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They are useful in calculating variances in a dataset with potentially noisy outliers. The Huber-White implemented here is identical to the "HC0" sandwich operator in the R module "sandwich".</p> -<p>The interfaces for robust linear, logistic, and multinomial logistic regression are similar. Each regression type has its own training function. The regression results are saved in an output table with small differences, depending on the regression type.</p> -<dl class="section warning"><dt>Warning</dt><dd>Please note that the interface for Cox proportional hazards, unlike the interface of other regression methods, accepts an output model table produced by <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef">coxph_train()</a> function.</dd></dl> -<p><a class="anchor" id="train_linregr"></a></p><dl class="section user"><dt>Robust Linear Regression Training Function</dt><dd></dd></dl> -<p>The <a class="el" href="robust_8sql__in.html#a390473d2fd45e268f0fc13ca971b49b4">robust_variance_linregr()</a> function has the following syntax: </p><pre class="syntax"> -robust_variance_linregr( source_table, - out_table, - dependent_varname, - independent_varname, - grouping_cols - ) -</pre> <dl class="arglist"> -<dt>source_table </dt> -<dd>VARCHAR. The name of the table containing the training data. </dd> -<dt>out_table </dt> -<dd><p class="startdd">VARCHAR. Name of the generated table containing the output model. The output table contains the following columns. </p><table class="output"> -<tr> -<th>coef </th><td>DOUBLE PRECISION[]. Vector of the coefficients of the regression. </td></tr> -<tr> -<th>std_err </th><td>DOUBLE PRECISION[]. Vector of the standard error of the coefficients. </td></tr> -<tr> -<th>t_stats </th><td>DOUBLE PRECISION[]. Vector of the t-stats of the coefficients. </td></tr> -<tr> -<th>p_values </th><td>DOUBLE PRECISION[]. Vector of the p-values of the coefficients. </td></tr> -</table> -<p class="enddd">A summary table named <out_table>_summary is also created, which is the same as the summary table created by linregr_train function. Please refer to the documentation for linear regression for details. </p> -</dd> -<dt>dependent_varname </dt> -<dd>VARCHAR. The name of the column containing the dependent variable. </dd> -<dt>independent_varname </dt> -<dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list. </dd> -<dt>grouping_cols (optional) </dt> -<dd>VARCHAR, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. Default value: NULL. </dd> -</dl> -<p><a class="anchor" id="train_logregr"></a></p><dl class="section user"><dt>Robust Logistic Regression Training Function</dt><dd></dd></dl> -<p>The <a class="el" href="robust_8sql__in.html#abc20ec2c5e74f268e7727c33a4bb9054">robust_variance_logregr()</a> function has the following syntax: </p><pre class="syntax"> -robust_variance_logregr( source_table, - out_table, - dependent_varname, - independent_varname, - grouping_cols, - max_iter, - optimizer, - tolerance, - verbose_mode - ) -</pre> <dl class="arglist"> -<dt>source_table </dt> -<dd>VARCHAR. The name of the table containing the training data. </dd> -<dt>out_table </dt> -<dd><p class="startdd">VARCHAR. Name of the generated table containing the output model. The output table has the following columns: </p><table class="output"> -<tr> -<th>coef </th><td>Vector of the coefficients of the regression. </td></tr> -<tr> -<th>std_err </th><td>Vector of the standard error of the coefficients. </td></tr> -<tr> -<th>z_stats </th><td>Vector of the z-stats of the coefficients. </td></tr> -<tr> -<th>p_values </th><td>Vector of the p-values of the coefficients. </td></tr> -</table> -<p class="enddd">A summary table named <out_table>_summary is also created, which is the same as the summary table created by logregr_train function. Please refer to the documentation for logistic regression for details. </p> -</dd> -<dt>dependent_varname </dt> -<dd>VARCHAR. The name of the column containing the independent variable. </dd> -<dt>independent_varname </dt> -<dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list. </dd> -<dt>grouping_cols (optional) </dt> -<dd>VARCHAR, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. </dd> -<dt>max_iter (optional) </dt> -<dd>INTEGER, default: 20. The maximum number of iterations that are allowed. </dd> -<dt>optimizer </dt> -<dd>VARCHAR, default: 'fista'. Name of optimizer, either 'fista' or 'igd'. </dd> -<dt>tolerance (optional) </dt> -<dd>DOUBLE PRECISION, default: 1e-6. The criteria to end iterations. Both the 'fista' and 'igd' optimizers compute the average difference between the coefficients of two consecutive iterations, and when the difference is smaller than tolerance or the iteration number is larger than max_iter, the