src/ports/postgres/modules/pmml/table_to_pmml.sql_in - madlib - Git at Google

 /* ------------------------------------------------------------------------
  * @file table_to_pmml.sql_in
  *
  * @brief Convert model table from MADlib to PMML
  * @date June 2014
  *
  * @sa PMML Export module description \ref grp_pmml.
  *
  *//* -------------------------------------------------------------------*/

 m4_include(`SQLCommon.m4')

 /**
 @addtogroup grp_pmml

 <div class="toc"><b>Contents</b><ul>
 <li class="level1"><a href="#function">PMML Export Function</a></li>
 <li class="level1"><a href="#examples">Examples</a></li>
 <li class="level1"><a href="#background">Background</a></li>
 <li class="level1"><a href="#related">Related Topics</a></li>
 </ul></div>

 @brief Implements the PMML XML standard to describe and exchange models
 produced by data mining and machine learning algorithms.


 @anchor function
 @par PMML Export Function
 The PMML export function in MADlib has the following syntax:
 <pre class="syntax">
 pmml ( model_table,
        name_spec
      )
 </pre>
 \b Arguments
 <DL class="arglist">
   <DT>model_table</DT>
   <DD>VARCHAR. The name of the table containing the model.</DD>

   <DT>name_spec (optional)</DT>
   <DD>VARCHAR or VARCHAR[]. Names to be used in the Data Dictionary of the PMML.
     See pmml() for detailed explanation.</DD>
 </DL>

 \b Output
   <DD>XML. The output of this function is a standard PMML document, some
     examples of which are covered in the next section.</DD>
 </DL>

 <DD>@note In PostgreSQL, users may be required to install their database
 with XML support in order to use this function.</DD>

 Usually the user wants to export the resulting PMML contents into a PMML file
 so that external softwares can use it. The following method can be used
 (Note: the user needs to use unaligned table output mode for psql with '-A'
 flag. And inside psql client, both '\\t' and '\\o' should be used):

 <pre class="example">
 \> \# under bash
 \> psql -A my_database
 \# -- in psql now
 \# \\t
 \# \\o test.pmml -- export to a file
 \# select madlib.pmml('tree_out');
 \# \\o
 \# \\t
 </pre>

 @anchor examples
 @examp
 -# Create the training data table.
 <pre class="example">
 CREATE TABLE patients( id integer NOT NULL,
                        second_attack integer,
                        treatment integer,
                        trait_anxiety integer);
 INSERT INTO patients(id, second_attack, treatment, trait_anxiety) VALUES
 ( 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>
 -# Train a regression model using logregr_train().
 <pre class="example">
 SELECT madlib.logregr_train(
         'patients',
         'patients_logregr',
         'second_attack',
         'ARRAY[1, treatment, trait_anxiety]');
 </pre>
 -# View the PMML export for this model.
 <pre class="example">
 SELECT madlib.pmml('patients_logregr');
 </pre>
 Result:
 <pre class="result">
 <?xml version="1.0" standalone="yes"?>
 <PMML version="4.1" xmlns="http://www.dmg.org/pmml-v4-1.html">
   <Header copyright="redacted for this example">
     <Extension extender="MADlib" name="user" value="gpadmin"/>
     <Application name="MADlib" version="1.7"/>
     <Timestamp>
       2014-06-13 17:30:14.527899 PDT
     </Timestamp>
   </Header>
   <DataDictionary numberOfFields="4">
     <DataField dataType="boolean" name="second_attack_pmml_prediction" optype="categorical"/>
     <DataField dataType="double" name="1" optype="continuous"/>
     <DataField dataType="double" name="treatment" optype="continuous"/>
     <DataField dataType="double" name="trait_anxiety" optype="continuous"/>
   </DataDictionary>
   <RegressionModel functionName="classification" normalizationMethod="softmax">
     <MiningSchema>
       <MiningField name="second_attack_pmml_prediction" usageType="predicted"/>
       <MiningField name="1"/>
       <MiningField name="treatment"/>
       <MiningField name="trait_anxiety"/>
     </MiningSchema>
     <RegressionTable intercept="0.0" targetCategory="True">
       <NumericPredictor coefficient="-6.36346994178" name="1"/>
       <NumericPredictor coefficient="-1.02410605239" name="treatment"/>
       <NumericPredictor coefficient="0.119044916669" name="trait_anxiety"/>
     </RegressionTable>
     <RegressionTable intercept="0.0" targetCategory="False"/>
   </RegressionModel>
 </PMML>
 </pre>

