Day3-LandCoverClassification-Pavia-University.ipynb

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  {
   "cell_type": "markdown",
   "id": "354b97bf",
   "metadata": {},
   "source": [
    "# Modal Symbolic Learning: Day 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c216d7ad",
   "metadata": {},
   "source": [
    "## Symbolic Land Cover Classification (*unimodal data*, *multimodal learning*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b627caf4",
   "metadata": {
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     "text": [
      "\u001b[32m\u001b[1m  Activating\u001b[22m\u001b[39m project at `~/Desktop/modal-symbolic-learning-course`\n",
      "\u001b[32m\u001b[1m    Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General`\n",
      "\u001b[32m\u001b[1m    Updating\u001b[22m\u001b[39m git-repo `https://github.com/JuliaRegistries/General.git`\n",
      "\u001b[32m\u001b[1m  No Changes\u001b[22m\u001b[39m to `~/Desktop/modal-symbolic-learning-course/Project.toml`\n",
      "\u001b[32m\u001b[1m  No Changes\u001b[22m\u001b[39m to `~/Desktop/modal-symbolic-learning-course/Manifest.toml`\n"
     ]
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    {
     "name": "stdout",
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     "text": [
      "\u001b[32m\u001b[1mStatus\u001b[22m\u001b[39m `~/Desktop/modal-symbolic-learning-course/Project.toml`\n",
      "  \u001b[90m[acdeb78f] \u001b[39mCatch22 v0.4.5\n",
      "  \u001b[90m[a93c6f00] \u001b[39mDataFrames v1.6.1\n",
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      "  \u001b[90m[7806a523] \u001b[39mDecisionTree v0.12.4\n",
      "  \u001b[90m[7073ff75] \u001b[39mIJulia v1.24.2\n",
      "  \u001b[90m[6a3955dd] \u001b[39mImageFiltering v0.7.8\n",
      "  \u001b[90m[033835bb] \u001b[39mJLD2 v0.4.38\n",
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      "\u001b[33m⌅\u001b[39m \u001b[90m[add582a8] \u001b[39mMLJ v0.19.5\n",
      "  \u001b[90m[c6f25543] \u001b[39mMLJDecisionTreeInterface v0.4.0\n",
      "  \u001b[90m[e54bda2e] \u001b[39mModalDecisionTrees v0.3.6\n",
      "  \u001b[90m[91a5bcdd] \u001b[39mPlots v1.39.0\n",
      "  \u001b[90m[7b3b3b3f] \u001b[39mSole v0.3.1\n",
      "  \u001b[90m[b002da8f] \u001b[39mSoleLogics v0.6.14\n",
      "  \u001b[90m[4249d9c7] \u001b[39mSoleModels v0.5.6\n",
      "  \u001b[90m[2913bbd2] \u001b[39mStatsBase v0.34.2\n",
      "  \u001b[90m[9a3f8284] \u001b[39mRandom\n",
      "\u001b[36m\u001b[1mInfo\u001b[22m\u001b[39m Packages marked with \u001b[33m⌅\u001b[39m have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`\n"
     ]
    }
   ],
   "source": [
    "using Pkg\n",
    "Pkg.activate(\".\")\n",
    "Pkg.instantiate()\n",
    "Pkg.update()\n",
    "Pkg.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ea6609c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries for statistics & Machine Learning\n",
    "using Random\n",
    "using DataFrames\n",
    "using Plots\n",
    "using StatsBase\n",
    "\n",
    "using MLJ\n",
    "using Sole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9fcb64e8",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Load LandCoverDataset: Pavia University...\n",
      "window_size         = (3, 3)\n",
      "pad_window_size     = (5, 5)\n",
      "ninstances_per_class  = 40\n",
      "ninstances_per_class_strategy = updownsampling\n",
      "flattened           = false\n",
      "apply_filter        = false\n",
      "seed                = 1\n",
      "\n",
      "Image size: (610, 340, 103)\n",
      "class_counts_d = [(\"Asphalt\", 1 => 6631), (\"Meadows\", 2 => 18649), (\"Gravel\", 3 => 2099), (\"Trees\", 4 => 3064), (\"Painted metal sheets\", 5 => 1345), (\"Bare Soil\", 6 => 5029), (\"Bitumen\", 7 => 1330), (\"Self-Blocking Bricks\", 8 => 3682), (\"Shadows\", 9 => 947)]\n",
      "no_class_counts = 164624\n",
      "n_classes = 9\n",
      "ninstances = 40 * 9 = 360\n",
      "effective_class_counts_d = [(\"Asphalt\", 1 => 40), (\"Meadows\", 2 => 40), (\"Gravel\", 3 => 40), (\"Trees\", 4 => 40), (\"Painted metal sheets\", 5 => 40), (\"Bare Soil\", 6 => 40), (\"Bitumen\", 7 => 40), (\"Self-Blocking Bricks\", 8 => 40), (\"Shadows\", 9 => 40)]\n",
      "countmap(labels) = Dict(5 => 40, 4 => 40, 6 => 40, 7 => 40, 2 => 40, 9 => 40, 8 => 40, 3 => 40, 1 => 40)\n"
     ]
    }
   ],
   "source": [
    "include(\"land-cover.jl\")\n",
    "data_dir = \"data/\"\n",
    "\n",
    "X_df, y = LandCoverDataset(\n",
    "    \"Pavia University\";\n",
    "    window_size          = 3,\n",
    "    ninstances_per_class = 40,\n",
    "    pad_window_size      = 5,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18acdc30",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String, Int64} with 9 entries:\n",
       "  \"Self-Blocking Bricks\" => 40\n",
       "  \"Bitumen\"              => 40\n",
       "  \"Gravel\"               => 40\n",
       "  \"Bare Soil\"            => 40\n",
       "  \"Painted metal sheets\" => 40\n",
       "  \"Shadows\"              => 40\n",
       "  \"Trees\"                => 40\n",
       "  \"Asphalt\"              => 40\n",
       "  \"Meadows\"              => 40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countmap(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9c05b81",
   "metadata": {
    "scrolled": false
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   "outputs": [
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      "text/latex": [
       "\\begin{tabular}{r|ccccccccccccc}\n",
       "\t& V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & \\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t2 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t3 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t4 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t5 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t6 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t7 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t8 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t10 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t11 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t12 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t13 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t14 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t15 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t16 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t17 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t18 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t19 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t20 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t21 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t22 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t23 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t24 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t25 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t26 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t27 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t28 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t29 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t30 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & 9 & $\\dots$ \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ &  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m360×103 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m V1    \u001b[0m\u001b[1m V2    \u001b[0m\u001b[1m V3    \u001b[0m\u001b[1m V4    \u001b[0m\u001b[1m V5    \u001b[0m\u001b[1m V6    \u001b[0m\u001b[1m V7    \u001b[0m\u001b[1m V8    \u001b[0m\u001b[1m V9    \u001b[0m\u001b[1m V10   \u001b[0m\u001b[1m V\u001b[0m ⋯\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m I\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │     9      9      9      9      9      9      9      9      9      9    ⋯\n",
       "   2 │     9      9      9      9      9      9      9      9      9      9\n",
       "   3 │     9      9      9      9      9      9      9      9      9      9\n",
       "   4 │     9      9      9      9      9      9      9      9      9      9\n",
       "   5 │     9      9      9      9      9      9      9      9      9      9    ⋯\n",
       "   6 │     9      9      9      9      9      9      9      9      9      9\n",
       "   7 │     9      9      9      9      9      9      9      9      9      9\n",
       "   8 │     9      9      9      9      9      9      9      9      9      9\n",
       "   9 │     9      9      9      9      9      9      9      9      9      9    ⋯\n",
       "  10 │     9      9      9      9      9      9      9      9      9      9\n",
       "  11 │     9      9      9      9      9      9      9      9      9      9\n",
       "  ⋮  │   ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋱\n",
       " 351 │     9      9      9      9      9      9      9      9      9      9\n",
       " 352 │     9      9      9      9      9      9      9      9      9      9    ⋯\n",
       " 353 │     9      9      9      9      9      9      9      9      9      9\n",
       " 354 │     9      9      9      9      9      9      9      9      9      9\n",
       " 355 │     9      9      9      9      9      9      9      9      9      9\n",
       " 356 │     9      9      9      9      9      9      9      9      9      9    ⋯\n",
       " 357 │     9      9      9      9      9      9      9      9      9      9\n",
       " 358 │     9      9      9      9      9      9      9      9      9      9\n",
       " 359 │     9      9      9      9      9      9      9      9      9      9\n",
       " 360 │     9      9      9      9      9      9      9      9      9      9    ⋯\n",
