# -*- coding: utf-8 -*- from __future__ import absolute_import import numpy as np import random from six.moves import range def pad_sequences(sequences, maxlen=None, dtype='int32', padding='pre', truncating='pre', value=0.): """Pads each sequence to the same length (length of the longest sequence). If maxlen is provided, any sequence longer than maxlen is truncated to maxlen. Truncation happens off either the beginning (default) or the end of the sequence. Supports post-padding and pre-padding (default). # Arguments sequences: list of lists where each element is a sequence maxlen: int, maximum length dtype: type to cast the resulting sequence. padding: 'pre' or 'post', pad either before or after each sequence. truncating: 'pre' or 'post', remove values from sequences larger than maxlen either in the beginning or in the end of the sequence value: float, value to pad the sequences to the desired value. # Returns x: numpy array with dimensions (number_of_sequences, maxlen) # Raises ValueError: in case of invalid values for `truncating` or `padding`, or in case of invalid shape for a `sequences` entry. """ lengths = [len(s) for s in sequences] nb_samples = len(sequences) if maxlen is None: maxlen = np.max(lengths) # take the sample shape from the first non empty sequence # checking for consistency in the main loop below. sample_shape = tuple() for s in sequences: if len(s) > 0: sample_shape = np.asarray(s).shape[1:] break x = (np.ones((nb_samples, maxlen) + sample_shape) * value).astype(dtype) for idx, s in enumerate(sequences): if not len(s): continue # empty list/array was found if truncating == 'pre': trunc = s[-maxlen:] elif truncating == 'post': trunc = s[:maxlen] else: raise ValueError('Truncating type "%s" not understood' % truncating) # check `trunc` has expected shape trunc = np.asarray(trunc, dtype=dtype) if trunc.shape[1:] != sample_shape: raise ValueError('Shape of sample %s of sequence at position %s is different from expected shape %s' % (trunc.shape[1:], idx, sample_shape)) if padding == 'post': x[idx, :len(trunc)] = trunc elif padding == 'pre': x[idx, -len(trunc):] = trunc else: raise ValueError('Padding type "%s" not understood' % padding) return x def make_sampling_table(size, sampling_factor=1e-5): """Generates a word rank-based probabilistic sampling table. This generates an array where the ith element is the probability that a word of rank i would be sampled, according to the sampling distribution used in word2vec. The word2vec formula is: p(word) = min(1, sqrt(word.frequency/sampling_factor) / (word.frequency/sampling_factor)) We assume that the word frequencies follow Zipf's law (s=1) to derive a numerical approximation of frequency(rank): frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank)) where gamma is the Euler-Mascheroni constant. # Arguments size: int, number of possible words to sample. sampling_factor: the sampling factor in the word2vec formula. # Returns A 1D Numpy array of length `size` where the ith entry is the probability that a word of rank i should be sampled. """ gamma = 0.577 rank = np.array(list(range(size))) rank[0] = 1 inv_fq = rank * (np.log(rank) + gamma) + 0.5 - 1. / (12. * rank) f = sampling_factor * inv_fq return np.minimum(1., f / np.sqrt(f)) def skipgrams(sequence, vocabulary_size, window_size=4, negative_samples=1., shuffle=True, categorical=False, sampling_table=None): """Generates skipgram word pairs. Takes a sequence (list of indexes of words), returns couples of [word_index, other_word index] and labels (1s or 0s), where label = 1 if 'other_word' belongs to the context of 'word', and label=0 if 'other_word' is randomly sampled # Arguments sequence: a word sequence (sentence), encoded as a list of word indices (integers). If using a `sampling_table`, word indices are expected to match the rank of the words in a reference dataset (e.g. 10 would encode the 10-th most frequently occuring token). Note that index 0 is expected to be a non-word and will be skipped. vocabulary_size: int. maximum possible word index + 1 window_size: int. actually half-window. The window of a word wi will be [i-window_size, i+window_size+1] negative_samples: float >= 0. 0 for no negative (=random) samples. 1 for same number as positive samples. etc. shuffle: whether to shuffle the word couples before returning them. categorical: bool. if False, labels will be integers (eg. [0, 1, 1 .. ]), if True labels will be categorical eg. [[1,0],[0,1],[0,1] .. ] sampling_table: 1D array of size `vocabulary_size` where the entry i encodes the probabibily to sample a word of rank i. # Returns couples, labels: where `couples` are int pairs and `labels` are either 0 or 1. # Note By convention, index 0 in the vocabulary is a non-word and will be skipped. """ couples = [] labels = [] for i, wi in enumerate(sequence): if not wi: continue if sampling_table is not None: if sampling_table[wi] < random.random(): continue window_start = max(0, i - window_size) window_end = min(len(sequence), i + window_size + 1) for j in range(window_start, window_end): if j != i: wj = sequence[j] if not wj: continue couples.append([wi, wj]) if categorical: labels.append([0, 1]) else: labels.append(1) if negative_samples > 0: nb_negative_samples = int(len(labels) * negative_samples) words = [c[0] for c in couples] random.shuffle(words) couples += [[words[i % len(words)], random.randint(1, vocabulary_size - 1)] for i in range(nb_negative_samples)] if categorical: labels += [[1, 0]] * nb_negative_samples else: labels += [0] * nb_negative_samples if shuffle: seed = random.randint(0, 10e6) random.seed(seed) random.shuffle(couples) random.seed(seed) random.shuffle(labels) return couples, labels