metric_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b50f6b7b-7982-4c3a-a17e-2388dad82bb8",
   "metadata": {},
   "source": [
    "# Imports and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a50199f-5c50-4331-ab48-a01a2dee83f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import special_data_reader\n",
    "import stats_utils\n",
    "import model_utils\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe74fe79-f59b-40d2-b3db-59eb20a7e6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pulls = special_data_reader.read_data(f'Data/Raw/pulls.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "246dbc1e-5a5e-4b4b-956c-9edc5d8278d4",
   "metadata": {},
   "source": [
    "# Goodness of fit analysis for null distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e85d2ed-07a7-49db-861d-b418443a71ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = pulls[(pulls['grouped'] == 'f') & (pulls['rarity'] == 5) & (pulls['guaranteed'] == 'f') \n",
    "                    & (pulls['bannerId'] > 200000) & (pulls['bannerId'] < 300000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "414f036c-5e1f-438f-85ab-3deed1633db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_counts, expected_counts = stats_utils.data_for_metric(\n",
    "    filtered_data, prob=0.006, pity_cutoff=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9812989d-77a4-4c84-9aff-1078b42b5ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_counts = true_counts[:-1]\n",
    "expected_counts = expected_counts[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3acd6672-8bb1-4bd4-9763-120354730f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.bar(list(range(len(true_counts))), true_counts, label='True counts')\n",
    "plt.bar(list(range(len(expected_counts))), expected_counts, label='Expected counts')\n",
    "plt.title('Expected successful draw distribution vs. reality')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cfcd169-22ae-415b-a073-252eec4e67d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "error = stats_utils.null_metric(\n",
    "    pulls, prob=0.006, pity_cutoff=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa627c19-f8da-426f-bb07-850f9d253af9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7187049222868278"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbe0e3f-cd7f-4461-a3da-c8024dcfb491",
   "metadata": {},
   "source": [
    "# GOF for fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d99e8198-0b27-4985-bb18-6837de613166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs = model_utils.get_probs(pulls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a635b0b9-4c9a-4683-b5b0-212393a8d7b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8815432098765431\n",
      "gauss sum: 0.9248687107765219\n",
      "geom sum: 0.4831354130670022\n"
     ]
    }
   ],
   "source": [
    "gaussian_mean = 76\n",
    "gaussian_var = 2.45\n",
    "cutoff = 73\n",
    "\n",
    "fit_data = model_utils.fit_mixture(probs, gaussian_mean, gaussian_var, cutoff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86d1ba15-58dc-40c7-a36b-c1262b19e87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_data = np.array(fit_data) * true_counts.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5e43c74-932c-410b-a603-4af32580203d",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (91,) (89,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/vq/l7sv019x0bl58shj2vtv61qh0000gn/T/ipykernel_62894/152598330.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrue_counts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mabs_difference\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabsolute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue_counts\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mfit_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mabs_difference\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (91,) (89,) "
     ]
    }
   ],
   "source": [
    "total = true_counts.sum()\n",
    "\n",
    "abs_difference = np.absolute(true_counts - fit_data).sum()\n",
    "abs_difference /= total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0b2004d0-d2ce-412b-a22d-7815f727ab2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "626ac0bb-a068-4999-b348-cfcbfdc961b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = range(1, len(fit_data) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b5f4a7cd-9b46-4eeb-8071-c93f12ccf96e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(inp, true_counts[1:-1])\n",
    "plt.show()\n",
    "\n",
    "plt.bar(inp, fit_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71801a5f-e501-4f74-97ed-f9ad4997fd08",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}