def fit(self, dataset, epochs=10, dev=None):
"""
Trains a BIST model on an annotated dataset in CoNLL file format.
Args:
dataset (str): Path to input dataset for training, formatted in CoNLL/U format.
epochs (int, optional): Number of learning iterations.
dev (str, optional): Path to development dataset for conducting evaluations.
"""
if dev:
dev = validate_existing_filepath(dev)
dataset = validate_existing_filepath(dataset)
validate((epochs, int, 0, None))
print("\nRunning fit on " + dataset + "...\n")
words, w2i, pos, rels = utils.vocab(dataset)
self.params = words, w2i, pos, rels, self.options
from nlp_architect.models.bist.mstlstm import MSTParserLSTM
self.model = MSTParserLSTM(*self.params)
for epoch in range(epochs):
print("Starting epoch", epoch + 1)
self.model.train(dataset)
if dev:
ext = dev.rindex(".")
res_path = dev[:ext] + "_epoch_" + str(epoch +
1) + "_pred" + dev[ext:]
utils.write_conll(res_path, self.model.predict(dev))
utils.run_eval(dev, res_path)
def load(self, path):
"""Loads and initializes a BIST model from file."""
with open(os.path.join(os.path.dirname(path), 'params.json'),
'r') as file:
self.params = json.load(file)
self.model = MSTParserLSTM(*self.params)
self.model.model.populate(path)
def load(self, path):
"""Loads and initializes a BIST model from file."""
with open(path.parent / 'params.json') as file:
self.params = json.load(file)
from nlp_architect.models.bist.mstlstm import MSTParserLSTM
self.model = MSTParserLSTM(*self.params)
self.model.model.populate(str(path))
def fit(self, dataset, epochs=10, dev=None):
"""
Trains a BIST model on an annotated dataset in CoNLL file format.
Args:
dataset (str): Path to input dataset for training, formatted in CoNLL/U format.
epochs (int, optional): Number of learning iterations.
dev (str, optional): Path to development dataset for conducting evaluations.
"""
if dev:
dev = validate_existing_filepath(dev)
dataset = validate_existing_filepath(dataset)
validate((epochs, int, 0, None))
print('\nRunning fit on ' + dataset + '...\n')
words, w2i, pos, rels = utils.vocab(dataset)
self.params = words, w2i, pos, rels, self.options
self.model = MSTParserLSTM(*self.params)
for epoch in range(epochs):
print('Starting epoch', epoch + 1)
self.model.train(dataset)
if dev:
ext = dev.rindex('.')
res_path = dev[:ext] + '_epoch_' + str(epoch + 1) + '_pred' + dev[ext:]
utils.write_conll(res_path, self.model.predict(dev))
utils.run_eval(dev, res_path)
class BISTModel(object):
"""
BIST parser model class.
This class handles training, prediction, loading and saving of a BIST parser model.
After the model is initialized, it accepts a CoNLL formatted dataset as input, and learns to
output dependencies for new input.
Args:
activation (str, optional): Activation function to use.
lstm_layers (int, optional): Number of LSTM layers to use.
lstm_dims (int, optional): Number of LSTM dimensions to use.
pos_dims (int, optional): Number of part-of-speech embedding dimensions to use.
Attributes:
model (MSTParserLSTM): The underlying LSTM model.
params (tuple): Additional parameters and resources for the model.
options (dict): User model options.
"""
def __init__(self,
activation="tanh",
lstm_layers=2,
lstm_dims=125,
pos_dims=25):
validate(
(activation, str),
(lstm_layers, int, 0, None),
(lstm_dims, int, 0, 1000),
(pos_dims, int, 0, 1000),
)
self.options = get_options_dict(activation, lstm_dims, lstm_layers,
pos_dims)
self.params = None
self.model = None
def fit(self, dataset, epochs=10, dev=None):
"""
Trains a BIST model on an annotated dataset in CoNLL file format.
Args:
dataset (str): Path to input dataset for training, formatted in CoNLL/U format.
epochs (int, optional): Number of learning iterations.
dev (str, optional): Path to development dataset for conducting evaluations.
"""
if dev:
dev = validate_existing_filepath(dev)
dataset = validate_existing_filepath(dataset)
validate((epochs, int, 0, None))
print("\nRunning fit on " + dataset + "...\n")
words, w2i, pos, rels = utils.vocab(dataset)
self.params = words, w2i, pos, rels, self.options
from nlp_architect.models.bist.mstlstm import MSTParserLSTM
self.model = MSTParserLSTM(*self.params)
for epoch in range(epochs):
print("Starting epoch", epoch + 1)
self.model.train(dataset)
if dev:
ext = dev.rindex(".")
res_path = dev[:ext] + "_epoch_" + str(epoch +
1) + "_pred" + dev[ext:]
utils.write_conll(res_path, self.model.predict(dev))
utils.run_eval(dev, res_path)
def predict(self, dataset, evaluate=False):
"""
Runs inference with the BIST model on a dataset in CoNLL file format.
Args:
dataset (str): Path to input CoNLL file.
evaluate (bool, optional): Write prediction and evaluation files to dataset's folder.
Returns:
res (list of list of ConllEntry): The list of input sentences with predicted
dependencies attached.
