Radi4 · February 21, 2018 14:06
diff --git a/Leaderboard.ipynb b/Leaderboard.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": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoding_dim_vars = [55, 50, 45, 40, 35, 30, 25, 20, 15]\n",
    "mse_vars = [0.0016985443405195194, 0.0016319910826800641, 0.0016116470808785277, 0.0015985778907972822, 0.0016373105322479613, 0.0016457742816988015, 0.0016772023516865354, 0.0016114161782412179, 0.0016173533817821755]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": 6,
   "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": "iVBORw0KGgoAAAANSUhEUgAAAf4AAAFoCAYAAAC2UJl7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPwAABiH4AQAwCMEPAIBBCH4AAAzy/wAE9BPixnT5jAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3026024e0>"
      ]
     },
     "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()"
   ]
  }
 ],
 "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
 }
	{
	"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": 3,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"encoding_dim_vars = [55, 50, 45, 40, 35, 30, 25, 20, 15]\n",
	"mse_vars = [0.0016985443405195194, 0.0016319910826800641, 0.0016116470808785277, 0.0015985778907972822, 0.0016373105322479613, 0.0016457742816988015, 0.0016772023516865354, 0.0016114161782412179, 0.0016173533817821755]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"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": 6,
	"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": "iVBORw0KGgoAAAANSUhEUgAAAf4AAAFoCAYAAAC2UJl7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPwAABiH4AQAwCMEPAIBBCH4AAAzy/wAE9BPixnT5jAAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7fb3026024e0>"
	]
	},
	"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()"
	]
	}
	],
	"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
	}