from ltlib import filelog
from ltlib import conlldata
from ltlib import viterbi
from ltlib.features import NormEmbeddingFeature, SennaCapsFeature
from ltlib.features import windowed_inputs
from ltlib.callbacks import token_evaluator, EpochTimer
from ltlib.layers import concat, inputs_and_embeddings
from ltlib.settings import cli_settings, log_settings
from ltlib.optimizers import get_optimizer
from ltlib.util import unique
from ltlib.output import save_token_predictions_multi_output
from config import Defaults
config = cli_settings(['datadir', 'datasets','wordvecs'], Defaults)
assert len(config.filter_nums) == len(config.filter_sizes)
datasets = config.datasets.split(',')
data = []
max_fs = []
max_vfs = []
for ind, dataset in enumerate(datasets):
data_path = config.datadir + '/' + dataset
data.append(conlldata.load_dir(data_path, config))
max_fs.append((0.0,0.0))
max_vfs.append((0.0,0.0))
max_y = 0
def main(argv):
config = cli_settings(['datadir', 'wordvecs'], Defaults)
data = load_dir(config.datadir, config)
force_oov = set(l.strip()
for l in open(config.oov)) if config.oov else None
w2v = NormEmbeddingFeature.from_file(config.wordvecs,
max_rank=config.max_vocab_size,
vocabulary=data.vocabulary,
force_oov=force_oov,
name='text')
# Add word vector features to tokens
features = [w2v]
data.tokens.add_features(features)
# Summarize word vector featurizer statistics (OOV etc.)
logging.info(features[0].summary())
# Create inputs at document level
data.documents.add_inputs([
FixedWidthInput(config.doc_size, f['<PADDING>'], f.name)
for f in features
])
# Create keras input and embedding for each feature
inputs, embeddings = inputs_and_embeddings(features, config)
# Combine and reshape for convolution
seq = concat(embeddings)
cshape = (config.doc_size, sum(f.output_dim for f in features)
) #calculating the size of documents and all features.
seq = Reshape((1, ) + cshape)(seq)
#seq = Reshape((1, config.doc_size, w2v.output_dim))(embeddings) #old way of doing the above
# Convolution(s)
convLayers = []
for filter_size, filter_num in zip(config.filter_sizes,
config.filter_nums):
seq2 = Convolution2D(filter_num,
filter_size,
cshape[1],
border_mode='valid',
activation='relu',
dim_ordering='th')(seq)
seq2 = MaxPooling2D(pool_size=(config.doc_size - filter_size + 1, 1),
dim_ordering='th')(seq2)
seq2 = Flatten()(seq2)
convLayers.append(seq2)
seq = concat(convLayers)
if config.drop_prob:
seq = Dropout(config.drop_prob)(seq)
for s in config.hidden_sizes:
seq = Dense(s, activation='relu')(seq)
out = Dense(data.documents.target_dim,
W_regularizer=W_regularizer(config),
activation='softmax')(seq)
model = Model(input=inputs, output=out)
if config.verbosity != 0:
logging.info(model.summary())
optimizer = get_optimizer(config)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy', f1, prec, rec])
weights, results = [], {}
callbacks = [
EpochTimer(),
WeightStore(weights),
document_evaluator(data.train, label='train', results=results),
document_evaluator(data.devel, label='devel', results=results),
]
if config.test:
callbacks.append(
document_evaluator(data.test, label='test', results=results))
hist = model.fit(data.train.documents.inputs,
data.train.documents.targets,
validation_data=(
data.devel.documents.inputs,
data.devel.documents.targets,
),
batch_size=config.batch_size,
nb_epoch=config.epochs,
verbose=config.verbosity,
callbacks=callbacks)
# logging.info(history.history)
for k, values in results.items():
s = lambda v: str(v) if not isinstance(v, float) else '{:.4f}'.format(v
)
logging.info('\t'.join(s(i) for i in [k] + values))
evalsets = [data.devel] + ([data.test] if config.test else [])
for s in evalsets:
logging.info('last epoch, {}: {}'.format(
s.name, evaluation_summary(model, s, 0, config)))
