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sklearn/decomposition/_lda.py
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Christine P. Chai
DOC: Add the Stochastic Variational Inference paper link (#32227)
21 сен 2025, 07:34
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21 сен 2025, 07:34
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""" ============================================================= Online Latent Dirichlet Allocation with variational inference ============================================================= This implementation is modified from Matthew D. Hoffman's onlineldavb code Link: https://github.com/blei-lab/onlineldavb """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause from numbers import Integral, Real import numpy as np import scipy.sparse as sp from joblib import effective_n_jobs from scipy.special import gammaln, logsumexp from sklearn.base import ( BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context, ) from sklearn.decomposition._online_lda_fast import ( _dirichlet_expectation_1d as cy_dirichlet_expectation_1d, ) from sklearn.decomposition._online_lda_fast import _dirichlet_expectation_2d from sklearn.decomposition._online_lda_fast import mean_change as cy_mean_change from sklearn.utils import check_random_state, gen_batches, gen_even_slices from sklearn.utils._param_validation import Interval, StrOptions from sklearn.utils.parallel import Parallel, delayed from sklearn.utils.validation import check_is_fitted, check_non_negative, validate_data EPS = np.finfo(float).eps def _update_doc_distribution( X, exp_topic_word_distr, doc_topic_prior, max_doc_update_iter, mean_change_tol, cal_sstats, random_state, ): """E-step: update document-topic distribution. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. exp_topic_word_distr : ndarray of shape (n_topics, n_features) Exponential value of expectation of log topic word distribution. In the literature, this is `exp(E[log(beta)])`. doc_topic_prior : float Prior of document topic distribution `theta`. max_doc_update_iter : int Max number of iterations for updating document topic distribution in the E-step. mean_change_tol : float Stopping tolerance for updating document topic distribution in E-step. cal_sstats : bool Parameter that indicate to calculate sufficient statistics or not. Set `cal_sstats` to `True` when we need to run M-step. random_state : RandomState instance or None Parameter that indicate how to initialize document topic distribution. Set `random_state` to None will initialize document topic distribution to a constant number. Returns ------- (doc_topic_distr, suff_stats) : `doc_topic_distr` is unnormalized topic distribution for each document. In the literature, this is `gamma`. we can calculate `E[log(theta)]` from it. `suff_stats` is expected sufficient statistics for the M-step. When `cal_sstats == False`, this will be None. """ is_sparse_x = sp.issparse(X) n_samples, n_features = X.shape n_topics = exp_topic_word_distr.shape[0] if random_state: doc_topic_distr = random_state.gamma(100.0, 0.01, (n_samples, n_topics)).astype( X.dtype, copy=False ) else: doc_topic_distr = np.ones((n_samples, n_topics), dtype=X.dtype) # In the literature, this is `exp(E[log(theta)])` exp_doc_topic = np.exp(_dirichlet_expectation_2d(doc_topic_distr)) # diff on `component_` (only calculate it when `cal_diff` is True) suff_stats = ( np.zeros(exp_topic_word_distr.shape, dtype=X.dtype) if cal_sstats else None ) if is_sparse_x: X_data = X.data X_indices = X.indices X_indptr = X.indptr # These cython functions are called in a nested loop on usually very small arrays # (length=n_topics). In that case, finding the appropriate signature of the # fused-typed function can be more costly than its execution, hence the dispatch # is done outside of the loop. ctype = "float" if X.dtype == np.float32 else "double" mean_change = cy_mean_change[ctype] dirichlet_expectation_1d = cy_dirichlet_expectation_1d[ctype] eps = np.finfo(X.dtype).eps for idx_d in range(n_samples): if is_sparse_x: ids = X_indices[X_indptr[idx_d] : X_indptr[idx_d + 1]] cnts = X_data[X_indptr[idx_d] : X_indptr[idx_d + 1]] else: ids = np.nonzero(X[idx_d, :])[0] cnts = X[idx_d, ids] doc_topic_d = doc_topic_distr[idx_d, :] # The next one is a copy, since the inner loop overwrites it. exp_doc_topic_d = exp_doc_topic[idx_d, :].copy() exp_topic_word_d = exp_topic_word_distr[:, ids] # Iterate between `doc_topic_d` and `norm_phi` until convergence for _ in range(0, max_doc_update_iter): last_d = doc_topic_d # The optimal phi_{dwk} is proportional to # exp(E[log(theta_{dk})]) * exp(E[log(beta_{dw})]). norm_phi = np.dot(exp_doc_topic_d, exp_topic_word_d) + eps doc_topic_d = exp_doc_topic_d * np.dot(cnts / norm_phi, exp_topic_word_d.T) # Note: adds doc_topic_prior to doc_topic_d, in-place. dirichlet_expectation_1d(doc_topic_d, doc_topic_prior, exp_doc_topic_d) if mean_change(last_d, doc_topic_d) < mean_change_tol: break doc_topic_distr[idx_d, :] = doc_topic_d # Contribution of document d to the expected sufficient # statistics for the M step. if cal_sstats: norm_phi = np.dot(exp_doc_topic_d, exp_topic_word_d) + eps suff_stats[:, ids] += np.outer(exp_doc_topic_d, cnts / norm_phi) return (doc_topic_distr, suff_stats) class LatentDirichletAllocation( ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator ): """Latent Dirichlet Allocation with online variational Bayes algorithm. The implementation is based on [1]_ and [2]_. .. versionadded:: 0.17 Read more in the :ref:`User Guide <LatentDirichletAllocation>`. Parameters ---------- n_components : int, default=10 Number of topics. .. versionchanged:: 0.19 ``n_topics`` was renamed to ``n_components`` doc_topic_prior : float, default=None Prior of document topic distribution `theta`. If the value is None, defaults to `1 / n_components`. In [1]_, this is called `alpha`. topic_word_prior : float, default=None Prior of topic word distribution `beta`. If the value is None, defaults to `1 / n_components`. In [1]_, this is called `eta`. learning_method : {'batch', 'online'}, default='batch' Method used to update `_component`. Only used in :meth:`fit` method. In general, if the data size is large, the online update will be much faster than the batch update. Valid options: - 'batch': Batch variational Bayes method. Use all training data in each EM update. Old `components_` will be overwritten in each iteration. - 'online': Online variational Bayes method. In each EM update, use mini-batch of training data to update the ``components_`` variable incrementally. The learning rate is controlled by the ``learning_decay`` and the ``learning_offset`` parameters. .. versionchanged:: 0.20 The default learning method is now ``"batch"``. learning_decay : float, default=0.7 It is a parameter that control learning rate in the online learning method. The value should be set between (0.5, 1.0] to guarantee asymptotic convergence. When the value is 0.0 and batch_size is ``n_samples``, the update method is same as batch learning. In the literature, this is called kappa. learning_offset : float, default=10.0 A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0. In the literature, this is called tau_0. max_iter : int, default=10 The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the :meth:`fit` method, and not the :meth:`partial_fit` method. batch_size : int, default=128 Number of documents to use in each EM iteration. Only used in online learning. evaluate_every : int, default=-1 How often to evaluate perplexity. Only used in `fit` method. set it to 0 or negative number to not evaluate perplexity in training at all. Evaluating perplexity can help you check convergence in training process, but it will also increase total training time. Evaluating perplexity in every iteration might increase training time up to two-fold. total_samples : int, default=1e6 Total number of documents. Only used in the :meth:`partial_fit` method. perp_tol : float, default=1e-1 Perplexity tolerance. Only used when ``evaluate_every`` is greater than 0. mean_change_tol : float, default=1e-3 Stopping tolerance for updating document topic distribution in E-step. max_doc_update_iter : int, default=100 Max number of iterations for updating document topic distribution in the E-step. n_jobs : int, default=None The number of jobs to use in the E-step. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. verbose : int, default=0 Verbosity level. random_state : int, RandomState instance or None, default=None Pass an int for reproducible results across multiple function calls. See :term:`Glossary <random_state>`. Attributes ---------- components_ : ndarray of shape (n_components, n_features) Variational parameters for topic word distribution. Since the complete conditional for topic word distribution is a Dirichlet, ``components_[i, j]`` can be viewed as pseudocount that represents the number of times word `j` was assigned to topic `i`. It can also be viewed as distribution over the words for each topic after normalization: ``model.components_ / model.components_.sum(axis=1)[:, np.newaxis]``. exp_dirichlet_component_ : ndarray of shape (n_components, n_features) Exponential value of expectation of log topic word distribution. In the literature, this is `exp(E[log(beta)])`. n_batch_iter_ : int Number of iterations of the EM step. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_iter_ : int Number of passes over the dataset. bound_ : float Final perplexity score on training set. doc_topic_prior_ : float Prior of document topic distribution `theta`. If the value is None, it is `1 / n_components`. random_state_ : RandomState instance RandomState instance that is generated either from a seed, the random number generator or by `np.random`. topic_word_prior_ : float Prior of topic word distribution `beta`. If the value is None, it is `1 / n_components`. See Also -------- sklearn.discriminant_analysis.LinearDiscriminantAnalysis: A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes' rule. References ---------- .. [1] "Online Learning for Latent Dirichlet Allocation", Matthew D. Hoffman, David M. Blei, Francis Bach, 2010. https://github.com/blei-lab/onlineldavb .. [2] "Stochastic Variational Inference", Matthew D. Hoffman, David M. Blei, Chong Wang, John Paisley, 2013. https://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf Examples -------- >>> from sklearn.decomposition import LatentDirichletAllocation >>> from sklearn.datasets import make_multilabel_classification >>> # This produces a feature matrix of token counts, similar to what >>> # CountVectorizer would produce on text. >>> X, _ = make_multilabel_classification(random_state=0) >>> lda = LatentDirichletAllocation(n_components=5, ... random_state=0) >>> lda.fit(X) LatentDirichletAllocation(...) >>> # get topics for some given samples: >>> lda.transform(X[-2:]) array([[0.00360392, 0.25499205, 0.0036211 , 0.64236448, 0.09541846], [0.15297572, 0.00362644, 0.44412786, 0.39568399, 0.003586 ]]) """ _parameter_constraints: dict = { "n_components": [Interval(Integral, 0, None, closed="neither")], "doc_topic_prior": [None, Interval(Real, 0, 1, closed="both")], "topic_word_prior": [None, Interval(Real, 0, 1, closed="both")], "learning_method": [StrOptions({"batch", "online"})], "learning_decay": [Interval(Real, 0, 1, closed="both")], "learning_offset": [Interval(Real, 1.0, None, closed="left")], "max_iter": [Interval(Integral, 0, None, closed="left")], "batch_size": [Interval(Integral, 0, None, closed="neither")], "evaluate_every": [Interval(Integral, None, None, closed="neither")], "total_samples": [Interval(Real, 0, None, closed="neither")], "perp_tol": [Interval(Real, 0, None, closed="left")], "mean_change_tol": [Interval(Real, 0, None, closed="left")], "max_doc_update_iter": [Interval(Integral, 0, None, closed="left")], "n_jobs": [None, Integral], "verbose": ["verbose"], "random_state": ["random_state"], } def __init__( self, n_components=10, *, doc_topic_prior=None, topic_word_prior=None, learning_method="batch", learning_decay=0.7, learning_offset=10.0, max_iter=10, batch_size=128, evaluate_every=-1, total_samples=1e6, perp_tol=1e-1, mean_change_tol=1e-3, max_doc_update_iter=100, n_jobs=None, verbose=0, random_state=None, ): self.n_components = n_components self.doc_topic_prior = doc_topic_prior self.topic_word_prior = topic_word_prior self.learning_method = learning_method self.learning_decay = learning_decay self.learning_offset = learning_offset self.max_iter = max_iter self.batch_size = batch_size self.evaluate_every = evaluate_every self.total_samples = total_samples self.perp_tol = perp_tol self.mean_change_tol = mean_change_tol self.max_doc_update_iter = max_doc_update_iter self.n_jobs = n_jobs self.verbose = verbose self.random_state = random_state def _init_latent_vars(self, n_features, dtype=np.float64): """Initialize latent variables.""" self.random_state_ = check_random_state(self.random_state) self.n_batch_iter_ = 1 self.n_iter_ = 0 if self.doc_topic_prior is None: self.doc_topic_prior_ = 1.0 / self.n_components else: self.doc_topic_prior_ = self.doc_topic_prior if self.topic_word_prior is None: self.topic_word_prior_ = 1.0 / self.n_components else: self.topic_word_prior_ = self.topic_word_prior init_gamma = 100.0 init_var = 1.0 / init_gamma # In the literature, this is called `lambda` self.components_ = self.random_state_.gamma( init_gamma, init_var, (self.n_components, n_features) ).astype(dtype, copy=False) # In the literature, this is `exp(E[log(beta)])` self.exp_dirichlet_component_ = np.exp( _dirichlet_expectation_2d(self.components_) ) def _e_step(self, X, cal_sstats, random_init, parallel=None): """E-step in EM update. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. cal_sstats : bool Parameter that indicate whether to calculate sufficient statistics or not. Set ``cal_sstats`` to True when we need to run M-step. random_init : bool Parameter that indicate whether to initialize document topic distribution randomly in the E-step. Set it to True in training steps. parallel : joblib.Parallel, default=None Pre-initialized instance of joblib.Parallel. Returns ------- (doc_topic_distr, suff_stats) : `doc_topic_distr` is unnormalized topic distribution for each document. In the literature, this is called `gamma`. `suff_stats` is expected sufficient statistics for the M-step. When `cal_sstats == False`, it will be None. """ # Run e-step in parallel random_state = self.random_state_ if random_init else None # TODO: make Parallel._effective_n_jobs public instead? n_jobs = effective_n_jobs(self.n_jobs) if parallel is None: parallel = Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) results = parallel( delayed(_update_doc_distribution)( X[idx_slice, :], self.exp_dirichlet_component_, self.doc_topic_prior_, self.max_doc_update_iter, self.mean_change_tol, cal_sstats, random_state, ) for idx_slice in gen_even_slices(X.shape[0], n_jobs) ) # merge result doc_topics, sstats_list = zip(*results) doc_topic_distr = np.vstack(doc_topics) if cal_sstats: # This step finishes computing the sufficient statistics for the # M-step. suff_stats = np.zeros(self.components_.shape, dtype=self.components_.dtype) for sstats in sstats_list: suff_stats += sstats suff_stats *= self.exp_dirichlet_component_ else: suff_stats = None return (doc_topic_distr, suff_stats) def _em_step(self, X, total_samples, batch_update, parallel=None): """EM update for 1 iteration. update `component_` by batch VB or online VB. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. total_samples : int Total number of documents. It is only used when batch_update is `False`. batch_update : bool Parameter that controls updating method. `True` for batch learning, `False` for online learning. parallel : joblib.Parallel, default=None Pre-initialized instance of joblib.Parallel Returns ------- doc_topic_distr : ndarray of shape (n_samples, n_components) Unnormalized document topic distribution. """ # E-step _, suff_stats = self._e_step( X, cal_sstats=True, random_init=True, parallel=parallel ) # M-step if batch_update: self.components_ = self.topic_word_prior_ + suff_stats else: # online update # In the literature, the weight is `rho` weight = np.power( self.learning_offset + self.n_batch_iter_, -self.learning_decay ) doc_ratio = float(total_samples) / X.shape[0] self.components_ *= 1 - weight self.components_ += weight * ( self.topic_word_prior_ + doc_ratio * suff_stats ) # update `component_` related variables self.exp_dirichlet_component_ = np.exp( _dirichlet_expectation_2d(self.components_) ) self.n_batch_iter_ += 1 return def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.positive_only = True tags.input_tags.sparse = True tags.transformer_tags.preserves_dtype = ["float32", "float64"] return tags def _check_non_neg_array(self, X, reset_n_features, whom): """check X format check X format and make sure no negative value in X. Parameters ---------- X : array-like or sparse matrix """ dtype = [np.float64, np.float32] if reset_n_features else self.components_.dtype X = validate_data( self, X, reset=reset_n_features, accept_sparse="csr", dtype=dtype, ) check_non_negative(X, whom) return X @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y=None): """Online VB with Mini-Batch update. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. y : Ignored Not used, present here for API consistency by convention. Returns ------- self Partially fitted estimator. """ first_time = not hasattr(self, "components_") X = self._check_non_neg_array( X, reset_n_features=first_time, whom="LatentDirichletAllocation.partial_fit" ) n_samples, n_features = X.shape batch_size = self.batch_size # initialize parameters or check if first_time: self._init_latent_vars(n_features, dtype=X.dtype) if n_features != self.components_.shape[1]: raise ValueError( "The provided data has %d dimensions while " "the model was trained with feature size %d." % (n_features, self.components_.shape[1]) ) n_jobs = effective_n_jobs(self.n_jobs) with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel: for idx_slice in gen_batches(n_samples, batch_size): self._em_step( X[idx_slice, :], total_samples=self.total_samples, batch_update=False, parallel=parallel, ) return self @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Learn model for the data X with variational Bayes method. When `learning_method` is 'online', use mini-batch update. Otherwise, use batch update. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. y : Ignored Not used, present here for API consistency by convention. Returns ------- self Fitted estimator. """ X = self._check_non_neg_array( X, reset_n_features=True, whom="LatentDirichletAllocation.fit" ) n_samples, n_features = X.shape max_iter = self.max_iter evaluate_every = self.evaluate_every learning_method = self.learning_method batch_size = self.batch_size # initialize parameters self._init_latent_vars(n_features, dtype=X.dtype) # change to perplexity later last_bound = None n_jobs = effective_n_jobs(self.n_jobs) with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel: for i in range(max_iter): if learning_method == "online": for idx_slice in gen_batches(n_samples, batch_size): self._em_step( X[idx_slice, :], total_samples=n_samples, batch_update=False, parallel=parallel, ) else: # batch update self._em_step( X, total_samples=n_samples, batch_update=True, parallel=parallel ) # check perplexity if evaluate_every > 0 and (i + 1) % evaluate_every == 0: doc_topics_distr, _ = self._e_step( X, cal_sstats=False, random_init=False, parallel=parallel ) bound = self._perplexity_precomp_distr( X, doc_topics_distr, sub_sampling=False ) if self.verbose: print( "iteration: %d of max_iter: %d, perplexity: %.4f" % (i + 1, max_iter, bound) ) if last_bound and abs(last_bound - bound) < self.perp_tol: break last_bound = bound elif self.verbose: print("iteration: %d of max_iter: %d" % (i + 1, max_iter)) self.n_iter_ += 1 # calculate final perplexity value on train set doc_topics_distr, _ = self._e_step( X, cal_sstats=False, random_init=False, parallel=parallel ) self.bound_ = self._perplexity_precomp_distr( X, doc_topics_distr, sub_sampling=False ) return self def _unnormalized_transform(self, X): """Transform data X according to fitted model. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. Returns ------- doc_topic_distr : ndarray of shape (n_samples, n_components) Document topic distribution for X. """ doc_topic_distr, _ = self._e_step(X, cal_sstats=False, random_init=False) return doc_topic_distr def transform(self, X, *, normalize=True): """Transform data X according to the fitted model. .. versionchanged:: 0.18 `doc_topic_distr` is now normalized. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. normalize : bool, default=True Whether to normalize the document topic distribution. Returns ------- doc_topic_distr : ndarray of shape (n_samples, n_components) Document topic distribution for X. """ check_is_fitted(self) X = self._check_non_neg_array( X, reset_n_features=False, whom="LatentDirichletAllocation.transform" ) doc_topic_distr = self._unnormalized_transform(X) if normalize: doc_topic_distr /= doc_topic_distr.sum(axis=1)[:, np.newaxis] return doc_topic_distr def fit_transform(self, X, y=None, *, normalize=True): """ Fit to data, then transform it. Fits transformer to `X` and `y` and returns a transformed version of `X`. Parameters ---------- X : array-like of shape (n_samples, n_features) Input samples. y : array-like of shape (n_samples,) or (n_samples, n_outputs), \ default=None Target values (None for unsupervised transformations). normalize : bool, default=True Whether to normalize the document topic distribution in `transform`. Returns ------- X_new : ndarray array of shape (n_samples, n_components) Transformed array. """ return self.fit(X, y).transform(X, normalize=normalize) def _approx_bound(self, X, doc_topic_distr, sub_sampling): """Estimate the variational bound. Estimate the variational bound over "all documents" using only the documents passed in as X. Since log-likelihood of each word cannot be computed directly, we use this bound to estimate it. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. doc_topic_distr : ndarray of shape (n_samples, n_components) Document topic distribution. In the literature, this is called gamma. sub_sampling : bool, default=False Compensate for subsampling of documents. It is used in calculate bound in online learning. Returns ------- score : float """ def _loglikelihood(prior, distr, dirichlet_distr, size): # calculate log-likelihood score = np.sum((prior - distr) * dirichlet_distr) score += np.sum(gammaln(distr) - gammaln(prior)) score += np.sum(gammaln(prior * size) - gammaln(np.sum(distr, 1))) return score is_sparse_x = sp.issparse(X) n_samples, n_components = doc_topic_distr.shape n_features = self.components_.shape[1] score = 0 dirichlet_doc_topic = _dirichlet_expectation_2d(doc_topic_distr) dirichlet_component_ = _dirichlet_expectation_2d(self.components_) doc_topic_prior = self.doc_topic_prior_ topic_word_prior = self.topic_word_prior_ if is_sparse_x: X_data = X.data X_indices = X.indices X_indptr = X.indptr # E[log p(docs | theta, beta)] for idx_d in range(0, n_samples): if is_sparse_x: ids = X_indices[X_indptr[idx_d] : X_indptr[idx_d + 1]] cnts = X_data[X_indptr[idx_d] : X_indptr[idx_d + 1]] else: ids = np.nonzero(X[idx_d, :])[0] cnts = X[idx_d, ids] temp = ( dirichlet_doc_topic[idx_d, :, np.newaxis] + dirichlet_component_[:, ids] ) norm_phi = logsumexp(temp, axis=0) score += np.dot(cnts, norm_phi) # compute E[log p(theta | alpha) - log q(theta | gamma)] score += _loglikelihood( doc_topic_prior, doc_topic_distr, dirichlet_doc_topic, self.n_components ) # Compensate for the subsampling of the population of documents if sub_sampling: doc_ratio = float(self.total_samples) / n_samples score *= doc_ratio # E[log p(beta | eta) - log q (beta | lambda)] score += _loglikelihood( topic_word_prior, self.components_, dirichlet_component_, n_features ) return score def score(self, X, y=None): """Calculate approximate log-likelihood as score. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. y : Ignored Not used, present here for API consistency by convention. Returns ------- score : float Use approximate bound as score. """ check_is_fitted(self) X = self._check_non_neg_array( X, reset_n_features=False, whom="LatentDirichletAllocation.score" ) doc_topic_distr = self._unnormalized_transform(X) score = self._approx_bound(X, doc_topic_distr, sub_sampling=False) return score def _perplexity_precomp_distr(self, X, doc_topic_distr=None, sub_sampling=False): """Calculate approximate perplexity for data X with ability to accept precomputed doc_topic_distr Perplexity is defined as exp(-1. * log-likelihood per word) Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. doc_topic_distr : ndarray of shape (n_samples, n_components), \ default=None Document topic distribution. If it is None, it will be generated by applying transform on X. Returns ------- score : float Perplexity score. """ if doc_topic_distr is None: doc_topic_distr = self._unnormalized_transform(X) else: n_samples, n_components = doc_topic_distr.shape if n_samples != X.shape[0]: raise ValueError( "Number of samples in X and doc_topic_distr do not match." ) if n_components != self.n_components: raise ValueError("Number of topics does not match.") current_samples = X.shape[0] bound = self._approx_bound(X, doc_topic_distr, sub_sampling) if sub_sampling: word_cnt = X.sum() * (float(self.total_samples) / current_samples) else: word_cnt = X.sum() perword_bound = bound / word_cnt return np.exp(-1.0 * perword_bound) def perplexity(self, X, sub_sampling=False): """Calculate approximate perplexity for data X. Perplexity is defined as exp(-1. * log-likelihood per word) .. versionchanged:: 0.19 *doc_topic_distr* argument has been deprecated and is ignored because user no longer has access to unnormalized distribution Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document word matrix. sub_sampling : bool Do sub-sampling or not. Returns ------- score : float Perplexity score. """ check_is_fitted(self) X = self._check_non_neg_array( X, reset_n_features=True, whom="LatentDirichletAllocation.perplexity" ) return self._perplexity_precomp_distr(X, sub_sampling=sub_sampling) @property def _n_features_out(self): """Number of transformed output features.""" return self.components_.shape[0]