       "\u001b[36m                                                 93 columns and 339 rows omitted\u001b[0m"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length.(X_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "52f9800b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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style = \"text-align: left;\">V61</th><th style = \"text-align: left;\">V62</th><th style = \"text-align: left;\">V63</th><th style = \"text-align: left;\">V64</th><th style = \"text-align: left;\">V65</th><th style = \"text-align: left;\">V66</th><th style = \"text-align: left;\">V67</th><th style = \"text-align: left;\">V68</th><th style = \"text-align: left;\">V69</th><th style = \"text-align: left;\">V70</th><th style = \"text-align: left;\">V71</th><th style = \"text-align: left;\">V72</th><th style = \"text-align: left;\">V73</th><th style = \"text-align: left;\">V74</th><th style = \"text-align: left;\">V75</th><th style = \"text-align: left;\">V76</th><th style = \"text-align: left;\">V77</th><th style = \"text-align: left;\">V78</th><th style = \"text-align: left;\">V79</th><th style = \"text-align: left;\">V80</th><th style = \"text-align: left;\">V81</th><th style = \"text-align: left;\">V82</th><th style = \"text-align: left;\">V83</th><th style = \"text-align: left;\">V84</th><th style = \"text-align: left;\">V85</th><th style = \"text-align: left;\">V86</th><th style = \"text-align: left;\">V87</th><th style = \"text-align: left;\">V88</th><th style = \"text-align: left;\">V89</th><th style = \"text-align: left;\">V90</th><th style = \"text-align: left;\">V91</th><th style = \"text-align: left;\">V92</th><th style = \"text-align: left;\">V93</th><th style = \"text-align: left;\">V94</th><th style = \"text-align: left;\">V95</th><th style = \"text-align: left;\">V96</th><th style = \"text-align: left;\">V97</th><th style = \"text-align: left;\">V98</th><th style = \"text-align: left;\">V99</th><th style = \"text-align: left;\">V100</th><th style = \"text-align: right;\">&ctdot;</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: left;\">Array…</th><th title = \"Matrix{Float64}\" style = \"text-align: right;\">&ctdot;</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">[1186.0 1294.0 1291.0; 1701.0 1705.0 1373.0; 1823.0 1359.0 1616.0]</td><td style = \"text-align: left;\">[1069.0 1177.0 1369.0; 1708.0 1463.0 1041.0; 2055.0 1229.0 1440.0]</td><td style = \"text-align: left;\">[1176.0 1179.0 1265.0; 1686.0 1190.0 939.0; 1967.0 1256.0 1245.0]</td><td style = \"text-align: left;\">[1318.0 1256.0 1273.0; 1528.0 1106.0 1133.0; 1829.0 1267.0 1085.0]</td><td style = \"text-align: left;\">[1278.0 1286.0 1326.0; 1414.0 1082.0 1308.0; 1759.0 1231.0 1031.0]</td><td style = \"text-align: left;\">[1183.0 1290.0 1254.0; 1436.0 1130.0 1411.0; 1740.0 1150.0 1087.0]</td><td style = \"text-align: left;\">[1120.0 1238.0 1177.0; 1423.0 1268.0 1329.0; 1731.0 1165.0 1165.0]</td><td style = \"text-align: left;\">[1199.0 1184.0 1084.0; 1346.0 1293.0 1228.0; 1745.0 1214.0 1167.0]</td><td style = \"text-align: left;\">[1296.0 1155.0 1078.0; 1261.0 1224.0 1212.0; 1757.0 1198.0 1164.0]</td><td style = \"text-align: left;\">[1299.0 1168.0 1152.0; 1306.0 1287.0 1178.0; 1767.0 1163.0 1159.0]</td><td style = \"text-align: left;\">[1252.0 1282.0 1255.0; 1342.0 1319.0 1167.0; 1725.0 1160.0 1151.0]</td><td style = \"text-align: left;\">[1258.0 1318.0 1273.0; 1356.0 1207.0 1198.0; 1733.0 1179.0 1111.0]</td><td style = \"text-align: left;\">[1264.0 1306.0 1240.0; 1359.0 1186.0 1203.0; 1793.0 1161.0 1078.0]</td><td style = \"text-align: left;\">[1233.0 1304.0 1273.0; 1356.0 1178.0 1205.0; 1827.0 1127.0 1086.0]</td><td style = \"text-align: left;\">[1277.0 1252.0 1276.0; 1346.0 1182.0 1187.0; 1790.0 1173.0 1096.0]</td><td style = \"text-align: left;\">[1303.0 1229.0 1252.0; 1343.0 1199.0 1185.0; 1795.0 1194.0 1089.0]</td><td style = \"text-align: left;\">[1300.0 1287.0 1239.0; 1349.0 1207.0 1230.0; 1813.0 1192.0 1099.0]</td><td style = \"text-align: left;\">[1313.0 1346.0 1220.0; 1343.0 1209.0 1268.0; 1827.0 1169.0 1123.0]</td><td style = \"text-align: left;\">[1339.0 1375.0 1242.0; 1361.0 1225.0 1274.0; 1832.0 1186.0 1139.0]</td><td style = \"text-align: left;\">[1339.0 1397.0 1300.0; 1386.0 1261.0 1254.0; 1802.0 1235.0 1129.0]</td><td style = \"text-align: left;\">[1361.0 1367.0 1302.0; 1386.0 1269.0 1277.0; 1775.0 1243.0 1131.0]</td><td style = \"text-align: left;\">[1412.0 1368.0 1317.0; 1364.0 1250.0 1323.0; 1773.0 1261.0 1172.0]</td><td style = \"text-align: left;\">[1442.0 1402.0 1340.0; 1380.0 1265.0 1346.0; 1813.0 1266.0 1235.0]</td><td style = \"text-align: left;\">[1442.0 1421.0 1353.0; 1444.0 1311.0 1349.0; 1828.0 1262.0 1243.0]</td><td style = \"text-align: left;\">[1439.0 1437.0 1399.0; 1505.0 1320.0 1350.0; 1810.0 1261.0 1267.0]</td><td 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377.0 202.0; 288.0 137.0 95.0; 121.0 97.0 79.0]</td><td style = \"text-align: left;\">[832.0 380.0 203.0; 269.0 140.0 95.0; 132.0 97.0 93.0]</td><td style = \"text-align: left;\">[844.0 372.0 213.0; 245.0 142.0 94.0; 136.0 87.0 111.0]</td><td style = \"text-align: left;\">[850.0 377.0 227.0; 246.0 149.0 106.0; 138.0 86.0 109.0]</td><td style = \"text-align: left;\">[846.0 393.0 221.0; 271.0 157.0 112.0; 129.0 91.0 110.0]</td><td style = \"text-align: left;\">[841.0 393.0 217.0; 285.0 159.0 111.0; 125.0 97.0 121.0]</td><td style = \"text-align: left;\">[833.0 404.0 234.0; 292.0 152.0 110.0; 143.0 114.0 123.0]</td><td style = \"text-align: left;\">[838.0 427.0 258.0; 316.0 158.0 131.0; 159.0 142.0 129.0]</td><td style = \"text-align: left;\">[849.0 456.0 279.0; 330.0 192.0 158.0; 172.0 166.0 129.0]</td><td style = \"text-align: left;\">[849.0 465.0 286.0; 335.0 204.0 153.0; 197.0 168.0 107.0]</td><td style = \"text-align: left;\">[851.0 464.0 258.0; 341.0 216.0 156.0; 209.0 173.0 109.0]</td><td style = \"text-align: left;\">[849.0 450.0 255.0; 316.0 214.0 167.0; 198.0 159.0 151.0]</td><td style = \"text-align: left;\">[842.0 456.0 267.0; 296.0 181.0 170.0; 187.0 147.0 161.0]</td><td style = \"text-align: left;\">[843.0 455.0 261.0; 303.0 174.0 174.0; 171.0 155.0 144.0]</td><td style = \"text-align: left;\">[846.0 442.0 263.0; 323.0 188.0 178.0; 178.0 158.0 164.0]</td><td style = \"text-align: left;\">[825.0 448.0 273.0; 328.0 195.0 180.0; 184.0 162.0 175.0]</td><td style = \"text-align: left;\">[791.0 467.0 273.0; 324.0 190.0 185.0; 177.0 163.0 164.0]</td><td style = \"text-align: left;\">[784.0 474.0 282.0; 337.0 204.0 215.0; 181.0 183.0 184.0]</td><td style = \"text-align: left;\">[787.0 490.0 335.0; 384.0 248.0 269.0; 249.0 242.0 234.0]</td><td style = \"text-align: left;\">[803.0 489.0 325.0; 375.0 266.0 261.0; 257.0 226.0 221.0]</td><td style = \"text-align: left;\">[823.0 473.0 286.0; 316.0 248.0 227.0; 231.0 193.0 184.0]</td><td style = \"text-align: left;\">[811.0 460.0 285.0; 288.0 226.0 231.0; 219.0 189.0 163.0]</td><td style = \"text-align: left;\">[805.0 447.0 278.0; 280.0 223.0 228.0; 214.0 190.0 159.0]</td><td style = \"text-align: left;\">[813.0 427.0 250.0; 297.0 221.0 209.0; 216.0 185.0 152.0]</td><td style = \"text-align: left;\">[797.0 431.0 251.0; 321.0 223.0 213.0; 224.0 196.0 162.0]</td><td style = \"text-align: left;\">[766.0 449.0 279.0; 325.0 229.0 222.0; 212.0 200.0 185.0]</td><td style = \"text-align: left;\">[759.0 435.0 286.0; 327.0 220.0 211.0; 207.0 209.0 186.0]</td><td style = \"text-align: left;\">[758.0 424.0 271.0; 338.0 221.0 194.0; 225.0 215.0 168.0]</td><td style = \"text-align: left;\">[717.0 432.0 270.0; 350.0 231.0 202.0; 249.0 215.0 166.0]</td><td style = \"text-align: left;\">[694.0 442.0 280.0; 369.0 239.0 214.0; 246.0 210.0 167.0]</td><td style = \"text-align: left;\">[733.0 458.0 291.0; 378.0 244.0 236.0; 240.0 206.0 161.0]</td><td style = \"text-align: left;\">[751.0 484.0 315.0; 389.0 240.0 257.0; 269.0 241.0 192.0]</td><td style = \"text-align: left;\">[735.0 508.0 332.0; 406.0 233.0 287.0; 307.0 281.0 244.0]</td><td style = \"text-align: left;\">[711.0 456.0 325.0; 405.0 220.0 280.0; 295.0 277.0 255.0]</td><td style = \"text-align: left;\">[683.0 367.0 282.0; 363.0 190.0 243.0; 247.0 248.0 218.0]</td><td style = \"text-align: right;\">&ctdot;</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& V1 & V2 & \\\\\n",
       "\t\\hline\n",
       "\t& Array… & Array… & \\\\\n",
       "\t\\hline\n",
       "\t1 & [1186.0 1294.0 1291.0; 1701.0 1705.0 1373.0; 1823.0 1359.0 1616.0] & [1069.0 1177.0 1369.0; 1708.0 1463.0 1041.0; 2055.0 1229.0 1440.0] & $\\dots$ \\\\\n",
       "\t2 & [1153.0 1201.0 1563.0; 784.0 1618.0 1508.0; 1173.0 1071.0 1434.0] & [1024.0 1258.0 1480.0; 1024.0 1548.0 1368.0; 1110.0 1152.0 1193.0] & $\\dots$ \\\\\n",
       "\t3 & [880.0 804.0 727.0; 672.0 865.0 1067.0; 540.0 757.0 803.0] & [726.0 486.0 771.0; 498.0 629.0 957.0; 676.0 886.0 817.0] & $\\dots$ \\\\\n",
       "\t4 & [1454.0 1082.0 1291.0; 512.0 985.0 936.0; 1430.0 1400.0 1098.0] & [1178.0 876.0 1229.0; 741.0 1014.0 1064.0; 1124.0 1259.0 1282.0] & $\\dots$ \\\\\n",
       "\t5 & [1603.0 1091.0 594.0; 1003.0 1260.0 891.0; 1222.0 801.0 863.0] & [1602.0 1092.0 689.0; 804.0 1240.0 827.0; 1002.0 941.0 736.0] & $\\dots$ \\\\\n",
       "\t6 & [966.0 632.0 605.0; 482.0 669.0 511.0; 427.0 811.0 640.0] & [1171.0 630.0 451.0; 446.0 507.0 471.0; 425.0 438.0 687.0] & $\\dots$ \\\\\n",
       "\t7 & [1219.0 1459.0 1247.0; 1377.0 483.0 1501.0; 1187.0 773.0 1630.0] & [1377.0 1259.0 1255.0; 1298.0 885.0 1456.0; 1380.0 975.0 1521.0] & $\\dots$ \\\\\n",
       "\t8 & [510.0 686.0 863.0; 637.0 748.0 988.0; 815.0 686.0 872.0] & [365.0 726.0 761.0; 810.0 639.0 713.0; 875.0 542.0 782.0] & $\\dots$ \\\\\n",
       "\t9 & [541.0 814.0 1198.0; 857.0 1166.0 812.0; 1317.0 620.0 1044.0] & [530.0 676.0 910.0; 697.0 848.0 546.0; 1170.0 713.0 905.0] & $\\dots$ \\\\\n",