computation stops. </dd> -<dt>verbose_mode (optional) </dt> -<dd>BOOLEAN, default: FALSE. Whether the regression fit should print any warning messages. </dd> -</dl> -<p><a class="anchor" id="train_mlogregr"></a></p><dl class="section user"><dt>Robust Multinomial Logistic Regression Function</dt><dd></dd></dl> -<p>The <a class="el" href="robust_8sql__in.html#a1f27c072a4ef885a55825f75d12b3bd8">robust_variance_mlogregr()</a> function has the following syntax: </p><pre class="syntax"> -robust_variance_mlogregr( source_table, - out_table, - dependent_varname, - independent_varname, - ref_category, - grouping_cols, - optimizer_params, - verbose_mode - ) -</pre> <dl class="arglist"> -<dt>source_table </dt> -<dd>VARCHAR. The name of the table containing training data, properly qualified. </dd> -<dt>out_table </dt> -<dd><p class="startdd">VARCHAR. The name of the table where the regression model will be stored. The output table has the following columns: </p><table class="output"> -<tr> -<th>category </th><td>The category. </td></tr> -<tr> -<th>ref_category </th><td>The refererence category used for modeling. </td></tr> -<tr> -<th>coef </th><td>Vector of the coefficients of the regression. </td></tr> -<tr> -<th>std_err </th><td>Vector of the standard error of the coefficients. </td></tr> -<tr> -<th>z_stats </th><td>Vector of the z-stats of the coefficients. </td></tr> -<tr> -<th>p_values </th><td>Vector of the p-values of the coefficients. </td></tr> -</table> -<p class="enddd">A summary table named <out_table>_summary is also created, which is the same as the summary table created by mlogregr_train function. Please refer to the documentation for multinomial logistic regression for details. </p> -</dd> -<dt>dependent_varname </dt> -<dd>VARCHAR. The name of the column containing the dependent variable. </dd> -<dt>independent_varname </dt> -<dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list. The <em>independent_varname</em> can be the name of a column that contains an array of numeric values. It can also be a string with the format 'ARRAY[1, x1, x2, x3]', where <em>x1</em>, <em>x2</em> and <em>x3</em> are each column names. </dd> -<dt>ref_category (optional) </dt> -<dd>INTEGER, default: 0. The reference category. </dd> -<dt>grouping_cols (optional) </dt> -<dd>VARCHAR, default: NULL. <em>Not currently implemented. Any non-NULL value is ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. </dd> -<dt>optimizer_params (optional) </dt> -<dd>TEXT, default: NULL, which uses the default values of optimizer parameters: max_iter=20, optimizer='newton', tolerance=1e-4. It should be a string that contains pairs of 'key=value' separated by commas. </dd> -<dt>verbose_mode (optional) </dt> -<dd>BOOLEAN, default FALSE. <em>Not currently implemented.</em> TRUE if the regression fit should print warning messages. </dd> -</dl> -<p><a class="anchor" id="robust_variance_coxph"></a></p><dl class="section user"><dt>Robust Variance Function For Cox Proportional Hazards</dt><dd></dd></dl> -<p>The <a class="el" href="clustered__variance__coxph_8sql__in.html#abaeae5d6cd30db4b06a49d24d714812e">robust_variance_coxph()</a> function has the following syntax: </p><pre class="syntax"> -robust_variance_coxph(model_table, output_table) -</pre><p><b>Arguments</b> </p><dl class="arglist"> -<dt>model_table </dt> -<dd>TEXT. The name of the model table, which is exactaly the same as the 'output_table' parameter of <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef" title="Compute cox-regression coefficients and diagnostic statistics. ">coxph_train()</a> function. </dd> -<dt>output_table </dt> -<dd>TEXT. The name of the table where the output is saved. It has the following columns: <table class="output"> -<tr> -<th>coef </th><td>FLOAT8[]. Vector of the coefficients. </td></tr> -<tr> -<th>loglikelihood </th><td>FLOAT8. Log-likelihood value of the MLE estimate. </td></tr> -<tr> -<th>std_err </th><td>FLOAT8[]. Vector of the standard error of the coefficients. </td></tr> -<tr> -<th>robust_se </th><td>FLOAT8[]. Vector of the robust standard errors of the coefficients. </td></tr> -<tr> -<th>robust_z </th><td>FLOAT8[]. Vector of the robust z-stats of the coefficients. </td></tr> -<tr> -<th>robust_p </th><td>FLOAT8[]. Vector of the robust p-values of the coefficients. </td></tr> -<tr> -<th>hessian </th><td>FLOAT8[]. The Hessian matrix. </td></tr> -</table> -</dd> -</dl> -<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl> -<p><b> Logistic Regression Example </b></p><ol type="1"> -<li>View online help for the logistic regression training function. <pre class="example"> -SELECT madlib.robust_variance_logregr(); -</pre></li> -<li>Create the training data table. <pre class="example"> -DROP TABLE IF EXISTS patients; -CREATE TABLE patients (id INTEGER