http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/getting_started/permanent-functions.html ---------------------------------------------------------------------- diff --git a/userguide/getting_started/permanent-functions.html b/userguide/getting_started/permanent-functions.html index 8bb30b7..e344abd 100644 --- a/userguide/getting_started/permanent-functions.html +++ b/userguide/getting_started/permanent-functions.html @@ -2215,7 +2215,7 @@ specific language governing permissions and limitations under the License. --> -<p>Hive v0.13 or later supports <a href="https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/DropFunction" target="_blank">permanent functions</a> that live across sessions.</p> +<p>Hive v0.13 or later supports <a href="https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/DropFunction" target="_blank">permanent functions</a> that lives across sessions.</p> <p>Permanent functions are useful when you are using Hive through Hiveserver or to avoid hivemall installation for each session.</p> <!-- toc --><div id="toc" class="toc"> @@ -2229,15 +2229,15 @@ <h1 id="put-hivemall-jar-to-hdfs">Put hivemall jar to HDFS</h1> <p>First, put hivemall jar to HDFS as follows:</p> <pre><code class="lang-sh">hadoop fs -mkdir -p /apps/hivemall -hadoop fs -put hivemall-with-dependencies.jar /apps/hivemall +hadoop fs -put hivemall-all-xxx.jar /apps/hivemall </code></pre> <h1 id="create-permanent-functions">Create permanent functions</h1> <p><em>The following is an auxiliary step to define functions for hivemall databases, not for the default database.</em></p> <pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">DATABASE</span> <span class="hljs-keyword">IF</span> <span class="hljs-keyword">NOT</span> <span class="hljs-keyword">EXISTS</span> hivemall; <span class="hljs-keyword">USE</span> hivemall; </code></pre> -<p>Then, create permanent functions using <a href="https://github.com/myui/hivemall/blob/master/resources/ddl/define-all-as-permanent.hive" target="_blank">define-all-as-permanent.hive</a>, a DDL script to define permanent UDFs.</p> -<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:hivemall_jar=hdfs:///apps/hivemall/hivemall-<span class="hljs-keyword">with</span>-dependencies.jar; +<p>Then, create permanent functions using <a href="https://github.com/apache/incubator-hivemall/blob/master/resources/ddl/define-all-as-permanent.hive" target="_blank">define-all-as-permanent.hive</a>, a DDL script to define permanent UDFs.</p> +<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:hivemall_jar=hdfs:///apps/hivemall/hivemall-all-xxx.jar.jar; source /tmp/define-all-as-permanent.hive; </code></pre> @@ -2303,7 +2303,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; 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}); </script> </div> @@ -2311,7 +2311,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda - <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/3.1.1/anchor.min.js"></script> + <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.1.0/anchor.min.js"></script> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/misc/approx.html ---------------------------------------------------------------------- diff --git a/userguide/misc/approx.html b/userguide/misc/approx.html index c5fefd9..04ee48a 100644 --- a/userguide/misc/approx.html +++ b/userguide/misc/approx.html @@ -2227,7 +2227,7 @@ </div><!-- tocstop --> <h1 id="approximate-counting-using-hyperloglog">Approximate Counting using HyperLogLog</h1> -<p><code>count(distinct value)</code> can often cause memory exhausted errors where input data and the cardinarity of value are large.</p> +<p><code>count(distinct value)</code> can often cause memory exhausted errors where input data and the cardinality of value are large.</p> <p><a href="https://en.wikipedia.org/wiki/HyperLogLog" target="_blank">HyperLogLog</a> is an efficient algorithm for approximating the number of distinct elements in a <a href="https://en.wikipedia.org/wiki/Multiset" target="_blank">multiset</a>. 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}); </script> </div> @@ -2493,7 +2493,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda - <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/3.1.1/anchor.min.js"></script> + <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.1.0/anchor.min.js"></script> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/misc/tokenizer.html ---------------------------------------------------------------------- diff --git a/userguide/misc/tokenizer.html b/userguide/misc/tokenizer.html index 31109df..509a82e 100644 --- a/userguide/misc/tokenizer.html +++ b/userguide/misc/tokenizer.html @@ -2266,7 +2266,7 @@ <blockquote> <p>["日本","経済","新聞","関西","国際","空港"]</p> </blockquote> -<p>Note that you can pass <code>null</code> to each of the third and fourth argument to explicitly use Kuromoji's default stop words and stop tags. </p> +<p>Note that you can pass <code>null</code> to each of the third and fourth argument to explicitly use Kuromoji's <a href="https://github.com/apache/lucene-solr/blob/master/lucene/analysis/kuromoji/src/resources/org/apache/lucene/analysis/ja/stopwords.txt" target="_blank">default stop words</a> and <a href="https://github.com/apache/lucene-solr/blob/master/lucene/analysis/kuromoji/src/resources/org/apache/lucene/analysis/ja/stoptags.txt" target="_blank">stop tags</a>.