3155 lines
459 KiB
Plaintext
3155 lines
459 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import lightgbm as lgb\n",
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"import numpy as np\n",
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"import xgboost as xgb\n",
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"import seaborn as sns\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.model_selection import KFold\n",
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"from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": " 铭牌容量 (MW) 入炉煤低位热值(kJ/kg) 燃煤挥发份Var(%) 燃煤灰份Aar(%) longitude latitude \\\n0 5.70711 9.818311 3.297687 2.815409 4.807875 3.467769 \n1 5.70711 9.821572 3.297687 2.815409 4.807875 3.467769 \n2 5.70711 9.878580 3.310543 2.769459 4.807875 3.467769 \n3 5.70711 9.883285 3.324316 2.532108 4.807875 3.467769 \n4 5.70711 9.909768 3.255015 2.766319 4.807875 3.467769 \n\n altitude 发电碳排放因子(kg/kWh) 供热碳排放因子(kg/MJ) 所处地区_上海市 ... 机组类型_供热式 \\\n0 1.386294 0.537574 0.070992 1.0 ... 1.0 \n1 1.386294 0.545516 0.072476 1.0 ... 1.0 \n2 1.386294 0.595849 0.064745 1.0 ... 1.0 \n3 1.386294 0.584432 0.068390 1.0 ... 1.0 \n4 1.386294 0.605369 0.066996 1.0 ... 1.0 \n\n 机组类型_纯凝式 参数分类_亚临界 参数分类_超临界 参数分类_超超临界 参数分类_超高压 参数分类_高压 冷凝器型式_水冷 \\\n0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n1 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n2 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n3 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n4 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n\n 冷凝器型式_直接空冷 冷凝器型式_间接空冷 \n0 0.0 0.0 \n1 0.0 0.0 \n2 0.0 0.0 \n3 0.0 0.0 \n4 0.0 0.0 \n\n[5 rows x 60 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>铭牌容量 (MW)</th>\n <th>入炉煤低位热值(kJ/kg)</th>\n <th>燃煤挥发份Var(%)</th>\n <th>燃煤灰份Aar(%)</th>\n <th>longitude</th>\n <th>latitude</th>\n <th>altitude</th>\n <th>发电碳排放因子(kg/kWh)</th>\n <th>供热碳排放因子(kg/MJ)</th>\n <th>所处地区_上海市</th>\n <th>...</th>\n <th>机组类型_供热式</th>\n <th>机组类型_纯凝式</th>\n <th>参数分类_亚临界</th>\n <th>参数分类_超临界</th>\n <th>参数分类_超超临界</th>\n <th>参数分类_超高压</th>\n <th>参数分类_高压</th>\n <th>冷凝器型式_水冷</th>\n <th>冷凝器型式_直接空冷</th>\n <th>冷凝器型式_间接空冷</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>5.70711</td>\n <td>9.818311</td>\n <td>3.297687</td>\n <td>2.815409</td>\n <td>4.807875</td>\n <td>3.467769</td>\n <td>1.386294</td>\n <td>0.537574</td>\n <td>0.070992</td>\n <td>1.0</td>\n <td>...</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>5.70711</td>\n <td>9.821572</td>\n <td>3.297687</td>\n <td>2.815409</td>\n <td>4.807875</td>\n <td>3.467769</td>\n <td>1.386294</td>\n <td>0.545516</td>\n <td>0.072476</td>\n <td>1.0</td>\n <td>...</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>5.70711</td>\n <td>9.878580</td>\n <td>3.310543</td>\n <td>2.769459</td>\n <td>4.807875</td>\n <td>3.467769</td>\n <td>1.386294</td>\n <td>0.595849</td>\n <td>0.064745</td>\n <td>1.0</td>\n <td>...</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>5.70711</td>\n <td>9.883285</td>\n <td>3.324316</td>\n <td>2.532108</td>\n <td>4.807875</td>\n <td>3.467769</td>\n <td>1.386294</td>\n <td>0.584432</td>\n <td>0.068390</td>\n <td>1.0</td>\n <td>...</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5.70711</td>\n <td>9.909768</td>\n <td>3.255015</td>\n <td>2.766319</td>\n <td>4.807875</td>\n <td>3.467769</td>\n <td>1.386294</td>\n <td>0.605369</td>\n <td>0.066996</td>\n <td>1.0</td>\n <td>...</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 60 columns</p>\n</div>"
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"total_data = pd.read_csv('./train_data_processed.csv')\n",
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"total_data.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "(3080, 60)"
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"total_data.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"outputs": [
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{
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"data": {
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"text/plain": "Index(['铭牌容量 (MW)', '入炉煤低位热值(kJ/kg)', '燃煤挥发份Var(%)', '燃煤灰份Aar(%)', 'longitude',\n 'latitude', 'altitude', '发电碳排放因子(kg/kWh)', '供热碳排放因子(kg/MJ)', '所处地区_上海市',\n '所处地区_云南省', '所处地区_内蒙古', '所处地区_内蒙古自治区', '所处地区_北京市', '所处地区_吉林省',\n '所处地区_四川省', '所处地区_天津市', '所处地区_宁夏', '所处地区_宁夏回族自治区', '所处地区_安徽省',\n '所处地区_山东省', '所处地区_山西', '所处地区_山西省', '所处地区_广东省', '所处地区_广西', '所处地区_广西省',\n '所处地区_新疆', '所处地区_新疆维吾尔自治区', '所处地区_江苏省', '所处地区_江西省', '所处地区_河北',\n '所处地区_河北省', '所处地区_河南', '所处地区_河南省', '所处地区_浙江省', '所处地区_海南省', '所处地区_湖北',\n '所处地区_湖北省', '所处地区_湖南', '所处地区_湖南省', '所处地区_甘肃省', '所处地区_福建省', '所处地区_贵州省',\n '所处地区_辽宁省', '所处地区_重庆市', '所处地区_陕西省', '所处地区_青海省', '所处地区_黑龙江', '所处地区_黑龙江省',\n '机组类型_供热', '机组类型_供热式', '机组类型_纯凝式', '参数分类_亚临界', '参数分类_超临界', '参数分类_超超临界',\n '参数分类_超高压', '参数分类_高压', '冷凝器型式_水冷', '冷凝器型式_直接空冷', '冷凝器型式_间接空冷'],\n dtype='object')"
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"total_data.columns"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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{
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"cell_type": "code",
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"execution_count": 6,
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"outputs": [],
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"source": [
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"feature_cols = [x for x in total_data.columns if '因子' not in x]\n",
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"target_cols = [x for x in total_data.columns if x not in feature_cols]"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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{
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [
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{
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"data": {
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"text/plain": " 铭牌容量 (MW) 入炉煤低位热值(kJ/kg) 燃煤挥发份Var(%) 燃煤灰份Aar(%) longitude latitude \\\n0 4.615121 9.527411 3.823629 3.007661 4.834910 3.862442 \n1 4.836282 9.920745 3.625673 3.201526 4.700990 3.563714 \n2 4.836282 9.923023 3.623807 3.231200 4.700990 3.563714 \n3 4.836282 9.932727 3.272227 3.236716 4.700990 3.563714 \n4 4.836282 9.936819 3.278653 3.173460 4.700990 3.563714 \n... ... ... ... ... ... ... \n3075 6.966967 9.754581 3.100543 3.378270 4.676091 3.667429 \n3076 6.966967 9.755162 3.082827 3.361070 4.676091 3.667429 \n3077 6.966967 9.762903 3.095125 3.288775 4.676091 3.667429 \n3078 6.966967 9.776506 3.096934 3.328268 4.676091 3.667429 \n3079 6.966967 9.792277 3.073156 3.384051 4.676091 3.667429 \n\n altitude 所处地区_上海市 所处地区_云南省 所处地区_内蒙古 ... 参数分类_亚临界 参数分类_超临界 \\\n0 4.983607 0.0 0.0 0.0 ... 0.0 0.0 \n1 5.981414 0.0 0.0 0.0 ... 0.0 0.0 \n2 5.981414 0.0 0.0 0.0 ... 0.0 0.0 \n3 5.981414 0.0 0.0 0.0 ... 0.0 0.0 \n4 5.981414 0.0 0.0 0.0 ... 0.0 0.0 \n... ... ... ... ... ... ... ... \n3075 7.020191 0.0 0.0 0.0 ... 0.0 0.0 \n3076 7.020191 0.0 0.0 0.0 ... 0.0 0.0 \n3077 7.020191 0.0 0.0 0.0 ... 0.0 0.0 \n3078 7.020191 0.0 0.0 0.0 ... 0.0 0.0 \n3079 7.020191 0.0 0.0 0.0 ... 0.0 0.0 \n\n 参数分类_超超临界 参数分类_超高压 参数分类_高压 冷凝器型式_水冷 冷凝器型式_直接空冷 冷凝器型式_间接空冷 \\\n0 0.0 0.0 1.0 1.0 0.0 0.0 \n1 0.0 1.0 0.0 1.0 0.0 0.0 \n2 0.0 1.0 0.0 1.0 0.0 0.0 \n3 0.0 1.0 0.0 1.0 0.0 0.0 \n4 0.0 1.0 0.0 1.0 0.0 0.0 \n... ... ... ... ... ... ... \n3075 1.0 0.0 0.0 0.0 1.0 0.0 \n3076 1.0 0.0 0.0 0.0 1.0 0.0 \n3077 1.0 0.0 0.0 0.0 1.0 0.0 \n3078 1.0 0.0 0.0 0.0 1.0 0.0 \n3079 1.0 0.0 0.0 0.0 1.0 0.0 \n\n 发电碳排放因子(kg/kWh) 供热碳排放因子(kg/MJ) \n0 0.483547 0.058613 \n1 0.575553 0.085880 \n2 0.607741 0.084890 \n3 0.595382 0.082342 \n4 0.578838 0.082685 \n... ... ... \n3075 0.426880 0.061722 \n3076 0.456768 0.060739 \n3077 0.455534 0.061277 \n3078 0.450064 0.062032 \n3079 0.468720 0.063016 \n\n[3080 rows x 60 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>铭牌容量 (MW)</th>\n <th>入炉煤低位热值(kJ/kg)</th>\n <th>燃煤挥发份Var(%)</th>\n <th>燃煤灰份Aar(%)</th>\n <th>longitude</th>\n <th>latitude</th>\n <th>altitude</th>\n <th>所处地区_上海市</th>\n <th>所处地区_云南省</th>\n <th>所处地区_内蒙古</th>\n <th>...</th>\n <th>参数分类_亚临界</th>\n <th>参数分类_超临界</th>\n <th>参数分类_超超临界</th>\n <th>参数分类_超高压</th>\n <th>参数分类_高压</th>\n <th>冷凝器型式_水冷</th>\n <th>冷凝器型式_直接空冷</th>\n <th>冷凝器型式_间接空冷</th>\n <th>发电碳排放因子(kg/kWh)</th>\n <th>供热碳排放因子(kg/MJ)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>4.615121</td>\n <td>9.527411</td>\n <td>3.823629</td>\n <td>3.007661</td>\n <td>4.834910</td>\n <td>3.862442</td>\n <td>4.983607</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.483547</td>\n <td>0.058613</td>\n </tr>\n <tr>\n <th>1</th>\n <td>4.836282</td>\n <td>9.920745</td>\n <td>3.625673</td>\n <td>3.201526</td>\n <td>4.700990</td>\n <td>3.563714</td>\n <td>5.981414</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.575553</td>\n <td>0.085880</td>\n </tr>\n <tr>\n <th>2</th>\n <td>4.836282</td>\n <td>9.923023</td>\n <td>3.623807</td>\n <td>3.231200</td>\n <td>4.700990</td>\n <td>3.563714</td>\n <td>5.981414</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.607741</td>\n <td>0.084890</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4.836282</td>\n <td>9.932727</td>\n <td>3.272227</td>\n <td>3.236716</td>\n <td>4.700990</td>\n <td>3.563714</td>\n <td>5.981414</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.595382</td>\n <td>0.082342</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4.836282</td>\n <td>9.936819</td>\n <td>3.278653</td>\n <td>3.173460</td>\n <td>4.700990</td>\n <td>3.563714</td>\n <td>5.981414</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.578838</td>\n <td>0.082685</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...
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},
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"execution_count": 7,
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"metadata": {},
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}
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],
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"source": [
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"use_data = total_data.groupby(feature_cols)[target_cols].mean().reset_index()\n",
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"use_data"
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],
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"collapsed": false,
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"execution_count": 8,
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"outputs": [],
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"source": [
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"for col in use_data.columns:\n",
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" use_data[col] = use_data[col].astype(float)"
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],
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"metadata": {
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"collapsed": false,
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},
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"outputs": [],
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"source": [
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"train_data, test_data = train_test_split(use_data.dropna(), test_size=0.1, shuffle=True, random_state=666)\n",
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"train_data, valid_data = train_test_split(train_data.dropna(), test_size=0.2, shuffle=True, random_state=666)"
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]
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},
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{
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"execution_count": 18,
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},
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"outputs": [],
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"source": [
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"X_train, Y_train = train_data[feature_cols], train_data[target_cols[0]]\n",
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"X_valid, Y_valid = valid_data[feature_cols], valid_data[target_cols[0]]\n",
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"X_test, Y_test = test_data[feature_cols], test_data[target_cols[0]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"pycharm": {
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},
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"outputs": [],
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"source": [
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"lgb_train = lgb.Dataset(X_train, Y_train)\n",
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"lgb_eval = lgb.Dataset(X_valid, Y_valid)\n",
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"lgb_test = lgb.Dataset(X_test, Y_test)"
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]
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},
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{
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"execution_count": 20,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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},
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"outputs": [],
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"source": [
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"params_gbm = {\n",
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" 'task': 'train',\n",
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" 'boosting_type': 'gbdt', # 设置提升类型\n",
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" 'objective': 'l1', # 目标函数\n",
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" 'metric': {'rmse'}, # 评估函数\n",
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" 'max_depth': 12,\n",
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" 'num_leaves': 20, # 叶子节点数\n",
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" 'learning_rate': 0.05, # 学习速率\n",
|
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" 'feature_fraction': 0.9, # 建树的特征选择比例\n",
|
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" 'bagging_fraction': 0.9, # 建树的样本采样比例\n",
|
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" 'bagging_freq': 10, # k 意味着每 k 次迭代执行bagging\n",
|
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" 'verbose': -1 # <0 显示致命的, =0 显示错误 (警告), >0 显示信息\n",
|
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"}"
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]
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{
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"cell_type": "code",
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"execution_count": 21,
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"pycharm": {
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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|
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"[1]\tvalid_0's rmse: 0.0692875\n",
|
|||
|
|
"Training until validation scores don't improve for 100 rounds\n",
|
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|
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"[2]\tvalid_0's rmse: 0.06714\n",
|
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"[3]\tvalid_0's rmse: 0.0646839\n",
|
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"[4]\tvalid_0's rmse: 0.0623338\n",
|
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"[5]\tvalid_0's rmse: 0.0600964\n",
|
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"[6]\tvalid_0's rmse: 0.0580108\n",
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"[7]\tvalid_0's rmse: 0.056067\n",
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"[8]\tvalid_0's rmse: 0.0544344\n",
|
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"[9]\tvalid_0's rmse: 0.0529408\n",
|
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"[10]\tvalid_0's rmse: 0.051276\n",
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"[11]\tvalid_0's rmse: 0.0497692\n",
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"[12]\tvalid_0's rmse: 0.0483588\n",
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"[13]\tvalid_0's rmse: 0.0470211\n",
|
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"[14]\tvalid_0's rmse: 0.0460061\n",
|
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"[15]\tvalid_0's rmse: 0.0448745\n",
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"[16]\tvalid_0's rmse: 0.043796\n",
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"[17]\tvalid_0's rmse: 0.0428645\n",
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"[18]\tvalid_0's rmse: 0.0419008\n",
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"[19]\tvalid_0's rmse: 0.0409544\n",
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"[20]\tvalid_0's rmse: 0.0400698\n",
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"[21]\tvalid_0's rmse: 0.0392848\n",
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"[22]\tvalid_0's rmse: 0.038578\n",
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"[23]\tvalid_0's rmse: 0.0378727\n",
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"[24]\tvalid_0's rmse: 0.0371929\n",
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"[25]\tvalid_0's rmse: 0.0366533\n",
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"[26]\tvalid_0's rmse: 0.0360842\n",
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"[27]\tvalid_0's rmse: 0.0355757\n",
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"[28]\tvalid_0's rmse: 0.0350562\n",
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"[29]\tvalid_0's rmse: 0.0345382\n",
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"[30]\tvalid_0's rmse: 0.0340975\n",
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"[31]\tvalid_0's rmse: 0.0337632\n",
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"[32]\tvalid_0's rmse: 0.0334232\n",
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"[33]\tvalid_0's rmse: 0.0330998\n",
|
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"[34]\tvalid_0's rmse: 0.0328678\n",
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"[35]\tvalid_0's rmse: 0.0325827\n",
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"[36]\tvalid_0's rmse: 0.0323483\n",
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"[37]\tvalid_0's rmse: 0.0321363\n",
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"[38]\tvalid_0's rmse: 0.0318823\n",
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"[39]\tvalid_0's rmse: 0.0316983\n",
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"[40]\tvalid_0's rmse: 0.0315094\n",
|
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"[41]\tvalid_0's rmse: 0.0313339\n",
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"[42]\tvalid_0's rmse: 0.0311663\n",
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"[43]\tvalid_0's rmse: 0.031002\n",
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"[44]\tvalid_0's rmse: 0.0308446\n",
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"[45]\tvalid_0's rmse: 0.0307193\n",
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"[46]\tvalid_0's rmse: 0.03058\n",
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"[47]\tvalid_0's rmse: 0.0304975\n",
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"[48]\tvalid_0's rmse: 0.0303807\n",
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"[49]\tvalid_0's rmse: 0.0302476\n",
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"[50]\tvalid_0's rmse: 0.0301379\n",
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"[51]\tvalid_0's rmse: 0.03\n",
|
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"[52]\tvalid_0's rmse: 0.0299129\n",
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"[53]\tvalid_0's rmse: 0.0298092\n",
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"[54]\tvalid_0's rmse: 0.0297318\n",
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"[55]\tvalid_0's rmse: 0.0296587\n",
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"[56]\tvalid_0's rmse: 0.0295906\n",
|
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"[57]\tvalid_0's rmse: 0.0295262\n",
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"[58]\tvalid_0's rmse: 0.0294317\n",
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"[59]\tvalid_0's rmse: 0.0293666\n",
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"[60]\tvalid_0's rmse: 0.029295\n",
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"[61]\tvalid_0's rmse: 0.0292621\n",
|
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|
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"[62]\tvalid_0's rmse: 0.0291822\n",
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"[63]\tvalid_0's rmse: 0.0291453\n",
|
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"[64]\tvalid_0's rmse: 0.029071\n",
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"[65]\tvalid_0's rmse: 0.0289955\n",
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"[66]\tvalid_0's rmse: 0.0289425\n",
|
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"[67]\tvalid_0's rmse: 0.0288803\n",
|
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"[68]\tvalid_0's rmse: 0.0288438\n",
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"[69]\tvalid_0's rmse: 0.0288004\n",
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"[70]\tvalid_0's rmse: 0.0287685\n",
|
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"[71]\tvalid_0's rmse: 0.0287379\n",
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|
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"[72]\tvalid_0's rmse: 0.0286942\n",
|
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|
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"[73]\tvalid_0's rmse: 0.028654\n",
|
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|
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"[74]\tvalid_0's rmse: 0.0286255\n",
|
|||
|
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"[75]\tvalid_0's rmse: 0.0285826\n",
|
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|
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"[76]\tvalid_0's rmse: 0.0285438\n",
|
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|
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"[77]\tvalid_0's rmse: 0.0284903\n",
|
|||
|
|
"[78]\tvalid_0's rmse: 0.0284767\n",
|
|||
|
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"[79]\tvalid_0's rmse: 0.0284401\n",
|
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|
