mirror of
https://github.com/explosion/spaCy.git
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Merge remote-tracking branch 'origin/develop' into feature/parser-history-model
This commit is contained in:
Matthew Honnibal
commit
09442d25ec
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@ -1,281 +0,0 @@
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"""This script expects something like a binary sentiment data set, such as
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that available here: `http://www.cs.cornell.edu/people/pabo/movie-review-data/`
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It expects a directory structure like: `data_dir/train/{pos|neg}`
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and `data_dir/test/{pos|neg}`. Put (say) 90% of the files in the former
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and the remainder in the latter.
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"""
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from __future__ import unicode_literals
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from __future__ import print_function
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from __future__ import division
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from collections import defaultdict
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from pathlib import Path
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import numpy
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import plac
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import spacy.en
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def read_data(nlp, data_dir):
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for subdir, label in (('pos', 1), ('neg', 0)):
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for filename in (data_dir / subdir).iterdir():
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text = filename.open().read()
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doc = nlp(text)
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if len(doc) >= 1:
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yield doc, label
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def partition(examples, split_size):
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examples = list(examples)
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numpy.random.shuffle(examples)
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n_docs = len(examples)
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split = int(n_docs * split_size)
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return examples[:split], examples[split:]
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def minibatch(data, bs=24):
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for i in range(0, len(data), bs):
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yield data[i:i+bs]
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class Extractor(object):
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def __init__(self, nlp, vector_length, dropout=0.3):
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self.nlp = nlp
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self.dropout = dropout
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self.vector = numpy.zeros((vector_length, ))
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def doc2bow(self, doc, dropout=None):
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if dropout is None:
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dropout = self.dropout
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bow = defaultdict(int)
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all_words = defaultdict(int)
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for word in doc:
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if numpy.random.random() >= dropout and not word.is_punct:
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bow[word.lower] += 1
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all_words[word.lower] += 1
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if sum(bow.values()) >= 1:
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return bow
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else:
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return all_words
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def bow2vec(self, bow, E):
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self.vector.fill(0)
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n = 0
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for orth_id, freq in bow.items():
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self.vector += self.nlp.vocab[self.nlp.vocab.strings[orth_id]].vector * freq
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# Apply the fine-tuning we've learned
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if orth_id < E.shape[0]:
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self.vector += E[orth_id] * freq
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n += freq
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return self.vector / n
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class NeuralNetwork(object):
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def __init__(self, depth, width, n_classes, n_vocab, extracter, optimizer):
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self.depth = depth
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self.width = width
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self.n_classes = n_classes
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self.weights = Params.random(depth, width, width, n_classes, n_vocab)
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self.doc2bow = extracter.doc2bow
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self.bow2vec = extracter.bow2vec
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self.optimizer = optimizer
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self._gradient = Params.zero(depth, width, width, n_classes, n_vocab)
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self._activity = numpy.zeros((depth, width))
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def train(self, batch):
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activity = self._activity
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gradient = self._gradient
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activity.fill(0)
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gradient.data.fill(0)
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loss = 0
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word_freqs = defaultdict(int)
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for doc, label in batch:
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word_ids = self.doc2bow(doc)
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vector = self.bow2vec(word_ids, self.weights.E)
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self.forward(activity, vector)
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loss += self.backprop(vector, gradient, activity, word_ids, label)
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for w, freq in word_ids.items():
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word_freqs[w] += freq
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self.optimizer(self.weights, gradient, len(batch), word_freqs)
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return loss
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def predict(self, doc):
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actv = self._activity
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actv.fill(0)
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W = self.weights.W
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b = self.weights.b
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E = self.weights.E
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vector = self.bow2vec(self.doc2bow(doc, dropout=0.0), E)
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self.forward(actv, vector)
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return numpy.argmax(softmax(actv[-1], W[-1], b[-1]))
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def forward(self, actv, in_):
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actv.fill(0)
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W = self.weights.W; b = self.weights.b
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actv[0] = relu(in_, W[0], b[0])
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for i in range(1, self.depth):
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actv[i] = relu(actv[i-1], W[i], b[i])
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def backprop(self, input_vector, gradient, activity, ids, label):
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W = self.weights.W
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b = self.weights.b
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target = numpy.zeros(self.n_classes)
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target[label] = 1.0
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pred = softmax(activity[-1], W[-1], b[-1])
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delta = pred - target
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for i in range(self.depth, 0, -1):
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gradient.b[i] += delta
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gradient.W[i] += numpy.outer(delta, activity[i-1])
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delta = d_relu(activity[i-1]) * W[i].T.dot(delta)
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gradient.b[0] += delta
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gradient.W[0] += numpy.outer(delta, input_vector)
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tuning = W[0].T.dot(delta).reshape((self.width,)) / len(ids)
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for w, freq in ids.items():
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if w < gradient.E.shape[0]:
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gradient.E[w] += tuning * freq
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return -sum(target * numpy.log(pred))
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def softmax(actvn, W, b):
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w = W.dot(actvn) + b
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ew = numpy.exp(w - max(w))
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return (ew / sum(ew)).ravel()
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def relu(actvn, W, b):
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x = W.dot(actvn) + b
