{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import pandas as pd\n", "import lightgbm as lgb\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "#新增加的两行\n", "from pylab import mpl\n", "\n", "# 设置显示中文字体\n", "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n", "\n", "mpl.rcParams[\"axes.unicode_minus\"] = False" ] }, { "cell_type": "code", "execution_count": 2, "outputs": [ { "data": { "text/plain": " days 发电量（千瓦时） 供热量（吉焦） 机组运行时间（小时） 硫分（%） 脱硫剂使用量（吨） 脱硫设施运行时间（小时） \\\n0 2018-10-01 156796.0 6536.83 24.0 0.51 5.06 24.0 \n1 2018-10-02 133984.0 2484.64 24.0 0.51 5.04 24.0 \n2 2018-10-03 134023.0 3020.83 24.0 0.51 5.04 24.0 \n3 2018-10-04 124765.0 5599.23 24.0 0.51 5.03 24.0 \n4 2018-10-05 134414.0 4702.65 24.0 0.51 5.06 24.0 \n\n 脱硝还原剂消耗量（吨） 脱硝运行时间（小时） 燃料消耗量（吨） ... cSO2 cO2 \\\n0 2.98 24.0 323 ... 2.148473e+07 3.745944e+07 \n1 2.97 24.0 218 ... 1.587722e+07 2.832146e+07 \n2 2.95 24.0 212 ... 2.829086e+07 3.174159e+07 \n3 2.98 24.0 223 ... 1.030569e+07 2.511504e+07 \n4 3.01 24.0 243 ... 1.830254e+06 4.106346e+07 \n\n csmoke flow rNOx rO2 temp rSO2 \\\n0 6.519466e+05 162345.192917 28.981417 9.900000 51.250000 5.581667 \n1 3.656575e+05 140175.330833 22.220750 9.400000 50.679167 4.364167 \n2 5.181773e+05 154686.184167 24.816708 8.550000 52.808333 7.580000 \n3 2.299438e+06 120345.545833 21.875125 10.202083 48.854167 2.808958 \n4 6.230433e+06 162533.103542 25.605917 11.497917 45.783333 0.393333 \n\n rsmoke day_of_year \n0 0.209167 273 \n1 0.190417 274 \n2 0.139583 275 \n3 0.893333 276 \n4 2.141875 277 \n\n[5 rows x 165 columns]", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
days发电量（千瓦时）供热量（吉焦）机组运行时间（小时）硫分（%）脱硫剂使用量（吨）脱硫设施运行时间（小时）脱硝还原剂消耗量（吨）脱硝运行时间（小时）燃料消耗量（吨）...cSO2cO2csmokeflowrNOxrO2temprSO2rsmokeday_of_year
02018-10-01156796.06536.8324.00.515.0624.02.9824.0323...2.148473e+073.745944e+076.519466e+05162345.19291728.9814179.90000051.2500005.5816670.209167273
12018-10-02133984.02484.6424.00.515.0424.02.9724.0218...1.587722e+072.832146e+073.656575e+05140175.33083322.2207509.40000050.6791674.3641670.190417274
22018-10-03134023.03020.8324.00.515.0424.02.9524.0212...2.829086e+073.174159e+075.181773e+05154686.18416724.8167088.55000052.8083337.5800000.139583275
32018-10-04124765.05599.2324.00.515.0324.02.9824.0223...1.030569e+072.511504e+072.299438e+06120345.54583321.87512510.20208348.8541672.8089580.893333276
42018-10-05134414.04702.6524.00.515.0624.03.0124.0243...1.830254e+064.106346e+076.230433e+06162533.10354225.60591711.49791745.7833330.3933332.141875277
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" }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total_data = pd.read_csv('./data/train_data.csv')\n", "total_data.head()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 3, "outputs": [], "source": [ "# 先去掉一些异常值\n", "use_data = total_data[(total_data['机组运行时间（小时）'] == 24) & (total_data['脱硝运行时间（小时）'] == 24)].set_index('days').drop(\n", " columns=['机组运行时间（小时）', '脱硫设施运行时间（小时）', '脱硝运行时间（小时）'])\n", "use_data['week_of_year'] = use_data.day_of_year / 7\n", "use_data.drop(columns=['day_of_year'], inplace=True)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 4, "outputs": [ { "data": { "text/plain": "['rNOx', 'rO2', 'rSO2', 'rsmoke']" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r_cols = [x for x in use_data.columns if x.startswith('r')]\n", "r_cols" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 5, "outputs": [], "source": [ "for col in use_data.columns:\n", " if use_data[col].max() > 10:\n", " use_data[col] = np.log1p(use_data[col])\n", " if col == '燃料消耗量（吨）':\n", " use_data[col] = np.log1p(use_data[col])" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 6, "outputs": [ { "data": { "text/plain": "Index(['cNOx', 'cSO2', 'cO2', 'csmoke', 'flow', 'rNOx', 'rO2', 'temp', 'rSO2',\n 'rsmoke', 'week_of_year'],\n dtype='object')" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_cols = [x for x in use_data.columns if x != '燃料消耗量（吨）']\n", "feature_cols = [x for x in feature_cols if '（' not in x]\n", "feature_cols = use_data.columns[-11:]\n", "feature_cols" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 7, "outputs": [], "source": [ "train_data, valid_data = train_test_split(use_data, test_size=0.15, shuffle=True, random_state=666)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 8, "outputs": [], "source": [ "X_train, Y_train = train_data[feature_cols], train_data['燃料消耗量（吨）']\n", "X_valid, Y_valid = valid_data[feature_cols], valid_data['燃料消耗量（吨）']\n", "X_test, Y_test = valid_data[feature_cols], valid_data['燃料消耗量（吨）']" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 9, "outputs": [], "source": [ "lgb_train = lgb.Dataset(X_train, Y_train)\n", "lgb_eval = lgb.Dataset(X_valid, Y_valid)\n", "lgb_test = lgb.Dataset(X_test, Y_test)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 10, "outputs": [], "source": [ "params = {\n", " 'task': 'train',\n", " 'boosting_type': 'gbdt', # 设置提升类型\n", " 'objective': 'regression_l2', # 目标函数\n", " 'metric': {'rmse'}, # 评估函数\n", " 'max_depth': 10,\n", " 'num_leaves': 31, # 叶子节点数\n", " 'learning_rate': 0.01, # 学习速率\n", " 'feature_fraction': 0.9, # 建树的特征选择比例\n", " 'bagging_fraction': 0.8, # 建树的样本采样比例\n", " 'bagging_freq': 5, # k 意味着每 k 次迭代执行bagging\n", " 'verbose': -1 # <0 显示致命的, =0 显示错误 (警告), >0 显示信息\n", "}" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 11, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1]\tvalid_0's rmse: 0.0361047\n", "Training until validation scores don't improve for 100 rounds\n", "[2]\tvalid_0's rmse: 0.0359398\n", "[3]\tvalid_0's rmse: 0.0358123\n", "[4]\tvalid_0's rmse: 0.0356709\n", "[5]\tvalid_0's rmse: 0.0355485\n", "[6]\tvalid_0's rmse: 0.035388\n", "[7]\tvalid_0's rmse: 0.0352781\n", "[8]\tvalid_0's rmse: 0.0351619\n", "[9]\tvalid_0's rmse: 0.035026\n", "[10]\tvalid_0's rmse: 0.0348736\n", "[11]\tvalid_0's rmse: 0.0347371\n", "[12]\tvalid_0's rmse: 0.0346186\n", "[13]\tvalid_0's rmse: 0.0345018\n", "[14]\tvalid_0's rmse: 0.0343709\n", "[15]\tvalid_0's rmse: 0.0342329\n", "[16]\tvalid_0's rmse: 0.0341227\n", "[17]\tvalid_0's rmse: 0.0340256\n", "[18]\tvalid_0's rmse: 0.033924\n", "[19]\tvalid_0's rmse: 0.0338187\n", "[20]\tvalid_0's rmse: 0.0337153\n", "[21]\tvalid_0's rmse: 0.033616\n", "[22]\tvalid_0's rmse: 0.0335067\n", "[23]\tvalid_0's rmse: 0.0334041\n", "[24]\tvalid_0's rmse: 0.0332995\n", "[25]\tvalid_0's rmse: 0.0331921\n", "[26]\tvalid_0's rmse: 0.0330884\n", "[27]\tvalid_0's rmse: 0.032978\n", "[28]\tvalid_0's rmse: 0.0328855\n", "[29]\tvalid_0's rmse: 0.032781\n", "[30]\tvalid_0's rmse: 0.032678\n", "[31]\tvalid_0's rmse: 0.0325855\n", "[32]\tvalid_0's rmse: 0.0325005\n", "[33]\tvalid_0's rmse: 0.0324143\n", "[34]\tvalid_0's rmse: 0.0323269\n", "[35]\tvalid_0's rmse: 0.0322377\n", "[36]\tvalid_0's rmse: 0.0321433\n", "[37]\tvalid_0's rmse: 0.0320294\n", "[38]\tvalid_0's rmse: 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rmse: 0.024653\n", "[276]\tvalid_0's rmse: 0.0246436\n", "[277]\tvalid_0's rmse: 0.0246298\n", "[278]\tvalid_0's rmse: 0.0246208\n", "[279]\tvalid_0's rmse: 0.0246118\n", "[280]\tvalid_0's rmse: 0.0245976\n", "[281]\tvalid_0's rmse: 0.0245997\n", "[282]\tvalid_0's