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Posted to commits@madlib.apache.org by ri...@apache.org on 2017/05/16 20:29:28 UTC
[13/51] [partial] incubator-madlib-site git commit: Add v1.11 docs
http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/b5b51c69/docs/v1.11/group__grp__unsupervised.html
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diff --git a/docs/v1.11/group__grp__unsupervised.html b/docs/v1.11/group__grp__unsupervised.html
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+<title>MADlib: Unsupervised Learning</title>
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+ <td style="padding-left: 0.5em;">
+ <div id="projectname">
+ <span id="projectnumber">1.11</span>
+ </div>
+ <div id="projectbrief">User Documentation for MADlib</div>
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+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0"
+ name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+ <div class="summary">
+<a href="#groups">Modules</a> </div>
+ <div class="headertitle">
+<div class="title">Unsupervised Learning</div> </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods for unsupervised learning tasks </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__association__rules"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__association__rules.html">Association Rules</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:group__grp__clustering"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__clustering.html">Clustering</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:group__grp__topic__modelling"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__topic__modelling.html">Topic Modelling</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+</table>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+ <ul>
+ <li class="footer">Generated on Tue May 16 2017 13:24:38 for MADlib by
+ <a href="http://www.doxygen.org/index.html">
+ <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li>
+ </ul>
+</div>
+</body>
+</html>
http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/b5b51c69/docs/v1.11/group__grp__unsupervised.js
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/b5b51c69/docs/v1.11/group__grp__utilities.html
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diff --git a/docs/v1.11/group__grp__utilities.html b/docs/v1.11/group__grp__utilities.html
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+<title>MADlib: Developer Database Functions</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+ <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+ <td style="padding-left: 0.5em;">
+ <div id="projectname">
+ <span id="projectnumber">1.11</span>
+ </div>
+ <div id="projectbrief">User Documentation for MADlib</div>
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+ onmouseover="return searchBox.OnSearchSelectShow()"
+ onmouseout="return searchBox.OnSearchSelectHide()"
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+<div class="title">Developer Database Functions<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a></div></div> </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> <ul>
+<li>
+<a href="#utilities">Utility Functions</a> </li>
+<li>
+<a href="#rel;ated">Related Topics</a> </li>
+</ul>
+</div><p>The utility module consists of useful utility functions to assist data scientists in using the product. Several of these functions can be used while implementing new algorithms.</p>
+<p><a class="anchor" id="utilities"></a></p><dl class="section user"><dt>Utility Functions</dt><dd></dd></dl>
+<table class="output">
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#a9ce7ffa76acc8a0638e9bc48be99f36a" title="Return MADlib build information. ">version()</a> </th><td><p class="starttd">Return MADlib build information. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#a76cfdbed8a6782f37f50710d855434bd" title="Raise an exception if the given condition is not satisfied. ">assert()</a> </th><td><p class="starttd">Raise an exception if the given condition is not satisfied. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#aa90ffaebb43a5cbcb5c48281cbadd3b0" title="Check if a SQL statement raises an error. ">check_if_raises_error()</a> </th><td><p class="starttd">Check if a SQL statement raises an error. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#ac1f509bd94c6309b59eb4d479fb2a470" title="Check if a column exists in a table. ">check_if_col_exists()</a> </th><td><p class="starttd">Check if a column exists in a table. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#a48bed63cab78a5446fa4c122eaae3a40" title="Check if a floating-point number is NaN (not a number) ">isnan()</a> </th><td><p class="starttd">Check if a floating-point number is NaN (not a number) </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#a56501b6f9fabe65d7a6a6beb70a0e000" title="Create the temporary schema if it does not exist yet. ">create_schema_pg_temp()</a> </th><td><p class="starttd">Create the temporary schema if it does not exist yet. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#ab194d6a1222672c314ea39b78aad8fd0" title="Create volatile noop function. ">noop()</a> </th><td><p class="starttd">Create volatile noop function. