def test_spacy_integration(caplog):
"""Run a simple e2e parse using spaCy as our parser.
The point of this test is to actually use the DB just as would be
done in a notebook by a user.
"""
# caplog.set_level(logging.INFO)
logger = logging.getLogger(__name__)
PARALLEL = 2 # Travis only gives 2 cores
session = Meta.init('postgres://localhost:5432/' + ATTRIBUTE).Session()
docs_path = 'tests/data/html_simple/'
pdf_path = 'tests/data/pdf_simple/'
max_docs = 2
doc_preprocessor = HTMLPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = OmniParser(
structural=True, lingual=True, visual=False, pdf_path=pdf_path)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
docs = session.query(Document).order_by(Document.name).all()
for doc in docs:
logger.info("Doc: {}".format(doc.name))
for phrase in doc.phrases:
logger.info(" Phrase: {}".format(phrase.text))
assert session.query(Document).count() == 2
assert session.query(Phrase).count() == 81
def parse(docs_path, pdf_path, max_docs):
doc_preprocessor = HTMLPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = OmniParser(
structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path,
blacklist=['style', 'script', 'meta', 'noscript'])
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
def test_parse_document_md(caplog):
"""Unit test of OmniParser on a single document.
This tests both the structural and visual parse of the document. This
also serves as a test of single-threaded parsing.
"""
logger = logging.getLogger(__name__)
session = Meta.init('postgres://localhost:5432/' + ATTRIBUTE).Session()
PARALLEL = 1
max_docs = 2
docs_path = 'tests/data/html_simple/'
pdf_path = 'tests/data/pdf_simple/'
# Preprocessor for the Docs
preprocessor = HTMLPreprocessor(docs_path, max_docs=max_docs)
# Create an OmniParser and parse the md document
omni = OmniParser(structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path)
omni.apply(preprocessor, parallelism=PARALLEL)
# Grab the md document
doc = session.query(Document).order_by(Document.name).all()[1]
logger.info("Doc: {}".format(doc))
for phrase in doc.phrases:
logger.info(" Phrase: {}".format(phrase.text))
header = doc.phrases[0]
# Test structural attributes
assert header.xpath == '/html/body/h1'
assert header.html_tag == 'h1'
assert header.html_attrs == ['id=sample-markdown']
# Test visual attributes
assert header.page == [1, 1]
assert header.top == [35, 35]
assert header.bottom == [61, 61]
assert header.right == [111, 231]
assert header.left == [35, 117]
# Test lingual attributes
assert header.ner_tags == ['O', 'O']
assert header.dep_labels == ['compound', 'ROOT']
# 44 phrases expected in the "md" document.
assert len(doc.phrases) == 45
def test_parse_document_diseases(caplog):
"""Unit test of OmniParser on a single document.
This tests both the structural and visual parse of the document.
"""
caplog.set_level(logging.INFO)
logger = logging.getLogger(__name__)
session = SnorkelSession()
PARALLEL = 2
max_docs = 2
docs_path = 'tests/data/html_simple/'
pdf_path = 'tests/data/pdf_simple/'
# Preprocessor for the Docs
preprocessor = HTMLPreprocessor(docs_path, max_docs=max_docs)
# Create an OmniParser and parse the md document
omni = OmniParser(structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path)
omni.apply(preprocessor, parallel=PARALLEL)
# Grab the diseases document
doc = session.query(Document).order_by(Document.name).all()[0]
logger.info("Doc: {}".format(doc))
for phrase in doc.phrases:
logger.info(" Phrase: {}".format(phrase.text))
phrase = sorted(doc.phrases)[11]
logger.info(" {}".format(phrase))
# Test structural attributes
assert phrase.xpath == '/html/body/table[1]/tbody/tr[3]/td[1]/p'
assert phrase.html_tag == 'p'
assert phrase.html_attrs == ['class=s6', 'style=padding-top: 1pt']
# Test visual attributes
assert phrase.page == [1, 1, 1]
assert phrase.top == [342, 296, 356]
assert phrase.left == [318, 369, 318]
# Test lingual attributes
assert phrase.ner_tags == ['O', 'O', 'GPE']
assert phrase.dep_labels == ['ROOT', 'prep', 'pobj']
# 44 phrases expected in the "diseases" document.
assert len(doc.phrases) == 36
def test_simple_tokenizer(caplog):
"""Unit test of OmniParser on a single document with lingual features off.
