Kaggle（3）：Predict CO2 Emissions in Rwanda

Kaggle（3）：Predict 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 = 1.07

2. 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)]

extrp

	ID_LAT_LON_YEAR_WEEK	latitude	longitude	year	week_no	SulphurDioxide_SO2_column_number_density	SulphurDioxide_SO2_column_number_density_amf	SulphurDioxide_SO2_slant_column_number_density	SulphurDioxide_cloud_fraction	SulphurDioxide_sensor_azimuth_angle	...	Cloud_cloud_top_height	Cloud_cloud_base_pressure	Cloud_cloud_base_height	Cloud_cloud_optical_depth	Cloud_surface_albedo	Cloud_sensor_azimuth_angle	Cloud_sensor_zenith_angle	Cloud_solar_azimuth_angle	Cloud_solar_zenith_angle	emission
53	ID_-0.510_29.290_2020_00	-0.510	29.290	2020	0	0.000064	0.970290	0.000073	0.163462	-100.602665	...	5388.602054	60747.063530	4638.602176	6.287709	0.283116	-13.291375	33.679610	-140.309173	30.053447	3.753601
54	ID_-0.510_29.290_2020_01	-0.510	29.290	2020	1	NaN	NaN	NaN	NaN	NaN	...	6361.488754	53750.174162	5361.488754	19.167269	0.317732	-30.474972	48.119754	-139.437777	30.391957	4.051966
55	ID_-0.510_29.290_2020_02	-0.510	29.290	2020	2	-0.000361	0.668526	-0.000231	0.086199	73.269733	...	5320.715902	61012.625000	4320.715861	48.203733	0.265554	-12.461150	35.809728	-137.854449	29.100415	4.154116
56	ID_-0.510_29.290_2020_03	-0.510	29.290	2020	3	0.000597	0.553729	0.000331	0.149257	73.522247	...	6219.319294	55704.782998	5219.319269	12.809350	0.267030	16.381079	35.836898	-139.017754	26.265561	4.165751
57	ID_-0.510_29.290_2020_04	-0.510	29.290	2020	4	0.000107	1.045238	0.000112	0.224283	77.588455	...	6348.560006	54829.331776	5348.560014	35.283981	0.268983	-12.193650	47.092968	-134.474279	27.061321	4.233635
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
78965	ID_-3.299_30.301_2020_48	-3.299	30.301	2020	48	0.000114	1.123935	0.000125	0.149885	74.376836	...	6092.323722	57479.397776	5169.185142	15.331296	0.261608	16.309625	39.924967	-132.258700	30.393604	26.929207
78966	ID_-3.299_30.301_2020_49	-3.299	30.301	2020	49	0.000051	0.617927	0.000031	0.213135	72.364738	...	5992.053006	57739.300155	4992.053006	27.214085	0.276616	-0.287656	45.624810	-134.460418	30.911741	26.606790
78967	ID_-3.299_30.301_2020_50	-3.299	30.301	2020	50	-0.000235	0.633192	-0.000149	0.257000	-99.141518	...	6104.231241	56954.517231	5181.570213	26.270365	0.260574	-50.411241	37.645974	-132.193161	32.516685	27.256273
78968	ID_-3.299_30.301_2020_51	-3.299	30.301	2020	51	NaN	NaN	NaN	NaN	NaN	...	4855.537585	64839.955718	3858.187453	14.519789	0.248484	30.840922	39.529722	-138.964016	28.574091	25.591976
78969	ID_-3.299_30.301_2020_52	-3.299	30.301	2020	52	0.000025	1.103025	0.000028	0.265622	-99.811790	...	5345.679464	62098.716546	4345.679397	13.082162	0.283677	-13.002957	38.243055	-136.660958	29.584058	25.559870

