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Posted to commits@hivemall.apache.org by my...@apache.org on 2018/12/26 10:23:15 UTC
[27/33] incubator-hivemall-site git commit: Update tutorial for
general classifier/regressor
http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d9012d92/userguide/binaryclass/news20_adagrad.html
----------------------------------------------------------------------
diff --git a/userguide/binaryclass/news20_adagrad.html b/userguide/binaryclass/news20_adagrad.html
index f4d3f39..d2742b2 100644
--- a/userguide/binaryclass/news20_adagrad.html
+++ b/userguide/binaryclass/news20_adagrad.html
@@ -100,7 +100,7 @@
<link rel="next" href="news20_rf.html" />
- <link rel="prev" href="news20_scw.html" />
+ <link rel="prev" href="news20_generic.html" />
</head>
@@ -972,7 +972,7 @@
<b>6.2.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -980,13 +980,28 @@
</li>
- <li class="chapter " data-level="6.2.2" data-path="a9a_lr.html">
+ <li class="chapter " data-level="6.2.2" data-path="a9a_generic.html">
- <a href="a9a_lr.html">
+ <a href="a9a_generic.html">
<b>6.2.2.</b>
+ General Binary Classifier
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="6.2.3" data-path="a9a_lr.html">
+
+ <a href="a9a_lr.html">
+
+
+ <b>6.2.3.</b>
+
Logistic Regression
</a>
@@ -995,14 +1010,14 @@
</li>
- <li class="chapter " data-level="6.2.3" data-path="a9a_minibatch.html">
+ <li class="chapter " data-level="6.2.4" data-path="a9a_minibatch.html">
<a href="a9a_minibatch.html">
- <b>6.2.3.</b>
+ <b>6.2.4.</b>
- Mini-batch gradient descent
+ Mini-batch Gradient Descent
</a>
@@ -1038,7 +1053,7 @@
<b>6.3.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1076,13 +1091,28 @@
</li>
- <li class="chapter active" data-level="6.3.4" data-path="news20_adagrad.html">
+ <li class="chapter " data-level="6.3.4" data-path="news20_generic.html">
- <a href="news20_adagrad.html">
+ <a href="news20_generic.html">
<b>6.3.4.</b>
+ General Binary Classifier
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter active" data-level="6.3.5" data-path="news20_adagrad.html">
+
+ <a href="news20_adagrad.html">
+
+
+ <b>6.3.5.</b>
+
AdaGradRDA, AdaGrad, AdaDelta
</a>
@@ -1091,12 +1121,12 @@
</li>
- <li class="chapter " data-level="6.3.5" data-path="news20_rf.html">
+ <li class="chapter " data-level="6.3.6" data-path="news20_rf.html">
<a href="news20_rf.html">
- <b>6.3.5.</b>
+ <b>6.3.6.</b>
Random Forest
@@ -1134,7 +1164,7 @@
<b>6.4.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1185,7 +1215,7 @@
<b>6.5.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1236,7 +1266,7 @@
<b>6.6.1.</b>
- Data pareparation
+ Data Pareparation
</a>
@@ -1302,7 +1332,7 @@
<b>6.8.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1360,7 +1390,7 @@
<b>7.1.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1375,7 +1405,7 @@
<b>7.1.2.</b>
- Data preparation for one-vs-the-rest classifiers
+ Data Preparation for one-vs-the-rest classifiers
</a>
@@ -1435,7 +1465,7 @@
<b>7.1.6.</b>
- one-vs-the-rest classifier
+ one-vs-the-rest Classifier
</a>
@@ -1559,7 +1589,7 @@
<b>8.2.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1567,13 +1597,28 @@
</li>
- <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_arow.html">
+ <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_generic.html">
- <a href="../regression/e2006_arow.html">
+ <a href="../regression/e2006_generic.html">
<b>8.2.2.</b>
+ General Regessor
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="8.2.3" data-path="../regression/e2006_arow.html">
+
+ <a href="../regression/e2006_arow.html">
+
+
+ <b>8.2.3.</b>
+
Passive Aggressive, AROW
</a>
@@ -1610,7 +1655,7 @@
<b>8.3.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1698,7 +1743,7 @@
<b>9.1.1.</b>
- Item-based collaborative filtering
+ Item-based Collaborative Filtering
</a>
@@ -1734,7 +1779,7 @@
<b>9.2.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1749,7 +1794,7 @@
<b>9.2.2.</b>
- LSH/MinHash and Jaccard similarity
+ LSH/MinHash and Jaccard Similarity
