- Timestamp:
- 10/14/16 15:51:10 (8 years ago)
- Location:
- branches/HeuristicLab.GoalSeekingProblem
- Files:
-
- 15 added
- 5 edited
- 2 copied
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- Unmodified
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branches/HeuristicLab.GoalSeekingProblem/HeuristicLab.GoalSeekingProblem.Views/3.4/Plugin.cs
r14329 r14333 22 22 using HeuristicLab.PluginInfrastructure; 23 23 24 namespace HeuristicLab.GoalSeekingProblem { 25 [Plugin("HeuristicLab.GoalSeekingProblem", "3.4.0.0")] 26 [PluginFile("HeuristicLab.GoalSeekingProblem-3.4.dll", PluginFileType.Assembly)] 27 [PluginDependency("HeuristicLab.Collections", "3.3")] 24 namespace HeuristicLab.GoalSeekingProblem.Views { 25 [Plugin("HeuristicLab.GoalSeekingProblem.Views", "3.4.0.0")] 26 [PluginFile("HeuristicLab.GoalSeekingProblem.Views-3.4.dll", PluginFileType.Assembly)] 28 27 [PluginDependency("HeuristicLab.Common", "3.3")] 29 28 [PluginDependency("HeuristicLab.Core", "3.3")] 30 [PluginDependency("HeuristicLab.Data", "3.3")] 31 [PluginDependency("HeuristicLab.Encodings.RealVectorEncoding", "3.3")] 32 [PluginDependency("HeuristicLab.Operators", "3.3")] 33 [PluginDependency("HeuristicLab.Optimization", "3.3")] 34 [PluginDependency("HeuristicLab.Parameters", "3.3")] 35 [PluginDependency("HeuristicLab.Persistence", "3.3")] 36 [PluginDependency("HeuristicLab.Problems.DataAnalysis", "3.4")] 37 [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic", "3.4")] 38 [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic.Regression", "3.4")] 29 [PluginDependency("HeuristicLab.MainForm", "3.3")] 30 [PluginDependency("HeuristicLab.MainForm.WindowsForms", "3.3")] 31 39 32 public class HeuristicLabProcessParameterOptimizationPlugin : PluginBase { 40 33 } -
branches/HeuristicLab.GoalSeekingProblem/HeuristicLab.GoalSeekingProblem.Views/3.4/Plugin.cs.frame
r14329 r14333 22 22 using HeuristicLab.PluginInfrastructure; 23 23 24 namespace HeuristicLab.GoalSeekingProblem { 25 [Plugin("HeuristicLab.GoalSeekingProblem", "3.4.0.0")] 26 [PluginFile("HeuristicLab.GoalSeekingProblem-3.4.dll", PluginFileType.Assembly)] 27 [PluginDependency("HeuristicLab.Collections", "3.3")] 24 namespace HeuristicLab.GoalSeekingProblem.Views { 25 [Plugin("HeuristicLab.GoalSeekingProblem.Views", "3.4.0.0")] 26 [PluginFile("HeuristicLab.GoalSeekingProblem.Views-3.4.dll", PluginFileType.Assembly)] 28 27 [PluginDependency("HeuristicLab.Common", "3.3")] 29 28 [PluginDependency("HeuristicLab.Core", "3.3")] 30 [PluginDependency("HeuristicLab.Data", "3.3")] 31 [PluginDependency("HeuristicLab.Encodings.RealVectorEncoding", "3.3")] 32 [PluginDependency("HeuristicLab.Operators", "3.3")] 33 [PluginDependency("HeuristicLab.Optimization", "3.3")] 34 [PluginDependency("HeuristicLab.Parameters", "3.3")] 35 [PluginDependency("HeuristicLab.Persistence", "3.3")] 36 [PluginDependency("HeuristicLab.Problems.DataAnalysis", "3.4")] 37 [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic", "3.4")] 38 [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic.Regression", "3.4")] 29 [PluginDependency("HeuristicLab.MainForm", "3.3")] 30 [PluginDependency("HeuristicLab.MainForm.WindowsForms", "3.3")] 31 39 32 public class HeuristicLabProcessParameterOptimizationPlugin : PluginBase { 40 33 } -
branches/HeuristicLab.GoalSeekingProblem/HeuristicLab.GoalSeekingProblem/3.4/GoalSeekingProblem.csproj
r14324 r14333 189 189 <None Include="Plugin.cs.frame" /> 190 190 <Compile Include="Analyzers\BestSolutionAnalyzer.cs" /> 191 <Compile Include="GoalParameter.cs" /> 192 <Compile Include="GoalSeekingUtil.cs" /> 193 <Compile Include="InputParameter.cs" /> 191 194 <Compile Include="IGoalSeekingProblem.cs" /> 192 195 <Compile Include="Plugin.cs" /> -
branches/HeuristicLab.GoalSeekingProblem/HeuristicLab.GoalSeekingProblem/3.4/IGoalSeekingProblem.cs
r14324 r14333 22 22 using System; 23 23 using System.Collections.Generic; 24 using HeuristicLab.Core;25 using HeuristicLab.Data;26 24 using HeuristicLab.Optimization; 27 25 using HeuristicLab.Problems.DataAnalysis; … … 29 27 namespace HeuristicLab.GoalSeeking { 30 28 public interface IGoalSeekingProblem : IProblem { 31 IRegressionProblemData ProblemData { get; set; } 32 29 void Configure(IRegressionProblemData problemData, int row); 33 30 //rounding ? 34 31 IEnumerable<double> GetEstimatedGoalValues(IEnumerable<double> parameterValues, bool round = false); 35 int Row { get; set; }36 32 37 33 //Models? … … 39 35 void AddModel(IRegressionModel model); 40 36 void RemoveModel(IRegressionModel model); 37 38 //targets and parameters 39 IEnumerable<GoalParameter> Goals { get; } 40 IEnumerable<InputParameter> Inputs { get; } 41 42 //events 41 43 event EventHandler ModelsChanged; 42 43 #region targets44 double GetTargetGoal(string target);45 void SetTargetGoal(string target, double goal);46 47 //variance ?48 double GetTargetWeight(string target);49 void SetTargetWeight(string target, double weight);50 51 double GetTargetVariance(string target);52 void SetTargetVariance(string target, double variance);53 54 double GetTargetStepSize(string target);55 void SetTargetStepSize(string target, double stepSize);56 57 44 event EventHandler TargetsChanged; 58 #endregion59 60 #region parameters61 ICheckedItemList<StringValue> ControllableParameters { get; }62 DoubleMatrix ControllableParameterBounds { get; }63 64 IEnumerable<string> GetControllableParameters();65 void SetControllableParameters(IEnumerable<string> parameterNames);66 67 bool GetParameterActive(string parameter);68 void SetParameterActive(string parameter, bool active);69 70 double[] GetParameterBounds(string parameterName);71 void SetParameterBounds(string parameterName, double min, double max, double step);72 // set get ParameterStepSize, set get parameters active73 // parameters changed event74 double GetParameterStepSize(string parameter);75 void SetParameterStepSize(string parameter, double stepSize);76 77 45 event EventHandler ParametersChanged; 78 #endregion79 46 } 80 47 } -
branches/HeuristicLab.GoalSeekingProblem/HeuristicLab.GoalSeekingProblem/3.4/MultiObjectiveGoalSeekingProblem.cs
r14325 r14333 23 23 using System.Collections.Generic; 24 24 using System.Linq; 25 using System.Windows.Forms;26 25 using HeuristicLab.Collections; 27 26 using HeuristicLab.Common; … … 39 38 [StorableClass] 40 39 public sealed class MultiObjectiveGoalSeekingProblem : MultiObjectiveBasicProblem<RealVectorEncoding>, IGoalSeekingProblem { 40 #region parameter names 41 41 private const string ModifiableDatasetParameterName = "Dataset"; 42 private const string ProblemDataParameterName = "ProblemData"; 43 private const string ControllableParametersParameterName = "ControllableParameters"; 44 private const string ControllableParameterBoundsParameterName = "ControllableParameterBounds"; 45 private const string TargetGoalsParameterName = "TargetGoals"; 46 private const string TargetsParameterName = "Targets"; 47 private const string ModelCollectionParameterName = "ModelCollection"; 48 private const string RowParameterName = "Row"; 49 // these parameters are used by the pareto folding analyzer 42 private const string InputsParameterName = "Inputs"; 43 private const string GoalsParameterName = "Goals"; 44 private const string ModelsParameterName = "Models"; 50 45 private const string QualitySumCutoffParameterName = "QualitySumCutoff"; 46 #endregion 51 47 52 48 #region parameters 49 public IValueParameter<CheckedItemList<InputParameter>> InputsParameter { 50 get { return (IValueParameter<CheckedItemList<InputParameter>>)Parameters[InputsParameterName]; } 51 } 52 public IValueParameter<CheckedItemList<GoalParameter>> GoalsParameter { 53 get { return (IValueParameter<CheckedItemList<GoalParameter>>)Parameters[GoalsParameterName]; } 54 } 55 public IFixedValueParameter<ItemCollection<IRegressionModel>> ModelsParameter { 56 get { return (IFixedValueParameter<ItemCollection<IRegressionModel>>)Parameters[ModelsParameterName]; } 57 } 53 58 public IFixedValueParameter<DoubleValue> QualitySumCutoffParameter { 54 59 get { return (IFixedValueParameter<DoubleValue>)Parameters[QualitySumCutoffParameterName]; } 55 60 } 56 public IValueParameter<IRegressionProblemData> ProblemDataParameter { 57 get { return (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; } 58 } 59 public IValueParameter<CheckedItemList<StringValue>> ControllableParametersParameter { 60 get { return (IValueParameter<CheckedItemList<StringValue>>)Parameters[ControllableParametersParameterName]; } 61 } 62 public IValueParameter<DoubleMatrix> ControllableParameterBoundsParameter { 63 get { return (IValueParameter<DoubleMatrix>)Parameters[ControllableParameterBoundsParameterName]; } 64 } 65 public IFixedValueParameter<ItemCollection<IRegressionModel>> ModelCollectionParameter { 66 get { return (IFixedValueParameter<ItemCollection<IRegressionModel>>)Parameters[ModelCollectionParameterName]; } 67 } 68 public IValueParameter<CheckedItemList<StringValue>> TargetsParameter { 69 get { return (IValueParameter<CheckedItemList<StringValue>>)Parameters[TargetsParameterName]; } 70 } 71 public IValueParameter<DoubleMatrix> TargetGoalsParameter { 72 get { return (IValueParameter<DoubleMatrix>)Parameters[TargetGoalsParameterName]; } 73 } 74 public IFixedValueParameter<IntValue> RowParameter { 75 get { return (IFixedValueParameter<IntValue>)Parameters[RowParameterName]; } 76 } 77 #endregion 78 79 #region parameter properties 80 private IItemCollection<IRegressionModel> ModelCollection { 81 get { return ModelCollectionParameter.Value; } 82 } 83 public DoubleMatrix TargetGoals { 84 get { return TargetGoalsParameter.Value; } 85 set { TargetGoalsParameter.Value = value; } 86 } 87 public double QualitySumCutoff { 88 get { return QualitySumCutoffParameter.Value.Value; } 89 set { QualitySumCutoffParameter.Value.Value = value; } 90 } 91 #endregion 92 93 #region IProcessParameterOptimizationProblem properties 94 [Storable] 95 private IRegressionProblemData problemData; 96 public IRegressionProblemData ProblemData { 97 get { return problemData; } 98 set { 99 if (value == null || value == problemData) return; 100 var variables = value.Dataset.DoubleVariables.ToList(); 101 if (Models.Any()) { 102 var targets = Models.Select(x => x.TargetVariable); 103 var hashset = new HashSet<string>(variables); 104 foreach (var target in targets) { 105 if (!hashset.Contains(target)) { 106 throw new ArgumentException(string.Format("Incompatible problem data. Target \"{0}\" is missing.", target)); 107 } 108 } 109 } 110 problemData = value; 111 dataset = new ModifiableDataset(variables, variables.Select(x => new List<double> { ProblemData.Dataset.GetDoubleValue(x, Row) })); 112 ProblemDataParameter.Value = ProblemData; 113 UpdateControllableParameters(); 114 UpdateTargetList(); 115 } 116 } 117 118 public int Row { 119 get { return RowParameter.Value.Value; } 120 set { RowParameter.Value.Value = value; } 121 } 122 61 #endregion 62 63 #region IGoalSeekingProblem implementation 123 64 public IEnumerable<IRegressionModel> Models { 124 get { return ModelCollectionParameter.Value; } 125 } 126 127 #region targets 128 public ICheckedItemList<StringValue> TargetList { 129 get { return TargetsParameter.Value; } 130 set { TargetsParameter.Value = (CheckedItemList<StringValue>)value; } 131 } 132 // convenience method 133 private IEnumerable<string> ActiveTargets { 134 get { return TargetList.CheckedItems.Select(x => x.Value.Value); } 135 } 136 #endregion 137 138 #region parameters 139 public ICheckedItemList<StringValue> ControllableParameters { 140 get { return ControllableParametersParameter.Value; } 141 set { ControllableParametersParameter.Value = (CheckedItemList<StringValue>)value; } 142 } 143 // convenience method 144 private IEnumerable<string> ActiveParameters { 145 get { return ControllableParameters.CheckedItems.Select(x => x.Value.Value); } 146 } 147 public DoubleMatrix ControllableParameterBounds { 148 get { return ControllableParameterBoundsParameter.Value; } 149 set { ControllableParameterBoundsParameter.Value = value; } 150 } 151 #endregion 152 #endregion 153 154 #region IProcessParameterOptimizationProblem methods 155 #region models 65 get { return ModelsParameter.Value; } 66 } 67 68 public IEnumerable<GoalParameter> Goals { 69 get { return GoalsParameter.Value; } 70 } 71 72 public IEnumerable<InputParameter> Inputs { 73 get { return InputsParameter.Value; } 74 } 75 76 public void AddModel(IRegressionModel model) { 77 var models = ModelsParameter.Value; 78 models.Add(model); 79 GoalSeekingUtil.RaiseEvent(this, ModelsChanged); 80 } 81 82 public void RemoveModel(IRegressionModel model) { 83 var models = ModelsParameter.Value; 84 models.Remove(model); 85 GoalSeekingUtil.RaiseEvent(this, ModelsChanged); 86 } 87 88 public void Configure(IRegressionProblemData problemData, int row) { 89 GoalSeekingUtil.Configure(Goals, Inputs, problemData, row); 90 } 91 156 92 public IEnumerable<double> GetEstimatedGoalValues(IEnumerable<double> parameterValues, bool round = false) { 157 93 var ds = (ModifiableDataset)dataset.Clone(); 158 foreach (var parameter in Active Parameters.Zip(parameterValues, (p, v) => new { Name = p, Value = v })) {94 foreach (var parameter in ActiveInputs.Zip(parameterValues, (p, v) => new { Name = p.Name, Value = v })) { 159 95 ds.SetVariableValue(parameter.Value, parameter.Name, 0); 160 96 } 161 97 var rows = new[] { 0 }; // actually just one row 162 163 98 var estimatedValues = 164 round ? Active Targets.Select(t => RoundToNearestStepMultiple(GetModels(t).Average(m => m.GetEstimatedValues(ds, rows).Single()), GetTargetStepSize(t)))165 : Active Targets.Select(t => GetModels(t).Average(m => m.GetEstimatedValues(ds, rows).Single()));99 round ? ActiveGoals.Select(t => RoundToNearestStepMultiple(GetModels(t.Name).Average(m => m.GetEstimatedValues(ds, rows).Single()), t.Step)) 100 : ActiveGoals.Select(t => GetModels(t.Name).Average(m => m.GetEstimatedValues(ds, rows).Single())); 166 101 return estimatedValues; 167 102 } 168 103 169 public void AddModel(IRegressionModel model) {170 var target = model.TargetVariable;171 CheckIfDatasetContainsTarget(target);172 ModelCollection.Add(model);173 OnModelsChanged(this, EventArgs.Empty);174 }175 176 // method which throws an exception that can be caught in the event handler if the check fails177 private void CheckIfDatasetContainsTarget(string target) {178 if (dataset.DoubleVariables.All(x => x != target))179 throw new ArgumentException(string.Format("Model target \"{0}\" does not exist in the dataset.", target));180 }181 182 public void RemoveModel(IRegressionModel model) {183 ModelCollection.Remove(model);184 OnModelsChanged(this, EventArgs.Empty);185 }186 187 104 public event EventHandler ModelsChanged; 188 private void OnModelsChanged(object sender, EventArgs args) {189 var changed = ModelsChanged;190 if (changed == null) return;191 changed(sender, args);192 }193 #endregion194 195 #region targets196 public bool GetTargetActive(string target) {197 var item = TargetList.SingleOrDefault(x => x.Value == target);198 if (item == null)199 throw new ArgumentException(string.Format("SetTargetActive: Invalid target name {0}", target));200 return TargetList.ItemChecked(item);201 }202 203 public void SetTargetActive(string target, bool active) {204 var item = TargetList.SingleOrDefault(x => x.Value == target);205 if (item == null)206 throw new ArgumentException(string.Format("SetTargetActive: Invalid target name {0}", target));207 TargetList.SetItemCheckedState(item, active);208 OnTargetsChanged(this, EventArgs.Empty);209 }210 211 public double GetTargetGoal(string target) {212 if (!IsValidTarget(target))213 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target));214 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count();215 return TargetGoals[i, 0];216 }217 218 public void SetTargetGoal(string target, double goal) {219 if (!IsValidTarget(target))220 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target));221 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count();222 TargetGoals[i, 0] = goal;223 OnTargetsChanged(this, EventArgs.Empty);224 }225 226 public double GetTargetWeight(string target) {227 if (!IsValidTarget(target))228 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target));229 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count();230 return TargetGoals[i, 1];231 }232 233 public void SetTargetWeight(string target, double weight) {234 if (!IsValidTarget(target))235 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target));236 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count();237 TargetGoals[i, 1] = weight;238 OnTargetsChanged(this, EventArgs.Empty);239 }240 241 public double GetTargetVariance(string target) {242 if (!IsValidTarget(target))243 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target));244 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count();245 return TargetGoals[i, 2];246 }247 248 public void SetTargetVariance(string target, double variance) {249 if (!IsValidTarget(target))250 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target));251 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count();252 