notebook version: 3 (4 Jul 2024)
new in this version: added check if downloaded data is indeed in 30MIN time resolution

• This notebook can be used to download data from the database InfluxDB

• Data are stored to a .csv file in this folder

Auto-settings

Data settings

DIRCONF = r'L:\Sync\luhk_work\20 - CODING\22 - POET\configs'
# DIRCONF = r'P:\Flux\RDS_calculations\_scripts\_configs\configs'  # Folder with configuration files: needed e.g. for connection to database
TIMEZONE_OFFSET_TO_UTC_HOURS = 1  # Timezone, e.g. "1" is translated to timezone "UTC+01:00" (CET, winter time)
REQUIRED_TIME_RESOLUTION = '30min'  # 30MIN time resolution

Imports

import importlib.metadata
from datetime import datetime

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
import seaborn as sns
from pathlib import Path
from diive.core.io.files import save_parquet

sns.set_theme('notebook')
from diive.core.plotting.timeseries import TimeSeries
from dbc_influxdb import dbcInflux
from diive.core.plotting.heatmap_datetime import HeatmapDateTime
from diive.core.times.times import TimestampSanitizer
from diive.pkgs.createvar.potentialradiation import potrad
from diive.pkgs.gapfilling.xgboost_ts import XGBoostTS
from diive.core.io.files import load_parquet
import warnings

warnings.filterwarnings(action='ignore', category=FutureWarning)
warnings.filterwarnings(action='ignore', category=UserWarning)
dt_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
version_diive = importlib.metadata.version("diive")
print(f"diive version: v{version_diive}")
version_dbc = importlib.metadata.version("dbc_influxdb")
print(f"dbc-influxdb version: v{version_dbc}")
dbc = dbcInflux(dirconf=DIRCONF)  # Connect to database

diive version: v0.85.0
dbc-influxdb version: v0.12.0
Reading configuration files was successful.
Connection to database works.

LOAD DATA from files

filename_data_simple_2005_2024 = "15.1_CH-CHA_meteo6_2005-2024.parquet"
data_simple_2005_2024 = load_parquet(filepath=filename_data_simple_2005_2024)
data_simple_2005_2024

Loaded .parquet file 15.1_CH-CHA_meteo6_2005-2024.parquet (0.066 seconds).
    --> Detected time resolution of <30 * Minutes> / 30min 

	G_GF1_0.03_1	LW_IN_T1_2_1	LW_OUT_T1_2_1	PA_GF1_0.9_1	FLAG_PA_GF1_0.9_1_ISFILLED	PPFD_IN_T1_2_2	...	SW_OUT_T1_2_1	TA_T1_2_1	FLAG_TA_T1_2_1_ISFILLED	TS_GF1_0.04_1	TS_GF1_0.15_1	TS_GF1_0.4_1
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	NaN	NaN	NaN	978.100000	1.0	NaN	...	NaN	1.566667	3.0	NaN	NaN	NaN
2005-01-01 00:45:00	NaN	NaN	NaN	977.933333	1.0	NaN	...	NaN	1.533333	3.0	NaN	NaN	NaN
2005-01-01 01:15:00	NaN	NaN	NaN	977.900000	1.0	NaN	...	NaN	1.566667	3.0	NaN	NaN	NaN
2005-01-01 01:45:00	NaN	NaN	NaN	977.833333	1.0	NaN	...	NaN	1.566667	3.0	NaN	NaN	NaN
2005-01-01 02:15:00	NaN	NaN	NaN	977.833333	1.0	NaN	...	NaN	1.500000	3.0	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	NaN	304.613900	NaN	983.370890	NaN	0.0	...	NaN	-1.919472	NaN	NaN	NaN	NaN
2024-12-31 22:15:00	NaN	303.039890	NaN	983.052160	NaN	0.0	...	NaN	-2.104678	NaN	NaN	NaN	NaN
2024-12-31 22:45:00	NaN	302.093633	NaN	982.851140	NaN	0.0	...	NaN	-2.089444	NaN	NaN	NaN	NaN
2024-12-31 23:15:00	NaN	302.217307	NaN	982.896827	NaN	0.0	...	NaN	-2.355761	NaN	NaN	NaN	NaN
2024-12-31 23:45:00	NaN	298.392973	NaN	982.856613	NaN	0.0	...	NaN	-2.578839	NaN	NaN	NaN	NaN

350640 rows × 21 columns

Plot

data_simple_2005_2024.plot(subplots=True, x_compat=True, title="Merged data", figsize=(20, 14));

Gap-filling

SW_IN

_df = pd.DataFrame()
_df['SW_IN_T1_2_1'] = data_simple_2005_2024[['SW_IN_T1_2_1']].copy()
_df['SW_IN_POT'] = potrad(timestamp_index=_df.index,
                          lat=47.210227,  # CH-CHA
                          lon=8.410645,  # CH-CHA
                          utc_offset=1)
_df

	SW_IN_T1_2_1	SW_IN_POT
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	3.0	0.0
2005-01-01 00:45:00	3.0	0.0
2005-01-01 01:15:00	3.0	0.0
2005-01-01 01:45:00	3.0	0.0
2005-01-01 02:15:00	0.0	0.0
...	...	...
2024-12-31 21:45:00	0.0	0.0
2024-12-31 22:15:00	0.0	0.0
2024-12-31 22:45:00	0.0	0.0
2024-12-31 23:15:00	0.0	0.0
2024-12-31 23:45:00	0.0	0.0

350640 rows × 2 columns

Set nighttime data to zero

# Detect nighttime
from diive.pkgs.createvar.daynightflag import DaytimeNighttimeFlag
dnf = DaytimeNighttimeFlag(
    timestamp_index=_df.index,
    nighttime_threshold=0.001,
    lat=47.210227,  # CH-CHA
    lon=8.410645,  # CH-CHA
    utc_offset=1)
nighttimeflag = dnf.get_nighttime_flag()
nighttime_ix = nighttimeflag == 1

Some nighttime data is above or below zero:

HeatmapDateTime(_df.loc[nighttime_ix, 'SW_IN_T1_2_1']).plot()

_df.loc[nighttime_ix, 'SW_IN_T1_2_1'].describe()

count    175280.000000
mean          0.281561
std           1.762171
min          -0.158850
25%           0.000000
50%           0.000000
75%           0.000000
max         246.000000
Name: SW_IN_T1_2_1, dtype: float64

_df.loc[nighttime_ix, 'SW_IN_T1_2_1'] = 0
_df.loc[nighttime_ix, 'SW_IN_T1_2_1'].describe()

count    175280.0
mean          0.0
std           0.0
min           0.0
25%           0.0
50%           0.0
75%           0.0
max           0.0
Name: SW_IN_T1_2_1, dtype: float64

HeatmapDateTime(_df.loc[nighttime_ix, 'SW_IN_T1_2_1']).plot()

HeatmapDateTime(_df['SW_IN_T1_2_1']).plot()

Fill

xgb = XGBoostTS(
    input_df=_df,
    target_col='SW_IN_T1_2_1',
    perm_n_repeats=10,
    include_timestamp_as_features=True,
    add_continuous_record_number=True,
    n_estimators=1000,
    random_state=42,
    early_stopping_rounds=50,
    n_jobs=-1
)

Adding new data columns ...
++ Added new columns with timestamp info: ['.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK'] 
++ Added new column .RECORDNUMBER with record numbers from 1 to 350640.

xgb.reduce_features()
xgb.report_feature_reduction()

Feature reduction based on permutation importance ...
[0]	validation_0-rmse:185.09994	validation_1-rmse:185.09994
[1]	validation_0-rmse:154.12846	validation_1-rmse:154.12846
[2]	validation_0-rmse:136.30653	validation_1-rmse:136.30653
[3]	validation_0-rmse:126.53401	validation_1-rmse:126.53401
[4]	validation_0-rmse:121.24482	validation_1-rmse:121.24482
[5]	validation_0-rmse:118.42406	validation_1-rmse:118.42406
[6]	validation_0-rmse:116.93448	validation_1-rmse:116.93448
[7]	validation_0-rmse:115.92969	validation_1-rmse:115.92969
[8]	validation_0-rmse:115.23145	validation_1-rmse:115.23145
[9]	validation_0-rmse:114.63395	validation_1-rmse:114.63395
[10]	validation_0-rmse:114.28162	validation_1-rmse:114.28162
[11]	validation_0-rmse:113.73770	validation_1-rmse:113.73770
[12]	validation_0-rmse:113.32689	validation_1-rmse:113.32689
[13]	validation_0-rmse:113.19577	validation_1-rmse:113.19577
[14]	validation_0-rmse:112.96676	validation_1-rmse:112.96676
[15]	validation_0-rmse:112.73606	validation_1-rmse:112.73606
[16]	validation_0-rmse:112.48335	validation_1-rmse:112.48335
[17]	validation_0-rmse:112.24649	validation_1-rmse:112.24649
[18]	validation_0-rmse:111.61597	validation_1-rmse:111.61597
[19]	validation_0-rmse:111.23004	validation_1-rmse:111.23004
[20]	validation_0-rmse:111.11617	validation_1-rmse:111.11617
[21]	validation_0-rmse:110.89508	validation_1-rmse:110.89508
[22]	validation_0-rmse:110.43816	validation_1-rmse:110.43816
[23]	validation_0-rmse:110.14462	validation_1-rmse:110.14462
[24]	validation_0-rmse:109.82039	validation_1-rmse:109.82039
[25]	validation_0-rmse:109.52867	validation_1-rmse:109.52867
[26]	validation_0-rmse:109.16376	validation_1-rmse:109.16376
[27]	validation_0-rmse:108.98647	validation_1-rmse:108.98647
[28]	validation_0-rmse:108.59392	validation_1-rmse:108.59392
[29]	validation_0-rmse:108.38679	validation_1-rmse:108.38679
[30]	validation_0-rmse:108.10723	validation_1-rmse:108.10723
[31]	validation_0-rmse:107.98273	validation_1-rmse:107.98273
[32]	validation_0-rmse:107.78704	validation_1-rmse:107.78704
[33]	validation_0-rmse:107.67915	validation_1-rmse:107.67915
[34]	validation_0-rmse:107.59724	validation_1-rmse:107.59724
[35]	validation_0-rmse:107.36288	validation_1-rmse:107.36288
[36]	validation_0-rmse:107.24611	validation_1-rmse:107.24611
[37]	validation_0-rmse:107.13180	validation_1-rmse:107.13180
[38]	validation_0-rmse:106.88882	validation_1-rmse:106.88882
[39]	validation_0-rmse:106.66754	validation_1-rmse:106.66754
[40]	validation_0-rmse:106.32001	validation_1-rmse:106.32001
[41]	validation_0-rmse:106.20929	validation_1-rmse:106.20929
[42]	validation_0-rmse:105.90313	validation_1-rmse:105.90313
[43]	validation_0-rmse:105.82066	validation_1-rmse:105.82066
[44]	validation_0-rmse:105.69052	validation_1-rmse:105.69052
[45]	validation_0-rmse:105.54170	validation_1-rmse:105.54170
[46]	validation_0-rmse:105.38900	validation_1-rmse:105.38900
[47]	validation_0-rmse:105.29007	validation_1-rmse:105.29007
[48]	validation_0-rmse:104.96938	validation_1-rmse:104.96938
[49]	validation_0-rmse:104.85564	validation_1-rmse:104.85564
[50]	validation_0-rmse:104.77598	validation_1-rmse:104.77598
[51]	validation_0-rmse:104.65056	validation_1-rmse:104.65056
[52]	validation_0-rmse:104.57097	validation_1-rmse:104.57097
[53]	validation_0-rmse:104.51103	validation_1-rmse:104.51103
[54]	validation_0-rmse:104.29341	validation_1-rmse:104.29341
[55]	validation_0-rmse:104.18283	validation_1-rmse:104.18283
[56]	validation_0-rmse:103.99416	validation_1-rmse:103.99416
[57]	validation_0-rmse:103.85454	validation_1-rmse:103.85454
[58]	validation_0-rmse:103.79718	validation_1-rmse:103.79718
[59]	validation_0-rmse:103.59580	validation_1-rmse:103.59580
[60]	validation_0-rmse:103.52363	validation_1-rmse:103.52363
[61]	validation_0-rmse:103.31070	validation_1-rmse:103.31070
[62]	validation_0-rmse:103.13865	validation_1-rmse:103.13865
[63]	validation_0-rmse:103.05637	validation_1-rmse:103.05637
[64]	validation_0-rmse:102.84352	validation_1-rmse:102.84352
[65]	validation_0-rmse:102.75766	validation_1-rmse:102.75766
[66]	validation_0-rmse:102.69421	validation_1-rmse:102.69421
[67]	validation_0-rmse:102.54546	validation_1-rmse:102.54546
[68]	validation_0-rmse:102.44428	validation_1-rmse:102.44428
[69]	validation_0-rmse:102.30090	validation_1-rmse:102.30090
[70]	validation_0-rmse:102.05886	validation_1-rmse:102.05886
[71]	validation_0-rmse:101.83065	validation_1-rmse:101.83065
[72]	validation_0-rmse:101.65072	validation_1-rmse:101.65072
[73]	validation_0-rmse:101.57503	validation_1-rmse:101.57503
[74]	validation_0-rmse:101.47135	validation_1-rmse:101.47135
[75]	validation_0-rmse:101.39923	validation_1-rmse:101.39923
[76]	validation_0-rmse:101.36518	validation_1-rmse:101.36518
[77]	validation_0-rmse:101.32554	validation_1-rmse:101.32554
[78]	validation_0-rmse:101.08988	validation_1-rmse:101.08988
[79]	validation_0-rmse:100.92074	validation_1-rmse:100.92074
[80]	validation_0-rmse:100.84085	validation_1-rmse:100.84085
[81]	validation_0-rmse:100.80778	validation_1-rmse:100.80778
[82]	validation_0-rmse:100.67857	validation_1-rmse:100.67857
[83]	validation_0-rmse:100.57954	validation_1-rmse:100.57954
[84]	validation_0-rmse:100.38545	validation_1-rmse:100.38545
[85]	validation_0-rmse:100.18837	validation_1-rmse:100.18837
[86]	validation_0-rmse:100.08876	validation_1-rmse:100.08876
[87]	validation_0-rmse:100.00404	validation_1-rmse:100.00404
[88]	validation_0-rmse:99.97361	validation_1-rmse:99.97361
[89]	validation_0-rmse:99.81747	validation_1-rmse:99.81747
[90]	validation_0-rmse:99.72842	validation_1-rmse:99.72842
[91]	validation_0-rmse:99.64551	validation_1-rmse:99.64551
[92]	validation_0-rmse:99.54063	validation_1-rmse:99.54063
[93]	validation_0-rmse:99.39758	validation_1-rmse:99.39758
[94]	validation_0-rmse:99.27048	validation_1-rmse:99.27048
[95]	validation_0-rmse:99.21485	validation_1-rmse:99.21485
[96]	validation_0-rmse:99.05297	validation_1-rmse:99.05297
[97]	validation_0-rmse:98.96003	validation_1-rmse:98.96003
[98]	validation_0-rmse:98.92777	validation_1-rmse:98.92777
[99]	validation_0-rmse:98.79235	validation_1-rmse:98.79235
[100]	validation_0-rmse:98.71846	validation_1-rmse:98.71846
[101]	validation_0-rmse:98.62597	validation_1-rmse:98.62597
[102]	validation_0-rmse:98.53782	validation_1-rmse:98.53782
[103]	validation_0-rmse:98.38305	validation_1-rmse:98.38305
[104]	validation_0-rmse:98.25682	validation_1-rmse:98.25682
[105]	validation_0-rmse:98.17701	validation_1-rmse:98.17701
[106]	validation_0-rmse:98.07447	validation_1-rmse:98.07447
[107]	validation_0-rmse:97.99124	validation_1-rmse:97.99124
[108]	validation_0-rmse:97.88834	validation_1-rmse:97.88834
[109]	validation_0-rmse:97.79163	validation_1-rmse:97.79163
[110]	validation_0-rmse:97.72239	validation_1-rmse:97.72239
[111]	validation_0-rmse:97.67729	validation_1-rmse:97.67729
[112]	validation_0-rmse:97.60591	validation_1-rmse:97.60591
[113]	validation_0-rmse:97.54079	validation_1-rmse:97.54079
[114]	validation_0-rmse:97.37064	validation_1-rmse:97.37064
[115]	validation_0-rmse:97.21470	validation_1-rmse:97.21470
[116]	validation_0-rmse:97.08222	validation_1-rmse:97.08222
[117]	validation_0-rmse:96.99639	validation_1-rmse:96.99639
[118]	validation_0-rmse:96.88954	validation_1-rmse:96.88954
[119]	validation_0-rmse:96.82927	validation_1-rmse:96.82927
[120]	validation_0-rmse:96.75706	validation_1-rmse:96.75706
[121]	validation_0-rmse:96.74022	validation_1-rmse:96.74022
[122]	validation_0-rmse:96.70368	validation_1-rmse:96.70368
[123]	validation_0-rmse:96.57014	validation_1-rmse:96.57014
[124]	validation_0-rmse:96.50018	validation_1-rmse:96.50018
[125]	validation_0-rmse:96.36240	validation_1-rmse:96.36240
[126]	validation_0-rmse:96.23118	validation_1-rmse:96.23118
[127]	validation_0-rmse:96.14793	validation_1-rmse:96.14793
[128]	validation_0-rmse:96.06794	validation_1-rmse:96.06794
[129]	validation_0-rmse:96.05060	validation_1-rmse:96.05060
[130]	validation_0-rmse:95.96283	validation_1-rmse:95.96283
[131]	validation_0-rmse:95.93195	validation_1-rmse:95.93195
[132]	validation_0-rmse:95.77245	validation_1-rmse:95.77245
[133]	validation_0-rmse:95.68567	validation_1-rmse:95.68567
[134]	validation_0-rmse:95.61351	validation_1-rmse:95.61351
[135]	validation_0-rmse:95.56431	validation_1-rmse:95.56431
[136]	validation_0-rmse:95.45833	validation_1-rmse:95.45833
[137]	validation_0-rmse:95.39358	validation_1-rmse:95.39358
[138]	validation_0-rmse:95.28882	validation_1-rmse:95.28882
[139]	validation_0-rmse:95.22765	validation_1-rmse:95.22765
[140]	validation_0-rmse:95.19902	validation_1-rmse:95.19902
[141]	validation_0-rmse:94.97059	validation_1-rmse:94.97059
[142]	validation_0-rmse:94.89584	validation_1-rmse:94.89584
[143]	validation_0-rmse:94.86383	validation_1-rmse:94.86383
[144]	validation_0-rmse:94.81250	validation_1-rmse:94.81250
[145]	validation_0-rmse:94.74577	validation_1-rmse:94.74577
[146]	validation_0-rmse:94.63249	validation_1-rmse:94.63249
[147]	validation_0-rmse:94.52036	validation_1-rmse:94.52036
[148]	validation_0-rmse:94.41464	validation_1-rmse:94.41464
[149]	validation_0-rmse:94.36360	validation_1-rmse:94.36360
[150]	validation_0-rmse:94.31671	validation_1-rmse:94.31671
[151]	validation_0-rmse:94.23376	validation_1-rmse:94.23376
[152]	validation_0-rmse:94.11304	validation_1-rmse:94.11304
[153]	validation_0-rmse:94.05421	validation_1-rmse:94.05421
[154]	validation_0-rmse:93.93566	validation_1-rmse:93.93566
[155]	validation_0-rmse:93.79253	validation_1-rmse:93.79253
[156]	validation_0-rmse:93.67400	validation_1-rmse:93.67400
[157]	validation_0-rmse:93.59455	validation_1-rmse:93.59455
[158]	validation_0-rmse:93.46415	validation_1-rmse:93.46415
[159]	validation_0-rmse:93.41323	validation_1-rmse:93.41323
[160]	validation_0-rmse:93.28352	validation_1-rmse:93.28352
[161]	validation_0-rmse:93.19910	validation_1-rmse:93.19910
[162]	validation_0-rmse:92.97845	validation_1-rmse:92.97845
[163]	validation_0-rmse:92.83474	validation_1-rmse:92.83474
[164]	validation_0-rmse:92.72622	validation_1-rmse:92.72622
[165]	validation_0-rmse:92.55734	validation_1-rmse:92.55734
[166]	validation_0-rmse:92.41439	validation_1-rmse:92.41439
[167]	validation_0-rmse:92.27319	validation_1-rmse:92.27319
[168]	validation_0-rmse:92.18238	validation_1-rmse:92.18238
[169]	validation_0-rmse:92.09044	validation_1-rmse:92.09044
[170]	validation_0-rmse:92.01903	validation_1-rmse:92.01903
[171]	validation_0-rmse:91.97633	validation_1-rmse:91.97633
[172]	validation_0-rmse:91.90855	validation_1-rmse:91.90855
[173]	validation_0-rmse:91.82227	validation_1-rmse:91.82227
[174]	validation_0-rmse:91.75642	validation_1-rmse:91.75642
[175]	validation_0-rmse:91.68508	validation_1-rmse:91.68508
[176]	validation_0-rmse:91.63113	validation_1-rmse:91.63113
[177]	validation_0-rmse:91.58390	validation_1-rmse:91.58390
[178]	validation_0-rmse:91.51633	validation_1-rmse:91.51633
[179]	validation_0-rmse:91.44910	validation_1-rmse:91.44910
[180]	validation_0-rmse:91.33009	validation_1-rmse:91.33009
[181]	validation_0-rmse:91.23938	validation_1-rmse:91.23938
[182]	validation_0-rmse:91.17797	validation_1-rmse:91.17797
[183]	validation_0-rmse:91.16614	validation_1-rmse:91.16614
[184]	validation_0-rmse:91.15427	validation_1-rmse:91.15427
[185]	validation_0-rmse:91.09898	validation_1-rmse:91.09898
[186]	validation_0-rmse:91.07660	validation_1-rmse:91.07660
[187]	validation_0-rmse:90.98847	validation_1-rmse:90.98847
[188]	validation_0-rmse:90.98015	validation_1-rmse:90.98015
[189]	validation_0-rmse:90.94033	validation_1-rmse:90.94033
[190]	validation_0-rmse:90.89704	validation_1-rmse:90.89704
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[747]	validation_0-rmse:68.91598	validation_1-rmse:68.91598
[748]	validation_0-rmse:68.87973	validation_1-rmse:68.87973
[749]	validation_0-rmse:68.86068	validation_1-rmse:68.86068
[750]	validation_0-rmse:68.82211	validation_1-rmse:68.82211
[751]	validation_0-rmse:68.79252	validation_1-rmse:68.79252
[752]	validation_0-rmse:68.76144	validation_1-rmse:68.76144
[753]	validation_0-rmse:68.74953	validation_1-rmse:68.74953
[754]	validation_0-rmse:68.72418	validation_1-rmse:68.72418
[755]	validation_0-rmse:68.70488	validation_1-rmse:68.70488
[756]	validation_0-rmse:68.69714	validation_1-rmse:68.69714
[757]	validation_0-rmse:68.66776	validation_1-rmse:68.66776
[758]	validation_0-rmse:68.66059	validation_1-rmse:68.66059
[759]	validation_0-rmse:68.62021	validation_1-rmse:68.62021
[760]	validation_0-rmse:68.58760	validation_1-rmse:68.58760
[761]	validation_0-rmse:68.55151	validation_1-rmse:68.55151
[762]	validation_0-rmse:68.51850	validation_1-rmse:68.51850
[763]	validation_0-rmse:68.49214	validation_1-rmse:68.49214
[764]	validation_0-rmse:68.44796	validation_1-rmse:68.44796
[765]	validation_0-rmse:68.42238	validation_1-rmse:68.42238
[766]	validation_0-rmse:68.39061	validation_1-rmse:68.39061
[767]	validation_0-rmse:68.37852	validation_1-rmse:68.37852
[768]	validation_0-rmse:68.37065	validation_1-rmse:68.37065
[769]	validation_0-rmse:68.34665	validation_1-rmse:68.34665
[770]	validation_0-rmse:68.32280	validation_1-rmse:68.32280
[771]	validation_0-rmse:68.29570	validation_1-rmse:68.29570
[772]	validation_0-rmse:68.26375	validation_1-rmse:68.26375
[773]	validation_0-rmse:68.23581	validation_1-rmse:68.23581
[774]	validation_0-rmse:68.20696	validation_1-rmse:68.20696
[775]	validation_0-rmse:68.18782	validation_1-rmse:68.18782
[776]	validation_0-rmse:68.17755	validation_1-rmse:68.17755
[777]	validation_0-rmse:68.14399	validation_1-rmse:68.14399
[778]	validation_0-rmse:68.13230	validation_1-rmse:68.13230
[779]	validation_0-rmse:68.10977	validation_1-rmse:68.10977
[780]	validation_0-rmse:68.06074	validation_1-rmse:68.06074
[781]	validation_0-rmse:68.02674	validation_1-rmse:68.02674
[782]	validation_0-rmse:68.01628	validation_1-rmse:68.01628
[783]	validation_0-rmse:67.98563	validation_1-rmse:67.98563
[784]	validation_0-rmse:67.96980	validation_1-rmse:67.96980
[785]	validation_0-rmse:67.95375	validation_1-rmse:67.95375
[786]	validation_0-rmse:67.94434	validation_1-rmse:67.94434
[787]	validation_0-rmse:67.90770	validation_1-rmse:67.90770
[788]	validation_0-rmse:67.88928	validation_1-rmse:67.88928
[789]	validation_0-rmse:67.88036	validation_1-rmse:67.88036
[790]	validation_0-rmse:67.85227	validation_1-rmse:67.85227
[791]	validation_0-rmse:67.82437	validation_1-rmse:67.82437
[792]	validation_0-rmse:67.77736	validation_1-rmse:67.77736
[793]	validation_0-rmse:67.75404	validation_1-rmse:67.75404
[794]	validation_0-rmse:67.71998	validation_1-rmse:67.71998
[795]	validation_0-rmse:67.67724	validation_1-rmse:67.67724
[796]	validation_0-rmse:67.64349	validation_1-rmse:67.64349
[797]	validation_0-rmse:67.61939	validation_1-rmse:67.61939
[798]	validation_0-rmse:67.59663	validation_1-rmse:67.59663
[799]	validation_0-rmse:67.56784	validation_1-rmse:67.56784
[800]	validation_0-rmse:67.54103	validation_1-rmse:67.54103
[801]	validation_0-rmse:67.51345	validation_1-rmse:67.51345
[802]	validation_0-rmse:67.47197	validation_1-rmse:67.47197
[803]	validation_0-rmse:67.45628	validation_1-rmse:67.45628
[804]	validation_0-rmse:67.43626	validation_1-rmse:67.43626
[805]	validation_0-rmse:67.41682	validation_1-rmse:67.41682
[806]	validation_0-rmse:67.39210	validation_1-rmse:67.39210
[807]	validation_0-rmse:67.36078	validation_1-rmse:67.36078
[808]	validation_0-rmse:67.34325	validation_1-rmse:67.34325
[809]	validation_0-rmse:67.31342	validation_1-rmse:67.31342
[810]	validation_0-rmse:67.28649	validation_1-rmse:67.28649
[811]	validation_0-rmse:67.25785	validation_1-rmse:67.25785
[812]	validation_0-rmse:67.23091	validation_1-rmse:67.23091
[813]	validation_0-rmse:67.21091	validation_1-rmse:67.21091
[814]	validation_0-rmse:67.19453	validation_1-rmse:67.19453
[815]	validation_0-rmse:67.17409	validation_1-rmse:67.17409
[816]	validation_0-rmse:67.14020	validation_1-rmse:67.14020
[817]	validation_0-rmse:67.12354	validation_1-rmse:67.12354
[818]	validation_0-rmse:67.10666	validation_1-rmse:67.10666
[819]	validation_0-rmse:67.08063	validation_1-rmse:67.08063
[820]	validation_0-rmse:67.05846	validation_1-rmse:67.05846
[821]	validation_0-rmse:67.03666	validation_1-rmse:67.03666
[822]	validation_0-rmse:67.00232	validation_1-rmse:67.00232
[823]	validation_0-rmse:66.97644	validation_1-rmse:66.97644
[824]	validation_0-rmse:66.93928	validation_1-rmse:66.93928
[825]	validation_0-rmse:66.90388	validation_1-rmse:66.90388
[826]	validation_0-rmse:66.87824	validation_1-rmse:66.87824
[827]	validation_0-rmse:66.83908	validation_1-rmse:66.83908
[828]	validation_0-rmse:66.81186	validation_1-rmse:66.81186
[829]	validation_0-rmse:66.77984	validation_1-rmse:66.77984
[830]	validation_0-rmse:66.75443	validation_1-rmse:66.75443
[831]	validation_0-rmse:66.72411	validation_1-rmse:66.72411
[832]	validation_0-rmse:66.71063	validation_1-rmse:66.71063
[833]	validation_0-rmse:66.69757	validation_1-rmse:66.69757
[834]	validation_0-rmse:66.66419	validation_1-rmse:66.66419
[835]	validation_0-rmse:66.62499	validation_1-rmse:66.62499
[836]	validation_0-rmse:66.61254	validation_1-rmse:66.61254
[837]	validation_0-rmse:66.57628	validation_1-rmse:66.57628
[838]	validation_0-rmse:66.54827	validation_1-rmse:66.54827
[839]	validation_0-rmse:66.52206	validation_1-rmse:66.52206
[840]	validation_0-rmse:66.50300	validation_1-rmse:66.50300
[841]	validation_0-rmse:66.48295	validation_1-rmse:66.48295
[842]	validation_0-rmse:66.45554	validation_1-rmse:66.45554
[843]	validation_0-rmse:66.44810	validation_1-rmse:66.44810
[844]	validation_0-rmse:66.43269	validation_1-rmse:66.43269
[845]	validation_0-rmse:66.39650	validation_1-rmse:66.39650
[846]	validation_0-rmse:66.36100	validation_1-rmse:66.36100
[847]	validation_0-rmse:66.32138	validation_1-rmse:66.32138
[848]	validation_0-rmse:66.29216	validation_1-rmse:66.29216
[849]	validation_0-rmse:66.27423	validation_1-rmse:66.27423
[850]	validation_0-rmse:66.25761	validation_1-rmse:66.25761
[851]	validation_0-rmse:66.22816	validation_1-rmse:66.22816
[852]	validation_0-rmse:66.19463	validation_1-rmse:66.19463
[853]	validation_0-rmse:66.18334	validation_1-rmse:66.18334
[854]	validation_0-rmse:66.16116	validation_1-rmse:66.16116
[855]	validation_0-rmse:66.13451	validation_1-rmse:66.13451
[856]	validation_0-rmse:66.11852	validation_1-rmse:66.11852
[857]	validation_0-rmse:66.08844	validation_1-rmse:66.08844
[858]	validation_0-rmse:66.06520	validation_1-rmse:66.06520
[859]	validation_0-rmse:66.04235	validation_1-rmse:66.04235
[860]	validation_0-rmse:65.99811	validation_1-rmse:65.99811
[861]	validation_0-rmse:65.97011	validation_1-rmse:65.97011
[862]	validation_0-rmse:65.95907	validation_1-rmse:65.95907
[863]	validation_0-rmse:65.93954	validation_1-rmse:65.93954
[864]	validation_0-rmse:65.92194	validation_1-rmse:65.92194
[865]	validation_0-rmse:65.89862	validation_1-rmse:65.89862
[866]	validation_0-rmse:65.87153	validation_1-rmse:65.87153
[867]	validation_0-rmse:65.85048	validation_1-rmse:65.85048
[868]	validation_0-rmse:65.82454	validation_1-rmse:65.82454
[869]	validation_0-rmse:65.79920	validation_1-rmse:65.79920
[870]	validation_0-rmse:65.76495	validation_1-rmse:65.76495
[871]	validation_0-rmse:65.75738	validation_1-rmse:65.75738
[872]	validation_0-rmse:65.72397	validation_1-rmse:65.72397
[873]	validation_0-rmse:65.69343	validation_1-rmse:65.69343
[874]	validation_0-rmse:65.67907	validation_1-rmse:65.67907
[875]	validation_0-rmse:65.67038	validation_1-rmse:65.67038
[876]	validation_0-rmse:65.65504	validation_1-rmse:65.65504
[877]	validation_0-rmse:65.62717	validation_1-rmse:65.62717
[878]	validation_0-rmse:65.60652	validation_1-rmse:65.60652
[879]	validation_0-rmse:65.58859	validation_1-rmse:65.58859
[880]	validation_0-rmse:65.57011	validation_1-rmse:65.57011
[881]	validation_0-rmse:65.55110	validation_1-rmse:65.55110
[882]	validation_0-rmse:65.52940	validation_1-rmse:65.52940
[883]	validation_0-rmse:65.48576	validation_1-rmse:65.48576
[884]	validation_0-rmse:65.45863	validation_1-rmse:65.45863
[885]	validation_0-rmse:65.45562	validation_1-rmse:65.45562
[886]	validation_0-rmse:65.44426	validation_1-rmse:65.44426
[887]	validation_0-rmse:65.43555	validation_1-rmse:65.43555
[888]	validation_0-rmse:65.41352	validation_1-rmse:65.41352
[889]	validation_0-rmse:65.40173	validation_1-rmse:65.40173
[890]	validation_0-rmse:65.39652	validation_1-rmse:65.39652
[891]	validation_0-rmse:65.39441	validation_1-rmse:65.39441
[892]	validation_0-rmse:65.36817	validation_1-rmse:65.36817
[893]	validation_0-rmse:65.36057	validation_1-rmse:65.36057
[894]	validation_0-rmse:65.33465	validation_1-rmse:65.33465
[895]	validation_0-rmse:65.31255	validation_1-rmse:65.31255
[896]	validation_0-rmse:65.26655	validation_1-rmse:65.26655
[897]	validation_0-rmse:65.25698	validation_1-rmse:65.25698
[898]	validation_0-rmse:65.22713	validation_1-rmse:65.22713
[899]	validation_0-rmse:65.20714	validation_1-rmse:65.20714
[900]	validation_0-rmse:65.18782	validation_1-rmse:65.18782
[901]	validation_0-rmse:65.16240	validation_1-rmse:65.16240
[902]	validation_0-rmse:65.13613	validation_1-rmse:65.13613
[903]	validation_0-rmse:65.12542	validation_1-rmse:65.12542
[904]	validation_0-rmse:65.09252	validation_1-rmse:65.09252
[905]	validation_0-rmse:65.06766	validation_1-rmse:65.06766
[906]	validation_0-rmse:65.04596	validation_1-rmse:65.04596
[907]	validation_0-rmse:65.00073	validation_1-rmse:65.00073
[908]	validation_0-rmse:64.95703	validation_1-rmse:64.95703
[909]	validation_0-rmse:64.92804	validation_1-rmse:64.92804
[910]	validation_0-rmse:64.91040	validation_1-rmse:64.91040
[911]	validation_0-rmse:64.87843	validation_1-rmse:64.87843
[912]	validation_0-rmse:64.85505	validation_1-rmse:64.85505
[913]	validation_0-rmse:64.83147	validation_1-rmse:64.83147
[914]	validation_0-rmse:64.79677	validation_1-rmse:64.79677
[915]	validation_0-rmse:64.77500	validation_1-rmse:64.77500
[916]	validation_0-rmse:64.75322	validation_1-rmse:64.75322
[917]	validation_0-rmse:64.74870	validation_1-rmse:64.74870
[918]	validation_0-rmse:64.71685	validation_1-rmse:64.71685
[919]	validation_0-rmse:64.70216	validation_1-rmse:64.70216
[920]	validation_0-rmse:64.67673	validation_1-rmse:64.67673
[921]	validation_0-rmse:64.67349	validation_1-rmse:64.67349
[922]	validation_0-rmse:64.65668	validation_1-rmse:64.65668
[923]	validation_0-rmse:64.63454	validation_1-rmse:64.63454
[924]	validation_0-rmse:64.59136	validation_1-rmse:64.59136
[925]	validation_0-rmse:64.56888	validation_1-rmse:64.56888
[926]	validation_0-rmse:64.53769	validation_1-rmse:64.53769
[927]	validation_0-rmse:64.51712	validation_1-rmse:64.51712
[928]	validation_0-rmse:64.49035	validation_1-rmse:64.49035
[929]	validation_0-rmse:64.47629	validation_1-rmse:64.47629
[930]	validation_0-rmse:64.46063	validation_1-rmse:64.46063
[931]	validation_0-rmse:64.44754	validation_1-rmse:64.44754
[932]	validation_0-rmse:64.43414	validation_1-rmse:64.43414
[933]	validation_0-rmse:64.41035	validation_1-rmse:64.41035
[934]	validation_0-rmse:64.38683	validation_1-rmse:64.38683
[935]	validation_0-rmse:64.36742	validation_1-rmse:64.36742
[936]	validation_0-rmse:64.35144	validation_1-rmse:64.35144
[937]	validation_0-rmse:64.30974	validation_1-rmse:64.30974
[938]	validation_0-rmse:64.29326	validation_1-rmse:64.29326
[939]	validation_0-rmse:64.27760	validation_1-rmse:64.27760
[940]	validation_0-rmse:64.26825	validation_1-rmse:64.26825
[941]	validation_0-rmse:64.25621	validation_1-rmse:64.25621
[942]	validation_0-rmse:64.24766	validation_1-rmse:64.24766
[943]	validation_0-rmse:64.22764	validation_1-rmse:64.22764
[944]	validation_0-rmse:64.21202	validation_1-rmse:64.21202
[945]	validation_0-rmse:64.20248	validation_1-rmse:64.20248
[946]	validation_0-rmse:64.17783	validation_1-rmse:64.17783
[947]	validation_0-rmse:64.16466	validation_1-rmse:64.16466
[948]	validation_0-rmse:64.15874	validation_1-rmse:64.15874
[949]	validation_0-rmse:64.13141	validation_1-rmse:64.13141
[950]	validation_0-rmse:64.11884	validation_1-rmse:64.11884
[951]	validation_0-rmse:64.10970	validation_1-rmse:64.10970
[952]	validation_0-rmse:64.08906	validation_1-rmse:64.08906
[953]	validation_0-rmse:64.06002	validation_1-rmse:64.06002
[954]	validation_0-rmse:64.03760	validation_1-rmse:64.03760
[955]	validation_0-rmse:64.00221	validation_1-rmse:64.00221
[956]	validation_0-rmse:63.98118	validation_1-rmse:63.98118
[957]	validation_0-rmse:63.96523	validation_1-rmse:63.96523
[958]	validation_0-rmse:63.94410	validation_1-rmse:63.94410
[959]	validation_0-rmse:63.92796	validation_1-rmse:63.92796
[960]	validation_0-rmse:63.91382	validation_1-rmse:63.91382
[961]	validation_0-rmse:63.90185	validation_1-rmse:63.90185
[962]	validation_0-rmse:63.87376	validation_1-rmse:63.87376
[963]	validation_0-rmse:63.85906	validation_1-rmse:63.85906
[964]	validation_0-rmse:63.84629	validation_1-rmse:63.84629
[965]	validation_0-rmse:63.82509	validation_1-rmse:63.82509
[966]	validation_0-rmse:63.80025	validation_1-rmse:63.80025
[967]	validation_0-rmse:63.78279	validation_1-rmse:63.78279
[968]	validation_0-rmse:63.75172	validation_1-rmse:63.75172
[969]	validation_0-rmse:63.72385	validation_1-rmse:63.72385
[970]	validation_0-rmse:63.70342	validation_1-rmse:63.70342
[971]	validation_0-rmse:63.68833	validation_1-rmse:63.68833
[972]	validation_0-rmse:63.67054	validation_1-rmse:63.67054
[973]	validation_0-rmse:63.66746	validation_1-rmse:63.66746
[974]	validation_0-rmse:63.65133	validation_1-rmse:63.65133
[975]	validation_0-rmse:63.64009	validation_1-rmse:63.64009
[976]	validation_0-rmse:63.62297	validation_1-rmse:63.62297
[977]	validation_0-rmse:63.60890	validation_1-rmse:63.60890
[978]	validation_0-rmse:63.59255	validation_1-rmse:63.59255
[979]	validation_0-rmse:63.57069	validation_1-rmse:63.57069
[980]	validation_0-rmse:63.55206	validation_1-rmse:63.55206
[981]	validation_0-rmse:63.52605	validation_1-rmse:63.52605
[982]	validation_0-rmse:63.51738	validation_1-rmse:63.51738
[983]	validation_0-rmse:63.50316	validation_1-rmse:63.50316
[984]	validation_0-rmse:63.48602	validation_1-rmse:63.48602
[985]	validation_0-rmse:63.47019	validation_1-rmse:63.47019
[986]	validation_0-rmse:63.44021	validation_1-rmse:63.44021
[987]	validation_0-rmse:63.42384	validation_1-rmse:63.42384
[988]	validation_0-rmse:63.41953	validation_1-rmse:63.41953
[989]	validation_0-rmse:63.40356	validation_1-rmse:63.40356
[990]	validation_0-rmse:63.39302	validation_1-rmse:63.39302
[991]	validation_0-rmse:63.37929	validation_1-rmse:63.37929
[992]	validation_0-rmse:63.35635	validation_1-rmse:63.35635
[993]	validation_0-rmse:63.32394	validation_1-rmse:63.32394
[994]	validation_0-rmse:63.30817	validation_1-rmse:63.30817
[995]	validation_0-rmse:63.28671	validation_1-rmse:63.28671
[996]	validation_0-rmse:63.26444	validation_1-rmse:63.26444
[997]	validation_0-rmse:63.21869	validation_1-rmse:63.21869
[998]	validation_0-rmse:63.19145	validation_1-rmse:63.19145
[999]	validation_0-rmse:63.17957	validation_1-rmse:63.17957
>>> Calculating feature importances (permutation importance, 10 repeats) ...
>>> Setting threshold for feature rejection to 0.04310191036847396.
>>> Removing rejected features from model data ...