</a>
@@ -1764,7 +1809,7 @@
<b>9.2.3.</b>
- LSH/MinHash and brute-force search
+ LSH/MinHash and Brute-force Search
</a>
@@ -1815,7 +1860,7 @@
<b>9.3.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1830,7 +1875,7 @@
<b>9.3.2.</b>
- Item-based collaborative filtering
+ Item-based Collaborative Filtering
</a>
@@ -1875,7 +1920,7 @@
<b>9.3.5.</b>
- SLIM for fast top-k recommendation
+ SLIM for fast top-k Recommendation
</a>
@@ -1890,7 +1935,7 @@
<b>9.3.6.</b>
- 10-fold cross validation (Matrix Factorization)
+ 10-fold Cross Validation (Matrix Factorization)
</a>
@@ -2080,7 +2125,7 @@
<b>13.2.1.</b>
- a9a tutorial for DataFrame
+ a9a Tutorial for DataFrame
</a>
@@ -2095,7 +2140,7 @@
<b>13.2.2.</b>
- a9a tutorial for SQL
+ a9a Tutorial for SQL
</a>
@@ -2131,7 +2176,7 @@
<b>13.3.1.</b>
- E2006-tfidf regression tutorial for DataFrame
+ E2006-tfidf Regression Tutorial for DataFrame
</a>
@@ -2146,7 +2191,7 @@
<b>13.3.2.</b>
- E2006-tfidf regression tutorial for SQL
+ E2006-tfidf Regression Tutorial for SQL
</a>
@@ -2166,7 +2211,7 @@
<b>13.4.</b>
- Generic features
+ Generic Features
</a>
@@ -2182,7 +2227,7 @@
<b>13.4.1.</b>
- Top-k join processing
+ Top-k Join Processing
</a>
@@ -2197,7 +2242,7 @@
<b>13.4.2.</b>
- Other utility functions
+ Other Utility Functions
</a>
@@ -2320,36 +2365,41 @@
<!-- toc --><div id="toc" class="toc">
<ul>
-<li><a href="#udf-preparation">UDF preparation</a></li>
+<li><a href="#adagradrda">AdaGradRDA</a><ul>
<li><a href="#model-building">model building</a></li>
<li><a href="#prediction">prediction</a></li>
<li><a href="#evaluation">evaluation</a></li>
+</ul>
+</li>
+<li><a href="#adagrad">AdaGrad</a><ul>
<li><a href="#model-building-1">model building</a></li>
<li><a href="#prediction-1">prediction</a></li>
<li><a href="#evaluation-1">evaluation</a></li>
+</ul>
+</li>
+<li><a href="#adadelta">AdaDelta</a><ul>
<li><a href="#model-building-2">model building</a></li>
<li><a href="#prediction-2">prediction</a></li>
<li><a href="#evaluation-2">evaluation</a></li>
</ul>
+</li>
+</ul>
</div><!-- tocstop -->
-<div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div class="panel-body"><p>This feature is supported since Hivemall <code>v0.3-beta2</code> or later.</p></div></div>
-<h2 id="udf-preparation">UDF preparation</h2>
-<pre><code>add jar ./tmp/hivemall-with-dependencies.jar;
-source ./tmp/define-all.hive;
-
-use news20;
-</code></pre><h1 id="adagradrda">[AdaGradRDA]</h1>
+<div class="panel panel-warning"><div class="panel-heading"><h3 class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> Caution</h3></div><div class="panel-body"><p><code>train_adagrad()</code> became deprecated since v0.5.0 release. Use smarter <a href="a9a_generic.html">general classifier</a> instead.</p></div></div>
+<h1 id="adagradrda">AdaGradRDA</h1>
<div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div class="panel-body"><p>The current AdaGradRDA implmenetation can only be applied to classification, not to regression, because it uses hinge loss for the loss function.</p></div></div>
<h2 id="model-building">model building</h2>
-<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> news20b_adagrad_rda_model1;
+<pre><code class="lang-sql"><span class="hljs-keyword">use</span> news20;
+
+<span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> news20b_adagrad_rda_model1;
<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> news20b_adagrad_rda_model1 <span class="hljs-keyword">as</span>
<span class="hljs-keyword">select</span>
feature,
voted_avg(weight) <span class="hljs-keyword">as</span> weight
<span class="hljs-keyword">from</span>
(<span class="hljs-keyword">select</span>
- train_adagrad_rda(addBias(features),label) <span class="hljs-keyword">as</span> (feature,weight)
+ train_adagrad_rda(add_bias(features),label) <span class="hljs-keyword">as</span> (feature,weight)
<span class="hljs-keyword">from</span>
news20b_train_x3
) t
@@ -2384,8 +2434,8 @@ use news20;
<p>SCW1 0.9661729383506805 </p>
<p>ADAGRAD+RDA 0.9677742193755005</p>
