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Ignore:
Timestamp:
09/27/18 09:51:35 (6 years ago)
Author:
fholzing
Message:

#2904: Overwrote trunk-changes with branch-changes

File:
1 edited

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  • branches/2904_CalculateImpacts/3.4/Implementation/Classification/ClassificationSolutionVariableImpactsCalculator.cs

    r16188 r16189  
    2323
    2424using System;
     25using System.Collections;
    2526using System.Collections.Generic;
    2627using System.Linq;
    … …  
    3637  [Item("ClassificationSolution Impacts Calculator", "Calculation of the impacts of input variables for any classification solution")]
    3738  public sealed class ClassificationSolutionVariableImpactsCalculator : ParameterizedNamedItem {
     39    #region Parameters/Properties
    3840    public enum ReplacementMethodEnum {
    3941      Median,
    … …  
    5456
    5557    private const string ReplacementParameterName = "Replacement Method";
     58    private const string FactorReplacementParameterName = "Factor Replacement Method";
    5659    private const string DataPartitionParameterName = "DataPartition";
    5760
    5861    public IFixedValueParameter<EnumValue<ReplacementMethodEnum>> ReplacementParameter {
    5962      get { return (IFixedValueParameter<EnumValue<ReplacementMethodEnum>>)Parameters[ReplacementParameterName]; }
     63    }
     64    public IFixedValueParameter<EnumValue<FactorReplacementMethodEnum>> FactorReplacementParameter {
     65      get { return (IFixedValueParameter<EnumValue<FactorReplacementMethodEnum>>)Parameters[FactorReplacementParameterName]; }
    6066    }
    6167    public IFixedValueParameter<EnumValue<DataPartitionEnum>> DataPartitionParameter {
    … …  
    6773      set { ReplacementParameter.Value.Value = value; }
    6874    }
     75    public FactorReplacementMethodEnum FactorReplacementMethod {
     76      get { return FactorReplacementParameter.Value.Value; }
     77      set { FactorReplacementParameter.Value.Value = value; }
     78    }
    6979    public DataPartitionEnum DataPartition {
    7080      get { return DataPartitionParameter.Value.Value; }
    7181      set { DataPartitionParameter.Value.Value = value; }
    7282    }
    73 
    74 
     83    #endregion
     84
     85    #region Ctor/Cloner
    7586    [StorableConstructor]
    7687    private ClassificationSolutionVariableImpactsCalculator(bool deserializing) : base(deserializing) { }
    7788    private ClassificationSolutionVariableImpactsCalculator(ClassificationSolutionVariableImpactsCalculator original, Cloner cloner)
    7889      : base(original, cloner) { }
     90    public ClassificationSolutionVariableImpactsCalculator()
     91      : base() {
     92      Parameters.Add(new FixedValueParameter<EnumValue<ReplacementMethodEnum>>(ReplacementParameterName, "The replacement method for variables during impact calculation.", new EnumValue<ReplacementMethodEnum>(ReplacementMethodEnum.Shuffle)));
     93      Parameters.Add(new FixedValueParameter<EnumValue<FactorReplacementMethodEnum>>(FactorReplacementParameterName, "The replacement method for factor variables during impact calculation.", new EnumValue<FactorReplacementMethodEnum>(FactorReplacementMethodEnum.Best)));
     94      Parameters.Add(new FixedValueParameter<EnumValue<DataPartitionEnum>>(DataPartitionParameterName, "The data partition on which the impacts are calculated.", new EnumValue<DataPartitionEnum>(DataPartitionEnum.Training)));
     95    }
     96
    7997    public override IDeepCloneable Clone(Cloner cloner) {
    8098      return new ClassificationSolutionVariableImpactsCalculator(this, cloner);
    8199    }
    82 
    83     public ClassificationSolutionVariableImpactsCalculator()
    84       : base() {
    85       Parameters.Add(new FixedValueParameter<EnumValue<ReplacementMethodEnum>>(ReplacementParameterName, "The replacement method for variables during impact calculation.", new EnumValue<ReplacementMethodEnum>(ReplacementMethodEnum.Median)));
    86       Parameters.Add(new FixedValueParameter<EnumValue<DataPartitionEnum>>(DataPartitionParameterName, "The data partition on which the impacts are calculated.", new EnumValue<DataPartitionEnum>(DataPartitionEnum.Training)));
    87     }
     100    #endregion
    88101
    89102    //mkommend: annoying name clash with static method, open to better naming suggestions
    90103    public IEnumerable<Tuple<string, double>> Calculate(IClassificationSolution solution) {
