LDA¶
Online Latent Dirichlet Allocation with Infinite Vocabulary.
Latent Dirichlet allocation (LDA) is a probabilistic approach for exploring topics in document collections. The key advantage of this variant is that it assumes an infinite vocabulary, meaning that the set of tokens does not have to known in advance, as opposed to the implementation from sklearn The results produced by this implementation are identical to those from the original implementation proposed by the method's authors.
This class takes as input token counts. Therefore, it requires you to tokenize beforehand. You can do so by using a feature_extraction.BagOfWords
instance, as shown in the example below.
Parameters¶
-
n_components – defaults to
10
Number of topics of the latent Drichlet allocation.
-
number_of_documents – defaults to
1000000.0
Estimated number of documents.
-
alpha_theta – defaults to
0.5
Hyper-parameter of the Dirichlet distribution of topics.
-
alpha_beta – defaults to
100.0
Hyper-parameter of the Dirichlet process of distribution over words.
-
tau – defaults to
64.0
Learning inertia to prevent premature convergence.
-
kappa – defaults to
0.75
The learning rate kappa controls how quickly new parameters estimates replace the old ones. kappa ∈ (0.5, 1] is required for convergence.
-
vocab_prune_interval – defaults to
10
Interval at which to refresh the words topics distribution.
-
number_of_samples – defaults to
10
Number of iteration to computes documents topics distribution.
-
ranking_smooth_factor – defaults to
1e-12
-
burn_in_sweeps – defaults to
5
Number of iteration necessaries while analyzing a document before updating document topics distribution.
-
maximum_size_vocabulary – defaults to
4000
Maximum size of the stored vocabulary.
-
seed (int) – defaults to
None
Random number seed used for reproducibility.
Attributes¶
-
counter (int)
The current number of observed documents.
-
truncation_size_prime (int)
Number of distincts words stored in the vocabulary. Updated before processing a document.
-
truncation_size (int)
Number of distincts words stored in the vocabulary. Updated after processing a document.
-
word_to_index (dict)
Words as keys and indexes as values.
-
index_to_word (dict)
Indexes as keys and words as values.
-
nu_1 (dict)
Weights of the words. Component of the variational inference.
-
nu_2 (dict)
Weights of the words. Component of the variational inference.
Examples¶
>>> from river import compose
>>> from river import feature_extraction
>>> from river import preprocessing
>>> X = [
... 'weather cold',
... 'weather hot dry',
... 'weather cold rainy',
... 'weather hot',
... 'weather cold humid',
... ]
>>> lda = compose.Pipeline(
... feature_extraction.BagOfWords(),
... preprocessing.LDA(
... n_components=2,
... number_of_documents=60,
... seed=42
... )
... )
>>> for x in X:
... lda = lda.learn_one(x)
... topics = lda.transform_one(x)
... print(topics)
{0: 0.5, 1: 2.5}
{0: 1.5, 1: 2.5}
{0: 3.5, 1: 0.5}
{0: 1.5, 1: 1.5}
{0: 2.5, 1: 1.5}
Methods¶
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy
if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
learn_one
Update with a set of features x
.
A lot of transformers don't actually have to do anything during the learn_one
step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_one
can override this method.
Parameters
- x (dict)
Returns
Transformer: self
learn_transform_one
Equivalent to lda.learn_one(x).transform_one(x)
s, but faster.
Parameters
- x (dict)
Returns
dict: Component attributions for the input document.
transform_one
Transform a set of features x
.
Parameters
- x (dict)
Returns
dict: The transformed values.