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#aab5ea2cd6201e38e24ca62b0fb91fb7a" title="Drop all tables matching pattern 'madlib_temp' in a given schema. ">cleanup_madlib_temp_tables()</a> </th><td><p class="starttd">Drop all tables matching pattern 'madlib_temp' in a given schema. </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p>Note: If the function <a class="el" href="utilities_8sql__in.html#aab5ea2cd6201e38e24ca62b0fb91fb7a" title="Drop all tables matching pattern 'madlib_temp' in a given schema. ">cleanup_madlib_temp_tables()</a> gives an Out-of-memory error, then the number of tables to be dropped is too high to execute in one transaction. In such a case, please follow the instructions provided with the error to execute the command in multiple transactions.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="utilities_8sql__in.html" title="SQL functions for carrying out routine tasks. ">utilities.sql_in</a> documenting the SQL functions. </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+ <ul>
+ <li class="footer">Generated on Tue May 16 2017 13:24:39 for MADlib by
+ <a href="http://www.doxygen.org/index.html">
+ <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li>
+ </ul>
+</div>
+</body>
+</html>
http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/b5b51c69/docs/v1.11/group__grp__utility__functions.html
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diff --git a/docs/v1.11/group__grp__utility__functions.html b/docs/v1.11/group__grp__utility__functions.html
new file mode 100644
index 0000000..effb301
--- /dev/null
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+
+<div class="header">
+ <div class="summary">
+<a href="#groups">Modules</a> </div>
+ <div class="headertitle">
+<div class="title">Utility Functions</div> </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__utilities"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__utilities.html">Developer Database Functions</a></td></tr>
+<tr class="memdesc:group__grp__utilities"><td class="mdescLeft"> </td><td class="mdescRight">Provides a collection of user-defined functions for performing common tasks in the database. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:group__grp__linear__solver"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__linear__solver.html">Linear Solvers</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:group__grp__path"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__path.html">Path</a></td></tr>
+<tr class="memdesc:group__grp__path"><td class="mdescLeft"> </td><td class="mdescRight">Path Functions. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:group__grp__pmml"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__pmml.html">PMML Export</a></td></tr>
+<tr class="memdesc:group__grp__pmml"><td class="mdescLeft"> </td><td class="mdescRight">Implements the PMML XML standard to describe and exchange models produced by data mining and machine learning algorithms. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:group__grp__sessionize"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__sessionize.html">Sessionize</a></td></tr>
+<tr class="memdesc:group__grp__sessionize"><td class="mdescLeft"> </td><td class="mdescRight">Sessionize. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:group__grp__text__analysis"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__text__analysis.html">Text Analysis</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
+</table>
+</div><!-- contents -->
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/b5b51c69/docs/v1.11/group__grp__utility__functions.js
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+var group__grp__utility__functions =
+[
+ [ "Developer Database Functions", "group__grp__utilities.html", null ],
+ [ "Linear Solvers", "group__grp__linear__solver.html", "group__grp__linear__solver" ],
+ [ "Path", "group__grp__path.html", null ],
+ [ "PMML Export", "group__grp__pmml.html", null ],
+ [ "Sessionize", "group__grp__sessionize.html", null ],
+ [ "Text Analysis", "group__grp__text__analysis.html", "group__grp__text__analysis" ]
+];
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/b5b51c69/docs/v1.11/group__grp__validation.html
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+<title>MADlib: Cross Validation</title>
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+ <div id="projectbrief">User Documentation for MADlib</div>
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+ <div class="headertitle">
+<div class="title">Cross Validation<div class="ingroups"><a class="el" href="group__grp__mdl.html">Model Evaluation</a></div></div> </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> <ul>
+<li>
+<a href="#cvfunction">Cross-Validation Function</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#notes">Notes</a> </li>
+<li>
+<a href="#background">Technical Background</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Estimates the fit of a predictive model given a data set and specifications for the training, prediction, and error estimation functions.</p>
+<p>Cross validation, sometimes called rotation estimation, is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and you want to estimate how accurately a predictive model will perform in practice.</p>