    91       return CalculateImpacts(solution, DataPartition, ReplacementMethod);
     104      return CalculateImpacts(solution, ReplacementMethod, FactorReplacementMethod, DataPartition);
    92105    }
    93106
    94107    public static IEnumerable<Tuple<string, double>> CalculateImpacts(
    95108      IClassificationSolution solution,
    96       DataPartitionEnum data = DataPartitionEnum.Training,
    97       ReplacementMethodEnum replacementMethod = ReplacementMethodEnum.Median,
     109      ReplacementMethodEnum replacementMethod = ReplacementMethodEnum.Shuffle,
     110      FactorReplacementMethodEnum factorReplacementMethod = FactorReplacementMethodEnum.Best,
     111      DataPartitionEnum dataPartition = DataPartitionEnum.Training) {
     112
     113      IEnumerable<int> rows = GetPartitionRows(dataPartition, solution.ProblemData);
     114      IEnumerable<double> estimatedClassValues = solution.GetEstimatedClassValues(rows);
     115      return CalculateImpacts(solution.Model, solution.ProblemData, estimatedClassValues, rows, replacementMethod, factorReplacementMethod);
     116    }
     117
     118    public static IEnumerable<Tuple<string, double>> CalculateImpacts(
     119     IClassificationModel model,
     120     IClassificationProblemData problemData,
     121     IEnumerable<double> estimatedClassValues,
     122     IEnumerable<int> rows,
     123     ReplacementMethodEnum replacementMethod = ReplacementMethodEnum.Shuffle,
     124     FactorReplacementMethodEnum factorReplacementMethod = FactorReplacementMethodEnum.Best) {
     125
     126      //fholzing: try and catch in case a different dataset is loaded, otherwise statement is neglectable
     127      var missingVariables = model.VariablesUsedForPrediction.Except(problemData.Dataset.VariableNames);
     128      if (missingVariables.Any()) {
     129        throw new InvalidOperationException(string.Format("Can not calculate variable impacts, because the model uses inputs missing in the dataset ({0})", string.Join(", ", missingVariables)));
     130      }
     131      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
     132      var originalQuality = CalculateQuality(targetValues, estimatedClassValues);
     133
     134      var impacts = new Dictionary<string, double>();
     135      var inputvariables = new HashSet<string>(problemData.AllowedInputVariables.Union(model.VariablesUsedForPrediction));
     136      var modifiableDataset = ((Dataset)(problemData.Dataset).Clone()).ToModifiable();
     137
     138      foreach (var inputVariable in inputvariables) {
     139        impacts[inputVariable] = CalculateImpact(inputVariable, model, problemData, modifiableDataset, rows, replacementMethod, factorReplacementMethod, targetValues, originalQuality);
     140      }
     141
     142      return impacts.Select(i => Tuple.Create(i.Key, i.Value));
     143    }
     144
     145    public static double CalculateImpact(string variableName,
     146      IClassificationModel model,
     147      IClassificationProblemData problemData,
     148      ModifiableDataset modifiableDataset,
     149      IEnumerable<int> rows,
     150      ReplacementMethodEnum replacementMethod = ReplacementMethodEnum.Shuffle,
     151      FactorReplacementMethodEnum factorReplacementMethod = FactorReplacementMethodEnum.Best,
     152      IEnumerable<double> targetValues = null,
     153      double quality = double.NaN) {
     154
     155      if (!model.VariablesUsedForPrediction.Contains(variableName)) { return 0.0; }
     156      if (!problemData.Dataset.VariableNames.Contains(variableName)) {
     157        throw new InvalidOperationException(string.Format("Can not calculate variable impact, because the model uses inputs missing in the dataset ({0})", variableName));
     158      }
     159
     160      if (targetValues == null) {
     161        targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
     162      }
     163      if (quality == double.NaN) {
     164        quality = CalculateQuality(model.GetEstimatedClassValues(modifiableDataset, rows), targetValues);
     165      }
     166
     167      IList originalValues = null;
     168      IList replacementValues = GetReplacementValues(modifiableDataset, variableName, model, rows, targetValues, out originalValues, replacementMethod, factorReplacementMethod);
     169
     170      double newValue = CalculateQualityForReplacement(model, modifiableDataset, variableName, originalValues, rows, replacementValues, targetValues);
