Class Classification

Hi,
I'd like to ask if how would you know that a class characteristics of a material is linked to QM.
I have a problem in QS61 wherein I created a class characteristics in batch class "FIFO" for material A and that charcateristics becomes a default value in QS61.

All batch characteristics using catalogs and numerics and linked to MIC's should appear in QS61.
If you've created a characteristic for use in batch search strategys, (FIFO sounds like one of these), you might want to create separate batch search strategy class.
In addition, SAP already provides several standard characteristics that can be used in a FIFO strategy so you shouldn't have to create your own. Look at the characteristics that start with LOBM_*.
One of these is batch creation date which is often used in FIFO search strategies for sorting.
Craig

Similar Messages

What is class & classifications

Hi
My requirement is to create tables same as standard tables. So they told me to create tables using class & classifications.
I dont know what is class & classifications. Please help me to create tables using this and also what is the tcode for that.

If people ask me to do something I never did, I tell them and ask if they can explain what it is...

Bapi for class classification

hi ,
how to upload the class classification details in ca01 using bapi_routing_create.
thanks in advance
Edited by: Arun on Jul 18, 2011 1:46 PM

Jessica,
Try looking at the following: BAPI_BUPA_FRG0040_CREATE
Note that if you already have maintained Sales Classifications then you'll have to use the _CHANGE version.
It has the importing structure of type BAPIBUS1006040_SALESCLASS and so you could for example populate it's fields as shown below and using the structure when calling the above BAPI:
DATA
       IS_COMPETITOR
       IS_PROSPECT
       IS_CONSUMER
       IS_CUSTOMER                    X
       CUSTOMER_SINCE
       IS_COD_CUSTOMER
       INDUSTRY
       IS_RENTED
       ACCOUNT_GROUP                  0001
       NIELSEN_ID
       CLASSIFIC
       ATTRIBUTE
       ATTRIB_2
       ATTRIB_3
       ATTRIB_4
       ATTRIB_5
       ATTRIB_6
       ATTRIB_7
       ATTRIB_8
       ATTRIB_9
       ATTRIB_10
Regards,
Brad

Classes & Classifications

Hi,
What are classes & classifications and what is their relationship to Items or Fixed assets?. Should I upload Classes and classifications before a intila stock upload?
Edited by: Csaba Szommer on Oct 13, 2011 8:34 AM

hi..
classes are the group of characteristics....and these classes we assign in classification tab of material master...
example..1..
while creating the release procedure we define the classes and these classes having characteristic..like plant value, purchasing org value..suppose these 2 are the characterics ..
we will create the class and define these 2 characteristics inside that class now...we will use this class in config release procedure...
Exp 2. in batch management..suppose we have created one characteristics SLED and we have assigned this to the class...and now material which we want to have identify by their shelf life date can be assigned to that class in classification tab in material master...
so..Classes are the combination of similar type of properties....and for that we need to define the properties....
so while uploading the intial stock of these SLED material ..the systems ask for date of manufactiring for these material so that it can calculate the shelf life date for the material...
hope u get it,,
Thanks

Class & classification in master table

Hi
I want to create one dealer master table. How to create classification class in dealer master table. please help me.

Hello, I now the question is really old but maybe the answer is usefull for someone else.
1) go into KLAH
complete the Class Type and/or the Class.
Here you get the Internal class number.
2) Then go to KSSK and input internal class number and the object
The object is the material or customer with the following format:
- material number (0000000000000XXXXX)
- customer number (XXXXXXXXX)
Done.

Revenue classes,classification rules in incentive compensation

Hi all,
Iam new to incentive compensation , can any one explain about revenue classes and classification rules and their role in calculating commission .
I want to know about how siblings are useful and about input and output expression .
Thank you,
Sandeep Reddy

Hello Danielle,
Dont get so frustrated. Cheer up...
Slight modification to your setup.
Define Joe's Pizza Shop as Revenue class and also in the hierarchy.
Capture these fields in the rule set.
158785 - child value
252155 - child value.
I guess you are pulling your orders into CN_COMMSSION_HEADERS_ALL table.
Go to tables>> and search for this table and click on columns and from the top dropdown select "classification"
and then 'enable that columns, put a user name as 'Location' or some thing which contains this above values.
so that you can see that columns 'Location when you define your ruleset. and feed these above 2 values so that the transactions can be captured/classified on the basis of above 2 values for calculation.
Cheers,
A.P.

Change Characteristic values in Material class Classification

Hi,
Is there any standard program to change Characteristic vaules in Material Classification?
Thanks,
Shiva

Thanks for your help.
Points will be awarded.
Shiva

Transfer of Classes( Classification data) - through ALE

Hi experts,
I am trying to transfer Classes from a 3.1i system to a ECC 6.0 system through ALE distribution.
Almost all the classes got transferred but in a few of the classes, the status of the idoc remains at 64 - waiting to be processed.
When I scheduled the processing of these idocs in background mode they result in a dump.
Dump:
The exception, which is assigned to class 'CX_SY_ARITHMETIC_OVERFLOW', was not
caught in
procedure "MAINTAIN_ALLO_VALUES_VIA_API" "(FORM)", nor was it propagated by a
RAISING clause.
This dump is happening because sy-index value is crossing 2147483647 ( which is the max allowed value).
I could not find any relevant notes for it. Does anyone know what is causing this issue and how to correct it.
FYI - There are around 3000 values attached to a characteristic which is attached to the class.
Thanks and Regards,
Karthik

Hi Karthrik,
Did you never get answer on this topic or did you make a request to SAP in the meanwhile? I got same dump and I am looking for solution, too.
Br
Markku

DMS Migration (DIR, charateristics, classes)

Hi All,
I have a requirement to migrate the Document Info Records, characteristics, classes, classification and the original documents from R/3 4.7 to ECC 6 Server, as well as move the original docs from the old KPRO server to the new KPRO Server.
So far i was able to find the IDOC type that i can use for characteristics, classes and classification as well as the reports to do a mass distribution.
My biggest problem is migrating the documents and to also ensure that, the links point to the new server. Your help would be really appreciated.
Thank you in Advance,
Danny

Hi,
U can use the message class DOCMAS.
Also consider that you would need to transfer the following message types where applicable:
Classification data needs to be ALEu2019d to the new system, in the sequence set out below:
1.     Characteristics and Characteristic Values
Most objects for variant configuration are dependent on characteristics. For this reason, characteristics must be transferred first.
u2022     Characteristics with value hierarchies, long texts for characteristic values, or linked documents may lead to problems during transfer.
u2022     Message type: CHRMAS
u2022     Availability: as of R/3 3.0
2.     Classes
When you use ALE to distribute classes, the characteristic assignments are also transferred.
u2022     Message type: CLSMAS
u2022     Availability: as of R/3 3.0
3.     Variant Table Structures
These are the variant tables that are created to support data maintenance.
u2022     Message type: VTAMAS
u2022     Availability: as of R/3 3.1
4.     Variant Table Contents
Once the structures of the variant tables have been distributed, their contents can be transferred.
u2022     Message type: VTMMAS
u2022     Availability: as of R/3 3.1
5.     User-Defined Functions (Variant Functions, VC Functions)
User-defined functions in variant configuration let you use function modules that you have written, to check and infer characteristic values.
The distribution of functions only transfers the framework (texts, characteristics, and so on). The function modules that belong to the functions must be transferred first, using the usual R/3 transport system.
u2022     Message type: VFNMAS
u2022     Availability: as of R/3 4.5
6.     Object Dependencies (Except Constraints)
Dependencies (preconditions, selection conditions, procedures, and actions) usually refer to characteristics, characteristic values, variant tables, and variant functions. For this reason, dependencies must be distributed after this other master data. The dependencies transferred here are global dependencies. Local dependencies are transferred with the objects to which they are assigned. For example, if you created a selection condition as a local dependency for a BOM item, this dependency is transferred when you use ALE to distribute the BOM (bill of material).
u2022     Message type: KNOMAS
u2022     Availability: as of R/3 3.1
7.     Constraints
Constraints can only be distributed as of R/3 4.5.
u2022     Message type: KNOMAS
u2022     Availability: as of R/3 4.5
8.     Constraint Nets
Constraint nets can only be distributed as of R/3 4.5.
u2022     Message type: DEPNET
u2022     Availability: as of R/3 4.5
9.     Assignment of Dependencies to Characteristics and Characteristic Values
u2022     The characteristics are transferred once more to do this. Start ALE distribution for characteristics again, and the system transfers the assignments.
See point 1: Characteristics and Characteristic Values
Regards,
Freddie.