rmse: 0.0246013\n", "[283]\tvalid_0's rmse: 0.0245843\n", "[284]\tvalid_0's rmse: 0.024582\n", "[285]\tvalid_0's rmse: 0.0245842\n", "[286]\tvalid_0's rmse: 0.0245838\n", "[287]\tvalid_0's rmse: 0.0245806\n", "[288]\tvalid_0's rmse: 0.0245793\n", "[289]\tvalid_0's rmse: 0.0245811\n", "[290]\tvalid_0's rmse: 0.0245856\n", "[291]\tvalid_0's rmse: 0.0245711\n", "[292]\tvalid_0's rmse: 0.0245609\n", "[293]\tvalid_0's rmse: 0.0245539\n", "[294]\tvalid_0's rmse: 0.0245484\n", "[295]\tvalid_0's rmse: 0.0245374\n", "[296]\tvalid_0's rmse: 0.0245338\n", "[297]\tvalid_0's rmse: 0.0245291\n", "[298]\tvalid_0's rmse: 0.0245297\n", "[299]\tvalid_0's rmse: 0.0245264\n", "[300]\tvalid_0's rmse: 0.0245232\n", "[301]\tvalid_0's rmse: 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rmse: 0.0241972\n", "[355]\tvalid_0's rmse: 0.0241909\n", "[356]\tvalid_0's rmse: 0.0241854\n", "[357]\tvalid_0's rmse: 0.0241721\n", "[358]\tvalid_0's rmse: 0.024171\n", "[359]\tvalid_0's rmse: 0.0241659\n", "[360]\tvalid_0's rmse: 0.0241622\n", "[361]\tvalid_0's rmse: 0.0241609\n", "[362]\tvalid_0's rmse: 0.0241598\n", "[363]\tvalid_0's rmse: 0.0241542\n", "[364]\tvalid_0's rmse: 0.024147\n", "[365]\tvalid_0's rmse: 0.0241429\n", "[366]\tvalid_0's rmse: 0.0241384\n", "[367]\tvalid_0's rmse: 0.0241319\n", "[368]\tvalid_0's rmse: 0.0241298\n", "[369]\tvalid_0's rmse: 0.0241277\n", "[370]\tvalid_0's rmse: 0.0241209\n", "[371]\tvalid_0's rmse: 0.0241138\n", "[372]\tvalid_0's rmse: 0.0241083\n", "[373]\tvalid_0's rmse: 0.0241056\n", "[374]\tvalid_0's rmse: 0.0240979\n", "[375]\tvalid_0's rmse: 0.0240917\n", "[376]\tvalid_0's rmse: 0.0240815\n", "[377]\tvalid_0's rmse: 0.0240701\n", "[378]\tvalid_0's rmse: 0.0240618\n", "[379]\tvalid_0's rmse: 0.0240512\n", "[380]\tvalid_0's rmse: 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"[433]\tvalid_0's rmse: 0.0239484\n", "[434]\tvalid_0's rmse: 0.0239519\n", "[435]\tvalid_0's rmse: 0.0239532\n", "[436]\tvalid_0's rmse: 0.023957\n", "[437]\tvalid_0's rmse: 0.0239521\n", "[438]\tvalid_0's rmse: 0.023949\n", "[439]\tvalid_0's rmse: 0.023945\n", "[440]\tvalid_0's rmse: 0.0239412\n", "[441]\tvalid_0's rmse: 0.0239376\n", "[442]\tvalid_0's rmse: 0.0239317\n", "[443]\tvalid_0's rmse: 0.0239337\n", "[444]\tvalid_0's rmse: 0.0239286\n", "[445]\tvalid_0's rmse: 0.0239233\n", "[446]\tvalid_0's rmse: 0.0239192\n", "[447]\tvalid_0's rmse: 0.0239162\n", "[448]\tvalid_0's rmse: 0.0239115\n", "[449]\tvalid_0's rmse: 0.0239118\n", "[450]\tvalid_0's rmse: 0.0239079\n", "[451]\tvalid_0's rmse: 0.0239096\n", "[452]\tvalid_0's rmse: 0.0239117\n", "[453]\tvalid_0's rmse: 0.0239158\n", "[454]\tvalid_0's rmse: 0.023916\n", "[455]\tvalid_0's rmse: 0.0239156\n", "[456]\tvalid_0's rmse: 0.0239162\n", "[457]\tvalid_0's rmse: 0.0239135\n", "[458]\tvalid_0's rmse: 0.0239121\n", 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"[563]\tvalid_0's rmse: 0.0237081\n", "[564]\tvalid_0's rmse: 0.0237097\n", "[565]\tvalid_0's rmse: 0.0237125\n", "[566]\tvalid_0's rmse: 0.02371\n", "[567]\tvalid_0's rmse: 0.0237057\n", "[568]\tvalid_0's rmse: 0.0237037\n", "[569]\tvalid_0's rmse: 0.0236994\n", "[570]\tvalid_0's rmse: 0.0236973\n", "[571]\tvalid_0's rmse: 0.0237007\n", "[572]\tvalid_0's rmse: 0.0237056\n", "[573]\tvalid_0's rmse: 0.0237081\n", "[574]\tvalid_0's rmse: 0.0237099\n", "[575]\tvalid_0's rmse: 0.0237144\n", "[576]\tvalid_0's rmse: 0.0237134\n", "[577]\tvalid_0's rmse: 0.0237125\n", "[578]\tvalid_0's rmse: 0.023717\n", "[579]\tvalid_0's rmse: 0.0237166\n", "[580]\tvalid_0's rmse: 0.023721\n", "[581]\tvalid_0's rmse: 