     171      double impact = quality - newValue;
     172
     173      return impact;
     174    }
     175
     176    private static IList GetReplacementValues(ModifiableDataset modifiableDataset,
     177      string variableName,
     178      IClassificationModel model,
     179      IEnumerable<int> rows,
     180      IEnumerable<double> targetValues,
     181      out IList originalValues,
     182      ReplacementMethodEnum replacementMethod = ReplacementMethodEnum.Shuffle,
    98183      FactorReplacementMethodEnum factorReplacementMethod = FactorReplacementMethodEnum.Best) {
    99184
    100       var problemData = solution.ProblemData;
    101       var dataset = problemData.Dataset;
    102 
    103       IEnumerable<int> rows;
    104       IEnumerable<double> targetValues;
    105       double originalAccuracy;
    106 
    107       OnlineCalculatorError error;
    108 
    109       switch (data) {
    110         case DataPartitionEnum.All:
    111           rows = problemData.AllIndices;
    112           targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.AllIndices).ToList();
    113           originalAccuracy = OnlineAccuracyCalculator.Calculate(targetValues, solution.EstimatedClassValues, out error);
    114           if (error != OnlineCalculatorError.None) throw new InvalidOperationException("Error during accuracy calculation.");
    115           break;
    116         case DataPartitionEnum.Training:
    117           rows = problemData.TrainingIndices;
    118           targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).ToList();
    119           originalAccuracy = OnlineAccuracyCalculator.Calculate(targetValues, solution.EstimatedTrainingClassValues, out error);
    120           if (error != OnlineCalculatorError.None) throw new InvalidOperationException("Error during accuracy calculation.");
    121           break;
    122         case DataPartitionEnum.Test:
    123           rows = problemData.TestIndices;
    124           targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices).ToList();
    125           originalAccuracy = OnlineAccuracyCalculator.Calculate(targetValues, solution.EstimatedTestClassValues, out error);
    126           if (error != OnlineCalculatorError.None) throw new InvalidOperationException("Error during accuracy calculation.");
    127           break;
    128         default: throw new ArgumentException(string.Format("DataPartition {0} cannot be handled.", data));
    129       }
    130 
    131       var impacts = new Dictionary<string, double>();
    132       var modifiableDataset = ((Dataset)dataset).ToModifiable();
    133 
    134       var inputvariables = new HashSet<string>(problemData.AllowedInputVariables.Union(solution.Model.VariablesUsedForPrediction));
    135       var allowedInputVariables = dataset.VariableNames.Where(v => inputvariables.Contains(v)).ToList();
    136 
    137       // calculate impacts for double variables
    138       foreach (var inputVariable in allowedInputVariables.Where(problemData.Dataset.VariableHasType<double>)) {
    139         var newEstimates = EvaluateModelWithReplacedVariable(solution.Model, inputVariable, modifiableDataset, rows, replacementMethod);
    140         var newAccuracy = OnlineAccuracyCalculator.Calculate(targetValues, newEstimates, out error);
    141         if (error != OnlineCalculatorError.None) throw new InvalidOperationException("Error during R² calculation with replaced inputs.");
    142 
    143         impacts[inputVariable] = originalAccuracy - newAccuracy;
    144       }
    145 
    146       // calculate impacts for string variables
    147       foreach (var inputVariable in allowedInputVariables.Where(problemData.Dataset.VariableHasType<string>)) {
    148         if (factorReplacementMethod == FactorReplacementMethodEnum.Best) {
    149           // try replacing with all possible values and find the best replacement value
    150           var smallestImpact = double.PositiveInfinity;
    151           foreach (var repl in problemData.Dataset.GetStringValues(inputVariable, rows).Distinct()) {
    152             var newEstimates = EvaluateModelWithReplacedVariable(solution.Model, inputVariable, modifiableDataset, rows,
    153               Enumerable.Repeat(repl, dataset.Rows));
    154             var newAccuracy = OnlineAccuracyCalculator.Calculate(targetValues, newEstimates, out error);
    155             if (error != OnlineCalculatorError.None)
    156               throw new InvalidOperationException("Error during accuracy calculation with replaced inputs.");