NOT NULL, second_attack INTEGER, - treatment INTEGER, trait_anxiety INTEGER); -COPY patients FROM STDIN WITH DELIMITER '|'; - 1 | 1 | 1 | 70 - 3 | 1 | 1 | 50 - 5 | 1 | 0 | 40 - 7 | 1 | 0 | 75 - 9 | 1 | 0 | 70 - 11 | 0 | 1 | 65 - 13 | 0 | 1 | 45 - 15 | 0 | 1 | 40 - 17 | 0 | 0 | 55 - 19 | 0 | 0 | 50 - 2 | 1 | 1 | 80 - 4 | 1 | 0 | 60 - 6 | 1 | 0 | 65 - 8 | 1 | 0 | 80 - 10 | 1 | 0 | 60 - 12 | 0 | 1 | 50 - 14 | 0 | 1 | 35 - 16 | 0 | 1 | 50 - 18 | 0 | 0 | 45 - 20 | 0 | 0 | 60 -\. -</pre></li> -<li>Run the logistic regression training function and compute the robust logistic variance of the regression: <pre class="example"> -DROP TABLE IF EXISTS patients_logregr; -SELECT madlib.robust_variance_logregr( 'patients', - 'patients_logregr', - 'second_attack', - 'ARRAY[1, treatment, trait_anxiety]' - ); -</pre></li> -<li>View the regression results. <pre class="example"> -\x on -Expanded display is on. -SELECT * FROM patients_logregr; -</pre> Result: <pre class="result"> - -[ RECORD 1 ]------------------------------------------------------- - coef | {-6.36346994178179,-1.02410605239327,0.119044916668605} - std_err | {3.45872062333648,1.1716192578234,0.0534328864185018} - z_stats | {-1.83983346294192,-0.874094587943036,2.22793348156809} - p_values | {0.0657926909738889,0.382066744585541,0.0258849510757339} -</pre> Alternatively, unnest the arrays in the results for easier reading of output. <pre class="example"> -\x off -SELECT unnest(array['intercept', 'treatment', 'trait_anxiety' ]) as attribute, - unnest(coef) as coefficient, - unnest(std_err) as standard_error, - unnest(z_stats) as z_stat, - unnest(p_values) as pvalue -FROM patients_logregr; -</pre></li> -</ol> -<p><b> Cox Proportional Hazards Example </b></p><ol type="1"> -<li>View online help for the robust Cox Proportional hazards training method. <pre class="example"> -SELECT madlib.robust_variance_coxph(); -</pre></li> -<li>Create an input data set. <pre class="example"> -DROP TABLE IF EXISTS sample_data; -CREATE TABLE sample_data ( - id INTEGER NOT NULL, - grp DOUBLE PRECISION, - wbc DOUBLE PRECISION, - timedeath INTEGER, - status BOOLEAN -); -COPY sample_data FROM STDIN DELIMITER '|'; - 0 | 0 | 1.45 | 35 | t - 1 | 0 | 1.47 | 34 | t - 3 | 0 | 2.2 | 32 | t - 4 | 0 | 1.78 | 25 | t - 5 | 0 | 2.57 | 23 | t - 6 | 0 | 2.32 | 22 | t - 7 | 0 | 2.01 | 20 | t - 8 | 0 | 2.05 | 19 | t - 9 | 0 | 2.16 | 17 | t - 10 | 0 | 3.6 | 16 | t - 11 | 1 | 2.3 | 15 | t - 12 | 0 | 2.88 | 13 | t - 13 | 1 | 1.5 | 12 | t - 14 | 0 | 2.6 | 11 | t - 15 | 0 | 2.7 | 10 | t - 16 | 0 | 2.8 | 9 | t - 17 | 1 | 2.32 | 8 | t - 18 | 0 | 4.43 | 7 | t - 19 | 0 | 2.31 | 6 | t - 20 | 1 | 3.49 | 5 | t - 21 | 1 | 2.42 | 4 | t - 22 | 1 | 4.01 | 3 | t - 23 | 1 | 4.91 | 2 | t - 24 | 1 | 5 | 1 | t -\. -</pre></li> -<li>Run the Cox regression function. <pre class="example"> -SELECT madlib.coxph_train( 'sample_data', - 'sample_cox', - 'timedeath', - 'ARRAY[grp,wbc]', - 'status' - ); -</pre></li> -<li>Run the Robust Cox regression function. <pre class="example"> -SELECT madlib.robust_variance_coxph( 'sample_cox', - 'sample_robust_cox' - ); -</pre></li> -<li>View the results of the robust Cox regression. <pre class="example"> -\x on -SELECT * FROM sample_robust_cox; -</pre> Results: <pre class="result"> --[ RECORD 1 ]-+---------------------------------------------------------------------------- -coef | {2.54407073265105,1.67172094780081} -loglikelihood | -37.8532498733452 -std_err | {0.677180599295459,0.387195514577754} -robust_se | {0.621095581073685,0.274773521439328} -robust_z | {4.09610180811965,6.08399579058399} -robust_p | {4.2016521208424e-05,1.17223683104729e-09} -hessian | {{2.78043065745405,-2.25848560642669},{-2.25848560642669,8.50472838284265}} -</pre></li> -</ol> -<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl> -<p>When doing regression analysis, we are sometimes interested in the variance of the computed coefficients \( \boldsymbol c \). While the built-in regression functions provide variance estimates, we may prefer a <em>robust</em> variance estimate.</p> -<p>The robust variance calculation can be expressed in a sandwich formation, which is the form </p><p class="formulaDsp"> -\[ S( \boldsymbol c) = B( \boldsymbol c) M( \boldsymbol c) B( \boldsymbol c) \] -</p> -<p> where \( B( \boldsymbol c)\) and \( M( \boldsymbol c)\) are matrices. The \( B( \boldsymbol c) \) matrix, also known as the bread, is relatively straight forward, and can be computed as </p><p class="formulaDsp"> -\[ B( \boldsymbol c) = n\left(\sum_i^n -H(y_i, x_i, \boldsymbol c) \right)^{-1} \] -</p> -<p> where \( H \) is the hessian matrix.