Storage class not allowed in storage type

Hi All,
I am getting this error while confirming the TO.
It stays Storage class is not allowed in Storage type but how can this be configured in OMM2.
or do we need to define any sequence in OMLY.

Hi
Have u defined your material as Hazardous material? is so, yes, u have to do settings in OMLY.
"Storage class: Classifies hazardous materials with regard to their storage conditions. A check can be carried out to find out whether hazardous materials that belong to a specific storage class may be placed into a particular storage type".
Rgds
Ramesh

How to link material idoc to class & characteristics ?

Hi Experts,
Please help me to link material idoc to class & classification.
I am new to idoc.
Thanks in advance..

Hi Nikhil,
Thanks for your reply.
I know message types CLSMAS u2014 Class master CLFMAS u2014 Classification data.
I have been told that to research, while sending idoc how material would be linked to class & classification.

Tables to view classification tab

Hi,
Suppose in material master I am maintaining classification tab and hence incorporating classification values. In what tables do I refer in order to find the above values. Do I refer to KLAH table or do I refer to MARA/MARC table.

Hi,
You will not get the values of clasificaiton directly in a table. You have to link many tables and this will be a time consuming job.
Better use Function Module "CLAF_CLASSIFICATION_OF_OBJECTS" - Get Class & Classification data for Object
Here pass following;
CLASSTYPE                       001
CLINT                                 For MATNR from KSSK Table
OBJECT                             MATNR

ECM for Classification

Dear All.
We have implimented ECM now we are using same for Material Master,BOM & Tasklist, now we want to use Class 001-Material in Material master so we defined class & characteristic for same.
Our requirement is we require to capture the changes done in Charecteristic value with refrance to Change no. so for taht i have created CC having object type Material,Characteristic,Characteristic of Class & Classification. But while i am going to MM02 with change no. in Classification view system geving me message as "Change no. ingnored for class type 001" Message no-CL141.
Please suggest what configration require for same??
Regards.
Dev123....

Hi Dev123
The reason why you receive the message CL141 is probably because 'ECH (time)' isnt set for the class type 001
You can check this in your system by following the path :
(Transaction ) O1CL ->
                        Object Table -> MARA
                        Class Type -> 001
                        Details
                        ECH(times)
                        Objects -> ECH(times)
IF ECH ( times ) is unticked then this leads to the popup you
receive . Inorder to resolve the issue these flags need to be set.
I Hope the information helps.
Enda.

Performance issue - Pricing Report

Hi Experts,
I have developed an ALV report and I need to improve the performance in production.
I have a relatively complex issue here. I have to fetch data for pricing from a pricing condition from all the respective underlying condition tables (AXXX). Then for every material, I have to display the material class classification characteristics and their values maintained in the material master as well as batch classification characteristics and their corresponding values maintained for every batch.
For example:
In this way, if the condition is having 10 materials, every material has say 10 material class characteristic and 12 batch class characteristics then the total number of rows in the out put should be 130 (one for material and 12 rows for classifications).
How can I optimize the output so that the performance is optimized.
I am also fetching other data for the output like stock and sales order quantity against every material.
I have tried minimizing loops and used select for all entries(I have read a few threads which suggests otherwise). Read statements use binary search. No select * queries.
Warm Regards,
Abdullah

I would not sign a lot of the recommendations going around here. Seems like a complex report you are doing there, so here is my personal generic recommendations:
- always use index when selecting from database tables
- avoid redundant database accesses to unbuffered tables, only read fields you really need for processing
- use sorted or hashed tables for reads or loops and access them by key
- use ASSIGNING rather than INTO when reading or looping (small gain)
- use SE30 and if necessary ST05 to fine tune if not yet satisfied with the runtime
Thomas