 Alternatively, the above can also be invoked as below if custom names are needed
 for fields in the Data Dictionary:
 <pre class="example">
 SELECT madlib.pmml('patients_logregr',
                    'out_attack~1+in_trait_anxiety+in_treatment');
 </pre>

 \b Note: If the second argument of 'pmml' function is not specified, a default suffix "_pmml_prediction" will be automatically append to the column name to be predicted. This can help avoid name conflicts.

 The following example demonstrates grouping columns in the model table for the same dataset as the previous example.

 -# Train a different regression model with 'treatment' as the grouping column.
 <pre class="example">
 SELECT madlib.logregr_train(
         'patients',
         'patients_logregr_grouping',
         'second_attack',
         'ARRAY[1, trait_anxiety]',
         'treatment');
 </pre>
 -# View the PMML export for this model.
 <pre class="example">
 SELECT madlib.pmml('patients_logregr_grouping',
                    ARRAY['second_attack','1','in_trait_anxiety']);
 </pre>
 Result:
 <pre class="result">
 <?xml version="1.0" standalone="yes"?>
  <PMML version="4.1" xmlns="http://www.dmg.org/pmml-v4-1.html">
    <Header copyright="redacted for this example">
      <Extension extender="MADlib" name="user" value="gpadmin"/>
      <Application name="MADlib" version="1.7"/>
      <Timestamp>
        2014-06-13 17:37:55.786307 PDT
      </Timestamp>
    </Header>
    <DataDictionary numberOfFields="4">
      <DataField dataType="boolean" name="second_attack" optype="categorical"/>
      <DataField dataType="double" name="1" optype="continuous"/>
      <DataField dataType="double" name="in_trait_anxiety" optype="continuous"/>
      <DataField dataType="string" name="treatment" optype="categorical"/>
    </DataDictionary>
    <MiningModel functionName="classification">
      <MiningSchema>
        <MiningField name="second_attack" usageType="predicted"/>
        <MiningField name="1"/>
        <MiningField name="in_trait_anxiety"/>
        <MiningField name="treatment"/>
      </MiningSchema>
      <Segmentation multipleModelMethod="selectFirst">
        <Segment>
          <SimplePredicate field="treatment" operator="equal" value="1"/>
          <RegressionModel functionName="classification" normalizationMethod="softmax">
            <MiningSchema>
              <MiningField name="second_attack" usageType="predicted"/>
              <MiningField name="1"/>
              <MiningField name="in_trait_anxiety"/>
            </MiningSchema>
            <RegressionTable intercept="0.0" targetCategory="True">
              <NumericPredictor coefficient="-8.02068430057" name="1"/>
              <NumericPredictor coefficient="0.130090428526" name="in_trait_anxiety"/>
            </RegressionTable>
            <RegressionTable intercept="0.0" targetCategory="False"/>
          </RegressionModel>
        </Segment>
        <Segment>
          <SimplePredicate field="treatment" operator="equal" value="0"/>
          <RegressionModel functionName="classification" normalizationMethod="softmax">
            <MiningSchema>
              <MiningField name="second_attack" usageType="predicted"/>
              <MiningField name="1"/>
              <MiningField name="in_trait_anxiety"/>
            </MiningSchema>
            <RegressionTable intercept="0.0" targetCategory="True">
              <NumericPredictor coefficient="-5.75043192191" name="1"/>
              <NumericPredictor coefficient="0.108282446319" name="in_trait_anxiety"/>
            </RegressionTable>
            <RegressionTable intercept="0.0" targetCategory="False"/>
          </RegressionModel>
        </Segment>
      </Segmentation>
    </MiningModel>
  </PMML>
 </pre>

 \b Note: MADlib currently supports PMML export for Linear Regression,
 Logistic Regression, Generalized Linear Regression Model, Multinomial Logistic
 Regression, Ordinal Linear Regression, Decision Tree and Random Forests.

