/loss": "min"
},
"checkpoint_freq": 1,
"checkpoint_runway": 2,
"clear_intermediate_checkpoints": True,
"clear_all_checkpoints": True,
},
},
}
emmental.Meta.update_config(config=config)
# Generate word embedding module
arity = 2
# Geneate special tokens
specials = []
for i in range(arity):
specials += [f"~~[[{i}", f"{i}]]~~"]
emb_layer = EmbeddingModule(word_counter=word_counter,
word_dim=300,
specials=specials)
diffs = train_marginals.max(axis=1) - train_marginals.min(axis=1)
train_idxs = np.where(diffs > 1e-6)[0]
train_dataloader = EmmentalDataLoader(
task_to_label_dict={ATTRIBUTE: "labels"},
dataset=FonduerDataset(
ATTRIBUTE,
train_cands[0],
F_train[0],
emb_layer.word2id,
train_marginals,
train_idxs,
),
split="train",
batch_size=100,
shuffle=True,
)
tasks = create_task(ATTRIBUTE,
2,
F_train[0].shape[1],
2,
emb_layer,
model="LogisticRegression")
model = EmmentalModel(name=f"{ATTRIBUTE}_task")
for task in tasks:
model.add_task(task)
emmental_learner = EmmentalLearner()
emmental_learner.learn(model, [train_dataloader])
test_dataloader = EmmentalDataLoader(
task_to_label_dict={ATTRIBUTE: "labels"},
dataset=FonduerDataset(ATTRIBUTE, test_cands[0], F_test[0],
emb_layer.word2id, 2),
split="test",
batch_size=100,
shuffle=False,
)
test_preds = model.predict(test_dataloader, return_preds=True)
positive = np.where(
np.array(test_preds["probs"][ATTRIBUTE])[:, TRUE] > 0.6)
true_pred = [test_cands[0][_] for _ in positive[0]]
pickle_file = "tests/data/parts_by_doc_dict.pkl"
with open(pickle_file, "rb") as f:
parts_by_doc = pickle.load(f)
(TP, FP, FN) = entity_level_f1(true_pred,
gold_file,
ATTRIBUTE,
test_docs,
parts_by_doc=parts_by_doc)
tp_len = len(TP)
fp_len = len(FP)
fn_len = len(FN)
prec = tp_len / (tp_len + fp_len) if tp_len + fp_len > 0 else float("nan")
rec = tp_len / (tp_len + fn_len) if tp_len + fn_len > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
logger.info(f"prec: {prec}")
logger.info(f"rec: {rec}")
logger.info(f"f1: {f1}")
assert f1 < 0.7 and f1 > 0.3
stg_temp_lfs_2 = [
LF_to_left,
LF_test_condition_aligned,
LF_collector_aligned,
LF_current_aligned,
LF_voltage_row_temp,
LF_voltage_row_part,
LF_typ_row,
LF_complement_left_row,
LF_too_many_numbers_row,
LF_temp_on_high_page_num,
LF_temp_outside_table,
LF_not_temp_relevant,
]
labeler.update(split=0,
lfs=[stg_temp_lfs_2, ce_v_max_lfs],
parallelism=PARALLEL)
assert session.query(Label).count() == 6478
assert session.query(LabelKey).count() == 16
L_train = labeler.get_label_matrices(train_cands)
assert L_train[0].shape == (3493, 16)
label_model = LabelModel()
label_model.fit(L_train=L_train[0], n_epochs=500, log_freq=100)
train_marginals = label_model.predict_proba(L_train[0])
diffs = train_marginals.max(axis=1) - train_marginals.min(axis=1)
train_idxs = np.where(diffs > 1e-6)[0]
train_dataloader = EmmentalDataLoader(
task_to_label_dict={ATTRIBUTE: "labels"},
dataset=FonduerDataset(
ATTRIBUTE,
train_cands[0],
F_train[0],
emb_layer.word2id,
train_marginals,
train_idxs,
),
split="train",
batch_size=100,
shuffle=True,
)
valid_dataloader = EmmentalDataLoader(
task_to_label_dict={ATTRIBUTE: "labels"},
dataset=FonduerDataset(
ATTRIBUTE,
train_cands[0],
F_train[0],
emb_layer.word2id,
np.argmax(train_marginals, axis=1),
train_idxs,
),
split="valid",
batch_size=100,
shuffle=False,
)
emmental.Meta.reset()
emmental.init(fonduer.Meta.log_path)
emmental.Meta.update_config(config=config)
tasks = create_task(ATTRIBUTE,
2,
F_train[0].shape[1],
2,
emb_layer,
model="LogisticRegression")
model = EmmentalModel(name=f"{ATTRIBUTE}_task")
for task in tasks:
model.add_task(task)
emmental_learner = EmmentalLearner()
emmental_learner.learn(model, [train_dataloader, valid_dataloader])
test_preds = model.predict(test_dataloader, return_preds=True)
positive = np.where(
np.array(test_preds["probs"][ATTRIBUTE])[:, TRUE] > 0.7)
