interactive_plot_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.152526Z",
     "start_time": "2018-10-25T22:13:37.503680Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "try:\n",
    "    # To enable interactive mode you should install ipywidgets\n",
    "    # https://github.com/jupyter-widgets/ipywidgets\n",
    "    from ipywidgets import SelectMultiple, interact\n",
    "\n",
    "    INTERACTIVE = True\n",
    "except ImportError:\n",
    "    INTERACTIVE = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.238695Z",
     "start_time": "2018-10-25T22:13:39.160165Z"
    }
   },
   "outputs": [],
   "source": [
    "regression_example_dir = Path().absolute().parents[1] / \"regression\"\n",
    "df_train = pd.read_csv(str(regression_example_dir / \"regression.train\"), header=None, sep=\"\\t\")\n",
    "df_test = pd.read_csv(str(regression_example_dir / \"regression.test\"), header=None, sep=\"\\t\")\n",
    "\n",
    "y_train = df_train[0]\n",
    "y_test = df_test[0]\n",
    "X_train = df_train.drop(0, axis=1)\n",
    "X_test = df_test.drop(0, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dataset object for LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_train = lgb.Dataset(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    feature_name=[f\"f{i + 1}\" for i in range(X_train.shape[-1])],\n",
    "    categorical_feature=[21],\n",
    ")\n",
    "lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.243104Z",
     "start_time": "2018-10-25T22:13:39.240578Z"
    }
   },
   "outputs": [],
   "source": [
    "params = {\"num_leaves\": 5, \"metric\": [\"l1\", \"l2\"], \"verbose\": -1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.336630Z",
     "start_time": "2018-10-25T22:13:39.246006Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10]\ttraining's l1: 0.457448\ttraining's l2: 0.217995\tvalid_1's l1: 0.456464\tvalid_1's l2: 0.21641\n",
      "[20]\ttraining's l1: 0.436869\ttraining's l2: 0.205099\tvalid_1's l1: 0.434057\tvalid_1's l2: 0.201616\n",
      "[30]\ttraining's l1: 0.421302\ttraining's l2: 0.197421\tvalid_1's l1: 0.417019\tvalid_1's l2: 0.192514\n",
      "[40]\ttraining's l1: 0.411107\ttraining's l2: 0.192856\tvalid_1's l1: 0.406303\tvalid_1's l2: 0.187258\n",
      "[50]\ttraining's l1: 0.403695\ttraining's l2: 0.189593\tvalid_1's l1: 0.398997\tvalid_1's l2: 0.183688\n",
      "[60]\ttraining's l1: 0.398704\ttraining's l2: 0.187043\tvalid_1's l1: 0.393977\tvalid_1's l2: 0.181009\n",