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return x * (x > 0)
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def d_relu(x):
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return x > 0
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class Adagrad(object):
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def __init__(self, lr, rho):
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self.eps = 1e-3
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# initial learning rate
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self.learning_rate = lr
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self.rho = rho
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# stores sum of squared gradients
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#self.h = numpy.zeros(self.dim)
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#self._curr_rate = numpy.zeros(self.h.shape)
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self.h = None
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self._curr_rate = None
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def __call__(self, weights, gradient, batch_size, word_freqs):
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if self.h is None:
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self.h = numpy.zeros(gradient.data.shape)
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self._curr_rate = numpy.zeros(gradient.data.shape)
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self.L2_penalty(gradient, weights, word_freqs)
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update = self.rescale(gradient.data / batch_size)
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weights.data -= update
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def rescale(self, gradient):
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if self.h is None:
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self.h = numpy.zeros(gradient.data.shape)
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self._curr_rate = numpy.zeros(gradient.data.shape)
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self._curr_rate.fill(0)
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self.h += gradient ** 2
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self._curr_rate = self.learning_rate / (numpy.sqrt(self.h) + self.eps)
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return self._curr_rate * gradient
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def L2_penalty(self, gradient, weights, word_freqs):
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# L2 Regularization
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for i in range(len(weights.W)):
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gradient.W[i] += weights.W[i] * self.rho
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gradient.b[i] += weights.b[i] * self.rho
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for w, freq in word_freqs.items():
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if w < gradient.E.shape[0]:
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gradient.E[w] += weights.E[w] * self.rho
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class Params(object):
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@classmethod
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def zero(cls, depth, n_embed, n_hidden, n_labels, n_vocab):
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return cls(depth, n_embed, n_hidden, n_labels, n_vocab, lambda x: numpy.zeros((x,)))
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@classmethod
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def random(cls, depth, nE, nH, nL, nV):
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return cls(depth, nE, nH, nL, nV, lambda x: (numpy.random.rand(x) * 2 - 1) * 0.08)
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def __init__(self, depth, n_embed, n_hidden, n_labels, n_vocab, initializer):
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nE = n_embed; nH = n_hidden; nL = n_labels; nV = n_vocab
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n_weights = sum([
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(nE * nH) + nH,
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(nH * nH + nH) * depth,
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(nH * nL) + nL,
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(nV * nE)
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])
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self.data = initializer(n_weights)
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self.W = []
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self.b = []
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i = self._add_layer(0, nE, nH)
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for _ in range(1, depth):
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i = self._add_layer(i, nH, nH)
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i = self._add_layer(i, nL, nH)
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self.E = self.data[i : i + (nV * nE)].reshape((nV, nE))
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self.E.fill(0)
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def _add_layer(self, start, x, y):
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end = start + (x * y)
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self.W.append(self.data[start : end].reshape((x, y)))
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self.b.append(self.data[end : end + x].reshape((x, )))
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return end + x
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@plac.annotations(
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data_dir=("Data directory", "positional", None, Path),
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n_iter=("Number of iterations (epochs)", "option", "i", int),
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width=("Size of hidden layers", "option", "H", int),
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depth=("Depth", "option", "d", int),
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dropout=("Drop-out rate", "option", "r", float),
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rho=("Regularization penalty", "option", "p", float),
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eta=("Learning rate", "option", "e", float),
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batch_size=("Batch size", "option", "b", int),
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vocab_size=("Number of words to fine-tune", "option", "w", int),
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)
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def main(data_dir, depth=3, width=300, n_iter=5, vocab_size=40000,
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batch_size=24, dropout=0.3, rho=1e-5, eta=0.005):
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n_classes = 2
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print("Loading")
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nlp = spacy.en.English(parser=False)
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train_data, dev_data = partition(read_data(nlp, data_dir / 'train'), 0.8)
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print("Begin training")
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extracter = Extractor(nlp, width, dropout=0.3)
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optimizer = Adagrad(eta, rho)
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model = NeuralNetwork(depth, width, n_classes, vocab_size, extracter, optimizer)
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prev_best = 0
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best_weights = None
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for epoch in range(n_iter):
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numpy.random.shuffle(train_data)
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train_loss = 0.0
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for batch in minibatch(train_data, bs=batch_size):
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train_loss += model.train(batch)
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n_correct = sum(model.predict(x) == y for x, y in dev_data)
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print(epoch, train_loss, n_correct / len(dev_data))
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if n_correct >= prev_best:
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best_weights = model.weights.data.copy()
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prev_best = n_correct
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model.weights.data = best_weights
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print("Evaluating")
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eval_data = list(read_data(nlp, data_dir / 'test'))
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n_correct = sum(model.predict(x) == y for x, y in eval_data)
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print(n_correct / len(eval_data))
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if __name__ == '__main__':
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#import cProfile
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#import pstats
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#cProfile.runctx("main(Path('data/aclImdb'))", globals(), locals(), "Profile.prof")
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#s = pstats.Stats("Profile.prof")
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#s.strip_dirs().sort_stats("time").print_stats(100)
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plac.call(main)
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@ -140,7 +140,8 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
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with nlp.use_params(optimizer.averages):
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dill.dump(nlp, file_, -1)
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except:
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pass
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print("Error saving model")
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def _render_parses(i, to_render):
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to_render[0].user_data['title'] = "Batch %d" % i
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@ -57,7 +57,6 @@ def test_en_lemmatizer_punct(en_lemmatizer):
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def test_en_lemmatizer_lemma_assignment(EN):
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text = "Bananas in pyjamas are geese."
