http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ce452ddb/userguide/regression/kddcup12tr2_dataset.html ---------------------------------------------------------------------- diff --git a/userguide/regression/kddcup12tr2_dataset.html b/userguide/regression/kddcup12tr2_dataset.html index 5955a2e..e07dd00 100644 --- a/userguide/regression/kddcup12tr2_dataset.html +++ b/userguide/regression/kddcup12tr2_dataset.html @@ -125,7 +125,7 @@ <li> - <a href="http://hivemall.incubator.apache.org/"; target="_blank" class="custom-link"><i class="fa fa-home"></i> Home</a> + <a href="https://hivemall.incubator.apache.org/"; target="_blank" class="custom-link"><i class="fa fa-home"></i> Home</a> </li> @@ -2282,7 +2282,7 @@ under the License. --> <p>The task is predicting the click through rate (CTR) of advertisement, meaning that we are to predict the probability of each ad being clicked. -<a href="http://www.kddcup2012.org/c/kddcup2012-track2"; target="_blank">http://www.kddcup2012.org/c/kddcup2012-track2</a></p> +<a href="https://www.kaggle.com/c/kddcup2012-track2"; target="_blank">https://www.kaggle.com/c/kddcup2012-track2</a></p> <hr> <p><strong>Dataset</strong> </p> <table> @@ -2498,7 +2498,7 @@ hadoop fs -put descriptionid_tokensid.txt /kddcup2012/track2/description/tokensi ) <span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> orc tblproperties (<span class="hljs-string">"orc.compress"</span>=<span class="hljs-string">"SNAPPY"</span>); </code></pre> <p><em>Caution: Joining between training table and user table takes a long time. Consider not to use gender and age and avoid joins if your Hadoop cluster is small.</em></p> -<p><a href="https://github.com/myui/hivemall/blob/master/resources/examples/kddtrack2/kddconv.awk"; target="_blank">kddconv.awk</a></p> +<p><a href="https://github.com/apache/incubator-hivemall/blob/master/resources/examples/kddtrack2/kddconv.awk"; target="_blank">kddconv.awk</a></p> <pre><code class="lang-sql">add file /tmp/kddconv.awk; <span class="hljs-comment">-- SET mapred.reduce.tasks=64;</span> @@ -2585,7 +2585,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; gitbook.push(function() { - gitbook.page.hasChanged({"page":{"title":"Data preparation","level":"8.3.1","depth":2,"next":{"title":"Logistic Regression, Passive Aggressive","level":"8.3.2","depth":2,"path":"regression/kddcup12tr2_lr.md","ref":"regression/kddcup12tr2_lr.md","articles":[]},"previous":{"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":[]}]},"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":{},"fo ntsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"http://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,"bot tom":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":"regression/kddcup12tr2_dataset.md","mtime":"2018-08-29T08:55:00.285Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-09-05T09:13:37.462Z"},"basePath":"..","book":{"language":""}}); + gitbook.page.hasChanged({"page":{"title":"Data preparation","level":"8.3.1","depth":2,"next":{"title":"Logistic Regression, Passive Aggressive","level":"8.3.2","depth":2,"path":"regression/kddcup12tr2_lr.md","ref":"regression/kddcup12tr2_lr.md","articles":[]},"previous":{"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":[]}]},"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":{},"fo ntsettings":{"theme":"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,"bo ttom":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":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"regression/kddcup12tr2_dataset.md","mtime":"2018-09-07T06:02:35.259Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-09-07T06:06:37.693Z"},"basePath":"..","book":{"language":""}}); 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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ce452ddb/userguide/regression/kddcup12tr2_lr.html ---------------------------------------------------------------------- diff --git a/userguide/regression/kddcup12tr2_lr.html b/userguide/regression/kddcup12tr2_lr.html index 75fba23..1b82bbe 100644 --- a/userguide/regression/kddcup12tr2_lr.html +++ b/userguide/regression/kddcup12tr2_lr.html @@ -125,7 +125,7 @@ <li> - <a href="http://hivemall.incubator.apache.org/"; target="_blank" class="custom-link"><i class="fa fa-home"></i> Home</a> + <a href="https://hivemall.incubator.apache.org/"; target="_blank" class="custom-link"><i class="fa fa-home"></i> Home</a> </li> @@ -2281,7 +2281,7 @@ specific language governing permissions and limitations under the License. --> -<p>The task is predicting the click through rate (CTR) of advertisement, meaning that we are to predict the probability of each ad being clicked.<br><a href="http://www.kddcup2012.org/c/kddcup2012-track2"; target="_blank">http://www.kddcup2012.org/c/kddcup2012-track2</a></p> +<p>The task is predicting the click through rate (CTR) of advertisement, meaning that we are to predict the probability of each ad being clicked.<br><a href="https://www.kaggle.com/c/kddcup2012-track2"; target="_blank">https://www.kaggle.com/c/kddcup2012-track2</a></p> <p><em>Caution: This example just shows a baseline result. Use token tables and amplifier to get better AUC score.</em></p> <hr> <h1 id="logistic-regression">Logistic Regression</h1> @@ -2336,7 +2336,8 @@ group by order by rowid ASC; </code></pre><h2 id="evaluation">Evaluation</h2> -<p><a href="https://github.com/myui/hivemall/blob/master/resources/examples/kddtrack2/scoreKDD.py"; target="_blank">scoreKDD.py</a></p> +<p>You can download scoreKDD.py from <a href="https://www.kaggle.com/c/kddcup2012-track2/data"; target="_blank">KDD Cup 2012, Track 2 site</a>. After logging-in to Kaggle, download +scoreKDD.py.