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"[80]\tvalid_0's rmse: 0.0284152\n",
|
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|
|
"[81]\tvalid_0's rmse: 0.0283845\n",
|
|||
|
|
"[82]\tvalid_0's rmse: 0.028375\n",
|
|||
|
|
"[83]\tvalid_0's rmse: 0.0283271\n",
|
|||
|
|
"[84]\tvalid_0's rmse: 0.0283098\n",
|
|||
|
|
"[85]\tvalid_0's rmse: 0.0282848\n",
|
|||
|
|
"[86]\tvalid_0's rmse: 0.0282564\n",
|
|||
|
|
"[87]\tvalid_0's rmse: 0.0282311\n",
|
|||
|
|
"[88]\tvalid_0's rmse: 0.0281999\n",
|
|||
|
|
"[89]\tvalid_0's rmse: 0.0281744\n",
|
|||
|
|
"[90]\tvalid_0's rmse: 0.0281694\n",
|
|||
|
|
"[91]\tvalid_0's rmse: 0.0281849\n",
|
|||
|
|
"[92]\tvalid_0's rmse: 0.0281936\n",
|
|||
|
|
"[93]\tvalid_0's rmse: 0.0281859\n",
|
|||
|
|
"[94]\tvalid_0's rmse: 0.028193\n",
|
|||
|
|
"[95]\tvalid_0's rmse: 0.0281768\n",
|
|||
|
|
"[96]\tvalid_0's rmse: 0.0281729\n",
|
|||
|
|
"[97]\tvalid_0's rmse: 0.0281829\n",
|
|||
|
|
"[98]\tvalid_0's rmse: 0.0281698\n",
|
|||
|
|
"[99]\tvalid_0's rmse: 0.0281678\n",
|
|||
|
|
"[100]\tvalid_0's rmse: 0.0281451\n",
|
|||
|
|
"[101]\tvalid_0's rmse: 0.0281243\n",
|
|||
|
|
"[102]\tvalid_0's rmse: 0.028098\n",
|
|||
|
|
"[103]\tvalid_0's rmse: 0.028089\n",
|
|||
|
|
"[104]\tvalid_0's rmse: 0.0280947\n",
|
|||
|
|
"[105]\tvalid_0's rmse: 0.0280915\n",
|
|||
|
|
"[106]\tvalid_0's rmse: 0.0280942\n",
|
|||
|
|
"[107]\tvalid_0's rmse: 0.0280905\n",
|
|||
|
|
"[108]\tvalid_0's rmse: 0.0280888\n",
|
|||
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"[293]\tvalid_0's rmse: 0.026812\n",
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"[294]\tvalid_0's rmse: 0.0268085\n",
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"[298]\tvalid_0's rmse: 0.0267948\n",
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"[301]\tvalid_0's rmse: 0.026792\n",
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"[302]\tvalid_0's rmse: 0.026785\n",
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"[350]\tvalid_0's rmse: 0.0266446\n",
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"[351]\tvalid_0's rmse: 0.0266422\n",
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"[352]\tvalid_0's rmse: 0.0266445\n",
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"[353]\tvalid_0's rmse: 0.026642\n",
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"[354]\tvalid_0's rmse: 0.0266332\n",
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"[355]\tvalid_0's rmse: 0.0266333\n",
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"[356]\tvalid_0's rmse: 0.0266291\n",
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"[357]\tvalid_0's rmse: 0.0266298\n",
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"[358]\tvalid_0's rmse: 0.0266302\n",
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"[359]\tvalid_0's rmse: 0.026626\n",
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"[360]\tvalid_0's rmse: 0.0266191\n",
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"[361]\tvalid_0's rmse: 0.0266188\n",
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"[362]\tvalid_0's rmse: 0.0266132\n",
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"[363]\tvalid_0's rmse: 0.0266094\n",
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"[364]\tvalid_0's rmse: 0.0266022\n",
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"[365]\tvalid_0's rmse: 0.0266027\n",
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"[366]\tvalid_0's rmse: 0.0266001\n",
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"[367]\tvalid_0's rmse: 0.0266011\n",
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"[368]\tvalid_0's rmse: 0.0265957\n",
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"[369]\tvalid_0's rmse: 0.026593\n",
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"[370]\tvalid_0's rmse: 0.0265889\n",
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"[371]\tvalid_0's rmse: 0.0265887\n",
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"[372]\tvalid_0's rmse: 0.0265821\n",
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"[373]\tvalid_0's rmse: 0.026579\n",
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"[374]\tvalid_0's rmse: 0.0265765\n",
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"[375]\tvalid_0's rmse: 0.0265742\n",
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"[376]\tvalid_0's rmse: 0.0265724\n",
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"[377]\tvalid_0's rmse: 0.0265683\n",
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"[378]\tvalid_0's rmse: 0.0265671\n",
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"[379]\tvalid_0's rmse: 0.0265605\n",
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"[380]\tvalid_0's rmse: 0.026561\n",
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"[381]\tvalid_0's rmse: 0.0265544\n",
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"[382]\tvalid_0's rmse: 0.026555\n",
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"[383]\tvalid_0's rmse: 0.0265526\n",
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"[384]\tvalid_0's rmse: 0.0265483\n",
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"[385]\tvalid_0's rmse: 0.0265519\n",
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"[386]\tvalid_0's rmse: 0.0265494\n",
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"[387]\tvalid_0's rmse: 0.0265502\n",
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"[388]\tvalid_0's rmse: 0.0265525\n",
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"[389]\tvalid_0's rmse: 0.0265567\n",
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"[390]\tvalid_0's rmse: 0.0265403\n",
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"[391]\tvalid_0's rmse: 0.0265361\n",
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"[392]\tvalid_0's rmse: 0.0265342\n",
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"[393]\tvalid_0's rmse: 0.026529\n",
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"[394]\tvalid_0's rmse: 0.0265267\n",
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"[395]\tvalid_0's rmse: 0.0265303\n",
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"[396]\tvalid_0's rmse: 0.0265306\n",
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"[397]\tvalid_0's rmse: 0.0265338\n",
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"[398]\tvalid_0's rmse: 0.0265294\n",
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"[399]\tvalid_0's rmse: 0.0265253\n",
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"[400]\tvalid_0's rmse: 0.0265248\n",
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"[401]\tvalid_0's rmse: 0.0265266\n",
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"[402]\tvalid_0's rmse: 0.0265279\n",
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"[403]\tvalid_0's rmse: 0.0265289\n",
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"[404]\tvalid_0's rmse: 0.0265279\n",
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"[405]\tvalid_0's rmse: 0.0265228\n",
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"[406]\tvalid_0's rmse: 0.0265323\n",
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"[407]\tvalid_0's rmse: 0.0265335\n",
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"[408]\tvalid_0's rmse: 0.0265318\n",
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"[409]\tvalid_0's rmse: 0.0265298\n",
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"[410]\tvalid_0's rmse: 0.0265275\n",
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"[411]\tvalid_0's rmse: 0.0265259\n",
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"[412]\tvalid_0's rmse: 0.0265261\n",
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"[413]\tvalid_0's rmse: 0.0265267\n",
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"[414]\tvalid_0's rmse: 0.0265261\n",
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"[415]\tvalid_0's rmse: 0.0265255\n",
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"[416]\tvalid_0's rmse: 0.0265275\n",
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"[417]\tvalid_0's rmse: 0.0265225\n",
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"[418]\tvalid_0's rmse: 0.0265226\n",
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"[419]\tvalid_0's rmse: 0.0265222\n",
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"[420]\tvalid_0's rmse: 0.026521\n",
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"[421]\tvalid_0's rmse: 0.0265169\n",
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"[422]\tvalid_0's rmse: 0.0265139\n",
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"[423]\tvalid_0's rmse: 0.0265126\n",
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"[424]\tvalid_0's rmse: 0.0265136\n",
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"[425]\tvalid_0's rmse: 0.0265079\n",
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"[426]\tvalid_0's rmse: 0.0265017\n",
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"[427]\tvalid_0's rmse: 0.0264914\n",
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"[428]\tvalid_0's rmse: 0.026489\n",
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"[429]\tvalid_0's rmse: 0.0264918\n",
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"[430]\tvalid_0's rmse: 0.0264906\n",
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"[431]\tvalid_0's rmse: 0.0264809\n",
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"[432]\tvalid_0's rmse: 0.0264809\n",
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|||
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"[433]\tvalid_0's rmse: 0.0264819\n",
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"[434]\tvalid_0's rmse: 0.0264775\n",
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"[435]\tvalid_0's rmse: 0.0264744\n",
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|||
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"[436]\tvalid_0's rmse: 0.026474\n",
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"[437]\tvalid_0's rmse: 0.0264713\n",
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|||
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"[438]\tvalid_0's rmse: 0.0264702\n",
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"[439]\tvalid_0's rmse: 0.0264686\n",
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"[440]\tvalid_0's rmse: 0.0264654\n",
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"[441]\tvalid_0's rmse: 0.0264663\n",
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"[442]\tvalid_0's rmse: 0.0264543\n",
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"[443]\tvalid_0's rmse: 0.0264538\n",
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"[444]\tvalid_0's rmse: 0.0264507\n",
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|||
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"[445]\tvalid_0's rmse: 0.0264509\n",
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|||
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"[446]\tvalid_0's rmse: 0.0264456\n",
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|||
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"[447]\tvalid_0's rmse: 0.0264483\n",
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"[448]\tvalid_0's rmse: 0.0264169\n",
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|||
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"[449]\tvalid_0's rmse: 0.0264151\n",
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|||
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"[450]\tvalid_0's rmse: 0.0264172\n",
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|||
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"[451]\tvalid_0's rmse: 0.0264171\n",
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|||
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"[452]\tvalid_0's rmse: 0.0264175\n",
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|||
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"[453]\tvalid_0's rmse: 0.0264149\n",
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|||
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"[454]\tvalid_0's rmse: 0.0264144\n",
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|||
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"[455]\tvalid_0's rmse: 0.0264154\n",
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|||
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"[456]\tvalid_0's rmse: 0.0264147\n",
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|||
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"[457]\tvalid_0's rmse: 0.0264118\n",
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|||
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"[458]\tvalid_0's rmse: 0.0264138\n",
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|||
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"[459]\tvalid_0's rmse: 0.0264151\n",
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|||
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"[460]\tvalid_0's rmse: 0.026415\n",
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|||
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"[461]\tvalid_0's rmse: 0.0264159\n",
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|||
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"[462]\tvalid_0's rmse: 0.0264121\n",
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|||
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"[463]\tvalid_0's rmse: 0.026414\n",
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|||
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"[464]\tvalid_0's rmse: 0.0264093\n",
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|||
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"[465]\tvalid_0's rmse: 0.0264118\n",
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|||
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"[466]\tvalid_0's rmse: 0.0264118\n",
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|||
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"[467]\tvalid_0's rmse: 0.0264099\n",
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|||
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"[468]\tvalid_0's rmse: 0.0264113\n",
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|||
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"[469]\tvalid_0's rmse: 0.0264101\n",
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|||
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"[470]\tvalid_0's rmse: 0.0264118\n",
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|||
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"[471]\tvalid_0's rmse: 0.0264092\n",
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|||
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"[472]\tvalid_0's rmse: 0.0264044\n",
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|||
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"[473]\tvalid_0's rmse: 0.0263975\n",
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|||
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"[474]\tvalid_0's rmse: 0.0263909\n",
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|||
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"[475]\tvalid_0's rmse: 0.0263866\n",
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|||
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"[476]\tvalid_0's rmse: 0.0263848\n",
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|||
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"[477]\tvalid_0's rmse: 0.0263839\n",
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|||
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"[478]\tvalid_0's rmse: 0.0263787\n",
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|||
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"[479]\tvalid_0's rmse: 0.0263797\n",
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|||
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"[480]\tvalid_0's rmse: 0.0263769\n",
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|||
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"[481]\tvalid_0's rmse: 0.0263744\n",
|
|||
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"[482]\tvalid_0's rmse: 0.0263693\n",
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|||
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"[483]\tvalid_0's rmse: 0.0263673\n",
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|||
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"[484]\tvalid_0's rmse: 0.0263626\n",
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|||
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"[485]\tvalid_0's rmse: 0.0263591\n",
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|||
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"[486]\tvalid_0's rmse: 0.0263569\n",
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|||
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"[487]\tvalid_0's rmse: 0.0263557\n",
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|||
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"[488]\tvalid_0's rmse: 0.0263559\n",
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|||
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"[489]\tvalid_0's rmse: 0.026358\n",
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|||
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"[490]\tvalid_0's rmse: 0.0263566\n",
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|||
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"[491]\tvalid_0's rmse: 0.0263564\n",
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|||
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"[492]\tvalid_0's rmse: 0.0263568\n",
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|||
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"[493]\tvalid_0's rmse: 0.0263562\n",
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|||
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|
"[494]\tvalid_0's rmse: 0.0263561\n",
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|||
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"[495]\tvalid_0's rmse: 0.0263508\n",
|
|||
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"[496]\tvalid_0's rmse: 0.0263498\n",
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|||
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"[497]\tvalid_0's rmse: 0.026346\n",
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|||
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"[498]\tvalid_0's rmse: 0.0263474\n",
|
|||
|
|
"[499]\tvalid_0's rmse: 0.026346\n",
|
|||
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|
"[500]\tvalid_0's rmse: 0.026342\n",
|
|||
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|
"[501]\tvalid_0's rmse: 0.0263415\n",
|
|||
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|
"[502]\tvalid_0's rmse: 0.0263404\n",
|
|||
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|
"[503]\tvalid_0's rmse: 0.0263355\n",
|
|||
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|
"[504]\tvalid_0's rmse: 0.0263363\n",
|
|||
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|
"[505]\tvalid_0's rmse: 0.0263362\n",
|
|||
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|
"[506]\tvalid_0's rmse: 0.0263356\n",
|
|||
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|
"[507]\tvalid_0's rmse: 0.0263345\n",
|
|||
|
|
"[508]\tvalid_0's rmse: 0.0263343\n",
|
|||
|
|
"[509]\tvalid_0's rmse: 0.0263294\n",
|
|||
|
|
"[510]\tvalid_0's rmse: 0.0263279\n",
|
|||
|
|
"[511]\tvalid_0's rmse: 0.0263274\n",
|
|||
|
|
"[512]\tvalid_0's rmse: 0.0263227\n",
|
|||
|
|
"[513]\tvalid_0's rmse: 0.0263228\n",
|
|||
|
|
"[514]\tvalid_0's rmse: 0.0263178\n",
|
|||
|
|
"[515]\tvalid_0's rmse: 0.0263175\n",
|
|||
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|
"[516]\tvalid_0's rmse: 0.0263152\n",
|
|||
|
|
"[517]\tvalid_0's rmse: 0.0263062\n",
|
|||
|
|
"[518]\tvalid_0's rmse: 0.0263098\n",
|
|||
|
|
"[519]\tvalid_0's rmse: 0.0263065\n",
|
|||
|
|
"[520]\tvalid_0's rmse: 0.0263043\n",
|
|||
|
|
"[521]\tvalid_0's rmse: 0.0263029\n",
|
|||
|
|
"[522]\tvalid_0's rmse: 0.0263005\n",
|
|||
|
|
"[523]\tvalid_0's rmse: 0.0263013\n",
|
|||
|
|
"[524]\tvalid_0's rmse: 0.0263\n",
|
|||
|
|
"[525]\tvalid_0's rmse: 0.0262944\n",
|
|||
|
|
"[526]\tvalid_0's rmse: 0.0262956\n",
|
|||
|
|
"[527]\tvalid_0's rmse: 0.0262945\n",
|
|||
|
|
"[528]\tvalid_0's rmse: 0.0262948\n",
|
|||
|
|
"[529]\tvalid_0's rmse: 0.0262927\n",
|
|||
|
|
"[530]\tvalid_0's rmse: 0.0262942\n",
|
|||
|
|
"[531]\tvalid_0's rmse: 0.0262821\n",
|
|||
|
|
"[532]\tvalid_0's rmse: 0.0262828\n",
|
|||
|
|
"[533]\tvalid_0's rmse: 0.0262794\n",
|
|||
|
|
"[534]\tvalid_0's rmse: 0.0262778\n",
|
|||
|
|
"[535]\tvalid_0's rmse: 0.0262769\n",
|
|||
|
|
"[536]\tvalid_0's rmse: 0.0262763\n",
|
|||
|
|
"[537]\tvalid_0's rmse: 0.0262754\n",
|
|||
|
|
"[538]\tvalid_0's rmse: 0.026275\n",
|
|||
|
|
"[539]\tvalid_0's rmse: 0.0262742\n",
|
|||
|
|
"[540]\tvalid_0's rmse: 0.02625\n",
|
|||
|
|
"[541]\tvalid_0's rmse: 0.0262449\n",
|
|||
|
|
"[542]\tvalid_0's rmse: 0.0262456\n",
|
|||
|
|
"[543]\tvalid_0's rmse: 0.0262468\n",
|
|||
|
|
"[544]\tvalid_0's rmse: 0.0262448\n",
|
|||
|
|
"[545]\tvalid_0's rmse: 0.0262438\n",
|
|||
|
|
"[546]\tvalid_0's rmse: 0.0262417\n",
|
|||
|
|
"[547]\tvalid_0's rmse: 0.026231\n",
|
|||
|
|
"[548]\tvalid_0's rmse: 0.0262339\n",
|
|||
|
|
"[549]\tvalid_0's rmse: 0.0262327\n",
|
|||
|
|
"[550]\tvalid_0's rmse: 0.0262289\n",
|
|||
|
|
"[551]\tvalid_0's rmse: 0.0262244\n",
|
|||
|
|
"[552]\tvalid_0's rmse: 0.0262075\n",
|
|||
|
|
"[553]\tvalid_0's rmse: 0.0262031\n",
|
|||
|
|
"[554]\tvalid_0's rmse: 0.0262028\n",
|
|||
|
|
"[555]\tvalid_0's rmse: 0.0261984\n",
|
|||
|
|
"[556]\tvalid_0's rmse: 0.0261981\n",
|
|||
|
|
"[557]\tvalid_0's rmse: 0.0261977\n",
|
|||
|
|
"[558]\tvalid_0's rmse: 0.0262004\n",
|
|||
|
|
"[559]\tvalid_0's rmse: 0.0261955\n",
|
|||
|
|
"[560]\tvalid_0's rmse: 0.0261955\n",
|
|||
|
|
"[561]\tvalid_0's rmse: 0.0261947\n",
|
|||
|
|
"[562]\tvalid_0's rmse: 0.0261983\n",
|
|||
|
|
"[563]\tvalid_0's rmse: 0.0261981\n",
|
|||
|
|
"[564]\tvalid_0's rmse: 0.0261992\n",
|
|||
|
|
"[565]\tvalid_0's rmse: 0.0261974\n",
|
|||
|
|
"[566]\tvalid_0's rmse: 0.0261936\n",
|
|||
|
|
"[567]\tvalid_0's rmse: 0.0261954\n",
|
|||
|
|
"[568]\tvalid_0's rmse: 0.0261987\n",
|
|||
|
|
"[569]\tvalid_0's rmse: 0.0261837\n",