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(axis=1)
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"] = extrp

train

	ID_LAT_LON_YEAR_WEEK	latitude	longitude	year	week_no	SulphurDioxide_SO2_column_number_density	SulphurDioxide_SO2_column_number_density_amf	SulphurDioxide_SO2_slant_column_number_density	SulphurDioxide_cloud_fraction	SulphurDioxide_sensor_azimuth_angle	...	Cloud_cloud_base_pressure	Cloud_cloud_base_height	Cloud_cloud_optical_depth	Cloud_surface_albedo	Cloud_sensor_azimuth_angle	Cloud_sensor_zenith_angle	Cloud_solar_azimuth_angle	Cloud_solar_zenith_angle	emission	week
0	ID_-0.510_29.290_2019_00	-0.510	29.290	2019	0	-0.000108	0.603019	-0.000065	0.255668	-98.593887	...	61085.809570	2615.120483	15.568533	0.272292	-12.628986	35.632416	-138.786423	30.752140	3.750994	0
1	ID_-0.510_29.290_2019_01	-0.510	29.290	2019	1	0.000021	0.728214	0.000014	0.130988	16.592861	...	66969.478735	3174.572424	8.690601	0.256830	30.359375	39.557633	-145.183930	27.251779	4.025176	1
2	ID_-0.510_29.290_2019_02	-0.510	29.290	2019	2	0.000514	0.748199	0.000385	0.110018	72.795837	...	60068.894448	3516.282669	21.103410	0.251101	15.377883	30.401823	-142.519545	26.193296	4.231381	2
3	ID_-0.510_29.290_2019_03	-0.510	29.290	2019	3	NaN	NaN	NaN	NaN	NaN	...	51064.547339	4180.973322	15.386899	0.262043	-11.293399	24.380357	-132.665828	28.829155	4.305286	3
4	ID_-0.510_29.290_2019_04	-0.510	29.290	2019	4	-0.000079	0.676296	-0.000048	0.121164	4.121269	...	63751.125781	3355.710107	8.114694	0.235847	38.532263	37.392979	-141.509805	22.204612	4.347317	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
79018	ID_-3.299_30.301_2021_48	-3.299	30.301	2021	48	0.000284	1.195643	0.000340	0.191313	72.820518	...	60657.101913	4590.879504	20.245954	0.304797	-35.140368	40.113533	-129.935508	32.095214	29.404171	154
79019	ID_-3.299_30.301_2021_49	-3.299	30.301	2021	49	0.000083	1.130868	0.000063	0.177222	-12.856753	...	60168.191528	4659.130378	6.104610	0.314015	4.667058	47.528435	-134.252871	30.771469	29.186497	155
79020	ID_-3.299_30.301_2021_50	-3.299	30.301	2021	50	NaN	NaN	NaN	NaN	NaN	...	56596.027209	5222.646823	14.817885	0.288058	-0.340922	35.328098	-134.731723	30.716166	29.131205	156
79021	ID_-3.299_30.301_2021_51	-3.299	30.301	2021	51	-0.000034	0.879397	-0.000028	0.184209	-100.344827	...	46533.348194	6946.858022	32.594768	0.274047	8.427699	48.295652	-139.447849	29.112868	28.125792	157
79022	ID_-3.299_30.301_2021_52	-3.299	30.301	2021	52	-0.000091	0.871951	-0.000079	0.000000	76.825638	...	47771.681887	6553.295018	19.464032	0.226276	-12.808528	47.923441	-136.299984	30.246387	27.239302	158

