API - Natural Language Processing¶
Natural Language Processing and Word Representation.
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Generate a training batch for the Skip-Gram model. |
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Sample an index from a probability array. |
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Sample from |
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Simple vocabulary wrapper, see create_vocab(). |
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Create Vocabulary class from a given vocabulary and its id-word, word-id convert. |
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Seperate a sentence string into a list of string words, add start_word and end_word, see |
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Creates the vocabulary of word to word_id. |
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Read context from file without any preprocessing. |
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Read list format context from a file. |
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Reads through an analogy question file, return its id format. |
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Build vocabulary. |
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Given a dictionary that maps word to integer id. |
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Build the words dictionary and replace rare words with ‘UNK’ token. |
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Save the vocabulary to a file so the model can be reloaded. |
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Convert a list of string (words) to IDs. |
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Convert a list of integer to strings (words). |
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Very basic tokenizer: split the sentence into a list of tokens. |
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Create vocabulary file (if it does not exist yet) from data file. |
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Initialize vocabulary from file, return the word_to_id (dictionary) and id_to_word (list). |
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Convert a string to list of integers representing token-ids. |
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Tokenize data file and turn into token-ids using given vocabulary file. |
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Calculate the bleu score for hypotheses and references using the MOSES ulti-bleu.perl script. |
Iteration function for training embedding matrix¶
-
tensorlayer.nlp.
generate_skip_gram_batch
(data, batch_size, num_skips, skip_window, data_index=0)[source]¶ Generate a training batch for the Skip-Gram model.
See Word2Vec example.
- Parameters
data (list of data) – To present context, usually a list of integers.
batch_size (int) – Batch size to return.
num_skips (int) – How many times to reuse an input to generate a label.
skip_window (int) – How many words to consider left and right.
data_index (int) – Index of the context location. This code use data_index to instead of yield like
tl.iterate
.
- Returns
batch (list of data) – Inputs.
labels (list of data) – Labels
data_index (int) – Index of the context location.
Examples
Setting num_skips=2, skip_window=1, use the right and left words. In the same way, num_skips=4, skip_window=2 means use the nearby 4 words.
>>> data = [1,2,3,4,5,6,7,8,9,10,11] >>> batch, labels, data_index = tl.nlp.generate_skip_gram_batch(data=data, batch_size=8, num_skips=2, skip_window=1, data_index=0) >>> print(batch) [2 2 3 3 4 4 5 5] >>> print(labels) [[3] [1] [4] [2] [5] [3] [4] [6]]
Sampling functions¶
Simple sampling¶
-
tensorlayer.nlp.
sample
(a=None, temperature=1.0)[source]¶ Sample an index from a probability array.
- Parameters
a (list of float) – List of probabilities.
temperature (float or None) –
- The higher the more uniform. When a = [0.1, 0.2, 0.7],
temperature = 0.7, the distribution will be sharpen [0.05048273, 0.13588945, 0.81362782]
temperature = 1.0, the distribution will be the same [0.1, 0.2, 0.7]
temperature = 1.5, the distribution will be filtered [0.16008435, 0.25411807, 0.58579758]
If None, it will be
np.argmax(a)
Notes
No matter what is the temperature and input list, the sum of all probabilities will be one. Even if input list = [1, 100, 200], the sum of all probabilities will still be one.
For large vocabulary size, choice a higher temperature or
tl.nlp.sample_top
to avoid error.
Vector representations of words¶
Simple vocabulary class¶
Vocabulary class¶
-
class
tensorlayer.nlp.
Vocabulary
(vocab_file, start_word='<S>', end_word='</S>', unk_word='<UNK>', pad_word='<PAD>')[source]¶ Create Vocabulary class from a given vocabulary and its id-word, word-id convert. See create_vocab() and
tutorial_tfrecord3.py
.- Parameters
vocab_file (str) – The file contains the vocabulary (can be created via
tl.nlp.create_vocab
), where the words are the first whitespace-separated token on each line (other tokens are ignored) and the word ids are the corresponding line numbers.start_word (str) – Special word denoting sentence start.
end_word (str) – Special word denoting sentence end.
unk_word (str) – Special word denoting unknown words.
