model-06-build-lgb-plot_for_mae_vs_lr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216, got 192\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    [[0.003, 0.125680],\n",
    "              [0.005, 0.125800],\n",
    "              [0.01, 0.126068],\n",
    "              [0.007, 0.125906],\n",
    "              [0.030, 0.127243],\n",
    "              [0.050, 0.127984],\n",
    "              [0.070, 0.129298],\n",
    "             ], columns=['learning_rate', 'mae']).sort_values('learning_rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>learning_rate</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.125680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005</td>\n",
       "      <td>0.125800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.125906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.126068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.030</td>\n",
       "      <td>0.127243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.050</td>\n",
       "      <td>0.127984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.070</td>\n",
       "      <td>0.129298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   learning_rate       mae\n",
       "0          0.003  0.125680\n",
       "1          0.005  0.125800\n",
       "3          0.007  0.125906\n",
       "2          0.010  0.126068\n",
       "4          0.030  0.127243\n",
       "5          0.050  0.127984\n",
       "6          0.070  0.129298"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['learning_rate'], df['mae'])\n",
    "plt.xlabel('learning rate'); plt.ylabel('CV MAE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}