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Este tutorial ilustra cómo generar incrustaciones desde un módulo TensorFlow Hub (TF-Hub) dados los datos de entrada y crear un índice aproximado de vecinos más cercanos (ANN) utilizando las incrustaciones extraídas. Luego, el índice se puede utilizar para la comparación y recuperación de similitudes en tiempo real.
Cuando se trata de un gran corpus de datos, no es eficiente realizar una coincidencia exacta escaneando todo el repositorio para encontrar los elementos más similares a una consulta determinada en tiempo real. Por lo tanto, utilizamos un algoritmo de coincidencia de similitud aproximada que nos permite compensar un poco de precisión en la búsqueda de coincidencias exactas del vecino más cercano para obtener un aumento significativo en la velocidad.
En este tutorial, mostramos un ejemplo de búsqueda de texto en tiempo real sobre un corpus de titulares de noticias para encontrar los titulares que más se parecen a una consulta. A diferencia de la búsqueda de palabras clave, esto captura la similitud semántica codificada en el texto incrustado.
Los pasos de este tutorial son:
- Descargue datos de muestra.
- Genere incrustaciones para los datos usando un módulo TF-Hub
- Cree un índice ANN para las incrustaciones.
- Utilice el índice para comparar similitudes
Usamos Apache Beam para generar las incrustaciones desde el módulo TF-Hub. También utilizamos la biblioteca ANNOY de Spotify para crear el índice aproximado de vecinos más cercanos.
Más modelos
Para modelos que tienen la misma arquitectura pero fueron entrenados en un lenguaje diferente, consulte esta colección. Aquí puede encontrar todas las incrustaciones de texto que están alojadas actualmente en tfhub.dev .
Configuración
Instale las bibliotecas necesarias.
pip install -q apache_beampip install -q 'scikit_learn~=0.23.0' # For gaussian_random_matrix.pip install -q annoy
Importar las bibliotecas requeridas
import os
import sys
import pickle
from collections import namedtuple
from datetime import datetime
import numpy as np
import apache_beam as beam
from apache_beam.transforms import util
import tensorflow as tf
import tensorflow_hub as hub
import annoy
from sklearn.random_projection import gaussian_random_matrix
print('TF version: {}'.format(tf.__version__))
print('TF-Hub version: {}'.format(hub.__version__))
print('Apache Beam version: {}'.format(beam.__version__))
TF version: 2.4.0 TF-Hub version: 0.11.0 Apache Beam version: 2.26.0
1. Descargar datos de muestra
El conjunto de datos A Million News Headlines contiene titulares de noticias publicados durante un período de 15 años procedentes de la prestigiosa Australian Broadcasting Corp. (ABC). Este conjunto de datos de noticias tiene un registro histórico resumido de eventos notables en el mundo desde principios de 2003 hasta finales de 2017 con un enfoque más granular en Australia.
Formato : datos de dos columnas separados por tabulaciones: 1) fecha de publicación y 2) texto del título. Sólo nos interesa el texto del título.
wget 'https://dataverse.harvard.edu/api/access/datafile/3450625?format=tab&gbrecs=true' -O raw.tsvwc -l raw.tsvhead raw.tsv
--2021-01-07 12:50:08-- https://dataverse.harvard.edu/api/access/datafile/3450625?format=tab&gbrecs=true Resolving dataverse.harvard.edu (dataverse.harvard.edu)... 206.191.184.198 Connecting to dataverse.harvard.edu (dataverse.harvard.edu)|206.191.184.198|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 57600231 (55M) [text/tab-separated-values] Saving to: ‘raw.tsv’ raw.tsv 100%[===================>] 54.93M 14.7MB/s in 4.4s 2021-01-07 12:50:14 (12.4 MB/s) - ‘raw.tsv’ saved [57600231/57600231] 1103664 raw.tsv publish_date headline_text 20030219 "aba decides against community broadcasting licence" 20030219 "act fire witnesses must be aware of defamation" 20030219 "a g calls for infrastructure protection summit" 20030219 "air nz staff in aust strike for pay rise" 20030219 "air nz strike to affect australian travellers" 20030219 "ambitious olsson wins triple jump" 20030219 "antic delighted with record breaking barca" 20030219 "aussie qualifier stosur wastes four memphis match" 20030219 "aust addresses un security council over iraq"
Para simplificar, solo mantenemos el texto del título y eliminamos la fecha de publicación.