       "\t10 & [856.0 302.0 568.0; 463.0 460.0 870.0; 631.0 762.0 801.0] & [698.0 291.0 598.0; 144.0 563.0 822.0; 494.0 484.0 525.0] & $\\dots$ \\\\\n",
       "\t11 & [589.0 838.0 602.0; 1055.0 877.0 1041.0; 747.0 1016.0 833.0] & [671.0 617.0 508.0; 800.0 688.0 749.0; 659.0 839.0 655.0] & $\\dots$ \\\\\n",
       "\t12 & [967.0 1086.0 1269.0; 1147.0 1269.0 1194.0; 907.0 1052.0 1016.0] & [1125.0 1005.0 1265.0; 1267.0 1122.0 1452.0; 992.0 988.0 1412.0] & $\\dots$ \\\\\n",
       "\t13 & [663.0 1111.0 382.0; 627.0 642.0 816.0; 893.0 474.0 438.0] & [560.0 940.0 488.0; 488.0 548.0 802.0; 1093.0 577.0 388.0] & $\\dots$ \\\\\n",
       "\t14 & [1025.0 1010.0 1261.0; 770.0 832.0 1078.0; 1001.0 1136.0 1701.0] & [979.0 965.0 1223.0; 715.0 991.0 1066.0; 1129.0 1078.0 1447.0] & $\\dots$ \\\\\n",
       "\t15 & [807.0 1086.0 994.0; 554.0 825.0 731.0; 534.0 770.0 565.0] & [668.0 1223.0 816.0; 552.0 838.0 704.0; 574.0 760.0 346.0] & $\\dots$ \\\\\n",
       "\t16 & [686.0 701.0 602.0; 595.0 264.0 84.0; 254.0 403.0 717.0] & [684.0 394.0 424.0; 430.0 252.0 360.0; 0.0 463.0 772.0] & $\\dots$ \\\\\n",
       "\t17 & [1243.0 1287.0 1005.0; 1113.0 772.0 1365.0; 962.0 1350.0 933.0] & [1199.0 1400.0 1074.0; 1203.0 1089.0 1358.0; 1244.0 1274.0 1244.0] & $\\dots$ \\\\\n",
       "\t18 & [1000.0 388.0 604.0; 1024.0 536.0 1136.0; 683.0 310.0 1241.0] & [753.0 570.0 556.0; 1145.0 534.0 840.0; 727.0 546.0 872.0] & $\\dots$ \\\\\n",
       "\t19 & [695.0 626.0 733.0; 597.0 621.0 578.0; 879.0 979.0 871.0] & [433.0 590.0 512.0; 430.0 442.0 367.0; 713.0 902.0 551.0] & $\\dots$ \\\\\n",
       "\t20 & [562.0 888.0 573.0; 886.0 951.0 720.0; 775.0 561.0 768.0] & [341.0 384.0 713.0; 518.0 821.0 602.0; 524.0 581.0 382.0] & $\\dots$ \\\\\n",
       "\t21 & [1042.0 1573.0 1137.0; 1482.0 1360.0 899.0; 1531.0 1324.0 1436.0] & [1318.0 1304.0 1193.0; 1312.0 1169.0 985.0; 1436.0 1190.0 1510.0] & $\\dots$ \\\\\n",
       "\t22 & [1034.0 1024.0 1086.0; 783.0 792.0 783.0; 776.0 865.0 843.0] & [700.0 656.0 829.0; 650.0 906.0 898.0; 610.0 639.0 470.0] & $\\dots$ \\\\\n",
       "\t23 & [737.0 858.0 436.0; 1122.0 592.0 695.0; 991.0 323.0 637.0] & [664.0 698.0 169.0; 958.0 600.0 472.0; 843.0 303.0 262.0] & $\\dots$ \\\\\n",
       "\t24 & [1417.0 1060.0 1208.0; 1539.0 909.0 1026.0; 645.0 990.0 992.0] & [1330.0 1296.0 1302.0; 1379.0 1097.0 1204.0; 816.0 993.0 1056.0] & $\\dots$ \\\\\n",
       "\t25 & [955.0 1087.0 911.0; 1096.0 1402.0 1469.0; 738.0 775.0 1177.0] & [1171.0 999.0 979.0; 1042.0 1221.0 1360.0; 864.0 813.0 1306.0] & $\\dots$ \\\\\n",
       "\t26 & [546.0 1330.0 1232.0; 1251.0 1354.0 942.0; 1315.0 1701.0 661.0] & [532.0 915.0 1247.0; 840.0 1511.0 1128.0; 1541.0 1455.0 836.0] & $\\dots$ \\\\\n",
       "\t27 & [856.0 1083.0 700.0; 1023.0 1178.0 874.0; 843.0 533.0 1142.0] & [697.0 904.0 667.0; 825.0 1138.0 921.0; 748.0 848.0 997.0] & $\\dots$ \\\\\n",
       "\t28 & [1798.0 916.0 1238.0; 1024.0 1009.0 830.0; 166.0 911.0 801.0] & [1850.0 880.0 715.0; 906.0 875.0 636.0; 443.0 606.0 463.0] & $\\dots$ \\\\\n",
       "\t29 & [1361.0 907.0 963.0; 1284.0 1396.0 1286.0; 1257.0 937.0 1392.0] & [1278.0 1052.0 1303.0; 1252.0 1465.0 1596.0; 1361.0 1123.0 1396.0] & $\\dots$ \\\\\n",
       "\t30 & [895.0 758.0 842.0; 477.0 1373.0 870.0; 817.0 266.0 1184.0] & [882.0 661.0 829.0; 690.0 1102.0 867.0; 645.0 381.0 1047.0] & $\\dots$ \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ &  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m360×103 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m V1                                \u001b[0m\u001b[1m V2                                \u001b[0m\u001b[1m V\u001b[0m ⋯\n",
       "     │\u001b[90m Array…                            \u001b[0m\u001b[90m Array…                            \u001b[0m\u001b[90m A\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │ [1186.0 1294.0 1291.0; 1701.0 17…  [1069.0 1177.0 1369.0; 1708.0 14…  [ ⋯\n",
       "   2 │ [1153.0 1201.0 1563.0; 784.0 161…  [1024.0 1258.0 1480.0; 1024.0 15…  [\n",
       "   3 │ [880.0 804.0 727.0; 672.0 865.0 …  [726.0 486.0 771.0; 498.0 629.0 …  [\n",
       "   4 │ [1454.0 1082.0 1291.0; 512.0 985…  [1178.0 876.0 1229.0; 741.0 1014…  [\n",
       "   5 │ [1603.0 1091.0 594.0; 1003.0 126…  [1602.0 1092.0 689.0; 804.0 1240…  [ ⋯\n",
       "   6 │ [966.0 632.0 605.0; 482.0 669.0 …  [1171.0 630.0 451.0; 446.0 507.0…  [\n",
       "   7 │ [1219.0 1459.0 1247.0; 1377.0 48…  [1377.0 1259.0 1255.0; 1298.0 88…  [\n",
       "   8 │ [510.0 686.0 863.0; 637.0 748.0 …  [365.0 726.0 761.0; 810.0 639.0 …  [\n",
       "   9 │ [541.0 814.0 1198.0; 857.0 1166.…  [530.0 676.0 910.0; 697.0 848.0 …  [ ⋯\n",
       "  10 │ [856.0 302.0 568.0; 463.0 460.0 …  [698.0 291.0 598.0; 144.0 563.0 …  [\n",
       "  11 │ [589.0 838.0 602.0; 1055.0 877.0…  [671.0 617.0 508.0; 800.0 688.0 …  [\n",
       "  ⋮  │                 ⋮                                  ⋮                    ⋱\n",
       " 351 │ [1300.0 1477.0 1108.0; 969.0 108…  [1289.0 1252.0 1139.0; 1172.0 10…  [\n",
       " 352 │ [1505.0 1452.0 916.0; 1285.0 803…  [1230.0 1355.0 1085.0; 1304.0 10…  [ ⋯\n",
       " 353 │ [1290.0 889.0 1183.0; 1408.0 673…  [1281.0 1061.0 1176.0; 989.0 949…  [\n",
       " 354 │ [728.0 888.0 643.0; 162.0 586.0 …  [493.0 729.0 754.0; 485.0 367.0 …  [\n",
       " 355 │ [462.0 546.0 801.0; 168.0 350.0 …  [539.0 362.0 800.0; 171.0 603.0 …  [\n",
       " 356 │ [1072.0 652.0 1195.0; 1118.0 102…  [1049.0 724.0 962.0; 1128.0 1006…  [ ⋯\n",
       " 357 │ [1310.0 727.0 908.0; 759.0 824.0…  [1364.0 780.0 947.0; 524.0 676.0…  [\n",
       " 358 │ [1980.0 1828.0 1224.0; 1003.0 85…  [2001.0 1745.0 1178.0; 1065.0 63…  [\n",
       " 359 │ [1672.0 1170.0 1006.0; 1785.0 12…  [1656.0 1129.0 873.0; 1746.0 118…  [\n",
       " 360 │ [1206.0 494.0 969.0; 1037.0 587.…  [1140.0 818.0 659.0; 879.0 698.0…  [ ⋯\n",
       "\u001b[36m                                                101 columns and 339 rows omitted\u001b[0m"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df = broadcast(values->Matrix{Float64}(values), X_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c7cebae9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>360×927 DataFrame</span></div><div style = \"float: right;\"><span style = \"font-style: italic;\">827 columns and 335 rows omitted</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">V1[1][1]</th><th style = \"text-align: left;\">V1[1][2]</th><th style = \"text-align: left;\">V1[1][3]</th><th style = \"text-align: left;\">V1[2][1]</th><th style = \"text-align: left;\">V1[2][2]</th><th style = \"text-align: left;\">V1[2][3]</th><th style = \"text-align: left;\">V1[3][1]</th><th style = \"text-align: left;\">V1[3][2]</th><th style = \"text-align: left;\">V1[3][3]</th><th style = \"text-align: left;\">V2[1][1]</th><th style = \"text-align: left;\">V2[1][2]</th><th style = \"text-align: left;\">V2[1][3]</th><th style = \"text-align: left;\">V2[2][1]</th><th style = \"text-align: left;\">V2[2][2]</th><th style = \"text-align: left;\">V2[2][3]</th><th style = \"text-align: left;\">V2[3][1]</th><th style = \"text-align: left;\">V2[3][2]</th><th style = \"text-align: left;\">V2[3][3]</th><th style = \"text-align: left;\">V3[1][1]</th><th style = \"text-align: left;\">V3[1][2]</th><th style = \"text-align: left;\">V3[1][3]</th><th style = \"text-align: left;\">V3[2][1]</th><th style = \"text-align: left;\">V3[2][2]</th><th style = \"text-align: left;\">V3[2][3]</th><th style = \"text-align: left;\">V3[3][1]</th><th style = \"text-align: left;\">V3[3][2]</th><th style = \"text-align: left;\">V3[3][3]</th><th style = \"text-align: left;\">V4[1][1]</th><th style = \"text-align: left;\">V4[1][2]</th><th style = \"text-align: left;\">V4[1][3]</th><th style = \"text-align: left;\">V4[2][1]</th><th style = \"text-align: left;\">V4[2][2]</th><th style = \"text-align: left;\">V4[2][3]</th><th style = \"text-align: left;\">V4[3][1]</th><th style = \"text-align: left;\">V4[3][2]</th><th style = \"text-align: left;\">V4[3][3]</th><th style = \"text-align: left;\">V5[1][1]</th><th style = \"text-align: left;\">V5[1][2]</th><th style = \"text-align: left;\">V5[1][3]</th><th style = \"text-align: left;\">V5[2][1]</th><th style = \"text-align: left;\">V5[2][2]</th><th style = \"text-align: left;\">V5[2][3]</th><th style = \"text-align: left;\">V5[3][1]</th><th style = \"text-align: left;\">V5[3][2]</th><th style = \"text-align: left;\">V5[3][3]</th><th style = \"text-align: left;\">V6[1][1]</th><th style = \"text-align: left;\">V6[1][2]</th><th style = \"text-align: left;\">V6[1][3]</th><th style = \"text-align: left;\">V6[2][1]</th><th style = \"text-align: left;\">V6[2][2]</th><th style = \"text-align: left;\">V6[2][3]</th><th style = \"text-align: left;\">V6[3][1]</th><th style = \"text-align: left;\">V6[3][2]</th><th style = \"text-align: left;\">V6[3][3]</th><th style = \"text-align: left;\">V7[1][1]</th><th style = \"text-align: left;\">V7[1][2]</th><th style = \"text-align: left;\">V7[1][3]</th><th style = \"text-align: left;\">V7[2][1]</th><th style = \"text-align: left;\">V7[2][2]</th><th style = \"text-align: left;\">V7[2][3]</th><th style = \"text-align: left;\">V7[3][1]</th><th style = \"text-align: left;\">V7[3][2]</th><th style = \"text-align: left;\">V7[3][3]</th><th style = \"text-align: left;\">V8[1][1]</th><th style = \"text-align: left;\">V8[1][2]</th><th style = \"text-align: left;\">V8[1][3]</th><th style = \"text-align: left;\">V8[2][1]</th><th style = \"text-align: left;\">V8[2][2]</th><th style = \"text-align: left;\">V8[2][3]</th><th style = \"text-align: left;\">V8[3][1]</th><th style = \"text-align: left;\">V8[3][2]</th><th style = \"text-align: left;\">V8[3][3]</th><th style = \"text-align: left;\">V9[1][1]</th><th style = \"text-align: left;\">V9[1][2]</th><th style = \"text-align: left;\">V9[1][3]</th><th style = \"text-align: left;\">V9[2][1]</th><th style = \"text-align: left;\">V9[2][2]</th><th style = \"text-align: left;\">V9[2][3]</th><th style = \"text-align: left;\">V9[3][1]</th><th style = \"text-align: left;\">V9[3][2]</th><th style = \"text-align: left;\">V9[3][3]</th><th style = \"text-align: left;\">V10[1][1]</th><th style = \"text-align: left;\">V10[1][2]</th><th style = \"text-align: left;\">V10[1][3]</th><th style = \"text-align: left;\">V10[2][1]</th><th style = \"text-align: left;\">V10[2][2]</th><th style = \"text-align: left;\">V10[2][3]</th><th style = \"text-align: left;\">V10[3][1]</th><th style = \"text-align: left;\">V10[3][2]</th><th style = \"text-align: left;\">V10[3][3]</th><th style = \"text-align: left;\">V11[1][1]</th><th style = \"text-align: left;\">V11[1][2]</th><th style = \"text-align: left;\">V11[1][3]</th><th style = \"text-align: left;\">V11[2][1]</th><th style = \"text-align: left;\">V11[2][2]</th><th style = \"text-align: 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\"text-align: right;\">60.0</td><td style = \"text-align: right;\">94.0</td><td style = \"text-align: right;\">798.0</td><td style = \"text-align: right;\">&ctdot;</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccccccccc}\n",