</p> <pre><code class="lang-sh">hadoop fs -getmerge /user/hive/warehouse/kdd12track2.db/lr_predict lr_predict.tbl gawk -F <span class="hljs-string">"\t"</span> <span class="hljs-string">'{print $2;}'</span> lr_predict.tbl > lr_predict.submit @@ -2486,7 +2487,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; gitbook.push(function() { - gitbook.page.hasChanged({"page":{"title":"Logistic Regression, Passive Aggressive","level":"8.3.2","depth":2,"next":{"title":"Logistic Regression with amplifier","level":"8.3.3","depth":2,"path":"regression/kddcup12tr2_lr_amplify.md","ref":"regression/kddcup12tr2_lr_amplify.md","articles":[]},"previous":{"title":"Data preparation","level":"8.3.1","depth":2,"path":"regression/kddcup12tr2_dataset.md","ref":"regression/kddcup12tr2_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":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"http://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"; 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}); </script> </div> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ce452ddb/userguide/regression/kddcup12tr2_lr_amplify.html ---------------------------------------------------------------------- diff --git a/userguide/regression/kddcup12tr2_lr_amplify.html b/userguide/regression/kddcup12tr2_lr_amplify.html index 059c3ab..f7913ae 100644 --- a/userguide/regression/kddcup12tr2_lr_amplify.html +++ b/userguide/regression/kddcup12tr2_lr_amplify.html @@ -125,7 +125,7 @@ <li> - <a href="http://hivemall.incubator.apache.org/"; target="_blank" class="custom-link"><i class="fa fa-home"></i> Home</a> + <a href="https://hivemall.incubator.apache.org/"; target="_blank" class="custom-link"><i class="fa fa-home"></i> Home</a> </li> @@ -2282,7 +2282,7 @@ under the License. --> <p>This article explains <em>amplify</em> technique that is useful for improving prediction score.</p> -<p>Iterations are mandatory in machine learning (e.g., in <a href="http://en.wikipedia.org/wiki/Stochastic_gradient_descent"; target="_blank">stochastic gradient descent</a>) to get good prediction models. However, MapReduce is known to be not suited for iterative algorithms because IN/OUT of each MapReduce job is through HDFS.</p> +<p>Iterations are mandatory in machine learning (e.g., in <a href="https://en.wikipedia.org/wiki/Stochastic_gradient_descent"; target="_blank">stochastic gradient descent</a>) to get good prediction models. However, MapReduce is known to be not suited for iterative algorithms because IN/OUT of each MapReduce job is through HDFS.</p> <p>In this example, we show how Hivemall deals with this problem. We use <a href="kddcup12tr2_dataset.html">KDD Cup 2012, Track 2 Task</a> as an example.</p> <p><strong>WARNING</strong>: rand_amplify() is supported in v0.2-beta1 and later.</p> <hr> @@ -2325,7 +2325,7 @@ So, we recommend users to use an amplified view for training as follows:</p> <p>Using <em>trainning_x3</em> instead of the plain training table results in higher and better AUC (0.746214) in <a href="kddcup12tr2_lr.html#evaluation">this example</a>.</p> <p>A problem in amplify() is that the shuffle (copy) and merge phase of the stage 1 could become a bottleneck. When the training table is so large that involves 100 Map tasks, the merge operator needs to merge at least 100 files by (external) merge sort! </p> -<p>Note that the actual bottleneck is not M/R iterations but shuffling training instance. Iteration without shuffling (as in <a href="http://spark.incubator.apache.org/examples.html"; target="_blank">the Spark example</a>) causes very slow convergence and results in requiring more iterations. Shuffling cannot be avoided even in iterative MapReduce variants.</p> +<p>Note that the actual bottleneck is not M/R iterations but shuffling training instance. Iteration without shuffling (as in <a href="https://spark.incubator.apache.org/examples.html"; target="_blank">the Spark example</a>) causes very slow convergence and results in requiring more iterations. Shuffling cannot be avoided even in iterative MapReduce variants.</p> <p><img src="../resources/images/amplify_elapsed.png" alt="amplify elapsed"></p> <hr> <h1 id="amplify-and-shuffle-training-examples-in-each-map-task">Amplify and shuffle training examples in each Map task</h1> @@ -2430,7 +2430,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; gitbook.push(function() { - gitbook.page.hasChanged({"page":{"title":"Logistic Regression with amplifier","level":"8.3.3","depth":2,"next":{"title":"AdaGrad, AdaDelta","level":"8.3.4","depth":2,"path":"regression/kddcup12tr2_adagrad.md","ref":"regression/kddcup12tr2_adagrad.md","articles":[]},"previous":{"title":"Logistic Regression, Passive Aggressive","level":"8.3.2","depth":2,"path":"regression/kddcup12tr2_lr.md","ref":"regression/kddcup12tr2_lr.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":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"http://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":"style s/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}},"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":"regression/kddcup12tr2_lr_amplify.md","mtime":"2018-08-29T08:55:00.285Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-09-05T09:13:37.462Z"},"basePath":"..","book":{"language":""}}); 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