"""
dataset = validate_existing_filepath(dataset)
validate((evaluate, bool))
print("\nRunning predict on " + dataset + "...\n")
res = list(self.model.predict(conll_path=dataset))
if evaluate:
ext = dataset.rindex(".")
pred_path = dataset[:ext] + "_pred" + dataset[ext:]
utils.write_conll(pred_path, res)
utils.run_eval(dataset, pred_path)
return res
def predict_conll(self, dataset):
"""
Runs inference with the BIST model on a dataset in CoNLL object format.
Args:
dataset (list of list of ConllEntry): Input in the form of ConllEntry objects.
Returns:
res (list of list of ConllEntry): The list of input sentences with predicted
dependencies attached.
"""
res = None
if hasattr(dataset, "__iter__"):
res = list(self.model.predict(conll=dataset))
return res
def load(self, path):
"""Loads and initializes a BIST model from file."""
with open(path.parent / "params.json") as file:
self.params = json.load(file)
from nlp_architect.models.bist.mstlstm import MSTParserLSTM
self.model = MSTParserLSTM(*self.params)
self.model.model.populate(str(path))
def save(self, path):
"""Saves the BIST model to file."""
print("Saving")
with open(os.path.join(os.path.dirname(path), "params.json"),
"w") as file:
json.dump(self.params, file)
self.model.model.save(path)
def load(self, path):
"""Loads and initializes a BIST model from file."""
with open(path.parent / 'params.json') as file:
self.params = json.load(file)
self.model = MSTParserLSTM(*self.params)
self.model.model.populate(str(path))
class BISTModel(object):
"""
BIST parser model class.
This class handles training, prediction, loading and saving of a BIST parser model.
After the model is initialized, it accepts a CoNLL formatted dataset as input, and learns to
output dependencies for new input.
Args:
activation (str, optional): Activation function to use.
lstm_layers (int, optional): Number of LSTM layers to use.
lstm_dims (int, optional): Number of LSTM dimensions to use.
pos_dims (int, optional): Number of part-of-speech embedding dimensions to use.
Attributes:
model (MSTParserLSTM): The underlying LSTM model.
params (tuple): Additional parameters and resources for the model.
options (dict): User model options.
"""
def __init__(self, activation='tanh', lstm_layers=2, lstm_dims=125, pos_dims=25):
validate((activation, str), (lstm_layers, int, 0, None), (lstm_dims, int, 0, 1000),
(pos_dims, int, 0, 1000))
self.options = get_options_dict(activation, lstm_dims, lstm_layers, pos_dims)
self.params = None
self.model = None
def fit(self, dataset, epochs=10, dev=None):
"""
Trains a BIST model on an annotated dataset in CoNLL file format.
Args:
dataset (str): Path to input dataset for training, formatted in CoNLL/U format.
epochs (int, optional): Number of learning iterations.
dev (str, optional): Path to development dataset for conducting evaluations.
"""
if dev:
dev = validate_existing_filepath(dev)
dataset = validate_existing_filepath(dataset)
validate((epochs, int, 0, None))
print('\nRunning fit on ' + dataset + '...\n')
words, w2i, pos, rels = utils.vocab(dataset)
self.params = words, w2i, pos, rels, self.options
self.model = MSTParserLSTM(*self.params)
for epoch in range(epochs):
print('Starting epoch', epoch + 1)
self.model.train(dataset)
if dev:
ext = dev.rindex('.')
res_path = dev[:ext] + '_epoch_' + str(epoch + 1) + '_pred' + dev[ext:]
utils.write_conll(res_path, self.model.predict(dev))
utils.run_eval(dev, res_path)
def predict(self, dataset, evaluate=False):
"""
Runs inference with the BIST model on a dataset in CoNLL file format.
Args:
dataset (str): Path to input CoNLL file.
evaluate (bool, optional): Write prediction and evaluation files to dataset's folder.
Returns:
res (list of list of ConllEntry): The list of input sentences with predicted
dependencies attached.
"""
dataset = validate_existing_filepath(dataset)
validate((evaluate, bool))
print('\nRunning predict on ' + dataset + '...\n')
res = list(self.model.predict(conll_path=dataset))
if evaluate:
ext = dataset.rindex('.')
pred_path = dataset[:ext] + '_pred' + dataset[ext:]
utils.write_conll(pred_path, res)
utils.run_eval(dataset, pred_path)
return res
def predict_conll(self, dataset):
"""
Runs inference with the BIST model on a dataset in CoNLL object format.
Args:
dataset (list of list of ConllEntry): Input in the form of ConllEntry objects.
Returns:
res (list of list of ConllEntry): The list of input sentences with predicted
dependencies attached.
"""
if hasattr(dataset, '__iter__'):
return list(self.model.predict(conll=dataset))
def load(self, path):
"""Loads and initializes a BIST model from file."""
with open(os.path.join(os.path.dirname(path), 'params.json'), 'r') as file:
self.params = json.load(file)
self.model = MSTParserLSTM(*self.params)
self.model.model.populate(path)
def save(self, path):
"""Saves the BIST model to file."""
print("Saving")
with open(os.path.join(os.path.dirname(path), 'params.json'), 'w') as file:
json.dump(self.params, file)
self.model.model.save(path)
def load(self, path):
"""Loads and initializes a BIST model from file."""
with open(os.path.join(os.path.dirname(path), 'params.json'), 'r') as file:
self.params = json.load(file)
self.model = MSTParserLSTM(*self.params)
self.model.model.populate(path)