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doc = EN.make_doc(text)
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EN.tensorizer(doc)
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assert all(t.lemma_ == '' for t in doc)
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EN.tagger(doc)
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assert all(t.lemma_ != '' for t in doc)
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@ -52,12 +52,13 @@ def test_en_models_vectors(example):
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# this isn't a perfect test since this could in principle fail
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# in a sane model as well,
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# but that's very unlikely and a good indicator if something is wrong
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vector0 = example[0].vector
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vector1 = example[1].vector
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vector2 = example[2].vector
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assert not numpy.array_equal(vector0,vector1)
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assert not numpy.array_equal(vector0,vector2)
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assert not numpy.array_equal(vector1,vector2)
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if example.vocab.vectors_length:
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vector0 = example[0].vector
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vector1 = example[1].vector
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vector2 = example[2].vector
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assert not numpy.array_equal(vector0,vector1)
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assert not numpy.array_equal(vector0,vector2)
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assert not numpy.array_equal(vector1,vector2)
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@pytest.mark.xfail
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@ -22,7 +22,6 @@ def test_issue429(EN):
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matcher = Matcher(EN.vocab)
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matcher.add('TEST', merge_phrases, [{'ORTH': 'a'}])
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doc = EN.make_doc('a b c')
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EN.tensorizer(doc)
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EN.tagger(doc)
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matcher(doc)
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EN.entity(doc)
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@ -124,9 +124,9 @@ p Check whether the matcher contains rules for a match ID.
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+aside-code("Example").
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matcher = PhraseMatcher(nlp.vocab)
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assert len(matcher) == 0
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assert 'OBAMA' not in matcher
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matcher.add('OBAMA', None, nlp(u"Barack Obama"))
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assert len(matcher) == 1
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assert 'OBAMA' in matcher
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+table(["Name", "Type", "Description"])
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+row
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@ -6,14 +6,14 @@ p
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| end-to-end from raw text, with no "gold standard" pre-processing, over
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| text from a mix of genres where possible.
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+under-construction
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+aside("Methodology")
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| The evaluation was conducted on raw text with no gold standard
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| information. The parser, tagger and entity recognizer were trained on the
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| #[+a("https://www.gabormelli.com/RKB/OntoNotes_Corpus") OntoNotes 5]
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| corpus, the word vectors on #[+a("http://commoncrawl.org") Common Crawl].
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+h(4, "benchmarks-models-english") English
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+table(["Model", "spaCy", "Type", "UAS", "NER F", "POS", "WPS", "Size"])
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+row
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+cell #[+a("/models/en#en_core_web_sm") #[code en_core_web_sm]] 2.0.0a5
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@ -46,3 +46,25 @@ p
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+cell #[code en_core_web_md] 1.2.1
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each data in ["1.x", "linear", 90.6, 81.4, 96.7, "18.8k", "1 GB"]
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+cell.u-text-right=data
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+h(4, "benchmarks-models-spanish") Spanish
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+table(["Model", "spaCy", "Type", "UAS", "NER F", "POS", "WPS", "Size"])
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+row
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+cell #[+a("/models/es#es_core_web_sm") #[code es_core_web_sm]] 2.0.0a0
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+cell.u-text-right 2.x
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+cell.u-text-right neural
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+cell.u-text-right #[strong 90.1]
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+cell.u-text-right 89.0
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+cell.u-text-right #[strong 96.7]
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+cell.u-text-right #[em n/a]
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+cell.u-text-right #[strong 36 MB]
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+row("divider")
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+cell #[code es_core_web_md] 1.1.0
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each data in ["1.x", "linear", 87.5]
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+cell.u-text-right=data
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+cell #[strong 94.2]
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+cell #[strong 96.7]
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+cell.u-text-right #[em n/a]
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+cell.u-text-right 377 MB
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|
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