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* Merge neuralnet branch into sense-tagger

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Matthew Honnibal 2015-07-01 22:38:22 +02:00
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261
bin/parser/nn_train.py Executable file
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#!/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)

3
setup.py
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@ -151,7 +151,8 @@ MOD_NAMES = ['spacy.parts_of_speech', 'spacy.strings',
'spacy.lexeme', 'spacy.vocab', 'spacy.tokens', 'spacy.spans',
'spacy.morphology',
'spacy.syntax.stateclass',
'spacy._ml', 'spacy.tokenizer', 'spacy.en.attrs',
'spacy._ml', 'spacy._theano',
'spacy.tokenizer', 'spacy.en.attrs',
'spacy.en.pos', 'spacy.syntax.parser',
'spacy.syntax.transition_system',
'spacy.syntax.arc_eager',

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spacy/_bu_nn.pyx Normal file
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"""Feed-forward neural network, using Thenao."""
import os
import sys
import time
import numpy
import theano
import theano.tensor as T
import gzip
import cPickle
def load_data(dataset):
''' Loads the dataset
:type dataset: string
:param dataset: the path to the dataset (here MNIST)
'''
#############
# LOAD DATA #
#############
# Download the MNIST dataset if it is not present
data_dir, data_file = os.path.split(dataset)
if data_dir == "" and not os.path.isfile(dataset):
# Check if dataset is in the data directory.
new_path = os.path.join(
os.path.split(__file__)[0],
"..",
"data",
dataset
)
if os.path.isfile(new_path) or data_file == 'mnist.pkl.gz':
dataset = new_path
if (not os.path.isfile(dataset)) and data_file == 'mnist.pkl.gz':
import urllib
origin = (
'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'
)
print 'Downloading data from %s' % origin
urllib.urlretrieve(origin, dataset)
print '... loading data'
# Load the dataset
f = gzip.open(dataset, 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()
#train_set, valid_set, test_set format: tuple(input, target)
#input is an numpy.ndarray of 2 dimensions (a matrix),
#each row corresponding to an example. target is a
#numpy.ndarray of 1 dimension (vector)) that have the same length as
#the number of rows in the input. It should give the target
#target to the example with the same index in the input.
def shared_dataset(data_xy, borrow=True):
""" Function that loads the dataset into shared variables
The reason we store our dataset in shared variables is to allow
Theano to copy it into the GPU memory (when code is run on GPU).
Since copying data into the GPU is slow, copying a minibatch everytime
is needed (the default behaviour if the data is not in a shared
variable) would lead to a large decrease in performance.
"""
data_x, data_y = data_xy
shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX),
borrow=borrow)
# When storing data on the GPU it has to be stored as floats
# therefore we will store the labels as ``floatX`` as well
# (``shared_y`` does exactly that). But during our computations
# we need them as ints (we use labels as index, and if they are
# floats it doesn't make sense) therefore instead of returning
# ``shared_y`` we will have to cast it to int. This little hack
# lets ous get around this issue
return shared_x, T.cast(shared_y, 'int32')
test_set_x, test_set_y = shared_dataset(test_set)
valid_set_x, valid_set_y = shared_dataset(valid_set)
train_set_x, train_set_y = shared_dataset(train_set)
rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y),
(test_set_x, test_set_y)]
return rval
class LogisticRegression(object):
"""Multi-class Logistic Regression Class
The logistic regression is fully described by a weight matrix :math:`W`
and bias vector :math:`b`. Classification is done by projecting data
points onto a set of hyperplanes, the distance to which is used to
determine a class membership probability.
"""
def __init__(self, input, n_in, n_out):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# start-snippet-1
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(
value=numpy.zeros((n_in, n_out),
dtype=theano.config.floatX
),
name='W',
borrow=True
)
# initialize the baises b as a vector of n_out 0s
self.b = theano.shared(
value=numpy.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
# symbolic expression for computing the matrix of class-membership
# probabilities
# Where:
# W is a matrix where column-k represent the separation hyper plain for
# class-k
# x is a matrix where row-j represents input training sample-j
# b is a vector where element-k represent the free parameter of hyper
# plain-k
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# symbolic description of how to compute prediction as class whose
# probability is maximal
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
# end-snippet-1
# parameters of the model
self.params = [self.W, self.b]
def neg_ll(self, y):
"""Return the mean of the negative log-likelihood of the prediction
of this model under a given target distribution.
.. math::
\frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
\frac{1}{|\mathcal{D}|} \sum_{i=0}^{|\mathcal{D}|}
\log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
\ell (\theta=\{W,b\}, \mathcal{D})
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
Note: we use the mean instead of the sum so that
the learning rate is less dependent on the batch size
"""
# start-snippet-2
# y.shape[0] is (symbolically) the number of rows in y, i.e.,
# number of examples (call it n) in the minibatch
# T.arange(y.shape[0]) is a symbolic vector which will contain
# [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of
# Log-Probabilities (call it LP) with one row per example and
# one column per class LP[T.arange(y.shape[0]),y] is a vector
# v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,
# LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is
# the mean (across minibatch examples) of the elements in v,
# i.e., the mean log-likelihood across the minibatch.
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
# end-snippet-2
def errors(self, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero one
loss over the size of the minibatch
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
