values_train = np.array(values_train, dtype="float32")
labels_train = np.array(labels_train, dtype="float32")
values_test = np.array(values_test, dtype="float32")
labels_test = np.array(labels_test, dtype="float32")
else:
# initialize word vector embedder
embedder = WordVectorEmbedder(embedder_model)
# initialize lists (will be converted later into numpy arrays)
values = []
labels = []
# get equal-sized subsets of each class
data_sampler = DataSampler(klass, file_path=data_params["path"], num_classes=2)
data = data_sampler.sample_balanced(
min_samples=data_args.get("min_samples", None),
rng_seed=data_args.get("load", {}).get("rng_seed", None),
)
# load dataset
logger.info("processing {} samples from {}...".format(len(data), data_params["path"]))
profile_results = timed_dataload(loader, data, data_args, embedder, values, labels)
# store loading time
seconds_loading = profile_results.timer.total_tt
# shuffle if necessary
if data_args["shuffle_after_load"]:
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)
# load dataset
logger.info("processing {} samples from {}...".format(len(data), data_params['path']))
profile_results = timed_dataload(data, data_args, values, labels)
# store loading time
seconds_loading = profile_results.timer.total_tt
# shuffle if necessary
if data_args['shuffle_after_load']:
indices = np.arange(len(labels))
np.random.shuffle(indices)
values = [values[i] for i in indices]
labels = [labels[i] for i in indices]
# shuffle if necessary
if data_args['shuffle_after_load']:
np.random.shuffle(values_train)
np.random.shuffle(labels_train)
np.random.shuffle(values_test)
np.random.shuffle(labels_test)
else:
# initialize lists (will be converted later into numpy arrays)
values = []
labels = []
# get equal-sized subsets of each class
data_sampler = DataSampler(klass, file_path=data_params['path'], num_classes=2)
data = data_sampler.sample_balanced(min_samples=data_args.get('min_samples', None), rng_seed=data_args.get('load', {}).get('rng_seed', None))
# load dataset
logger.info("processing {} samples from {}...".format(len(data), data_params['path']))
profile_results = timed_dataload(data, data_args, values, labels)
# store loading time
seconds_loading = profile_results.timer.total_tt
# convert into nparray for sklearn
values = np.array(values, dtype="float32")
labels = np.array(labels, dtype="float32")
logger.info("Loaded {} samples...".format(len(values)))
# shuffle if necessary
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)
# load dataset
logger.info("processing {} samples from {}...".format(
len(data), data_params['path']))
profile_results = timed_dataload(data, data_args, values, labels)
# store loading time
seconds_loading = profile_results.timer.total_tt
# shuffle if necessary
if data_args['shuffle_after_load']:
indices = np.arange(len(labels))
np.random.shuffle(indices)