    157 
    158             var impact = originalAccuracy - newAccuracy;
    159             if (impact < smallestImpact) smallestImpact = impact;
    160           }
    161           impacts[inputVariable] = smallestImpact;
    162         } else {
    163           // for replacement methods shuffle and mode
    164           // calculate impacts for factor variables
    165 
    166           var newEstimates = EvaluateModelWithReplacedVariable(solution.Model, inputVariable, modifiableDataset, rows,
    167             factorReplacementMethod);
    168           var newAccuracy = OnlineAccuracyCalculator.Calculate(targetValues, newEstimates, out error);
    169           if (error != OnlineCalculatorError.None)
    170             throw new InvalidOperationException("Error during accuracy calculation with replaced inputs.");
    171 
    172           impacts[inputVariable] = originalAccuracy - newAccuracy;
    173         }
    174       } // foreach
    175       return impacts.OrderByDescending(i => i.Value).Select(i => Tuple.Create(i.Key, i.Value));
    176     }
    177 
    178     private static IEnumerable<double> EvaluateModelWithReplacedVariable(IClassificationModel model, string variable, ModifiableDataset dataset, IEnumerable<int> rows, ReplacementMethodEnum replacement = ReplacementMethodEnum.Median) {
    179       var originalValues = dataset.GetReadOnlyDoubleValues(variable).ToList();
     185      IList replacementValues = null;
     186      if (modifiableDataset.VariableHasType<double>(variableName)) {
     187        originalValues = modifiableDataset.GetReadOnlyDoubleValues(variableName).ToList();
     188        replacementValues = GetReplacementValuesForDouble(modifiableDataset, rows, (List<double>)originalValues, replacementMethod);
     189      } else if (modifiableDataset.VariableHasType<string>(variableName)) {
     190        originalValues = modifiableDataset.GetReadOnlyStringValues(variableName).ToList();
     191        replacementValues = GetReplacementValuesForString(model, modifiableDataset, variableName, rows, (List<string>)originalValues, targetValues, factorReplacementMethod);
     192      } else {
     193        throw new NotSupportedException("Variable not supported");
     194      }
     195
     196      return replacementValues;
     197    }
     198
     199    private static IList GetReplacementValuesForDouble(ModifiableDataset modifiableDataset,
     200      IEnumerable<int> rows,
     201      List<double> originalValues,
     202      ReplacementMethodEnum replacementMethod = ReplacementMethodEnum.Shuffle) {
     203
     204      IRandom random = new FastRandom(31415);
     205      List<double> replacementValues;
    180206      double replacementValue;
    181       List<double> replacementValues;
    182       IRandom rand;
    183 
    184       switch (replacement) {
     207
     208      switch (replacementMethod) {
    185209        case ReplacementMethodEnum.Median:
    186210          replacementValue = rows.Select(r => originalValues[r]).Median();
    187           replacementValues = Enumerable.Repeat(replacementValue, dataset.Rows).ToList();
     211          replacementValues = Enumerable.Repeat(replacementValue, modifiableDataset.Rows).ToList();
    188212          break;
    189213        case ReplacementMethodEnum.Average:
    190214          replacementValue = rows.Select(r => originalValues[r]).Average();
    191           replacementValues = Enumerable.Repeat(replacementValue, dataset.Rows).ToList();
     215          replacementValues = Enumerable.Repeat(replacementValue, modifiableDataset.Rows).ToList();
    192216          break;
    193217        case ReplacementMethodEnum.Shuffle:
    194218          // new var has same empirical distribution but the relation to y is broken
    195           rand = new FastRandom(31415);
    196219          // prepare a complete column for the dataset
    197           replacementValues = Enumerable.Repeat(double.NaN, dataset.Rows).ToList();
     220          replacementValues = Enumerable.Repeat(double.NaN, modifiableDataset.Rows).ToList();
    198221          // shuffle only the selected rows
    199           var shuffledValues = rows.Select(r => originalValues[r]).Shuffle(rand).ToList();
     222          var shuffledValues = rows.Select(r => originalValues[r]).Shuffle(random).ToList();
    200223          int i = 0;
    201224          // update column values
    … …  
    207230          var avg = rows.Select(r => originalValues[r]).Average();
