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new 9e32dd98 PMML: Update user docs
9e32dd98 is described below
commit 9e32dd985043bb829cb1690b9c0493a71fc8796d
Author: Nikhil Kak <n...@vmware.com>
AuthorDate: Fri Mar 1 16:21:50 2024 -0800
PMML: Update user docs
JIRA: MADLIB-1517
Starting 0cd28f9733927d63beaefc9488db7f8bfdb3bd80, we no longer include
intercept as a predictor in the pmml file. User docs need to be updated to
incorporate these changes
This commit makes the following changes to the user docs:
1. Remove the predictor variable "1" from the namespec expression
2. Add a note about non array independent variable expressions
---
.../postgres/modules/pmml/table_to_pmml.sql_in | 154 +++++++++++----------
1 file changed, 81 insertions(+), 73 deletions(-)
diff --git a/src/ports/postgres/modules/pmml/table_to_pmml.sql_in
b/src/ports/postgres/modules/pmml/table_to_pmml.sql_in
index 37ecef5c..09b078c6 100644
--- a/src/ports/postgres/modules/pmml/table_to_pmml.sql_in
+++ b/src/ports/postgres/modules/pmml/table_to_pmml.sql_in
@@ -111,31 +111,29 @@ SELECT madlib.pmml('patients_logregr');
Result:
<pre class="result">
<?xml version="1.0" standalone="yes"?>
-<PMML version="4.1" xmlns="http://www.dmg.org/pmml-v4-1.html";>
+<PMML version="4.1" xmlns="http://www.dmg.org/PMML-4_1";>
<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>
+ <Application name="MADlib" version="2.1.0"/>
+ <Timestamp>2024-03-01 16:32:49.798404 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 numberOfFields="3">
+ <DataField name="second_attack_pmml_prediction" optype="categorical"
dataType="boolean">
+ <Value value="True"/>
+ <Value value="False"/>
+ </DataField>
+ <DataField name="treatment" optype="continuous" dataType="double"/>
+ <DataField name="trait_anxiety" optype="continuous" dataType="double"/>
</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 intercept="-6.363469941781809" targetCategory="True">
+ <NumericPredictor name="treatment" coefficient="-1.0241060523932681"/>
+ <NumericPredictor name="trait_anxiety"
coefficient="0.11904491666860519"/>
</RegressionTable>
<RegressionTable intercept="0.0" targetCategory="False"/>
</RegressionModel>
@@ -146,10 +144,25 @@ 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');
+ 'out_attack~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.
+\b Note: 1. 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.
+
+\b Note: 2. While training regression models, it is possible to use a non
array expression. Consider this example:
+<pre>
+-- Create a table where a column named 'x' is an array of the independent
variables
+CREATE TABLE patients2 AS SELECT second_attack AS y, ARRAY[1, treatment,
trait_anxiety] AS x from patients;
+
+-- Now use the columns 'x' and 'y' created in the previous step
+SELECT madlib.logregr_train(
+ 'patients2',
+ 'patients_logregr2',
+ 'y',
+ 'x');
+</pre>
+In such scenarios, the pmml code always assumes that the intercept variable
"1," was already included in the independent variable
+expression. If it is not included, the exported PMML would be incorrect.
The following example demonstrates grouping columns in the model table for the
same dataset as the previous example.
@@ -165,66 +178,61 @@ SELECT madlib.logregr_train(
-# View the PMML export for this model.
<pre class="example">
SELECT madlib.pmml('patients_logregr_grouping',
- ARRAY['second_attack','1','in_trait_anxiety']);
+ ARRAY['second_attack','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>
+<PMML version="4.1" xmlns="http://www.dmg.org/PMML-4_1";>
+ <Header copyright="redacted for this example">
+ <Extension extender="MADlib" name="user" value="gpadmin"/>
+ <Application name="MADlib" version="2.1.0"/>
+ <Timestamp>2024-03-01 16:33:49.804054 PDT</Timestamp>
+ </Header>
+ <DataDictionary numberOfFields="3">
+ <DataField name="second_attack" optype="categorical" dataType="boolean">
+ <Value value="True"/>
+ <Value value="False"/>
+ </DataField>
+ <DataField name="in_trait_anxiety" optype="continuous" dataType="double"/>
+ <DataField name="treatment" optype="categorical" dataType="string"/>
+ </DataDictionary>
+ <MiningModel functionName="classification">
+ <MiningSchema>
+ <MiningField name="second_attack" usageType="predicted"/>
+ <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="in_trait_anxiety"/>
+ </MiningSchema>
+ <RegressionTable intercept="-8.020684300569357"
targetCategory="True">
+ <NumericPredictor name="in_trait_anxiety"
coefficient="0.13009042852646274"/>
+ </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="in_trait_anxiety"/>
+ </MiningSchema>
+ <RegressionTable intercept="-5.750431921908941"
targetCategory="True">
+ <NumericPredictor name="in_trait_anxiety"
coefficient="0.10828244631865602"/>
+ </RegressionTable>
+ <RegressionTable intercept="0.0" targetCategory="False"/>
+ </RegressionModel>
+ </Segment>
+ </Segmentation>
+ </MiningModel>
+</PMML>
</pre>
\b Note: MADlib currently supports PMML export for Linear Regression,