Dmtreedemo.java error

hi,
i got error in the following programme in java named dmdemotree.java the code and the error are as mentioned below
i have installed oracle 10g r2 and i have used JDK 1.4.2 softwares , i have set classpath for jdm.jar and ojdm_api.jar available in oracle 10g r2 software ,successfully compiled but at execution stage i got error as
F:\Mallari\DATA MINING demos\java\samples>java dmtreedemo localhost:1521:orcl scott tiger
--- Build Model - using cost matrix ---
javax.datamining.JDMException: Generic Error.
at oracle.dmt.jdm.resource.OraExceptionHandler.createException(OraExcept
ionHandler.java:142)
at oracle.dmt.jdm.resource.OraExceptionHandler.createException(OraExcept
ionHandler.java:91)
at oracle.dmt.jdm.OraDMObject.createException(OraDMObject.java:111)
at oracle.dmt.jdm.base.OraTask.saveObjectInDatabase(OraTask.java:204)
at oracle.dmt.jdm.OraMiningObject.saveObjectInDatabase(OraMiningObject.j
ava:164)
at oracle.dmt.jdm.resource.OraPersistanceManagerImpl.saveObject(OraPersi
stanceManagerImpl.java:245)
at oracle.dmt.jdm.resource.OraConnection.saveObject(OraConnection.java:3
83)
at dmtreedemo.executeTask(dmtreedemo.java:622)
at dmtreedemo.buildModel(dmtreedemo.java:304)
at dmtreedemo.main(dmtreedemo.java:199)
Caused by: java.sql.SQLException: Unsupported feature
at oracle.jdbc.dbaccess.DBError.throwSqlException(DBError.java:134)
at oracle.jdbc.dbaccess.DBError.throwSqlException(DBError.java:179)
at oracle.jdbc.dbaccess.DBError.throwSqlException(DBError.java:269)
at oracle.jdbc.dbaccess.DBError.throwUnsupportedFeatureSqlException(DBEr
ror.java:690)
at oracle.jdbc.driver.OracleCallableStatement.setString(OracleCallableSt
atement.java:1337)
at oracle.dmt.jdm.utils.OraSQLUtils.createCallableStatement(OraSQLUtils.
java:126)
at oracle.dmt.jdm.utils.OraSQLUtils.executeCallableStatement(OraSQLUtils
.java:532)
at oracle.dmt.jdm.scheduler.OraProgramJob.createJob(OraProgramJob.java:7
7)
at oracle.dmt.jdm.scheduler.OraJob.saveJob(OraJob.java:107)
at oracle.dmt.jdm.scheduler.OraProgramJob.saveJob(OraProgramJob.java:85)
at oracle.dmt.jdm.scheduler.OraProgramJob.saveJob(OraProgramJob.java:290
at oracle.dmt.jdm.base.OraTask.saveObjectInDatabase(OraTask.java:199)
... 6 more
SO PLZ HELP ME OUT IN THIS , I WILL BE VERY THANK FULL
===========================================================
the sample code is
// Copyright (c) 2004, 2005, Oracle. All rights reserved.
// File: dmtreedemo.java
* This demo program describes how to use the Oracle Data Mining (ODM) Java API
* to solve a classification problem using Decision Tree (DT) algorithm.
* PROBLEM DEFINITION
* How to predict whether a customer responds or not to the new affinity card
* program using a classifier based on DT algorithm?
* DATA DESCRIPTION
* Data for this demo is composed from base tables in the Sales History (SH)
* schema. The SH schema is an Oracle Database Sample Schema that has the customer
* demographics, purchasing, and response details for the previous affinity card
* programs. Data exploration and preparing the data is a common step before
* doing data mining. Here in this demo, the following views are created in the user
* schema using CUSTOMERS, COUNTRIES, and SUPPLIMENTARY_DEMOGRAPHICS tables.
* MINING_DATA_BUILD_V:
* This view collects the previous customers' demographics, purchasing, and affinity
* card response details for building the model.
* MINING_DATA_TEST_V:
* This view collects the previous customers' demographics, purchasing, and affinity
* card response details for testing the model.
* MINING_DATA_APPLY_V:
* This view collects the prospective customers' demographics and purchasing
* details for predicting response for the new affinity card program.
* DATA MINING PROCESS
* Prepare Data:
* 1. Missing Value treatment for predictors
* See dmsvcdemo.java for a definition of missing values, and the steps to be
* taken for missing value imputation. SVM interprets all NULL values for a
* given attribute as "sparse". Sparse data is not suitable for decision
* trees, but it will accept sparse data nevertheless. Decision Tree
* implementation in ODM handles missing predictor values (by penalizing
* predictors which have missing values) and missing target values (by simple
* discarding records with missing target values). We skip missing values
* treatment in this demo.
* 2. Outlier/Clipping treatment for predictors
* See dmsvcdemo.java for a discussion on outlier treatment. For decision
* trees, outlier treatment is not really necessary. We skip outlier treatment
* in this demo.
* 3. Binning high cardinality data
* No data preparation for the types we accept is necessary - even for high
* cardinality predictors. Preprocessing to reduce the cardinality
* (e.g., binning) can improve the performance of the build, but it could
* penalize the accuracy of the resulting model.
* The PrepareData() method in this demo program illustrates the preparation of the
* build, test, and apply data. We skip PrepareData() since the decision tree
* algorithm is very capable of handling data which has not been specially
* prepared. For this demo, no data preparation will be performed.
* Build Model:
* Mining Model is the prime object in data mining. The buildModel() method
* illustrates how to build a classification model using DT algorithm.
* Test Model:
* Classification model performance can be evaluated by computing test
* metrics like accuracy, confusion matrix, lift and ROC. The testModel() or
* computeTestMetrics() method illustrates how to perform a test operation to
* compute various metrics.
* Apply Model:
* Predicting the target attribute values is the prime function of
* classification models. The applyModel() method illustrates how to
* predict the customer response for affinity card program.
* EXECUTING DEMO PROGRAM
* Refer to Oracle Data Mining Administrator's Guide
* for guidelines for executing this demo program.
// Generic Java api imports
import java.math.BigDecimal;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.sql.Statement;
import java.text.DecimalFormat;
import java.text.MessageFormat;
import java.util.Collection;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Stack;
// Java Data Mining (JDM) standard api imports
import javax.datamining.ExecutionHandle;
import javax.datamining.ExecutionState;
import javax.datamining.ExecutionStatus;
import javax.datamining.JDMException;
import javax.datamining.MiningAlgorithm;
import javax.datamining.MiningFunction;
import javax.datamining.NamedObject;
import javax.datamining.SizeUnit;
import javax.datamining.algorithm.tree.TreeHomogeneityMetric;
import javax.datamining.algorithm.tree.TreeSettings;
import javax.datamining.algorithm.tree.TreeSettingsFactory;
import javax.datamining.base.AlgorithmSettings;
import javax.datamining.base.Model;
import javax.datamining.base.Task;
import javax.datamining.data.AttributeDataType;
import javax.datamining.data.CategoryProperty;
import javax.datamining.data.CategorySet;
import javax.datamining.data.CategorySetFactory;
import javax.datamining.data.ModelSignature;
import javax.datamining.data.PhysicalAttribute;
import javax.datamining.data.PhysicalAttributeFactory;
import javax.datamining.data.PhysicalAttributeRole;
import javax.datamining.data.PhysicalDataSet;
import javax.datamining.data.PhysicalDataSetFactory;
import javax.datamining.data.SignatureAttribute;
import javax.datamining.modeldetail.tree.TreeModelDetail;
import javax.datamining.modeldetail.tree.TreeNode;
import javax.datamining.resource.Connection;
import javax.datamining.resource.ConnectionFactory;
import javax.datamining.resource.ConnectionSpec;
import javax.datamining.rule.Predicate;
import javax.datamining.rule.Rule;
import javax.datamining.supervised.classification.ClassificationApplySettings;