      "[70]\ttraining's l1: 0.394876\ttraining's l2: 0.184982\tvalid_1's l1: 0.389805\tvalid_1's l2: 0.178803\n",
      "[80]\ttraining's l1: 0.391147\ttraining's l2: 0.1828\tvalid_1's l1: 0.386476\tvalid_1's l2: 0.176799\n",
      "[90]\ttraining's l1: 0.388101\ttraining's l2: 0.180817\tvalid_1's l1: 0.384404\tvalid_1's l2: 0.175775\n",
      "[100]\ttraining's l1: 0.385174\ttraining's l2: 0.179171\tvalid_1's l1: 0.382929\tvalid_1's l2: 0.175321\n"
     ]
    }
   ],
   "source": [
    "evals_result = {}  # to record eval results for plotting\n",
    "gbm = lgb.train(\n",
    "    params,\n",
    "    lgb_train,\n",
    "    num_boost_round=100,\n",
    "    valid_sets=[lgb_train, lgb_test],\n",
    "    callbacks=[lgb.log_evaluation(10), lgb.record_evaluation(evals_result)],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot metrics recorded during training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.809203Z",
     "start_time": "2018-10-25T22:13:39.338985Z"
    }
   },
   "outputs": [],
   "source": [
    "def render_metric(metric_name):\n",
    "    lgb.plot_metric(evals_result, metric=metric_name, figsize=(10, 5))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if INTERACTIVE:\n",
    "    # create widget to switch between metrics\n",
    "    interact(render_metric, metric_name=params[\"metric\"])\n",
    "else:\n",
    "    render_metric(params[\"metric\"][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.958548Z",
     "start_time": "2018-10-25T22:13:39.811530Z"
    }
   },
   "outputs": [],
   "source": [
    "def render_plot_importance(importance_type, max_features=10, ignore_zero=True, precision=3):\n",
    "    lgb.plot_importance(\n",
    "        gbm,\n",
    "        importance_type=importance_type,\n",
    "        max_num_features=max_features,\n",
    "        ignore_zero=ignore_zero,\n",
    "        figsize=(12, 8),\n",
    "        precision=precision,\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QZ+a44uYJxVdXc84Gzu7OdQcPGsDCbr7fpRqrUqnU/ec+9Q570prsS+uxJ5LK8pMSJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCgliRJkkowUEuSJEklGKglSZKkEgzUkiRJUgkGakmSJKkEA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJKmbVq5cya677srBBx8MwJNPPsmUKVPYbrvtOPLII3njjTeaXKGk3jSwUReOiFOA2cAOwIJieBkwOzMfLua0A0uBlUBnZu5Rz7VXdKxk3Jm39HjN2nCnT+zkGHvSUuxJa7IvrefbBwzp9jkXX3wxEyZM4JVXXgHgjDPO4LTTTuOoo47ipJNO4sorr2T27Nk9XaqkFtXIFeqTgQOBfYAPZeYk4B+Ay9aaNy0zJ9cbpiVJaqbFixdzyy23cMIJJwCQmdx1110cfvjhAMyaNYsbb7yxmSVK6mUNCdQRcSkwHpgPTMnMl4pDPwXGNOIxJUnqDaeeeirnn38+G21U/U/oiy++yLBhwxg4sPpH3zFjxvDUU081s0RJvawhgTozTwKeprr6/LWaQ8cDP6idCtweEfdHxImNqEWSpJ5y8803M2rUKHbfffc1Y5n5lnkR0ZtlSWqyhu2hXltETKMaqKfWDO+TmU9HxCjgjoj4n8z80TrOPxE4EWDEiJHMmdjZ8JpVvy0HV/eGqnXYk9ZkX1rPsmXLqFQqdc295ppruP3227nhhht44403ePXVVzn66KNZsmQJd955JwMGDOCXv/wlm266ad3XVNe60xf1Dnuybr0SqCNiEnAFMCMzX1w9nplPF9+fj4jvAXsCXQbqzLyMYv/12PHb5oULeu3fAqrD6RM7sSetxZ60JvvSer59wBDa2trqmls7r1KpMHfuXG6++WY+9alPsWTJEo466iiuvfZajj322Lqvqa5VKhV/hi3Gnqxbw982LyLGAjcAn83MR2vGh0TEZqtvAx8FHml0PZIk9bTzzjuPf/qnf2LbbbflxRdf5Pjjj292SZJ6UW8sk8wB3gNcUuwpW/32eFsC3yvGBgL/npm31XPBwYMGsPDcgxpUrjZEpVKhfWZbs8tQDXvSmuxL69nQP2G3tbWtWa0bP3489957b88VJalPaVigzsxxxc0Tiq+1jz8B7NKox5ckSZJ6g5+UKEmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCgliRJkkowUEuSJEklGKglSZKkEgzUkiRJUgkGakmSJKkEA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJA5tdgCRJ3fXaa6+x33778frrr9PZ2cnhhx/O3//937PvvvuydOlSAJ5//nn23HNPbrzxxiZXK+mdrmGBOiJOAWYDOwALiuFlwOzMfDgitge+U3PKeGBOZl60vmuv6FjJuDNv6emSVcLpEzs5xp60FHvSmuzLurWfe1DdczfZZBPuuusuhg4dSkdHB1OnTmXGjBn8+Mc/XjPnsMMO45BDDmlEqZL0Jo1coT4ZmAGMBn6dmS9FxAzgMmBKZi4EJgNExADgKeB7DaxHkvQOEREMHToUgI6ODjo6OoiINceXLl3KXXfdxbe+9a1mlSipH2nIHuqIuJTqivN8quH5peLQT4ExXZyyP/CbzPxtI+qRJL3zrFy5ksmTJzNq1CimT5/OlClT1hz73ve+x/7778/mm2/exAol9RcNCdSZeRLwNDAtM79Wc+h44AddnHIUcE0japEkvTMNGDCAhx56iMWLF3PvvffyyCOPrDl2zTXXcPTRRzexOkn9SWRmYy4c0Q7skZkvFPenAZcAUzPzxZp5G1MN3ztl5nNvc70TgRMBRowYufuciy5vSN3aMFsOhudWNLsK1bInrcm+rNvErbfY4HPnzZvHpptuypFHHskf/vAHPve5z3Hdddex8cYbr/fcZcuWrdk+otZhX1pPf+zJtGnT7s/MPdY3r1fe5SMiJgFXADNqw3RhBvDA24VpgMy8jOr+a8aO3zYvXOAblLSS0yd2Yk9aiz1pTfZl3dpnttU9d8mSJQwaNIhhw4axYsUKzjrrLM444wza2tq49NJL+cQnPsFHP/rRuq5VqVRoa6v/sdU77EvrsSfr1vDf6hExFrgB+GxmPtrFlKNxu4ckqRueeeYZZs2axcqVK1m1ahVHHHEEBx98MADXXnstZ555ZpMrlNSf9MYyyRzgPcAlxSuwO1cvnUfEu4DpwJ/3Qh2SpHeISZMm8eCDD3Z5rFKp9G4xkvq9hgXqzBxX3Dyh+OpqzqtUw3a3DB40gIXdeL9SNV6lUunWn2vVePakNdkXSXrn8aPHJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCgliRJkkowUEuSJEklGKglSZKkEgzUkiRJUgkGakmSJKkEA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWpH7itddeY88992SXXXZhp5124uyzzwZg5syZbL/99uy8884cd9xxdHR0NLlSSepbBjby4hFxCjAb2AFYUAwvA2Zn5sMRsSnwI2CTopbrM/Ps9V13RcdKxp15S4Oq1oY4fWInx9iTlmJPWlNP96X93IPqnrvJJptw1113MXToUDo6Opg6dSozZsxg5syZXH311QB8+tOf5oorrmD27Nk9VqMkvdM1NFADJwMzgNHArzPzpYiYAVwGTAFeBz6cmcsiYhDwXxHxg8z8aYPrkqR+JyIYOnQoAB0dHXR0dBARHHjggWvm7LnnnixevLhZJUpSn9SwLR8RcSkwHpgPTMnMl4pDPwXGAGTVsmJ8UPGVjapJkvq7lStXMnnyZEaNGsX06dOZMmXKmmMdHR1cddVVHHDAAU2sUJL6noYF6sw8CXgamJaZX6s5dDzwg9V3ImJARDwEPA/ckZk/a1RNktTfDRgwgIceeojFixdz77338sgjj6w5dvLJJ7Pffvux7777NrFCSep7IrN7C8IR8W7gfZn5izrmtgN7ZOYLxf1pwCXA1Mx8ca25w4DvAV/IzEe6uNaJwIkAI0aM3H3ORZd3q2411paD4bkVza5CtexJa+rpvkzceosNPnfevHlsuummHHnkkcybN4/HHnuML3/5y2y0Uf96vfqyZcvWbIVR67Avrac/9mTatGn3Z+Ye65tX1x7qiKgAHy/mPwQsiYi7M/N/11tQREwCrgBmrB2mATLz5eJxDgDeEqgz8zKqe68ZO37bvHBBo7d/qztOn9iJPWkt9qQ19XRf2me21T13yZIlDBo0iGHDhrFixQrOOusszjjjDB5//HEWLlzInXfeyeDBg3ustr6iUqnQ1tbW7DK0FvvSeuzJutX7W32LzHwlIk4AvpWZZ0fEeleoV4uIscANwGcz89Ga8ZFARxGmBwMfAc7rRv2SpDo988wzzJo1i5UrV7Jq1SqOOOIIDj74YAYOHMg222zD3nvvDcAnP/lJ5syZ0+RqJanvqDdQD4yI0cARwN9uwOPMAd4DXBIRAJ3F8vloYF5EDKC6n/s/MvPmDbi+JGk9Jk2axIMPPviW8c7OziZUI0nvHPUG6i8DPwTuycyfR8R44LH1nZSZ44qbJxRfax//BbBrnTWsMXjQABZ2471X1XiVSqVbf3pW49mT1mRfJOmdp65AnZnXAdfV3H8COKxRRUmSJEl9RV0v5Y6ID0TEnRHxSHF/UkT8n8aWJkmSJLW+et8b6XLgS0AHrNmqcVSjipIkSZL6inoD9bsy8961xnwViyRJkvq9egP1CxHxJxQfCx4RhwPPNKwqSZIkqY+o910+/oLqh6rsEBFPAU8CMxtWlSRJktRHrDdQR8RGVD8+/CMRMQTYKDOXNr40SZIkqfWtd8tHZq4CPl/cXm6YliRJkv6o3j3Ud0TEX0XE+yJi+OqvhlYmSZIk9QH17qE+rvj+FzVjCYzv2XIkSZKkvqXeT0p8f6MLkSRJkvqiugJ1RHyuq/HM/NeeLUeSJEnqW+rd8vHBmtubAvsDDwAGakmSJPVr9W75+ELt/YjYAriqIRVJkiRJfUi97/KxtleB7XqyEEmSJKkvqncP9U0UHztONYTvCFzXqKIkSZKkvqLePdRza253Ar/NzMUNqEeSJEnqU+rd8nFgZt5dfN2TmYsj4ryGViZJkiT1AfUG6uldjM3oyUIkSZKkvuhtt3xExGzgZGB8RPyi5tBmwD2NLEySJEnqC9a3h/rfgR8AXwXOrBlfmpm/b1hVkiRJUh/xtoE6M/8A/AE4GiAiRlH9YJehETE0M3/X+BIlSZKk1lXXHuqI+FhEPAY8CdwNtFNduZYkSZL6tXpflPiPwF7Ao5n5fqofPe4eakmSJPV79Qbqjsx8EdgoIjbKzP8EJjewLkmSJKlPqPeDXV6OiKHAj4F/i4jnqX7AiyRJktSv1btCfQjwKnAqcBvwG+BjjSpKkiRJ6ivqWqHOzOURsQ2wXWbOi4h3AQMaW5okSZLU+up9l48/A64H/qUY2hq4sVFFSZIkSX1FvVs+/gLYB3gFIDMfA0Y1qihJUs977bXX2HPPPdlll13YaaedOPvsswGYOXMm22+/PTvvvDPHHXccHR0dTa5UkvqWel+U+HpmvhERAETEQCA39EEj4hRgNvAAcDlwETAIeCEzP7S+81d0rGTcmbds6MOrAU6f2Mkx9qSl2JPW1NN9aT/3oLrnbrLJJtx1110MHTqUjo4Opk6dyowZM5g5cyZXX301AJ/+9Ke54oormD17do/VKEnvdPUG6rsj4m+AwRExHTgZuKnE454MzABeAv4bOCAzf1d8EqMkqQEigqFDhwLQ0dFBR0cHEcGBBx64Zs6ee+7J4sWLm1WiJPVJ9W75OBNYAiwA/hy4Ffg/G/KAEXEpMB6YT3UryQ2rP8I8M5/fkGtKkuqzcuVKJk+ezKhRo5g+fTpTpkxZc6yjo4OrrrqKAw44oIkVSlLf87aBOiLGAmTmqsy8PDM/lZmHF7c3aMtHZp4EPA1MA0YC746ISkTcHxGf25BrSpLqM2DAAB566CEWL17MvffeyyOPPLLm2Mknn8x+++3Hvvvu28QKJanvibfLxRHxQGbuVtz+bmYe1iMPGtEO7AH8XfF9f2Aw8BPgoMx8tItzTgROBBgxYuTucy66vCdKUQ/ZcjA8t6LZVaiWPWlNPd2XiVtvscHnzps3j0033ZQjjzySefPm8dhjj/HlL3+ZjTaq94+X7wzLli1bsxVGrcO+tJ7+2JNp06bdn5l7rG/e+vZQR83t8eVK6tJiqi9EXA4sj4gfAbsAbwnUmXkZcBnA2PHb5oUL6t3+rd5w+sRO7ElrsSetqaf70j6zre65S5YsYdCgQQwbNowVK1Zw1llnccYZZ/D444+zcOFC7rzzTgYPHtxjtfUVlUqFtra2ZpehtdiX1mNP1m19v9VzHbd7yveBfy7eNWRjYArwtQY8jiT1e8888wyzZs1i5cqVrFq1iiOOOIKDDz6YgQMHss0227D33nsD8MlPfpI5c+Y0uVpJ6jvWF6h3iYhXqK5UDy5uU9zPzNy8zINn5q8j4jbgF8Aq4IrMfGQ9p0mSNsCkSZN48MEH3zLe2dnZhGok6Z3jbQN1Zjbk48Uzc1zN7QuAC7pz/uBBA1jYjfdeVeNVKpVu/elZjWdPWpN9kaR3nv71yhNJkiSphxmoJUmSpBIM1JLtERTUAAAWb0lEQVQkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCgliRJkkowUEuSJEklGKglSZKkEgzUkiRJUgkGakmSJKkEA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC2pT1i0aBHTpk1jwoQJ7LTTTlx88cVvOj537lwighdeeKFJFUqS+quBzXjQiDgFmA08kJkzI+KDwE+BIzPz+vWdv6JjJePOvKXRZaobTp/YyTH2pKX0hZ60n3tQ3XMHDhzIhRdeyG677cbSpUvZfffdmT59OjvuuCOLFi3ijjvuYOzYsQ2sVpKkrjVrhfpk4MAiTA8AzgN+2KRaJPUBo0ePZrfddgNgs802Y8KECTz11FMAnHbaaZx//vlERDNLlCT1U72+Qh0RlwLjgfkR8U0gge8CH+ztWiT1Te3t7Tz44INMmTKF+fPns/XWW7PLLrs0uyxJUj/V64E6M0+KiAOAacAmwL8DH8ZALakOy5Yt47DDDuOiiy5i4MCBnHPOOdx+++3NLkuS1I81ZQ91jYuAMzJz5fr+VBsRJwInAowYMZI5Ezt7oTzVa8vB1T27ah19oSeVSqVb8zs7O/nSl77ElClTGD58ONdeey2PPvoo22+/PQBLlixhp5124hvf+AbDhw9vQMXlLVu2rNvPW41lT1qTfWk99mTdIjN7/0Ej2oE9gJ8Dq5P0COBV4MTMvPHtzh87ftvc6IiL326KetnpEzu5cEGz/32mWn2hJ915UWJmMmvWLIYPH85FF13U5Zxx48Zx3333MWLEiJ4qscdVKhXa2tqaXYZq2JPWZF9aT3/sSUTcn5l7rG9eU982LzPfn5njMnMccD1w8vrCtKT+6Z577uGqq67irrvuYvLkyUyePJlbb7212WVJktT0LR+SVJepU6eyvr+otbe3904xkiTVaEqgLlak1x47pt7zBw8awMJu/KlYjVepVGif2dbsMlTDnkiS1Dv8pERJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCgliRJkkowUEuSJEklGKglSZKkEgzUkiRJUgkGakmSJKkEA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS2qIRYsWMW3aNCZMmMBOO+3ExRdfDMAXv/hFdthhByZNmsShhx7Kyy+/3ORKJUkqZ2AzHjQiTgFmA78C3gvsBvxtZs6t5/wVHSsZd+YtDaxQ3XX6xE6OsSctpRE9aT/3oLrnDhw4kAsvvJDddtuNpUuXsvvuuzN9+nSmT5/OV7/6VQYOHMgZZ5zBV7/6Vc4777werVOSpN7UlEANnAzMAJYD2wCfaFIdkhpk9OjRjB49GoDNNtuMCRMm8NRTT/HRj350zZy99tqL66+/vlklSpLUI3p9y0dEXAqMB+YDMzPz50BHb9chqfe0t7fz4IMPMmXKlDeNf/Ob32TGjBlNqkqSpJ7R6yvUmXlSRBwATMvMF3r78SX1rmXLlnHYYYdx0UUXsfnmm68ZP+eccxg4cCAzZ85sYnWSJJUXmdn7DxrRDuyxOlBHxN8By95uD3VEnAicCDBixMjd51x0eS9UqnptORieW9HsKlSrET2ZuPUW3Zrf2dnJl770JT74wQ9yxBFHrBm/7bbbuOmmm7jwwgvZdNNNe7bIFrds2TKGDh3a7DJUw560JvvSevpjT6ZNm3Z/Zu6xvnnN2kPdbZl5GXAZwNjx2+aFC/pM6f3C6RM7sSetpRE9aZ/ZVvfczGTWrFnss88+XHTRRWvGb7vtNubPn8/dd9/NyJEje7S+vqBSqdDW1tbsMlTDnrQm+9J67Mm6mYAkNcQ999zDVVddxcSJE5k8eTIAX/nKVzjllFN4/fXXmT59OlB9YeKll17azFIlSSqlqYE6IrYC7gM2B1ZFxKnAjpn5SjPrklTe1KlT6WpL2YEHHtiEaiRJapymBOrMHFdzd0x3zx88aAALu/F+uGq8SqXSre0Aajx7IklS7/CTEiVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCgliRJkkowUEuSJEklGKglSZKkEgzUkiRJUgkGakmSJKkEA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCglrpw3HHHMWrUKHbeeee3HJs7dy4RwQsvvNCEyiRJUqsZ2MiLR8QpwGzgV8B7gd2Av83MuTVzTgNOABJYABybma+93XVXdKxk3Jm3NKxudd/pEzs5psV70n7uQXXPPeaYY/j85z/P5z73uTeNL1q0iDvuuIOxY8f2dHmSJKmPavQK9cnAgVRD9SnA3NqDEbF1Mb5HZu4MDACOanBN0nrtt99+DB8+/C3jp512Gueffz4R0YSqJElSK2pYoI6IS4HxwHxgZmb+HOjoYupAYHBEDATeBTzdqJqkMubPn8/WW2/NLrvs0uxSJElSC2nYlo/MPCkiDgCmZWaXm00z86mImAv8DlgB3J6ZtzeqJmlDvfrqq5xzzjncfrv/85QkSW/W0D3U6xMR7wYOAd4PvAxcFxGfycyru5h7InAiwIgRI5kzsbNXa9Xb23JwdR91K6tUKt2a/+yzz7J8+XIqlQpPPPEEjz76KNtvvz0AS5YsYaedduIb3/hGl1tDWsGyZcu6/ZzVePal9diT1mRfWo89WbemBmrgI8CTmbkEICJuAP4UeEugzszLgMsAxo7fNi9c0OzSVev0iZ20ek/aZ7Z1b357O0OGDKGtrY22tjaOO+64NcfGjRvHfffdx4gRI3q4yp5TqVRoa2trdhlai31pPfakNdmX1mNP1q3Zb5v3O2CviHhXVF/ltT/w6ybXJHH00Uez9957s3DhQsaMGcOVV17Z7JIkSVKL6pUlxYjYCrgP2BxYFRGnAjtm5s8i4nrgAaATeJBiFVpqpmuuueZtj7e3t/dOIZIkqeU1NFBn5riau2PWMeds4OzuXHfwoAEs7MZ7CqvxKpVKt7dUSJIkvRM0e8uHJEmS1KcZqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCgliRJkkowUEuSJEklGKglSZKkEgzUkiRJUgkGakmSJKkEA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqYWCzC9gQKzpWMu7MW5pdhmqcPrGTY1q8J+3nHlT33OOOO46bb76ZUaNG8cgjj7zp2Ny5c/niF7/IkiVLGDFiRE+XKUmS+piGrlBHxCkR8euI+G5E/CQiXo+Iv1przgERsTAiHo+IMxtZj1SvY445httuu+0t44sWLeKOO+5g7NixTahKkiS1okZv+TgZOBCYDZwCzK09GBEDgP8HzAB2BI6OiB0bXJO0Xvvttx/Dhw9/y/hpp53G+eefT0Q0oSpJktSKGhaoI+JSYDwwH5iZmT8HOtaatifweGY+kZlvANcChzSqJqmM+fPns/XWW7PLLrs0uxRJktRCGraHOjNPiogDgGmZ+cI6pm0NLKq5vxiY0tXEiDgROBFgxIiRzJnY2ZPlqqQtB1f3UbeySqXSrfnPPvssy5cvp1Kp8Nprr3HGGWdwwQUXrLl/zz33sMUWWzSm2B6wbNmybj9nNZ59aT32pDXZl9ZjT9at2S9K7Orv5tnVxMy8DLgMYOz4bfPCBc0uXbVOn9hJq/ekfWZb9+a3tzNkyBDa2tpYsGABL774Ip///OcBeOGFF/jCF77Avffey1ZbbdWAasurVCq0tbU1uwytxb60HnvSmuxL67En69bsBLQYeF/N/THA002qRVqniRMn8vzzz6+5P27cOO677z7f5UOSJDX9fah/DmwXEe+PiI2Bo6juuZaa6uijj2bvvfdm4cKFjBkzhiuvvLLZJUmSpBbVKyvUEbEVcB+wObAqIk4FdszMVyLi88APgQHANzPzl+u73uBBA1jYjfcUVuNVKpVub6loZddcc83bHm9vb++dQiRJUstraKDOzHE1d8esY86twK2NrEOSJElqlGZv+ZAkSZL6NAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklSCgVqSJEkqwUAtSZIklWCgliRJkkowUEuSJEklGKglSZKkEgzUkiRJUgkGakmSJKkEA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLkiRJJRioJUmSpBIM1JIkSVIJBmpJkiSpBAO1JEmSVIKBWpIkSSrBQC1JkiSVYKCWJEmSSjBQS5IkSSUYqCVJkqQSDNSSJElSCQZqSZIkqQQDtSRJklRCZGaza+i2iFgKLGx2HXqTEcALzS5Cb2JPWpN9aT32pDXZl9bTH3uyTWaOXN+kgb1RSQMszMw9ml2E/igi7rMnrcWetCb70nrsSWuyL63HnqybWz4kSZKkEgzUkiRJUgl9NVBf1uwC9Bb2pPXYk9ZkX1qPPWlN9qX12JN16JMvSpQkSZJaRV9doZYkSZJaQp8K1BFxQEQsjIjHI+LMZtfTX0XENyPi+Yh4pGZseETcERGPFd/f3cwa+5uIeF9E/GdE/DoifhkRf1mM25cmiYhNI+LeiHi46MnfF+Pvj4ifFT35TkRs3Oxa+6OIGBARD0bEzcV9+9JEEdEeEQsi4qGIuK8Y8/dXk0XEsIi4PiL+p/jvy972pWt9JlBHxADg/wEzgB2BoyNix+ZW1W99GzhgrbEzgTszczvgzuK+ek8ncHpmTgD2Av6i+P+HfWme14EPZ+YuwGTggIjYCzgP+FrRk5eA45tYY3/2l8Cva+7bl+ablpmTa96Wzd9fzXcxcFtm7gDsQvX/M/alC30mUAN7Ao9n5hOZ+QZwLXBIk2vqlzLzR8Dv1xo+BJhX3J4HfKJXi+rnMvOZzHyguL2U6i+9rbEvTZNVy4q7g4qvBD4MXF+M25MmiIgxwEHAFcX9wL60In9/NVFEbA7sB1wJkJlvZObL2Jcu9aVAvTWwqOb+4mJMrWHLzHwGquEOGNXkevqtiBgH7Ar8DPvSVMW2goeA54E7gN8AL2dmZzHF32PNcRHw18Cq4v57sC/NlsDtEXF/RJxYjPn7q7nGA0uAbxXbo66IiCHYly71pUAdXYz5FiVSjYgYCnwXODUzX2l2Pf1dZq7MzMnAGKp/ZZvQ1bTerap/i4iDgecz8/7a4S6m2pfetU9m7kZ1W+dfRMR+zS5IDAR2A76RmbsCy3F7xzr1pUC9GHhfzf0xwNNNqkVv9VxEjAYovj/f5Hr6nYgYRDVM/1tm3lAM25cWUPyZtEJ1f/uwiBhYHPL3WO/bB/h4RLRT3Tr4Yaor1valiTLz6eL788D3qP4D1N9fzbUYWJyZPyvuX081YNuXLvSlQP1zYLvildgbA0cB85tck/5oPjCruD0L+H4Ta+l3ij2gVwK/zsx/qjlkX5okIkZGxLDi9mDgI1T3tv8ncHgxzZ70ssz8UmaOycxxVP87cldmzsS+NE1EDImIzVbfBj4KPIK/v5oqM58FFkXE9sXQ/sCvsC9d6lMf7BIRB1JdSRgAfDMzz2lySf1SRFwDtAEjgOeAs4Ebgf8AxgK/Az6VmWu/cFENEhFTgR8DC/jjvtC/obqP2r40QURMovqCnQFUFy/+IzO/HBHjqa6MDgceBD6Tma83r9L+KyLagL/KzIPtS/MUP/vvFXcHAv+emedExHvw91dTRcRkqi/e3Rh4AjiW4vcZ9uVN+lSgliRJklpNX9ryIUmSJLUcA7UkSZJUgoFakiRJKsFALUmSJJVgoJYkSZJKMFBLUh0iYmVEPFTzNW4DrjEsIk7u+erWXP/jEdGrn2QWEZ+IiB178zElqdX4tnmSVIeIWJaZQ0teYxxwc2bu3M3zBmTmyjKP3QjFJwteQfU5Xd/seiSpWVyhlqQNFBEDIuKCiPh5RPwiIv68GB8aEXdGxAMRsSAiDilOORf4k2KF+4KIaIuIm2uu988RcUxxuz0i5kTEfwGfiog/iYjbIuL+iPhxROzQRT3HRMQ/F7e/HRHfiIj/jIgnIuJDEfHNiPh1RHy75pxlEXFhUeudETGyGJ8cET8tntf3IuLdxXglIr4SEXcDZwAfBy4ontOfRMSfFT+PhyPiuxHxrpp6/m9E/HdRz+E1Nfx18XN6OCLOLcbW+3wlqVUMbHYBktRHDI6Ih4rbT2bmocDxwB8y84MRsQlwT0TcDiwCDs3MVyJiBPDTiJgPnAnsnJmTYc0n9b2d1zJzajH3TuCkzHwsIqYAlwAfXs/57y7mfBy4CdgHOAH4eURMzsyHgCHAA5l5ekTMofrJp58H/hX4QmbeHRFfLsZPLa47LDM/VNS1HTUr1BHxcmZeXtz+x+Jn9PXivNHAVGAHqh9ffH1EzAA+AUzJzFcjYngx97INeL6S1BQGakmqz4rVQbjGR4FJNautWwDbAYuBr0TEflQ/Cn5rYMsNeMzvQHXFG/hT4LqIWH1skzrOvykzMyIWAM9l5oLier8ExgEPFfV9p5h/NXBDRGxBNTTfXYzPA65bu6512LkI0sOAocAPa47dmJmrgF9FxOqfx0eAb2XmqwCZ+fsSz1eSmsJALUkbLqiu4v7wTYPVbRsjgd0zsyMi2oFNuzi/kzdvvVt7zvLi+0bAy10E+vV5vfi+qub26vvr+v1fzwtrlr/NsW8Dn8jMh4ufQ1sX9UD1Z7f6+9qPuaHPV5Kawj3UkrThfgjMjohBABHxgYgYQnWl+vkiTE8DtinmLwU2qzn/t8COEbFJsSq8f1cPkpmvAE9GxKeKx4mI2KWHnsNGwOoV9k8D/5WZfwBeioh9i/HPAnd3dTJvfU6bAc8UP5OZdTz+7cBxNXuthzf4+UpSjzNQS9KGuwL4FfBARDwC/AvVld9/A/aIiPuohsr/AcjMF6nus34kIi7IzEXAfwC/KM558G0eayZwfEQ8DPwSOORt5nbHcmCniLif6h7lLxfjs6i+2PAXwOSa8bVdC3wxIh6MiD8BzgJ+BtxB8bzfTmbeRnU/9X3FHvW/Kg416vlKUo/zbfMkqR+LHng7QEnq71yhliRJkkpwhVqSJEkqwRVqSZIkqQQDtSRJklSCgVqSJP3/7daxAAAAAMAgf+tB7C2KgEGoAQBgEGoAABiEGgAAhgA3ESOFhO8zCgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if INTERACTIVE:\n",