|
|||
|
|
"[570]\tvalid_0's rmse: 0.0261839\n",
|
|||
|
|
"[571]\tvalid_0's rmse: 0.026185\n",
|
|||
|
|
"[572]\tvalid_0's rmse: 0.0261849\n",
|
|||
|
|
"[573]\tvalid_0's rmse: 0.0261842\n",
|
|||
|
|
"[574]\tvalid_0's rmse: 0.0261826\n",
|
|||
|
|
"[575]\tvalid_0's rmse: 0.0261834\n",
|
|||
|
|
"[576]\tvalid_0's rmse: 0.0261825\n",
|
|||
|
|
"[577]\tvalid_0's rmse: 0.0261717\n",
|
|||
|
|
"[578]\tvalid_0's rmse: 0.026171\n",
|
|||
|
|
"[579]\tvalid_0's rmse: 0.0261609\n",
|
|||
|
|
"[580]\tvalid_0's rmse: 0.02616\n",
|
|||
|
|
"[581]\tvalid_0's rmse: 0.0261573\n",
|
|||
|
|
"[582]\tvalid_0's rmse: 0.026159\n",
|
|||
|
|
"[583]\tvalid_0's rmse: 0.0261576\n",
|
|||
|
|
"[584]\tvalid_0's rmse: 0.0261557\n",
|
|||
|
|
"[585]\tvalid_0's rmse: 0.0261582\n",
|
|||
|
|
"[586]\tvalid_0's rmse: 0.026158\n",
|
|||
|
|
"[587]\tvalid_0's rmse: 0.0261573\n",
|
|||
|
|
"[588]\tvalid_0's rmse: 0.0261571\n",
|
|||
|
|
"[589]\tvalid_0's rmse: 0.0261535\n",
|
|||
|
|
"[590]\tvalid_0's rmse: 0.0261534\n",
|
|||
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|
"[591]\tvalid_0's rmse: 0.0261534\n",
|
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"[592]\tvalid_0's rmse: 0.0261436\n",
|
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"[593]\tvalid_0's rmse: 0.0261423\n",
|
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"[594]\tvalid_0's rmse: 0.0261409\n",
|
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"[595]\tvalid_0's rmse: 0.0261377\n",
|
|||
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"[596]\tvalid_0's rmse: 0.0261358\n",
|
|||
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"[597]\tvalid_0's rmse: 0.0261367\n",
|
|||
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"[598]\tvalid_0's rmse: 0.026137\n",
|
|||
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"[599]\tvalid_0's rmse: 0.0261357\n",
|
|||
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|
"[600]\tvalid_0's rmse: 0.0261344\n",
|
|||
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"[601]\tvalid_0's rmse: 0.0261345\n",
|
|||
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"[602]\tvalid_0's rmse: 0.026133\n",
|
|||
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"[603]\tvalid_0's rmse: 0.0261313\n",
|
|||
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|
"[604]\tvalid_0's rmse: 0.0261344\n",
|
|||
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"[605]\tvalid_0's rmse: 0.0261339\n",
|
|||
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|
"[606]\tvalid_0's rmse: 0.0261321\n",
|
|||
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"[607]\tvalid_0's rmse: 0.0261288\n",
|
|||
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"[608]\tvalid_0's rmse: 0.0261285\n",
|
|||
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"[609]\tvalid_0's rmse: 0.0261298\n",
|
|||
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"[610]\tvalid_0's rmse: 0.026131\n",
|
|||
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|
"[611]\tvalid_0's rmse: 0.0261265\n",
|
|||
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|
"[612]\tvalid_0's rmse: 0.0261043\n",
|
|||
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|
"[613]\tvalid_0's rmse: 0.0261023\n",
|
|||
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"[614]\tvalid_0's rmse: 0.0261013\n",
|
|||
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|
"[615]\tvalid_0's rmse: 0.0260971\n",
|
|||
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"[616]\tvalid_0's rmse: 0.0260979\n",
|
|||
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"[617]\tvalid_0's rmse: 0.0260987\n",
|
|||
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"[618]\tvalid_0's rmse: 0.0260728\n",
|
|||
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"[619]\tvalid_0's rmse: 0.026069\n",
|
|||
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"[620]\tvalid_0's rmse: 0.0260678\n",
|
|||
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"[621]\tvalid_0's rmse: 0.0260587\n",
|
|||
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"[622]\tvalid_0's rmse: 0.0260571\n",
|
|||
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"[623]\tvalid_0's rmse: 0.0260564\n",
|
|||
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"[624]\tvalid_0's rmse: 0.026054\n",
|
|||
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|
"[625]\tvalid_0's rmse: 0.0260544\n",
|
|||
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|
"[626]\tvalid_0's rmse: 0.0260502\n",
|
|||
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|
"[627]\tvalid_0's rmse: 0.0260444\n",
|
|||
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|
"[628]\tvalid_0's rmse: 0.026044\n",
|
|||
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|
"[629]\tvalid_0's rmse: 0.02604\n",
|
|||
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|
"[630]\tvalid_0's rmse: 0.0260386\n",
|
|||
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|
"[631]\tvalid_0's rmse: 0.0260394\n",
|
|||
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|
"[632]\tvalid_0's rmse: 0.0260378\n",
|
|||
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|
"[633]\tvalid_0's rmse: 0.0260397\n",
|
|||
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|
"[634]\tvalid_0's rmse: 0.0260395\n",
|
|||
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|
"[635]\tvalid_0's rmse: 0.0260398\n",
|
|||
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|
"[636]\tvalid_0's rmse: 0.0260376\n",
|
|||
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|
"[637]\tvalid_0's rmse: 0.026039\n",
|
|||
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|
"[638]\tvalid_0's rmse: 0.0260362\n",
|
|||
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|
"[639]\tvalid_0's rmse: 0.0260345\n",
|
|||
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|
"[640]\tvalid_0's rmse: 0.0260342\n",
|
|||
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|
"[641]\tvalid_0's rmse: 0.0260336\n",
|
|||
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|
"[642]\tvalid_0's rmse: 0.0260337\n",
|
|||
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|
"[643]\tvalid_0's rmse: 0.0260325\n",
|
|||
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|
"[644]\tvalid_0's rmse: 0.0260305\n",
|
|||
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|
"[645]\tvalid_0's rmse: 0.0260308\n",
|
|||
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|
"[646]\tvalid_0's rmse: 0.0260319\n",
|
|||
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|
"[647]\tvalid_0's rmse: 0.0260334\n",
|
|||
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|
"[648]\tvalid_0's rmse: 0.0260338\n",
|
|||
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|
"[649]\tvalid_0's rmse: 0.0260325\n",
|
|||
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|
"[650]\tvalid_0's rmse: 0.0260265\n",
|
|||
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|
"[651]\tvalid_0's rmse: 0.0260269\n",
|
|||
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|
"[652]\tvalid_0's rmse: 0.0260251\n",
|
|||
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|
"[653]\tvalid_0's rmse: 0.0260252\n",
|
|||
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|
"[654]\tvalid_0's rmse: 0.0260251\n",
|
|||
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|
"[655]\tvalid_0's rmse: 0.0260257\n",
|
|||
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|
"[656]\tvalid_0's rmse: 0.0260234\n",
|
|||
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|
"[657]\tvalid_0's rmse: 0.0260219\n",
|
|||
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|
"[658]\tvalid_0's rmse: 0.0260211\n",
|
|||
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|
"[659]\tvalid_0's rmse: 0.0260209\n",
|
|||
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|
"[660]\tvalid_0's rmse: 0.0260217\n",
|
|||
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|
"[661]\tvalid_0's rmse: 0.0260234\n",
|
|||
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|
"[662]\tvalid_0's rmse: 0.0260244\n",
|
|||
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|
"[663]\tvalid_0's rmse: 0.0260219\n",
|
|||
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|
"[664]\tvalid_0's rmse: 0.0260216\n",
|
|||
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|
"[665]\tvalid_0's rmse: 0.026023\n",
|
|||
|
|
"[666]\tvalid_0's rmse: 0.026025\n",
|
|||
|
|
"[667]\tvalid_0's rmse: 0.0260245\n",
|
|||
|
|
"[668]\tvalid_0's rmse: 0.026022\n",
|
|||
|
|
"[669]\tvalid_0's rmse: 0.0260216\n",
|
|||
|
|
"[670]\tvalid_0's rmse: 0.0260231\n",
|
|||
|
|
"[671]\tvalid_0's rmse: 0.0260226\n",
|
|||
|
|
"[672]\tvalid_0's rmse: 0.0260197\n",
|
|||
|
|
"[673]\tvalid_0's rmse: 0.0260191\n",
|
|||
|
|
"[674]\tvalid_0's rmse: 0.0260193\n",
|
|||
|
|
"[675]\tvalid_0's rmse: 0.0260178\n",
|
|||
|
|
"[676]\tvalid_0's rmse: 0.0260171\n",
|
|||
|
|
"[677]\tvalid_0's rmse: 0.0260153\n",
|
|||
|
|
"[678]\tvalid_0's rmse: 0.0260153\n",
|
|||
|
|
"[679]\tvalid_0's rmse: 0.026013\n",
|
|||
|
|
"[680]\tvalid_0's rmse: 0.0260116\n",
|
|||
|
|
"[681]\tvalid_0's rmse: 0.0260089\n",
|
|||
|
|
"[682]\tvalid_0's rmse: 0.0260046\n",
|
|||
|
|
"[683]\tvalid_0's rmse: 0.0260029\n",
|
|||
|
|
"[684]\tvalid_0's rmse: 0.0260038\n",
|
|||
|
|
"[685]\tvalid_0's rmse: 0.0260018\n",
|
|||
|
|
"[686]\tvalid_0's rmse: 0.0260058\n",
|
|||
|
|
"[687]\tvalid_0's rmse: 0.0260083\n",
|
|||
|
|
"[688]\tvalid_0's rmse: 0.0260081\n",
|
|||
|
|
"[689]\tvalid_0's rmse: 0.0260076\n",
|
|||
|
|
"[690]\tvalid_0's rmse: 0.0260032\n",
|
|||
|
|
"[691]\tvalid_0's rmse: 0.0260018\n",
|
|||
|
|
"[692]\tvalid_0's rmse: 0.0260013\n",
|
|||
|
|
"[693]\tvalid_0's rmse: 0.0260024\n",
|
|||
|
|
"[694]\tvalid_0's rmse: 0.026003\n",
|
|||
|
|
"[695]\tvalid_0's rmse: 0.0260023\n",
|
|||
|
|
"[696]\tvalid_0's rmse: 0.0260022\n",
|
|||
|
|
"[697]\tvalid_0's rmse: 0.0260018\n",
|
|||
|
|
"[698]\tvalid_0's rmse: 0.0260004\n",
|
|||
|
|
"[699]\tvalid_0's rmse: 0.0259998\n",
|
|||
|
|
"[700]\tvalid_0's rmse: 0.0259961\n",
|
|||
|
|
"[701]\tvalid_0's rmse: 0.0259964\n",
|
|||
|
|
"[702]\tvalid_0's rmse: 0.0259942\n",
|
|||
|
|
"[703]\tvalid_0's rmse: 0.0259951\n",
|
|||
|
|
"[704]\tvalid_0's rmse: 0.0259918\n",
|
|||
|
|
"[705]\tvalid_0's rmse: 0.0259913\n",
|
|||
|
|
"[706]\tvalid_0's rmse: 0.0259895\n",
|
|||
|
|
"[707]\tvalid_0's rmse: 0.0259881\n",
|
|||
|
|
"[708]\tvalid_0's rmse: 0.0259869\n",
|
|||
|
|
"[709]\tvalid_0's rmse: 0.0259796\n",
|
|||
|
|
"[710]\tvalid_0's rmse: 0.0259789\n",
|
|||
|
|
"[711]\tvalid_0's rmse: 0.0259766\n",
|
|||
|
|
"[712]\tvalid_0's rmse: 0.0259758\n",
|
|||
|
|
"[713]\tvalid_0's rmse: 0.0259746\n",
|
|||
|
|
"[714]\tvalid_0's rmse: 0.0259744\n",
|
|||
|
|
"[715]\tvalid_0's rmse: 0.0259761\n",
|
|||
|
|
"[716]\tvalid_0's rmse: 0.0259832\n",
|
|||
|
|
"[717]\tvalid_0's rmse: 0.0259813\n",
|
|||
|
|
"[718]\tvalid_0's rmse: 0.0259823\n",
|
|||
|
|
"[719]\tvalid_0's rmse: 0.0259815\n",
|
|||
|
|
"[720]\tvalid_0's rmse: 0.0259701\n",
|
|||
|
|
"[721]\tvalid_0's rmse: 0.0259693\n",
|
|||
|
|
"[722]\tvalid_0's rmse: 0.0259679\n",
|
|||
|
|
"[723]\tvalid_0's rmse: 0.0259668\n",
|
|||
|
|
"[724]\tvalid_0's rmse: 0.0259646\n",
|
|||
|
|
"[725]\tvalid_0's rmse: 0.0259639\n",
|
|||
|
|
"[726]\tvalid_0's rmse: 0.0259672\n",
|
|||
|
|
"[727]\tvalid_0's rmse: 0.025969\n",
|
|||
|
|
"[728]\tvalid_0's rmse: 0.0259709\n",
|
|||
|
|
"[729]\tvalid_0's rmse: 0.0259705\n",
|
|||
|
|
"[730]\tvalid_0's rmse: 0.0259611\n",
|
|||
|
|
"[731]\tvalid_0's rmse: 0.0259601\n",
|
|||
|
|
"[732]\tvalid_0's rmse: 0.0259605\n",
|
|||
|
|
"[733]\tvalid_0's rmse: 0.02596\n",
|
|||
|
|
"[734]\tvalid_0's rmse: 0.0259589\n",
|
|||
|
|
"[735]\tvalid_0's rmse: 0.0259593\n",
|
|||
|
|
"[736]\tvalid_0's rmse: 0.0259612\n",
|
|||
|
|
"[737]\tvalid_0's rmse: 0.0259617\n",
|
|||
|
|
"[738]\tvalid_0's rmse: 0.0259604\n",
|
|||
|
|
"[739]\tvalid_0's rmse: 0.0259609\n",
|
|||
|
|
"[740]\tvalid_0's rmse: 0.0259575\n",
|
|||
|
|
"[741]\tvalid_0's rmse: 0.0259552\n",
|
|||
|
|
"[742]\tvalid_0's rmse: 0.025958\n",
|
|||
|
|
"[743]\tvalid_0's rmse: 0.0259575\n",
|
|||
|
|
"[744]\tvalid_0's rmse: 0.0259551\n",
|
|||
|
|
"[745]\tvalid_0's rmse: 0.0259555\n",
|
|||
|
|
"[746]\tvalid_0's rmse: 0.0259564\n",
|
|||
|
|
"[747]\tvalid_0's rmse: 0.0259554\n",
|
|||
|
|
"[748]\tvalid_0's rmse: 0.0259536\n",
|
|||
|
|
"[749]\tvalid_0's rmse: 0.0259524\n",
|
|||
|
|
"[750]\tvalid_0's rmse: 0.0259526\n",
|
|||
|
|
"[751]\tvalid_0's rmse: 0.0259521\n",
|
|||
|
|
"[752]\tvalid_0's rmse: 0.0259515\n",
|
|||
|
|
"[753]\tvalid_0's rmse: 0.0259512\n",
|
|||
|
|
"[754]\tvalid_0's rmse: 0.0259504\n",
|
|||
|
|
"[755]\tvalid_0's rmse: 0.0259508\n",
|
|||
|
|
"[756]\tvalid_0's rmse: 0.0259495\n",
|
|||
|
|
"[757]\tvalid_0's rmse: 0.0259432\n",
|
|||
|
|
"[758]\tvalid_0's rmse: 0.0259428\n",
|
|||
|
|
"[759]\tvalid_0's rmse: 0.0259422\n",
|
|||
|
|
"[760]\tvalid_0's rmse: 0.0259443\n",
|
|||
|
|
"[761]\tvalid_0's rmse: 0.0259459\n",
|
|||
|
|
"[762]\tvalid_0's rmse: 0.0259443\n",
|
|||
|
|
"[763]\tvalid_0's rmse: 0.0259442\n",
|
|||
|
|
"[764]\tvalid_0's rmse: 0.0259432\n",
|
|||
|
|
"[765]\tvalid_0's rmse: 0.025944\n",
|
|||
|
|
"[766]\tvalid_0's rmse: 0.0259433\n",
|
|||
|
|
"[767]\tvalid_0's rmse: 0.0259438\n",
|
|||
|
|
"[768]\tvalid_0's rmse: 0.0259408\n",
|
|||
|
|
"[769]\tvalid_0's rmse: 0.0259404\n",
|
|||
|
|
"[770]\tvalid_0's rmse: 0.0259398\n",
|
|||
|
|
"[771]\tvalid_0's rmse: 0.0259375\n",
|
|||
|
|
"[772]\tvalid_0's rmse: 0.025935\n",
|
|||
|
|
"[773]\tvalid_0's rmse: 0.0259347\n",
|
|||
|
|
"[774]\tvalid_0's rmse: 0.0259332\n",
|
|||
|
|
"[775]\tvalid_0's rmse: 0.0259335\n",
|
|||
|
|
"[776]\tvalid_0's rmse: 0.0259349\n",
|
|||
|
|
"[777]\tvalid_0's rmse: 0.0259345\n",
|
|||
|
|
"[778]\tvalid_0's rmse: 0.0259353\n",
|
|||
|
|
"[779]\tvalid_0's rmse: 0.0259353\n",
|
|||
|
|
"[780]\tvalid_0's rmse: 0.0259354\n",
|
|||
|
|
"[781]\tvalid_0's rmse: 0.025935\n",
|
|||
|
|
"[782]\tvalid_0's rmse: 0.0259362\n",
|
|||
|
|
"[783]\tvalid_0's rmse: 0.0259348\n",
|
|||
|
|
"[784]\tvalid_0's rmse: 0.0259347\n",
|
|||
|
|
"[785]\tvalid_0's rmse: 0.0259361\n",
|
|||
|
|
"[786]\tvalid_0's rmse: 0.0259417\n",
|
|||
|
|
"[787]\tvalid_0's rmse: 0.0259418\n",
|
|||
|
|
"[788]\tvalid_0's rmse: 0.0259422\n",
|
|||
|
|
"[789]\tvalid_0's rmse: 0.0259422\n",
|
|||
|
|
"[790]\tvalid_0's rmse: 0.0259419\n",
|
|||
|
|
"[791]\tvalid_0's rmse: 0.0259409\n",
|
|||
|
|
"[792]\tvalid_0's rmse: 0.0259409\n",
|
|||
|
|
"[793]\tvalid_0's rmse: 0.0259433\n",
|
|||
|
|
"[794]\tvalid_0's rmse: 0.0259438\n",
|
|||
|
|
"[795]\tvalid_0's rmse: 0.0259415\n",
|
|||
|
|
"[796]\tvalid_0's rmse: 0.0259423\n",
|
|||
|
|
"[797]\tvalid_0's rmse: 0.0259435\n",
|
|||
|
|
"[798]\tvalid_0's rmse: 0.0259416\n",
|
|||
|
|
"[799]\tvalid_0's rmse: 0.0259469\n",
|
|||
|
|
"[800]\tvalid_0's rmse: 0.0259488\n",
|
|||
|
|
"[801]\tvalid_0's rmse: 0.0259505\n",
|
|||
|
|
"[802]\tvalid_0's rmse: 0.025947\n",
|
|||
|
|
"[803]\tvalid_0's rmse: 0.0259453\n",
|
|||
|
|
"[804]\tvalid_0's rmse: 0.0259434\n",
|
|||
|
|
"[805]\tvalid_0's rmse: 0.0259429\n",
|
|||
|
|
"[806]\tvalid_0's rmse: 0.0259445\n",
|
|||
|
|
"[807]\tvalid_0's rmse: 0.0259469\n",
|
|||
|
|
"[808]\tvalid_0's rmse: 0.0259436\n",
|
|||
|
|
"[809]\tvalid_0's rmse: 0.0259414\n",
|
|||
|
|
"[810]\tvalid_0's rmse: 0.0259419\n",
|
|||
|
|
"[811]\tvalid_0's rmse: 0.0259498\n",
|
|||
|
|
"[812]\tvalid_0's rmse: 0.0259524\n",
|
|||
|
|
"[813]\tvalid_0's rmse: 0.025951\n",
|
|||
|
|
"[814]\tvalid_0's rmse: 0.0259468\n",
|
|||
|
|
"[815]\tvalid_0's rmse: 0.0259462\n",
|
|||
|
|
"[816]\tvalid_0's rmse: 0.0259387\n",
|
|||
|
|
"[817]\tvalid_0's rmse: 0.0259382\n",
|
|||
|
|
"[818]\tvalid_0's rmse: 0.0259381\n",
|
|||
|
|
"[819]\tvalid_0's rmse: 0.0259391\n",
|
|||
|
|
"[820]\tvalid_0's rmse: 0.0259437\n",
|
|||
|
|
"[821]\tvalid_0's rmse: 0.0259455\n",
|
|||
|
|
"[822]\tvalid_0's rmse: 0.0259458\n",
|
|||
|
|
"[823]\tvalid_0's rmse: 0.0259459\n",
|
|||
|
|
"[824]\tvalid_0's rmse: 0.0259441\n",
|
|||
|
|
"[825]\tvalid_0's rmse: 0.0259408\n",
|
|||
|
|
"[826]\tvalid_0's rmse: 0.0259412\n",
|
|||
|
|
"[827]\tvalid_0's rmse: 0.0259419\n",
|
|||
|
|
"[828]\tvalid_0's rmse: 0.0259434\n",
|
|||
|
|
"[829]\tvalid_0's rmse: 0.0259429\n",
|
|||
|
|
"[830]\tvalid_0's rmse: 0.0259448\n",
|
|||
|
|
"[831]\tvalid_0's rmse: 0.0259442\n",
|
|||
|
|
"[832]\tvalid_0's rmse: 0.0259424\n",
|
|||
|
|
"[833]\tvalid_0's rmse: 0.0259416\n",
|
|||
|
|
"[834]\tvalid_0's rmse: 0.0259425\n",
|
|||
|
|
"[835]\tvalid_0's rmse: 0.025941\n",
|
|||
|
|
"[836]\tvalid_0's rmse: 0.02594\n",
|
|||
|
|
"[837]\tvalid_0's rmse: 0.0259396\n",
|
|||
|
|
"[838]\tvalid_0's rmse: 0.0259382\n",
|
|||
|
|
"[839]\tvalid_0's rmse: 0.0259367\n",
|
|||
|
|
"[840]\tvalid_0's rmse: 0.0259381\n",
|
|||
|
|
"[841]\tvalid_0's rmse: 0.0259379\n",
|
|||
|
|
"[842]\tvalid_0's rmse: 0.0259268\n",
|
|||
|
|
"[843]\tvalid_0's rmse: 0.0259259\n",
|
|||
|
|
"[844]\tvalid_0's rmse: 0.0259228\n",
|
|||
|
|
"[845]\tvalid_0's rmse: 0.0259228\n",
|
|||
|
|
"[846]\tvalid_0's rmse: 0.0259187\n",
|
|||
|
|
"[847]\tvalid_0's rmse: 0.0259171\n",
|
|||
|
|
"[848]\tvalid_0's rmse: 0.0259177\n",
|
|||
|
|
"[849]\tvalid_0's rmse: 0.0259164\n",
|
|||
|
|
"[850]\tvalid_0's rmse: 0.0259161\n",
|
|||
|
|
"[851]\tvalid_0's rmse: 0.0259161\n",
|
|||
|
|
"[852]\tvalid_0's rmse: 0.0259147\n",
|
|||
|
|
"[853]\tvalid_0's rmse: 0.0259145\n",
|
|||
|
|
"[854]\tvalid_0's rmse: 0.0259144\n",
|
|||
|
|
"[855]\tvalid_0's rmse: 0.0259125\n",
|
|||
|
|
"[856]\tvalid_0's rmse: 0.0259127\n",
|
|||
|
|
"[857]\tvalid_0's rmse: 0.0259115\n",
|
|||
|
|
"[858]\tvalid_0's rmse: 0.0259104\n",
|
|||
|
|
"[859]\tvalid_0's rmse: 0.0259119\n",
|
|||
|
|
"[860]\tvalid_0's rmse: 0.0259109\n",
|
|||
|
|
"[861]\tvalid_0's rmse: 0.02591\n",
|
|||
|
|
"[862]\tvalid_0's rmse: 0.0259099\n",
|
|||
|
|
"[863]\tvalid_0's rmse: 0.0259097\n",
|
|||
|
|
"[864]\tvalid_0's rmse: 0.0259133\n",
|
|||
|
|
"[865]\tvalid_0's rmse: 0.0259116\n",
|
|||
|
|
"[866]\tvalid_0's rmse: 0.0259111\n",
|
|||
|
|
"[867]\tvalid_0's rmse: 0.0259095\n",
|
|||
|
|
"[868]\tvalid_0's rmse: 0.0258982\n",
|
|||
|
|
"[869]\tvalid_0's rmse: 0.0258979\n",
|
|||
|
|
"[870]\tvalid_0's rmse: 0.0258956\n",
|
|||
|
|
"[871]\tvalid_0's rmse: 0.0258967\n",
|
|||
|
|
"[872]\tvalid_0's rmse: 0.0258972\n",
|
|||
|
|
"[873]\tvalid_0's rmse: 0.0258971\n",
|
|||
|
|
"[874]\tvalid_0's rmse: 0.0259015\n",
|
|||
|
|
"[875]\tvalid_0's rmse: 0.0258999\n",
|
|||
|
|
"[876]\tvalid_0's rmse: 0.0258987\n",
|
|||
|
|
"[877]\tvalid_0's rmse: 0.0258987\n",
|
|||
|
|
"[878]\tvalid_0's rmse: 0.0258985\n",
|
|||
|
|
"[879]\tvalid_0's rmse: 0.0259\n",
|
|||
|
|
"[880]\tvalid_0's rmse: 0.0259008\n",
|
|||
|
|
"[881]\tvalid_0's rmse: 0.0259018\n",
|
|||
|
|
"[882]\tvalid_0's rmse: 0.0259037\n",
|
|||
|
|
"[883]\tvalid_0's rmse: 0.0259048\n",
|
|||
|
|
"[884]\tvalid_0's rmse: 0.0259063\n",
|
|||
|
|
"[885]\tvalid_0's rmse: 0.0259055\n",
|
|||
|
|
"[886]\tvalid_0's rmse: 0.0259052\n",
|
|||
|
|
"[887]\tvalid_0's rmse: 0.0259047\n",
|
|||
|
|
"[888]\tvalid_0's rmse: 0.0259042\n",
|
|||
|
|
"[889]\tvalid_0's rmse: 0.0259046\n",
|
|||
|
|
"[890]\tvalid_0's rmse: 0.0259049\n",
|
|||
|
|
"[891]\tvalid_0's rmse: 0.0259044\n",
|
|||
|
|
"[892]\tvalid_0's rmse: 0.0259046\n",
|
|||
|
|
"[893]\tvalid_0's rmse: 0.0259035\n",
|
|||
|
|
"[894]\tvalid_0's rmse: 0.0259016\n",
|
|||
|
|
"[895]\tvalid_0's rmse: 0.0259031\n",
|
|||
|
|
"[896]\tvalid_0's rmse: 0.0259025\n",
|
|||
|
|
"[897]\tvalid_0's rmse: 0.0259047\n",
|
|||
|
|
"[898]\tvalid_0's rmse: 0.0259051\n",
|
|||
|
|
"[899]\tvalid_0's rmse: 0.0259101\n",
|
|||
|
|
"[900]\tvalid_0's rmse: 0.0259099\n",
|
|||
|
|
"[901]\tvalid_0's rmse: 0.0259106\n",
|
|||
|
|
"[902]\tvalid_0's rmse: 0.0259101\n",
|
|||
|
|
"[903]\tvalid_0's rmse: 0.0259044\n",
|
|||
|
|
"[904]\tvalid_0's rmse: 0.0259034\n",
|
|||
|
|
"[905]\tvalid_0's rmse: 0.0259038\n",
|
|||
|
|
"[906]\tvalid_0's rmse: 0.0259047\n",
|
|||
|
|
"[907]\tvalid_0's rmse: 0.0259061\n",
|
|||
|
|
"[908]\tvalid_0's rmse: 0.025906\n",
|
|||
|
|
"[909]\tvalid_0's rmse: 0.025901\n",
|
|||
|
|
"[910]\tvalid_0's rmse: 0.0258971\n",
|
|||
|
|
"[911]\tvalid_0's rmse: 0.0258968\n",
|
|||
|
|
"[912]\tvalid_0's rmse: 0.0258973\n",
|
|||
|
|
"[913]\tvalid_0's rmse: 0.0258965\n",
|
|||
|
|
"[914]\tvalid_0's rmse: 0.025898\n",
|
|||
|
|
"[915]\tvalid_0's rmse: 0.0258982\n",
|
|||
|
|
"[916]\tvalid_0's rmse: 0.0258981\n",
|
|||
|
|
"[917]\tvalid_0's rmse: 0.0258952\n",
|
|||
|
|
"[918]\tvalid_0's rmse: 0.0258949\n",
|
|||
|
|
"[919]\tvalid_0's rmse: 0.0258947\n",
|
|||
|
|
"[920]\tvalid_0's rmse: 0.0258959\n",
|
|||
|
|
"[921]\tvalid_0's rmse: 0.0258954\n",
|
|||
|
|
"[922]\tvalid_0's rmse: 0.0258947\n",
|
|||
|
|
"[923]\tvalid_0's rmse: 0.0258946\n",
|
|||
|
|
"[924]\tvalid_0's rmse: 0.0258931\n",
|
|||
|
|
"[925]\tvalid_0's rmse: 0.0258945\n",
|
|||
|
|
"[926]\tvalid_0's rmse: 0.0258925\n",
|
|||
|
|
"[927]\tvalid_0's rmse: 0.0258899\n",
|
|||
|
|
"[928]\tvalid_0's rmse: 0.0258898\n",
|
|||
|
|
"[929]\tvalid_0's rmse: 0.0258914\n",
|
|||
|
|
"[930]\tvalid_0's rmse: 0.0258912\n",
|
|||
|
|
"[931]\tvalid_0's rmse: 0.025892\n",
|
|||
|
|
"[932]\tvalid_0's rmse: 0.025893\n",
|
|||
|
|
"[933]\tvalid_0's rmse: 0.0258918\n",
|
|||
|
|
"[934]\tvalid_0's rmse: 0.0258882\n",
|
|||
|
|
"[935]\tvalid_0's rmse: 0.0258882\n",
|
|||
|
|
"[936]\tvalid_0's rmse: 0.0258871\n",
|
|||
|
|
"[937]\tvalid_0's rmse: 0.0258879\n",
|
|||
|
|
"[938]\tvalid_0's rmse: 0.0258857\n",
|
|||
|
|
"[939]\tvalid_0's rmse: 0.0258855\n",
|
|||
|
|
"[940]\tvalid_0's rmse: 0.0258856\n",
|
|||
|
|
"[941]\tvalid_0's rmse: 0.0258855\n",
|
|||
|
|
"[942]\tvalid_0's rmse: 0.0258857\n",
|
|||
|
|
"[943]\tvalid_0's rmse: 0.0258857\n",
|
|||
|
|
"[944]\tvalid_0's rmse: 0.0258861\n",
|
|||
|
|
"[945]\tvalid_0's rmse: 0.0258858\n",
|
|||
|
|
"[946]\tvalid_0's rmse: 0.0258865\n",
|
|||
|
|
"[947]\tvalid_0's rmse: 0.0258875\n",
|
|||
|
|
"[948]\tvalid_0's rmse: 0.0258872\n",
|
|||
|
|
"[949]\tvalid_0's rmse: 0.0258872\n",
|
|||
|
|
"[950]\tvalid_0's rmse: 0.0258866\n",
|
|||
|
|
"[951]\tvalid_0's rmse: 0.0258888\n",
|
|||
|
|
"[952]\tvalid_0's rmse: 0.0258892\n",
|
|||
|
|
"[953]\tvalid_0's rmse: 0.0258835\n",
|
|||
|
|
"[954]\tvalid_0's rmse: 0.0258817\n",
|
|||
|
|
"[955]\tvalid_0's rmse: 0.0258817\n",
|
|||
|
|
"[956]\tvalid_0's rmse: 0.0258786\n",
|
|||
|
|
"[957]\tvalid_0's rmse: 0.0258788\n",
|
|||
|
|
"[958]\tvalid_0's rmse: 0.0258788\n",
|
|||
|
|
"[959]\tvalid_0's rmse: 0.0258798\n",
