云建站系统前三名,aap手机网站建设,临沂网站建设吧,网页设计作业唐诗宋词代码Kaggle#xff08;3#xff09;#xff1a;Predict CO2 Emissions in Rwanda 1. Introduction
在本次竞赛中#xff0c;我们的任务是预测非洲 497 个不同地点 2022 年的二氧化碳排放量。 在训练数据中#xff0c;我们有 2019-2021 年的二氧化碳排放量
本笔记本的内容3Predict CO2 Emissions in Rwanda 1. Introduction
在本次竞赛中我们的任务是预测非洲 497 个不同地点 2022 年的二氧化碳排放量。 在训练数据中我们有 2019-2021 年的二氧化碳排放量
本笔记本的内容
1.通过平滑消除2020年一次性的新冠疫情趋势。 或者用 2019 年和 2021 年的平均值来估算 2020 年也是一种有效的方法但此处未实施 2. 观察靠近最大排放位置的位置也具有较高的排放水平。 执行 K-Means 聚类以根据数据点的位置对数据点进行聚类。 这允许具有相似排放的数据点被分组在一起 3. 以 2019 年和 2020 年为训练数据用一些集成模型进行实验以测试其在 2021 年数据上的 CV
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math
from tqdm import tqdm
from sklearn.preprocessing import SplineTransformer
from holidays import CountryHoliday
from tqdm.notebook import tqdm
from typing import Listfrom category_encoders import OneHotEncoder, MEstimateEncoder, GLMMEncoder, OrdinalEncoder
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold, RepeatedKFold, TimeSeriesSplit, train_test_split, cross_val_score
from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor
from sklearn.ensemble import HistGradientBoostingRegressor, VotingRegressor, StackingRegressor
from sklearn.svm import SVR, LinearSVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, SGDRegressor, LogisticRegression
from sklearn.linear_model import PassiveAggressiveRegressor, ARDRegression
from sklearn.linear_model import TheilSenRegressor, RANSACRegressor, HuberRegressor
from sklearn.cross_decomposition import PLSRegression
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, roc_auc_score, roc_curve
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import FunctionTransformer, StandardScaler, MinMaxScaler, LabelEncoder, SplineTransformer
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer, KNNImputer
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import squareform
from sklearn.feature_selection import RFECV
from sklearn.decomposition import PCA
from xgboost import XGBRegressor, XGBClassifier
import lightgbm as lgbm
from lightgbm import LGBMRegressor, LGBMClassifier
from lightgbm import log_evaluation, early_stopping, record_evaluation
from catboost import CatBoostRegressor, CatBoostClassifier, Pool
from sklearn import set_config
from sklearn.multioutput import MultiOutputClassifier
from datetime import datetime, timedelta
import gcimport warnings
warnings.filterwarnings(ignore)set_config(transform_output pandas)pal sns.color_palette(viridis)pd.set_option(display.max_rows, 100)M 1.072. Examine Data
2.1
在这里我们试图平滑 2020 年的数据以消除新冠趋势
1.使用平滑导入的数据集 2. 使用 2019 年和 2021 年值的平均值 [https://www.kaggle.com/code/kacperrabczewski/rwanda-co2-step-by-step-guide]
extrp pd.read_csv(./data/PS3E20_train_covid_updated)
extrp extrp[(extrp[year] 2020)]extrpID_LAT_LON_YEAR_WEEKlatitudelongitudeyearweek_noSulphurDioxide_SO2_column_number_densitySulphurDioxide_SO2_column_number_density_amfSulphurDioxide_SO2_slant_column_number_densitySulphurDioxide_cloud_fractionSulphurDioxide_sensor_azimuth_angle...Cloud_cloud_top_heightCloud_cloud_base_pressureCloud_cloud_base_heightCloud_cloud_optical_depthCloud_surface_albedoCloud_sensor_azimuth_angleCloud_sensor_zenith_angleCloud_solar_azimuth_angleCloud_solar_zenith_angleemission53ID_-0.510_29.290_2020_00-0.51029.290202000.0000640.9702900.0000730.163462-100.602665...5388.60205460747.0635304638.6021766.2877090.283116-13.29137533.679610-140.30917330.0534473.75360154ID_-0.510_29.290_