<commit_before>import collections
import math
import os
import random
import zipfile
import numpy as np
import urllib.request
import tensorflow as tf

url = 'http://mattmahoney.net/dc/'


def maybe_download(filename, expected_bytes):
    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)

def read_data(filename):
    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))

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
            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
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0


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
    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
        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]])

batch_size = 128
embedding_size = 128
skip_window = 1
num_skips = 2


valid_size = 16
valid_window = 100
valid_examples = np.random.choice(valid_window, valid_size, replace = False)
num_sampled = 64<commit_msg>Finish the code of 7-1<commit_after>import collections
import math
import os
import random
import zipfile
import numpy as np
import urllib.request
import tensorflow as tf

url = 'http://mattmahoney.net/dc/'


def maybe_download(filename, expected_bytes):
    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)

def read_data(filename):
    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))

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
            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
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0


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
    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
        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]])

batch_size = 128
embedding_size = 128
skip_window = 1
num_skips = 2


valid_size = 16
valid_window = 100
valid_examples = np.random.choice(valid_window, valid_size, replace = False)
num_sampled = 64

graph = tf.Graph()
with graph.as_default():


    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)


    with tf.device('/cpu:0'):
        embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        embed = tf.nn.embedding_lookup(embeddings, train_inputs)


        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]))
    

    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))

    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)


    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)


    init = tf.global_variables_initializer()

num_steps = 1000001


with tf.Session(graph = graph) as session:
    init.run()
    print("Initialized")


    average_loss = 0
    for step in range(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 = session.run([optimizer, loss], feed_dict = feed_dict)
        average_loss += loss_val

        if step % 2000 == 0:
            if step > 0:
                average_loss /= 2000
            print("Average loss at step ", step, ": ", average_loss)
            average_loss = 0
        
        if step % 10000 == 0:
            sim = similarity.eval()
            for i in range(valid_size):
                valid_word = reverse_dictionary[valid_examples[i]]
                top_k = 8
                nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                log_str = "Nearest to %s: " % valid_word
                for k in range(top_k):
                    close_word = reverse_dictionary[nearest[k]]
                    log_str = "%s %s, " % (log_str, close_word)
                print(log_str)
    final_embeddings = normalized_embeddings.eval()
{'fraction_non_alphanumeric': 0.11229031153714482, 'fraction_numerical': 0.0197420974552094, 'mean_word_length': 3.0857808857808857, 'pattern_counts': {'":': 1, '<': 5, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 4, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6222181', 'n_tokens_mistral': 2958, 'n_tokens_neox': 2750, 'n_words': 679}