{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8214e1d8",
   "metadata": {},
   "source": [
    "# How to get an Entity 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.entity_tile import EntityTile\n",
    "from sorbetto.flavor.entity_flavor import EntityFlavor\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 Entity Tile for first rank"
   ]
  },
  {
   "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 = EntityFlavor(rank=1, entity_list=[entity1, entity2, entity3])\n",
    "\n",
    "# Create the tile object\n",
    "tile = EntityTile(\n",
    "    parameterization=ParameterizationDefault(),\n",
    "    flavor=flavor,\n",
    "    name=\"Entity Tile for 1st rank\",\n",
    ")\n",
    "\n",
    "# Draw the entity tile\n",
    "fig, ax = tile.draw()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "trail",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}