339 lines
16 KiB
Python
339 lines
16 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Author: Jan Zikes, Radim Rehurek
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# Copyright (C) 2014 Radim Rehurek <me@radimrehurek.com>
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# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
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"""Online Latent Dirichlet Allocation (LDA) in Python, using all CPU cores to parallelize and speed up model training.
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The parallelization uses multiprocessing; in case this doesn't work for you for some reason,
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try the :class:`gensim.models.ldamodel.LdaModel` class which is an equivalent, but more straightforward and single-core
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implementation.
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The training algorithm:
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* is **streamed**: training documents may come in sequentially, no random access required,
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* runs in **constant memory** w.r.t. the number of documents: size of the
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training corpus does not affect memory footprint, can process corpora larger than RAM
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Wall-clock `performance on the English Wikipedia <http://radimrehurek.com/gensim/wiki.html>`_ (2G corpus positions,
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3.5M documents, 100K features, 0.54G non-zero entries in the final bag-of-words matrix), requesting 100 topics:
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====================================================== ==============
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algorithm training time
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====================================================== ==============
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LdaMulticore(workers=1) 2h30m
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LdaMulticore(workers=2) 1h24m
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LdaMulticore(workers=3) 1h6m
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old LdaModel() 3h44m
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simply iterating over input corpus = I/O overhead 20m
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====================================================== ==============
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(Measured on `this i7 server <http://www.hetzner.de/en/hosting/produkte_rootserver/ex40ssd>`_
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with 4 physical cores, so that optimal `workers=3`, one less than the number of cores.)
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This module allows both LDA model estimation from a training corpus and inference of topic distribution on new,
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unseen documents. The model can also be updated with new documents for online training.
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The core estimation code is based on the `onlineldavb.py script
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<https://github.com/blei-lab/onlineldavb/blob/master/onlineldavb.py>`_, by `Hoffman, Blei, Bach:
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Online Learning for Latent Dirichlet Allocation, NIPS 2010 <http://www.cs.princeton.edu/~mdhoffma>`_.
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Usage examples
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--------------
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The constructor estimates Latent Dirichlet Allocation model parameters based on a training corpus
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>>> from gensim.test.utils import common_corpus, common_dictionary
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>>>
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>>> lda = LdaMulticore(common_corpus, id2word=common_dictionary, num_topics=10)
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Save a model to disk, or reload a pre-trained model
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>>> from gensim.test.utils import datapath
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>>>
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>>> # Save model to disk.
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>>> temp_file = datapath("model")
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>>> lda.save(temp_file)
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>>>
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>>> # Load a potentially pretrained model from disk.
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>>> lda = LdaModel.load(temp_file)
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Query, or update the model using new, unseen documents
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>>> other_texts = [
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... ['computer', 'time', 'graph'],
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... ['survey', 'response', 'eps'],
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... ['human', 'system', 'computer']
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... ]
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>>> other_corpus = [common_dictionary.doc2bow(text) for text in other_texts]
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>>>
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>>> unseen_doc = other_corpus[0]
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>>> vector = lda[unseen_doc] # get topic probability distribution for a document
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>>>
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>>> # Update the model by incrementally training on the new corpus.
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>>> lda.update(other_corpus) # update the LDA model with additional documents
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"""
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import logging
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import numpy as np
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from gensim import utils
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from gensim.models.ldamodel import LdaModel, LdaState
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import six
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from six.moves import queue, xrange
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from multiprocessing import Pool, Queue, cpu_count
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logger = logging.getLogger(__name__)
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class LdaMulticore(LdaModel):
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"""An optimized implementation of the LDA algorithm, able to harness the power of multicore CPUs.
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Follows the similar API as the parent class :class:`~gensim.models.ldamodel.LdaModel`.
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"""
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def __init__(self, corpus=None, num_topics=100, id2word=None, workers=None,
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chunksize=2000, passes=1, batch=False, alpha='symmetric',
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eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50,
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gamma_threshold=0.001, random_state=None, minimum_probability=0.01,
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minimum_phi_value=0.01, per_word_topics=False, dtype=np.float32):
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"""
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Parameters
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----------
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corpus : {iterable of list of (int, float), scipy.sparse.csc}, optional
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Stream of document vectors or sparse matrix of shape (`num_terms`, `num_documents`).
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If not given, the model is left untrained (presumably because you want to call
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:meth:`~gensim.models.ldamodel.LdaModel.update` manually).
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num_topics : int, optional
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The number of requested latent topics to be extracted from the training corpus.
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id2word : {dict of (int, str), :class:`gensim.corpora.dictionary.Dictionary`}
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Mapping from word IDs to words. It is used to determine the vocabulary size, as well as for
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debugging and topic printing.