 In Ordinal Regression, the signs of feature coefficients will be different in
 PMML export and in the default output model table from ordinal(). This is due
 to the difference of model settings.

 @anchor background
 @par Background
 The Predictive Model Markup Language (PMML) is an XML-based file format that
 provides a way for applications to describe and exchange models produced by
 data mining and machine learning algorithms. A PMML file comprises the
 following components:
 - Header: Contains general information of the model, such as copyright
 information and model description.
 - Data Dictionary: Contains definitions of fields used in the model.
 - Data Transformations: Contains transformations for mapping user data into
 a form that can be used by the mining model.
 - Model: Contains definitions of the data mining model, which includes
 attributes such as the model name, function name, and algorithm name.
 - Mining Schema: Contains specific information for the fields used in
 the model, which includes the name and usage type.
 - Targets: Allows for post-processing of the predicted value.
 - Output: Allows for naming of output fields expected from the model.

 MADlib follows the PMML v4.1 standard. For more details about PMML, see
 http://www.dmg.org/v4-1/GeneralStructure.html.


 @anchor related
 @par Related Topics

 File table_to_pmml.sql_in documenting the PMML export functions.

 \ref grp_linreg

 \ref grp_logreg

 \ref grp_glm

 \ref grp_ordinal

 \ref grp_multinom

 \ref grp_decision_tree

 \ref grp_random_forest

 */


 /**
  * @brief Given the model constructed from a data mining algorithm,
  * this function converts the model into PMML format.
  *
  * @param model_table Model table that contains the output of a training algorithm.
  *
  * @return An XML document corresponding to the PMML of the input model.
  *
  * The names in the Data Dictionary are generated based on the names
  * used in the model table.
  * If the fields are represented as an array of expressions without array
  * concatenation, the names in the Data Dictionary will match the source table,
  * except that a suffix '_pmml_prediction' is added to the predicted variable.
  * If the field is an array of FLOAT8 values, the names in the Data
  * Dictionary will be indexed using the array name, like 'ind[1]', 'ind[2]', etc.
  * In all other cases, the names will simply be 'x[1]', 'x[2]', and so on.
  */
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pmml(
     model_table varchar
 ) RETURNS xml AS $$
 PythonFunction(pmml, table_to_pmml, table_to_pmml)
 $$ LANGUAGE plpythonu
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `READS SQL DATA', `');

 /**
  * @brief Given the model constructed from a data mining algorithm,
  * this function converts the model into PMML format.
  *
  * @param model_table Model table that contains the output of a training algorithm.
  * @param name_spec Names to be used in the Data Dictionary, given as
  * a string containing an expression such as 'y ~ x1 + x2 + x3'.
  *
  * @return An XML document corresponding to the PMML of the input model.
  */
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pmml(
     model_table varchar
     , name_spec varchar
 ) RETURNS xml AS $$
 PythonFunction(pmml, table_to_pmml, table_to_pmml)
 $$ LANGUAGE plpythonu
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `READS SQL DATA', `');

 /**
  * @brief Given the model constructed from a data mining algorithm,
  * this function converts the model into PMML format.
  *
  * @param model_table Model table that contains the output of a training algorithm.
  * @param name_spec Names to be used in the Data Dictionary given as
  * an array of strings, such as ARRAY['y', 'x1', 'x2', ...].
  *
  * @return An XML document corresponding to the PMML of the input model.
  */
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pmml(
     model_table varchar
     , name_spec varchar[]
 ) RETURNS xml AS $$
 PythonFunction(pmml, table_to_pmml, table_to_pmml)
 $$ LANGUAGE plpythonu
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `READS SQL DATA', `');