+<p>The cross-validation function provided by this module is very flexible and can work with algorithms you want to cross validate, including algorithms you write yourself. Among the inputs to the cross-validation function are specifications of the modelling, prediction, and error metric functions. These three-part specifications include the name of the function, an array of arguments to pass to the function, and an array of the data types of the arguments. This makes it possible to use functions from other MADlib modules or user-defined functions that you supply.</p>
+<ul>
+<li>The modelling (training) function takes in a given data set with independent and dependent variables and produces a model, which is stored in an output table.</li>
+<li>The prediction function takes in the model generated by the modelling function and a different data set with independent variables, and produces a prediction of the dependent variables based on the model, which is stored in an output table. The prediction function should take a unique ID column name in the data table as one of the inputs, so that the prediction result can be compared with the validation values. Note: Prediction function in some MADlib modules do not save results into an output table. These prediction functions are not suitable for cross-validation.</li>
+<li>The error metric function compares the prediction results with the known values of the dependent variables in the data set that was fed into the prediction function. It computes the error metric using the specified error metric function, storing the results in a table.</li>
+</ul>
+<p>Other inputs include the output table name, k value for the k-fold cross validation, and how many folds to try. For example, you can choose to run a simple validation instead of a full cross validation.</p>
+<p><a class="anchor" id="cvfunction"></a></p><dl class="section user"><dt>Cross-Validation Function</dt><dd></dd></dl>
+<pre class="syntax">
+cross_validation_general( modelling_func,
+ modelling_params,
+ modelling_params_type,
+ param_explored,
+ explore_values,
+ predict_func,
+ predict_params,
+ predict_params_type,
+ metric_func,
+ metric_params,
+ metric_params_type,
+ data_tbl,
+ data_id,
+ id_is_random,
+ validation_result,
+ data_cols,
+ fold_num
+ )</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>modelling_func </dt>
+<dd>VARCHAR. The name of the function that trains the model. </dd>
+<dt>modelling_params </dt>
+<dd>VARCHAR[]. An array of parameters to supply to the modelling function. </dd>
+<dt>modelling_params_type </dt>
+<dd>VARCHAR[]. An array of data type names for each of the parameters supplied to the modelling function. </dd>
+<dt>param_explored </dt>
+<dd>VARCHAR. The name of the parameter that will be checked to find the optimum value. The name must appear in the <em>modelling_params</em> array. </dd>
+<dt>explore_values </dt>
+<dd>VARCHAR. The name of the parameter whose values are to be studied. </dd>
+<dt>predict_func </dt>
+<dd>VARCHAR. The name of the prediction function. </dd>
+<dt>predict_params </dt>
+<dd>VARCHAR[]. An array of parameters to supply to the prediction function. </dd>
+<dt>predict_params_type </dt>
+<dd>VARCHAR[]. An array of data type names for each of the parameters supplied to the prediction function. </dd>
+<dt>metric_func </dt>
+<dd>VARCHAR. The name of the function for measuring errors. </dd>
+<dt>metric_params </dt>
+<dd>VARCHAR[]. An array of parameters to supply to the error metric function. </dd>
+<dt>metric_params_type </dt>
+<dd>VARCHAR[]. An array of data type names for each of the parameters supplied to the metric function. </dd>
+<dt>data_tbl </dt>
+<dd>VARCHAR. The name of the data table that will be split into training and validation parts. </dd>
+<dt>data_id </dt>
+<dd><p class="startdd">VARCHAR. The name of the column containing a unique ID associated with each row, or NULL if the table has no such column.</p>
+<p class="enddd">Ideally, the data set has a unique ID for each row, so that it is easier to partition the data set into the training part and the validation part. Set the <em>id_is_random</em> argument to inform the cross-validation function whether the ID value is randomly assigned to each row. If it is not randomly assigned, the cross-validation function generates a random ID for each row. </p>
+</dd>
+<dt>id_is_random </dt>
+<dd>BOOLEAN. TRUE if the provided ID is randomly assigned to each row. </dd>
+<dt>validation_result </dt>
+<dd>VARCHAR. The name of the table to store the output of the cross-validation function. The output table has the following columns: <table class="output">
+<tr>
+<th>param_explored </th><td>The name of the parameter checked to find the optimum value. This is the same name specified in the <em>param_explored</em> argument of the <em><a class="el" href="cross__validation_8sql__in.html#a2a7791b05f51e8748ab7b6ccf328a7e2">cross_validation_general()</a></em> function. </td></tr>
+<tr>
+<th>average error </th><td>The average of the errors computed by the error metric function. </td></tr>
+<tr>
+<th>standard deviation of error </th><td>The standard deviation of the errors. </td></tr>
+</table>
+</dd>
+<dt>data_cols </dt>
+<dd><p class="startdd">A comma-separated list of names of data columns to use in the calculation. When its value is NULL, the function will automatically figure out all the column names of the data table. This is only used if the <em>data_id</em> argument is NULL, otherwise it is ignored.</p>