0.0237188\n", "[582]\tvalid_0's rmse: 0.0237185\n", "[583]\tvalid_0's rmse: 0.0237182\n", "[584]\tvalid_0's rmse: 0.0237154\n", "[585]\tvalid_0's rmse: 0.0237144\n", "[586]\tvalid_0's rmse: 0.0237223\n", "[587]\tvalid_0's rmse: 0.0237258\n", "[588]\tvalid_0's rmse: 0.0237284\n", "[589]\tvalid_0's rmse: 0.0237351\n", "[590]\tvalid_0's rmse: 0.0237396\n", "[591]\tvalid_0's rmse: 0.0237399\n", "[592]\tvalid_0's rmse: 0.0237394\n", "[593]\tvalid_0's rmse: 0.0237347\n", "[594]\tvalid_0's rmse: 0.0237336\n", "[595]\tvalid_0's rmse: 0.0237299\n", "[596]\tvalid_0's rmse: 0.0237252\n", "[597]\tvalid_0's rmse: 0.0237205\n", "[598]\tvalid_0's rmse: 0.0237181\n", "[599]\tvalid_0's rmse: 0.0237155\n", "[600]\tvalid_0's rmse: 0.0237113\n", "[601]\tvalid_0's rmse: 0.0237135\n", "[602]\tvalid_0's rmse: 0.0237156\n", "[603]\tvalid_0's rmse: 0.0237178\n", "[604]\tvalid_0's rmse: 0.0237191\n", "[605]\tvalid_0's rmse: 0.0237146\n", "[606]\tvalid_0's rmse: 0.0237114\n", "[607]\tvalid_0's rmse: 0.0237142\n", "[608]\tvalid_0's rmse: 0.023715\n", "[609]\tvalid_0's rmse: 0.0237133\n", "[610]\tvalid_0's rmse: 0.0237139\n", "[611]\tvalid_0's rmse: 0.0237155\n", "[612]\tvalid_0's rmse: 0.0237169\n", "[613]\tvalid_0's rmse: 0.0237189\n", "[614]\tvalid_0's rmse: 0.0237202\n", "[615]\tvalid_0's rmse: 0.0237216\n", "[616]\tvalid_0's rmse: 0.0237216\n", "[617]\tvalid_0's rmse: 0.0237165\n", "[618]\tvalid_0's rmse: 0.0237185\n", "[619]\tvalid_0's rmse: 0.0237152\n", "[620]\tvalid_0's rmse: 0.0237125\n", "[621]\tvalid_0's rmse: 0.0237107\n", "[622]\tvalid_0's rmse: 0.0237079\n", "[623]\tvalid_0's rmse: 0.0237081\n", "[624]\tvalid_0's rmse: 0.0237024\n", "[625]\tvalid_0's rmse: 0.0237027\n", "[626]\tvalid_0's rmse: 0.0237078\n", "[627]\tvalid_0's rmse: 0.0237062\n", "[628]\tvalid_0's rmse: 0.0237076\n", "[629]\tvalid_0's rmse: 0.0237107\n", "[630]\tvalid_0's rmse: 0.0237141\n", "[631]\tvalid_0's rmse: 0.02372\n", "[632]\tvalid_0's rmse: 0.0237201\n", "[633]\tvalid_0's rmse: 0.0237182\n", "[634]\tvalid_0's rmse: 0.023717\n", "[635]\tvalid_0's rmse: 0.023711\n", "[636]\tvalid_0's rmse: 0.0237045\n", "[637]\tvalid_0's rmse: 0.0237\n", "[638]\tvalid_0's rmse: 0.0236946\n", "[639]\tvalid_0's rmse: 0.0236963\n", "[640]\tvalid_0's rmse: 0.023692\n", "[641]\tvalid_0's rmse: 0.0236891\n", "[642]\tvalid_0's rmse: 0.0236864\n", "[643]\tvalid_0's rmse: 0.0236813\n", "[644]\tvalid_0's rmse: 0.0236781\n", "[645]\tvalid_0's rmse: 0.0236752\n", "[646]\tvalid_0's rmse: 0.0236743\n", "[647]\tvalid_0's rmse: 0.0236729\n", "[648]\tvalid_0's rmse: 0.0236707\n", "[649]\tvalid_0's rmse: 0.0236687\n", "[650]\tvalid_0's rmse: 0.0236681\n", "[651]\tvalid_0's rmse: 0.0236664\n", "[652]\tvalid_0's rmse: 0.0236637\n", "[653]\tvalid_0's rmse: 0.0236597\n", "[654]\tvalid_0's rmse: 0.0236582\n", "[655]\tvalid_0's rmse: 0.0236561\n", "[656]\tvalid_0's rmse: 0.0236593\n", "[657]\tvalid_0's rmse: 0.0236551\n", "[658]\tvalid_0's rmse: 0.0236587\n", "[659]\tvalid_0's rmse: 0.0236519\n", "[660]\tvalid_0's rmse: 0.0236453\n", "[661]\tvalid_0's rmse: 0.0236416\n", "[662]\tvalid_0's rmse: 0.023636\n", "[663]\tvalid_0's rmse: 0.0236345\n", "[664]\tvalid_0's rmse: 0.0236303\n", "[665]\tvalid_0's rmse: 0.0236267\n", "[666]\tvalid_0's rmse: 0.0236234\n", "[667]\tvalid_0's rmse: 0.0236212\n", "[668]\tvalid_0's rmse: 0.0236192\n", "[669]\tvalid_0's rmse: 0.023616\n", "[670]\tvalid_0's rmse: 0.0236154\n", "[671]\tvalid_0's rmse: 0.0236192\n", "[672]\tvalid_0's rmse: 0.0236205\n", "[673]\tvalid_0's rmse: 0.0236209\n", "[674]\tvalid_0's rmse: 0.0236217\n", "[675]\tvalid_0's rmse: 0.0236231\n", "[676]\tvalid_0's