</blockquote>
-<h1 id="adagrad">[AdaGrad]</h1>
-<p><em>Note that AdaGrad is better suited for a regression problem because the current implementation only support logistic loss.</em></p>
+<h1 id="adagrad">AdaGrad</h1>
+<div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div class="panel-body"><p>AdaGrad is better suited for a binary classification problem because the current implementation only support logistic loss.</p></div></div>
<h2 id="model-building">model building</h2>
<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> news20b_adagrad_model1;
<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> news20b_adagrad_model1 <span class="hljs-keyword">as</span>
@@ -2394,7 +2444,7 @@ use news20;
voted_avg(weight) <span class="hljs-keyword">as</span> weight
<span class="hljs-keyword">from</span>
(<span class="hljs-keyword">select</span>
- adagrad(addBias(features),convert_label(label)) <span class="hljs-keyword">as</span> (feature,weight)
+ train_adagrad_regr(add_bias(features),convert_label(label)) <span class="hljs-keyword">as</span> (feature,weight)
<span class="hljs-keyword">from</span>
news20b_train_x3
) t
@@ -2428,7 +2478,7 @@ use news20;
<blockquote>
<p>0.9549639711769415 (adagrad)</p>
</blockquote>
-<h1 id="adadelta">[AdaDelta]</h1>
+<h1 id="adadelta">AdaDelta</h1>
<div class="panel panel-warning"><div class="panel-heading"><h3 class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> Caution</h3></div><div class="panel-body"><p>AdaDelta can only be applied for regression problem because the current implementation only support logistic loss.</p></div></div>
<h2 id="model-building">model building</h2>
<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> news20b_adadelta_model1;
@@ -2438,7 +2488,7 @@ use news20;
voted_avg(weight) <span class="hljs-keyword">as</span> weight
<span class="hljs-keyword">from</span>
(<span class="hljs-keyword">select</span>
- adadelta(addBias(features),convert_label(label)) <span class="hljs-keyword">as</span> (feature,weight)
+ adadelta(add_bias(features),convert_label(label)) <span class="hljs-keyword">as</span> (feature,weight)
<span class="hljs-keyword">from</span>
news20b_train_x3
) t
@@ -2528,7 +2578,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
<script>
var gitbook = gitbook || [];
gitbook.push(function() {
- gitbook.page.hasChanged({"page":{"title":"AdaGradRDA, AdaGrad, AdaDelta","level":"6.3.4","depth":2,"next":{"title":"Random Forest","level":"6.3.5","depth":2,"path":"binaryclass/news20_rf.md","ref":"binaryclass/news20_rf.md","articles":[]},"previous":{"title":"CW, AROW, SCW","level":"6.3.3","depth":2,"path":"binaryclass/news20_scw.md","ref":"binaryclass/news20_scw.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall/"},"
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+ gitbook.page.hasChanged({"page":{"title":"AdaGradRDA, AdaGrad, AdaDelta","level":"6.3.5","depth":2,"next":{"title":"Random Forest","level":"6.3.6","depth":2,"path":"binaryclass/news20_rf.md","ref":"binaryclass/news20_rf.md","articles":[]},"previous":{"title":"General Binary Classifier","level":"6.3.4","depth":2,"path":"binaryclass/news20_generic.md","ref":"binaryclass/news20_generic.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/inc
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});
</script>
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@@ -2558,7 +2608,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
- <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.1.1/anchor.min.js"></script>
+ <script src="../gitbook/gitbook-plugin-anchorjs/anchor.min.js"></script>
http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d9012d92/userguide/binaryclass/news20_dataset.html
----------------------------------------------------------------------
diff --git a/userguide/binaryclass/news20_dataset.html b/userguide/binaryclass/news20_dataset.html
index 6b26de4..26537ad 100644
--- a/userguide/binaryclass/news20_dataset.html
+++ b/userguide/binaryclass/news20_dataset.html
@@ -4,7 +4,7 @@