    "    # create widget for interactive feature importance plot\n",
    "    interact(\n",
    "        render_plot_importance,\n",
    "        importance_type=[\"split\", \"gain\"],\n",
    "        max_features=(1, X_train.shape[-1]),\n",
    "        precision=(0, 10),\n",
    "    )\n",
    "else:\n",
    "    render_plot_importance(importance_type=\"split\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot split value histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def render_histogram(feature):\n",
    "    lgb.plot_split_value_histogram(gbm, feature=feature, bins=\"auto\", figsize=(10, 5))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if INTERACTIVE:\n",
    "    # create widget for interactive split value histogram\n",
    "    interact(render_histogram, feature=gbm.feature_name())\n",
    "else:\n",
    "    render_histogram(feature=\"f26\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:40.027803Z",
     "start_time": "2018-10-25T22:13:39.960713Z"
    }
   },
   "source": [
    "## Plot trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def render_tree(tree_index, show_info, precision=3):\n",
    "    show_info = None if \"None\" in show_info else show_info\n",
    "    return lgb.create_tree_digraph(gbm, tree_index=tree_index, show_info=show_info, precision=precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x7200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if INTERACTIVE:\n",
    "    # create widget to switch between trees and control info in nodes\n",
    "    interact(\n",
    "        render_tree,\n",
    "        tree_index=(0, gbm.num_trees() - 1),\n",
    "        show_info=SelectMultiple(  # allow multiple values to be selected\n",
    "            options=[\n",
    "                \"None\",\n",
    "                \"split_gain\",\n",
    "                \"internal_value\",\n",
    "                \"internal_count\",\n",
    "                \"internal_weight\",\n",
    "                \"leaf_count\",\n",
    "                \"leaf_weight\",\n",
    "                \"data_percentage\",\n",
    "            ],\n",
    "            value=[\"None\"],\n",
    "        ),\n",
    "        precision=(0, 10),\n",
    "    )\n",
    "    tree = None\n",
    "else:\n",
    "    tree = render_tree(53, [\"None\"])\n",
    "tree"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.11.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}