       "\t& V1[1][1] & V1[1][2] & V1[1][3] & V1[2][1] & V1[2][2] & V1[2][3] & V1[3][1] & V1[3][2] & \\\\\n",
       "\t\\hline\n",
       "\t& Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & 1186.0 & 1701.0 & 1823.0 & 1294.0 & 1705.0 & 1359.0 & 1291.0 & 1373.0 & $\\dots$ \\\\\n",
       "\t2 & 1153.0 & 784.0 & 1173.0 & 1201.0 & 1618.0 & 1071.0 & 1563.0 & 1508.0 & $\\dots$ \\\\\n",
       "\t3 & 880.0 & 672.0 & 540.0 & 804.0 & 865.0 & 757.0 & 727.0 & 1067.0 & $\\dots$ \\\\\n",
       "\t4 & 1454.0 & 512.0 & 1430.0 & 1082.0 & 985.0 & 1400.0 & 1291.0 & 936.0 & $\\dots$ \\\\\n",
       "\t5 & 1603.0 & 1003.0 & 1222.0 & 1091.0 & 1260.0 & 801.0 & 594.0 & 891.0 & $\\dots$ \\\\\n",
       "\t6 & 966.0 & 482.0 & 427.0 & 632.0 & 669.0 & 811.0 & 605.0 & 511.0 & $\\dots$ \\\\\n",
       "\t7 & 1219.0 & 1377.0 & 1187.0 & 1459.0 & 483.0 & 773.0 & 1247.0 & 1501.0 & $\\dots$ \\\\\n",
       "\t8 & 510.0 & 637.0 & 815.0 & 686.0 & 748.0 & 686.0 & 863.0 & 988.0 & $\\dots$ \\\\\n",
       "\t9 & 541.0 & 857.0 & 1317.0 & 814.0 & 1166.0 & 620.0 & 1198.0 & 812.0 & $\\dots$ \\\\\n",
       "\t10 & 856.0 & 463.0 & 631.0 & 302.0 & 460.0 & 762.0 & 568.0 & 870.0 & $\\dots$ \\\\\n",
       "\t11 & 589.0 & 1055.0 & 747.0 & 838.0 & 877.0 & 1016.0 & 602.0 & 1041.0 & $\\dots$ \\\\\n",
       "\t12 & 967.0 & 1147.0 & 907.0 & 1086.0 & 1269.0 & 1052.0 & 1269.0 & 1194.0 & $\\dots$ \\\\\n",
       "\t13 & 663.0 & 627.0 & 893.0 & 1111.0 & 642.0 & 474.0 & 382.0 & 816.0 & $\\dots$ \\\\\n",
       "\t14 & 1025.0 & 770.0 & 1001.0 & 1010.0 & 832.0 & 1136.0 & 1261.0 & 1078.0 & $\\dots$ \\\\\n",
       "\t15 & 807.0 & 554.0 & 534.0 & 1086.0 & 825.0 & 770.0 & 994.0 & 731.0 & $\\dots$ \\\\\n",
       "\t16 & 686.0 & 595.0 & 254.0 & 701.0 & 264.0 & 403.0 & 602.0 & 84.0 & $\\dots$ \\\\\n",
       "\t17 & 1243.0 & 1113.0 & 962.0 & 1287.0 & 772.0 & 1350.0 & 1005.0 & 1365.0 & $\\dots$ \\\\\n",
       "\t18 & 1000.0 & 1024.0 & 683.0 & 388.0 & 536.0 & 310.0 & 604.0 & 1136.0 & $\\dots$ \\\\\n",
       "\t19 & 695.0 & 597.0 & 879.0 & 626.0 & 621.0 & 979.0 & 733.0 & 578.0 & $\\dots$ \\\\\n",
       "\t20 & 562.0 & 886.0 & 775.0 & 888.0 & 951.0 & 561.0 & 573.0 & 720.0 & $\\dots$ \\\\\n",
       "\t21 & 1042.0 & 1482.0 & 1531.0 & 1573.0 & 1360.0 & 1324.0 & 1137.0 & 899.0 & $\\dots$ \\\\\n",
       "\t22 & 1034.0 & 783.0 & 776.0 & 1024.0 & 792.0 & 865.0 & 1086.0 & 783.0 & $\\dots$ \\\\\n",
       "\t23 & 737.0 & 1122.0 & 991.0 & 858.0 & 592.0 & 323.0 & 436.0 & 695.0 & $\\dots$ \\\\\n",
       "\t24 & 1417.0 & 1539.0 & 645.0 & 1060.0 & 909.0 & 990.0 & 1208.0 & 1026.0 & $\\dots$ \\\\\n",
       "\t25 & 955.0 & 1096.0 & 738.0 & 1087.0 & 1402.0 & 775.0 & 911.0 & 1469.0 & $\\dots$ \\\\\n",
       "\t26 & 546.0 & 1251.0 & 1315.0 & 1330.0 & 1354.0 & 1701.0 & 1232.0 & 942.0 & $\\dots$ \\\\\n",
       "\t27 & 856.0 & 1023.0 & 843.0 & 1083.0 & 1178.0 & 533.0 & 700.0 & 874.0 & $\\dots$ \\\\\n",
       "\t28 & 1798.0 & 1024.0 & 166.0 & 916.0 & 1009.0 & 911.0 & 1238.0 & 830.0 & $\\dots$ \\\\\n",
       "\t29 & 1361.0 & 1284.0 & 1257.0 & 907.0 & 1396.0 & 937.0 & 963.0 & 1286.0 & $\\dots$ \\\\\n",
       "\t30 & 895.0 & 477.0 & 817.0 & 758.0 & 1373.0 & 266.0 & 842.0 & 870.0 & $\\dots$ \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ &  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m360×927 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m V1[1][1] \u001b[0m\u001b[1m V1[1][2] \u001b[0m\u001b[1m V1[1][3] \u001b[0m\u001b[1m V1[2][1] \u001b[0m\u001b[1m V1[2][2] \u001b[0m\u001b[1m V1[2][3] \u001b[0m\u001b[1m V1[3][1] \u001b[0m\u001b[1m V\u001b[0m ⋯\n",
       "     │\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m F\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │   1186.0    1701.0    1823.0    1294.0    1705.0    1359.0    1291.0    ⋯\n",
       "   2 │   1153.0     784.0    1173.0    1201.0    1618.0    1071.0    1563.0\n",
       "   3 │    880.0     672.0     540.0     804.0     865.0     757.0     727.0\n",
       "   4 │   1454.0     512.0    1430.0    1082.0     985.0    1400.0    1291.0\n",
       "   5 │   1603.0    1003.0    1222.0    1091.0    1260.0     801.0     594.0    ⋯\n",
       "   6 │    966.0     482.0     427.0     632.0     669.0     811.0     605.0\n",
       "   7 │   1219.0    1377.0    1187.0    1459.0     483.0     773.0    1247.0\n",
       "   8 │    510.0     637.0     815.0     686.0     748.0     686.0     863.0\n",
       "   9 │    541.0     857.0    1317.0     814.0    1166.0     620.0    1198.0    ⋯\n",
       "  10 │    856.0     463.0     631.0     302.0     460.0     762.0     568.0\n",
       "  11 │    589.0    1055.0     747.0     838.0     877.0    1016.0     602.0\n",
       "  ⋮  │    ⋮         ⋮         ⋮         ⋮         ⋮         ⋮         ⋮        ⋱\n",
       " 351 │   1300.0     969.0    1005.0    1477.0    1080.0     591.0    1108.0\n",
       " 352 │   1505.0    1285.0     958.0    1452.0     803.0    1101.0     916.0    ⋯\n",
       " 353 │   1290.0    1408.0    1292.0     889.0     673.0    1147.0    1183.0\n",
       " 354 │    728.0     162.0     519.0     888.0     586.0     955.0     643.0\n",
       " 355 │    462.0     168.0     570.0     546.0     350.0     813.0     801.0\n",
       " 356 │   1072.0    1118.0    1557.0     652.0    1023.0     780.0    1195.0    ⋯\n",
       " 357 │   1310.0     759.0     607.0     727.0     824.0     804.0     908.0\n",
       " 358 │   1980.0    1003.0    1269.0    1828.0     859.0     780.0    1224.0\n",
       " 359 │   1672.0    1785.0    1511.0    1170.0    1247.0    1431.0    1006.0\n",
       " 360 │   1206.0    1037.0     503.0     494.0     587.0     382.0     969.0    ⋯\n",
       "\u001b[36m                                                920 columns and 339 rows omitted\u001b[0m"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's unwind the spatial axes\n",
    "X_df_static = Matrix(X_df)\n",
    "cols = []\n",
    "for i_var in 1:size(X_df_static, 2)\n",
    "    var_unroll = cat(X_df_static[:,i_var]...; dims = 3)\n",
    "    append!(cols, eachrow(reshape(var_unroll, (9, nrow(X_df)))))\n",
    "end\n",
    "X_df_static = DataFrame(cols, [\"$n[$i][$j]\" for n in names(X_df) for i in 1:3 for j in 1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "49d439a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "● MultiModalDataset{DataFrame}\n",
       "   └─ dimensionalities: (2, 0)\n",
       "- Modality 1 / 2\n",
       "   └─ dimensionality: 2\n",
       "\u001b[1m360×103 SubDataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m V1                                \u001b[0m\u001b[1m V2                                \u001b[0m\u001b[1m V\u001b[0m ⋯\n",
       "     │\u001b[90m Array…                            \u001b[0m\u001b[90m Array…                            \u001b[0m\u001b[90m A\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │ [1186.0 1294.0 1291.0; 1701.0 17…  [1069.0 1177.0 1369.0; 1708.0 14…  [ ⋯\n",
       "   2 │ [1153.0 1201.0 1563.0; 784.0 161…  [1024.0 1258.0 1480.0; 1024.0 15…  [\n",
       "   3 │ [880.0 804.0 727.0; 672.0 865.0 …  [726.0 486.0 771.0; 498.0 629.0 …  [\n",
       "   4 │ [1454.0 1082.0 1291.0; 512.0 985…  [1178.0 876.0 1229.0; 741.0 1014…  [\n",
       "   5 │ [1603.0 1091.0 594.0; 1003.0 126…  [1602.0 1092.0 689.0; 804.0 1240…  [ ⋯\n",
       "   6 │ [966.0 632.0 605.0; 482.0 669.0 …  [1171.0 630.0 451.0; 446.0 507.0…  [\n",
       "   7 │ [1219.0 1459.0 1247.0; 1377.0 48…  [1377.0 1259.0 1255.0; 1298.0 88…  [\n",
       "   8 │ [510.0 686.0 863.0; 637.0 748.0 …  [365.0 726.0 761.0; 810.0 639.0 …  [\n",
       "   9 │ [541.0 814.0 1198.0; 857.0 1166.…  [530.0 676.0 910.0; 697.0 848.0 …  [ ⋯\n",
       "  10 │ [856.0 302.0 568.0; 463.0 460.0 …  [698.0 291.0 598.0; 144.0 563.0 …  [\n",
       "  11 │ [589.0 838.0 602.0; 1055.0 877.0…  [671.0 617.0 508.0; 800.0 688.0 …  [\n",
       "  ⋮  │                 ⋮                                  ⋮                    ⋱\n",
       " 351 │ [1300.0 1477.0 1108.0; 969.0 108…  [1289.0 1252.0 1139.0; 1172.0 10…  [\n",
       " 352 │ [1505.0 1452.0 916.0; 1285.0 803…  [1230.0 1355.0 1085.0; 1304.0 10…  [ ⋯\n",
       " 353 │ [1290.0 889.0 1183.0; 1408.0 673…  [1281.0 1061.0 1176.0; 989.0 949…  [\n",
       " 354 │ [728.0 888.0 643.0; 162.0 586.0 …  [493.0 729.0 754.0; 485.0 367.0 …  [\n",
       " 355 │ [462.0 546.0 801.0; 168.0 350.0 …  [539.0 362.0 800.0; 171.0 603.0 …  [\n",
       " 356 │ [1072.0 652.0 1195.0; 1118.0 102…  [1049.0 724.0 962.0; 1128.0 1006…  [ ⋯\n",
       " 357 │ [1310.0 727.0 908.0; 759.0 824.0…  [1364.0 780.0 947.0; 524.0 676.0…  [\n",
       " 358 │ [1980.0 1828.0 1224.0; 1003.0 85…  [2001.0 1745.0 1178.0; 1065.0 63…  [\n",
