http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d9012d92/userguide/regression/e2006_arow.html ---------------------------------------------------------------------- diff --git a/userguide/regression/e2006_arow.html b/userguide/regression/e2006_arow.html index c95408e..473cbce 100644 --- a/userguide/regression/e2006_arow.html +++ b/userguide/regression/e2006_arow.html @@ -100,7 +100,7 @@ <link rel="next" href="kddcup12tr2.html" /> - <link rel="prev" href="e2006_dataset.html" /> + <link rel="prev" href="e2006_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="../binaryclass/a9a_lr.html"> + <li class="chapter " data-level="6.2.2" data-path="../binaryclass/a9a_generic.html"> - <a href="../binaryclass/a9a_lr.html"> + <a href="../binaryclass/a9a_generic.html"> <b>6.2.2.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.3" data-path="../binaryclass/a9a_lr.html"> + + <a href="../binaryclass/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="../binaryclass/a9a_minibatch.html"> + <li class="chapter " data-level="6.2.4" data-path="../binaryclass/a9a_minibatch.html"> <a href="../binaryclass/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="../binaryclass/news20_adagrad.html"> + <li class="chapter " data-level="6.3.4" data-path="../binaryclass/news20_generic.html"> - <a href="../binaryclass/news20_adagrad.html"> + <a href="../binaryclass/news20_generic.html"> <b>6.3.4.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.3.5" data-path="../binaryclass/news20_adagrad.html"> + + <a href="../binaryclass/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="../binaryclass/news20_rf.html"> + <li class="chapter " data-level="6.3.6" data-path="../binaryclass/news20_rf.html"> <a href="../binaryclass/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 active" data-level="8.2.2" data-path="e2006_arow.html"> + <li class="chapter " data-level="8.2.2" data-path="e2006_generic.html"> - <a href="e2006_arow.html"> + <a href="e2006_generic.html"> <b>8.2.2.</b> + General Regessor + + </a> + + + + </li> + + <li class="chapter active" data-level="8.2.3" data-path="e2006_arow.html"> + + <a href="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> @@ -2317,11 +2362,40 @@ specific language governing permissions and limitations under the License. --> -<p><a href="https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html#E2006-tfidf"; target="_blank">https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html#E2006-tfidf</a></p> -<hr> -<h1 id="pa1a">[PA1a]</h1> +<!-- toc --><div id="toc" class="toc"> + +<ul> +<li><a href="#pa1a">PA1a</a><ul> +<li><a href="#training">Training</a></li> +<li><a href="#prediction">prediction</a></li> +<li><a href="#evaluation">evaluation</a></li> +</ul> +</li> +<li><a href="#pa2a">PA2a</a><ul> +<li><a href="#training-1">Training</a></li> +<li><a href="#prediction-1">prediction</a></li> +<li><a href="#evaluation-1">evaluation</a></li> +</ul> +</li> +<li><a href="#arow">AROW</a><ul> +<li><a href="#training-2">Training</a></li> +<li><a href="#prediction-2">prediction</a></li> +<li><a href="#evaluation-2">evaluation</a></li> +</ul> +</li> +<li><a href="#arowe">AROWe</a><ul> +<li><a href="#training-3">Training</a></li> +<li><a href="#prediction-3">prediction</a></li> +<li><a href="#evaluation-3">evaluation</a></li> +</ul> +</li> +</ul> + +</div><!-- tocstop --> +<h1 id="pa1a">PA1a</h1> <h2 id="training">Training</h2> <pre><code class="lang-sql"><span class="hljs-keyword">set</span> mapred.reduce.tasks=<span class="hljs-number">64</span>; + <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> e2006tfidf_pa1a_model ; <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> e2006tfidf_pa1a_model <span class="hljs-keyword">as</span> <span class="hljs-keyword">select</span> @@ -2334,9 +2408,11 @@ e2006tfidf_train_x3 ) t <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> feature; + +<span class="hljs-comment">-- reset to the default setting</span> <span class="hljs-keyword">set</span> mapred.reduce.tasks=<span class="hljs-number">-1</span>; </code></pre> -<p><em>Caution: Do not use voted_avg() for regression. voted_avg() is for classification.</em></p> +<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>Do not use <code>voted_avg()</code> for regression. <code>voted_avg()</code> is for classification.</p></div></div> <h2 id="prediction">prediction</h2> <pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span class="hljs-keyword">or</span> <span class="hljs-keyword">replace</span> <span class="hljs-keyword">view</span> e2006tfidf_pa1a_predict <span class="hljs-keyword">as</span> @@ -2350,35 +2426,42 @@ t.