def test_unseen_word_ending():
""" The last character should come with a </w> even if it wasn't seen as the last letter of a
word in the training set.
"""
encoder = Encoder(pct_bpe=1, ngram_max=4)
encoder.fit(test_corpus)
assert encoder.tokenize('import toolz') == [SOW, 'impo', 'rt', EOW, SOW, 'tool', 'z', EOW]
def test_mixed_encoder():
encoder = Encoder(vocab_size=1000, pct_bpe=0.98, ngram_max=4)
encoder.fit(test_corpus)
assert encoder.tokenize('import this yield toolz') == [
'import', SOW, 'th', 'is', EOW, SOW, 'yiel', 'd', EOW, SOW, 'tool',
'z', EOW
]
def test_bpe_encoder_fit():
""" Encoer should be able to fit to provided text data. """
encoder = Encoder(pct_bpe=1, ngram_max=4)
encoder.fit(test_corpus)
assert encoder.tokenize('from toolz import reduce') == [
SOW, 'f', 'ro', 'm', EOW, SOW, 'tool', 'z', EOW, SOW, 'impo', 'rt',
EOW, SOW, 'redu', 'ce', EOW
]
def main(corpus_path):
# type: (str) -> None
""" Loads corpus, learns word and BPE vocab, and writes to stdout. Assumes corpus is
line-separated text.
"""
with open(corpus_path) as infile:
lines = list(map(str.strip, infile))
encoder = Encoder(silent=True)
encoder.fit(lines)
print(json.dumps(encoder.vocabs_to_dict()))
def test_encoder_creation_graceful_failure(vocab_size):
""" Min vocab size is 1. Anything lower should ValueError """
died = False
try:
Encoder(vocab_size=vocab_size)
except ValueError:
died = True
assert died, "Encoder should have raised a ValueError for < 1 vocab size"
def test_strict_mode():
strict_encoder = Encoder(pct_bpe=1, strict=True)
strict_encoder.fit(test_corpus)
failed = False
idxs = [[9]]
try:
list(strict_encoder.inverse_transform(idxs))
except ValueError:
failed = True
assert failed, 'Should have failed to inverse transform word due to strict mode'
non_strict_encoder = Encoder(pct_bpe=1, strict=False)
non_strict_encoder.fit(test_corpus)
failed = False
idxs = [[9]]
try:
list(non_strict_encoder.inverse_transform(idxs))
except ValueError:
failed = True
assert not failed, 'Should not have failed to inverse transform word due to non-strict mode'
def test_unknown_char_handling():
encoder = Encoder(pct_bpe=1)
encoder.fit(test_corpus)
result = list(encoder.inverse_transform(encoder.transform([';'])))[0]
assert encoder.UNK in result
assert ';' not in result
def load(self, model_name, force_update=False):
"""
Use this function to automatically download and load a new model. It will automatically check online if a newer version is available.
:param model_name: identifier for the model to be loaded
:param force_update: use this flag to trigger forceful model update - useful if you have an unhealthy model store
:return: True if the process was successful and False if something failed
"""
try:
URL_PREFIX = "https://github.com/adobe/tripod/raw/master/data/trained/"
model_prefix = os.path.join(self._model_store, model_name)
must_download = force_update or not os.path.exists(
model_prefix + '.best') or not os.path.exists(model_prefix +
'.encodings')
model_name_suffixes = ['-aa', '-ab', '-ac', '-ad']
if must_download:
# download file parts
for model_name_suffix in model_name_suffixes:
url = "{0}{1}.zip{2}".format(URL_PREFIX, model_name,
model_name_suffix)
print(url)
download_target = model_prefix + '.zip' + model_name_suffix
self._download_with_progress_bar(url, download_target)
sys.stdout.write('\n')
# concatenate zip
download_target = model_prefix + '.zip'
f_out = open(download_target, 'wb')
for model_name_suffix in model_name_suffixes:
download_part = model_prefix + '.zip' + model_name_suffix
f_in = open(download_part, 'rb')
f_out.write(f_in.read())
f_in.close()
f_out.close()
zipfile = ZipFile(download_target, "r")
zipfile.extractall(self._model_store)
zipfile.close()
sys.stdout.write("\nModel extracted successfully.")