       " 359 │ [1672.0 1170.0 1006.0; 1785.0 12…  [1656.0 1129.0 873.0; 1746.0 118…  [\n",
       " 360 │ [1206.0 494.0 969.0; 1037.0 587.…  [1140.0 818.0 659.0; 879.0 698.0…  [ ⋯\n",
       "\u001b[36m                                                101 columns and 339 rows omitted\u001b[0m\n",
       "- Modality 2 / 2\n",
       "   └─ dimensionality: 0\n",
       "\u001b[1m360×927 SubDataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m V1[1][1] \u001b[0m\u001b[1m V1[1][2] \u001b[0m\u001b[1m V1[1][3] \u001b[0m\u001b[1m V1[2][1] \u001b[0m\u001b[1m V1[2][2] \u001b[0m\u001b[1m V1[2][3] \u001b[0m\u001b[1m V1[3][1] \u001b[0m\u001b[1m V\u001b[0m ⋯\n",
       "     │\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m F\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │   1186.0    1701.0    1823.0    1294.0    1705.0    1359.0    1291.0    ⋯\n",
       "   2 │   1153.0     784.0    1173.0    1201.0    1618.0    1071.0    1563.0\n",
       "   3 │    880.0     672.0     540.0     804.0     865.0     757.0     727.0\n",
       "   4 │   1454.0     512.0    1430.0    1082.0     985.0    1400.0    1291.0\n",
       "   5 │   1603.0    1003.0    1222.0    1091.0    1260.0     801.0     594.0    ⋯\n",
       "   6 │    966.0     482.0     427.0     632.0     669.0     811.0     605.0\n",
       "   7 │   1219.0    1377.0    1187.0    1459.0     483.0     773.0    1247.0\n",
       "   8 │    510.0     637.0     815.0     686.0     748.0     686.0     863.0\n",
       "   9 │    541.0     857.0    1317.0     814.0    1166.0     620.0    1198.0    ⋯\n",
       "  10 │    856.0     463.0     631.0     302.0     460.0     762.0     568.0\n",
       "  11 │    589.0    1055.0     747.0     838.0     877.0    1016.0     602.0\n",
       "  ⋮  │    ⋮         ⋮         ⋮         ⋮         ⋮         ⋮         ⋮        ⋱\n",
       " 351 │   1300.0     969.0    1005.0    1477.0    1080.0     591.0    1108.0\n",
       " 352 │   1505.0    1285.0     958.0    1452.0     803.0    1101.0     916.0    ⋯\n",
       " 353 │   1290.0    1408.0    1292.0     889.0     673.0    1147.0    1183.0\n",
       " 354 │    728.0     162.0     519.0     888.0     586.0     955.0     643.0\n",
       " 355 │    462.0     168.0     570.0     546.0     350.0     813.0     801.0\n",
       " 356 │   1072.0    1118.0    1557.0     652.0    1023.0     780.0    1195.0    ⋯\n",
       " 357 │   1310.0     759.0     607.0     727.0     824.0     804.0     908.0\n",
       " 358 │   1980.0    1003.0    1269.0    1828.0     859.0     780.0    1224.0\n",
       " 359 │   1672.0    1785.0    1511.0    1170.0    1247.0    1431.0    1006.0\n",
       " 360 │   1206.0    1037.0     503.0     494.0     587.0     382.0     969.0    ⋯\n",
       "\u001b[36m                                                920 columns and 339 rows omitted\u001b[0m\n"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using SoleData\n",
    "\n",
    "X_multimodal = MultiModalDataset([X_df, X_df_static])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bc1235d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModalDecisionTree(\n",
       "  max_depth = nothing, \n",
       "  min_samples_leaf = 4, \n",
       "  min_purity_increase = 0.002, \n",
       "  max_purity_at_leaf = Inf, \n",
       "  max_modal_depth = nothing, \n",
       "  relations = :RCC8, \n",
       "  features = nothing, \n",
       "  conditions = nothing, \n",
       "  featvaltype = Float64, \n",
       "  initconditions = nothing, \n",
       "  downsize = ModalDecisionTrees.MLJInterface.var\"#downsize#43\"(), \n",
       "  print_progress = false, \n",
       "  rng = Random._GLOBAL_RNG(), \n",
       "  display_depth = nothing, \n",
       "  min_samples_split = nothing, \n",
       "  n_subfeatures = identity, \n",
       "  post_prune = false, \n",
       "  merge_purity_threshold = nothing, \n",
       "  feature_importance = :split)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using ModalDecisionTrees\n",
    "\n",
    "model = ModalDecisionTree(; relations = :RCC8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "18e9bda4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecomputing logiset...\n",
      "\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mScalarOneStepMemoset: Found globalrel in relations in a single-world case.\n",
      "\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ SoleModels ~/.julia/packages/SoleModels/xvfwj/src/logisets/scalar/onestep-memoset.jl:304\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "694.598215 seconds (7.75 G allocations: 729.113 GiB, 11.43% gc time, 12.83% compilation time: <1% of which was recompilation)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PerformanceEvaluation object with these fields:\n",
       "  model, measure, operation, measurement, per_fold,\n",
       "  per_observation, fitted_params_per_fold,\n",
       "  report_per_fold, train_test_rows, resampling, repeats\n",
       "Extract:\n",
       "┌────────────┬──────────────┬─────────────┬─────────┬───────────────────────────\n",
       "│\u001b[22m measure    \u001b[0m│\u001b[22m operation    \u001b[0m│\u001b[22m measurement \u001b[0m│\u001b[22m 1.96*SE \u001b[0m│\u001b[22m per_fold                \u001b[0m ⋯\n",
       "├────────────┼──────────────┼─────────────┼─────────┼───────────────────────────\n",
       "│ Accuracy() │ predict_mode │ 0.775       │ 0.0537  │ [0.778, 0.833, 0.611, 0. ⋯\n",
       "└────────────┴──────────────┴─────────────┴─────────┴───────────────────────────\n",
       "\u001b[36m                                                                1 column omitted\u001b[0m\n"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train in cross-validation!\n",
    "e = @time evaluate!(machine(model, X_multimodal, y);\n",
    "    resampling=StratifiedCV(rng = Random.Xoshiro(1), shuffle=true, nfolds = 10),\n",
    "    measures=[accuracy],\n",
    "    verbosity=0,\n",
    "    check_measure=false\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3a463fb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-element Vector{Vector{Float64}}:\n",
       " [0.7777777777777778, 0.8333333333333334, 0.6111111111111112, 0.9166666666666666, 0.7777777777777778, 0.7777777777777778, 0.6944444444444444, 0.75, 0.7777777777777778, 0.8333333333333334]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test accuracies per fold\n",
    "e.per_fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "534b72f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10-element Vector{DecisionTree{String, MultiFormula{Atom{ScalarCondition{Int64, SoleModels.UnivariateValue, ScalarMetaCondition{SoleModels.UnivariateValue, typeof(>=)}}}}, SoleModels.ConstantModel{String}}}:\n",
       " ▣ {2}(V923 ≥ 1714)\n",
       "├✔ {2}(V52 ≥ 1122)\n",
       "│├✔ {2}(V248 ≥ 1882)\n",
       "││├✔ Painted metal sheets\n",
       "││└✘ {1}(⟨G⟩(min[V47] ≥ 2103))\n",
       "││ ├✔ Gravel\n",
       "││ └✘ {2}(V178 ≥ 1476)\n",
       "││  ├✔ Self-Blocking Bricks\n",
       "││  └✘ {1}(⟨G⟩(min[V2] < 1021))\n",
       "││   ├✔ Self-Blocking Bricks\n",
       "││   └✘ Gravel\n",
       "│└✘ {1}(⟨G⟩(min[V68] < 563))\n",
       "│ ├✔ Trees\n",
       "│ └✘ {1}(⟨G⟩(min[V29] ≥ 1347))\n",
       "│  ├✔ Bare Soil\n",
       "│  └✘ {2}(V662 ≥ 1915)\n",
       "│   ├✔ Meadows\n",
       "│   └✘ {2}(V63 ≥ 579)\n",
       "│    ├✔ Meadows\n",
       "│    └✘ Bare Soil\n",
       "└✘ {1}(⟨G⟩(min[V6] < 567))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V593 ≥ 1343)\n",
       "  ├✔ {2}(V917 ≥ 1490)\n",
       "  │├✔ Gravel\n",
       "  │└✘ {1}(⟨G⟩(min[V3] < 754))\n",
       "  │ ├✔ Asphalt\n",
       "  │ └✘ Bitumen\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V923 ≥ 1714)\n",
       "├✔ {2}(V44 ≥ 1109)\n",
       "│├✔ {1}(⟨G⟩(min[V38] ≥ 2424))\n",
       "││├✔ Painted metal sheets\n",
       "││└✘ {1}(⟨G⟩(min[V67] ≥ 2152))\n",
       "││ ├✔ Gravel\n",
       "││ └✘ {2}(V743 ≥ 1919)\n",
       "││  ├✔ Self-Blocking Bricks\n",
       "││  └✘ Gravel\n",
       "│└✘ {1}(⟨G⟩(min[V44] < 543))\n",
       "│ ├✔ {1}(⟨G⟩((min[V44] < 543) ∧ ⟨N̅T̅P̅P̅⟩(min[V28] ≥ 620)))\n",
       "│ │├✔ Meadows\n",
       "│ │└✘ Trees\n",
       "│ └✘ {1}(⟨G⟩(min[V29] ≥ 1347))\n",
       "│  ├✔ Bare Soil\n",
       "│  └✘ {2}(V662 ≥ 1908)\n",
       "│   ├✔ Meadows\n",
       "│   └✘ Bare Soil\n",
       "└✘ {1}(⟨G⟩(min[V6] < 567))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V593 ≥ 1343)\n",
       "  ├✔ {2}(V667 ≥ 1686)\n",
       "  │├✔ Gravel\n",
       "  │└✘ {2}(V201 ≥ 1266)\n",
       "  │ ├✔ Bitumen\n",
       "  │ └✘ Asphalt\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V923 ≥ 1714)\n",
       "├✔ {2}(V52 ≥ 1114)\n",
       "│├✔ {1}(⟨G⟩(min[V38] ≥ 2267))\n",
       "││├✔ {1}(⟨G⟩((min[V38] ≥ 2267) ∧ (min[V1] ≥ 2642)))\n",
       "│││├✔ Painted metal sheets\n",
       "│││└✘ Bare Soil\n",
       "││└✘ {2}(V167 ≥ 1475)\n",
       "││ ├✔ {1}(⟨G⟩(min[V4] < 1185))\n",
       "││ │├✔ Self-Blocking Bricks\n",
       "││ │└✘ Gravel\n",
       "││ └✘ Gravel\n",
       "│└✘ {1}(⟨G⟩(min[V68] < 563))\n",
       "│ ├✔ Trees\n",
       "│ └✘ {1}(⟨G⟩(min[V29] ≥ 1347))\n",
       "│  ├✔ Bare Soil\n",
       "│  └✘ {2}(V877 ≥ 2483)\n",
       "│   ├✔ Meadows\n",
       "│   └✘ Bare Soil\n",
       "└✘ {1}(⟨G⟩(min[V5] < 516))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V593 ≥ 1343)\n",
       "  ├✔ {2}(V917 ≥ 1490)\n",
       "  │├✔ {2}(V106 ≥ 1346)\n",
       "  ││├✔ Bitumen\n",
       "  ││└✘ Gravel\n",
       "  │└✘ Bitumen\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V923 ≥ 1714)\n",
       "├✔ {2}(V52 ≥ 1114)\n",
       "│├✔ {2}(V248 ≥ 1882)\n",
       "││├✔ {1}(⟨G⟩(min[V1] ≥ 2642))\n",
       "│││├✔ Painted metal sheets\n",
       "│││└✘ Bare Soil\n",
       "││└✘ {1}(⟨G⟩(min[V48] ≥ 2114))\n",
       "││ ├✔ Gravel\n",