</p> -<p>The \( M( \boldsymbol c)\) matrix has several variations, each with different robustness properties. The form implemented here is the Huber-White sandwich operator, which takes the form </p><p class="formulaDsp"> -\[ M_{H} =\frac{1}{n} \sum_i^n \psi(y_i,x_i, \boldsymbol c)^T \psi(y_i,x_i, \boldsymbol c). \] -</p> -<p>The above method for calculating robust variance (Huber-White estimates) is implemented for linear regression, logistic regression, and multinomial logistic regression. It is useful in calculating variances in a dataset with potentially noisy outliers. The Huber-White implemented here is identical to the "HC0" sandwich operator in the R module "sandwich".</p> -<p>When multinomial logistic regression is computed before the multinomial robust regression, it uses a default reference category of zero and the regression coefficients are included in the output table. The regression coefficients in the output are in the same order as the multinomial logistic regression function, which is described below. For a problem with \( K \) dependent variables \( (1, ..., K) \) and \( J \) categories \( (0, ..., J-1) \), let \( {m_{k,j}} \) denote the coefficient for dependent variable \( k \) and category \( j \) . The output is \( {m_{k_1, j_0}, m_{k_1, j_1} \ldots m_{k_1, j_{J-1}}, m_{k_2, j_0}, m_{k_2, j_1} \ldots m_{k_K, j_{J-1}}} \). The order is NOT CONSISTENT with the multinomial regression marginal effect calculation with function <em>marginal_mlogregr</em>. This is deliberate because the interfaces of all multinomial regressions (robust, clustered, ...) will be moved to match that used in marginal.</p> -<p>The robust variance of Cox proportional hazards is more complex because coeeficients are trained by maximizing a partial log-likelihood. Therefore, one cannot directly use the formula for \( M( \boldsymbol c) \) as in Huber-White robust estimator. Extra terms are needed. See [4] for details.</p> -<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl> -<p>[1] vce(cluster) function in STATA: <a href="http://www.stata.com/help.cgi?vce_option";>http://www.stata.com/help.cgi?vce_option</a></p> -<p>[2] clustered estimators in R: <a href="http://people.su.se/~ma/clustering.pdf";>http://people.su.se/~ma/clustering.pdf</a></p> -<p>[3] Achim Zeileis: Object-oriented Computation of Sandwich Estimators. Research Report Series / Department of Statistics and Mathematics, 37. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna. <a href="http://cran.r-project.org/web/packages/sandwich/vignettes/sandwich-OOP.pdf";>http://cran.r-project.org/web/packages/sandwich/vignettes/sandwich-OOP.pdf</a></p> -<p>[4] D. Y. Lin and L . J. Wei, <em>The Robust Inference for the Cox Proportional Hazards Model</em>, Journal of the American Statistical Association, Vol. 84, No. 408, p.1074 (1989).</p> -<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="robust_8sql__in.html" title="SQL functions for robust variance linear and logistic regression. ">robust.sql_in</a> documenting the SQL functions File <a class="el" href="robust__variance__coxph_8sql__in.html" title="SQL functions for robust cox proportional hazards regression. ">robust_variance_coxph.sql_in</a> documenting more the SQL functions</dd></dl> -</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 Mon Aug 6 2018 21:55:39 for MADlib by - <a href="http://www.doxygen.org/index.html";> - <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.14 </li> - </ul> -</div> -</body> -</html>
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There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl> -<p>The random sampling module consists of useful utility functions for sampling operations. These functions can be used while implementing new algorithms.</p> -<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Functions</dt><dd></dd></dl> -<p>Sample a single row according to weights. </p><pre class="syntax"> -weighted_sample( value, - weight - ) -</pre><p><b>Arguments</b> </p><dl class="arglist"> -<dt>value </dt> -<dd>BIGINT or FLOAT8[]. Value of row. Uniqueness is not enforced. If a value occurs multiple times, the probability of sampling this value is proportional to the sum of its weights. </dd> -<dt>weight </dt> -<dd>FLOAT8. Weight for row. A negative value here is treated has zero weight. </dd> -</dl> -<p>Refer to the file for documentation on each of the utility functions.</p> -<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl> -<dl class="section see"><dt>See also</dt><dd>File <a class="el" href="sample_8sql__in.html" title="SQL functions for random sampling. 