TargetGoals[i, 2] = variance;253 OnTargetsChanged(this, EventArgs.Empty);254 }255 256 public double GetTargetStepSize(string target) {257 if (!IsValidTarget(target))258 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target));259 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count();260 return TargetGoals[i, 3];261 }262 263 public void SetTargetStepSize(string target, double stepSize) {264 if (!IsValidTarget(target))265 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target));266 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count();267 TargetGoals[i, 3] = stepSize;268 OnTargetsChanged(this, EventArgs.Empty);269 }270 271 105 public event EventHandler TargetsChanged; 272 private void OnTargetsChanged(object sender, EventArgs args) {273 var changed = TargetsChanged;274 if (changed == null) return;275 changed(sender, args);276 }277 #endregion // targets278 279 #region process parameters280 /// <summary>281 /// Returns the parameter bounds (min and max) and the step size for the specified parameter282 /// </summary>283 /// <param name="parameterName"></param>284 /// <returns>A double array containing the values (min, max, step) in this order</returns>285 public double[] GetParameterBounds(string parameterName) {286 var index = ControllableParameters.TakeWhile(x => x.Value != parameterName).Count();287 if (index < ControllableParameters.Count) {288 var min = ControllableParameterBounds[index, 0];289 var max = ControllableParameterBounds[index, 1];290 var step = ControllableParameterBounds[index, 2];291 return new[] { min, max, step };292 }293 throw new ArgumentException(string.Format("GetParameterBounds: Unknown parameter {0}.", parameterName));294 }295 296 public void SetParameterBounds(string parameterName, double min, double max, double step) {297 int i = ControllableParameterBounds.RowNames.TakeWhile(x => x != parameterName).Count();298 if (i < ControllableParameterBounds.Rows) {299 ControllableParameterBounds[i, 0] = min;300 ControllableParameterBounds[i, 1] = max;301 ControllableParameterBounds[i, 2] = step;302 UpdateEncoding();303 OnParametersChanged(this, EventArgs.Empty);304 } else {305 throw new ArgumentException(string.Format("SetParameterBounds: Invalid parameter name {0}", parameterName));306 }307 308 }309 310 public double GetParameterStepSize(string parameter) {311 int i = ControllableParameterBounds.RowNames.TakeWhile(x => x != parameter).Count();312 if (i < ControllableParameterBounds.Rows)313 return ControllableParameterBounds[i, 2];314 throw new ArgumentException(string.Format("GetParameterStepSize: Invalid parameter name {0}", parameter));315 }316 317 public void SetParameterStepSize(string parameter, double stepSize) {318 int i = ControllableParameterBounds.RowNames.TakeWhile(x => x != parameter).Count();319 if (i < ControllableParameterBounds.Rows) {320 ControllableParameterBounds[i, 2] = stepSize;321 OnParametersChanged(this, EventArgs.Empty);322 return;323 }324 throw new ArgumentException(string.Format("SetParameterStepSize: Invalid parameter name {0}", parameter));325 }326 327 public bool GetParameterActive(string parameter) {328 var item = ControllableParameters.SingleOrDefault(x => x.Value == parameter);329 if (item == null)330 throw new ArgumentException(string.Format("GetParameterActive: Invalid target name {0}", parameter));331 return ControllableParameters.ItemChecked(item);332 }333 334 public void SetParameterActive(string parameter, bool active) {335 var item = ControllableParameters.SingleOrDefault(x => x.Value == parameter);336 if (item == null)337 throw new ArgumentException(string.Format("SetParameterActive: Invalid target name {0}", parameter));338 ControllableParameters.SetItemCheckedState(item, active);339 OnParametersChanged(this, EventArgs.Empty);340 }341 342 public void SetControllableParameters(IEnumerable<string> parameterNames) {343 ControllableParameters = new CheckedItemList<StringValue>();344 foreach (var v in parameterNames) {345 ControllableParameters.Add(new StringValue(v), false);346 }347 ControllableParameters.CheckedItemsChanged += ControllableParameters_OnItemsChanged;348 ControllableParameterBounds = new DoubleMatrix(ControllableParameters.Count, 3);349 ControllableParameterBounds.RowNames = GetControllableParameters();350 ControllableParameterBounds.ColumnNames = new[] { "Min", "Max", "Step" };351 352 for (int i = 0; i < ControllableParameters.Count; ++i) {353 var itemName = ControllableParameters[i].Value;354 var values = ProblemData.Dataset.GetReadOnlyDoubleValues(itemName).Where(x => !double.IsNaN(x) && !double.IsInfinity(x)).ToList();355 if (!values.Any()) continue;356 357 // add a 20% margin to allow the optimization algorithm more freedom of exploration358 ControllableParameterBounds[i, 0] = 0.8 * values.Min(); // min359 ControllableParameterBounds[i, 1] = 1.2 * values.Max(); // max360 ControllableParameterBounds[i, 2] = 1e-6; // step361 }362 OnParametersChanged(this, EventArgs.Empty);363 }364 365 public IEnumerable<string> GetControllableParameters() {366 return ControllableParameters.Select(x => x.Value);367 }368 369 106 public event EventHandler ParametersChanged; 370 private void OnParametersChanged(object sender, EventArgs args) { 371 var changed = ParametersChanged; 372 if (changed == null) return; 373 changed(sender, args); 374 } 375 #endregion // process parameters 376 #endregion // IGoalSeekingProblem methods 377 378 #region data members 107 #endregion 108 109 private IEnumerable<GoalParameter> ActiveGoals { 110 get { return Goals.Where(x => x.Active); } 111 } 112 private IEnumerable<InputParameter> ActiveInputs { 113 get { return Inputs.Where(x => x.Active); } 114 } 115 private double QualitySumCutoff { 116 get { return QualitySumCutoffParameter.Value.Value; } 117 } 118 379 119 [Storable] 380 120 private ModifiableDataset dataset; // modifiable dataset … … 389 129 get { return (ValueParameter<BoolArray>)Parameters["Maximization"]; } 390 130 } 391 #endregion392 131 393 132 #region constructors … … 397 136 private MultiObjectiveGoalSeekingProblem(MultiObjectiveGoalSeekingProblem original, Cloner cloner) : base(original, cloner) { 398 137 this.dataset = cloner.Clone(original.dataset); 399 this.problemData = cloner.Clone(original.problemData);400 138 401 139 RegisterEvents(); … … 408 146 [StorableHook(HookType.AfterDeserialization)] 409 147 private void AfterDeserialization() { 410 if (Parameters.ContainsKey("Accuracy"))411 Parameters.Remove("Accuracy");412 413 if (!Parameters.ContainsKey(QualitySumCutoffParameterName)) {414 Parameters.Add(new FixedValueParameter<DoubleValue>(QualitySumCutoffParameterName, new DoubleValue(0.2)));415 QualitySumCutoffParameter.Hidden = true;416 }417 418 if (ProblemData == null && Parameters.ContainsKey(ProblemDataParameterName)) {419 ProblemData = ProblemDataParameter.Value;420 }421 422 if (!Parameters.ContainsKey(ModifiableDatasetParameterName)) {423 Parameters.Add(new ValueParameter<IDataset>(ModifiableDatasetParameterName, dataset) { Hidden = true });424 }425 426 // backwards-compatibility427 if (Parameters.ContainsKey("Models")) {428 var solutions = ((IFixedValueParameter<ItemCollection<IRegressionSolution>>)Parameters["Models"]).Value;429 var models = new ItemCollection<IRegressionModel>();430 foreach (var solution in solutions) {431 var model = solution.Model;432 model.TargetVariable = solution.ProblemData.TargetVariable;433 models.Add(model);434 }435 if (Parameters.ContainsKey(ModelCollectionParameterName))436 Parameters.Remove(ModelCollectionParameterName);437 Parameters.Add(new FixedValueParameter<ItemCollection<IRegressionModel>>(ModelCollectionParameterName, models));438 }439 440 148 RegisterEvents(); 441 149 } 442 150 443 151 public MultiObjectiveGoalSeekingProblem() { 444 Parameters.Add(new ValueParameter<IRegressionProblemData>(ProblemDataParameterName, new RegressionProblemData()));152 dataset = new ModifiableDataset(); 445 153 Parameters.Add(new ValueParameter<IDataset>(ModifiableDatasetParameterName, dataset) { Hidden = true }); 446 Parameters.Add(new ValueParameter<CheckedItemList<StringValue>>(ControllableParametersParameterName)); 447 Parameters.Add(new ValueParameter<CheckedItemList<StringValue>>(TargetsParameterName)); 448 Parameters.Add(new ValueParameter<DoubleMatrix>(ControllableParameterBoundsParameterName)); 449 Parameters.Add(new FixedValueParameter<ItemCollection<IRegressionModel>>(ModelCollectionParameterName, new ItemCollection<IRegressionModel>())); 450 Parameters.Add(new ValueParameter<DoubleMatrix>(TargetGoalsParameterName)); // model