Merge pull request #1008 from KhiopsML/989-khiops_raw_gui-not-active-despite-driver-being-installed

989 khiops raw gui not active despite driver being installed

Author: bruno-at-orange

CMakeLists.txt

@@ -24,6 +24,12 @@ cmake_policy(SET CMP0058 NEW)
 cmake_policy(SET CMP0063 NEW)
 cmake_policy(SET CMP0083 NEW)
 
+# De-duplicate libraries on link lines (CMake 3.29+); silences Apple ld "ignoring duplicate libraries" warnings that
+# arise from diamond static-lib deps
+if(POLICY CMP0156)
+  cmake_policy(SET CMP0156 NEW)
+endif()
+
 # Do not add warning flags for MSVC cmake_policy(SET CMP0092 NEW)
 
 # Warn about in-source builds

src/Learning/DTForest/DTAttributeSelection.cpp

@@ -313,7 +313,7 @@ ObjectArray* DTAttributeSelection::GetTreeAttributesFromLevels(const int nMaxAtt
 	// Repris de l'algo de Weighted Random Sampling with a reservoir Information Processing Letters 97(2006) 181-185
 
 	DTTreeAttribute* dtAttribute;
-	int nAttribute, nMax;
+	int nAttribute;
 
 	if (nMaxAttributesNumber <= 0)
 		return NULL;
@@ -325,9 +325,6 @@ ObjectArray* DTAttributeSelection::GetTreeAttributesFromLevels(const int nMaxAtt
 
 	ivSortedListIndexes.SetSize(0);
 
-	nMax = (nMaxAttributesNumber > oaTreeAttributeSelection.GetSize()) ? oaTreeAttributeSelection.GetSize()
-									   : nMaxAttributesNumber;
-
 	for (nAttribute = 0; nAttribute < nMaxAttributesNumber; nAttribute++)
 	{
 		dtAttribute = cast(DTTreeAttribute*, oaTreeAttributeSelection.GetAt(nAttribute));

src/Learning/DTForest/DTDecisionTreeCreationTask.cpp

@@ -649,7 +649,6 @@ boolean DTDecisionTreeCreationTask::SlaveProcess()
 	DTDecisionTreeSpec* reportTreeSpec = NULL;
 	KWLearningSpec* learningSpecTree = NULL;
 	KWLearningSpec* slaveLearningSpec = NULL;
-	int nNativeVariableNumber;
 	ALString sVariableName;
 	ALString sTmp;
 	ALString sMessage;
@@ -666,7 +665,6 @@ boolean DTDecisionTreeCreationTask::SlaveProcess()
 	DTAttributeSelection* attributegenerator;
 	boolean bRegressionWithEqualFreqDiscretization = false;
 	boolean bRegressionWithMODLDiscretization = false;
-	int nBuidTreeNumber;
 	int nOldSeedTree;
 
 	if (randomForestParameter.GetDecisionTreeParameter()->GetVerboseMode())
@@ -676,6 +674,7 @@ boolean DTDecisionTreeCreationTask::SlaveProcess()
 	}
 
 #ifdef GENERATE_PYTHON_REPORTING_TRACES
+	int nBuidTreeNumber;
 	const int oldMaxErrorFlowNumber = Global::GetMaxErrorFlowNumber();
 	Global::ActivateErrorFlowControl();
 	Global::SetMaxErrorFlowNumber(10000);
@@ -760,14 +759,13 @@ boolean DTDecisionTreeCreationTask::SlaveProcess()
 			 IntToString(GetTaskIndex() + 1) + "\t" + ALString("Grant memory : \t") +
 			 ALString(LongintToHumanReadableString(GetSlaveResourceGrant()->GetMemory())));
 	DTTimer_SlaveProcess.Start();
+	nBuidTreeNumber = 0;
 #endif
 
 	KWClass* kwcUpdatedClass = shared_learningSpec.GetLearningSpec()->GetClass();
 
 	randomForestParameter.CopyFrom(input_forestParameter->GetForestParameter());
 
-	nBuidTreeNumber = 0;
-
 	// Calcul des arbres
 	if (bOk and not TaskProgression::IsInterruptionRequested())
 	{
@@ -820,9 +818,6 @@ boolean DTDecisionTreeCreationTask::SlaveProcess()
 				    learningSpecTree, &blOrigine, shared_odAttributeStats->GetObjectDictionary());
 			}
 
