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}); </script> </div> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d2019181/userguide/clustering/lda.html ---------------------------------------------------------------------- diff --git a/userguide/clustering/lda.html b/userguide/clustering/lda.html index e12404a..c9af401 100644 --- a/userguide/clustering/lda.html +++ b/userguide/clustering/lda.html @@ -2095,7 +2095,7 @@ docid, word ) <span class="hljs-keyword">select</span> - train_lda(feature, <span class="hljs-string">"-topic 2 -iter 20"</span>) <span class="hljs-keyword">as</span> (label, word, lambda) + train_lda(feature, <span class="hljs-string">"-topics 2 -iter 20"</span>) <span class="hljs-keyword">as</span> (label, word, lambda) <span class="hljs-keyword">from</span> ( <span class="hljs-keyword">select</span> docid, collect_set(word_count) <span class="hljs-keyword">as</span> feature <span class="hljs-keyword">from</span> word_counts @@ -2104,7 +2104,7 @@ ) t ; </code></pre> -<p>Here, an option <code>-topic 2</code> specifies the number of topics we assume in the set of documents.</p> +<p>Here, an option <code>-topics 2</code> specifies the number of topics we assume in the set of documents.</p> <p>Notice that <code>order by docid</code> ensures building a LDA model precisely in a single node. In case that you like to launch <code>train_lda</code> in parallel, following query hopefully returns similar (but might be slightly approximated) result:</p> <pre><code class="lang-sql">with word_counts as ( <span class="hljs-comment">-- same as above</span> @@ -2113,7 +2113,7 @@ label, word, <span class="hljs-keyword">avg</span>(lambda) <span class="hljs-keyword">as</span> lambda <span class="hljs-keyword">from</span> ( <span class="hljs-keyword">select</span> - train_lda(feature, <span class="hljs-string">"-topic 2 -iter 20"</span>) <span class="hljs-keyword">as</span> (label, word, lambda) + train_lda(feature, <span class="hljs-string">"-topics 2 -iter 20"</span>) <span class="hljs-keyword">as</span> (label, word, lambda) <span class="hljs-keyword">from</span> ( <span class="hljs-keyword">select</span> docid, collect_set(f) <span class="hljs-keyword">as</span> feature <span class="hljs-keyword">from</span> word_counts @@ -2244,7 +2244,7 @@ ) <span class="hljs-keyword">select</span> t.docid, - lda_predict(t.word, t.<span class="hljs-keyword">value</span>, m.label, m.lambda, <span class="hljs-string">"-topic 2"</span>) <span class="hljs-keyword">as</span> probabilities + lda_predict(t.word, t.<span class="hljs-keyword">value</span>, m.label, m.lambda, <span class="hljs-string">"-topics 2"</span>) <span class="hljs-keyword">as</span> probabilities <span class="hljs-keyword">from</span> <span class="hljs-keyword">test</span> t <span class="hljs-keyword">JOIN</span> lda_model m <span class="hljs-keyword">ON</span> (t.word = m.word) @@ -2270,7 +2270,7 @@ </tr> </tbody> </table> -<p>Importantly, an option <code>-topic</code> should be set to the same value as you set for training.</p> +<p>Importantly, an option <code>-topics</code> is expected to be the same value as you set for training.</p> <p>Since the probabilities are sorted in descending order, a label of the most promising topic is easily obtained as:</p> <pre><code class="lang-sql"><span class="hljs-keyword">select</span> docid, probabilities[<span class="hljs-number">0</span>].label <span class="hljs-keyword">from</span> topic @@ -2350,7 +2350,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; 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showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"http://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"clustering/lda.md","mtime":"2017-05-09T08:04:19.000Z","type":"markdown"},"gitbook":{"version":"3.2.2","time":"2017-05-09T08:21:43.808Z"},"basePath":"..","book":{"language":""}}); }); </script> </div> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d2019181/userguide/clustering/plsa.html ---------------------------------------------------------------------- diff --git a/userguide/clustering/plsa.html b/userguide/clustering/plsa.html index bf9c7a7..2cc49c5 100644 --- a/userguide/clustering/plsa.html +++ b/userguide/clustering/plsa.html @@ -2238,7 +2238,13 @@ topic <span class="hljs-keyword">as</span> ( </code></pre> <p>This value controls <strong>how much iterative model update is affected by the old results</strong>.</p> <p>From an algorithmic point of view, training pLSA (and LDA) iteratively repeats certain operations and updates the target value (i.e., probability obtained as a result of <code>train_plsa()</code>). This iterative procedure gradually makes the probabilities more accurate. What <code>alpha</code> does is to control the degree of the change of probabilities in each step.</p> -<p>Normally, <code>alpha</code> is set to a small value from 0.0 to 0.5 (default is 0.5). +<p>Importantly, pLSA is likely to overfit single mini-batch. As a result, <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>P</mi><mo>(</mo><mi>w</mi><mi mathvariant="normal">∣</mi><mi>z</mi><mo>)</mo></mrow><annotation encoding="application/x-tex">P(w|z)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><span class="mord mathit" style="margin-right:0.13889em;">P</span><span class="mopen">(</span><span class="mord mathit" style="margin-right:0.02691em;">w</span><span class="mord mathrm">∣</span><span class="mord mathit" style="margin-right:0.04398em;">z</span><span class="mclose">)</span></span></span></span> could be particularly bad values (i.e., <span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo>(</mo><mi>w</mi><mi mathvarian t="normal">∣</mi><mi>z</mi><mo>)</mo><mo>=</mo><mn>0</mn></mrow><annotation encoding="application/x-tex">(w|z) = 0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="strut" style="height:0.75em;"></span><span class="strut bottom" style="height:1em;vertical-align:-0.25em;"></span><span class="base textstyle uncramped"><span class="mopen">(</span><span class="mord mathit" style="margin-right:0.02691em;">w</span><span class="mord mathrm">∣</span><span class="mord mathit" style="margin-right:0.04398em;">z</span><span class="mclose">)</span><span class="mrel">=</span><span class="mord mathrm">0</span></span></span></span>), and <code>train_plsa()</code> sometimes fails with an exception like:</p> +<pre><code>Perplexity would be Infinity. Try different mini-batch size `-s`, larger `-delta` and/or larger `-alpha`. +</code></pre><p>In that case, you need to try different hyper-parameters to avoid overfitting as the exception suggests.</p> +<p>For instance, <a href="http://qwone.com/~jason/20Newsgroups/"; target="_blank">20 newsgroups dataset</a> which consists of 10906 realistic documents empirically requires the following options:</p> +<pre><code class="lang-sql"><span class="hljs-keyword">SELECT</span> train_plsa(features, <span class="hljs-string">"-topics 20 -iter 10 -s 128 -delta 0.01 -alpha 512 -eps 0.1"</span>) +</code></pre> +<p>Clearly, <code>alpha</code> is much larger than <code>0.01</code> which was used for the dummy data above. Let you keep in mind that an appropriate value of <code>alpha</code> highly depends on the number of documents and mini-batch size. <div id="page-footer"><hr><!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. 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