@Override
protected AnalyzerOutPutDataBaseUpdate doPredict(
DataBaseAnalyticSource source, PredictorConfig config) throws AnalysisException {
DataSet dataSet = null;
try {
dataSet = getDataSet(source, source.getAnalyticConfig());
config.getTrainedModel().getModel().apply(dataSet);
} catch (Exception e) {
logger.error(e);
if (e instanceof WrongUsedException) {
throw new AnalysisError(this, (WrongUsedException) e);
} else if (e instanceof AnalysisError) {
throw (AnalysisError) e;
} else {
throw new AnalysisException(e);
}
}
AnalyzerOutPutDataBaseUpdate result = new AnalyzerOutPutDataBaseUpdate();
result.setDataset(dataSet);
// set url user pwd ,schema, table
fillDBInfo(result, (DataBaseAnalyticSource) source);
Model model = config.getTrainedModel().getModel();
result.setUpdatedColumns(((NBModel) model).getUpdateColumns());
// good, bad ,
result.setAnalyticNodeMetaInfo(createNodeMetaInfo(config.getLocale()));
return result;
}
@Override
protected Model train(AnalyticSource analyticSource) throws AnalysisException {
ResultSet rs = null;
Statement st = null;
try {
IDataSourceInfo dataSourceInfo =
DataSourceInfoFactory.createConnectionInfo(analyticSource.getDataSourceType());
dbtype = dataSourceInfo.getDBType();
RandomForestModel lastResult = null;
RandomForestIMP randomForestImpl = null;
// if (dbtype.equalsIgnoreCase(DataSourceInfoOracle.dBType)) {
// randomForestTrainer = new AdaboostOracle();
//
// } else
if (dbtype.equalsIgnoreCase(DataSourceInfoGreenplum.dBType)
|| dbtype.equalsIgnoreCase(DataSourceInfoPostgres.dBType)) {
randomForestImpl = new RandomForestGreenplum();
} else if (dbtype.equalsIgnoreCase(DataSourceInfoOracle.dBType)) {
randomForestImpl = new RandomForestOracle();
} else if (dbtype.equalsIgnoreCase(DataSourceInfoDB2.dBType)) {
randomForestImpl = new RandomForestDB2();
((RandomForestDB2) randomForestImpl)
.setConnection(((DataBaseAnalyticSource) analyticSource).getConnection());
} else if (dbtype.equalsIgnoreCase(DataSourceInfoNZ.dBType)) {
randomForestImpl = new RandomForestNZ();
} else {
throw new AnalysisException("Databse type is not supported for Random Forest:" + dbtype);
// return null;
}
try {
dataSet =
getDataSet((DataBaseAnalyticSource) analyticSource, analyticSource.getAnalyticConfig());
} catch (OperatorException e1) {
logger.error(e1);
throw new OperatorException(e1.getLocalizedMessage());
}
setSpecifyColumn(dataSet, analyticSource.getAnalyticConfig());
dataSet.computeAllColumnStatistics();
RandomForestConfig rfConfig = (RandomForestConfig) analyticSource.getAnalyticConfig();
String dbSystem = ((DataBaseAnalyticSource) analyticSource).getDataBaseInfo().getSystem();
String url = ((DataBaseAnalyticSource) analyticSource).getDataBaseInfo().getUrl();
String userName = ((DataBaseAnalyticSource) analyticSource).getDataBaseInfo().getUserName();
String password = ((DataBaseAnalyticSource) analyticSource).getDataBaseInfo().getPassword();
String inputSchema = ((DataBaseAnalyticSource) analyticSource).getTableInfo().getSchema();
String tableName = ((DataBaseAnalyticSource) analyticSource).getTableInfo().getTableName();
String useSSL = ((DataBaseAnalyticSource) analyticSource).getDataBaseInfo().getUseSSL();
String sampleWithReplacement = rfConfig.getSampleWithReplacement();
long timeStamp = System.currentTimeMillis();
pnewTable = "pnew" + timeStamp;
sampleTable = "s" + timeStamp;
String dependentColumn = rfConfig.getDependentColumn();
String columnNames = rfConfig.getColumnNames();
String[] totalColumns = columnNames.split(",");
int subSize = Integer.parseInt(rfConfig.getNodeColumnNumber());
int forestSize = Integer.parseInt(rfConfig.getForestSize());
Connection conncetion = null;
if (dbtype.equalsIgnoreCase(DataSourceInfoGreenplum.dBType)
|| dbtype.equalsIgnoreCase(DataSourceInfoPostgres.dBType)) {
lastResult = new RandomForestModelGreenplum(dataSet);
} else if (dbtype.equalsIgnoreCase(DataSourceInfoOracle.dBType)) {
lastResult = new RandomForestModelOracle(dataSet);
} else if (dbtype.equalsIgnoreCase(DataSourceInfoDB2.dBType)) {
lastResult = new RandomForestModelDB2(dataSet);
} else if (dbtype.equalsIgnoreCase(DataSourceInfoNZ.dBType)) {
lastResult = new RandomForestModelNZ(dataSet);
}
lastResult.setColumnNames(columnNames);
lastResult.setDependColumn(dependentColumn);
lastResult.setTableName(tableName);
conncetion = ((DataBaseAnalyticSource) analyticSource).getConnection();