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This is commonly used when classes are imbalanced, to ensure that subclasses are adequately represented in the sample. <br /></td></tr> -<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> -<tr class="memitem:group__grp__strs"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__strs.html">Stratified Sampling</a></td></tr> -<tr class="memdesc:group__grp__strs"><td class="mdescLeft"> </td><td class="mdescRight">A method for independently sampling subpopulations (strata). <br /></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 Mon Aug 6 2018 21:55:39 for MADlib by - <a href="http://www.doxygen.org/index.html";> - <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.14 </li> - </ul> -</div> -</body> -</html> http://git-wip-us.apache.org/repos/asf/madlib-site/blob/573d66d8/docs/rc/group__grp__sampling.js ---------------------------------------------------------------------- diff --git a/docs/rc/group__grp__sampling.js b/docs/rc/group__grp__sampling.js deleted file mode 100644 index 719fc9d..0000000 --- a/docs/rc/group__grp__sampling.js +++ /dev/null @@ -1,5 +0,0 @@ -var group__grp__sampling = -[ - [ "Balanced Sampling", "group__grp__balance__sampling.html", null ], - [ "Stratified Sampling", "group__grp__strs.html", null ] -]; \ No newline at end of file http://git-wip-us.apache.org/repos/asf/madlib-site/blob/573d66d8/docs/rc/group__grp__sessionize.html ---------------------------------------------------------------------- diff --git a/docs/rc/group__grp__sessionize.html b/docs/rc/group__grp__sessionize.html deleted file mode 100644 index 158a778..0000000 --- a/docs/rc/group__grp__sessionize.html +++ /dev/null @@ -1,285 +0,0 @@ -<!-- HTML header for doxygen 1.8.4--> -<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";> -<html xmlns="http://www.w3.org/1999/xhtml";> -<head> -<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> -<meta http-equiv="X-UA-Compatible" content="IE=9"/> -<meta name="generator" content="Doxygen 1.8.14"/> -<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/> -<title>MADlib: Sessionize</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> -<link href="navtree.css" rel="stylesheet" type="text/css"/> -<script type="text/javascript" src="resize.js"></script> -<script type="text/javascript" src="navtreedata.js"></script> -<script type="text/javascript" src="navtree.js"></script> -<script type="text/javascript"> -/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ - $(document).ready(initResizable); -/* @license-end */</script> -<link href="search/search.css" rel="stylesheet" type="text/css"/> -<script type="text/javascript" src="search/searchdata.js"></script> -<script type="text/javascript" src="search/search.js"></script> -<script type="text/javascript"> -/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ - $(document).ready(function() { init_search(); }); -/* @license-end */ -</script> -<script type="text/x-mathjax-config"> - MathJax.Hub.Config({ - extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"], - jax: ["input/TeX","output/HTML-CSS"], -}); -</script><script type="text/javascript" async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";></script> -<!-- hack in the navigation tree --> -<script type="text/javascript" src="eigen_navtree_hacks.js"></script> -<link href="doxygen.css" rel="stylesheet" type="text/css" /> -<link href="madlib_extra.css" rel="stylesheet" type="text/css"/> -<!-- google analytics --> -<script> - (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ - (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), - m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) - })(window,document,'script','//www.google-analytics.com/analytics.js','ga'); - ga('create', 'UA-45382226-1', 'madlib.apache.org'); - ga('send', 'pageview'); -</script> -</head> -<body> -<div id="top"><!-- do not remove this div, it is closed by doxygen! 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A defined period of inactivity indicates the end of one session and beginning of the next session. Sessions can be useful in many domains including web analytics [1], network security, manufacturing, finance, and operational analytics.</p> -<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd><pre class="syntax"> -sessionize( - source_table, - output_table, - partition_expr, - time_stamp, - max_time, - output_cols, - create_view -) -</pre></dd></dl> -<p><b>Arguments</b> </p><dl class="arglist"> -<dt>source_table </dt> -<dd><p class="startdd">VARCHAR. Name of the source table that contains the data to be sessionized.</p> -<p class="enddd"></p> -</dd> -<dt>output_table </dt> -<dd><p class="startdd">VARCHAR. Name of the output view or table. (The parameter create_view described below defines whether the output is actually a view or a table.) In addition to the columns in the source_table, the output also contains a new column called session_id: </p><ul> -<li> -session_id=1,2,...n where n is the number of the session in the partition. </li> -</ul> -<p class="enddd"></p> -</dd> -<dt>partition_expr </dt> -<dd><p class="startdd">VARCHAR. The 'partition_expr' is a single column or a list of comma-separated columns/expressions to divide all rows into groups, or partitions. Sessionization is applied across the rows that fall into the same partition. This parameter can be set to NULL or '' to indicate the sessionization operation is to be applied to the whole input table.