    208231          var stdDev = rows.Select(r => originalValues[r]).StandardDeviation();
    209           rand = new FastRandom(31415);
    210232          // prepare a complete column for the dataset
    211           replacementValues = Enumerable.Repeat(double.NaN, dataset.Rows).ToList();
     233          replacementValues = Enumerable.Repeat(double.NaN, modifiableDataset.Rows).ToList();
    212234          // update column values
    213235          foreach (var r in rows) {
    214             replacementValues[r] = NormalDistributedRandom.NextDouble(rand, avg, stdDev);
     236            replacementValues[r] = NormalDistributedRandom.NextDouble(random, avg, stdDev);
    215237          }
    216238          break;
    217239
    218240        default:
    219           throw new ArgumentException(string.Format("ReplacementMethod {0} cannot be handled.", replacement));
    220       }
    221 
    222       return EvaluateModelWithReplacedVariable(model, variable, dataset, rows, replacementValues);
    223     }
    224 
    225     private static IEnumerable<double> EvaluateModelWithReplacedVariable(
    226       IClassificationModel model, string variable, ModifiableDataset dataset,
    227       IEnumerable<int> rows,
    228       FactorReplacementMethodEnum replacement = FactorReplacementMethodEnum.Shuffle) {
    229       var originalValues = dataset.GetReadOnlyStringValues(variable).ToList();
    230       List<string> replacementValues;
    231       IRandom rand;
    232 
    233       switch (replacement) {
     241          throw new ArgumentException(string.Format("ReplacementMethod {0} cannot be handled.", replacementMethod));
     242      }
     243
     244      return replacementValues;
     245    }
     246
     247    private static IList GetReplacementValuesForString(IClassificationModel model,
     248      ModifiableDataset modifiableDataset,
     249      string variableName,
     250      IEnumerable<int> rows,
     251      List<string> originalValues,
     252      IEnumerable<double> targetValues,
     253      FactorReplacementMethodEnum factorReplacementMethod = FactorReplacementMethodEnum.Shuffle) {
     254
     255      List<string> replacementValues = null;
     256      IRandom random = new FastRandom(31415);
     257
     258      switch (factorReplacementMethod) {
     259        case FactorReplacementMethodEnum.Best:
     260          // try replacing with all possible values and find the best replacement value
     261          var bestQuality = double.NegativeInfinity;
     262          foreach (var repl in modifiableDataset.GetStringValues(variableName, rows).Distinct()) {
     263            List<string> curReplacementValues = Enumerable.Repeat(repl, modifiableDataset.Rows).ToList();
     264            //fholzing: this result could be used later on (theoretically), but is neglected for better readability/method consistency
     265            var newValue = CalculateQualityForReplacement(model, modifiableDataset, variableName, originalValues, rows, curReplacementValues, targetValues);
     266            var curQuality = newValue;
     267
     268            if (curQuality > bestQuality) {
     269              bestQuality = curQuality;
     270              replacementValues = curReplacementValues;
     271            }
     272          }
     273          break;
    234274        case FactorReplacementMethodEnum.Mode:
    235275          var mostCommonValue = rows.Select(r => originalValues[r])
    … …  
    237277            .OrderByDescending(g => g.Count())
    238278            .First().Key;
    239           replacementValues = Enumerable.Repeat(mostCommonValue, dataset.Rows).ToList();
     279          replacementValues = Enumerable.Repeat(mostCommonValue, modifiableDataset.Rows).ToList();
    240280          break;
    241281        case FactorReplacementMethodEnum.Shuffle:
    242282          // new var has same empirical distribution but the relation to y is broken
    243           rand = new FastRandom(31415);
    244283          // prepare a complete column for the dataset
    245           replacementValues = Enumerable.Repeat(string.Empty, dataset.Rows).ToList();
     284          replacementValues = Enumerable.Repeat(string.Empty, modifiableDataset.Rows).ToList();
    246285          // shuffle only the selected rows
    247           var shuffledValues = rows.Select(r => originalValues[r]).Shuffle(rand).ToList();
     286          var shuffledValues = rows.Select(r => originalValues[r]).Shuffle(random).ToList();