# start-snippet-1
class HiddenLayer(object):
def __init__(self, rng, input, n_in, n_out, W=None, b=None,
activation=T.tanh):
"""
Typical hidden layer of a MLP: units are fully-connected and have
sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
and the bias vector b is of shape (n_out,).
NOTE : The nonlinearity used here is tanh
Hidden unit activation is given by: tanh(dot(input,W) + b)
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dmatrix
:param input: a symbolic tensor of shape (n_examples, n_in)
:type n_in: int
:param n_in: dimensionality of input
:type n_out: int
:param n_out: number of hidden units
:type activation: theano.Op or function
:param activation: Non linearity to be applied in the hidden
layer
"""
self.input = input
# end-snippet-1
# `W` is initialized with `W_values` which is uniformely sampled
# from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))
# for tanh activation function
# the output of uniform if converted using asarray to dtype
# theano.config.floatX so that the code is runable on GPU
# Note : optimal initialization of weights is dependent on the
# activation function used (among other things).
# For example, results presented in [Xavier10] suggest that you
# should use 4 times larger initial weights for sigmoid
# compared to tanh
# We have no info for other function, so we use the same as
# tanh.
if W is None:
W_values = numpy.asarray(
rng.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)
),
dtype=theano.config.floatX
)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
W = theano.shared(value=W_values, name='W', borrow=True)
if b is None:
b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
b = theano.shared(value=b_values, name='b', borrow=True)
self.W = W
self.b = b
lin_output = T.dot(input, self.W) + self.b
self.output = (
lin_output if activation is None
else activation(lin_output)
)
# parameters of the model
self.params = [self.W, self.b]
# start-snippet-2
class MLP(object):
"""Multi-Layer Perceptron Class
A multilayer perceptron is a feedforward artificial neural network model
that has one layer or more of hidden units and nonlinear activations.
Intermediate layers usually have as activation function tanh or the
sigmoid function (defined here by a ``HiddenLayer`` class) while the
top layer is a softmax layer (defined here by a ``LogisticRegression``
class).
"""
def __init__(self, rng, input, n_in, n_hidden, n_out):
"""Initialize the parameters for the multilayer perceptron
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_hidden: int
:param n_hidden: number of hidden units
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# Since we are dealing with a one hidden layer MLP, this will translate
# into a HiddenLayer with a tanh activation function connected to the
# LogisticRegression layer; the activation function can be replaced by
# sigmoid or any other nonlinear function
self.hidden = HiddenLayer(
rng=rng,
input=input,
n_in=n_in,
n_out=n_hidden,
activation=T.tanh
)
# The logistic regression layer gets as input the hidden units
# of the hidden layer
self.maxent = LogisticRegression(
input=self.hidden.output,
n_in=n_hidden,
n_out=n_out
)
# L1 norm ; one regularization option is to enforce L1 norm to
# be small
self.L1 = abs(self.hidden.W).sum() + abs(self.maxent.W).sum()
# square of L2 norm ; one regularization option is to enforce
# square of L2 norm to be small
self.L2_sqr = (self.hidden.W ** 2).sum() + (self.maxent.W ** 2).sum()
# negative log likelihood of the MLP is given by the negative
# log likelihood of the output of the model, computed in the
# logistic regression layer
self.neg_ll = self.maxent.neg_ll
# same holds for the function computing the number of errors
self.errors = self.maxent.errors
# the parameters of the model are the parameters of the two layer it is
# made out of
self.params = self.hidden.params + self.maxent.params
def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
dataset='mnist.pkl.gz', batch_size=1, n_hidden=500):
"""
Demonstrate stochastic gradient descent optimization for a multilayer
perceptron
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient
:type L1_reg: float
:param L1_reg: L1-norm's weight when added to the cost (see
regularization)
:type L2_reg: float
:param L2_reg: L2-norm's weight when added to the cost (see
regularization)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
rng = numpy.random.RandomState(1234)
# construct the MLP class
mlp = MLP(
rng=rng,
input=x,
n_in=28 * 28,
n_hidden=n_hidden,
n_out=10
)
# the cost we minimize during training is the negative log likelihood of
# the model plus the regularization terms (L1 and L2); cost is expressed
# here symbolically
# compiling a Theano function that computes the mistakes that are made
# by the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=mlp.maxent.errors(y),
givens={
x: test_set_x[index:index+1],
y: test_set_y[index:index+1]
}
)
validate_model = theano.function(
inputs=[index],
outputs=mlp.maxent.errors(y),
givens={
x: valid_set_x[index:index+1],
y: valid_set_y[index:index+1]
}
)
# compute the gradient of cost with respect to theta (sotred in params)
# the resulting gradients will be stored in a list gparams
cost = mlp.neg_ll(y) + L1_reg * mlp.L1 + L2_reg * mlp.L2_sqr
gparams = [T.grad(cost, param) for param in mlp.params]
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
updates = [(mlp.params[i], mlp.params[i] - (learning_rate * gparams[i]))
for i in xrange(len(gparams))]
# compiling a Theano function `train_model` that returns the cost, but
# in the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index:index+1],
y: train_set_y[index:index+1]
}
)
# end-snippet-5
###############
# TRAIN MODEL #
###############
print '... training'
start_time = time.clock()
n_examples = train_set_x.get_value(borrow=True).shape[0]
n_dev_examples = valid_set_x.get_value(borrow=True).shape[0]
n_test_examples = test_set_x.get_value(borrow=True).shape[0]
for epoch in range(1, n_epochs+1):
for idx in xrange(n_examples):
train_model(idx)
# compute zero-one loss on validation set
error = numpy.mean(map(validate_model, xrange(n_dev_examples)))
print('epoch %i, validation error %f %%' % (epoch, error * 100))
end_time = time.clock()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
if __name__ == '__main__':
test_mlp()

