LDA主题模型¶
LDA(Latent Dirichlet Allocation)在主题模型中占有非常重要的地位，常用来文本分类。
# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
"""
This example implements amortized Latent Dirichlet Allocation [1],
demonstrating how to marginalize out discrete assignment variables in a Pyro
model. This model and inference algorithm treat documents as vectors of
categorical variables (vectors of word ids), and collapses word-topic
assignments using Pyro's enumeration. We use PyTorch's reparametrized Gamma and
Dirichlet distributions [2], avoiding the need for Laplace approximations as in
[1]. Following [1] we use the Adam optimizer and clip gradients.
**References:**
[1] Akash Srivastava, Charles Sutton. ICLR 2017.
"Autoencoding Variational Inference for Topic Models"
https://arxiv.org/pdf/1703.01488.pdf
[2] Martin Jankowiak, Fritz Obermeyer. ICML 2018.
"Pathwise gradients beyond the reparametrization trick"
https://arxiv.org/pdf/1806.01851.pdf
"""
import argparse
import functools
import logging
import torch
from torch import nn
from torch.distributions import constraints
import pyro
import pyro.distributions as dist
from pyro.infer import SVI, JitTraceEnum_ELBO, TraceEnum_ELBO
from pyro.optim import ClippedAdam
logging.basicConfig(format='%(relativeCreated) 9d %(message)s', level=logging.INFO)
# This is a fully generative model of a batch of documents.
# data is a [num_words_per_doc, num_documents] shaped array of word ids
# (specifically it is not a histogram). We assume in this simple example
# that all documents have the same number of words.
def model(data=None, args=None, batch_size=None):
# Globals.
with pyro.plate("topics", args.num_topics):
topic_weights = pyro.sample("topic_weights", dist.Gamma(1. / args.num_topics, 1.))
topic_words = pyro.sample("topic_words",
dist.Dirichlet(torch.ones(args.num_words) / args.num_words))
# Locals.
with pyro.plate("documents", args.num_docs) as ind:
if data is not None:
with pyro.util.ignore_jit_warnings():
assert data.shape == (args.num_words_per_doc, args.num_docs)
data = data[:, ind]
doc_topics = pyro.sample("doc_topics", dist.Dirichlet(topic_weights))
with pyro.plate("words", args.num_words_per_doc):
# The word_topics variable is marginalized out during inference,
# achieved by specifying infer={"enumerate": "parallel"} and using
# TraceEnum_ELBO for inference. Thus we can ignore this variable in
# the guide.
word_topics = pyro.sample("word_topics", dist.Categorical(doc_topics),
infer={"enumerate": "parallel"})
data = pyro.sample("doc_words", dist.Categorical(topic_words[word_topics]),
obs=data)
return topic_weights, topic_words, data
# We will use amortized inference of the local topic variables, achieved by a
# multi-layer perceptron. We'll wrap the guide in an nn.Module.
def make_predictor(args):
layer_sizes = ([args.num_words] +
[int(s) for s in args.layer_sizes.split('-')] +
[args.num_topics])
logging.info('Creating MLP with sizes {}'.format(layer_sizes))
layers = []
for in_size, out_size in zip(layer_sizes, layer_sizes[1:]):
layer = nn.Linear(in_size, out_size)
layer.weight.data.normal_(0, 0.001)
layer.bias.data.normal_(0, 0.001)
layers.append(layer)
layers.append(nn.Sigmoid())
layers.append(nn.Softmax(dim=-1))
return nn.Sequential(*layers)
def parametrized_guide(predictor, data, args, batch_size=None):
# Use a conjugate guide for global variables.
topic_weights_posterior = pyro.param(
"topic_weights_posterior",
lambda: torch.ones(args.num_topics),
constraint=constraints.positive)
topic_words_posterior = pyro.param(
"topic_words_posterior",
lambda: torch.ones(args.num_topics, args.num_words),
constraint=constraints.greater_than(0.5))
with pyro.plate("topics", args.num_topics):
pyro.sample("topic_weights", dist.Gamma(topic_weights_posterior, 1.))
pyro.sample("topic_words", dist.Dirichlet(topic_words_posterior))
# Use an amortized guide for local variables.
pyro.module("predictor", predictor)
with pyro.plate("documents", args.num_docs, batch_size) as ind:
data = data[:, ind]
# The neural network will operate on histograms rather than word
# index vectors, so we'll convert the raw data to a histogram.
counts = (torch.zeros(args.num_words, ind.size(0))
.scatter_add(0, data, torch.ones(data.shape)))
doc_topics = predictor(counts.transpose(0, 1))
pyro.sample("doc_topics", dist.Delta(doc_topics, event_dim=1))
def main(args):
logging.info('Generating data')
pyro.set_rng_seed(0)
pyro.clear_param_store()
pyro.enable_validation(__debug__)
# We can generate synthetic data directly by calling the model.
true_topic_weights, true_topic_words, data = model(args=args)
# We'll train using SVI.
logging.info('-' * 40)
logging.info('Training on {} documents'.format(args.num_docs))
predictor = make_predictor(args)
guide = functools.partial(parametrized_guide, predictor)
Elbo = JitTraceEnum_ELBO if args.jit else TraceEnum_ELBO
elbo = Elbo(max_plate_nesting=2)
optim = ClippedAdam({'lr': args.learning_rate})
svi = SVI(model, guide, optim, elbo)
logging.info('Step\tLoss')
for step in range(args.num_steps):
loss = svi.step(data, args=args, batch_size=args.batch_size)
if step % 10 == 0:
logging.info('{: >5d}\t{}'.format(step, loss))
loss = elbo.loss(model, guide, data, args=args)
logging.info('final loss = {}'.format(loss))
if __name__ == '__main__':
assert pyro.__version__.startswith('1.3.0')
parser = argparse.ArgumentParser(description="Amortized Latent Dirichlet Allocation")
parser.add_argument("-t", "--num-topics", default=8, type=int)
parser.add_argument("-w", "--num-words", default=1024, type=int)
parser.add_argument("-d", "--num-docs", default=1000, type=int)
parser.add_argument("-wd", "--num-words-per-doc", default=64, type=int)
parser.add_argument("-n", "--num-steps", default=1000, type=int)
parser.add_argument("-l", "--layer-sizes", default="100-100")
parser.add_argument("-lr", "--learning-rate", default=0.01, type=float)
parser.add_argument("-b", "--batch-size", default=32, type=int)
parser.add_argument('--jit', action='store_true')
args = parser.parse_args()
main(args)