loader = klass(data_params["path"])
data_args = data_params["args"]
load_args = data_args.get("load", {})
data = loader.load_data(**load_args)
# test all vector models
for embedder_model in data_args["models"]:
# identify prebuilt model if exists
if isinstance(embedder_model, dict):
# initialize word vector embedder
embedder_model, prebuilt_model_params = embedder_model.items().pop()
prebuilt_path_model = prebuilt_model_params.get("model", None)
model_args = prebuilt_model_params.get("args", {})
embedder = WordVectorEmbedder(embedder_model, model_fullpath=prebuilt_path_model, model_args=model_args)
# update embedder parameters
if prebuilt_path_model:
model_path_dir, model_path_filename, model_path_filext = WordVectorBuilder.filename_components(
prebuilt_path_model
)
embedder.model_subset = model_path_filename
# training data (custom or default)
if prebuilt_model_params.get("train", None):
prebuilt_path_train = prebuilt_model_params.get("train")
else:
prebuilt_path_train = WordVectorBuilder.filename_train(prebuilt_path_model)
with open(prebuilt_path_train, "rb") as f:
data_train = pickle.load(f)
def main():
model_defaults = {
'imdb': {
'data_filename' : "",
'hdf5_name' : "imdb_split.hd5"},
'amazon': {
'data_filename' : "reviews_Health_and_Personal_Care.json.gz",
'hdf5_name' : "health_personal_split.hd5"
},
'sentiment140': {
'data_filename' : "sentiment140.csv",
'hdf5_name' : "sentiment140_split.hd5"
},
'open_weiboscope': {
'data_filename' : "",
'hdf5_name' : "open_weiboscope.hd5"
},
}
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument("dataset", help="Name of dataset (one of amazon, imdb, sentiment140, open_weiboscope)")
arg_parser.add_argument("--working_dir", "-w", default=".",
help="Directory where data and results should be put, default PWD.")
#arg_parser.add_argument("embedding", choices=('glove','word2vec'), required=False)
vector_group=arg_parser.add_mutually_exclusive_group()
vector_group.add_argument("--glove", nargs=1, metavar="LOCATION",help="Use glove, object at this path")
vector_group.add_argument("--word2vec", nargs=1, metavar="LOCATION",help="Use word2vec, object at this path")
arg_parser.add_argument("--results_dir", "-r", default=None, help="custom subfolder to store results and weights in (defaults to dataset)")
arg_parser.add_argument("--data_path", "-d", default=None, help="custom path to original data, partially overrides working_dir")
model_types=arg_parser.add_mutually_exclusive_group()
model_types.add_argument("--cnn", default=True, action="store_true", help="Use convolutional model")
model_types.add_argument("--lstm", action="store_true", help="Use LSTM")
arg_parser.add_argument("--hdf5_path", "-5", default=None, help="custom path to split data in HDF5")
arg_parser.add_argument("--weights_path", default=None, help="path to weights to initialize model with")
arg_parser.add_argument("--gpu_id", "-g", default=0, type=int, help="GPU device ID (integer)")
arg_parser.add_argument("--learning_rate", default=0.01, type=float, help="Learning rate, default 0.01")
arg_parser.add_argument("--momentum_coef", default=0.9, type=float, help="Momentum coefficient, default 0.9")
arg_parser.add_argument("--batch_size", default=128, type=int, help="Batch size")
arg_parser.add_argument("--nframes", default=256, type=int, help="Frame buffer size for CREPE. 256 or 1024.")