target weights 451 Parameters.Add(new FixedValueParameter<IntValue>(RowParameterName)); 154 Parameters.Add(new ValueParameter<CheckedItemList<InputParameter>>(InputsParameterName)); 155 Parameters.Add(new ValueParameter<CheckedItemList<GoalParameter>>(GoalsParameterName)); 156 Parameters.Add(new FixedValueParameter<ItemCollection<IRegressionModel>>(ModelsParameterName, new ItemCollection<IRegressionModel>())); 452 157 Parameters.Add(new FixedValueParameter<DoubleValue>(QualitySumCutoffParameterName, new DoubleValue(0.2))); 453 454 158 QualitySumCutoffParameter.Hidden = true; 455 456 // when the problem is created, the problem data parameter will be set to a default value457 // set the internal property to the same value458 ProblemData = ProblemDataParameter.Value;459 460 UpdateControllableParameters();461 UpdateTargetList();159 EncodingParameter.Hidden = true; 160 EvaluatorParameter.Hidden = true; 161 SolutionCreatorParameter.Hidden = true; 162 MaximizationParameter.Hidden = true; 163 GoalSeekingUtil.UpdateInputs(InputsParameter.Value, Models, InputParameterChanged); 164 Encoding = GoalSeekingUtil.CreateEncoding(ActiveInputs); 165 GoalSeekingUtil.UpdateTargets(GoalsParameter.Value, Models, GoalParameterChanged); 462 166 RegisterEvents(); 463 167 } … … 466 170 public override double[] Evaluate(Individual individual, IRandom random) { 467 171 var vector = individual.RealVector(); 468 vector.ElementNames = ActiveParameters; 469 172 vector.ElementNames = ActiveInputs.Select(x => x.Name); 470 173 int i = 0; 471 174 // round vector according to parameter step sizes 472 foreach (var parameter in ControllableParameters.CheckedItems) { 473 var step = ControllableParameterBounds[parameter.Index, 2]; 474 vector[i] = RoundToNearestStepMultiple(vector[i], step); 175 foreach (var parameter in ActiveInputs) { 176 vector[i] = RoundToNearestStepMultiple(vector[i], parameter.Step); 475 177 ++i; 476 178 } 477 179 var estimatedValues = GetEstimatedGoalValues(vector, round: true); 478 var qualities = TargetList.CheckedItems.Zip(estimatedValues, (t, v) => new { Name = t.Value.Value, Index = t.Index, EstimatedValue = v }) 479 .Select(target => { 480 var goal = TargetGoals[target.Index, 0]; 481 var weight = TargetGoals[target.Index, 1]; 482 var variance = TargetGoals[target.Index, 2]; 483 return weight * Math.Pow(target.EstimatedValue - goal, 2) / variance; 484 }); 180 var qualities = ActiveGoals.Zip(estimatedValues, (t, v) => new { Target = t, EstimatedValue = v }) 181 .Select(x => x.Target.Weight * Math.Pow(x.EstimatedValue - x.Target.Goal, 2) / x.Target.Variance); 485 182 return qualities.ToArray(); 486 183 } 487 184 185 #region pareto analyzer 488 186 public override void Analyze(Individual[] individuals, double[][] qualities, ResultCollection results, IRandom random) { 489 187 var matrix = FilterFrontsByQualitySum(individuals, qualities, Math.Max(QualitySumCutoff, qualities.Min(x => x.Sum()))); … … 498 196 499 197 private DoubleMatrix FilterFrontsByQualitySum(Individual[] individuals, double[][] qualities, double qualitySumCutoff) { 500 var activeParameters = Active Parameters.ToList();501 var active Targets = ActiveTargets.ToList();198 var activeParameters = ActiveInputs.ToList(); 199 var activeGoals = ActiveGoals.ToList(); 502 200 var filteredModels = new List<double[]>(); 503 201 var rowNames = new List<string>(); 504 202 // build list of column names by combining target and parameter names (with their respective original and estimated values) 505 203 var columnNames = new List<string> { "Quality Sum" }; 506 foreach (var target in active Targets) {507 columnNames.Add(target );508 columnNames.Add(target + " (estimated)");509 } 510 foreach (var controllableParameter in activeParameters) {511 columnNames.Add( controllableParameter);512 columnNames.Add( controllableParameter+ " (estimated)");513 columnNames.Add( controllableParameter+ " (deviation)");204 foreach (var target in activeGoals) { 205 columnNames.Add(target.Name); 206 columnNames.Add(target.Name + " (estimated)"); 207 } 208 foreach (var parameter in activeParameters) { 209 columnNames.Add(parameter.Name); 210 columnNames.Add(parameter.Name + " (estimated)"); 211 columnNames.Add(parameter.Name + " (deviation)"); 514 212 } 515 213 // filter models based on their quality sum; remove duplicate models … … 524 222 rowValues[0] = qualitySum; 525 223 int offset = 1; 526 for (int j = 0; j < active Targets.Count * 2; j += 2) {224 for (int j = 0; j < activeGoals.Count * 2; j += 2) { 527 225 int k = j + offset; 528 var goal = GetTargetGoal(activeTargets[j / 2]);226 var goal = activeGoals[j / 2].Goal; 529 227 rowValues[k] = goal; // original value 530 228 rowValues[k + 1] = estimatedValues[j / 2]; // estimated value 531 229 } 532 offset += active Targets.Count * 2;230 offset += activeGoals.Count * 2; 533 231 for (int j = 0; j < activeParameters.Count * 3; j += 3) { 534 232 int k = j + offset; 535 rowValues[k] = problemData.Dataset.GetDoubleValue(columnNames[k], Row);233 rowValues[k] = 0; // TODO: figure this out and fix 536 234 rowValues[k + 1] = vector[j / 3]; 537 235 rowValues[k + 2] = rowValues[k + 1] - rowValues[k]; … … 550 248 return matrix; 551 249 } 250 #endregion 552 251 553 252 #region event handlers 554 253 private void RegisterEvents() { 555 ProblemDataParameter.ValueChanged += OnProblemDataChanged; 556 ModelCollectionParameter.Value.ItemsAdded += ModelCollection_OnItemsAdded; 557 ModelCollectionParameter.Value.ItemsRemoved += ModelCollection_OnItemsRemoved; 558 RowParameter.Value.ValueChanged += OnRowChanged; 559 ControllableParameters.CheckedItemsChanged += ControllableParameters_OnItemsChanged; 560 ControllableParameterBounds.ItemChanged += ControllableParameterBounds_ItemChanged; 561 } 562 563 private void OnRowChanged(object o, EventArgs e) { 564 // set variables in the modifiable dataset according to the new row 565 foreach (var v in dataset.DoubleVariables) 566 dataset.SetVariableValue(ProblemData.Dataset.GetDoubleValue(v, Row), v, 0); 567 // set the correct targets 568 UpdateTargetList(); 569 } 570 571 private void OnProblemDataChanged(object o, EventArgs e) { 572 try { 573 ProblemData = ProblemDataParameter.Value; 574 } 575 catch (ArgumentException exception) { 576 MessageBox.Show(exception.Message, "Update Problem Data", MessageBoxButtons.OK, MessageBoxIcon.Error); 577 ProblemDataParameter.Value = problemData; 578 } 579 } 580 581 private void ModelCollection_OnItemsAdded(object sender, CollectionItemsChangedEventArgs<IRegressionModel> e) { 582 if (e.Items == null) return; 583 584 var collection = (IObservableCollection<IRegressionModel>)sender; 585 var newItems = e.Items.ToList(); 586 587 foreach (var model in e.Items) { 588 try { 589 CheckIfDatasetContainsTarget(model.TargetVariable); 590 } 591 catch (ArgumentException exception) { 592 MessageBox.Show(exception.Message, "Add Model", MessageBoxButtons.OK, MessageBoxIcon.Error); 593 newItems.Remove(model); 594 collection.Remove(model); 595 } 596 } 597 UpdateTargetList(); 598 OnModelsChanged(this, EventArgs.Empty); 599 } 600 601 private void ModelCollection_OnItemsRemoved(object sender, CollectionItemsChangedEventArgs<IRegressionModel> e) { 602 if (e.Items == null) return; 603 UpdateTargetList(); 604 OnModelsChanged(this, EventArgs.Empty); 605 } 606 607 private void ControllableParameters_OnItemsChanged(object o, CollectionItemsChangedEventArgs<IndexedItem<StringValue>> e) { 608 UpdateEncoding(); 609 } 610 611 private void ControllableParameterBounds_ItemChanged(object o, EventArgs e) { 612 UpdateEncoding(); 254 ModelsParameter.Value.ItemsAdded += ModelCollection_ItemsChanged; 255 ModelsParameter.Value.ItemsRemoved += ModelCollection_ItemsChanged; 256 GoalsParameter.Value.CheckedItemsChanged += GoalSeekingUtil.Goals_CheckedItemsChanged; 257 InputsParameter.Value.CheckedItemsChanged += GoalSeekingUtil.Inputs_CheckedItemsChanged; 258 } 259 260 private void ModelCollection_ItemsChanged(object sender, CollectionItemsChangedEventArgs<IRegressionModel> e) { 261 if (e.Items == null || !e.Items.Any()) return; 262 GoalSeekingUtil.UpdateInputs(InputsParameter.Value, Models, InputParameterChanged); 263 Encoding = GoalSeekingUtil.CreateEncoding(ActiveInputs); 264 GoalSeekingUtil.UpdateTargets(GoalsParameter.Value, Models, GoalParameterChanged); 265 GoalSeekingUtil.RaiseEvent(this, ModelsChanged); 266 } 267 268 private void InputParameterChanged(object sender, EventArgs args) { 269 var inputParameter = (InputParameter)sender; 270 var inputs = InputsParameter.Value; 271 if (inputs.ItemChecked(inputParameter) != inputParameter.Active) 272 inputs.SetItemCheckedState(inputParameter, inputParameter.Active); 273 Encoding = GoalSeekingUtil.CreateEncoding(ActiveInputs); 274 } 275 276 private void GoalParameterChanged(object sender, EventArgs args) { 277 var goalParameter = (GoalParameter)sender; 278 var goals = GoalsParameter.Value; 279 if (goals.ItemChecked(goalParameter) != goalParameter.Active) 280 goals.SetItemCheckedState(goalParameter, goalParameter.Active); 613 281 } 614 282 #endregion 615 283 616 284 #region helper methods 617 private void UpdateControllableParameters() { 618 if (ProblemData == null) return; 619 var variablesUsedForPrediction = ModelCollection.Any() 620 ? ModelCollection.SelectMany(x => x.VariablesUsedForPrediction).Distinct() 621 : ProblemData.Dataset.DoubleVariables; 622 SetControllableParameters(variablesUsedForPrediction); 623 } 624 625 private void UpdateTargetList() { 626 if (ProblemData == null) return; 627 if (!Models.Any()) { 628 TargetGoals = new DoubleMatrix(); 629 maximization = new[] { false }; 630 MaximizationParameter.Value = (BoolArray)new BoolArray(maximization).AsReadOnly(); 631 TargetList = new CheckedItemList<StringValue>(); 632 return; 633 } 634 635 var targetNames = Models.Select(x => x.TargetVariable).Distinct().ToList(); 636 var oldTargetGoals = (DoubleMatrix)TargetGoals.Clone(); 637 var oldRowIndices = oldTargetGoals.RowNames.Select((x, i) => new { x, i }).ToDictionary(x => x.x, x => x.i); 638 TargetGoals = new DoubleMatrix(targetNames.Count, 4); 639 TargetGoals.RowNames = targetNames; 640 TargetGoals.ColumnNames = new[] { "Goal", "Weight", "Variance", "Step size" }; 641 642 TargetList = new CheckedItemList<StringValue>(); 643 for (int i = 0; i < targetNames.Count; ++i) { 644 TargetList.Add(new StringValue(targetNames[i]), true); 645 int rowIndex; 646 if (oldRowIndices.TryGetValue(targetNames[i], out rowIndex)) { 647 for (int j = 0; j < TargetGoals.Columns; ++j) 648 TargetGoals[i, j] = oldTargetGoals[rowIndex, j]; 649 } else { 650 TargetGoals[i, 0] = ProblemData.Dataset.GetDoubleValue(targetNames[i], Row); 651 TargetGoals[i, 1] = 1.0; 652 TargetGoals[i, 2] = ProblemData.Dataset.GetReadOnlyDoubleValues(targetNames[i]).Variance(); 653 TargetGoals[i, 3] = 1e-6; 654 } 655 } 656 maximization = new bool[targetNames.Count]; 657 MaximizationParameter.Value = (BoolArray)new BoolArray(maximization).AsReadOnly(); 658 } 659 660 private void UpdateEncoding() { 661 var activeParameters = ActiveParameters.ToList(); 662 if (Encoding == null) 663 Encoding = new RealVectorEncoding(activeParameters.Count); 664 else 665 Encoding.Length = activeParameters.Count; 666 667 Encoding.Bounds = new DoubleMatrix(activeParameters.Count, 2); // only two columns: min and max 668 Encoding.Bounds.RowNames = activeParameters; 669 Encoding.Bounds.ColumnNames = new[] { "Min.", "Max." }; 670 671 int i = 0; 672 foreach (var item in ControllableParameters.CheckedItems) { 673 var index = item.Index; 674 Encoding.Bounds[i, 0] = ControllableParameterBounds[index, 0]; 675 Encoding.Bounds[i, 1] = ControllableParameterBounds[index, 1]; 676 ++i; 677 } 678 } 679 680 private bool IsValidTarget(string target) { 681 return TargetList.Any(x => x.Value == target); 285 // method which throws an exception that can be caught in the event handler if the check fails 286 private void CheckIfDatasetContainsTarget(string target) { 287 if (dataset.DoubleVariables.All(x => x != target)) 288 throw new ArgumentException(string.Format("Model target \"{0}\" does not exist in the dataset.", target)); 682 289 } 683 290 private static double RoundToNearestStepMultiple(double value, double step) { … … 685 292 } 686 293 private IEnumerable<IRegressionModel> GetModels(string target) { 687 return Model Collection.Where(x => x.TargetVariable == target);294 return Models.Where(x => x.TargetVariable == target); 688 295 } 689 296 private class DoubleEqualityComparer : IEqualityComparer<double> { -
branches/HeuristicLab.GoalSeekingProblem/HeuristicLab.GoalSeekingProblem/3.4/Properties/AssemblyInfo.cs
r14324 r14333 54 54 // [assembly: AssemblyVersion("1.0.*")] 55 55 [assembly: AssemblyVersion("3.3.0.0")] 56 [assembly: AssemblyFileVersion("3.3.11.1432 1")]56 [assembly: AssemblyFileVersion("3.3.11.14325")] -
branches/HeuristicLab.GoalSeekingProblem/HeuristicLab.GoalSeekingProblem/3.4/SingleObjectiveGoalSeekingProblem.cs
r14324 r14333 23 23 using System.Collections.Generic; 24 24 using System.Linq; 25 using System.Windows.Forms;26 25 using HeuristicLab.Collections; 27 26 using HeuristicLab.Common; 28 27 using HeuristicLab.Core; 29 using HeuristicLab.Data;30 28 using HeuristicLab.Encodings.RealVectorEncoding; 31 29 using HeuristicLab.Optimization; … … 41 39 #region parameter names 42 40 private const string ModifiableDatasetParameterName = "Dataset"; 43 private const string ProblemDataParameterName = "ProblemData"; 44 private const string ControllableParametersParameterName = "ControllableParameters"; 45 private const string ControllableParameterBoundsParameterName = "ControllableParameterBounds"; 46 private const string TargetGoalsParameterName = "TargetGoals"; 47 private const string TargetsParameterName = "Targets"; 48 private const string ModelCollectionParameterName = "ModelCollection"; 49 private const string RowParameterName = "Row"; 50 // these parameters are used by the pareto folding analyzer 51 private const string AllowedRangesParameterName = "AllowedRanges"; 52 private const string QualitySumCutoffParameterName = "QualitySumCutoff"; 41 private const string InputsParameterName = "Inputs"; 42 private const string GoalsParameterName = "Goals"; 43 private const string ModelsParameterName = "Models"; 53 44 #endregion 54 45 55 46 #region parameters 56 public IValueParameter<RealVector> AllowedRangesParameter { 57 get { return (IValueParameter<RealVector>)Parameters[AllowedRangesParameterName]; } 58 } 59 public IFixedValueParameter<DoubleValue> QualitySumCutoffParameter { 60 get { return (IFixedValueParameter<DoubleValue>)Parameters[QualitySumCutoffParameterName]; } 61 } 62 public IValueParameter<IRegressionProblemData> ProblemDataParameter { 63 get { return (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; } 64 } 65 public IValueParameter<CheckedItemList<StringValue>> ControllableParametersParameter { 66 get { return (IValueParameter<CheckedItemList<StringValue>>)Parameters[ControllableParametersParameterName]; } 67 } 68 public IValueParameter<DoubleMatrix> ControllableParameterBoundsParameter { 69 get { return (IValueParameter<DoubleMatrix>)Parameters[ControllableParameterBoundsParameterName]; } 70 } 71 public IFixedValueParameter<ItemCollection<IRegressionModel>> ModelCollectionParameter { 72 get { return (IFixedValueParameter<ItemCollection<IRegressionModel>>)Parameters[ModelCollectionParameterName]; } 73 } 74 public IValueParameter<CheckedItemList<StringValue>> TargetsParameter { 75 get { return (IValueParameter<CheckedItemList<StringValue>>)Parameters[TargetsParameterName]; } 76 } 77 public IValueParameter<DoubleMatrix> TargetGoalsParameter { 78 get { return (IValueParameter<DoubleMatrix>)Parameters[TargetGoalsParameterName]; } 79 } 80 public IFixedValueParameter<IntValue> RowParameter { 81 get { return (IFixedValueParameter<IntValue>)Parameters[RowParameterName]; } 82 } 83 #endregion 84 85 #region parameter properties 86 private IItemCollection<IRegressionModel> ModelCollection { 87 get { return ModelCollectionParameter.Value; } 88 } 89 public DoubleMatrix TargetGoals { 90 get { return TargetGoalsParameter.Value; } 91 set { TargetGoalsParameter.Value = value; } 92 } 93 #endregion 94 95 #region IProcessParameterOptimizationProblem properties 96 [Storable] 97 private IRegressionProblemData problemData; 98 public IRegressionProblemData ProblemData { 99 get { return problemData; } 100 set { 101 if (value == null || value == problemData) return; 102 var variables = value.Dataset.DoubleVariables.ToList(); 103 if (Models.Any()) { 104 var targets = Models.Select(x => x.TargetVariable); 105 var hashset = new HashSet<string>(variables); 106 foreach (var target in targets) { 107 if (!hashset.Contains(target)) { 108 throw new ArgumentException(string.Format("Incompatible problem data. Target \"{0}\" is missing.", target)); 109 } 110 } 111 } 112 problemData = value; 113 dataset = new ModifiableDataset(variables, variables.Select(x => new List<double> { ProblemData.Dataset.GetDoubleValue(x, Row) })); 114 ProblemDataParameter.Value = ProblemData; 115 UpdateControllableParameters(); 116 UpdateTargetList(); 117 } 118 } 119 120 public int Row { 121 get { return RowParameter.Value.Value; } 122 set { RowParameter.Value.Value = value; } 123 } 124 47 public IValueParameter<CheckedItemList<InputParameter>> InputsParameter { 48 get { return (IValueParameter<CheckedItemList<InputParameter>>)Parameters[InputsParameterName]; } 49 } 50 public IValueParameter<CheckedItemList<GoalParameter>> GoalsParameter { 51 get { return (IValueParameter<CheckedItemList<GoalParameter>>)Parameters[GoalsParameterName]; } 52 } 53 public IFixedValueParameter<ItemCollection<IRegressionModel>> ModelsParameter { 54 get { return (IFixedValueParameter<ItemCollection<IRegressionModel>>)Parameters[ModelsParameterName]; } 55 } 56 #endregion 57 58 #region IGoalSeekingProblem implementation 125 59 public IEnumerable<IRegressionModel> Models { 126 get { return ModelCollectionParameter.Value; } 127 } 128 129 #region targets 130 public ICheckedItemList<StringValue> TargetList { 131 get { return TargetsParameter.Value; } 132 set { TargetsParameter.Value = (CheckedItemList<StringValue>)value; } 133 } 134 private IEnumerable<string> ActiveTargets { 135 get { return TargetList.CheckedItems.Select(x => x.Value.Value); } 136 } 137 #endregion 138 139 #region parameters 140 private IEnumerable<string> ActiveParameters { 141 get { return ControllableParameters.CheckedItems.Select(x => x.Value.Value); } 142 } 143 public ICheckedItemList<StringValue> ControllableParameters { 144 get { return ControllableParametersParameter.Value; } 145 set { ControllableParametersParameter.Value = (CheckedItemList<StringValue>)value; } 146 } 147 public DoubleMatrix ControllableParameterBounds { 148 get { return ControllableParameterBoundsParameter.Value; } 149 set { ControllableParameterBoundsParameter.Value = value; } 150 } 151 #endregion 152 #endregion 153 154 #region IProcessParameterOptimizationProblem methods 155 #region solutions 60 get { return ModelsParameter.Value; } 61 } 62 63 public IEnumerable<GoalParameter> Goals { 64 get { return GoalsParameter.Value; } 65 } 66 67 public IEnumerable<InputParameter> Inputs { 68 get { return InputsParameter.Value; } 69 } 70 71 public void AddModel(IRegressionModel model) { 72 var models = ModelsParameter.Value; 73 models.Add(model); 74 GoalSeekingUtil.RaiseEvent(this, ModelsChanged); 75 } 76 77 public void RemoveModel(IRegressionModel model) { 78 var models = ModelsParameter.Value; 79 models.Remove(model); 80 GoalSeekingUtil.RaiseEvent(this, ModelsChanged); 81 } 82 83 public void Configure(IRegressionProblemData problemData, int row) { 84 GoalSeekingUtil.Configure(Goals, Inputs, problemData, row); 85 } 86 156 87 public IEnumerable<double> GetEstimatedGoalValues(IEnumerable<double> parameterValues, bool round = false) { 157 88 var ds = (ModifiableDataset)dataset.Clone(); 158 foreach (var parameter in Active Parameters.Zip(parameterValues, (p, v) => new { Name = p, Value = v })) {89 foreach (var parameter in ActiveInputs.Zip(parameterValues, (p, v) => new { Name = p.Name, Value = v })) { 159 90 ds.SetVariableValue(parameter.Value, parameter.Name, 0); 160 91 } 161 92 var rows = new[] { 0 }; // actually just one row 162 163 93 var estimatedValues = 164 round ? ActiveTargets.Select(t => RoundToNearestStepMultiple(GetModels(t).Average(m => m.GetEstimatedValues(ds, rows).Single()), GetTargetStepSize(t))) 165 : ActiveTargets.Select(t => GetModels(t).Average(m => m.GetEstimatedValues(ds, rows).Single())); 166 94 round ? ActiveGoals.Select(t => RoundToNearestStepMultiple(GetModels(t.Name).Average(m => m.GetEstimatedValues(ds, rows).Single()), t.Step)) 95 : ActiveGoals.Select(t => GetModels(t.Name).Average(m => m.GetEstimatedValues(ds, rows).Single())); 167 96 return estimatedValues; 168 97 } 169 98 170 public void AddModel(IRegressionModel model) { 171 var target = model.TargetVariable; 172 CheckIfDatasetContainsTarget(target); 173 ModelCollection.Add(model); 174 OnModelsChanged(this, EventArgs.Empty); 175 } 176 99 public event EventHandler ModelsChanged; 100 public event EventHandler TargetsChanged; 101 public event EventHandler ParametersChanged; 102 #endregion 103 104 private IEnumerable<GoalParameter> ActiveGoals { 105 get { return Goals.Where(x => x.Active); } 106 } 107 private IEnumerable<InputParameter> ActiveInputs { 108 get { return Inputs.Where(x => x.Active); } 109 } 110 111 [Storable] 112 private ModifiableDataset dataset; // modifiable dataset 113 114 public override bool Maximization { 115 get { return false; } 116 } 117 118 #region constructors 119 [StorableConstructor] 120 private SingleObjectiveGoalSeekingProblem(bool deserializing) : base(deserializing) { } 121 122 private SingleObjectiveGoalSeekingProblem(SingleObjectiveGoalSeekingProblem original, Cloner cloner) : base(original, cloner) { 123 this.dataset = cloner.Clone(original.dataset); 124 RegisterEvents(); 125 } 126 127 public override IDeepCloneable Clone(Cloner cloner) { 128 return new SingleObjectiveGoalSeekingProblem(this, cloner); 129 } 130 131 [StorableHook(HookType.AfterDeserialization)] 132 private void AfterDeserialization() { 133 RegisterEvents(); 134 } 135 136 public SingleObjectiveGoalSeekingProblem() { 137 dataset = new ModifiableDataset(); 138 Parameters.Add(new ValueParameter<IDataset>(ModifiableDatasetParameterName, dataset) { Hidden = true }); 139 Parameters.Add(new ValueParameter<CheckedItemList<InputParameter>>(InputsParameterName)); 140 Parameters.Add(new ValueParameter<CheckedItemList<GoalParameter>>(GoalsParameterName)); 141 Parameters.Add(new FixedValueParameter<ItemCollection<IRegressionModel>>(ModelsParameterName, new ItemCollection<IRegressionModel>())); 142 EncodingParameter.Hidden = true; 143 EvaluatorParameter.Hidden = true; 144 SolutionCreatorParameter.Hidden = true; 145 GoalSeekingUtil.UpdateInputs(InputsParameter.Value, Models, InputParameterChanged); 146 Encoding = GoalSeekingUtil.CreateEncoding(ActiveInputs); 147 GoalSeekingUtil.UpdateTargets(GoalsParameter.Value, Models, GoalParameterChanged); 148 RegisterEvents(); 149 } 150 #endregion 151 152 public override double Evaluate(Individual individual, IRandom random) { 153 var vector = individual.RealVector(); 154 vector.ElementNames = ActiveInputs.Select(x => x.Name); 155 int i = 0; 156 // round vector according to parameter step sizes 157 foreach (var parameter in ActiveInputs) { 158 vector[i] = RoundToNearestStepMultiple(vector[i], parameter.Step); 159 ++i; 160 } 161 var estimatedValues = GetEstimatedGoalValues(vector, round: true); 162 var quality = ActiveGoals.Zip(estimatedValues, (t, v) => new { Target = t, EstimatedValue = v }) 163 .Average(x => x.Target.Weight * Math.Pow(x.EstimatedValue - x.Target.Goal, 2) / x.Target.Variance); 164 return quality; 165 } 166 #region event handlers 167 168 private void RegisterEvents() { 169 ModelsParameter.Value.ItemsAdded += ModelCollection_ItemsChanged; 170 ModelsParameter.Value.ItemsRemoved += ModelCollection_ItemsChanged; 171 GoalsParameter.Value.CheckedItemsChanged += GoalSeekingUtil.Goals_CheckedItemsChanged; 172 InputsParameter.Value.CheckedItemsChanged += GoalSeekingUtil.Inputs_CheckedItemsChanged; 173 } 174 175 private void ModelCollection_ItemsChanged(object sender, CollectionItemsChangedEventArgs<IRegressionModel> e) { 176 if (e.Items == null || !e.Items.Any()) return; 177 GoalSeekingUtil.UpdateInputs(InputsParameter.Value, Models, InputParameterChanged); 178 Encoding = GoalSeekingUtil.CreateEncoding(ActiveInputs); 179 GoalSeekingUtil.UpdateTargets(GoalsParameter.Value, Models, GoalParameterChanged); 180 GoalSeekingUtil.RaiseEvent(this, ModelsChanged); 181 } 182 183 private void InputParameterChanged(object sender, EventArgs args) { 184 var inputParameter = (InputParameter)sender; 185 var inputs = InputsParameter.Value; 186 if (inputs.ItemChecked(inputParameter) != inputParameter.Active) 187 inputs.SetItemCheckedState(inputParameter, inputParameter.Active); 188 Encoding = GoalSeekingUtil.CreateEncoding(ActiveInputs); 189 } 190 191 private void GoalParameterChanged(object sender, EventArgs args) { 192 var goalParameter = (GoalParameter)sender; 193 var goals = GoalsParameter.Value; 194 if (goals.ItemChecked(goalParameter) != goalParameter.Active) 195 goals.SetItemCheckedState(goalParameter, goalParameter.Active); 196 } 197 #endregion 198 199 #region helper methods 177 200 // method which throws an exception that can be caught in the event handler if the check fails 178 201 private void CheckIfDatasetContainsTarget(string target) { … … 181 204 } 182 205 183 public void RemoveModel(IRegressionModel model) { 184 ModelCollection.Remove(model); 