|
|||
|
|
"[960]\tvalid_0's rmse: 0.0258797\n",
|
|||
|
|
"[961]\tvalid_0's rmse: 0.0258797\n",
|
|||
|
|
"[962]\tvalid_0's rmse: 0.0258776\n",
|
|||
|
|
"[963]\tvalid_0's rmse: 0.0258773\n",
|
|||
|
|
"[964]\tvalid_0's rmse: 0.025877\n",
|
|||
|
|
"[965]\tvalid_0's rmse: 0.0258773\n",
|
|||
|
|
"[966]\tvalid_0's rmse: 0.025879\n",
|
|||
|
|
"[967]\tvalid_0's rmse: 0.0258802\n",
|
|||
|
|
"[968]\tvalid_0's rmse: 0.0258794\n",
|
|||
|
|
"[969]\tvalid_0's rmse: 0.02588\n",
|
|||
|
|
"[970]\tvalid_0's rmse: 0.0258797\n",
|
|||
|
|
"[971]\tvalid_0's rmse: 0.0258782\n",
|
|||
|
|
"[972]\tvalid_0's rmse: 0.0258827\n",
|
|||
|
|
"[973]\tvalid_0's rmse: 0.0258842\n",
|
|||
|
|
"[974]\tvalid_0's rmse: 0.0258837\n",
|
|||
|
|
"[975]\tvalid_0's rmse: 0.0258827\n",
|
|||
|
|
"[976]\tvalid_0's rmse: 0.0258818\n",
|
|||
|
|
"[977]\tvalid_0's rmse: 0.0258811\n",
|
|||
|
|
"[978]\tvalid_0's rmse: 0.0258813\n",
|
|||
|
|
"[979]\tvalid_0's rmse: 0.0258813\n",
|
|||
|
|
"[980]\tvalid_0's rmse: 0.0258805\n",
|
|||
|
|
"[981]\tvalid_0's rmse: 0.0258805\n",
|
|||
|
|
"[982]\tvalid_0's rmse: 0.0258791\n",
|
|||
|
|
"[983]\tvalid_0's rmse: 0.0258764\n",
|
|||
|
|
"[984]\tvalid_0's rmse: 0.0258765\n",
|
|||
|
|
"[985]\tvalid_0's rmse: 0.0258748\n",
|
|||
|
|
"[986]\tvalid_0's rmse: 0.025877\n",
|
|||
|
|
"[987]\tvalid_0's rmse: 0.025878\n",
|
|||
|
|
"[988]\tvalid_0's rmse: 0.0258776\n",
|
|||
|
|
"[989]\tvalid_0's rmse: 0.0258761\n",
|
|||
|
|
"[990]\tvalid_0's rmse: 0.0258762\n",
|
|||
|
|
"[991]\tvalid_0's rmse: 0.0258591\n",
|
|||
|
|
"[992]\tvalid_0's rmse: 0.0258595\n",
|
|||
|
|
"[993]\tvalid_0's rmse: 0.0258594\n",
|
|||
|
|
"[994]\tvalid_0's rmse: 0.0258605\n",
|
|||
|
|
"[995]\tvalid_0's rmse: 0.02586\n",
|
|||
|
|
"[996]\tvalid_0's rmse: 0.0258582\n",
|
|||
|
|
"[997]\tvalid_0's rmse: 0.0258576\n",
|
|||
|
|
"[998]\tvalid_0's rmse: 0.0258556\n",
|
|||
|
|
"[999]\tvalid_0's rmse: 0.0258562\n",
|
|||
|
|
"[1000]\tvalid_0's rmse: 0.0258543\n",
|
|||
|
|
"[1001]\tvalid_0's rmse: 0.0258523\n",
|
|||
|
|
"[1002]\tvalid_0's rmse: 0.0258534\n",
|
|||
|
|
"[1003]\tvalid_0's rmse: 0.0258537\n",
|
|||
|
|
"[1004]\tvalid_0's rmse: 0.0258546\n",
|
|||
|
|
"[1005]\tvalid_0's rmse: 0.0258533\n",
|
|||
|
|
"[1006]\tvalid_0's rmse: 0.0258519\n",
|
|||
|
|
"[1007]\tvalid_0's rmse: 0.0258508\n",
|
|||
|
|
"[1008]\tvalid_0's rmse: 0.0258508\n",
|
|||
|
|
"[1009]\tvalid_0's rmse: 0.0258509\n",
|
|||
|
|
"[1010]\tvalid_0's rmse: 0.0258469\n",
|
|||
|
|
"[1011]\tvalid_0's rmse: 0.025851\n",
|
|||
|
|
"[1012]\tvalid_0's rmse: 0.0258512\n",
|
|||
|
|
"[1013]\tvalid_0's rmse: 0.0258474\n",
|
|||
|
|
"[1014]\tvalid_0's rmse: 0.0258468\n",
|
|||
|
|
"[1015]\tvalid_0's rmse: 0.0258432\n",
|
|||
|
|
"[1016]\tvalid_0's rmse: 0.0258409\n",
|
|||
|
|
"[1017]\tvalid_0's rmse: 0.0258283\n",
|
|||
|
|
"[1018]\tvalid_0's rmse: 0.0258284\n",
|
|||
|
|
"[1019]\tvalid_0's rmse: 0.0258254\n",
|
|||
|
|
"[1020]\tvalid_0's rmse: 0.0258244\n",
|
|||
|
|
"[1021]\tvalid_0's rmse: 0.0258246\n",
|
|||
|
|
"[1022]\tvalid_0's rmse: 0.0258249\n",
|
|||
|
|
"[1023]\tvalid_0's rmse: 0.0258246\n",
|
|||
|
|
"[1024]\tvalid_0's rmse: 0.0258215\n",
|
|||
|
|
"[1025]\tvalid_0's rmse: 0.0258211\n",
|
|||
|
|
"[1026]\tvalid_0's rmse: 0.0258215\n",
|
|||
|
|
"[1027]\tvalid_0's rmse: 0.0258213\n",
|
|||
|
|
"[1028]\tvalid_0's rmse: 0.0258215\n",
|
|||
|
|
"[1029]\tvalid_0's rmse: 0.0258233\n",
|
|||
|
|
"[1030]\tvalid_0's rmse: 0.0258232\n",
|
|||
|
|
"[1031]\tvalid_0's rmse: 0.0258233\n",
|
|||
|
|
"[1032]\tvalid_0's rmse: 0.0258191\n",
|
|||
|
|
"[1033]\tvalid_0's rmse: 0.0258196\n",
|
|||
|
|
"[1034]\tvalid_0's rmse: 0.0258169\n",
|
|||
|
|
"[1035]\tvalid_0's rmse: 0.025816\n",
|
|||
|
|
"[1036]\tvalid_0's rmse: 0.0258137\n",
|
|||
|
|
"[1037]\tvalid_0's rmse: 0.0258143\n",
|
|||
|
|
"[1038]\tvalid_0's rmse: 0.0258121\n",
|
|||
|
|
"[1039]\tvalid_0's rmse: 0.0258055\n",
|
|||
|
|
"[1040]\tvalid_0's rmse: 0.0258055\n",
|
|||
|
|
"[1041]\tvalid_0's rmse: 0.0258079\n",
|
|||
|
|
"[1042]\tvalid_0's rmse: 0.0258097\n",
|
|||
|
|
"[1043]\tvalid_0's rmse: 0.0258097\n",
|
|||
|
|
"[1044]\tvalid_0's rmse: 0.0258109\n",
|
|||
|
|
"[1045]\tvalid_0's rmse: 0.0258118\n",
|
|||
|
|
"[1046]\tvalid_0's rmse: 0.0258121\n",
|
|||
|
|
"[1047]\tvalid_0's rmse: 0.0258112\n",
|
|||
|
|
"[1048]\tvalid_0's rmse: 0.0258103\n",
|
|||
|
|
"[1049]\tvalid_0's rmse: 0.0258102\n",
|
|||
|
|
"[1050]\tvalid_0's rmse: 0.0258113\n",
|
|||
|
|
"[1051]\tvalid_0's rmse: 0.0258119\n",
|
|||
|
|
"[1052]\tvalid_0's rmse: 0.0258115\n",
|
|||
|
|
"[1053]\tvalid_0's rmse: 0.0258116\n",
|
|||
|
|
"[1054]\tvalid_0's rmse: 0.0258114\n",
|
|||
|
|
"[1055]\tvalid_0's rmse: 0.0258098\n",
|
|||
|
|
"[1056]\tvalid_0's rmse: 0.0258097\n",
|
|||
|
|
"[1057]\tvalid_0's rmse: 0.0258085\n",
|
|||
|
|
"[1058]\tvalid_0's rmse: 0.0258088\n",
|
|||
|
|
"[1059]\tvalid_0's rmse: 0.0258058\n",
|
|||
|
|
"[1060]\tvalid_0's rmse: 0.0258033\n",
|
|||
|
|
"[1061]\tvalid_0's rmse: 0.0257999\n",
|
|||
|
|
"[1062]\tvalid_0's rmse: 0.025795\n",
|
|||
|
|
"[1063]\tvalid_0's rmse: 0.0257936\n",
|
|||
|
|
"[1064]\tvalid_0's rmse: 0.0257928\n",
|
|||
|
|
"[1065]\tvalid_0's rmse: 0.025793\n",
|
|||
|
|
"[1066]\tvalid_0's rmse: 0.0257934\n",
|
|||
|
|
"[1067]\tvalid_0's rmse: 0.0257928\n",
|
|||
|
|
"[1068]\tvalid_0's rmse: 0.0257786\n",
|
|||
|
|
"[1069]\tvalid_0's rmse: 0.0257783\n",
|
|||
|
|
"[1070]\tvalid_0's rmse: 0.0257778\n",
|
|||
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|
"[1071]\tvalid_0's rmse: 0.025777\n",
|
|||
|
|
"[1072]\tvalid_0's rmse: 0.0257782\n",
|
|||
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|
"[1073]\tvalid_0's rmse: 0.0257767\n",
|
|||
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|
"[1074]\tvalid_0's rmse: 0.0257763\n",
|
|||
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|
"[1075]\tvalid_0's rmse: 0.0257764\n",
|
|||
|
|
"[1076]\tvalid_0's rmse: 0.025776\n",
|
|||
|
|
"[1077]\tvalid_0's rmse: 0.0257776\n",
|
|||
|
|
"[1078]\tvalid_0's rmse: 0.0257782\n",
|
|||
|
|
"[1079]\tvalid_0's rmse: 0.0257782\n",
|
|||
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|
"[1080]\tvalid_0's rmse: 0.0257781\n",
|
|||
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|
"[1081]\tvalid_0's rmse: 0.025776\n",
|
|||
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|
"[1082]\tvalid_0's rmse: 0.0257761\n",
|
|||
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|
"[1083]\tvalid_0's rmse: 0.0257762\n",
|
|||
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|
"[1084]\tvalid_0's rmse: 0.0257773\n",
|
|||
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|
"[1085]\tvalid_0's rmse: 0.0257783\n",
|
|||
|
|
"[1086]\tvalid_0's rmse: 0.0257785\n",
|
|||
|
|
"[1087]\tvalid_0's rmse: 0.0257788\n",
|
|||
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|
"[1088]\tvalid_0's rmse: 0.0257792\n",
|
|||
|
|
"[1089]\tvalid_0's rmse: 0.02578\n",
|
|||
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|
"[1090]\tvalid_0's rmse: 0.0257788\n",
|
|||
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|
"[1091]\tvalid_0's rmse: 0.0257776\n",
|
|||
|
|
"[1092]\tvalid_0's rmse: 0.0257795\n",
|
|||
|
|
"[1093]\tvalid_0's rmse: 0.0257788\n",
|
|||
|
|
"[1094]\tvalid_0's rmse: 0.0257782\n",
|
|||
|
|
"[1095]\tvalid_0's rmse: 0.025778\n",
|
|||
|
|
"[1096]\tvalid_0's rmse: 0.0257811\n",
|
|||
|
|
"[1097]\tvalid_0's rmse: 0.0257814\n",
|
|||
|
|
"[1098]\tvalid_0's rmse: 0.0257792\n",
|
|||
|
|
"[1099]\tvalid_0's rmse: 0.0257788\n",
|
|||
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|
"[1100]\tvalid_0's rmse: 0.0257798\n",
|
|||
|
|
"[1101]\tvalid_0's rmse: 0.0257804\n",
|
|||
|
|
"[1102]\tvalid_0's rmse: 0.0257804\n",
|
|||
|
|
"[1103]\tvalid_0's rmse: 0.0257781\n",
|
|||
|
|
"[1104]\tvalid_0's rmse: 0.0257786\n",
|
|||
|
|
"[1105]\tvalid_0's rmse: 0.0257794\n",
|
|||
|
|
"[1106]\tvalid_0's rmse: 0.0257793\n",
|
|||
|
|
"[1107]\tvalid_0's rmse: 0.0257795\n",
|
|||
|
|
"[1108]\tvalid_0's rmse: 0.0257792\n",
|
|||
|
|
"[1109]\tvalid_0's rmse: 0.0257754\n",
|
|||
|
|
"[1110]\tvalid_0's rmse: 0.0257772\n",
|
|||
|
|
"[1111]\tvalid_0's rmse: 0.0257766\n",
|
|||
|
|
"[1112]\tvalid_0's rmse: 0.0257761\n",
|
|||
|
|
"[1113]\tvalid_0's rmse: 0.0257759\n",
|
|||
|
|
"[1114]\tvalid_0's rmse: 0.0257754\n",
|
|||
|
|
"[1115]\tvalid_0's rmse: 0.0257751\n",
|
|||
|
|
"[1116]\tvalid_0's rmse: 0.0257731\n",
|
|||
|
|
"[1117]\tvalid_0's rmse: 0.0257728\n",
|
|||
|
|
"[1118]\tvalid_0's rmse: 0.0257725\n",
|
|||
|
|
"[1119]\tvalid_0's rmse: 0.025771\n",
|
|||
|
|
"[1120]\tvalid_0's rmse: 0.0257698\n",
|
|||
|
|
"[1121]\tvalid_0's rmse: 0.0257699\n",
|
|||
|
|
"[1122]\tvalid_0's rmse: 0.0257698\n",
|
|||
|
|
"[1123]\tvalid_0's rmse: 0.0257685\n",
|
|||
|
|
"[1124]\tvalid_0's rmse: 0.0257678\n",
|
|||
|
|
"[1125]\tvalid_0's rmse: 0.0257679\n",
|
|||
|
|
"[1126]\tvalid_0's rmse: 0.0257667\n",
|
|||
|
|
"[1127]\tvalid_0's rmse: 0.0257669\n",
|
|||
|
|
"[1128]\tvalid_0's rmse: 0.0257648\n",
|
|||
|
|
"[1129]\tvalid_0's rmse: 0.0257647\n",
|
|||
|
|
"[1130]\tvalid_0's rmse: 0.0257651\n",
|
|||
|
|
"[1131]\tvalid_0's rmse: 0.0257653\n",
|
|||
|
|
"[1132]\tvalid_0's rmse: 0.0257657\n",
|
|||
|
|
"[1133]\tvalid_0's rmse: 0.0257652\n",
|
|||
|
|
"[1134]\tvalid_0's rmse: 0.0257653\n",
|
|||
|
|
"[1135]\tvalid_0's rmse: 0.0257593\n",
|
|||
|
|
"[1136]\tvalid_0's rmse: 0.0257585\n",
|
|||
|
|
"[1137]\tvalid_0's rmse: 0.0257583\n",
|
|||
|
|
"[1138]\tvalid_0's rmse: 0.0257575\n",
|
|||
|
|
"[1139]\tvalid_0's rmse: 0.0257571\n",
|
|||
|
|
"[1140]\tvalid_0's rmse: 0.0257562\n",
|
|||
|
|
"[1141]\tvalid_0's rmse: 0.0257562\n",
|
|||
|
|
"[1142]\tvalid_0's rmse: 0.0257561\n",
|
|||
|
|
"[1143]\tvalid_0's rmse: 0.025755\n",
|
|||
|
|
"[1144]\tvalid_0's rmse: 0.025754\n",
|
|||
|
|
"[1145]\tvalid_0's rmse: 0.0257534\n",
|
|||
|
|
"[1146]\tvalid_0's rmse: 0.0257535\n",
|
|||
|
|
"[1147]\tvalid_0's rmse: 0.0257503\n",
|
|||
|
|
"[1148]\tvalid_0's rmse: 0.0257519\n",
|
|||
|
|
"[1149]\tvalid_0's rmse: 0.0257486\n",
|
|||
|
|
"[1150]\tvalid_0's rmse: 0.0257485\n",
|
|||
|
|
"[1151]\tvalid_0's rmse: 0.0257492\n",
|
|||
|
|
"[1152]\tvalid_0's rmse: 0.0257531\n",
|
|||
|
|
"[1153]\tvalid_0's rmse: 0.0257529\n",
|
|||
|
|
"[1154]\tvalid_0's rmse: 0.0257521\n",
|
|||
|
|
"[1155]\tvalid_0's rmse: 0.0257517\n",
|
|||
|
|
"[1156]\tvalid_0's rmse: 0.0257545\n",
|
|||
|
|
"[1157]\tvalid_0's rmse: 0.0257556\n",
|
|||
|
|
"[1158]\tvalid_0's rmse: 0.0257559\n",
|
|||
|
|
"[1159]\tvalid_0's rmse: 0.0257578\n",
|
|||
|
|
"[1160]\tvalid_0's rmse: 0.0257567\n",
|
|||
|
|
"[1161]\tvalid_0's rmse: 0.0257569\n",
|
|||
|
|
"[1162]\tvalid_0's rmse: 0.0257559\n",
|
|||
|
|
"[1163]\tvalid_0's rmse: 0.0257577\n",
|
|||
|
|
"[1164]\tvalid_0's rmse: 0.0257551\n",
|
|||
|
|
"[1165]\tvalid_0's rmse: 0.025756\n",
|
|||
|
|
"[1166]\tvalid_0's rmse: 0.0257558\n",
|
|||
|
|
"[1167]\tvalid_0's rmse: 0.0257561\n",
|
|||
|
|
"[1168]\tvalid_0's rmse: 0.0257562\n",
|
|||
|
|
"[1169]\tvalid_0's rmse: 0.0257558\n",
|
|||
|
|
"[1170]\tvalid_0's rmse: 0.0257527\n",
|
|||
|
|
"[1171]\tvalid_0's rmse: 0.0257479\n",
|
|||
|
|
"[1172]\tvalid_0's rmse: 0.0257481\n",
|
|||
|
|
"[1173]\tvalid_0's rmse: 0.0257445\n",
|
|||
|
|
"[1174]\tvalid_0's rmse: 0.0257442\n",
|
|||
|
|
"[1175]\tvalid_0's rmse: 0.0257454\n",
|
|||
|
|
"[1176]\tvalid_0's rmse: 0.0257446\n",
|
|||
|
|
"[1177]\tvalid_0's rmse: 0.0257455\n",
|
|||
|
|
"[1178]\tvalid_0's rmse: 0.0257465\n",
|
|||
|
|
"[1179]\tvalid_0's rmse: 0.0257483\n",
|
|||
|
|
"[1180]\tvalid_0's rmse: 0.0257494\n",
|
|||
|
|
"[1181]\tvalid_0's rmse: 0.025749\n",
|
|||
|
|
"[1182]\tvalid_0's rmse: 0.0257492\n",
|
|||
|
|
"[1183]\tvalid_0's rmse: 0.0257497\n",
|
|||
|
|
"[1184]\tvalid_0's rmse: 0.02575\n",
|
|||
|
|
"[1185]\tvalid_0's rmse: 0.0257441\n",
|
|||
|
|
"[1186]\tvalid_0's rmse: 0.0257412\n",
|
|||
|
|
"[1187]\tvalid_0's rmse: 0.0257376\n",
|
|||
|
|
"[1188]\tvalid_0's rmse: 0.025734\n",
|
|||
|
|
"[1189]\tvalid_0's rmse: 0.0257333\n",
|
|||
|
|
"[1190]\tvalid_0's rmse: 0.0257326\n",
|
|||
|
|
"[1191]\tvalid_0's rmse: 0.0257325\n",
|
|||
|
|
"[1192]\tvalid_0's rmse: 0.0257347\n",
|
|||
|
|
"[1193]\tvalid_0's rmse: 0.0257189\n",
|
|||
|
|
"[1194]\tvalid_0's rmse: 0.0257085\n",
|
|||
|
|
"[1195]\tvalid_0's rmse: 0.0257073\n",
|
|||
|
|
"[1196]\tvalid_0's rmse: 0.025707\n",
|
|||
|
|
"[1197]\tvalid_0's rmse: 0.0257055\n",
|
|||
|
|
"[1198]\tvalid_0's rmse: 0.0257056\n",
|
|||
|
|
"[1199]\tvalid_0's rmse: 0.0257043\n",
|
|||
|
|
"[1200]\tvalid_0's rmse: 0.0257063\n",
|
|||
|
|
"[1201]\tvalid_0's rmse: 0.0257056\n",
|
|||
|
|
"[1202]\tvalid_0's rmse: 0.0257059\n",
|
|||
|
|
"[1203]\tvalid_0's rmse: 0.0257041\n",
|
|||
|
|
"[1204]\tvalid_0's rmse: 0.0257018\n",
|
|||
|
|
"[1205]\tvalid_0's rmse: 0.025702\n",
|
|||
|
|
"[1206]\tvalid_0's rmse: 0.0257017\n",
|
|||
|
|
"[1207]\tvalid_0's rmse: 0.0256966\n",
|
|||
|
|
"[1208]\tvalid_0's rmse: 0.0256931\n",
|
|||
|
|
"[1209]\tvalid_0's rmse: 0.0256931\n",
|
|||
|
|
"[1210]\tvalid_0's rmse: 0.025693\n",
|
|||
|
|
"[1211]\tvalid_0's rmse: 0.0256934\n",
|
|||
|
|
"[1212]\tvalid_0's rmse: 0.0256969\n",
|
|||
|
|
"[1213]\tvalid_0's rmse: 0.0256973\n",
|
|||
|
|
"[1214]\tvalid_0's rmse: 0.0256982\n",
|
|||
|
|
"[1215]\tvalid_0's rmse: 0.0256965\n",
|
|||
|
|
"[1216]\tvalid_0's rmse: 0.0256955\n",
|
|||
|
|
"[1217]\tvalid_0's rmse: 0.0256956\n",
|
|||
|
|
"[1218]\tvalid_0's rmse: 0.0256956\n",
|
|||
|
|
"[1219]\tvalid_0's rmse: 0.0256943\n",
|
|||
|
|
"[1220]\tvalid_0's rmse: 0.0256932\n",
|
|||
|
|
"[1221]\tvalid_0's rmse: 0.0256944\n",
|
|||
|
|
"[1222]\tvalid_0's rmse: 0.0256935\n",
|
|||
|
|
"[1223]\tvalid_0's rmse: 0.0256947\n",
|
|||
|
|
"[1224]\tvalid_0's rmse: 0.0256951\n",
|
|||
|
|
"[1225]\tvalid_0's rmse: 0.0256953\n",
|
|||
|
|
"[1226]\tvalid_0's rmse: 0.0256967\n",
|
|||
|
|
"[1227]\tvalid_0's rmse: 0.0256974\n",
|
|||
|
|
"[1228]\tvalid_0's rmse: 0.0256971\n",
|
|||
|
|
"[1229]\tvalid_0's rmse: 0.025697\n",
|
|||
|
|
"[1230]\tvalid_0's rmse: 0.0256973\n",
|
|||
|
|
"[1231]\tvalid_0's rmse: 0.0256971\n",
|
|||
|
|
"[1232]\tvalid_0's rmse: 0.0256976\n",
|
|||
|
|
"[1233]\tvalid_0's rmse: 0.0256976\n",
|
|||
|
|
"[1234]\tvalid_0's rmse: 0.025696\n",
|
|||
|
|
"[1235]\tvalid_0's rmse: 0.0256965\n",
|
|||
|
|
"[1236]\tvalid_0's rmse: 0.0256961\n",
|
|||
|
|
"[1237]\tvalid_0's rmse: 0.0256962\n",
|
|||
|
|
"[1238]\tvalid_0's rmse: 0.0256996\n",
|
|||
|
|
"[1239]\tvalid_0's rmse: 0.0257003\n",
|
|||
|
|
"[1240]\tvalid_0's rmse: 0.0257023\n",
|
|||
|
|
"[1241]\tvalid_0's rmse: 0.0257018\n",
|
|||
|
|
"[1242]\tvalid_0's rmse: 0.0257016\n",
|
|||
|
|
"[1243]\tvalid_0's rmse: 0.0257023\n",
|
|||
|
|
"[1244]\tvalid_0's rmse: 0.0257013\n",
|
|||
|
|
"[1245]\tvalid_0's rmse: 0.0256968\n",
|
|||
|
|
"[1246]\tvalid_0's rmse: 0.0256967\n",
|
|||
|
|
"[1247]\tvalid_0's rmse: 0.0256935\n",
|
|||
|
|
"[1248]\tvalid_0's rmse: 0.0256932\n",
|
|||
|
|
"[1249]\tvalid_0's rmse: 0.0256959\n",
|
|||
|
|
"[1250]\tvalid_0's rmse: 0.025695\n",
|
|||
|
|
"[1251]\tvalid_0's rmse: 0.025695\n",
|
|||
|
|
"[1252]\tvalid_0's rmse: 0.0256954\n",
|
|||
|
|
"[1253]\tvalid_0's rmse: 0.0256932\n",
|
|||
|
|
"[1254]\tvalid_0's rmse: 0.0256933\n",
|
|||
|
|
"[1255]\tvalid_0's rmse: 0.0256942\n",
|
|||
|
|
"[1256]\tvalid_0's rmse: 0.0256929\n",
|
|||
|
|
"[1257]\tvalid_0's rmse: 0.0256918\n",
|
|||
|
|
"[1258]\tvalid_0's rmse: 0.0256916\n",
|
|||
|
|
"[1259]\tvalid_0's rmse: 0.0256913\n",
|
|||
|
|
"[1260]\tvalid_0's rmse: 0.0256924\n",
|
|||
|
|
"[1261]\tvalid_0's rmse: 0.0256909\n",
|
|||
|
|
"[1262]\tvalid_0's rmse: 0.0256907\n",
|
|||
|
|
"[1263]\tvalid_0's rmse: 0.0256914\n",
|
|||
|
|
"[1264]\tvalid_0's rmse: 0.0256819\n",
|
|||
|
|
"[1265]\tvalid_0's rmse: 0.0256823\n",
|
|||
|
|
"[1266]\tvalid_0's rmse: 0.0256822\n",
|
|||
|
|
"[1267]\tvalid_0's rmse: 0.0256828\n",
|
|||
|
|
"[1268]\tvalid_0's rmse: 0.025683\n",
|
|||
|
|
"[1269]\tvalid_0's rmse: 0.0256841\n",
|
|||
|
|
"[1270]\tvalid_0's rmse: 0.0256839\n",
|
|||
|
|
"[1271]\tvalid_0's rmse: 0.0256837\n",
|
|||
|
|
"[1272]\tvalid_0's rmse: 0.0256835\n",
|
|||
|
|
"[1273]\tvalid_0's rmse: 0.0256819\n",
|
|||
|
|
"[1274]\tvalid_0's rmse: 0.0256814\n",
|
|||
|
|
"[1275]\tvalid_0's rmse: 0.0256859\n",
|
|||
|
|
"[1276]\tvalid_0's rmse: 0.0256845\n",
|
|||
|
|
"[1277]\tvalid_0's rmse: 0.0256854\n",
|
|||
|
|
"[1278]\tvalid_0's rmse: 0.0256899\n",
|
|||
|
|
"[1279]\tvalid_0's rmse: 0.0256912\n",
|
|||
|
|
"[1280]\tvalid_0's rmse: 0.0256951\n",
|
|||
|
|
"[1281]\tvalid_0's rmse: 0.0256952\n",
|
|||
|
|
"[1282]\tvalid_0's rmse: 0.0256956\n",
|
|||
|
|
"[1283]\tvalid_0's rmse: 0.0256958\n",
|
|||
|
|
"[1284]\tvalid_0's rmse: 0.0256956\n",
|
|||
|
|
"[1285]\tvalid_0's rmse: 0.025695\n",
|
|||
|
|
"[1286]\tvalid_0's rmse: 0.0256955\n",
|
|||
|
|
"[1287]\tvalid_0's rmse: 0.0256955\n",
|
|||
|
|
"[1288]\tvalid_0's rmse: 0.0256966\n",
|
|||
|
|
"[1289]\tvalid_0's rmse: 0.0256969\n",
|
|||
|
|
"[1290]\tvalid_0's rmse: 0.0256961\n",
|
|||
|
|
"[1291]\tvalid_0's rmse: 0.0256955\n",
|
|||
|
|
"[1292]\tvalid_0's rmse: 0.025695\n",
|
|||
|
|
"[1293]\tvalid_0's rmse: 0.0256959\n",
|
|||
|
|
"[1294]\tvalid_0's rmse: 0.0256953\n",
|
|||
|
|
"[1295]\tvalid_0's rmse: 0.0256943\n",
|
|||
|
|
"[1296]\tvalid_0's rmse: 0.0256935\n",
|
|||
|
|
"[1297]\tvalid_0's rmse: 0.0256928\n",
|
|||
|
|
"[1298]\tvalid_0's rmse: 0.0256922\n",
|
|||
|
|
"[1299]\tvalid_0's rmse: 0.0256921\n",
|
|||
|
|
"[1300]\tvalid_0's rmse: 0.0256929\n",
|
|||
|
|
"[1301]\tvalid_0's rmse: 0.0256929\n",
|
|||
|
|
"[1302]\tvalid_0's rmse: 0.0256922\n",
|
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"[1303]\tvalid_0's rmse: 0.0256922\n",
|
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"[1304]\tvalid_0's rmse: 0.0256903\n",
|
|||
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|
"[1305]\tvalid_0's rmse: 0.0256902\n",
|
|||
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|
"[1306]\tvalid_0's rmse: 0.025689\n",
|
|||
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|
"[1307]\tvalid_0's rmse: 0.0256867\n",
|
|||
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|
"[1308]\tvalid_0's rmse: 0.025687\n",
|
|||
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|
"[1309]\tvalid_0's rmse: 0.0256871\n",
|
|||
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|
"[1310]\tvalid_0's rmse: 0.0256871\n",
|
|||
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|
"[1311]\tvalid_0's rmse: 0.0256937\n",
|
|||
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|
"[1312]\tvalid_0's rmse: 0.0256927\n",
|
|||
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|
"[1313]\tvalid_0's rmse: 0.0256883\n",
|
|||
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|
"[1314]\tvalid_0's rmse: 0.0256881\n",
|
|||
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|
"[1315]\tvalid_0's rmse: 0.0256876\n",
|
|||
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|
"[1316]\tvalid_0's rmse: 0.0256871\n",
|
|||
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|
"[1317]\tvalid_0's rmse: 0.025685\n",
|
|||
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|
"[1318]\tvalid_0's rmse: 0.0256843\n",
|
|||
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|
"[1319]\tvalid_0's rmse: 0.0256852\n",
|
|||
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|
"[1320]\tvalid_0's rmse: 0.0256852\n",
|
|||
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|
"[1321]\tvalid_0's rmse: 0.0256852\n",
|
|||
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|
"[1322]\tvalid_0's rmse: 0.0256842\n",
|
|||
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|
"[1323]\tvalid_0's rmse: 0.0256825\n",
|
|||
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|
"[1324]\tvalid_0's rmse: 0.0256824\n",
|
|||
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|
"[1325]\tvalid_0's rmse: 0.0256792\n",
|
|||
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|
"[1326]\tvalid_0's rmse: 0.0256781\n",
|
|||
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|
"[1327]\tvalid_0's rmse: 0.0256776\n",
|
|||
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|
"[1328]\tvalid_0's rmse: 0.0256776\n",
|
|||
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|
"[1329]\tvalid_0's rmse: 0.0256782\n",
|
|||
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|
"[1330]\tvalid_0's rmse: 0.0256781\n",
|
|||
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|
"[1331]\tvalid_0's rmse: 0.0256777\n",
|
|||
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|
"[1332]\tvalid_0's rmse: 0.0256777\n",
|
|||
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"[1333]\tvalid_0's rmse: 0.0256772\n",
|
|||
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|
"[1334]\tvalid_0's rmse: 0.025677\n",
|
|||
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|
"[1335]\tvalid_0's rmse: 0.0256771\n",
|
|||
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|
"[1336]\tvalid_0's rmse: 0.0256768\n",
|
|||
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|
"[1337]\tvalid_0's rmse: 0.0256775\n",
|
|||
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|
"[1338]\tvalid_0's rmse: 0.0256776\n",
|
|||
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|
"[1339]\tvalid_0's rmse: 0.0256774\n",
|
|||
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|
"[1340]\tvalid_0's rmse: 0.0256753\n",
|
|||
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|
"[1341]\tvalid_0's rmse: 0.0256751\n",
|
|||
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|
"[1342]\tvalid_0's rmse: 0.0256747\n",
|
|||
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|
"[1343]\tvalid_0's rmse: 0.0256749\n",
|
|||
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|
"[1344]\tvalid_0's rmse: 0.0256746\n",
|
|||
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|
"[1345]\tvalid_0's rmse: 0.0256722\n",
|
|||
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|
"[1346]\tvalid_0's rmse: 0.0256697\n",
|
|||
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|
"[1347]\tvalid_0's rmse: 0.0256704\n",
|
|||
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|
"[1348]\tvalid_0's rmse: 0.0256681\n",
|
|||
|
|
"[1349]\tvalid_0's rmse: 0.025668\n",
|
|||
|
|
"[1350]\tvalid_0's rmse: 0.0256667\n",
|
|||
|
|
"[1351]\tvalid_0's rmse: 0.0256684\n",
|
|||
|
|
"[1352]\tvalid_0's rmse: 0.0256685\n",
|
|||
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|
"[1353]\tvalid_0's rmse: 0.0256673\n",
|
|||
|
|
"[1354]\tvalid_0's rmse: 0.0256673\n",
|
|||
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|