<span class="hljs-keyword">rowid</span>; </code></pre> <h2 id="evaluation">evaluation</h2> -<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> e2006tfidf_pa1a_submit; -<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> e2006tfidf_pa1a_submit <span class="hljs-keyword">as</span> -<span class="hljs-keyword">select</span> - t.target <span class="hljs-keyword">as</span> actual, - p.predicted <span class="hljs-keyword">as</span> predicted -<span class="hljs-keyword">from</span> - e2006tfidf_test t <span class="hljs-keyword">JOIN</span> e2006tfidf_pa1a_predict p - <span class="hljs-keyword">on</span> (t.<span class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>); - -<span class="hljs-keyword">select</span> <span class="hljs-keyword">avg</span>(actual), <span class="hljs-keyword">avg</span>(predicted) <span class="hljs-keyword">from</span> e2006tfidf_pa1a_submit; -</code></pre> -<blockquote> -<p>-3.8200363760415414 -3.8869923258589476</p> -</blockquote> -<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:mean_actual=<span class="hljs-number">-3.8200363760415414</span>; - +<pre><code class="lang-sql">WITH submit as ( + <span class="hljs-keyword">select</span> + t.target <span class="hljs-keyword">as</span> actual, + p.predicted <span class="hljs-keyword">as</span> predicted + <span class="hljs-keyword">from</span> + e2006tfidf_test t + <span class="hljs-keyword">JOIN</span> e2006tfidf_pa1a_predict p + <span class="hljs-keyword">on</span> (t.<span class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>) +) <span class="hljs-keyword">select</span> - <span class="hljs-keyword">sqrt</span>(<span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(predicted - actual,<span class="hljs-number">2.0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>)) <span class="hljs-keyword">as</span> RMSE, - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(predicted - actual,<span class="hljs-number">2.0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> MSE, - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">abs</span>(predicted - actual))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> MAE, - <span class="hljs-number">1</span> - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(actual - predicted,<span class="hljs-number">2.0</span>)) / <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(actual - ${mean_actual},<span class="hljs-number">2.0</span>)) <span class="hljs-keyword">as</span> R2 + rmse(predicted, actual) <span class="hljs-keyword">as</span> RMSE, + mse(predicted, actual) <span class="hljs-keyword">as</span> MSE, + mae(predicted, actual) <span class="hljs-keyword">as</span> MAE, + r2(predicted, actual) <span class="hljs-keyword">as</span> R2 <span class="hljs-keyword">from</span> - e2006tfidf_pa1a_submit; + submit; </code></pre> -<blockquote> -<p>0.3797959864675519 0.14424499133686086 0.23846059576113587 0.5010367946980386</p> -</blockquote> -<hr> -<h1 id="pa2a">[PA2a]</h1> +<table> +<thead> +<tr> +<th style="text-align:center">rmse</th> +<th style="text-align:center">mse</th> +<th style="text-align:center">mae</th> +<th style="text-align:center">r2</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">0.3797959864675519</td> +<td style="text-align:center">0.14424499133686086</td> +<td style="text-align:center">0.23846059576113587</td> +<td style="text-align:center">0.5010367946980386</td> +</tr> +</tbody> +</table> +<h1 id="pa2a">PA2a</h1> <h2 id="training">Training</h2> <pre><code class="lang-sql"><span class="hljs-keyword">set</span> mapred.reduce.tasks=<span class="hljs-number">64</span>; <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> e2006tfidf_pa2a_model; @@ -2408,35 +2491,42 @@ t.<span class="hljs-keyword">rowid</span>; </code></pre> <h2 id="evaluation">evaluation</h2> -<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> e2006tfidf_pa2a_submit; -<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> e2006tfidf_pa2a_submit <span class="hljs-keyword">as</span> -<span class="hljs-keyword">select</span> - t.target <span class="hljs-keyword">as</span> actual, - pd.predicted <span class="hljs-keyword">as</span> predicted -<span class="hljs-keyword">from</span> - e2006tfidf_test t <span class="hljs-keyword">JOIN</span> e2006tfidf_pa2a_predict pd - <span class="hljs-keyword">on</span> (t.<span class="hljs-keyword">rowid</span> = pd.<span class="hljs-keyword">rowid</span>); - -<span class="hljs-keyword">select</span> <span class="hljs-keyword">avg</span>(actual), <span class="hljs-keyword">avg</span>(predicted) <span class="hljs-keyword">from</span> e2006tfidf_pa2a_submit; -</code></pre> -<blockquote> -<p>-3.8200363760415414 -3.9124877451612488</p> -</blockquote> -<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:mean_actual=<span class="hljs-number">-3.8200363760415414</span>; - +<pre><code class="lang-sql">WITH submit as ( + <span class="hljs-keyword">select</span> + t.target <span class="hljs-keyword">as</span> actual, + p.predicted <span class="hljs-keyword">as</span> predicted + <span class="hljs-keyword">from</span> + e2006tfidf_test t + <span class="hljs-keyword">JOIN</span> e2006tfidf_pa2a_predict p + <span class="hljs-keyword">on</span> (t.