    248287          int i = 0;
    249288          // update column values
    … …  
    253292          break;
    254293        default:
    255           throw new ArgumentException(string.Format("FactorReplacementMethod {0} cannot be handled.", replacement));
    256       }
    257 
    258       return EvaluateModelWithReplacedVariable(model, variable, dataset, rows, replacementValues);
    259     }
    260 
    261     private static IEnumerable<double> EvaluateModelWithReplacedVariable(IClassificationModel model, string variable,
    262       ModifiableDataset dataset, IEnumerable<int> rows, IEnumerable<double> replacementValues) {
    263       var originalValues = dataset.GetReadOnlyDoubleValues(variable).ToList();
    264       dataset.ReplaceVariable(variable, replacementValues.ToList());
     294          throw new ArgumentException(string.Format("FactorReplacementMethod {0} cannot be handled.", factorReplacementMethod));
     295      }
     296
     297      return replacementValues;
     298    }
     299
     300    private static double CalculateQualityForReplacement(
     301      IClassificationModel model,
     302      ModifiableDataset modifiableDataset,
     303      string variableName,
     304      IList originalValues,
     305      IEnumerable<int> rows,
     306      IList replacementValues,
     307      IEnumerable<double> targetValues) {
     308
     309      modifiableDataset.ReplaceVariable(variableName, replacementValues);
     310      var discModel = model as IDiscriminantFunctionClassificationModel;
     311      if (discModel != null) {
     312        var problemData = new ClassificationProblemData(modifiableDataset, modifiableDataset.VariableNames, model.TargetVariable);
     313        discModel.RecalculateModelParameters(problemData, rows);
     314      }
     315
    265316      //mkommend: ToList is used on purpose to avoid lazy evaluation that could result in wrong estimates due to variable replacements
    266       var estimates = model.GetEstimatedClassValues(dataset, rows).ToList();
    267       dataset.ReplaceVariable(variable, originalValues);
    268 
    269       return estimates;
    270     }
    271     private static IEnumerable<double> EvaluateModelWithReplacedVariable(IClassificationModel model, string variable,
    272       ModifiableDataset dataset, IEnumerable<int> rows, IEnumerable<string> replacementValues) {
    273       var originalValues = dataset.GetReadOnlyStringValues(variable).ToList();
    274       dataset.ReplaceVariable(variable, replacementValues.ToList());
    275       //mkommend: ToList is used on purpose to avoid lazy evaluation that could result in wrong estimates due to variable replacements
    276       var estimates = model.GetEstimatedClassValues(dataset, rows).ToList();
    277       dataset.ReplaceVariable(variable, originalValues);
    278 
    279       return estimates;
     317      var estimates = model.GetEstimatedClassValues(modifiableDataset, rows).ToList();
     318      var ret = CalculateQuality(targetValues, estimates);
     319      modifiableDataset.ReplaceVariable(variableName, originalValues);
     320
     321      return ret;
     322    }
     323
     324    public static double CalculateQuality(IEnumerable<double> targetValues, IEnumerable<double> estimatedClassValues) {
     325      OnlineCalculatorError errorState;
     326      var ret = OnlineAccuracyCalculator.Calculate(targetValues, estimatedClassValues, out errorState);
     327      if (errorState != OnlineCalculatorError.None) { throw new InvalidOperationException("Error during calculation with replaced inputs."); }
     328      return ret;
     329    }
     330
     331    public static IEnumerable<int> GetPartitionRows(DataPartitionEnum dataPartition, IClassificationProblemData problemData) {
     332      IEnumerable<int> rows;
     333
     334      switch (dataPartition) {
     335        case DataPartitionEnum.All:
     336          rows = problemData.AllIndices;
     337          break;
     338        case DataPartitionEnum.Test:
     339          rows = problemData.TestIndices;
     340          break;
     341        case DataPartitionEnum.Training:
     342          rows = problemData.TrainingIndices;
     343          break;
     344        default:
     345          throw new NotSupportedException("DataPartition not supported");
     346      }
     347
     348      return rows;
    280349    }
    281350  }
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