8
spacy/_ml.pxd
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@ -5,6 +5,7 @@ from cymem.cymem cimport Pool
from thinc.learner cimport LinearModel
from thinc.features cimport Extractor, Feature
from thinc.typedefs cimport atom_t, feat_t, weight_t, class_t
from thinc.api cimport ExampleC
from preshed.maps cimport PreshMapArray
@ -14,9 +15,14 @@ from .tokens cimport Tokens
cdef int arg_max(const weight_t* scores, const int n_classes) nogil
cdef int arg_max_if_true(const weight_t* scores, const int* is_valid, int n_classes) nogil
cdef int arg_max_if_zero(const weight_t* scores, const int* costs, int n_classes) nogil
cdef class Model:
cdef int n_classes
cdef readonly int n_classes
cdef readonly int n_feats
cdef const weight_t* score(self, atom_t* context) except NULL
cdef int set_scores(self, weight_t* scores, atom_t* context) except -1

36
spacy/_ml.pyx
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@ -10,6 +10,7 @@ import cython
import numpy.random
from thinc.features cimport Feature, count_feats
from thinc.api cimport Example
cdef int arg_max(const weight_t* scores, const int n_classes) nogil:
@ -23,17 +24,52 @@ cdef int arg_max(const weight_t* scores, const int n_classes) nogil:
return best
cdef int arg_max_if_true(const weight_t* scores, const int* is_valid,
const int n_classes) nogil:
cdef int i
cdef int best = 0
cdef weight_t mode = -900000
for i in range(n_classes):
if is_valid[i] and scores[i] > mode:
mode = scores[i]
best = i
return best
cdef int arg_max_if_zero(const weight_t* scores, const int* costs,
const int n_classes) nogil:
cdef int i
cdef int best = 0
cdef weight_t mode = -900000
for i in range(n_classes):
if costs[i] == 0 and scores[i] > mode:
mode = scores[i]
best = i
return best
cdef class Model:
def __init__(self, n_classes, templates, model_loc=None):
if model_loc is not None and path.isdir(model_loc):
model_loc = path.join(model_loc, 'model')
self.n_classes = n_classes
self._extractor = Extractor(templates)
self.n_feats = self._extractor.n_templ
self._model = LinearModel(n_classes, self._extractor.n_templ)
self.model_loc = model_loc
if self.model_loc and path.exists(self.model_loc):
self._model.load(self.model_loc, freq_thresh=0)
def predict(self, Example eg):
self.set_scores(eg.c.scores, eg.c.atoms)
eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
def train(self, Example eg):
self.predict(eg)
eg.c.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes)
eg.c.cost = eg.c.costs[eg.c.guess]
self.update(eg.c.atoms, eg.c.guess, eg.c.best, eg.c.cost)
cdef const weight_t* score(self, atom_t* context) except NULL:
cdef int n_feats
feats = self._extractor.get_feats(context, &n_feats)

3
spacy/_nn.py Normal file
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@ -0,0 +1,3 @@
"""Feed-forward neural network, using Thenao."""

146
spacy/_nn.pyx Normal file
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@ -0,0 +1,146 @@
"""Feed-forward neural network, using Thenao."""
import os
import sys
import time
import numpy
import theano
import theano.tensor as T
import plac
from spacy.gold import read_json_file
from spacy.gold import GoldParse
from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
def build_model(n_classes, n_vocab, n_hidden, n_word_embed, n_tag_embed):
# allocate symbolic variables for the data
words = T.vector('words')
tags = T.vector('tags')
word_e = _init_embedding(n_words, n_word_embed)
tag_e = _init_embedding(n_tags, n_tag_embed)
label_e = _init_embedding(n_labels, n_label_embed)
maxent_W, maxent_b = _init_maxent_weights(n_hidden, n_classes)
hidden_W, hidden_b = _init_hidden_weights(28*28, n_hidden, T.tanh)
params = [hidden_W, hidden_b, maxent_W, maxent_b, word_e, tag_e, label_e]
x = T.concatenate([
T.flatten(word_e[word_indices], outdim=1),
T.flatten(tag_e[tag_indices], outdim=1)])
p_y_given_x = feed_layer(
T.nnet.softmax,
maxent_W,
maxent_b,
feed_layer(
T.tanh,
hidden_W,
hidden_b,
x))[0]
guess = T.argmax(p_y_given_x)
cost = (
-T.log(p_y_given_x[y])
+ L1(L1_reg, maxent_W, hidden_W, word_e, tag_e)
+ L2(L2_reg, maxent_W, hidden_W, wod_e, tag_e)
)
train_model = theano.function(
inputs=[words, tags, y],
outputs=guess,
updates=[update(learning_rate, param, cost) for param in params]
)
evaluate_model = theano.function(
inputs=[x, y],
outputs=T.neq(y, T.argmax(p_y_given_x[0])),
)
return train_model, evaluate_model
def _init_embedding(vocab_size, n_dim):
embedding = 0.2 * numpy.random.uniform(-1.0, 1.0, (vocab_size+1, n_dim))
return theano.shared(embedding).astype(theano.config.floatX)
def _init_maxent_weights(n_hidden, n_out):
weights = numpy.zeros((n_hidden, 10), dtype=theano.config.floatX)
bias = numpy.zeros((10,), dtype=theano.config.floatX)
return (
theano.shared(name='W', borrow=True, value=weights),
theano.shared(name='b', borrow=True, value=bias)
)
def _init_hidden_weights(n_in, n_out, activation=T.tanh):
rng = numpy.random.RandomState(1234)
weights = numpy.asarray(
rng.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)
),
dtype=theano.config.floatX
)
bias = numpy.zeros((n_out,), dtype=theano.config.floatX)
return (
theano.shared(value=weights, name='W', borrow=True),
theano.shared(value=bias, name='b', borrow=True)
)
def feed_layer(activation, weights, bias, input):
return activation(T.dot(input, weights) + bias)
def L1(L1_reg, w1, w2):
return L1_reg * (abs(w1).sum() + abs(w2).sum())
def L2(L2_reg, w1, w2):
return L2_reg * ((w1 ** 2).sum() + (w2 ** 2).sum())
def update(eta, param, cost):
return (param, param - (eta * T.grad(cost, param)))
def main(train_loc, eval_loc, model_dir):
learning_rate = 0.01
L1_reg = 0.00
L2_reg = 0.0001
print "... reading the data"
gold_train = list(read_json_file(train_loc))
print '... building the model'
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
train_model, evaluate_model = build_model(n_hidden, len(POS_TAGS), learning_rate,
L1_reg, L2_reg)
print '... training'
for epoch in range(1, n_epochs+1):
for raw_text, sents in gold_tuples:
for (ids, words, tags, ner, heads, deps), _ in sents:
tokens = nlp.tokenizer.tokens_from_list(words)
for t in tokens:
guess = train_model([t.orth], [t.tag])
loss += guess != t.tag
print loss
# compute zero-one loss on validation set
#error = numpy.mean([evaluate_model(x, y) for x, y in dev_examples])
#print('epoch %i, validation error %f %%' % (epoch, error * 100))
if __name__ == '__main__':
plac.call(main)

13
spacy/_theano.pxd Normal file
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@ -0,0 +1,13 @@
from ._ml cimport Model
from thinc.nn cimport InputLayer
cdef class TheanoModel(Model):
cdef InputLayer input_layer
cdef object train_func
cdef object predict_func
cdef object debug
cdef public float eta
cdef public float mu
cdef public float t