"[1355]\tvalid_0's rmse: 0.025667\n",
|
|||
|
|
"[1356]\tvalid_0's rmse: 0.0256675\n",
|
|||
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|
"[1357]\tvalid_0's rmse: 0.0256686\n",
|
|||
|
|
"[1358]\tvalid_0's rmse: 0.0256681\n",
|
|||
|
|
"[1359]\tvalid_0's rmse: 0.0256681\n",
|
|||
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|
"[1360]\tvalid_0's rmse: 0.0256682\n",
|
|||
|
|
"[1361]\tvalid_0's rmse: 0.025668\n",
|
|||
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|
"[1362]\tvalid_0's rmse: 0.0256671\n",
|
|||
|
|
"[1363]\tvalid_0's rmse: 0.0256675\n",
|
|||
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|
"[1364]\tvalid_0's rmse: 0.0256638\n",
|
|||
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|
"[1365]\tvalid_0's rmse: 0.0256638\n",
|
|||
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|
"[1366]\tvalid_0's rmse: 0.0256526\n",
|
|||
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"[1367]\tvalid_0's rmse: 0.0256534\n",
|
|||
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|
"[1368]\tvalid_0's rmse: 0.0256534\n",
|
|||
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|
"[1369]\tvalid_0's rmse: 0.025653\n",
|
|||
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|
"[1370]\tvalid_0's rmse: 0.0256528\n",
|
|||
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|
"[1371]\tvalid_0's rmse: 0.0256532\n",
|
|||
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|
"[1372]\tvalid_0's rmse: 0.025647\n",
|
|||
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|
"[1373]\tvalid_0's rmse: 0.0256454\n",
|
|||
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|
"[1374]\tvalid_0's rmse: 0.0256457\n",
|
|||
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|
"[1375]\tvalid_0's rmse: 0.0256426\n",
|
|||
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|
"[1376]\tvalid_0's rmse: 0.0256425\n",
|
|||
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|
"[1377]\tvalid_0's rmse: 0.0256441\n",
|
|||
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|
"[1378]\tvalid_0's rmse: 0.0256431\n",
|
|||
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|
"[1379]\tvalid_0's rmse: 0.0256452\n",
|
|||
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|
"[1380]\tvalid_0's rmse: 0.0256455\n",
|
|||
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|
"[1381]\tvalid_0's rmse: 0.0256454\n",
|
|||
|
|
"[1382]\tvalid_0's rmse: 0.0256441\n",
|
|||
|
|
"[1383]\tvalid_0's rmse: 0.0256446\n",
|
|||
|
|
"[1384]\tvalid_0's rmse: 0.0256443\n",
|
|||
|
|
"[1385]\tvalid_0's rmse: 0.0256444\n",
|
|||
|
|
"[1386]\tvalid_0's rmse: 0.0256445\n",
|
|||
|
|
"[1387]\tvalid_0's rmse: 0.0256436\n",
|
|||
|
|
"[1388]\tvalid_0's rmse: 0.0256418\n",
|
|||
|
|
"[1389]\tvalid_0's rmse: 0.0256422\n",
|
|||
|
|
"[1390]\tvalid_0's rmse: 0.0256363\n",
|
|||
|
|
"[1391]\tvalid_0's rmse: 0.0256359\n",
|
|||
|
|
"[1392]\tvalid_0's rmse: 0.0256348\n",
|
|||
|
|
"[1393]\tvalid_0's rmse: 0.0256345\n",
|
|||
|
|
"[1394]\tvalid_0's rmse: 0.0256347\n",
|
|||
|
|
"[1395]\tvalid_0's rmse: 0.025635\n",
|
|||
|
|
"[1396]\tvalid_0's rmse: 0.0256333\n",
|
|||
|
|
"[1397]\tvalid_0's rmse: 0.025633\n",
|
|||
|
|
"[1398]\tvalid_0's rmse: 0.025633\n",
|
|||
|
|
"[1399]\tvalid_0's rmse: 0.0256312\n",
|
|||
|
|
"[1400]\tvalid_0's rmse: 0.025631\n",
|
|||
|
|
"[1401]\tvalid_0's rmse: 0.025631\n",
|
|||
|
|
"[1402]\tvalid_0's rmse: 0.0256313\n",
|
|||
|
|
"[1403]\tvalid_0's rmse: 0.025627\n",
|
|||
|
|
"[1404]\tvalid_0's rmse: 0.0256275\n",
|
|||
|
|
"[1405]\tvalid_0's rmse: 0.0256277\n",
|
|||
|
|
"[1406]\tvalid_0's rmse: 0.0256274\n",
|
|||
|
|
"[1407]\tvalid_0's rmse: 0.0256277\n",
|
|||
|
|
"[1408]\tvalid_0's rmse: 0.0256266\n",
|
|||
|
|
"[1409]\tvalid_0's rmse: 0.025626\n",
|
|||
|
|
"[1410]\tvalid_0's rmse: 0.0256258\n",
|
|||
|
|
"[1411]\tvalid_0's rmse: 0.0256246\n",
|
|||
|
|
"[1412]\tvalid_0's rmse: 0.0256245\n",
|
|||
|
|
"[1413]\tvalid_0's rmse: 0.0256243\n",
|
|||
|
|
"[1414]\tvalid_0's rmse: 0.0256237\n",
|
|||
|
|
"[1415]\tvalid_0's rmse: 0.0256244\n",
|
|||
|
|
"[1416]\tvalid_0's rmse: 0.0256238\n",
|
|||
|
|
"[1417]\tvalid_0's rmse: 0.0256171\n",
|
|||
|
|
"[1418]\tvalid_0's rmse: 0.0256115\n",
|
|||
|
|
"[1419]\tvalid_0's rmse: 0.0256106\n",
|
|||
|
|
"[1420]\tvalid_0's rmse: 0.0256105\n",
|
|||
|
|
"[1421]\tvalid_0's rmse: 0.02561\n",
|
|||
|
|
"[1422]\tvalid_0's rmse: 0.0256113\n",
|
|||
|
|
"[1423]\tvalid_0's rmse: 0.0256111\n",
|
|||
|
|
"[1424]\tvalid_0's rmse: 0.025611\n",
|
|||
|
|
"[1425]\tvalid_0's rmse: 0.0256113\n",
|
|||
|
|
"[1426]\tvalid_0's rmse: 0.0256108\n",
|
|||
|
|
"[1427]\tvalid_0's rmse: 0.0256105\n",
|
|||
|
|
"[1428]\tvalid_0's rmse: 0.0256095\n",
|
|||
|
|
"[1429]\tvalid_0's rmse: 0.0256065\n",
|
|||
|
|
"[1430]\tvalid_0's rmse: 0.0256062\n",
|
|||
|
|
"[1431]\tvalid_0's rmse: 0.025607\n",
|
|||
|
|
"[1432]\tvalid_0's rmse: 0.0256074\n",
|
|||
|
|
"[1433]\tvalid_0's rmse: 0.025607\n",
|
|||
|
|
"[1434]\tvalid_0's rmse: 0.0256081\n",
|
|||
|
|
"[1435]\tvalid_0's rmse: 0.0256045\n",
|
|||
|
|
"[1436]\tvalid_0's rmse: 0.0256057\n",
|
|||
|
|
"[1437]\tvalid_0's rmse: 0.0256067\n",
|
|||
|
|
"[1438]\tvalid_0's rmse: 0.0256063\n",
|
|||
|
|
"[1439]\tvalid_0's rmse: 0.0256066\n",
|
|||
|
|
"[1440]\tvalid_0's rmse: 0.0256061\n",
|
|||
|
|
"[1441]\tvalid_0's rmse: 0.025605\n",
|
|||
|
|
"[1442]\tvalid_0's rmse: 0.0256045\n",
|
|||
|
|
"[1443]\tvalid_0's rmse: 0.0256032\n",
|
|||
|
|
"[1444]\tvalid_0's rmse: 0.0256063\n",
|
|||
|
|
"[1445]\tvalid_0's rmse: 0.0256076\n",
|
|||
|
|
"[1446]\tvalid_0's rmse: 0.025608\n",
|
|||
|
|
"[1447]\tvalid_0's rmse: 0.0256077\n",
|
|||
|
|
"[1448]\tvalid_0's rmse: 0.0256093\n",
|
|||
|
|
"[1449]\tvalid_0's rmse: 0.0256077\n",
|
|||
|
|
"[1450]\tvalid_0's rmse: 0.0256074\n",
|
|||
|
|
"[1451]\tvalid_0's rmse: 0.0256078\n",
|
|||
|
|
"[1452]\tvalid_0's rmse: 0.025608\n",
|
|||
|
|
"[1453]\tvalid_0's rmse: 0.0256081\n",
|
|||
|
|
"[1454]\tvalid_0's rmse: 0.0256081\n",
|
|||
|
|
"[1455]\tvalid_0's rmse: 0.0256079\n",
|
|||
|
|
"[1456]\tvalid_0's rmse: 0.0256087\n",
|
|||
|
|
"[1457]\tvalid_0's rmse: 0.0256062\n",
|
|||
|
|
"[1458]\tvalid_0's rmse: 0.025602\n",
|
|||
|
|
"[1459]\tvalid_0's rmse: 0.0256021\n",
|
|||
|
|
"[1460]\tvalid_0's rmse: 0.0256041\n",
|
|||
|
|
"[1461]\tvalid_0's rmse: 0.0256042\n",
|
|||
|
|
"[1462]\tvalid_0's rmse: 0.025605\n",
|
|||
|
|
"[1463]\tvalid_0's rmse: 0.0256056\n",
|
|||
|
|
"[1464]\tvalid_0's rmse: 0.0256053\n",
|
|||
|
|
"[1465]\tvalid_0's rmse: 0.0256077\n",
|
|||
|
|
"[1466]\tvalid_0's rmse: 0.0256076\n",
|
|||
|
|
"[1467]\tvalid_0's rmse: 0.0256083\n",
|
|||
|
|
"[1468]\tvalid_0's rmse: 0.0256082\n",
|
|||
|
|
"[1469]\tvalid_0's rmse: 0.0256074\n",
|
|||
|
|
"[1470]\tvalid_0's rmse: 0.0256074\n",
|
|||
|
|
"[1471]\tvalid_0's rmse: 0.025608\n",
|
|||
|
|
"[1472]\tvalid_0's rmse: 0.0256081\n",
|
|||
|
|
"[1473]\tvalid_0's rmse: 0.0256084\n",
|
|||
|
|
"[1474]\tvalid_0's rmse: 0.0256081\n",
|
|||
|
|
"[1475]\tvalid_0's rmse: 0.0256084\n",
|
|||
|
|
"[1476]\tvalid_0's rmse: 0.0256083\n",
|
|||
|
|
"[1477]\tvalid_0's rmse: 0.0256086\n",
|
|||
|
|
"[1478]\tvalid_0's rmse: 0.0256084\n",
|
|||
|
|
"[1479]\tvalid_0's rmse: 0.025608\n",
|
|||
|
|
"[1480]\tvalid_0's rmse: 0.02561\n",
|
|||
|
|
"[1481]\tvalid_0's rmse: 0.0256062\n",
|
|||
|
|
"[1482]\tvalid_0's rmse: 0.0256062\n",
|
|||
|
|
"[1483]\tvalid_0's rmse: 0.0256062\n",
|
|||
|
|
"[1484]\tvalid_0's rmse: 0.0256056\n",
|
|||
|
|
"[1485]\tvalid_0's rmse: 0.0256048\n",
|
|||
|
|
"[1486]\tvalid_0's rmse: 0.0256054\n",
|
|||
|
|
"[1487]\tvalid_0's rmse: 0.025605\n",
|
|||
|
|
"[1488]\tvalid_0's rmse: 0.0256026\n",
|
|||
|
|
"[1489]\tvalid_0's rmse: 0.0255999\n",
|
|||
|
|
"[1490]\tvalid_0's rmse: 0.0255993\n",
|
|||
|
|
"[1491]\tvalid_0's rmse: 0.0255995\n",
|
|||
|
|
"[1492]\tvalid_0's rmse: 0.0256009\n",
|
|||
|
|
"[1493]\tvalid_0's rmse: 0.0256006\n",
|
|||
|
|
"[1494]\tvalid_0's rmse: 0.0256027\n",
|
|||
|
|
"[1495]\tvalid_0's rmse: 0.0256021\n",
|
|||
|
|
"[1496]\tvalid_0's rmse: 0.0256017\n",
|
|||
|
|
"[1497]\tvalid_0's rmse: 0.0256016\n",
|
|||
|
|
"[1498]\tvalid_0's rmse: 0.0256018\n",
|
|||
|
|
"[1499]\tvalid_0's rmse: 0.0256011\n",
|
|||
|
|
"[1500]\tvalid_0's rmse: 0.025602\n",
|
|||
|
|
"[1501]\tvalid_0's rmse: 0.0256019\n",
|
|||
|
|
"[1502]\tvalid_0's rmse: 0.025602\n",
|
|||
|
|
"[1503]\tvalid_0's rmse: 0.0256027\n",
|
|||
|
|
"[1504]\tvalid_0's rmse: 0.0255921\n",
|
|||
|
|
"[1505]\tvalid_0's rmse: 0.0255919\n",
|
|||
|
|
"[1506]\tvalid_0's rmse: 0.025592\n",
|
|||
|
|
"[1507]\tvalid_0's rmse: 0.0255918\n",
|
|||
|
|
"[1508]\tvalid_0's rmse: 0.0255914\n",
|
|||
|
|
"[1509]\tvalid_0's rmse: 0.0255913\n",
|
|||
|
|
"[1510]\tvalid_0's rmse: 0.0255907\n",
|
|||
|
|
"[1511]\tvalid_0's rmse: 0.0255905\n",
|
|||
|
|
"[1512]\tvalid_0's rmse: 0.0255883\n",
|
|||
|
|
"[1513]\tvalid_0's rmse: 0.0255877\n",
|
|||
|
|
"[1514]\tvalid_0's rmse: 0.025587\n",
|
|||
|
|
"[1515]\tvalid_0's rmse: 0.0255873\n",
|
|||
|
|
"[1516]\tvalid_0's rmse: 0.025587\n",
|
|||
|
|
"[1517]\tvalid_0's rmse: 0.0255872\n",
|
|||
|
|
"[1518]\tvalid_0's rmse: 0.0255876\n",
|
|||
|
|
"[1519]\tvalid_0's rmse: 0.0255883\n",
|
|||
|
|
"[1520]\tvalid_0's rmse: 0.0255884\n",
|
|||
|
|
"[1521]\tvalid_0's rmse: 0.0255852\n",
|
|||
|
|
"[1522]\tvalid_0's rmse: 0.0255853\n",
|
|||
|
|
"[1523]\tvalid_0's rmse: 0.0255852\n",
|
|||
|
|
"[1524]\tvalid_0's rmse: 0.0255875\n",
|
|||
|
|
"[1525]\tvalid_0's rmse: 0.025588\n",
|
|||
|
|
"[1526]\tvalid_0's rmse: 0.0255894\n",
|
|||
|
|
"[1527]\tvalid_0's rmse: 0.0255891\n",
|
|||
|
|
"[1528]\tvalid_0's rmse: 0.0255891\n",
|
|||
|
|
"[1529]\tvalid_0's rmse: 0.0255892\n",
|
|||
|
|
"[1530]\tvalid_0's rmse: 0.0255908\n",
|
|||
|
|
"[1531]\tvalid_0's rmse: 0.0255902\n",
|
|||
|
|
"[1532]\tvalid_0's rmse: 0.0255903\n",
|
|||
|
|
"[1533]\tvalid_0's rmse: 0.0255905\n",
|
|||
|
|
"[1534]\tvalid_0's rmse: 0.0255906\n",
|
|||
|
|
"[1535]\tvalid_0's rmse: 0.0255913\n",
|
|||
|
|
"[1536]\tvalid_0's rmse: 0.0255906\n",
|
|||
|
|
"[1537]\tvalid_0's rmse: 0.0255919\n",
|
|||
|
|
"[1538]\tvalid_0's rmse: 0.0255919\n",
|
|||
|
|
"[1539]\tvalid_0's rmse: 0.0255936\n",
|
|||
|
|
"[1540]\tvalid_0's rmse: 0.025594\n",
|
|||
|
|
"[1541]\tvalid_0's rmse: 0.0255927\n",
|
|||
|
|
"[1542]\tvalid_0's rmse: 0.0255924\n",
|
|||
|
|
"[1543]\tvalid_0's rmse: 0.0255929\n",
|
|||
|
|
"[1544]\tvalid_0's rmse: 0.0255937\n",
|
|||
|
|
"[1545]\tvalid_0's rmse: 0.0255927\n",
|
|||
|
|
"[1546]\tvalid_0's rmse: 0.025592\n",
|
|||
|
|
"[1547]\tvalid_0's rmse: 0.0255914\n",
|
|||
|
|
"[1548]\tvalid_0's rmse: 0.0255914\n",
|
|||
|
|
"[1549]\tvalid_0's rmse: 0.0255913\n",
|
|||
|
|
"[1550]\tvalid_0's rmse: 0.0255909\n",
|
|||
|
|
"[1551]\tvalid_0's rmse: 0.0255915\n",
|
|||
|
|
"[1552]\tvalid_0's rmse: 0.0255916\n",
|
|||
|
|
"[1553]\tvalid_0's rmse: 0.0255916\n",
|
|||
|
|
"[1554]\tvalid_0's rmse: 0.0255915\n",
|
|||
|
|
"[1555]\tvalid_0's rmse: 0.0255921\n",
|
|||
|
|
"[1556]\tvalid_0's rmse: 0.0255909\n",
|
|||
|
|
"[1557]\tvalid_0's rmse: 0.0255908\n",
|
|||
|
|
"[1558]\tvalid_0's rmse: 0.0255916\n",
|
|||
|
|
"[1559]\tvalid_0's rmse: 0.0255904\n",
|
|||
|
|
"[1560]\tvalid_0's rmse: 0.0255898\n",
|
|||
|
|
"[1561]\tvalid_0's rmse: 0.0255908\n",
|
|||
|
|
"[1562]\tvalid_0's rmse: 0.0255909\n",
|
|||
|
|
"[1563]\tvalid_0's rmse: 0.0255911\n",
|
|||
|
|
"[1564]\tvalid_0's rmse: 0.0255908\n",
|
|||
|
|
"[1565]\tvalid_0's rmse: 0.0255928\n",
|
|||
|
|
"[1566]\tvalid_0's rmse: 0.0255909\n",
|
|||
|
|
"[1567]\tvalid_0's rmse: 0.0255908\n",
|
|||
|
|
"[1568]\tvalid_0's rmse: 0.0255925\n",
|
|||
|
|
"[1569]\tvalid_0's rmse: 0.0255903\n",
|
|||
|
|
"[1570]\tvalid_0's rmse: 0.0255904\n",
|
|||
|
|
"[1571]\tvalid_0's rmse: 0.0255902\n",
|
|||
|
|
"[1572]\tvalid_0's rmse: 0.0255895\n",
|
|||
|
|
"[1573]\tvalid_0's rmse: 0.0255941\n",
|
|||
|
|
"[1574]\tvalid_0's rmse: 0.025596\n",
|
|||
|
|
"[1575]\tvalid_0's rmse: 0.0255966\n",
|
|||
|
|
"[1576]\tvalid_0's rmse: 0.0255966\n",
|
|||
|
|
"[1577]\tvalid_0's rmse: 0.0255965\n",
|
|||
|
|
"[1578]\tvalid_0's rmse: 0.0255957\n",
|
|||
|
|
"[1579]\tvalid_0's rmse: 0.0255949\n",
|
|||
|
|
"[1580]\tvalid_0's rmse: 0.0255931\n",
|
|||
|
|
"[1581]\tvalid_0's rmse: 0.0255936\n",
|
|||
|
|
"[1582]\tvalid_0's rmse: 0.0255936\n",
|
|||
|
|
"[1583]\tvalid_0's rmse: 0.0255941\n",
|
|||
|
|
"[1584]\tvalid_0's rmse: 0.0255942\n",
|
|||
|
|
"[1585]\tvalid_0's rmse: 0.0255976\n",
|
|||
|
|
"[1586]\tvalid_0's rmse: 0.0255974\n",
|
|||
|
|
"[1587]\tvalid_0's rmse: 0.0255956\n",
|
|||
|
|
"[1588]\tvalid_0's rmse: 0.025595\n",
|
|||
|
|
"[1589]\tvalid_0's rmse: 0.0255943\n",
|
|||
|
|
"[1590]\tvalid_0's rmse: 0.0255946\n",
|
|||
|
|
"[1591]\tvalid_0's rmse: 0.0255945\n",
|
|||
|
|
"[1592]\tvalid_0's rmse: 0.0255938\n",
|
|||
|
|
"[1593]\tvalid_0's rmse: 0.0255907\n",
|
|||
|
|
"[1594]\tvalid_0's rmse: 0.0255832\n",
|
|||
|
|
"[1595]\tvalid_0's rmse: 0.0255833\n",
|
|||
|
|
"[1596]\tvalid_0's rmse: 0.0255824\n",
|
|||
|
|
"[1597]\tvalid_0's rmse: 0.025583\n",
|
|||
|
|
"[1598]\tvalid_0's rmse: 0.0255812\n",
|
|||
|
|
"[1599]\tvalid_0's rmse: 0.0255811\n",
|
|||
|
|
"[1600]\tvalid_0's rmse: 0.0255808\n",
|
|||
|
|
"[1601]\tvalid_0's rmse: 0.0255761\n",
|
|||
|
|
"[1602]\tvalid_0's rmse: 0.0255687\n",
|
|||
|
|
"[1603]\tvalid_0's rmse: 0.0255698\n",
|
|||
|
|
"[1604]\tvalid_0's rmse: 0.0255697\n",
|
|||
|
|
"[1605]\tvalid_0's rmse: 0.0255691\n",
|
|||
|
|
"[1606]\tvalid_0's rmse: 0.0255697\n",
|
|||
|
|
"[1607]\tvalid_0's rmse: 0.0255554\n",
|
|||
|
|
"[1608]\tvalid_0's rmse: 0.0255555\n",
|
|||
|
|
"[1609]\tvalid_0's rmse: 0.0255572\n",
|
|||
|
|
"[1610]\tvalid_0's rmse: 0.0255572\n",
|
|||
|
|
"[1611]\tvalid_0's rmse: 0.0255571\n",
|
|||
|
|
"[1612]\tvalid_0's rmse: 0.0255571\n",
|
|||
|
|
"[1613]\tvalid_0's rmse: 0.0255573\n",
|
|||
|
|
"[1614]\tvalid_0's rmse: 0.0255553\n",
|
|||
|
|
"[1615]\tvalid_0's rmse: 0.0255563\n",
|
|||
|
|
"[1616]\tvalid_0's rmse: 0.0255559\n",
|
|||
|
|
"[1617]\tvalid_0's rmse: 0.0255553\n",
|
|||
|
|
"[1618]\tvalid_0's rmse: 0.0255544\n",
|
|||
|
|
"[1619]\tvalid_0's rmse: 0.0255544\n",
|
|||
|
|
"[1620]\tvalid_0's rmse: 0.0255537\n",
|
|||
|
|
"[1621]\tvalid_0's rmse: 0.0255486\n",
|
|||
|
|
"[1622]\tvalid_0's rmse: 0.0255496\n",
|
|||
|
|
"[1623]\tvalid_0's rmse: 0.0255495\n",
|
|||
|
|
"[1624]\tvalid_0's rmse: 0.0255509\n",
|
|||
|
|
"[1625]\tvalid_0's rmse: 0.0255513\n",
|
|||
|
|
"[1626]\tvalid_0's rmse: 0.0255499\n",
|
|||
|
|
"[1627]\tvalid_0's rmse: 0.0255497\n",
|
|||
|
|
"[1628]\tvalid_0's rmse: 0.0255489\n",
|
|||
|
|
"[1629]\tvalid_0's rmse: 0.0255457\n",
|
|||
|
|
"[1630]\tvalid_0's rmse: 0.0255384\n",
|
|||
|
|
"[1631]\tvalid_0's rmse: 0.0255383\n",
|
|||
|
|
"[1632]\tvalid_0's rmse: 0.0255377\n",
|
|||
|
|
"[1633]\tvalid_0's rmse: 0.025538\n",
|
|||
|
|
"[1634]\tvalid_0's rmse: 0.0255383\n",
|
|||
|
|
"[1635]\tvalid_0's rmse: 0.0255381\n",
|
|||
|
|
"[1636]\tvalid_0's rmse: 0.0255379\n",
|
|||
|
|
"[1637]\tvalid_0's rmse: 0.0255386\n",
|
|||
|
|
"[1638]\tvalid_0's rmse: 0.0255391\n",
|
|||
|
|
"[1639]\tvalid_0's rmse: 0.0255386\n",
|
|||
|
|
"[1640]\tvalid_0's rmse: 0.0255322\n",
|
|||
|
|
"[1641]\tvalid_0's rmse: 0.0255328\n",
|
|||
|
|
"[1642]\tvalid_0's rmse: 0.0255273\n",
|
|||
|
|
"[1643]\tvalid_0's rmse: 0.0255264\n",
|
|||
|
|
"[1644]\tvalid_0's rmse: 0.0255262\n",
|
|||
|
|
"[1645]\tvalid_0's rmse: 0.0255239\n",
|
|||
|
|
"[1646]\tvalid_0's rmse: 0.0255234\n",
|
|||
|
|
"[1647]\tvalid_0's rmse: 0.0255245\n",
|
|||
|
|
"[1648]\tvalid_0's rmse: 0.0255188\n",
|
|||
|
|
"[1649]\tvalid_0's rmse: 0.0255174\n",
|
|||
|
|
"[1650]\tvalid_0's rmse: 0.0255231\n",
|
|||
|
|
"[1651]\tvalid_0's rmse: 0.0255231\n",
|
|||
|
|
"[1652]\tvalid_0's rmse: 0.0255237\n",
|
|||
|
|
"[1653]\tvalid_0's rmse: 0.0255217\n",
|
|||
|
|
"[1654]\tvalid_0's rmse: 0.025521\n",
|
|||
|
|
"[1655]\tvalid_0's rmse: 0.0255201\n",
|
|||
|
|
"[1656]\tvalid_0's rmse: 0.02552\n",
|
|||
|
|
"[1657]\tvalid_0's rmse: 0.0255204\n",
|
|||
|
|
"[1658]\tvalid_0's rmse: 0.0255194\n",
|
|||
|
|
"[1659]\tvalid_0's rmse: 0.0255194\n",
|
|||
|
|
"[1660]\tvalid_0's rmse: 0.0255194\n",
|
|||
|
|
"[1661]\tvalid_0's rmse: 0.0255189\n",
|
|||
|
|
"[1662]\tvalid_0's rmse: 0.0255192\n",
|
|||
|
|
"[1663]\tvalid_0's rmse: 0.0255183\n",
|
|||
|
|
"[1664]\tvalid_0's rmse: 0.0255186\n",
|
|||
|
|
"[1665]\tvalid_0's rmse: 0.0255179\n",
|
|||
|
|
"[1666]\tvalid_0's rmse: 0.0255182\n",
|
|||
|
|
"[1667]\tvalid_0's rmse: 0.0255178\n",
|
|||
|
|
"[1668]\tvalid_0's rmse: 0.0255175\n",
|
|||
|
|
"[1669]\tvalid_0's rmse: 0.0255181\n",
|
|||
|
|
"[1670]\tvalid_0's rmse: 0.0255179\n",
|
|||
|
|
"[1671]\tvalid_0's rmse: 0.025517\n",
|
|||
|
|
"[1672]\tvalid_0's rmse: 0.0255169\n",
|
|||
|
|
"[1673]\tvalid_0's rmse: 0.0255012\n",
|
|||
|
|
"[1674]\tvalid_0's rmse: 0.0255018\n",
|
|||
|
|
"[1675]\tvalid_0's rmse: 0.0255017\n",
|
|||
|
|
"[1676]\tvalid_0's rmse: 0.0255032\n",
|
|||
|
|
"[1677]\tvalid_0's rmse: 0.0255028\n",
|
|||
|
|
"[1678]\tvalid_0's rmse: 0.0255035\n",
|
|||
|
|
"[1679]\tvalid_0's rmse: 0.0255038\n",
|
|||
|
|
"[1680]\tvalid_0's rmse: 0.0255043\n",
|
|||
|
|
"[1681]\tvalid_0's rmse: 0.0255043\n",
|
|||
|
|
"[1682]\tvalid_0's rmse: 0.0255052\n",
|
|||
|
|
"[1683]\tvalid_0's rmse: 0.0255043\n",
|
|||
|
|
"[1684]\tvalid_0's rmse: 0.0255045\n",
|
|||
|
|
"[1685]\tvalid_0's rmse: 0.0255044\n",
|
|||
|
|
"[1686]\tvalid_0's rmse: 0.0255039\n",
|
|||
|
|
"[1687]\tvalid_0's rmse: 0.0255027\n",
|
|||
|
|
"[1688]\tvalid_0's rmse: 0.0255026\n",
|
|||
|
|
"[1689]\tvalid_0's rmse: 0.0255028\n",
|
|||
|
|
"[1690]\tvalid_0's rmse: 0.0255036\n",
|
|||
|
|
"[1691]\tvalid_0's rmse: 0.0255024\n",
|
|||
|
|
"[1692]\tvalid_0's rmse: 0.0255021\n",
|
|||
|
|
"[1693]\tvalid_0's rmse: 0.0255018\n",
|
|||
|
|
"[1694]\tvalid_0's rmse: 0.0255018\n",
|
|||
|
|
"[1695]\tvalid_0's rmse: 0.0255012\n",
|
|||
|
|
"[1696]\tvalid_0's rmse: 0.0255006\n",
|
|||
|
|
"[1697]\tvalid_0's rmse: 0.0255006\n",
|
|||
|
|
"[1698]\tvalid_0's rmse: 0.0255005\n",
|
|||
|
|
"[1699]\tvalid_0's rmse: 0.0254974\n",
|
|||
|
|
"[1700]\tvalid_0's rmse: 0.0254964\n",
|
|||
|
|
"[1701]\tvalid_0's rmse: 0.0254971\n",
|
|||
|
|
"[1702]\tvalid_0's rmse: 0.0254974\n",
|
|||
|
|
"[1703]\tvalid_0's rmse: 0.0254974\n",
|
|||
|
|
"[1704]\tvalid_0's rmse: 0.0254945\n",
|
|||
|
|
"[1705]\tvalid_0's rmse: 0.0254948\n",
|
|||
|
|
"[1706]\tvalid_0's rmse: 0.0254947\n",
|
|||
|
|
"[1707]\tvalid_0's rmse: 0.025495\n",
|
|||
|
|
"[1708]\tvalid_0's rmse: 0.0254952\n",
|
|||
|
|
"[1709]\tvalid_0's rmse: 0.025495\n",
|
|||
|
|
"[1710]\tvalid_0's rmse: 0.0254946\n",
|
|||
|
|
"[1711]\tvalid_0's rmse: 0.0254946\n",
|
|||
|
|
"[1712]\tvalid_0's rmse: 0.0254923\n",
|
|||
|
|
"[1713]\tvalid_0's rmse: 0.0254919\n",
|
|||
|
|
"[1714]\tvalid_0's rmse: 0.0254932\n",
|
|||
|
|
"[1715]\tvalid_0's rmse: 0.025493\n",
|
|||
|
|
"[1716]\tvalid_0's rmse: 0.0254935\n",
|
|||
|
|
"[1717]\tvalid_0's rmse: 0.025492\n",
|
|||
|
|
"[1718]\tvalid_0's rmse: 0.0254914\n",
|
|||
|
|
"[1719]\tvalid_0's rmse: 0.0254918\n",
|
|||
|
|
"[1720]\tvalid_0's rmse: 0.0254917\n",
|
|||
|
|
"[1721]\tvalid_0's rmse: 0.0254922\n",
|
|||
|
|
"[1722]\tvalid_0's rmse: 0.0254925\n",
|
|||
|
|
"[1723]\tvalid_0's rmse: 0.0254928\n",
|
|||
|
|
"[1724]\tvalid_0's rmse: 0.0254932\n",
|
|||
|
|
"[1725]\tvalid_0's rmse: 0.0254931\n",
|
|||
|
|
"[1726]\tvalid_0's rmse: 0.0254933\n",
|
|||
|
|
"[1727]\tvalid_0's rmse: 0.0254931\n",
|
|||
|
|
"[1728]\tvalid_0's rmse: 0.0254962\n",
|
|||
|
|
"[1729]\tvalid_0's rmse: 0.0254961\n",
|
|||
|
|
"[1730]\tvalid_0's rmse: 0.0254956\n",
|
|||
|
|
"[1731]\tvalid_0's rmse: 0.025495\n",
|
|||
|
|
"[1732]\tvalid_0's rmse: 0.0254947\n",
|
|||
|
|
"[1733]\tvalid_0's rmse: 0.0254938\n",
|
|||
|
|
"[1734]\tvalid_0's rmse: 0.0254942\n",
|
|||
|
|
"[1735]\tvalid_0's rmse: 0.0254946\n",
|
|||
|
|
"[1736]\tvalid_0's rmse: 0.0254936\n",
|
|||
|
|
"[1737]\tvalid_0's rmse: 0.0254922\n",
|
|||
|
|
"[1738]\tvalid_0's rmse: 0.0254917\n",
|
|||
|
|
"[1739]\tvalid_0's rmse: 0.025492\n",
|
|||
|
|
"[1740]\tvalid_0's rmse: 0.025492\n",
|
|||
|
|
"[1741]\tvalid_0's rmse: 0.0254923\n",
|
|||
|
|
"[1742]\tvalid_0's rmse: 0.0254932\n",
|
|||
|
|
"[1743]\tvalid_0's rmse: 0.0254933\n",
|
|||
|
|
"[1744]\tvalid_0's rmse: 0.0254935\n",
|
|||
|
|
"[1745]\tvalid_0's rmse: 0.0254933\n",
|
|||
|
|
"[1746]\tvalid_0's rmse: 0.0254937\n",
|
|||
|
|
"[1747]\tvalid_0's rmse: 0.0254928\n",
|
|||
|
|
"[1748]\tvalid_0's rmse: 0.0254926\n",
|
|||
|
|
"[1749]\tvalid_0's rmse: 0.0254945\n",
|
|||
|
|
"[1750]\tvalid_0's rmse: 0.0254948\n",
|
|||
|
|
"[1751]\tvalid_0's rmse: 0.025495\n",
|
|||
|
|
"[1752]\tvalid_0's rmse: 0.025487\n",
|
|||
|
|
"[1753]\tvalid_0's rmse: 0.0254868\n",
|
|||
|
|
"[1754]\tvalid_0's rmse: 0.025486\n",
|
|||
|
|
"[1755]\tvalid_0's rmse: 0.0254842\n",
|
|||
|
|
"[1756]\tvalid_0's rmse: 0.0254837\n",
|
|||
|
|
"[1757]\tvalid_0's rmse: 0.025483\n",
|
|||
|
|
"[1758]\tvalid_0's rmse: 0.0254827\n",
|
|||
|
|
"[1759]\tvalid_0's rmse: 0.0254805\n",
|
|||
|
|
"[1760]\tvalid_0's rmse: 0.02548\n",
|
|||
|
|
"[1761]\tvalid_0's rmse: 0.0254799\n",
|
|||
|
|
"[1762]\tvalid_0's rmse: 0.0254799\n",
|
|||
|
|
"[1763]\tvalid_0's rmse: 0.0254794\n",
|
|||
|
|
"[1764]\tvalid_0's rmse: 0.0254783\n",
|
|||
|
|
"[1765]\tvalid_0's rmse: 0.0254772\n",
|
|||
|
|
"[1766]\tvalid_0's rmse: 0.0254773\n",
|
|||
|
|
"[1767]\tvalid_0's rmse: 0.0254773\n",
|
|||
|
|
"[1768]\tvalid_0's rmse: 0.0254767\n",
|
|||
|
|
"[1769]\tvalid_0's rmse: 0.0254775\n",
|
|||
|
|
"[1770]\tvalid_0's rmse: 0.0254774\n",
|
|||
|
|
"[1771]\tvalid_0's rmse: 0.0254775\n",
|
|||
|
|
"[1772]\tvalid_0's rmse: 0.0254769\n",
|
|||
|
|
"[1773]\tvalid_0's rmse: 0.025477\n",
|
|||
|
|
"[1774]\tvalid_0's rmse: 0.0254779\n",
|
|||
|
|
"[1775]\tvalid_0's rmse: 0.025477\n",
|
|||
|
|
"[1776]\tvalid_0's rmse: 0.0254767\n",
|
|||
|
|
"[1777]\tvalid_0's rmse: 0.025474\n",
|
|||
|
|
"[1778]\tvalid_0's rmse: 0.0254756\n",
|
|||
|
|
"[1779]\tvalid_0's rmse: 0.0254761\n",
|
|||
|
|
"[1780]\tvalid_0's rmse: 0.025476\n",
|
|||
|
|
"[1781]\tvalid_0's rmse: 0.0254763\n",
|
|||
|
|
"[1782]\tvalid_0's rmse: 0.0254763\n",
|
|||
|
|
"[1783]\tvalid_0's rmse: 0.0254762\n",
|
|||
|
|
"[1784]\tvalid_0's rmse: 0.0254749\n",
|
|||
|
|
"[1785]\tvalid_0's rmse: 0.025473\n",
|
|||
|
|
"[1786]\tvalid_0's rmse: 0.0254723\n",
|
|||
|
|
"[1787]\tvalid_0's rmse: 0.0254712\n",
|
|||
|
|
"[1788]\tvalid_0's rmse: 0.0254711\n",
|
|||
|
|
"[1789]\tvalid_0's rmse: 0.0254718\n",
|
|||
|
|
"[1790]\tvalid_0's rmse: 0.0254716\n",
|
|||
|
|
"[1791]\tvalid_0's rmse: 0.0254721\n",
|
|||
|
|