 -- Help messages -------------------------------------------------------
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pmml()
 RETURNS TEXT AS $$
     PythonFunction(pmml, table_to_pmml, pmml_help_msg)
 $$ LANGUAGE plpythonu IMMUTABLE
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
	/* ------------------------------------------------------------------------
	* @file table_to_pmml.sql_in
	*
	* @brief Convert model table from MADlib to PMML
	* @date June 2014
	*
	* @sa PMML Export module description \ref grp_pmml.
	*
	// -------------------------------------------------------------------*/

	m4_include(`SQLCommon.m4')

	/**
	@addtogroup grp_pmml

	<div class="toc"><b>Contents</b><ul>
	<li class="level1"><a href="#function">PMML Export Function</a></li>
	<li class="level1"><a href="#examples">Examples</a></li>
	<li class="level1"><a href="#background">Background</a></li>
	<li class="level1"><a href="#related">Related Topics</a></li>
	</ul></div>

	@brief Implements the PMML XML standard to describe and exchange models
	produced by data mining and machine learning algorithms.


	@anchor function
	@par PMML Export Function
	The PMML export function in MADlib has the following syntax:
	<pre class="syntax">
	pmml ( model_table,
	name_spec
	)
	</pre>
	\b Arguments
	<DL class="arglist">
	<DT>model_table</DT>
	<DD>VARCHAR. The name of the table containing the model.</DD>

	<DT>name_spec (optional)</DT>
	<DD>VARCHAR or VARCHAR[]. Names to be used in the Data Dictionary of the PMML.
	See pmml() for detailed explanation.</DD>
	</DL>

	\b Output
	<DD>XML. The output of this function is a standard PMML document, some
	examples of which are covered in the next section.</DD>
	</DL>

	<DD>@note In PostgreSQL, users may be required to install their database
	with XML support in order to use this function.</DD>

	Usually the user wants to export the resulting PMML contents into a PMML file
	so that external softwares can use it. The following method can be used
	(Note: the user needs to use unaligned table output mode for psql with '-A'
	flag. And inside psql client, both '\\t' and '\\o' should be used):

	<pre class="example">
	\> \# under bash
	\> psql -A my_database
	\# -- in psql now
	\# \\t
	\# \\o test.pmml -- export to a file
	\# select madlib.pmml('tree_out');
	\# \\o
	\# \\t
	</pre>

	@anchor examples
	@examp
	-# Create the training data table.
	<pre class="example">
	CREATE TABLE patients( id integer NOT NULL,
	second_attack integer,
	treatment integer,
	trait_anxiety integer);
	INSERT INTO patients(id, second_attack, treatment, trait_anxiety) VALUES
	( 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>
	-# Train a regression model using logregr_train().
	<pre class="example">
	SELECT madlib.logregr_train(
	'patients',
	'patients_logregr',
	'second_attack',
	'ARRAY[1, treatment, trait_anxiety]');
	</pre>
	-# View the PMML export for this model.
	<pre class="example">
	SELECT madlib.pmml('patients_logregr');
	</pre>
	Result:
	<pre class="result">
	<?xml version="1.0" standalone="yes"?>
	<PMML version="4.1" xmlns="http://www.dmg.org/pmml-v4-1.html">
	<Header copyright="redacted for this example">
	<Extension extender="MADlib" name="user" value="gpadmin"/>
	<Application name="MADlib" version="1.7"/>
	<Timestamp>
	2014-06-13 17:30:14.527899 PDT
	</Timestamp>
	</Header>
	<DataDictionary numberOfFields="4">
	<DataField dataType="boolean" name="second_attack_pmml_prediction" optype="categorical"/>
	<DataField dataType="double" name="1" optype="continuous"/>
	<DataField dataType="double" name="treatment" optype="continuous"/>
	<DataField dataType="double" name="trait_anxiety" optype="continuous"/>
	</DataDictionary>
	<RegressionModel functionName="classification" normalizationMethod="softmax">
	<MiningSchema>
	<MiningField name="second_attack_pmml_prediction" usageType="predicted"/>
	<MiningField name="1"/>
	<MiningField name="treatment"/>
	<MiningField name="trait_anxiety"/>
	</MiningSchema>
	<RegressionTable intercept="0.0" targetCategory="True">
	<NumericPredictor coefficient="-6.36346994178" name="1"/>
	<NumericPredictor coefficient="-1.02410605239" name="treatment"/>
	<NumericPredictor coefficient="0.119044916669" name="trait_anxiety"/>
	</RegressionTable>
	<RegressionTable intercept="0.0" targetCategory="False"/>
	</RegressionModel>
	</PMML>
	</pre>