true_pred = [test_cands[0][_] for _ in positive[0]]
(TP, FP, FN) = entity_level_f1(true_pred,
gold_file,
ATTRIBUTE,
test_docs,
parts_by_doc=parts_by_doc)
tp_len = len(TP)
fp_len = len(FP)
fn_len = len(FN)
prec = tp_len / (tp_len + fp_len) if tp_len + fp_len > 0 else float("nan")
rec = tp_len / (tp_len + fn_len) if tp_len + fn_len > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
logger.info(f"prec: {prec}")
logger.info(f"rec: {rec}")
logger.info(f"f1: {f1}")
assert f1 > 0.7
# Testing LSTM
emmental.Meta.reset()
emmental.init(fonduer.Meta.log_path)
emmental.Meta.update_config(config=config)
tasks = create_task(ATTRIBUTE,
2,
F_train[0].shape[1],
2,
emb_layer,
model="LSTM")
model = EmmentalModel(name=f"{ATTRIBUTE}_task")
for task in tasks:
model.add_task(task)
emmental_learner = EmmentalLearner()
emmental_learner.learn(model, [train_dataloader])
test_preds = model.predict(test_dataloader, return_preds=True)
positive = np.where(
np.array(test_preds["probs"][ATTRIBUTE])[:, TRUE] > 0.7)
true_pred = [test_cands[0][_] for _ in positive[0]]
(TP, FP, FN) = entity_level_f1(true_pred,
gold_file,
ATTRIBUTE,
test_docs,
parts_by_doc=parts_by_doc)
tp_len = len(TP)
fp_len = len(FP)
fn_len = len(FN)
prec = tp_len / (tp_len + fp_len) if tp_len + fp_len > 0 else float("nan")
rec = tp_len / (tp_len + fn_len) if tp_len + fn_len > 0 else float("nan")
f1 = 2 * (prec * rec) / (prec + rec) if prec + rec > 0 else float("nan")
logger.info(f"prec: {prec}")
logger.info(f"rec: {rec}")
logger.info(f"f1: {f1}")
assert f1 > 0.7
def test_cand_gen_cascading_delete():
"""Test cascading the deletion of candidates."""
# 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/"
pdf_path = "tests/data/pdf/"
# Parsing
logger.info("Parsing...")
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(
session, structural=True, lingual=True, visual=True, pdf_path=pdf_path
)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
assert session.query(Document).count() == max_docs
assert session.query(Sentence).count() == 799
docs = session.query(Document).order_by(Document.name).all()
# 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.clear_all()
mention_extractor.apply(docs, parallelism=PARALLEL)
assert session.query(Mention).count() == 93
assert session.query(Part).count() == 70
assert session.query(Temp).count() == 23
part = session.query(Part).order_by(Part.id).all()[0]
temp = session.query(Temp).order_by(Temp.id).all()[0]
logger.info(f"Part: {part.context}")
logger.info(f"Temp: {temp.context}")
# 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).count() == 1432
assert session.query(Candidate).count() == 1432
assert docs[0].name == "112823"
assert len(docs[0].parts) == 70
assert len(docs[0].temps) == 23
# Delete from parent class should cascade to child
x = session.query(Candidate).first()
session.query(Candidate).filter_by(id=x.id).delete(synchronize_session="fetch")
assert session.query(Candidate).count() == 1431
assert session.query(PartTemp).count() == 1431
# Test that deletion of a Candidate does not delete the Mention
x = session.query(PartTemp).first()
session.query(PartTemp).filter_by(id=x.id).delete(synchronize_session="fetch")
assert session.query(PartTemp).count() == 1430
assert session.query(Temp).count() == 23
assert session.query(Part).count() == 70
# Clearing Mentions should also delete Candidates
mention_extractor.clear()
assert session.query(Mention).count() == 0
assert session.query(Part).count() == 0
assert session.query(Temp).count() == 0
assert session.query(PartTemp).count() == 0
assert session.query(Candidate).count() == 0
def __init__(self, session):
self.session = session
self.Email = mention_subclass("Email")
self.Email_C = candidate_subclass("Email_C", [self.Email])
def test_feature_extraction():
"""Test extracting candidates from mentions from documents."""