+<p>If the data set has no unique ID for each row, the cross-validation function copies the data set to a temporary table with a randomly assigned ID column. Setting this argument to the list of independent and dependent variables that are to be used in the calculation minimizes the copying workload by only copying the required data.</p>
+<p class="enddd">The new temporary table is dropped after the computation has finished. </p>
+</dd>
+<dt><em>fold_num</em> </dt>
+<dd>INTEGER, default: 10. Value of k. How many folds validation? Each validation uses 1/fold_num fraction of the data for validation. </dd>
+</dl>
+<p>The parameter arrays for the modelling, prediction and metric functions can include the following special keywords:</p>
+<ul>
+<li><em>%data%</em> – The argument position for training/validation data</li>
+<li><em>%model%</em> – The argument position for the output/input of modelling/prediction function</li>
+<li><em>%id%</em> – The argument position of the unique ID column (user-provided or generated by the cross-validation function, as described above)</li>
+<li><em>%prediction%</em> – The argument position for the output/input of prediction/metric function</li>
+<li><em>%error%</em> – The argument position for the output of the metric function</li>
+</ul>
+<p><b>Note</b>: If the argument <em>explore_values</em> is NULL or has zero length, then the cross-validation function will only run a data folding.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<p>This example uses cross validation with an elastic net regression to find the best value of the regularization parameter.</p>
+<ol type="1">
+<li>Populate the table <code>cvtest</code> with 101 dimensional independent variables <code>val</code>, and dependent variable <code>dep</code>.</li>
+<li>Run the general cross-validation function. <pre class="example">
+SELECT madlib.cross_validation_general
+ ( 'madlib.elastic_net_train',
+ '{%data%, %model%, dep, indep, gaussian, 1, lambda, TRUE, NULL, fista,
+ "{eta = 2, max_stepsize = 2, use_active_set = t}",
+ NULL, 2000, 1e-6}'::varchar[],
+ '{varchar, varchar, varchar, varchar, varchar, double precision,
+ double precision, boolean, varchar, varchar, varchar, varchar,
+ integer, double precision}'::varchar[],
+ 'lambda',
+ '{0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20,
+ 0.22, 0.24, 0.26, 0.28, 0.30, 0.32, 0.34, 0.36}'::varchar[],
+ 'madlib.elastic_net_predict',
+ '{%model%, %data%, %id%, %prediction%}'::varchar[],
+ '{text, text, text, text}'::varchar[],
+ 'madlib.mse_error',
+ '{%prediction%, %data%, %id%, dep, %error%}'::varchar[],
+ '{varchar, varchar, varchar, varchar, varchar}'::varchar[],
+ 'cvtest',
+ NULL::varchar,
+ FALSE,
+ 'valid_rst_tbl',
+ '{indep, dep}'::varchar[],
+ 10
+);
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<p><em>max_locks_per_transaction</em>, which usually is set to the default value of 64, limits the number of tables that can be dropped inside a single transaction (the cross-validation function). Thus, the number of different values of <em>param_explored</em> (or the length of the <em>explored_values</em> array) cannot be too large. For 10-fold cross validation, the limit of <code>length(<em>explored_values</em>)</code> is around 40. If the limit is exceeded, you may get an "out of shared memory" error because <em>max_locks_per_transaction</em> is exceeded.</p>
+<p>One way to overcome this limitation is to run the cross-validation function multiple times, with each run covering a different region of values of the parameter.</p>
+<p>In the future, MADlib may implement cross-validation functions for each individual applicable module, where it is possible to optimize the calculation to avoid dropping tables and prevent exceeding the <em>max_locks_per_transaction</em> limitation. Since module-specific cross-validation functions depend upon the implementation details of the modules to perform the optimization, they will not be as flexible as the generalized cross-validation function provided here.</p>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>One round of cross validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross validation are performed using different partitions, and the validation results are averaged over the rounds.</p>
+<p>In k-fold cross validation, the original sample is randomly partitioned into k equal sized subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k−1 subsamples are used as training data. The cross-validation process is repeated k times (the folds), with each of the k subsamples used exactly once as the validation data. The k results from the folds can be averaged (or otherwise combined) to produce a single estimation. The advantage of this method over repeated random sub-sampling is that all observations are used for both training and validation, and each observation is used for validation exactly once. 10-fold cross validation is commonly used, but in general k remains an unfixed parameter.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="cross__validation_8sql__in.html" title="SQL functions for cross validation. ">cross_validation.sql_in</a> documenting the SQL functions. </p>
+</div><!-- contents -->
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