-			// Calcul du nombre de variables initiales
-			nNativeVariableNumber = slaveLearningSpec->GetInitialAttributeNumber();
-
 			// creation pour un slice de selection :
 			// - des arbres dttree
 			// - des rapport KWAttributeStats
@@ -904,9 +899,9 @@ boolean DTDecisionTreeCreationTask::SlaveProcess()
 						    learningSpecTree, &blOrigine,
 						    shared_odAttributeStats->GetObjectDictionary());
 					}
-
+#ifdef GENERATE_PYTHON_REPORTING_TRACES
 					nBuidTreeNumber++;
-
+#endif
 					nOldSeedTree = GetRandomSeed();
 					SetRandomSeed(attributegenerator->GetRandomSeed());
 

src/Learning/DTForest/DTForestAttributeSelection.cpp

@@ -51,7 +51,6 @@ void DTForestAttributeSelection::Initialization(const ObjectDictionary* odInputA
 {
 	require(odInputAttributeStats != NULL);
 
-	longint nNbInstence = 0;
 	DTTreeAttribute* taAttribute;
 	KWAttributeStats* attributeStats;
 	KWAttribute* attribute;
@@ -63,10 +62,9 @@ void DTForestAttributeSelection::Initialization(const ObjectDictionary* odInputA
 	odInputAttributeStats->ExportObjectArray(&oaAttributeStats);
 	nOriginalAttributesNumber = oaAttributeStats.GetSize();
 
-	// nombre d'objet dasn la base
+	// nombre d'objet dans la base
 	attributeStats = cast(KWAttributeStats*, oaAttributeStats.GetAt(0));
-	nNbInstence = attributeStats->GetInstanceNumber();
-	//	cout << "les attibut : " << nOriginalAttributesNumber << endl;
+
 	for (nAttribute = 0; nAttribute < nOriginalAttributesNumber; nAttribute++)
 	{
 		attributeStats = cast(KWAttributeStats*, oaAttributeStats.GetAt(nAttribute));
@@ -102,7 +100,6 @@ void DTForestAttributeSelection::BuildForestUniformSelections(int nmaxselectionn
 		nMaxSelectionNumber = nmaxselectionnumber;
 		int nSelection, nAttribute;
 		int nTinf, nTnull;
-		int nKinf, nKnull;
 		int nAttributesNumber;
 		int nSeed = nRandomSeed;
 		ObjectArray oaVariablesNull;
@@ -116,20 +113,16 @@ void DTForestAttributeSelection::BuildForestUniformSelections(int nmaxselectionn
 
 		Clean();
 
-		nKinf = 0;
-		nKnull = 0;
 		// calcul des ensemble de variable inf et null
 		for (nAttribute = 0; nAttribute < nOrigineAttributesNumber; nAttribute++)
 		{
 			taAttribute = cast(DTTreeAttribute*, oaOriginalAttributesUsed.GetAt(nAttribute));
 			if (taAttribute->dLevel == 0.0)
 			{
-				nKnull++;
 				oaVariablesNull.Add(taAttribute);
 			}
 			else
 			{
-				nKinf++;
 				oaVariablesInf.Add(taAttribute);
 			}
 		}
@@ -140,7 +133,6 @@ void DTForestAttributeSelection::BuildForestUniformSelections(int nmaxselectionn
 			// le dictionnaire de lecture de la base (i.e : flaggues avec SetUsed(false) et SetLoaded(false)
 			// ) D'autre part, on  a deja verifie que les attributs restants peuvent etre traites en memoire
 			// (et le cas echeant, on a deja decharge les attributs les moins informatifs)
-
 			if (dPct == 0.0)
 				nAttributesNumber =
 				    (int)(sqrtl(nOrigineAttributesNumber) + log2l(nOrigineAttributesNumber)); // MB
@@ -173,7 +165,7 @@ void DTForestAttributeSelection::BuildForestUniformSelections(int nmaxselectionn
 			else
 				oaOriginalAttributesUsed.Shuffle();
 
-			// cretation nouvelle liste de variables
+			// creation nouvelle liste de variables
 			svRandattibuteselection = new DTAttributeSelection;
 
 			svRandattibuteselection->SetRandomSeed(nSeed);
@@ -758,13 +750,7 @@ boolean DTAttributeSelectionsSlices::Check() const
 			slice = cast(KWDataTableSlice*, oaSlices.GetAt(nSlice));
 
 			// Erreur si probleme d'ordre
-			// if (previousSlice != NULL and previousSlice->CompareLexicographicOrder(slice) >= 0)
-			//{
-			//	AddError("Wrong ordering between " + slice->GetClassLabel() + "s " +
-			//		 previousSlice->GetObjectLabel() + " and " + slice->GetObjectLabel());
-			//	bOk = false;
-			//	break;
-			//}
+			assert(previousSlice == NULL or previousSlice->CompareLexicographicOrder(slice) < 0);
 			previousSlice = slice;
 