PARALLEL = 1
max_docs = 1
session = Meta.init(CONN_STRING).Session()
docs_path = "tests/data/html/"
pdf_path = "tests/data/pdf/"
# Parsing
logger.info("Parsing...")
doc_preprocessor = HTMLDocPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = Parser(session,
structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
assert session.query(Document).count() == max_docs
assert session.query(Sentence).count() == 799
docs = session.query(Document).order_by(Document.name).all()
# Mention Extraction
part_ngrams = MentionNgrams(n_max=1)
temp_ngrams = MentionNgrams(n_max=1)
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 docs[0].name == "112823"
assert session.query(Part).count() == 58
assert session.query(Temp).count() == 16
part = session.query(Part).order_by(Part.id).all()[0]
temp = session.query(Temp).order_by(Temp.id).all()[0]
logger.info(f"Part: {part.context}")
logger.info(f"Temp: {temp.context}")
# Candidate Extraction
PartTemp = candidate_subclass("PartTemp", [Part, Temp])
candidate_extractor = CandidateExtractor(session, [PartTemp])
candidate_extractor.apply(docs, split=0, parallelism=PARALLEL)
n_cands = session.query(PartTemp).count()
# Featurization based on default feature library
featurizer = Featurizer(session, [PartTemp])
# Test that featurization default feature library
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
n_default_feats = session.query(FeatureKey).count()
featurizer.clear(train=True)
# Example feature extractor
def feat_ext(candidates):
candidates = candidates if isinstance(candidates,
list) else [candidates]
for candidate in candidates:
yield candidate.id, f"cand_id_{candidate.id}", 1
# Featurization with one extra feature extractor
feature_extractors = FeatureExtractor(customize_feature_funcs=[feat_ext])
featurizer = Featurizer(session, [PartTemp],
feature_extractors=feature_extractors)
# Test that featurization default feature library with one extra feature extractor
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
n_default_w_customized_features = session.query(FeatureKey).count()
featurizer.clear(train=True)
# Featurization with only textual feature
feature_extractors = FeatureExtractor(features=["textual"])
featurizer = Featurizer(session, [PartTemp],
feature_extractors=feature_extractors)
# Test that featurization textual feature library
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
n_textual_features = session.query(FeatureKey).count()
featurizer.clear(train=True)
# Featurization with only tabular feature
feature_extractors = FeatureExtractor(features=["tabular"])
featurizer = Featurizer(session, [PartTemp],
feature_extractors=feature_extractors)
# Test that featurization tabular feature library
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
n_tabular_features = session.query(FeatureKey).count()
featurizer.clear(train=True)
# Featurization with only structural feature
feature_extractors = FeatureExtractor(features=["structural"])
featurizer = Featurizer(session, [PartTemp],
feature_extractors=feature_extractors)
# Test that featurization structural feature library
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
n_structural_features = session.query(FeatureKey).count()
featurizer.clear(train=True)
# Featurization with only visual feature
feature_extractors = FeatureExtractor(features=["visual"])
featurizer = Featurizer(session, [PartTemp],
feature_extractors=feature_extractors)
# Test that featurization visual feature library
featurizer.apply(split=0, train=True, parallelism=PARALLEL)
n_visual_features = session.query(FeatureKey).count()
featurizer.clear(train=True)
assert (n_default_feats == n_textual_features + n_tabular_features +
n_structural_features + n_visual_features)
assert n_default_w_customized_features == n_default_feats + n_cands
def __init__(self):
self.Email = mention_subclass("Email")
self.Email_C = candidate_subclass("Email_C", [self.Email])
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