185 OnModelsChanged(this, EventArgs.Empty); 186 } 187 188 public event EventHandler ModelsChanged; 189 private void OnModelsChanged(object sender, EventArgs args) { 190 var changed = ModelsChanged; 191 if (changed == null) return; 192 changed(sender, args); 193 } 194 #endregion 195 196 #region targets 197 public bool GetTargetActive(string target) { 198 var item = TargetList.SingleOrDefault(x => x.Value == target); 199 if (item == null) 200 throw new ArgumentException(string.Format("SetTargetActive: Invalid target name {0}", target)); 201 return TargetList.ItemChecked(item); 202 } 203 204 public void SetTargetActive(string target, bool active) { 205 var item = TargetList.SingleOrDefault(x => x.Value == target); 206 if (item == null) 207 throw new ArgumentException(string.Format("SetTargetActive: Invalid target name {0}", target)); 208 TargetList.SetItemCheckedState(item, active); 209 OnTargetsChanged(this, EventArgs.Empty); 210 } 211 212 public double GetTargetGoal(string target) { 213 if (!IsValidTarget(target)) 214 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target)); 215 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count(); 216 return TargetGoals[i, 0]; 217 } 218 219 public void SetTargetGoal(string target, double goal) { 220 if (!IsValidTarget(target)) 221 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target)); 222 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count(); 223 TargetGoals[i, 0] = goal; 224 OnTargetsChanged(this, EventArgs.Empty); 225 } 226 227 public double GetTargetWeight(string target) { 228 if (!IsValidTarget(target)) 229 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target)); 230 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count(); 231 return TargetGoals[i, 1]; 232 } 233 234 public void SetTargetWeight(string target, double weight) { 235 if (!IsValidTarget(target)) 236 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target)); 237 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count(); 238 TargetGoals[i, 1] = weight; 239 OnTargetsChanged(this, EventArgs.Empty); 240 } 241 242 public double GetTargetVariance(string target) { 243 if (!IsValidTarget(target)) 244 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target)); 245 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count(); 246 return TargetGoals[i, 2]; 247 } 248 249 public void SetTargetVariance(string target, double variance) { 250 if (!IsValidTarget(target)) 251 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target)); 252 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count(); 253 TargetGoals[i, 2] = variance; 254 OnTargetsChanged(this, EventArgs.Empty); 255 } 256 257 public double GetTargetStepSize(string target) { 258 if (!IsValidTarget(target)) 259 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target)); 260 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count(); 261 return TargetGoals[i, 3]; 262 } 263 264 public void SetTargetStepSize(string target, double stepSize) { 265 if (!IsValidTarget(target)) 266 throw new ArgumentException(string.Format("The target variable name \"{0}\" does not exist in the dataset.", target)); 267 int i = TargetGoals.RowNames.TakeWhile(x => x != target).Count(); 268 TargetGoals[i, 3] = stepSize; 269 OnTargetsChanged(this, EventArgs.Empty); 270 } 271 272 public event EventHandler TargetsChanged; 273 private void OnTargetsChanged(object sender, EventArgs args) { 274 var changed = TargetsChanged; 275 if (changed == null) return; 276 changed(sender, args); 277 } 278 #endregion // targets 279 280 #region process parameters 281 /// <summary> 282 /// Returns the parameter bounds (min and max) and the step size for the specified parameter 283 /// </summary> 284 /// <param name="parameterName"></param> 285 /// <returns>A double array containing the values (min, max, step) in this order</returns> 286 public double[] GetParameterBounds(string parameterName) { 287 var index = ControllableParameters.TakeWhile(x => x.Value != parameterName).Count(); 288 if (index < ControllableParameters.Count) { 289 var min = ControllableParameterBounds[index, 0]; 290 var max = ControllableParameterBounds[index, 1]; 291 var step = ControllableParameterBounds[index, 2]; 292 return new[] { min, max, step }; 293 } 294 throw new ArgumentException(string.Format("GetParameterBounds: Unknown parameter {0}.", parameterName)); 295 } 296 297 public void SetParameterBounds(string parameterName, double min, double max, double step) { 298 int i = ControllableParameterBounds.RowNames.TakeWhile(x => x != parameterName).Count(); 299 if (i < ControllableParameterBounds.Rows) { 300 ControllableParameterBounds[i, 0] = min; 301 ControllableParameterBounds[i, 1] = max; 302 ControllableParameterBounds[i, 2] = step; 303 UpdateEncoding(); 304 OnParametersChanged(this, EventArgs.Empty); 305 } else { 306 throw new ArgumentException(string.Format("SetParameterBounds: Invalid parameter name {0}", parameterName)); 307 } 308 } 309 310 public double GetParameterStepSize(string parameter) { 311 int i = ControllableParameterBounds.RowNames.TakeWhile(x => x != parameter).Count(); 312 if (i < ControllableParameterBounds.Rows) 313 return ControllableParameterBounds[i, 2]; 314 throw new ArgumentException(string.Format("GetParameterStepSize: Invalid parameter name {0}", parameter)); 315 } 316 317 public void SetParameterStepSize(string parameter, double stepSize) { 318 int i = ControllableParameterBounds.RowNames.TakeWhile(x => x != parameter).Count(); 319 if (i < ControllableParameterBounds.Rows) { 320 ControllableParameterBounds[i, 2] = stepSize; 321 OnParametersChanged(this, EventArgs.Empty); 322 return; 323 } 324 throw new ArgumentException(string.Format("SetParameterStepSize: Invalid parameter name {0}", parameter)); 325 } 326 327 public bool GetParameterActive(string parameter) { 328 var item = ControllableParameters.SingleOrDefault(x => x.Value == parameter); 329 if (item == null) 330 throw new ArgumentException(string.Format("GetParameterActive: Invalid target name {0}", parameter)); 331 return ControllableParameters.ItemChecked(item); 332 } 333 334 public void SetParameterActive(string parameter, bool active) { 335 var item = ControllableParameters.SingleOrDefault(x => x.Value == parameter); 336 if (item == null) 337 throw new ArgumentException(string.Format("SetParameterActive: Invalid target name {0}", parameter)); 338 ControllableParameters.SetItemCheckedState(item, active); 339 OnParametersChanged(this, EventArgs.Empty); 340 } 341 342 public void SetControllableParameters(IEnumerable<string> parameterNames) { 343 ControllableParameters = new CheckedItemList<StringValue>(); 344 foreach (var v in parameterNames) { 345 ControllableParameters.Add(new StringValue(v), false); 346 } 347 ControllableParameters.CheckedItemsChanged += ControllableParameters_OnItemsChanged; 348 ControllableParameterBounds = new DoubleMatrix(ControllableParameters.Count, 3); 349 ControllableParameterBounds.RowNames = GetControllableParameters(); 350 ControllableParameterBounds.ColumnNames = new[] { "Min", "Max", "Step" }; 351 352 for (int i = 0; i < ControllableParameters.Count; ++i) { 353 var itemName = ControllableParameters[i].Value; 354 var values = ProblemData.Dataset.GetReadOnlyDoubleValues(itemName).Where(x => !double.IsNaN(x) && !double.IsInfinity(x)).ToList(); 355 if (!values.Any()) continue; 356 357 // add a 20% margin to allow the optimization algorithm more freedom of exploration 358 ControllableParameterBounds[i, 0] = 0.8 * values.Min(); // min 359 ControllableParameterBounds[i, 1] = 1.2 * values.Max(); // max 360 ControllableParameterBounds[i, 2] = 1e-6; // step 361 } 362 OnParametersChanged(this, EventArgs.Empty); 363 } 364 365 public IEnumerable<string> GetControllableParameters() { 366 return ControllableParameters.Select(x => x.Value); 367 } 368 369 public event EventHandler ParametersChanged; 370 private void OnParametersChanged(object sender, EventArgs args) { 371 var changed = ParametersChanged; 372 if (changed == null) return; 373 changed(sender, args); 374 } 375 #endregion // process parameters 376 #endregion // IGoalSeekingProblem methods 377 378 #region data members 379 [Storable] 380 private ModifiableDataset dataset; // modifiable dataset 381 382 public override bool Maximization { 383 get { return false; } 384 } 385 #endregion 386 387 #region constructors 388 [StorableConstructor] 389 private SingleObjectiveGoalSeekingProblem(bool deserializing) : base(deserializing) { } 390 391 private SingleObjectiveGoalSeekingProblem(SingleObjectiveGoalSeekingProblem