{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8214e1d8",
   "metadata": {},
   "source": [
    "# How to get a Ranking Tile with Sorbetto? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c47066c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de2b0c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sorbetto.performance.two_class_classification_performance import (\n",
    "    TwoClassClassificationPerformance,\n",
    ")\n",
    "from sorbetto.core.entity import Entity\n",
    "from sorbetto.tile.ranking_tile import RankingTile\n",
    "from sorbetto.flavor.ranking_flavor import RankingFlavor\n",
    "from sorbetto.parameterization.parameterization_default import ParameterizationDefault"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d3fd08",
   "metadata": {},
   "source": [
    "## Define entity 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6473e1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the performance\n",
    "proba_tn = 0.4\n",
    "proba_fp = 0.045\n",
    "proba_fn = 0.055\n",
    "proba_tp = 0.5\n",
    "\n",
    "# Create a performance object\n",
    "performance1 = TwoClassClassificationPerformance(\n",
    "    ptn=proba_tn, pfp=proba_fp, pfn=proba_fn, ptp=proba_tp\n",
    ")\n",
    "\n",
    "# Create an entity object\n",
    "entity1 = Entity(performance1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b0b7e9",
   "metadata": {},
   "source": [
    "## Define entity 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d19f6f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the performance\n",
    "proba_tn = 0.3\n",
    "proba_fp = 0.055\n",
    "proba_fn = 0.045\n",
    "proba_tp = 0.6\n",
    "\n",
    "# Create a performance object\n",
    "performance2 = TwoClassClassificationPerformance(\n",
    "    ptn=proba_tn, pfp=proba_fp, pfn=proba_fn, ptp=proba_tp\n",
    ")\n",
    "\n",
    "# Create an entity object\n",
    "entity2 = Entity(performance2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d688c1d5",
   "metadata": {},
   "source": [
    "## Define entity 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a79015c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the performance\n",
    "proba_tn = 0.3\n",
    "proba_fp = 0.16\n",
    "proba_fn = 0.14\n",
    "proba_tp = 0.4\n",
    "\n",
    "# Create a performance object\n",
    "performance3 = TwoClassClassificationPerformance(\n",
    "    ptn=proba_tn, pfp=proba_fp, pfn=proba_fn, ptp=proba_tp\n",
    ")\n",
    "\n",
    "# Create an entity object\n",
    "entity3 = Entity(performance3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9d9bf83",
   "metadata": {},
   "source": [
    "## Get Ranking Tile for entity 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88f18a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the flavor object\n",
    "flavor = RankingFlavor(entity=entity1, entity_list=[entity1, entity2, entity3])\n",
    "\n",
    "# Create the tile object\n",
    "tile = RankingTile(\n",
    "    parameterization=ParameterizationDefault(),\n",
    "    flavor=flavor,\n",
    "    name=\"Ranking Tile for entity 1\",\n",
    ")\n",
    "\n",
    "# Draw the entity tile\n",
    "fig, ax = tile.draw()\n",
    "plt.show()"
   ]
  }
 ],
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