-
vocab
¶ A dictionary that maps word to ID.
- Type
dictionary
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reverse_vocab
¶ A list that maps ID to word.
- Type
list of int
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start_id
¶ For start ID.
- Type
int
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end_id
¶ For end ID.
- Type
int
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unk_id
¶ For unknown ID.
- Type
int
-
pad_id
¶ For Padding ID.
- Type
int
Examples
The vocab file looks like follow, includes start_word , end_word …
>>> a 969108 >>> <S> 586368 >>> </S> 586368 >>> . 440479 >>> on 213612 >>> of 202290 >>> the 196219 >>> in 182598 >>> with 152984 >>> and 139109 >>> is 97322
Process sentence¶
-
tensorlayer.nlp.
process_sentence
(sentence, start_word='<S>', end_word='</S>')[source]¶ Seperate a sentence string into a list of string words, add start_word and end_word, see
create_vocab()
andtutorial_tfrecord3.py
.- Parameters
sentence (str) – A sentence.
start_word (str or None) – The start word. If None, no start word will be appended.
end_word (str or None) – The end word. If None, no end word will be appended.
- Returns
A list of strings that separated into words.
- Return type
list of str
Examples
>>> c = "how are you?" >>> c = tl.nlp.process_sentence(c) >>> print(c) ['<S>', 'how', 'are', 'you', '?', '</S>']
Notes
You have to install the following package.
Create vocabulary¶
-
tensorlayer.nlp.
create_vocab
(sentences, word_counts_output_file, min_word_count=1)[source]¶ Creates the vocabulary of word to word_id.
See
tutorial_tfrecord3.py
.The vocabulary is saved to disk in a text file of word counts. The id of each word in the file is its corresponding 0-based line number.
- Parameters
sentences (list of list of str) – All sentences for creating the vocabulary.
word_counts_output_file (str) – The file name.
min_word_count (int) – Minimum number of occurrences for a word.
- Returns
The simple vocabulary object, see
Vocabulary
for more.- Return type
Examples
Pre-process sentences
>>> captions = ["one two , three", "four five five"] >>> processed_capts = [] >>> for c in captions: >>> c = tl.nlp.process_sentence(c, start_word="<S>", end_word="</S>") >>> processed_capts.append(c) >>> print(processed_capts) ...[['<S>', 'one', 'two', ',', 'three', '</S>'], ['<S>', 'four', 'five', 'five', '</S>']]
Create vocabulary
>>> tl.nlp.create_vocab(processed_capts, word_counts_output_file='vocab.txt', min_word_count=1) Creating vocabulary. Total words: 8 Words in vocabulary: 8 Wrote vocabulary file: vocab.txt
Get vocabulary object
>>> vocab = tl.nlp.Vocabulary('vocab.txt', start_word="<S>", end_word="</S>", unk_word="<UNK>") INFO:tensorflow:Initializing vocabulary from file: vocab.txt [TL] Vocabulary from vocab.txt : <S> </S> <UNK> vocabulary with 10 words (includes start_word, end_word, unk_word) start_id: 2 end_id: 3 unk_id: 9 pad_id: 0
Read words from file¶
Simple read file¶
Read file¶
-
tensorlayer.nlp.
read_words
(filename='nietzsche.txt', replace=None)[source]¶ Read list format context from a file.
For customized read_words method, see
tutorial_generate_text.py
.- Parameters
filename (str) – a file path.
replace (list of str) – replace original string by target string.
- Returns
The context in a list (split using space).
- Return type
list of str
Read analogy question file¶
-
tensorlayer.nlp.
read_analogies_file
(eval_file='questions-words.txt', word2id=None)[source]¶ Reads through an analogy question file, return its id format.
- Parameters
eval_file (str) – The file name.
word2id (dictionary) – a dictionary that maps word to ID.
