Source code for nlp_architect.models.bist_parser

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# Copyright 2017-2018 Intel Corporation
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import json
import os

from nlp_architect.models.bist import utils
from nlp_architect.models.bist.utils import get_options_dict
from nlp_architect.utils.io import validate, validate_existing_filepath


[docs]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
[docs] 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)
[docs] 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
[docs] 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
[docs] 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))
[docs] 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)