	Alternatively, the above can also be invoked as below if custom names are needed
	for fields in the Data Dictionary:
	<pre class="example">
	SELECT madlib.pmml('patients_logregr',
	'out_attack~1+in_trait_anxiety+in_treatment');
	</pre>

	\b Note: If the second argument of 'pmml' function is not specified, a default suffix "_pmml_prediction" will be automatically append to the column name to be predicted. This can help avoid name conflicts.

	The following example demonstrates grouping columns in the model table for the same dataset as the previous example.

	-# Train a different regression model with 'treatment' as the grouping column.
	<pre class="example">
	SELECT madlib.logregr_train(
	'patients',
	'patients_logregr_grouping',
	'second_attack',
	'ARRAY[1, trait_anxiety]',
	'treatment');
	</pre>
	-# View the PMML export for this model.
	<pre class="example">
	SELECT madlib.pmml('patients_logregr_grouping',
	ARRAY['second_attack','1','in_trait_anxiety']);
	</pre>
	Result:
	<pre class="result">
	<?xml version="1.0" standalone="yes"?>
	<PMML version="4.1" xmlns="http://www.dmg.org/pmml-v4-1.html">
	<Header copyright="redacted for this example">
	<Extension extender="MADlib" name="user" value="gpadmin"/>
	<Application name="MADlib" version="1.7"/>
	<Timestamp>
	2014-06-13 17:37:55.786307 PDT
	</Timestamp>
	</Header>
	<DataDictionary numberOfFields="4">
	<DataField dataType="boolean" name="second_attack" optype="categorical"/>
	<DataField dataType="double" name="1" optype="continuous"/>
	<DataField dataType="double" name="in_trait_anxiety" optype="continuous"/>
	<DataField dataType="string" name="treatment" optype="categorical"/>
	</DataDictionary>
	<MiningModel functionName="classification">
	<MiningSchema>
	<MiningField name="second_attack" usageType="predicted"/>
	<MiningField name="1"/>
	<MiningField name="in_trait_anxiety"/>
	<MiningField name="treatment"/>
	</MiningSchema>
	<Segmentation multipleModelMethod="selectFirst">
	<Segment>
	<SimplePredicate field="treatment" operator="equal" value="1"/>
	<RegressionModel functionName="classification" normalizationMethod="softmax">
	<MiningSchema>
	<MiningField name="second_attack" usageType="predicted"/>
	<MiningField name="1"/>
	<MiningField name="in_trait_anxiety"/>
	</MiningSchema>
	<RegressionTable intercept="0.0" targetCategory="True">
	<NumericPredictor coefficient="-8.02068430057" name="1"/>
	<NumericPredictor coefficient="0.130090428526" name="in_trait_anxiety"/>
	</RegressionTable>
	<RegressionTable intercept="0.0" targetCategory="False"/>
	</RegressionModel>
	</Segment>
	<Segment>
	<SimplePredicate field="treatment" operator="equal" value="0"/>
	<RegressionModel functionName="classification" normalizationMethod="softmax">
	<MiningSchema>
	<MiningField name="second_attack" usageType="predicted"/>
	<MiningField name="1"/>
	<MiningField name="in_trait_anxiety"/>
	</MiningSchema>
	<RegressionTable intercept="0.0" targetCategory="True">
	<NumericPredictor coefficient="-5.75043192191" name="1"/>
	<NumericPredictor coefficient="0.108282446319" name="in_trait_anxiety"/>
	</RegressionTable>
	<RegressionTable intercept="0.0" targetCategory="False"/>
	</RegressionModel>
	</Segment>
	</Segmentation>
	</MiningModel>
	</PMML>
	</pre>

	\b Note: MADlib currently supports PMML export for Linear Regression,
	Logistic Regression, Generalized Linear Regression Model, Multinomial Logistic
	Regression, Ordinal Linear Regression, Decision Tree and Random Forests.