2020_01-0.51029.29020201NaNNaNNaNNaNNaN...6361.48875453750.1741625361.48875419.1672690.317732-30.47497248.119754-139.43777730.3919574.05196655ID_-0.510_29.290_2020_02-0.51029.29020202-0.0003610.668526-0.0002310.08619973.269733...5320.71590261012.6250004320.71586148.2037330.265554-12.46115035.809728-137.85444929.1004154.15411656ID_-0.510_29.290_2020_03-0.51029.290202030.0005970.5537290.0003310.14925773.522247...6219.31929455704.7829985219.31926912.8093500.26703016.38107935.836898-139.01775426.2655614.16575157ID_-0.510_29.290_2020_04-0.51029.290202040.0001071.0452380.0001120.22428377.588455...6348.56000654829.3317765348.56001435.2839810.268983-12.19365047.092968-134.47427927.0613214.233635..................................................................78965ID_-3.299_30.301_2020_48-3.29930.3012020480.0001141.1239350.0001250.14988574.376836...6092.32372257479.3977765169.18514215.3312960.26160816.30962539.924967-132.25870030.39360426.92920778966ID_-3.299_30.301_2020_49-3.29930.3012020490.0000510.6179270.0000310.21313572.364738...5992.05300657739.3001554992.05300627.2140850.276616-0.28765645.624810-134.46041830.91174126.60679078967ID_-3.299_30.301_2020_50-3.29930.301202050-0.0002350.633192-0.0001490.257000-99.141518...6104.23124156954.5172315181.57021326.2703650.260574-50.41124137.645974-132.19316132.51668527.25627378968ID_-3.299_30.301_2020_51-3.29930.301202051NaNNaNNaNNaNNaN...4855.53758564839.9557183858.18745314.5197890.24848430.84092239.529722-138.96401628.57409125.59197678969ID_-3.299_30.301_2020_52-3.29930.3012020520.0000251.1030250.0000280.265622-99.811790...5345.67946462098.7165464345.67939713.0821620.283677-13.00295738.243055-136.66095829.58405825.559870
26341 rows × 76 columns
DATA_DIR ./data/
train pd.read_csv(DATA_DIR train.csv)
test pd.read_csv(DATA_DIR test.csv)def add_features(df):#df[week] df[year].astype(str) - df[week_no].astype(str)#df[date] df[week].apply(lambda x: get_date_from_week_string(x))#df df.drop(columns [week])df[week] (df[year] - 2019) * 53 df[week_no]#df[lat_long] df[latitude].astype(str) # df[longitude].astype(str)return dftrain add_features(train)
test add_features(test)2.2
对预测进行一些有风险的后处理。
假设数据点的 MAX max(2019 年排放量、2020 年排放量、2021 年排放量)。
如果 2021 年排放量 2019 年排放量我们将 MAX * 1.07 分配给预测否则我们只分配 MAX。 参考https://www.kaggle.com/competitions/playground-series-s3e20/discussion/430152
vals set()
for x in train[[latitude, longitude]].values:vals.add(tuple(x))vals list(vals)zeros []for lat, long in vals:subset train[(train[latitude] lat) (train[longitude] long)]em_vals subset[emission].valuesif all(x 0 for x in em_vals):zeros.append([lat, long])test[2021_emission] test[week_no]
test[2020_emission] test[week_no]
test[2019_emission] test[week_no]for lat, long in vals:test.loc[(test.latitude lat) (test.longitude long), 2021_emission] train.loc[(train.latitude lat) (train.longitude long) (train.year 2021) (train.week_no 48), emission].valuestest.loc[(test.latitude lat) (test.longitude long), 2020_emission] train.loc[(train.latitude lat) (train.longitude long) (train.year 2020) (train.week_no 48), emission].valuestest.loc[(test.latitude lat) (test.longitude long), 2019_emission] train.loc[(train.latitude lat) (train.longitude long) (train.year 2019) (train.week_no 48), emission].values#print(train.loc[(train.latitude lat) (train.longitude long) (train.year 2021), emission])test[ratio] (test[2021_emission] / test[2019_emission]).replace(np.nan, 0)
