Commit 2408e31b by manetta

adding the previous algolit work on the word2vec_basic script

parent cb831700
Word2Vec Algolit extensions
===========================
This is an annoted version of the word2vec_basic.py script.
Added Algolit extension:
- input text = Frankenstein (cleaned version of the book)
- writes a dictionary.txt
- possibility to pick your own test words
- writes a log.txt of the training proces
Procedure:
1. download data (word2vec)
1. read data from plain text file (algolit)
2. create a dictionary and replace rare words with UNK token
def test(input):
"""
Test documentation line.
"""
print input
test('test input')
print test.__doc__
\ No newline at end of file
# /usr/bin/env
l = [2255, 0, 298, 23, 2632, 2671, 2579, 2838, 12, 26, 1954, 33, 24, 1, 923, 4, 1709, 0, 32, 52, 0, 2, 15, 198, 52, 4718, 3762, 17, 84, 0, 19, 419, 19, 154, 49, 0, 19, 337, 1, 952, 4, 1, 2255, 3889, 0, 3891, 15, 26, 1954, 49, 0, 32, 4326, 12, 12, 3277, 298, 49, 1, 753, 3266, 12, 1503, 2632, 2671, 2579, 2838, 12, 2260, 1869, 4006, 758, 0, 1954, 0, 12, 448, 1019, 12, 12, 0, 4, 26, 2255, 3889, 1954, 298, 12, 12, 12, 12 , 662, 23, 4870, 0, 0, 0, 0, 0, 2, 4668, 0, 0, 0, 23, 4392, 4882, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 298, 12, 49, 1, 753, 3266, 12, 12, 23, 12, 2632, 2671, 2579, 2838, 12, 12, 12, 12, 289, 3826, 12, 12, 1558, 2431, 0, 3658, 758, 12, 5, 1984, 2151, 431, 12, 17, 44, 0, 5, 481, 10, 52, 1846, 127, 949, 1, 1681, 4, 38, 911, 18, 17, 29, 1428, 15, 96, 379, 2997, 3, 285, 225, 2114, 2, 6, 78, 478, 33, 5, 1761, 6, 113, 31 2, 4, 6, 3012, 2, 4017, 1914, 8, 1, 767, 4, 6, 663, 12, 3, 65, 255, 199, 780, 4, 1126, 2, 21, 3, 1528, 8, 1, 1393, 4, 2431, 3, 164, 7, 267, 1401, 873, 1182, 66, 6, 1221, 18, 0, 6, 2518, 2, 1713, 11, 15, 275, 72, 17, 566, 26, 310, 26, 873, 18, 127, 1360, 27, 1, 1466, 89, 18, 3, 65 , 2048, 1498, 11, 7, 4783, 4, 92, 2757, 0, 2741, 23, 26, 306, 4, 410, 6, 3123, 240, 54, 3131, 2, 2767, 3, 2198, 8, 648, 5, 30, 1232, 10, 1, 2168, 33, 1, 1715, 4, 1663, 2, 1392, 19, 177, 2560, 406, 5, 6, 383, 21, 1, 2167, 4, 305, 2, 275, 105, 928, 1, 178, 33, 233, 1477, 70, 3448, 3893, 464, 0, 1, 1378, 2, 0, 7, 1825, 0, 4650, 15, 35, 393, 6, 312, 3, 44, 587, 59, 1484, 8, 1491, 0, 602, 2, 1663, 50, 1701, 2, 0, 141, 7, 444, 238, 51, 84, 30, 1975, 5, 7, 491, 3084, 8, 1826, 2, 8, 305, 77, 2167, 779, 242, 25, 1, 0, 3404, 70, 3225, 2, 1240, 84, 30, 280, 2900, 2]
# print len(l)
This diff could not be displayed because it is too large.
# Copyright 2015 The TensorFlow Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import math
import os
import random
import zipfile
import numpy as np
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
# Step 1: Download the data.
url = 'http://mattmahoney.net/dc/'
def maybe_download(filename, expected_bytes):
"""Download a file if not present, and make sure it's the right size."""
if not os.path.exists(filename):
filename, _ = urllib.request.urlretrieve(url + filename, filename)
statinfo = os.stat(filename)
if statinfo.st_size == expected_bytes:
print('Found and verified', filename)
else:
print(statinfo.st_size)
raise Exception(
'Failed to verify ' + filename + '. Can you get to it with a browser?')
return filename
filename = maybe_download('text8.zip', 31344016)
# Read the data into a list of strings.
def read_data(filename):
"""Extract the first file enclosed in a zip file as a list of words"""
with zipfile.ZipFile(filename) as f:
data = tf.compat.as_str(f.read(f.namelist()[0])).split()
return data
words = read_data(filename)
print('Data size', len(words))
# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 50000
def build_dataset(words):
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, count, dictionary, reverse_dictionary = build_dataset(words)
del words # Hint to reduce memory.
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
data_index = 0
# Step 3: Function to generate a training batch for the skip-gram model.
def generate_batch(batch_size, num_skips, skip_window):
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
batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
for i in range(8):
print(batch[i], reverse_dictionary[batch[i]],
'->', labels[i, 0], reverse_dictionary[labels[i, 0]])
# Step 4: Build and train a skip-gram model.
batch_size = 128
embedding_size = 128 # Dimension of the embedding vector.
skip_window = 1 # How many words to consider left and right.
num_skips = 2 # How many times to reuse an input to generate a label.
# 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_size = 16 # Random set of words to evaluate similarity on.
valid_window = 100 # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)
num_sampled = 64 # Number of negative examples to sample.
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(weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed,
num_sampled=num_sampled,
num_classes=vocabulary_size))
# Construct the SGD optimizer using a learning rate of 1.0.
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)
# Add variable initializer.
init = tf.global_variables_initializer()
# Step 5: Begin training.
num_steps = 100001
with tf.Session(graph=graph) as session:
# We must initialize all variables before we use them.
init.run()
print("Initialized")
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}
# We perform one update step by evaluating the optimizer op (including it
# in the list of returned values for session.run()
_, loss_val = session.run([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 step % 10000 == 0:
sim = similarity.eval()
for i in xrange(valid_size):
valid_word = 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 = reverse_dictionary[nearest[k]]
log_str = "%s %s," % (log_str, close_word)
print(log_str)
final_embeddings = normalized_embeddings.eval()
# Step 6: Visualize the embeddings.
def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
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)
try:
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
plot_only = 500
low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
labels = [reverse_dictionary[i] for i in xrange(plot_only)]
plot_with_labels(low_dim_embs, labels)
except ImportError:
print("Please install sklearn, matplotlib, and scipy to visualize embeddings.")
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