 			// Erreur si tranche deja utilisee

src/Learning/DTForest/DTGrouperMODLOptimization.cpp

@@ -1325,7 +1325,6 @@ void DTGrouperMODL::FastPostOptimizeGroups(KWFrequencyTable* kwftSource, KWFrequ
 	int nStepNumber;
 	int nModalityNumber;
 	int nGroupNumber;
-	int nTargetNumber;
 	IntVector ivModalityIndexes;
 	IntVector ivGroupIndexes;
 	DoubleVector dvGroupCosts;
@@ -1362,10 +1361,9 @@ void DTGrouperMODL::FastPostOptimizeGroups(KWFrequencyTable* kwftSource, KWFrequ
 	if (bPrintInitialTables)
 		cout << *kwftSource << endl << *kwftTarget << endl;
 
-	// Memorisation des nombre de modalites, groupes et classes cibles
+	// Memorisation des nombre de modalites et groupes
 	nModalityNumber = kwftSource->GetFrequencyVectorNumber();
 	nGroupNumber = kwftTarget->GetFrequencyVectorNumber();
-	nTargetNumber = kwftSource->GetFrequencyVectorSize();
 
 	// Initialisation des vecteurs d'index des modalites et des groupes
 	ivModalityIndexes.SetSize(nModalityNumber);
@@ -1521,7 +1519,6 @@ void DTGrouperMODL::FastPostOptimizeGroupsWithGarbage(KWFrequencyTable* kwftSour
 	int nStepNumber;
 	int nModalityNumber;
 	int nGroupNumber;
-	int nTargetNumber;
 	IntVector ivModalityIndexes;
 	IntVector ivGroupIndexes;
 	DoubleVector dvGroupCosts;
@@ -1569,10 +1566,10 @@ void DTGrouperMODL::FastPostOptimizeGroupsWithGarbage(KWFrequencyTable* kwftSour
 	if (bPrintInitialTables)
 		cout << *kwftSource << endl << *kwftTarget << endl;
 
-	// Memorisation des nombre de modalites, groupes et classes cibles
+	// Memorisation des nombre de modalites et groupes
 	nModalityNumber = kwftSource->GetFrequencyVectorNumber();
 	nGroupNumber = kwftTarget->GetFrequencyVectorNumber();
-	nTargetNumber = kwftSource->GetFrequencyVectorSize();
+
 	// Initialisation du nombre de modalites du groupe poubelle
 	nGarbageModalityNumber = cast(KWFrequencyVector*, frequencyList->GetHead())->GetModalityNumber();
 	nNewGarbageModalityNumber = 0;
@@ -2035,7 +2032,6 @@ void DTGrouperMODL::PostOptimizeGroupsWithGarbageSearch(ObjectArray* oaInitialGr
 	boolean bContinue;
 	int nModalityNumber;
 	int nGroupNumber;
-	int nTargetNumber;
 	DoubleVector dvGroupCosts;
 	DoubleVector dvGroupOutDeltaCosts;
 	DoubleVector dvGroupInDeltaCosts;
@@ -2077,10 +2073,9 @@ void DTGrouperMODL::PostOptimizeGroupsWithGarbageSearch(ObjectArray* oaInitialGr
 	if (bPrintInitialGroups)
 		cout << *oaInitialGroups << endl << *oaNewGroups << endl;
 
-	// Memorisation des nombre de modalites, groupes et classes cibles
+	// Memorisation des nombre de modalites et groupes
 	nModalityNumber = oaInitialGroups->GetSize();
 	nGroupNumber = oaNewGroups->GetSize();
-	nTargetNumber = cast(KWMODLGroup*, oaInitialGroups->GetAt(0))->GetFrequencyVector()->GetSize();
 	nTrueGroupNumber = nGroupNumber;
 
 	// Initialisation du nombre de modalites du groupe poubelle

src/Learning/KDDomainKnowledge/KDClassBuilder.cpp

@@ -899,20 +899,17 @@ void KDClassBuilder::DisplayUsedConstructedBlockRules(const SortedList* slUsedCo
 {
 	POSITION position;
 	KDSparseUsedConstructedBlockRule* usedConstructedBlockRule;
-	int nRule;
 
 	require(slUsedConstructedBlockRules != NULL);
 	require(slUsedConstructedBlockRules->GetCompareFunction() == KDSparseUsedConstructedBlockRuleCompareCostName);
 