79023 rows × 77 columns

test

	ID_LAT_LON_YEAR_WEEK	latitude	longitude	year	week_no	SulphurDioxide_SO2_column_number_density	SulphurDioxide_SO2_column_number_density_amf	SulphurDioxide_SO2_slant_column_number_density	SulphurDioxide_cloud_fraction	SulphurDioxide_sensor_azimuth_angle	...	Cloud_cloud_base_pressure	Cloud_cloud_base_height	Cloud_cloud_optical_depth	Cloud_surface_albedo	Cloud_sensor_azimuth_angle	Cloud_sensor_zenith_angle	Cloud_solar_azimuth_angle	Cloud_solar_zenith_angle	week	lazy_pred
0	ID_-0.510_29.290_2022_00	-0.510	29.290	2022	0	NaN	NaN	NaN	NaN	NaN	...	41047.937500	7472.313477	7.935617	0.240773	-100.113792	33.697044	-133.047546	33.779583	159	3.753601
1	ID_-0.510_29.290_2022_01	-0.510	29.290	2022	1	0.000456	0.691164	0.000316	0.000000	76.239196	...	54915.708579	5476.147161	11.448437	0.293119	-30.510319	42.402593	-138.632822	31.012380	160	4.051966
2	ID_-0.510_29.290_2022_02	-0.510	29.290	2022	2	0.000161	0.605107	0.000106	0.079870	-42.055341	...	39006.093750	7984.795703	10.753179	0.267130	39.087361	45.936480	-144.784988	26.743361	161	4.231381
3	ID_-0.510_29.290_2022_03	-0.510	29.290	2022	3	0.000350	0.696917	0.000243	0.201028	72.169566	...	57646.368368	5014.724115	11.764556	0.304679	-24.465127	42.140419	-135.027891	29.604774	162	4.305286
4	ID_-0.510_29.290_2022_04	-0.510	29.290	2022	4	-0.000317	0.580527	-0.000184	0.204352	76.190865	...	52896.541873	5849.280394	13.065317	0.284221	-12.907850	30.122641	-135.500119	26.276807	163	4.347317
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
24348	ID_-3.299_30.301_2022_44	-3.299	30.301	2022	44	-0.000618	0.745549	-0.000461	0.234492	72.306198	...	55483.459980	5260.120056	30.398508	0.180046	-25.528588	45.284576	-116.521412	29.992562	203	30.327420
24349	ID_-3.299_30.301_2022_45	-3.299	30.301	2022	45	NaN	NaN	NaN	NaN	NaN	...	53589.917383	5678.951521	19.223844	0.177833	-13.380005	43.770351	-122.405759	29.017975	204	30.811167
24350	ID_-3.299_30.301_2022_46	-3.299	30.301	2022	46	NaN	NaN	NaN	NaN	NaN	...	62646.761340	4336.282491	13.801194	0.219471	-5.072065	33.226455	-124.530639	30.187472	205	31.162886
24351	ID_-3.299_30.301_2022_47	-3.299	30.301	2022	47	0.000071	1.003805	0.000077	0.205077	74.327427	...	50728.313991	6188.578464	27.887489	0.247275	-0.668714	45.885617	-129.006797	30.427455	206	31.439606
24352	ID_-3.299_30.301_2022_48	-3.299	30.301	2022	48	NaN	NaN	NaN	NaN	NaN	...	46260.039092	6777.863819	23.771269	0.239684	-40.826139	30.680056	-124.895473	34.457720	207	29.944366

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(data=train, x="week", y="emission", label="Emission", alpha=0.7, color='blue')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))

497

Insights

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_index=False)['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 x==0 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])), axis=1)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"])

train

	latitude	longitude	year	week_no	emission	kmeans_group	distance_to_max_emission
0	-0.510	29.290	2019	0	3.750994	6	207.849890
1	-0.510	29.290	2019	1	4.025176	6	207.849890
2	-0.510	29.290	2019	2	4.231381	6	207.849890
3	-0.510	29.290	2019	3	4.305286	6	207.849890
4	-0.510	29.290	2019	4	4.347317	6	207.849890
...	...	...	...	...	...	...	...
79018	-3.299	30.301	2021	48	29.404171	6	157.630611
79019	-3.299	30.301	2021	49	29.186497	6	157.630611
79020	-3.299	30.301	2021	50	29.131205	6	157.630611
79021	-3.299	30.301	2021	51	28.125792	6	157.630611
79022	-3.299	30.301	2021	52	27.239302	6	157.630611

79023 rows × 7 columns

test

	latitude	longitude	year	week_no	lazy_pred	kmeans_group	distance_to_max_emission
0	-0.510	29.290	2022	0	3.753601	6	207.849890
1	-0.510	29.290	2022	1	4.051966	6	207.849890
2	-0.510	29.290	2022	2	4.231381	6	207.849890
3	-0.510	29.290	2022	3	4.305286	6	207.849890
4	-0.510	29.290	2022	4	4.347317	6	207.849890
...	...	...	...	...	...	...	...
24348	-3.299	30.301	2022	44	30.327420	6	157.630611
24349	-3.299	30.301	2022	45	30.811167	6	157.630611
24350	-3.299	30.301	2022	46	31.162886	6	157.630611
24351	-3.299	30.301	2022	47	31.439606	6	157.630611
24352	-3.299	30.301	2022	48	29.944366	6	157.630611

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, squared=False)

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(f"RMSE 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(f"RMSE 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(f"RMSE 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(f"RMSE 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(f"RMSE 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(f"RMSE 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+: 15.108200701163458

6. 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_pred

  0%|          | 0/7 [00:00<?, ?it/s]

test["emission"] = test["emission"] * 1.07

test.to_csv('submission.csv', index=False)