52
spacy/_theano.pyx Normal file
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@ -0,0 +1,52 @@
from thinc.api cimport Example, ExampleC
from thinc.typedefs cimport weight_t
from ._ml cimport arg_max_if_true
from ._ml cimport arg_max_if_zero
import numpy
from os import path
cdef class TheanoModel(Model):
def __init__(self, n_classes, input_spec, train_func, predict_func, model_loc=None,
eta=0.001, mu=0.9, debug=None):
if model_loc is not None and path.isdir(model_loc):
model_loc = path.join(model_loc, 'model')
self.eta = eta
self.mu = mu
self.t = 1
initializer = lambda: 0.2 * numpy.random.uniform(-1.0, 1.0)
self.input_layer = InputLayer(input_spec, initializer)
self.train_func = train_func
self.predict_func = predict_func
self.debug = debug
self.n_classes = n_classes
self.n_feats = len(self.input_layer)
self.model_loc = model_loc
def predict(self, Example eg):
self.input_layer.fill(eg.embeddings, eg.atoms, use_avg=True)
theano_scores = self.predict_func(eg.embeddings)[0]
cdef int i
for i in range(self.n_classes):
eg.c.scores[i] = theano_scores[i]
eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
def train(self, Example eg):
self.input_layer.fill(eg.embeddings, eg.atoms, use_avg=False)
theano_scores, update, y, loss = self.train_func(eg.embeddings, eg.costs,
self.eta, self.mu)
self.input_layer.update(update, eg.atoms, self.t, self.eta, self.mu)
for i in range(self.n_classes):
eg.c.scores[i] = theano_scores[i]
eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
eg.c.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes)
eg.c.cost = eg.c.costs[eg.c.guess]
eg.c.loss = loss
self.t += 1
def end_training(self):
pass

29
spacy/syntax/arc_eager.pyx
View File

@ -398,7 +398,8 @@ cdef class ArcEager(TransitionSystem):
n_valid += output[i]
assert n_valid >= 1
cdef int set_costs(self, int* output, StateClass stcls, GoldParse gold) except -1:
cdef int set_costs(self, bint* is_valid, int* costs,
StateClass stcls, GoldParse gold) except -1:
cdef int i, move, label
cdef label_cost_func_t[N_MOVES] label_cost_funcs
cdef move_cost_func_t[N_MOVES] move_cost_funcs
@ -423,30 +424,14 @@ cdef class ArcEager(TransitionSystem):
n_gold = 0
for i in range(self.n_moves):
if self.c[i].is_valid(stcls, self.c[i].label):
is_valid[i] = True
move = self.c[i].move
label = self.c[i].label
if move_costs[move] == -1:
move_costs[move] = move_cost_funcs[move](stcls, &gold.c)
output[i] = move_costs[move] + label_cost_funcs[move](stcls, &gold.c, label)
n_gold += output[i] == 0
costs[i] = move_costs[move] + label_cost_funcs[move](stcls, &gold.c, label)
n_gold += costs[i] == 0
else:
output[i] = 9000
is_valid[i] = False
costs[i] = 9000
assert n_gold >= 1
cdef Transition best_valid(self, const weight_t* scores, StateClass stcls) except *:
cdef bint[N_MOVES] is_valid
is_valid[SHIFT] = Shift.is_valid(stcls, -1)
is_valid[REDUCE] = Reduce.is_valid(stcls, -1)
is_valid[LEFT] = LeftArc.is_valid(stcls, -1)
is_valid[RIGHT] = RightArc.is_valid(stcls, -1)
is_valid[BREAK] = Break.is_valid(stcls, -1)
cdef Transition best
cdef weight_t score = MIN_SCORE
cdef int i
for i in range(self.n_moves):
if scores[i] > score and is_valid[self.c[i].move]:
best = self.c[i]
score = scores[i]
assert best.clas < self.n_moves
assert score > MIN_SCORE, (stcls.stack_depth(), stcls.buffer_length(), stcls.is_final(), stcls._b_i, stcls.length)
return best

17
spacy/syntax/joint.pxd Normal file
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@ -0,0 +1,17 @@
from cymem.cymem cimport Pool
from thinc.typedefs cimport weight_t
from .stateclass cimport StateClass
from .transition_system cimport TransitionSystem, Transition
from ..gold cimport GoldParseC
cdef class ArcEager(TransitionSystem):
pass
cdef int push_cost(StateClass stcls, const GoldParseC* gold, int target) nogil
cdef int arc_cost(StateClass stcls, const GoldParseC* gold, int head, int child) nogil