=================
FEATURE REDUCTION
=================

- Target variable: SW_IN_T1_2_1

- The random variable .RANDOM was added to the original features, used as benchmark for detecting relevant feature importances.

PERMUTATION IMPORTANCE (mean) across all splits of TimeSeriesSplit:

               PERM_IMPORTANCE   PERM_SD
SW_IN_POT             1.630007  0.004663
.DOY                  0.525178  0.004180
.RECORDNUMBER         0.490144  0.002605
.WEEK                 0.295492  0.001627
.YEARDOY              0.282347  0.001769
.YEARMONTH            0.276780  0.001507
.YEARWEEK             0.271208  0.000979
.RANDOM               0.043102  0.000310
.YEAR                 0.028569  0.000118
.HOUR                 0.017962  0.000118
.MONTH                0.013074  0.000197
.SEASON               0.008000  0.000127

- These results are from feature reduction. Note that feature importances for the final model are calculated during gap-filling.

--> 11 original input features (before feature reduction): ['SW_IN_POT', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
--> 4 rejected features (during feature reduction): ['.YEAR', '.SEASON', '.MONTH', '.HOUR']
--> 7 accepted features (after feature reduction): ['SW_IN_POT', '.DOY', '.RECORDNUMBER', '.WEEK', '.YEARDOY', '.YEARMONTH', '.YEARWEEK']

xgb.trainmodel(showplot_scores=False, showplot_importance=False)
xgb.report_traintest()

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2005-01-01 00:45:00 and 2024-12-31 23:45:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:185.11802	validation_1-rmse:184.35022
[1]	validation_0-rmse:154.17017	validation_1-rmse:153.54128
[2]	validation_0-rmse:136.36258	validation_1-rmse:135.88500
[3]	validation_0-rmse:126.52564	validation_1-rmse:126.15580
[4]	validation_0-rmse:121.27460	validation_1-rmse:121.04368
[5]	validation_0-rmse:118.47730	validation_1-rmse:118.38885
[6]	validation_0-rmse:116.98657	validation_1-rmse:116.91071
[7]	validation_0-rmse:115.93105	validation_1-rmse:115.93634
[8]	validation_0-rmse:115.26781	validation_1-rmse:115.35900
[9]	validation_0-rmse:114.90300	validation_1-rmse:115.02270
[10]	validation_0-rmse:114.39260	validation_1-rmse:114.55386
[11]	validation_0-rmse:113.90200	validation_1-rmse:114.10400
[12]	validation_0-rmse:113.58487	validation_1-rmse:113.81515
[13]	validation_0-rmse:113.14914	validation_1-rmse:113.45330
[14]	validation_0-rmse:112.85873	validation_1-rmse:113.22469
[15]	validation_0-rmse:112.68333	validation_1-rmse:113.13115
[16]	validation_0-rmse:112.28168	validation_1-rmse:112.84127
[17]	validation_0-rmse:112.01332	validation_1-rmse:112.63764
[18]	validation_0-rmse:111.73322	validation_1-rmse:112.43446
[19]	validation_0-rmse:111.17583	validation_1-rmse:111.90060
[20]	validation_0-rmse:110.85038	validation_1-rmse:111.66811
[21]	validation_0-rmse:110.56450	validation_1-rmse:111.44285
[22]	validation_0-rmse:110.34817	validation_1-rmse:111.27071
[23]	validation_0-rmse:110.02465	validation_1-rmse:110.97777
[24]	validation_0-rmse:109.93603	validation_1-rmse:110.90457
[25]	validation_0-rmse:109.62699	validation_1-rmse:110.62249
[26]	validation_0-rmse:109.23021	validation_1-rmse:110.32175
[27]	validation_0-rmse:109.10695	validation_1-rmse:110.21918
[28]	validation_0-rmse:108.81809	validation_1-rmse:109.99304
[29]	validation_0-rmse:108.66971	validation_1-rmse:109.85961
[30]	validation_0-rmse:108.46127	validation_1-rmse:109.69785
[31]	validation_0-rmse:108.24126	validation_1-rmse:109.49695
[32]	validation_0-rmse:108.10803	validation_1-rmse:109.41657
[33]	validation_0-rmse:107.78793	validation_1-rmse:109.12429
[34]	validation_0-rmse:107.57544	validation_1-rmse:108.96108
[35]	validation_0-rmse:107.39765	validation_1-rmse:108.83279
[36]	validation_0-rmse:107.29375	validation_1-rmse:108.75072
[37]	validation_0-rmse:107.02678	validation_1-rmse:108.52388
[38]	validation_0-rmse:106.80414	validation_1-rmse:108.36262
[39]	validation_0-rmse:106.57670	validation_1-rmse:108.18462
[40]	validation_0-rmse:106.41872	validation_1-rmse:108.03792
[41]	validation_0-rmse:106.28774	validation_1-rmse:107.91877
[42]	validation_0-rmse:106.02169	validation_1-rmse:107.68246
[43]	validation_0-rmse:105.86752	validation_1-rmse:107.58962
[44]	validation_0-rmse:105.76186	validation_1-rmse:107.50375
[45]	validation_0-rmse:105.68864	validation_1-rmse:107.43246
[46]	validation_0-rmse:105.46403	validation_1-rmse:107.25195
[47]	validation_0-rmse:105.38416	validation_1-rmse:107.19984
[48]	validation_0-rmse:105.21083	validation_1-rmse:107.04792
[49]	validation_0-rmse:104.92321	validation_1-rmse:106.79261
[50]	validation_0-rmse:104.75558	validation_1-rmse:106.70657
[51]	validation_0-rmse:104.65021	validation_1-rmse:106.64496
[52]	validation_0-rmse:104.49951	validation_1-rmse:106.55144
[53]	validation_0-rmse:104.39543	validation_1-rmse:106.44742
[54]	validation_0-rmse:104.25432	validation_1-rmse:106.33093
[55]	validation_0-rmse:104.22601	validation_1-rmse:106.31525
[56]	validation_0-rmse:104.09024	validation_1-rmse:106.28268
[57]	validation_0-rmse:103.98640	validation_1-rmse:106.20355
[58]	validation_0-rmse:103.81095	validation_1-rmse:106.06483
[59]	validation_0-rmse:103.58616	validation_1-rmse:105.84937
[60]	validation_0-rmse:103.48275	validation_1-rmse:105.78134
[61]	validation_0-rmse:103.44817	validation_1-rmse:105.75798
[62]	validation_0-rmse:103.31301	validation_1-rmse:105.63338
[63]	validation_0-rmse:103.18033	validation_1-rmse:105.51759
[64]	validation_0-rmse:103.07352	validation_1-rmse:105.43948
[65]	validation_0-rmse:102.78616	validation_1-rmse:105.22365
[66]	validation_0-rmse:102.54774	validation_1-rmse:105.00280
[67]	validation_0-rmse:102.39124	validation_1-rmse:104.88209
[68]	validation_0-rmse:102.26387	validation_1-rmse:104.78015
[69]	validation_0-rmse:102.20396	validation_1-rmse:104.73908
[70]	validation_0-rmse:102.03176	validation_1-rmse:104.61031
[71]	validation_0-rmse:101.86514	validation_1-rmse:104.47565
[72]	validation_0-rmse:101.68067	validation_1-rmse:104.29494
[73]	validation_0-rmse:101.44656	validation_1-rmse:104.08942
[74]	validation_0-rmse:101.28445	validation_1-rmse:103.96874
[75]	validation_0-rmse:101.18538	validation_1-rmse:103.89657
[76]	validation_0-rmse:100.95231	validation_1-rmse:103.71793
[77]	validation_0-rmse:100.82498	validation_1-rmse:103.63452
[78]	validation_0-rmse:100.71174	validation_1-rmse:103.54115
[79]	validation_0-rmse:100.66005	validation_1-rmse:103.49768
[80]	validation_0-rmse:100.63274	validation_1-rmse:103.47200
[81]	validation_0-rmse:100.60808	validation_1-rmse:103.45783
[82]	validation_0-rmse:100.52945	validation_1-rmse:103.45690
[83]	validation_0-rmse:100.44752	validation_1-rmse:103.40279
[84]	validation_0-rmse:100.32678	validation_1-rmse:103.30662
[85]	validation_0-rmse:100.26314	validation_1-rmse:103.25188
[86]	validation_0-rmse:100.24039	validation_1-rmse:103.23568
[87]	validation_0-rmse:100.20029	validation_1-rmse:103.21128
[88]	validation_0-rmse:100.10729	validation_1-rmse:103.14679
[89]	validation_0-rmse:99.97283	validation_1-rmse:103.04499
[90]	validation_0-rmse:99.85284	validation_1-rmse:102.94041
[91]	validation_0-rmse:99.66406	validation_1-rmse:102.76488
[92]	validation_0-rmse:99.54745	validation_1-rmse:102.68264
[93]	validation_0-rmse:99.52567	validation_1-rmse:102.66809
[94]	validation_0-rmse:99.47409	validation_1-rmse:102.62861
[95]	validation_0-rmse:99.31069	validation_1-rmse:102.50852
[96]	validation_0-rmse:99.23375	validation_1-rmse:102.53655
[97]	validation_0-rmse:99.11839	validation_1-rmse:102.45793
[98]	validation_0-rmse:99.04044	validation_1-rmse:102.38232
[99]	validation_0-rmse:98.91922	validation_1-rmse:102.27574
[100]	validation_0-rmse:98.76434	validation_1-rmse:102.12618
[101]	validation_0-rmse:98.59487	validation_1-rmse:102.00492
[102]	validation_0-rmse:98.55390	validation_1-rmse:101.97578
[103]	validation_0-rmse:98.40414	validation_1-rmse:101.85765
[104]	validation_0-rmse:98.29797	validation_1-rmse:101.78094
[105]	validation_0-rmse:98.17811	validation_1-rmse:101.67353
[106]	validation_0-rmse:98.12822	validation_1-rmse:101.62907
[107]	validation_0-rmse:98.05771	validation_1-rmse:101.57421
[108]	validation_0-rmse:97.91734	validation_1-rmse:101.49471
[109]	validation_0-rmse:97.77077	validation_1-rmse:101.37693
[110]	validation_0-rmse:97.64086	validation_1-rmse:101.26721
[111]	validation_0-rmse:97.49759	validation_1-rmse:101.17762
[112]	validation_0-rmse:97.34346	validation_1-rmse:101.04020
[113]	validation_0-rmse:97.19209	validation_1-rmse:100.91169
[114]	validation_0-rmse:97.09364	validation_1-rmse:100.84242
[115]	validation_0-rmse:97.07776	validation_1-rmse:100.83165
[116]	validation_0-rmse:96.99096	validation_1-rmse:100.78157
[117]	validation_0-rmse:96.89081	validation_1-rmse:100.70848
[118]	validation_0-rmse:96.82187	validation_1-rmse:100.67523
[119]	validation_0-rmse:96.74895	validation_1-rmse:100.63468
[120]	validation_0-rmse:96.67899	validation_1-rmse:100.63475
[121]	validation_0-rmse:96.62308	validation_1-rmse:100.65265
[122]	validation_0-rmse:96.60921	validation_1-rmse:100.64247
[123]	validation_0-rmse:96.57198	validation_1-rmse:100.65196
[124]	validation_0-rmse:96.48595	validation_1-rmse:100.57307
[125]	validation_0-rmse:96.40968	validation_1-rmse:100.50627
[126]	validation_0-rmse:96.37232	validation_1-rmse:100.51965
[127]	validation_0-rmse:96.31613	validation_1-rmse:100.49132
[128]	validation_0-rmse:96.30103	validation_1-rmse:100.48261
[129]	validation_0-rmse:96.25688	validation_1-rmse:100.46325
[130]	validation_0-rmse:96.21718	validation_1-rmse:100.43136
[131]	validation_0-rmse:96.12588	validation_1-rmse:100.35386
[132]	validation_0-rmse:96.00679	validation_1-rmse:100.27006
[133]	validation_0-rmse:95.90265	validation_1-rmse:100.20592
[134]	validation_0-rmse:95.82181	validation_1-rmse:100.15524
[135]	validation_0-rmse:95.73071	validation_1-rmse:100.07756
[136]	validation_0-rmse:95.66318	validation_1-rmse:100.01943
[137]	validation_0-rmse:95.61805	validation_1-rmse:99.99151
[138]	validation_0-rmse:95.57443	validation_1-rmse:99.96687
[139]	validation_0-rmse:95.53057	validation_1-rmse:99.93617
[140]	validation_0-rmse:95.47154	validation_1-rmse:99.87175
[141]	validation_0-rmse:95.33623	validation_1-rmse:99.80632
[142]	validation_0-rmse:95.22834	validation_1-rmse:99.75784
[143]	validation_0-rmse:95.05939	validation_1-rmse:99.61194
[144]	validation_0-rmse:95.02124	validation_1-rmse:99.59472
[145]	validation_0-rmse:94.85632	validation_1-rmse:99.46324
[146]	validation_0-rmse:94.75442	validation_1-rmse:99.35788
[147]	validation_0-rmse:94.68010	validation_1-rmse:99.29612
[148]	validation_0-rmse:94.63399	validation_1-rmse:99.28045
[149]	validation_0-rmse:94.58038	validation_1-rmse:99.25491
[150]	validation_0-rmse:94.49792	validation_1-rmse:99.20059
[151]	validation_0-rmse:94.40384	validation_1-rmse:99.13627
[152]	validation_0-rmse:94.35996	validation_1-rmse:99.13456
[153]	validation_0-rmse:94.28721	validation_1-rmse:99.08308
[154]	validation_0-rmse:94.17061	validation_1-rmse:99.00955
[155]	validation_0-rmse:94.12108	validation_1-rmse:98.99911
[156]	validation_0-rmse:94.10410	validation_1-rmse:98.98287
[157]	validation_0-rmse:94.07236	validation_1-rmse:98.96205
[158]	validation_0-rmse:93.94999	validation_1-rmse:98.85611
[159]	validation_0-rmse:93.88887	validation_1-rmse:98.80812
[160]	validation_0-rmse:93.86399	validation_1-rmse:98.80491
[161]	validation_0-rmse:93.80986	validation_1-rmse:98.76489
[162]	validation_0-rmse:93.76371	validation_1-rmse:98.74502
[163]	validation_0-rmse:93.68814	validation_1-rmse:98.70195
[164]	validation_0-rmse:93.59225	validation_1-rmse:98.61775
[165]	validation_0-rmse:93.54072	validation_1-rmse:98.58556
[166]	validation_0-rmse:93.44966	validation_1-rmse:98.50449
[167]	validation_0-rmse:93.37684	validation_1-rmse:98.44449
[168]	validation_0-rmse:93.32845	validation_1-rmse:98.41916
[169]	validation_0-rmse:93.25388	validation_1-rmse:98.34782
[170]	validation_0-rmse:93.21427	validation_1-rmse:98.34436
[171]	validation_0-rmse:93.14787	validation_1-rmse:98.28669
[172]	validation_0-rmse:93.09246	validation_1-rmse:98.25060
[173]	validation_0-rmse:93.04668	validation_1-rmse:98.21369
[174]	validation_0-rmse:92.99656	validation_1-rmse:98.17966
[175]	validation_0-rmse:92.94656	validation_1-rmse:98.14663
[176]	validation_0-rmse:92.83418	validation_1-rmse:98.05076
[177]	validation_0-rmse:92.71685	validation_1-rmse:97.95888
[178]	validation_0-rmse:92.68798	validation_1-rmse:97.93735
[179]	validation_0-rmse:92.66812	validation_1-rmse:97.92628
[180]	validation_0-rmse:92.62737	validation_1-rmse:97.90451
[181]	validation_0-rmse:92.60867	validation_1-rmse:97.88806
[182]	validation_0-rmse:92.58568	validation_1-rmse:97.87479
[183]	validation_0-rmse:92.48689	validation_1-rmse:97.78928
[184]	validation_0-rmse:92.47528	validation_1-rmse:97.78444
[185]	validation_0-rmse:92.43318	validation_1-rmse:97.78404
[186]	validation_0-rmse:92.31065	validation_1-rmse:97.69502
[187]	validation_0-rmse:92.18737	validation_1-rmse:97.58763
[188]	validation_0-rmse:92.07679	validation_1-rmse:97.51765
[189]	validation_0-rmse:91.98435	validation_1-rmse:97.42226
[190]	validation_0-rmse:91.86090	validation_1-rmse:97.32178
[191]	validation_0-rmse:91.81041	validation_1-rmse:97.31414
[192]	validation_0-rmse:91.73271	validation_1-rmse:97.27655
[193]	validation_0-rmse:91.61064	validation_1-rmse:97.21178
[194]	validation_0-rmse:91.57581	validation_1-rmse:97.21436
[195]	validation_0-rmse:91.45429	validation_1-rmse:97.10830
[196]	validation_0-rmse:91.41346	validation_1-rmse:97.09001
[197]	validation_0-rmse:91.29875	validation_1-rmse:97.01263
[198]	validation_0-rmse:91.28984	validation_1-rmse:97.00830
[199]	validation_0-rmse:91.18882	validation_1-rmse:96.91603
[200]	validation_0-rmse:91.10528	validation_1-rmse:96.86620
[201]	validation_0-rmse:91.04865	validation_1-rmse:96.83571
[202]	validation_0-rmse:90.96182	validation_1-rmse:96.75971
[203]	validation_0-rmse:90.90567	validation_1-rmse:96.71647
[204]	validation_0-rmse:90.83762	validation_1-rmse:96.64879
[205]	validation_0-rmse:90.79659	validation_1-rmse:96.66217
[206]	validation_0-rmse:90.74986	validation_1-rmse:96.64755
[207]	validation_0-rmse:90.66278	validation_1-rmse:96.58187
[208]	validation_0-rmse:90.62998	validation_1-rmse:96.55465
[209]	validation_0-rmse:90.51527	validation_1-rmse:96.46841
[210]	validation_0-rmse:90.46872	validation_1-rmse:96.46704
[211]	validation_0-rmse:90.41367	validation_1-rmse:96.44090
[212]	validation_0-rmse:90.37765	validation_1-rmse:96.42922
[213]	validation_0-rmse:90.32443	validation_1-rmse:96.40890
[214]	validation_0-rmse:90.29537	validation_1-rmse:96.40002
[215]	validation_0-rmse:90.20870	validation_1-rmse:96.32057
[216]	validation_0-rmse:90.11847	validation_1-rmse:96.28767
[217]	validation_0-rmse:90.06400	validation_1-rmse:96.21624
[218]	validation_0-rmse:90.02515	validation_1-rmse:96.21284
[219]	validation_0-rmse:89.91219	validation_1-rmse:96.13442
[220]	validation_0-rmse:89.79809	validation_1-rmse:96.05171
[221]	validation_0-rmse:89.73461	validation_1-rmse:96.01500
[222]	validation_0-rmse:89.68798	validation_1-rmse:95.99812
[223]	validation_0-rmse:89.63681	validation_1-rmse:95.99863
[224]	validation_0-rmse:89.60260	validation_1-rmse:95.99986
[225]	validation_0-rmse:89.54895	validation_1-rmse:95.98271
[226]	validation_0-rmse:89.48507	validation_1-rmse:95.95722
[227]	validation_0-rmse:89.44950	validation_1-rmse:95.94174
[228]	validation_0-rmse:89.37029	validation_1-rmse:95.88946
[229]	validation_0-rmse:89.31223	validation_1-rmse:95.85753
[230]	validation_0-rmse:89.23853	validation_1-rmse:95.79594
[231]	validation_0-rmse:89.21752	validation_1-rmse:95.78113
[232]	validation_0-rmse:89.15346	validation_1-rmse:95.75345
[233]	validation_0-rmse:89.09483	validation_1-rmse:95.70171
[234]	validation_0-rmse:89.05686	validation_1-rmse:95.69280
[235]	validation_0-rmse:89.00185	validation_1-rmse:95.69258
[236]	validation_0-rmse:88.88771	validation_1-rmse:95.58904
[237]	validation_0-rmse:88.85102	validation_1-rmse:95.56736
[238]	validation_0-rmse:88.81759	validation_1-rmse:95.55953
[239]	validation_0-rmse:88.72077	validation_1-rmse:95.50285
[240]	validation_0-rmse:88.68437	validation_1-rmse:95.48494
[241]	validation_0-rmse:88.65621	validation_1-rmse:95.47185
[242]	validation_0-rmse:88.63162	validation_1-rmse:95.45714
[243]	validation_0-rmse:88.56481	validation_1-rmse:95.42344
[244]	validation_0-rmse:88.52236	validation_1-rmse:95.40537
[245]	validation_0-rmse:88.46667	validation_1-rmse:95.38015
[246]	validation_0-rmse:88.41209	validation_1-rmse:95.32082
[247]	validation_0-rmse:88.29744	validation_1-rmse:95.26059
[248]	validation_0-rmse:88.25698	validation_1-rmse:95.27592
[249]	validation_0-rmse:88.21383	validation_1-rmse:95.27360
[250]	validation_0-rmse:88.16735	validation_1-rmse:95.25344
[251]	validation_0-rmse:88.10341	validation_1-rmse:95.21310
[252]	validation_0-rmse:88.03153	validation_1-rmse:95.16728
[253]	validation_0-rmse:87.96668	validation_1-rmse:95.13962
[254]	validation_0-rmse:87.90452	validation_1-rmse:95.07997
[255]	validation_0-rmse:87.86879	validation_1-rmse:95.08286
[256]	validation_0-rmse:87.79709	validation_1-rmse:95.02726
[257]	validation_0-rmse:87.73813	validation_1-rmse:94.98301
[258]	validation_0-rmse:87.69616	validation_1-rmse:94.96609
[259]	validation_0-rmse:87.66161	validation_1-rmse:94.97481
[260]	validation_0-rmse:87.62651	validation_1-rmse:94.94783
[261]	validation_0-rmse:87.57866	validation_1-rmse:94.92121
[262]	validation_0-rmse:87.53949	validation_1-rmse:94.89732
[263]	validation_0-rmse:87.49597	validation_1-rmse:94.90369
[264]	validation_0-rmse:87.45584	validation_1-rmse:94.86511
[265]	validation_0-rmse:87.43558	validation_1-rmse:94.85934
[266]	validation_0-rmse:87.41807	validation_1-rmse:94.84840
[267]	validation_0-rmse:87.41181	validation_1-rmse:94.84725
[268]	validation_0-rmse:87.40124	validation_1-rmse:94.84166
[269]	validation_0-rmse:87.35337	validation_1-rmse:94.80899
[270]	validation_0-rmse:87.30997	validation_1-rmse:94.77132
[271]	validation_0-rmse:87.28557	validation_1-rmse:94.75883
[272]	validation_0-rmse:87.22776	validation_1-rmse:94.72388
[273]	validation_0-rmse:87.14098	validation_1-rmse:94.64671
[274]	validation_0-rmse:87.12717	validation_1-rmse:94.64138
[275]	validation_0-rmse:87.07035	validation_1-rmse:94.62337
[276]	validation_0-rmse:87.04116	validation_1-rmse:94.63067
[277]	validation_0-rmse:87.01126	validation_1-rmse:94.63627
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[834]	validation_0-rmse:72.23840	validation_1-rmse:88.74311
[835]	validation_0-rmse:72.22662	validation_1-rmse:88.74756
[836]	validation_0-rmse:72.20567	validation_1-rmse:88.74776
[837]	validation_0-rmse:72.18231	validation_1-rmse:88.74209
[838]	validation_0-rmse:72.16481	validation_1-rmse:88.74002
[839]	validation_0-rmse:72.14730	validation_1-rmse:88.74721
[840]	validation_0-rmse:72.12056	validation_1-rmse:88.74147
[841]	validation_0-rmse:72.10746	validation_1-rmse:88.73332
[842]	validation_0-rmse:72.07828	validation_1-rmse:88.71787
[843]	validation_0-rmse:72.05292	validation_1-rmse:88.71662
[844]	validation_0-rmse:72.04446	validation_1-rmse:88.70970
[845]	validation_0-rmse:72.02764	validation_1-rmse:88.70478
[846]	validation_0-rmse:72.01740	validation_1-rmse:88.70433
[847]	validation_0-rmse:72.00209	validation_1-rmse:88.69034
[848]	validation_0-rmse:71.98751	validation_1-rmse:88.68625
[849]	validation_0-rmse:71.97320	validation_1-rmse:88.68127
[850]	validation_0-rmse:71.94335	validation_1-rmse:88.66268
[851]	validation_0-rmse:71.92524	validation_1-rmse:88.66152
[852]	validation_0-rmse:71.91402	validation_1-rmse:88.66925
[853]	validation_0-rmse:71.90431	validation_1-rmse:88.66519
[854]	validation_0-rmse:71.89364	validation_1-rmse:88.66308
[855]	validation_0-rmse:71.88181	validation_1-rmse:88.66864
[856]	validation_0-rmse:71.85453	validation_1-rmse:88.66369
[857]	validation_0-rmse:71.84218	validation_1-rmse:88.65749
[858]	validation_0-rmse:71.82874	validation_1-rmse:88.65718
[859]	validation_0-rmse:71.81395	validation_1-rmse:88.64783
[860]	validation_0-rmse:71.78381	validation_1-rmse:88.62680
[861]	validation_0-rmse:71.76523	validation_1-rmse:88.62207
[862]	validation_0-rmse:71.75045	validation_1-rmse:88.62388
[863]	validation_0-rmse:71.73966	validation_1-rmse:88.61965
[864]	validation_0-rmse:71.72112	validation_1-rmse:88.60924
[865]	validation_0-rmse:71.70517	validation_1-rmse:88.59951
[866]	validation_0-rmse:71.69093	validation_1-rmse:88.60530
[867]	validation_0-rmse:71.66739	validation_1-rmse:88.59448
[868]	validation_0-rmse:71.65640	validation_1-rmse:88.59385
[869]	validation_0-rmse:71.65360	validation_1-rmse:88.59296
[870]	validation_0-rmse:71.63424	validation_1-rmse:88.58418
[871]	validation_0-rmse:71.60423	validation_1-rmse:88.56621
[872]	validation_0-rmse:71.57144	validation_1-rmse:88.54672
[873]	validation_0-rmse:71.53673	validation_1-rmse:88.55036
[874]	validation_0-rmse:71.52676	validation_1-rmse:88.55217
[875]	validation_0-rmse:71.50999	validation_1-rmse:88.54093
[876]	validation_0-rmse:71.50093	validation_1-rmse:88.53611
[877]	validation_0-rmse:71.48005	validation_1-rmse:88.53495
[878]	validation_0-rmse:71.46319	validation_1-rmse:88.53705
[879]	validation_0-rmse:71.45054	validation_1-rmse:88.53982
[880]	validation_0-rmse:71.44185	validation_1-rmse:88.54006
[881]	validation_0-rmse:71.42562	validation_1-rmse:88.53456
[882]	validation_0-rmse:71.39954	validation_1-rmse:88.52206
[883]	validation_0-rmse:71.38872	validation_1-rmse:88.52295
[884]	validation_0-rmse:71.36434	validation_1-rmse:88.51340
[885]	validation_0-rmse:71.34298	validation_1-rmse:88.53077
[886]	validation_0-rmse:71.33332	validation_1-rmse:88.52596
[887]	validation_0-rmse:71.31934	validation_1-rmse:88.52220
[888]	validation_0-rmse:71.29909	validation_1-rmse:88.49855
[889]	validation_0-rmse:71.27316	validation_1-rmse:88.48832
[890]	validation_0-rmse:71.25425	validation_1-rmse:88.47858
[891]	validation_0-rmse:71.22562	validation_1-rmse:88.45785
[892]	validation_0-rmse:71.22166	validation_1-rmse:88.45550
[893]	validation_0-rmse:71.22042	validation_1-rmse:88.45582
[894]	validation_0-rmse:71.20067	validation_1-rmse:88.46092
[895]	validation_0-rmse:71.17753	validation_1-rmse:88.45027
[896]	validation_0-rmse:71.16462	validation_1-rmse:88.44211
[897]	validation_0-rmse:71.14919	validation_1-rmse:88.43877
[898]	validation_0-rmse:71.12141	validation_1-rmse:88.42804
[899]	validation_0-rmse:71.10301	validation_1-rmse:88.42950
[900]	validation_0-rmse:71.08118	validation_1-rmse:88.42890
[901]	validation_0-rmse:71.06844	validation_1-rmse:88.42313
[902]	validation_0-rmse:71.06062	validation_1-rmse:88.42777
[903]	validation_0-rmse:71.04370	validation_1-rmse:88.42105
[904]	validation_0-rmse:71.03178	validation_1-rmse:88.41930
[905]	validation_0-rmse:71.01902	validation_1-rmse:88.42221
[906]	validation_0-rmse:70.99846	validation_1-rmse:88.41699
[907]	validation_0-rmse:70.98524	validation_1-rmse:88.40730
[908]	validation_0-rmse:70.97042	validation_1-rmse:88.41443
[909]	validation_0-rmse:70.96348	validation_1-rmse:88.42251
[910]	validation_0-rmse:70.94885	validation_1-rmse:88.43182
[911]	validation_0-rmse:70.92924	validation_1-rmse:88.42748
[912]	validation_0-rmse:70.91766	validation_1-rmse:88.42810
[913]	validation_0-rmse:70.88858	validation_1-rmse:88.43405
[914]	validation_0-rmse:70.87404	validation_1-rmse:88.43557
[915]	validation_0-rmse:70.84368	validation_1-rmse:88.42494
[916]	validation_0-rmse:70.83414	validation_1-rmse:88.42304
[917]	validation_0-rmse:70.81546	validation_1-rmse:88.42887
[918]	validation_0-rmse:70.80793	validation_1-rmse:88.43097
[919]	validation_0-rmse:70.78601	validation_1-rmse:88.42015
[920]	validation_0-rmse:70.77203	validation_1-rmse:88.41661
[921]	validation_0-rmse:70.76345	validation_1-rmse:88.41315
[922]	validation_0-rmse:70.75454	validation_1-rmse:88.41507
[923]	validation_0-rmse:70.75217	validation_1-rmse:88.41516
[924]	validation_0-rmse:70.73246	validation_1-rmse:88.40305
[925]	validation_0-rmse:70.71541	validation_1-rmse:88.39448
[926]	validation_0-rmse:70.69108	validation_1-rmse:88.38546
[927]	validation_0-rmse:70.67424	validation_1-rmse:88.38824
[928]	validation_0-rmse:70.65878	validation_1-rmse:88.39035
[929]	validation_0-rmse:70.63436	validation_1-rmse:88.37747
[930]	validation_0-rmse:70.61629	validation_1-rmse:88.37625
[931]	validation_0-rmse:70.59583	validation_1-rmse:88.36769
[932]	validation_0-rmse:70.58037	validation_1-rmse:88.37044
[933]	validation_0-rmse:70.56662	validation_1-rmse:88.37020
[934]	validation_0-rmse:70.55580	validation_1-rmse:88.36396
[935]	validation_0-rmse:70.55093	validation_1-rmse:88.36226
[936]	validation_0-rmse:70.54169	validation_1-rmse:88.37070
[937]	validation_0-rmse:70.53603	validation_1-rmse:88.37118
[938]	validation_0-rmse:70.52379	validation_1-rmse:88.36863
[939]	validation_0-rmse:70.51959	validation_1-rmse:88.36875
[940]	validation_0-rmse:70.51483	validation_1-rmse:88.36386
[941]	validation_0-rmse:70.50291	validation_1-rmse:88.35538
[942]	validation_0-rmse:70.47476	validation_1-rmse:88.34149
[943]	validation_0-rmse:70.46565	validation_1-rmse:88.35107
[944]	validation_0-rmse:70.44399	validation_1-rmse:88.34983
[945]	validation_0-rmse:70.42341	validation_1-rmse:88.35537
[946]	validation_0-rmse:70.40214	validation_1-rmse:88.36157
[947]	validation_0-rmse:70.38437	validation_1-rmse:88.35894
[948]	validation_0-rmse:70.36933	validation_1-rmse:88.36609
[949]	validation_0-rmse:70.35500	validation_1-rmse:88.36111
[950]	validation_0-rmse:70.34191	validation_1-rmse:88.35282
[951]	validation_0-rmse:70.32492	validation_1-rmse:88.34758
[952]	validation_0-rmse:70.30882	validation_1-rmse:88.33976
[953]	validation_0-rmse:70.29475	validation_1-rmse:88.34486
[954]	validation_0-rmse:70.28104	validation_1-rmse:88.33484
[955]	validation_0-rmse:70.26260	validation_1-rmse:88.32716
[956]	validation_0-rmse:70.24324	validation_1-rmse:88.32184
[957]	validation_0-rmse:70.23110	validation_1-rmse:88.32483
[958]	validation_0-rmse:70.21684	validation_1-rmse:88.31848
[959]	validation_0-rmse:70.19993	validation_1-rmse:88.31807
[960]	validation_0-rmse:70.18768	validation_1-rmse:88.31795
[961]	validation_0-rmse:70.16460	validation_1-rmse:88.31693
[962]	validation_0-rmse:70.15318	validation_1-rmse:88.31628
[963]	validation_0-rmse:70.14460	validation_1-rmse:88.31867
[964]	validation_0-rmse:70.12959	validation_1-rmse:88.32346
[965]	validation_0-rmse:70.11684	validation_1-rmse:88.33089
[966]	validation_0-rmse:70.11036	validation_1-rmse:88.32996
[967]	validation_0-rmse:70.10619	validation_1-rmse:88.33399
[968]	validation_0-rmse:70.09063	validation_1-rmse:88.33269
[969]	validation_0-rmse:70.07652	validation_1-rmse:88.33209
[970]	validation_0-rmse:70.06430	validation_1-rmse:88.33848
[971]	validation_0-rmse:70.03608	validation_1-rmse:88.32028
[972]	validation_0-rmse:70.01984	validation_1-rmse:88.31929
[973]	validation_0-rmse:70.00921	validation_1-rmse:88.32131
[974]	validation_0-rmse:69.99093	validation_1-rmse:88.33055
[975]	validation_0-rmse:69.95958	validation_1-rmse:88.31590
[976]	validation_0-rmse:69.94079	validation_1-rmse:88.30959
[977]	validation_0-rmse:69.90713	validation_1-rmse:88.30837
[978]	validation_0-rmse:69.88411	validation_1-rmse:88.30494
[979]	validation_0-rmse:69.86473	validation_1-rmse:88.29292
[980]	validation_0-rmse:69.85100	validation_1-rmse:88.29146
[981]	validation_0-rmse:69.83784	validation_1-rmse:88.29024
[982]	validation_0-rmse:69.81769	validation_1-rmse:88.27353
[983]	validation_0-rmse:69.80734	validation_1-rmse:88.26822
[984]	validation_0-rmse:69.80092	validation_1-rmse:88.26862
[985]	validation_0-rmse:69.78084	validation_1-rmse:88.27339
[986]	validation_0-rmse:69.77299	validation_1-rmse:88.27026
[987]	validation_0-rmse:69.75735	validation_1-rmse:88.26021
[988]	validation_0-rmse:69.75414	validation_1-rmse:88.25931
[989]	validation_0-rmse:69.73166	validation_1-rmse:88.25780
[990]	validation_0-rmse:69.71929	validation_1-rmse:88.26344
[991]	validation_0-rmse:69.70773	validation_1-rmse:88.25005
[992]	validation_0-rmse:69.70255	validation_1-rmse:88.25109
[993]	validation_0-rmse:69.68226	validation_1-rmse:88.24618
[994]	validation_0-rmse:69.67687	validation_1-rmse:88.24344
[995]	validation_0-rmse:69.66020	validation_1-rmse:88.23921
[996]	validation_0-rmse:69.64014	validation_1-rmse:88.22356
[997]	validation_0-rmse:69.61785	validation_1-rmse:88.20860
[998]	validation_0-rmse:69.60782	validation_1-rmse:88.21235
[999]	validation_0-rmse:69.59782	validation_1-rmse:88.20783
>>> Using model to predict target SW_IN_T1_2_1 in unseen test data ...
>>> Using model to calculate permutation importance based on unseen test data ...
>>> Calculating prediction scores based on predicting unseen test data of SW_IN_T1_2_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

