predict
Predict top LDA topics of documents
Syntax
Description
___ = predict(___,
specifies additional options using one or more name-value pair arguments.Name,Value
)
Examples
Predict Top LDA Topics of Documents
To reproduce the results in this example, set rng
to 'default'
.
rng('default')
Load the example data. The file sonnetsPreprocessed.txt
contains preprocessed versions of Shakespeare's sonnets. The file contains one sonnet per line, with words separated by a space. Extract the text from sonnetsPreprocessed.txt
, split the text into documents at newline characters, and then tokenize the documents.
filename = "sonnetsPreprocessed.txt";
str = extractFileText(filename);
textData = split(str,newline);
documents = tokenizedDocument(textData);
Create a bag-of-words model using bagOfWords
.
bag = bagOfWords(documents)
bag = bagOfWords with properties: Counts: [154×3092 double] Vocabulary: ["fairest" "creatures" "desire" "increase" "thereby" "beautys" "rose" "might" "never" "die" "riper" "time" "decease" "tender" "heir" "bear" "memory" "thou" "contracted" … ] NumWords: 3092 NumDocuments: 154
Fit an LDA model with 20 topics.
numTopics = 20; mdl = fitlda(bag,numTopics)
Initial topic assignments sampled in 0.513255 seconds. ===================================================================================== | Iteration | Time per | Relative | Training | Topic | Topic | | | iteration | change in | perplexity | concentration | concentration | | | (seconds) | log(L) | | | iterations | ===================================================================================== | 0 | 0.04 | | 1.159e+03 | 5.000 | 0 | | 1 | 0.05 | 5.4884e-02 | 8.028e+02 | 5.000 | 0 | | 2 | 0.04 | 4.7400e-03 | 7.778e+02 | 5.000 | 0 | | 3 | 0.04 | 3.4597e-03 | 7.602e+02 | 5.000 | 0 | | 4 | 0.03 | 3.4662e-03 | 7.430e+02 | 5.000 | 0 | | 5 | 0.03 | 2.9259e-03 | 7.288e+02 | 5.000 | 0 | | 6 | 0.03 | 6.4180e-05 | 7.291e+02 | 5.000 | 0 | =====================================================================================
mdl = ldaModel with properties: NumTopics: 20 WordConcentration: 1 TopicConcentration: 5 CorpusTopicProbabilities: [0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500] DocumentTopicProbabilities: [154×20 double] TopicWordProbabilities: [3092×20 double] Vocabulary: ["fairest" "creatures" "desire" "increase" "thereby" "beautys" "rose" "might" "never" "die" "riper" "time" "decease" "tender" "heir" "bear" "memory" "thou" … ] TopicOrder: 'initial-fit-probability' FitInfo: [1×1 struct]
Predict the top topics for an array of new documents.
newDocuments = tokenizedDocument([ "what's in a name? a rose by any other name would smell as sweet." "if music be the food of love, play on."]); topicIdx = predict(mdl,newDocuments)
topicIdx = 2×1
19
8
Visualize the predicted topics using word clouds.
figure subplot(1,2,1) wordcloud(mdl,topicIdx(1)); title("Topic " + topicIdx(1)) subplot(1,2,2) wordcloud(mdl,topicIdx(2)); title("Topic " + topicIdx(2))
Predict Top LDA Topics of Word Count Matrix
Load the example data. sonnetsCounts.mat
contains a matrix of word counts and a corresponding vocabulary of preprocessed versions of Shakespeare's sonnets.
load sonnetsCounts.mat
size(counts)
ans = 1×2
154 3092
Fit an LDA model with 20 topics. To reproduce the results in this example, set rng
to 'default'
.
rng('default')
numTopics = 20;
mdl = fitlda(counts,numTopics)
Initial topic assignments sampled in 0.066217 seconds. ===================================================================================== | Iteration | Time per | Relative | Training | Topic | Topic | | | iteration | change in | perplexity | concentration | concentration | | | (seconds) | log(L) | | | iterations | ===================================================================================== | 0 | 0.01 | | 1.159e+03 | 5.000 | 0 | | 1 | 0.02 | 5.4884e-02 | 8.028e+02 | 5.000 | 0 | | 2 | 0.02 | 4.7400e-03 | 7.778e+02 | 5.000 | 0 | | 3 | 0.02 | 3.4597e-03 | 7.602e+02 | 5.000 | 0 | | 4 | 0.02 | 3.4662e-03 | 7.430e+02 | 5.000 | 0 | | 5 | 0.02 | 2.9259e-03 | 7.288e+02 | 5.000 | 0 | | 6 | 0.02 | 6.4180e-05 | 7.291e+02 | 5.000 | 0 | =====================================================================================
mdl = ldaModel with properties: NumTopics: 20 WordConcentration: 1 TopicConcentration: 5 CorpusTopicProbabilities: [0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500] DocumentTopicProbabilities: [154x20 double] TopicWordProbabilities: [3092x20 double] Vocabulary: ["1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" ... ] (1x3092 string) TopicOrder: 'initial-fit-probability' FitInfo: [1x1 struct]
Predict the top topics for the first 5 documents in counts
.