- Returns
A
[n_examples, 4]
numpy array containing the analogy question’s word IDs.- Return type
numpy.array
Examples
The file should be in this format
>>> : capital-common-countries >>> Athens Greece Baghdad Iraq >>> Athens Greece Bangkok Thailand >>> Athens Greece Beijing China >>> Athens Greece Berlin Germany >>> Athens Greece Bern Switzerland >>> Athens Greece Cairo Egypt >>> Athens Greece Canberra Australia >>> Athens Greece Hanoi Vietnam >>> Athens Greece Havana Cuba
Get the tokenized analogy question data
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> data, count, dictionary, reverse_dictionary = tl.nlp.build_words_dataset(words, vocabulary_size, True) >>> analogy_questions = tl.nlp.read_analogies_file(eval_file='questions-words.txt', word2id=dictionary) >>> print(analogy_questions) [[ 3068 1248 7161 1581] [ 3068 1248 28683 5642] [ 3068 1248 3878 486] ..., [ 1216 4309 19982 25506] [ 1216 4309 3194 8650] [ 1216 4309 140 312]]
Build vocabulary, word dictionary and word tokenization¶
Build dictionary from word to id¶
-
tensorlayer.nlp.
build_vocab
(data)[source]¶ Build vocabulary.
Given the context in list format. Return the vocabulary, which is a dictionary for word to id. e.g. {‘campbell’: 2587, ‘atlantic’: 2247, ‘aoun’: 6746 …. }
- Parameters
data (list of str) – The context in list format
- Returns
that maps word to unique ID. e.g. {‘campbell’: 2587, ‘atlantic’: 2247, ‘aoun’: 6746 …. }
- Return type
dictionary
References
Examples
>>> data_path = os.getcwd() + '/simple-examples/data' >>> train_path = os.path.join(data_path, "ptb.train.txt") >>> word_to_id = build_vocab(read_txt_words(train_path))
Build dictionary from id to word¶
Build dictionaries for id to word etc¶
-
tensorlayer.nlp.
build_words_dataset
(words=None, vocabulary_size=50000, printable=True, unk_key='UNK')[source]¶ Build the words dictionary and replace rare words with ‘UNK’ token. The most common word has the smallest integer id.
- Parameters
words (list of str or byte) – The context in list format. You may need to do preprocessing on the words, such as lower case, remove marks etc.
vocabulary_size (int) – The maximum vocabulary size, limiting the vocabulary size. Then the script replaces rare words with ‘UNK’ token.
printable (boolean) – Whether to print the read vocabulary size of the given words.
unk_key (str) – Represent the unknown words.
- Returns
data (list of int) – The context in a list of ID.
count (list of tuple and list) –
- Pair words and IDs.
count[0] is a list : the number of rare words
count[1:] are tuples : the number of occurrence of each word
e.g. [[‘UNK’, 418391], (b’the’, 1061396), (b’of’, 593677), (b’and’, 416629), (b’one’, 411764)]
dictionary (dictionary) – It is word_to_id that maps word to ID.
reverse_dictionary (a dictionary) – It is id_to_word that maps ID to word.
Examples
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> vocabulary_size = 50000 >>> data, count, dictionary, reverse_dictionary = tl.nlp.build_words_dataset(words, vocabulary_size)
References
Save vocabulary¶
-
tensorlayer.nlp.
save_vocab
(count=None, name='vocab.txt')[source]¶ Save the vocabulary to a file so the model can be reloaded.
- Parameters
count (a list of tuple and list) – count[0] is a list : the number of rare words, count[1:] are tuples : the number of occurrence of each word, e.g. [[‘UNK’, 418391], (b’the’, 1061396), (b’of’, 593677), (b’and’, 416629), (b’one’, 411764)]
Examples
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> vocabulary_size = 50000 >>> data, count, dictionary, reverse_dictionary = tl.nlp.build_words_dataset(words, vocabulary_size, True) >>> tl.nlp.save_vocab(count, name='vocab_text8.txt') >>> vocab_text8.txt UNK 418391 the 1061396 of 593677 and 416629 one 411764 in 372201 a 325873 to 316376
Convert words to IDs and IDs to words¶
These functions can be done by Vocabulary
class.