================================
MODEL TRAINING & TESTING RESULTS
================================

## DATA
  > target: SW_IN_T1_2_1
  > features: 7 ['SW_IN_POT', '.DOY', '.RECORDNUMBER', '.WEEK', '.YEARDOY', '.YEARMONTH', '.YEARWEEK']
  > 350640 records (with missing)
  > 350615 available records for target and all features (no missing values)
  > training on 262961 records (75.0%) of 262961 features between 2005-01-01 00:45:00 and 2024-12-31 23:45:00
  > testing on 87654 unseen records (25.0%) of SW_IN_T1_2_1 between 2005-01-01 00:15:00 and 2024-12-31 19:45:00

## MODEL
  > the model was trained on training data (262961 records)
  > the model was tested on test data (87654 values)
  > estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
  > parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}
  > number of features used in model:  7
  > names of features used in model:  ['SW_IN_POT', '.DOY', '.RECORDNUMBER', '.WEEK', '.YEARDOY', '.YEARMONTH', '.YEARWEEK']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of SW_IN_T1_2_1 (87654 records).
  > feature importances are showing permutation importances from 10 repeats

               PERM_IMPORTANCE   PERM_SD
SW_IN_POT             1.730021  0.012578
.DOY                  0.436865  0.003421
.YEARDOY              0.343745  0.003580
.RECORDNUMBER         0.281731  0.002395
.WEEK                 0.220007  0.002405
.YEARMONTH            0.201281  0.002110
.YEARWEEK             0.179003  0.001558


## MODEL SCORES
  All scores were calculated based on unseen test data (87654 records).
  > MAE:  45.42199531804341 (mean absolute error)
  > MedAE:  10.314247131347656 (median absolute error)
  > MSE:  7780.621801041423 (mean squared error)
  > RMSE:  88.20783299141536 (root mean squared error)
  > MAXE:  906.8031234751562 (max error)
  > MAPE:  12515707243646994.000 (mean absolute percentage error)
  > R2:  0.858896961060538

xgb.fillgaps(showplot_scores=False, showplot_importance=False)
xgb.report_gapfilling()

Gap-filling using final model ...
>>> Using final model on all data to predict target SW_IN_T1_2_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting SW_IN_T1_2_1 ...
>>> Predicting target SW_IN_T1_2_1 where all features are available ... predicted 350640 records.
>>> Collecting results for final model ...
>>> Filling 25 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable SW_IN_T1_2_1_gfXG ...
>>> Restoring original timestamp in results ...
>>> Combining predictions from full model and fallback model ...

===================
GAP-FILLING RESULTS
===================

Model scores and feature importances were calculated from high-quality predicted targets (25 values, SW_IN_T1_2_1_gfXG where flag=1) in comparison to observed targets (350615 values, SW_IN_T1_2_1).

## TARGET
- first timestamp:  2005-01-01 00:15:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 350640 values)
- target column (observed):  SW_IN_T1_2_1
- missing records (observed):  25 (cross-check from flag: 25)
- target column (gap-filled):  SW_IN_T1_2_1_gfXG  (350640 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_SW_IN_T1_2_1_gfXG_ISFILLED
  > flag 0 ... observed targets (350615 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (25 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['SW_IN_POT', '.DOY', '.YEARDOY', '.RECORDNUMBER', '.WEEK', '.YEARMONTH', '.YEARWEEK']
- number of features used in model:  7
- permutation importances were calculated from 10 repeats.

               PERM_IMPORTANCE   PERM_SD
SW_IN_POT             1.764539  0.004482
.DOY                  0.462762  0.001235
.YEARDOY              0.348317  0.001705
.RECORDNUMBER         0.293486  0.001738
.WEEK                 0.226191  0.001059
.YEARMONTH            0.209859  0.001645
.YEARWEEK             0.184757  0.001219

## MODEL
The model was trained on a training set with test size 25.00%.
- estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
- parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}

## MODEL SCORES
- MAE:  38.77751383082156 (mean absolute error)
- MedAE:  9.864006996154785 (median absolute error)
- MSE:  5578.050197859053 (mean squared error)
- RMSE:  74.68634545791521 (root mean squared error)
- MAXE:  906.8031234751562 (max error)
- MAPE:  12419886362664158.000 (mean absolute percentage error)
- R2:  0.8994482674251935

Results

results = xgb.gapfilling_df_
results

	.PREDICTIONS_FULLMODEL	SW_IN_T1_2_1	.GAP_PREDICTIONS	FLAG_SW_IN_T1_2_1_gfXG_ISFILLED	SW_IN_T1_2_1_gfXG	.PREDICTIONS_FALLBACK	.GAPFILLED_CUMULATIVE	.PREDICTIONS
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	-1.159734	0.0	NaN	0	0.0	None	0.000000e+00	-1.159734
2005-01-01 00:45:00	-1.159734	0.0	NaN	0	0.0	None	0.000000e+00	-1.159734
2005-01-01 01:15:00	-1.159734	0.0	NaN	0	0.0	None	0.000000e+00	-1.159734
2005-01-01 01:45:00	-1.159734	0.0	NaN	0	0.0	None	0.000000e+00	-1.159734
2005-01-01 02:15:00	-1.159734	0.0	NaN	0	0.0	None	0.000000e+00	-1.159734
...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	-0.738784	0.0	NaN	0	0.0	None	4.988461e+07	-0.738784
2024-12-31 22:15:00	-0.738784	0.0	NaN	0	0.0	None	4.988461e+07	-0.738784
2024-12-31 22:45:00	-0.738784	0.0	NaN	0	0.0	None	4.988461e+07	-0.738784
2024-12-31 23:15:00	-0.738784	0.0	NaN	0	0.0	None	4.988461e+07	-0.738784
2024-12-31 23:45:00	-0.738784	0.0	NaN	0	0.0	None	4.988461e+07	-0.738784

350640 rows × 8 columns

Add to data

# Combine data
data_simple_2005_2024['SW_IN_T1_2_1'] = results['SW_IN_T1_2_1_gfXG'].copy()
data_simple_2005_2024['SW_IN_T1_2_1']

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    0.0
2005-01-01 00:45:00    0.0
2005-01-01 01:15:00    0.0
2005-01-01 01:45:00    0.0
2005-01-01 02:15:00    0.0
                      ... 
2024-12-31 21:45:00    0.0
2024-12-31 22:15:00    0.0
2024-12-31 22:45:00    0.0
2024-12-31 23:15:00    0.0
2024-12-31 23:45:00    0.0
Freq: 30min, Name: SW_IN_T1_2_1, Length: 350640, dtype: float64

# Combine flags
data_simple_2005_2024['FLAG_SW_IN_T1_2_1_ISFILLED'] = data_simple_2005_2024['FLAG_SW_IN_T1_2_1_ISFILLED'].combine_first(results['FLAG_SW_IN_T1_2_1_gfXG_ISFILLED'])
data_simple_2005_2024['FLAG_SW_IN_T1_2_1_ISFILLED']

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    3.0
2005-01-01 00:45:00    3.0
2005-01-01 01:15:00    3.0
2005-01-01 01:45:00    3.0
2005-01-01 02:15:00    3.0
                      ... 
2024-12-31 21:45:00    0.0
2024-12-31 22:15:00    0.0
2024-12-31 22:45:00    0.0
2024-12-31 23:15:00    0.0
2024-12-31 23:45:00    0.0
Freq: 30min, Name: FLAG_SW_IN_T1_2_1_ISFILLED, Length: 350640, dtype: float64

title = "Short-wave incoming radiation"
data_simple_2005_2024[['SW_IN_T1_2_1', 'FLAG_SW_IN_T1_2_1_ISFILLED']].plot(x_compat=True, title=title, subplots=True, figsize=(20, 6));

TA

_df = pd.DataFrame()
_df['SW_IN_T1_2_1'] = data_simple_2005_2024['SW_IN_T1_2_1'].copy()
_df['TA_T1_2_1'] = data_simple_2005_2024['TA_T1_2_1'].copy()
_df

	SW_IN_T1_2_1	TA_T1_2_1
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	0.0	1.566667
2005-01-01 00:45:00	0.0	1.533333
2005-01-01 01:15:00	0.0	1.566667
2005-01-01 01:45:00	0.0	1.566667
2005-01-01 02:15:00	0.0	1.500000
...	...	...
2024-12-31 21:45:00	0.0	-1.919472
2024-12-31 22:15:00	0.0	-2.104678
2024-12-31 22:45:00	0.0	-2.089444
2024-12-31 23:15:00	0.0	-2.355761
2024-12-31 23:45:00	0.0	-2.578839

350640 rows × 2 columns

Fill

xgb = XGBoostTS(
    input_df=_df,
    target_col='TA_T1_2_1',
    perm_n_repeats=10,
    include_timestamp_as_features=True,
    add_continuous_record_number=True,
    n_estimators=1000,
    random_state=42,
    early_stopping_rounds=50,
    n_jobs=-1
)

Adding new data columns ...
++ Added new columns with timestamp info: ['.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK'] 
++ Added new column .RECORDNUMBER with record numbers from 1 to 350640.

xgb.reduce_features()
xgb.report_feature_reduction()

