{ "cells": [ { "cell_type": "code", "execution_count": 2, "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": 3, "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": 3, "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": 4, "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": 5, "outputs": [ { "data": { "text/plain": "['rNOx', 'rO2', 'rSO2', 'rsmoke']" }, "execution_count": 5, "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": 6, "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": 7, "outputs": [ { "data": { "text/plain": "Index(['cNOx', 'cSO2', 'cO2', 'csmoke', 'flow', 'rNOx', 'rO2', 'temp', 'rSO2',\n 'rsmoke', 'week_of_year'],\n dtype='object')" }, "execution_count": 7, "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": 8, "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": 9, "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": 10, "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": 11, "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": 12, "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": 15, "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": 16, "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": 13, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE: 5.55E+02\n", "RMSE: 23.55984288394111\n", "MAE: 17.576455710512402\n", "MAPE: 0.03501471248540124\n", "R_2: 0.9704326876428987\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": 20, "outputs": [], "source": [ "import seaborn as sns" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 23, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\zhaojh\\AppData\\Local\\Temp\\ipykernel_1488\\3332664617.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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}, "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=[40, 20])\n", "ax = sns.barplot(x=feature_importance['fea_name'], y=feature_importance['fea_imp'])\n", "ax.set_xticklabels(labels=feature_cols, rotation=45, fontsize=6)\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": 18, "outputs": [], "source": [ "import seaborn as sns" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 22, "outputs": [], "source": [ "from sklearn.preprocessing import MinMaxScaler" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "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 }