import javax.datamining.supervised.classification.ClassificationApplySettingsFactory;
import javax.datamining.supervised.classification.ClassificationModel;
import javax.datamining.supervised.classification.ClassificationSettings;
import javax.datamining.supervised.classification.ClassificationSettingsFactory;
import javax.datamining.supervised.classification.ClassificationTestMetricOption;
import javax.datamining.supervised.classification.ClassificationTestMetrics;
import javax.datamining.supervised.classification.ClassificationTestMetricsTask;
import javax.datamining.supervised.classification.ClassificationTestMetricsTaskFactory;
import javax.datamining.supervised.classification.ClassificationTestTaskFactory;
import javax.datamining.supervised.classification.ConfusionMatrix;
import javax.datamining.supervised.classification.CostMatrix;
import javax.datamining.supervised.classification.CostMatrixFactory;
import javax.datamining.supervised.classification.Lift;
import javax.datamining.supervised.classification.ReceiverOperatingCharacterics;
import javax.datamining.task.BuildTask;
import javax.datamining.task.BuildTaskFactory;
import javax.datamining.task.apply.DataSetApplyTask;
import javax.datamining.task.apply.DataSetApplyTaskFactory;
// Oracle Java Data Mining (JDM) implemented api imports
import oracle.dmt.jdm.algorithm.tree.OraTreeSettings;
import oracle.dmt.jdm.resource.OraConnection;
import oracle.dmt.jdm.resource.OraConnectionFactory;
import oracle.dmt.jdm.modeldetail.tree.OraTreeModelDetail;
public class dmtreedemo
//Connection related data members
private static Connection m_dmeConn;
private static ConnectionFactory m_dmeConnFactory;
//Object factories used in this demo program
private static PhysicalDataSetFactory m_pdsFactory;
private static PhysicalAttributeFactory m_paFactory;
private static ClassificationSettingsFactory m_clasFactory;
private static TreeSettingsFactory m_treeFactory;
private static BuildTaskFactory m_buildFactory;
private static DataSetApplyTaskFactory m_dsApplyFactory;
private static ClassificationTestTaskFactory m_testFactory;
private static ClassificationApplySettingsFactory m_applySettingsFactory;
private static CostMatrixFactory m_costMatrixFactory;
private static CategorySetFactory m_catSetFactory;
private static ClassificationTestMetricsTaskFactory m_testMetricsTaskFactory;
// Global constants
private static DecimalFormat m_df = new DecimalFormat("##.####");
private static String m_costMatrixName = null;
public static void main( String args[] )
try
if ( args.length != 3 ) {
System.out.println("Usage: java dmsvrdemo <Host name>:<Port>:<SID> <User Name> <Password>");
return;
String uri = args[0];
String name = args[1];
String password = args[2];
// 1. Login to the Data Mining Engine
m_dmeConnFactory = new OraConnectionFactory();
ConnectionSpec connSpec = m_dmeConnFactory.getConnectionSpec();
connSpec.setURI("jdbc:oracle:thin:@"+uri);
connSpec.setName(name);
connSpec.setPassword(password);
m_dmeConn = m_dmeConnFactory.getConnection(connSpec);
// 2. Clean up all previuosly created demo objects
clean();
// 3. Initialize factories for mining objects
initFactories();
m_costMatrixName = createCostMatrix();
// 4. Build model with supplied cost matrix
buildModel();
// 5. Test model - To compute accuracy and confusion matrix, lift result
// and ROC for the model from apply output data.
// Please see dnnbdemo.java to see how to test the model
// with a test input data and cost matrix.
// Test the model with cost matrix
computeTestMetrics("DT_TEST_APPLY_OUTPUT_COST_JDM",
"dtTestMetricsWithCost_jdm", m_costMatrixName);
// Test the model without cost matrix
computeTestMetrics("DT_TEST_APPLY_OUTPUT_JDM",
"dtTestMetrics_jdm", null);
// 6. Apply the model
applyModel();
} catch(Exception anyExp) {
anyExp.printStackTrace(System.out);
} finally {
try {
//6. Logout from the Data Mining Engine
m_dmeConn.close();
} catch(Exception anyExp1) { }//Ignore
* Initialize all object factories used in the demo program.
* @exception JDMException if factory initalization failed
public static void initFactories() throws JDMException
m_pdsFactory = (PhysicalDataSetFactory)m_dmeConn.getFactory(
"javax.datamining.data.PhysicalDataSet");
m_paFactory = (PhysicalAttributeFactory)m_dmeConn.getFactory(
"javax.datamining.data.PhysicalAttribute");
m_clasFactory = (ClassificationSettingsFactory)m_dmeConn.getFactory(
"javax.datamining.supervised.classification.ClassificationSettings");
m_treeFactory = (TreeSettingsFactory) m_dmeConn.getFactory(
"javax.datamining.algorithm.tree.TreeSettings");
m_buildFactory = (BuildTaskFactory)m_dmeConn.getFactory(
"javax.datamining.task.BuildTask");
m_dsApplyFactory = (DataSetApplyTaskFactory)m_dmeConn.getFactory(
"javax.datamining.task.apply.DataSetApplyTask");
m_testFactory = (ClassificationTestTaskFactory)m_dmeConn.getFactory(
"javax.datamining.supervised.classification.ClassificationTestTask");
m_applySettingsFactory = (ClassificationApplySettingsFactory)m_dmeConn.getFactory(
"javax.datamining.supervised.classification.ClassificationApplySettings");
m_costMatrixFactory = (CostMatrixFactory)m_dmeConn.getFactory(
"javax.datamining.supervised.classification.CostMatrix");
m_catSetFactory = (CategorySetFactory)m_dmeConn.getFactory(
"javax.datamining.data.CategorySet" );
m_testMetricsTaskFactory = (ClassificationTestMetricsTaskFactory)m_dmeConn.getFactory(
"javax.datamining.supervised.classification.ClassificationTestMetricsTask");
* This method illustrates how to build a mining model using the
* MINING_DATA_BUILD_V dataset and classification settings with
* DT algorithm.
* @exception JDMException if model build failed
public static void buildModel() throws JDMException
System.out.println("---------------------------------------------------");
System.out.println("--- Build Model - using cost matrix ---");
System.out.println("---------------------------------------------------");
// 1. Create & save PhysicalDataSpecification
PhysicalDataSet buildData =
m_pdsFactory.create("MINING_DATA_BUILD_V", false);
PhysicalAttribute pa = m_paFactory.create("CUST_ID",
AttributeDataType.integerType, PhysicalAttributeRole.caseId );
buildData.addAttribute(pa);
m_dmeConn.saveObject("treeBuildData_jdm", buildData, true);
//2. Create & save Mining Function Settings
// Create tree algorithm settings
TreeSettings treeAlgo = m_treeFactory.create();
// By default, tree algorithm will have the following settings:
// treeAlgo.setBuildHomogeneityMetric(TreeHomogeneityMetric.gini);
// treeAlgo.setMaxDepth(7);
// ((OraTreeSettings)treeAlgo).setMinDecreaseInImpurity(0.1, SizeUnit.percentage);
// treeAlgo.setMinNodeSize( 0.05, SizeUnit.percentage );
// treeAlgo.setMinNodeSize( 10, SizeUnit.count );
// ((OraTreeSettings)treeAlgo).setMinDecreaseInImpurity(20, SizeUnit.count);
// Set cost matrix. A cost matrix is used to influence the weighting of
// misclassification during model creation (and scoring).
// See Oracle Data Mining Concepts Guide for more details.
String costMatrixName = m_costMatrixName;
// Create ClassificationSettings
ClassificationSettings buildSettings = m_clasFactory.create();
buildSettings.setAlgorithmSettings(treeAlgo);
buildSettings.setCostMatrixName(costMatrixName);
buildSettings.setTargetAttributeName("AFFINITY_CARD");
m_dmeConn.saveObject("treeBuildSettings_jdm", buildSettings, true);
// 3. Create, save & execute Build Task
BuildTask buildTask = m_buildFactory.create(
"treeBuildData_jdm", // Build data specification
"treeBuildSettings_jdm", // Mining function settings name
"treeModel_jdm" // Mining model name
buildTask.setDescription("treeBuildTask_jdm");
executeTask(buildTask, "treeBuildTask_jdm");
//4. Restore the model from the DME and explore the details of the model
ClassificationModel model =