arg_parser.add_argument("--rng_seed",default=None,type=float, help="Random number seed")
arg_parser.add_argument("--do_evals", default=False, action="store_true")
arg_parser.add_argument("--log_level", default=logging.INFO, type=int)
args = arg_parser.parse_args()
logging.getLogger().setLevel(args.log_level)
dataset_name = args.dataset
args.working_dir = os.path.abspath(args.working_dir)
if not args.results_dir:
args.results_dir = dataset_name
args.results_dir = os.path.join(args.working_dir, args.results_dir)
if not args.data_path:
args.data_path = os.path.join(args.working_dir, model_defaults[dataset_name]['data_filename'])
if not args.hdf5_path:
args.hdf5_path = os.path.join(args.working_dir, model_defaults[dataset_name]['hdf5_name'])
# dataset-specific arguments to do_model
model_args = {
'sentiment140' : {
'min_length' : 70,
'max_length' : 150,
'normalizer_fun' : normalize_tweet,
'variant' : 'tweet_character',
},
'imdb' : {
'normalizer_fun' : normalize_imdb,
},
'amazon' : {
'normalizer_fun' : data_utils.normalize,
},
'open_weiboscope' : {
'normalizer_fun' : data_utils.normalize,
'balance_labels' : True,
'max_records' : 2e6,
},
}
# use 50 words in embedding-based models of microblogs,
# otherwise use 99 words for other embedding-based models
if dataset_name in ('sentiment140','open_weiboscope'):
embedding_nr_words = 50
else:
embedding_nr_words = 99
# CNN or LSTM
if args.cnn:
model_args[dataset_name]['model_type'] = 'cnn'
# character-based by default (overridden for embedding-based, below)
model_args[dataset_name]['transformer_fun'] = data_utils.to_one_hot
elif args.lstm:
model_args[dataset_name]['model_type'] = 'lstm'
# for character-based LSTM, re-reverse data
if not (args.glove or args.word2vec):
model_args[dataset_name]['transformer_fun'] = reverse_one_hot
# set default hidden size (overridden for embedding-based models below)
model_args[dataset_name]['hidden_size'] = 10
model_args[dataset_name]['nframes']=args.nframes
# parameters for embedding-based models
if args.glove or args.word2vec:
model_args[dataset_name]['sequence_length'] = embedding_nr_words
model_args[dataset_name]['crepe_variant'] = 'embedding{}'.format(embedding_nr_words)
model_args[dataset_name]['hidden_size'] = 200
if args.glove:
glove_path = os.path.abspath(args.glove[0])
if not os.path.isfile(glove_path):
model_downloader = ModelDownloader('glove')
glove_url = model_downloader.data_location['twitter-2b']['url']
glove_dir = os.path.dirname(glove_path)
glove_file = os.path.basename(glove_path)
model_downloader.download_and_save_vectors(glove_url, glove_dir, glove_file)
glove_embedder = WordVectorEmbedder("glove",glove_path)
model_args[dataset_name]['transformer_fun'] = \
lambda x: glove_embedder.embed_words_into_vectors(
transform_for_vectors(x), embedding_nr_words)
model_args[dataset_name]['vocab_size'] = 200
if args.word2vec:
w2v_embedder = WordVectorEmbedder("word2vec", os.path.abspath(args.word2vec[0]))
model_args[dataset_name]['transformer_fun'] = \
lambda x: w2v_embedder.embed_words_into_vectors(
transform_for_vectors(x), embedding_nr_words)
model_args[dataset_name]['vocab_size'] = 300
try:
logger.debug(model_args[dataset_name]['normalizer_fun'])
except KeyError:
logger.debug("No custom normalization fn specified")
do_model(dataset_name,
args.working_dir,
args.results_dir,
args.data_path,
args.hdf5_path,
gpu_id=args.gpu_id,
learning_rate=args.learning_rate,
momentum_coef=args.momentum_coef,
batch_size=args.batch_size,
rng_seed=args.rng_seed,
**model_args[dataset_name])
if args.do_evals:
do_evaluations(args.results_dir)
def main():
model_defaults = {
'imdb': {
'data_filename': "",
'hdf5_name': "imdb_split.hd5"
},
'amazon': {
'data_filename': "reviews_Health_and_Personal_Care.json.gz",
'hdf5_name': "health_personal_split.hd5"
},
'sentiment140': {
'data_filename': "sentiment140.csv",
'hdf5_name': "sentiment140_split.hd5"
},
'open_weiboscope': {
'data_filename': "",
'hdf5_name': "open_weiboscope.hd5"
},
}
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument(
"dataset",
help=
"Name of dataset (one of amazon, imdb, sentiment140, open_weiboscope)")
arg_parser.add_argument(
"--working_dir",
"-w",
default=".",
help="Directory where data and results should be put, default PWD.")