Feature reduction based on permutation importance ...
[0]	validation_0-rmse:6.58013	validation_1-rmse:6.58013
[1]	validation_0-rmse:5.36035	validation_1-rmse:5.36035
[2]	validation_0-rmse:4.60877	validation_1-rmse:4.60877
[3]	validation_0-rmse:4.13898	validation_1-rmse:4.13898
[4]	validation_0-rmse:3.85629	validation_1-rmse:3.85629
[5]	validation_0-rmse:3.68049	validation_1-rmse:3.68049
[6]	validation_0-rmse:3.57062	validation_1-rmse:3.57062
[7]	validation_0-rmse:3.49049	validation_1-rmse:3.49049
[8]	validation_0-rmse:3.43829	validation_1-rmse:3.43829
[9]	validation_0-rmse:3.39443	validation_1-rmse:3.39443
[10]	validation_0-rmse:3.36784	validation_1-rmse:3.36784
[11]	validation_0-rmse:3.34869	validation_1-rmse:3.34869
[12]	validation_0-rmse:3.31166	validation_1-rmse:3.31166
[13]	validation_0-rmse:3.28019	validation_1-rmse:3.28019
[14]	validation_0-rmse:3.26258	validation_1-rmse:3.26258
[15]	validation_0-rmse:3.24914	validation_1-rmse:3.24914
[16]	validation_0-rmse:3.23781	validation_1-rmse:3.23781
[17]	validation_0-rmse:3.20941	validation_1-rmse:3.20941
[18]	validation_0-rmse:3.18173	validation_1-rmse:3.18173
[19]	validation_0-rmse:3.17306	validation_1-rmse:3.17306
[20]	validation_0-rmse:3.15875	validation_1-rmse:3.15875
[21]	validation_0-rmse:3.12903	validation_1-rmse:3.12903
[22]	validation_0-rmse:3.12077	validation_1-rmse:3.12077
[23]	validation_0-rmse:3.09944	validation_1-rmse:3.09944
[24]	validation_0-rmse:3.08541	validation_1-rmse:3.08541
[25]	validation_0-rmse:3.06867	validation_1-rmse:3.06867
[26]	validation_0-rmse:3.05047	validation_1-rmse:3.05047
[27]	validation_0-rmse:3.03113	validation_1-rmse:3.03113
[28]	validation_0-rmse:3.01529	validation_1-rmse:3.01529
[29]	validation_0-rmse:3.00338	validation_1-rmse:3.00338
[30]	validation_0-rmse:2.99737	validation_1-rmse:2.99737
[31]	validation_0-rmse:2.99096	validation_1-rmse:2.99096
[32]	validation_0-rmse:2.98650	validation_1-rmse:2.98650
[33]	validation_0-rmse:2.97453	validation_1-rmse:2.97453
[34]	validation_0-rmse:2.95019	validation_1-rmse:2.95019
[35]	validation_0-rmse:2.93314	validation_1-rmse:2.93314
[36]	validation_0-rmse:2.91214	validation_1-rmse:2.91214
[37]	validation_0-rmse:2.90687	validation_1-rmse:2.90687
[38]	validation_0-rmse:2.89218	validation_1-rmse:2.89218
[39]	validation_0-rmse:2.87678	validation_1-rmse:2.87678
[40]	validation_0-rmse:2.87004	validation_1-rmse:2.87004
[41]	validation_0-rmse:2.86443	validation_1-rmse:2.86443
[42]	validation_0-rmse:2.85765	validation_1-rmse:2.85765
[43]	validation_0-rmse:2.84911	validation_1-rmse:2.84911
[44]	validation_0-rmse:2.83752	validation_1-rmse:2.83752
[45]	validation_0-rmse:2.82765	validation_1-rmse:2.82765
[46]	validation_0-rmse:2.81877	validation_1-rmse:2.81877
[47]	validation_0-rmse:2.81667	validation_1-rmse:2.81667
[48]	validation_0-rmse:2.80776	validation_1-rmse:2.80776
[49]	validation_0-rmse:2.79109	validation_1-rmse:2.79109
[50]	validation_0-rmse:2.78880	validation_1-rmse:2.78880
[51]	validation_0-rmse:2.78458	validation_1-rmse:2.78458
[52]	validation_0-rmse:2.77018	validation_1-rmse:2.77018
[53]	validation_0-rmse:2.75689	validation_1-rmse:2.75689
[54]	validation_0-rmse:2.74963	validation_1-rmse:2.74963
[55]	validation_0-rmse:2.74360	validation_1-rmse:2.74360
[56]	validation_0-rmse:2.73598	validation_1-rmse:2.73598
[57]	validation_0-rmse:2.72369	validation_1-rmse:2.72369
[58]	validation_0-rmse:2.71919	validation_1-rmse:2.71919
[59]	validation_0-rmse:2.71561	validation_1-rmse:2.71561
[60]	validation_0-rmse:2.71005	validation_1-rmse:2.71005
[61]	validation_0-rmse:2.70154	validation_1-rmse:2.70154
[62]	validation_0-rmse:2.69594	validation_1-rmse:2.69594
[63]	validation_0-rmse:2.68705	validation_1-rmse:2.68705
[64]	validation_0-rmse:2.68147	validation_1-rmse:2.68147
[65]	validation_0-rmse:2.67449	validation_1-rmse:2.67449
[66]	validation_0-rmse:2.66966	validation_1-rmse:2.66966
[67]	validation_0-rmse:2.66395	validation_1-rmse:2.66395
[68]	validation_0-rmse:2.65617	validation_1-rmse:2.65617
[69]	validation_0-rmse:2.64860	validation_1-rmse:2.64860
[70]	validation_0-rmse:2.64334	validation_1-rmse:2.64334
[71]	validation_0-rmse:2.63912	validation_1-rmse:2.63912
[72]	validation_0-rmse:2.63236	validation_1-rmse:2.63236
[73]	validation_0-rmse:2.62246	validation_1-rmse:2.62246
[74]	validation_0-rmse:2.61831	validation_1-rmse:2.61831
[75]	validation_0-rmse:2.61516	validation_1-rmse:2.61516
[76]	validation_0-rmse:2.60673	validation_1-rmse:2.60673
[77]	validation_0-rmse:2.60415	validation_1-rmse:2.60415
[78]	validation_0-rmse:2.59800	validation_1-rmse:2.59800
[79]	validation_0-rmse:2.59166	validation_1-rmse:2.59166
[80]	validation_0-rmse:2.58824	validation_1-rmse:2.58824
[81]	validation_0-rmse:2.58072	validation_1-rmse:2.58072
[82]	validation_0-rmse:2.57717	validation_1-rmse:2.57717
[83]	validation_0-rmse:2.57418	validation_1-rmse:2.57418
[84]	validation_0-rmse:2.56727	validation_1-rmse:2.56727
[85]	validation_0-rmse:2.55737	validation_1-rmse:2.55737
[86]	validation_0-rmse:2.55325	validation_1-rmse:2.55325
[87]	validation_0-rmse:2.54445	validation_1-rmse:2.54445
[88]	validation_0-rmse:2.54139	validation_1-rmse:2.54139
[89]	validation_0-rmse:2.53662	validation_1-rmse:2.53662
[90]	validation_0-rmse:2.53266	validation_1-rmse:2.53266
[91]	validation_0-rmse:2.53030	validation_1-rmse:2.53030
[92]	validation_0-rmse:2.51890	validation_1-rmse:2.51890
[93]	validation_0-rmse:2.51588	validation_1-rmse:2.51588
[94]	validation_0-rmse:2.51357	validation_1-rmse:2.51357
[95]	validation_0-rmse:2.51143	validation_1-rmse:2.51143
[96]	validation_0-rmse:2.50740	validation_1-rmse:2.50740
[97]	validation_0-rmse:2.50361	validation_1-rmse:2.50361
[98]	validation_0-rmse:2.49962	validation_1-rmse:2.49962
[99]	validation_0-rmse:2.49467	validation_1-rmse:2.49467
[100]	validation_0-rmse:2.49074	validation_1-rmse:2.49074
[101]	validation_0-rmse:2.48834	validation_1-rmse:2.48834
[102]	validation_0-rmse:2.48551	validation_1-rmse:2.48551
[103]	validation_0-rmse:2.48217	validation_1-rmse:2.48217
[104]	validation_0-rmse:2.48042	validation_1-rmse:2.48042
[105]	validation_0-rmse:2.47625	validation_1-rmse:2.47625
[106]	validation_0-rmse:2.47423	validation_1-rmse:2.47423
[107]	validation_0-rmse:2.46697	validation_1-rmse:2.46697
[108]	validation_0-rmse:2.46191	validation_1-rmse:2.46191
[109]	validation_0-rmse:2.45538	validation_1-rmse:2.45538
[110]	validation_0-rmse:2.45231	validation_1-rmse:2.45231
[111]	validation_0-rmse:2.45071	validation_1-rmse:2.45071
[112]	validation_0-rmse:2.44793	validation_1-rmse:2.44793
[113]	validation_0-rmse:2.44564	validation_1-rmse:2.44564
[114]	validation_0-rmse:2.44438	validation_1-rmse:2.44438
[115]	validation_0-rmse:2.44197	validation_1-rmse:2.44197
[116]	validation_0-rmse:2.43881	validation_1-rmse:2.43881
[117]	validation_0-rmse:2.43229	validation_1-rmse:2.43229
[118]	validation_0-rmse:2.43053	validation_1-rmse:2.43053
[119]	validation_0-rmse:2.42706	validation_1-rmse:2.42706
[120]	validation_0-rmse:2.42371	validation_1-rmse:2.42371
[121]	validation_0-rmse:2.41971	validation_1-rmse:2.41971
[122]	validation_0-rmse:2.41863	validation_1-rmse:2.41863
[123]	validation_0-rmse:2.41469	validation_1-rmse:2.41469
[124]	validation_0-rmse:2.40567	validation_1-rmse:2.40567
[125]	validation_0-rmse:2.39927	validation_1-rmse:2.39927
[126]	validation_0-rmse:2.39375	validation_1-rmse:2.39375
[127]	validation_0-rmse:2.38812	validation_1-rmse:2.38812
[128]	validation_0-rmse:2.38072	validation_1-rmse:2.38072
[129]	validation_0-rmse:2.37701	validation_1-rmse:2.37701
[130]	validation_0-rmse:2.37500	validation_1-rmse:2.37500
[131]	validation_0-rmse:2.37181	validation_1-rmse:2.37181
[132]	validation_0-rmse:2.36640	validation_1-rmse:2.36640
[133]	validation_0-rmse:2.36457	validation_1-rmse:2.36457
[134]	validation_0-rmse:2.36165	validation_1-rmse:2.36165
[135]	validation_0-rmse:2.36033	validation_1-rmse:2.36033
[136]	validation_0-rmse:2.35683	validation_1-rmse:2.35683
[137]	validation_0-rmse:2.35498	validation_1-rmse:2.35498
[138]	validation_0-rmse:2.35112	validation_1-rmse:2.35112
[139]	validation_0-rmse:2.34785	validation_1-rmse:2.34785
[140]	validation_0-rmse:2.34570	validation_1-rmse:2.34570
[141]	validation_0-rmse:2.34215	validation_1-rmse:2.34215
[142]	validation_0-rmse:2.33493	validation_1-rmse:2.33493
[143]	validation_0-rmse:2.33329	validation_1-rmse:2.33329
[144]	validation_0-rmse:2.32938	validation_1-rmse:2.32938
[145]	validation_0-rmse:2.32486	validation_1-rmse:2.32486
[146]	validation_0-rmse:2.31953	validation_1-rmse:2.31953
[147]	validation_0-rmse:2.31541	validation_1-rmse:2.31541
[148]	validation_0-rmse:2.31253	validation_1-rmse:2.31253
[149]	validation_0-rmse:2.30896	validation_1-rmse:2.30896
[150]	validation_0-rmse:2.30444	validation_1-rmse:2.30444
[151]	validation_0-rmse:2.30348	validation_1-rmse:2.30348
[152]	validation_0-rmse:2.30189	validation_1-rmse:2.30189
[153]	validation_0-rmse:2.30018	validation_1-rmse:2.30018
[154]	validation_0-rmse:2.29792	validation_1-rmse:2.29792
[155]	validation_0-rmse:2.29324	validation_1-rmse:2.29324
[156]	validation_0-rmse:2.29159	validation_1-rmse:2.29159
[157]	validation_0-rmse:2.28842	validation_1-rmse:2.28842
[158]	validation_0-rmse:2.28501	validation_1-rmse:2.28501
[159]	validation_0-rmse:2.28179	validation_1-rmse:2.28179
[160]	validation_0-rmse:2.27894	validation_1-rmse:2.27894
[161]	validation_0-rmse:2.27824	validation_1-rmse:2.27824
[162]	validation_0-rmse:2.27569	validation_1-rmse:2.27569
[163]	validation_0-rmse:2.27350	validation_1-rmse:2.27350
[164]	validation_0-rmse:2.26909	validation_1-rmse:2.26909
[165]	validation_0-rmse:2.26572	validation_1-rmse:2.26572
[166]	validation_0-rmse:2.26065	validation_1-rmse:2.26065
[167]	validation_0-rmse:2.25677	validation_1-rmse:2.25677
[168]	validation_0-rmse:2.25600	validation_1-rmse:2.25600
[169]	validation_0-rmse:2.25227	validation_1-rmse:2.25227
[170]	validation_0-rmse:2.24946	validation_1-rmse:2.24946
[171]	validation_0-rmse:2.24478	validation_1-rmse:2.24478
[172]	validation_0-rmse:2.23986	validation_1-rmse:2.23986
[173]	validation_0-rmse:2.23887	validation_1-rmse:2.23887
[174]	validation_0-rmse:2.23658	validation_1-rmse:2.23658
[175]	validation_0-rmse:2.23082	validation_1-rmse:2.23082
[176]	validation_0-rmse:2.22724	validation_1-rmse:2.22724
[177]	validation_0-rmse:2.22379	validation_1-rmse:2.22379
[178]	validation_0-rmse:2.21965	validation_1-rmse:2.21965
[179]	validation_0-rmse:2.21742	validation_1-rmse:2.21742
[180]	validation_0-rmse:2.21498	validation_1-rmse:2.21498
[181]	validation_0-rmse:2.21413	validation_1-rmse:2.21413
[182]	validation_0-rmse:2.21267	validation_1-rmse:2.21267
[183]	validation_0-rmse:2.21151	validation_1-rmse:2.21151
[184]	validation_0-rmse:2.20890	validation_1-rmse:2.20890
[185]	validation_0-rmse:2.20786	validation_1-rmse:2.20786
[186]	validation_0-rmse:2.20496	validation_1-rmse:2.20496
[187]	validation_0-rmse:2.20328	validation_1-rmse:2.20328
[188]	validation_0-rmse:2.20114	validation_1-rmse:2.20114
[189]	validation_0-rmse:2.19920	validation_1-rmse:2.19920
[190]	validation_0-rmse:2.19542	validation_1-rmse:2.19542
[191]	validation_0-rmse:2.19260	validation_1-rmse:2.19260
[192]	validation_0-rmse:2.18943	validation_1-rmse:2.18943
[193]	validation_0-rmse:2.18598	validation_1-rmse:2.18598
[194]	validation_0-rmse:2.18071	validation_1-rmse:2.18071
[195]	validation_0-rmse:2.17896	validation_1-rmse:2.17896
[196]	validation_0-rmse:2.17744	validation_1-rmse:2.17744
[197]	validation_0-rmse:2.17476	validation_1-rmse:2.17476
[198]	validation_0-rmse:2.17334	validation_1-rmse:2.17334
[199]	validation_0-rmse:2.17102	validation_1-rmse:2.17102
[200]	validation_0-rmse:2.16802	validation_1-rmse:2.16802
[201]	validation_0-rmse:2.16699	validation_1-rmse:2.16699
[202]	validation_0-rmse:2.16452	validation_1-rmse:2.16452
[203]	validation_0-rmse:2.16243	validation_1-rmse:2.16243
[204]	validation_0-rmse:2.16121	validation_1-rmse:2.16121
[205]	validation_0-rmse:2.16007	validation_1-rmse:2.16007
[206]	validation_0-rmse:2.15823	validation_1-rmse:2.15823
[207]	validation_0-rmse:2.15780	validation_1-rmse:2.15780
[208]	validation_0-rmse:2.15479	validation_1-rmse:2.15479
[209]	validation_0-rmse:2.15174	validation_1-rmse:2.15174
[210]	validation_0-rmse:2.15006	validation_1-rmse:2.15006
[211]	validation_0-rmse:2.14852	validation_1-rmse:2.14852
[212]	validation_0-rmse:2.14759	validation_1-rmse:2.14759
[213]	validation_0-rmse:2.14646	validation_1-rmse:2.14646
[214]	validation_0-rmse:2.14337	validation_1-rmse:2.14337
[215]	validation_0-rmse:2.14191	validation_1-rmse:2.14191
[216]	validation_0-rmse:2.14055	validation_1-rmse:2.14055
[217]	validation_0-rmse:2.13974	validation_1-rmse:2.13974
[218]	validation_0-rmse:2.13867	validation_1-rmse:2.13867
[219]	validation_0-rmse:2.13794	validation_1-rmse:2.13794
[220]	validation_0-rmse:2.13576	validation_1-rmse:2.13576
[221]	validation_0-rmse:2.13416	validation_1-rmse:2.13416
[222]	validation_0-rmse:2.13154	validation_1-rmse:2.13154
[223]	validation_0-rmse:2.12848	validation_1-rmse:2.12848
[224]	validation_0-rmse:2.12667	validation_1-rmse:2.12667
[225]	validation_0-rmse:2.12332	validation_1-rmse:2.12332
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[782]	validation_0-rmse:1.62637	validation_1-rmse:1.62637
[783]	validation_0-rmse:1.62595	validation_1-rmse:1.62595
[784]	validation_0-rmse:1.62560	validation_1-rmse:1.62560
[785]	validation_0-rmse:1.62543	validation_1-rmse:1.62543
[786]	validation_0-rmse:1.62510	validation_1-rmse:1.62510
[787]	validation_0-rmse:1.62437	validation_1-rmse:1.62437
[788]	validation_0-rmse:1.62419	validation_1-rmse:1.62419
[789]	validation_0-rmse:1.62338	validation_1-rmse:1.62338
[790]	validation_0-rmse:1.62263	validation_1-rmse:1.62263
[791]	validation_0-rmse:1.62222	validation_1-rmse:1.62222
[792]	validation_0-rmse:1.62194	validation_1-rmse:1.62194
[793]	validation_0-rmse:1.62179	validation_1-rmse:1.62179
[794]	validation_0-rmse:1.62130	validation_1-rmse:1.62130
[795]	validation_0-rmse:1.62071	validation_1-rmse:1.62071
[796]	validation_0-rmse:1.62049	validation_1-rmse:1.62049
[797]	validation_0-rmse:1.62032	validation_1-rmse:1.62032
[798]	validation_0-rmse:1.61976	validation_1-rmse:1.61976
[799]	validation_0-rmse:1.61939	validation_1-rmse:1.61939
[800]	validation_0-rmse:1.61882	validation_1-rmse:1.61882
[801]	validation_0-rmse:1.61831	validation_1-rmse:1.61831
[802]	validation_0-rmse:1.61791	validation_1-rmse:1.61791
[803]	validation_0-rmse:1.61742	validation_1-rmse:1.61742
[804]	validation_0-rmse:1.61675	validation_1-rmse:1.61675
[805]	validation_0-rmse:1.61644	validation_1-rmse:1.61644
[806]	validation_0-rmse:1.61522	validation_1-rmse:1.61522
[807]	validation_0-rmse:1.61476	validation_1-rmse:1.61476
[808]	validation_0-rmse:1.61369	validation_1-rmse:1.61369
[809]	validation_0-rmse:1.61344	validation_1-rmse:1.61344
[810]	validation_0-rmse:1.61313	validation_1-rmse:1.61313
[811]	validation_0-rmse:1.61249	validation_1-rmse:1.61249
[812]	validation_0-rmse:1.61206	validation_1-rmse:1.61206
[813]	validation_0-rmse:1.61158	validation_1-rmse:1.61158
[814]	validation_0-rmse:1.61116	validation_1-rmse:1.61116
[815]	validation_0-rmse:1.61061	validation_1-rmse:1.61061
[816]	validation_0-rmse:1.61025	validation_1-rmse:1.61025
[817]	validation_0-rmse:1.61008	validation_1-rmse:1.61008
[818]	validation_0-rmse:1.60953	validation_1-rmse:1.60953
[819]	validation_0-rmse:1.60892	validation_1-rmse:1.60892
[820]	validation_0-rmse:1.60816	validation_1-rmse:1.60816
[821]	validation_0-rmse:1.60793	validation_1-rmse:1.60793
[822]	validation_0-rmse:1.60762	validation_1-rmse:1.60762
[823]	validation_0-rmse:1.60701	validation_1-rmse:1.60701
[824]	validation_0-rmse:1.60665	validation_1-rmse:1.60665
[825]	validation_0-rmse:1.60656	validation_1-rmse:1.60656
[826]	validation_0-rmse:1.60617	validation_1-rmse:1.60617
[827]	validation_0-rmse:1.60578	validation_1-rmse:1.60578
[828]	validation_0-rmse:1.60544	validation_1-rmse:1.60544
[829]	validation_0-rmse:1.60479	validation_1-rmse:1.60479
[830]	validation_0-rmse:1.60455	validation_1-rmse:1.60455
[831]	validation_0-rmse:1.60435	validation_1-rmse:1.60435
[832]	validation_0-rmse:1.60408	validation_1-rmse:1.60408
[833]	validation_0-rmse:1.60371	validation_1-rmse:1.60371
[834]	validation_0-rmse:1.60315	validation_1-rmse:1.60315
[835]	validation_0-rmse:1.60308	validation_1-rmse:1.60308
[836]	validation_0-rmse:1.60272	validation_1-rmse:1.60272
[837]	validation_0-rmse:1.60252	validation_1-rmse:1.60252
[838]	validation_0-rmse:1.60212	validation_1-rmse:1.60212
[839]	validation_0-rmse:1.60147	validation_1-rmse:1.60147
[840]	validation_0-rmse:1.60111	validation_1-rmse:1.60111
[841]	validation_0-rmse:1.60068	validation_1-rmse:1.60068
[842]	validation_0-rmse:1.60020	validation_1-rmse:1.60020
[843]	validation_0-rmse:1.59985	validation_1-rmse:1.59985
[844]	validation_0-rmse:1.59966	validation_1-rmse:1.59966
[845]	validation_0-rmse:1.59897	validation_1-rmse:1.59897
[846]	validation_0-rmse:1.59878	validation_1-rmse:1.59878
[847]	validation_0-rmse:1.59860	validation_1-rmse:1.59860
[848]	validation_0-rmse:1.59799	validation_1-rmse:1.59799
[849]	validation_0-rmse:1.59754	validation_1-rmse:1.59754
[850]	validation_0-rmse:1.59689	validation_1-rmse:1.59689
[851]	validation_0-rmse:1.59670	validation_1-rmse:1.59670
[852]	validation_0-rmse:1.59607	validation_1-rmse:1.59607
[853]	validation_0-rmse:1.59567	validation_1-rmse:1.59567
[854]	validation_0-rmse:1.59503	validation_1-rmse:1.59503
[855]	validation_0-rmse:1.59470	validation_1-rmse:1.59470
[856]	validation_0-rmse:1.59430	validation_1-rmse:1.59430
[857]	validation_0-rmse:1.59386	validation_1-rmse:1.59386
[858]	validation_0-rmse:1.59352	validation_1-rmse:1.59352
[859]	validation_0-rmse:1.59337	validation_1-rmse:1.59337
[860]	validation_0-rmse:1.59319	validation_1-rmse:1.59319
[861]	validation_0-rmse:1.59275	validation_1-rmse:1.59275
[862]	validation_0-rmse:1.59237	validation_1-rmse:1.59237
[863]	validation_0-rmse:1.59222	validation_1-rmse:1.59222
[864]	validation_0-rmse:1.59171	validation_1-rmse:1.59171
[865]	validation_0-rmse:1.59137	validation_1-rmse:1.59137
[866]	validation_0-rmse:1.59088	validation_1-rmse:1.59088
[867]	validation_0-rmse:1.59056	validation_1-rmse:1.59056
[868]	validation_0-rmse:1.58998	validation_1-rmse:1.58998
[869]	validation_0-rmse:1.58928	validation_1-rmse:1.58928
[870]	validation_0-rmse:1.58908	validation_1-rmse:1.58908
[871]	validation_0-rmse:1.58890	validation_1-rmse:1.58890
[872]	validation_0-rmse:1.58878	validation_1-rmse:1.58878
[873]	validation_0-rmse:1.58831	validation_1-rmse:1.58831
[874]	validation_0-rmse:1.58788	validation_1-rmse:1.58788
[875]	validation_0-rmse:1.58707	validation_1-rmse:1.58707
[876]	validation_0-rmse:1.58627	validation_1-rmse:1.58627
[877]	validation_0-rmse:1.58585	validation_1-rmse:1.58585
[878]	validation_0-rmse:1.58541	validation_1-rmse:1.58541
[879]	validation_0-rmse:1.58506	validation_1-rmse:1.58506
[880]	validation_0-rmse:1.58463	validation_1-rmse:1.58463
[881]	validation_0-rmse:1.58440	validation_1-rmse:1.58440
[882]	validation_0-rmse:1.58405	validation_1-rmse:1.58405
[883]	validation_0-rmse:1.58339	validation_1-rmse:1.58339
[884]	validation_0-rmse:1.58296	validation_1-rmse:1.58296
[885]	validation_0-rmse:1.58229	validation_1-rmse:1.58229
[886]	validation_0-rmse:1.58185	validation_1-rmse:1.58185
[887]	validation_0-rmse:1.58140	validation_1-rmse:1.58140
[888]	validation_0-rmse:1.58105	validation_1-rmse:1.58105
[889]	validation_0-rmse:1.58067	validation_1-rmse:1.58067
[890]	validation_0-rmse:1.58034	validation_1-rmse:1.58034
[891]	validation_0-rmse:1.57992	validation_1-rmse:1.57992
[892]	validation_0-rmse:1.57954	validation_1-rmse:1.57954
[893]	validation_0-rmse:1.57895	validation_1-rmse:1.57895
[894]	validation_0-rmse:1.57855	validation_1-rmse:1.57855
[895]	validation_0-rmse:1.57819	validation_1-rmse:1.57819
[896]	validation_0-rmse:1.57804	validation_1-rmse:1.57804
[897]	validation_0-rmse:1.57763	validation_1-rmse:1.57763
[898]	validation_0-rmse:1.57681	validation_1-rmse:1.57681
[899]	validation_0-rmse:1.57623	validation_1-rmse:1.57623
[900]	validation_0-rmse:1.57603	validation_1-rmse:1.57603
[901]	validation_0-rmse:1.57548	validation_1-rmse:1.57548
[902]	validation_0-rmse:1.57524	validation_1-rmse:1.57524
[903]	validation_0-rmse:1.57484	validation_1-rmse:1.57484
[904]	validation_0-rmse:1.57442	validation_1-rmse:1.57442
[905]	validation_0-rmse:1.57402	validation_1-rmse:1.57402
[906]	validation_0-rmse:1.57379	validation_1-rmse:1.57379
[907]	validation_0-rmse:1.57364	validation_1-rmse:1.57364
[908]	validation_0-rmse:1.57305	validation_1-rmse:1.57305
[909]	validation_0-rmse:1.57286	validation_1-rmse:1.57286
[910]	validation_0-rmse:1.57248	validation_1-rmse:1.57248
[911]	validation_0-rmse:1.57179	validation_1-rmse:1.57179
[912]	validation_0-rmse:1.57160	validation_1-rmse:1.57160
[913]	validation_0-rmse:1.57150	validation_1-rmse:1.57150
[914]	validation_0-rmse:1.57059	validation_1-rmse:1.57059
[915]	validation_0-rmse:1.57012	validation_1-rmse:1.57012
[916]	validation_0-rmse:1.56957	validation_1-rmse:1.56957
[917]	validation_0-rmse:1.56927	validation_1-rmse:1.56927
[918]	validation_0-rmse:1.56832	validation_1-rmse:1.56832
[919]	validation_0-rmse:1.56797	validation_1-rmse:1.56797
[920]	validation_0-rmse:1.56783	validation_1-rmse:1.56783
[921]	validation_0-rmse:1.56738	validation_1-rmse:1.56738
[922]	validation_0-rmse:1.56700	validation_1-rmse:1.56700
[923]	validation_0-rmse:1.56670	validation_1-rmse:1.56670
[924]	validation_0-rmse:1.56639	validation_1-rmse:1.56639
[925]	validation_0-rmse:1.56590	validation_1-rmse:1.56590
[926]	validation_0-rmse:1.56563	validation_1-rmse:1.56563
[927]	validation_0-rmse:1.56505	validation_1-rmse:1.56505
[928]	validation_0-rmse:1.56460	validation_1-rmse:1.56460
[929]	validation_0-rmse:1.56428	validation_1-rmse:1.56428
[930]	validation_0-rmse:1.56371	validation_1-rmse:1.56371
[931]	validation_0-rmse:1.56299	validation_1-rmse:1.56299
[932]	validation_0-rmse:1.56249	validation_1-rmse:1.56249
[933]	validation_0-rmse:1.56193	validation_1-rmse:1.56193
[934]	validation_0-rmse:1.56162	validation_1-rmse:1.56162
[935]	validation_0-rmse:1.56098	validation_1-rmse:1.56098
[936]	validation_0-rmse:1.56053	validation_1-rmse:1.56053
[937]	validation_0-rmse:1.56020	validation_1-rmse:1.56020
[938]	validation_0-rmse:1.55952	validation_1-rmse:1.55952
[939]	validation_0-rmse:1.55909	validation_1-rmse:1.55909
[940]	validation_0-rmse:1.55884	validation_1-rmse:1.55884
[941]	validation_0-rmse:1.55862	validation_1-rmse:1.55862
[942]	validation_0-rmse:1.55810	validation_1-rmse:1.55810
[943]	validation_0-rmse:1.55747	validation_1-rmse:1.55747
[944]	validation_0-rmse:1.55665	validation_1-rmse:1.55665
[945]	validation_0-rmse:1.55580	validation_1-rmse:1.55580
[946]	validation_0-rmse:1.55548	validation_1-rmse:1.55548
[947]	validation_0-rmse:1.55520	validation_1-rmse:1.55520
[948]	validation_0-rmse:1.55463	validation_1-rmse:1.55463
[949]	validation_0-rmse:1.55375	validation_1-rmse:1.55375
[950]	validation_0-rmse:1.55337	validation_1-rmse:1.55337
[951]	validation_0-rmse:1.55309	validation_1-rmse:1.55309
[952]	validation_0-rmse:1.55270	validation_1-rmse:1.55270
[953]	validation_0-rmse:1.55227	validation_1-rmse:1.55227
[954]	validation_0-rmse:1.55196	validation_1-rmse:1.55196
[955]	validation_0-rmse:1.55129	validation_1-rmse:1.55129
[956]	validation_0-rmse:1.55087	validation_1-rmse:1.55087
[957]	validation_0-rmse:1.55073	validation_1-rmse:1.55073
[958]	validation_0-rmse:1.54986	validation_1-rmse:1.54986
[959]	validation_0-rmse:1.54952	validation_1-rmse:1.54952
[960]	validation_0-rmse:1.54915	validation_1-rmse:1.54915
[961]	validation_0-rmse:1.54898	validation_1-rmse:1.54898
[962]	validation_0-rmse:1.54861	validation_1-rmse:1.54861
[963]	validation_0-rmse:1.54809	validation_1-rmse:1.54809
[964]	validation_0-rmse:1.54791	validation_1-rmse:1.54791
[965]	validation_0-rmse:1.54738	validation_1-rmse:1.54738
[966]	validation_0-rmse:1.54710	validation_1-rmse:1.54710
[967]	validation_0-rmse:1.54656	validation_1-rmse:1.54656
[968]	validation_0-rmse:1.54644	validation_1-rmse:1.54644
[969]	validation_0-rmse:1.54613	validation_1-rmse:1.54613
[970]	validation_0-rmse:1.54564	validation_1-rmse:1.54564
[971]	validation_0-rmse:1.54527	validation_1-rmse:1.54527
[972]	validation_0-rmse:1.54503	validation_1-rmse:1.54503
[973]	validation_0-rmse:1.54473	validation_1-rmse:1.54473
[974]	validation_0-rmse:1.54450	validation_1-rmse:1.54450
[975]	validation_0-rmse:1.54413	validation_1-rmse:1.54413
[976]	validation_0-rmse:1.54377	validation_1-rmse:1.54377
[977]	validation_0-rmse:1.54353	validation_1-rmse:1.54353
[978]	validation_0-rmse:1.54318	validation_1-rmse:1.54318
[979]	validation_0-rmse:1.54300	validation_1-rmse:1.54300
[980]	validation_0-rmse:1.54281	validation_1-rmse:1.54281
[981]	validation_0-rmse:1.54209	validation_1-rmse:1.54209
[982]	validation_0-rmse:1.54162	validation_1-rmse:1.54162
[983]	validation_0-rmse:1.54104	validation_1-rmse:1.54104
[984]	validation_0-rmse:1.54094	validation_1-rmse:1.54094
[985]	validation_0-rmse:1.54026	validation_1-rmse:1.54026
[986]	validation_0-rmse:1.53984	validation_1-rmse:1.53984
[987]	validation_0-rmse:1.53966	validation_1-rmse:1.53966
[988]	validation_0-rmse:1.53939	validation_1-rmse:1.53939
[989]	validation_0-rmse:1.53896	validation_1-rmse:1.53896
[990]	validation_0-rmse:1.53872	validation_1-rmse:1.53872
[991]	validation_0-rmse:1.53841	validation_1-rmse:1.53841
[992]	validation_0-rmse:1.53822	validation_1-rmse:1.53822
[993]	validation_0-rmse:1.53781	validation_1-rmse:1.53781
[994]	validation_0-rmse:1.53753	validation_1-rmse:1.53753
[995]	validation_0-rmse:1.53691	validation_1-rmse:1.53691
[996]	validation_0-rmse:1.53620	validation_1-rmse:1.53620
[997]	validation_0-rmse:1.53590	validation_1-rmse:1.53590
[998]	validation_0-rmse:1.53563	validation_1-rmse:1.53563
[999]	validation_0-rmse:1.53543	validation_1-rmse:1.53543
>>> Calculating feature importances (permutation importance, 10 repeats) ...
>>> Setting threshold for feature rejection to 0.00426857069701615.
>>> Removing rejected features from model data ...

=================
FEATURE REDUCTION
=================

- Target variable: TA_T1_2_1

- The random variable .RANDOM was added to the original features, used as benchmark for detecting relevant feature importances.

PERMUTATION IMPORTANCE (mean) across all splits of TimeSeriesSplit:

               PERM_IMPORTANCE   PERM_SD
.DOY                  0.690096  0.001582
.HOUR                 0.317070  0.000758
.SEASON               0.288100  0.000662
.RECORDNUMBER         0.256753  0.000629
.YEARDOY              0.208535  0.000577
SW_IN_T1_2_1          0.192914  0.000776
.YEARWEEK             0.189991  0.000494
.YEARMONTH            0.184747  0.000833
.WEEK                 0.121119  0.000425
.YEAR                 0.034190  0.000152
.MONTH                0.018300  0.000093
.RANDOM               0.004269  0.000015

- These results are from feature reduction. Note that feature importances for the final model are calculated during gap-filling.

--> 11 original input features (before feature reduction): ['SW_IN_T1_2_1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
--> 5 rejected features (during feature reduction): None.
--> 11 accepted features (after feature reduction): ['.DOY', '.HOUR', '.SEASON', '.RECORDNUMBER', '.YEARDOY', 'SW_IN_T1_2_1', '.YEARWEEK', '.YEARMONTH', '.WEEK', '.YEAR', '.MONTH']

xgb.trainmodel(showplot_scores=False, showplot_importance=False)
xgb.report_traintest()

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2005-01-01 00:45:00 and 2024-12-31 23:15:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:6.58013	validation_1-rmse:6.54892
[1]	validation_0-rmse:5.36035	validation_1-rmse:5.34068
[2]	validation_0-rmse:4.60877	validation_1-rmse:4.59894
[3]	validation_0-rmse:4.13898	validation_1-rmse:4.13697
[4]	validation_0-rmse:3.85629	validation_1-rmse:3.86163
[5]	validation_0-rmse:3.68049	validation_1-rmse:3.69165
[6]	validation_0-rmse:3.57062	validation_1-rmse:3.58579
[7]	validation_0-rmse:3.49049	validation_1-rmse:3.50914
[8]	validation_0-rmse:3.43830	validation_1-rmse:3.46077
[9]	validation_0-rmse:3.39444	validation_1-rmse:3.41779
[10]	validation_0-rmse:3.36785	validation_1-rmse:3.39268
[11]	validation_0-rmse:3.34869	validation_1-rmse:3.37643
[12]	validation_0-rmse:3.31167	validation_1-rmse:3.33997
[13]	validation_0-rmse:3.28020	validation_1-rmse:3.31099
[14]	validation_0-rmse:3.26259	validation_1-rmse:3.29438
[15]	validation_0-rmse:3.24915	validation_1-rmse:3.28155
[16]	validation_0-rmse:3.23782	validation_1-rmse:3.27054
[17]	validation_0-rmse:3.20942	validation_1-rmse:3.24174
[18]	validation_0-rmse:3.18174	validation_1-rmse:3.21432
[19]	validation_0-rmse:3.17309	validation_1-rmse:3.20656
[20]	validation_0-rmse:3.15879	validation_1-rmse:3.19274
[21]	validation_0-rmse:3.11854	validation_1-rmse:3.15401
[22]	validation_0-rmse:3.11002	validation_1-rmse:3.14570
[23]	validation_0-rmse:3.09463	validation_1-rmse:3.13073
[24]	validation_0-rmse:3.09066	validation_1-rmse:3.12679
[25]	validation_0-rmse:3.07951	validation_1-rmse:3.11622
[26]	validation_0-rmse:3.06360	validation_1-rmse:3.10085
[27]	validation_0-rmse:3.04284	validation_1-rmse:3.07911
[28]	validation_0-rmse:3.03116	validation_1-rmse:3.06739
[29]	validation_0-rmse:3.01495	validation_1-rmse:3.05203
[30]	validation_0-rmse:2.99727	validation_1-rmse:3.03561
[31]	validation_0-rmse:2.98276	validation_1-rmse:3.02189
[32]	validation_0-rmse:2.97694	validation_1-rmse:3.01699
[33]	validation_0-rmse:2.96428	validation_1-rmse:3.00450
[34]	validation_0-rmse:2.95153	validation_1-rmse:2.99140
[35]	validation_0-rmse:2.94113	validation_1-rmse:2.98216
[36]	validation_0-rmse:2.92215	validation_1-rmse:2.96425
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[864]	validation_0-rmse:1.59205	validation_1-rmse:1.76209
[865]	validation_0-rmse:1.59179	validation_1-rmse:1.76189
[866]	validation_0-rmse:1.59115	validation_1-rmse:1.76138
[867]	validation_0-rmse:1.59086	validation_1-rmse:1.76107
[868]	validation_0-rmse:1.59068	validation_1-rmse:1.76106
[869]	validation_0-rmse:1.59018	validation_1-rmse:1.76059
[870]	validation_0-rmse:1.58983	validation_1-rmse:1.76031
[871]	validation_0-rmse:1.58902	validation_1-rmse:1.75987
[872]	validation_0-rmse:1.58859	validation_1-rmse:1.75941
[873]	validation_0-rmse:1.58835	validation_1-rmse:1.75937
[874]	validation_0-rmse:1.58817	validation_1-rmse:1.75922
[875]	validation_0-rmse:1.58793	validation_1-rmse:1.75909
[876]	validation_0-rmse:1.58774	validation_1-rmse:1.75898
[877]	validation_0-rmse:1.58732	validation_1-rmse:1.75864
[878]	validation_0-rmse:1.58701	validation_1-rmse:1.75826
[879]	validation_0-rmse:1.58646	validation_1-rmse:1.75779
[880]	validation_0-rmse:1.58591	validation_1-rmse:1.75733
[881]	validation_0-rmse:1.58551	validation_1-rmse:1.75716
[882]	validation_0-rmse:1.58525	validation_1-rmse:1.75703
[883]	validation_0-rmse:1.58493	validation_1-rmse:1.75686
[884]	validation_0-rmse:1.58456	validation_1-rmse:1.75665
[885]	validation_0-rmse:1.58433	validation_1-rmse:1.75654
[886]	validation_0-rmse:1.58402	validation_1-rmse:1.75636
[887]	validation_0-rmse:1.58390	validation_1-rmse:1.75623
[888]	validation_0-rmse:1.58355	validation_1-rmse:1.75596
[889]	validation_0-rmse:1.58338	validation_1-rmse:1.75587
[890]	validation_0-rmse:1.58282	validation_1-rmse:1.75549
[891]	validation_0-rmse:1.58230	validation_1-rmse:1.75516
[892]	validation_0-rmse:1.58178	validation_1-rmse:1.75452
[893]	validation_0-rmse:1.58162	validation_1-rmse:1.75443
[894]	validation_0-rmse:1.58120	validation_1-rmse:1.75416
[895]	validation_0-rmse:1.58096	validation_1-rmse:1.75391
[896]	validation_0-rmse:1.58054	validation_1-rmse:1.75362
[897]	validation_0-rmse:1.58005	validation_1-rmse:1.75319
[898]	validation_0-rmse:1.57964	validation_1-rmse:1.75278
[899]	validation_0-rmse:1.57947	validation_1-rmse:1.75273
[900]	validation_0-rmse:1.57902	validation_1-rmse:1.75244
[901]	validation_0-rmse:1.57856	validation_1-rmse:1.75210
[902]	validation_0-rmse:1.57840	validation_1-rmse:1.75201
[903]	validation_0-rmse:1.57792	validation_1-rmse:1.75171
[904]	validation_0-rmse:1.57730	validation_1-rmse:1.75121
[905]	validation_0-rmse:1.57690	validation_1-rmse:1.75081
[906]	validation_0-rmse:1.57654	validation_1-rmse:1.75071
[907]	validation_0-rmse:1.57647	validation_1-rmse:1.75067
[908]	validation_0-rmse:1.57618	validation_1-rmse:1.75054
[909]	validation_0-rmse:1.57559	validation_1-rmse:1.75011
[910]	validation_0-rmse:1.57525	validation_1-rmse:1.74987
[911]	validation_0-rmse:1.57461	validation_1-rmse:1.74937
[912]	validation_0-rmse:1.57381	validation_1-rmse:1.74871
[913]	validation_0-rmse:1.57349	validation_1-rmse:1.74862
[914]	validation_0-rmse:1.57310	validation_1-rmse:1.74853
[915]	validation_0-rmse:1.57274	validation_1-rmse:1.74826
[916]	validation_0-rmse:1.57222	validation_1-rmse:1.74807
[917]	validation_0-rmse:1.57190	validation_1-rmse:1.74806
[918]	validation_0-rmse:1.57176	validation_1-rmse:1.74794
[919]	validation_0-rmse:1.57110	validation_1-rmse:1.74747
[920]	validation_0-rmse:1.57051	validation_1-rmse:1.74702
[921]	validation_0-rmse:1.57021	validation_1-rmse:1.74678
[922]	validation_0-rmse:1.57001	validation_1-rmse:1.74664
[923]	validation_0-rmse:1.56971	validation_1-rmse:1.74648
[924]	validation_0-rmse:1.56923	validation_1-rmse:1.74620
[925]	validation_0-rmse:1.56890	validation_1-rmse:1.74593
[926]	validation_0-rmse:1.56836	validation_1-rmse:1.74543
[927]	validation_0-rmse:1.56810	validation_1-rmse:1.74519
[928]	validation_0-rmse:1.56768	validation_1-rmse:1.74490
[929]	validation_0-rmse:1.56709	validation_1-rmse:1.74440
[930]	validation_0-rmse:1.56624	validation_1-rmse:1.74379
[931]	validation_0-rmse:1.56582	validation_1-rmse:1.74366
[932]	validation_0-rmse:1.56529	validation_1-rmse:1.74326
[933]	validation_0-rmse:1.56493	validation_1-rmse:1.74304
[934]	validation_0-rmse:1.56423	validation_1-rmse:1.74248
[935]	validation_0-rmse:1.56371	validation_1-rmse:1.74212
[936]	validation_0-rmse:1.56333	validation_1-rmse:1.74164
[937]	validation_0-rmse:1.56290	validation_1-rmse:1.74121
[938]	validation_0-rmse:1.56231	validation_1-rmse:1.74059
[939]	validation_0-rmse:1.56218	validation_1-rmse:1.74057
[940]	validation_0-rmse:1.56170	validation_1-rmse:1.74024
[941]	validation_0-rmse:1.56135	validation_1-rmse:1.74005
[942]	validation_0-rmse:1.56087	validation_1-rmse:1.73986
[943]	validation_0-rmse:1.56052	validation_1-rmse:1.73970
[944]	validation_0-rmse:1.56013	validation_1-rmse:1.73944
[945]	validation_0-rmse:1.55993	validation_1-rmse:1.73934
[946]	validation_0-rmse:1.55979	validation_1-rmse:1.73923
[947]	validation_0-rmse:1.55970	validation_1-rmse:1.73922
[948]	validation_0-rmse:1.55945	validation_1-rmse:1.73911
[949]	validation_0-rmse:1.55899	validation_1-rmse:1.73877
[950]	validation_0-rmse:1.55829	validation_1-rmse:1.73814
[951]	validation_0-rmse:1.55779	validation_1-rmse:1.73771
[952]	validation_0-rmse:1.55749	validation_1-rmse:1.73750
[953]	validation_0-rmse:1.55705	validation_1-rmse:1.73708
[954]	validation_0-rmse:1.55675	validation_1-rmse:1.73686
[955]	validation_0-rmse:1.55657	validation_1-rmse:1.73673
[956]	validation_0-rmse:1.55643	validation_1-rmse:1.73672
[957]	validation_0-rmse:1.55598	validation_1-rmse:1.73638
[958]	validation_0-rmse:1.55559	validation_1-rmse:1.73607
[959]	validation_0-rmse:1.55549	validation_1-rmse:1.73604
[960]	validation_0-rmse:1.55469	validation_1-rmse:1.73555
[961]	validation_0-rmse:1.55394	validation_1-rmse:1.73494
[962]	validation_0-rmse:1.55374	validation_1-rmse:1.73481
[963]	validation_0-rmse:1.55332	validation_1-rmse:1.73460
[964]	validation_0-rmse:1.55315	validation_1-rmse:1.73448
[965]	validation_0-rmse:1.55291	validation_1-rmse:1.73438
[966]	validation_0-rmse:1.55273	validation_1-rmse:1.73430
[967]	validation_0-rmse:1.55258	validation_1-rmse:1.73423
[968]	validation_0-rmse:1.55250	validation_1-rmse:1.73421
[969]	validation_0-rmse:1.55241	validation_1-rmse:1.73416
[970]	validation_0-rmse:1.55233	validation_1-rmse:1.73414
[971]	validation_0-rmse:1.55204	validation_1-rmse:1.73400
[972]	validation_0-rmse:1.55155	validation_1-rmse:1.73369
[973]	validation_0-rmse:1.55123	validation_1-rmse:1.73356
[974]	validation_0-rmse:1.55095	validation_1-rmse:1.73334
[975]	validation_0-rmse:1.55068	validation_1-rmse:1.73330
[976]	validation_0-rmse:1.55030	validation_1-rmse:1.73302
[977]	validation_0-rmse:1.54997	validation_1-rmse:1.73297
[978]	validation_0-rmse:1.54975	validation_1-rmse:1.73278
[979]	validation_0-rmse:1.54908	validation_1-rmse:1.73233
[980]	validation_0-rmse:1.54845	validation_1-rmse:1.73168
[981]	validation_0-rmse:1.54829	validation_1-rmse:1.73165
[982]	validation_0-rmse:1.54808	validation_1-rmse:1.73145
[983]	validation_0-rmse:1.54794	validation_1-rmse:1.73137
[984]	validation_0-rmse:1.54742	validation_1-rmse:1.73083
[985]	validation_0-rmse:1.54728	validation_1-rmse:1.73080
[986]	validation_0-rmse:1.54676	validation_1-rmse:1.73030
[987]	validation_0-rmse:1.54649	validation_1-rmse:1.73029
[988]	validation_0-rmse:1.54613	validation_1-rmse:1.73001
[989]	validation_0-rmse:1.54584	validation_1-rmse:1.72992
[990]	validation_0-rmse:1.54568	validation_1-rmse:1.72978
[991]	validation_0-rmse:1.54537	validation_1-rmse:1.72955
[992]	validation_0-rmse:1.54513	validation_1-rmse:1.72936
[993]	validation_0-rmse:1.54503	validation_1-rmse:1.72935
[994]	validation_0-rmse:1.54477	validation_1-rmse:1.72930
[995]	validation_0-rmse:1.54440	validation_1-rmse:1.72909
[996]	validation_0-rmse:1.54425	validation_1-rmse:1.72902
[997]	validation_0-rmse:1.54350	validation_1-rmse:1.72827
[998]	validation_0-rmse:1.54278	validation_1-rmse:1.72772
[999]	validation_0-rmse:1.54209	validation_1-rmse:1.72720
>>> Using model to predict target TA_T1_2_1 in unseen test data ...
>>> Using model to calculate permutation importance based on unseen test data ...
>>> Calculating prediction scores based on predicting unseen test data of TA_T1_2_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

================================
MODEL TRAINING & TESTING RESULTS
================================

## DATA
  > target: TA_T1_2_1
  > features: 11 ['.DOY', '.HOUR', '.SEASON', '.RECORDNUMBER', '.YEARDOY', 'SW_IN_T1_2_1', '.YEARWEEK', '.YEARMONTH', '.WEEK', '.YEAR', '.MONTH']
  > 350640 records (with missing)
  > 350447 available records for target and all features (no missing values)
  > training on 262835 records (75.0%) of 262835 features between 2005-01-01 00:45:00 and 2024-12-31 23:15:00
  > testing on 87612 unseen records (25.0%) of TA_T1_2_1 between 2005-01-01 00:15:00 and 2024-12-31 23:45:00

## MODEL
  > the model was trained on training data (262835 records)
  > the model was tested on test data (87612 values)
  > estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
  > parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}
  > number of features used in model:  11
  > names of features used in model:  ['.DOY', '.HOUR', '.SEASON', '.RECORDNUMBER', '.YEARDOY', 'SW_IN_T1_2_1', '.YEARWEEK', '.YEARMONTH', '.WEEK', '.YEAR', '.MONTH']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of TA_T1_2_1 (87612 records).
  > feature importances are showing permutation importances from 10 repeats

               PERM_IMPORTANCE   PERM_SD
.DOY                  0.771144  0.002806
.SEASON               0.304357  0.001794
.HOUR                 0.265438  0.001582
.YEARDOY              0.180848  0.000919
SW_IN_T1_2_1          0.170660  0.000966
.RECORDNUMBER         0.162792  0.000692
.YEARWEEK             0.159954  0.000563
.WEEK                 0.135289  0.000600
.YEARMONTH            0.128779  0.000824
.MONTH                0.012186  0.000129
.YEAR                 0.004215  0.000072


## MODEL SCORES
  All scores were calculated based on unseen test data (87612 records).
  > MAE:  1.3086510101407869 (mean absolute error)
  > MedAE:  1.0264122083325806 (median absolute error)
  > MSE:  2.983207444710757 (mean squared error)
  > RMSE:  1.727196411735144 (root mean squared error)
  > MAXE:  14.304340839468995 (max error)
  > MAPE:  510443699952.678 (mean absolute percentage error)
  > R2:  0.9577378280968553

xgb.fillgaps(showplot_scores=False, showplot_importance=False)
xgb.report_gapfilling()

Gap-filling using final model ...
>>> Using final model on all data to predict target TA_T1_2_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting TA_T1_2_1 ...
>>> Predicting target TA_T1_2_1 where all features are available ... predicted 350640 records.
>>> Collecting results for final model ...
>>> Filling 193 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable TA_T1_2_1_gfXG ...
>>> Restoring original timestamp in results ...
>>> Combining predictions from full model and fallback model ...

