LeaderBorad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>Team                    </th><th style=\"text-align: right;\">  Alpha</th><th style=\"text-align: right;\">        Mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Девушки                 </td><td style=\"text-align: right;\"> 0.246 </td><td style=\"text-align: right;\">0.0016     </td></tr>\n",
       "<tr><td>Петр                    </td><td style=\"text-align: right;\"> 0.91  </td><td style=\"text-align: right;\">0.00165    </td></tr>\n",
       "<tr><td>Валентин, Валерия, Костя</td><td style=\"text-align: right;\"> 0.3279</td><td style=\"text-align: right;\">1.43573e+13</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "from IPython.display import HTML\n",
    "HTML(tabulate([[\"Девушки\", 0.246, 0.0016], [\"Петр\", 0.91, 0.00165], [\"Валентин, Валерия, Костя\", 0.3279, 14357335228541.21]],\n",
    "              headers= ['Team', 'Alpha', 'Mse'], tablefmt='html'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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_vars = [0.0016985443405195194, 0.0016319910826800641, 0.0016116470808785277, 0.0015985778907972822, 0.0016373105322479613, 0.0016457742816988015, 0.0016772023516865354, 0.0016114161782412179, 0.0016173533817821755, 0.0016297283644772596, 0.001500232661950016, 0.0016556636955857697, 0.001711736573417589, 0.001557603151840187, 0.0016428516817796877, 0.001704868406824342, 0.0015853440518005682, 0.0016839128978147]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 18\n"
     ]
    }
   ],
   "source": [
    "print (len(encoding_dim_vars), len(mse_vars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "x = np.array(encoding_dim_vars)\n",
    "y = np.array(mse_vars)\n",
    "y *= 100;\n",
    "x = x / 61."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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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CAOGEsSRAZOEQvReBuCEMEAiMJQEiE/91e8ENYWCKULnxBYDAIuA7UZw9RjOzRippUD9F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\nySduqrKpCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc985117710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.title(\"MSE vs Alpha\")\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"MSE * 100\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding_dim_small = encoding_dim_vars[7:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse_small = mse_vars[7:]\n",
    "mse_2 = [0.0016148057549385073, 0.0016000556621725331, 0.0016159719614639821, 0.001651689882996602, 0.0016380432239572942, 0.0016322856500307387, 0.0015942311836629284, 0.001695856637655292, 0.0017701174438159617, 0.0016558190676838318, 0.0016156968204925836]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(encoding_dim_small)\n",
    "y = np.array(mse_small)\n",
    "y *= 100;\n",
    "z = np.array(mse_2)\n",
    "z *= 100\n",
