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": [
    "import warnings\n",
    "warnings.filterwarnings('ignore') #suppress lgb userwarnings\n",
    "\n",
    "import lightgbm as lgb\n",
    "import pandas as pd\n",
    "import graphviz\n",
    "\n",
    "if lgb.compat.MATPLOTLIB_INSTALLED:\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib inline\n",
    "else:\n",
    "    raise ImportError('You need to install matplotlib to run this notebook.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.158186Z",
     "start_time": "2018-10-25T22:13:39.154477Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lightgbm version: 2.2.1\n",
      "pandas version: 0.23.3\n"
     ]
    }
   ],
   "source": [
    "print('lightgbm version: ' + lgb.__version__)\n",
    "print('pandas version: ' + pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.238695Z",
     "start_time": "2018-10-25T22:13:39.160165Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../../regression/regression.train', header=None, sep='\\t')\n",
    "df_test = pd.read_csv('../../regression/regression.test', header=None, sep='\\t')\n",
    "\n",
    "y_train = df_train[0].values\n",
    "y_test = df_test[0].values\n",
    "X_train = df_train.drop(0, axis=1).values\n",
    "X_test = df_test.drop(0, axis=1).values\n",
    "\n",
    "# create dataset for lightgbm\n",
    "lgb_train = lgb.Dataset(X_train, y_train)\n",
    "lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "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 = {\n",
    "    'num_leaves': 5,\n",
    "    'metric': ('l1', 'l2'),\n",
    "    'verbose': 0\n",
    "}"
   ]
  },
  {
   "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 l2: 0.217995\ttraining's l1: 0.457448\tvalid_1's l2: 0.21641\tvalid_1's l1: 0.456464\n",
      "[20]\ttraining's l2: 0.205099\ttraining's l1: 0.436869\tvalid_1's l2: 0.201616\tvalid_1's l1: 0.434057\n",
      "[30]\ttraining's l2: 0.197421\ttraining's l1: 0.421302\tvalid_1's l2: 0.192514\tvalid_1's l1: 0.417019\n",
      "[40]\ttraining's l2: 0.192856\ttraining's l1: 0.411107\tvalid_1's l2: 0.187258\tvalid_1's l1: 0.406303\n",
      "[50]\ttraining's l2: 0.189593\ttraining's l1: 0.403695\tvalid_1's l2: 0.183688\tvalid_1's l1: 0.398997\n",
      "[60]\ttraining's l2: 0.187043\ttraining's l1: 0.398704\tvalid_1's l2: 0.181009\tvalid_1's l1: 0.393977\n",
      "[70]\ttraining's l2: 0.184982\ttraining's l1: 0.394876\tvalid_1's l2: 0.178803\tvalid_1's l1: 0.389805\n",
      "[80]\ttraining's l2: 0.1828\ttraining's l1: 0.391147\tvalid_1's l2: 0.176799\tvalid_1's l1: 0.386476\n",
      "[90]\ttraining's l2: 0.180817\ttraining's l1: 0.388101\tvalid_1's l2: 0.175775\tvalid_1's l1: 0.384404\n",
      "[100]\ttraining's l2: 0.179171\ttraining's l1: 0.385174\tvalid_1's l2: 0.175321\tvalid_1's l1: 0.382929\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "evals_result = {}  # to record eval results for plotting\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=100,\n",
    "                valid_sets=[lgb_train, lgb_test],\n",
    "                feature_name=['f' + str(i + 1) for i in range(28)],\n",
    "                categorical_feature=[21],\n",
    "                evals_result=evals_result,\n",
    "                verbose_eval=10)"
   ]
  },
  {
   "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": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lgb.plot_metric(evals_result, metric='l1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.958548Z",
     "start_time": "2018-10-25T22:13:39.811530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lgb.plot_importance(gbm, max_num_features=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:40.027803Z",
     "start_time": "2018-10-25T22:13:39.960713Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting 84th tree with graphviz\n"
     ]
    },
    {
     "data": {
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       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
       " -->\n",
       "<!-- Title: Tree84 Pages: 1 -->\n",
       "<svg width=\"531pt\" height=\"463pt\"\n",
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       "<!-- split3&#45;&gt;leaf3 -->\n",
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       "<title>split3&#45;&gt;leaf3</title>\n",
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       "<!-- split3&#45;&gt;leaf4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>split3&#45;&gt;leaf4</title>\n",
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x117a7f208>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Plotting 84th tree with graphviz')\n",
    "lgb.create_tree_digraph(gbm, tree_index=83, name='Tree84', precision=4)"
   ]
  }
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