===================
GAP-FILLING RESULTS
===================

Model scores and feature importances were calculated from high-quality predicted targets (193 values, TA_T1_2_1_gfXG where flag=1) in comparison to observed targets (350447 values, TA_T1_2_1).

## TARGET
- first timestamp:  2005-01-01 00:15:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 350640 values)
- target column (observed):  TA_T1_2_1
- missing records (observed):  193 (cross-check from flag: 193)
- target column (gap-filled):  TA_T1_2_1_gfXG  (350640 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_TA_T1_2_1_gfXG_ISFILLED
  > flag 0 ... observed targets (350447 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (193 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['.DOY', '.SEASON', '.HOUR', '.YEARDOY', 'SW_IN_T1_2_1', '.RECORDNUMBER', '.YEARWEEK', '.WEEK', '.YEARMONTH', '.MONTH', '.YEAR']
- number of features used in model:  11
- permutation importances were calculated from 10 repeats.

               PERM_IMPORTANCE   PERM_SD
.DOY                  0.774335  0.001391
.SEASON               0.302882  0.001007
.HOUR                 0.267670  0.000883
.YEARDOY              0.180432  0.000565
SW_IN_T1_2_1          0.173023  0.000335
.RECORDNUMBER         0.162385  0.000230
.YEARWEEK             0.160514  0.000311
.WEEK                 0.133567  0.000355
.YEARMONTH            0.127686  0.000235
.MONTH                0.012299  0.000036
.YEAR                 0.004079  0.000035

## MODEL
The model was trained on a training set with test size 25.00%.
- estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
- parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}

## MODEL SCORES
- MAE:  1.1976371817198224 (mean absolute error)
- MedAE:  0.9264980378011067 (median absolute error)
- MSE:  2.5293335678454474 (mean squared error)
- RMSE:  1.5903878671083502 (root mean squared error)
- MAXE:  14.304340839468995 (max error)
- MAPE:  340381448834.361 (mean absolute percentage error)
- R2:  0.9644377814155215

Results

results = xgb.gapfilling_df_
results

	.PREDICTIONS_FULLMODEL	TA_T1_2_1	.GAP_PREDICTIONS	FLAG_TA_T1_2_1_gfXG_ISFILLED	TA_T1_2_1_gfXG	.PREDICTIONS_FALLBACK	.GAPFILLED_CUMULATIVE	.PREDICTIONS
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	3.541210	1.566667	NaN	0	1.566667	None	1.566667e+00	3.541210
2005-01-01 00:45:00	3.541210	1.533333	NaN	0	1.533333	None	3.100000e+00	3.541210
2005-01-01 01:15:00	3.764976	1.566667	NaN	0	1.566667	None	4.666667e+00	3.764976
2005-01-01 01:45:00	3.764976	1.566667	NaN	0	1.566667	None	6.233333e+00	3.764976
2005-01-01 02:15:00	2.921293	1.500000	NaN	0	1.500000	None	7.733333e+00	2.921293
...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	-2.075586	-1.919472	NaN	0	-1.919472	None	3.502278e+06	-2.075586
2024-12-31 22:15:00	-2.026122	-2.104678	NaN	0	-2.104678	None	3.502276e+06	-2.026122
2024-12-31 22:45:00	-2.026122	-2.089444	NaN	0	-2.089444	None	3.502274e+06	-2.026122
2024-12-31 23:15:00	-2.331489	-2.355761	NaN	0	-2.355761	None	3.502272e+06	-2.331489
2024-12-31 23:45:00	-2.331489	-2.578839	NaN	0	-2.578839	None	3.502269e+06	-2.331489

350640 rows × 8 columns

Add to data

# Combine data
data_simple_2005_2024['TA_T1_2_1'] = results['TA_T1_2_1_gfXG'].copy()
data_simple_2005_2024['TA_T1_2_1']

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    1.566667
2005-01-01 00:45:00    1.533333
2005-01-01 01:15:00    1.566667
2005-01-01 01:45:00    1.566667
2005-01-01 02:15:00    1.500000
                         ...   
2024-12-31 21:45:00   -1.919472
2024-12-31 22:15:00   -2.104678
2024-12-31 22:45:00   -2.089444
2024-12-31 23:15:00   -2.355761
2024-12-31 23:45:00   -2.578839
Freq: 30min, Name: TA_T1_2_1, Length: 350640, dtype: float64

# Combine flags
data_simple_2005_2024['FLAG_TA_T1_2_1_ISFILLED'] = data_simple_2005_2024['FLAG_TA_T1_2_1_ISFILLED'].combine_first(results['FLAG_TA_T1_2_1_gfXG_ISFILLED'])
data_simple_2005_2024['FLAG_TA_T1_2_1_ISFILLED']

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    3.0
2005-01-01 00:45:00    3.0
2005-01-01 01:15:00    3.0
2005-01-01 01:45:00    3.0
2005-01-01 02:15:00    3.0
                      ... 
2024-12-31 21:45:00    0.0
2024-12-31 22:15:00    0.0
2024-12-31 22:45:00    0.0
2024-12-31 23:15:00    0.0
2024-12-31 23:45:00    0.0
Freq: 30min, Name: FLAG_TA_T1_2_1_ISFILLED, Length: 350640, dtype: float64

title = "Air temperature"
data_simple_2005_2024[['TA_T1_2_1', 'FLAG_TA_T1_2_1_ISFILLED']].plot(x_compat=True, title=title, subplots=True, figsize=(20, 6));

PPFD

_df = pd.DataFrame()
_df['PPFD_IN_T1_2_2'] = data_simple_2005_2024[['PPFD_IN_T1_2_2']].copy()
_df['SW_IN_T1_2_1'] = data_simple_2005_2024[['SW_IN_T1_2_1']].copy()
_df

	PPFD_IN_T1_2_2	SW_IN_T1_2_1
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	NaN	0.0
2005-01-01 00:45:00	NaN	0.0
2005-01-01 01:15:00	NaN	0.0
2005-01-01 01:45:00	NaN	0.0
2005-01-01 02:15:00	NaN	0.0
...	...	...
2024-12-31 21:45:00	0.0	0.0
2024-12-31 22:15:00	0.0	0.0
2024-12-31 22:45:00	0.0	0.0
2024-12-31 23:15:00	0.0	0.0
2024-12-31 23:45:00	0.0	0.0

350640 rows × 2 columns

Set nighttime data to zero

# Detect nighttime
from diive.pkgs.createvar.daynightflag import DaytimeNighttimeFlag
dnf = DaytimeNighttimeFlag(
    timestamp_index=_df.index,    
    lat=47.210227,  # CH-CHA
    lon=8.410645,  # CH-CHA
    utc_offset=1,
    nighttime_threshold=0.001)
nighttimeflag = dnf.get_nighttime_flag()
nighttime_ix = nighttimeflag == 1

Some nighttime data is above or below zero:

HeatmapDateTime(_df.loc[nighttime_ix, 'PPFD_IN_T1_2_2']).plot()

_df.loc[nighttime_ix, 'PPFD_IN_T1_2_2'].describe()

count    167652.000000
mean          0.346994
std           3.748432
min         -42.389820
25%          -0.168090
50%           0.000000
75%           0.000000
max         254.000000
Name: PPFD_IN_T1_2_2, dtype: float64

_df.loc[nighttime_ix, 'PPFD_IN_T1_2_2'] = 0
_df.loc[nighttime_ix, 'PPFD_IN_T1_2_2'].describe()

count    175280.0
mean          0.0
std           0.0
min           0.0
25%           0.0
50%           0.0
75%           0.0
max           0.0
Name: PPFD_IN_T1_2_2, dtype: float64

HeatmapDateTime(_df.loc[nighttime_ix, 'PPFD_IN_T1_2_2']).plot()

HeatmapDateTime(_df['PPFD_IN_T1_2_2']).plot()

Fill

xgb = XGBoostTS(
    input_df=_df,
    target_col='PPFD_IN_T1_2_2',
    perm_n_repeats=10,
    include_timestamp_as_features=True,
    add_continuous_record_number=True,
    n_estimators=1000,
    random_state=42,
    early_stopping_rounds=50,
    n_jobs=-1
)

Adding new data columns ...
++ Added new columns with timestamp info: ['.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK'] 
++ Added new column .RECORDNUMBER with record numbers from 1 to 350640.

xgb.reduce_features()
xgb.report_feature_reduction()

Feature reduction based on permutation importance ...
[0]	validation_0-rmse:325.23732	validation_1-rmse:325.23732
[1]	validation_0-rmse:229.11486	validation_1-rmse:229.11486
[2]	validation_0-rmse:162.29953	validation_1-rmse:162.29953
[3]	validation_0-rmse:116.22189	validation_1-rmse:116.22189
[4]	validation_0-rmse:84.81768	validation_1-rmse:84.81768
[5]	validation_0-rmse:63.89091	validation_1-rmse:63.89091
[6]	validation_0-rmse:50.34313	validation_1-rmse:50.34313
[7]	validation_0-rmse:41.93109	validation_1-rmse:41.93109
[8]	validation_0-rmse:36.89622	validation_1-rmse:36.89622
[9]	validation_0-rmse:33.88641	validation_1-rmse:33.88641
[10]	validation_0-rmse:32.15637	validation_1-rmse:32.15637
[11]	validation_0-rmse:31.06934	validation_1-rmse:31.06934
[12]	validation_0-rmse:30.32861	validation_1-rmse:30.32861
[13]	validation_0-rmse:29.85210	validation_1-rmse:29.85210
[14]	validation_0-rmse:29.38520	validation_1-rmse:29.38520
[15]	validation_0-rmse:29.10881	validation_1-rmse:29.10881
[16]	validation_0-rmse:28.89576	validation_1-rmse:28.89576
[17]	validation_0-rmse:28.67629	validation_1-rmse:28.67629
[18]	validation_0-rmse:27.61129	validation_1-rmse:27.61129
[19]	validation_0-rmse:27.45010	validation_1-rmse:27.45010
[20]	validation_0-rmse:27.05827	validation_1-rmse:27.05827
[21]	validation_0-rmse:26.92968	validation_1-rmse:26.92968
[22]	validation_0-rmse:26.74150	validation_1-rmse:26.74150
[23]	validation_0-rmse:25.98934	validation_1-rmse:25.98934
[24]	validation_0-rmse:25.89394	validation_1-rmse:25.89394
[25]	validation_0-rmse:25.81129	validation_1-rmse:25.81129
[26]	validation_0-rmse:25.68321	validation_1-rmse:25.68321
[27]	validation_0-rmse:25.56458	validation_1-rmse:25.56458
[28]	validation_0-rmse:25.50611	validation_1-rmse:25.50611
[29]	validation_0-rmse:25.31716	validation_1-rmse:25.31716
[30]	validation_0-rmse:25.26619	validation_1-rmse:25.26619
[31]	validation_0-rmse:25.17827	validation_1-rmse:25.17827
[32]	validation_0-rmse:24.99436	validation_1-rmse:24.99436
[33]	validation_0-rmse:24.80584	validation_1-rmse:24.80584
[34]	validation_0-rmse:24.69920	validation_1-rmse:24.69920
[35]	validation_0-rmse:24.61161	validation_1-rmse:24.61161
[36]	validation_0-rmse:24.44045	validation_1-rmse:24.44045
[37]	validation_0-rmse:24.09383	validation_1-rmse:24.09383
[38]	validation_0-rmse:23.98184	validation_1-rmse:23.98184
[39]	validation_0-rmse:23.91147	validation_1-rmse:23.91147
[40]	validation_0-rmse:23.82869	validation_1-rmse:23.82869
[41]	validation_0-rmse:23.72384	validation_1-rmse:23.72384
[42]	validation_0-rmse:23.50802	validation_1-rmse:23.50802
[43]	validation_0-rmse:23.30224	validation_1-rmse:23.30224
[44]	validation_0-rmse:23.11414	validation_1-rmse:23.11414
[45]	validation_0-rmse:22.89405	validation_1-rmse:22.89405
[46]	validation_0-rmse:22.85172	validation_1-rmse:22.85172
[47]	validation_0-rmse:22.77942	validation_1-rmse:22.77942
[48]	validation_0-rmse:22.60177	validation_1-rmse:22.60177
[49]	validation_0-rmse:22.55924	validation_1-rmse:22.55924
[50]	validation_0-rmse:22.51415	validation_1-rmse:22.51415
[51]	validation_0-rmse:22.49164	validation_1-rmse:22.49164
[52]	validation_0-rmse:22.43381	validation_1-rmse:22.43381
[53]	validation_0-rmse:22.37959	validation_1-rmse:22.37959
[54]	validation_0-rmse:22.26734	validation_1-rmse:22.26734
[55]	validation_0-rmse:22.14601	validation_1-rmse:22.14601
[56]	validation_0-rmse:22.10158	validation_1-rmse:22.10158
[57]	validation_0-rmse:22.04391	validation_1-rmse:22.04391
[58]	validation_0-rmse:21.96226	validation_1-rmse:21.96226
[59]	validation_0-rmse:21.93287	validation_1-rmse:21.93287
[60]	validation_0-rmse:21.89663	validation_1-rmse:21.89663
[61]	validation_0-rmse:21.88077	validation_1-rmse:21.88077
[62]	validation_0-rmse:21.84089	validation_1-rmse:21.84089
[63]	validation_0-rmse:21.76557	validation_1-rmse:21.76557
[64]	validation_0-rmse:21.66634	validation_1-rmse:21.66634
[65]	validation_0-rmse:21.59513	validation_1-rmse:21.59513
[66]	validation_0-rmse:21.55169	validation_1-rmse:21.55169
[67]	validation_0-rmse:21.48051	validation_1-rmse:21.48051
[68]	validation_0-rmse:21.34820	validation_1-rmse:21.34820
[69]	validation_0-rmse:21.30320	validation_1-rmse:21.30320
[70]	validation_0-rmse:21.28464	validation_1-rmse:21.28464
[71]	validation_0-rmse:21.25278	validation_1-rmse:21.25278
[72]	validation_0-rmse:21.22200	validation_1-rmse:21.22200
[73]	validation_0-rmse:21.02341	validation_1-rmse:21.02341
[74]	validation_0-rmse:20.98292	validation_1-rmse:20.98292
[75]	validation_0-rmse:20.90164	validation_1-rmse:20.90164
[76]	validation_0-rmse:20.77617	validation_1-rmse:20.77617
[77]	validation_0-rmse:20.71614	validation_1-rmse:20.71614
[78]	validation_0-rmse:20.69473	validation_1-rmse:20.69473
[79]	validation_0-rmse:20.60632	validation_1-rmse:20.60632
[80]	validation_0-rmse:20.49832	validation_1-rmse:20.49832
[81]	validation_0-rmse:20.45575	validation_1-rmse:20.45575
[82]	validation_0-rmse:20.31136	validation_1-rmse:20.31136
[83]	validation_0-rmse:20.29686	validation_1-rmse:20.29686
[84]	validation_0-rmse:20.22197	validation_1-rmse:20.22197
[85]	validation_0-rmse:20.20349	validation_1-rmse:20.20349
[86]	validation_0-rmse:20.18671	validation_1-rmse:20.18671
[87]	validation_0-rmse:20.15988	validation_1-rmse:20.15988
[88]	validation_0-rmse:20.05117	validation_1-rmse:20.05117
[89]	validation_0-rmse:20.03630	validation_1-rmse:20.03630
[90]	validation_0-rmse:20.01599	validation_1-rmse:20.01599
[91]	validation_0-rmse:19.99655	validation_1-rmse:19.99655
[92]	validation_0-rmse:19.97684	validation_1-rmse:19.97684
[93]	validation_0-rmse:19.93412	validation_1-rmse:19.93412
[94]	validation_0-rmse:19.90412	validation_1-rmse:19.90412
[95]	validation_0-rmse:19.86974	validation_1-rmse:19.86974
[96]	validation_0-rmse:19.69079	validation_1-rmse:19.69079
[97]	validation_0-rmse:19.58252	validation_1-rmse:19.58252
[98]	validation_0-rmse:19.46602	validation_1-rmse:19.46602
[99]	validation_0-rmse:19.39489	validation_1-rmse:19.39489
[100]	validation_0-rmse:19.33497	validation_1-rmse:19.33497
[101]	validation_0-rmse:19.31238	validation_1-rmse:19.31238
[102]	validation_0-rmse:19.23653	validation_1-rmse:19.23653
[103]	validation_0-rmse:19.22474	validation_1-rmse:19.22474
[104]	validation_0-rmse:19.19147	validation_1-rmse:19.19147
[105]	validation_0-rmse:19.04643	validation_1-rmse:19.04643
[106]	validation_0-rmse:19.01838	validation_1-rmse:19.01838
[107]	validation_0-rmse:18.98251	validation_1-rmse:18.98251
[108]	validation_0-rmse:18.93977	validation_1-rmse:18.93977
[109]	validation_0-rmse:18.91998	validation_1-rmse:18.91998
[110]	validation_0-rmse:18.88299	validation_1-rmse:18.88299
[111]	validation_0-rmse:18.84274	validation_1-rmse:18.84274
[112]	validation_0-rmse:18.71749	validation_1-rmse:18.71749
[113]	validation_0-rmse:18.66832	validation_1-rmse:18.66832
[114]	validation_0-rmse:18.65477	validation_1-rmse:18.65477
[115]	validation_0-rmse:18.62735	validation_1-rmse:18.62735
[116]	validation_0-rmse:18.62037	validation_1-rmse:18.62037
[117]	validation_0-rmse:18.51517	validation_1-rmse:18.51517
[118]	validation_0-rmse:18.49771	validation_1-rmse:18.49771
[119]	validation_0-rmse:18.47728	validation_1-rmse:18.47728
[120]	validation_0-rmse:18.46705	validation_1-rmse:18.46705
[121]	validation_0-rmse:18.46114	validation_1-rmse:18.46114
[122]	validation_0-rmse:18.42949	validation_1-rmse:18.42949
[123]	validation_0-rmse:18.40906	validation_1-rmse:18.40906
[124]	validation_0-rmse:18.36940	validation_1-rmse:18.36940
[125]	validation_0-rmse:18.34741	validation_1-rmse:18.34741
[126]	validation_0-rmse:18.32024	validation_1-rmse:18.32024
[127]	validation_0-rmse:18.30009	validation_1-rmse:18.30009
[128]	validation_0-rmse:18.21767	validation_1-rmse:18.21767
[129]	validation_0-rmse:18.19902	validation_1-rmse:18.19902
[130]	validation_0-rmse:18.13430	validation_1-rmse:18.13430
[131]	validation_0-rmse:18.12550	validation_1-rmse:18.12550
[132]	validation_0-rmse:18.11015	validation_1-rmse:18.11015
[133]	validation_0-rmse:18.08187	validation_1-rmse:18.08187
[134]	validation_0-rmse:18.04328	validation_1-rmse:18.04328
[135]	validation_0-rmse:18.02783	validation_1-rmse:18.02783
[136]	validation_0-rmse:17.99411	validation_1-rmse:17.99411
[137]	validation_0-rmse:17.98145	validation_1-rmse:17.98145
[138]	validation_0-rmse:17.96693	validation_1-rmse:17.96693
[139]	validation_0-rmse:17.95180	validation_1-rmse:17.95180
[140]	validation_0-rmse:17.93327	validation_1-rmse:17.93327
[141]	validation_0-rmse:17.92089	validation_1-rmse:17.92089
[142]	validation_0-rmse:17.85956	validation_1-rmse:17.85956
[143]	validation_0-rmse:17.85223	validation_1-rmse:17.85223
[144]	validation_0-rmse:17.77038	validation_1-rmse:17.77038
[145]	validation_0-rmse:17.72640	validation_1-rmse:17.72640
[146]	validation_0-rmse:17.63253	validation_1-rmse:17.63253
[147]	validation_0-rmse:17.61314	validation_1-rmse:17.61314
[148]	validation_0-rmse:17.59229	validation_1-rmse:17.59229
[149]	validation_0-rmse:17.57835	validation_1-rmse:17.57835
[150]	validation_0-rmse:17.56318	validation_1-rmse:17.56318
[151]	validation_0-rmse:17.52169	validation_1-rmse:17.52169
[152]	validation_0-rmse:17.51491	validation_1-rmse:17.51491
[153]	validation_0-rmse:17.50174	validation_1-rmse:17.50174
[154]	validation_0-rmse:17.48610	validation_1-rmse:17.48610
[155]	validation_0-rmse:17.48100	validation_1-rmse:17.48100
[156]	validation_0-rmse:17.46549	validation_1-rmse:17.46549
[157]	validation_0-rmse:17.42663	validation_1-rmse:17.42663
[158]	validation_0-rmse:17.40137	validation_1-rmse:17.40137
[159]	validation_0-rmse:17.39235	validation_1-rmse:17.39235
[160]	validation_0-rmse:17.38107	validation_1-rmse:17.38107
[161]	validation_0-rmse:17.37711	validation_1-rmse:17.37711
[162]	validation_0-rmse:17.35678	validation_1-rmse:17.35678
[163]	validation_0-rmse:17.33097	validation_1-rmse:17.33097
[164]	validation_0-rmse:17.31296	validation_1-rmse:17.31296
[165]	validation_0-rmse:17.28154	validation_1-rmse:17.28154
[166]	validation_0-rmse:17.23627	validation_1-rmse:17.23627
[167]	validation_0-rmse:17.18413	validation_1-rmse:17.18413
[168]	validation_0-rmse:17.11274	validation_1-rmse:17.11274
[169]	validation_0-rmse:17.10047	validation_1-rmse:17.10047
[170]	validation_0-rmse:17.08703	validation_1-rmse:17.08703
[171]	validation_0-rmse:17.03920	validation_1-rmse:17.03920
[172]	validation_0-rmse:17.02573	validation_1-rmse:17.02573
[173]	validation_0-rmse:16.99503	validation_1-rmse:16.99503
[174]	validation_0-rmse:16.97932	validation_1-rmse:16.97932
[175]	validation_0-rmse:16.96080	validation_1-rmse:16.96080
[176]	validation_0-rmse:16.93767	validation_1-rmse:16.93767
[177]	validation_0-rmse:16.91120	validation_1-rmse:16.91120
[178]	validation_0-rmse:16.89281	validation_1-rmse:16.89281
[179]	validation_0-rmse:16.85882	validation_1-rmse:16.85882
[180]	validation_0-rmse:16.84819	validation_1-rmse:16.84819
[181]	validation_0-rmse:16.83773	validation_1-rmse:16.83773
[182]	validation_0-rmse:16.82801	validation_1-rmse:16.82801
[183]	validation_0-rmse:16.80383	validation_1-rmse:16.80383
[184]	validation_0-rmse:16.77088	validation_1-rmse:16.77088
[185]	validation_0-rmse:16.76141	validation_1-rmse:16.76141
[186]	validation_0-rmse:16.74242	validation_1-rmse:16.74242
[187]	validation_0-rmse:16.72845	validation_1-rmse:16.72845
[188]	validation_0-rmse:16.68735	validation_1-rmse:16.68735
[189]	validation_0-rmse:16.63743	validation_1-rmse:16.63743
[190]	validation_0-rmse:16.63505	validation_1-rmse:16.63505
[191]	validation_0-rmse:16.63088	validation_1-rmse:16.63088
[192]	validation_0-rmse:16.62448	validation_1-rmse:16.62448
[193]	validation_0-rmse:16.60762	validation_1-rmse:16.60762
[194]	validation_0-rmse:16.59288	validation_1-rmse:16.59288
[195]	validation_0-rmse:16.56429	validation_1-rmse:16.56429
[196]	validation_0-rmse:16.54772	validation_1-rmse:16.54772
[197]	validation_0-rmse:16.48664	validation_1-rmse:16.48664
[198]	validation_0-rmse:16.47057	validation_1-rmse:16.47057
[199]	validation_0-rmse:16.46075	validation_1-rmse:16.46075
[200]	validation_0-rmse:16.44295	validation_1-rmse:16.44295
[201]	validation_0-rmse:16.43760	validation_1-rmse:16.43760
[202]	validation_0-rmse:16.38772	validation_1-rmse:16.38772
[203]	validation_0-rmse:16.37879	validation_1-rmse:16.37879
[204]	validation_0-rmse:16.36946	validation_1-rmse:16.36946
[205]	validation_0-rmse:16.36150	validation_1-rmse:16.36150
[206]	validation_0-rmse:16.33134	validation_1-rmse:16.33134
[207]	validation_0-rmse:16.26498	validation_1-rmse:16.26498
[208]	validation_0-rmse:16.25646	validation_1-rmse:16.25646
[209]	validation_0-rmse:16.23185	validation_1-rmse:16.23185
[210]	validation_0-rmse:16.22273	validation_1-rmse:16.22273
[211]	validation_0-rmse:16.20715	validation_1-rmse:16.20715
[212]	validation_0-rmse:16.17504	validation_1-rmse:16.17504
[213]	validation_0-rmse:16.15121	validation_1-rmse:16.15121
[214]	validation_0-rmse:16.08250	validation_1-rmse:16.08250
[215]	validation_0-rmse:16.07213	validation_1-rmse:16.07213
[216]	validation_0-rmse:16.04159	validation_1-rmse:16.04159
[217]	validation_0-rmse:16.01847	validation_1-rmse:16.01847
[218]	validation_0-rmse:16.00331	validation_1-rmse:16.00331
[219]	validation_0-rmse:15.99785	validation_1-rmse:15.99785
[220]	validation_0-rmse:15.98435	validation_1-rmse:15.98435
[221]	validation_0-rmse:15.97209	validation_1-rmse:15.97209
[222]	validation_0-rmse:15.94593	validation_1-rmse:15.94593
[223]	validation_0-rmse:15.94246	validation_1-rmse:15.94246
[224]	validation_0-rmse:15.92297	validation_1-rmse:15.92297
[225]	validation_0-rmse:15.90358	validation_1-rmse:15.90358
[226]	validation_0-rmse:15.85613	validation_1-rmse:15.85613
[227]	validation_0-rmse:15.84409	validation_1-rmse:15.84409
[228]	validation_0-rmse:15.81660	validation_1-rmse:15.81660
[229]	validation_0-rmse:15.79060	validation_1-rmse:15.79060
[230]	validation_0-rmse:15.77386	validation_1-rmse:15.77386
[231]	validation_0-rmse:15.73101	validation_1-rmse:15.73101
[232]	validation_0-rmse:15.67614	validation_1-rmse:15.67614
[233]	validation_0-rmse:15.65280	validation_1-rmse:15.65280
[234]	validation_0-rmse:15.60750	validation_1-rmse:15.60750
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[791]	validation_0-rmse:9.90899	validation_1-rmse:9.90899
[792]	validation_0-rmse:9.90051	validation_1-rmse:9.90051
[793]	validation_0-rmse:9.89787	validation_1-rmse:9.89787
[794]	validation_0-rmse:9.89134	validation_1-rmse:9.89134
[795]	validation_0-rmse:9.88798	validation_1-rmse:9.88798
[796]	validation_0-rmse:9.88321	validation_1-rmse:9.88321
[797]	validation_0-rmse:9.87926	validation_1-rmse:9.87926
[798]	validation_0-rmse:9.87486	validation_1-rmse:9.87486
[799]	validation_0-rmse:9.86930	validation_1-rmse:9.86930
[800]	validation_0-rmse:9.86529	validation_1-rmse:9.86529
[801]	validation_0-rmse:9.85579	validation_1-rmse:9.85579
[802]	validation_0-rmse:9.85231	validation_1-rmse:9.85231
[803]	validation_0-rmse:9.84737	validation_1-rmse:9.84737
[804]	validation_0-rmse:9.83709	validation_1-rmse:9.83709
[805]	validation_0-rmse:9.83486	validation_1-rmse:9.83486
[806]	validation_0-rmse:9.82456	validation_1-rmse:9.82456
[807]	validation_0-rmse:9.81740	validation_1-rmse:9.81740
[808]	validation_0-rmse:9.81076	validation_1-rmse:9.81076
[809]	validation_0-rmse:9.80105	validation_1-rmse:9.80105
[810]	validation_0-rmse:9.79695	validation_1-rmse:9.79695
[811]	validation_0-rmse:9.79270	validation_1-rmse:9.79270
[812]	validation_0-rmse:9.77442	validation_1-rmse:9.77442
[813]	validation_0-rmse:9.77040	validation_1-rmse:9.77040
[814]	validation_0-rmse:9.76166	validation_1-rmse:9.76166
[815]	validation_0-rmse:9.75580	validation_1-rmse:9.75580
[816]	validation_0-rmse:9.75141	validation_1-rmse:9.75141
[817]	validation_0-rmse:9.74693	validation_1-rmse:9.74693
[818]	validation_0-rmse:9.74449	validation_1-rmse:9.74449
[819]	validation_0-rmse:9.74008	validation_1-rmse:9.74008
[820]	validation_0-rmse:9.73680	validation_1-rmse:9.73680
[821]	validation_0-rmse:9.73296	validation_1-rmse:9.73296
[822]	validation_0-rmse:9.72817	validation_1-rmse:9.72817
[823]	validation_0-rmse:9.72423	validation_1-rmse:9.72423
[824]	validation_0-rmse:9.71862	validation_1-rmse:9.71862
[825]	validation_0-rmse:9.70962	validation_1-rmse:9.70962
[826]	validation_0-rmse:9.70546	validation_1-rmse:9.70546
[827]	validation_0-rmse:9.69915	validation_1-rmse:9.69915
[828]	validation_0-rmse:9.69781	validation_1-rmse:9.69781
[829]	validation_0-rmse:9.69391	validation_1-rmse:9.69391
[830]	validation_0-rmse:9.69110	validation_1-rmse:9.69110
[831]	validation_0-rmse:9.68944	validation_1-rmse:9.68944
[832]	validation_0-rmse:9.68675	validation_1-rmse:9.68675
[833]	validation_0-rmse:9.67756	validation_1-rmse:9.67756
[834]	validation_0-rmse:9.67199	validation_1-rmse:9.67199
[835]	validation_0-rmse:9.66834	validation_1-rmse:9.66834
[836]	validation_0-rmse:9.66580	validation_1-rmse:9.66580
[837]	validation_0-rmse:9.66238	validation_1-rmse:9.66238
[838]	validation_0-rmse:9.65972	validation_1-rmse:9.65972
[839]	validation_0-rmse:9.64896	validation_1-rmse:9.64896
[840]	validation_0-rmse:9.64418	validation_1-rmse:9.64418
[841]	validation_0-rmse:9.64108	validation_1-rmse:9.64108
[842]	validation_0-rmse:9.63538	validation_1-rmse:9.63538
[843]	validation_0-rmse:9.62537	validation_1-rmse:9.62537
[844]	validation_0-rmse:9.61906	validation_1-rmse:9.61906
[845]	validation_0-rmse:9.61613	validation_1-rmse:9.61613
[846]	validation_0-rmse:9.61297	validation_1-rmse:9.61297
[847]	validation_0-rmse:9.60786	validation_1-rmse:9.60786
[848]	validation_0-rmse:9.59904	validation_1-rmse:9.59904
[849]	validation_0-rmse:9.59280	validation_1-rmse:9.59280
[850]	validation_0-rmse:9.58576	validation_1-rmse:9.58576
[851]	validation_0-rmse:9.58297	validation_1-rmse:9.58297
[852]	validation_0-rmse:9.58067	validation_1-rmse:9.58067
[853]	validation_0-rmse:9.57876	validation_1-rmse:9.57876
[854]	validation_0-rmse:9.57582	validation_1-rmse:9.57582
[855]	validation_0-rmse:9.57318	validation_1-rmse:9.57318
[856]	validation_0-rmse:9.56617	validation_1-rmse:9.56617
[857]	validation_0-rmse:9.56381	validation_1-rmse:9.56381
[858]	validation_0-rmse:9.55832	validation_1-rmse:9.55832
[859]	validation_0-rmse:9.55516	validation_1-rmse:9.55516
[860]	validation_0-rmse:9.54962	validation_1-rmse:9.54962
[861]	validation_0-rmse:9.54666	validation_1-rmse:9.54666
[862]	validation_0-rmse:9.54477	validation_1-rmse:9.54477
[863]	validation_0-rmse:9.53908	validation_1-rmse:9.53908
[864]	validation_0-rmse:9.51901	validation_1-rmse:9.51901
[865]	validation_0-rmse:9.51766	validation_1-rmse:9.51766
[866]	validation_0-rmse:9.51483	validation_1-rmse:9.51483
[867]	validation_0-rmse:9.51191	validation_1-rmse:9.51191
[868]	validation_0-rmse:9.51058	validation_1-rmse:9.51058
[869]	validation_0-rmse:9.50784	validation_1-rmse:9.50784
[870]	validation_0-rmse:9.50378	validation_1-rmse:9.50378
[871]	validation_0-rmse:9.49941	validation_1-rmse:9.49941
[872]	validation_0-rmse:9.49641	validation_1-rmse:9.49641
[873]	validation_0-rmse:9.49330	validation_1-rmse:9.49330
[874]	validation_0-rmse:9.49224	validation_1-rmse:9.49224
[875]	validation_0-rmse:9.48973	validation_1-rmse:9.48973
[876]	validation_0-rmse:9.48676	validation_1-rmse:9.48676
[877]	validation_0-rmse:9.48075	validation_1-rmse:9.48075
[878]	validation_0-rmse:9.47761	validation_1-rmse:9.47761
[879]	validation_0-rmse:9.46698	validation_1-rmse:9.46698
[880]	validation_0-rmse:9.46104	validation_1-rmse:9.46104
[881]	validation_0-rmse:9.45793	validation_1-rmse:9.45793
[882]	validation_0-rmse:9.44493	validation_1-rmse:9.44493
[883]	validation_0-rmse:9.43668	validation_1-rmse:9.43668
[884]	validation_0-rmse:9.43159	validation_1-rmse:9.43159
[885]	validation_0-rmse:9.42911	validation_1-rmse:9.42911
[886]	validation_0-rmse:9.42555	validation_1-rmse:9.42555
[887]	validation_0-rmse:9.42091	validation_1-rmse:9.42091
[888]	validation_0-rmse:9.41221	validation_1-rmse:9.41221
[889]	validation_0-rmse:9.40517	validation_1-rmse:9.40517
[890]	validation_0-rmse:9.40103	validation_1-rmse:9.40103
[891]	validation_0-rmse:9.39818	validation_1-rmse:9.39818
[892]	validation_0-rmse:9.39613	validation_1-rmse:9.39613
[893]	validation_0-rmse:9.38914	validation_1-rmse:9.38914
[894]	validation_0-rmse:9.38351	validation_1-rmse:9.38351
[895]	validation_0-rmse:9.37715	validation_1-rmse:9.37715
[896]	validation_0-rmse:9.37315	validation_1-rmse:9.37315
[897]	validation_0-rmse:9.36950	validation_1-rmse:9.36950
[898]	validation_0-rmse:9.36653	validation_1-rmse:9.36653
[899]	validation_0-rmse:9.36527	validation_1-rmse:9.36527
[900]	validation_0-rmse:9.36149	validation_1-rmse:9.36149
[901]	validation_0-rmse:9.35478	validation_1-rmse:9.35478
[902]	validation_0-rmse:9.35373	validation_1-rmse:9.35373
[903]	validation_0-rmse:9.35019	validation_1-rmse:9.35019
[904]	validation_0-rmse:9.34855	validation_1-rmse:9.34855
[905]	validation_0-rmse:9.34407	validation_1-rmse:9.34407
[906]	validation_0-rmse:9.34195	validation_1-rmse:9.34195
[907]	validation_0-rmse:9.32745	validation_1-rmse:9.32745
[908]	validation_0-rmse:9.32335	validation_1-rmse:9.32335
[909]	validation_0-rmse:9.32245	validation_1-rmse:9.32245
[910]	validation_0-rmse:9.32132	validation_1-rmse:9.32132
[911]	validation_0-rmse:9.31645	validation_1-rmse:9.31645
[912]	validation_0-rmse:9.31605	validation_1-rmse:9.31605
[913]	validation_0-rmse:9.30997	validation_1-rmse:9.30997
[914]	validation_0-rmse:9.30499	validation_1-rmse:9.30499
[915]	validation_0-rmse:9.30213	validation_1-rmse:9.30213
[916]	validation_0-rmse:9.29542	validation_1-rmse:9.29542
[917]	validation_0-rmse:9.29280	validation_1-rmse:9.29280
[918]	validation_0-rmse:9.28770	validation_1-rmse:9.28770
[919]	validation_0-rmse:9.28167	validation_1-rmse:9.28167
[920]	validation_0-rmse:9.28027	validation_1-rmse:9.28027
[921]	validation_0-rmse:9.27850	validation_1-rmse:9.27850
[922]	validation_0-rmse:9.26691	validation_1-rmse:9.26691
[923]	validation_0-rmse:9.26209	validation_1-rmse:9.26209
[924]	validation_0-rmse:9.25920	validation_1-rmse:9.25920
[925]	validation_0-rmse:9.25289	validation_1-rmse:9.25289
[926]	validation_0-rmse:9.24686	validation_1-rmse:9.24686
[927]	validation_0-rmse:9.24139	validation_1-rmse:9.24139
[928]	validation_0-rmse:9.23413	validation_1-rmse:9.23413
[929]	validation_0-rmse:9.23026	validation_1-rmse:9.23026
[930]	validation_0-rmse:9.22337	validation_1-rmse:9.22337
[931]	validation_0-rmse:9.22064	validation_1-rmse:9.22064
[932]	validation_0-rmse:9.21519	validation_1-rmse:9.21519
[933]	validation_0-rmse:9.21167	validation_1-rmse:9.21167
[934]	validation_0-rmse:9.20762	validation_1-rmse:9.20762
[935]	validation_0-rmse:9.20196	validation_1-rmse:9.20196
[936]	validation_0-rmse:9.19828	validation_1-rmse:9.19828
[937]	validation_0-rmse:9.19506	validation_1-rmse:9.19506
[938]	validation_0-rmse:9.19174	validation_1-rmse:9.19174
[939]	validation_0-rmse:9.19029	validation_1-rmse:9.19029
[940]	validation_0-rmse:9.18928	validation_1-rmse:9.18928
[941]	validation_0-rmse:9.18638	validation_1-rmse:9.18638
[942]	validation_0-rmse:9.18528	validation_1-rmse:9.18528
[943]	validation_0-rmse:9.17673	validation_1-rmse:9.17673
[944]	validation_0-rmse:9.17326	validation_1-rmse:9.17326
[945]	validation_0-rmse:9.16158	validation_1-rmse:9.16158
[946]	validation_0-rmse:9.15496	validation_1-rmse:9.15496
[947]	validation_0-rmse:9.15202	validation_1-rmse:9.15202
[948]	validation_0-rmse:9.15085	validation_1-rmse:9.15085
[949]	validation_0-rmse:9.14840	validation_1-rmse:9.14840
[950]	validation_0-rmse:9.14468	validation_1-rmse:9.14468
[951]	validation_0-rmse:9.13791	validation_1-rmse:9.13791
[952]	validation_0-rmse:9.13591	validation_1-rmse:9.13591
[953]	validation_0-rmse:9.13337	validation_1-rmse:9.13337
[954]	validation_0-rmse:9.13170	validation_1-rmse:9.13170
[955]	validation_0-rmse:9.12825	validation_1-rmse:9.12825
[956]	validation_0-rmse:9.12428	validation_1-rmse:9.12428
[957]	validation_0-rmse:9.10639	validation_1-rmse:9.10639
[958]	validation_0-rmse:9.10039	validation_1-rmse:9.10039
[959]	validation_0-rmse:9.09489	validation_1-rmse:9.09489
[960]	validation_0-rmse:9.08056	validation_1-rmse:9.08056
[961]	validation_0-rmse:9.07420	validation_1-rmse:9.07420
[962]	validation_0-rmse:9.06596	validation_1-rmse:9.06596
[963]	validation_0-rmse:9.06267	validation_1-rmse:9.06267
[964]	validation_0-rmse:9.05368	validation_1-rmse:9.05368
[965]	validation_0-rmse:9.05144	validation_1-rmse:9.05144
[966]	validation_0-rmse:9.04739	validation_1-rmse:9.04739
[967]	validation_0-rmse:9.04514	validation_1-rmse:9.04514
[968]	validation_0-rmse:9.03779	validation_1-rmse:9.03779
[969]	validation_0-rmse:9.02942	validation_1-rmse:9.02942
[970]	validation_0-rmse:9.02489	validation_1-rmse:9.02489
[971]	validation_0-rmse:9.02254	validation_1-rmse:9.02254
[972]	validation_0-rmse:9.01753	validation_1-rmse:9.01753
[973]	validation_0-rmse:9.01591	validation_1-rmse:9.01591
[974]	validation_0-rmse:9.01092	validation_1-rmse:9.01092
[975]	validation_0-rmse:9.00633	validation_1-rmse:9.00633
[976]	validation_0-rmse:9.00179	validation_1-rmse:9.00179
[977]	validation_0-rmse:9.00011	validation_1-rmse:9.00011
[978]	validation_0-rmse:8.99801	validation_1-rmse:8.99801
[979]	validation_0-rmse:8.99568	validation_1-rmse:8.99568
[980]	validation_0-rmse:8.99286	validation_1-rmse:8.99286
[981]	validation_0-rmse:8.98328	validation_1-rmse:8.98328
[982]	validation_0-rmse:8.97755	validation_1-rmse:8.97755
[983]	validation_0-rmse:8.97516	validation_1-rmse:8.97516
[984]	validation_0-rmse:8.97096	validation_1-rmse:8.97096
[985]	validation_0-rmse:8.96659	validation_1-rmse:8.96659
[986]	validation_0-rmse:8.96127	validation_1-rmse:8.96127
[987]	validation_0-rmse:8.95305	validation_1-rmse:8.95305
[988]	validation_0-rmse:8.94882	validation_1-rmse:8.94882
[989]	validation_0-rmse:8.94645	validation_1-rmse:8.94645
[990]	validation_0-rmse:8.94373	validation_1-rmse:8.94373
[991]	validation_0-rmse:8.94108	validation_1-rmse:8.94108
[992]	validation_0-rmse:8.93948	validation_1-rmse:8.93948
[993]	validation_0-rmse:8.93692	validation_1-rmse:8.93692
[994]	validation_0-rmse:8.93519	validation_1-rmse:8.93519
[995]	validation_0-rmse:8.93251	validation_1-rmse:8.93251
[996]	validation_0-rmse:8.92924	validation_1-rmse:8.92924
[997]	validation_0-rmse:8.92634	validation_1-rmse:8.92634
[998]	validation_0-rmse:8.91895	validation_1-rmse:8.91895
[999]	validation_0-rmse:8.91715	validation_1-rmse:8.91715
>>> Calculating feature importances (permutation importance, 10 repeats) ...
>>> Setting threshold for feature rejection to 0.001294121446179275.
>>> Removing rejected features from model data ...

