# coding=utf-8 """Latent Dirichlet allocation using collapsed Gibbs sampling""" from __future__ import absolute_import, division, unicode_literals # noqa import logging import sys import numpy as np import guidedlda._guidedlda import guidedlda.utils import random logger = logging.getLogger('guidedlda') PY2 = sys.version_info[0] == 2 if PY2: range = xrange class GuidedLDA: """Guided Latent Dirichlet allocation using collapsed Gibbs sampling Parameters ---------- n_topics : int Number of topics n_iter : int, default 2000 Number of sampling iterations alpha : float, default 0.1 Dirichlet parameter for distribution over topics eta : float, default 0.01 Dirichlet parameter for distribution over words random_state : int or RandomState, optional The generator used for the initial topics. Attributes ---------- `components_` : array, shape = [n_topics, n_features] Point estimate of the topic-word distributions (Phi in literature) `topic_word_` : Alias for `components_` `word_topic_` : array, shape = [n_features, n_topics] Point estimate of the word-topic distributions `nzw_` : array, shape = [n_topics, n_features] Matrix of counts recording topic-word assignments in final iteration. `ndz_` : array, shape = [n_samples, n_topics] Matrix of counts recording document-topic assignments in final iteration. `doc_topic_` : array, shape = [n_samples, n_features] Point estimate of the document-topic distributions (Theta in literature) `nz_` : array, shape = [n_topics] Array of topic assignment counts in final iteration. Examples -------- >>> import numpy >>> X = numpy.array([[1,1], [2, 1], [3, 1], [4, 1], [5, 8], [6, 1]]) >>> import lda >>> model = lda.LDA(n_topics=2, random_state=0, n_iter=100) >>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE LDA(alpha=... >>> model.components_ array([[ 0.85714286, 0.14285714], [ 0.45 , 0.55 ]]) >>> model.loglikelihood() #doctest: +ELLIPSIS -40.395... References ---------- Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent Dirichlet Allocation." Journal of Machine Learning Research 3 (2003): 993–1022. Griffiths, Thomas L., and Mark Steyvers. "Finding Scientific Topics." Proceedings of the National Academy of Sciences 101 (2004): 5228–5235. doi:10.1073/pnas.0307752101. Wallach, Hanna, David Mimno, and Andrew McCallum. "Rethinking LDA: Why Priors Matter." In Advances in Neural Information Processing Systems 22, edited by Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, 1973–1981, 2009. Buntine, Wray. "Estimating Likelihoods for Topic Models." In Advances in Machine Learning, First Asian Conference on Machine Learning (2009): 51–64. doi:10.1007/978-3-642-05224-8_6. """ def __init__(self, n_topics, n_iter=2000, alpha=0.01, eta=0.01, random_state=None, refresh=10): self.n_topics = n_topics self.n_iter = n_iter self.alpha = alpha self.eta = eta # if random_state is None, check_random_state(None) does nothing # other than return the current numpy RandomState self.random_state = random_state self.refresh = refresh if alpha <= 0 or eta <= 0: raise ValueError("alpha and eta must be greater than zero") # random numbers that are reused rng = guidedlda.utils.check_random_state(random_state) if random_state: random.seed(random_state) self._rands = rng.rand(1024**2 // 8) # 1MiB of random variates # configure console logging if not already configured if len(logger.handlers) == 1 and isinstance(logger.handlers[0], logging.NullHandler): logging.basicConfig(level=logging.INFO) def fit(self, X, y=None, seed_topics={}, seed_confidence=0): """Fit the model with X. Parameters ---------- X: array-like, shape (n_samples, n_features) Training data, where n_samples in the number of samples and n_features is the number of features. Sparse matrix allowed. Returns ------- self : object Returns the instance itself. """ self._fit(X, seed_topics=seed_topics, seed_confidence=seed_confidence) return self def fit_transform(self, X, y=None, seed_topics={}, seed_confidence=0): """Apply dimensionality reduction on X Parameters ---------- X : array-like, shape (n_samples, n_features) New data, where n_samples in the number of samples and n_features is the number of features. Sparse matrix allowed. Returns ------- doc_topic : array-like, shape (n_samples, n_topics) Point estimate of the document-topic distributions """ if isinstance(X, np.ndarray): # in case user passes a (non-sparse) array of shape (n_features,) # turn it into an array of shape (1, n_features) X = np.atleast_2d(X) self._fit(X, seed_topics=seed_topics, seed_confidence=seed_confidence) return self.doc_topic_ def transform(self, X, max_iter=20, tol=1e-16): """Transform the data X according to previously fitted model Parameters ---------- X : array-like, shape (n_samples, n_features) New data, where n_samples in the number of samples and n_features is the number of features. max_iter : int, optional Maximum number of iterations in iterated-pseudocount estimation. tol: double, optional Tolerance value used in stopping condition. Returns ------- doc_topic : array-like, shape (n_samples, n_topics) Point estimate of the document-topic distributions Note ---- This uses the "iterated pseudo-counts" approach described in Wallach et al. (2009) and discussed in Buntine (2009). """ if isinstance(X, np.ndarray): # in case user passes a (non-sparse) array of shape (n_features,) # turn it into an array of shape (1, n_features) X = np.atleast_2d(X) doc_topic = np.empty((X.shape[0], self.n_topics)) WS, DS = guidedlda.utils.matrix_to_lists(X) # TODO: this loop is parallelizable for d in np.unique(DS): doc_topic[d] = self._transform_single(WS[DS == d], max_iter, tol) return doc_topic def _transform_single(self, doc, max_iter, tol): """Transform a single document according to the previously fit model Parameters ---------- X : 1D numpy array of integers Each element represents a word in the document max_iter : int Maximum number of iterations in iterated-pseudocount estimation. tol: double Tolerance value used in stopping condition. Returns ------- doc_topic : 1D numpy array of length n_topics Point estimate of the topic distributions for document Note ---- See Note in `transform` documentation. """ PZS = np.zeros((len(doc), self.n_topics)) for iteration in range(max_iter + 1): # +1 is for initialization PZS_new = self.components_[:, doc].T PZS_new *= (PZS.sum(axis=0) - PZS + self.alpha) PZS_new /= PZS_new.sum(axis=1)[:, np.newaxis] # vector to single column matrix delta_naive = np.abs(PZS_new - PZS).sum() logger.debug('transform iter {}, delta {}'.format(iteration, delta_naive)) PZS = PZS_new if delta_naive < tol: break theta_doc = PZS.sum(axis=0) / PZS.sum() assert len(theta_doc) == self.n_topics assert theta_doc.shape == (self.n_topics,) return theta_doc def _fit(self, X, seed_topics, seed_confidence): """Fit the model to the data X Parameters ---------- X: array-like, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. Sparse matrix allowed. """ random_state = guidedlda.utils.check_random_state(self.random_state) rands = self._rands.copy() self._initialize(X, seed_topics, seed_confidence) for it in range(self.n_iter): # FIXME: using numpy.roll with a random shift might be faster random_state.shuffle(rands) if it % self.refresh == 0: ll = self.loglikelihood() logger.info("<{}> log likelihood: {:.0f}".format(it, ll)) # keep track of loglikelihoods for monitoring convergence self.loglikelihoods_.append(ll) self._sample_topics(rands) ll = self.loglikelihood() logger.info("<{}> log likelihood: {:.0f}".format(self.n_iter - 1, ll)) # note: numpy /= is integer division self.components_ = (self.nzw_ + self.eta).astype(float) self.components_ /= np.sum(self.components_, axis=1)[:, np.newaxis] self.topic_word_ = self.components_ self.word_topic_ = (self.nzw_ + self.eta).astype(float) self.word_topic_ /= np.sum(self.word_topic_, axis=0)[np.newaxis, :] self.word_topic_ = self.word_topic_.T self.doc_topic_ = (self.ndz_ + self.alpha).astype(float) self.doc_topic_ /= np.sum(self.doc_topic_, axis=1)[:, np.newaxis] # delete attributes no longer needed after fitting to save memory and reduce clutter del self.WS del self.DS del self.ZS return self def _initialize(self, X, seed_topics, seed_confidence): """Initialize the document topic distribution. topic word distribution, etc. Parameters ---------- seed_topics: type=dict, value={2:0, 256:0, 412:1, 113:1} """ D, W = X.shape N = int(X.sum()) n_topics = self.n_topics n_iter = self.n_iter logger.info("n_documents: {}".format(D)) logger.info("vocab_size: {}".format(W)) logger.info("n_words: {}".format(N)) logger.info("n_topics: {}".format(n_topics)) logger.info("n_iter: {}".format(n_iter)) self.beta = 0.1 self.nzw_ = nzw_ = np.zeros((n_topics, W), dtype=np.intc) # + self.beta self.ndz_ = ndz_ = np.zeros((D, n_topics), dtype=np.intc) # + self.alpha self.nz_ = nz_ = np.zeros(n_topics, dtype=np.intc)# + W * self.beta self.WS, self.DS = WS, DS = guidedlda.utils.matrix_to_lists(X) self.ZS = ZS = np.empty_like(self.WS, dtype=np.intc) np.testing.assert_equal(N, len(WS)) # seeded Initialization count_testing = 0 for i in range(N): w, d = WS[i], DS[i] if w not in seed_topics: continue # check if seeded initialization if w in seed_topics and random.random() < seed_confidence: z_new = seed_topics[w] else: z_new = i % n_topics ZS[i] = z_new ndz_[d, z_new] += 1 nzw_[z_new, w] += 1 nz_[z_new] += 1 # Non seeded Initialization for i in range(N): w, d = WS[i], DS[i] if w in seed_topics: continue z_new = i % n_topics ZS[i] = z_new ndz_[d, z_new] += 1 nzw_[z_new, w] += 1 nz_[z_new] += 1 self.loglikelihoods_ = [] self.nzw_ = nzw_.astype(np.intc) self.ndz_ = ndz_.astype(np.intc) self.nz_ = nz_.astype(np.intc) def purge_extra_matrices(self): """Clears out word topic. document topic. and internal matrix. Once this method is used. don't call fit_transform again. Just use the model for predictions. """ del self.topic_word_ del self.word_topic_ del self.doc_topic_ del self.nzw_ del self.ndz_ del self.nz_ def loglikelihood(self): """Calculate complete log likelihood, log p(w,z) Formula used is log p(w,z) = log p(w|z) + log p(z) """ nzw, ndz, nz = self.nzw_, self.ndz_, self.nz_ alpha = self.alpha eta = self.eta nd = np.sum(ndz, axis=1).astype(np.intc) return guidedlda._guidedlda._loglikelihood(nzw, ndz, nz, nd, alpha, eta) def _sample_topics(self, rands): """Samples all topic assignments. Called once per iteration.""" n_topics, vocab_size = self.nzw_.shape alpha = np.repeat(self.alpha, n_topics).astype(np.float64) eta = np.repeat(self.eta, vocab_size).astype(np.float64) guidedlda._guidedlda._sample_topics(self.WS, self.DS, self.ZS, self.nzw_, self.ndz_, self.nz_, alpha, eta, rands)