CLEAR (Maaya Sakamoto) Score by Hideki Saito

CLEAR.csv

CLEAR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CLEAR (Maaya Sakamoto) Score by Hideki Saito"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attempt</th>\n",
       "      <th>Score</th>\n",
       "      <th>Vector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>88.364</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>89.329</td>\n",
       "      <td>0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>89.471</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>90.357</td>\n",
       "      <td>0.886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>90.408</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>89.743</td>\n",
       "      <td>-0.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>90.822</td>\n",
       "      <td>0.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>91.008</td>\n",
       "      <td>0.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>91.310</td>\n",
       "      <td>0.302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>90.710</td>\n",
       "      <td>-0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>91.311</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>90.862</td>\n",
       "      <td>-0.449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>89.373</td>\n",
       "      <td>-1.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>92.083</td>\n",
       "      <td>0.772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>92.068</td>\n",
       "      <td>-0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>91.577</td>\n",
       "      <td>-0.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>92.312</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>91.919</td>\n",
       "      <td>-0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>90.638</td>\n",
       "      <td>-1.674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>92.098</td>\n",
       "      <td>-0.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>91.100</td>\n",
       "      <td>-1.212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Attempt   Score  Vector\n",
       "0         1  88.364   0.000\n",
       "1         2  89.329   0.965\n",
       "2         3  89.471   0.142\n",
       "3         4  90.357   0.886\n",
       "4         5  90.408   0.051\n",
       "5         6  89.743  -0.665\n",
       "6         7  90.822   0.414\n",
       "7         8  91.008   0.186\n",
       "8         9  91.310   0.302\n",
       "9        10  90.710  -0.600\n",
       "10       11  91.311   0.001\n",
       "11       12  90.862  -0.449\n",
       "12       13  89.373  -1.938\n",
       "13       14  92.083   0.772\n",
       "14       15  92.068  -0.015\n",
       "15       16  91.577  -0.506\n",
       "16       17  92.312   0.229\n",
       "17       18  91.919  -0.393\n",
       "18       19  90.638  -1.674\n",
       "19       20  92.098  -0.214\n",
       "20       21  91.100  -1.212"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attempt = []\n",
    "score = []\n",
    "\n",
    "csv = pd.read_csv(\"CLEAR.csv\")\n",
    "csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = np.polyfit(csv['Attempt'],csv['Score'], 2)\n",
    "fit_fn = np.poly1d(fit)\n",
    "\n",
    "regressionSpace = np.linspace(1,csv['Attempt'].__len__())\n",
    "r2 = r2_score(csv['Score'], fit_fn(csv['Attempt']))\n",
    "\n",
    "rSquared = \"Fit: \"+str(round(fit[2],2)) + \" + \" + str(round(fit[1],2)) +\"x\" + \" + \" + \\\n",
    "str(round(fit[0],2)) + \"x^2 r^2 = \" + str(round(r2,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 139.5, 139.5, -0.5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0c4dedc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, facecolor='white', figsize=(10,8))\n",
    "csv.plot(kind='scatter', x='Attempt', y='Score', ax=ax, color='red', linewidth=0)\n",
    "plt.plot(regressionSpace, fit_fn(regressionSpace), color='blue')\n",
    "plt.suptitle('CLEAR (Maaya Sakamoto) Score by Hideki Saito', fontsize=14, fontweight='bold')\n",
    "plt.title(rSquared)\n",
    "\n",
    "ax.set_clip_on(False)\n",
    "plt.grid(True, linestyle='dashed')\n",
    "plt.plot(regressionSpace, fit_fn(regressionSpace))\n",
    "plt.plot(attempt, score, 'ro')\n",
    "\n",
    "im = plt.imread('./hslogo.png')\n",
    "imageax = fig.add_axes([0.83, 0.88, 0.06, 0.09], anchor='NE', zorder=5)\n",
    "imageax.imshow(im)\n",
    "imageax.axis('off')\n"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

hslogo.png