#arg_parser.add_argument("embedding", choices=('glove','word2vec'), required=False)
vector_group = arg_parser.add_mutually_exclusive_group()
vector_group.add_argument("--glove",
nargs=1,
metavar="LOCATION",
help="Use glove, object at this path")
vector_group.add_argument("--word2vec",
nargs=1,
metavar="LOCATION",
help="Use word2vec, object at this path")
arg_parser.add_argument(
"--results_dir",
"-r",
default=None,
help=
"custom subfolder to store results and weights in (defaults to dataset)"
)
arg_parser.add_argument(
"--data_path",
"-d",
default=None,
help="custom path to original data, partially overrides working_dir")
model_types = arg_parser.add_mutually_exclusive_group()
model_types.add_argument("--cnn",
default=True,
action="store_true",
help="Use convolutional model")
model_types.add_argument("--lstm", action="store_true", help="Use LSTM")
arg_parser.add_argument("--hdf5_path",
"-5",
default=None,
help="custom path to split data in HDF5")
arg_parser.add_argument("--weights_path",
default=None,
help="path to weights to initialize model with")
arg_parser.add_argument("--gpu_id",
"-g",
default=0,
type=int,
help="GPU device ID (integer)")
arg_parser.add_argument("--learning_rate",
default=0.01,
type=float,
help="Learning rate, default 0.01")
arg_parser.add_argument("--momentum_coef",
default=0.9,
type=float,
help="Momentum coefficient, default 0.9")
arg_parser.add_argument("--batch_size",
default=128,
type=int,
help="Batch size")
arg_parser.add_argument("--nframes",
default=256,
type=int,
help="Frame buffer size for CREPE. 256 or 1024.")
arg_parser.add_argument("--rng_seed",
default=None,
type=float,
help="Random number seed")
arg_parser.add_argument("--do_evals", default=False, action="store_true")
arg_parser.add_argument("--log_level", default=logging.INFO, type=int)
args = arg_parser.parse_args()
logging.getLogger().setLevel(args.log_level)
dataset_name = args.dataset
args.working_dir = os.path.abspath(args.working_dir)
if not args.results_dir:
args.results_dir = dataset_name
args.results_dir = os.path.join(args.working_dir, args.results_dir)
if not args.data_path:
args.data_path = os.path.join(
args.working_dir, model_defaults[dataset_name]['data_filename'])
if not args.hdf5_path:
args.hdf5_path = os.path.join(
args.working_dir, model_defaults[dataset_name]['hdf5_name'])
# dataset-specific arguments to do_model
model_args = {
'sentiment140': {
'min_length': 70,
'max_length': 150,
'normalizer_fun': normalize_tweet,
'variant': 'tweet_character',
},
'imdb': {
'normalizer_fun': normalize_imdb,
},
'amazon': {
'normalizer_fun': data_utils.normalize,
},
'open_weiboscope': {
'normalizer_fun': data_utils.normalize,
'balance_labels': True,
'max_records': 2e6,
},
}
# use 50 words in embedding-based models of microblogs,
# otherwise use 99 words for other embedding-based models
if dataset_name in ('sentiment140', 'open_weiboscope'):
embedding_nr_words = 50
else:
embedding_nr_words = 99
# CNN or LSTM
if args.cnn:
model_args[dataset_name]['model_type'] = 'cnn'
# character-based by default (overridden for embedding-based, below)
model_args[dataset_name]['transformer_fun'] = data_utils.to_one_hot
elif args.lstm:
model_args[dataset_name]['model_type'] = 'lstm'
# for character-based LSTM, re-reverse data
if not (args.glove or args.word2vec):
model_args[dataset_name]['transformer_fun'] = reverse_one_hot
# set default hidden size (overridden for embedding-based models below)
model_args[dataset_name]['hidden_size'] = 10
model_args[dataset_name]['nframes'] = args.nframes