(ClassificationModel)m_dmeConn.retrieveObject(
"treeModel_jdm", NamedObject.model);
// Display model build settings
ClassificationSettings retrievedBuildSettings =
(ClassificationSettings)model.getBuildSettings();
if(buildSettings == null)
System.out.println("Failure to restore build settings.");
else
displayBuildSettings(retrievedBuildSettings, "treeBuildSettings_jdm");
// Display model signature
displayModelSignature((Model)model);
// Display model detail
TreeModelDetail treeModelDetails = (TreeModelDetail) model.getModelDetail();
displayTreeModelDetailsExtensions(treeModelDetails);
* Create and save cost matrix.
* Consider an example where it costs $10 to mail a promotion to a
* prospective customer and if the prospect becomes a customer, the
* typical sale including the promotion, is worth $100. Then the cost
* of missing a customer (i.e. missing a $100 sale) is 10x that of
* incorrectly indicating that a person is good prospect (spending
* $10 for the promo). In this case, all prediction errors made by
* the model are NOT equal. To act on what the model determines to
* be the most likely (probable) outcome may be a poor choice.
* Suppose that the probability of a BUY reponse is 10% for a given
* prospect. Then the expected revenue from the prospect is:
* .10 * $100 - .90 * $10 = $1.
* The optimal action, given the cost matrix, is to simply mail the
* promotion to the customer, because the action is profitable ($1).
* In contrast, without the cost matrix, all that can be said is
* that the most likely response is NO BUY, so don't send the
* promotion. This shows that cost matrices can be very important.
* The caveat in all this is that the model predicted probabilities
* may NOT be accurate. For binary targets, a systematic approach to
* this issue exists. It is ROC, illustrated below.
* With ROC computed on a test set, the user can see how various model
* predicted probability thresholds affect the action of mailing a promotion.
* Suppose I promote when the probability to BUY exceeds 5, 10, 15%, etc.
* what return can I expect? Note that the answer to this question does
* not rely on the predicted probabilities being accurate, only that
* they are in approximately the correct rank order.
* Assuming that the predicted probabilities are accurate, provide the
* cost matrix table name as input to the RANK_APPLY procedure to get
* appropriate costed scoring results to determine the most appropriate
* action.
* In this demo, we will create the following cost matrix
* ActualTarget PredictedTarget Cost
* 0 0 0
* 0 1 1
* 1 0 8
* 1 1 0
private static String createCostMatrix() throws JDMException
String costMatrixName = "treeCostMatrix";
// Create categorySet
CategorySet catSet = m_catSetFactory.create(AttributeDataType.integerType);
// Add category values
catSet.addCategory(new Integer(0), CategoryProperty.valid);
catSet.addCategory(new Integer(1), CategoryProperty.valid);
// Create cost matrix
CostMatrix costMatrix = m_costMatrixFactory.create(catSet);
// ActualTarget PredictedTarget Cost
costMatrix.setValue(new Integer(0), new Integer(0), 0);
costMatrix.setValue(new Integer(0), new Integer(1), 1);
costMatrix.setValue(new Integer(1), new Integer(0), 8);
costMatrix.setValue(new Integer(1), new Integer(1), 0);
//save cost matrix
m_dmeConn.saveObject(costMatrixName, costMatrix, true);
return costMatrixName;
* This method illustrates how to compute test metrics using
* an apply output table that has actual and predicted target values. Here the
* apply operation is done on the MINING_DATA_TEST_V dataset. It creates
* an apply output table with actual and predicted target values. Using
* ClassificationTestMetricsTask test metrics are computed. This produces
* the same test metrics results as ClassificationTestTask.
* @param applyOutputName apply output table name
* @param testResultName test result name
* @param costMatrixName table name of the supplied cost matrix
* @exception JDMException if model test failed
public static void computeTestMetrics(String applyOutputName,
String testResultName, String costMatrixName) throws JDMException
if (costMatrixName != null) {
System.out.println("---------------------------------------------------");
System.out.println("--- Test Model - using apply output table ---");
System.out.println("--- - using cost matrix table ---");
System.out.println("---------------------------------------------------");
else {
System.out.println("---------------------------------------------------");
System.out.println("--- Test Model - using apply output table ---");
System.out.println("--- - using no cost matrix table ---");
System.out.println("---------------------------------------------------");
// 1. Do the apply on test data to create an apply output table
// Create & save PhysicalDataSpecification
PhysicalDataSet applyData =
m_pdsFactory.create( "MINING_DATA_TEST_V", false );
PhysicalAttribute pa = m_paFactory.create("CUST_ID",
AttributeDataType.integerType, PhysicalAttributeRole.caseId );
applyData.addAttribute( pa );
m_dmeConn.saveObject( "treeTestApplyData_jdm", applyData, true );
// 2 Create & save ClassificationApplySettings
ClassificationApplySettings clasAS = m_applySettingsFactory.create();
HashMap sourceAttrMap = new HashMap();
sourceAttrMap.put( "AFFINITY_CARD", "AFFINITY_CARD" );
clasAS.setSourceDestinationMap( sourceAttrMap );
m_dmeConn.saveObject( "treeTestApplySettings_jdm", clasAS, true);
// 3 Create, store & execute apply Task
DataSetApplyTask applyTask = m_dsApplyFactory.create(
"treeTestApplyData_jdm",
"treeModel_jdm",
"treeTestApplySettings_jdm",
applyOutputName);
if(executeTask(applyTask, "treeTestApplyTask_jdm"))
// Compute test metrics on new created apply output table
// 4. Create & save PhysicalDataSpecification
PhysicalDataSet applyOutputData = m_pdsFactory.create(
applyOutputName, false );
applyOutputData.addAttribute( pa );
m_dmeConn.saveObject( "treeTestApplyOutput_jdm", applyOutputData, true );
// 5. Create a ClassificationTestMetricsTask
ClassificationTestMetricsTask testMetricsTask =
m_testMetricsTaskFactory.create( "treeTestApplyOutput_jdm", // apply output data used as input
"AFFINITY_CARD", // actual target column
"PREDICTION", // predicted target column
testResultName // test metrics result name
testMetricsTask.computeMetric( // enable confusion matrix computation
ClassificationTestMetricOption.confusionMatrix, true );
testMetricsTask.computeMetric( // enable lift computation
ClassificationTestMetricOption.lift, true );
testMetricsTask.computeMetric( // enable ROC computation
ClassificationTestMetricOption.receiverOperatingCharacteristics, true );
testMetricsTask.setPositiveTargetValue( new Integer(1) );
testMetricsTask.setNumberOfLiftQuantiles( 10 );
testMetricsTask.setPredictionRankingAttrName( "PROBABILITY" );
if (costMatrixName != null) {
testMetricsTask.setCostMatrixName(costMatrixName);
displayTable(costMatrixName, "", "order by ACTUAL_TARGET_VALUE, PREDICTED_TARGET_VALUE");
// Store & execute the task
boolean isTaskSuccess = executeTask(testMetricsTask, "treeTestMetricsTask_jdm");
if( isTaskSuccess ) {
// Restore & display test metrics
ClassificationTestMetrics testMetrics = (ClassificationTestMetrics)
m_dmeConn.retrieveObject( testResultName, NamedObject.testMetrics );
// Display classification test metrics
displayTestMetricDetails(testMetrics);
* This method illustrates how to apply the mining model on the
* MINING_DATA_APPLY_V dataset to predict customer
* response. After completion of the task apply output table with the
* predicted results is created at the user specified location.
* @exception JDMException if model apply failed
public static void applyModel() throws JDMException
System.out.println("---------------------------------------------------");
System.out.println("--- Apply Model ---");