<span class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>) +) <span class="hljs-keyword">select</span> - <span class="hljs-keyword">sqrt</span>(<span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(predicted - actual,<span class="hljs-number">2.0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>)) <span class="hljs-keyword">as</span> RMSE, - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(predicted - actual,<span class="hljs-number">2.0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> MSE, - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">abs</span>(predicted - actual))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> MAE, - <span class="hljs-number">1</span> - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(actual - predicted,<span class="hljs-number">2.0</span>)) / <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(actual - ${mean_actual},<span class="hljs-number">2.0</span>)) <span class="hljs-keyword">as</span> R2 + rmse(predicted, actual) <span class="hljs-keyword">as</span> RMSE, + mse(predicted, actual) <span class="hljs-keyword">as</span> MSE, + mae(predicted, actual) <span class="hljs-keyword">as</span> MAE, + r2(predicted, actual) <span class="hljs-keyword">as</span> R2 <span class="hljs-keyword">from</span> - e2006tfidf_pa2a_submit; + submit; </code></pre> -<blockquote> -<p>0.38538660838804495 0.14852283792484033 0.2466732002711477 0.48623913673053565</p> -</blockquote> -<hr> -<h1 id="arow">[AROW]</h1> +<table> +<thead> +<tr> +<th style="text-align:center">rmse</th> +<th style="text-align:center">mse</th> +<th style="text-align:center">mae</th> +<th style="text-align:center">r2</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">0.38538660838804495</td> +<td style="text-align:center">0.14852283792484033</td> +<td style="text-align:center">0.2466732002711477</td> +<td style="text-align:center">0.48623913673053565</td> +</tr> +</tbody> +</table> +<h1 id="arow">AROW</h1> <h2 id="training">Training</h2> <pre><code class="lang-sql"><span class="hljs-keyword">set</span> mapred.reduce.tasks=<span class="hljs-number">64</span>; <span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> e2006tfidf_arow_model ; @@ -2468,35 +2558,42 @@ t.<span class="hljs-keyword">rowid</span>; </code></pre> <h2 id="evaluation">evaluation</h2> -<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> e2006tfidf_arow_submit; -<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> e2006tfidf_arow_submit <span class="hljs-keyword">as</span> -<span class="hljs-keyword">select</span> - t.target <span class="hljs-keyword">as</span> actual, - p.predicted <span class="hljs-keyword">as</span> predicted -<span class="hljs-keyword">from</span> - e2006tfidf_test t <span class="hljs-keyword">JOIN</span> e2006tfidf_arow_predict p - <span class="hljs-keyword">on</span> (t.<span class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>); - -<span class="hljs-keyword">select</span> <span class="hljs-keyword">avg</span>(actual), <span class="hljs-keyword">avg</span>(predicted) <span class="hljs-keyword">from</span> e2006tfidf_arow_submit; -</code></pre> -<blockquote> -<p>-3.8200363760415414 -3.8692518911517433</p> -</blockquote> -<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:mean_actual=<span class="hljs-number">-3.8200363760415414</span>; - +<pre><code class="lang-sql">WITH submit as ( + <span class="hljs-keyword">select</span> + t.target <span class="hljs-keyword">as</span> actual, + p.predicted <span class="hljs-keyword">as</span> predicted + <span class="hljs-keyword">from</span> + e2006tfidf_test t + <span class="hljs-keyword">JOIN</span> e2006tfidf_arow_predict p + <span class="hljs-keyword">on</span> (t.