"""
caplog.set_level(logging.INFO)
logger = logging.getLogger(__name__)
session = Meta.init('postgres://localhost:5432/' + ATTRIBUTE).Session()
PARALLEL = 2
max_docs = 2
docs_path = 'tests/data/html_simple/'
pdf_path = 'tests/data/pdf_simple/'
# Preprocessor for the Docs
preprocessor = HTMLPreprocessor(docs_path, max_docs=max_docs)
omni = OmniParser(
structural=True, lingual=False, visual=True, pdf_path=pdf_path)
omni.apply(preprocessor, parallelism=PARALLEL)
doc = session.query(Document).order_by(Document.name).all()[1]
logger.info("Doc: {}".format(doc))
for i, phrase in enumerate(doc.phrases):
logger.info(" Phrase[{}]: {}".format(i, phrase.text))
header = doc.phrases[0]
# Test structural attributes
assert header.xpath == '/html/body/h1'
assert header.html_tag == 'h1'
assert header.html_attrs == ['id=sample-markdown']
# Test lingual attributes
assert header.ner_tags == ['', '']
assert header.dep_labels == ['', '']
assert header.dep_parents == [0, 0]
assert header.lemmas == ['', '']
assert header.pos_tags == ['', '']
assert len(doc.phrases) == 44
from fonduer import candidate_subclass
Org_Fig = candidate_subclass('Org_Fig', ['organic', 'figure'])
from fonduer import HTMLPreprocessor, OmniParser
docs_path = os.environ[
'FONDUERHOME'] + '/tutorials/organic_synthesis_figures/data/html/'
pdf_path = os.environ[
'FONDUERHOME'] + '/tutorials/organic_synthesis_figures/data/pdf/'
max_docs = float(4)
doc_preprocessor = HTMLPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = OmniParser(structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
from fonduer import Document, Phrase, Figure
docs = session.query(Document).order_by(Document.name).all()
ld = len(docs)
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):
def test_e2e(caplog):
"""Run an end-to-end test on 20 documents of the hardware domain."""
caplog.set_level(logging.INFO)
PARALLEL = 2
max_docs = 12
session = SnorkelSession()
Part_Attr = candidate_subclass('Part_Attr', ['part', 'attr'])
docs_path = 'tests/e2e/data/html/'
pdf_path = 'tests/e2e/data/pdf/'
doc_preprocessor = HTMLPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = OmniParser(
structural=True, lingual=True, visual=True, pdf_path=pdf_path)
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
num_docs = session.query(Document).count()
logger.info("Docs: {}".format(num_docs))
assert num_docs == max_docs
num_phrases = session.query(Phrase).count()
logger.info("Phrases: {}".format(num_phrases))
# assert num_phrases == 20
# Divid into test and train
docs = session.query(Document).order_by(Document.name).all()
ld = len(docs)
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])
attr_matcher = RegexMatchSpan(
rgx=r'(?:[1][5-9]|20)[05]', longest_match_only=False)
### Transistor Naming Conventions as Regular Expressions ###
eeca_rgx = r'([ABC][A-Z][WXYZ]?[0-9]{3,5}(?:[A-Z]){0,5}[0-9]?[A-Z]?(?:-[A-Z0-9]{1,7})?(?:[-][A-Z0-9]{1,2})?(?:\/DG)?)'
jedec_rgx = r'(2N\d{3,4}[A-Z]{0,5}[0-9]?[A-Z]?)'
jis_rgx = r'(2S[ABCDEFGHJKMQRSTVZ]{1}[\d]{2,4})'
others_rgx = r'((?:NSVBC|SMBT|MJ|MJE|MPS|MRF|RCA|TIP|ZTX|ZT|ZXT|TIS|TIPL|DTC|MMBT|SMMBT|PZT|FZT|STD|BUV|PBSS|KSC|CXT|FCX|CMPT){1}[\d]{2,4}[A-Z]{0,5}(?:-[A-Z0-9]{0,6})?(?:[-][A-Z0-9]{0,1})?)'
part_rgx = '|'.join([eeca_rgx, jedec_rgx, jis_rgx, others_rgx])
part_rgx_matcher = RegexMatchSpan(rgx=part_rgx, longest_match_only=True)
def get_digikey_parts_set(path):
"""
Reads in the digikey part dictionary and yeilds each part.