Model result = null;
try {
st = conncetion.createStatement();
} catch (SQLException e) {
logger.error(e);
throw new AnalysisException(e);
}
// Iterator<String> dependvalueIterator = dataSet.getColumns()
// .getLabel().getMapping().getValues().iterator();
if (dataSet.getColumns().getLabel() instanceof NominalColumn) {
if (dataSet.getColumns().getLabel().getMapping().getValues().size() <= 1) {
String e =
SDKLanguagePack.getMessage(
SDKLanguagePack.ADABOOST_SAMPLE_ERRINFO, rfConfig.getLocale());
logger.error(e);
throw new AnalysisException(e);
}
if (dataSet.getColumns().getLabel().getMapping().getValues().size()
> AlpineMinerConfig.ADABOOST_MAX_DEPENDENT_COUNT) {
String e =
SDKLanguagePack.getMessage(
SDKLanguagePack.ADABOOST_MAX_DEPENDENT_COUNT_ERRINFO, rfConfig.getLocale());
logger.error(e);
throw new AnalysisException(e);
}
}
try {
randomForestImpl.randomForestTrainInit(
inputSchema, tableName, timeStamp, dependentColumn, st, dataSet);
} catch (SQLException e) {
logger.error(e);
throw new AnalysisException(e);
}
CartConfig config = new CartConfig();
config.setDependentColumn(dependentColumn);
config.setConfidence(rfConfig.getConfidence());
config.setMaximal_depth(rfConfig.getMaximal_depth());
config.setMinimal_leaf_size(rfConfig.getMinimal_leaf_size());
config.setMinimal_size_for_split(rfConfig.getMinimal_size_for_split());
config.setNo_pre_pruning("true");
config.setNo_pruning("true");
for (int i = 0; i < forestSize; i++) {
CartTrainer analyzer = new CartTrainer();
if (sampleWithReplacement == Resources.TrueOpt) {
randomForestImpl.randomForestSample(
inputSchema,
timeStamp + "" + i,
dependentColumn,
st,
rs,
pnewTable,
sampleTable + i,
rfConfig.getLocale());
} else {
randomForestImpl.randomForestSampleNoReplace(
inputSchema,
timeStamp + "" + i,
dependentColumn,
st,
rs,
pnewTable,
sampleTable + i,
rfConfig.getLocale(),
dataSet.size());
}
String subColumns = getSubColumns(totalColumns, subSize);
config.setColumnNames(subColumns);
DataBaseAnalyticSource tempsource =
new DataBaseAnalyticSource(
dbSystem, url, userName, password, inputSchema, sampleTable + i, useSSL);
tempsource.setAnalyticConfiguration(config);
tempsource.setConenction(conncetion);
result =
((AnalyzerOutPutTrainModel) analyzer.doAnalysis(tempsource))
.getEngineModel()
.getModel();
String OOBTable = "OOB" + sampleTable + i;
randomForestImpl.generateOOBTable(
inputSchema, OOBTable, pnewTable, sampleTable + i, st, rs);
DataBaseAnalyticSource tempPredictSource =
new DataBaseAnalyticSource(
dbSystem, url, userName, password, inputSchema, OOBTable, useSSL);
String predictOutTable = "OOBPredict" + sampleTable;
EngineModel em = new EngineModel();
em.setModel(result);
PredictorConfig tempconfig = new PredictorConfig(em);
tempconfig.setDropIfExist(dropIfExists);
tempconfig.setOutputSchema(inputSchema);
tempconfig.setOutputTable(predictOutTable);
tempPredictSource.setAnalyticConfiguration(tempconfig);
tempPredictSource.setConenction(conncetion);
AbstractDBModelPredictor predictor = new CartPredictor();
predictor.doAnalysis(tempPredictSource); // use the weak alg , do
double OOBError = 0.0;
if (result instanceof DecisionTreeModel) {
OOBError =
randomForestImpl.getOOBError(
tempPredictSource, dependentColumn, "P(" + dependentColumn + ")");
lastResult.getOobEstimateError().add(OOBError);
} else if (result instanceof RegressionTreeModel) {
OOBError = randomForestImpl.getMSE(tempPredictSource, "P(" + dependentColumn + ")");
lastResult.getOobLoss().add(OOBError);
double OOBMape =
randomForestImpl.getMAPE(
tempPredictSource, dependentColumn, "P(" + dependentColumn + ")");
lastResult.getOobMape().add(OOBMape);
} else {
OOBError = Double.NaN;
lastResult.getOobLoss().add(OOBError);
}
lastResult.addModel((SingleModel) result);
randomForestImpl.clearTrainResult(inputSchema, sampleTable + i);
randomForestImpl.clearTrainResult(inputSchema, predictOutTable);
randomForestImpl.clearTrainResult(inputSchema, OOBTable);
}
return lastResult;
} catch (Exception e) {
logger.error(e);
if (e instanceof WrongUsedException) {
throw new AnalysisError(this, (WrongUsedException) e);
} else if (e instanceof AnalysisError) {
throw (AnalysisError) e;
} else {
throw new AnalysisException(e);
}
} finally {
try {
if (st != null) {
st.close();
}
if (rs != null) {
rs.close();
}
} catch (SQLException e) {
logger.error(e);
throw new AnalysisException(e.getLocalizedMessage());
}
}
}