452
spacy/syntax/joint.pyx Normal file
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@ -0,0 +1,452 @@
# cython: profile=True
from __future__ import unicode_literals
import ctypes
import os
from ..structs cimport TokenC
from .transition_system cimport do_func_t, get_cost_func_t
from .transition_system cimport move_cost_func_t, label_cost_func_t
from ..gold cimport GoldParse
from ..gold cimport GoldParseC
from libc.stdint cimport uint32_t
from libc.string cimport memcpy
from cymem.cymem cimport Pool
from .stateclass cimport StateClass
DEF NON_MONOTONIC = True
DEF USE_BREAK = True
DEF USE_ROOT_ARC_SEGMENT = True
cdef weight_t MIN_SCORE = -90000
# Break transition from here
# http://www.aclweb.org/anthology/P13-1074
cdef enum:
SHIFT
REDUCE
LEFT
RIGHT
BREAK
N_MOVES
MOVE_NAMES = [None] * N_MOVES
MOVE_NAMES[SHIFT] = 'S'
MOVE_NAMES[REDUCE] = 'D'
MOVE_NAMES[LEFT] = 'L'
MOVE_NAMES[RIGHT] = 'R'
MOVE_NAMES[BREAK] = 'B'
# Helper functions for the arc-eager oracle
cdef int push_cost(StateClass stcls, const GoldParseC* gold, int target) nogil:
cdef int cost = 0
cdef int i, S_i
for i in range(stcls.stack_depth()):
S_i = stcls.S(i)
if gold.heads[target] == S_i:
cost += 1
if gold.heads[S_i] == target and (NON_MONOTONIC or not stcls.has_head(S_i)):
cost += 1
cost += Break.is_valid(stcls, -1) and Break.move_cost(stcls, gold) == 0
return cost
cdef int pop_cost(StateClass stcls, const GoldParseC* gold, int target) nogil:
cdef int cost = 0
cdef int i, B_i
for i in range(stcls.buffer_length()):
B_i = stcls.B(i)
cost += gold.heads[B_i] == target
cost += gold.heads[target] == B_i
if gold.heads[B_i] == B_i or gold.heads[B_i] < target:
break
cost += Break.is_valid(stcls, -1) and Break.move_cost(stcls, gold) == 0
return cost
cdef int arc_cost(StateClass stcls, const GoldParseC* gold, int head, int child) nogil:
if arc_is_gold(gold, head, child):
return 0
elif stcls.H(child) == gold.heads[child]:
return 1
# Head in buffer
elif gold.heads[child] >= stcls.B(0) and stcls.B(1) != -1:
return 1
else:
return 0
cdef bint arc_is_gold(const GoldParseC* gold, int head, int child) nogil:
if gold.labels[child] == -1:
return True
elif USE_ROOT_ARC_SEGMENT and _is_gold_root(gold, head) and _is_gold_root(gold, child):
return True
elif gold.heads[child] == head:
return True
else:
return False
cdef bint label_is_gold(const GoldParseC* gold, int head, int child, int label) nogil:
if gold.labels[child] == -1:
return True
elif label == -1:
return True
elif gold.labels[child] == label:
return True
else:
return False
cdef bint _is_gold_root(const GoldParseC* gold, int word) nogil:
return gold.labels[word] == -1 or gold.heads[word] == word
cdef class Shift:
@staticmethod
cdef bint is_valid(StateClass st, int label) nogil:
return st.buffer_length() >= 2 and not st.shifted[st.B(0)] and not st.B_(0).sent_start
@staticmethod
cdef int transition(StateClass st, int label) nogil:
st.push()
st.fast_forward()
@staticmethod
cdef int cost(StateClass st, const GoldParseC* gold, int label) nogil:
return Shift.move_cost(st, gold) + Shift.label_cost(st, gold, label)
@staticmethod
cdef inline int move_cost(StateClass s, const GoldParseC* gold) nogil:
return push_cost(s, gold, s.B(0))
@staticmethod
cdef inline int label_cost(StateClass s, const GoldParseC* gold, int label) nogil:
return 0
cdef class Reduce:
@staticmethod
cdef bint is_valid(StateClass st, int label) nogil:
return st.stack_depth() >= 2
@staticmethod
cdef int transition(StateClass st, int label) nogil:
if st.has_head(st.S(0)):
st.pop()
else:
st.unshift()
st.fast_forward()
@staticmethod
cdef int cost(StateClass s, const GoldParseC* gold, int label) nogil:
return Reduce.move_cost(s, gold) + Reduce.label_cost(s, gold, label)
@staticmethod
cdef inline int move_cost(StateClass st, const GoldParseC* gold) nogil:
return pop_cost(st, gold, st.S(0))
@staticmethod
cdef inline int label_cost(StateClass s, const GoldParseC* gold, int label) nogil:
return 0
cdef class LeftArc:
@staticmethod
cdef bint is_valid(StateClass st, int label) nogil:
return not st.B_(0).sent_start
@staticmethod
cdef int transition(StateClass st, int label) nogil:
st.add_arc(st.B(0), st.S(0), label)
st.pop()
st.fast_forward()
@staticmethod
cdef int cost(StateClass s, const GoldParseC* gold, int label) nogil:
return LeftArc.move_cost(s, gold) + LeftArc.label_cost(s, gold, label)
@staticmethod
cdef inline int move_cost(StateClass s, const GoldParseC* gold) nogil:
cdef int cost = 0
if arc_is_gold(gold, s.B(0), s.S(0)):
return 0
else:
# Account for deps we might lose between S0 and stack
if not s.has_head(s.S(0)):
for i in range(1, s.stack_depth()):
cost += gold.heads[s.S(i)] == s.S(0)
cost += gold.heads[s.S(0)] == s.S(i)
return pop_cost(s, gold, s.S(0)) + arc_cost(s, gold, s.B(0), s.S(0))
@staticmethod
cdef inline int label_cost(StateClass s, const GoldParseC* gold, int label) nogil:
return arc_is_gold(gold, s.B(0), s.S(0)) and not label_is_gold(gold, s.B(0), s.S(0), label)
cdef class RightArc:
@staticmethod
cdef bint is_valid(StateClass st, int label) nogil:
return not st.B_(0).sent_start
@staticmethod
cdef int transition(StateClass st, int label) nogil:
st.add_arc(st.S(0), st.B(0), label)
st.push()
st.fast_forward()
@staticmethod
cdef inline int cost(StateClass s, const GoldParseC* gold, int label) nogil:
return RightArc.move_cost(s, gold) + RightArc.label_cost(s, gold, label)
@staticmethod
cdef inline int move_cost(StateClass s, const GoldParseC* gold) nogil:
if arc_is_gold(gold, s.S(0), s.B(0)):
return 0
elif s.shifted[s.B(0)]:
return push_cost(s, gold, s.B(0))
else:
return push_cost(s, gold, s.B(0)) + arc_cost(s, gold, s.S(0), s.B(0))
@staticmethod
cdef int label_cost(StateClass s, const GoldParseC* gold, int label) nogil:
return arc_is_gold(gold, s.S(0), s.B(0)) and not label_is_gold(gold, s.S(0), s.B(0), label)
cdef class Break:
@staticmethod
cdef bint is_valid(StateClass st, int label) nogil:
cdef int i
if not USE_BREAK:
return False
elif st.at_break():
return False
elif st.B(0) == 0:
return False
elif st.stack_depth() < 1:
return False
elif (st.S(0) + 1) != st.B(0):
# Must break at the token boundary
return False
else:
return True
@staticmethod
cdef int transition(StateClass st, int label) nogil:
st.set_break(st.B(0))
st.fast_forward()
@staticmethod