"""
all_parts = set()
with open(path, "r") as csvinput:
reader = csv.reader(csvinput)
for line in reader:
(part, url) = line
all_parts.add(part)
return all_parts
### Dictionary of known transistor parts ###
dict_path = 'tests/e2e/data/digikey_part_dictionary.csv'
part_dict_matcher = DictionaryMatch(d=get_digikey_parts_set(dict_path))
def common_prefix_length_diff(str1, str2):
for i in range(min(len(str1), len(str2))):
if str1[i] != str2[i]:
return min(len(str1), len(str2)) - i
return 0
def part_file_name_conditions(attr):
file_name = attr.sentence.document.name
if len(file_name.split('_')) != 2: return False
if attr.get_span()[0] == '-': return False
name = attr.get_span().replace('-', '')
return any(char.isdigit() for char in name) and any(
char.isalpha() for char in name) and common_prefix_length_diff(
file_name.split('_')[1], name) <= 2
add_rgx = '^[A-Z0-9\-]{5,15}$'
part_file_name_lambda_matcher = LambdaFunctionMatcher(
func=part_file_name_conditions)
part_file_name_matcher = Intersect(
RegexMatchSpan(rgx=add_rgx, longest_match_only=True),
part_file_name_lambda_matcher)
part_matcher = Union(part_rgx_matcher, part_dict_matcher,
part_file_name_matcher)
part_ngrams = OmniNgramsPart(parts_by_doc=None, n_max=3)
attr_ngrams = OmniNgramsTemp(n_max=2)
def stg_temp_filter(c):
(part, attr) = c
if same_table((part, attr)):
return (is_horz_aligned((part, attr)) or is_vert_aligned(
(part, attr)))
return True
candidate_filter = stg_temp_filter
candidate_extractor = CandidateExtractor(
Part_Attr, [part_ngrams, attr_ngrams], [part_matcher, attr_matcher],
candidate_filter=candidate_filter)
candidate_extractor.apply(train_docs, split=0, parallelism=PARALLEL)
train_cands = session.query(Part_Attr).filter(Part_Attr.split == 0).all()
logger.info("Number of candidates: {}".format(len(train_cands)))
for i, docs in enumerate([dev_docs, test_docs]):
candidate_extractor.apply(docs, split=i + 1)
logger.info("Number of candidates: {}".format(
session.query(Part_Attr).filter(Part_Attr.split == i + 1).count()))
featurizer = BatchFeatureAnnotator(Part_Attr)
F_train = featurizer.apply(
split=0, replace_key_set=True, parallelism=PARALLEL)
logger.info(F_train.shape)
F_dev = featurizer.apply(
split=1, replace_key_set=False, parallelism=PARALLEL)
logger.info(F_dev.shape)
F_test = featurizer.apply(
split=2, replace_key_set=False, parallelism=PARALLEL)
logger.info(F_test.shape)
gold_file = 'tests/e2e/data/hardware_tutorial_gold.csv'
load_hardware_labels(
session, Part_Attr, gold_file, ATTRIBUTE, annotator_name='gold')
def LF_storage_row(c):
return 1 if 'storage' in get_row_ngrams(c.attr) else 0
def LF_temperature_row(c):
return 1 if 'temperature' in get_row_ngrams(c.attr) else 0
def LF_operating_row(c):
return 1 if 'operating' in get_row_ngrams(c.attr) else 0
def LF_tstg_row(c):
return 1 if overlap(['tstg', 'stg', 'ts'], list(
get_row_ngrams(c.attr))) else 0
def LF_to_left(c):
return 1 if 'to' in get_left_ngrams(c.attr, window=2) else 0
def LF_negative_number_left(c):
return 1 if any([
re.match(r'-\s*\d+', ngram)
for ngram in get_left_ngrams(c.attr, window=4)
]) else 0
stg_temp_lfs = [
LF_storage_row, LF_operating_row, LF_temperature_row, LF_tstg_row,
LF_to_left, LF_negative_number_left
]
labeler = BatchLabelAnnotator(Part_Attr, lfs=stg_temp_lfs)
L_train = labeler.apply(split=0, clear=True, parallelism=PARALLEL)
logger.info(L_train.shape)
L_gold_train = load_gold_labels(session, annotator_name='gold', split=0)
gen_model = GenerativeModel()
gen_model.train(
L_train,
epochs=500,
decay=0.9,
step_size=0.001 / L_train.shape[0],
reg_param=0)
logger.info("LF Accuracy: {}".format(gen_model.weights.lf_accuracy))
L_gold_dev = load_gold_labels(session, annotator_name='gold', split=1)