epoch = get_best_epoch(results, 'devel', config)
model.set_weights(weights[epoch])
if config.threshold:
threshold = results['devel/maxf-threshold'][epoch]
else:
threshold = 0.0
for s in evalsets:
logging.info('best devel epoch th {} ({}), {}: {}'.format(
threshold, config.target_metric, s.name,
evaluation_summary(model, s, threshold, config)))
from ltlib import filelog
from ltlib import conlldata
from ltlib import viterbi
from ltlib.features import NormEmbeddingFeature, SennaCapsFeature
from ltlib.features import windowed_inputs
from ltlib.callbacks import token_evaluator, EpochTimer
from ltlib.layers import concat, inputs_and_embeddings
from ltlib.settings import cli_settings, log_settings
from ltlib.optimizers import get_optimizer
from ltlib.output import save_token_predictions
from baseline_config import Defaults
config = cli_settings(['datadir', 'wordvecs'], Defaults)
data = conlldata.load_dir(config.datadir, config)
vmapper = viterbi.get_prediction_mapper(data.train.sentences, config)
w2v = NormEmbeddingFeature.from_file(config.wordvecs,
max_rank=config.max_vocab_size,
vocabulary=data.vocabulary,
name='words')
features = [w2v]
if config.word_features:
features.append(SennaCapsFeature(name='caps'))
data.tokens.add_features(features)
data.tokens.add_inputs(windowed_inputs(config.window_size, features))
def main(argv):
global data
config = cli_settings(['datadir', 'wordvecs'], Defaults)
##load_dir(config.datadir, config)
print("finished reading data")
force_oov = set(l.strip()
for l in open(config.oov)) if config.oov else None
w2v = NormEmbeddingFeature.from_file(config.wordvecs,
max_rank=config.max_vocab_size,
vocabulary=data.vocabulary,
force_oov=force_oov,
name='text')
# Add word vector features to tokens
print("finished reading embeddings")
features = [w2v]
data.tokens.add_features(features)
# Summarize word vector featurizer statistics (OOV etc.)
# Create inputs at document level
data.documents.add_inputs([
FixedWidthInput(config.doc_size, f['<PADDING>'], f.name)
for f in features
])
# Create keras input and embedding for each feature
inputs, embeddings = inputs_and_embeddings(features, config)
# Combine and reshape for convolution
seq = concat(embeddings)
cshape = (config.doc_size, sum(f.output_dim for f in features)
) #calculating the size of documents and all features.
seq = Reshape((1, ) + cshape)(seq)
#seq = Reshape((1, config.doc_size, w2v.output_dim))(embeddings) #old way of doing the above
# Convolution(s)
convLayers = []
for filter_size, filter_num in zip(config.filter_sizes,
config.filter_nums):
seq2 = Convolution2D(filter_num,
filter_size,
cshape[1],
border_mode='valid',
activation='relu',
dim_ordering='th')(seq)
seq2 = MaxPooling2D(pool_size=(config.doc_size - filter_size + 1, 1),
dim_ordering='th')(seq2)
seq2 = Flatten()(seq2)
convLayers.append(seq2)
seq = concat(convLayers)
if config.drop_prob:
seq = Dropout(config.drop_prob)(seq)
for s in config.hidden_sizes:
seq = Dense(s, activation='relu')(seq)
out = Dense(data.documents.target_dim,
W_regularizer=W_regularizer(config),
activation='sigmoid')(seq)
model = Model(input=inputs, output=out)
optimizer = get_optimizer(config)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer
#metrics=['accuracy', f1, prec, rec]
)
weights, results = [], {}
callbacks = [
EpochTimer(),
#WeightStore(weights),
#document_evaluator(data.train, label='train', results=results),
evaluator(data.devel, label='devel', results=results)
]
#if config.test:
#callbacks.append(document_evaluator(data.test, label='test',
# results=results))
hist = model.fit(data.train.documents.inputs,
data.train.documents.targets,
validation_data=(
data.devel.documents.inputs,
data.devel.documents.targets,
),
batch_size=config.batch_size,
nb_epoch=config.epochs,
verbose=config.verbosity,
callbacks=callbacks)