Dmtreedemo.java error

Question

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) {}

Answer 1

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