Comment fournir correctement les données d'entrée pour Accord.NET DecisionTrees

Question

J'essaie d'apprendre l'apprentissage automatique et en particulier les arbres de décision, j'ai copié ce morceau de code sur le site Web de l'Accord .Net et il ne semble pas fonctionner pour moi, et je ne peux pas comprendre pourquoi. L'erreur qu'il me donne est sur la ligne 40 en disant: "System.IndexOutOfRangeException: 'L'index était en dehors des limites du tableau.'" Je ne suis pas sûr de ce que je me trompe, l'ensemble de données qu'il utilise est trouvé ici: https://en.wikipedia.org/wiki/Iris_flower_data_set Peut-être que j'ai du mal à lui donner l'ensemble de données correctement? Par la façon dont je suis l'aide communautaire Visual Studio 2017.

Voici le code:

using Accord.MachineLearning.DecisionTrees; 
using Accord.MachineLearning.DecisionTrees.Learning; 
using Accord.MachineLearning.DecisionTrees.Rules; 
using Accord.Math; 
using Accord.Math.Optimization.Losses; 
using Accord.Statistics.Filters; 
using ConsoleApp2.Properties; 
using System; 
using System.Collections.Generic; 
using System.Linq; 
using System.Text; 
using System.Threading.Tasks; 

namespace ConsoleApp2 
{ 
    class Program 
    { 
     static void Main(string[] args) 
     { 
      // In this example, we will process the famous Fisher's Iris dataset in 
      // which the task is to classify weather the features of an Iris flower 
      // belongs to an Iris setosa, an Iris versicolor, or an Iris virginica: 
      // 
      // - https://en.wikipedia.org/wiki/Iris_flower_data_set 
      // 

      // First, let's load the dataset into an array of text that we can process 
      // In this example, we will process the famous Fisher's Iris dataset in 
      // which the task is to classify weather the features of an Iris flower 
      // belongs to an Iris setosa, an Iris versicolor, or an Iris virginica: 
      // 
      // - https://en.wikipedia.org/wiki/Iris_flower_data_set 
      // 

      // First, let's load the dataset into an array of text that we can process 
      string[][] text = Resources.iris_data.Split(new[] { "\r\n" }, 
       StringSplitOptions.RemoveEmptyEntries).Apply(x => x.Split(',')); 

      // The first four columns contain the flower features 
      double [][] inputs = text.GetColumns(0, 1, 2, 3).To<double[][]>(); 

      // The last column contains the expected flower type 
      string[] labels = text.GetColumn(4); 

      // Since the labels are represented as text, the first step is to convert 
      // those text labels into integer class labels, so we can process them 
      // more easily. For this, we will create a codebook to encode class labels: 
      // 
      var codebook = new Codification("Output", labels); 

      // With the codebook, we can convert the labels: 
      int[] outputs = codebook.Translate("Output", labels); 

      // Let's declare the names of our input variables: 
      DecisionVariable[] features = 
      { 
       new DecisionVariable("sepal length", DecisionVariableKind.Continuous), 
       new DecisionVariable("sepal width", DecisionVariableKind.Continuous), 
       new DecisionVariable("petal length", DecisionVariableKind.Continuous), 
       new DecisionVariable("petal width", DecisionVariableKind.Continuous), 
      }; 

      // Now, we can finally create our tree for the 3 classes: 
      var tree = new DecisionTree(inputs: features, classes: 3); 

      // And we can use the C4.5 for learning: 
      var teacher = new C45Learning(tree); 

      // And finally induce the tree: 
      teacher.Learn(inputs, outputs); 

      // To get the estimated class labels, we can use 
      int[] predicted = tree.Decide(inputs); 

      // And the classification error (of 0.0266) can be computed as 
      double error = new ZeroOneLoss(outputs).Loss(tree.Decide(inputs)); 

      // Moreover, we may decide to convert our tree to a set of rules: 
      DecisionSet rules = tree.ToRules(); 