cdef int cost(StateClass s, const GoldParseC* gold, int label) nogil:
return Break.move_cost(s, gold) + Break.label_cost(s, gold, label)
@staticmethod
cdef inline int move_cost(StateClass s, const GoldParseC* gold) nogil:
cdef int cost = 0
cdef int i, j, S_i, B_i
for i in range(s.stack_depth()):
S_i = s.S(i)
for j in range(s.buffer_length()):
B_i = s.B(j)
cost += gold.heads[S_i] == B_i
cost += gold.heads[B_i] == S_i
# Check for sentence boundary --- if it's here, we can't have any deps
# between stack and buffer, so rest of action is irrelevant.
s0_root = _get_root(s.S(0), gold)
b0_root = _get_root(s.B(0), gold)
if s0_root != b0_root or s0_root == -1 or b0_root == -1:
return cost
else:
return cost + 1
@staticmethod
cdef inline int label_cost(StateClass s, const GoldParseC* gold, int label) nogil:
return 0
cdef int _get_root(int word, const GoldParseC* gold) nogil:
while gold.heads[word] != word and gold.labels[word] != -1 and word >= 0:
word = gold.heads[word]
if gold.labels[word] == -1:
return -1
else:
return word
cdef class ArcEager(TransitionSystem):
@classmethod
def get_labels(cls, gold_parses):
move_labels = {SHIFT: {'': True}, REDUCE: {'': True}, RIGHT: {'ROOT': True},
LEFT: {'ROOT': True}, BREAK: {'ROOT': True}}
for raw_text, sents in gold_parses:
for (ids, words, tags, heads, labels, iob), ctnts in sents:
for child, head, label in zip(ids, heads, labels):
if label.upper() == 'ROOT':
label = 'ROOT'
if label != 'ROOT':
if head < child:
move_labels[RIGHT][label] = True
elif head > child:
move_labels[LEFT][label] = True
return move_labels
cdef int preprocess_gold(self, GoldParse gold) except -1:
for i in range(gold.length):
if gold.heads[i] is None: # Missing values
gold.c.heads[i] = i
gold.c.labels[i] = -1
else:
label = gold.labels[i]
if label.upper() == 'ROOT':
label = 'ROOT'
gold.c.heads[i] = gold.heads[i]
gold.c.labels[i] = self.strings[label]
for end, brackets in gold.brackets.items():
for start, label_strs in brackets.items():
gold.c.brackets[start][end] = 1
for label_str in label_strs:
# Add the encoded label to the set
gold.brackets[end][start].add(self.strings[label_str])
cdef Transition lookup_transition(self, object name) except *:
if '-' in name:
move_str, label_str = name.split('-', 1)
label = self.label_ids[label_str]
else:
label = 0
move = MOVE_NAMES.index(move_str)
for i in range(self.n_moves):
if self.c[i].move == move and self.c[i].label == label:
return self.c[i]
def move_name(self, int move, int label):
label_str = self.strings[label]
if label_str:
return MOVE_NAMES[move] + '-' + label_str
else:
return MOVE_NAMES[move]
cdef Transition init_transition(self, int clas, int move, int label) except *:
# TODO: Apparent Cython bug here when we try to use the Transition()
# constructor with the function pointers
cdef Transition t
t.score = 0
t.clas = clas
t.move = move
t.label = label
if move == SHIFT:
t.is_valid = Shift.is_valid
t.do = Shift.transition
t.get_cost = Shift.cost
elif move == REDUCE:
t.is_valid = Reduce.is_valid
t.do = Reduce.transition
t.get_cost = Reduce.cost
elif move == LEFT:
t.is_valid = LeftArc.is_valid
t.do = LeftArc.transition
t.get_cost = LeftArc.cost
elif move == RIGHT:
t.is_valid = RightArc.is_valid
t.do = RightArc.transition
t.get_cost = RightArc.cost
elif move == BREAK:
t.is_valid = Break.is_valid
t.do = Break.transition
t.get_cost = Break.cost
else:
raise Exception(move)
return t
cdef int initialize_state(self, StateClass st) except -1:
# Ensure sent_start is set to 0 throughout
for i in range(st.length):
st._sent[i].sent_start = False
st._sent[i].l_edge = i
st._sent[i].r_edge = i
st.fast_forward()
cdef int finalize_state(self, StateClass st) except -1:
cdef int root_label = self.strings['ROOT']
for i in range(st.length):
if st._sent[i].head == 0 and st._sent[i].dep == 0:
st._sent[i].dep = root_label
# If we're not using the Break transition, we segment via root-labelled
# arcs between the root words.
elif USE_ROOT_ARC_SEGMENT and st._sent[i].dep == root_label:
st._sent[i].head = 0
cdef int set_valid(self, bint* output, StateClass stcls) except -1:
cdef bint[N_MOVES] is_valid
is_valid[SHIFT] = Shift.is_valid(stcls, -1)
is_valid[REDUCE] = Reduce.is_valid(stcls, -1)
is_valid[LEFT] = LeftArc.is_valid(stcls, -1)
is_valid[RIGHT] = RightArc.is_valid(stcls, -1)
is_valid[BREAK] = Break.is_valid(stcls, -1)
cdef int i
n_valid = 0
for i in range(self.n_moves):
output[i] = is_valid[self.c[i].move]
n_valid += output[i]
assert n_valid >= 1
cdef int set_costs(self, int* output, StateClass stcls, GoldParse gold) except -1:
cdef int i, move, label
cdef label_cost_func_t[N_MOVES] label_cost_funcs
cdef move_cost_func_t[N_MOVES] move_cost_funcs
cdef int[N_MOVES] move_costs
for i in range(N_MOVES):
move_costs[i] = -1
move_cost_funcs[SHIFT] = Shift.move_cost
move_cost_funcs[REDUCE] = Reduce.move_cost
move_cost_funcs[LEFT] = LeftArc.move_cost
move_cost_funcs[RIGHT] = RightArc.move_cost
move_cost_funcs[BREAK] = Break.move_cost
label_cost_funcs[SHIFT] = Shift.label_cost
label_cost_funcs[REDUCE] = Reduce.label_cost
label_cost_funcs[LEFT] = LeftArc.label_cost
label_cost_funcs[RIGHT] = RightArc.label_cost
label_cost_funcs[BREAK] = Break.label_cost
cdef int* labels = gold.c.labels
cdef int* heads = gold.c.heads
n_gold = 0
for i in range(self.n_moves):
if self.c[i].is_valid(stcls, self.c[i].label):
move = self.c[i].move
label = self.c[i].label
if move_costs[move] == -1:
move_costs[move] = move_cost_funcs[move](stcls, &gold.c)
output[i] = move_costs[move] + label_cost_funcs[move](stcls, &gold.c, label)
n_gold += output[i] == 0
else:
output[i] = 9000
assert n_gold >= 1
cdef Transition best_valid(self, const weight_t* scores, StateClass stcls) except *:
cdef bint[N_MOVES] is_valid
is_valid[SHIFT] = Shift.is_valid(stcls, -1)
is_valid[REDUCE] = Reduce.is_valid(stcls, -1)
is_valid[LEFT] = LeftArc.is_valid(stcls, -1)
is_valid[RIGHT] = RightArc.is_valid(stcls, -1)
is_valid[BREAK] = Break.is_valid(stcls, -1)
cdef Transition best
cdef weight_t score = MIN_SCORE
cdef int i
for i in range(self.n_moves):
if scores[i] > score and is_valid[self.c[i].move]:
best = self.c[i]
score = scores[i]
assert best.clas < self.n_moves
assert score > MIN_SCORE, (stcls.stack_depth(), stcls.buffer_length(), stcls.is_final(), stcls._b_i, stcls.length)
return best