sys.stdout.flush()
if os.path.exists(model_prefix + '.bpe'):
self._bpe = BPEEncoder.load(model_prefix + '.bpe')
self._encodings = Encodings()
self._encodings.load(model_prefix + '.encodings')
self._model = TripodModel2(self._encodings)
self._model.load(model_prefix + '.best')
self._model.to(self._device)
self._model.eval()
self._loaded = True
return True
except:
return False
def test_mixed_encoder_word_in_other_word():
""" Ensure that a word is correctly decoded when it contains another word """
encoder = Encoder(vocab_size=1000, pct_bpe=0.98, ngram_max=4)
encoder.fit(test_corpus)
text = 'imimportport this yield toolz'
idxs = list(encoder.transform([text]))
idxs[0][1] = encoder.word_vocab['import']
rebuilt = next(encoder.inverse_transform(idxs))
assert rebuilt == 'import' + text[1:]
def test_common_byte_pair_collisions():
""" Ensure common byte pairs like "as" don't pull from word vocab when they are subword """
encoder = Encoder(vocab_size=200, pct_bpe=0.9, ngram_max=2)
encoder.fit(test_corpus + ["as"] * 10)
word = next(encoder.transform(["8 miles as the crow flies."]))
assert encoder.bpe_vocab["as"] not in word
assert encoder.word_vocab["as"] in word
subword = next(encoder.transform(["Basted turkey legs."]))
assert encoder.word_vocab["as"] not in subword
assert encoder.bpe_vocab["as"] in subword
def test_fixed_length_encoding():
encoder = Encoder(pct_bpe=1, required_tokens=[PAD])
encoder.fit(test_corpus)
result = list(encoder.transform([''], fixed_length=10))
assert len(result) == 1
assert len(result[0]) == 10
result = list(encoder.transform(['', 'import ' * 50], fixed_length=10))
assert len(result) == 2
assert len(result[0]) == 10
assert len(result[1]) == 10
def test_dump_and_load():
""" Should be able to dump encoder to dict, then load it again. """
encoder = Encoder(pct_bpe=1, ngram_max=4)
encoder.fit(test_corpus)
assert encoder.tokenize('from toolz import reduce') == [
SOW, 'f', 'ro', 'm', EOW, SOW, 'tool', 'z', EOW, SOW, 'impo', 'rt',
EOW, SOW, 'redu', 'ce', EOW
]
encoder_d = encoder.vocabs_to_dict()
new_encoder = Encoder.from_dict(encoder_d)
assert new_encoder.tokenize('from toolz import reduce') == [
SOW, 'f', 'ro', 'm', EOW, SOW, 'tool', 'z', EOW, SOW, 'impo', 'rt',
EOW, SOW, 'redu', 'ce', EOW
]
def test_inverse_transform():
encoder = Encoder(pct_bpe=1)
encoder.fit(test_corpus)
transform = lambda text: next(encoder.inverse_transform(encoder.transform([text])))
assert transform('this is how we do it') == 'this is how we do it'
assert transform('looking at the promotional stuff, it looks good.') == \
'looking at the promotional stuff {} it looks good .'.format(UNK)
assert transform('almost nothing should be recognized! let\'s see...') == \
'almost nothing should be recognized {unk} let {unk} s see ...'.format(unk=UNK)
assert transform("Vizzini: He didn't fall? INCONCEIVABLE!") == \
"vizzini {unk} he didn {unk} t fall {unk} inconceivable {unk}".format(unk=UNK)
def test_subword_tokenize():
encoder = Encoder(pct_bpe=1)
encoder.fit(test_corpus)
assert list(encoder.subword_tokenize('this')) == [SOW, 'th', 'is', EOW]
def test_required_tokens():
""" Should be able to require tokens to be present in encoder """
encoder = Encoder(required_tokens=['cats', 'dogs'])
encoder.fit(test_corpus)
assert 'cats' in encoder.word_vocab
assert 'dogs' in encoder.word_vocab
def test_single_letter_tokenizing():
""" Should yield single letters when untrained """
encoder = Encoder()
assert encoder.tokenize('single letters') == \
[SOW] + [UNK] * len('single') + [EOW, SOW] + [UNK] * len('letters') + [EOW]
def encoder_for_lines(lines):
""" Calculate BPE encoder for provided lines of text """
encoder = Encoder(vocab_size=VOCAB_SIZE, required_tokens=[START])
encoder.fit(lines)
encoder.save('latest_encoder.json')
return encoder
def test_encoder_creation(vocab_size):
""" Should be able to instantiate an Encoder with expected params """
Encoder(vocab_size=vocab_size)
def test_tokenize():
encoder = Encoder(pct_bpe=1)
encoder.fit(test_corpus)
assert list(encoder.tokenize('this is how')) == [
SOW, 'th', 'is', EOW, SOW, 'is', EOW, SOW, 'ho', 'w', EOW
]
def test_basic_transform():
encoder = Encoder(pct_bpe=1)
encoder.fit(test_corpus)
assert len(list(encoder.transform(['this']))[0]) == 4
def test_encoder_learning_from_random_sentences(sentences):
encoder = Encoder()
encoder.fit(test_corpus)
encoded = encoder.transform(sentences)