rmse: 0.0236199\n", "[677]\tvalid_0's rmse: 0.0236168\n", "[678]\tvalid_0's rmse: 0.0236158\n", "[679]\tvalid_0's rmse: 0.0236132\n", "[680]\tvalid_0's rmse: 0.023615\n", "[681]\tvalid_0's rmse: 0.0236104\n", "[682]\tvalid_0's rmse: 0.0236094\n", "[683]\tvalid_0's rmse: 0.023608\n", "[684]\tvalid_0's rmse: 0.0236061\n", "[685]\tvalid_0's rmse: 0.0236054\n", "[686]\tvalid_0's rmse: 0.0236006\n", "[687]\tvalid_0's rmse: 0.0235964\n", "[688]\tvalid_0's rmse: 0.0235926\n", "[689]\tvalid_0's rmse: 0.0235928\n", "[690]\tvalid_0's rmse: 0.0235919\n", "[691]\tvalid_0's rmse: 0.0235965\n", "[692]\tvalid_0's rmse: 0.023596\n", "[693]\tvalid_0's rmse: 0.0235979\n", "[694]\tvalid_0's rmse: 0.0235969\n", "[695]\tvalid_0's rmse: 0.0235965\n", "[696]\tvalid_0's rmse: 0.0235954\n", "[697]\tvalid_0's rmse: 0.0235889\n", "[698]\tvalid_0's rmse: 0.0235825\n", "[699]\tvalid_0's rmse: 0.0235817\n", "[700]\tvalid_0's rmse: 0.0235755\n", "[701]\tvalid_0's rmse: 0.0235773\n", "[702]\tvalid_0's rmse: 0.0235708\n", "[703]\tvalid_0's rmse: 0.0235742\n", "[704]\tvalid_0's rmse: 0.0235722\n", "[705]\tvalid_0's rmse: 0.0235752\n", "[706]\tvalid_0's rmse: 0.0235732\n", "[707]\tvalid_0's rmse: 0.0235712\n", "[708]\tvalid_0's rmse: 0.023572\n", "[709]\tvalid_0's rmse: 0.0235776\n", "[710]\tvalid_0's rmse: 0.0235757\n", "[711]\tvalid_0's rmse: 0.0235846\n", "[712]\tvalid_0's rmse: 0.0235865\n", "[713]\tvalid_0's rmse: 0.0235889\n", "[714]\tvalid_0's rmse: 0.0235902\n", "[715]\tvalid_0's rmse: 0.0235915\n", "[716]\tvalid_0's rmse: 0.0235899\n", "[717]\tvalid_0's rmse: 0.0235843\n", "[718]\tvalid_0's rmse: 0.0235871\n", "[719]\tvalid_0's rmse: 0.0235815\n", "[720]\tvalid_0's rmse: 0.0235834\n", "[721]\tvalid_0's rmse: 0.0235796\n", "[722]\tvalid_0's rmse: 0.0235709\n", "[723]\tvalid_0's rmse: 0.0235679\n", "[724]\tvalid_0's rmse: 0.0235597\n", "[725]\tvalid_0's rmse: 0.023558\n", "[726]\tvalid_0's rmse: 0.0235521\n", "[727]\tvalid_0's rmse: 0.0235506\n", "[728]\tvalid_0's rmse: 0.0235456\n", "[729]\tvalid_0's rmse: 0.0235434\n", "[730]\tvalid_0's rmse: 0.023542\n", "[731]\tvalid_0's rmse: 0.0235465\n", "[732]\tvalid_0's rmse: 0.0235518\n", "[733]\tvalid_0's rmse: 0.0235577\n", "[734]\tvalid_0's rmse: 0.0235628\n", "[735]\tvalid_0's rmse: 0.023568\n", "[736]\tvalid_0's rmse: 0.023567\n", "[737]\tvalid_0's rmse: 0.0235687\n", "[738]\tvalid_0's rmse: 0.023567\n", "[739]\tvalid_0's rmse: 0.0235689\n", "[740]\tvalid_0's rmse: 0.0235683\n", "[741]\tvalid_0's rmse: 0.0235687\n", "[742]\tvalid_0's rmse: 0.0235667\n", "[743]\tvalid_0's rmse: 0.023565\n", "[744]\tvalid_0's rmse: 0.0235647\n", "[745]\tvalid_0's rmse: 0.0235639\n", "[746]\tvalid_0's rmse: 0.0235626\n", "[747]\tvalid_0's rmse: 0.0235621\n", "[748]\tvalid_0's rmse: 0.0235655\n", "[749]\tvalid_0's rmse: 0.0235651\n", "[750]\tvalid_0's rmse: 0.0235626\n", "[751]\tvalid_0's rmse: 0.0235604\n", "[752]\tvalid_0's rmse: 0.0235554\n", "[753]\tvalid_0's rmse: 0.023553\n", "[754]\tvalid_0's rmse: 0.0235507\n", "[755]\tvalid_0's rmse: 0.0235485\n", "[756]\tvalid_0's rmse: 0.0235475\n", "[757]\tvalid_0's rmse: 0.0235471\n", "[758]\tvalid_0's rmse: 0.023547\n", "[759]\tvalid_0's rmse: 0.023546\n", "[760]\tvalid_0's rmse: 0.023548\n", "[761]\tvalid_0's rmse: 0.0235404\n", "[762]\tvalid_0's rmse: 0.0235322\n", "[763]\tvalid_0's rmse: 0.0235255\n", "[764]\tvalid_0's rmse: 0.0235206\n", "[765]\tvalid_0's rmse: 0.0235189\n", "[766]\tvalid_0's rmse: 0.0235161\n", "[767]\tvalid_0's rmse: 0.0235131\n", "[768]\tvalid_0's rmse: 0.0235113\n", "[769]\tvalid_0's rmse: 0.0235116\n", "[770]\tvalid_0's rmse: 