<head>
<meta charset="UTF-8">
<meta content="text/html; charset=utf-8" http-equiv="Content-Type">
- <title>Data preparation · Hivemall User Manual</title>
+ <title>Data Preparation · Hivemall User Manual</title>
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
<meta name="description" content="">
<meta name="generator" content="GitBook 3.2.3">
@@ -972,7 +972,7 @@
<b>6.2.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -980,13 +980,28 @@
</li>
- <li class="chapter " data-level="6.2.2" data-path="a9a_lr.html">
+ <li class="chapter " data-level="6.2.2" data-path="a9a_generic.html">
- <a href="a9a_lr.html">
+ <a href="a9a_generic.html">
<b>6.2.2.</b>
+ General Binary Classifier
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="6.2.3" data-path="a9a_lr.html">
+
+ <a href="a9a_lr.html">
+
+
+ <b>6.2.3.</b>
+
Logistic Regression
</a>
@@ -995,14 +1010,14 @@
</li>
- <li class="chapter " data-level="6.2.3" data-path="a9a_minibatch.html">
+ <li class="chapter " data-level="6.2.4" data-path="a9a_minibatch.html">
<a href="a9a_minibatch.html">
- <b>6.2.3.</b>
+ <b>6.2.4.</b>
- Mini-batch gradient descent
+ Mini-batch Gradient Descent
</a>
@@ -1038,7 +1053,7 @@
<b>6.3.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1076,13 +1091,28 @@
</li>
- <li class="chapter " data-level="6.3.4" data-path="news20_adagrad.html">
+ <li class="chapter " data-level="6.3.4" data-path="news20_generic.html">
- <a href="news20_adagrad.html">
+ <a href="news20_generic.html">
<b>6.3.4.</b>
+ General Binary Classifier
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="6.3.5" data-path="news20_adagrad.html">
+
+ <a href="news20_adagrad.html">
+
+
+ <b>6.3.5.</b>
+
AdaGradRDA, AdaGrad, AdaDelta
</a>
@@ -1091,12 +1121,12 @@
</li>
- <li class="chapter " data-level="6.3.5" data-path="news20_rf.html">
+ <li class="chapter " data-level="6.3.6" data-path="news20_rf.html">
<a href="news20_rf.html">
- <b>6.3.5.</b>
+ <b>6.3.6.</b>
Random Forest
@@ -1134,7 +1164,7 @@
<b>6.4.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1185,7 +1215,7 @@
<b>6.5.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1236,7 +1266,7 @@
<b>6.6.1.</b>
- Data pareparation
+ Data Pareparation
</a>
@@ -1302,7 +1332,7 @@
<b>6.8.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1360,7 +1390,7 @@
<b>7.1.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1375,7 +1405,7 @@
<b>7.1.2.</b>
- Data preparation for one-vs-the-rest classifiers
+ Data Preparation for one-vs-the-rest classifiers
</a>
@@ -1435,7 +1465,7 @@
<b>7.1.6.</b>
- one-vs-the-rest classifier
+ one-vs-the-rest Classifier
</a>
@@ -1559,7 +1589,7 @@
<b>8.2.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1567,13 +1597,28 @@
</li>
- <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_arow.html">
+ <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_generic.html">
- <a href="../regression/e2006_arow.html">
+ <a href="../regression/e2006_generic.html">
<b>8.2.2.</b>
+ General Regessor
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="8.2.3" data-path="../regression/e2006_arow.html">
+
+ <a href="../regression/e2006_arow.html">
+
+
+ <b>8.2.3.</b>
+
Passive Aggressive, AROW
</a>
@@ -1610,7 +1655,7 @@
<b>8.3.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1698,7 +1743,7 @@
<b>9.1.1.</b>
- Item-based collaborative filtering
+ Item-based Collaborative Filtering
</a>
@@ -1734,7 +1779,7 @@
<b>9.2.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1749,7 +1794,7 @@
<b>9.2.2.</b>
- LSH/MinHash and Jaccard similarity
+ LSH/MinHash and Jaccard Similarity
</a>
@@ -1764,7 +1809,7 @@
<b>9.2.3.</b>
- LSH/MinHash and brute-force search
+ LSH/MinHash and Brute-force Search
</a>
@@ -1815,7 +1860,7 @@
<b>9.3.1.</b>
- Data preparation
+ Data Preparation
</a>
@@ -1830,7 +1875,7 @@
<b>9.3.2.</b>
- Item-based collaborative filtering
+ Item-based Collaborative Filtering
</a>
@@ -1875,7 +1920,7 @@
<b>9.3.5.</b>
- SLIM for fast top-k recommendation
+ SLIM for fast top-k Recommendation
</a>
@@ -1890,7 +1935,7 @@
<b>9.3.6.</b>