System.out.println("---------------------------------------------------");
System.out.println("---------------------------------------------------");
System.out.println("--- Business case 1 ---");
System.out.println("--- Find the 10 customers who live in Italy ---");
System.out.println("--- that are least expensive to be convinced to ---");
System.out.println("--- use an affinity card. ---");
System.out.println("---------------------------------------------------");
// 1. Create & save PhysicalDataSpecification
PhysicalDataSet applyData =
m_pdsFactory.create( "MINING_DATA_APPLY_V", false );
PhysicalAttribute pa = m_paFactory.create("CUST_ID",
AttributeDataType.integerType, PhysicalAttributeRole.caseId );
applyData.addAttribute( pa );
m_dmeConn.saveObject( "treeApplyData_jdm", applyData, true );
// 2. Create & save ClassificationApplySettings
ClassificationApplySettings clasAS = m_applySettingsFactory.create();
// Add source attributes
HashMap sourceAttrMap = new HashMap();
sourceAttrMap.put( "COUNTRY_NAME", "COUNTRY_NAME" );
clasAS.setSourceDestinationMap( sourceAttrMap );
// Add cost matrix
clasAS.setCostMatrixName( m_costMatrixName );
m_dmeConn.saveObject( "treeApplySettings_jdm", clasAS, true);
// 3. Create, store & execute apply Task
DataSetApplyTask applyTask = m_dsApplyFactory.create(
"treeApplyData_jdm", "treeModel_jdm",
"treeApplySettings_jdm", "TREE_APPLY_OUTPUT1_JDM");
executeTask(applyTask, "treeApplyTask_jdm");
// 4. Display apply result -- Note that APPLY results do not need to be
// reverse transformed, as done in the case of model details. This is
// because class values of a classification target were not (required to
// be) binned or normalized.
// Find the 10 customers who live in Italy that are least expensive to be
// convinced to use an affinity card.
displayTable("TREE_APPLY_OUTPUT1_JDM",
"where COUNTRY_NAME='Italy' and ROWNUM < 11 ",
"order by COST");
System.out.println("---------------------------------------------------");
System.out.println("--- Business case 2 ---");
System.out.println("--- List ten customers (ordered by their id) ---");
System.out.println("--- along with likelihood and cost to use or ---");
System.out.println("--- reject the affinity card. ---");
System.out.println("---------------------------------------------------");
// 1. Create & save PhysicalDataSpecification
applyData =
m_pdsFactory.create( "MINING_DATA_APPLY_V", false );
pa = m_paFactory.create("CUST_ID",
AttributeDataType.integerType, PhysicalAttributeRole.caseId );
applyData.addAttribute( pa );
m_dmeConn.saveObject( "treeApplyData_jdm", applyData, true );
// 2. Create & save ClassificationApplySettings
clasAS = m_applySettingsFactory.create();
// Add cost matrix
clasAS.setCostMatrixName( m_costMatrixName );
m_dmeConn.saveObject( "treeApplySettings_jdm", clasAS, true);
// 3. Create, store & execute apply Task
applyTask = m_dsApplyFactory.create(
"treeApplyData_jdm", "treeModel_jdm",
"treeApplySettings_jdm", "TREE_APPLY_OUTPUT2_JDM");
executeTask(applyTask, "treeApplyTask_jdm");
// 4. Display apply result -- Note that APPLY results do not need to be
// reverse transformed, as done in the case of model details. This is
// because class values of a classification target were not (required to
// be) binned or normalized.
// List ten customers (ordered by their id) along with likelihood and cost
// to use or reject the affinity card (Note: while this example has a
// binary target, such a query is useful in multi-class classification -
// Low, Med, High for example).
displayTable("TREE_APPLY_OUTPUT2_JDM",
"where ROWNUM < 21",
"order by CUST_ID, PREDICTION");
System.out.println("---------------------------------------------------");
System.out.println("--- Business case 3 ---");
System.out.println("--- Find the customers who work in Tech support ---");
System.out.println("--- and are under 25 who is going to response ---");
System.out.println("--- to the new affinity card program. ---");
System.out.println("---------------------------------------------------");
// 1. Create & save PhysicalDataSpecification
applyData =
m_pdsFactory.create( "MINING_DATA_APPLY_V", false );
pa = m_paFactory.create("CUST_ID",
AttributeDataType.integerType, PhysicalAttributeRole.caseId );
applyData.addAttribute( pa );
m_dmeConn.saveObject( "treeApplyData_jdm", applyData, true );
// 2. Create & save ClassificationApplySettings
clasAS = m_applySettingsFactory.create();
// Add source attributes
sourceAttrMap = new HashMap();
sourceAttrMap.put( "AGE", "AGE" );
sourceAttrMap.put( "OCCUPATION", "OCCUPATION" );
clasAS.setSourceDestinationMap( sourceAttrMap );
m_dmeConn.saveObject( "treeApplySettings_jdm", clasAS, true);
// 3. Create, store & execute apply Task
applyTask = m_dsApplyFactory.create(
"treeApplyData_jdm", "treeModel_jdm",
"treeApplySettings_jdm", "TREE_APPLY_OUTPUT3_JDM");
executeTask(applyTask, "treeApplyTask_jdm");
// 4. Display apply result -- Note that APPLY results do not need to be
// reverse transformed, as done in the case of model details. This is
// because class values of a classification target were not (required to
// be) binned or normalized.
// Find the customers who work in Tech support and are under 25 who is
// going to response to the new affinity card program.
displayTable("TREE_APPLY_OUTPUT3_JDM",
"where OCCUPATION = 'TechSup' " +
"and AGE < 25 " +
"and PREDICTION = 1 ",
"order by CUST_ID");
* This method stores the given task with the specified name in the DMS
* and submits the task for asynchronous execution in the DMS. After
* completing the task successfully it returns true. If there is a task
* failure, then it prints error description and returns false.
* @param taskObj task object
* @param taskName name of the task
* @return boolean returns true when the task is successful
* @exception JDMException if task execution failed
public static boolean executeTask(Task taskObj, String taskName)
throws JDMException
boolean isTaskSuccess = false;
m_dmeConn.saveObject(taskName, taskObj, true);
ExecutionHandle execHandle = m_dmeConn.execute(taskName);
System.out.print(taskName + " is started, please wait. ");
//Wait for completion of the task
ExecutionStatus status = execHandle.waitForCompletion(Integer.MAX_VALUE);
//Check the status of the task after completion
isTaskSuccess = status.getState().equals(ExecutionState.success);
if( isTaskSuccess ) //Task completed successfully
System.out.println(taskName + " is successful.");
else //Task failed
System.out.println(taskName + " failed.\nFailure Description: " +
status.getDescription() );
return isTaskSuccess;
private static void displayBuildSettings(
ClassificationSettings clasSettings, String buildSettingsName)
System.out.println("BuildSettings Details from the "
+ buildSettingsName + " table:");
displayTable(buildSettingsName, "", "order by SETTING_NAME");
System.out.println("BuildSettings Details from the "
+ buildSettingsName + " model build settings object:");
String objName = clasSettings.getName();
if(objName != null)
System.out.println("Name = " + objName);
String objDescription = clasSettings.getDescription();
if(objDescription != null)
System.out.println("Description = " + objDescription);
java.util.Date creationDate = clasSettings.getCreationDate();
String creator = clasSettings.getCreatorInfo();
String targetAttrName = clasSettings.getTargetAttributeName();
System.out.println("Target attribute name = " + targetAttrName);
AlgorithmSettings algoSettings = clasSettings.getAlgorithmSettings();
if(algoSettings == null)
System.out.println("Failure: clasSettings.getAlgorithmSettings() returns null");
MiningAlgorithm algo = algoSettings.getMiningAlgorithm();
if(algo == null) System.out.println("Failure: algoSettings.getMiningAlgorithm() returns null");
