173 lines
4.2 KiB
Markdown
173 lines
4.2 KiB
Markdown
<!--
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@license Apache-2.0
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Copyright (c) 2018 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# LDA
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> [Latent Dirichlet Allocation][lda] via collapsed Gibbs sampling.
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<section class="intro">
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var lda = require( '@stdlib/nlp/lda' );
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```
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#### lda( docs, K\[, options] )
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[Latent Dirichlet Allocation][lda] via collapsed Gibbs sampling. To create a model, call the `lda` function by passing it an `array` of `strings` and the number of topics `K` that should be identified.
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```javascript
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var model;
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var docs;
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docs = [
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'I loved you first',
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'For one is both and both are one in love',
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'You never see my pain',
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'My love is such that rivers cannot quench',
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'See a lot of pain, a lot of tears'
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];
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model = lda( docs, 2 );
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// returns {}
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```
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After initialization, model parameters are estimated by calling the `.fit()` method, which performs collapsed Gibbs sampling.
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The model object contains the following methods:
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#### model.fit( iter, burnin, thin )
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<!-- run-disable -->
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```javascript
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model.fit( 1000, 100, 10 );
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```
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The `iter` parameter denotes the number of sampling iterations. While a common choice, one thousand iterations might not always be appropriate. Empirical diagnostics can be used to assess whether the constructed Markov Chain has converged. `burnin` denotes the number of estimates that are thrown away at the beginning, whereas `thin` controls the number of estimates discarded in-between iterations.
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#### model.getTerms( k\[, no = 10] )
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Returns the `no` terms with the highest probabilities for chosen topic `k`.
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<!-- run-disable -->
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```javascript
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var words = model.getTerms( 0, 3 );
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/* returns
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[
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{ 'word': 'both', 'prob': 0.06315008476532499 },
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{ 'word': 'pain', 'prob': 0.05515729517235543 },
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{ 'word': 'one', 'prob': 0.05486669737616135 }
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]
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*/
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```
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</section>
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<!-- /.usage -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var sotu = require( '@stdlib/datasets/sotu' );
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var roundn = require( '@stdlib/math/base/special/roundn' );
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var stopwords = require( '@stdlib/datasets/stopwords-en' );
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var lowercase = require( '@stdlib/string/lowercase' );
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var lda = require( '@stdlib/nlp/lda' );
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var speeches;
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var words;
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var terms;
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var model;
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var str;
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var i;
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var j;
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words = stopwords();
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for ( i = 0; i < words.length; i++ ) {
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words[ i ] = new RegExp( '\\b'+words[ i ]+'\\b', 'gi' );
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}
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speeches = sotu({
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'range': [ 1930, 2010 ]
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});
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for ( i = 0; i < speeches.length; i++ ) {
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str = lowercase( speeches[ i ].text );
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for ( j = 0; j < words.length; j++ ) {
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str = str.replace( words[ j ], '' );
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}
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speeches[ i ] = str;
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}
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model = lda( speeches, 3 );
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model.fit( 1000, 100, 10 );
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for ( i = 0; i <= 80; i++ ) {
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str = 'Year: ' + (1930+i) + '\t';
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str += 'Topic 1: ' + roundn( model.avgTheta.get( i, 0 ), -3 ) + '\t';
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str += 'Topic 2: ' + roundn( model.avgTheta.get( i, 1 ), -3 ) + '\t';
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str += 'Topic 3: ' + roundn( model.avgTheta.get( i, 2 ), -3 );
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console.log( str );
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}
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terms = model.getTerms( 0, 20 );
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for ( i = 0; i < terms.length; i++ ) {
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terms[ i ] = terms[ i ].word;
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}
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console.log( 'Words most associated with first topic:\n ' + terms.join( ', ' ) );
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terms = model.getTerms( 1, 20 );
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for ( i = 0; i < terms.length; i++ ) {
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terms[ i ] = terms[ i ].word;
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}
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console.log( 'Words most associated with second topic:\n ' + terms.join( ', ' ) );
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terms = model.getTerms( 2, 20 );
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for ( i = 0; i < terms.length; i++ ) {
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terms[ i ] = terms[ i ].word;
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}
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console.log( 'Words most associated with third topic:\n ' + terms.join( ', ' ) );
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```
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</section>
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<!-- /.examples -->
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<section class="links">
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[lda]: https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
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</section>
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<!-- /.links -->
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