Program.cs

using DecisionTree;
using Microsoft.Data.Analysis;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Unit4.CollectionsLib;
namespace DTModel
{
	internal class Program
	{

		static void Main(string[] args)
		{
			DataFrame df = DataFrame.LoadCsv("Car.csv");
			df = df.DropNulls();
			var (trainDf, testDf) = TrainTestSplit(df, 0.2, seed: 42);
			RandomForest fr = new RandomForest(trainDf, "owner", 100);
			DataFrame pred = fr.Pred(testDf);
			Console.WriteLine(pred.ToString());
			DataFrame.SaveCsv(pred, "pred.csv", ',');
		}
		public static (DataFrame train, DataFrame test) TrainTestSplit(DataFrame df, double testSize = 0.2, int? seed = null)
		{
			Random rand = seed.HasValue ? new Random(seed.Value) : new Random();
			int rowCount = (int)df.Rows.Count;
			int testCount = (int)(rowCount * testSize);
			int trainCount = rowCount - testCount;
			List<int> indices = Enumerable.Range(0, rowCount).ToList();
			for (int i = indices.Count - 1; i > 0; i--)
			{
				int j = rand.Next(i + 1);
				int temp = indices[i];
				indices[i] = indices[j];
				indices[j] = temp;
			}
			var trainIndices = indices.Take(trainCount).Select(i => (long)i);
			var testIndices = indices.Skip(trainCount).Select(i => (long)i);
			DataFrame trainDf = df[trainIndices];
			DataFrame testDf = df[testIndices];
			return (trainDf, testDf);
		}
	}
}