    "x = x / 61."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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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pSSUlJfrRj34kt9vtaYuIiFB5efmQFAj4a0/FKb127IznecN5h1759cdqv+jU\n6sXczQIAV/MZACoqKoayDmBAHJ0uVZ20e22rOnlORfMns7YCAFyFcVGEhZY2hxrPO7y2NbV2qKXN\nexsAmIoAgLCQMDpGyWO8X/SXFB/LBYEA8GcIAAgLMSOiNCMz1WvbjMyxDP8DwJ/xeQ0AEGqKc67c\nmVJ18pyaWjuUFB+rO6Zfr/z/+7lhrgwAgg8BAGEjKjJSqxdnqmj+ZM88ABOuT5Td3jrcpQFA0CEA\nIOzEjIjSuKS44S4DAIIa1wAAAGAgAgAAAAYiAAAAYCACAAAABiIAAABgIAIAAAAGIgAAAGAgAgAA\nAAYiAAAAYCACAAAABiIAAABgIAIAAAAGIgAAAGAgAgAAAAYiAAAAYCACAAAABiIAAABgIAIAAAAG\nIgAAAGAgAgAAAAYiAAAAYCACAAAABiIAAABgIAIAAAAGIgAAAGCggAaAyspKLVmyRLm5udqxY0eP\n9pqaGhUXFysrK0slJSXd2nJycpSfn6+CggIVFhYGskwg5Dk6XfqsqV2OTtdwlwIgRFgCtWOXy6XN\nmzdr586dslqtWrlypXJycpSRkeF5T2JiojZu3Kjy8nKv+9i9e7eSk5MDVSIQ8lyXL2tPxSlVnbSr\n8bxDyWNiNCMzVRu+OGO4SwMQ5AI2AlBdXa2JEycqPT1d0dHRysvL6/FBn5KSouzsbFksAcshQFjb\nU3FKrx07o4bzDrklNZx36LVjZ/SjX5wY7tIABLmABYD6+nqlpaV5nlutVtXX1/dpH2vWrFFhYaH2\n7Nkz2OUBIc/R6VLVSbvXtiPvf8rpAADXFLRfvUtLS2W1WtXQ0KA1a9Zo0qRJmj179jW3SUqKk8US\n1eu+U1PjB6vMkEK/w8un5y6osdXhte1c80VFRY9Q6thRQ1zV8AvX490b+m2Wweh3wAKA1WpVXV2d\n53l9fb2sVmuftpeunCbIzc1VdXV1rwGgqam91/2mpsbLbm/1u45wQb/Dj6vTpeT4GDWc7xkCxiaO\nlMvZGbZ99yWcj/e10G+zXN3vgQSBgJ0CmDZtmmpra3X69Gk5nU7ZbDbl5OT4tW17e7va2to8j19/\n/XVNmTIlUKUCISlmRJRmZKZ6bZuTdZ1iRvQ+GgbAXAEbAbBYLNq0aZPWrl0rl8uloqIiTZkyRaWl\npZKkVatWyW63q6ioSG1tbYqMjNTu3bt18OBBNTU1af369ZKu3E2wfPlyzZs3L1ClAiGrOOfKXTVV\nJ8+pqbWAEdLlAAAKfElEQVRDSfGxmpE5Vg/lT1Vj44Vhrg5AMItwu93u4S5isPgzFMSQkVlM6bej\n06WWNocSRscoZkSUMf3+c/TbLPR7YKcAgvYiQAD+ixkRpXFJccNdBoAQwlTAAAAYiAAAAICBCAAA\nABiIAAAAgIEIAAAAGIgAAACAgQgAAAAYiAAAAICBCAAAABiIAAAAgIEIAAAAGIgAAACAgQgAAAAY\niAAAAICBCAAAABiIAAAAgIEIAAAAGIgAAACAgQgAAAAYiAAAAICBCAAAABiIAAAAgIEIAAAAGIgA\nAACAgQgAAAAYiAAAAICBCAAAABiIAAAAgIEIAAAAGIgAAACAgQgAAAAYiAAAAICBCAAAABiIAAAA\ngIEIAAAAGIgAAACAgQgAAAAYiAAAAICBCAAAABiIAAAAgIEIAAAAGIgAAACAgQgAAAAYiAAAAICB\nCAAAABiIAAAAgIEIAAAAGIgAAACAgQgAAAAYiAAAAICBCAAAABgooAGgsrJSS5YsUW5urnbs2NGj\nvaamRsXFxcrKylJJSUmPdpfLpRUrVuiRRx4JZJkAABjHEqgdu1wubd68WTt37pTVatXKlSuVk5Oj\njIwMz3sSExO1ceNGlZeXe93Hj3/8Y02ePFltbW2BKhMAACMFbASgurpaEydOVHp6uqKjo5WXl9fj\ngz4lJUXZ2dmyWHrmkLq6Oh06dEgrV64MVIkAABgrYAGgvr5eaWlpnudWq1X19fV+b79t2zY99dRT\niozkMgUAAAZbwE4BDMSvfvUrJScnKysrS0ePHvV7u6SkOFksUb2+LzU1fiDlhSz6bRb6bRb6bZbB\n6HfAAoDValVdXZ3neX19vaxWq1/bvvPOO6qoqFBlZaUcDofa2tr05JNP6p//+Z+vuV1TU3uv+05N\njZfd3upXHeGEfpuFfpuFfpvl6n4PJAgEbHx92rRpqq2t1enTp+V0OmWz2ZSTk+PXtl/5yldUWVmp\niooKPffcc5ozZ06vH/4AAMB/ARsBsFgs2rRpk9auXSuXy6WioiJNmTJFpaWlkqRVq1bJbrerqKhI\nbW1tioyM1O7du3Xw4EGNHj06UGUBAABJEW632z3cRQwWf4aCGDIyC/02C/02C/0O0lMAAAAgeBEA\nAAAwEAEAAAADEQAAADAQAQAAAAMRAAAAMBABAAAAAxEAAAAwEAEAAAADEQAAADAQAQAAAAMRAAAA\nMBABAAAAAxEAAAAwEAEAAAADEQAAADAQAQAAAAMRAAAAMBABAAAAA0W43W73cBcBAACGFiMAAAAY\niAAAAICBCAAAABiIAAAAgIEIAAAAGIgAAACAgcIqAFRWVmrJkiXKzc3Vjh07erS73W5t2bJFubm5\nys/P14kTJ/zeNpgNpN85OTnKz89XQUGBCgsLh7LsAeut3zU1NSouLlZWVpZKSkr6tG0wG0i/w/l4\nv/LKK8rPz1d+fr7uu+8+ffjhh35vG8wG0u9wPt6vvfZat74dO3bM722D2UD63efj7Q4Tly5dci9a\ntMj9hz/8we1wONz5+fnujz76qNt7Dh065H744Yfdly9fdldVVblXrlzp97bBaiD9drvd7oULF7ob\nGhqGuuwB86ff586dc7/33nvu5557zv3DH/6wT9sGq4H02+0O7+P99ttvu5ubm91u95XfeVP+fvvq\nt9sd3se7ra3NffnyZbfb7XZ/8MEH7iVLlvi9bbAaSL/d7r4f77AZAaiurtbEiROVnp6u6Oho5eXl\nqby8vNt7ysvLtWLFCkVEROiWW27R+fPn9dlnn/m1bbAaSL9DmT/9TklJUXZ2tiwWS5+3DVYD6Xco\n86ffM2fOVEJCgiTplltuUV1dnd/bBquB9DuU+dPvUaNGKSIiQpJ08eJFz+NwP96++t0fYRMA6uvr\nlZaW5nlutVpVX19/zfekpaWpvr7er22D1UD63WXNmjUqLCzUnj17Al/wIBnIMQv3490bE473Sy+9\npHnz5vVr22AykH53Cefj/T//8z9aunSpHnnkEW3btq1P2wajgfS7S1+Od/h8RUC/lJaWymq1qqGh\nQWvWrNGkSZM0e/bs4S4LAWLC8T5y5Iheeukl/fSnPx3uUoaUt36H+/HOzc1Vbm6u3nrrLT3//PPa\ntWvXcJc0JHz1u6/HO2xGAKxWa7ehr/r6elmt1mu+p66uTlar1a9tg9VA+t3VJl0ZNs7NzVV1dfUQ\nVD1wAzlm4X68e9teCt/j/eGHH+of/uEf9IMf/EBJSUl92jYYDaTfXdtL4Xu8u8yePVunT59WY2Oj\nEce7y9X97tpe8v94h00AmDZtmmpra3X69Gk5nU7ZbDbl5OR0e09OTo727dsnt9utd999V/Hx8Ro3\nbpxf2wargfS7vb1dbW1tkqT29na9/vrrmjJlynB0o88GcszC/Xj7Eu7H++zZs/rSl76kZ599Vjfc\ncEOftg1WA+l3uB/vTz75RO7/v5bdiRMn5HQ6lZSUFPbH21e/+3O8w+YUgMVi0aZNm7R27Vq5XC4V\nFRVpypQpKi0tlSStWrVK8+fP1+HDh5Wbm6uRI0d6zp342jYUDKTfDQ0NWr9+vSTJ5XJp+fLlPc4f\nBit/+m2321VUVKS2tjZFRkZq9+7dOnjwoEaPHh3Wx9tXv5uamsL6eH//+99Xc3OzvvWtb0mSoqKi\nVFZWFvZ/v331O9z/fv/yl7/U/v37ZbFYFBsbq+9973uKiIgI++Ptq9/9Od4sBwwAgIHC5hQAAADw\nHwEAAAADEQAAADAQAQAAAAMRAAAAMBABAIBHS0uLsrOztWXLFs9r//Iv/6LvfOc7vW5bVlamxx57\nLJDlARhEBAAAHgcOHND06dNls9nkdDqHuxwAAUQAAODx8ssv62//9m914403el1BraysTGvWrNGj\njz6qZcuW6cEHH+y2WElbW5ueeOIJ5eXl6b777pPdbpck/e53v9Pq1at1zz33aNmyZcbM2Q4EMwIA\nAElX5pNvbm7WnDlzVFhYqJdfftnr+95++2199atf1cGDB/X5z39eW7du9bQdP35cX/va12Sz2ZSR\nkaH/+I//kCSNHz9eu3bt0t69e/Xzn/9cP/vZz1RTUzMk/QLgHQEAgKQrS8kWFBQoIiJCd955p6qr\nq70uRXrrrbdq0qRJkqR7771XR44c8bTNnDlT1113nSRp+vTp+sMf/iBJ6ujo0De+8Q3l5+dr1apV\n+uyzz/Thhx8OQa8A+BI2awEA6D+n06kDBw4oOjpa+/fvlyR1dnaqrKysT/uJiYnxPI6KipLL5ZIk\nPffcc0pNTdW3v/1tWSwWPfTQQ3I4HIPXAQB9xggAAJWXl+uGG25QZWWlKioqVFFRoR/96Efau3dv\nj/e+8847qq2tlXTlmoE5c+b0uv/W1lalpaXJYrHo5MmTOnbs2GB3AUAfMQIAQC+//LLy8/O7vTZj\nxgxdvnxZv/3tb5WVleV5febMmfrOd76jTz75RGPHjtX27dt73f+6dev01a9+VS+99JJuuOEGzZ49\ne9D7AKBvWA0QgN/Kysp06NAhvfDCC8NdCoAB4hQAAAAGYgQAAAADMQIAAICBCAAAABiIAAAAgIEI\nAAAAGIgAAACAgQgAAAAY6P8BTEkSBkwvF78AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc93eb96048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.scatter(x, z)\n",
    "plt.title(\"MSE vs Alpha\")\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"MSE * 100\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "enc_dim = [1, 2, 4, 5, 6, 7, 8, 9]\n",
    "mape = [291, 297, 263, 330, 377, 288, 290, 315]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array(enc_dim)\n",
    "y = np.array(mape)\n",
    "x = x / 61."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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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JZHkAABgrYEv0kmS321VcXKzm5mYtX75cJ06c0ObNm7V169Ybsv/o6OEKDTVrCT8mJjLY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\nxAZPYbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc93e9d1048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.title(\"MPE vs Alpha\")\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"MPE\")\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
}