!rm -r corpus
!mkdir corpus
with open('corpus/text.txt', 'w') as out_file:
with open('raw.tsv', 'r') as in_file:
for line in in_file:
headline = line.split('\t')[1].strip().strip('"')
out_file.write(headline+"\n")
rm: cannot remove 'corpus': No such file or directory
tail corpus/text.txt
severe storms forecast for nye in south east queensland snake catcher pleads for people not to kill reptiles south australia prepares for party to welcome new year strikers cool off the heat with big win in adelaide stunning images from the sydney to hobart yacht the ashes smiths warners near miss liven up boxing day test timelapse: brisbanes new year fireworks what 2017 meant to the kids of australia what the papodopoulos meeting may mean for ausus who is george papadopoulos the former trump campaign aide
2. Genere incrustaciones para los datos.
En este tutorial, utilizamos el modelo de lenguaje de red neuronal (NNLM) para generar incrustaciones para los datos de los titulares. Las incrustaciones de oraciones se pueden usar fácilmente para calcular la similitud de significado a nivel de oración. Ejecutamos el proceso de generación de incrustación utilizando Apache Beam.
Método de extracción por incrustación
embed_fn = None
def generate_embeddings(text, module_url, random_projection_matrix=None):
# Beam will run this function in different processes that need to
# import hub and load embed_fn (if not previously loaded)
global embed_fn
if embed_fn is None:
embed_fn = hub.load(module_url)
embedding = embed_fn(text).numpy()
if random_projection_matrix is not None:
embedding = embedding.dot(random_projection_matrix)
return text, embedding
Convertir a tf.Método de ejemplo
def to_tf_example(entries):
examples = []
text_list, embedding_list = entries
for i in range(len(text_list)):
text = text_list[i]
embedding = embedding_list[i]
features = {
'text': tf.train.Feature(
bytes_list=tf.train.BytesList(value=[text.encode('utf-8')])),
'embedding': tf.train.Feature(
float_list=tf.train.FloatList(value=embedding.tolist()))
}
example = tf.train.Example(
features=tf.train.Features(
feature=features)).SerializeToString(deterministic=True)
examples.append(example)
return examples
Tubería de haz
def run_hub2emb(args):
'''Runs the embedding generation pipeline'''
options = beam.options.pipeline_options.PipelineOptions(**args)
args = namedtuple("options", args.keys())(*args.values())
with beam.Pipeline(args.runner, options=options) as pipeline:
(
pipeline
| 'Read sentences from files' >> beam.io.ReadFromText(
file_pattern=args.data_dir)
| 'Batch elements' >> util.BatchElements(
min_batch_size=args.batch_size, max_batch_size=args.batch_size)
| 'Generate embeddings' >> beam.Map(
generate_embeddings, args.module_url, args.random_projection_matrix)
| 'Encode to tf example' >> beam.FlatMap(to_tf_example)
| 'Write to TFRecords files' >> beam.io.WriteToTFRecord(
file_path_prefix='{}/emb'.format(args.output_dir),
file_name_suffix='.tfrecords')
)
Generación de matriz de peso de proyección aleatoria
La proyección aleatoria es una técnica simple pero poderosa que se utiliza para reducir la dimensionalidad de un conjunto de puntos que se encuentran en el espacio euclidiano. Para obtener información teórica, consulte el lema de Johnson-Lindenstrauss .
Reducir la dimensionalidad de las incrustaciones con proyección aleatoria significa menos tiempo necesario para construir y consultar el índice ANN.