<span class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>) +) <span class="hljs-keyword">select</span> - <span class="hljs-keyword">sqrt</span>(<span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(predicted - actual,<span class="hljs-number">2.0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>)) <span class="hljs-keyword">as</span> RMSE, - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(predicted - actual,<span class="hljs-number">2.0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> MSE, - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">abs</span>(predicted - actual))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> MAE, - <span class="hljs-number">1</span> - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(actual - predicted,<span class="hljs-number">2.0</span>)) / <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(actual - ${mean_actual},<span class="hljs-number">2.0</span>)) <span class="hljs-keyword">as</span> R2 + rmse(predicted, actual) <span class="hljs-keyword">as</span> RMSE, + mse(predicted, actual) <span class="hljs-keyword">as</span> MSE, + mae(predicted, actual) <span class="hljs-keyword">as</span> MAE, + r2(predicted, actual) <span class="hljs-keyword">as</span> R2 <span class="hljs-keyword">from</span> - e2006tfidf_arow_submit; + submit; </code></pre> -<blockquote> -<p>0.37862513029019407 0.14335698928726642 0.2368787001269389 0.5041085155590119</p> -</blockquote> -<hr> -<h1 id="arowe">[AROWe]</h1> +<table> +<thead> +<tr> +<th style="text-align:center">rmse</th> +<th style="text-align:center">mse</th> +<th style="text-align:center">mae</th> +<th style="text-align:center">r2</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">0.37862513029019407</td> +<td style="text-align:center">0.14335698928726642</td> +<td style="text-align:center">0.2368787001269389</td> +<td style="text-align:center">0.5041085155590119</td> +</tr> +</tbody> +</table> +<h1 id="arowe">AROWe</h1> <p>AROWe is a modified version of AROW that uses Hinge loss (epsilion = 0.1)</p> <h2 id="training">Training</h2> <pre><code class="lang-sql"><span class="hljs-keyword">set</span> mapred.reduce.tasks=<span class="hljs-number">64</span>; @@ -2529,33 +2626,41 @@ t.<span class="hljs-keyword">rowid</span>; </code></pre> <h2 id="evaluation">evaluation</h2> -<pre><code class="lang-sql"><span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> e2006tfidf_arowe_submit; -<span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> e2006tfidf_arowe_submit <span class="hljs-keyword">as</span> -<span class="hljs-keyword">select</span> - t.target <span class="hljs-keyword">as</span> actual, - p.predicted <span class="hljs-keyword">as</span> predicted -<span class="hljs-keyword">from</span> - e2006tfidf_test t <span class="hljs-keyword">JOIN</span> e2006tfidf_arowe_predict p - <span class="hljs-keyword">on</span> (t.<span class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>); - -<span class="hljs-keyword">select</span> <span class="hljs-keyword">avg</span>(actual), <span class="hljs-keyword">avg</span>(predicted) <span class="hljs-keyword">from</span> e2006tfidf_arowe_submit; -</code></pre> -<blockquote> -<p>-3.8200363760415414 -3.86494905688414</p> -</blockquote> -<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:mean_actual=<span class="hljs-number">-3.8200363760415414</span>; - +<pre><code class="lang-sql">WITH submit as ( + <span class="hljs-keyword">select</span> + t.target <span class="hljs-keyword">as</span> actual, + p.predicted <span class="hljs-keyword">as</span> predicted + <span class="hljs-keyword">from</span> + e2006tfidf_test t + <span class="hljs-keyword">JOIN</span> e2006tfidf_arowe_predict p + <span class="hljs-keyword">on</span> (t.<span class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>) +) <span class="hljs-keyword">select</span> - <span class="hljs-keyword">sqrt</span>(<span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(predicted - actual,<span class="hljs-number">2.0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>)) <span class="hljs-keyword">as</span> RMSE, - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(predicted - actual,<span class="hljs-number">2.0</span>))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> MSE, - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">abs</span>(predicted - actual))/<span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) <span class="hljs-keyword">as</span> MAE, - <span class="hljs-number">1</span> - <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(actual - predicted,<span class="hljs-number">2.0</span>)) / <span class="hljs-keyword">sum</span>(<span class="hljs-keyword">pow</span>(actual - ${mean_actual},<span class="hljs-number">2.0</span>)) <span class="hljs-keyword">as</span> R2 + rmse(predicted, actual) <span class="hljs-keyword">as</span> RMSE, + mse(predicted, actual) <span class="hljs-keyword">as</span> MSE, + mae(predicted, actual) <span class="hljs-keyword">as</span> MAE, + r2(predicted, actual) <span class="hljs-keyword">as</span> R2 <span class="hljs-keyword">from</span> - e2006tfidf_arowe_submit; + submit; </code></pre> -<blockquote> -<p>0.37789148212861856 0.14280197226536404 0.2357339155291536 0.5060283955470721</p> -</blockquote> +<table> +<thead> +<tr> +<th style="text-align:center">rmse</th> +<th style="text-align:center">mse</th> +<th style="text-align:center">mae</th> +<th style="text-align:center">r2</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">0.37789148212861856</td> +<td style="text-align:center">0.14280197226536404</td> +<td style="text-align:center">0.2357339155291536</td> +<td style="text-align:center">0.5060283955470721</td> +</tr> +</tbody> +</table> <p><div id="page-footer" class="localized-footer"><hr><!