      // And using the codebook, we can inspect the tree reasoning: 
      string ruleText = rules.ToString(codebook, "Output", 
       System.Globalization.CultureInfo.InvariantCulture); 

      // The output is: 
      string expected = @"Iris-setosa =: (petal length <= 2.45) 
Iris-versicolor =: (petal length > 2.45) && (petal width <= 1.75) && (sepal length <= 7.05) && (sepal width <= 2.85) 
Iris-versicolor =: (petal length > 2.45) && (petal width <= 1.75) && (sepal length <= 7.05) && (sepal width > 2.85) 
Iris-versicolor =: (petal length > 2.45) && (petal width > 1.75) && (sepal length <= 5.95) && (sepal width > 3.05) 
Iris-virginica =: (petal length > 2.45) && (petal width <= 1.75) && (sepal length > 7.05) 
Iris-virginica =: (petal length > 2.45) && (petal width > 1.75) && (sepal length > 5.95) 
Iris-virginica =: (petal length > 2.45) && (petal width > 1.75) && (sepal length <= 5.95) && (sepal width <= 3.05) 
"; 

      Console.WriteLine("expected"); 
      Console.ReadLine(); 

     } 
    } 
} 

Answer 1

A en juger par l'exemple de code lui-même, tout ce que vous avez besoin est une classe static contenant vos données dans le format CSV:

static public class Resources 
    { 
     public static string iris_data = 
@"7.9,3.8,6.4,2,I. virginica 
7.7,3.8,6.7,2.2,I. virginica 
7.7,2.6,6.9,2.3,I. virginica 
7.7,2.8,6.7,2,I. virginica 
7.7,3,6.1,2.3,I. virginica 
7.6,3,6.6,2.1,I. virginica 
7.4,2.8,6.1,1.9,I. virginica 
7.3,2.9,6.3,1.8,I. virginica 
7.2,3.6,6.1,2.5,I. virginica 
7.2,3.2,6,1.8,I. virginica 
7.2,3,5.8,1.6,I. virginica 
7.1,3,5.9,2.1,I. virginica 
7,3.2,4.7,1.4,I. versicolor 
6.9,3.1,4.9,1.5,I. versicolor 
6.9,3.2,5.7,2.3,I. virginica 
6.9,3.1,5.4,2.1,I. virginica 
6.9,3.1,5.1,2.3,I. virginica 
6.8,2.8,4.8,1.4,I. versicolor 
6.8,3,5.5,2.1,I. virginica 
6.8,3.2,5.9,2.3,I. virginica 
6.7,3.1,4.4,1.4,I. versicolor 
6.7,3,5,1.7,I. versicolor 
6.7,3.1,4.7,1.5,I. versicolor 
6.7,2.5,5.8,1.8,I. virginica 
6.7,3.3,5.7,2.1,I. virginica 
6.7,3.1,5.6,2.4,I. virginica 
6.7,3.3,5.7,2.5,I. virginica 
6.7,3,5.2,2.3,I. virginica 
6.6,2.9,4.6,1.3,I. versicolor 
6.6,3,4.4,1.4,I. versicolor 
6.5,2.8,4.6,1.5,I. versicolor 
6.5,3,5.8,2.2,I. virginica 
6.5,3.2,5.1,2,I. virginica 
6.5,3,5.5,1.8,I. virginica 
6.5,3,5.2,2,I. virginica 
6.4,3.2,4.5,1.5,I. versicolor 
6.4,2.9,4.3,1.3,I. versicolor 
6.4,2.7,5.3,1.9,I. virginica 
6.4,3.2,5.3,2.3,I. virginica 
6.4,2.8,5.6,2.1,I. virginica 
6.4,2.8,5.6,2.2,I. virginica 
6.4,3.1,5.5,1.8,I. virginica 
6.3,3.3,4.7,1.6,I. versicolor 
6.3,2.5,4.9,1.5,I. versicolor 
6.3,2.3,4.4,1.3,I. versicolor 
6.3,3.3,6,2.5,I. virginica 
6.3,2.9,5.6,1.8,I. virginica 
6.3,2.7,4.9,1.8,I. virginica 
6.3,2.8,5.1,1.5,I. virginica 
6.3,3.4,5.6,2.4,I. virginica 
6.3,2.5,5,1.9,I. virginica 
6.2,2.2,4.5,1.5,I. versicolor 
6.2,2.9,4.3,1.3,I. versicolor 
6.2,2.8,4.8,1.8,I. virginica 
6.2,3.4,5.4,2.3,I. virginica 
6.1,2.9,4.7,1.4,I. versicolor 
6.1,2.8,4,1.3,I. versicolor 
6.1,2.8,4.7,1.2,I. versicolor 
6.1,3,4.6,1.4,I. versicolor 
6.1,3,4.9,1.8,I. virginica 
6.1,2.6,5.6,1.4,I. virginica 
6,2.2,4,1,I. versicolor 
6,2.9,4.5,1.5,I. versicolor 
6,2.7,5.1,1.6,I. versicolor 
6,3.4,4.5,1.6,I. versicolor 
6,2.2,5,1.5,I. virginica 
6,3,4.8,1.8,I. virginica 
5.9,3,4.2,1.5,I. versicolor 
5.9,3.2,4.8,1.8,I. versicolor 
5.9,3,5.1,1.8,I. virginica 
5.8,4,1.2,0.2,I. setosa 
5.8,2.7,4.1,1,I. versicolor 
5.8,2.7,3.9,1.2,I. versicolor 
5.8,2.6,4,1.2,I. versicolor 
5.8,2.7,5.1,1.9,I. virginica 
5.8,2.8,5.1,2.4,I. virginica 
5.8,2.7,5.1,1.9,I. virginica 
5.7,4.4,1.5,0.4,I. setosa 
5.7,3.8,1.7,0.3,I. setosa 
5.7,2.8,4.5,1.3,I. versicolor 
5.7,2.6,3.5,1,I. versicolor 
5.7,3,4.2,1.2,I. versicolor 
5.7,2.9,4.2,1.3,I. versicolor 
5.7,2.8,4.1,1.3,I. versicolor 
5.7,2.5,5,2,I. virginica 
5.6,2.9,3.6,1.3,I. versicolor 
5.6,3,4.5,1.5,I. versicolor 
5.6,2.5,3.9,1.1,I. versicolor 
5.6,3,4.1,1.3,I. versicolor 
5.6,2.7,4.2,1.3,I. versicolor 
5.6,2.8,4.9,2,I. virginica 
5.5,4.2,1.4,0.2,I. setosa 
5.5,3.5,1.3,0.2,I. setosa 
5.5,2.3,4,1.3,I. versicolor 
5.5,2.4,3.8,1.1,I. versicolor 
5.5,2.4,3.7,1,I. versicolor 
5.5,2.5,4,1.3,I. versicolor 
5.5,2.6,4.4,1.2,I. versicolor 
5.4,3.9,1.7,0.4,I. setosa 
5.4,3.7,1.5,0.2,I. setosa 
5.4,3.9,1.3,0.4,I. setosa 
5.4,3.4,1.7,0.2,I. setosa 
5.4,3.4,1.5,0.4,I. setosa 
5.4,3,4.5,1.5,I. versicolor 
5.3,3.7,1.5,0.2,I. setosa 
5.2,3.5,1.5,0.2,I. setosa 
5.2,3.4,1.4,0.2,I. setosa 
5.2,4.1,1.5,0.1,I. setosa 
5.2,2.7,3.9,1.4,I. versicolor 
5.1,3.5,1.4,0.2,I. setosa 
5.1,3.5,1.4,0.3,I. setosa 
5.1,3.8,1.5,0.3,I. setosa 
5.1,3.7,1.5,0.4,I. setosa 
5.1,3.3,1.7,0.5,I. setosa 
5.1,3.4,1.5,0.2,I. setosa 
5.1,3.8,1.9,0.4,I. setosa 
5.1,3.8,1.6,0.2,I. setosa 
5.1,2.5,3,1.1,I. versicolor 
5,3.6,1.4,0.2,I. setosa 
5,3.4,1.5,0.2,I. setosa 
5,3,1.6,0.2,I. setosa 
5,3.4,1.6,0.4,I. setosa 
5,3.2,1.2,0.2,I. setosa 
5,3.5,1.3,0.3,I. setosa 
5,3.5,1.6,0.6,I. setosa 
5,3.3,1.4,0.2,I. setosa 
5,2,3.5,1,I. versicolor 
5,2.3,3.3,1,I. versicolor 
4.9,3,1.4,0.2,I. setosa 
4.9,3.1,1.5,0.1,I. setosa 
4.9,3.1,1.5,0.2,I. setosa 
4.9,3.6,1.4,0.1,I. setosa 
4.9,2.4,3.3,1,I. versicolor 
4.9,2.5,4.5,1.7,I. virginica 
4.8,3.4,1.6,0.2,I. setosa 
4.8,3,1.4,0.1,I. setosa 
4.8,3.4,1.9,0.2,I. setosa 
4.8,3.1,1.6,0.2,I. setosa 
4.8,3,1.4,0.3,I. setosa 
4.7,3.2,1.3,0.2,I. setosa 
4.7,3.2,1.6,0.2,I. setosa 
4.6,3.1,1.5,0.2,I. setosa 
4.6,3.4,1.4,0.3,I. setosa 
4.6,3.6,1,0.2,I. setosa 
4.6,3.2,1.4,0.2,I. setosa 
4.5,2.3,1.3,0.3,I. setosa 
4.4,2.9,1.4,0.2,I. setosa 
4.4,3,1.3,0.2,I. setosa 
4.4,3.2,1.3,0.2,I. setosa 
4.3,3,1.1,0.1,I. setosa 
"; 
    } 