train_marginals = gen_model.marginals(L_train)
disc_model = SparseLogisticRegression()
disc_model.train(F_train, train_marginals, n_epochs=200, lr=0.001)
L_gold_test = load_gold_labels(session, annotator_name='gold', split=2)
test_candidates = [
F_test.get_candidate(session, i) for i in range(F_test.shape[0])
]
test_score = disc_model.predictions(F_test)
true_pred = [
test_candidates[_] for _ in np.nditer(np.where(test_score > 0))
]
pickle_file = 'tests/e2e/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("prec: {}".format(prec))
logger.info("rec: {}".format(rec))
logger.info("f1: {}".format(f1))
assert f1 < 0.7 and f1 > 0.4
def LF_test_condition_aligned(c):
return -1 if overlap(['test', 'condition'],
list(get_aligned_ngrams(c.attr))) else 0
def LF_collector_aligned(c):
return -1 if overlap([
'collector', 'collector-current', 'collector-base',
'collector-emitter'
], list(get_aligned_ngrams(c.attr))) else 0
def LF_current_aligned(c):
return -1 if overlap(['current', 'dc', 'ic'],
list(get_aligned_ngrams(c.attr))) else 0
def LF_voltage_row_temp(c):
return -1 if overlap(['voltage', 'cbo', 'ceo', 'ebo', 'v'],
list(get_aligned_ngrams(c.attr))) else 0
def LF_voltage_row_part(c):
return -1 if overlap(['voltage', 'cbo', 'ceo', 'ebo', 'v'],
list(get_aligned_ngrams(c.attr))) else 0
def LF_typ_row(c):
return -1 if overlap(['typ', 'typ.'],
list(get_row_ngrams(c.attr))) else 0
def LF_complement_left_row(c):
return -1 if (overlap(['complement', 'complementary'],
chain.from_iterable([
get_row_ngrams(c.part),
get_left_ngrams(c.part, window=10)
]))) else 0
def LF_too_many_numbers_row(c):
num_numbers = list(get_row_ngrams(c.attr,
attrib="ner_tags")).count('number')
return -1 if num_numbers >= 3 else 0
def LF_temp_on_high_page_num(c):
return -1 if c.attr.get_attrib_tokens('page')[0] > 2 else 0
def LF_temp_outside_table(c):
return -1 if not c.attr.sentence.is_tabular() is None else 0
def LF_not_temp_relevant(c):
return -1 if not overlap(
['storage', 'temperature', 'tstg', 'stg', 'ts'],
list(get_aligned_ngrams(c.attr))) else 0
stg_temp_lfs_2 = [
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 = BatchLabelAnnotator(Part_Attr, lfs=stg_temp_lfs_2)
L_train = labeler.apply(
split=0,
clear=False,
update_keys=True,
update_values=True,
parallelism=PARALLEL)
gen_model = GenerativeModel()
gen_model.train(
L_train,
epochs=500,
decay=0.9,
step_size=0.001 / L_train.shape[0],
reg_param=0)
train_marginals = gen_model.marginals(L_train)
disc_model = SparseLogisticRegression()
disc_model.train(F_train, train_marginals, n_epochs=200, lr=0.001)
test_candidates = [
F_test.get_candidate(session, i) for i in range(F_test.shape[0])
]
test_score = disc_model.predictions(F_test)
true_pred = [
test_candidates[_] for _ in np.nditer(np.where(test_score > 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("prec: {}".format(prec))
logger.info("rec: {}".format(rec))
logger.info("f1: {}".format(f1))
assert f1 > 0.7
Org_Fig = candidate_subclass('Org_Fig', ['product', 'figure'])
from fonduer import HTMLPreprocessor, OmniParser
docs_path = os.environ[
'FONDUERHOME'] + 'tutorials/organic_synthesis_figures/data/html/'
pdf_path = os.environ[
'FONDUERHOME'] + 'tutorials/organic_synthesis_figures/data/pdf/'
max_docs = float(10)
doc_preprocessor = HTMLPreprocessor(docs_path, max_docs=max_docs)
corpus_parser = OmniParser(
structural=True,
lingual=True,
visual=True,
pdf_path=pdf_path,
# flatten=['sup', 'sub', 'small'],
# ignore=['italic', 'bold'],
blacklist=['style', 'script', 'meta', 'noscript'])
corpus_parser.apply(doc_preprocessor, parallelism=PARALLEL)
from fonduer import Document
# divide train/dev/test
docs = session.query(Document).order_by(Document.name).all()
ld = len(docs)
train_docs = set()
dev_docs = set()
test_docs = set()