0.0235096\n", "[771]\tvalid_0's rmse: 0.0235095\n", "[772]\tvalid_0's rmse: 0.0235065\n", "[773]\tvalid_0's rmse: 0.0235049\n", "[774]\tvalid_0's rmse: 0.023504\n", "[775]\tvalid_0's rmse: 0.0235031\n", "[776]\tvalid_0's rmse: 0.0235041\n", "[777]\tvalid_0's rmse: 0.0235034\n", "[778]\tvalid_0's rmse: 0.0235044\n", "[779]\tvalid_0's rmse: 0.0235023\n", "[780]\tvalid_0's rmse: 0.0235033\n", "[781]\tvalid_0's rmse: 0.023505\n", "[782]\tvalid_0's rmse: 0.0235067\n", "[783]\tvalid_0's rmse: 0.0235069\n", "[784]\tvalid_0's rmse: 0.023507\n", "[785]\tvalid_0's rmse: 0.0235084\n", "[786]\tvalid_0's rmse: 0.0235073\n", "[787]\tvalid_0's rmse: 0.0235103\n", "[788]\tvalid_0's rmse: 0.0235107\n", "[789]\tvalid_0's rmse: 0.0235102\n", "[790]\tvalid_0's rmse: 0.0235127\n", "[791]\tvalid_0's rmse: 0.0235112\n", "[792]\tvalid_0's rmse: 0.0235102\n", "[793]\tvalid_0's rmse: 0.0235093\n", "[794]\tvalid_0's rmse: 0.0235079\n", "[795]\tvalid_0's rmse: 0.0235067\n", "[796]\tvalid_0's rmse: 0.0235103\n", "[797]\tvalid_0's rmse: 0.0235153\n", "[798]\tvalid_0's rmse: 0.0235183\n", "[799]\tvalid_0's rmse: 0.0235219\n", "[800]\tvalid_0's rmse: 0.0235219\n", "[801]\tvalid_0's rmse: 0.0235216\n", "[802]\tvalid_0's rmse: 0.0235214\n", "[803]\tvalid_0's rmse: 0.0235148\n", "[804]\tvalid_0's rmse: 0.0235062\n", "[805]\tvalid_0's rmse: 0.0235034\n", "[806]\tvalid_0's rmse: 0.0235047\n", "[807]\tvalid_0's rmse: 0.0235054\n", "[808]\tvalid_0's rmse: 0.0235062\n", "[809]\tvalid_0's rmse: 0.023506\n", "[810]\tvalid_0's rmse: 0.0235021\n", "[811]\tvalid_0's rmse: 0.0235045\n", "[812]\tvalid_0's rmse: 0.0235052\n", "[813]\tvalid_0's rmse: 0.023506\n", "[814]\tvalid_0's rmse: 0.0235077\n", "[815]\tvalid_0's rmse: 0.0235097\n", "[816]\tvalid_0's rmse: 0.0235131\n", "[817]\tvalid_0's rmse: 0.0235155\n", "[818]\tvalid_0's rmse: 0.0235176\n", "[819]\tvalid_0's rmse: 0.0235196\n", "[820]\tvalid_0's rmse: 0.0235238\n", "[821]\tvalid_0's rmse: 0.0235239\n", "[822]\tvalid_0's rmse: 0.0235244\n", "[823]\tvalid_0's rmse: 0.0235239\n", "[824]\tvalid_0's rmse: 0.0235241\n", "[825]\tvalid_0's rmse: 0.0235212\n", "[826]\tvalid_0's rmse: 0.0235226\n", "[827]\tvalid_0's rmse: 0.023523\n", "[828]\tvalid_0's rmse: 0.023524\n", "[829]\tvalid_0's rmse: 0.0235217\n", "[830]\tvalid_0's rmse: 0.0235221\n", "[831]\tvalid_0's rmse: 0.023523\n", "[832]\tvalid_0's rmse: 0.023519\n", "[833]\tvalid_0's rmse: 0.0235185\n", "[834]\tvalid_0's rmse: 0.0235189\n", "[835]\tvalid_0's rmse: 0.0235118\n", "[836]\tvalid_0's rmse: 0.0235092\n", "[837]\tvalid_0's rmse: 0.0235095\n", "[838]\tvalid_0's rmse: 0.0235097\n", "[839]\tvalid_0's rmse: 0.02351\n", "[840]\tvalid_0's rmse: 0.0235108\n", "[841]\tvalid_0's rmse: 0.0235141\n", "[842]\tvalid_0's rmse: 0.0235179\n", "[843]\tvalid_0's rmse: 0.0235229\n", "[844]\tvalid_0's rmse: 0.0235268\n", "[845]\tvalid_0's rmse: 0.0235272\n", "[846]\tvalid_0's rmse: 0.0235265\n", "[847]\tvalid_0's rmse: 0.0235274\n", "[848]\tvalid_0's rmse: 0.0235266\n", "[849]\tvalid_0's rmse: 0.0235251\n", "[850]\tvalid_0's rmse: 0.0235253\n", "[851]\tvalid_0's rmse: 0.0235213\n", "[852]\tvalid_0's rmse: 0.023517\n", "[853]\tvalid_0's rmse: 0.0235131\n", "[854]\tvalid_0's rmse: 0.0235091\n", "[855]\tvalid_0's rmse: 0.0235049\n", "[856]\tvalid_0's rmse: 0.0235052\n", "[857]\tvalid_0's rmse: 0.0235\n", "[858]\tvalid_0's rmse: 0.0235013\n", "[859]\tvalid_0's rmse: 0.0235023\n", "[860]\tvalid_0's rmse: 0.0235047\n", "[861]\tvalid_0's rmse: 0.023503\n", "[862]\tvalid_0's rmse: 0.0234994\n", "[863]\tvalid_0's rmse: 0.0234981\n", "[864]\tvalid_0's rmse: 0.0234936\n", "[865]\tvalid_0's