Source code for skipgram

# Copyright 2015 Google Inc. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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# ==============================================================================

from __future__ import absolute_import
from __future__ import print_function
import collections
import math
import os
import random
import zipfile

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin
import matplotlib.pyplot as plt
import tensorflow as tf

# Read the data into a string.
[docs]def read_data(filename): """ :param filename: A zip file to open and read from :return: A list of the space delimited tokens from the textfile. """ f = zipfile.ZipFile(filename) for name in f.namelist(): return f.close()
[docs]def build_dataset(words, vocabulary_size): """ :param words: A list of word tokens from a text file :param vocabulary_size: How many word tokens to keep. :return: data (text transformed into list of word ids 'UNK'=0), count (list of pairs (word:word_count) indexed by word id), dictionary (word:id hashmap), reverse_dictionary (id:word hashmap) """ count = [['UNK', -1]] count.extend(collections.Counter(words).most_common(vocabulary_size - 1)) dictionary = dict() for word, _ in count: dictionary[word] = len(dictionary) data = list() unk_count = 0 for word in words: if word in dictionary: index = dictionary[word] else: index = 0 # dictionary['UNK'] unk_count += 1 data.append(index) count[0][1] = unk_count reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) return data, count, dictionary, reverse_dictionary
data_index = 0 # Step 3: Function to generate a training batch for the skip-gram model.
[docs]def generate_batch(data, batch_size, num_skips, skip_window): """ :param data: list of word ids corresponding to text :param batch_size: Size of batch to retrieve :param num_skips: How many times to reuse an input to generate a label. :param skip_window: How many words to consider left and right. :return: """ global data_index assert batch_size % num_skips == 0 assert num_skips <= 2 * skip_window batch = np.ndarray(shape=(batch_size), dtype=np.int32) labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2 * skip_window + 1 # [ skip_window target skip_window ] buffer = collections.deque(maxlen=span) for _ in range(span): buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) for i in range(batch_size // num_skips): target = skip_window # target label at the center of the buffer targets_to_avoid = [skip_window] for j in range(num_skips): while target in targets_to_avoid: target = random.randint(0, span - 1) targets_to_avoid.append(target) batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[target] buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) return batch, labels
# Step 4: Build and train a skip-gram model.
[docs]class SkipGramVecs(object): """ Trains a skip gram model from `Distributed Representations of Words and Phrases and their Compositionality`_ :param textfile: Plain text file or zip file with plain text files. :param vocabulary_size: How many words to use from text :param batch_size: mini-batch size :param embedding_size: Dimension of the embedding vector. :param skip_window: How many words to consider left and right. :param num_skips: How many times to reuse an input to generate a label. :param valid_size: Random set of words to evaluate similarity on. :param valid_window: Only pick dev samples in the head of the distribution. :param num_sampled: Number of negative examples to sample. :param num_steps: How many mini-batch steps to take :param verbose: Whether to calculate and print similarities for a sample of words """ def __init__(self, textfile, vocabulary_size=12735, batch_size=128, embedding_size=128, skip_window=1, num_skips=2, valid_size=16, valid_window=100, num_sampled=64, num_steps=100000, verbose=False): if not textfile.endswith('.zip'): ziptext = textfile + '.zip' os.system('zip ' + ziptext + ' ' + textfile) textfile = ziptext words = read_data(textfile) print('Data size', len(words)), self.count, self.dictionary, self.reverse_dictionary = build_dataset(words, vocabulary_size) del words # Hint to reduce memory. batch, labels = generate_batch(, batch_size=8, num_skips=2, skip_window=1) for i in range(8): print(batch[i], '->', labels[i, 0]) print(self.reverse_dictionary[batch[i]], '->', self.reverse_dictionary[labels[i, 0]]) # We pick a random validation set to sample nearest neighbors. Here we limit the # validation samples to the words that have a low numeric ID, which by # construction are also the most frequent. valid_examples = np.array(random.sample(np.arange(valid_window), valid_size)) graph = tf.Graph() with graph.as_default(): # Input data. train_inputs = tf.placeholder(tf.int32, shape=[batch_size]) train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1]) valid_dataset = tf.constant(valid_examples, dtype=tf.int32) # Ops and variables pinned to the CPU because of missing GPU implementation with tf.device('/cpu:0'): # Look up embeddings for inputs. embeddings = tf.Variable( tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) embed = tf.nn.embedding_lookup(embeddings, train_inputs) # Construct the variables for the NCE loss nce_weights = tf.Variable( tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size))) nce_biases = tf.Variable(tf.zeros([vocabulary_size])) # Compute the average NCE loss for the batch. # tf.nce_loss automatically draws a new sample of the negative labels each # time we evaluate the loss. loss = tf.reduce_mean( tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels, num_sampled, vocabulary_size)) optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) # Compute the cosine similarity between minibatch examples and all embeddings. norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) normalized_embeddings = embeddings / norm valid_embeddings = tf.nn.embedding_lookup( normalized_embeddings, valid_dataset) similarity = tf.matmul( valid_embeddings, normalized_embeddings, transpose_b=True) with tf.Session(graph=graph) as session: # We must initialize all variables before we use them. tf.initialize_all_variables().run() average_loss = 0 for step in xrange(num_steps): batch_inputs, batch_labels = generate_batch(, batch_size, num_skips, skip_window) feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels} _, loss_val =[optimizer, loss], feed_dict=feed_dict) average_loss += loss_val if step % 2000 == 0: if step > 0: average_loss /= 2000 # The average loss is an estimate of the loss over the last 2000 batches. print("Average loss at step ", step, ": ", average_loss) average_loss = 0 # Note that this is expensive (~20% slowdown if computed every 500 steps) if verbose and step % 10000 == 0: sim = similarity.eval() for i in xrange(valid_size): valid_word = self.reverse_dictionary[valid_examples[i]] top_k = 8 # number of nearest neighbors nearest = (-sim[i, :]).argsort()[1:top_k + 1] log_str = "Nearest to %s:" % valid_word for k in xrange(top_k): close_word = self.reverse_dictionary[nearest[k]] log_str = "%s %s," % (log_str, close_word) print(log_str) self.final_embeddings = normalized_embeddings.eval()
[docs] def plot_embeddings(self, filename='tsne.png', num_terms=500): """ Plot tsne reduction of learned word embeddings in 2-space. :param filename: File to save plot to. :param num_terms: How many words to plot. """ plot_tsne(self.final_embeddings, [self.reverse_dictionary[i] for i in xrange(num_terms)], filename, num_terms)
[docs]def plot_tsne(embeddings, labels, filename='tsne.png', num_terms=500): """ Makes tsne plot to visualize word embeddings. Need sklearn, matplotlib for this to work. :param filename: Location to save labeled tsne plots :param num_terms: Num of words to plot """ try: from sklearn.manifold import TSNE tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000) low_dim_embs = tsne.fit_transform(embeddings[:num_terms, :]) assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings" plt.figure(figsize=(18, 18)) # in inches for i, label in enumerate(labels): x, y = low_dim_embs[i, :] plt.scatter(x, y) plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom') plt.savefig(filename) except ImportError: print("Please install sklearn and matplotlib to visualize embeddings.")