test[pos_ratio] test[ratio].apply(lambda x: max(x, 1))
test[pos_ratio] test[pos_ratio].apply(lambda x: 1.07 if x 1 else x)
test[max] test[[2019_emission, 2020_emission, 2021_emission]].max(axis1)
test[lazy_pred] test[max] * test[pos_ratio]
test test.drop(columns [ratio, pos_ratio, max, 2019_emission, 2020_emission, 2021_emission])train.loc[train.year 2020, emission] extrptrainID_LAT_LON_YEAR_WEEKlatitudelongitudeyearweek_noSulphurDioxide_SO2_column_number_densitySulphurDioxide_SO2_column_number_density_amfSulphurDioxide_SO2_slant_column_number_densitySulphurDioxide_cloud_fractionSulphurDioxide_sensor_azimuth_angle...Cloud_cloud_base_pressureCloud_cloud_base_heightCloud_cloud_optical_depthCloud_surface_albedoCloud_sensor_azimuth_angleCloud_sensor_zenith_angleCloud_solar_azimuth_angleCloud_solar_zenith_angleemissionweek0ID_-0.510_29.290_2019_00-0.51029.29020190-0.0001080.603019-0.0000650.255668-98.593887...61085.8095702615.12048315.5685330.272292-12.62898635.632416-138.78642330.7521403.75099401ID_-0.510_29.290_2019_01-0.51029.290201910.0000210.7282140.0000140.13098816.592861...66969.4787353174.5724248.6906010.25683030.35937539.557633-145.18393027.2517794.02517612ID_-0.510_29.290_2019_02-0.51029.290201920.0005140.7481990.0003850.11001872.795837...60068.8944483516.28266921.1034100.25110115.37788330.401823-142.51954526.1932964.23138123ID_-0.510_29.290_2019_03-0.51029.29020193NaNNaNNaNNaNNaN...51064.5473394180.97332215.3868990.262043-11.29339924.380357-132.66582828.8291554.30528634ID_-0.510_29.290_2019_04-0.51029.29020194-0.0000790.676296-0.0000480.1211644.121269...63751.1257813355.7101078.1146940.23584738.53226337.392979-141.50980522.2046124.3473174..................................................................79018ID_-3.299_30.301_2021_48-3.29930.3012021480.0002841.1956430.0003400.19131372.820518...60657.1019134590.87950420.2459540.304797-35.14036840.113533-129.93550832.09521429.40417115479019ID_-3.299_30.301_2021_49-3.29930.3012021490.0000831.1308680.0000630.177222-12.856753...60168.1915284659.1303786.1046100.3140154.66705847.528435-134.25287130.77146929.18649715579020ID_-3.299_30.301_2021_50-3.29930.301202150NaNNaNNaNNaNNaN...56596.0272095222.64682314.8178850.288058-0.34092235.328098-134.73172330.71616629.13120515679021ID_-3.299_30.301_2021_51-3.29930.301202151-0.0000340.879397-0.0000280.184209-100.344827...46533.3481946946.85802232.5947680.2740478.42769948.295652-139.44784929.11286828.12579215779022ID_-3.299_30.301_2021_52-3.29930.301202152-0.0000910.871951-0.0000790.00000076.825638...47771.6818876553.29501819.4640320.226276-12.80852847.923441-136.29998430.24638727.239302158
79023 rows × 77 columns
testID_LAT_LON_YEAR_WEEKlatitudelongitudeyearweek_noSulphurDioxide_SO2_column_number_densitySulphurDioxide_SO2_column_number_density_amfSulphurDioxide_SO2_slant_column_number_densitySulphurDioxide_cloud_fractionSulphurDioxide_sensor_azimuth_angle...Cloud_cloud_base_pressureCloud_cloud_base_heightCloud_cloud_optical_depthCloud_surface_albedoCloud_sensor_azimuth_angleCloud_sensor_zenith_angleCloud_solar_azimuth_angleCloud_solar_zenith_angleweeklazy_pred0ID_-0.510_29.290_2022_00-0.51029.29020220NaNNaNNaNNaNNaN...41047.9375007472.3134777.9356170.240773-100.11379233.697044-133.04754633.7795831593.7536011ID_-0.510_29.290_2022_01-0.51029.290202210.0004560.6911640.0003160.00000076.239196...54915.7085795476.14716111.4484370