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workers : int, optional
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Number of workers processes to be used for parallelization. If None all available cores
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(as estimated by `workers=cpu_count()-1` will be used. **Note** however that for
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hyper-threaded CPUs, this estimation returns a too high number -- set `workers`
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directly to the number of your **real** cores (not hyperthreads) minus one, for optimal performance.
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chunksize : int, optional
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Number of documents to be used in each training chunk.
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passes : int, optional
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Number of passes through the corpus during training.
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alpha : {np.ndarray, str}, optional
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Can be set to an 1D array of length equal to the number of expected topics that expresses
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our a-priori belief for the each topics' probability.
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Alternatively default prior selecting strategies can be employed by supplying a string:
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* 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
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* 'default': Learns an assymetric prior from the corpus.
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eta : {float, np.array, str}, optional
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A-priori belief on word probability, this can be:
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* scalar for a symmetric prior over topic/word probability,
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* vector of length num_words to denote an asymmetric user defined probability for each word,
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* matrix of shape (num_topics, num_words) to assign a probability for each word-topic combination,
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* the string 'auto' to learn the asymmetric prior from the data.
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decay : float, optional
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A number between (0.5, 1] to weight what percentage of the previous lambda value is forgotten
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when each new document is examined. Corresponds to Kappa from
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`Matthew D. Hoffman, David M. Blei, Francis Bach:
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"Online Learning for Latent Dirichlet Allocation NIPS'10" <https://www.di.ens.fr/~fbach/mdhnips2010.pdf>`_.
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offset : float, optional
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Hyper-parameter that controls how much we will slow down the first steps the first few iterations.
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Corresponds to Tau_0 from `Matthew D. Hoffman, David M. Blei, Francis Bach:
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"Online Learning for Latent Dirichlet Allocation NIPS'10" <https://www.di.ens.fr/~fbach/mdhnips2010.pdf>`_.
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eval_every : int, optional
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Log perplexity is estimated every that many updates. Setting this to one slows down training by ~2x.
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iterations : int, optional
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Maximum number of iterations through the corpus when inferring the topic distribution of a corpus.
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gamma_threshold : float, optional
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Minimum change in the value of the gamma parameters to continue iterating.
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minimum_probability : float, optional
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Topics with a probability lower than this threshold will be filtered out.
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random_state : {np.random.RandomState, int}, optional
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Either a randomState object or a seed to generate one. Useful for reproducibility.
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minimum_phi_value : float, optional
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if `per_word_topics` is True, this represents a lower bound on the term probabilities.
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per_word_topics : bool
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If True, the model also computes a list of topics, sorted in descending order of most likely topics for
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each word, along with their phi values multiplied by the feature length (i.e. word count).
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dtype : {numpy.float16, numpy.float32, numpy.float64}, optional
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Data-type to use during calculations inside model. All inputs are also converted.
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"""
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self.workers = max(1, cpu_count() - 1) if workers is None else workers
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self.batch = batch
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if isinstance(alpha, six.string_types) and alpha == 'auto':
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raise NotImplementedError("auto-tuning alpha not implemented in multicore LDA; use plain LdaModel.")
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super(LdaMulticore, self).__init__(
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corpus=corpus, num_topics=num_topics,
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id2word=id2word, chunksize=chunksize, passes=passes, alpha=alpha, eta=eta,
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decay=decay, offset=offset, eval_every=eval_every, iterations=iterations,
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gamma_threshold=gamma_threshold, random_state=random_state, minimum_probability=minimum_probability,
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minimum_phi_value=minimum_phi_value, per_word_topics=per_word_topics, dtype=dtype
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)
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def update(self, corpus, chunks_as_numpy=False):
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"""Train the model with new documents, by EM-iterating over `corpus` until the topics converge
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(or until the maximum number of allowed iterations is reached).
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Train the model with new documents, by EM-iterating over the corpus until the topics converge, or until
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the maximum number of allowed iterations is reached. `corpus` must be an iterable. The E step is distributed
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into the several processes.
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Notes
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-----
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This update also supports updating an already trained model (`self`)
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with new documents from `corpus`; the two models are then merged in
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proportion to the number of old vs. new documents. This feature is still
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experimental for non-stationary input streams.
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For stationary input (no topic drift in new documents), on the other hand,
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this equals the online update of Hoffman et al. and is guaranteed to
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converge for any `decay` in (0.5, 1.0>.
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Parameters
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----------
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corpus : {iterable of list of (int, float), scipy.sparse.csc}, optional
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Stream of document vectors or sparse matrix of shape (`num_terms`, `num_documents`) used to update the
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model.
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chunks_as_numpy : bool
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Whether each chunk passed to the inference step should be a np.ndarray or not. Numpy can in some settings
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turn the term IDs into floats, these will be converted back into integers in inference, which incurs a
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performance hit. For distributed computing it may be desirable to keep the chunks as `numpy.ndarray`.
