/*
* Create mining model
*/
private void CreateMiningModel(Microsoft.AnalysisServices.MiningStructure objStructure, string sName, string sAlgorithm,
List <string> lsAtrPredict, List <string> lsMeasurePredict, List <bool> lbPredictItems, int parOne, int parTwo)
{
Microsoft.AnalysisServices.MiningModel myMiningModel = objStructure.CreateMiningModel(true, sName);
/* Notes:
* Each mining column must have its' input and predict columns
* Input and key columns are added automatically when they are created in the mining structure
* Predict columns can be added in the mining model
* An input column can be also a predict column
*/
myMiningModel.Algorithm = sAlgorithm;
switch (sAlgorithm)
{
case MiningModelAlgorithms.MicrosoftClustering:
myMiningModel.AlgorithmParameters.Add("CLUSTERING_METHOD", parOne);
if (parTwo > 0)
{
myMiningModel.AlgorithmParameters.Add("CLUSTER_COUNT", parTwo);
}
break;
//case MiningModelAlgorithms.MicrosoftTimeSeries:
// myMiningModel.AlgorithmParameters.Add("PERIODICITY_HINT", "{12}"); // {12} represents the number of months for prediction
// break;
case MiningModelAlgorithms.MicrosoftNaiveBayes:
break;
case MiningModelAlgorithms.MicrosoftDecisionTrees:
myMiningModel.AlgorithmParameters.Add("SCORE_METHOD", parOne);
myMiningModel.AlgorithmParameters.Add("SPLIT_METHOD", parTwo);
break;
}
/***************** Predict columns *****************/
// add optional predict columns
if (lsAtrPredict.Count != 0)
{
// predict columns
for (int i = 0; i < lsAtrPredict.Count; i++)
{
Microsoft.AnalysisServices.MiningModelColumn modelColumn = myMiningModel.Columns.GetByName(lsAtrPredict[i]);
modelColumn.SourceColumnID = lsAtrPredict[i];
if (lbPredictItems[i] == true)
{
modelColumn.Usage = MiningModelColumnUsages.PredictOnly;
}
else
{
modelColumn.Usage = MiningModelColumnUsages.Predict;
}
}
}
myMiningModel.Update();
}
/*
* Create mining model with custom fields and algorithm
*/
private void CreateCustomModel(MiningStructure objStructure, string sAlgorithm, string sModelName, string sKeyColumn, List <string> lPredictColumns, List <bool> lbPredictColumns, int parOne, int parTwo)
{
// drop existing model
if (objStructure.MiningModels.ContainsName(sModelName))
{
objStructure.MiningModels[sModelName].Drop();
}
// Detailed description of the model algorithms is here:
// http://msdn.microsoft.com/en-us/library/ms175595.aspx
// More customisation for these algorithms can be found here:
// http://msdn.microsoft.com/en-us/library/cc280427.aspx
// Also a model example can be found here:
// http://msdn.microsoft.com/en-us/library/ms345087(v=SQL.100).aspx
Microsoft.AnalysisServices.MiningModel myMiningModel = objStructure.CreateMiningModel(true, sModelName);
myMiningModel.Algorithm = sAlgorithm;
switch (sAlgorithm)
{
case MiningModelAlgorithms.MicrosoftClustering:
myMiningModel.AlgorithmParameters.Add("CLUSTERING_METHOD", parOne);
myMiningModel.AlgorithmParameters.Add("CLUSTER_COUNT", parTwo);
break;
//case MiningModelAlgorithms.MicrosoftTimeSeries:
// myMiningModel.AlgorithmParameters.Add("PERIODICITY_HINT", "{12}"); // {12} represents the number of months for prediction
// break;
case MiningModelAlgorithms.MicrosoftNaiveBayes:
break;
case MiningModelAlgorithms.MicrosoftDecisionTrees:
myMiningModel.AlgorithmParameters.Add("SCORE_METHOD", parOne);
myMiningModel.AlgorithmParameters.Add("SPLIT_METHOD", parTwo);
break;
}
/***************** Predict columns *****************/
// add optional predict columns
if (lPredictColumns.Count != 0)
{
// predict columns
for (int i = 0; i < lPredictColumns.Count; i++)
{
Microsoft.AnalysisServices.MiningModelColumn modelColumn = myMiningModel.Columns.GetByName(lPredictColumns[i]);
modelColumn.SourceColumnID = lPredictColumns[i];
if (lbPredictColumns[i] == true)
{
modelColumn.Usage = MiningModelColumnUsages.PredictOnly;
}
else
{
modelColumn.Usage = MiningModelColumnUsages.Predict;
}
}
}
myMiningModel.Update();
}
