servo/notebooks/decision.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "source": [
    "#r \"nuget:Microsoft.ML\"\n",
    "#r \"nuget:Microsoft.ML.AutoML\""
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "source": [
    "using Microsoft.ML;\n",
    "using Microsoft.ML.Data;\n",
    "using Microsoft.ML.AutoML;\n",
    "using System.IO;"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "source": [
    "class NeedsInput {\n",
    "    [LoadColumn(0)] public string Action { get; set; }\n",
    "    [LoadColumn(1)] public string Need { get; set; }\n",
    "    [LoadColumn(2,3)] public float[] Threshold { get; set; }\n",
    "}\n",
    "\n",
    "class NeedsOutput {\n",
    "    public int Score { get; set; }\n",
    "}"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "source": [
    "var dataPath = Path.Combine(Environment.CurrentDirectory, \"data\", \"choice.csv\");"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "source": [
    "var ctx = new MLContext();\n",
    "var trainingData = ctx.Data.LoadFromTextFile<NeedsInput>(dataPath, hasHeader: true);\n",
    "var recSettings = new RecommendationExperimentSettings();"
   ],
   "outputs": []
  }
 ],
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   "file_extension": ".cs",
   "mimetype": "text/x-csharp",
   "name": "C#",
   "pygments_lexer": "csharp",
   "version": "8.0"
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