21
spacy/syntax/ner.pyx
View File

@ -128,27 +128,6 @@ cdef class BiluoPushDown(TransitionSystem):
raise Exception(move)
return t
cdef Transition best_valid(self, const weight_t* scores, StateClass stcls) except *:
cdef int best = -1
cdef weight_t score = -90000
cdef const Transition* m
cdef int i
for i in range(self.n_moves):
m = &self.c[i]
if m.is_valid(stcls, m.label) and scores[i] > score:
best = i
score = scores[i]
assert best >= 0
cdef Transition t = self.c[best]
t.score = score
return t
cdef int set_valid(self, bint* output, StateClass stcls) except -1:
cdef int i
for i in range(self.n_moves):
m = &self.c[i]
output[i] = m.is_valid(stcls, m.label)
cdef class Missing:
@staticmethod

9
spacy/syntax/parser.pxd
View File

@ -8,9 +8,6 @@ from ..tokens cimport Tokens, TokenC
cdef class Parser:
cdef readonly object cfg
cdef readonly Model model
cdef readonly TransitionSystem moves
cdef int _greedy_parse(self, Tokens tokens) except -1
cdef int _beam_parse(self, Tokens tokens) except -1
cdef public object cfg
cdef public Model model
cdef public TransitionSystem moves

195
spacy/syntax/parser.pyx
View File

@ -19,17 +19,10 @@ from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport hash64
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
from util import Config
from thinc.features cimport Extractor
from thinc.features cimport Feature
from thinc.features cimport count_feats
from thinc.api cimport Example
from thinc.learner cimport LinearModel
from thinc.search cimport Beam
from thinc.search cimport MaxViolation
from ..tokens cimport Tokens, TokenC
from ..strings cimport StringStore
@ -58,6 +51,8 @@ def get_templates(name):
return pf.ner
elif name == 'debug':
return pf.unigrams
elif name.startswith('embed'):
return (pf.words, pf.tags, pf.labels)
else:
return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
pf.tree_shape + pf.trigrams)
@ -68,39 +63,103 @@ cdef class Parser:
assert os.path.exists(model_dir) and os.path.isdir(model_dir)
self.cfg = Config.read(model_dir, 'config')
self.moves = transition_system(strings, self.cfg.labels)
templates = get_templates(self.cfg.features)
self.model = Model(self.moves.n_moves, templates, model_dir)
self.model = Model(self.moves.n_moves, self.cfg.templates, model_dir)
def __call__(self, Tokens tokens):
if self.cfg.get('beam_width', 1) < 1:
self._greedy_parse(tokens)
else:
self._beam_parse(tokens)
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
self.moves.initialize_state(stcls)
cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
self.model.n_feats, self.model.n_feats)
eg.scores[0] = 10
assert eg.c.scores[0] == 10
while not stcls.is_final():
memset(eg.c.scores, 0, eg.c.nr_class * sizeof(weight_t))
self.moves.set_valid(<bint*>eg.c.is_valid, stcls)
fill_context(eg.c.atoms, stcls)
self.model.predict(eg)
self.moves.c[eg.c.guess].do(stcls, self.moves.c[eg.c.guess].label)
self.moves.finalize_state(stcls)
tokens.set_parse(stcls._sent)
def train(self, Tokens tokens, GoldParse gold):
self.moves.preprocess_gold(gold)
if self.cfg.beam_width < 1:
return self._greedy_train(tokens, gold)
else:
return self._beam_train(tokens, gold)
cdef int _greedy_parse(self, Tokens tokens) except -1:
cdef atom_t[CONTEXT_SIZE] context
cdef int n_feats
cdef Pool mem = Pool()
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
self.moves.initialize_state(stcls)
cdef Transition guess
cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
self.model.n_feats, self.model.n_feats)
cdef weight_t loss = 0
words = [w.orth_ for w in tokens]
cdef Transition G
while not stcls.is_final():
fill_context(context, stcls)
scores = self.model.score(context)
guess = self.moves.best_valid(scores, stcls)
#print self.moves.move_name(guess.move, guess.label), stcls.print_state(words)
guess.do(stcls, guess.label)
assert stcls._s_i >= 0
self.moves.finalize_state(stcls)
tokens.set_parse(stcls._sent)
memset(eg.c.scores, 0, eg.c.nr_class * sizeof(weight_t))
self.moves.set_costs(<bint*>eg.c.is_valid, eg.c.costs, stcls, gold)
fill_context(eg.c.atoms, stcls)
self.model.train(eg)
G = self.moves.c[eg.c.guess]
#if eg.c.cost != 0:
# print self.moves.move_name(G.move, G.label), stcls.print_state(words)
self.moves.c[eg.c.guess].do(stcls, self.moves.c[eg.c.guess].label)
loss += eg.c.loss
return loss
# These are passed as callbacks to thinc.search.Beam
"""
cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
dest = <StateClass>_dest
src = <StateClass>_src
moves = <const Transition*>_moves
dest.clone(src)
moves[clas].do(dest, moves[clas].label)
cdef void* _init_state(Pool mem, int length, void* tokens) except NULL:
cdef StateClass st = StateClass.init(<const TokenC*>tokens, length)
st.fast_forward()
Py_INCREF(st)
return <void*>st
cdef int _check_final_state(void* _state, void* extra_args) except -1:
return (<StateClass>_state).is_final()
def _cleanup(Beam beam):
for i in range(beam.width):
Py_XDECREF(<PyObject*>beam._states[i].content)
Py_XDECREF(<PyObject*>beam._parents[i].content)
cdef hash_t _hash_state(void* _state, void* _) except 0:
return <hash_t>_state
#state = <const State*>_state
#cdef atom_t[10] rep
#rep[0] = state.stack[0] if state.stack_len >= 1 else 0
#rep[1] = state.stack[-1] if state.stack_len >= 2 else 0
#rep[2] = state.stack[-2] if state.stack_len >= 3 else 0
#rep[3] = state.i
#rep[4] = state.sent[state.stack[0]].l_kids if state.stack_len >= 1 else 0
#rep[5] = state.sent[state.stack[0]].r_kids if state.stack_len >= 1 else 0
#rep[6] = state.sent[state.stack[0]].dep if state.stack_len >= 1 else 0
#rep[7] = state.sent[state.stack[-1]].dep if state.stack_len >= 2 else 0
#if get_left(state, get_n0(state), 1) != NULL:
# rep[8] = get_left(state, get_n0(state), 1).dep
#else:
# rep[8] = 0
#rep[9] = state.sent[state.i].l_kids
#return hash64(rep, sizeof(atom_t) * 10, 0)
cdef int _beam_parse(self, Tokens tokens) except -1:
cdef Beam beam = Beam(self.moves.n_moves, self.cfg.beam_width)
@ -114,30 +173,6 @@ cdef class Parser:
tokens.set_parse(state._sent)
_cleanup(beam)
def _greedy_train(self, Tokens tokens, GoldParse gold):
cdef Pool mem = Pool()
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
self.moves.initialize_state(stcls)
cdef int cost
cdef const Feature* feats
cdef const weight_t* scores
cdef Transition guess
cdef Transition best
cdef atom_t[CONTEXT_SIZE] context
loss = 0
words = [w.orth_ for w in tokens]
history = []
while not stcls.is_final():
fill_context(context, stcls)
scores = self.model.score(context)
guess = self.moves.best_valid(scores, stcls)
best = self.moves.best_gold(scores, stcls, gold)
cost = guess.get_cost(stcls, &gold.c, guess.label)
self.model.update(context, guess.clas, best.clas, cost)
guess.do(stcls, guess.label)
loss += cost
return loss
def _beam_train(self, Tokens tokens, GoldParse gold_parse):
cdef Beam pred = Beam(self.moves.n_moves, self.cfg.beam_width)
@ -200,50 +235,4 @@ cdef class Parser:
count_feats(counts[clas], feats, n_feats, inc)
self.moves.c[clas].do(stcls, self.moves.c[clas].label)
# These are passed as callbacks to thinc.search.Beam
cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
dest = <StateClass>_dest
src = <StateClass>_src
moves = <const Transition*>_moves
dest.clone(src)
moves[clas].do(dest, moves[clas].label)
cdef void* _init_state(Pool mem, int length, void* tokens) except NULL:
cdef StateClass st = StateClass.init(<const TokenC*>tokens, length)
st.fast_forward()
Py_INCREF(st)
return <void*>st
cdef int _check_final_state(void* _state, void* extra_args) except -1:
return (<StateClass>_state).is_final()
def _cleanup(Beam beam):
for i in range(beam.width):
Py_XDECREF(<PyObject*>beam._states[i].content)
Py_XDECREF(<PyObject*>beam._parents[i].content)
cdef hash_t _hash_state(void* _state, void* _) except 0:
return <hash_t>_state
#state = <const State*>_state
#cdef atom_t[10] rep
#rep[0] = state.stack[0] if state.stack_len >= 1 else 0
#rep[1] = state.stack[-1] if state.stack_len >= 2 else 0
#rep[2] = state.stack[-2] if state.stack_len >= 3 else 0
#rep[3] = state.i
#rep[4] = state.sent[state.stack[0]].l_kids if state.stack_len >= 1 else 0
#rep[5] = state.sent[state.stack[0]].r_kids if state.stack_len >= 1 else 0
#rep[6] = state.sent[state.stack[0]].dep if state.stack_len >= 1 else 0
#rep[7] = state.sent[state.stack[-1]].dep if state.stack_len >= 2 else 0
#if get_left(state, get_n0(state), 1) != NULL:
# rep[8] = get_left(state, get_n0(state), 1).dep
#else:
# rep[8] = 0
#rep[9] = state.sent[state.i].l_kids
#return hash64(rep, sizeof(atom_t) * 10, 0)
"""

