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  {
   "cell_type": "markdown",
   "id": "7f035fb5-8e89-47cc-ab5d-4c2c43ab279b",
   "metadata": {},
   "source": [
    "# Reward curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3db2c79-0a3f-462a-a314-b91301d6dec0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "\n",
    "from __future__ import annotations\n",
    "\n",
    "import my_nb_path  # isort: split\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from smallmatter.ds import SimpleMatrixPlotter\n",
    "\n",
    "from flight_sales.flight_sales_gym import reward_functions\n",
    "from flight_sales.plot import plot_contours\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c18475-da97-412e-aced-d83967c22ce7",
   "metadata": {},
   "source": [
    "## Contour plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec6b885d-d328-4ad4-8dc1-b2c3e43e6723",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_contours()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cafdd24d-f8e2-46af-8574-d60e5d67e51b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Plots by dimension\n",
    "\n",
    "Here're the helper functions to plot by prices, or by tickets sold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f6dbd74-6136-455f-817a-03e2a5fb4f26",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_hspace():\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.set_visible(False)\n",
    "    fig.set_figheight(1e-4)  # figure height in inches\n",
    "\n",
    "\n",
    "def plot_prices(f):\n",
    "    \"\"\"Evaluate the behavior of reward function ``f()``.\n",
    "\n",
    "    Arguments:\n",
    "        f: the reward function.\n",
    "    \"\"\"\n",
    "    max_fare = 20\n",
    "    daily_seats_quota = 20\n",
    "    prices = [round(i * max_fare, 1) for i in np.linspace(0.0, 1.0, 11)]\n",
    "\n",
    "    def del_smp(s):\n",
    "        s.fig.clf()\n",
    "        plt.close(s.fig)\n",
    "        del s.fig\n",
    "        s.fig = None\n",
    "\n",
    "    smp = SimpleMatrixPlotter(4, init_figcount=len(prices), figsize=(4, 2.5))\n",
    "    for price in prices:\n",
    "        rewards = [f(sold, price)[0] for sold in range(daily_seats_quota + 1)]\n",
    "        # import ipdb; ipdb.set_trace()\n",
    "        ax = smp.pop()\n",
    "        ax.plot(rewards)\n",
    "        ax.set_title(f\"{price=}\")\n",
    "        ax.set_xlabel(\"Tickets sold\")\n",
    "        ax.set_ylabel(\"Rewards\")\n",
    "\n",
    "    smp.trim()\n",
    "    smp.fig.suptitle(f\"Reward function: {f.__name__}()\", fontweight=\"bold\")\n",
    "    smp.fig.tight_layout()\n",
    "    plot_hspace()\n",
    "\n",
    "\n",
    "def plot_solds(f):\n",
    "    \"\"\"Evaluate the behavior of reward function ``f()``.\n",
    "\n",
    "    Arguments:\n",
    "        f: the reward function.\n",
    "    \"\"\"\n",
    "    max_fare = 20\n",
    "    daily_seats_quota = 20\n",
    "    prices = [round(i * max_fare, 1) for i in np.linspace(0.0, 1.0, 11)]\n",
    "\n",
    "    def del_smp(s):\n",
    "        s.fig.clf()\n",
    "        plt.close(s.fig)\n",
    "        del s.fig\n",
    "        s.fig = None\n",
    "\n",
    "    smp = SimpleMatrixPlotter(4, init_figcount=(daily_seats_quota + 1), figsize=(4, 2))\n",
    "    for sold in range(daily_seats_quota + 1):\n",
    "        rewards = [f(sold, price)[0] for price in prices]\n",
    "        ax = smp.pop()\n",
    "        ax.plot(prices, rewards)\n",
    "        ax.set_title(f\"{sold=}\")\n",
    "        ax.set_xlabel(\"Price\")\n",
    "        ax.set_ylabel(\"Rewards\")\n",
    "\n",
    "    smp.trim()\n",
    "    smp.fig.suptitle(f\"Reward function: {f.__name__}()\", fontweight=\"bold\")\n",
    "    smp.fig.tight_layout()\n",
    "    plot_hspace()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1606de-51e2-444b-8584-5431f71f1b53",
   "metadata": {},
   "source": [
    "### Without jitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d3f1189-81e7-4487-83cd-de33f39ac811",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_prices(reward_functions[\"revenue_0_20_no_jitter\"])\n",
    "plot_prices(reward_functions[\"revenue_0_05_no_jitter\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a86161d-98c1-4fca-b2e9-5d4b67970ab7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_solds(reward_functions[\"revenue_0_20_no_jitter\"])\n",
    "plot_solds(reward_functions[\"revenue_0_05_no_jitter\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "546f12da-2425-4bdb-bc5b-ddd2adbecd9e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_prices(reward_functions[\"profit_no_jitter\"])\n",
    "plot_solds(reward_functions[\"profit_no_jitter\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6336d87-1cae-44ba-bd08-dbad91f6defe",
   "metadata": {},
   "source": [
    "### With jitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb082e46-6e09-456e-83e8-4221ae8b35b1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_prices(reward_functions[\"revenue_0_02\"])\n",
    "plot_solds(reward_functions[\"revenue_0_02\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc037552-48e3-40c8-9400-3b06035e8453",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_prices(reward_functions[\"profit\"])\n",
    "plot_solds(reward_functions[\"profit\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dffaad91-e38e-49b1-a95f-a4c6dd0266a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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