=================
FEATURE REDUCTION
=================

- Target variable: PPFD_IN_T1_2_2

- The random variable .RANDOM was added to the original features, used as benchmark for detecting relevant feature importances.

PERMUTATION IMPORTANCE (mean) across all splits of TimeSeriesSplit:

               PERM_IMPORTANCE   PERM_SD
SW_IN_T1_2_1          1.535705  0.002414
.HOUR                 0.044561  0.000229
.DOY                  0.018830  0.000306
.SEASON               0.007105  0.000165
.YEARMONTH            0.005537  0.000069
.RECORDNUMBER         0.003914  0.000054
.YEARDOY              0.003877  0.000054
.WEEK                 0.003643  0.000071
.YEARWEEK             0.002312  0.000020
.RANDOM               0.001294  0.000068
.MONTH                0.000846  0.000059
.YEAR                 0.000465  0.000022

- These results are from feature reduction. Note that feature importances for the final model are calculated during gap-filling.

--> 11 original input features (before feature reduction): ['SW_IN_T1_2_1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
--> 2 rejected features (during feature reduction): ['.YEAR', '.MONTH']
--> 9 accepted features (after feature reduction): ['SW_IN_T1_2_1', '.HOUR', '.DOY', '.SEASON', '.YEARMONTH', '.RECORDNUMBER', '.YEARDOY', '.WEEK', '.YEARWEEK']

xgb.trainmodel(showplot_scores=False, showplot_importance=False)
xgb.report_traintest()

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2005-01-01 00:45:00 and 2024-12-31 23:45:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:325.23732	validation_1-rmse:324.60019
[1]	validation_0-rmse:229.11486	validation_1-rmse:228.50045
[2]	validation_0-rmse:162.29953	validation_1-rmse:161.59285
[3]	validation_0-rmse:116.22189	validation_1-rmse:115.30561
[4]	validation_0-rmse:84.81769	validation_1-rmse:83.57315
[5]	validation_0-rmse:63.89092	validation_1-rmse:62.23451
[6]	validation_0-rmse:50.34317	validation_1-rmse:48.27809
[7]	validation_0-rmse:41.93144	validation_1-rmse:39.50949
[8]	validation_0-rmse:36.94021	validation_1-rmse:34.22487
[9]	validation_0-rmse:33.92395	validation_1-rmse:31.11069
[10]	validation_0-rmse:32.19361	validation_1-rmse:29.33318
[11]	validation_0-rmse:31.18447	validation_1-rmse:28.22811
[12]	validation_0-rmse:30.49785	validation_1-rmse:27.46488
[13]	validation_0-rmse:30.01732	validation_1-rmse:27.00855
[14]	validation_0-rmse:29.67170	validation_1-rmse:26.70187
[15]	validation_0-rmse:29.42053	validation_1-rmse:26.43001
[16]	validation_0-rmse:28.30793	validation_1-rmse:25.91028
[17]	validation_0-rmse:28.14511	validation_1-rmse:25.75757
[18]	validation_0-rmse:27.96116	validation_1-rmse:25.61285
[19]	validation_0-rmse:27.85436	validation_1-rmse:25.53663
[20]	validation_0-rmse:27.73935	validation_1-rmse:25.42889
[21]	validation_0-rmse:27.51673	validation_1-rmse:25.19451
[22]	validation_0-rmse:26.88744	validation_1-rmse:25.02994
[23]	validation_0-rmse:26.72593	validation_1-rmse:24.85665
[24]	validation_0-rmse:26.55398	validation_1-rmse:24.82180
[25]	validation_0-rmse:26.36519	validation_1-rmse:24.64407
[26]	validation_0-rmse:26.24544	validation_1-rmse:24.57047
[27]	validation_0-rmse:26.16152	validation_1-rmse:24.54550
[28]	validation_0-rmse:26.06913	validation_1-rmse:24.50818
[29]	validation_0-rmse:26.01037	validation_1-rmse:24.45399
[30]	validation_0-rmse:25.79331	validation_1-rmse:24.20556
[31]	validation_0-rmse:25.57699	validation_1-rmse:24.17058
[32]	validation_0-rmse:25.38768	validation_1-rmse:24.03451
[33]	validation_0-rmse:25.23870	validation_1-rmse:23.97826
[34]	validation_0-rmse:25.18216	validation_1-rmse:23.90692
[35]	validation_0-rmse:25.00462	validation_1-rmse:23.95039
[36]	validation_0-rmse:24.73227	validation_1-rmse:23.79459
[37]	validation_0-rmse:24.61120	validation_1-rmse:23.71168
[38]	validation_0-rmse:24.46915	validation_1-rmse:23.71713
[39]	validation_0-rmse:24.34513	validation_1-rmse:23.66372
[40]	validation_0-rmse:24.28613	validation_1-rmse:23.64725
[41]	validation_0-rmse:24.21564	validation_1-rmse:23.64073
[42]	validation_0-rmse:24.09139	validation_1-rmse:23.58160
[43]	validation_0-rmse:23.93488	validation_1-rmse:23.56539
[44]	validation_0-rmse:23.84104	validation_1-rmse:23.51395
[45]	validation_0-rmse:23.81071	validation_1-rmse:23.49470
[46]	validation_0-rmse:23.77343	validation_1-rmse:23.48133
[47]	validation_0-rmse:23.68574	validation_1-rmse:23.46666
[48]	validation_0-rmse:23.64010	validation_1-rmse:23.45331
[49]	validation_0-rmse:23.42549	validation_1-rmse:23.51470
[50]	validation_0-rmse:23.36681	validation_1-rmse:23.47778
[51]	validation_0-rmse:23.33490	validation_1-rmse:23.45422
[52]	validation_0-rmse:23.22322	validation_1-rmse:23.43492
[53]	validation_0-rmse:23.17293	validation_1-rmse:23.39082
[54]	validation_0-rmse:23.07782	validation_1-rmse:23.41384
[55]	validation_0-rmse:23.01566	validation_1-rmse:23.41436
[56]	validation_0-rmse:22.84912	validation_1-rmse:23.32054
[57]	validation_0-rmse:22.82265	validation_1-rmse:23.31321
[58]	validation_0-rmse:22.80023	validation_1-rmse:23.29853
[59]	validation_0-rmse:22.72548	validation_1-rmse:23.30375
[60]	validation_0-rmse:22.68767	validation_1-rmse:23.26569
[61]	validation_0-rmse:22.53370	validation_1-rmse:23.25841
[62]	validation_0-rmse:22.44629	validation_1-rmse:23.25173
[63]	validation_0-rmse:22.35200	validation_1-rmse:23.24770
[64]	validation_0-rmse:22.33725	validation_1-rmse:23.23204
[65]	validation_0-rmse:22.29248	validation_1-rmse:23.19853
[66]	validation_0-rmse:22.23841	validation_1-rmse:23.19748
[67]	validation_0-rmse:22.19295	validation_1-rmse:23.15352
[68]	validation_0-rmse:22.12446	validation_1-rmse:23.18342
[69]	validation_0-rmse:22.01950	validation_1-rmse:23.21106
[70]	validation_0-rmse:21.99775	validation_1-rmse:23.20762
[71]	validation_0-rmse:21.94168	validation_1-rmse:23.15597
[72]	validation_0-rmse:21.87730	validation_1-rmse:23.13576
[73]	validation_0-rmse:21.80014	validation_1-rmse:23.11067
[74]	validation_0-rmse:21.76623	validation_1-rmse:23.09568
[75]	validation_0-rmse:21.74576	validation_1-rmse:23.08758
[76]	validation_0-rmse:21.70285	validation_1-rmse:23.08000
[77]	validation_0-rmse:21.68835	validation_1-rmse:23.07299
[78]	validation_0-rmse:21.66733	validation_1-rmse:23.07370
[79]	validation_0-rmse:21.65122	validation_1-rmse:23.05657
[80]	validation_0-rmse:21.57929	validation_1-rmse:23.06913
[81]	validation_0-rmse:21.56339	validation_1-rmse:23.05620
[82]	validation_0-rmse:21.50936	validation_1-rmse:23.05682
[83]	validation_0-rmse:21.47458	validation_1-rmse:23.04162
[84]	validation_0-rmse:21.44059	validation_1-rmse:23.02650
[85]	validation_0-rmse:21.40115	validation_1-rmse:23.01871
[86]	validation_0-rmse:21.35492	validation_1-rmse:23.02295
[87]	validation_0-rmse:21.29991	validation_1-rmse:23.03165
[88]	validation_0-rmse:21.23127	validation_1-rmse:23.03907
[89]	validation_0-rmse:21.15871	validation_1-rmse:23.01329
[90]	validation_0-rmse:21.12883	validation_1-rmse:23.01399
[91]	validation_0-rmse:21.11791	validation_1-rmse:23.01450
[92]	validation_0-rmse:21.08857	validation_1-rmse:23.00742
[93]	validation_0-rmse:21.03022	validation_1-rmse:22.97336
[94]	validation_0-rmse:21.00883	validation_1-rmse:22.97636
[95]	validation_0-rmse:20.99097	validation_1-rmse:22.96019
[96]	validation_0-rmse:20.95800	validation_1-rmse:22.97465
[97]	validation_0-rmse:20.94188	validation_1-rmse:22.96439
[98]	validation_0-rmse:20.92108	validation_1-rmse:22.96165
[99]	validation_0-rmse:20.89501	validation_1-rmse:22.94923
[100]	validation_0-rmse:20.84276	validation_1-rmse:22.94209
[101]	validation_0-rmse:20.82474	validation_1-rmse:22.92929
[102]	validation_0-rmse:20.77582	validation_1-rmse:22.91527
[103]	validation_0-rmse:20.75857	validation_1-rmse:22.91020
[104]	validation_0-rmse:20.70070	validation_1-rmse:22.89023
[105]	validation_0-rmse:20.65029	validation_1-rmse:22.87544
[106]	validation_0-rmse:20.62584	validation_1-rmse:22.87303
[107]	validation_0-rmse:20.55681	validation_1-rmse:22.88437
[108]	validation_0-rmse:20.51199	validation_1-rmse:22.90060
[109]	validation_0-rmse:20.49404	validation_1-rmse:22.89031
[110]	validation_0-rmse:20.45209	validation_1-rmse:22.92455
[111]	validation_0-rmse:20.43075	validation_1-rmse:22.92921
[112]	validation_0-rmse:20.42291	validation_1-rmse:22.92550
[113]	validation_0-rmse:20.38573	validation_1-rmse:22.94416
[114]	validation_0-rmse:20.36625	validation_1-rmse:22.93031
[115]	validation_0-rmse:20.30898	validation_1-rmse:22.91732
[116]	validation_0-rmse:20.27921	validation_1-rmse:22.91213
[117]	validation_0-rmse:20.21352	validation_1-rmse:22.97593
[118]	validation_0-rmse:20.18945	validation_1-rmse:22.97833
[119]	validation_0-rmse:20.18088	validation_1-rmse:22.96704
[120]	validation_0-rmse:20.16939	validation_1-rmse:22.96299
[121]	validation_0-rmse:20.15575	validation_1-rmse:22.94508
[122]	validation_0-rmse:20.14289	validation_1-rmse:22.93985
[123]	validation_0-rmse:20.09541	validation_1-rmse:22.95333
[124]	validation_0-rmse:20.07239	validation_1-rmse:22.94720
[125]	validation_0-rmse:19.98676	validation_1-rmse:22.98510
[126]	validation_0-rmse:19.97987	validation_1-rmse:22.98179
[127]	validation_0-rmse:19.94801	validation_1-rmse:22.98363
[128]	validation_0-rmse:19.93133	validation_1-rmse:22.97826
[129]	validation_0-rmse:19.91060	validation_1-rmse:22.97478
[130]	validation_0-rmse:19.88338	validation_1-rmse:22.96881
[131]	validation_0-rmse:19.86997	validation_1-rmse:22.96714
[132]	validation_0-rmse:19.78024	validation_1-rmse:22.96458
[133]	validation_0-rmse:19.73206	validation_1-rmse:22.95246
[134]	validation_0-rmse:19.66906	validation_1-rmse:22.95959
[135]	validation_0-rmse:19.60172	validation_1-rmse:22.95570
[136]	validation_0-rmse:19.55827	validation_1-rmse:22.95799
[137]	validation_0-rmse:19.54133	validation_1-rmse:22.94358
[138]	validation_0-rmse:19.52216	validation_1-rmse:22.93629
[139]	validation_0-rmse:19.50194	validation_1-rmse:22.91826
[140]	validation_0-rmse:19.48812	validation_1-rmse:22.92157
[141]	validation_0-rmse:19.47886	validation_1-rmse:22.91920
[142]	validation_0-rmse:19.46081	validation_1-rmse:22.91552
[143]	validation_0-rmse:19.43980	validation_1-rmse:22.91690
[144]	validation_0-rmse:19.40570	validation_1-rmse:22.92098
[145]	validation_0-rmse:19.36861	validation_1-rmse:22.92284
[146]	validation_0-rmse:19.32941	validation_1-rmse:22.91557
[147]	validation_0-rmse:19.31682	validation_1-rmse:22.91560
[148]	validation_0-rmse:19.30315	validation_1-rmse:22.90199
[149]	validation_0-rmse:19.27693	validation_1-rmse:22.89530
[150]	validation_0-rmse:19.25173	validation_1-rmse:22.87410
[151]	validation_0-rmse:19.24504	validation_1-rmse:22.87488
[152]	validation_0-rmse:19.23713	validation_1-rmse:22.87011
[153]	validation_0-rmse:19.23267	validation_1-rmse:22.86640
[154]	validation_0-rmse:19.19311	validation_1-rmse:22.86017
[155]	validation_0-rmse:19.11475	validation_1-rmse:22.90894
[156]	validation_0-rmse:19.07167	validation_1-rmse:22.92515
[157]	validation_0-rmse:19.06195	validation_1-rmse:22.91650
[158]	validation_0-rmse:19.03836	validation_1-rmse:22.91560
[159]	validation_0-rmse:18.96387	validation_1-rmse:22.97244
[160]	validation_0-rmse:18.90170	validation_1-rmse:22.99649
[161]	validation_0-rmse:18.89099	validation_1-rmse:22.99707
[162]	validation_0-rmse:18.88477	validation_1-rmse:22.99241
[163]	validation_0-rmse:18.82355	validation_1-rmse:23.02423
[164]	validation_0-rmse:18.75592	validation_1-rmse:23.03169
[165]	validation_0-rmse:18.74098	validation_1-rmse:23.02416
[166]	validation_0-rmse:18.72826	validation_1-rmse:23.01563
[167]	validation_0-rmse:18.70524	validation_1-rmse:23.01413
[168]	validation_0-rmse:18.63354	validation_1-rmse:23.06582
[169]	validation_0-rmse:18.53506	validation_1-rmse:23.06609
[170]	validation_0-rmse:18.49898	validation_1-rmse:23.06075
[171]	validation_0-rmse:18.46975	validation_1-rmse:23.07529
[172]	validation_0-rmse:18.42263	validation_1-rmse:23.03376
[173]	validation_0-rmse:18.41065	validation_1-rmse:23.03757
[174]	validation_0-rmse:18.39519	validation_1-rmse:23.03032
[175]	validation_0-rmse:18.37525	validation_1-rmse:23.02753
[176]	validation_0-rmse:18.36072	validation_1-rmse:23.02506
[177]	validation_0-rmse:18.34415	validation_1-rmse:23.03351
[178]	validation_0-rmse:18.32389	validation_1-rmse:23.03611
[179]	validation_0-rmse:18.31400	validation_1-rmse:23.03572
[180]	validation_0-rmse:18.30407	validation_1-rmse:23.03392
[181]	validation_0-rmse:18.29037	validation_1-rmse:23.02873
[182]	validation_0-rmse:18.27976	validation_1-rmse:23.02577
[183]	validation_0-rmse:18.25826	validation_1-rmse:23.03496
[184]	validation_0-rmse:18.23772	validation_1-rmse:23.03144
[185]	validation_0-rmse:18.22304	validation_1-rmse:23.03236
[186]	validation_0-rmse:18.21258	validation_1-rmse:23.02725
[187]	validation_0-rmse:18.19115	validation_1-rmse:23.02816
[188]	validation_0-rmse:18.13015	validation_1-rmse:23.03088
[189]	validation_0-rmse:18.11553	validation_1-rmse:23.02502
[190]	validation_0-rmse:18.10333	validation_1-rmse:23.02919
[191]	validation_0-rmse:18.09774	validation_1-rmse:23.02745
[192]	validation_0-rmse:18.09216	validation_1-rmse:23.02594
[193]	validation_0-rmse:18.07722	validation_1-rmse:23.02085
[194]	validation_0-rmse:18.06157	validation_1-rmse:23.01686
[195]	validation_0-rmse:18.05357	validation_1-rmse:23.00415
[196]	validation_0-rmse:18.02605	validation_1-rmse:22.96965
[197]	validation_0-rmse:18.00425	validation_1-rmse:22.94668
[198]	validation_0-rmse:17.99971	validation_1-rmse:22.94533
[199]	validation_0-rmse:17.98521	validation_1-rmse:22.94572
[200]	validation_0-rmse:17.97572	validation_1-rmse:22.94050
[201]	validation_0-rmse:17.95445	validation_1-rmse:22.95948
[202]	validation_0-rmse:17.94936	validation_1-rmse:22.95533
[203]	validation_0-rmse:17.94103	validation_1-rmse:22.94859
>>> Using model to predict target PPFD_IN_T1_2_2 in unseen test data ...
>>> Using model to calculate permutation importance based on unseen test data ...
>>> Calculating prediction scores based on predicting unseen test data of PPFD_IN_T1_2_2 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

================================
MODEL TRAINING & TESTING RESULTS
================================

## DATA
  > target: PPFD_IN_T1_2_2
  > features: 9 ['SW_IN_T1_2_1', '.HOUR', '.DOY', '.SEASON', '.YEARMONTH', '.RECORDNUMBER', '.YEARDOY', '.WEEK', '.YEARWEEK']
  > 350640 records (with missing)
  > 342906 available records for target and all features (no missing values)
  > training on 257179 records (75.0%) of 257179 features between 2005-01-01 00:45:00 and 2024-12-31 23:45:00
  > testing on 85727 unseen records (25.0%) of PPFD_IN_T1_2_2 between 2005-01-01 00:15:00 and 2024-12-31 22:45:00

## MODEL
  > the model was trained on training data (257179 records)
  > the model was tested on test data (85727 values)
  > estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
  > parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}
  > number of features used in model:  9
  > names of features used in model:  ['SW_IN_T1_2_1', '.HOUR', '.DOY', '.SEASON', '.YEARMONTH', '.RECORDNUMBER', '.YEARDOY', '.WEEK', '.YEARWEEK']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of PPFD_IN_T1_2_2 (85727 records).
  > feature importances are showing permutation importances from 10 repeats

               PERM_IMPORTANCE   PERM_SD
SW_IN_T1_2_1          1.651919  0.007398
.HOUR                 0.025003  0.000275
.DOY                  0.013744  0.000271
.YEARMONTH            0.002533  0.000083
.RECORDNUMBER         0.001932  0.000075
.YEARWEEK             0.001458  0.000089
.WEEK                 0.001264  0.000034
.YEARDOY              0.000851  0.000016
.SEASON               0.000318  0.000008


## MODEL SCORES
  All scores were calculated based on unseen test data (85727 records).
  > MAE:  6.702566437104721 (mean absolute error)
  > MedAE:  1.1350681781768799 (median absolute error)
  > MSE:  522.5875728344951 (mean squared error)
  > RMSE:  22.860174383291458 (root mean squared error)
  > MAXE:  1595.8807373046875 (max error)
  > MAPE:  1239158145864955.750 (mean absolute percentage error)
  > R2:  0.9975550221254795

xgb.fillgaps(showplot_scores=False, showplot_importance=False)
xgb.report_gapfilling()

Gap-filling using final model ...
>>> Using final model on all data to predict target PPFD_IN_T1_2_2 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting PPFD_IN_T1_2_2 ...
>>> Predicting target PPFD_IN_T1_2_2 where all features are available ... predicted 350640 records.
>>> Collecting results for final model ...
>>> Filling 7734 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable PPFD_IN_T1_2_2_gfXG ...
>>> Restoring original timestamp in results ...
>>> Combining predictions from full model and fallback model ...

===================
GAP-FILLING RESULTS
===================

Model scores and feature importances were calculated from high-quality predicted targets (7734 values, PPFD_IN_T1_2_2_gfXG where flag=1) in comparison to observed targets (342906 values, PPFD_IN_T1_2_2).

## TARGET
- first timestamp:  2005-01-01 00:15:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 350640 values)
- target column (observed):  PPFD_IN_T1_2_2
- missing records (observed):  7734 (cross-check from flag: 7734)
- target column (gap-filled):  PPFD_IN_T1_2_2_gfXG  (350640 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_PPFD_IN_T1_2_2_gfXG_ISFILLED
  > flag 0 ... observed targets (342906 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (7734 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['SW_IN_T1_2_1', '.HOUR', '.DOY', '.YEARMONTH', '.RECORDNUMBER', '.YEARWEEK', '.WEEK', '.YEARDOY', '.SEASON']
- number of features used in model:  9
- permutation importances were calculated from 10 repeats.

