source: branches/3140_NumberSymbol/HeuristicLab.Problems.DataAnalysis/3.4/DatasetExtensions.cs @ 18140

#region License Information
/* HeuristicLab
 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;

namespace HeuristicLab.Problems.DataAnalysis {
  public static class DatasetExtensions {
    public static double[,] ToArray(this IDataset dataset, IEnumerable<string> variables, IEnumerable<int> rows) {
      return ToArray(dataset,
        variables,
        transformations: variables.Select(_ => (ITransformation<double>)null), // no transform
        rows: rows);
    }
    public static double[,] ToArray(this IDataset dataset, IEnumerable<string> variables,
      IEnumerable<ITransformation<double>> transformations, IEnumerable<int> rows) {
      string[] variablesArr = variables.ToArray();
      int[] rowsArr = rows.ToArray();
      ITransformation<double>[] transformArr = transformations.ToArray();
      if (transformArr.Length != variablesArr.Length)
        throw new ArgumentException("Number of variables and number of transformations must match.");

      double[,] matrix = new double[rowsArr.Length, variablesArr.Length];

      for (int i = 0; i < variablesArr.Length; i++) {
        var origValues = dataset.GetDoubleValues(variablesArr[i], rowsArr);
        var values = transformArr[i] != null ? transformArr[i].Apply(origValues) : origValues;
        int row = 0;
        foreach (var value in values) {
          matrix[row, i] = value;
          row++;
        }
      }

      return matrix;
    }

    /// <summary>
    /// Prepares a binary data matrix from a number of factors and specified factor values
    /// </summary>
    /// <param name="dataset">A dataset that contains the variable values</param>
    /// <param name="factorVariables">An enumerable of categorical variables (factors). For each variable an enumerable of values must be specified.</param>
    /// <param name="rows">An enumerable of row indices for the dataset</param>
    /// <returns></returns>
    /// <remarks>Factor variables (categorical variables) are split up into multiple binary variables one for each specified value.</remarks>
    public static double[,] ToArray(
      this IDataset dataset,
      IEnumerable<KeyValuePair<string, IEnumerable<string>>> factorVariables,
      IEnumerable<int> rows) {
      // check input variables. Only string variables are allowed.
      var invalidInputs =
        factorVariables.Select(kvp => kvp.Key).Where(name => !dataset.VariableHasType<string>(name));
      if (invalidInputs.Any())
        throw new NotSupportedException("Unsupported inputs: " + string.Join(", ", invalidInputs));

      int numBinaryColumns = factorVariables.Sum(kvp => kvp.Value.Count());

      List<int> rowsList = rows.ToList();
      double[,] matrix = new double[rowsList.Count, numBinaryColumns];

      int col = 0;
      foreach (var kvp in factorVariables) {
        var varName = kvp.Key;
        var cats = kvp.Value;
        if (!cats.Any()) continue;
        foreach (var cat in cats) {
          var values = dataset.GetStringValues(varName, rows);
          int row = 0;
          foreach (var value in values) {
            matrix[row, col] = value == cat ? 1 : 0;
            row++;
          }
          col++;
        }
      }
      return matrix;
    }

    public static IntervalCollection GetVariableRanges(this IDataset dataset, bool ignoreNaNs = true) {
      IntervalCollection variableRanges = new IntervalCollection();
      foreach (var variable in dataset.DoubleVariables) { // ranges can only be calculated for double variables
        var values = dataset.GetDoubleValues(variable);

        if (ignoreNaNs) {
          values = values.Where(v => !double.IsNaN(v));

          if (!values.Any()) { //handle values with only NaNs explicitly
            var emptyInterval = new Interval(double.NaN, double.NaN);
            variableRanges.AddInterval(variable, emptyInterval);
            continue;
          }
        }

        var interval = Interval.GetInterval(values);
        variableRanges.AddInterval(variable, interval);
      }

      return variableRanges;
    }

    public static IEnumerable<KeyValuePair<string, IEnumerable<string>>> GetFactorVariableValues(
      this IDataset ds, IEnumerable<string> factorVariables, IEnumerable<int> rows) {
      return from factor in factorVariables
             let distinctValues = ds.GetStringValues(factor, rows).Distinct().ToArray()
             // 1 distinct value => skip (constant)
             // 2 distinct values => only take one of the two values
             // >=3 distinct values => create a binary value for each value
             let reducedValues = distinctValues.Length <= 2
               ? distinctValues.Take(distinctValues.Length - 1)
               : distinctValues
             select new KeyValuePair<string, IEnumerable<string>>(factor, reducedValues);
    }
  }
}