System.out.println("Algorithm Name: " + algo.name());
MiningFunction function = clasSettings.getMiningFunction();
if(function == null) System.out.println("Failure: clasSettings.getMiningFunction() returns null");
System.out.println("Function Name: " + function.name());
try {
String costMatrixName = clasSettings.getCostMatrixName();
if(costMatrixName != null) {
System.out.println("Cost Matrix Details from the " + costMatrixName
+ " table:");
displayTable(costMatrixName, "", "order by ACTUAL_TARGET_VALUE, PREDICTED_TARGET_VALUE");
} catch(Exception jdmExp)
System.out.println("Failure: clasSettings.getCostMatrixName()throws exception");
jdmExp.printStackTrace();
// List of DT algorithm settings
// treeAlgo.setBuildHomogeneityMetric(TreeHomogeneityMetric.gini);
// treeAlgo.setMaxDepth(7);
// ((OraTreeSettings)treeAlgo).setMinDecreaseInImpurity(0.1, SizeUnit.percentage);
// treeAlgo.setMinNodeSize( 0.05, SizeUnit.percentage );
// treeAlgo.setMinNodeSize( 10, SizeUnit.count );
// ((OraTreeSettings)treeAlgo).setMinDecreaseInImpurity(20, SizeUnit.count);
TreeHomogeneityMetric homogeneityMetric = ((OraTreeSettings)algoSettings).getBuildHomogeneityMetric();
System.out.println("Homogeneity Metric: " + homogeneityMetric.name());
int intValue = ((OraTreeSettings)algoSettings).getMaxDepth();
System.out.println("Max Depth: " + intValue);
double doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSizeForSplit(SizeUnit.percentage);
System.out.println("MinNodeSizeForSplit (percentage): " + m_df.format(doubleValue));
doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSizeForSplit(SizeUnit.count);
System.out.println("MinNodeSizeForSplit (count): " + m_df.format(doubleValue));
doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSize();
SizeUnit unit = ((OraTreeSettings)algoSettings).getMinNodeSizeUnit();
System.out.println("Min Node Size (" + unit.name() +"): " + m_df.format(doubleValue));
doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSize( SizeUnit.count );
System.out.println("Min Node Size (" + SizeUnit.count.name() +"): " + m_df.format(doubleValue));
doubleValue = ((OraTreeSettings)algoSettings).getMinNodeSize( SizeUnit.percentage );
System.out.println("Min Node Size (" + SizeUnit.percentage.name() +"): " + m_df.format(doubleValue));
* This method displayes DT model signature.
* @param model model object
* @exception JDMException if failed to retrieve model signature
public static void displayModelSignature(Model model) throws JDMException
String modelName = model.getName();
System.out.println("Model Name: " + modelName);
ModelSignature modelSignature = model.getSignature();
System.out.println("ModelSignature Deatils: ( Attribute Name, Attribute Type )");
MessageFormat mfSign = new MessageFormat(" ( {0}, {1} )");
String[] vals = new String[3];
Collection sortedSet = modelSignature.getAttributes();
Iterator attrIterator = sortedSet.iterator();
while(attrIterator.hasNext())
SignatureAttribute attr = (SignatureAttribute)attrIterator.next();
vals[0] = attr.getName();
vals[1] = attr.getDataType().name();
System.out.println( mfSign.format(vals) );
* This method displayes DT model details.
* @param treeModelDetails tree model details object
* @exception JDMException if failed to retrieve model details
public static void displayTreeModelDetailsExtensions(TreeModelDetail treeModelDetails)
throws JDMException
System.out.println( "\nTreeModelDetail: Model name=" + "treeModel_jdm" );
TreeNode root = treeModelDetails.getRootNode();
System.out.println( "\nRoot node: " + root.getIdentifier() );
// get the info for the tree model
int treeDepth = ((OraTreeModelDetail) treeModelDetails).getTreeDepth();
System.out.println( "Tree depth: " + treeDepth );
int totalNodes = ((OraTreeModelDetail) treeModelDetails).getNumberOfNodes();
System.out.println( "Total number of nodes: " + totalNodes );
int totalLeaves = ((OraTreeModelDetail) treeModelDetails).getNumberOfLeafNodes();
System.out.println( "Total number of leaf nodes: " + totalLeaves );
Stack nodeStack = new Stack();
nodeStack.push( root);
while( !nodeStack.empty() )
TreeNode node = (TreeNode) nodeStack.pop();
// display this node
int nodeId = node.getIdentifier();
long caseCount = node.getCaseCount();
Object prediction = node.getPrediction();
int level = node.getLevel();
int children = node.getNumberOfChildren();
TreeNode parent = node.getParent();
System.out.println( "\nNode id=" + nodeId + " at level " + level );
if( parent != null )
System.out.println( "parent: " + parent.getIdentifier() +
", children=" + children );
System.out.println( "Case count: " + caseCount + ", prediction: " + prediction );
Predicate predicate = node.getPredicate();
System.out.println( "Predicate: " + predicate.toString() );
Predicate[] surrogates = node.getSurrogates();
if( surrogates != null )
for( int i=0; i<surrogates.length; i++ )
System.out.println( "Surrogate[" + i + "]: " + surrogates[i] );
// add child nodes in the stack
if( children > 0 )
TreeNode[] childNodes = node.getChildren();
for( int i=0; i<childNodes.length; i++ )
nodeStack.push( childNodes[i] );
TreeNode[] allNodes = treeModelDetails.getNodes();
System.out.print( "\nNode identifiers by getNodes():" );
for( int i=0; i<allNodes.length; i++ )
System.out.print( " " + allNodes.getIdentifier() );
System.out.println();
// display the node identifiers
int[] nodeIds = treeModelDetails.getNodeIdentifiers();
System.out.print( "Node identifiers by getNodeIdentifiers():" );
for( int i=0; i<nodeIds.length; i++ )
System.out.print( " " + nodeIds[i] );
System.out.println();
TreeNode node = treeModelDetails.getNode(nodeIds.length-1);
System.out.println( "Node identifier by getNode(" + (nodeIds.length-1) +
"): " + node.getIdentifier() );
Rule rule2 = treeModelDetails.getRule(nodeIds.length-1);
System.out.println( "Rule identifier by getRule(" + (nodeIds.length-1) +
"): " + rule2.getRuleIdentifier() );
// get the rules and display them
Collection ruleColl = treeModelDetails.getRules();
Iterator ruleIterator = ruleColl.iterator();
while( ruleIterator.hasNext() )
Rule rule = (Rule) ruleIterator.next();
int ruleId = rule.getRuleIdentifier();
Predicate antecedent = (Predicate) rule.getAntecedent();
Predicate consequent = (Predicate) rule.getConsequent();
System.out.println( "\nRULE " + ruleId + ": support=" +
rule.getSupport() + " (abs=" + rule.getAbsoluteSupport() +
"), confidence=" + rule.getConfidence() );
System.out.println( antecedent );
System.out.println( "=======>" );
System.out.println( consequent );
* Display classification test metrics object
* @param testMetrics classification test metrics object
* @exception JDMException if failed to retrieve test metric details
public static void displayTestMetricDetails(
ClassificationTestMetrics testMetrics) throws JDMException
// Retrieve Oracle ABN model test metrics deatils extensions
// Test Metrics Name
System.out.println("Test Metrics Name = " + testMetrics.getName());
// Model Name
System.out.println("Model Name = " + testMetrics.getModelName());
// Test Data Name
System.out.println("Test Data Name = " + testMetrics.getTestDataName());
// Accuracy
System.out.println("Accuracy = " + m_df.format(testMetrics.getAccuracy().doubleValue()));
// Confusion Matrix
ConfusionMatrix confusionMatrix = testMetrics.getConfusionMatrix();
Collection categories = confusionMatrix.getCategories();
Iterator xIterator = categories.iterator();
System.out.println("Confusion Matrix: Accuracy = " + m_df.format(confusionMatrix.getAccuracy()));
System.out.println("Confusion Matrix: Error = " + m_df.format(confusionMatrix.getError()));
System.out.println("Confusion Matrix:( Actual, Prection, Value )");
MessageFormat mf = new MessageFormat(" ( {0}, {1}, {2} )");
String[] vals = new String[3];
while(xIterator.hasNext())
Object actual = xIterator.next();