rmse: 0.0234924\n", "[866]\tvalid_0's rmse: 0.0234934\n", "[867]\tvalid_0's rmse: 0.0234969\n", "[868]\tvalid_0's rmse: 0.0234978\n", "[869]\tvalid_0's rmse: 0.0235012\n", "[870]\tvalid_0's rmse: 0.0235047\n", "[871]\tvalid_0's rmse: 0.0235039\n", "[872]\tvalid_0's rmse: 0.0235025\n", "[873]\tvalid_0's rmse: 0.0235049\n", "[874]\tvalid_0's rmse: 0.0235041\n", "[875]\tvalid_0's rmse: 0.0235059\n", "[876]\tvalid_0's rmse: 0.0235107\n", "[877]\tvalid_0's rmse: 0.0235122\n", "[878]\tvalid_0's rmse: 0.0235171\n", "[879]\tvalid_0's rmse: 0.0235214\n", "[880]\tvalid_0's rmse: 0.0235213\n", "[881]\tvalid_0's rmse: 0.023521\n", "[882]\tvalid_0's rmse: 0.0235209\n", "[883]\tvalid_0's rmse: 0.0235202\n", "[884]\tvalid_0's rmse: 0.0235197\n", "[885]\tvalid_0's rmse: 0.0235206\n", "[886]\tvalid_0's rmse: 0.0235238\n", "[887]\tvalid_0's rmse: 0.0235254\n", "[888]\tvalid_0's rmse: 0.0235276\n", "[889]\tvalid_0's rmse: 0.0235311\n", "[890]\tvalid_0's rmse: 0.0235339\n", "[891]\tvalid_0's rmse: 0.0235356\n", "[892]\tvalid_0's rmse: 0.0235324\n", "[893]\tvalid_0's rmse: 0.0235339\n", "[894]\tvalid_0's rmse: 0.0235337\n", "[895]\tvalid_0's rmse: 0.0235358\n", "[896]\tvalid_0's rmse: 0.0235353\n", "[897]\tvalid_0's rmse: 0.0235357\n", "[898]\tvalid_0's rmse: 0.0235341\n", "[899]\tvalid_0's rmse: 0.0235337\n", "[900]\tvalid_0's rmse: 0.023535\n", "[901]\tvalid_0's rmse: 0.0235356\n", "[902]\tvalid_0's rmse: 0.0235366\n", "[903]\tvalid_0's rmse: 0.0235374\n", "[904]\tvalid_0's rmse: 0.023538\n", "[905]\tvalid_0's rmse: 0.0235402\n", "[906]\tvalid_0's rmse: 0.023542\n", "[907]\tvalid_0's rmse: 0.0235439\n", "[908]\tvalid_0's rmse: 0.0235459\n", "[909]\tvalid_0's rmse: 0.0235494\n", "[910]\tvalid_0's rmse: 0.0235523\n", "[911]\tvalid_0's rmse: 0.0235503\n", "[912]\tvalid_0's rmse: 0.0235494\n", "[913]\tvalid_0's rmse: 0.0235473\n", "[914]\tvalid_0's rmse: 0.0235462\n", "[915]\tvalid_0's rmse: 0.0235449\n", "[916]\tvalid_0's rmse: 0.0235459\n", "[917]\tvalid_0's rmse: 0.023544\n", "[918]\tvalid_0's rmse: 0.0235448\n", "[919]\tvalid_0's rmse: 0.023543\n", "[920]\tvalid_0's rmse: 0.0235422\n", "[921]\tvalid_0's rmse: 0.0235431\n", "[922]\tvalid_0's rmse: 0.023542\n", "[923]\tvalid_0's rmse: 0.0235415\n", "[924]\tvalid_0's rmse: 0.0235385\n", "[925]\tvalid_0's rmse: 0.0235385\n", "[926]\tvalid_0's rmse: 0.0235357\n", "[927]\tvalid_0's rmse: 0.0235344\n", "[928]\tvalid_0's rmse: 0.0235349\n", "[929]\tvalid_0's rmse: 0.0235341\n", "[930]\tvalid_0's rmse: 0.0235303\n", "[931]\tvalid_0's rmse: 0.0235341\n", "[932]\tvalid_0's rmse: 0.023536\n", "[933]\tvalid_0's rmse: 0.0235349\n", "[934]\tvalid_0's rmse: 0.0235368\n", "[935]\tvalid_0's rmse: 0.0235389\n", "[936]\tvalid_0's rmse: 0.0235382\n", "[937]\tvalid_0's rmse: 0.0235344\n", "[938]\tvalid_0's rmse: 0.0235317\n", "[939]\tvalid_0's rmse: 0.0235276\n", "[940]\tvalid_0's rmse: 0.0235269\n", "[941]\tvalid_0's rmse: 0.0235273\n", "[942]\tvalid_0's rmse: 0.0235316\n", "[943]\tvalid_0's rmse: 0.0235336\n", "[944]\tvalid_0's rmse: 0.0235346\n", "[945]\tvalid_0's rmse: 0.0235367\n", "[946]\tvalid_0's rmse: 0.023537\n", "[947]\tvalid_0's rmse: 0.023538\n", "[948]\tvalid_0's rmse: 0.023539\n", "[949]\tvalid_0's rmse: 0.0235394\n", "[950]\tvalid_0's rmse: 0.0235403\n", "[951]\tvalid_0's rmse: 0.0235336\n", "[952]\tvalid_0's rmse: 0.0235269\n", "[953]\tvalid_0's rmse: 0.0235204\n", "[954]\tvalid_0's rmse: 0.0235217\n", "[955]\tvalid_0's rmse: 0.0235153\n", "[956]\tvalid_0's rmse: 0.0235105\n", "[957]\tvalid_0's rmse: 0.023508\n", "[958]\tvalid_0's rmse: 0.0235124\n", "[959]\tvalid_0's