En este tutorial utilizamos la proyección aleatoria gaussiana de la biblioteca Scikit-learn .
def generate_random_projection_weights(original_dim, projected_dim):
random_projection_matrix = None
random_projection_matrix = gaussian_random_matrix(
n_components=projected_dim, n_features=original_dim).T
print("A Gaussian random weight matrix was creates with shape of {}".format(random_projection_matrix.shape))
print('Storing random projection matrix to disk...')
with open('random_projection_matrix', 'wb') as handle:
pickle.dump(random_projection_matrix,
handle, protocol=pickle.HIGHEST_PROTOCOL)
return random_projection_matrix
Establecer parámetros
Si desea crear un índice utilizando el espacio de incrustación original sin proyección aleatoria, establezca el parámetro projected_dim en None . Tenga en cuenta que esto ralentizará el paso de indexación para incrustaciones de alta dimensión.
module_url = 'https://tfhub.dev/google/nnlm-en-dim128/2'
projected_dim = 64
Ejecutar canalización
import tempfile
output_dir = tempfile.mkdtemp()
original_dim = hub.load(module_url)(['']).shape[1]
random_projection_matrix = None
if projected_dim:
random_projection_matrix = generate_random_projection_weights(
original_dim, projected_dim)
args = {
'job_name': 'hub2emb-{}'.format(datetime.utcnow().strftime('%y%m%d-%H%M%S')),
'runner': 'DirectRunner',
'batch_size': 1024,
'data_dir': 'corpus/*.txt',
'output_dir': output_dir,
'module_url': module_url,
'random_projection_matrix': random_projection_matrix,
}
print("Pipeline args are set.")
args
A Gaussian random weight matrix was creates with shape of (128, 64)
Storing random projection matrix to disk...
Pipeline args are set.
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function gaussian_random_matrix is deprecated; gaussian_random_matrix is deprecated in 0.22 and will be removed in version 0.24.
warnings.warn(msg, category=FutureWarning)
{'job_name': 'hub2emb-210107-125029',
'runner': 'DirectRunner',
'batch_size': 1024,
'data_dir': 'corpus/*.txt',
'output_dir': '/tmp/tmp0g361gzp',
'module_url': 'https://tfhub.dev/google/nnlm-en-dim128/2',
'random_projection_matrix': array([[-0.1349755 , -0.12082699, 0.07092581, ..., -0.02680793,
-0.0459312 , -0.20462361],
[-0.06197901, 0.01832142, 0.21362496, ..., 0.06641898,
0.14553738, -0.117217 ],
[ 0.03452009, 0.14239163, 0.01371371, ..., 0.10422342,
0.02966668, -0.07094185],
...,
[ 0.03384223, 0.05102025, 0.01941788, ..., -0.07500625,
0.09584965, -0.08593636],
[ 0.11010087, -0.10597793, 0.06668758, ..., -0.0518654 ,
-0.14681441, 0.08449293],
[ 0.26909502, -0.0291555 , 0.04305639, ..., -0.02295843,
0.1164921 , -0.04828371]])}
print("Running pipeline...")
%time run_hub2emb(args)
print("Pipeline is done.")
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features. Running pipeline... Warning:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7efcac3599d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Warning:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7efcac3599d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Warning:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7efcac475598> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Warning:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7efcac475598> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be. CPU times: user 9min 4s, sys: 10min 14s, total: 19min 19s Wall time: 2min 30s Pipeline is done.
ls {output_dir}
emb-00000-of-00001.tfrecords
Lea algunas de las incrustaciones generadas...
embed_file = os.path.join(output_dir, 'emb-00000-of-00001.tfrecords')
sample = 5
# Create a description of the features.
feature_description = {
'text': tf.io.FixedLenFeature([], tf.string),
'embedding': tf.io.FixedLenFeature([projected_dim], tf.float32)
}
def _parse_example(example):
# Parse the input `tf.Example` proto using the dictionary above.
return tf.io.parse_single_example(example, feature_description)
dataset = tf.data.TFRecordDataset(embed_file)
for record in dataset.take(sample).map(_parse_example):
print("{}: {}".format(record['text'].numpy().decode('utf-8'), record['embedding'].numpy()[:10]))
headline_text: [ 0.07743962 -0.10065071 -0.03604915 0.03902601 0.02538098 -0.01991337 -0.11972483 0.03102058 0.16498186 -0.04299153] aba decides against community broadcasting licence: [ 0.02420221 -0.07736929 0.05655728 -0.18739551 0.11344934 0.12652674 -0.18189304 0.00422473 0.13149698 0.01910412] act fire witnesses must be aware of defamation: [-0.17413895 -0.05418579 0.07769868 0.05096476 0.08622053 0.33112594 0.04067763 0.00448784 0.15882017 0.33829722] a g calls for infrastructure protection summit: [ 0.16939437 -0.18585566 -0.14201084 -0.21779229 -0.1374832 0.14933842 -0.19583155 0.12921487 0.09811856 0.099967 ] air nz staff in aust strike for pay rise: [ 0.0230642 -0.03269081 0.18271443 0.23761444 -0.01575144 0.06109515 -0.01963143 -0.05211507 0.06050447 -0.20023327]
3. Cree el índice ANN para las incrustaciones
ANNOY (Vecinos más cercanos aproximados, oh sí) es una biblioteca de C++ con enlaces de Python para buscar puntos en el espacio que estén cerca de un punto de consulta determinado. También crea grandes estructuras de datos basadas en archivos de solo lectura que se asignan a la memoria. Spotify lo crea y lo utiliza para recomendaciones de música. Si estás interesado puedes jugar junto con otras alternativas a ANNOY como NGT , FAISS , etc.
def build_index(embedding_files_pattern, index_filename, vector_length,
metric='angular', num_trees=100):
'''Builds an ANNOY index'''
annoy_index = annoy.AnnoyIndex(vector_length, metric=metric)
# Mapping between the item and its identifier in the index
mapping = {}
embed_files = tf.io.gfile.glob(embedding_files_pattern)
num_files = len(embed_files)
print('Found {} embedding file(s).'.format(num_files))
item_counter = 0
for i, embed_file in enumerate(embed_files):
print('Loading embeddings in file {} of {}...'.format(i+1, num_files))
dataset = tf.data.TFRecordDataset(embed_file)
for record in dataset.map(_parse_example):
text = record['text'].numpy().decode("utf-8")
embedding = record['embedding'].numpy()
mapping[item_counter] = text
annoy_index.add_item(item_counter, embedding)
item_counter += 1
if item_counter % 100000 == 0:
print('{} items loaded to the index'.format(item_counter))
print('A total of {} items added to the index'.format(item_counter))
print('Building the index with {} trees...'.format(num_trees))
annoy_index.build(n_trees=num_trees)
print('Index is successfully built.')
print('Saving index to disk...')
annoy_index.save(index_filename)
print('Index is saved to disk.')
print("Index file size: {} GB".format(
round(os.path.getsize(index_filename) / float(1024 ** 3), 2)))
annoy_index.unload()
print('Saving mapping to disk...')
with open(index_filename + '.mapping', 'wb') as handle:
pickle.dump(mapping, handle, protocol=pickle.HIGHEST_PROTOCOL)
print('Mapping is saved to disk.')
print("Mapping file size: {} MB".format(
round(os.path.getsize(index_filename + '.mapping') / float(1024 ** 2), 2)))
embedding_files = "{}/emb-*.tfrecords".format(output_dir)
embedding_dimension = projected_dim
index_filename = "index"
!rm {index_filename}
!rm {index_filename}.mapping
%time build_index(embedding_files, index_filename, embedding_dimension)
rm: cannot remove 'index': No such file or directory rm: cannot remove 'index.mapping': No such file or directory Found 1 embedding file(s). Loading embeddings in file 1 of 1... 100000 items loaded to the index 200000 items loaded to the index 300000 items loaded to the index 400000 items loaded to the index 500000 items loaded to the index 600000 items loaded to the index 700000 items loaded to the index 800000 items loaded to the index 900000 items loaded to the index 1000000 items loaded to the index 1100000 items loaded to the index A total of 1103664 items added to the index Building the index with 100 trees... Index is successfully built. Saving index to disk... Index is saved to disk. Index file size: 1.61 GB Saving mapping to disk... Mapping is saved to disk. Mapping file size: 50.61 MB CPU times: user 9min 54s, sys: 53.9 s, total: 10min 48s Wall time: 5min 5s
ls
corpus random_projection_matrix index raw.tsv index.mapping tf2_semantic_approximate_nearest_neighbors.ipynb
4. Utilice el índice para la coincidencia de similitudes
Ahora podemos usar el índice ANN para encontrar titulares de noticias que estén semánticamente cerca de una consulta de entrada.
Cargue el índice y los archivos de mapeo.
index = annoy.AnnoyIndex(embedding_dimension)
index.load(index_filename, prefault=True)
print('Annoy index is loaded.')
with open(index_filename + '.mapping', 'rb') as handle:
mapping = pickle.load(handle)
print('Mapping file is loaded.')
Annoy index is loaded. /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: The default argument for metric will be removed in future version of Annoy. Please pass metric='angular' explicitly. """Entry point for launching an IPython kernel. Mapping file is loaded.
Método de coincidencia de similitud
def find_similar_items(embedding, num_matches=5):
'''Finds similar items to a given embedding in the ANN index'''
ids = index.get_nns_by_vector(
embedding, num_matches, search_k=-1, include_distances=False)
items = [mapping[i] for i in ids]
return items
Extraer incrustación de una consulta determinada
# Load the TF-Hub module
print("Loading the TF-Hub module...")
%time embed_fn = hub.load(module_url)
print("TF-Hub module is loaded.")
random_projection_matrix = None
if os.path.exists('random_projection_matrix'):
print("Loading random projection matrix...")
with open('random_projection_matrix', 'rb') as handle:
random_projection_matrix = pickle.load(handle)
print('random projection matrix is loaded.')
def extract_embeddings(query):
'''Generates the embedding for the query'''
query_embedding = embed_fn([query])[0].numpy()
if random_projection_matrix is not None:
query_embedding = query_embedding.dot(random_projection_matrix)
return query_embedding
Loading the TF-Hub module... CPU times: user 757 ms, sys: 619 ms, total: 1.38 s Wall time: 1.37 s TF-Hub module is loaded. Loading random projection matrix... random projection matrix is loaded.
extract_embeddings("Hello Machine Learning!")[:10]
array([ 0.12164804, 0.0162079 , -0.15466002, -0.14580576, 0.03926325,
-0.10124508, -0.1333948 , 0.0515029 , -0.14688903, -0.09971556])
Ingrese una consulta para encontrar los artículos más similares
query = "confronting global challenges"
print("Generating embedding for the query...")
%time query_embedding = extract_embeddings(query)
print("")
print("Finding relevant items in the index...")
%time items = find_similar_items(query_embedding, 10)
print("")
print("Results:")
print("=========")
for item in items:
print(item)
Generating embedding for the query... CPU times: user 5.18 ms, sys: 596 µs, total: 5.77 ms Wall time: 2.19 ms Finding relevant items in the index... CPU times: user 555 µs, sys: 327 µs, total: 882 µs Wall time: 601 µs Results: ========= confronting global challenges emerging nations to help struggling global economy g7 warns of increasing global economic crisis world struggling to cope with global terrorism companies health to struggle amid global crisis external risks biggest threat to economy asian giants unite to tackle global crisis g7 ministers warn of slowing global growth experts to discuss global warming threat scientists warn of growing natural disasters
¿Querer aprender más?
Puede obtener más información sobre TensorFlow en tensorflow.org y consultar la documentación de la API de TF-Hub en tensorflow.org/hub . Encuentre los módulos de TensorFlow Hub disponibles en tfhub.dev , incluidos más módulos de incrustación de texto y módulos de vectores de características de imágenes.
Consulte también el curso intensivo de aprendizaje automático , que es la introducción práctica y rápida al aprendizaje automático de Google.