 	// Parcours des regles de la liste
 	ost << "Used constructed block rules\t" << slUsedConstructedBlockRules->GetCount() << endl;
-	nRule = 0;
 	position = slUsedConstructedBlockRules->GetHeadPosition();
 	while (position != NULL)
 	{
 		usedConstructedBlockRule =
 		    cast(KDSparseUsedConstructedBlockRule*, slUsedConstructedBlockRules->GetNext(position));
-		nRule++;
 
 		// Affichage de la regle
 		ost << *usedConstructedBlockRule->GetConstructedBlockRule() << "\t";

src/Learning/KDDomainKnowledge/KDMultiTableFeatureConstruction.cpp

@@ -2117,7 +2117,6 @@ void KDMultiTableFeatureConstruction::BuildAllSelectionRulesFromSelectionOperand
 	double dSelectionOperandProb;
 	KDClassSelectionStats* classSelectionStats;
 	KDClassSelectionOperandStats* classSelectionOperandStats;
-	boolean bRuleFiltered;
 	int nInitialRuleNumber;
 
 	require(selectionRule != NULL);
@@ -2437,7 +2436,6 @@ void KDMultiTableFeatureConstruction::BuildAllSelectionRulesFromSelectionOperand
 		}
 	}
 	// Parcours des tailles de selection
-	bRuleFiltered = false;
 	for (nSize = 1; nSize <= dvSelectionSizeRandomDrawingNumbers.GetSize(); nSize++)
 	{
 		dSelectionRandomDrawingNumber = dvSelectionSizeRandomDrawingNumbers.GetAt(nSize - 1);
@@ -2477,8 +2475,7 @@ void KDMultiTableFeatureConstruction::BuildAllSelectionRulesFromSelectionOperand
 			oaAllSelectionParts.SetSize(0);
 
 			// Filtrage des regles pour ne garder que les regles de probabilite suffisantes pour etre tirees
-			bRuleFiltered =
-			    FilterConstructedRulesForRandomDrawing(dRandomDrawingNumber, oaAllConstructedRules);
+			FilterConstructedRulesForRandomDrawing(dRandomDrawingNumber, oaAllConstructedRules);
 
 			// Nettoyage des selections d'operandes
 			oaSelectionOperandIndexedFrequencies.DeleteAll();

src/Learning/KNITransfer/KNIDatabaseTransferView.cpp

@@ -35,17 +35,15 @@ void KNIDatabaseTransferView::KNITransferDatabase()
 {
 	KNIMTRecodingOperands recodingOperands;
 	KWMTDatabaseTextFile* sourceMTDatabase;
-	KWMTDatabaseTextFile* targetMTDatabase;
 	KWDatabase* workingTargetDatabase;
 	Timer timerTransfer;
 	int nMapping;
 	KWMTDatabaseMapping* mapping;
 	int nRecordNumber;
 	ALString sTmp;
 
-	// Acces aux bases source et cible
+	// Acces aux bases source
 	sourceMTDatabase = cast(KWMTDatabaseTextFile*, sourceDatabase);
-	targetMTDatabase = cast(KWMTDatabaseTextFile*, targetDatabase);
 
 	// On passe par une autre table en sortie, pour pouvoir specifier son chemin si elle n'en a pas
 	workingTargetDatabase = targetDatabase->Clone();

src/Learning/KNITransfer/KNITransferProblemView.cpp

@@ -54,6 +54,7 @@ void KNITransferProblemView::SetObject(Object* object)
 
 	// Acces a l'objet edite
 	learningProblem = cast(KNITransferProblem*, object);
+	(void)learningProblem; // Evite un warning si on n'utilise pas l'objet dans cette methode
 }
 
 KNITransferProblem* KNITransferProblemView::GetTransferProblem()

src/Learning/KWDRRuleLibrary/KWDRDateTime.cpp

@@ -1237,21 +1237,19 @@ TimestampTZ KWDRBuildTimestampTZ::ComputeTimestampTZResult(const KWObject* kwoOb
 	TimestampTZ tstzTimestamp;
 	Continuous cMinutes;
 	int nMinutes;
-	boolean bOk;
 
 	require(IsCompiled());
 
 	// Modifie la time zone que si les operandes sont valides
 	tsTimestamp = GetFirstOperand()->GetTimestampValue(kwoObject);
-	bOk = false;
 	tstzTimestamp.Reset();
 	if (tsTimestamp.Check())
 	{
 		cMinutes = GetSecondOperand()->GetContinuousValue(kwoObject);
 		if (cMinutes != KWContinuous::GetMissingValue())
 		{
 			nMinutes = int(floor(cMinutes + 0.5));
-			bOk = tstzTimestamp.Init(tsTimestamp, nMinutes);
+			tstzTimestamp.Init(tsTimestamp, nMinutes);
 		}
 	}
 	return tstzTimestamp;