</p> <p>If you have a large custom dictionary as an external file, <code>userDict</code> can also be <code>const string userDictURL</code> which indicates URL of the external file on somewhere like Amazon S3:</p> <pre><code class="lang-sql"><span class="hljs-keyword">select</span> tokenize_ja(<span class="hljs-string">"日本経済新聞&関西国際空港"</span>, <span class="hljs-string">"normal"</span>, <span class="hljs-literal">null</span>, <span class="hljs-literal">null</span>, <span class="hljs-string">"https://raw.githubusercontent.com/atilika/kuromoji/909fd6b32bf4e9dc86b7599de5c9b50ca8f004a1/kuromoji-core/src/test/resources/userdict.txt"</span>); 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}); </script> </div> @@ -2416,7 +2416,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda - <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/3.1.1/anchor.min.js"></script> + <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.1.0/anchor.min.js"></script> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/multiclass/iris_randomforest.html ---------------------------------------------------------------------- diff --git a/userguide/multiclass/iris_randomforest.html b/userguide/multiclass/iris_randomforest.html index 0745306..151a218 100644 --- a/userguide/multiclass/iris_randomforest.html +++ b/userguide/multiclass/iris_randomforest.html @@ -2299,15 +2299,17 @@ $ sed '/^$/d' iris.data | hadoop fs -put - /dataset/iris/raw/iris.data <span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> SEQUENCEFILE <span class="hljs-keyword">AS</span> <span class="hljs-keyword">select</span> - train_randomforest_classifier(features, label) - <span class="hljs-comment">-- hivemall v0.4.1-alpha.2 and before</span> + train_randomforest_classifier(features, label) + <span class="hljs-comment">-- v0.5.0 and later</span> + <span class="hljs-comment">-- train_randomforest_classifier(features, label) as (model_id, model_weight, model, var_importance, oob_errors, oob_tests)</span> + <span class="hljs-comment">-- v0.4.1-alpha.2 and before</span> <span class="hljs-comment">-- train_randomforest_classifier(features, label) as (pred_model, var_importance, oob_errors, oob_tests)</span> - <span class="hljs-comment">-- hivemall v0.4.1 and later</span> + <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span> <span class="hljs-comment">-- train_randomforest_classifier(features, label) as (model_id, model_type, pred_model, var_importance, oob_errors, oob_tests)</span> <span class="hljs-keyword">from</span> training; </code></pre> -<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>The default <code>TEXTFILE</code> should not be used for model table when using Javascript output through <code>-output javascript</code> option.</p></div></div> +<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>Note that model storage format is different between versions as seen the above.</p></div></div> <pre><code class="lang-sql">hive> desc extended model; </code></pre> <table> @@ -2384,7 +2386,7 @@ usage: train_randomforest_classifier(array<double|string> features, int features [default: ceil(sqrt(x[0].length))]. int(num_variables * x[0].length) is - considered if num_variable is (0,1 + considered if num_variable is (0,1] </code></pre> <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>-num_trees</code> controls the number of trees for each task, not the total number of trees.</p></div></div> <h3 id="parallelize-training">Parallelize Training</h3> @@ -2416,7 +2418,7 @@ usage: train_randomforest_classifier(array<double|string> features, int <p>[6.837674865013268,4.1317115752776665,24.331571871930226,25.677497925673062] 0.056666666666666664</p> </blockquote> <h1 id="prediction">Prediction</h1> -<pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:classification=<span class="hljs-literal">true</span>; +<pre><code class="lang-sql"><span class="hljs-comment">-- set hivevar:classification=true;</span> <span class="hljs-keyword">set</span> hive.<span class="hljs-keyword">auto</span>.<span class="hljs-keyword">convert</span>.<span class="hljs-keyword">join</span>=<span class="hljs-literal">true</span>; <span class="hljs-keyword">set</span> hive.mapjoin.optimized.hashtable=<span class="hljs-literal">false</span>; @@ -2425,18 +2427,19 @@ usage: train_randomforest_classifier(array<double|string> features, int <span class="hljs-keyword">SELECT</span> <span class="hljs-keyword">rowid</span>, <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span> - <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + <span class="hljs-comment">-- v0.5.0 or later</span> rf_ensemble(predicted.<span class="hljs-keyword">value</span>, predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> predicted <span class="hljs-comment">-- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)</span> <span class="hljs-keyword">FROM</span> ( <span class="hljs-keyword">SELECT</span> <span class="hljs-keyword">rowid</span>, - <span class="hljs-comment">-- hivemall v0.4.1 and later</span> + <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span> <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted</span> - <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + <span class="hljs-comment">-- v0.5.0 or later</span> p.model_weight, - tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, t.features, ${classification}) <span class="hljs-keyword">as</span> predicted - <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted -- to use the old model in v0.5-rc.1 or later</span> + tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, t.features, <span class="hljs-string">"-classification"</span>) <span class="hljs-keyword">as</span> predicted + <span class="hljs-comment">-- tree_predict(p.model_id, p.model, t.features, ${classification}) as predicted</span> + <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted -- to use the old model in v0.5.0 or later</span> <span class="hljs-keyword">FROM</span> <span class="hljs-keyword">model</span> p <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> <span class="hljs-comment">-- CROSS JOIN</span> @@ -2446,7 +2449,7 @@ usage: train_randomforest_classifier(array<double|string> features, int <span class="hljs-keyword">rowid</span> ; </code></pre> -<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>tree_predict_v1</code> is for the backward compatibility for using prediction models built before <code>v0.5-rc.1</code> on <code>v0.5-rc.1</code> or later.