def get_embedding_matrix(self, texts, embedding_fp):
self.fit_transform(texts)
if embedding_fp is None:
embedding_fp = Path(get_data_home(),
EMBEDDING_EN["name"]).expanduser()
if not embedding_fp.exists():
logging.warning("Warning: will start to download large "
"embedding file in 10 seconds.")
time.sleep(10)
download_embedding()
logging.info("Loading embedding matrix. "
"This can take several minutes.")
embedding = load_embedding(embedding_fp, n_jobs=self.n_jobs)
embedding_matrix = sample_embedding(embedding, self.word_index)
return embedding_matrix
def download_embedding(url=EMBEDDING_EN['url'],
name=EMBEDDING_EN['name'],
data_home=None):
"""Download word embedding file.
Download word embedding file, unzip the file and save to the
file system.
Parameters
----------
url: str
The URL of the gzipped word embedding file
name: str
The filename of the embedding file.
data_home: str
The location of the ASR datasets.
Default `asreview.utils.get_data_home()`
"""
if data_home is None:
data_home = get_data_home()
out_fp = Path(data_home, name)
logging.info(f'Start downloading: {url}')
r = urlopen(url)
compressed_file = io.BytesIO(r.read())
logging.info(f'Save embedding to {out_fp}')
decompressed_file = gzip.GzipFile(fileobj=compressed_file)
with open(out_fp, 'wb') as out_file:
for line in decompressed_file:
out_file.write(line)
def get_embedding_matrix(self):
if self.embedding_matrix is not None:
return self.embedding_matrix
if self.word_index is None:
self.get_Xy()
if self.embedding_fp is None:
self.embedding_fp = Path(get_data_home(),
EMBEDDING_EN["name"]).expanduser()
if not self.embedding_fp.exists():
print("Warning: will start to download large "
"embedding file in 10 seconds.")
time.sleep(10)
download_embedding()
# create features and labels
logging.info("Loading embedding matrix. "
"This can take several minutes.")
embedding = load_embedding(self.embedding_fp,
word_index=self.word_index)
self.embedding_matrix = sample_embedding(embedding, self.word_index)
return self.embedding_matrix
def get_reviewer(dataset,
mode='oracle',
model=DEFAULT_MODEL,
query_strategy=DEFAULT_QUERY_STRATEGY,
balance_strategy=DEFAULT_BALANCE_STRATEGY,
n_instances=DEFAULT_N_INSTANCES,
n_queries=1,
embedding_fp=None,
verbose=1,
prior_included=None,
prior_excluded=None,
n_prior_included=DEFAULT_N_PRIOR_INCLUDED,
n_prior_excluded=DEFAULT_N_PRIOR_EXCLUDED,
config_file=None,
src_log_fp=None,
**kwargs):
# Find the URL of the datasets if the dataset is an example dataset.
if dataset in DEMO_DATASETS.keys():
dataset = DEMO_DATASETS[dataset]
if src_log_fp is not None:
logger = Logger(log_fp=src_log_fp)
settings = logger.settings
else:
logger = None
settings = ASReviewSettings(model=model,
n_instances=n_instances,
n_queries=n_queries,
n_prior_included=n_prior_included,
n_prior_excluded=n_prior_excluded,
query_strategy=query_strategy,
balance_strategy=balance_strategy,
mode=mode,
data_fp=dataset)
settings.from_file(config_file)
model = settings.model
if model in ["lstm_base", "lstm_pool"]:
base_model = "RNN"
else:
base_model = "other"
# Check if mode is valid
if mode in AVAILABLE_REVIEW_CLASSES:
if verbose:
print(f"Start review in '{mode}' mode.")
else:
raise ValueError(f"Unknown mode '{mode}'.")
print(f"Model: '{model}'")
# if the provided file is a pickle file
if is_pickle(dataset):
with open(dataset, 'rb') as f:
data_obj = pickle.load(f)
if isinstance(data_obj, tuple) and len(data_obj) == 3:
X, y, embedding_matrix = data_obj
elif isinstance(data_obj, tuple) and len(data_obj) == 4:
X, y, embedding_matrix, _ = data_obj
else:
raise ValueError("Incorrect pickle object.")