, vous pouvez également comparer vos résultats réels et attendus:

Console.WriteLine("expected = \n{0}", expected); 
Console.WriteLine("ruleText = \n{0}", ruleText); 

Il devrait vous donner quelque chose comme ceci:

expected = 
Iris-setosa =: (2 <= 2.45) 
Iris-versicolor =: (2 > 2.45) && (3 <= 1.75) && (0 <= 7.05) && (1 <= 2.85) 
Iris-versicolor =: (2 > 2.45) && (3 <= 1.75) && (0 <= 7.05) && (1 > 2.85) 
Iris-versicolor =: (2 > 2.45) && (3 > 1.75) && (0 <= 5.95) && (1 > 3.05) 
Iris-virginica =: (2 > 2.45) && (3 <= 1.75) && (0 > 7.05) 
Iris-virginica =: (2 > 2.45) && (3 > 1.75) && (0 > 5.95) 
Iris-virginica =: (2 > 2.45) && (3 > 1.75) && (0 <= 5.95) && (1 <= 3.05) 

ruleText = 
I. virginica =: (2 > 2.45) && (3 <= 1.75) && (0 > 7.05) 
I. virginica =: (2 > 2.45) && (3 > 1.75) && (0 > 5.95) 
I. virginica =: (2 > 2.45) && (3 > 1.75) && (0 <= 5.95) && (1 <= 3.05) 
I. versicolor =: (2 > 2.45) && (3 <= 1.75) && (0 <= 7.05) && (1 <= 2.85) 
I. versicolor =: (2 > 2.45) && (3 <= 1.75) && (0 <= 7.05) && (1 > 2.85) 
I. versicolor =: (2 > 2.45) && (3 > 1.75) && (0 <= 5.95) && (1 > 3.05) 
I. setosa =: (2 <= 2.45)