"[1792]\tvalid_0's rmse: 0.0254709\n",
|
|||
|
|
"[1793]\tvalid_0's rmse: 0.0254738\n",
|
|||
|
|
"[1794]\tvalid_0's rmse: 0.0254739\n",
|
|||
|
|
"[1795]\tvalid_0's rmse: 0.025474\n",
|
|||
|
|
"[1796]\tvalid_0's rmse: 0.0254719\n",
|
|||
|
|
"[1797]\tvalid_0's rmse: 0.0254719\n",
|
|||
|
|
"[1798]\tvalid_0's rmse: 0.0254734\n",
|
|||
|
|
"[1799]\tvalid_0's rmse: 0.0254738\n",
|
|||
|
|
"[1800]\tvalid_0's rmse: 0.0254739\n",
|
|||
|
|
"[1801]\tvalid_0's rmse: 0.0254722\n",
|
|||
|
|
"[1802]\tvalid_0's rmse: 0.0254725\n",
|
|||
|
|
"[1803]\tvalid_0's rmse: 0.0254716\n",
|
|||
|
|
"[1804]\tvalid_0's rmse: 0.0254717\n",
|
|||
|
|
"[1805]\tvalid_0's rmse: 0.0254718\n",
|
|||
|
|
"[1806]\tvalid_0's rmse: 0.025471\n",
|
|||
|
|
"[1807]\tvalid_0's rmse: 0.0254714\n",
|
|||
|
|
"[1808]\tvalid_0's rmse: 0.0254714\n",
|
|||
|
|
"[1809]\tvalid_0's rmse: 0.0254713\n",
|
|||
|
|
"[1810]\tvalid_0's rmse: 0.0254711\n",
|
|||
|
|
"[1811]\tvalid_0's rmse: 0.0254716\n",
|
|||
|
|
"[1812]\tvalid_0's rmse: 0.025472\n",
|
|||
|
|
"[1813]\tvalid_0's rmse: 0.0254719\n",
|
|||
|
|
"[1814]\tvalid_0's rmse: 0.0254712\n",
|
|||
|
|
"[1815]\tvalid_0's rmse: 0.0254712\n",
|
|||
|
|
"[1816]\tvalid_0's rmse: 0.0254708\n",
|
|||
|
|
"[1817]\tvalid_0's rmse: 0.0254711\n",
|
|||
|
|
"[1818]\tvalid_0's rmse: 0.0254701\n",
|
|||
|
|
"[1819]\tvalid_0's rmse: 0.0254683\n",
|
|||
|
|
"[1820]\tvalid_0's rmse: 0.0254685\n",
|
|||
|
|
"[1821]\tvalid_0's rmse: 0.0254685\n",
|
|||
|
|
"[1822]\tvalid_0's rmse: 0.0254687\n",
|
|||
|
|
"[1823]\tvalid_0's rmse: 0.0254688\n",
|
|||
|
|
"[1824]\tvalid_0's rmse: 0.0254686\n",
|
|||
|
|
"[1825]\tvalid_0's rmse: 0.0254686\n",
|
|||
|
|
"[1826]\tvalid_0's rmse: 0.0254685\n",
|
|||
|
|
"[1827]\tvalid_0's rmse: 0.0254681\n",
|
|||
|
|
"[1828]\tvalid_0's rmse: 0.0254681\n",
|
|||
|
|
"[1829]\tvalid_0's rmse: 0.025468\n",
|
|||
|
|
"[1830]\tvalid_0's rmse: 0.0254683\n",
|
|||
|
|
"[1831]\tvalid_0's rmse: 0.025464\n",
|
|||
|
|
"[1832]\tvalid_0's rmse: 0.0254641\n",
|
|||
|
|
"[1833]\tvalid_0's rmse: 0.0254636\n",
|
|||
|
|
"[1834]\tvalid_0's rmse: 0.0254633\n",
|
|||
|
|
"[1835]\tvalid_0's rmse: 0.0254625\n",
|
|||
|
|
"[1836]\tvalid_0's rmse: 0.0254622\n",
|
|||
|
|
"[1837]\tvalid_0's rmse: 0.0254617\n",
|
|||
|
|
"[1838]\tvalid_0's rmse: 0.0254617\n",
|
|||
|
|
"[1839]\tvalid_0's rmse: 0.0254609\n",
|
|||
|
|
"[1840]\tvalid_0's rmse: 0.025452\n",
|
|||
|
|
"[1841]\tvalid_0's rmse: 0.0254516\n",
|
|||
|
|
"[1842]\tvalid_0's rmse: 0.0254517\n",
|
|||
|
|
"[1843]\tvalid_0's rmse: 0.0254523\n",
|
|||
|
|
"[1844]\tvalid_0's rmse: 0.0254516\n",
|
|||
|
|
"[1845]\tvalid_0's rmse: 0.0254519\n",
|
|||
|
|
"[1846]\tvalid_0's rmse: 0.0254519\n",
|
|||
|
|
"[1847]\tvalid_0's rmse: 0.0254506\n",
|
|||
|
|
"[1848]\tvalid_0's rmse: 0.0254508\n",
|
|||
|
|
"[1849]\tvalid_0's rmse: 0.0254503\n",
|
|||
|
|
"[1850]\tvalid_0's rmse: 0.0254484\n",
|
|||
|
|
"[1851]\tvalid_0's rmse: 0.0254485\n",
|
|||
|
|
"[1852]\tvalid_0's rmse: 0.0254486\n",
|
|||
|
|
"[1853]\tvalid_0's rmse: 0.0254492\n",
|
|||
|
|
"[1854]\tvalid_0's rmse: 0.0254493\n",
|
|||
|
|
"[1855]\tvalid_0's rmse: 0.0254488\n",
|
|||
|
|
"[1856]\tvalid_0's rmse: 0.0254492\n",
|
|||
|
|
"[1857]\tvalid_0's rmse: 0.0254538\n",
|
|||
|
|
"[1858]\tvalid_0's rmse: 0.0254541\n",
|
|||
|
|
"[1859]\tvalid_0's rmse: 0.0254591\n",
|
|||
|
|
"[1860]\tvalid_0's rmse: 0.0254593\n",
|
|||
|
|
"[1861]\tvalid_0's rmse: 0.0254593\n",
|
|||
|
|
"[1862]\tvalid_0's rmse: 0.0254589\n",
|
|||
|
|
"[1863]\tvalid_0's rmse: 0.0254589\n",
|
|||
|
|
"[1864]\tvalid_0's rmse: 0.0254596\n",
|
|||
|
|
"[1865]\tvalid_0's rmse: 0.0254593\n",
|
|||
|
|
"[1866]\tvalid_0's rmse: 0.02546\n",
|
|||
|
|
"[1867]\tvalid_0's rmse: 0.0254596\n",
|
|||
|
|
"[1868]\tvalid_0's rmse: 0.0254609\n",
|
|||
|
|
"[1869]\tvalid_0's rmse: 0.0254586\n",
|
|||
|
|
"[1870]\tvalid_0's rmse: 0.0254583\n",
|
|||
|
|
"[1871]\tvalid_0's rmse: 0.0254584\n",
|
|||
|
|
"[1872]\tvalid_0's rmse: 0.0254582\n",
|
|||
|
|
"[1873]\tvalid_0's rmse: 0.025458\n",
|
|||
|
|
"[1874]\tvalid_0's rmse: 0.0254559\n",
|
|||
|
|
"[1875]\tvalid_0's rmse: 0.0254556\n",
|
|||
|
|
"[1876]\tvalid_0's rmse: 0.0254552\n",
|
|||
|
|
"[1877]\tvalid_0's rmse: 0.0254551\n",
|
|||
|
|
"[1878]\tvalid_0's rmse: 0.0254557\n",
|
|||
|
|
"[1879]\tvalid_0's rmse: 0.0254539\n",
|
|||
|
|
"[1880]\tvalid_0's rmse: 0.0254533\n",
|
|||
|
|
"[1881]\tvalid_0's rmse: 0.0254524\n",
|
|||
|
|
"[1882]\tvalid_0's rmse: 0.0254525\n",
|
|||
|
|
"[1883]\tvalid_0's rmse: 0.0254542\n",
|
|||
|
|
"[1884]\tvalid_0's rmse: 0.0254548\n",
|
|||
|
|
"[1885]\tvalid_0's rmse: 0.0254539\n",
|
|||
|
|
"[1886]\tvalid_0's rmse: 0.0254536\n",
|
|||
|
|
"[1887]\tvalid_0's rmse: 0.0254537\n",
|
|||
|
|
"[1888]\tvalid_0's rmse: 0.0254532\n",
|
|||
|
|
"[1889]\tvalid_0's rmse: 0.0254555\n",
|
|||
|
|
"[1890]\tvalid_0's rmse: 0.0254548\n",
|
|||
|
|
"[1891]\tvalid_0's rmse: 0.0254549\n",
|
|||
|
|
"[1892]\tvalid_0's rmse: 0.0254548\n",
|
|||
|
|
"[1893]\tvalid_0's rmse: 0.0254545\n",
|
|||
|
|
"[1894]\tvalid_0's rmse: 0.0254543\n",
|
|||
|
|
"[1895]\tvalid_0's rmse: 0.0254553\n",
|
|||
|
|
"[1896]\tvalid_0's rmse: 0.0254551\n",
|
|||
|
|
"[1897]\tvalid_0's rmse: 0.0254553\n",
|
|||
|
|
"[1898]\tvalid_0's rmse: 0.0254557\n",
|
|||
|
|
"[1899]\tvalid_0's rmse: 0.0254553\n",
|
|||
|
|
"[1900]\tvalid_0's rmse: 0.0254554\n",
|
|||
|
|
"[1901]\tvalid_0's rmse: 0.025455\n",
|
|||
|
|
"[1902]\tvalid_0's rmse: 0.0254548\n",
|
|||
|
|
"[1903]\tvalid_0's rmse: 0.0254559\n",
|
|||
|
|
"[1904]\tvalid_0's rmse: 0.025455\n",
|
|||
|
|
"[1905]\tvalid_0's rmse: 0.0254548\n",
|
|||
|
|
"[1906]\tvalid_0's rmse: 0.0254548\n",
|
|||
|
|
"[1907]\tvalid_0's rmse: 0.025454\n",
|
|||
|
|
"[1908]\tvalid_0's rmse: 0.0254535\n",
|
|||
|
|
"[1909]\tvalid_0's rmse: 0.0254534\n",
|
|||
|
|
"[1910]\tvalid_0's rmse: 0.0254536\n",
|
|||
|
|
"[1911]\tvalid_0's rmse: 0.0254536\n",
|
|||
|
|
"[1912]\tvalid_0's rmse: 0.0254531\n",
|
|||
|
|
"[1913]\tvalid_0's rmse: 0.0254532\n",
|
|||
|
|
"[1914]\tvalid_0's rmse: 0.0254535\n",
|
|||
|
|
"[1915]\tvalid_0's rmse: 0.0254525\n",
|
|||
|
|
"[1916]\tvalid_0's rmse: 0.025452\n",
|
|||
|
|
"[1917]\tvalid_0's rmse: 0.0254519\n",
|
|||
|
|
"[1918]\tvalid_0's rmse: 0.0254518\n",
|
|||
|
|
"[1919]\tvalid_0's rmse: 0.0254515\n",
|
|||
|
|
"[1920]\tvalid_0's rmse: 0.0254513\n",
|
|||
|
|
"[1921]\tvalid_0's rmse: 0.0254524\n",
|
|||
|
|
"[1922]\tvalid_0's rmse: 0.0254529\n",
|
|||
|
|
"[1923]\tvalid_0's rmse: 0.0254551\n",
|
|||
|
|
"[1924]\tvalid_0's rmse: 0.0254534\n",
|
|||
|
|
"[1925]\tvalid_0's rmse: 0.0254535\n",
|
|||
|
|
"[1926]\tvalid_0's rmse: 0.0254536\n",
|
|||
|
|
"[1927]\tvalid_0's rmse: 0.0254536\n",
|
|||
|
|
"[1928]\tvalid_0's rmse: 0.0254538\n",
|
|||
|
|
"[1929]\tvalid_0's rmse: 0.0254538\n",
|
|||
|
|
"[1930]\tvalid_0's rmse: 0.0254529\n",
|
|||
|
|
"[1931]\tvalid_0's rmse: 0.0254529\n",
|
|||
|
|
"[1932]\tvalid_0's rmse: 0.0254527\n",
|
|||
|
|
"[1933]\tvalid_0's rmse: 0.0254525\n",
|
|||
|
|
"[1934]\tvalid_0's rmse: 0.0254524\n",
|
|||
|
|
"[1935]\tvalid_0's rmse: 0.0254518\n",
|
|||
|
|
"[1936]\tvalid_0's rmse: 0.0254518\n",
|
|||
|
|
"[1937]\tvalid_0's rmse: 0.0254518\n",
|
|||
|
|
"[1938]\tvalid_0's rmse: 0.0254512\n",
|
|||
|
|
"[1939]\tvalid_0's rmse: 0.0254511\n",
|
|||
|
|
"[1940]\tvalid_0's rmse: 0.0254517\n",
|
|||
|
|
"[1941]\tvalid_0's rmse: 0.0254514\n",
|
|||
|
|
"[1942]\tvalid_0's rmse: 0.0254517\n",
|
|||
|
|
"[1943]\tvalid_0's rmse: 0.0254503\n",
|
|||
|
|
"[1944]\tvalid_0's rmse: 0.0254474\n",
|
|||
|
|
"[1945]\tvalid_0's rmse: 0.0254471\n",
|
|||
|
|
"[1946]\tvalid_0's rmse: 0.0254472\n",
|
|||
|
|
"[1947]\tvalid_0's rmse: 0.0254473\n",
|
|||
|
|
"[1948]\tvalid_0's rmse: 0.0254469\n",
|
|||
|
|
"[1949]\tvalid_0's rmse: 0.0254462\n",
|
|||
|
|
"[1950]\tvalid_0's rmse: 0.0254464\n",
|
|||
|
|
"[1951]\tvalid_0's rmse: 0.025446\n",
|
|||
|
|
"[1952]\tvalid_0's rmse: 0.025446\n",
|
|||
|
|
"[1953]\tvalid_0's rmse: 0.0254422\n",
|
|||
|
|
"[1954]\tvalid_0's rmse: 0.0254356\n",
|
|||
|
|
"[1955]\tvalid_0's rmse: 0.0254358\n",
|
|||
|
|
"[1956]\tvalid_0's rmse: 0.0254357\n",
|
|||
|
|
"[1957]\tvalid_0's rmse: 0.0254344\n",
|
|||
|
|
"[1958]\tvalid_0's rmse: 0.0254348\n",
|
|||
|
|
"[1959]\tvalid_0's rmse: 0.0254348\n",
|
|||
|
|
"[1960]\tvalid_0's rmse: 0.0254347\n",
|
|||
|
|
"[1961]\tvalid_0's rmse: 0.0254346\n",
|
|||
|
|
"[1962]\tvalid_0's rmse: 0.0254346\n",
|
|||
|
|
"[1963]\tvalid_0's rmse: 0.0254344\n",
|
|||
|
|
"[1964]\tvalid_0's rmse: 0.0254341\n",
|
|||
|
|
"[1965]\tvalid_0's rmse: 0.0254337\n",
|
|||
|
|
"[1966]\tvalid_0's rmse: 0.0254337\n",
|
|||
|
|
"[1967]\tvalid_0's rmse: 0.0254335\n",
|
|||
|
|
"[1968]\tvalid_0's rmse: 0.0254336\n",
|
|||
|
|
"[1969]\tvalid_0's rmse: 0.0254336\n",
|
|||
|
|
"[1970]\tvalid_0's rmse: 0.0254333\n",
|
|||
|
|
"[1971]\tvalid_0's rmse: 0.0254335\n",
|
|||
|
|
"[1972]\tvalid_0's rmse: 0.0254333\n",
|
|||
|
|
"[1973]\tvalid_0's rmse: 0.0254328\n",
|
|||
|
|
"[1974]\tvalid_0's rmse: 0.0254329\n",
|
|||
|
|
"[1975]\tvalid_0's rmse: 0.0254329\n",
|
|||
|
|
"[1976]\tvalid_0's rmse: 0.0254334\n",
|
|||
|
|
"[1977]\tvalid_0's rmse: 0.0254333\n",
|
|||
|
|
"[1978]\tvalid_0's rmse: 0.0254336\n",
|
|||
|
|
"[1979]\tvalid_0's rmse: 0.0254342\n",
|
|||
|
|
"[1980]\tvalid_0's rmse: 0.0254343\n",
|
|||
|
|
"[1981]\tvalid_0's rmse: 0.0254338\n",
|
|||
|
|
"[1982]\tvalid_0's rmse: 0.0254341\n",
|
|||
|
|
"[1983]\tvalid_0's rmse: 0.0254341\n",
|
|||
|
|
"[1984]\tvalid_0's rmse: 0.0254343\n",
|
|||
|
|
"[1985]\tvalid_0's rmse: 0.0254342\n",
|
|||
|
|
"[1986]\tvalid_0's rmse: 0.0254341\n",
|
|||
|
|
"[1987]\tvalid_0's rmse: 0.0254347\n",
|
|||
|
|
"[1988]\tvalid_0's rmse: 0.025435\n",
|
|||
|
|
"[1989]\tvalid_0's rmse: 0.0254349\n",
|
|||
|
|
"[1990]\tvalid_0's rmse: 0.0254338\n",
|
|||
|
|
"[1991]\tvalid_0's rmse: 0.0254339\n",
|
|||
|
|
"[1992]\tvalid_0's rmse: 0.0254342\n",
|
|||
|
|
"[1993]\tvalid_0's rmse: 0.0254341\n",
|
|||
|
|
"[1994]\tvalid_0's rmse: 0.0254341\n",
|
|||
|
|
"[1995]\tvalid_0's rmse: 0.0254339\n",
|
|||
|
|
"[1996]\tvalid_0's rmse: 0.0254349\n",
|
|||
|
|
"[1997]\tvalid_0's rmse: 0.025434\n",
|
|||
|
|
"[1998]\tvalid_0's rmse: 0.0254327\n",
|
|||
|
|
"[1999]\tvalid_0's rmse: 0.0254326\n",
|
|||
|
|
"[2000]\tvalid_0's rmse: 0.025432\n",
|
|||
|
|
"Did not meet early stopping. Best iteration is:\n",
|
|||
|
|
"[2000]\tvalid_0's rmse: 0.025432\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"gbm = lgb.train(params_gbm, lgb_train, num_boost_round=2000, valid_sets=lgb_eval, early_stopping_rounds=100)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"y_pred = gbm.predict(X_test)\n",
|
|||
|
|
"y_true = Y_test.values"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE: 3.7E-04\n",
|
|||
|
|
"RMSE: 0.019\n",
|
|||
|
|
"MAE: 0.013\n",
|
|||
|
|
"MAPE: 2.64 %\n",
|
|||
|
|
"R_2: 0.93\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, '.1E'))\n",
|
|||
|
|
"print('RMSE:', round(RMSE, 3))\n",
|
|||
|
|
"print('MAE:', round(MAE, 3))\n",
|
|||
|
|
"print('MAPE:', round(MAPE*100, 2), '%')\n",
|
|||
|
|
"print('R_2:', round(R_2, 3)) #R方为负就说明拟合效果比平均值差a"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"dtrain = xgb.DMatrix(X_train, Y_train)\n",
|
|||
|
|
"dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
|
|||
|
|
"dtest = xgb.DMatrix(X_test, Y_test)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 102,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.model_selection import cross_val_score\n",
|
|||
|
|
"from xgboost import XGBRegressor\n",
|
|||
|
|
"from bayes_opt import BayesianOptimization"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 103,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def xgb_cv(max_depth, learning_rate, n_estimators, min_child_weight, subsample, colsample_bytree, reg_alpha, gamma):\n",
|
|||
|
|
" val = cross_val_score(estimator=XGBRegressor(max_depth=int(max_depth),\n",
|
|||
|
|
" learning_rate=learning_rate,\n",
|
|||
|
|
" n_estimators=int(n_estimators),\n",
|
|||
|
|
" min_child_weight=min_child_weight,\n",
|
|||
|
|
" subsample=max(min(subsample, 1), 0),\n",
|
|||
|
|
" colsample_bytree=max(min(colsample_bytree, 1), 0),\n",
|
|||
|
|
" reg_alpha=max(reg_alpha, 0), gamma=gamma, objective='reg:squarederror',\n",
|
|||
|
|
" booster='gbtree',\n",
|
|||
|
|
" seed=666), X=use_data[feature_cols], y=use_data.values[:1], scoring='r2',\n",
|
|||
|
|
" cv=10).max()\n",
|
|||
|
|
" return val"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 104,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"| iter | target | colsam... | gamma | learni... | max_depth | min_ch... | n_esti... | reg_alpha | subsample |\n",
|
|||
|
|
"-------------------------------------------------------------------------------------------------------------------------\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"ename": "ValueError",
|
|||
|
|
"evalue": "Found input variables with inconsistent numbers of samples: [3080, 1]",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
|||
|
|
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
|
|||
|
|
"\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_17148\\1576227182.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 7\u001B[0m \u001B[1;34m'reg_alpha'\u001B[0m\u001B[1;33m:\u001B[0m \u001B[1;33m(\u001B[0m\u001B[1;36m0.001\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m10\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 8\u001B[0m 'gamma': (0.001, 10)})\n\u001B[1;32m----> 9\u001B[1;33m \u001B[0mxgb_bo\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmaximize\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mn_iter\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m100\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minit_points\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m10\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
|
|||
|
|
"\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\bayes_opt\\bayesian_optimization.py\u001B[0m in \u001B[0;36mmaximize\u001B[1;34m(self, init_points, n_iter, acquisition_function, acq, kappa, kappa_decay, kappa_decay_delay, xi, **gp_params)\u001B[0m\n\u001B[0;32m 309\u001B[0m \u001B[0mx_probe\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msuggest\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mutil\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 310\u001B[0m \u001B[0miteration\u001B[0m \u001B[1;33m+=\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 311\u001B[1;33m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mprobe\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx_probe\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlazy\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 312\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 313\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_bounds_transformer\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0miteration\u001B[0m \u001B[1;33m>\u001B[0m \u001B[1;36m0\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
|||
|
|
"\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\bayes_opt\\bayesian_optimization.py\u001B[0m in \u001B[0;36mprobe\u001B[1;34m(self, params, lazy)\u001B[0m\n\u001B[0;32m 206\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_queue\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0madd\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 207\u001B[0m \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 208\u001B[1;33m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_space\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mprobe\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 209\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdispatch\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mEvents\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mOPTIMIZATION_STEP\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 210\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
|||
|
|
"\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\bayes_opt\\target_space.py\u001B[0m in \u001B[0;36mprobe\u001B[1;34m(self, params)\u001B[0m\n\u001B[0;32m 234\u001B[0m \u001B[0mx\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_as_array\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 235\u001B[0m \u001B[0mparams\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mzip\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_keys\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 236\u001B[1;33m \u001B[0mtarget\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtarget_func\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m**\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 237\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 238\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_constraint\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
|||
|
|
"\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_17148\\2288155185.py\u001B[0m in \u001B[0;36mxgb_cv\u001B[1;34m(max_depth, learning_rate, n_estimators, min_child_weight, subsample, colsample_bytree, reg_alpha, gamma)\u001B[0m\n\u001B[0;32m 9\u001B[0m \u001B[0mbooster\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'gbtree'\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 10\u001B[0m seed=666), X=use_data[feature_cols], y=use_data.values[:1], scoring='r2',\n\u001B[1;32m---> 11\u001B[1;33m cv=10).max()\n\u001B[0m\u001B[0;32m 12\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mval\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
|||
|
|
"\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001B[0m in \u001B[0;36mcross_val_score\u001B[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001B[0m\n\u001B[0;32m 518\u001B[0m \u001B[0mfit_params\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mfit_params\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 519\u001B[0m \u001B[0mpre_dispatch\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mpre_dispatch\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 520\u001B[1;33m \u001B[0merror_score\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0merror_score\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 521\u001B[0m )\n\u001B[0;32m 522\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mcv_results\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"test_score\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
|||
|
|
"\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001B[0m in \u001B[0;36mcross_validate\u001B[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001B[0m\n\u001B[0;32m 251\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 252\u001B[0m \"\"\"\n\u001B[1;32m--> 253\u001B[1;33m \u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mgroups\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mindexable\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mgroups\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 254\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 255\u001B[0m \u001B[0mcv\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcheck_cv\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcv\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mclassifier\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mis_classifier\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mestimator\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
|||
|
|
"\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\sklearn\\utils\\validation.py\u001B[0m in \u001B[0;36mindexable\u001B[1;34m(*iterables)\u001B[0m\n\u001B[0;32m 376\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 377\u001B[0m \u001B[0mresult\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[0m_make_indexable\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mX\u001B[0m \u001B[1;32min\u001B[0m \u001B[0miterables\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 378\u001B[1;33m \u001B[0mcheck_consistent_length\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0mresult\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 379\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mresult\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 380\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
|||
|
|
"\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\sklearn\\utils\\validation.py\u001B[0m in \u001B[0;36mcheck_consistent_length\u001B[1;34m(*arrays)\u001B[0m\n\u001B[0;32m 332\u001B[0m raise ValueError(\n\u001B[0;32m 333\u001B[0m \u001B[1;34m\"Found input variables with inconsistent numbers of samples: %r\"\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 334\u001B[1;33m \u001B[1;33m%\u001B[0m \u001B[1;33m[\u001B[0m\u001B[0mint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ml\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0ml\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mlengths\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 335\u001B[0m )\n\u001B[0;32m 336\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
|||
|
|
"\u001B[1;31mValueError\u001B[0m: Found input variables with inconsistent numbers of samples: [3080, 1]"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"xgb_bo = BayesianOptimization(xgb_cv, pbounds={'max_depth': (20, 60),\n",
|
|||
|
|
" 'learning_rate': (0.005, 0.1),\n",
|
|||
|
|
" 'n_estimators': (100, 2000),\n",
|
|||
|
|
" 'min_child_weight': (0, 30),\n",
|
|||
|
|
" 'subsample': (0.05, 1),\n",
|
|||
|
|
" 'colsample_bytree': (0.1, 1),\n",
|
|||
|
|
" 'reg_alpha': (0.001, 10),\n",
|
|||
|
|
" 'gamma': (0.001, 10)})\n",
|
|||
|
|
"xgb_bo.maximize(n_iter=100, init_points=10)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 105,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"params_xgb = {'objective': 'reg:squarederror',\n",
|
|||
|
|
" 'booster': 'gbtree',\n",
|
|||
|
|
" 'eta': 0.037,\n",
|
|||
|
|
" 'max_depth': 30,\n",
|
|||
|
|
" 'subsample': 1.0,\n",
|
|||
|
|
" 'colsample_bytree': 0.47,\n",
|
|||
|
|
" 'min_child_weight': 30,\n",
|
|||
|
|
" 'seed': 42}\n",
|
|||
|
|
"num_boost_round = 2000\n",
|
|||
|
|
"\n",
|
|||
|
|
"dtrain = xgb.DMatrix(X_train, Y_train)\n",
|
|||
|
|
"dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
|
|||
|
|
"watchlist = [(dtrain, 'train'), (dvalid, 'eval')]\n",
|
|||
|
|
"\n",
|
|||
|
|
"gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
|
|||
|
|
" early_stopping_rounds=100, verbose_eval=False)\n"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 106,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"y_pred_xgb = np.expm1(gb_model.predict(xgb.DMatrix(X_test)))\n",
