public void CreateMiningModel() { //connecting the server and database Server myServer = new Server(); myServer.Connect("DataSource=localhost;Catalog=FoodMart"); Database myDatabase = myServer.Databases["FoodMart"]; Cube myCube = myDatabase.Cubes["FoodMart 2000"]; CubeDimension myDimension = myCube.Dimensions["Customer"]; Microsoft.AnalysisServices.MiningStructure myMiningStructure = myDatabase.MiningStructures.Add("CustomerSegement", "CustomerSegement"); //Bind the mining structure to a cube. myMiningStructure.Source = new CubeDimensionBinding(".", myCube.ID, myDimension.ID); // Create the key column. CubeAttribute customerKey = myCube.Dimensions["Customer"].Attributes["Customer"]; ScalarMiningStructureColumn keyStructureColumn = CreateMiningStructureColumn(customerKey, true); myMiningStructure.Columns.Add(keyStructureColumn); //Member Card attribute CubeAttribute memberCard = myCube.Dimensions["Customer"].Attributes["Member Card"]; ScalarMiningStructureColumn memberCardStructureColumn = CreateMiningStructureColumn(memberCard, false); myMiningStructure.Columns.Add(memberCardStructureColumn); //Total Children attribute CubeAttribute totalChildren = myCube.Dimensions["Customer"].Attributes["Total Children"]; ScalarMiningStructureColumn totalChildrenStructureColumn = CreateMiningStructureColumn(totalChildren, false); myMiningStructure.Columns.Add(totalChildrenStructureColumn); //Store Sales measure ToDo: fix this! //Microsoft.AnalysisServices.Measure storeSales = myCube.MeasureGroups[0].Measures["Store Sales"]; //ScalarMiningStructureColumn storeSalesStructureColumn = CreateMiningStructureColumn(storeSales, false); //myMiningStructure.Columns.Add(storeSalesStructureColumn); //Create a mining model from the mining structure. By default, all the //structure columns are used. Nonkey columns are with usage input Microsoft.AnalysisServices.MiningModel myMiningModel = myMiningStructure.CreateMiningModel(true, "CustomerSegment"); //Set the algorithm to be clustering. myMiningModel.Algorithm = MiningModelAlgorithms.MicrosoftClustering; //Process structure and model try { myMiningStructure.Update(UpdateOptions.ExpandFull); myMiningStructure.Process(ProcessType.ProcessFull); } catch (Microsoft.AnalysisServices.OperationException e) { string err = e.Message; } }
/* * 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(); }
// Mining sample model private void CreateMarketBasketModel() { CubeAttribute basketAttribute; CubeAttribute itemAttribute; Server myServer = new Server(); myServer.Connect("DataSource=localhost;Catalog=FoodMart"); Database myDatabase = myServer.Databases["FoodMart"]; Cube myCube = myDatabase.Cubes["FoodMart 2000"]; CubeDimension myDimension = myCube.Dimensions["Customer"]; Microsoft.AnalysisServices.MiningStructure myMiningStructure = myDatabase.MiningStructures.Add("MarketBasket", "MarketBasket"); myMiningStructure.Source = new CubeDimensionBinding(".", myCube.ID, myDimension.ID); basketAttribute = myCube.Dimensions["Customer"].Attributes["Customer"]; itemAttribute = myCube.Dimensions["Product"].Attributes["Product"]; //basket structure column ScalarMiningStructureColumn basket = CreateMiningStructureColumn(basketAttribute, true); basket.Name = "Basket"; myMiningStructure.Columns.Add(basket); //item structure column - nested table ScalarMiningStructureColumn item = CreateMiningStructureColumn(itemAttribute, true); item.Name = "Item"; MeasureGroup measureGroup = myCube.MeasureGroups[0]; TableMiningStructureColumn purchases = CreateMiningStructureColumn(measureGroup); purchases.Name = "Purchases"; purchases.Columns.Add(item); myMiningStructure.Columns.Add(purchases); Microsoft.AnalysisServices.MiningModel myMiningModel = myMiningStructure.CreateMiningModel(); myMiningModel.Name = "MarketBasket"; myMiningModel.Columns["Purchases"].Usage = MiningModelColumnUsages.PredictOnly; myMiningModel.Algorithm = MiningModelAlgorithms.MicrosoftAssociationRules; }