</p></div></div> +<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>Left outer join without a join condition (i.e., <code>model p LEFT OUTER JOIN training t</code>) is a trick to fix the left table for cross join.</p><h4 id="caution">Caution</h4><p><code>tree_predict_v1</code> is for the backward compatibility for using prediction models built before <code>v0.5</code> on <code>v0.5</code> or later.</p></div></div> <h3 id="parallelize-prediction">Parallelize Prediction</h3> <p>The following query runs predictions in N-parallel. It would reduce elapsed time for prediction almost by N.</p> <pre><code class="lang-sql"><span class="hljs-keyword">SET</span> hivevar:classification=<span class="hljs-literal">true</span>; @@ -2454,28 +2457,30 @@ usage: train_randomforest_classifier(array<double|string> features, int <span class="hljs-keyword">SET</span> hive.mapjoin.optimized.hashtable=<span class="hljs-literal">false</span>; <span class="hljs-keyword">SET</span> mapred.reduce.tasks=<span class="hljs-number">8</span>; +<span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> predicted; <span class="hljs-keyword">create</span> <span class="hljs-keyword">table</span> predicted <span class="hljs-keyword">as</span> <span class="hljs-keyword">SELECT</span> <span class="hljs-keyword">rowid</span>, <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span> - <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + <span class="hljs-comment">-- v0.5.0 or later</span> rf_ensemble(predicted.<span class="hljs-keyword">value</span>, predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> predicted <span class="hljs-comment">-- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)</span> <span class="hljs-keyword">FROM</span> ( <span class="hljs-keyword">SELECT</span> t.<span class="hljs-keyword">rowid</span>, - <span class="hljs-comment">-- hivemall v0.4.1 and later</span> + <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span> <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted</span> - <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + <span class="hljs-comment">-- v0.5.0 or later</span> p.model_weight, - tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, t.features, ${classification}) <span class="hljs-keyword">as</span> predicted - <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted as predicted -- to use the old model in v0.5-rc.1 or later</span> + tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, t.features, <span class="hljs-string">"-classification"</span>) <span class="hljs-keyword">as</span> predicted + <span class="hljs-comment">-- tree_predict(p.model_id, p.model, t.features, ${classification}) as predicted</span> + <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted as predicted -- to use the old model in v0.5.0 or later</span> <span class="hljs-keyword">FROM</span> ( <span class="hljs-keyword">SELECT</span> - <span class="hljs-comment">-- hivemall v0.4.1 and later</span> + <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span> <span class="hljs-comment">-- model_id, model_type, pred_model</span> - <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span> + <span class="hljs-comment">-- v0.5.0 or later</span> model_id, model_weight, <span class="hljs-keyword">model</span> <span class="hljs-keyword">FROM</span> <span class="hljs-keyword">model</span> <span class="hljs-keyword">DISTRIBUTE</span> <span class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span class="hljs-number">1</span>) @@ -2494,13 +2499,13 @@ usage: train_randomforest_classifier(array<double|string> features, int <pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:total_cnt=<span class="hljs-number">150</span>; WITH t1 as ( -<span class="hljs-keyword">SELECT</span> - t.<span class="hljs-keyword">rowid</span>, - t.label <span class="hljs-keyword">as</span> actual, - p.predicted.label <span class="hljs-keyword">as</span> predicted -<span class="hljs-keyword">FROM</span> - predicted p - <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> training t <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> + t.<span class="hljs-keyword">rowid</span>, + t.label <span class="hljs-keyword">as</span> actual, + p.predicted.label <span class="hljs-keyword">as</span> predicted + <span class="hljs-keyword">FROM</span> + predicted p + <span class="hljs-keyword">LEFT</span> <span class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> training t <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">count</span>(<span class="hljs-number">1</span>) / ${total_cnt} @@ -2514,7 +2519,7 @@ WITH t1 as ( <p>0.98</p> </blockquote> <h1 id="graphvis-export">Graphvis export</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><code>tree_export</code> feature is supported from Hivemall v0.5-rc.1 or later. +<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><code>tree_export</code> feature is supported from Hivemall v0.5.0 or later. Better to limit tree depth on training by <code>-depth</code> option to plot a Decision Tree.</p></div></div> <p>Hivemall provide <code>tree_export</code> to export a decision tree into <a href="http://www.graphviz.org/" target="_blank">Graphviz</a> or human-readable Javascript format. 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