|
|||
|
|
"y_true_xgb = np.expm1(Y_test.values)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 107,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE: 1.1E-05\n",
|
|||
|
|
"RMSE: 0.003\n",
|
|||
|
|
"MAE: 0.002\n",
|
|||
|
|
"MAPE: 2.99 %\n",
|
|||
|
|
"R_2: 0.88\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"MSE = mean_squared_error(y_true_xgb, y_pred_xgb)\n",
|
|||
|
|
"RMSE = np.sqrt(mean_squared_error(y_true_xgb, y_pred_xgb))\n",
|
|||
|
|
"MAE = mean_absolute_error(y_true_xgb, y_pred_xgb)\n",
|
|||
|
|
"MAPE = mean_absolute_percentage_error(y_true_xgb, y_pred_xgb)\n",
|
|||
|
|
"R_2 = r2_score(y_true_xgb, y_pred_xgb)\n",
|
|||
|
|
"print('MSE:', format(MSE, '.1E'))\n",
|
|||
|
|
"print('RMSE:', round(RMSE, 3))\n",
|
|||
|
|
"print('MAE:', round(MAE, 3))\n",
|
|||
|
|
"print('MAPE:', round(MAPE*100, 2), '%')\n",
|
|||
|
|
"print('R_2:', round(R_2, 3)) #R方为负就说明拟合效果比平均值差a"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 108,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 109,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.47 %, R_2: 0.776\n",
|
|||
|
|
"MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.19 %, R_2: 0.83\n",
|
|||
|
|
"MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.87 %, R_2: 0.811\n",
|
|||
|
|
"MSE: 1.2E-05, RMSE: 0.003, MAE: 0.002, MAPE: 2.96 %, R_2: 0.861\n",
|
|||
|
|
"MSE: 1.9E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.65 %, R_2: 0.775\n",
|
|||
|
|
"MSE: 1.9E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.56 %, R_2: 0.789\n",
|
|||
|
|
"MSE: 2.3E-05, RMSE: 0.005, MAE: 0.002, MAPE: 3.05 %, R_2: 0.723\n",
|
|||
|
|
"MSE: 2.5E-05, RMSE: 0.005, MAE: 0.002, MAPE: 3.94 %, R_2: 0.717\n",
|
|||
|
|
"MSE: 1.0E-05, RMSE: 0.003, MAE: 0.002, MAPE: 2.9 %, R_2: 0.864\n",
|
|||
|
|
"MSE: 9.4E-06, RMSE: 0.003, MAE: 0.002, MAPE: 2.89 %, R_2: 0.881\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"kf = KFold(n_splits=10, shuffle=True, random_state=42)\n",
|
|||
|
|
"eva_list = list()\n",
|
|||
|
|
"for (train_index, test_index) in kf.split(use_data):\n",
|
|||
|
|
" train = use_data.loc[train_index]\n",
|
|||
|
|
" test = use_data.loc[test_index]\n",
|
|||
|
|
" train, valid = train_test_split(train, test_size=0.15, random_state=42)\n",
|
|||
|
|
" X_train, Y_train = train[feature_cols], train[target_cols[1]]\n",
|
|||
|
|
" X_valid, Y_valid = valid[feature_cols], valid[target_cols[1]]\n",
|
|||
|
|
" X_test, Y_test = test[feature_cols], test[target_cols[1]]\n",
|
|||
|
|
" dtrain = xgb.DMatrix(X_train, Y_train)\n",
|
|||
|
|
" dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
|
|||
|
|
" watchlist = [(dvalid, 'eval')]\n",
|
|||
|
|
" gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
|
|||
|
|
" early_stopping_rounds=100, verbose_eval=False)\n",
|
|||
|
|
" y_pred = gb_model.predict(xgb.DMatrix(X_test))\n",
|
|||
|
|
" y_true = Y_test.values\n",
|
|||
|
|
" 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, '.1E'), end=', ')\n",
|
|||
|
|
" print('RMSE:', round(RMSE, 3), end=', ')\n",
|
|||
|
|
" print('MAE:', round(MAE, 3), end=', ')\n",
|
|||
|
|
" print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n",
|
|||
|
|
" print('R_2:', round(R_2, 3)) #R方为负就说明拟合效果比平均值差\n",
|
|||
|
|
" eva_list.append([MSE, RMSE, MAE, MAPE, R_2])"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 110,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"record = pd.DataFrame.from_records(eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R2'])"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 120,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": " MSE RMSE MAE MAPE R2\n0 0.000018 0.004221 0.002394 0.034705 0.775560\n1 0.000018 0.004191 0.002405 0.031921 0.829931\n2 0.000018 0.004249 0.002235 0.038677 0.810649\n3 0.000012 0.003395 0.002090 0.029607 0.861337\n4 0.000019 0.004334 0.002302 0.036496 0.775066\n5 0.000019 0.004367 0.002260 0.035588 0.789063\n6 0.000023 0.004806 0.002272 0.030522 0.723082\n7 0.000025 0.004968 0.002401 0.039428 0.717094\n8 0.000010 0.003207 0.002037 0.029033 0.863679\n9 0.000009 0.003072 0.002008 0.028871 0.880821",
|
|||
|
|
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>MSE</th>\n <th>RMSE</th>\n <th>MAE</th>\n <th>MAPE</th>\n <th>R2</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.000018</td>\n <td>0.004221</td>\n <td>0.002394</td>\n <td>0.034705</td>\n <td>0.775560</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0.000018</td>\n <td>0.004191</td>\n <td>0.002405</td>\n <td>0.031921</td>\n <td>0.829931</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0.000018</td>\n <td>0.004249</td>\n <td>0.002235</td>\n <td>0.038677</td>\n <td>0.810649</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0.000012</td>\n <td>0.003395</td>\n <td>0.002090</td>\n <td>0.029607</td>\n <td>0.861337</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0.000019</td>\n <td>0.004334</td>\n <td>0.002302</td>\n <td>0.036496</td>\n <td>0.775066</td>\n </tr>\n <tr>\n <th>5</th>\n <td>0.000019</td>\n <td>0.004367</td>\n <td>0.002260</td>\n <td>0.035588</td>\n <td>0.789063</td>\n </tr>\n <tr>\n <th>6</th>\n <td>0.000023</td>\n <td>0.004806</td>\n <td>0.002272</td>\n <td>0.030522</td>\n <td>0.723082</td>\n </tr>\n <tr>\n <th>7</th>\n <td>0.000025</td>\n <td>0.004968</td>\n <td>0.002401</td>\n <td>0.039428</td>\n <td>0.717094</td>\n </tr>\n <tr>\n <th>8</th>\n <td>0.000010</td>\n <td>0.003207</td>\n <td>0.002037</td>\n <td>0.029033</td>\n <td>0.863679</td>\n </tr>\n <tr>\n <th>9</th>\n <td>0.000009</td>\n <td>0.003072</td>\n <td>0.002008</td>\n <td>0.028871</td>\n <td>0.880821</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|||
|
|
},
|
|||
|
|
"execution_count": 120,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"record"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 124,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": " MSE RMSE MAE MAPE R2\n8 0.00001 0.003207 0.002037 0.029033 0.863679",
|
|||
|
|
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>MSE</th>\n <th>RMSE</th>\n <th>MAE</th>\n <th>MAPE</th>\n <th>R2</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8</th>\n <td>0.00001</td>\n <td>0.003207</td>\n <td>0.002037</td>\n <td>0.029033</td>\n <td>0.863679</td>\n </tr>\n </tbody>\n</table>\n</div>"
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"execution_count": 124,
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"cell_type": "code",
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"execution_count": 126,
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"outputs": [],
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"source": [
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"index = [0, 1, 2, 3, 4, 5, 6, 8]"
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],
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"metadata": {
|
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"collapsed": false,
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{
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"cell_type": "code",
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"execution_count": 128,
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"outputs": [
|
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{
|
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"data": {
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"text/plain": "MSE 0.000017\nRMSE 0.004096\nMAE 0.002249\nMAPE 0.033319\nR2 0.803546\ndtype: float64"
|
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},
|
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"execution_count": 128,
|
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"metadata": {},
|
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"output_type": "execute_result"
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}
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],
|
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"source": [
|
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|
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"record.loc[index].mean()"
|
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],
|
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"metadata": {
|
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"collapsed": false,
|
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{
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"cell_type": "code",
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"execution_count": 63,
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"outputs": [
|
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{
|
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"data": {
|
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"text/plain": "MSE 0.000552\nRMSE 0.022978\nMAE 0.014251\nMAPE 0.034105\nR2 0.896138\ndtype: float64"
|
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},
|
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"execution_count": 63,
|
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"metadata": {},
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"output_type": "execute_result"
|
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}
|
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|
|
],
|
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"source": [
|
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"record.mean()"
|
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],
|
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"metadata": {
|
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"collapsed": false,
|
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"execution_count": 57,
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"outputs": [],
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"source": [
|
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|
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"import matplotlib.pyplot as plt\n",
|
|||
|
|
"#新增加的两行\n",
|
|||
|
|
"from pylab import mpl\n",
|
|||
|
|
"# 设置显示中文字体\n",
|
|||
|
|
"mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
|
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|
|
"\n",
|
|||
|
|
"mpl.rcParams[\"axes.unicode_minus\"] = False"
|
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],
|
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"metadata": {
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"collapsed": false,
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"outputs": [
|
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{
|
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"data": {
|
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"text/plain": "<Figure size 1600x1000 with 1 Axes>",
|
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|
"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(16, 10))\n",
|
|||
|
|
"plt.plot(range(len(y_true)), y_true, 'o-', label='真实值')\n",
|
|||
|
|
"plt.plot(range(len(y_pred)), y_pred, '*-', label='预测值')\n",
|
|||
|
|
"plt.legend(loc='best')\n",
|
|||
|
|
"plt.title('预测结果')\n",
|
|||
|
|
"plt.savefig('./figure/CO2排放强度预测结果.png')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 59,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"pd.DataFrame.from_records([y_pred, y_true]).T.to_csv('pred.csv')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 60,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"rst = pd.DataFrame.from_records(([y_true_xgb, y_pred_xgb])).T\n",
|
|||
|
|
"rst.columns = ['y_true', 'y_pred']"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 61,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"rst['mAP'] = abs(rst.y_pred - rst.y_true) / rst.y_true"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 62,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": " y_true y_pred mAP\n23 0.233161 0.228589 0.019609\n46 0.242031 0.260373 0.075782\n42 0.233845 0.215675 0.077700\n1 0.233773 0.237715 0.016864\n58 0.258407 0.259042 0.002460\n41 0.233404 0.246465 0.055956\n15 0.249245 0.248289 0.003837\n63 0.237670 0.284324 0.196296\n59 0.244008 0.242001 0.008228\n37 0.252681 0.251169 0.005983",
|
|||
|
|
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>y_true</th>\n <th>y_pred</th>\n <th>mAP</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>23</th>\n <td>0.233161</td>\n <td>0.228589</td>\n <td>0.019609</td>\n </tr>\n <tr>\n <th>46</th>\n <td>0.242031</td>\n <td>0.260373</td>\n <td>0.075782</td>\n </tr>\n <tr>\n <th>42</th>\n <td>0.233845</td>\n <td>0.215675</td>\n <td>0.077700</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0.233773</td>\n <td>0.237715</td>\n <td>0.016864</td>\n </tr>\n <tr>\n <th>58</th>\n <td>0.258407</td>\n <td>0.259042</td>\n <td>0.002460</td>\n </tr>\n <tr>\n <th>41</th>\n <td>0.233404</td>\n <td>0.246465</td>\n <td>0.055956</td>\n </tr>\n <tr>\n <th>15</th>\n <td>0.249245</td>\n <td>0.248289</td>\n <td>0.003837</td>\n </tr>\n <tr>\n <th>63</th>\n <td>0.237670</td>\n <td>0.284324</td>\n <td>0.196296</td>\n </tr>\n <tr>\n <th>59</th>\n <td>0.244008</td>\n <td>0.242001</td>\n <td>0.008228</td>\n </tr>\n <tr>\n <th>37</th>\n <td>0.252681</td>\n <td>0.251169</td>\n <td>0.005983</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|||
|
|
},
|
|||
|
|
"execution_count": 62,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"rst.sort_values(by='mAP').sample(10)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 63,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 1600x1000 with 1 Axes>",
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABREAAANBCAYAAAB3XPnSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3RU9bn/8c/cEy4JchVBVEShQiBSsApeAFFhCaJotNZSES8nclpEu2wbikB/6EHQZQWON27qEY7SoqUiQhE9BWwVjiAaRFAQ8aCYYLklgcx1//6YzM4MmUxmwiXs7PdrLdYke/ae2Rs2WbM+eZ7v4zAMwxAAAAAAAAAA1MLZ0CcAAAAAAAAA4PRGiAgAAAAAAAAgJUJEAAAAAAAAACkRIgIAAAAAAABIiRARAAAAAAAAQEqEiAAAAAAAAABSIkQEAAAAAAAAkBIhIgAAAAAAAICUCBEBAABwQvn9fh08eLDG9u3bt2vv3r1JjyktLdXvf/97lZSUnOzTAwAAQD24G/oEAAAA0DCOHDmiUCgkr9crh8OR8FwgEJDT6VRWVpbGjh2rK6+8UnfccYcOHjyo0tLSGq/VuXNnud3Rj5ZvvvmmZs6cqffffz9hn1mzZumTTz7RqlWr1KRJk4Tn/H6/lixZonvvvbfW8927d68GDBggp9Npnq9hGIpEInK5XOZ+kUhEvXr10uLFizP7CwEAAECtCBEBAAAauVGjRmnbtm3yeDxm+HbxxRerW7duev755+X1ehUMBhUOh81wz+/367bbbtPEiRN12WWXadq0afrss8+Un5+vqVOnqk2bNpKkUCikkpISrV+/Xi1atJAkeTweeb3ehHPYu3ev1qxZo2nTpiW8h8fjkdPpNEPA2GM4HFYgEJDX6zW3+Xw+SdJLL72kn/zkJ5Kk1atX6/e//73Wr19vvtfs2bO1YcOGE/8XCQAAYGOEiAAAAI3ck08+KcMw5PV6tWHDBhUVFenWW2/VlVdeqV/+8peSpOnTp2v37t169tlnaxw/evRo5eXl6ejRo/rhhx+Un5+vV155RZK0Z88eXX311fJ4PCooKNCQIUPUunXrGq/x7LPPqlevXho+fLg+++wzNWvWTBMnTqwR9g0ePDjh+xdffFH9+vWTJDmd6a/Ek8m+AAAAqBshIgAAQCPXrl07Pf/888rKytLcuXP1xBNP6Morr0zYZ9euXerevXvS4w3D0I9//GNJ0l//+ldJ0s6dO/XUU08ltB/7fL4aFYiS9MEHH+j111/Xn/70J0nS5MmTdfjwYb3yyivyer3yer0qLS3VkCFDtHz5crVv316RSER+v185OTnm68SCwbvvvjuhnTkYDCovL8/cLxwO65JLLsn47wkAAAC1I0QEAACwgUsvvVRjx47Veeedl1Dt16NHD0mSw+HQP/7xD82ZM0eGYUiStmzZonXr1mnmzJmaOXOmOnToYB5nGIZWr16dECI6HI4aaysePXpU48aNU05OjhYtWqQDBw5o69atWrhwoVq3bq1gMCifz6fs7GxJUlZWlpo2bapAIKCsrCx5PB7ztcLhsCRp/vz5dbYz/+///u8J+XsDAABAFH0eAAAAjZhhGDp69Kh69eqlRYsWKT8/X5FIxHze5/Np/vz52rx5sxYtWqQPPvhA8+fPN9cf7NGjh3JyclRQUKCtW7eax8WGqDRt2jTl+2dnZ2vEiBEaNWqUunXrprVr12rMmDHq3bu3tm7dql69eqlbt2666qqrJElXX321unbtqry8vBqDUeLPuy6Z7AsAAIC6UYkIAADQiP3www+6/PLL5XQ6zQrDF198UeFwWI8//rgZBh4+fFgFBQX67//+b0nVIeEZZ5yhF154QdOmTVO7du305ZdfJrx+LGxMZeLEiZKkKVOm6Oyzz9avfvUrSVLXrl21bt06ZWdn11jDMBgMJlQhxra5XC7ddddd5rbYdOaLLrooYd+ePXvWeV4AAABIHyEiAABAI9a6dWsVFxfL6/Xqscce05EjRzR+/HhdfvnlCesG+v1+SdK5556rHTt2JLyGx+NRUVGR3G63GUTu379fTZo0McO/2PbabNq0SX/605/08ssvm8Gj1+tVq1atkh7r8/nMIFOSvv/+e2VlZWnTpk0JLdPvvfeepkyZorVr1yYcbxiGysrK5PV60wo6AQAAkBohIgAAQCPmcDhqDDv517/+pRtvvDFhjcOysjK53W61bNky6essXbpUK1eu1LXXXitJKikpUbt27cznA4FAreewbt06Pfzww+ratauKi4v15ptvyu12a/LkyRo6dKh2796d9LgVK1aoc+fOkqShQ4fqyJEjtb5HbZWHM2bM0IgRI2o9DgAAAOlhTUQAAACb6datm6ZPny6puoJwx44dOvvss80qv1AolHDMokWLlJubqwsuuEBDhw7VRx99pLPOOku5ubl64IEH5PP5FA6HFQwGa7zfypUrdeDAAe3bt08ffvihsrKy1K9fP0nRisOioiJ9+umn5p8VK1ZIig5ZiVm9erU+/fRTbd++Xdu3b9fUqVOVm5uroqIitWjRQi+//LJatGihlStXavv27frss8/04YcfmqEnAAAAjg+ViAAAADYWqyBcvXq1Lr74YnN7rL1ZirYib9u2TdOnT1fXrl2Vn5+vwYMH66c//alWrFihsWPHSooGgseubShJ48eP1/jx49WmTZsazzmdTrnd7oSW41jlZPxrtWrVSlJ0XcT//M//1H/9139p5syZ5vlfeumlGj58uEaNGqVHH31UAwYM0BlnnFHvvxcAAAAkIkQEAACwsZkzZ+rIkSNauXKlXn75ZS1fvlx9+/bVm2++ae4ze/ZsDRo0SF27dpUkvfnmm/r+++/VuXNnjR8/XuXl5RozZoxefPFFSdJf//rXhPdo06aNjh49qh07dujrr7/WV199pR9++EETJkyQFA0GKyoqzP0rKytrnOeBAwe0YsUKzZs3Ty6XSy+//LJ69uyp1atXm/tMnDhRrVq10rhx49SlSxcNHz5cAwcO1LnnnnvC/r4AAADsihARAACgkTt69KhCoZBKSkrUtm3bhOfC4bB+97vf6f7779fFF1+sxx57TNOnT1dRUZE6d+6st99+W//85z+1aNEiSdEwb+bMmSooKNDgwYNVVFSkadOmqV+/furWrZskJbQ0BwIBXX311SotLZXD4VC7du107rnnKj8/X5IUiUT0+OOP6/HHH69x3rFWa8Mw9Itf/EL79u3TXXfdpbvuususVjy2hfr+++/X8OHDtWDBAj3zzDPm+wEAAOD4ECICAAA0cjt27NCYMWN03nnnma3Hu3fv1qRJk7Rt2zZNnDhRN9xwgyRp0qRJGj58uMaNG6ctW7borLPOUq9evdSnTx8FAgH98pe/VCgU0gMPPCBJ+tnPfqZly5bp7bffVrdu3TRp0iStXr3aHNri9Xr16KOPqnnz5rrwwgvVrFmzhHMLBAJ65JFH9POf/9zctmfPHl199dVmS7XD4dAzzzyjNm3aKDs7O+F4v99fYx3Gjh07atKkSXr44Ydr7A8AAID6cRixX/ECAADAVtasWaP8/Hzl5ubWeG7fvn1yuVxq2bKl/H6/fD6fDMPQihUr1KFDB/Xq1cvcd//+/eZU56VLl2rPnj266aabEqY/1+bbb79VTk6OmjdvfuIuDAAAACccISIAAAAAAACAlGqOzwMAAAAAAACAOISIAAAAAAAAAFIiRAQAAAAAAACQEiEiAAAAAAAAgJTcDX0CxyMSiSgUCsnpdMrhcDT06QAAAAAAAACWYhiGIpGI3G63nM7a6w0tHSKGQiEVFxc39GkAAAAAAAAAlpaXlyev11vr85YOEWPpaF5enlwuVwOfzYkXDodVXFzcaK8PjRv3L6yM+xdWxv0Lq+MehpVx/8LKuH/tK/Zvn6oKUbJ4iBhrYXa5XI36Bm/s14fGjfsXVsb9Cyvj/oXVcQ/Dyrh/YWXcv/ZV11KBDFYBAAAAAAAAkBIhIgAAAAAAAICUCBEBAAAAAAAApGTpNREBAAAAAABgbeFwWMFgsKFPo9HyeDwnZJ1
|
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},
|
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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|
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"plt.figure(figsize=(16, 10))\n",
|
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|
|
"plt.plot(range(len(y_true_xgb)), y_true_xgb, 'o-', label='真实值')\n",
|
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|
|
"plt.plot(range(len(y_pred_xgb)), y_pred_xgb, '*-', label='预测值')\n",
|
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|
|
"plt.legend(loc='best')\n",
|
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|
|
"plt.title('预测结果')\n",
|
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|
|
"plt.savefig('./figure/CO2排放强度预测结果.png')"
|
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],
|
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"metadata": {
|
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"collapsed": false,
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"pycharm": {
|
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"name": "#%%\n"
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}
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}
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},
|
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{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"## 煤种标准化工程"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%% md\n"
|
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|
}
|
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|
}
|
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},
|
|||
|
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{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 73,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"new_values = total_data.groupby(['煤种', '入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']).CO2_em_air.mean()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 74,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": " 煤种 入炉煤低位热值_new 燃煤挥发份Var_new 燃煤灰份Aar_new\n0 无烟煤 17050.00 6.51 31.330000\n1 无烟煤 18440.00 9.13 21.240189\n2 无烟煤 19335.65 7.06 21.400000\n3 无烟煤 20125.07 5.70 29.850000\n4 无烟煤 20463.30 5.70 29.790000\n.. ... ... ... ...\n622 贫煤 21772.91 10.66 26.320000\n623 贫煤 21907.00 10.64 28.100000\n624 贫煤 22042.72 12.96 25.690000\n625 贫煤 23215.00 11.00 19.310000\n626 贫煤 23791.00 11.00 19.310000\n\n[627 rows x 4 columns]",
|
|||
|
|
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>煤种</th>\n <th>入炉煤低位热值_new</th>\n <th>燃煤挥发份Var_new</th>\n <th>燃煤灰份Aar_new</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>无烟煤</td>\n <td>17050.00</td>\n <td>6.51</td>\n <td>31.330000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>无烟煤</td>\n <td>18440.00</td>\n <td>9.13</td>\n <td>21.240189</td>\n </tr>\n <tr>\n <th>2</th>\n <td>无烟煤</td>\n <td>19335.65</td>\n <td>7.06</td>\n <td>21.400000</td>\n </tr>\n <tr>\n <th>3</th>\n <td>无烟煤</td>\n <td>20125.07</td>\n <td>5.70</td>\n <td>29.850000</td>\n </tr>\n <tr>\n <th>4</th>\n <td>无烟煤</td>\n <td>20463.30</td>\n <td>5.70</td>\n <td>29.790000</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>622</th>\n <td>贫煤</td>\n <td>21772.91</td>\n <td>10.66</td>\n <td>26.320000</td>\n </tr>\n <tr>\n <th>623</th>\n <td>贫煤</td>\n <td>21907.00</td>\n <td>10.64</td>\n <td>28.100000</td>\n </tr>\n <tr>\n <th>624</th>\n <td>贫煤</td>\n <td>22042.72</td>\n <td>12.96</td>\n <td>25.690000</td>\n </tr>\n <tr>\n <th>625</th>\n <td>贫煤</td>\n <td>23215.00</td>\n <td>11.00</td>\n <td>19.310000</td>\n </tr>\n <tr>\n <th>626</th>\n <td>贫煤</td>\n <td>23791.00</td>\n <td>11.00</td>\n <td>19.310000</td>\n </tr>\n </tbody>\n</table>\n<p>627 rows × 4 columns</p>\n</div>"
|
|||
|
|
},
|
|||
|
|
"execution_count": 74,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"coal_df = new_values.reset_index().drop(columns='CO2_em_air')\n",
|
|||
|
|
"coal_df"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 75,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"coal_params_dict = dict()\n",
|
|||
|
|
"for coal_type in coal_df['煤种'].unique().tolist():\n",
|
|||
|
|
" options = coal_df[coal_df['煤种']==coal_type][['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']].values\n",
|
|||
|
|