/*--------------------------------- * Description: Create Mining Model * ----------------------------------- */ public static void CreateMM(Microsoft.AnalysisServices.MiningStructure ms, string strStockCode, bool bMulti) { if (ms.MiningModels.ContainsName(strStockCode)) { ms.MiningModels[strStockCode].Drop(); } Microsoft.AnalysisServices.MiningModel mm = ms.CreateMiningModel(true, strStockCode); mm.Algorithm = MiningModelAlgorithms.MicrosoftTimeSeries; InitialParameters(strStockCode); // 0.1:0.05 -> from 0 to (1-0.1)/0.05=18 mm.AlgorithmParameters.Add("COMPLEXITY_PENALTY", COMPLEXITY_PENALTY); // {5,20,60}, 0:0.1 -> from 0 to (1-0.1)/0.05=18 mm.AlgorithmParameters.Add("PERIODICITY_HINT", "{5,20,60}"); mm.AlgorithmParameters.Add("AUTO_DETECT_PERIODICITY", AUTO_DETECT_PERIODICITY); // Defeult: 1, 10 mm.AlgorithmParameters.Add("HISTORIC_MODEL_COUNT", HISTORIC_MODEL_COUNT); mm.AlgorithmParameters.Add("HISTORIC_MODEL_GAP", HISTORIC_MODEL_GAP); // Max, Min Time Series mm.AlgorithmParameters.Add("MAXIMUM_SERIES_VALUE", MAXIMUM_SERIES_VALUE); mm.AlgorithmParameters.Add("MINIMUM_SERIES_VALUE", MINIMUM_SERIES_VALUE); mm.AllowDrillThrough = true; mm.Columns["ID"].Usage = MiningModelColumnUsages.Key; mm.Columns["ClosePrice"].Usage = MiningModelColumnUsages.Predict; if (strStockCode.ToUpper() != "VNINDEX" && !bMulti) { mm.Columns["OpenPrice"].Usage = MiningModelColumnUsages.Input; mm.Columns["HighPrice"].Usage = MiningModelColumnUsages.Input; mm.Columns["LowPrice"].Usage = MiningModelColumnUsages.Input; //mm.Columns["Volume"].Usage = MiningModelColumnUsages.Input; } mm.Update(); // Update parameters into StockForecastModel UpdateForecastModel(strStockCode); }
public void AddMiningStructure() { Server srv = new Server(); srv.Connect("DataSource=CLARITY-7HYGMQM\\ANA;Initial Catalog=Adventure Works DW 2008"); Database db = srv.Databases["Adventure Works DW 2008"]; Cube myCube = db.Cubes["Adventure Works"]; CubeDimension myDimension = myCube.Dimensions.GetByName("Customer"); Microsoft.AnalysisServices.MiningStructure myMiningStructure = db.MiningStructures.Add("TestMining", "TestMining"); myMiningStructure.Source = new CubeDimensionBinding(".", myCube.ID, myDimension.ID); // get current mining models // Demo code foreach (Microsoft.AnalysisServices.MiningStructure ms in db.MiningStructures) { Console.WriteLine(ms.Name); foreach (Microsoft.AnalysisServices.MiningModel mm in ms.MiningModels) { Console.WriteLine(mm.Name); } } CubeAttribute basketAttribute; CubeAttribute itemAttribute; basketAttribute = myCube.Dimensions.GetByName("Customer").Attributes[0]; itemAttribute = myCube.Dimensions.GetByName("Product").Attributes[0]; //basket structure column ScalarMiningStructureColumn basket = CreateMiningStructureColumn(basketAttribute, true); basket.Name = "Basket"; myMiningStructure.Columns.Add(basket); //item structure column - nested table ScalarMiningStructureColumn item = CreateMiningStructureColumn(itemAttribute, true); item.Name = "Item"; MeasureGroup measureGroup = myCube.MeasureGroups[0]; TableMiningStructureColumn purchases = CreateMiningStructureColumn(measureGroup); purchases.Name = "Purchases"; purchases.Columns.Add(item); myMiningStructure.Columns.Add(purchases); Microsoft.AnalysisServices.MiningModel myMiningModel = myMiningStructure.CreateMiningModel(); myMiningModel.Name = "MarketBasket"; myMiningModel.Columns["Purchases"].Usage = MiningModelColumnUsages.PredictOnly; myMiningModel.Algorithm = MiningModelAlgorithms.MicrosoftAssociationRules; try { myMiningStructure.Update(UpdateOptions.ExpandFull); myMiningStructure.Process(ProcessType.ProcessFull); } catch (Microsoft.AnalysisServices.OperationException e) { this.sResult = e.StackTrace; Console.WriteLine(e.StackTrace); } }