original, Cloner cloner) : base(original, cloner) { 392 this.dataset = cloner.Clone(original.dataset); 393 this.problemData = cloner.Clone(original.problemData); 394 395 RegisterEvents(); 396 } 397 398 public override IDeepCloneable Clone(Cloner cloner) { 399 return new SingleObjectiveGoalSeekingProblem(this, cloner); 400 } 401 402 [StorableHook(HookType.AfterDeserialization)] 403 private void AfterDeserialization() { 404 // backwards-compatibility 405 if (Parameters.ContainsKey("Models")) { 406 var solutions = ((IFixedValueParameter<ItemCollection<IRegressionSolution>>)Parameters["Models"]).Value; 407 var models = new ItemCollection<IRegressionModel>(); 408 foreach (var solution in solutions) { 409 var model = solution.Model; 410 model.TargetVariable = solution.ProblemData.TargetVariable; 411 models.Add(model); 412 } 413 if (Parameters.ContainsKey(ModelCollectionParameterName)) 414 Parameters.Remove(ModelCollectionParameterName); 415 Parameters.Add(new FixedValueParameter<ItemCollection<IRegressionModel>>(ModelCollectionParameterName, models)); 416 } 417 418 RegisterEvents(); 419 } 420 421 public SingleObjectiveGoalSeekingProblem() { 422 Parameters.Add(new ValueParameter<IRegressionProblemData>(ProblemDataParameterName, new RegressionProblemData())); 423 Parameters.Add(new ValueParameter<IDataset>(ModifiableDatasetParameterName, dataset) { Hidden = true }); 424 Parameters.Add(new ValueParameter<CheckedItemList<StringValue>>(ControllableParametersParameterName)); 425 Parameters.Add(new ValueParameter<CheckedItemList<StringValue>>(TargetsParameterName)); 426 Parameters.Add(new ValueParameter<DoubleMatrix>(ControllableParameterBoundsParameterName)); 427 Parameters.Add(new FixedValueParameter<ItemCollection<IRegressionModel>>(ModelCollectionParameterName, new ItemCollection<IRegressionModel>())); 428 Parameters.Add(new ValueParameter<DoubleMatrix>(TargetGoalsParameterName)); // model target weights 429 Parameters.Add(new FixedValueParameter<IntValue>(RowParameterName)); 430 431 // when the problem is created, the problem data parameter will be set to a default value 432 // set the internal property to the same value 433 ProblemData = ProblemDataParameter.Value; 434 435 UpdateControllableParameters(); 436 UpdateTargetList(); 437 RegisterEvents(); 438 } 439 #endregion 440 441 public override double Evaluate(Individual individual, IRandom random) { 442 var vector = individual.RealVector(); 443 vector.ElementNames = ActiveParameters; 444 int i = 0; 445 // round vector according to parameter step sizes 446 foreach (var parameter in ControllableParameters.CheckedItems) { 447 var step = ControllableParameterBounds[parameter.Index, 2]; 448 vector[i] = RoundToNearestStepMultiple(vector[i], step); 449 ++i; 450 } 451 var estimatedValues = GetEstimatedGoalValues(vector, round: true); 452 var quality = TargetList.CheckedItems.Zip(estimatedValues, (t, v) => new { Name = t.Value.Value, Index = t.Index, EstimatedValue = v }) 453 .Average(target => { 454 var goal = TargetGoals[target.Index, 0]; 455 var weight = TargetGoals[target.Index, 1]; 456 var variance = TargetGoals[target.Index, 2]; 457 return weight * Math.Pow(target.EstimatedValue - goal, 2) / variance; 458 }); 459 return quality; 460 } 461 462 #region event handlers 463 private void RegisterEvents() { 464 ProblemDataParameter.ValueChanged += OnProblemDataChanged; 465 ModelCollectionParameter.Value.ItemsAdded += ModelCollection_OnItemsAdded; 466 ModelCollectionParameter.Value.ItemsRemoved += ModelCollection_OnItemsRemoved; 467 RowParameter.Value.ValueChanged += OnRowChanged; 468 ControllableParameters.CheckedItemsChanged += ControllableParameters_OnItemsChanged; 469 ControllableParameterBounds.ItemChanged += ControllableParameterBounds_ItemChanged; 470 } 471 472 private void OnRowChanged(object o, EventArgs e) { 473 // set variables in the modifiable dataset according to the new row 474 foreach (var v in dataset.DoubleVariables) 475 dataset.SetVariableValue(ProblemData.Dataset.GetDoubleValue(v, Row), v, 0); 476 // set the correct targets 477 UpdateTargetList(); 478 } 479 480 private void OnProblemDataChanged(object o, EventArgs e) { 481 try { 482 ProblemData = ProblemDataParameter.Value; 483 } 484 catch (ArgumentException exception) { 485 MessageBox.Show(exception.Message, "Update Problem Data", MessageBoxButtons.OK, MessageBoxIcon.Error); 486 ProblemDataParameter.Value = problemData; 487 } 488 } 489 490 private void ModelCollection_OnItemsAdded(object sender, CollectionItemsChangedEventArgs<IRegressionModel> e) { 491 if (e.Items == null) return; 492 493 var collection = (IObservableCollection<IRegressionModel>)sender; 494 var newItems = e.Items.ToList(); 495 496 foreach (var model in e.Items) { 497 try { 498 CheckIfDatasetContainsTarget(model.TargetVariable); 499 } 500 catch (ArgumentException exception) { 501 MessageBox.Show(exception.Message, "Add Model", MessageBoxButtons.OK, MessageBoxIcon.Error); 502 newItems.Remove(model); 503 collection.Remove(model); 504 } 505 } 506 UpdateTargetList(); 507 OnModelsChanged(this, EventArgs.Empty); 508 } 509 510 private void ModelCollection_OnItemsRemoved(object sender, CollectionItemsChangedEventArgs<IRegressionModel> e) { 511 if (e.Items == null) return; 512 UpdateTargetList(); 513 OnModelsChanged(this, EventArgs.Empty); 514 } 515 516 private void ControllableParameters_OnItemsChanged(object o, CollectionItemsChangedEventArgs<IndexedItem<StringValue>> e) { 517 UpdateEncoding(); 518 } 519 520 private void ControllableParameterBounds_ItemChanged(object o, EventArgs e) { 521 UpdateEncoding(); 522 } 523 #endregion 524 525 #region helper methods 526 private void UpdateControllableParameters() { 527 if (ProblemData == null) return; 528 var variablesUsedForPrediction = ModelCollection.Any() 529 ? ModelCollection.SelectMany(x => x.VariablesUsedForPrediction).Distinct() 530 : ProblemData.Dataset.DoubleVariables; 531 SetControllableParameters(variablesUsedForPrediction); 532 } 533 534 private void UpdateTargetList() { 535 if (ProblemData == null) return; 536 if (!Models.Any()) { 537 TargetGoals = new DoubleMatrix(); 538 TargetList = new CheckedItemList<StringValue>(); 539 return; 540 } 541 var targetNames = Models.Select(x => x.TargetVariable).Distinct().ToList(); 542 var oldTargetGoals = (DoubleMatrix)TargetGoals.Clone(); 543 var oldRowIndices = oldTargetGoals.RowNames.Select((x, i) => new { x, i }).ToDictionary(x => x.x, x => x.i); 544 TargetGoals = new DoubleMatrix(targetNames.Count, 4); 545 TargetGoals.RowNames = targetNames; 546 TargetGoals.ColumnNames = new[] { "Goal", "Weight", "Variance", "Step size" }; 547 548 TargetList = new CheckedItemList<StringValue>(); 549 for (int i = 0; i < targetNames.Count; ++i) { 550 TargetList.Add(new StringValue(targetNames[i]), true); 551 int rowIndex; 552 if (oldRowIndices.TryGetValue(targetNames[i], out rowIndex)) { 553 for (int j = 0; j < TargetGoals.Columns; ++j) 554 TargetGoals[i, j] = oldTargetGoals[rowIndex, j]; 555 } else { 556 TargetGoals[i, 0] = ProblemData.Dataset.GetDoubleValue(targetNames[i], Row); 557 TargetGoals[i, 1] = 1.0; 558 TargetGoals[i, 2] = ProblemData.Dataset.GetReadOnlyDoubleValues(targetNames[i]).Variance(); 559 TargetGoals[i, 3] = 1e-6; 560 } 561 } 562 } 563 564 private void UpdateEncoding() { 565 var activeParameters = ActiveParameters.ToList(); 566 if (Encoding == null) 567 Encoding = new RealVectorEncoding(activeParameters.Count); 568 else 569 Encoding.Length = activeParameters.Count; 570 571 Encoding.Bounds = new DoubleMatrix(activeParameters.Count, 2); // only two columns: min and max 572 Encoding.Bounds.RowNames = activeParameters; 573 Encoding.Bounds.ColumnNames = new[] { "Min.", "Max." }; 574 575 int i = 0; 576 foreach (var item in ControllableParameters.CheckedItems) { 577 var index = item.Index; 578 Encoding.Bounds[i, 0] = ControllableParameterBounds[index, 0]; 579 Encoding.Bounds[i, 1] = ControllableParameterBounds[index, 1]; 580 ++i; 581 } 582 } 583 private bool IsValidTarget(string target) { 584 return TargetList.Any(x => x.Value == target); 585 } 206 private IEnumerable<IRegressionModel> GetModels(string target) { 207 return Models.Where(x => x.TargetVariable == target); 208 } 209 586 210 private static double RoundToNearestStepMultiple(double value, double step) { 587 211 return step * (long)Math.Round(value / step); 588 212 } 589 private IEnumerable<IRegressionModel> GetModels(string target) {590 return ModelCollection.Where(x => x.TargetVariable == target);591 }592 213 #endregion 593 214 }
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