       "││ └✘ {2}(V742 ≥ 1915)\n",
       "││  ├✔ Self-Blocking Bricks\n",
       "││  └✘ {2}(V132 ≥ 1394)\n",
       "││   ├✔ Self-Blocking Bricks\n",
       "││   └✘ Gravel\n",
       "│└✘ {2}(V540 ≥ 428)\n",
       "│ ├✔ Meadows\n",
       "│ └✘ Trees\n",
       "└✘ {1}(⟨G⟩(min[V6] < 567))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V593 ≥ 1343)\n",
       "  ├✔ {2}(V876 ≥ 1600)\n",
       "  │├✔ Gravel\n",
       "  │└✘ {2}(V155 ≥ 1168)\n",
       "  │ ├✔ Bitumen\n",
       "  │ └✘ Asphalt\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V923 ≥ 1715)\n",
       "├✔ {2}(V34 ≥ 1009)\n",
       "│├✔ {2}(V248 ≥ 1882)\n",
       "││├✔ {1}(⟨G⟩(min[V1] ≥ 2642))\n",
       "│││├✔ Painted metal sheets\n",
       "│││└✘ Bare Soil\n",
       "││└✘ {1}(⟨G⟩(min[V48] ≥ 2114))\n",
       "││ ├✔ Gravel\n",
       "││ └✘ {2}(V197 ≥ 1459)\n",
       "││  ├✔ Self-Blocking Bricks\n",
       "││  └✘ Gravel\n",
       "│└✘ {2}(V540 ≥ 428)\n",
       "│ ├✔ {1}(⟨G⟩(min[V29] ≥ 1347))\n",
       "│ │├✔ Bare Soil\n",
       "│ │└✘ {2}(V662 ≥ 1908)\n",
       "│ │ ├✔ Meadows\n",
       "│ │ └✘ {1}(⟨G⟩(min[V37] ≥ 891))\n",
       "│ │  ├✔ Bare Soil\n",
       "│ │  └✘ Meadows\n",
       "│ └✘ {1}(⟨G⟩(min[V72] < 1534))\n",
       "│  ├✔ Trees\n",
       "│  └✘ Meadows\n",
       "└✘ {1}(⟨G⟩(min[V6] < 597))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V593 ≥ 1343)\n",
       "  ├✔ {2}(V876 ≥ 1600)\n",
       "  │├✔ Gravel\n",
       "  │└✘ {1}(⟨G⟩(min[V18] ≥ 1318))\n",
       "  │ ├✔ Bitumen\n",
       "  │ └✘ Asphalt\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V860 ≥ 1700)\n",
       "├✔ {2}(V52 ≥ 1114)\n",
       "│├✔ {1}(⟨G⟩(min[V38] ≥ 2424))\n",
       "││├✔ {1}(⟨G⟩((min[V38] ≥ 2424) ∧ (min[V1] ≥ 2769)))\n",
       "│││├✔ Painted metal sheets\n",
       "│││└✘ Bare Soil\n",
       "││└✘ {1}(⟨G⟩(min[V48] ≥ 2114))\n",
       "││ ├✔ Gravel\n",
       "││ └✘ {2}(V743 ≥ 1919)\n",
       "││  ├✔ Self-Blocking Bricks\n",
       "││  └✘ Gravel\n",
       "│└✘ {2}(V603 ≥ 606)\n",
       "│ ├✔ Meadows\n",
       "│ └✘ Trees\n",
       "└✘ {1}(⟨G⟩(min[V6] < 567))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V593 ≥ 1343)\n",
       "  ├✔ {2}(V142 ≥ 1281)\n",
       "  │├✔ Bitumen\n",
       "  │└✘ {2}(V917 ≥ 1401)\n",
       "  │ ├✔ Gravel\n",
       "  │ └✘ Bitumen\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V923 ≥ 1714)\n",
       "├✔ {2}(V52 ≥ 1114)\n",
       "│├✔ {2}(V248 ≥ 1882)\n",
       "││├✔ {1}(⟨G⟩(min[V1] ≥ 2642))\n",
       "│││├✔ Painted metal sheets\n",
       "│││└✘ Bare Soil\n",
       "││└✘ Self-Blocking Bricks\n",
       "│└✘ {2}(V540 ≥ 428)\n",
       "│ ├✔ {1}(⟨G⟩(min[V29] ≥ 1347))\n",
       "│ │├✔ Bare Soil\n",
       "│ │└✘ {1}(⟨G⟩(min[V85] < 1923))\n",
       "│ │ ├✔ Bare Soil\n",
       "│ │ └✘ {2}(V6 ≥ 969)\n",
       "│ │  ├✔ Bare Soil\n",
       "│ │  └✘ Meadows\n",
       "│ └✘ {1}(⟨G⟩(min[V72] < 1534))\n",
       "│  ├✔ Trees\n",
       "│  └✘ Meadows\n",
       "└✘ {1}(⟨G⟩(min[V6] < 567))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V593 ≥ 1343)\n",
       "  ├✔ {2}(V415 ≥ 1666)\n",
       "  │├✔ Gravel\n",
       "  │└✘ Bitumen\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V923 ≥ 1714)\n",
       "├✔ {2}(V52 ≥ 1114)\n",
       "│├✔ {2}(V248 ≥ 1882)\n",
       "││├✔ Painted metal sheets\n",
       "││└✘ Self-Blocking Bricks\n",
       "│└✘ {2}(V477 ≥ 482)\n",
       "│ ├✔ {1}(⟨G⟩(min[V29] ≥ 1348))\n",
       "│ │├✔ Bare Soil\n",
       "│ │└✘ {2}(V762 ≥ 2450)\n",
       "│ │ ├✔ Meadows\n",
       "│ │ └✘ Bare Soil\n",
       "│ └✘ Trees\n",
       "└✘ {1}(⟨G⟩(min[V4] < 496))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V575 ≥ 1369)\n",
       "  ├✔ {2}(V876 ≥ 1600)\n",
       "  │├✔ Gravel\n",
       "  │└✘ {2}(V155 ≥ 1168)\n",
       "  │ ├✔ Bitumen\n",
       "  │ └✘ Asphalt\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V923 ≥ 1714)\n",
       "├✔ {2}(V52 ≥ 1114)\n",
       "│├✔ {2}(V239 ≥ 1857)\n",
       "││├✔ {1}(⟨G⟩(min[V1] ≥ 2642))\n",
       "│││├✔ Painted metal sheets\n",
       "│││└✘ Bare Soil\n",
       "││└✘ {2}(V170 ≥ 1534)\n",
       "││ ├✔ Self-Blocking Bricks\n",
       "││ └✘ {2}(V14 ≥ 1205)\n",
       "││  ├✔ Gravel\n",
       "││  └✘ {2}(V528 ≥ 1925)\n",
       "││   ├✔ Gravel\n",
       "││   └✘ Self-Blocking Bricks\n",
       "│└✘ {2}(V540 ≥ 428)\n",
       "│ ├✔ {1}(⟨G⟩(min[V28] ≥ 1302))\n",
       "│ │├✔ Bare Soil\n",
       "│ │└✘ {1}(⟨G⟩(min[V102] < 2299))\n",
       "│ │ ├✔ Meadows\n",
       "│ │ └✘ Meadows\n",
       "│ └✘ Trees\n",
       "└✘ {1}(⟨G⟩(min[V6] < 567))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V593 ≥ 1343)\n",
       "  ├✔ Bitumen\n",
       "  └✘ Asphalt\n",
       "\n",
       " ▣ {2}(V923 ≥ 1649)\n",
       "├✔ {2}(V52 ≥ 1114)\n",
       "│├✔ {2}(V248 ≥ 1882)\n",
       "││├✔ {1}(⟨G⟩(min[V1] ≥ 2642))\n",
       "│││├✔ Painted metal sheets\n",
       "│││└✘ Bare Soil\n",
       "││└✘ {2}(V141 ≥ 1464)\n",
       "││ ├✔ Self-Blocking Bricks\n",
       "││ └✘ Gravel\n",
       "│└✘ {2}(V540 ≥ 428)\n",
       "│ ├✔ {1}(⟨G⟩(min[V29] ≥ 1347))\n",
       "│ │├✔ Bare Soil\n",
       "│ │└✘ {1}(⟨G⟩(min[V102] < 2299))\n",
       "│ │ ├✔ Bare Soil\n",
       "│ │ └✘ Meadows\n",
       "│ └✘ Trees\n",
       "└✘ {1}(⟨G⟩(min[V6] < 567))\n",
       " ├✔ Shadows\n",
       " └✘ {2}(V566 ≥ 1385)\n",
       "  ├✔ {2}(V845 ≥ 1661)\n",
       "  │├✔ Gravel\n",
       "  │└✘ {2}(V175 ≥ 1261)\n",
       "  │ ├✔ Bitumen\n",
       "  │ └✘ Asphalt\n",
       "  └✘ Asphalt\n"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trees = map((((train_idxs, test_idxs), rep),)->begin\n",
    "    predictions, tree_test = rep.sprinkle(slicedataset(X_multimodal, test_idxs), y[test_idxs]; simplify = true)\n",
    "    tree_test\n",
    "end, zip(e.train_test_rows, e.report_per_fold))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "38741669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134-element Vector{Rule{String, A, SoleModels.ConstantModel{String}} where A<:Formula}:\n",
       " ▣ {2}(V248 ≥ 1882)  🠮  Painted metal sheets\n",
       "\n",
       " ▣ {1}(⟨G⟩(min[V47] ≥ 2103)) ∧ {2}(V52 ≥ 1122) ∧ (V248 < 1882)  🠮  Gravel\n",
       "\n",
       " ▣ {1}([G](min[V47] < 2103)) ∧ {2}(V178 ≥ 1476) ∧ (V248 < 1882)  🠮  Self-Blocking Bricks\n",
       "\n",
       " ▣ {1}⟨G⟩(min[V2] < 1021) ∧ [G](min[V47] < 2103) ∧ {2}(V52 ≥ 1122) ∧ (V248 < 1882) ∧ (V178 < 1476)  🠮  Self-Blocking Bricks\n",
       "\n",
       " ▣ {1}[G](min[V47] < 2103) ∧ [G](min[V2] ≥ 1021) ∧ {2}(V52 ≥ 1122) ∧ (V248 < 1882) ∧ (V178 < 1476)  🠮  Gravel\n",
       "\n",
       " ▣ {1}(⟨G⟩(min[V68] < 563)) ∧ {2}(V923 ≥ 1714) ∧ (V52 < 1122)  🠮  Trees\n",
       "\n",
       " ▣ {1}⟨G⟩(min[V29] ≥ 1347) ∧ [G](min[V68] ≥ 563) ∧ {2}(V923 ≥ 1714) ∧ (V52 < 1122)  🠮  Bare Soil\n",
       "\n",
       " ▣ {1}[G](min[V68] ≥ 563) ∧ [G](min[V29] < 1347) ∧ {2}(V662 ≥ 1915) ∧ (V52 < 1122)  🠮  Meadows\n",
       "\n",
       " ▣ {1}[G](min[V68] ≥ 563) ∧ [G](min[V29] < 1347) ∧ {2}(V63 ≥ 579) ∧ (V52 < 1122) ∧ (V662 < 1915)  🠮  Meadows\n",
       "\n",
       " ▣ {1}[G](min[V68] ≥ 563) ∧ [G](min[V29] < 1347) ∧ {2}(V923 ≥ 1714) ∧ (V52 < 1122) ∧ (V662 < 1915) ∧ (V63 < 579)  🠮  Bare Soil\n",
       "\n",
       " ▣ {1}(⟨G⟩(min[V6] < 567)) ∧ {2}(V923 < 1714)  🠮  Shadows\n",
       "\n",
       " ▣ {1}([G](min[V6] ≥ 567)) ∧ {2}(V917 ≥ 1490) ∧ (V923 < 1714)  🠮  Gravel\n",
       "\n",
       " ▣ {1}⟨G⟩(min[V3] < 754) ∧ [G](min[V6] ≥ 567) ∧ {2}(V593 ≥ 1343) ∧ (V923 < 1714) ∧ (V917 < 1490)  🠮  Asphalt\n",
       "\n",
       " ⋮\n",
       " ▣ {1}([G](min[V1] < 2642)) ∧ {2}(V248 ≥ 1882)  🠮  Bare Soil\n",
       "\n",
       " ▣ {2}(V141 ≥ 1464) ∧ (V248 < 1882)  🠮  Self-Blocking Bricks\n",
       "\n",
       " ▣ {2}(V52 ≥ 1114) ∧ (V248 < 1882) ∧ (V141 < 1464)  🠮  Gravel\n",
       "\n",
       " ▣ {1}(⟨G⟩(min[V29] ≥ 1347)) ∧ {2}(V540 ≥ 428) ∧ (V52 < 1114)  🠮  Bare Soil\n",
       "\n",
       " ▣ {1}⟨G⟩(min[V102] < 2299) ∧ [G](min[V29] < 1347) ∧ {2}(V540 ≥ 428) ∧ (V52 < 1114)  🠮  Bare Soil\n",
       "\n",
       " ▣ {1}[G](min[V29] < 1347) ∧ [G](min[V102] ≥ 2299) ∧ {2}(V540 ≥ 428) ∧ (V52 < 1114)  🠮  Meadows\n",
       "\n",
       " ▣ {2}(V923 ≥ 1649) ∧ (V52 < 1114) ∧ (V540 < 428)  🠮  Trees\n",
       "\n",
       " ▣ {1}(⟨G⟩(min[V6] < 567)) ∧ {2}(V923 < 1649)  🠮  Shadows\n",
       "\n",
       " ▣ {1}([G](min[V6] ≥ 567)) ∧ {2}(V845 ≥ 1661) ∧ (V923 < 1649)  🠮  Gravel\n",
       "\n",
       " ▣ {1}([G](min[V6] ≥ 567)) ∧ {2}(V175 ≥ 1261) ∧ (V923 < 1649) ∧ (V845 < 1661)  🠮  Bitumen\n",
       "\n",
       " ▣ {1}([G](min[V6] ≥ 567)) ∧ {2}(V566 ≥ 1385) ∧ (V923 < 1649) ∧ (V845 < 1661) ∧ (V175 < 1261)  🠮  Asphalt\n",
       "\n",
       " ▣ {1}([G](min[V6] ≥ 567)) ∧ {2}(V923 < 1649) ∧ (V566 < 1385)  🠮  Asphalt\n"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = vcat(listrules.(trees)...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f995a6fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "▣ {2}(V248 ≥ 1882)  🠮  Painted metal sheets\n",
      "\n",
      "(:supporting_labels, :supporting_predictions, :shortform)\n"
     ]
    }
   ],
   "source": [
    "# Every symbolic model (including rules) can have has additional information attached\n",
    "println(rules[1])\n",
    "\n",
    "ruleinfo = SoleModels.info(rules[1])\n",
    "println(keys(ruleinfo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "34eb479e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ruleinfo[:supporting_predictions] |> length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0da7198c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134-element Vector{NamedTuple{(:ninstances, :confidence, :coverage), Tuple{Int64, Float64, Float64}}}:\n",
       " (ninstances = 7, confidence = 0.57, coverage = 0.19)\n",
       " (ninstances = 6, confidence = 0.67, coverage = 0.17)\n",
       " (ninstances = 6, confidence = 0.67, coverage = 0.17)\n",