	In Ordinal Regression, the signs of feature coefficients will be different in
	PMML export and in the default output model table from ordinal(). This is due
	to the difference of model settings.

	@anchor background
	@par Background
	The Predictive Model Markup Language (PMML) is an XML-based file format that
	provides a way for applications to describe and exchange models produced by
	data mining and machine learning algorithms. A PMML file comprises the
	following components:
	- Header: Contains general information of the model, such as copyright
	information and model description.
	- Data Dictionary: Contains definitions of fields used in the model.
	- Data Transformations: Contains transformations for mapping user data into
	a form that can be used by the mining model.
	- Model: Contains definitions of the data mining model, which includes
	attributes such as the model name, function name, and algorithm name.
	- Mining Schema: Contains specific information for the fields used in
	the model, which includes the name and usage type.
	- Targets: Allows for post-processing of the predicted value.
	- Output: Allows for naming of output fields expected from the model.

	MADlib follows the PMML v4.1 standard. For more details about PMML, see
	http://www.dmg.org/v4-1/GeneralStructure.html.


	@anchor related
	@par Related Topics

	File table_to_pmml.sql_in documenting the PMML export functions.

	\ref grp_linreg

	\ref grp_logreg

	\ref grp_glm

	\ref grp_ordinal

	\ref grp_multinom

	\ref grp_decision_tree

	\ref grp_random_forest

	*/


	/**
	* @brief Given the model constructed from a data mining algorithm,
	* this function converts the model into PMML format.
	*
	* @param model_table Model table that contains the output of a training algorithm.
	*
	* @return An XML document corresponding to the PMML of the input model.
	*
	* The names in the Data Dictionary are generated based on the names
	* used in the model table.
	* If the fields are represented as an array of expressions without array
	* concatenation, the names in the Data Dictionary will match the source table,
	* except that a suffix '_pmml_prediction' is added to the predicted variable.
	* If the field is an array of FLOAT8 values, the names in the Data
	* Dictionary will be indexed using the array name, like 'ind[1]', 'ind[2]', etc.
	* In all other cases, the names will simply be 'x[1]', 'x[2]', and so on.
	*/
	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pmml(
	model_table varchar
	) RETURNS xml AS $$
	PythonFunction(pmml, table_to_pmml, table_to_pmml)
	$$ LANGUAGE plpythonu
	m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `READS SQL DATA', `');

	/**
	* @brief Given the model constructed from a data mining algorithm,
	* this function converts the model into PMML format.
	*
	* @param model_table Model table that contains the output of a training algorithm.
	* @param name_spec Names to be used in the Data Dictionary, given as
	* a string containing an expression such as 'y ~ x1 + x2 + x3'.
	*
	* @return An XML document corresponding to the PMML of the input model.
	*/
	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pmml(
	model_table varchar
	, name_spec varchar
	) RETURNS xml AS $$
	PythonFunction(pmml, table_to_pmml, table_to_pmml)
	$$ LANGUAGE plpythonu
	m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `READS SQL DATA', `');

	/**
	* @brief Given the model constructed from a data mining algorithm,
	* this function converts the model into PMML format.
	*
	* @param model_table Model table that contains the output of a training algorithm.
	* @param name_spec Names to be used in the Data Dictionary given as
	* an array of strings, such as ARRAY['y', 'x1', 'x2', ...].
	*
	* @return An XML document corresponding to the PMML of the input model.
	*/
	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pmml(
	model_table varchar
	, name_spec varchar[]
	) RETURNS xml AS $$
	PythonFunction(pmml, table_to_pmml, table_to_pmml)
	$$ LANGUAGE plpythonu
	m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `READS SQL DATA', `');


	-- Help messages -------------------------------------------------------
	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pmml()
	RETURNS TEXT AS $$
	PythonFunction(pmml, table_to_pmml, pmml_help_msg)
	$$ LANGUAGE plpythonu IMMUTABLE
	m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');