.293119-30.51031942.402593-138.63282231.0123801604.0519662ID_-0.510_29.290_2022_02-0.51029.290202220.0001610.6051070.0001060.079870-42.055341...39006.0937507984.79570310.7531790.26713039.08736145.936480-144.78498826.7433611614.2313813ID_-0.510_29.290_2022_03-0.51029.290202230.0003500.6969170.0002430.20102872.169566...57646.3683685014.72411511.7645560.304679-24.46512742.140419-135.02789129.6047741624.3052864ID_-0.510_29.290_2022_04-0.51029.29020224-0.0003170.580527-0.0001840.20435276.190865...52896.5418735849.28039413.0653170.284221-12.90785030.122641-135.50011926.2768071634.347317..................................................................24348ID_-3.299_30.301_2022_44-3.29930.301202244-0.0006180.745549-0.0004610.23449272.306198...55483.4599805260.12005630.3985080.180046-25.52858845.284576-116.52141229.99256220330.32742024349ID_-3.299_30.301_2022_45-3.29930.301202245NaNNaNNaNNaNNaN...53589.9173835678.95152119.2238440.177833-13.38000543.770351-122.40575929.01797520430.81116724350ID_-3.299_30.301_2022_46-3.29930.301202246NaNNaNNaNNaNNaN...62646.7613404336.28249113.8011940.219471-5.07206533.226455-124.53063930.18747220531.16288624351ID_-3.299_30.301_2022_47-3.29930.3012022470.0000711.0038050.0000770.20507774.327427...50728.3139916188.57846427.8874890.247275-0.66871445.885617-129.00679730.42745520631.43960624352ID_-3.299_30.301_2022_48-3.29930.301202248NaNNaNNaNNaNNaN...46260.0390926777.86381923.7712690.239684-40.82613930.680056-124.89547334.45772020729.944366
24353 rows × 77 columns Insights
训练数据集有 79023 个观测值测试数据集有 24353 个观测值。 正如我们所观察到的某些列具有空值
3. EDA and Data Distribution
def plot_emission(train):plt.figure(figsize(15, 6))sns.lineplot(datatrain, xweek, yemission, labelEmission, alpha0.7, colorblue)plt.xlabel(Week)plt.ylabel(Emission)plt.title(Emission over time)plt.legend()plt.tight_layout()plt.show()plot_emission(train)sns.histplot(train[emission])4. Data Transformation
print(len(vals))497Insights
有 497 个独特的经纬度组合 4.1
大多数特征只是噪音我们可以将它们删除。(Reference: multiple discussion posts)
#train train.drop(columns [ID_LAT_LON_YEAR_WEEK, lat_long])
#test test.drop(columns [ID_LAT_LON_YEAR_WEEK, lat_long])train train[[latitude, longitude, year, week_no, emission]]
test test[[latitude, longitude, year, week_no, lazy_pred]]4.2
K Means 聚类 到最高排放量的距离
#https://www.kaggle.com/code/lucasboesen/simple-catboost-6-features-cv-21-7
from sklearn.cluster import KMeans
import haversine as hskm_train train.groupby(by[latitude, longitude], as_indexFalse)[emission].mean()
model KMeans(n_clusters 7, random_state 42)
model.fit(km_train)
yhat_train model.predict(km_train)
km_train[kmeans_group] yhat_train Own Groups
# Some locations have emission 0
km_train[is_zero] km_train[emission].apply(lambda x: no_emission_recorded if x0 else emission_recorded)# Distance to the highest emission location
max_lat_lon_emission km_train.loc[km_train[emission]km_train[emission].max(), [latitude, longitude]]
km_train[distance_to_max_emission] km_train.apply(lambda x: hs.haversine((x[latitude], x[longitude]), (max_lat_lon_emission[latitude].values[0], max_lat_lon_emission[longitude].values[0])), axis1)train train.merge(km_train[[latitude, longitude, kmeans_group, distance_to_max_emission]], on[latitude, longitude])
test test.merge(km_train[[latitude, longitude, kmeans_group, distance_to_max_emission]], on[latitude, longitude])
#train train.drop(columns [latitude, longitude])
