def test_WordEmbedEncoder():
# TODO we should mock this
word2vec = KeyedVectors.load_word2vec_format(
'data/nkjp+wiki-forms-all-300-skipg-ns.txt', limit=50)
chunks = [Chunk([Token('5'), Token('kilogramów'), Token('pomiodorów')])]
encoder = WordEmbedEncoder(word2vec)
X = encoder.fit_transform(chunks)
assert type(X) == np.ndarray
assert X.shape == (1, 3, 300)
def test_CTagsEncoder():
chunks = [
Chunk([
Token('5', ctags=['brev:pun', 'conj', 'prep:nom']),
Token('kilogramów', ctags=['qub']),
Token('pomidorów', ctags=['brev:pun', 'conj', 'prep:nom'])
])
]
encoder = CTagsEncoder(tagset.categories)
X = encoder.fit_transform(chunks)
assert type(X) == np.ndarray
assert X.shape == (1, 3, 88)
def load_chunks(fpath, limit=None):
with open(fpath, 'rb') as f:
events = pulldom.parse(f, parser=_create_parser())
chunk_id = 0
chunk_events = _start_events(events, 'chunk')
for chunk in tqdm(chunk_events, desc=f'Loading chunks from {fpath}'):
for chunk in _findall(chunk, 'chunk'):
if chunk_id == limit:
return
chunk_id += 1
tokens = []
for tok in _findall(chunk, 'tok'):
orth = _findvalue(tok, 'orth')
lemmas = []
ctags = []
disamb_lemma = None
disamb_ctag = None
for lex in _findall(tok, 'lex'):
lemma = _findvalue(lex, 'base')
ctag = _findvalue(lex, 'ctag')
if lex.getAttribute('disamb') == '1':
disamb_lemma = lemma
disamb_ctag = ctag
else:
lemmas.append(lemma)
ctags.append(ctag)
token = Token(orth, lemmas, ctags, disamb_lemma,
disamb_ctag)
tokens.append(token)
yield Chunk(tokens)
def test_MorphAnalyzer_unknown_word():
unknown_word = 'Kotkowicach'
chunks = [Chunk([Token(unknown_word)])]
empty_dict = Dictionary({})
analyzer = MorphAnalyzer(empty_dict)
analyzer.analyze(chunks)
token = chunks[0].tokens[0]
assert token.lemmas == [unknown_word]
assert token.ctags == ['ign']
def test_KerasInputFormatter():
# TODO Move somewhere else
# TODO we should mock this
word2vec = KeyedVectors.load_word2vec_format(
'data/nkjp+wiki-forms-all-300-skipg-ns.txt', limit=5)
u = KerasInputFormatter([('word2vec', WordEmbedEncoder(word2vec)),
('tag2vec', CTagsEncoder(tagset.categories))])
chunks = [
Chunk([
Token('5', ctags=['brev:pun', 'conj', 'prep:nom']),
Token('kilogramów', ctags=['qub']),
Token('pomidorów', ctags=['brev:pun', 'conj', 'prep:nom'])
])
]
X = u.fit_transform(chunks)
print(X)
def merge_chunks(analyzed_fpath, gold_fpath, limit=None):
analyzed_chunks = load_chunks(analyzed_fpath, limit)
gold_chunks = load_chunks(gold_fpath, limit)
for analyzed_chunk, gold_chunk in zip(analyzed_chunks, gold_chunks):
tokens = []
if len(analyzed_chunk.tokens) != len(gold_chunk.tokens):
raise ValueError('Invalid tokens number')
for analyzed, gold in zip(analyzed_chunk.tokens, gold_chunk.tokens):
merged = Token(analyzed.orth, analyzed.lemmas, analyzed.ctags,
gold.disamb_lemma, gold.disamb_ctag)
tokens.append(merged)
yield Chunk(tokens)
def test_MorphAnalyzer():
known_word = 'pomidorów'
chunks = [Chunk([Token(known_word)])]
dict = Dictionary(
{known_word: [DictEntry(known_word, 'pomidor', 'xyz', '')]})
analyzer = MorphAnalyzer(dict)
analyzer.analyze(chunks)
token = chunks[0].tokens[0]
assert token.lemmas == ['pomidor']
assert token.ctags == ['xyz']
def test_DisambCTagEncoder():
chunks = [
Chunk([
Token('5', disamb_ctag='brev:pun'),
Token('kilogramów', disamb_ctag='qub'),
Token('pomidorów', disamb_ctag='conj')
]),
Chunk([
Token('5', disamb_ctag='brev:pun'),
Token('kilogramów', disamb_ctag='qub'),
Token('pomidorów', disamb_ctag='conj'),
Token('i', disamb_ctag='conj'),
Token('ogórków', disamb_ctag='conj')
])
]
encoder = DisambCTagEncoder(tagset.categories)
y = encoder.fit_transform(chunks)
assert isinstance(y, dict)
pos = y['pos']
assert type(pos) == np.ndarray
assert pos.shape == (2, 5, 35)