</p> -<p class="enddd"></p> -</dd> -<dt>time_stamp </dt> -<dd><p class="startdd">VARCHAR. The time stamp column name that is used for sessionization calculation. <br /> - Note that the time_stamp column will be sorted in ascending order before the session reconstruction is done within a partition.</p> -<p class="enddd"></p> -</dd> -<dt>max_time </dt> -<dd><p class="startdd">INTERVAL. Maximum delta time (i.e., time out) between subsequent events that define a session. If the elapsed time between subsequent events is longer than max_time, a new session is created.</p> -<p class="enddd"><a class="anchor" id="note"></a></p><dl class="section note"><dt>Note</dt><dd>Note that max_time is of time type INTERVAL which is a PostgreSQL way of describing elapsed time. For more information on INTERVAL please refer to reference [2].</dd></dl> -</dd> -<dt>output_cols (optional) </dt> -<dd><p class="startdd">VARCHAR. An optional comma separated list of columns to be written to the output_table. Must be a valid SELECT expression. This is set to '*' by default, which means all columns in the input table will be written to the output_table plus the session_id column. <br /> - Note that this parameter could include a list containing the partition_expr or any other expressions of interest. E.g., '*, expr1, expr2, etc.' where this means output all columns from the input table plus the expressions listed plus the session_id column.</p> -<p class="enddd"></p> -</dd> -<dt>create_view (optional) </dt> -<dd>BOOLEAN default: TRUE. Determines whether to create a view or materialize the output as a table. If you only need session info once, creating a view could be significantly faster than materializing as a table. Please note that if you set create_view to NULL (allowed by PostgreSQL) it will get set to the default value of TRUE. </dd> -</dl> -<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl> -<p>The data set describes shopper behavior on a notional web site that sells beer and wine. A beacon fires an event to a log file when the shopper visits different pages on the site: landing page, beer selection page, wine selection page, and checkout. Each user is identified by a a user id, and every time a page is visited, the page and time stamp are logged.</p> -<p>Create the data table:</p> -<pre class="example"> -DROP TABLE IF EXISTS eventlog CASCADE; -- Using CASCADE in case you are running through this example more than once (views used below) -CREATE TABLE eventlog (event_timestamp TIMESTAMP, - user_id INT, - page TEXT, - revenue FLOAT); -INSERT INTO eventlog VALUES -('04/15/2015 02:19:00', 101331, 'CHECKOUT', 16), -('04/15/2015 02:17:00', 202201, 'WINE', 0), -('04/15/2015 03:18:00', 202201, 'BEER', 0), -('04/15/2015 01:03:00', 100821, 'LANDING', 0), -('04/15/2015 01:04:00', 100821, 'WINE', 0), -('04/15/2015 01:05:00', 100821, 'CHECKOUT', 39), -('04/15/2015 02:06:00', 100821, 'WINE', 0), -('04/15/2015 02:09:00', 100821, 'WINE', 0), -('04/15/2015 02:15:00', 101331, 'LANDING', 0), -('04/15/2015 02:16:00', 101331, 'WINE', 0), -('04/15/2015 02:17:00', 101331, 'HELP', 0), -('04/15/2015 02:18:00', 101331, 'WINE', 0), -('04/15/2015 02:29:00', 201881, 'LANDING', 0), -('04/15/2015 02:30:00', 201881, 'BEER', 0), -('04/15/2015 01:05:00', 202201, 'LANDING', 0), -('04/15/2015 01:06:00', 202201, 'HELP', 0), -('04/15/2015 01:09:00', 202201, 'LANDING', 0), -('04/15/2015 02:15:00', 202201, 'WINE', 0), -('04/15/2015 02:16:00', 202201, 'BEER', 0), -('04/15/2015 03:19:00', 202201, 'WINE', 0), -('04/15/2015 03:22:00', 202201, 'CHECKOUT', 21); -</pre><p>Sessionize the table by each user_id: </p><pre class="example"> - DROP VIEW IF EXISTS sessionize_output_view; - SELECT madlib.sessionize( - 'eventlog', -- Name of input table - 'sessionize_output_view', -- View to store sessionize results - 'user_id', -- Partition input table by user id - 'event_timestamp', -- Time column used to compute sessions - '0:30:0' -- Use 30 minute time out to define sessions - ); -SELECT * FROM sessionize_output_view ORDER BY user_id, event_timestamp; -</pre><p>Result: </p><pre class="result"> - event_timestamp | user_id | page | revenue | session_id ----------------------+---------+----------+---------+------------ - 2015-04-15 01:03:00 | 100821 | LANDING | 0 | 1 - 2015-04-15 01:04:00 | 100821 | WINE | 0 | 1 - 2015-04-15 01:05:00 | 100821 | CHECKOUT | 39 | 1 - 2015-04-15 02:06:00 | 100821 | WINE | 0 | 2 - 2015-04-15 02:09:00 | 100821 | WINE | 0 | 2 - 2015-04-15 02:15:00 | 101331 | LANDING | 0 | 