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"""
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try:
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lencorpus = len(corpus)
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except TypeError:
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logger.warning("input corpus stream has no len(); counting documents")
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lencorpus = sum(1 for _ in corpus)
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if lencorpus == 0:
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logger.warning("LdaMulticore.update() called with an empty corpus")
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return
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self.state.numdocs += lencorpus
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if not self.batch:
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updatetype = "online"
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updateafter = self.chunksize * self.workers
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else:
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updatetype = "batch"
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updateafter = lencorpus
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evalafter = min(lencorpus, (self.eval_every or 0) * updateafter)
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updates_per_pass = max(1, lencorpus / updateafter)
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logger.info(
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"running %s LDA training, %s topics, %i passes over the supplied corpus of %i documents, "
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"updating every %i documents, evaluating every ~%i documents, "
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"iterating %ix with a convergence threshold of %f",
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updatetype, self.num_topics, self.passes, lencorpus, updateafter,
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evalafter, self.iterations, self.gamma_threshold
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)
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if updates_per_pass * self.passes < 10:
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logger.warning(
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"too few updates, training might not converge; "
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"consider increasing the number of passes or iterations to improve accuracy"
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)
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job_queue = Queue(maxsize=2 * self.workers)
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result_queue = Queue()
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# rho is the "speed" of updating; TODO try other fncs
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# pass_ + num_updates handles increasing the starting t for each pass,
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# while allowing it to "reset" on the first pass of each update
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def rho():
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return pow(self.offset + pass_ + (self.num_updates / self.chunksize), -self.decay)
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logger.info("training LDA model using %i processes", self.workers)
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pool = Pool(self.workers, worker_e_step, (job_queue, result_queue,))
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for pass_ in xrange(self.passes):
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queue_size, reallen = [0], 0
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other = LdaState(self.eta, self.state.sstats.shape)
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def process_result_queue(force=False):
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"""
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Clear the result queue, merging all intermediate results, and update the
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LDA model if necessary.
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"""
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merged_new = False
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while not result_queue.empty():
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other.merge(result_queue.get())
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queue_size[0] -= 1
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merged_new = True
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if (force and merged_new and queue_size[0] == 0) or (not self.batch and (other.numdocs >= updateafter)):
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self.do_mstep(rho(), other, pass_ > 0)
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other.reset()
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if self.eval_every is not None and \
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((force and queue_size[0] == 0) or
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(self.eval_every != 0 and (self.num_updates / updateafter) % self.eval_every == 0)):
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self.log_perplexity(chunk, total_docs=lencorpus)
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chunk_stream = utils.grouper(corpus, self.chunksize, as_numpy=chunks_as_numpy)
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for chunk_no, chunk in enumerate(chunk_stream):
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reallen += len(chunk) # keep track of how many documents we've processed so far
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# put the chunk into the workers' input job queue
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chunk_put = False
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while not chunk_put:
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try:
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job_queue.put((chunk_no, chunk, self), block=False, timeout=0.1)
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chunk_put = True
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queue_size[0] += 1
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logger.info(
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"PROGRESS: pass %i, dispatched chunk #%i = documents up to #%i/%i, "
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"outstanding queue size %i",
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pass_, chunk_no, chunk_no * self.chunksize + len(chunk), lencorpus, queue_size[0]
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)
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except queue.Full:
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# in case the input job queue is full, keep clearing the
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# result queue, to make sure we don't deadlock
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process_result_queue()
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process_result_queue()
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# endfor single corpus pass
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# wait for all outstanding jobs to finish
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while queue_size[0] > 0:
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process_result_queue(force=True)
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if reallen != lencorpus:
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raise RuntimeError("input corpus size changed during training (don't use generators as input)")
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# endfor entire update
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pool.terminate()
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def worker_e_step(input_queue, result_queue):
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"""Perform E-step for each job.
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Parameters
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----------
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input_queue : queue of (int, list of (int, float), :class:`~gensim.models.lda_worker.Worker`)
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Each element is a job characterized by its ID, the corpus chunk to be processed in BOW format and the worker
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responsible for processing it.
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result_queue : queue of :class:`~gensim.models.ldamodel.LdaState`
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After the worker finished the job, the state of the resulting (trained) worker model is appended to this queue.
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"""
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logger.debug("worker process entering E-step loop")
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while True:
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logger.debug("getting a new job")
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|
chunk_no, chunk, worker_lda = input_queue.get()
|
||
|
|
logger.debug("processing chunk #%i of %i documents", chunk_no, len(chunk))
|
||
|
|
worker_lda.state.reset()
|
||
|
|
worker_lda.do_estep(chunk) # TODO: auto-tune alpha?
|
||
|
|
del chunk
|
||
|
|
logger.debug("processed chunk, queuing the result")
|
||
|
|
result_queue.put(worker_lda.state)
|
||
|
|
del worker_lda # free up some memory
|
||
|
|
logger.debug("result put")
|