LeaderBorad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoding_dim_vars = [55, 50, 45, 40, 35, 30, 25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 2, 1]\n",
    "mse_1 = [0.0016985443405195194, 0.0016319910826800641, 0.0016116470808785277, 0.0015985778907972822, 0.0016373105322479613, 0.0016457742816988015, 0.0016772023516865354, 0.001611416178241217, 0.0016173533817821755, 0.0016297283644772596, 0.001500232661950016, 0.0016556636955857697, 0.001711736573417589, 0.001557603151840187, 0.0016428516817796877, 0.001704868406824342, 0.0015853440518005682, 0.0016839128978147]\n",
    "mse_2 = [0.0016148057549385073, 0.0016000556621725331, 0.0016159719614639821, 0.0016516898829966021, 0.0016380432239572942, 0.0016322856500307387, 0.0015942311836629284, 0.001695856637655292, 0.0017701174438159617, 0.0016558190676838318, 0.001615696820492583, 0.0016234932587864234, 0.001698405322334231, 0.001552564946024114, 0.0016693240432043243, 0.001698433432443223, 0.0016085233722342342, 0.0016738578123415]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(encoding_dim_vars)\n",
    "y = np.array(mse_1)\n",
    "y *= 100;\n",
    "z = np.array(mse_2)\n",
    "z *= 100\n",
    "x = x / 61."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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552zZbDYNHz5cP/7xYsdrg4ICFRvbVTExF98fFhashIQ+WrBgri5cuKAlSxard+8ebarL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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ff5eeaef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, label =\"Девушки\")\n",
    "plt.scatter(x, z, label = \"Петр\")\n",
    "plt.title(\"MSE vs Alpha\")\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"MSE * 100\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    return np.mean(np.mean(np.abs(y_true - y_pred), axis = 0) / np.abs(np.mean(y_true, axis = 0))) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "enc_dim = [1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 40, 45, 50, 55, 61]\n",
    "mape_1 = [902.386, 924.632027779008, 976.8429457360012, 944.0732288879257, 1022.2850661513611, 952.1022673312397, 948.2301123498746, 993.8926674351683, 954.0436784602147, 938.235801234678, 928.7103458091423, 757.3954841324567, 505.8568901236891, 242.67027913445012, 2.945132145322553e-12]\n",
    "mape_2 = [901.407, 904.5504412568678, 1001.914440271646, 1027.7680775245012, 995.2271397891041, 925.0938274207464, 976.257157849064, 980.3664413662548, 960.3574021894561, 934.204689705432, 923.0468753106240, 738.2049876204586, 524.0213455346632, 218.08923556432156, 2.484329432778253e-12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array(enc_dim)\n",
    "y = np.array(mape_1)\n",
    "z = np.array(mape_2)\n",
    "x = x / 61."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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ETE5hLyIiYnIKexEREZNT2IuIiJicwl5ERMTkFPYiIiImp7AXERExOYW9iIiI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f903e1d0ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, label = \"Девушки\", alpha = 0.5)\n",
    "plt.scatter(x, z, label = \"Петр\", alpha = 0.5)\n",
    "plt.title(\"MAPE vs Alpha\")\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"MAPE\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}