#!/usr/bin/env python from __future__ import division from __future__ import unicode_literals import os from os import path import shutil import codecs import random import plac import cProfile import pstats import re import spacy.util from spacy.en import English from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir from spacy.syntax.util import Config from spacy.gold import read_json_file from spacy.gold import GoldParse from spacy.scorer import Scorer from spacy.syntax.parser import Parser, get_templates from spacy._theano import TheanoModel import theano import theano.tensor as T from theano.printing import Print import numpy from collections import OrderedDict, defaultdict theano.config.profile = False theano.config.floatX = 'float32' floatX = theano.config.floatX def L1(L1_reg, *weights): return L1_reg * sum(abs(w).sum() for w in weights) def L2(L2_reg, *weights): return L2_reg * sum((w ** 2).sum() for w in weights) def rms_prop(loss, params, eta=1.0, rho=0.9, eps=1e-6): updates = OrderedDict() for param in params: value = param.get_value(borrow=True) accu = theano.shared(np.zeros(value.shape, dtype=value.dtype), broadcastable=param.broadcastable) grad = T.grad(loss, param) accu_new = rho * accu + (1 - rho) * grad ** 2 updates[accu] = accu_new updates[param] = param - (eta * grad / T.sqrt(accu_new + eps)) return updates def relu(x): return x * (x > 0) def feed_layer(activation, weights, bias, input_): return activation(T.dot(input_, weights) + bias) def init_weights(n_in, n_out): rng = numpy.random.RandomState(1235) weights = numpy.asarray( rng.standard_normal(size=(n_in, n_out)) * numpy.sqrt(2.0 / n_in), dtype=theano.config.floatX ) bias = numpy.zeros((n_out,), dtype=theano.config.floatX) return [wrapper(weights, name='W'), wrapper(bias, name='b')] def compile_model(n_classes, n_hidden, n_in, optimizer): x = T.vector('x') costs = T.ivector('costs') loss = T.scalar('loss') maxent_W, maxent_b = init_weights(n_hidden, n_classes) hidden_W, hidden_b = init_weights(n_in, n_hidden) # Feed the inputs forward through the network p_y_given_x = feed_layer( T.nnet.softmax, maxent_W, maxent_b, feed_layer( relu, hidden_W, hidden_b, x)) loss = -T.log(T.sum(p_y_given_x[0] * T.eq(costs, 0)) + 1e-8) train_model = theano.function( name='train_model', inputs=[x, costs], outputs=[p_y_given_x[0], T.grad(loss, x), loss], updates=optimizer(loss, [maxent_W, maxent_b, hidden_W, hidden_b]), on_unused_input='warn' ) evaluate_model = theano.function( name='evaluate_model', inputs=[x], outputs=[ feed_layer( T.nnet.softmax, maxent_W, maxent_b, feed_layer( relu, hidden_W, hidden_b, x ) )[0] ] ) return train_model, evaluate_model def score_model(scorer, nlp, annot_tuples, verbose=False): tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1]) nlp.tagger(tokens) nlp.parser(tokens) gold = GoldParse(tokens, annot_tuples) scorer.score(tokens, gold, verbose=verbose) def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', eta=0.01, mu=0.9, nv_hidden=100, nv_word=10, nv_tag=10, nv_label=10, seed=0, n_sents=0, verbose=False): dep_model_dir = path.join(model_dir, 'deps') pos_model_dir = path.join(model_dir, 'pos') if path.exists(dep_model_dir): shutil.rmtree(dep_model_dir) if path.exists(pos_model_dir): shutil.rmtree(pos_model_dir) os.mkdir(dep_model_dir) os.mkdir(pos_model_dir) setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir) Config.write(dep_model_dir, 'config', seed=seed, templates=tuple(), labels=Language.ParserTransitionSystem.get_labels(gold_tuples), vector_lengths=(nv_word, nv_tag, nv_label), hidden_nodes=nv_hidden, eta=eta, mu=mu ) # Bake-in hyper-parameters optimizer = lambda loss, params: rms_prop(loss, params, eta=eta, rho=rho, eps=eps) nlp = Language(data_dir=model_dir) n_classes = nlp.parser.model.n_classes train, predict = compile_model(n_classes, nv_hidden, n_in, optimizer) nlp.parser.model = TheanoModel(n_classes, input_spec, train, predict, model_loc) if n_sents > 0: gold_tuples = gold_tuples[:n_sents] print "Itn.\tP.Loss\tUAS\tTag %\tToken %" log_loc = path.join(model_dir, 'job.log') for itn in range(n_iter): scorer = Scorer() loss = 0 for _, sents in gold_tuples: for annot_tuples, ctnt in sents: if len(annot_tuples[1]) == 1: continue score_model(scorer, nlp, annot_tuples) tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1]) nlp.tagger(tokens) gold = GoldParse(tokens, annot_tuples, make_projective=True) assert gold.is_projective loss += nlp.parser.train(tokens, gold) nlp.tagger.train(tokens, gold.tags) random.shuffle(gold_tuples) logline = '%d:\t%d\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc, scorer.token_acc) print logline with open(log_loc, 'aw') as file_: file_.write(logline + '\n') nlp.parser.model.end_training() nlp.tagger.model.end_training() nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt')) return nlp def evaluate(nlp, gold_tuples, gold_preproc=True): scorer = Scorer() for raw_text, sents in gold_tuples: for annot_tuples, brackets in sents: tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1]) nlp.tagger(tokens) nlp.parser(tokens) gold = GoldParse(tokens, annot_tuples) scorer.score(tokens, gold) return scorer @plac.annotations( train_loc=("Location of training file or directory"), dev_loc=("Location of development file or directory"), model_dir=("Location of output model directory",), eval_only=("Skip training, and only evaluate", "flag", "e", bool), n_sents=("Number of training sentences", "option", "n", int), n_iter=("Number of training iterations", "option", "i", int), verbose=("Verbose error reporting", "flag", "v", bool), nv_word=("Word vector length", "option", "W", int), nv_tag=("Tag vector length", "option", "T", int), nv_label=("Label vector length", "option", "L", int), nv_hidden=("Hidden nodes length", "option", "H", int), eta=("Learning rate", "option", "E", float), mu=("Momentum", "option", "M", float), ) def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, verbose=False, nv_word=10, nv_tag=10, nv_label=10, nv_hidden=10, eta=0.1, mu=0.9, eval_only=False): gold_train = list(read_json_file(train_loc, lambda doc: 'wsj' in doc['id'])) nlp = train(English, gold_train, model_dir, feat_set='embed', eta=eta, mu=mu, nv_word=nv_word, nv_tag=nv_tag, nv_label=nv_label, nv_hidden=nv_hidden, n_sents=n_sents, n_iter=n_iter, verbose=verbose) scorer = evaluate(nlp, list(read_json_file(dev_loc))) print 'TOK', 100-scorer.token_acc print 'POS', scorer.tags_acc print 'UAS', scorer.uas print 'LAS', scorer.las print 'NER P', scorer.ents_p print 'NER R', scorer.ents_r print 'NER F', scorer.ents_f if __name__ == '__main__': plac.call(main)