topicIdx = predict(mdl,counts(1:5,:))
topicIdx = 5×1
3
15
19
3
14
Calculate Topic Prediction Scores
To reproduce the results in this example, set rng
to 'default'
.
rng('default')
Load the example data. The file sonnetsPreprocessed.txt
contains preprocessed versions of Shakespeare's sonnets. The file contains one sonnet per line, with words separated by a space. Extract the text from sonnetsPreprocessed.txt
, split the text into documents at newline characters, and then tokenize the documents.
filename = "sonnetsPreprocessed.txt";
str = extractFileText(filename);
textData = split(str,newline);
documents = tokenizedDocument(textData);
Create a bag-of-words model using bagOfWords
.
bag = bagOfWords(documents)
bag = bagOfWords with properties: Counts: [154x3092 double] Vocabulary: ["fairest" "creatures" "desire" "increase" "thereby" "beautys" "rose" "might" "never" "die" "riper" "time" "decease" "tender" "heir" "bear" "memory" "thou" ... ] (1x3092 string) NumWords: 3092 NumDocuments: 154
Fit an LDA model with 20 topics. To suppress verbose output, set 'Verbose'
to 0.
numTopics = 20;
mdl = fitlda(bag,numTopics,'Verbose',0);
Predict the top topics for a new document. Specify the iteration limit to be 200.
newDocument = tokenizedDocument("what's in a name? a rose by any other name would smell as sweet."); iterationLimit = 200; [topicIdx,scores] = predict(mdl,newDocument, ... 'IterationLimit',iterationLimit)
topicIdx = 19
scores = 1×20
0.0250 0.0250 0.0250 0.0250 0.1250 0.0250 0.0250 0.0250 0.0250 0.0730 0.0250 0.0250 0.0770 0.0250 0.0250 0.0250 0.0250 0.0250 0.2250 0.1250
View the prediction scores in a bar chart.
figure bar(scores) title("LDA Topic Prediction Scores") xlabel("Topic Index") ylabel("Score")
Input Arguments
ldaMdl
— Input LDA model
ldaModel
object
Input LDA model, specified as an ldaModel
object.
documents
— Input documents
tokenizedDocument
array | string array of words | cell array of character vectors
Input documents, specified as a tokenizedDocument
array, a string array of words, or a cell array of
character vectors. If documents
is a
tokenizedDocument
, then it must be a column vector. If
documents
is a string array or a cell array of character
vectors, then it must be a row of the words of a single document.
Tip
To ensure that the function does not discard useful information, you must first preprocess the input documents using the same steps used to preprocess the documents used to train the model.
bag
— Input model
bagOfWords
object | bagOfNgrams
object
Input bag-of-words or bag-of-n-grams model, specified as a bagOfWords
object or a bagOfNgrams
object. If bag
is a
bagOfNgrams
object, then the function treats each n-gram as a
single word.
counts
— Frequency counts of words
matrix of nonnegative integers
Frequency counts of words, specified as a matrix of nonnegative integers. If you specify
'DocumentsIn'
to be 'rows'
, then the value
counts(i,j)
corresponds to the number of times the
jth word of the vocabulary appears in the ith
document. Otherwise, the value counts(i,j)
corresponds to the number
of times the ith word of the vocabulary appears in the
jth document.
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: 'IterationLimit',200
specifies the iteration limit to
be 200.
DocumentsIn
— Orientation of documents
'rows'
(default) | 'columns'
Orientation of documents in the word count matrix, specified as the comma-separated pair
consisting of 'DocumentsIn'
and one of the following:
'rows'
– Input is a matrix of word counts with rows corresponding to documents.'columns'
– Input is a transposed matrix of word counts with columns corresponding to documents.
This option only applies if you specify the input documents as a matrix of word counts.
Note
If you orient your word count matrix so that documents correspond to columns and specify
'DocumentsIn','columns'
, then you might experience a significant
reduction in optimization-execution time.
IterationLimit
— Maximum number of iterations
100
(default) | positive integer
Maximum number of iterations, specified as the comma-separated pair consisting of 'IterationLimit'
and a positive integer.
Example: 'IterationLimit',200
LogLikelihoodTolerance
— Relative tolerance on log-likelihood
0.0001
(default) | positive scalar
Relative tolerance on log-likelihood, specified as the comma-separated pair consisting
of 'LogLikelihoodTolerance'
and a positive scalar. The optimization
terminates when this tolerance is reached.
Example: 'LogLikelihoodTolerance',0.001
Output Arguments
topicIdx
— Predicted topic indices
vector of numeric indices
Predicted topic indices, returned as a vector of numeric indices.
score
— Predicted topic probabilities
matrix
Predicted topic probabilities, returned as a
D
-by-K
matrix, where
D
is the number of input documents and
K
is the number of topics in the LDA model.
score(i,j)
is the probability that topic
j
appears in document i
. Each row
of score
sums to one.
Version History
Introduced in R2017b
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