rmse: 0.023517\n", "[960]\tvalid_0's rmse: 0.0235178\n", "[961]\tvalid_0's rmse: 0.0235156\n", "[962]\tvalid_0's rmse: 0.0235136\n", "[963]\tvalid_0's rmse: 0.0235115\n", "[964]\tvalid_0's rmse: 0.023511\n", "[965]\tvalid_0's rmse: 0.023509\n", "Early stopping, best iteration is:\n", "[865]\tvalid_0's rmse: 0.0234924\n" ] } ], "source": [ "gbm = lgb.train(params, lgb_train, num_boost_round=2000, valid_sets=lgb_eval, early_stopping_rounds=100)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 12, "outputs": [], "source": [ "y_pred = np.expm1(np.expm1(gbm.predict(X_test)))\n", "y_true = np.expm1(np.expm1(Y_test))" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 13, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE: 5.74E+03\n", "RMSE: 75.76181372320752\n", "MAE: 54.761485656796516\n", "MAPE: 0.11813067374720394\n", "R_2: 0.6942490637163895\n" ] } ], "source": [ "MSE = mean_squared_error(y_true, y_pred)\n", "RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", "MAE = mean_absolute_error(y_true, y_pred)\n", "MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", "R_2 = r2_score(y_true, y_pred)\n", "print('MSE:', format(MSE, '.2E'))\n", "print('RMSE:', RMSE)\n", "print('MAE:', MAE)\n", "print('MAPE:', MAPE)\n", "print('R_2:', R_2) #R方为负就说明拟合效果比平均值差" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 14, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE: 5.74E+03\n", "RMSE: 75.76181372320752\n", "MAE: 54.761485656796516\n", "MAPE: 0.11813067374720394\n", "R_2: 0.6942490637163895\n" ] } ], "source": [ "MSE = mean_squared_error(y_true, y_pred)\n", "RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", "MAE = mean_absolute_error(y_true, y_pred)\n", "MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", "R_2 = r2_score(y_true, y_pred)\n", "print('MSE:', format(MSE, '.2E'))\n", "print('RMSE:', RMSE)\n", "print('MAE:', MAE)\n", "print('MAPE:', MAPE)\n", "print('R_2:', R_2) #R方为负就说明拟合效果比平均值差" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 15, "outputs": [], "source": [ "import seaborn as sns" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 20, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\zhaojh\\AppData\\Local\\Temp\\ipykernel_26460\\3712314790.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " ax.set_yticklabels(labels=[0, 2000, 4000, 6000, 8000, 10000, 12000, 14000, 16000], fontsize=6)\n" ] }, { "data": { "text/plain": "
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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "feature_importance = pd.DataFrame()\n", "feature_importance['fea_name'] = feature_cols\n", "feature_importance['fea_imp'] = gbm.feature_importance()\n", "feature_importance = feature_importance.sort_values('fea_imp', ascending=False)\n", "\n", "plt.figure(figsize=[20, 10])\n", "ax = sns.barplot(x=feature_importance['fea_name'], y=feature_importance['fea_imp'])\n", "ax.set_xticklabels(labels=feature_importance['fea_name'], rotation=45, fontsize=18)\n", "ax.set_yticklabels(labels=[0, 2000, 4000, 6000, 8000, 10000, 12000, 14000, 16000], fontsize=6)\n", "plt.xlabel('fea_name', fontsize=18)\n", "plt.ylabel('fea_imp', fontsize=18)\n", "plt.tight_layout()\n", "plt.savefig('./figure/特征重要性.png')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 17, "outputs": [], "source": [ "import seaborn as sns" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 18, "outputs": [], "source": [ "from sklearn.preprocessing import MinMaxScaler" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 18, "outputs": [], "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }