transform
Transform documents into lower-dimensional space
Syntax
Description
specifies additional options using one or more name-value pair arguments. These
name-value pairs only apply if the input model is an dscores
= transform(___,Name,Value
)ldaModel
object.
Examples
Transform Documents into LSA Semantic Space
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 LSA model with 20 components.
numCompnents = 20; mdl = fitlsa(bag,numCompnents)
mdl = lsaModel with properties: NumComponents: 20 ComponentWeights: [2.7866e+03 515.5889 443.6428 316.4191 295.4065 261.8927 226.1649 186.2160 170.6413 156.6033 151.5275 146.2553 141.6741 135.5318 134.1694 128.9931 124.2382 122.2931 116.5035 116.2590] DocumentScores: [154x20 double] WordScores: [3092x20 double] Vocabulary: ["fairest" "creatures" "desire" "increase" "thereby" "beautys" "rose" "might" "never" "die" "riper" "time" "decease" "tender" "heir" "bear" "memory" ... ] (1x3092 string) FeatureStrengthExponent: 2
Use transform
to transform the first 10 documents into the semantic space of the LSA model.
dscores = transform(mdl,documents(1:10))
dscores = 10×20
5.6059 -1.8559 0.9286 -0.7086 -0.4652 0.8340 0.6751 -0.0611 -0.2268 1.9320 -0.7289 -1.0864 0.7131 -0.0571 -0.3401 0.0940 -0.4406 1.7507 -1.1534 0.1785
7.3069 -2.3578 1.8359 -2.3442 -1.5776 2.0310 0.7948 -1.3411 1.1700 1.8839 0.0883 0.4734 -1.1244 0.6795 1.3585 -0.0247 0.3627 -0.5414 -0.0272 -0.0114
7.1056 -2.3508 -2.8837 -1.0688 -0.3462 0.6962 0.0334 0.0472 -0.4916 0.6496 -1.1959 -1.0171 -0.4020 1.2953 -0.4583 0.5984 -0.3890 1.1780 0.6413 0.6575
8.6292 -3.0471 -0.8512 -0.4356 -0.3055 -0.4671 -1.4219 0.8454 0.8270 0.4122 2.2082 -1.1770 1.7775 -2.2344 -2.7813 1.4979 0.7486 -2.0593 0.6376 1.0721
1.0434 1.7490 0.8703 -2.2315 -1.1221 -0.2848 -2.0522 0.6975 -1.7191 -0.2852 0.8879 0.9950 -0.5555 0.8842 -0.0360 1.0050 0.4158 0.5061 0.9602 0.4672
6.8358 -2.0806 -3.3798 -1.0452 -0.2075 -2.0970 -0.4477 -0.2080 -0.9532 1.6203 0.6653 0.0036 1.0825 0.6396 -0.2154 -0.0794 0.7108 1.8007 -4.0326 -0.3872
2.3847 0.3923 -0.4323 -1.5340 0.4023 1.0396 -1.0326 -0.3776 -0.2101 -1.0944 -0.7513 -0.2894 0.4303 0.1864 0.4922 0.4844 0.5191 -0.2378 0.9528 0.4817
3.7925 -0.3941 -4.4610 -0.4930 0.4651 -0.3404 -0.5493 -0.1470 -0.5065 0.2566 0.3394 -1.1529 -0.0391 -0.8800 -0.4712 0.9672 0.5457 -0.3639 -0.3085 0.5637
4.6522 0.7188 -1.1787 -0.8996 0.3360 -0.4531 -0.1935 -0.3328 0.8640 -1.6679 -0.8056 -2.1993 0.1808 0.0163 -0.9520 -0.8982 0.6603 3.6451 1.2412 1.9621
8.8218 -0.8168 -2.5101 1.1197 -0.8673 1.2336 0.0768 -0.1943 0.7629 -0.1222 0.3786 1.1611 0.2326 0.3415 -0.3327 -0.3792 1.7554 0.2526 -2.1574 -0.0193
Transform Documents into LDA Topic Mixtures
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 five topics.
numTopics = 5; mdl = fitlda(bag,numTopics)
Initial topic assignments sampled in 0.064998 seconds. ===================================================================================== | Iteration | Time per | Relative | Training | Topic | Topic | | | iteration | change in | perplexity | concentration | concentration | | | (seconds) | log(L) | | | iterations | ===================================================================================== | 0 | 0.01 | | 1.212e+03 | 1.250 | 0 | | 1 | 0.01 | 1.2300e-02 | 1.112e+03 | 1.250 | 0 | | 2 | 0.01 | 1.3254e-03 | 1.102e+03 | 1.250 | 0 | | 3 | 0.01 | 2.9402e-05 | 1.102e+03 | 1.250 | 0 | =====================================================================================
mdl = ldaModel with properties: NumTopics: 5 WordConcentration: 1 TopicConcentration: 1.2500 CorpusTopicProbabilities: [0.2000 0.2000 0.2000 0.2000 0.2000] DocumentTopicProbabilities: [154x5 double] TopicWordProbabilities: [3092x5 double] Vocabulary: ["fairest" "creatures" "desire" "increase" "thereby" "beautys" "rose" "might" "never" "die" "riper" "time" "decease" "tender" "heir" "bear" ... ] (1x3092 string) TopicOrder: 'initial-fit-probability' FitInfo: [1x1 struct]
Use transform
to transform the documents into a vector of topic probabilities. You can visualize these mixtures using stacked bar charts. View the topic mixtures of the first 10 documents.
topicMixtures = transform(mdl,documents(1:10)); figure barh(topicMixtures,'stacked') xlim([0 1]) title("Topic Mixtures") xlabel("Topic Probability") ylabel("Document") legend("Topic " + string(1:numTopics),'Location','northeastoutside')
Transform Word Count Matrix into LDA Topic Mixtures
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 0.072024 seconds. ===================================================================================== | Iteration | Time per | Relative | Training | Topic | Topic | | | iteration | change in | perplexity | concentration | concentration | | | (seconds) | log(L) | | | iterations | ===================================================================================== | 0 | 0.00 | | 1.159e+03 | 5.000 | 0 | | 1 | 0.02 | 5.4884e-02 | 8.028e+02 | 5.000 | 0 | | 2 | 0.03 | 4.7400e-03 | 7.778e+02 | 5.000 | 0 | | 3 | 0.02 | 3.4597e-03 | 7.602e+02 | 5.000 | 0 | | 4 | 0.03 | 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]
Use transform
to transform the documents into a vector of topic probabilities.
topicMixtures = transform(mdl,counts(1:10,:))
topicMixtures = 10×20
0.0167 0.0035 0.1645 0.0977 0.0433 0.0833 0.0987 0.0033 0.0299 0.0234 0.0033 0.0345 0.0235 0.0958 0.0667 0.0167 0.0300 0.0519 0.0833 0.0300
0.0711 0.0544 0.0116 0.0044 0.0033 0.0033 0.0431 0.0053 0.0145 0.0421 0.0971 0.0033 0.0040 0.1632 0.1784 0.0937 0.0683 0.0398 0.0954 0.0037
0.0293 0.0482 0.1078 0.0322 0.0036 0.0036 0.0464 0.0036 0.0064 0.0612 0.0036 0.0176 0.0036 0.0464 0.0906 0.1169 0.0888 0.1115 0.1180 0.0607
0.0055 0.0962 0.2403 0.0033 0.0296 0.1613 0.0164 0.0955 0.0163 0.0045 0.0172 0.0033 0.0415 0.0404 0.0342 0.0176 0.0417 0.0642 0.0033 0.0676
0.0341 0.0224 0.0341 0.0645 0.0948 0.0038 0.0189 0.1099 0.0187 0.0560 0.1045 0.0356 0.0668 0.1196 0.0038 0.0931 0.0493 0.0038 0.0038 0.0626
0.0445 0.0035 0.1167 0.0034 0.0446 0.0583 0.1268 0.0169 0.0034 0.1135 0.0034 0.0034 0.0047 0.0993 0.0909 0.0582 0.0308 0.0887 0.0856 0.0034
0.1720 0.0764 0.0090 0.0180 0.0325 0.1213 0.0036 0.0036 0.0505 0.0472 0.0348 0.0477 0.0039 0.0038 0.0122 0.0041 0.0036 0.1605 0.1487 0.0465
0.0043 0.0033 0.1248 0.0033 0.0299 0.0033 0.0690 0.1699 0.0695 0.0982 0.0033 0.0039 0.0620 0.0833 0.0040 0.0700 0.0033 0.1479 0.0033 0.0433
0.0412 0.0387 0.0555 0.0165 0.0166 0.0433 0.0033 0.0038 0.0048 0.0033 0.0473 0.0474 0.1290 0.1107 0.0089 0.0112 0.0167 0.1555 0.2423 0.0040
0.0362 0.0035 0.1117 0.0304 0.0034 0.1248 0.0439 0.0340 0.0168 0.0714 0.0034 0.0214 0.0056 0.0449 0.1438 0.0036 0.0290 0.1437 0.0980 0.0304
Input Arguments
lsaMdl
— Input LSA model
lsaModel
object
Input LSA model, specified as an lsaModel
object.
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
sets the iteration limit to
200.
Note
These name-value pairs only apply if the input model is an
ldaModel
object.
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
dscores
— Output document scores
matrix
Output document scores, returned as a matrix of score vectors.
Version History
Introduced in R2017b
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