List of Words to IDs¶
-
tensorlayer.nlp.
words_to_word_ids
(data=None, word_to_id=None, unk_key='UNK')[source]¶ Convert a list of string (words) to IDs.
- Parameters
data (list of string or byte) – The context in list format
word_to_id (a dictionary) – that maps word to ID.
unk_key (str) – Represent the unknown words.
- Returns
A list of IDs to represent the context.
- Return type
list of int
Examples
>>> words = tl.files.load_matt_mahoney_text8_dataset() >>> vocabulary_size = 50000 >>> data, count, dictionary, reverse_dictionary = tl.nlp.build_words_dataset(words, vocabulary_size, True) >>> context = [b'hello', b'how', b'are', b'you'] >>> ids = tl.nlp.words_to_word_ids(words, dictionary) >>> context = tl.nlp.word_ids_to_words(ids, reverse_dictionary) >>> print(ids) [6434, 311, 26, 207] >>> print(context) [b'hello', b'how', b'are', b'you']
References
List of IDs to Words¶
-
tensorlayer.nlp.
word_ids_to_words
(data, id_to_word)[source]¶ Convert a list of integer to strings (words).
- Parameters
data (list of int) – The context in list format.
id_to_word (dictionary) – a dictionary that maps ID to word.
- Returns
A list of string or byte to represent the context.
- Return type
list of str
Examples
see
tl.nlp.words_to_word_ids
Functions for translation¶
Word Tokenization¶
-
tensorlayer.nlp.
basic_tokenizer
(sentence, _WORD_SPLIT=re.compile(b'([., !?"\':;)(])'))[source]¶ Very basic tokenizer: split the sentence into a list of tokens.
- Parameters
sentence (tensorflow.python.platform.gfile.GFile Object) –
_WORD_SPLIT (regular expression for word spliting.) –
Examples
>>> see create_vocabulary >>> from tensorflow.python.platform import gfile >>> train_path = "wmt/giga-fren.release2" >>> with gfile.GFile(train_path + ".en", mode="rb") as f: >>> for line in f: >>> tokens = tl.nlp.basic_tokenizer(line) >>> tl.logging.info(tokens) >>> exit() [b'Changing', b'Lives', b'|', b'Changing', b'Society', b'|', b'How', b'It', b'Works', b'|', b'Technology', b'Drives', b'Change', b'Home', b'|', b'Concepts', b'|', b'Teachers', b'|', b'Search', b'|', b'Overview', b'|', b'Credits', b'|', b'HHCC', b'Web', b'|', b'Reference', b'|', b'Feedback', b'Virtual', b'Museum', b'of', b'Canada', b'Home', b'Page']
References
Code from
/tensorflow/models/rnn/translation/data_utils.py
Create or read vocabulary¶
-
tensorlayer.nlp.
create_vocabulary
(vocabulary_path, data_path, max_vocabulary_size, tokenizer=None, normalize_digits=True, _DIGIT_RE=re.compile(b'\\d'), _START_VOCAB=None)[source]¶ Create vocabulary file (if it does not exist yet) from data file.
Data file is assumed to contain one sentence per line. Each sentence is tokenized and digits are normalized (if normalize_digits is set). Vocabulary contains the most-frequent tokens up to max_vocabulary_size. We write it to vocabulary_path in a one-token-per-line format, so that later token in the first line gets id=0, second line gets id=1, and so on.
- Parameters
vocabulary_path (str) – Path where the vocabulary will be created.
data_path (str) – Data file that will be used to create vocabulary.
max_vocabulary_size (int) – Limit on the size of the created vocabulary.
tokenizer (function) – A function to use to tokenize each data sentence. If None, basic_tokenizer will be used.
normalize_digits (boolean) – If true, all digits are replaced by 0.