1 - 2015-04-15 02:16:00 | 101331 | WINE | 0 | 1 - 2015-04-15 02:17:00 | 101331 | HELP | 0 | 1 - 2015-04-15 02:18:00 | 101331 | WINE | 0 | 1 - 2015-04-15 02:19:00 | 101331 | CHECKOUT | 16 | 1 - 2015-04-15 02:29:00 | 201881 | LANDING | 0 | 1 - 2015-04-15 02:30:00 | 201881 | BEER | 0 | 1 - 2015-04-15 01:05:00 | 202201 | LANDING | 0 | 1 - 2015-04-15 01:06:00 | 202201 | HELP | 0 | 1 - 2015-04-15 01:09:00 | 202201 | LANDING | 0 | 1 - 2015-04-15 02:15:00 | 202201 | WINE | 0 | 2 - 2015-04-15 02:16:00 | 202201 | BEER | 0 | 2 - 2015-04-15 02:17:00 | 202201 | WINE | 0 | 2 - 2015-04-15 03:18:00 | 202201 | BEER | 0 | 3 - 2015-04-15 03:19:00 | 202201 | WINE | 0 | 3 - 2015-04-15 03:22:00 | 202201 | CHECKOUT | 21 | 3 -(21 rows) -</pre><p>Now let's say we want to see 3 minute sessions by a group of users with a certain range of user IDs. To do this, we need to sessionize the table based on a partition expression. Also, we want to persist a table output with a reduced set of columns in the table. </p><pre class="example"> - DROP TABLE IF EXISTS sessionize_output_table; - SELECT madlib.sessionize( - 'eventlog', -- Name of input table - 'sessionize_output_table', -- Table to store sessionize results - 'user_id < 200000', -- Partition input table by subset of users - 'event_timestamp', -- Order partitions in input table by time - '180', -- Use 180 second time out to define sessions (same as '0:03:0') - 'event_timestamp, user_id, user_id < 200000 AS "Department-A1"', -- Select only user_id and event_timestamp columns, along with the session id as output - 'f' -- create a table - ); - SELECT * FROM sessionize_output_table WHERE "Department-A1"='TRUE' ORDER BY event_timestamp; -</pre><p>Result showing 2 users and 3 total sessions across the group: </p><pre class="result"> - event_timestamp | user_id | Department-A1 | session_id ----------------------+---------+---------------+------------ - 2015-04-15 01:03:00 | 100821 | t | 1 - 2015-04-15 01:04:00 | 100821 | t | 1 - 2015-04-15 01:05:00 | 100821 | t | 1 - 2015-04-15 02:06:00 | 100821 | t | 2 - 2015-04-15 02:09:00 | 100821 | t | 2 - 2015-04-15 02:15:00 | 101331 | t | 3 - 2015-04-15 02:16:00 | 101331 | t | 3 - 2015-04-15 02:17:00 | 101331 | t | 3 - 2015-04-15 02:18:00 | 101331 | t | 3 - 2015-04-15 02:19:00 | 101331 | t | 3 -(10 rows) -</pre><p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl> -<p>NOTE: The following PostgreSQL link refers to documentation resources for the current PostgreSQL database version. Depending upon your database platform version, you may need to change "current" reference in the link to your database version.</p> -<p>If your database platform uses the Greenplum Database (or related variants), please check with the project community and/or your database vendor to identify the PostgreSQL version it is based on.</p> -<p>[1] Sesssions in web analytics <a href="https://en.wikipedia.org/wiki/Session_(web_analytics)">https://en.wikipedia.org/wiki/Session_(web_analytics)</a></p> -<p>[2] PostgreSQL date/time types <a href="https://www.postgresql.org/docs/current/static/datatype-datetime.html";>https://www.postgresql.org/docs/current/static/datatype-datetime.html</a> </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 Mon Aug 6 2018 21:55:39 for MADlib by - <a href="http://www.doxygen.org/index.html";> - <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.14 </li> - </ul> -</div> -</body> -</html> http://git-wip-us.apache.org/repos/asf/madlib-site/blob/573d66d8/docs/rc/group__grp__sketches.html ---------------------------------------------------------------------- diff --git a/docs/rc/group__grp__sketches.html b/docs/rc/group__grp__sketches.html deleted file mode 100644 index 95d9938..0000000 --- a/docs/rc/group__grp__sketches.html +++ /dev/null @@ -1,167 +0,0 @@ -<!-- HTML header for doxygen 1.8.4--> -<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";> -<html xmlns="http://www.w3.org/1999/xhtml";> -<head> -<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> -<meta http-equiv="X-UA-Compatible" content="IE=9"/> -<meta name="generator" content="Doxygen 1.8.14"/> -<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/> -<title>MADlib: Cardinality Estimators</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> -<link href="navtree.css" rel="stylesheet" type="text/css"/> -<script type="text/javascript" src="resize.js"></script> -<script type="text/javascript" src="navtreedata.js"></script> -<script type="text/javascript" src="navtree.js"></script> -<script type="text/javascript"> -/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ - $(document).ready(initResizable); -/* @license-end */</script> -<link href="search/search.css" rel="stylesheet" type="text/css"/> -<script type="text/javascript" src="search/searchdata.js"></script> -<script type="text/javascript" src="search/search.js"></script> -<script type="text/javascript"> -/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt GPL-v2 */ - $(document).ready(function() { init_search(); }); -/* @license-end */ -</script> -<script type="text/x-mathjax-config"> - MathJax.Hub.Config({ - extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"], - jax: ["input/TeX","output/HTML-CSS"], -}); -</script><script type="text/javascript" async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";></script> -<!