       " (ninstances = 6, confidence = 0.67, coverage = 0.17)\n",
       " (ninstances = 6, confidence = 0.67, coverage = 0.17)\n",
       " (ninstances = 5, confidence = 0.4, coverage = 0.14)\n",
       " (ninstances = 5, confidence = 0.4, coverage = 0.14)\n",
       " (ninstances = 5, confidence = 0.8, coverage = 0.14)\n",
       " (ninstances = 5, confidence = 0.6, coverage = 0.14)\n",
       " (ninstances = 5, confidence = 0.8, coverage = 0.14)\n",
       " (ninstances = 5, confidence = 0.8, coverage = 0.14)\n",
       " (ninstances = 5, confidence = 0.6, coverage = 0.14)\n",
       " (ninstances = 5, confidence = 0.6, coverage = 0.14)\n",
       " ⋮\n",
       " (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
       " (ninstances = 1, confidence = 1.0, coverage = 0.03)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort(readmetrics.(rules), by=x->x[:coverage], rev = true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2886c687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "▣ {2}(V52 ≥ 1114.0) ∧ (V248 < 1882.0)  🠮  Self-Blocking Bricks : (ninstances = 7, confidence = 0.57, coverage = 0.19)\n",
      "▣ {1}⟨G⟩(min[V44] < 543.0) ∧ [G]((min[V44] < 543.0) → [N̅T̅P̅P̅](min[V28] < 620.0)) ∧ {2}(V923 ≥ 1714.0) ∧ (V44 < 1109.0)  🠮  Trees : (ninstances = 6, confidence = 0.67, coverage = 0.17)\n",
      "▣ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Meadows : (ninstances = 6, confidence = 0.67, coverage = 0.17)\n",
      "▣ {2}(V52 ≥ 1114.0) ∧ (V248 < 1882.0)  🠮  Self-Blocking Bricks : (ninstances = 6, confidence = 0.67, coverage = 0.17)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1714.0)  🠮  Bitumen : (ninstances = 6, confidence = 0.67, coverage = 0.17)\n",
      "▣ {1}[G](min[V68] ≥ 563.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V923 ≥ 1714.0) ∧ (V52 < 1114.0) ∧ (V877 < 2483.0)  🠮  Bare Soil : (ninstances = 5, confidence = 0.4, coverage = 0.14)\n",
      "▣ {1}([G](min[V48] < 2114.0)) ∧ {2}(V197 ≥ 1459.0) ∧ (V248 < 1882.0)  🠮  Self-Blocking Bricks : (ninstances = 5, confidence = 0.4, coverage = 0.14)\n",
      "▣ {1}(⟨G⟩(min[V6] < 597.0)) ∧ {2}(V923 < 1715.0)  🠮  Shadows : (ninstances = 5, confidence = 0.8, coverage = 0.14)\n",
      "▣ {1}[G](min[V38] < 2424.0) ∧ [G](min[V48] < 2114.0) ∧ {2}(V52 ≥ 1114.0) ∧ (V743 < 1919.0)  🠮  Gravel : (ninstances = 5, confidence = 0.6, coverage = 0.14)\n",
      "▣ {2}(V603 ≥ 606.0) ∧ (V52 < 1114.0)  🠮  Meadows : (ninstances = 5, confidence = 0.8, coverage = 0.14)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1714.0) ∧ (V415 < 1666.0)  🠮  Bitumen : (ninstances = 5, confidence = 0.8, coverage = 0.14)\n",
      "▣ {1}([G](min[V29] < 1348.0)) ∧ {2}(V762 ≥ 2450.0) ∧ (V52 < 1114.0)  🠮  Meadows : (ninstances = 5, confidence = 0.6, coverage = 0.14)\n",
      "▣ {1}([G](min[V4] ≥ 496.0)) ∧ {2}(V923 < 1714.0) ∧ (V575 < 1369.0)  🠮  Asphalt : (ninstances = 5, confidence = 0.6, coverage = 0.14)\n",
      "▣ {2}(V248 ≥ 1882.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}[G](min[V68] ≥ 563.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V662 ≥ 1915.0) ∧ (V52 < 1122.0)  🠮  Meadows : (ninstances = 4, confidence = 0.5, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V6] < 567.0)) ∧ {2}(V923 < 1714.0)  🠮  Shadows : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V38] ≥ 2424.0)) ∧ {2}(V44 ≥ 1109.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V6] < 567.0)) ∧ {2}(V923 < 1714.0)  🠮  Shadows : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V923 < 1714.0) ∧ (V593 < 1343.0)  🠮  Asphalt : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩((min[V38] ≥ 2267.0) ∧ (min[V1] ≥ 2642.0))) ∧ {2}(V52 ≥ 1114.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}([G](min[V5] ≥ 516.0)) ∧ {2}(V106 ≥ 1346.0) ∧ (V923 < 1714.0)  🠮  Bitumen : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}([G](min[V5] ≥ 516.0)) ∧ {2}(V923 < 1714.0) ∧ (V593 < 1343.0)  🠮  Asphalt : (ninstances = 4, confidence = 0.75, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V1] ≥ 2642.0)) ∧ {2}(V248 ≥ 1882.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {2}(V923 ≥ 1714.0) ∧ (V52 < 1114.0) ∧ (V540 < 428.0)  🠮  Trees : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V6] < 567.0)) ∧ {2}(V923 < 1714.0)  🠮  Shadows : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V155 ≥ 1168.0) ∧ (V923 < 1714.0) ∧ (V876 < 1600.0)  🠮  Bitumen : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V1] ≥ 2642.0)) ∧ {2}(V248 ≥ 1882.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V72] < 1534.0)) ∧ {2}(V923 ≥ 1715.0) ∧ (V34 < 1009.0) ∧ (V540 < 428.0)  🠮  Trees : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}([G](min[V6] ≥ 597.0)) ∧ {2}(V923 < 1715.0) ∧ (V593 < 1343.0)  🠮  Asphalt : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V6] < 567.0)) ∧ {2}(V860 < 1700.0)  🠮  Shadows : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V860 < 1700.0) ∧ (V593 < 1343.0)  🠮  Asphalt : (ninstances = 4, confidence = 0.75, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V1] ≥ 2642.0)) ∧ {2}(V248 ≥ 1882.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V72] < 1534.0)) ∧ {2}(V923 ≥ 1714.0) ∧ (V52 < 1114.0) ∧ (V540 < 428.0)  🠮  Trees : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V6] < 567.0)) ∧ {2}(V923 < 1714.0)  🠮  Shadows : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {2}(V248 ≥ 1882.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}([G](min[V4] ≥ 496.0)) ∧ {2}(V155 ≥ 1168.0) ∧ (V923 < 1714.0) ∧ (V876 < 1600.0)  🠮  Bitumen : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V1] ≥ 2642.0)) ∧ {2}(V239 ≥ 1857.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {2}(V923 ≥ 1714.0) ∧ (V52 < 1114.0) ∧ (V540 < 428.0)  🠮  Trees : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V6] < 567.0)) ∧ {2}(V923 < 1714.0)  🠮  Shadows : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V1] ≥ 2642.0)) ∧ {2}(V248 ≥ 1882.0)  🠮  Painted metal sheets : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {2}(V141 ≥ 1464.0) ∧ (V248 < 1882.0)  🠮  Self-Blocking Bricks : (ninstances = 4, confidence = 0.75, coverage = 0.11)\n",
      "▣ {1}⟨G⟩(min[V102] < 2299.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Bare Soil : (ninstances = 4, confidence = 0.25, coverage = 0.11)\n",
      "▣ {2}(V923 ≥ 1649.0) ∧ (V52 < 1114.0) ∧ (V540 < 428.0)  🠮  Trees : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V6] < 567.0)) ∧ {2}(V923 < 1649.0)  🠮  Shadows : (ninstances = 4, confidence = 1.0, coverage = 0.11)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V923 < 1649.0) ∧ (V566 < 1385.0)  🠮  Asphalt : (ninstances = 4, confidence = 0.75, coverage = 0.11)\n",
      "▣ {1}(⟨G⟩(min[V68] < 563.0)) ∧ {2}(V923 ≥ 1714.0) ∧ (V52 < 1122.0)  🠮  Trees : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}⟨G⟩(min[V29] ≥ 1347.0) ∧ [G](min[V68] ≥ 563.0) ∧ {2}(V923 ≥ 1714.0) ∧ (V52 < 1122.0)  🠮  Bare Soil : (ninstances = 3, confidence = 0.67, coverage = 0.08)\n",
      "▣ {1}[G](min[V6] ≥ 567.0) ∧ [G](min[V3] ≥ 754.0) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1714.0) ∧ (V917 < 1490.0)  🠮  Bitumen : (ninstances = 3, confidence = 0.67, coverage = 0.08)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V923 < 1714.0) ∧ (V593 < 1343.0)  🠮  Asphalt : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}⟨G⟩(min[V29] ≥ 1347.0) ∧ [G](min[V44] ≥ 543.0) ∧ {2}(V923 ≥ 1714.0) ∧ (V44 < 1109.0)  🠮  Bare Soil : (ninstances = 3, confidence = 0.67, coverage = 0.08)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V201 ≥ 1266.0) ∧ (V923 < 1714.0) ∧ (V667 < 1686.0)  🠮  Bitumen : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}[G](min[V68] ≥ 563.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V877 ≥ 2483.0) ∧ (V52 < 1114.0)  🠮  Meadows : (ninstances = 3, confidence = 0.33, coverage = 0.08)\n",
      "▣ {1}(⟨G⟩(min[V5] < 516.0)) ∧ {2}(V923 < 1714.0)  🠮  Shadows : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V923 < 1714.0) ∧ (V593 < 1343.0)  🠮  Asphalt : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}⟨G⟩(min[V18] ≥ 1318.0) ∧ [G](min[V6] ≥ 597.0) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1715.0) ∧ (V876 < 1600.0)  🠮  Bitumen : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}(⟨G⟩((min[V38] ≥ 2424.0) ∧ (min[V1] ≥ 2769.0))) ∧ {2}(V52 ≥ 1114.0)  🠮  Painted metal sheets : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}⟨G⟩(min[V38] ≥ 2424.0) ∧ [G]((min[V38] ≥ 2424.0) → (min[V1] < 2769.0)) ∧ {2}(V52 ≥ 1114.0)  🠮  Bare Soil : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {2}(V860 ≥ 1700.0) ∧ (V52 < 1114.0) ∧ (V603 < 606.0)  🠮  Trees : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V142 ≥ 1281.0) ∧ (V860 < 1700.0)  🠮  Bitumen : (ninstances = 3, confidence = 0.67, coverage = 0.08)\n",
      "▣ {1}[G](min[V29] < 1347.0) ∧ [G](min[V85] ≥ 1923.0) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0) ∧ (V6 < 969.0)  🠮  Meadows : (ninstances = 3, confidence = 0.33, coverage = 0.08)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V923 < 1714.0) ∧ (V593 < 1343.0)  🠮  Asphalt : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {2}(V923 ≥ 1714.0) ∧ (V52 < 1114.0) ∧ (V477 < 482.0)  🠮  Trees : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}(⟨G⟩(min[V4] < 496.0)) ∧ {2}(V923 < 1714.0)  🠮  Shadows : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {2}(V170 ≥ 1534.0) ∧ (V239 < 1857.0)  🠮  Self-Blocking Bricks : (ninstances = 3, confidence = 0.33, coverage = 0.08)\n",
      "▣ {1}(⟨G⟩(min[V28] ≥ 1302.0)) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Bare Soil : (ninstances = 3, confidence = 0.67, coverage = 0.08)\n",
      "▣ {1}[G](min[V28] < 1302.0) ∧ [G](min[V102] ≥ 2299.0) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Meadows : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V175 ≥ 1261.0) ∧ (V923 < 1649.0) ∧ (V845 < 1661.0)  🠮  Bitumen : (ninstances = 3, confidence = 1.0, coverage = 0.08)\n",