_DIGIT_RE (regular expression function) – Default is
re.compile(br"\d")
._START_VOCAB (list of str) – The pad, go, eos and unk token, default is
[b"_PAD", b"_GO", b"_EOS", b"_UNK"]
.
References
Code from
/tensorflow/models/rnn/translation/data_utils.py
-
tensorlayer.nlp.
initialize_vocabulary
(vocabulary_path)[source]¶ Initialize vocabulary from file, return the word_to_id (dictionary) and id_to_word (list).
We assume the vocabulary is stored one-item-per-line, so a file will result in a vocabulary {“dog”: 0, “cat”: 1}, and this function will also return the reversed-vocabulary [“dog”, “cat”].
- Parameters
vocabulary_path (str) – Path to the file containing the vocabulary.
- Returns
vocab (dictionary) – a dictionary that maps word to ID.
rev_vocab (list of int) – a list that maps ID to word.
Examples
>>> Assume 'test' contains dog cat bird >>> vocab, rev_vocab = tl.nlp.initialize_vocabulary("test") >>> print(vocab) >>> {b'cat': 1, b'dog': 0, b'bird': 2} >>> print(rev_vocab) >>> [b'dog', b'cat', b'bird']
:raises ValueError : if the provided vocabulary_path does not exist.:
Convert words to IDs and IDs to words¶
-
tensorlayer.nlp.
sentence_to_token_ids
(sentence, vocabulary, tokenizer=None, normalize_digits=True, UNK_ID=3, _DIGIT_RE=re.compile(b'\\d'))[source]¶ Convert a string to list of integers representing token-ids.
For example, a sentence “I have a dog” may become tokenized into [“I”, “have”, “a”, “dog”] and with vocabulary {“I”: 1, “have”: 2, “a”: 4, “dog”: 7”} this function will return [1, 2, 4, 7].
- Parameters
sentence (tensorflow.python.platform.gfile.GFile Object) – The sentence in bytes format to convert to token-ids, see
basic_tokenizer()
anddata_to_token_ids()
.vocabulary (dictionary) – Mmapping tokens to integers.
tokenizer (function) – A function to use to tokenize each sentence. If None,
basic_tokenizer
will be used.normalize_digits (boolean) – If true, all digits are replaced by 0.
- Returns
The token-ids for the sentence.
- Return type
list of int
-
tensorlayer.nlp.
data_to_token_ids
(data_path, target_path, vocabulary_path, tokenizer=None, normalize_digits=True, UNK_ID=3, _DIGIT_RE=re.compile(b'\\d'))[source]¶ Tokenize data file and turn into token-ids using given vocabulary file.
This function loads data line-by-line from data_path, calls the above sentence_to_token_ids, and saves the result to target_path. See comment for sentence_to_token_ids on the details of token-ids format.
- Parameters
data_path (str) – Path to the data file in one-sentence-per-line format.
target_path (str) – Path where the file with token-ids will be created.
vocabulary_path (str) – Path to the vocabulary file.
tokenizer (function) – A function to use to tokenize each sentence. If None,
basic_tokenizer
will be used.normalize_digits (boolean) – If true, all digits are replaced by 0.
References
Code from
/tensorflow/models/rnn/translation/data_utils.py
Metrics¶
BLEU¶
-
tensorlayer.nlp.
moses_multi_bleu
(hypotheses, references, lowercase=False)[source]¶ Calculate the bleu score for hypotheses and references using the MOSES ulti-bleu.perl script.
- Parameters
hypotheses (numpy.array.string) – A numpy array of strings where each string is a single example.
references (numpy.array.string) – A numpy array of strings where each string is a single example.
lowercase (boolean) – If True, pass the “-lc” flag to the multi-bleu script
Examples
>>> hypotheses = ["a bird is flying on the sky"] >>> references = ["two birds are flying on the sky", "a bird is on the top of the tree", "an airplane is on the sky",] >>> score = tl.nlp.moses_multi_bleu(hypotheses, references)
- Returns
The BLEU score
- Return type
float
References