               PERM_IMPORTANCE   PERM_SD
SW_IN_T1_2_1          1.656898  0.002364
.HOUR                 0.025525  0.000134
.DOY                  0.014270  0.000175
.YEARMONTH            0.002839  0.000033
.RECORDNUMBER         0.002355  0.000072
.YEARWEEK             0.001550  0.000038
.WEEK                 0.001355  0.000029
.YEARDOY              0.000927  0.000022
.SEASON               0.000325  0.000006

## MODEL
The model was trained on a training set with test size 25.00%.
- estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
- parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}

## MODEL SCORES
- MAE:  6.298178917969939 (mean absolute error)
- MedAE:  1.1375821455451955 (median absolute error)
- MSE:  406.9287614051293 (mean squared error)
- RMSE:  20.17247534154223 (root mean squared error)
- MAXE:  1595.8807373046875 (max error)
- MAPE:  1232809676680468.250 (mean absolute percentage error)
- R2:  0.9981005030772517

Results

results = xgb.gapfilling_df_
results

	.PREDICTIONS_FULLMODEL	PPFD_IN_T1_2_2	.GAP_PREDICTIONS	FLAG_PPFD_IN_T1_2_2_gfXG_ISFILLED	PPFD_IN_T1_2_2_gfXG	.PREDICTIONS_FALLBACK	.GAPFILLED_CUMULATIVE	.PREDICTIONS
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	0.466701	0.0	NaN	0	0.0	None	0.000000e+00	0.466701
2005-01-01 00:45:00	0.466701	0.0	NaN	0	0.0	None	0.000000e+00	0.466701
2005-01-01 01:15:00	0.466701	0.0	NaN	0	0.0	None	0.000000e+00	0.466701
2005-01-01 01:45:00	0.466701	0.0	NaN	0	0.0	None	0.000000e+00	0.466701
2005-01-01 02:15:00	0.466701	0.0	NaN	0	0.0	None	0.000000e+00	0.466701
...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	0.018580	0.0	NaN	0	0.0	None	9.850744e+07	0.018580
2024-12-31 22:15:00	0.018580	0.0	NaN	0	0.0	None	9.850744e+07	0.018580
2024-12-31 22:45:00	0.018580	0.0	NaN	0	0.0	None	9.850744e+07	0.018580
2024-12-31 23:15:00	0.018580	0.0	NaN	0	0.0	None	9.850744e+07	0.018580
2024-12-31 23:45:00	0.018580	0.0	NaN	0	0.0	None	9.850744e+07	0.018580

350640 rows × 8 columns

Add to data

# Combine data
data_simple_2005_2024['PPFD_IN_T1_2_2'] = results['PPFD_IN_T1_2_2_gfXG'].copy()
data_simple_2005_2024['PPFD_IN_T1_2_2']

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    0.0
2005-01-01 00:45:00    0.0
2005-01-01 01:15:00    0.0
2005-01-01 01:45:00    0.0
2005-01-01 02:15:00    0.0
                      ... 
2024-12-31 21:45:00    0.0
2024-12-31 22:15:00    0.0
2024-12-31 22:45:00    0.0
2024-12-31 23:15:00    0.0
2024-12-31 23:45:00    0.0
Freq: 30min, Name: PPFD_IN_T1_2_2, Length: 350640, dtype: float64

# Combine flags not necessary, because PPFD was not gapfilled before
data_simple_2005_2024['FLAG_PPFD_IN_T1_2_2_ISFILLED'] = results['FLAG_PPFD_IN_T1_2_2_gfXG_ISFILLED'].copy()
data_simple_2005_2024['FLAG_PPFD_IN_T1_2_2_ISFILLED']

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    0
2005-01-01 00:45:00    0
2005-01-01 01:15:00    0
2005-01-01 01:45:00    0
2005-01-01 02:15:00    0
                      ..
2024-12-31 21:45:00    0
2024-12-31 22:15:00    0
2024-12-31 22:45:00    0
2024-12-31 23:15:00    0
2024-12-31 23:45:00    0
Freq: 30min, Name: FLAG_PPFD_IN_T1_2_2_ISFILLED, Length: 350640, dtype: int64

title = "PPFD_IN"
data_simple_2005_2024[['PPFD_IN_T1_2_2', 'FLAG_PPFD_IN_T1_2_2_ISFILLED']].plot(x_compat=True, title=title, subplots=True, figsize=(20, 6));

Calculate VPD (kPa)

from diive.pkgs.createvar.vpd import calc_vpd_from_ta_rh

vpd = calc_vpd_from_ta_rh(df=data_simple_2005_2024, rh_col='RH_T1_2_1', ta_col='TA_T1_2_1')
vpd

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    0.099893
2005-01-01 00:45:00    0.097606
2005-01-01 01:15:00    0.091683
2005-01-01 01:45:00    0.071157
2005-01-01 02:15:00    0.058333
                         ...   
2024-12-31 21:45:00    0.000011
2024-12-31 22:15:00    0.000011
2024-12-31 22:45:00    0.000011
2024-12-31 23:15:00    0.000010
2024-12-31 23:45:00    0.000010
Freq: 30min, Name: VPD, Length: 350640, dtype: float64

data_simple_2005_2024['VPD_T1_2_1'] = vpd

_df = pd.DataFrame()
_df['VPD_T1_2_1'] = data_simple_2005_2024[['VPD_T1_2_1']].copy()
_df['SW_IN_T1_2_1'] = data_simple_2005_2024[['SW_IN_T1_2_1']].copy()
_df['TA_T1_2_1'] = data_simple_2005_2024[['TA_T1_2_1']].copy()
_df

	VPD_T1_2_1	SW_IN_T1_2_1	TA_T1_2_1
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	0.099893	0.0	1.566667
2005-01-01 00:45:00	0.097606	0.0	1.533333
2005-01-01 01:15:00	0.091683	0.0	1.566667
2005-01-01 01:45:00	0.071157	0.0	1.566667
2005-01-01 02:15:00	0.058333	0.0	1.500000
...	...	...	...
2024-12-31 21:45:00	0.000011	0.0	-1.919472
2024-12-31 22:15:00	0.000011	0.0	-2.104678
2024-12-31 22:45:00	0.000011	0.0	-2.089444
2024-12-31 23:15:00	0.000010	0.0	-2.355761
2024-12-31 23:45:00	0.000010	0.0	-2.578839

350640 rows × 3 columns

_df['VPD_T1_2_1'].isnull().sum()

np.int64(194)

Fill

xgb = XGBoostTS(
    input_df=_df,
    target_col='VPD_T1_2_1',
    perm_n_repeats=10,
    include_timestamp_as_features=True,
    add_continuous_record_number=True,
    n_estimators=1000,
    random_state=42,
    early_stopping_rounds=50,
    n_jobs=-1
)

Adding new data columns ...
++ Added new columns with timestamp info: ['.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK'] 
++ Added new column .RECORDNUMBER with record numbers from 1 to 350640.

xgb.reduce_features()
xgb.report_feature_reduction()

Feature reduction based on permutation importance ...
[0]	validation_0-rmse:0.38740	validation_1-rmse:0.38740
[1]	validation_0-rmse:0.30020	validation_1-rmse:0.30020
[2]	validation_0-rmse:0.24424	validation_1-rmse:0.24424
[3]	validation_0-rmse:0.20921	validation_1-rmse:0.20921
[4]	validation_0-rmse:0.18789	validation_1-rmse:0.18789
[5]	validation_0-rmse:0.17460	validation_1-rmse:0.17460
[6]	validation_0-rmse:0.16615	validation_1-rmse:0.16615
[7]	validation_0-rmse:0.16075	validation_1-rmse:0.16075
[8]	validation_0-rmse:0.15708	validation_1-rmse:0.15708
[9]	validation_0-rmse:0.15420	validation_1-rmse:0.15420
[10]	validation_0-rmse:0.15209	validation_1-rmse:0.15209
[11]	validation_0-rmse:0.15004	validation_1-rmse:0.15004
[12]	validation_0-rmse:0.14856	validation_1-rmse:0.14856
[13]	validation_0-rmse:0.14742	validation_1-rmse:0.14742
[14]	validation_0-rmse:0.14634	validation_1-rmse:0.14634
[15]	validation_0-rmse:0.14559	validation_1-rmse:0.14559
[16]	validation_0-rmse:0.14466	validation_1-rmse:0.14466
[17]	validation_0-rmse:0.14367	validation_1-rmse:0.14367
[18]	validation_0-rmse:0.14305	validation_1-rmse:0.14305
[19]	validation_0-rmse:0.14255	validation_1-rmse:0.14255
[20]	validation_0-rmse:0.14186	validation_1-rmse:0.14186
[21]	validation_0-rmse:0.14124	validation_1-rmse:0.14124
[22]	validation_0-rmse:0.14079	validation_1-rmse:0.14079
[23]	validation_0-rmse:0.14028	validation_1-rmse:0.14028
[24]	validation_0-rmse:0.13961	validation_1-rmse:0.13961
[25]	validation_0-rmse:0.13897	validation_1-rmse:0.13897
[26]	validation_0-rmse:0.13861	validation_1-rmse:0.13861
[27]	validation_0-rmse:0.13825	validation_1-rmse:0.13825
[28]	validation_0-rmse:0.13733	validation_1-rmse:0.13733
[29]	validation_0-rmse:0.13638	validation_1-rmse:0.13638
[30]	validation_0-rmse:0.13601	validation_1-rmse:0.13601
[31]	validation_0-rmse:0.13573	validation_1-rmse:0.13573
[32]	validation_0-rmse:0.13539	validation_1-rmse:0.13539
[33]	validation_0-rmse:0.13513	validation_1-rmse:0.13513
[34]	validation_0-rmse:0.13446	validation_1-rmse:0.13446
[35]	validation_0-rmse:0.13436	validation_1-rmse:0.13436
[36]	validation_0-rmse:0.13406	validation_1-rmse:0.13406
[37]	validation_0-rmse:0.13367	validation_1-rmse:0.13367
[38]	validation_0-rmse:0.13345	validation_1-rmse:0.13345
[39]	validation_0-rmse:0.13293	validation_1-rmse:0.13293
[40]	validation_0-rmse:0.13271	validation_1-rmse:0.13271
[41]	validation_0-rmse:0.13232	validation_1-rmse:0.13232
[42]	validation_0-rmse:0.13203	validation_1-rmse:0.13203
[43]	validation_0-rmse:0.13163	validation_1-rmse:0.13163
[44]	validation_0-rmse:0.13143	validation_1-rmse:0.13143
[45]	validation_0-rmse:0.13112	validation_1-rmse:0.13112
[46]	validation_0-rmse:0.13100	validation_1-rmse:0.13100
[47]	validation_0-rmse:0.13057	validation_1-rmse:0.13057
[48]	validation_0-rmse:0.13021	validation_1-rmse:0.13021
[49]	validation_0-rmse:0.12964	validation_1-rmse:0.12964
[50]	validation_0-rmse:0.12949	validation_1-rmse:0.12949
[51]	validation_0-rmse:0.12907	validation_1-rmse:0.12907
[52]	validation_0-rmse:0.12879	validation_1-rmse:0.12879
[53]	validation_0-rmse:0.12804	validation_1-rmse:0.12804
[54]	validation_0-rmse:0.12771	validation_1-rmse:0.12771
[55]	validation_0-rmse:0.12705	validation_1-rmse:0.12705
[56]	validation_0-rmse:0.12664	validation_1-rmse:0.12664
[57]	validation_0-rmse:0.12616	validation_1-rmse:0.12616
[58]	validation_0-rmse:0.12583	validation_1-rmse:0.12583
[59]	validation_0-rmse:0.12557	validation_1-rmse:0.12557
[60]	validation_0-rmse:0.12534	validation_1-rmse:0.12534
[61]	validation_0-rmse:0.12527	validation_1-rmse:0.12527
[62]	validation_0-rmse:0.12476	validation_1-rmse:0.12476
[63]	validation_0-rmse:0.12459	validation_1-rmse:0.12459
[64]	validation_0-rmse:0.12442	validation_1-rmse:0.12442
[65]	validation_0-rmse:0.12406	validation_1-rmse:0.12406
[66]	validation_0-rmse:0.12389	validation_1-rmse:0.12389
[67]	validation_0-rmse:0.12361	validation_1-rmse:0.12361
[68]	validation_0-rmse:0.12324	validation_1-rmse:0.12324
[69]	validation_0-rmse:0.12291	validation_1-rmse:0.12291
[70]	validation_0-rmse:0.12268	validation_1-rmse:0.12268
[71]	validation_0-rmse:0.12262	validation_1-rmse:0.12262
[72]	validation_0-rmse:0.12250	validation_1-rmse:0.12250
[73]	validation_0-rmse:0.12244	validation_1-rmse:0.12244
[74]	validation_0-rmse:0.12230	validation_1-rmse:0.12230
[75]	validation_0-rmse:0.12208	validation_1-rmse:0.12208
[76]	validation_0-rmse:0.12199	validation_1-rmse:0.12199
[77]	validation_0-rmse:0.12189	validation_1-rmse:0.12189
[78]	validation_0-rmse:0.12166	validation_1-rmse:0.12166
[79]	validation_0-rmse:0.12129	validation_1-rmse:0.12129
[80]	validation_0-rmse:0.12111	validation_1-rmse:0.12111
[81]	validation_0-rmse:0.12086	validation_1-rmse:0.12086
[82]	validation_0-rmse:0.12074	validation_1-rmse:0.12074
[83]	validation_0-rmse:0.12058	validation_1-rmse:0.12058
[84]	validation_0-rmse:0.12046	validation_1-rmse:0.12046
[85]	validation_0-rmse:0.11996	validation_1-rmse:0.11996
[86]	validation_0-rmse:0.11977	validation_1-rmse:0.11977
[87]	validation_0-rmse:0.11935	validation_1-rmse:0.11935
[88]	validation_0-rmse:0.11917	validation_1-rmse:0.11917
[89]	validation_0-rmse:0.11909	validation_1-rmse:0.11909
[90]	validation_0-rmse:0.11894	validation_1-rmse:0.11894
[91]	validation_0-rmse:0.11874	validation_1-rmse:0.11874
[92]	validation_0-rmse:0.11847	validation_1-rmse:0.11847
[93]	validation_0-rmse:0.11802	validation_1-rmse:0.11802
[94]	validation_0-rmse:0.11787	validation_1-rmse:0.11787
[95]	validation_0-rmse:0.11783	validation_1-rmse:0.11783
[96]	validation_0-rmse:0.11760	validation_1-rmse:0.11760
[97]	validation_0-rmse:0.11751	validation_1-rmse:0.11751
[98]	validation_0-rmse:0.11735	validation_1-rmse:0.11735
[99]	validation_0-rmse:0.11716	validation_1-rmse:0.11716
[100]	validation_0-rmse:0.11695	validation_1-rmse:0.11695
[101]	validation_0-rmse:0.11682	validation_1-rmse:0.11682
[102]	validation_0-rmse:0.11661	validation_1-rmse:0.11661
[103]	validation_0-rmse:0.11651	validation_1-rmse:0.11651
[104]	validation_0-rmse:0.11645	validation_1-rmse:0.11645
[105]	validation_0-rmse:0.11633	validation_1-rmse:0.11633
[106]	validation_0-rmse:0.11602	validation_1-rmse:0.11602
[107]	validation_0-rmse:0.11593	validation_1-rmse:0.11593
[108]	validation_0-rmse:0.11588	validation_1-rmse:0.11588
[109]	validation_0-rmse:0.11573	validation_1-rmse:0.11573
[110]	validation_0-rmse:0.11553	validation_1-rmse:0.11553
[111]	validation_0-rmse:0.11540	validation_1-rmse:0.11540
[112]	validation_0-rmse:0.11515	validation_1-rmse:0.11515
[113]	validation_0-rmse:0.11504	validation_1-rmse:0.11504
[114]	validation_0-rmse:0.11483	validation_1-rmse:0.11483
[115]	validation_0-rmse:0.11481	validation_1-rmse:0.11481
[116]	validation_0-rmse:0.11463	validation_1-rmse:0.11463
[117]	validation_0-rmse:0.11451	validation_1-rmse:0.11451
[118]	validation_0-rmse:0.11444	validation_1-rmse:0.11444
[119]	validation_0-rmse:0.11431	validation_1-rmse:0.11431
[120]	validation_0-rmse:0.11401	validation_1-rmse:0.11401
[121]	validation_0-rmse:0.11389	validation_1-rmse:0.11389
[122]	validation_0-rmse:0.11372	validation_1-rmse:0.11372
[123]	validation_0-rmse:0.11360	validation_1-rmse:0.11360
[124]	validation_0-rmse:0.11352	validation_1-rmse:0.11352
[125]	validation_0-rmse:0.11345	validation_1-rmse:0.11345
[126]	validation_0-rmse:0.11334	validation_1-rmse:0.11334
[127]	validation_0-rmse:0.11320	validation_1-rmse:0.11320
[128]	validation_0-rmse:0.11312	validation_1-rmse:0.11312
[129]	validation_0-rmse:0.11294	validation_1-rmse:0.11294
[130]	validation_0-rmse:0.11278	validation_1-rmse:0.11278
[131]	validation_0-rmse:0.11256	validation_1-rmse:0.11256
[132]	validation_0-rmse:0.11233	validation_1-rmse:0.11233
[133]	validation_0-rmse:0.11218	validation_1-rmse:0.11218
[134]	validation_0-rmse:0.11209	validation_1-rmse:0.11209
[135]	validation_0-rmse:0.11187	validation_1-rmse:0.11187
[136]	validation_0-rmse:0.11176	validation_1-rmse:0.11176
[137]	validation_0-rmse:0.11162	validation_1-rmse:0.11162
[138]	validation_0-rmse:0.11155	validation_1-rmse:0.11155
[139]	validation_0-rmse:0.11135	validation_1-rmse:0.11135
[140]	validation_0-rmse:0.11102	validation_1-rmse:0.11102
[141]	validation_0-rmse:0.11090	validation_1-rmse:0.11090
[142]	validation_0-rmse:0.11063	validation_1-rmse:0.11063
[143]	validation_0-rmse:0.11059	validation_1-rmse:0.11059
[144]	validation_0-rmse:0.11050	validation_1-rmse:0.11050
[145]	validation_0-rmse:0.11046	validation_1-rmse:0.11046
[146]	validation_0-rmse:0.11038	validation_1-rmse:0.11038
[147]	validation_0-rmse:0.11033	validation_1-rmse:0.11033
[148]	validation_0-rmse:0.11028	validation_1-rmse:0.11028
[149]	validation_0-rmse:0.11010	validation_1-rmse:0.11010
[150]	validation_0-rmse:0.11003	validation_1-rmse:0.11003
[151]	validation_0-rmse:0.10987	validation_1-rmse:0.10987
[152]	validation_0-rmse:0.10973	validation_1-rmse:0.10973
[153]	validation_0-rmse:0.10965	validation_1-rmse:0.10965
[154]	validation_0-rmse:0.10959	validation_1-rmse:0.10959
[155]	validation_0-rmse:0.10950	validation_1-rmse:0.10950
[156]	validation_0-rmse:0.10929	validation_1-rmse:0.10929
[157]	validation_0-rmse:0.10918	validation_1-rmse:0.10918
[158]	validation_0-rmse:0.10906	validation_1-rmse:0.10906
[159]	validation_0-rmse:0.10884	validation_1-rmse:0.10884
[160]	validation_0-rmse:0.10876	validation_1-rmse:0.10876
[161]	validation_0-rmse:0.10863	validation_1-rmse:0.10863
[162]	validation_0-rmse:0.10847	validation_1-rmse:0.10847
[163]	validation_0-rmse:0.10840	validation_1-rmse:0.10840
[164]	validation_0-rmse:0.10823	validation_1-rmse:0.10823
[165]	validation_0-rmse:0.10811	validation_1-rmse:0.10811
[166]	validation_0-rmse:0.10801	validation_1-rmse:0.10801
[167]	validation_0-rmse:0.10788	validation_1-rmse:0.10788
[168]	validation_0-rmse:0.10778	validation_1-rmse:0.10778
[169]	validation_0-rmse:0.10772	validation_1-rmse:0.10772
[170]	validation_0-rmse:0.10762	validation_1-rmse:0.10762
[171]	validation_0-rmse:0.10745	validation_1-rmse:0.10745
[172]	validation_0-rmse:0.10727	validation_1-rmse:0.10727
[173]	validation_0-rmse:0.10708	validation_1-rmse:0.10708
[174]	validation_0-rmse:0.10703	validation_1-rmse:0.10703
[175]	validation_0-rmse:0.10690	validation_1-rmse:0.10690
[176]	validation_0-rmse:0.10671	validation_1-rmse:0.10671
[177]	validation_0-rmse:0.10660	validation_1-rmse:0.10660
[178]	validation_0-rmse:0.10649	validation_1-rmse:0.10649
[179]	validation_0-rmse:0.10641	validation_1-rmse:0.10641
[180]	validation_0-rmse:0.10629	validation_1-rmse:0.10629
[181]	validation_0-rmse:0.10623	validation_1-rmse:0.10623
[182]	validation_0-rmse:0.10609	validation_1-rmse:0.10609
[183]	validation_0-rmse:0.10601	validation_1-rmse:0.10601
[184]	validation_0-rmse:0.10589	validation_1-rmse:0.10589
[185]	validation_0-rmse:0.10582	validation_1-rmse:0.10582
[186]	validation_0-rmse:0.10576	validation_1-rmse:0.10576
[187]	validation_0-rmse:0.10569	validation_1-rmse:0.10569
[188]	validation_0-rmse:0.10561	validation_1-rmse:0.10561
[189]	validation_0-rmse:0.10555	validation_1-rmse:0.10555
[190]	validation_0-rmse:0.10545	validation_1-rmse:0.10545
[191]	validation_0-rmse:0.10543	validation_1-rmse:0.10543
[192]	validation_0-rmse:0.10521	validation_1-rmse:0.10521
[193]	validation_0-rmse:0.10513	validation_1-rmse:0.10513
[194]	validation_0-rmse:0.10503	validation_1-rmse:0.10503
[195]	validation_0-rmse:0.10494	validation_1-rmse:0.10494
[196]	validation_0-rmse:0.10489	validation_1-rmse:0.10489
[197]	validation_0-rmse:0.10474	validation_1-rmse:0.10474
[198]	validation_0-rmse:0.10458	validation_1-rmse:0.10458
[199]	validation_0-rmse:0.10450	validation_1-rmse:0.10450
[200]	validation_0-rmse:0.10440	validation_1-rmse:0.10440
[201]	validation_0-rmse:0.10432	validation_1-rmse:0.10432
[202]	validation_0-rmse:0.10427	validation_1-rmse:0.10427
[203]	validation_0-rmse:0.10419	validation_1-rmse:0.10419
[204]	validation_0-rmse:0.10409	validation_1-rmse:0.10409
[205]	validation_0-rmse:0.10400	validation_1-rmse:0.10400
[206]	validation_0-rmse:0.10393	validation_1-rmse:0.10393
[207]	validation_0-rmse:0.10390	validation_1-rmse:0.10390
[208]	validation_0-rmse:0.10376	validation_1-rmse:0.10376
[209]	validation_0-rmse:0.10365	validation_1-rmse:0.10365
[210]	validation_0-rmse:0.10339	validation_1-rmse:0.10339
[211]	validation_0-rmse:0.10326	validation_1-rmse:0.10326
[212]	validation_0-rmse:0.10316	validation_1-rmse:0.10316
[213]	validation_0-rmse:0.10306	validation_1-rmse:0.10306
[214]	validation_0-rmse:0.10295	validation_1-rmse:0.10295
[215]	validation_0-rmse:0.10287	validation_1-rmse:0.10287
[216]	validation_0-rmse:0.10278	validation_1-rmse:0.10278
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[774]	validation_0-rmse:0.07413	validation_1-rmse:0.07413
[775]	validation_0-rmse:0.07410	validation_1-rmse:0.07410
[776]	validation_0-rmse:0.07407	validation_1-rmse:0.07407
[777]	validation_0-rmse:0.07404	validation_1-rmse:0.07404
[778]	validation_0-rmse:0.07400	validation_1-rmse:0.07400
[779]	validation_0-rmse:0.07396	validation_1-rmse:0.07396
[780]	validation_0-rmse:0.07394	validation_1-rmse:0.07394
[781]	validation_0-rmse:0.07391	validation_1-rmse:0.07391
[782]	validation_0-rmse:0.07389	validation_1-rmse:0.07389
[783]	validation_0-rmse:0.07384	validation_1-rmse:0.07384
[784]	validation_0-rmse:0.07380	validation_1-rmse:0.07380
[785]	validation_0-rmse:0.07377	validation_1-rmse:0.07377
[786]	validation_0-rmse:0.07374	validation_1-rmse:0.07374
[787]	validation_0-rmse:0.07372	validation_1-rmse:0.07372
[788]	validation_0-rmse:0.07371	validation_1-rmse:0.07371
[789]	validation_0-rmse:0.07367	validation_1-rmse:0.07367
[790]	validation_0-rmse:0.07362	validation_1-rmse:0.07362
[791]	validation_0-rmse:0.07361	validation_1-rmse:0.07361
[792]	validation_0-rmse:0.07358	validation_1-rmse:0.07358
[793]	validation_0-rmse:0.07355	validation_1-rmse:0.07355
[794]	validation_0-rmse:0.07353	validation_1-rmse:0.07353
[795]	validation_0-rmse:0.07345	validation_1-rmse:0.07345
[796]	validation_0-rmse:0.07343	validation_1-rmse:0.07343
[797]	validation_0-rmse:0.07337	validation_1-rmse:0.07337
[798]	validation_0-rmse:0.07336	validation_1-rmse:0.07336
[799]	validation_0-rmse:0.07333	validation_1-rmse:0.07333
[800]	validation_0-rmse:0.07333	validation_1-rmse:0.07333
[801]	validation_0-rmse:0.07329	validation_1-rmse:0.07329
[802]	validation_0-rmse:0.07327	validation_1-rmse:0.07327
[803]	validation_0-rmse:0.07326	validation_1-rmse:0.07326
[804]	validation_0-rmse:0.07323	validation_1-rmse:0.07323
[805]	validation_0-rmse:0.07320	validation_1-rmse:0.07320
[806]	validation_0-rmse:0.07318	validation_1-rmse:0.07318
[807]	validation_0-rmse:0.07316	validation_1-rmse:0.07316
[808]	validation_0-rmse:0.07314	validation_1-rmse:0.07314
[809]	validation_0-rmse:0.07312	validation_1-rmse:0.07312
[810]	validation_0-rmse:0.07309	validation_1-rmse:0.07309
[811]	validation_0-rmse:0.07308	validation_1-rmse:0.07308
[812]	validation_0-rmse:0.07306	validation_1-rmse:0.07306
[813]	validation_0-rmse:0.07303	validation_1-rmse:0.07303
[814]	validation_0-rmse:0.07300	validation_1-rmse:0.07300
[815]	validation_0-rmse:0.07297	validation_1-rmse:0.07297
[816]	validation_0-rmse:0.07296	validation_1-rmse:0.07296
[817]	validation_0-rmse:0.07294	validation_1-rmse:0.07294
[818]	validation_0-rmse:0.07290	validation_1-rmse:0.07290
[819]	validation_0-rmse:0.07284	validation_1-rmse:0.07284
[820]	validation_0-rmse:0.07280	validation_1-rmse:0.07280
[821]	validation_0-rmse:0.07278	validation_1-rmse:0.07278
[822]	validation_0-rmse:0.07275	validation_1-rmse:0.07275
[823]	validation_0-rmse:0.07271	validation_1-rmse:0.07271
[824]	validation_0-rmse:0.07269	validation_1-rmse:0.07269
[825]	validation_0-rmse:0.07268	validation_1-rmse:0.07268
[826]	validation_0-rmse:0.07266	validation_1-rmse:0.07266
[827]	validation_0-rmse:0.07265	validation_1-rmse:0.07265
[828]	validation_0-rmse:0.07262	validation_1-rmse:0.07262
[829]	validation_0-rmse:0.07260	validation_1-rmse:0.07260
[830]	validation_0-rmse:0.07256	validation_1-rmse:0.07256
[831]	validation_0-rmse:0.07251	validation_1-rmse:0.07251
[832]	validation_0-rmse:0.07247	validation_1-rmse:0.07247
[833]	validation_0-rmse:0.07245	validation_1-rmse:0.07245
[834]	validation_0-rmse:0.07240	validation_1-rmse:0.07240
[835]	validation_0-rmse:0.07239	validation_1-rmse:0.07239
[836]	validation_0-rmse:0.07236	validation_1-rmse:0.07236
[837]	validation_0-rmse:0.07232	validation_1-rmse:0.07232
[838]	validation_0-rmse:0.07229	validation_1-rmse:0.07229
[839]	validation_0-rmse:0.07225	validation_1-rmse:0.07225
[840]	validation_0-rmse:0.07222	validation_1-rmse:0.07222
[841]	validation_0-rmse:0.07221	validation_1-rmse:0.07221
[842]	validation_0-rmse:0.07220	validation_1-rmse:0.07220
[843]	validation_0-rmse:0.07215	validation_1-rmse:0.07215
[844]	validation_0-rmse:0.07208	validation_1-rmse:0.07208
[845]	validation_0-rmse:0.07204	validation_1-rmse:0.07204
[846]	validation_0-rmse:0.07199	validation_1-rmse:0.07199
[847]	validation_0-rmse:0.07197	validation_1-rmse:0.07197
[848]	validation_0-rmse:0.07197	validation_1-rmse:0.07197
[849]	validation_0-rmse:0.07195	validation_1-rmse:0.07195
[850]	validation_0-rmse:0.07187	validation_1-rmse:0.07187
[851]	validation_0-rmse:0.07185	validation_1-rmse:0.07185
[852]	validation_0-rmse:0.07182	validation_1-rmse:0.07182
[853]	validation_0-rmse:0.07180	validation_1-rmse:0.07180
[854]	validation_0-rmse:0.07177	validation_1-rmse:0.07177
[855]	validation_0-rmse:0.07175	validation_1-rmse:0.07175
[856]	validation_0-rmse:0.07170	validation_1-rmse:0.07170
[857]	validation_0-rmse:0.07167	validation_1-rmse:0.07167
[858]	validation_0-rmse:0.07165	validation_1-rmse:0.07165
[859]	validation_0-rmse:0.07163	validation_1-rmse:0.07163
[860]	validation_0-rmse:0.07162	validation_1-rmse:0.07162
[861]	validation_0-rmse:0.07159	validation_1-rmse:0.07159
[862]	validation_0-rmse:0.07157	validation_1-rmse:0.07157
[863]	validation_0-rmse:0.07152	validation_1-rmse:0.07152
[864]	validation_0-rmse:0.07150	validation_1-rmse:0.07150
[865]	validation_0-rmse:0.07146	validation_1-rmse:0.07146
[866]	validation_0-rmse:0.07144	validation_1-rmse:0.07144
[867]	validation_0-rmse:0.07140	validation_1-rmse:0.07140
[868]	validation_0-rmse:0.07137	validation_1-rmse:0.07137
[869]	validation_0-rmse:0.07134	validation_1-rmse:0.07134
[870]	validation_0-rmse:0.07131	validation_1-rmse:0.07131
[871]	validation_0-rmse:0.07129	validation_1-rmse:0.07129
[872]	validation_0-rmse:0.07125	validation_1-rmse:0.07125
[873]	validation_0-rmse:0.07124	validation_1-rmse:0.07124
[874]	validation_0-rmse:0.07121	validation_1-rmse:0.07121
[875]	validation_0-rmse:0.07119	validation_1-rmse:0.07119
[876]	validation_0-rmse:0.07117	validation_1-rmse:0.07117
[877]	validation_0-rmse:0.07116	validation_1-rmse:0.07116
[878]	validation_0-rmse:0.07113	validation_1-rmse:0.07113
[879]	validation_0-rmse:0.07109	validation_1-rmse:0.07109
[880]	validation_0-rmse:0.07108	validation_1-rmse:0.07108
[881]	validation_0-rmse:0.07106	validation_1-rmse:0.07106
[882]	validation_0-rmse:0.07104	validation_1-rmse:0.07104
[883]	validation_0-rmse:0.07101	validation_1-rmse:0.07101
[884]	validation_0-rmse:0.07100	validation_1-rmse:0.07100
[885]	validation_0-rmse:0.07097	validation_1-rmse:0.07097
[886]	validation_0-rmse:0.07093	validation_1-rmse:0.07093
[887]	validation_0-rmse:0.07089	validation_1-rmse:0.07089
[888]	validation_0-rmse:0.07087	validation_1-rmse:0.07087
[889]	validation_0-rmse:0.07085	validation_1-rmse:0.07085
[890]	validation_0-rmse:0.07082	validation_1-rmse:0.07082
[891]	validation_0-rmse:0.07078	validation_1-rmse:0.07078
[892]	validation_0-rmse:0.07075	validation_1-rmse:0.07075
[893]	validation_0-rmse:0.07075	validation_1-rmse:0.07075
[894]	validation_0-rmse:0.07072	validation_1-rmse:0.07072
[895]	validation_0-rmse:0.07070	validation_1-rmse:0.07070
[896]	validation_0-rmse:0.07064	validation_1-rmse:0.07064
[897]	validation_0-rmse:0.07061	validation_1-rmse:0.07061
[898]	validation_0-rmse:0.07061	validation_1-rmse:0.07061
[899]	validation_0-rmse:0.07058	validation_1-rmse:0.07058
[900]	validation_0-rmse:0.07056	validation_1-rmse:0.07056
[901]	validation_0-rmse:0.07054	validation_1-rmse:0.07054
[902]	validation_0-rmse:0.07052	validation_1-rmse:0.07052
[903]	validation_0-rmse:0.07049	validation_1-rmse:0.07049
[904]	validation_0-rmse:0.07046	validation_1-rmse:0.07046
[905]	validation_0-rmse:0.07042	validation_1-rmse:0.07042
[906]	validation_0-rmse:0.07039	validation_1-rmse:0.07039
[907]	validation_0-rmse:0.07036	validation_1-rmse:0.07036
[908]	validation_0-rmse:0.07034	validation_1-rmse:0.07034
[909]	validation_0-rmse:0.07030	validation_1-rmse:0.07030
[910]	validation_0-rmse:0.07028	validation_1-rmse:0.07028
[911]	validation_0-rmse:0.07026	validation_1-rmse:0.07026
[912]	validation_0-rmse:0.07023	validation_1-rmse:0.07023
[913]	validation_0-rmse:0.07020	validation_1-rmse:0.07020
[914]	validation_0-rmse:0.07018	validation_1-rmse:0.07018
[915]	validation_0-rmse:0.07017	validation_1-rmse:0.07017
[916]	validation_0-rmse:0.07012	validation_1-rmse:0.07012
[917]	validation_0-rmse:0.07010	validation_1-rmse:0.07010
[918]	validation_0-rmse:0.07006	validation_1-rmse:0.07006
[919]	validation_0-rmse:0.07004	validation_1-rmse:0.07004
[920]	validation_0-rmse:0.07001	validation_1-rmse:0.07001
[921]	validation_0-rmse:0.06998	validation_1-rmse:0.06998
[922]	validation_0-rmse:0.06995	validation_1-rmse:0.06995
[923]	validation_0-rmse:0.06993	validation_1-rmse:0.06993
[924]	validation_0-rmse:0.06990	validation_1-rmse:0.06990
[925]	validation_0-rmse:0.06987	validation_1-rmse:0.06987
[926]	validation_0-rmse:0.06984	validation_1-rmse:0.06984
[927]	validation_0-rmse:0.06984	validation_1-rmse:0.06984
[928]	validation_0-rmse:0.06983	validation_1-rmse:0.06983
[929]	validation_0-rmse:0.06979	validation_1-rmse:0.06979
[930]	validation_0-rmse:0.06975	validation_1-rmse:0.06975
[931]	validation_0-rmse:0.06974	validation_1-rmse:0.06974
[932]	validation_0-rmse:0.06971	validation_1-rmse:0.06971
[933]	validation_0-rmse:0.06969	validation_1-rmse:0.06969
[934]	validation_0-rmse:0.06967	validation_1-rmse:0.06967
[935]	validation_0-rmse:0.06965	validation_1-rmse:0.06965
[936]	validation_0-rmse:0.06962	validation_1-rmse:0.06962
[937]	validation_0-rmse:0.06960	validation_1-rmse:0.06960
[938]	validation_0-rmse:0.06957	validation_1-rmse:0.06957
[939]	validation_0-rmse:0.06954	validation_1-rmse:0.06954
[940]	validation_0-rmse:0.06950	validation_1-rmse:0.06950
[941]	validation_0-rmse:0.06948	validation_1-rmse:0.06948
[942]	validation_0-rmse:0.06943	validation_1-rmse:0.06943
[943]	validation_0-rmse:0.06940	validation_1-rmse:0.06940
[944]	validation_0-rmse:0.06937	validation_1-rmse:0.06937
[945]	validation_0-rmse:0.06934	validation_1-rmse:0.06934
[946]	validation_0-rmse:0.06932	validation_1-rmse:0.06932
[947]	validation_0-rmse:0.06928	validation_1-rmse:0.06928
[948]	validation_0-rmse:0.06923	validation_1-rmse:0.06923
[949]	validation_0-rmse:0.06918	validation_1-rmse:0.06918
[950]	validation_0-rmse:0.06916	validation_1-rmse:0.06916
[951]	validation_0-rmse:0.06915	validation_1-rmse:0.06915
[952]	validation_0-rmse:0.06912	validation_1-rmse:0.06912
[953]	validation_0-rmse:0.06909	validation_1-rmse:0.06909
[954]	validation_0-rmse:0.06905	validation_1-rmse:0.06905
[955]	validation_0-rmse:0.06904	validation_1-rmse:0.06904
[956]	validation_0-rmse:0.06902	validation_1-rmse:0.06902
[957]	validation_0-rmse:0.06900	validation_1-rmse:0.06900
[958]	validation_0-rmse:0.06897	validation_1-rmse:0.06897
[959]	validation_0-rmse:0.06894	validation_1-rmse:0.06894
[960]	validation_0-rmse:0.06892	validation_1-rmse:0.06892
[961]	validation_0-rmse:0.06889	validation_1-rmse:0.06889
[962]	validation_0-rmse:0.06886	validation_1-rmse:0.06886
[963]	validation_0-rmse:0.06884	validation_1-rmse:0.06884
[964]	validation_0-rmse:0.06881	validation_1-rmse:0.06881
[965]	validation_0-rmse:0.06878	validation_1-rmse:0.06878
[966]	validation_0-rmse:0.06876	validation_1-rmse:0.06876
[967]	validation_0-rmse:0.06873	validation_1-rmse:0.06873
[968]	validation_0-rmse:0.06871	validation_1-rmse:0.06871
[969]	validation_0-rmse:0.06870	validation_1-rmse:0.06870
[970]	validation_0-rmse:0.06868	validation_1-rmse:0.06868
[971]	validation_0-rmse:0.06865	validation_1-rmse:0.06865
[972]	validation_0-rmse:0.06863	validation_1-rmse:0.06863
[973]	validation_0-rmse:0.06860	validation_1-rmse:0.06860
[974]	validation_0-rmse:0.06858	validation_1-rmse:0.06858
[975]	validation_0-rmse:0.06856	validation_1-rmse:0.06856
[976]	validation_0-rmse:0.06854	validation_1-rmse:0.06854
[977]	validation_0-rmse:0.06850	validation_1-rmse:0.06850
[978]	validation_0-rmse:0.06847	validation_1-rmse:0.06847
[979]	validation_0-rmse:0.06844	validation_1-rmse:0.06844
[980]	validation_0-rmse:0.06842	validation_1-rmse:0.06842
[981]	validation_0-rmse:0.06839	validation_1-rmse:0.06839
[982]	validation_0-rmse:0.06837	validation_1-rmse:0.06837
[983]	validation_0-rmse:0.06835	validation_1-rmse:0.06835
[984]	validation_0-rmse:0.06832	validation_1-rmse:0.06832
[985]	validation_0-rmse:0.06829	validation_1-rmse:0.06829
[986]	validation_0-rmse:0.06827	validation_1-rmse:0.06827
[987]	validation_0-rmse:0.06825	validation_1-rmse:0.06825
[988]	validation_0-rmse:0.06823	validation_1-rmse:0.06823
[989]	validation_0-rmse:0.06822	validation_1-rmse:0.06822
[990]	validation_0-rmse:0.06821	validation_1-rmse:0.06821
[991]	validation_0-rmse:0.06818	validation_1-rmse:0.06818
[992]	validation_0-rmse:0.06816	validation_1-rmse:0.06816
[993]	validation_0-rmse:0.06814	validation_1-rmse:0.06814
[994]	validation_0-rmse:0.06814	validation_1-rmse:0.06814
[995]	validation_0-rmse:0.06812	validation_1-rmse:0.06812
[996]	validation_0-rmse:0.06812	validation_1-rmse:0.06812
[997]	validation_0-rmse:0.06811	validation_1-rmse:0.06811
[998]	validation_0-rmse:0.06811	validation_1-rmse:0.06811
[999]	validation_0-rmse:0.06808	validation_1-rmse:0.06808
>>> Calculating feature importances (permutation importance, 10 repeats) ...
>>> Setting threshold for feature rejection to 0.004018037245814754.
>>> Removing rejected features from model data ...