      "▣ {1}(⟨G⟩(min[V47] ≥ 2103.0)) ∧ {2}(V52 ≥ 1122.0) ∧ (V248 < 1882.0)  🠮  Gravel : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {1}([G](min[V47] < 2103.0)) ∧ {2}(V178 ≥ 1476.0) ∧ (V248 < 1882.0)  🠮  Self-Blocking Bricks : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}⟨G⟩(min[V2] < 1021.0) ∧ [G](min[V47] < 2103.0) ∧ {2}(V52 ≥ 1122.0) ∧ (V248 < 1882.0) ∧ (V178 < 1476.0)  🠮  Self-Blocking Bricks : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {1}[G](min[V68] ≥ 563.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V923 ≥ 1714.0) ∧ (V52 < 1122.0) ∧ (V662 < 1915.0) ∧ (V63 < 579.0)  🠮  Bare Soil : (ninstances = 2, confidence = 0.0, coverage = 0.06)\n",
      "▣ {1}⟨G⟩(min[V67] ≥ 2152.0) ∧ [G](min[V38] < 2424.0) ∧ {2}(V44 ≥ 1109.0)  🠮  Gravel : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {1}[G](min[V38] < 2424.0) ∧ [G](min[V67] < 2152.0) ∧ {2}(V743 ≥ 1919.0)  🠮  Self-Blocking Bricks : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}[G](min[V38] < 2424.0) ∧ [G](min[V67] < 2152.0) ∧ {2}(V44 ≥ 1109.0) ∧ (V743 < 1919.0)  🠮  Gravel : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "▣ {1}(⟨G⟩((min[V44] < 543.0) ∧ ⟨N̅T̅P̅P̅⟩(min[V28] ≥ 620.0))) ∧ {2}(V923 ≥ 1714.0) ∧ (V44 < 1109.0)  🠮  Meadows : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}⟨G⟩(min[V4] < 1185.0) ∧ [G](min[V38] < 2267.0) ∧ {2}(V167 ≥ 1475.0)  🠮  Self-Blocking Bricks : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {1}([G](min[V38] < 2267.0)) ∧ {2}(V52 ≥ 1114.0) ∧ (V167 < 1475.0)  🠮  Gravel : (ninstances = 2, confidence = 0.0, coverage = 0.06)\n",
      "▣ {1}(⟨G⟩(min[V68] < 563.0)) ∧ {2}(V923 ≥ 1714.0) ∧ (V52 < 1114.0)  🠮  Trees : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}⟨G⟩(min[V29] ≥ 1347.0) ∧ [G](min[V68] ≥ 563.0) ∧ {2}(V923 ≥ 1714.0) ∧ (V52 < 1114.0)  🠮  Bare Soil : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {1}([G](min[V5] ≥ 516.0)) ∧ {2}(V917 ≥ 1490.0) ∧ (V923 < 1714.0) ∧ (V106 < 1346.0)  🠮  Gravel : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}([G](min[V1] < 2642.0)) ∧ {2}(V248 ≥ 1882.0)  🠮  Bare Soil : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}([G](min[V48] < 2114.0)) ∧ {2}(V742 ≥ 1915.0) ∧ (V248 < 1882.0)  🠮  Self-Blocking Bricks : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {1}([G](min[V48] < 2114.0)) ∧ {2}(V132 ≥ 1394.0) ∧ (V248 < 1882.0) ∧ (V742 < 1915.0)  🠮  Self-Blocking Bricks : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}([G](min[V48] < 2114.0)) ∧ {2}(V52 ≥ 1114.0) ∧ (V248 < 1882.0) ∧ (V742 < 1915.0) ∧ (V132 < 1394.0)  🠮  Gravel : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {1}[G](min[V29] < 1347.0) ∧ [G](min[V37] < 891.0) ∧ {2}(V540 ≥ 428.0) ∧ (V34 < 1009.0) ∧ (V662 < 1908.0)  🠮  Meadows : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}[G](min[V38] < 2424.0) ∧ [G](min[V48] < 2114.0) ∧ {2}(V743 ≥ 1919.0)  🠮  Self-Blocking Bricks : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V917 ≥ 1401.0) ∧ (V860 < 1700.0) ∧ (V142 < 1281.0)  🠮  Gravel : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {1}(⟨G⟩(min[V29] ≥ 1348.0)) ∧ {2}(V477 ≥ 482.0) ∧ (V52 < 1114.0)  🠮  Bare Soil : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}([G](min[V29] < 1348.0)) ∧ {2}(V477 ≥ 482.0) ∧ (V52 < 1114.0) ∧ (V762 < 2450.0)  🠮  Bare Soil : (ninstances = 2, confidence = 0.0, coverage = 0.06)\n",
      "▣ {2}(V14 ≥ 1205.0) ∧ (V239 < 1857.0) ∧ (V170 < 1534.0)  🠮  Gravel : (ninstances = 2, confidence = 0.5, coverage = 0.06)\n",
      "▣ {2}(V528 ≥ 1925.0) ∧ (V239 < 1857.0) ∧ (V170 < 1534.0) ∧ (V14 < 1205.0)  🠮  Gravel : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V923 < 1714.0) ∧ (V593 < 1343.0)  🠮  Asphalt : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}(⟨G⟩(min[V29] ≥ 1347.0)) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Bare Soil : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V845 ≥ 1661.0) ∧ (V923 < 1649.0)  🠮  Gravel : (ninstances = 2, confidence = 1.0, coverage = 0.06)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V566 ≥ 1385.0) ∧ (V923 < 1649.0) ∧ (V845 < 1661.0) ∧ (V175 < 1261.0)  🠮  Asphalt : (ninstances = 2, confidence = 0.0, coverage = 0.06)\n",
      "▣ {1}[G](min[V47] < 2103.0) ∧ [G](min[V2] ≥ 1021.0) ∧ {2}(V52 ≥ 1122.0) ∧ (V248 < 1882.0) ∧ (V178 < 1476.0)  🠮  Gravel : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}[G](min[V68] ≥ 563.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V63 ≥ 579.0) ∧ (V52 < 1122.0) ∧ (V662 < 1915.0)  🠮  Meadows : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V917 ≥ 1490.0) ∧ (V923 < 1714.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}⟨G⟩(min[V3] < 754.0) ∧ [G](min[V6] ≥ 567.0) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1714.0) ∧ (V917 < 1490.0)  🠮  Asphalt : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}[G](min[V44] ≥ 543.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V662 ≥ 1908.0) ∧ (V44 < 1109.0)  🠮  Meadows : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}[G](min[V44] ≥ 543.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V923 ≥ 1714.0) ∧ (V44 < 1109.0) ∧ (V662 < 1908.0)  🠮  Bare Soil : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V667 ≥ 1686.0) ∧ (V923 < 1714.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1714.0) ∧ (V667 < 1686.0) ∧ (V201 < 1266.0)  🠮  Asphalt : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}⟨G⟩(min[V38] ≥ 2267.0) ∧ [G]((min[V38] ≥ 2267.0) → (min[V1] < 2642.0)) ∧ {2}(V52 ≥ 1114.0)  🠮  Bare Soil : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}[G](min[V38] < 2267.0) ∧ [G](min[V4] ≥ 1185.0) ∧ {2}(V167 ≥ 1475.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V5] ≥ 516.0)) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1714.0) ∧ (V917 < 1490.0)  🠮  Bitumen : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}(⟨G⟩(min[V48] ≥ 2114.0)) ∧ {2}(V52 ≥ 1114.0) ∧ (V248 < 1882.0)  🠮  Gravel : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V876 ≥ 1600.0) ∧ (V923 < 1714.0)  🠮  Gravel : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1714.0) ∧ (V876 < 1600.0) ∧ (V155 < 1168.0)  🠮  Asphalt : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V1] < 2642.0)) ∧ {2}(V248 ≥ 1882.0)  🠮  Bare Soil : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}(⟨G⟩(min[V48] ≥ 2114.0)) ∧ {2}(V34 ≥ 1009.0) ∧ (V248 < 1882.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V48] < 2114.0)) ∧ {2}(V34 ≥ 1009.0) ∧ (V248 < 1882.0) ∧ (V197 < 1459.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}(⟨G⟩(min[V29] ≥ 1347.0)) ∧ {2}(V540 ≥ 428.0) ∧ (V34 < 1009.0)  🠮  Bare Soil : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V29] < 1347.0)) ∧ {2}(V662 ≥ 1908.0) ∧ (V34 < 1009.0)  🠮  Meadows : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}⟨G⟩(min[V37] ≥ 891.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V540 ≥ 428.0) ∧ (V34 < 1009.0) ∧ (V662 < 1908.0)  🠮  Bare Soil : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V72] ≥ 1534.0)) ∧ {2}(V923 ≥ 1715.0) ∧ (V34 < 1009.0) ∧ (V540 < 428.0)  🠮  Meadows : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V6] ≥ 597.0)) ∧ {2}(V876 ≥ 1600.0) ∧ (V923 < 1715.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}[G](min[V6] ≥ 597.0) ∧ [G](min[V18] < 1318.0) ∧ {2}(V593 ≥ 1343.0) ∧ (V923 < 1715.0) ∧ (V876 < 1600.0)  🠮  Asphalt : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}⟨G⟩(min[V48] ≥ 2114.0) ∧ [G](min[V38] < 2424.0) ∧ {2}(V52 ≥ 1114.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V593 ≥ 1343.0) ∧ (V860 < 1700.0) ∧ (V142 < 1281.0) ∧ (V917 < 1401.0)  🠮  Bitumen : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V1] < 2642.0)) ∧ {2}(V248 ≥ 1882.0)  🠮  Bare Soil : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}(⟨G⟩(min[V29] ≥ 1347.0)) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Bare Soil : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}⟨G⟩(min[V85] < 1923.0) ∧ [G](min[V29] < 1347.0) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Bare Soil : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}[G](min[V29] < 1347.0) ∧ [G](min[V85] ≥ 1923.0) ∧ {2}(V6 ≥ 969.0) ∧ (V52 < 1114.0)  🠮  Bare Soil : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V72] ≥ 1534.0)) ∧ {2}(V923 ≥ 1714.0) ∧ (V52 < 1114.0) ∧ (V540 < 428.0)  🠮  Meadows : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V6] ≥ 567.0)) ∧ {2}(V415 ≥ 1666.0) ∧ (V923 < 1714.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V4] ≥ 496.0)) ∧ {2}(V876 ≥ 1600.0) ∧ (V923 < 1714.0)  🠮  Gravel : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V4] ≥ 496.0)) ∧ {2}(V575 ≥ 1369.0) ∧ (V923 < 1714.0) ∧ (V876 < 1600.0) ∧ (V155 < 1168.0)  🠮  Asphalt : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V1] < 2642.0)) ∧ {2}(V239 ≥ 1857.0)  🠮  Bare Soil : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {2}(V52 ≥ 1114.0) ∧ (V239 < 1857.0) ∧ (V170 < 1534.0) ∧ (V14 < 1205.0) ∧ (V528 < 1925.0)  🠮  Self-Blocking Bricks : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {1}⟨G⟩(min[V102] < 2299.0) ∧ [G](min[V28] < 1302.0) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Meadows : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}([G](min[V1] < 2642.0)) ∧ {2}(V248 ≥ 1882.0)  🠮  Bare Soil : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n",
      "▣ {2}(V52 ≥ 1114.0) ∧ (V248 < 1882.0) ∧ (V141 < 1464.0)  🠮  Gravel : (ninstances = 1, confidence = 0.0, coverage = 0.03)\n",
      "▣ {1}[G](min[V29] < 1347.0) ∧ [G](min[V102] ≥ 2299.0) ∧ {2}(V540 ≥ 428.0) ∧ (V52 < 1114.0)  🠮  Meadows : (ninstances = 1, confidence = 1.0, coverage = 0.03)\n"
     ]
    }
   ],
   "source": [
    "# goodrules = filter(r->readmetrics(r)[:ninstances] > 1, rules)\n",
    "goodrules = sort(rules, by=r->readmetrics(r)[:coverage], rev = true)\n",
    "printmodel.(goodrules; show_metrics = true, threshold_digits = 4);"
   ]
  }
 ],
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