" coal_params_dict[coal_type] = options"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 76,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "{'无烟煤': array([[1.70500000e+04, 6.51000000e+00, 3.13300000e+01],\n [1.84400000e+04, 9.13000000e+00, 2.12401894e+01],\n [1.93356500e+04, 7.06000000e+00, 2.14000000e+01],\n [2.01250700e+04, 5.70000000e+00, 2.98500000e+01],\n [2.04633000e+04, 5.70000000e+00, 2.97900000e+01]]),\n '烟煤': array([[1.277100e+04, 2.126000e+01, 3.355000e+01],\n [1.500000e+04, 2.346000e+01, 1.904000e+01],\n [1.610000e+04, 2.333000e+01, 1.873000e+01],\n ...,\n [2.348751e+04, 2.927000e+01, 2.097000e+01],\n [2.365000e+04, 2.887000e+01, 7.910000e+00],\n [2.365614e+04, 2.927000e+01, 2.097000e+01]]),\n '褐煤': array([[1.059800e+04, 2.476000e+01, 2.179000e+01],\n [1.129000e+04, 4.764000e+01, 3.079000e+01],\n [1.160400e+04, 4.758000e+01, 3.025000e+01],\n [1.172435e+04, 4.601000e+01, 3.673000e+01],\n [1.203000e+04, 4.726000e+01, 3.119000e+01],\n [1.213546e+04, 4.642000e+01, 1.113000e+01],\n [1.217290e+04, 4.642000e+01, 1.113000e+01],\n [1.219256e+04, 4.642000e+01, 1.113000e+01],\n [1.221131e+04, 4.642000e+01, 1.113000e+01],\n [1.230939e+04, 4.642000e+01, 1.113000e+01],\n [1.233780e+04, 4.642000e+01, 1.113000e+01],\n [1.267400e+04, 4.324000e+01, 1.237000e+01],\n [1.278700e+04, 4.884000e+01, 4.117000e+01],\n [1.295100e+04, 2.228000e+01, 1.287000e+01],\n [1.299880e+04, 2.256000e+01, 1.716000e+01],\n [1.311100e+04, 2.367000e+01, 2.107000e+01],\n [1.313000e+04, 2.417000e+01, 1.630000e+01],\n [1.318000e+04, 2.445000e+01, 1.794000e+01],\n [1.320830e+04, 2.451000e+01, 1.429000e+01],\n [1.325722e+04, 1.703000e+01, 3.660000e+01],\n [1.327000e+04, 3.204000e+01, 1.709000e+01],\n [1.327300e+04, 2.364000e+01, 1.622000e+01],\n [1.327300e+04, 2.458000e+01, 1.261000e+01],\n [1.332771e+04, 4.090000e+01, 2.507000e+01],\n [1.333064e+04, 1.680000e+01, 3.741000e+01],\n [1.335883e+04, 2.301000e+01, 1.841000e+01],\n [1.336864e+04, 2.301000e+01, 1.841000e+01],\n [1.343787e+04, 2.336000e+01, 1.705000e+01],\n [1.344000e+04, 4.782000e+01, 2.290000e+01],\n [1.345749e+04, 2.388000e+01, 1.652000e+01],\n [1.357000e+04, 1.799000e+01, 2.177000e+01],\n [1.364000e+04, 2.526000e+01, 2.108000e+01],\n [1.365410e+04, 2.232000e+01, 1.171000e+01],\n [1.369000e+04, 4.771000e+01, 2.205000e+01],\n [1.382000e+04, 2.420000e+01, 1.104000e+01],\n [1.389597e+04, 2.232000e+01, 1.171000e+01],\n [1.390000e+04, 3.683000e+01, 4.441000e+01],\n [1.395400e+04, 2.310000e+01, 1.011000e+01],\n [1.396000e+04, 4.665000e+01, 1.890000e+01],\n [1.400000e+04, 4.520000e+01, 1.364000e+01],\n [1.404100e+04, 2.346000e+01, 1.046000e+01],\n [1.410900e+04, 4.520000e+01, 1.364000e+01],\n [1.412200e+04, 2.478000e+01, 1.916000e+01],\n [1.419900e+04, 4.733000e+01, 1.697000e+01],\n [1.433937e+04, 2.476000e+01, 3.371000e+01],\n [1.440000e+04, 2.589000e+01, 1.643000e+01],\n [1.442729e+04, 4.474000e+01, 1.193000e+01],\n [1.446814e+04, 2.484000e+01, 3.331000e+01],\n [1.448810e+04, 3.554000e+01, 1.171000e+01],\n [1.458200e+04, 2.834000e+01, 2.320000e+01],\n [1.460000e+04, 2.714000e+01, 4.346000e+01],\n [1.462400e+04, 4.613000e+01, 2.700000e+01],\n [1.463500e+04, 4.613000e+01, 2.700000e+01],\n [1.464000e+04, 4.439000e+01, 1.684000e+01],\n [1.470100e+04, 2.210000e+01, 4.588000e+01],\n [1.481078e+04, 4.501000e+01, 1.325000e+01],\n [1.489878e+04, 2.386000e+01, 3.161000e+01],\n [1.507938e+04, 4.501000e+01, 1.325000e+01],\n [1.512117e+04, 2.355000e+01, 1.472000e+01],\n [1.517400e+04, 3.126000e+01, 1.696000e+01],\n [1.523800e+04, 2.492000e+01, 2.378000e+01],\n [1.524041e+04, 2.355000e+01, 1.472000e+01],\n [1.528927e+04, 2.345000e+01, 1.554000e+01],\n [1.534700e+04, 2.492000e+01, 2.378000e+01],\n [1.536708e
|
|||
|
|
},
|
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"execution_count": 76,
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"metadata": {},
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}
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],
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"source": [
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"coal_params_dict"
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],
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"metadata": {
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"collapsed": false,
|
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"pycharm": {
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"name": "#%%\n"
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"execution_count": 77,
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"outputs": [
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{
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"data": {
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"text/plain": " 地区 所属集团 投产时间 机组容量 机组类型 参数分类 冷却方式 锅炉类型 时间 \\\n0 北京 华能 1998/1/20 0:00 165 供热式 超高压 水冷 煤粉 2016.0 \n1 北京 华能 1998/1/20 0:00 165 供热式 超高压 水冷 煤粉 2016.0 \n2 北京 华能 1998/12/20 0:00 220 供热式 超高压 水冷 煤粉 2016.0 \n3 北京 华能 1999/6/26 0:00 220 供热式 超高压 水冷 煤粉 2016.0 \n4 辽宁 大唐 2009/4/30 0:00 300 供热式 亚临界 水冷 煤粉 2016.0 \n.. .. ... ... ... ... ... ... ... ... \n847 新疆 NaN NaN 1320 纯凝式 超临界 间接空冷 煤粉 NaN \n848 辽宁 NaN NaN 700 供热式 超临界 水冷 煤粉 NaN \n849 内蒙 NaN NaN 700 供热式 超临界 直接空冷 煤粉 NaN \n850 山东 NaN NaN 40 供热式 超高压 水冷 循环流化床 NaN \n851 浙江 NaN NaN 70 供热式 超高压 水冷 循环流化床 NaN \n\n 发电量 ... 标煤量 出力系数 煤种 入炉煤低位热值 燃煤挥发份Var 燃煤灰份Aar \\\n0 51841.70000 ... 2.580497e+05 75.84 烟煤 23380.0 27.59 9.94 \n1 47387.95000 ... 2.126813e+05 74.50 烟煤 23380.0 27.59 9.94 \n2 115498.04000 ... 4.410925e+05 78.76 烟煤 23380.0 27.59 9.94 \n3 120884.07000 ... 4.707218e+05 81.41 烟煤 23380.0 27.59 9.94 \n4 111218.55000 ... 3.726990e+05 71.27 褐煤 14122.0 24.78 19.16 \n.. ... ... ... ... .. ... ... ... \n847 704381.26290 ... 2.283076e+06 NaN 褐煤 19970.0 35.33 9.05 \n848 350000.00000 ... 1.328747e+06 NaN 褐煤 14640.0 44.39 16.84 \n849 385000.00000 ... 1.362009e+06 NaN 褐煤 13960.0 46.65 18.90 \n850 17000.00000 ... 1.810834e+05 NaN 烟煤 21060.0 19.12 20.27 \n851 35788.81469 ... 3.502535e+05 NaN 烟煤 22021.0 19.12 21.77 \n\n CO2_em_air 入炉煤低位热值_new 燃煤挥发份Var_new 燃煤灰份Aar_new \n0 0.235066 23380.0 27.59 9.94 \n1 0.226207 23380.0 27.59 9.94 \n2 0.220954 23380.0 27.59 9.94 \n3 0.216298 23380.0 27.59 9.94 \n4 0.238755 14122.0 24.78 19.16 \n.. ... ... ... ... \n847 0.196452 19970.0 35.33 9.05 \n848 0.185688 14640.0 44.39 16.84 \n849 0.181214 13960.0 46.65 18.90 \n850 0.347570 21060.0 19.12 20.27 \n851 0.253057 22021.0 19.12 21.77 \n\n[852 rows x 21 columns]",
|
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>地区</th>\n <th>所属集团</th>\n <th>投产时间</th>\n <th>机组容量</th>\n <th>机组类型</th>\n <th>参数分类</th>\n <th>冷却方式</th>\n <th>锅炉类型</th>\n <th>时间</th>\n <th>发电量</th>\n <th>...</th>\n <th>标煤量</th>\n <th>出力系数</th>\n <th>煤种</th>\n <th>入炉煤低位热值</th>\n <th>燃煤挥发份Var</th>\n <th>燃煤灰份Aar</th>\n <th>CO2_em_air</th>\n <th>入炉煤低位热值_new</th>\n <th>燃煤挥发份Var_new</th>\n <th>燃煤灰份Aar_new</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>北京</td>\n <td>华能</td>\n <td>1998/1/20 0:00</td>\n <td>165</td>\n <td>供热式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>2016.0</td>\n <td>51841.70000</td>\n <td>...</td>\n <td>2.580497e+05</td>\n <td>75.84</td>\n <td>烟煤</td>\n <td>23380.0</td>\n <td>27.59</td>\n <td>9.94</td>\n <td>0.235066</td>\n <td>23380.0</td>\n <td>27.59</td>\n <td>9.94</td>\n </tr>\n <tr>\n <th>1</th>\n <td>北京</td>\n <td>华能</td>\n <td>1998/1/20 0:00</td>\n <td>165</td>\n <td>供热式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>2016.0</td>\n <td>47387.95000</td>\n <td>...</td>\n <td>2.126813e+05</td>\n <td>74.50</td>\n <td>烟煤</td>\n <td>23380.0</td>\n <td>27.59</td>\n <td>9.94</td>\n <td>0.226207</td>\n <td>23380.0</td>\n <td>27.59</td>\n <td>9.94</td>\n </tr>\n <tr>\n <th>2</th>\n <td>北京</td>\n <td>华能</td>\n <td>1998/12/20 0:00</td>\n <td>220</td>\n <td>供热式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>2016.0</td>\n <td>115498.04000</td>\n <td>...</td>\n <td>4.410925e+05</td>\n <td>78.76</td>\n <td>烟煤</td>\n <td>23380.0</td>\n <td>27.59</td>\n <td>9.94</td>\n <td>0.220954</td>\n <td>23380.0</td>\n <td>27.59</td>\n <td>9.94</td>\n </tr>\n <tr>\n <th>3</th>\n <td>北京</td>\n <td>华能</td>\n <td>1999/6/26 0:00</td>\n <td>220</td>\n <td>供热式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>2016.0</td>\n <td>120884.07000</td>\n <td>...</td>\n <td>4.707218e+05</td>\n <td>81.41</td>\n <td>烟煤</td>\n <td>23380.0</td>\n <td>27.59</td>\n <td>9.94</td>\n <td>0.216298</td>\n <td>23380.0</td>\n <td>27.59</td>\n <td>9.94</td>\n </tr>\n <tr>\n <th>4</th>\n <td>辽宁</td>\n <td>大唐</td>\n <td>2009/4/30 0:00</td>\n <td>300</td>\n <td>供热式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>2016.0</td>\n <td>111218.55000</td>\n <td>...</td>\n <td>3.726990e+05</td>\n <td>71.27</td>\n <td>褐煤</td>\n <td>14122.0</td>\n <td>24.78</td>\n <td>19.16</td>\n <td>0.238755</td>\n <td>14122.0</td>\n <td>24.78</td>\n <td>19.16</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</
|
|||
|
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},
|
|||
|
|
"execution_count": 77,
|
|||
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"metadata": {},
|
|||
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|
"output_type": "execute_result"
|
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|
|
}
|
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|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"total_data"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
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|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 78,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"new_use_data = total_data.groupby(use_col + ['煤种'])['CO2_em_air'].mean().reset_index().drop(columns=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new'])\n",
|
|||
|
|
"new_use_data.rename(columns={0:'CO2_em_air'}, inplace=True)\n",
|
|||
|
|
"new_use_data['coal_params'] = new_use_data['煤种'].apply(lambda x: coal_params_dict.get(x))\n",
|
|||
|
|
"new_use_data.drop(columns='煤种', inplace=True)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
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"pycharm": {
|
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"name": "#%%\n"
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}
|
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}
|
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},
|
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|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 79,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"new_data = new_use_data.explode(column='coal_params')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
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"pycharm": {
|
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|
"name": "#%%\n"
|
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}
|
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|
}
|
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},
|
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{
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"cell_type": "code",
|
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"execution_count": 80,
|
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"outputs": [
|
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{
|
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|
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"data": {
|
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|
|
"text/plain": " 地区 机组类型 参数分类 冷却方式 锅炉类型 机组容量 coal_params\n0 上海 纯凝式 亚临界 水冷 煤粉 320 [12771.0, 21.26, 33.55]\n0 上海 纯凝式 亚临界 水冷 煤粉 320 [15000.0, 23.46, 19.04]\n0 上海 纯凝式 亚临界 水冷 煤粉 320 [16100.0, 23.33, 18.73]\n0 上海 纯凝式 亚临界 水冷 煤粉 320 [16190.0, 23.33, 18.73]\n0 上海 纯凝式 亚临界 水冷 煤粉 320 [16641.0, 19.13, 39.12]\n.. ... ... ... ... ... ... ...\n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 [23253.68, 23.72, 18.45]\n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 [23380.0, 27.59, 9.94]\n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 [23487.51, 29.27, 20.97]\n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 [23650.0, 28.87, 7.91]\n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 [23656.14, 29.27, 20.97]\n\n[208875 rows x 7 columns]",
|
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>地区</th>\n <th>机组类型</th>\n <th>参数分类</th>\n <th>冷却方式</th>\n <th>锅炉类型</th>\n <th>机组容量</th>\n <th>coal_params</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>[12771.0, 21.26, 33.55]</td>\n </tr>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>[15000.0, 23.46, 19.04]</td>\n </tr>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>[16100.0, 23.33, 18.73]</td>\n </tr>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>[16190.0, 23.33, 18.73]</td>\n </tr>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>[16641.0, 19.13, 39.12]</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>[23253.68, 23.72, 18.45]</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>[23380.0, 27.59, 9.94]</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>[23487.51, 29.27, 20.97]</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>[23650.0, 28.87, 7.91]</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>[23656.14, 29.27, 20.97]</td>\n </tr>\n </tbody>\n</table>\n<p>208875 rows × 7 columns</p>\n</div>"
|
|||
|
|
},
|
|||
|
|
"execution_count": 80,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"new_data.drop(columns=['CO2_em_air'])"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 81,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"norm_data = pd.concat([new_data, new_data.coal_params.apply(pd.Series, index=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new'])], axis=1).drop(columns='coal_params')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 82,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": " 地区 机组类型 参数分类 冷却方式 锅炉类型 机组容量 CO2_em_air 入炉煤低位热值_new 燃煤挥发份Var_new \\\n0 上海 纯凝式 亚临界 水冷 煤粉 320 0.266602 12771.00 21.26 \n0 上海 纯凝式 亚临界 水冷 煤粉 320 0.266602 15000.00 23.46 \n0 上海 纯凝式 亚临界 水冷 煤粉 320 0.266602 16100.00 23.33 \n0 上海 纯凝式 亚临界 水冷 煤粉 320 0.266602 16190.00 23.33 \n0 上海 纯凝式 亚临界 水冷 煤粉 320 0.266602 16641.00 19.13 \n.. ... ... ... ... ... ... ... ... ... \n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 0.278763 23253.68 23.72 \n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 0.278763 23380.00 27.59 \n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 0.278763 23487.51 29.27 \n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 0.278763 23650.00 28.87 \n646 黑龙江 纯凝式 超高压 水冷 煤粉 210 0.278763 23656.14 29.27 \n\n 燃煤灰份Aar_new \n0 33.55 \n0 19.04 \n0 18.73 \n0 18.73 \n0 39.12 \n.. ... \n646 18.45 \n646 9.94 \n646 20.97 \n646 7.91 \n646 20.97 \n\n[208875 rows x 10 columns]",
|
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>地区</th>\n <th>机组类型</th>\n <th>参数分类</th>\n <th>冷却方式</th>\n <th>锅炉类型</th>\n <th>机组容量</th>\n <th>CO2_em_air</th>\n <th>入炉煤低位热值_new</th>\n <th>燃煤挥发份Var_new</th>\n <th>燃煤灰份Aar_new</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>0.266602</td>\n <td>12771.00</td>\n <td>21.26</td>\n <td>33.55</td>\n </tr>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>0.266602</td>\n <td>15000.00</td>\n <td>23.46</td>\n <td>19.04</td>\n </tr>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>0.266602</td>\n <td>16100.00</td>\n <td>23.33</td>\n <td>18.73</td>\n </tr>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>0.266602</td>\n <td>16190.00</td>\n <td>23.33</td>\n <td>18.73</td>\n </tr>\n <tr>\n <th>0</th>\n <td>上海</td>\n <td>纯凝式</td>\n <td>亚临界</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>320</td>\n <td>0.266602</td>\n <td>16641.00</td>\n <td>19.13</td>\n <td>39.12</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>0.278763</td>\n <td>23253.68</td>\n <td>23.72</td>\n <td>18.45</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>0.278763</td>\n <td>23380.00</td>\n <td>27.59</td>\n <td>9.94</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>0.278763</td>\n <td>23487.51</td>\n <td>29.27</td>\n <td>20.97</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>0.278763</td>\n <td>23650.00</td>\n <td>28.87</td>\n <td>7.91</td>\n </tr>\n <tr>\n <th>646</th>\n <td>黑龙江</td>\n <td>纯凝式</td>\n <td>超高压</td>\n <td>水冷</td>\n <td>煤粉</td>\n <td>210</td>\n <td>0.278763</td>\n <td>23656.14</td>\n <td>29.27</td>\n <td>20.97</td>\n </tr>\n </tbody>\n</table>\n<p>208875 rows × 10 columns</p>\n</div>"
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"source": [
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"norm_data"
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],
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"source": [
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"for col in num_cols:\n",
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" norm_data[col] = np.log1p(norm_data[col])\n",
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" # total_data[col] = (total_data[col] - total_data[col].min()) / (total_data[col].max() - total_data[col].min())\n",
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"norm_data_dummy = pd.get_dummies(norm_data, columns=object_cols)"
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{
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"data": {
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"text/plain": " 机组容量 CO2_em_air 入炉煤低位热值_new 燃煤挥发份Var_new 燃煤灰份Aar_new 地区_上海 \\\n0 5.771441 0.236338 9.455011 3.102791 3.542408 1 \n0 5.771441 0.236338 9.615872 3.197039 2.997730 1 \n0 5.771441 0.236338 9.686637 3.191710 2.982140 1 \n0 5.771441 0.236338 9.692211 3.191710 2.982140 1 \n0 5.771441 0.236338 9.719685 3.002211 3.691875 1 \n.. ... ... ... ... ... ... \n646 5.351858 0.245893 10.054262 3.207613 2.967847 0 \n646 5.351858 0.245893 10.059679 3.353057 2.392426 0 \n646 5.351858 0.245893 10.064267 3.410157 3.089678 0 \n646 5.351858 0.245893 10.071161 3.396855 2.187174 0 \n646 5.351858 0.245893 10.071420 3.410157 3.089678 0 \n\n 地区_云南 地区_内蒙 地区_北京 地区_吉林 ... 机组类型_纯凝式 参数分类_亚临界 参数分类_超临界 参数分类_超超临界 \\\n0 0 0 0 0 ... 1 1 0 0 \n0 0 0 0 0 ... 1 1 0 0 \n0 0 0 0 0 ... 1 1 0 0 \n0 0 0 0 0 ... 1 1 0 0 \n0 0 0 0 0 ... 1 1 0 0 \n.. ... ... ... ... ... ... ... ... ... \n646 0 0 0 0 ... 1 0 0 0 \n646 0 0 0 0 ... 1 0 0 0 \n646 0 0 0 0 ... 1 0 0 0 \n646 0 0 0 0 ... 1 0 0 0 \n646 0 0 0 0 ... 1 0 0 0 \n\n 参数分类_超高压 冷却方式_水冷 冷却方式_直接空冷 冷却方式_间接空冷 锅炉类型_循环流化床 锅炉类型_煤粉 \n0 0 1 0 0 0 1 \n0 0 1 0 0 0 1 \n0 0 1 0 0 0 1 \n0 0 1 0 0 0 1 \n0 0 1 0 0 0 1 \n.. ... ... ... ... ... ... \n646 1 1 0 0 0 1 \n646 1 1 0 0 0 1 \n646 1 1 0 0 0 1 \n646 1 1 0 0 0 1 \n646 1 1 0 0 0 1 \n\n[208875 rows x 45 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>机组容量</th>\n <th>CO2_em_air</th>\n <th>入炉煤低位热值_new</th>\n <th>燃煤挥发份Var_new</th>\n <th>燃煤灰份Aar_new</th>\n <th>地区_上海</th>\n <th>地区_云南</th>\n <th>地区_内蒙</th>\n <th>地区_北京</th>\n <th>地区_吉林</th>\n <th>...</th>\n <th>机组类型_纯凝式</th>\n <th>参数分类_亚临界</th>\n <th>参数分类_超临界</th>\n <th>参数分类_超超临界</th>\n <th>参数分类_超高压</th>\n <th>冷却方式_水冷</th>\n <th>冷却方式_直接空冷</th>\n <th>冷却方式_间接空冷</th>\n <th>锅炉类型_循环流化床</th>\n <th>锅炉类型_煤粉</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>5.771441</td>\n <td>0.236338</td>\n <td>9.455011</td>\n <td>3.102791</td>\n <td>3.542408</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>0</th>\n <td>5.771441</td>\n <td>0.236338</td>\n <td>9.615872</td>\n <td>3.197039</td>\n <td>2.997730</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>0</th>\n <td>5.771441</td>\n <td>0.236338</td>\n <td>9.686637</td>\n <td>3.191710</td>\n <td>2.982140</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>0</th>\n <td>5.771441</td>\n <td>0.236338</td>\n <td>9.692211</td>\n <td>3.191710</td>\n <td>2.982140</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>0</th>\n <td>5.771441</td>\n <td>0.236338</td>\n <td>9.719685</td>\n <td>3.002211</td>\n <td>3.691875</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>646</th>\n <td>5.351858</td>\n <td>0.245893</td>\n <td>10.054262</td>\n <td>3.207613</td>\n <td>2.967847</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n
|
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},
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"execution_count": 84,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"norm_data_dummy"
|
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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{
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"cell_type": "code",
|
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"execution_count": 85,
|
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"outputs": [],
|
|||
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"source": [
|
|||
|
|
"new_xgb_data = xgb.DMatrix(norm_data_dummy[feature_cols])"
|
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|
],
|
|||
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"metadata": {
|
|||
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|
"collapsed": false,
|
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"name": "#%%\n"
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{
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"cell_type": "code",
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"execution_count": 86,
|
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"outputs": [],
|
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"source": [
|
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"norm_data.drop(columns='CO2_em_air', inplace=True)"
|
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],
|
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"metadata": {
|
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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"cell_type": "code",
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"execution_count": 87,
|
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"outputs": [],
|
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"source": [
|
|||
|
|
"norm_data['co2_pred'] = gb_model.predict(new_xgb_data)\n",
|
|||
|
|
"normaled_data = norm_data.drop(columns=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']).groupby([x for x in use_col if x not in ['CO2_em_air', '入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']])['co2_pred'].mean()"
|
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],
|
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"metadata": {
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"collapsed": false,
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
|
|||
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|
"source": [
|
|||
|
|
"normaled_data.reset_index().to_csv('./data/去煤种化数据.csv', encoding='utf-8-sig', index=False)"
|
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.7.13 ('py37')",
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"version": "3.7.13"
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},
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"vscode": {
|
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"interpreter": {
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"hash": "993bd31d5df1020fab369d79a34ff0a2a159e1798f3e25d3ad4b7751d38184c9"
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}
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