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)

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

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 _more_tags(self):

return {

"preserves_dtype": [np.float64, np.float32],

"requires_positive_X": True,

}

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 = self._validate_data(

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):

"""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.

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)

doc_topic_distr /= doc_topic_distr.sum(axis=1)[:, np.newaxis]

return doc_topic_distr

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."""