#test test.drop(columns [latitude, longitude])trainlatitudelongitudeyearweek_noemissionkmeans_groupdistance_to_max_emission0-0.51029.290201903.7509946207.8498901-0.51029.290201914.0251766207.8498902-0.51029.290201924.2313816207.8498903-0.51029.290201934.3052866207.8498904-0.51029.290201944.3473176207.849890........................79018-3.29930.30120214829.4041716157.63061179019-3.29930.30120214929.1864976157.63061179020-3.29930.30120215029.1312056157.63061179021-3.29930.30120215128.1257926157.63061179022-3.29930.30120215227.2393026157.630611
79023 rows × 7 columns
testlatitudelongitudeyearweek_nolazy_predkmeans_groupdistance_to_max_emission0-0.51029.290202203.7536016207.8498901-0.51029.290202214.0519666207.8498902-0.51029.290202224.2313816207.8498903-0.51029.290202234.3052866207.8498904-0.51029.290202244.3473176207.849890........................24348-3.29930.30120224430.3274206157.63061124349-3.29930.30120224530.8111676157.63061124350-3.29930.30120224631.1628866157.63061124351-3.29930.30120224731.4396066157.63061124352-3.29930.30120224829.9443666157.630611
24353 rows × 7 columns
cat_params {n_estimators: 799, learning_rate: 0.09180872710592884,depth: 8, l2_leaf_reg: 1.0242996861886846, subsample: 0.38227256755249117, colsample_bylevel: 0.7183481537623551,random_state: 42,silent: True,
}lgb_params {n_estimators: 835, max_depth: 12, reg_alpha: 3.849279869880706, reg_lambda: 0.6840221712299135, min_child_samples: 10, subsample: 0.6810493885301987, learning_rate: 0.0916362259866008, colsample_bytree: 0.3133780298325982, colsample_bynode: 0.7966712089198238,random_state: 42,
}xgb_params {random_state: 42,
}rf_params {n_estimators: 263, max_depth: 41, min_samples_split: 10, min_samples_leaf: 3,random_state: 42,verbose: 0
}et_params {random_state: 42,verbose: 0
}5. Validate Performance on 2021 data
def rmse(a, b):return mean_squared_error(a, b, squaredFalse)validation train[train.year 2021]
clusters train[kmeans_group].unique()for i in range(len(clusters)):cluster clusters[i]print()print(f Cluster {cluster} )train_c train[train[kmeans_group] cluster]X_train train_c[train_c.year 2021].drop(columns [emission, kmeans_group])y_train train_c[train_c.year 2021][emission].copy()X_val train_c[train_c.year 2021].drop(columns [emission, kmeans_group])y_val train_c[train_c.year 2021][emission].copy()#catboost_reg CatBoostRegressor(**cat_params)catboost_reg.fit(X_train, y_train, eval_set(X_val, y_val))catboost_pred catboost_reg.predict(X_val) * Mprint(fRMSE of CatBoost: {rmse(catboost_pred, y_val)})#lightgbm_reg LGBMRegressor(**lgb_params,verbose-1)lightgbm_reg.fit(X_train, y_train, eval_set(X_val, y_val))lightgbm_pred lightgbm_reg.predict(X_val) * Mprint(fRMSE of LightGBM: {rmse(lightgbm_pred, y_val)})#xgb_reg XGBRegressor(**xgb_params)xgb_reg.fit(X_train, y_train, eval_set[(X_val, y_val)], verbose False)xgb_pred xgb_reg.predict(X_val) * Mprint(fRMSE of XGBoost: {rmse(xgb_pred, y_val)})#rf_reg RandomForestRegressor(**rf_params)rf_reg.fit(X_train, y_train)rf_pred rf_reg.predict(X_val) * Mprint(fRMSE of Random Forest: {rmse(rf_pred, y_val)})#et_reg ExtraTreesRegressor(**et_params)et_reg.fit(X_train, y_train)et_pred et_reg.predict(X_val) * Mprint(fRMSE of Extra Trees: {rmse(et_pred, y_val)})overall_pred lightgbm_pred #(catboost_pred lightgbm_pred) / 2validation.loc[validation[kmeans_group] cluster, emission] overall_predprint(fRMSE Overall: {rmse(overall_pred, y_val)})print()