- 10-fold cross validation (Matrix Factorization)
+ 10-fold Cross Validation (Matrix Factorization)
</a>
@@ -2080,7 +2125,7 @@
<b>13.2.1.</b>
- a9a tutorial for DataFrame
+ a9a Tutorial for DataFrame
</a>
@@ -2095,7 +2140,7 @@
<b>13.2.2.</b>
- a9a tutorial for SQL
+ a9a Tutorial for SQL
</a>
@@ -2131,7 +2176,7 @@
<b>13.3.1.</b>
- E2006-tfidf regression tutorial for DataFrame
+ E2006-tfidf Regression Tutorial for DataFrame
</a>
@@ -2146,7 +2191,7 @@
<b>13.3.2.</b>
- E2006-tfidf regression tutorial for SQL
+ E2006-tfidf Regression Tutorial for SQL
</a>
@@ -2166,7 +2211,7 @@
<b>13.4.</b>
- Generic features
+ Generic Features
</a>
@@ -2182,7 +2227,7 @@
<b>13.4.1.</b>
- Top-k join processing
+ Top-k Join Processing
</a>
@@ -2197,7 +2242,7 @@
<b>13.4.2.</b>
- Other utility functions
+ Other Utility Functions
</a>
@@ -2284,7 +2329,7 @@
<!-- Title -->
<h1>
<i class="fa fa-circle-o-notch fa-spin"></i>
- <a href=".." >Data preparation</a>
+ <a href=".." >Data Preparation</a>
</h1>
</div>
@@ -2352,11 +2397,6 @@ hadoop fs -copyFromLocal news20.test.t /dataset/news20-binary/test
<pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span class="hljs-keyword">database</span> news20;
<span class="hljs-keyword">use</span> news20;
-<span class="hljs-keyword">delete</span> jar /home/myui/tmp/hivemall.jar;
-add jar /home/myui/tmp/hivemall.jar;
-
-source /home/myui/tmp/define-all.hive;
-
<span class="hljs-keyword">Create</span> <span class="hljs-keyword">external</span> <span class="hljs-keyword">table</span> news20b_train (
<span class="hljs-keyword">rowid</span> <span class="hljs-built_in">int</span>,
label <span class="hljs-built_in">int</span>,
@@ -2375,10 +2415,10 @@ source /home/myui/tmp/define-all.hive;
<span class="hljs-keyword">select</span>
*
<span class="hljs-keyword">from</span> (
-<span class="hljs-keyword">select</span>
- amplify(<span class="hljs-number">3</span>, *) <span class="hljs-keyword">as</span> (<span class="hljs-keyword">rowid</span>, label, features)
-<span class="hljs-keyword">from</span>
- news20b_train
+ <span class="hljs-keyword">select</span>
+ amplify(<span class="hljs-number">3</span>, *) <span class="hljs-keyword">as</span> (<span class="hljs-keyword">rowid</span>, label, features)
+ <span class="hljs-keyword">from</span>
+ news20b_train
) t
CLUSTER <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(${<span class="hljs-keyword">seed</span>});
@@ -2386,11 +2426,8 @@ CLUSTER <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</sp
<span class="hljs-keyword">select</span>
<span class="hljs-keyword">rowid</span>,
label,
- <span class="hljs-keyword">cast</span>(<span class="hljs-keyword">split</span>(feature,<span class="hljs-string">":"</span>)[<span class="hljs-number">0</span>] <span class="hljs-keyword">as</span> <span class="hljs-built_in">int</span>) <span class="hljs-keyword">as</span> feature,
- <span class="hljs-keyword">cast</span>(<span class="hljs-keyword">split</span>(feature,<span class="hljs-string">":"</span>)[<span class="hljs-number">1</span>] <span class="hljs-keyword">as</span> <span class="hljs-built_in">float</span>) <span class="hljs-keyword">as</span> <span class="hljs-keyword">value</span>
- <span class="hljs-comment">-- hivemall v0.3.1 or later</span>
- <span class="hljs-comment">-- extract_feature(feature) as feature,</span>
- <span class="hljs-comment">-- extract_weight(feature) as value</span>
+ extract_feature(feature) <span class="hljs-keyword">as</span> feature,
+ extract_weight(feature) <span class="hljs-keyword">as</span> <span class="hljs-keyword">value</span>
<span class="hljs-keyword">from</span>
news20b_test LATERAL <span class="hljs-keyword">VIEW</span> explode(add_bias(features)) t <span class="hljs-keyword">AS</span> feature;
</code></pre>
@@ -2449,7 +2486,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
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@@ -2479,7 +2516,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda
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