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