# parameters for embedding-based models
if args.glove or args.word2vec:
model_args[dataset_name]['sequence_length'] = embedding_nr_words
model_args[dataset_name]['crepe_variant'] = 'embedding{}'.format(
embedding_nr_words)
model_args[dataset_name]['hidden_size'] = 200
if args.glove:
glove_path = os.path.abspath(args.glove[0])
if not os.path.isfile(glove_path):
model_downloader = ModelDownloader('glove')
glove_url = model_downloader.data_location['twitter-2b']['url']
glove_dir = os.path.dirname(glove_path)
glove_file = os.path.basename(glove_path)
model_downloader.download_and_save_vectors(glove_url, glove_dir,
glove_file)
glove_embedder = WordVectorEmbedder("glove", glove_path)
model_args[dataset_name]['transformer_fun'] = \
lambda x: glove_embedder.embed_words_into_vectors(
transform_for_vectors(x), embedding_nr_words)
model_args[dataset_name]['vocab_size'] = 200
if args.word2vec:
w2v_embedder = WordVectorEmbedder("word2vec",
os.path.abspath(args.word2vec[0]))
model_args[dataset_name]['transformer_fun'] = \
lambda x: w2v_embedder.embed_words_into_vectors(
transform_for_vectors(x), embedding_nr_words)
model_args[dataset_name]['vocab_size'] = 300
try:
logger.debug(model_args[dataset_name]['normalizer_fun'])
except KeyError:
logger.debug("No custom normalization fn specified")
do_model(dataset_name,
args.working_dir,
args.results_dir,
args.data_path,
args.hdf5_path,
gpu_id=args.gpu_id,
learning_rate=args.learning_rate,
momentum_coef=args.momentum_coef,
batch_size=args.batch_size,
rng_seed=args.rng_seed,
**model_args[dataset_name])
if args.do_evals:
do_evaluations(args.results_dir)
loader = klass(data_params['path'])
data_args = data_params['args']
load_args = data_args.get('load', {})
data = loader.load_data(load_args)
# test all vector models
for embedder_model in data_args['models']:
# identify prebuilt model if exists
prebuilt_path_model = None
if isinstance(embedder_model, dict):
embedder_model, prebuilt_model_params = embedder_model.items().pop()
prebuilt_path_model = prebuilt_model_params.get('model')
# initialize word vector embedder
embedder = WordVectorEmbedder(embedder_model, prebuilt_path_model)
# load pre-sampled data from disk
if prebuilt_path_model:
# training data (custom or default)
if prebuilt_model_params.get('train', None):
prebuilt_path_train = prebuilt_model_params.get('train')
else:
prebuilt_path_train = WordVectorBuilder.filename_train(prebuilt_path_model)
# testing data (custom or default)
if prebuilt_model_params.get('test', None):
prebuilt_path_test = prebuilt_model_params.get('test')
else:
prebuilt_path_test = WordVectorBuilder.filename_test(prebuilt_path_model)
for data_source, data_params in datasets.iteritems():
# prepare data loader
klass = data_params['class']
loader = klass(data_params['path'])
data_args = data_params['args']
data = loader.load_data()
# initialize lists (will be converted later into numpy arrays)
values = []
labels = []
# initialize vector embedder
prebuilt_model_path = data_args.get('models', {}).get(
embedder_model, {}).get('prebuilt_model_path', None)
embedder = WordVectorEmbedder(embedder_model, prebuilt_model_path)
# load pre-sampled data from disk
if prebuilt_model_path:
with open(WordVectorBuilder.filename_train(prebuilt_model_path),
'rb') as f:
data = pickle.load(f)
else:
# get equal-sized subsets of each class
min_samples = data_args['min_samples'] if data_args.has_key(
'min_samples') else None
data_sampler = DataSampler(klass,
file_path=data_params['path'],
num_classes=2)
data = data_sampler.sample_balanced(min_samples)
