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_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_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
from fonduer.candidates import MentionSentences
mention_sentence = MentionSentences()
def get_mention_spaces():
return [mention_sentence]