=================
FEATURE REDUCTION
=================

- Target variable: VPD_T1_2_1

- The random variable .RANDOM was added to the original features, used as benchmark for detecting relevant feature importances.

PERMUTATION IMPORTANCE (mean) across all splits of TimeSeriesSplit:

               PERM_IMPORTANCE   PERM_SD
TA_T1_2_1             3.168092  0.006758
.DOY                  0.218033  0.000714
.RECORDNUMBER         0.175360  0.000704
.YEARDOY              0.133052  0.000501
.YEARWEEK             0.113177  0.000256
SW_IN_T1_2_1          0.102864  0.000305
.YEARMONTH            0.098572  0.000315
.WEEK                 0.089854  0.000265
.HOUR                 0.069893  0.000367
.YEAR                 0.010686  0.000041
.SEASON               0.005804  0.000049
.MONTH                0.004451  0.000022
.RANDOM               0.004018  0.000027

- These results are from feature reduction. Note that feature importances for the final model are calculated during gap-filling.

--> 12 original input features (before feature reduction): ['SW_IN_T1_2_1', 'TA_T1_2_1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
--> 5 rejected features (during feature reduction): None.
--> 12 accepted features (after feature reduction): ['TA_T1_2_1', '.DOY', '.RECORDNUMBER', '.YEARDOY', '.YEARWEEK', 'SW_IN_T1_2_1', '.YEARMONTH', '.WEEK', '.HOUR', '.YEAR', '.SEASON', '.MONTH']

xgb.trainmodel(showplot_scores=False, showplot_importance=False)
xgb.report_traintest()

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2005-01-01 00:45:00 and 2024-12-31 23:15:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:0.38740	validation_1-rmse:0.38547
[1]	validation_0-rmse:0.30020	validation_1-rmse:0.29905
[2]	validation_0-rmse:0.24424	validation_1-rmse:0.24400
[3]	validation_0-rmse:0.20921	validation_1-rmse:0.20968
[4]	validation_0-rmse:0.18789	validation_1-rmse:0.18911
[5]	validation_0-rmse:0.17460	validation_1-rmse:0.17628
[6]	validation_0-rmse:0.16615	validation_1-rmse:0.16826
[7]	validation_0-rmse:0.16075	validation_1-rmse:0.16308
[8]	validation_0-rmse:0.15708	validation_1-rmse:0.15961
[9]	validation_0-rmse:0.15420	validation_1-rmse:0.15676
[10]	validation_0-rmse:0.15209	validation_1-rmse:0.15476
[11]	validation_0-rmse:0.15004	validation_1-rmse:0.15266
[12]	validation_0-rmse:0.14856	validation_1-rmse:0.15121
[13]	validation_0-rmse:0.14742	validation_1-rmse:0.15016
[14]	validation_0-rmse:0.14634	validation_1-rmse:0.14905
[15]	validation_0-rmse:0.14560	validation_1-rmse:0.14837
[16]	validation_0-rmse:0.14466	validation_1-rmse:0.14741
[17]	validation_0-rmse:0.14367	validation_1-rmse:0.14646
[18]	validation_0-rmse:0.14306	validation_1-rmse:0.14590
[19]	validation_0-rmse:0.14255	validation_1-rmse:0.14544
[20]	validation_0-rmse:0.14186	validation_1-rmse:0.14478
[21]	validation_0-rmse:0.14124	validation_1-rmse:0.14416
[22]	validation_0-rmse:0.14079	validation_1-rmse:0.14377
[23]	validation_0-rmse:0.14028	validation_1-rmse:0.14330
[24]	validation_0-rmse:0.13961	validation_1-rmse:0.14269
[25]	validation_0-rmse:0.13897	validation_1-rmse:0.14202
[26]	validation_0-rmse:0.13862	validation_1-rmse:0.14170
[27]	validation_0-rmse:0.13826	validation_1-rmse:0.14137
[28]	validation_0-rmse:0.13733	validation_1-rmse:0.14050
[29]	validation_0-rmse:0.13698	validation_1-rmse:0.14019
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[866]	validation_0-rmse:0.07249	validation_1-rmse:0.09204
[867]	validation_0-rmse:0.07247	validation_1-rmse:0.09202
[868]	validation_0-rmse:0.07242	validation_1-rmse:0.09199
[869]	validation_0-rmse:0.07240	validation_1-rmse:0.09197
[870]	validation_0-rmse:0.07238	validation_1-rmse:0.09196
[871]	validation_0-rmse:0.07234	validation_1-rmse:0.09191
[872]	validation_0-rmse:0.07229	validation_1-rmse:0.09189
[873]	validation_0-rmse:0.07227	validation_1-rmse:0.09187
[874]	validation_0-rmse:0.07225	validation_1-rmse:0.09186
[875]	validation_0-rmse:0.07221	validation_1-rmse:0.09184
[876]	validation_0-rmse:0.07216	validation_1-rmse:0.09182
[877]	validation_0-rmse:0.07213	validation_1-rmse:0.09182
[878]	validation_0-rmse:0.07211	validation_1-rmse:0.09182
[879]	validation_0-rmse:0.07207	validation_1-rmse:0.09180
[880]	validation_0-rmse:0.07204	validation_1-rmse:0.09179
[881]	validation_0-rmse:0.07201	validation_1-rmse:0.09177
[882]	validation_0-rmse:0.07197	validation_1-rmse:0.09177
[883]	validation_0-rmse:0.07195	validation_1-rmse:0.09176
[884]	validation_0-rmse:0.07193	validation_1-rmse:0.09174
[885]	validation_0-rmse:0.07189	validation_1-rmse:0.09173
[886]	validation_0-rmse:0.07186	validation_1-rmse:0.09171
[887]	validation_0-rmse:0.07183	validation_1-rmse:0.09168
[888]	validation_0-rmse:0.07180	validation_1-rmse:0.09168
[889]	validation_0-rmse:0.07177	validation_1-rmse:0.09167
[890]	validation_0-rmse:0.07174	validation_1-rmse:0.09164
[891]	validation_0-rmse:0.07171	validation_1-rmse:0.09163
[892]	validation_0-rmse:0.07169	validation_1-rmse:0.09163
[893]	validation_0-rmse:0.07167	validation_1-rmse:0.09161
[894]	validation_0-rmse:0.07165	validation_1-rmse:0.09160
[895]	validation_0-rmse:0.07160	validation_1-rmse:0.09155
[896]	validation_0-rmse:0.07159	validation_1-rmse:0.09155
[897]	validation_0-rmse:0.07157	validation_1-rmse:0.09153
[898]	validation_0-rmse:0.07156	validation_1-rmse:0.09153
[899]	validation_0-rmse:0.07153	validation_1-rmse:0.09151
[900]	validation_0-rmse:0.07152	validation_1-rmse:0.09150
[901]	validation_0-rmse:0.07151	validation_1-rmse:0.09150
[902]	validation_0-rmse:0.07149	validation_1-rmse:0.09147
[903]	validation_0-rmse:0.07148	validation_1-rmse:0.09147
[904]	validation_0-rmse:0.07146	validation_1-rmse:0.09146
[905]	validation_0-rmse:0.07141	validation_1-rmse:0.09143
[906]	validation_0-rmse:0.07139	validation_1-rmse:0.09141
[907]	validation_0-rmse:0.07137	validation_1-rmse:0.09141
[908]	validation_0-rmse:0.07135	validation_1-rmse:0.09140
[909]	validation_0-rmse:0.07133	validation_1-rmse:0.09139
[910]	validation_0-rmse:0.07130	validation_1-rmse:0.09138
[911]	validation_0-rmse:0.07128	validation_1-rmse:0.09137
[912]	validation_0-rmse:0.07126	validation_1-rmse:0.09136
[913]	validation_0-rmse:0.07123	validation_1-rmse:0.09136
[914]	validation_0-rmse:0.07120	validation_1-rmse:0.09132
[915]	validation_0-rmse:0.07117	validation_1-rmse:0.09131
[916]	validation_0-rmse:0.07115	validation_1-rmse:0.09132
[917]	validation_0-rmse:0.07114	validation_1-rmse:0.09131
[918]	validation_0-rmse:0.07110	validation_1-rmse:0.09129
[919]	validation_0-rmse:0.07109	validation_1-rmse:0.09127
[920]	validation_0-rmse:0.07106	validation_1-rmse:0.09125
[921]	validation_0-rmse:0.07103	validation_1-rmse:0.09123
[922]	validation_0-rmse:0.07098	validation_1-rmse:0.09120
[923]	validation_0-rmse:0.07093	validation_1-rmse:0.09119
[924]	validation_0-rmse:0.07092	validation_1-rmse:0.09119
[925]	validation_0-rmse:0.07090	validation_1-rmse:0.09118
[926]	validation_0-rmse:0.07089	validation_1-rmse:0.09118
[927]	validation_0-rmse:0.07088	validation_1-rmse:0.09117
[928]	validation_0-rmse:0.07085	validation_1-rmse:0.09115
[929]	validation_0-rmse:0.07084	validation_1-rmse:0.09115
[930]	validation_0-rmse:0.07082	validation_1-rmse:0.09114
[931]	validation_0-rmse:0.07080	validation_1-rmse:0.09112
[932]	validation_0-rmse:0.07079	validation_1-rmse:0.09112
[933]	validation_0-rmse:0.07077	validation_1-rmse:0.09111
[934]	validation_0-rmse:0.07075	validation_1-rmse:0.09109
[935]	validation_0-rmse:0.07072	validation_1-rmse:0.09108
[936]	validation_0-rmse:0.07071	validation_1-rmse:0.09107
[937]	validation_0-rmse:0.07069	validation_1-rmse:0.09105
[938]	validation_0-rmse:0.07063	validation_1-rmse:0.09100
[939]	validation_0-rmse:0.07062	validation_1-rmse:0.09101
[940]	validation_0-rmse:0.07058	validation_1-rmse:0.09100
[941]	validation_0-rmse:0.07056	validation_1-rmse:0.09099
[942]	validation_0-rmse:0.07055	validation_1-rmse:0.09098
[943]	validation_0-rmse:0.07055	validation_1-rmse:0.09098
[944]	validation_0-rmse:0.07053	validation_1-rmse:0.09098
[945]	validation_0-rmse:0.07053	validation_1-rmse:0.09098
[946]	validation_0-rmse:0.07051	validation_1-rmse:0.09097
[947]	validation_0-rmse:0.07047	validation_1-rmse:0.09097
[948]	validation_0-rmse:0.07044	validation_1-rmse:0.09096
[949]	validation_0-rmse:0.07042	validation_1-rmse:0.09095
[950]	validation_0-rmse:0.07041	validation_1-rmse:0.09095
[951]	validation_0-rmse:0.07039	validation_1-rmse:0.09094
[952]	validation_0-rmse:0.07037	validation_1-rmse:0.09094
[953]	validation_0-rmse:0.07033	validation_1-rmse:0.09091
[954]	validation_0-rmse:0.07031	validation_1-rmse:0.09090
[955]	validation_0-rmse:0.07029	validation_1-rmse:0.09089
[956]	validation_0-rmse:0.07026	validation_1-rmse:0.09089
[957]	validation_0-rmse:0.07025	validation_1-rmse:0.09088
[958]	validation_0-rmse:0.07023	validation_1-rmse:0.09087
[959]	validation_0-rmse:0.07021	validation_1-rmse:0.09086
[960]	validation_0-rmse:0.07020	validation_1-rmse:0.09085
[961]	validation_0-rmse:0.07018	validation_1-rmse:0.09085
[962]	validation_0-rmse:0.07014	validation_1-rmse:0.09083
[963]	validation_0-rmse:0.07013	validation_1-rmse:0.09082
[964]	validation_0-rmse:0.07011	validation_1-rmse:0.09081
[965]	validation_0-rmse:0.07008	validation_1-rmse:0.09081
[966]	validation_0-rmse:0.07006	validation_1-rmse:0.09081
[967]	validation_0-rmse:0.07005	validation_1-rmse:0.09080
[968]	validation_0-rmse:0.07003	validation_1-rmse:0.09078
[969]	validation_0-rmse:0.07002	validation_1-rmse:0.09078
[970]	validation_0-rmse:0.07000	validation_1-rmse:0.09076
[971]	validation_0-rmse:0.06998	validation_1-rmse:0.09075
[972]	validation_0-rmse:0.06998	validation_1-rmse:0.09075
[973]	validation_0-rmse:0.06994	validation_1-rmse:0.09073
[974]	validation_0-rmse:0.06992	validation_1-rmse:0.09073
[975]	validation_0-rmse:0.06988	validation_1-rmse:0.09071
[976]	validation_0-rmse:0.06984	validation_1-rmse:0.09067
[977]	validation_0-rmse:0.06981	validation_1-rmse:0.09067
[978]	validation_0-rmse:0.06979	validation_1-rmse:0.09065
[979]	validation_0-rmse:0.06976	validation_1-rmse:0.09064
[980]	validation_0-rmse:0.06973	validation_1-rmse:0.09063
[981]	validation_0-rmse:0.06968	validation_1-rmse:0.09059
[982]	validation_0-rmse:0.06964	validation_1-rmse:0.09058
[983]	validation_0-rmse:0.06961	validation_1-rmse:0.09055
[984]	validation_0-rmse:0.06957	validation_1-rmse:0.09051
[985]	validation_0-rmse:0.06955	validation_1-rmse:0.09050
[986]	validation_0-rmse:0.06953	validation_1-rmse:0.09049
[987]	validation_0-rmse:0.06951	validation_1-rmse:0.09049
[988]	validation_0-rmse:0.06949	validation_1-rmse:0.09048
[989]	validation_0-rmse:0.06948	validation_1-rmse:0.09047
[990]	validation_0-rmse:0.06946	validation_1-rmse:0.09047
[991]	validation_0-rmse:0.06944	validation_1-rmse:0.09047
[992]	validation_0-rmse:0.06942	validation_1-rmse:0.09045
[993]	validation_0-rmse:0.06937	validation_1-rmse:0.09044
[994]	validation_0-rmse:0.06933	validation_1-rmse:0.09041
[995]	validation_0-rmse:0.06932	validation_1-rmse:0.09040
[996]	validation_0-rmse:0.06930	validation_1-rmse:0.09039
[997]	validation_0-rmse:0.06928	validation_1-rmse:0.09038
[998]	validation_0-rmse:0.06926	validation_1-rmse:0.09039
[999]	validation_0-rmse:0.06922	validation_1-rmse:0.09038
>>> Using model to predict target VPD_T1_2_1 in unseen test data ...
>>> Using model to calculate permutation importance based on unseen test data ...
>>> Calculating prediction scores based on predicting unseen test data of VPD_T1_2_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

================================
MODEL TRAINING & TESTING RESULTS
================================

## DATA
  > target: VPD_T1_2_1
  > features: 12 ['TA_T1_2_1', '.DOY', '.RECORDNUMBER', '.YEARDOY', '.YEARWEEK', 'SW_IN_T1_2_1', '.YEARMONTH', '.WEEK', '.HOUR', '.YEAR', '.SEASON', '.MONTH']
  > 350640 records (with missing)
  > 350446 available records for target and all features (no missing values)
  > training on 262834 records (75.0%) of 262834 features between 2005-01-01 00:45:00 and 2024-12-31 23:15:00
  > testing on 87612 unseen records (25.0%) of VPD_T1_2_1 between 2005-01-01 00:15:00 and 2024-12-31 23:45:00

## MODEL
  > the model was trained on training data (262834 records)
  > the model was tested on test data (87612 values)
  > estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
  > parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}
  > number of features used in model:  12
  > names of features used in model:  ['TA_T1_2_1', '.DOY', '.RECORDNUMBER', '.YEARDOY', '.YEARWEEK', 'SW_IN_T1_2_1', '.YEARMONTH', '.WEEK', '.HOUR', '.YEAR', '.SEASON', '.MONTH']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of VPD_T1_2_1 (87612 records).
  > feature importances are showing permutation importances from 10 repeats

               PERM_IMPORTANCE   PERM_SD
TA_T1_2_1             3.290200  0.010010
.DOY                  0.247001  0.001878
.RECORDNUMBER         0.240649  0.001484
.YEARDOY              0.163264  0.001003
.YEARWEEK             0.156299  0.000989
.YEARMONTH            0.104850  0.000645
.WEEK                 0.089213  0.000491
SW_IN_T1_2_1          0.087231  0.000439
.HOUR                 0.068200  0.000586
.MONTH                0.004853  0.000114
.SEASON               0.003782  0.000081
.YEAR                 0.000162  0.000009


## MODEL SCORES
  All scores were calculated based on unseen test data (87612 records).
  > MAE:  0.05547026684241678 (mean absolute error)
  > MedAE:  0.02927470693985803 (median absolute error)
  > MSE:  0.008167713198996472 (mean squared error)
  > RMSE:  0.09037540151499451 (root mean squared error)
  > MAXE:  1.710828424508557 (max error)
  > MAPE:  13837239700097.121 (mean absolute percentage error)
  > R2:  0.9693215951141911

xgb.fillgaps(showplot_scores=False, showplot_importance=False)
xgb.report_gapfilling()

Gap-filling using final model ...
>>> Using final model on all data to predict target VPD_T1_2_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting VPD_T1_2_1 ...
>>> Predicting target VPD_T1_2_1 where all features are available ... predicted 350640 records.
>>> Collecting results for final model ...
>>> Filling 194 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable VPD_T1_2_1_gfXG ...
>>> Restoring original timestamp in results ...
>>> Combining predictions from full model and fallback model ...

===================
GAP-FILLING RESULTS
===================

Model scores and feature importances were calculated from high-quality predicted targets (194 values, VPD_T1_2_1_gfXG where flag=1) in comparison to observed targets (350446 values, VPD_T1_2_1).

## TARGET
- first timestamp:  2005-01-01 00:15:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 350640 values)
- target column (observed):  VPD_T1_2_1
- missing records (observed):  194 (cross-check from flag: 194)
- target column (gap-filled):  VPD_T1_2_1_gfXG  (350640 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_VPD_T1_2_1_gfXG_ISFILLED
  > flag 0 ... observed targets (350446 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (194 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['TA_T1_2_1', '.DOY', '.RECORDNUMBER', '.YEARDOY', '.YEARWEEK', '.YEARMONTH', 'SW_IN_T1_2_1', '.WEEK', '.HOUR', '.MONTH', '.SEASON', '.YEAR']
- number of features used in model:  12
- permutation importances were calculated from 10 repeats.

               PERM_IMPORTANCE   PERM_SD
TA_T1_2_1             3.276807  0.008096
.DOY                  0.248589  0.000674
.RECORDNUMBER         0.238711  0.001183
.YEARDOY              0.162079  0.000317
.YEARWEEK             0.154402  0.000438
.YEARMONTH            0.103686  0.000407
SW_IN_T1_2_1          0.091543  0.000411
.WEEK                 0.089954  0.000284
.HOUR                 0.070721  0.000208
.MONTH                0.004814  0.000040
.SEASON               0.003888  0.000037
.YEAR                 0.000160  0.000005

## MODEL
The model was trained on a training set with test size 25.00%.
- estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
- parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}

## MODEL SCORES
- MAE:  0.04704485837402436 (mean absolute error)
- MedAE:  0.026262745568987667 (median absolute error)
- MSE:  0.0056358676990734295 (mean squared error)
- RMSE:  0.07507241636628882 (root mean squared error)
- MAXE:  1.710828424508557 (max error)
- MAPE:  12581715157156.318 (mean absolute percentage error)
- R2:  0.9790245620819845

Results

results = xgb.gapfilling_df_
results

	.PREDICTIONS_FULLMODEL	VPD_T1_2_1	.GAP_PREDICTIONS	FLAG_VPD_T1_2_1_gfXG_ISFILLED	VPD_T1_2_1_gfXG	.PREDICTIONS_FALLBACK	.GAPFILLED_CUMULATIVE	.PREDICTIONS
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	0.083435	0.099893	NaN	0	0.099893	None	0.099893	0.083435
2005-01-01 00:45:00	0.083435	0.097606	NaN	0	0.097606	None	0.197499	0.083435
2005-01-01 01:15:00	0.067558	0.091683	NaN	0	0.091683	None	0.289182	0.067558
2005-01-01 01:45:00	0.067558	0.071157	NaN	0	0.071157	None	0.360339	0.067558
2005-01-01 02:15:00	0.058768	0.058333	NaN	0	0.058333	None	0.418672	0.058768
...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	-0.001123	0.000011	NaN	0	0.000011	None	109123.093210	-0.001123
2024-12-31 22:15:00	-0.002141	0.000011	NaN	0	0.000011	None	109123.093220	-0.002141
2024-12-31 22:45:00	-0.002141	0.000011	NaN	0	0.000011	None	109123.093231	-0.002141
2024-12-31 23:15:00	-0.002114	0.000010	NaN	0	0.000010	None	109123.093241	-0.002114
2024-12-31 23:45:00	-0.010980	0.000010	NaN	0	0.000010	None	109123.093251	-0.010980

350640 rows × 8 columns

Add to data

# Combine data
data_simple_2005_2024['VPD_T1_2_1'] = results['VPD_T1_2_1_gfXG'].copy()
data_simple_2005_2024['VPD_T1_2_1']

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    0.099893
2005-01-01 00:45:00    0.097606
2005-01-01 01:15:00    0.091683
2005-01-01 01:45:00    0.071157
2005-01-01 02:15:00    0.058333
                         ...   
2024-12-31 21:45:00    0.000011
2024-12-31 22:15:00    0.000011
2024-12-31 22:45:00    0.000011
2024-12-31 23:15:00    0.000010
2024-12-31 23:45:00    0.000010
Freq: 30min, Name: VPD_T1_2_1, Length: 350640, dtype: float64

# Combine flags not necessary, because PPFD was not gapfilled before
data_simple_2005_2024['FLAG_VPD_T1_2_1_ISFILLED'] = results['FLAG_VPD_T1_2_1_gfXG_ISFILLED'].copy()
data_simple_2005_2024['FLAG_VPD_T1_2_1_ISFILLED']

TIMESTAMP_MIDDLE
2005-01-01 00:15:00    0
2005-01-01 00:45:00    0
2005-01-01 01:15:00    0
2005-01-01 01:45:00    0
2005-01-01 02:15:00    0
                      ..
2024-12-31 21:45:00    0
2024-12-31 22:15:00    0
2024-12-31 22:45:00    0
2024-12-31 23:15:00    0
2024-12-31 23:45:00    0
Freq: 30min, Name: FLAG_VPD_T1_2_1_ISFILLED, Length: 350640, dtype: int64

title = "VPD"
data_simple_2005_2024[['VPD_T1_2_1', 'FLAG_VPD_T1_2_1_ISFILLED']].plot(x_compat=True, title=title, subplots=True, figsize=(20, 6));

Plots

_plot_df = data_simple_2005_2024.copy()
_plot_df = _plot_df.replace(-9999, np.nan)

Time series plot

_plot_df.plot(subplots=True, figsize=(20, 14), title="Downloaded data", alpha=.9, x_compat=True);

Heatmaps

for col in _plot_df.columns:
    series = _plot_df[col]
    series.name = col
    HeatmapDateTime(series, figsize=(6, 9)).show()

L:\Sync\luhk_work\20 - CODING\21 - DIIVE\diive\diive\core\plotting\heatmap_base.py:190: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig = plt.figure(facecolor='white', figsize=self.figsize)

Stats

data_simple_2005_2024.describe().T

	count	mean	std	min	25%	50%	75%	max
G_GF1_0.03_1	257233.0	-1.294757	13.078049	-47.055000	-8.839500	-4.027900	3.322900	144.000000
LW_IN_T1_2_1	334630.0	323.621286	44.292433	162.974472	296.259460	326.301376	355.175772	454.941700
LW_OUT_T1_2_1	264726.0	363.862656	49.620005	219.459778	325.215393	357.583692	393.994850	588.454834
PA_GF1_0.9_1	345963.0	968.040536	8.055630	926.800000	963.466667	968.410800	973.094743	1004.485000
FLAG_PA_GF1_0.9_1_ISFILLED	280512.0	0.697350	0.459406	0.000000	0.000000	1.000000	1.000000	1.000000
PPFD_IN_T1_2_2	350640.0	280.936127	466.513839	-23.974247	0.000000	0.000000	370.503738	2306.300049
PREC_RAIN_TOT_GF1_0.5_1	280512.0	0.064690	0.408662	0.000000	0.000000	0.000000	0.000000	36.480904
FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED	280512.0	0.063266	0.243457	0.000000	0.000000	0.000000	0.000000	2.000000
RH_T1_2_1	350446.0	84.492494	18.126598	9.561100	73.433333	92.133333	99.721975	100.000000
FLAG_RH_T1_2_1_ISFILLED	280439.0	0.242998	0.428894	0.000000	0.000000	0.000000	0.000000	1.000000
SWC_GF1_0.05_1	255743.0	50.799695	7.900669	24.511940	47.020676	53.084404	56.925919	62.706470
SWC_GF1_0.15_1	261403.0	42.715561	4.602187	24.468729	40.653645	44.082874	45.981319	50.708080
SWC_GF1_0.75_1	261394.0	43.008481	1.596751	37.764030	41.844761	42.524414	43.705841	48.056732
SW_IN_T1_2_1	350640.0	142.267317	235.536372	0.000000	0.000000	0.000000	188.463783	1140.900024
FLAG_SW_IN_T1_2_1_ISFILLED	350640.0	0.150650	0.616569	0.000000	0.000000	0.000000	0.000000	3.000000
SW_OUT_T1_2_1	265645.0	26.672217	49.826913	-29.716660	-3.722600	0.698370	39.070100	396.149994
TA_T1_2_1	350640.0	9.988219	8.436331	-18.246000	3.304501	9.683236	15.975257	36.348891
FLAG_TA_T1_2_1_ISFILLED	350640.0	0.190378	0.730601	0.000000	0.000000	0.000000	0.000000	3.000000
TS_GF1_0.04_1	261097.0	11.635724	6.528476	-2.576900	5.872834	11.853000	17.216000	29.000000
TS_GF1_0.15_1	261812.0	11.692454	6.248735	0.045730	5.830100	11.827865	17.303924	25.957001
TS_GF1_0.4_1	261914.0	11.546517	5.482197	1.378100	6.261362	11.589000	16.733999	21.813999
FLAG_PPFD_IN_T1_2_2_ISFILLED	350640.0	0.022057	0.146869	0.000000	0.000000	0.000000	0.000000	1.000000
VPD_T1_2_1	350640.0	0.311211	0.518800	-0.057524	0.002725	0.079559	0.372177	4.574110
FLAG_VPD_T1_2_1_ISFILLED	350640.0	0.000553	0.023515	0.000000	0.000000	0.000000	0.000000	1.000000

Keep 7 meteo variables

keepcols = [
    'LW_IN_T1_2_1',
    'PA_GF1_0.9_1',
    'FLAG_PA_GF1_0.9_1_ISFILLED',
    'PPFD_IN_T1_2_2',
    'FLAG_PPFD_IN_T1_2_2_ISFILLED',
    'VPD_T1_2_1',
    'FLAG_VPD_T1_2_1_ISFILLED',
    'SW_IN_T1_2_1',
    'FLAG_SW_IN_T1_2_1_ISFILLED',
    'TA_T1_2_1',
    'FLAG_TA_T1_2_1_ISFILLED',
    'RH_T1_2_1',
    'FLAG_RH_T1_2_1_ISFILLED'    
]
outdf = data_simple_2005_2024[keepcols].copy()
outdf

	LW_IN_T1_2_1	PA_GF1_0.9_1	FLAG_PA_GF1_0.9_1_ISFILLED	PPFD_IN_T1_2_2	FLAG_PPFD_IN_T1_2_2_ISFILLED	VPD_T1_2_1	...	SW_IN_T1_2_1	FLAG_SW_IN_T1_2_1_ISFILLED	TA_T1_2_1	FLAG_TA_T1_2_1_ISFILLED	RH_T1_2_1	FLAG_RH_T1_2_1_ISFILLED
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	NaN	978.100000	1.0	0.0	0	0.099893	...	0.0	3.0	1.566667	3.0	85.400000	1.0
2005-01-01 00:45:00	NaN	977.933333	1.0	0.0	0	0.097606	...	0.0	3.0	1.533333	3.0	85.700000	1.0
2005-01-01 01:15:00	NaN	977.900000	1.0	0.0	0	0.091683	...	0.0	3.0	1.566667	3.0	86.600000	1.0
2005-01-01 01:45:00	NaN	977.833333	1.0	0.0	0	0.071157	...	0.0	3.0	1.566667	3.0	89.600000	1.0
2005-01-01 02:15:00	NaN	977.833333	1.0	0.0	0	0.058333	...	0.0	3.0	1.500000	3.0	91.433333	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	304.613900	983.370890	NaN	0.0	0	0.000011	...	0.0	0.0	-1.919472	0.0	99.997990	NaN
2024-12-31 22:15:00	303.039890	983.052160	NaN	0.0	0	0.000011	...	0.0	0.0	-2.104678	0.0	99.997990	NaN
2024-12-31 22:45:00	302.093633	982.851140	NaN	0.0	0	0.000011	...	0.0	0.0	-2.089444	0.0	99.997990	NaN
2024-12-31 23:15:00	302.217307	982.896827	NaN	0.0	0	0.000010	...	0.0	0.0	-2.355761	0.0	99.997990	NaN
2024-12-31 23:45:00	298.392973	982.856613	NaN	0.0	0	0.000010	...	0.0	0.0	-2.578839	0.0	99.997990	NaN

350640 rows × 13 columns

Save measured and gap-filled data to parquet

OUTNAME = "16.1_CH-CHA_meteo7_gapfilled_2005-2024"
OUTPATH = r""
filepath = save_parquet(filename=OUTNAME, data=outdf, outpath=OUTPATH)
# outdf.to_csv(Path(OUTPATH) / f"{OUTNAME}.csv")

Saved file 16.1_CH-CHA_meteo7_gapfilled_2005-2024.parquet (0.196 seconds).

End of notebook.

dt_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(f"Finished. {dt_string}")

Finished. 2025-01-20 17:32:27