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file @@ -2611,7 +2716,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; gitbook.push(function() { - gitbook.page.hasChanged({"page":{"title":"Passive Aggressive, AROW","level":"8.2.2","depth":2,"next":{"title":"KDDCup 2012 Track 2 CTR Prediction Tutorial","level":"8.3","depth":1,"path":"regression/kddcup12tr2.md","ref":"regression/kddcup12tr2.md","articles":[{"title":"Data preparation","level":"8.3.1","depth":2,"path":"regression/kddcup12tr2_dataset.md","ref":"regression/kddcup12tr2_dataset.md","articles":[]},{"title":"Logistic Regression, Passive Aggressive","level":"8.3.2","depth":2,"path":"regression/kddcup12tr2_lr.md","ref":"regression/kddcup12tr2_lr.md","articles":[]},{"title":"Logistic Regression with amplifier","level":"8.3.3","depth":2,"path":"regression/kddcup12tr2_lr_amplify.md","ref":"regression/kddcup12tr2_lr_amplify.md","articles":[]},{"title":"AdaGrad, AdaDelta","level":"8.3.4","depth":2,"path":"regression/kddcup12tr2_adagrad.md","ref":"regression/kddcup12tr2_adagrad.md","articles":[]}]},"previous":{"title":"Data preparation","level":"8.2.1","depth":2,"pa th":"regression/e2006_dataset.md","ref":"regression/e2006_dataset.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/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"the me":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"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"},"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}},"struc ture":{"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":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"regression/e2006_arow.md","mtime":"2018-11-02T10:33:52.973Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-11-13T09:32:29.643Z"},"basePath":"..","book":{"language":""}}); 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}); </script> </div> @@ -2641,7 +2746,7 @@ 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/regression/e2006_dataset.html ---------------------------------------------------------------------- diff --git a/userguide/regression/e2006_dataset.html b/userguide/regression/e2006_dataset.html index 7305822..6215927 100644 --- a/userguide/regression/e2006_dataset.html +++ b/userguide/regression/e2006_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"> @@ -97,7 +97,7 @@ <link rel="shortcut icon" href="../gitbook/images/favicon.ico" type="image/x-icon"> - <link rel="next" href="e2006_arow.html" /> + <link rel="next" href="e2006_generic.html" /> <link rel="prev" href="e2006.html" /> @@ -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="../binaryclass/a9a_lr.html"> + <li class="chapter " data-level="6.2.2" data-path="../binaryclass/a9a_generic.html"> - <a href="../binaryclass/a9a_lr.html"> + <a href="../binaryclass/a9a_generic.html"> <b>6.2.2.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.3" data-path="../binaryclass/a9a_lr.html"> + + <a href="../binaryclass/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="../binaryclass/a9a_minibatch.html"> + <li class="chapter " data-level="6.2.4" data-path="../binaryclass/a9a_minibatch.html"> <a href="../binaryclass/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="../binaryclass/news20_adagrad.html"> + <li class="chapter " data-level="6.3.4" data-path="../binaryclass/news20_generic.html"> - <a href="../binaryclass/news20_adagrad.html"> + <a href="../binaryclass/news20_generic.html"> <b>6.3.4.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.3.5" data-path="../binaryclass/news20_adagrad.html"> + + <a href="../binaryclass/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="../binaryclass/news20_rf.html"> + <li class="chapter " data-level="6.3.6" data-path="../binaryclass/news20_rf.html"> <a href="../binaryclass/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="e2006_arow.html"> + <li class="chapter " data-level="8.2.2" data-path="e2006_generic.html"> - <a href="e2006_arow.html"> + <a href="e2006_generic.html"> <b>8.2.2.</b> + General Regessor + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.3" data-path="e2006_arow.html"> + + <a href="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> @@ -2432,7 +2477,7 @@ Apache Hivemall is an effort undergoing incubation 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