-- hack in the navigation tree --> -<script type="text/javascript" src="eigen_navtree_hacks.js"></script> -<link href="doxygen.css" rel="stylesheet" type="text/css" /> -<link href="madlib_extra.css" rel="stylesheet" type="text/css"/> -<!-- google analytics --> -<script> - (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ - (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), - m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) - })(window,document,'script','//www.google-analytics.com/analytics.js','ga'); - ga('create', 'UA-45382226-1', 'madlib.apache.org'); - ga('send', 'pageview'); -</script> -</head> -<body> -<div id="top"><!-- do not remove this div, it is closed by doxygen! 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Sketches can be formed in a single pass of the data, and used to approximate a variety of descriptive statistics.</p> -<p>We implement sketches as SQL User-Defined Aggregates (UDAs). Because they are single-pass, small-space and parallelized, a single query can use many sketches to gather summary statistics on many columns of a table efficiently.</p> -<p>This module currently implements user-defined aggregates based on three main sketch methods:</p><ul> -<li><em>Count-Min (CM)</em> sketches, which can be used to approximate a number of descriptive statistics including<ul> -<li><code>COUNT</code> of rows whose column value matches a given value in a set</li> -<li><code>COUNT</code> of rows whose column value falls in a range (*)</li> -<li>order statistics including <em>median</em> and <em>centiles</em> (*)</li> -<li><em>histograms</em>: both <em>equi-width</em> and <em>equi-depth</em> (*)</li> -</ul> -</li> -<li><em>Flajolet-Martin (FM)</em> sketches for approximating <code>COUNT(DISTINCT)</code>.</li> -<li><em>Most Frequent Value (MFV)</em> sketches, which output the most frequently-occuring values in a column, along with their associated counts.</li> -</ul> -<p><em>Note:</em> Features marked with a star (*) only work for discrete types that can be cast to int8.</p> -<p>The sketch methods consist of a number of SQL UDAs (user-defined aggregates) and UDFs (user-defined functions), to be used directly in SQL queries. </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__countmin"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__countmin.html">CountMin (Cormode-Muthukrishnan)</a></td></tr> -<tr class="memdesc:group__grp__countmin"><td class="mdescLeft"> </td><td class="mdescRight">Implements Cormode-Mathukrishnan <em>CountMin</em> sketches on integer values as a user-defined aggregate. <br /></td></tr> -<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> -<tr class="memitem:group__grp__fmsketch"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__fmsketch.html">FM (Flajolet-Martin)</a></td></tr> -<tr class="memdesc:group__grp__fmsketch"><td class="mdescLeft"> </td><td class="mdescRight">Implements Flajolet-Martin's distinct count estimation as a user-defined aggregate. <br /></td></tr> -<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr> -<tr class="memitem:group__grp__mfvsketch"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__mfvsketch.html">MFV (Most Frequent Values)</a></td></tr> -<tr class="memdesc:group__grp__mfvsketch"><td class="mdescLeft"> </td><td class="mdescRight">Implements the most frequent values variant of the CountMin sketch as a user-defined aggregate. <br /></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 Mon Aug 6 2018 21:55:39 for MADlib by - <a href="http://www.doxygen.org/index.html";> - <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.14 </li> - </ul> -</div> -</body> -</html> http://git-wip-us.apache.org/repos/asf/madlib-site/blob/573d66d8/docs/rc/group__grp__sketches.js ---------------------------------------------------------------------- diff --git a/docs/rc/group__grp__sketches.js b/docs/rc/group__grp__sketches.js deleted file mode 100644 index 1e443dd..0000000 --- a/docs/rc/group__grp__sketches.js +++ /dev/null @@ -1,6 +0,0 @@ -var group__grp__sketches = -[ - [ "CountMin (Cormode-Muthukrishnan)", "group__grp__countmin.html", null ], - [ "FM (Flajolet-Martin)", "group__grp__fmsketch.html", null ], - [ "MFV (Most Frequent Values)", "group__grp__mfvsketch.html", null ] -]; \ No newline at end of file