8
spacy/syntax/transition_system.pxd
View File

@ -46,9 +46,5 @@ cdef class TransitionSystem:
cdef int set_valid(self, bint* output, StateClass state) except -1
cdef int set_costs(self, int* output, StateClass state, GoldParse gold) except -1
cdef Transition best_valid(self, const weight_t* scores, StateClass stcls) except *
cdef Transition best_gold(self, const weight_t* scores, StateClass state,
GoldParse gold) except *
cdef int set_costs(self, bint* is_valid, int* costs,
StateClass state, GoldParse gold) except -1

35
spacy/syntax/transition_system.pyx
View File

@ -43,30 +43,17 @@ cdef class TransitionSystem:
cdef Transition init_transition(self, int clas, int move, int label) except *:
raise NotImplementedError
cdef Transition best_valid(self, const weight_t* scores, StateClass s) except *:
raise NotImplementedError
cdef int set_valid(self, bint* output, StateClass state) except -1:
raise NotImplementedError
cdef int set_costs(self, int* output, StateClass stcls, GoldParse gold) except -1:
cdef int set_valid(self, bint* is_valid, StateClass stcls) except -1:
cdef int i
for i in range(self.n_moves):
if self.c[i].is_valid(stcls, self.c[i].label):
output[i] = self.c[i].get_cost(stcls, &gold.c, self.c[i].label)
is_valid[i] = self.c[i].is_valid(stcls, self.c[i].label)
cdef int set_costs(self, bint* is_valid, int* costs,
StateClass stcls, GoldParse gold) except -1:
cdef int i
self.set_valid(is_valid, stcls)
for i in range(self.n_moves):
if is_valid[i]:
costs[i] = self.c[i].get_cost(stcls, &gold.c, self.c[i].label)
else:
output[i] = 9000
cdef Transition best_gold(self, const weight_t* scores, StateClass stcls,
GoldParse gold) except *:
cdef Transition best
cdef weight_t score = MIN_SCORE
cdef int i
for i in range(self.n_moves):
if self.c[i].is_valid(stcls, self.c[i].label):
cost = self.c[i].get_cost(stcls, &gold.c, self.c[i].label)
if scores[i] > score and cost == 0:
best = self.c[i]
score = scores[i]
assert score > MIN_SCORE
return best
costs[i] = 9000