else:
as_data = ASReviewData.from_file(dataset)
_, texts, labels = as_data.get_data()
# get the model
if base_model == "RNN":
if embedding_fp is None:
embedding_fp = Path(get_data_home(),
EMBEDDING_EN["name"]).expanduser()
if not embedding_fp.exists():
print("Warning: will start to download large "
"embedding file in 10 seconds.")
time.sleep(10)
download_embedding(verbose=verbose)
# create features and labels
X, word_index = text_to_features(texts)
y = labels
embedding = load_embedding(embedding_fp, word_index=word_index)
embedding_matrix = sample_embedding(embedding, word_index)
elif model.lower() in ['nb', 'svc', 'svm']:
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer())])
X = text_clf.fit_transform(texts)
y = labels
settings.fit_kwargs = {}
settings.query_kwargs = {}
if base_model == 'RNN':
if model == "lstm_base":
model_kwargs = lstm_base_model_defaults(settings, verbose)
create_lstm_model = create_lstm_base_model
elif model == "lstm_pool":
model_kwargs = lstm_pool_model_defaults(settings, verbose)
create_lstm_model = create_lstm_pool_model
else:
raise ValueError(f"Unknown model {model}")
settings.fit_kwargs = lstm_fit_defaults(settings, verbose)
settings.query_kwargs['verbose'] = verbose
# create the model
model = KerasClassifier(create_lstm_model(
embedding_matrix=embedding_matrix, **model_kwargs),
verbose=verbose)
elif model.lower() in ['nb']:
from asreview.models import create_nb_model
model = create_nb_model()
elif model.lower() in ['svm', 'svc']:
from asreview.models import create_svc_model
model = create_svc_model()
else:
raise ValueError('Model not found.')
# Pick query strategy
query_fn, query_str = get_query_strategy(settings)
if verbose:
print(f"Query strategy: {query_str}")
train_data_fn, train_method = get_balance_strategy(settings)
if verbose:
print(f"Using {train_method} method to obtain training data.")
# Initialize the review class.
if mode == "simulate":
reviewer = ReviewSimulate(X,
y,
model=model,
query_strategy=query_fn,
train_data_fn=train_data_fn,
n_instances=settings.n_instances,
n_queries=settings.n_queries,
verbose=verbose,
prior_included=prior_included,
prior_excluded=prior_excluded,
n_prior_included=settings.n_prior_included,
n_prior_excluded=settings.n_prior_excluded,
fit_kwargs=settings.fit_kwargs,
balance_kwargs=settings.balance_kwargs,
query_kwargs=settings.query_kwargs,
logger=logger,
**kwargs)
elif mode == "oracle":
reviewer = ReviewOracle(X,
model=model,
query_strategy=query_fn,
as_data=as_data,
train_data_fn=train_data_fn,
n_instances=settings.n_instances,
n_queries=settings.n_queries,
verbose=verbose,
prior_included=prior_included,
prior_excluded=prior_excluded,
fit_kwargs=settings.fit_kwargs,
balance_kwargs=settings.balance_kwargs,
query_kwargs=settings.query_kwargs,
logger=logger,
**kwargs)
elif mode == "minimal":
reviewer = MinimalReview(X,
model=model,
query_strategy=query_fn,
train_data_fn=train_data_fn,
n_instances=settings.n_instances,
n_queries=settings.n_queries,
verbose=verbose,
prior_included=prior_included,
prior_excluded=prior_excluded,
fit_kwargs=settings.fit_kwargs,
balance_kwargs=settings.balance_kwargs,
query_kwargs=settings.query_kwargs,
logger=logger,
**kwargs)
else:
raise ValueError("Error finding mode, should never come here...")
reviewer._logger.add_settings(settings)
return reviewer