print(f[DONE] RMSE of all clusters: {rmse(validation[emission], train[train.year 2021][emission])})
print(f[DONE] RMSE of all clusters Week 1-20: {rmse(validation[validation.week_no 21][emission], train[(train.year 2021) (train.week_no 21)][emission])})
print(f[DONE] RMSE of all clusters Week 21: {rmse(validation[validation.week_no 21][emission], train[(train.year 2021) (train.week_no 21)][emission])})Cluster 6
RMSE of CatBoost: 2.3575606902299895
RMSE of LightGBM: 2.2103640167714094
RMSE of XGBoost: 2.5018849673349863
RMSE of Random Forest: 2.6335510523545556
RMSE of Extra Trees: 3.0029623116826776
RMSE Overall: 2.2103640167714094
Cluster 5
RMSE of CatBoost: 19.175306730779514
RMSE of LightGBM: 17.910821889134688
RMSE of XGBoost: 19.6677120674706
RMSE of Random Forest: 18.856743714624777
RMSE of Extra Trees: 20.70417439300032
RMSE Overall: 17.910821889134688
Cluster 1
RMSE of CatBoost: 9.26195004601851
RMSE of LightGBM: 8.513309514506675
RMSE of XGBoost: 10.137965612920658
RMSE of Random Forest: 9.838001199034126
RMSE of Extra Trees: 11.043246766709913
RMSE Overall: 8.513309514506675
Cluster 4
RMSE of CatBoost: 44.564695183442716
RMSE of LightGBM: 43.946690922308754
RMSE of XGBoost: 50.18811358270916
RMSE of Random Forest: 46.39201148051631
RMSE of Extra Trees: 50.58999576441371
RMSE Overall: 43.946690922308754
Cluster 0
RMSE of CatBoost: 28.408461784012662
RMSE of LightGBM: 26.872533954605416
RMSE of XGBoost: 30.622689084145943
RMSE of Random Forest: 28.46657485784377
RMSE of Extra Trees: 31.733046766544884
RMSE Overall: 26.872533954605416
Cluster 3
RMSE of CatBoost: 263.29528869714665
RMSE of LightGBM: 326.12883397111284
RMSE of XGBoost: 336.5771065570381
RMSE of Random Forest: 303.9321016178147
RMSE of Extra Trees: 336.67756932119914
RMSE Overall: 326.12883397111284
Cluster 2
RMSE of CatBoost: 206.96165808156715
RMSE of LightGBM: 222.40891682146665
RMSE of XGBoost: 281.12604107718465
RMSE of Random Forest: 232.11332438348992
RMSE of Extra Trees: 281.29392713471816
RMSE Overall: 222.40891682146665[DONE] RMSE of all clusters: 23.275548123498453
[DONE] RMSE of all clusters Week 1-20: 31.92891146501802
[DONE] RMSE of all clusters Week 21: 15.1082007011634586. Predicting 2022 result
clusters train[kmeans_group].unique()for i in tqdm(range(len(clusters))):cluster clusters[i]train_c train[train[kmeans_group] cluster]if emission in test.columns:test_c test[test[kmeans_group] cluster].drop(columns [emission, kmeans_group, lazy_pred])else:test_c test[test[kmeans_group] cluster].drop(columns [kmeans_group, lazy_pred])X train_c.drop(columns [emission, kmeans_group])y train_c[emission].copy()#catboost_reg CatBoostRegressor(**cat_params)catboost_reg.fit(X, y)#print(test_c)catboost_pred catboost_reg.predict(test_c)#lightgbm_reg LGBMRegressor(**lgb_params,verbose-1)lightgbm_reg.fit(X, y)#print(test_c)lightgbm_pred lightgbm_reg.predict(test_c)##xgb_reg XGBRegressor(**xgb_params)#xgb_reg.fit(X, y, verbose False)#xgb_pred xgb_reg.predict(test)#rf_reg RandomForestRegressor(**rf_params)rf_reg.fit(X, y)rf_pred rf_reg.predict(test_c)##et_reg ExtraTreesRegressor(**et_params)#et_reg.fit(X, y)#et_pred et_reg.predict(test)overall_pred lightgbm_pred #(catboost_pred lightgbm_pred) / 2test.loc[test[kmeans_group] cluster, emission] overall_pred0%| | 0/7 [00:00?, ?it/s]test[emission] test[emission] * 1.07test.to_csv(submission.csv, indexFalse)