vals[0] = actual.toString();
Iterator yIterator = categories.iterator();
while(yIterator.hasNext())
Object predicted = yIterator.next();
vals[1] = predicted.toString();
long number = confusionMatrix.getNumberOfPredictions(actual, predicted);
vals[2] = Long.toString(number);
System.out.println(mf.format(vals));
// Lift
Lift lift = testMetrics.getLift();
System.out.println("Lift Details:");
System.out.println("Lift: Target Attribute Name = " + lift.getTargetAttributeName());
System.out.println("Lift: Positive Target Value = " + lift.getPositiveTargetValue());
System.out.println("Lift: Total Cases = " + lift.getTotalCases());
System.out.println("Lift: Total Positive Cases = " + lift.getTotalPositiveCases());
int numberOfQuantiles = lift.getNumberOfQuantiles();
System.out.println("Lift: Number Of Quantiles = " + numberOfQuantiles);
System.out.println("Lift: ( QUANTILE_NUMBER, QUANTILE_TOTAL_COUNT, QUANTILE_TARGET_COUNT, PERCENTAGE_RECORDS_CUMULATIVE,CUMULATIVE_LIFT,CUMULATIVE_TARGET_DENSITY,TARGETS_CUMULATIVE, NON_TARGETS_CUMULATIVE, LIFT_QUANTILE, TARGET_DENSITY )");
MessageFormat mfLift = new MessageFormat(" ( {0}, {1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}, {9} )");
String[] liftVals = new String[10];
for(int iQuantile=1; iQuantile<= numberOfQuantiles; iQuantile++)
liftVals[0] = Integer.toString(iQuantile); //QUANTILE_NUMBER
liftVals[1] = Long.toString(lift.getCases((iQuantile-1), iQuantile));//QUANTILE_TOTAL_COUNT
liftVals[2] = Long.toString(lift.getNumberOfPositiveCases((iQuantile-1), iQuantile));//QUANTILE_TARGET_COUNT
liftVals[3] = m_df.format(lift.getCumulativePercentageSize(iQuantile).doubleValue());//PERCENTAGE_RECORDS_CUMULATIVE
liftVals[4] = m_df.format(lift.getCumulativeLift(iQuantile).doubleValue());//CUMULATIVE_LIFT
liftVals[5] = m_df.format(lift.getCumulativeTargetDensity(iQuantile).doubleValue());//CUMULATIVE_TARGET_DENSITY
liftVals[6] = Long.toString(lift.getCumulativePositiveCases(iQuantile));//TARGETS_CUMULATIVE
liftVals[7] = Long.toString(lift.getCumulativeNegativeCases(iQuantile));//NON_TARGETS_CUMULATIVE
liftVals[8] = m_df.format(lift.getLift(iQuantile, iQuantile).doubleValue());//LIFT_QUANTILE
liftVals[9] = m_df.format(lift.getTargetDensity(iQuantile, iQuantile).doubleValue());//TARGET_DENSITY
System.out.println(mfLift.format(liftVals));
// ROC
ReceiverOperatingCharacterics roc = testMetrics.getROC();
System.out.println("ROC Details:");
System.out.println("ROC: Area Under Curve = " + m_df.format(roc.getAreaUnderCurve()));
int nROCThresh = roc.getNumberOfThresholdCandidates();
System.out.println("ROC: Number Of Threshold Candidates = " + nROCThresh);
System.out.println("ROC: ( INDEX, PROBABILITY, TRUE_POSITIVES, FALSE_NEGATIVES, FALSE_POSITIVES, TRUE_NEGATIVES, TRUE_POSITIVE_FRACTION, FALSE_POSITIVE_FRACTION )");
MessageFormat mfROC = new MessageFormat(" ( {0}, {1}, {2}, {3}, {4}, {5}, {6}, {7} )");
String[] rocVals = new String[8];
for(int iROC=1; iROC <= nROCThresh; iROC++)
rocVals[0] = Integer.toString(iROC); //INDEX
rocVals[1] = m_df.format(roc.getProbabilityThreshold(iROC));//PROBABILITY
rocVals[2] = Long.toString(roc.getPositives(iROC, true));//TRUE_POSITIVES
rocVals[3] = Long.toString(roc.getNegatives(iROC, false));//FALSE_NEGATIVES
rocVals[4] = Long.toString(roc.getPositives(iROC, false));//FALSE_POSITIVES
rocVals[5] = Long.toString(roc.getNegatives(iROC, true));//TRUE_NEGATIVES
rocVals[6] = m_df.format(roc.getHitRate(iROC));//TRUE_POSITIVE_FRACTION
rocVals[7] = m_df.format(roc.getFalseAlarmRate(iROC));//FALSE_POSITIVE_FRACTION
System.out.println(mfROC.format(rocVals));
private static void displayTable(String tableName, String whereCause, String orderByColumn)
StringBuffer emptyCol = new StringBuffer(" ");
java.sql.Connection dbConn =
((OraConnection)m_dmeConn).getDatabaseConnection();
PreparedStatement pStmt = null;
ResultSet rs = null;
try
pStmt = dbConn.prepareStatement("SELECT * FROM " + tableName + " " + whereCause + " " + orderByColumn);
rs = pStmt.executeQuery();
ResultSetMetaData rsMeta = rs.getMetaData();
int colCount = rsMeta.getColumnCount();
StringBuffer header = new StringBuffer();
System.out.println("Table : " + tableName);
//Build table header
for(int iCol=1; iCol<=colCount; iCol++)
String colName = rsMeta.getColumnName(iCol);
header.append(emptyCol.replace(0, colName.length(), colName));
emptyCol = new StringBuffer(" ");
System.out.println(header.toString());
//Write table data
while(rs.next())
StringBuffer rowContent = new StringBuffer();
for(int iCol=1; iCol<=colCount; iCol++)
int sqlType = rsMeta.getColumnType(iCol);
Object obj = rs.getObject(iCol);
String colContent = null;
if(obj instanceof java.lang.Number)
try
BigDecimal bd = (BigDecimal)obj;
if(bd.scale() > 5)
colContent = m_df.format(obj);
} else
colContent = bd.toString();
} catch(Exception anyExp) {
colContent = m_df.format(obj);
} else
if(obj == null)
colContent = "NULL";
else
colContent = obj.toString();
rowContent.append(" "+emptyCol.replace(0, colContent.length(), colContent));
emptyCol = new StringBuffer(" ");
System.out.println(rowContent.toString());
} catch(Exception anySqlExp) {
anySqlExp.printStackTrace();
}//Ignore
private static void createTableForTestMetrics(String applyOutputTableName,
String testDataName,
String testMetricsInputTableName)
//0. need to execute the following in the schema
String sqlCreate =
"create table " + testMetricsInputTableName + " as " +
"select a.id as id, prediction, probability, affinity_card " +
"from " + testDataName + " a, " + applyOutputTableName + " b " +
"where a.id = b.id";
java.sql.Connection dbConn = ((OraConnection) m_dmeConn).getDatabaseConnection();
Statement stmt = null;
try
stmt = dbConn.createStatement();
stmt.executeUpdate( sqlCreate );
catch( Exception anySqlExp )
System.out.println( anySqlExp.getMessage() );
anySqlExp.printStackTrace();
finally
try
stmt.close();
catch( SQLException sqlExp ) {}
private static void clean()
java.sql.Connection dbConn =
((OraConnection) m_dmeConn).getDatabaseConnection();
Statement stmt = null;
// Drop apply output table
try
stmt = dbConn.createStatement();
stmt.executeUpdate("DROP TABLE TREE_APPLY_OUTPUT1_JDM");
} catch(Exception anySqlExp) {}//Ignore
finally
try
stmt.close();
catch( SQLException sqlExp ) {}
try
stmt = dbConn.createStatement();
stmt.executeUpdate("DROP TABLE TREE_APPLY_OUTPUT2_JDM");
} catch(Exception anySqlExp) {}//Ignore
finally
try
stmt.close();
catch( SQLException sqlExp ) {}
try
stmt = dbConn.createStatement();
stmt.executeUpdate("DROP TABLE TREE_APPLY_OUTPUT3_JDM");
} catch(Exception anySqlExp) {}//Ignore
finally
try
stmt.close();
catch( SQLException sqlExp ) {}
// Drop apply output table created for test metrics task
try
stmt = dbConn.createStatement();
stmt.executeUpdate("DROP TABLE DT_TEST_APPLY_OUTPUT_COST_JDM");
} catch(Exception anySqlExp) {}//Ignore
finally
try
stmt.close();
catch( SQLException sqlExp ) {}
try
stmt = dbConn.createStatement();
stmt.executeUpdate("DROP TABLE DT_TEST_APPLY_OUTPUT_JDM");
} catch(Exception anySqlExp) {}//Ignore
finally
try
stmt.close();
catch( SQLException sqlExp ) {}
//Drop the model
try {
m_dmeConn.removeObject( "treeModel_jdm", NamedObject.model );
} catch(Exception jdmExp) {}
// drop test metrics result: created by TestMetricsTask
try {
m_dmeConn.removeObject( "dtTestMetricsWithCost_jdm", NamedObject.testMetrics );
} catch(Exception jdmExp) {}
try {
m_dmeConn.removeObject( "dtTestMetrics_jdm", NamedObject.testMetrics );
} catch(Exception jdmExp) {}

Hi
I am not sure whether this will help but someone else was getting an error with a java.sql.SQLexception: Unsupported feature. Here is a link to the fix: http://saloon.javaranch.com/cgi-bin/ubb/ultimatebb.cgi?ubb=get_topic&f=3&t=007947
Best wishes
Michael

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