{
 "cells": [
  {
   "source": [
    "# Tutorial 2 - Bank decision loan problem with fuzzy inputs"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "This tutorial uses the fuzzy inference system developed in Tutorial 1."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "\n",
    "os.chdir('/workspaces/fuzzy-expert')\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "source": [
    "## Specification of the fuzzy inference system"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0b87f10-2777-43f0-ab20-9c516cf20dd3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from fuzzy_expert.variable import FuzzyVariable\n",
    "from fuzzy_expert.rule import FuzzyRule\n",
    "from fuzzy_expert.inference import DecompositionalInference\n",
    "\n",
    "variables = {\n",
    "    \"score\": FuzzyVariable(\n",
    "        universe_range=(150, 200),\n",
    "        terms={\n",
    "            \"High\": [(175, 0), (180, 0.2), (185, 0.7), (190, 1)],\n",
    "            \"Low\": [(155, 1), (160, 0.8), (165, 0.5), (170, 0.2), (175, 0)],\n",
    "        },\n",
    "    ),\n",
    "    \"ratio\": FuzzyVariable(\n",
    "        universe_range=(0.1, 1),\n",
    "        terms={\n",
    "            \"Goodr\": [(0.3, 1), (0.4, 0.7), (0.41, 0.3), (0.42, 0)],\n",
    "            \"Badr\": [(0.44, 0), (0.45, 0.3), (0.5, 0.7), (0.7, 1)],\n",
    "        },\n",
    "    ),\n",
    "    #\n",
    "    \"credit\": FuzzyVariable(\n",
    "        universe_range=(0, 10),\n",
    "        terms={\n",
    "            \"Goodc\": [(2, 1), (3, 0.7), (4, 0.3), (5, 0)],\n",
    "            \"Badc\": [(5, 0), (6, 0.3), (7, 0.7), (8, 1)],\n",
    "        },\n",
    "    ),\n",
    "    #\n",
    "    \"decision\": FuzzyVariable(\n",
    "        universe_range=(0, 10),\n",
    "        terms={\n",
    "            \"Approve\": [(5, 0), (6, 0.3), (7, 0.7), (8, 1)],\n",
    "            \"Reject\": [(2, 1), (3, 0.7), (4, 0.3), (5, 0)],\n",
    "        },\n",
    "    ),\n",
    "}\n",
    "\n",
    "rules = [\n",
    "    FuzzyRule(\n",
    "        premise=[\n",
    "            (\"score\", \"High\"),\n",
    "            (\"AND\", \"ratio\", \"Goodr\"),\n",
    "            (\"AND\", \"credit\", \"Goodc\"),\n",
    "        ],\n",
    "        consequence=[(\"decision\", \"Approve\")],\n",
    "    ),\n",
    "    FuzzyRule(\n",
    "        premise=[\n",
    "            (\"score\", \"Low\"),\n",
    "            (\"AND\", \"ratio\", \"Badr\"),\n",
    "            (\"OR\", \"credit\", \"Badc\"),\n",
    "        ],\n",
    "        consequence=[(\"decision\", \"Reject\")],\n",
    "    )\n",
    "]\n",
    "\n",
    "model = DecompositionalInference(\n",
    "    and_operator=\"min\",\n",
    "    or_operator=\"max\",\n",
    "    implication_operator=\"Rc\",\n",
    "    composition_operator=\"max-min\",\n",
    "    production_link=\"max\",\n",
    "    defuzzification_operator=\"cog\",\n",
    ")\n",
    "\n"
   ]
  },
  {
   "source": [
    "## Computation with fuzzy inputs"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Fuzzy inputs are specified as a list of points (x, u), where x is a point in the universe of discourse and u is the corresponding value of the membership function. In the first case, the fuzziness of inputs are considered; however, values are according with an approval decision. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "model.plot(\n",
    "    variables=variables,\n",
    "    rules=rules,\n",
    "    score=[(180, 0.0), (190, 0.2), (195, 0.8), (200, 1.0)],\n",
    "    ratio=[(0.1, 1), (0.3, 1), (0.4, 0.6), (0.41, 0.2),  (0.42, 0)],\n",
    "    credit=[(0, 0), (1, 1), (2, 1), (3, 0.0), (4, 0.0)],\n",
    ")"
   ]
  },
  {
   "source": [
    "In the second case, the values are clearly related to a reject decision."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 9 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "model.plot(\n",
    "    variables=variables,\n",
    "    rules=rules,\n",
    "    score=[(150, 0.9), (155, 0.7), (160, 0.5), (165, 0.3), (170, 0.0)],\n",
    "    ratio=[(0.44, 0), (0.45, 0.3), (0.5, 0.5), (0.7, 0.7),  (1, 0.9)],\n",
    "    credit=[(6, 0), (7, 0.3), (8, 0.5), (9, 0.7), (10, 0.9)],\n",
    ")"
   ]
  },
  {
   "source": [
    "In the third case, two variables have good values and the third a bad value, causing indetermination in the result."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "model.plot(\n",
    "    variables=variables,\n",
    "    rules=rules,\n",
    "    score=[(185, 0.0), (190, 0.4), (195, 0.6), (200, 0.8)],\n",
    "    ratio=[(0.45, 0), (0.5, 0.4), (0.7, 0.6), (1, 0.8)],\n",
    "    credit=[(2, 1), (3, 0.8), (4, 0.6), (4.8, 0.0)],\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.5 64-bit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  },
  "interpreter": {
   "hash": "4cd7ab41f5fca4b9b44701077e38c5ffd31fe66a6cab21e0214b68d958d0e462"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}