Gap-fill meteo variables

Settings

Data settings

SITE_LAT = 47.478333   # CH-LAE
SITE_LON = 8.364389  # CH-LAE
TIMEZONE_OFFSET_TO_UTC_HOURS = 1  # Timezone, e.g. "1" is translated to timezone "UTC+01:00" (CET, winter time)

Imports

from datetime import datetime
import importlib.metadata
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
%matplotlib inline
import numpy as np
import pandas as pd
import seaborn as sns
from pathlib import Path
sns.set_theme('notebook')
from diive.core.plotting.timeseries import TimeSeries
from diive.core.io.files import load_parquet, save_parquet
from diive.core.plotting.heatmap_datetime import HeatmapDateTime
from diive.core.times.times import DetectFrequency
from diive.core.times.times import TimestampSanitizer
from diive.pkgs.createvar.potentialradiation import potrad
from diive.pkgs.gapfilling.xgboost_ts import XGBoostTS
from diive.pkgs.corrections.offsetcorrection import remove_relativehumidity_offset, remove_radiation_zero_offset
import warnings
from influxdb_client.client.warnings import MissingPivotFunction
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}")

diive version: v0.87.1

Load data from files

filename_data_simple_2004_2024 = "12.1_METEO6_NOT-GAPFILLED_2004-2024.parquet"
data_simple_2004_2024 = load_parquet(filepath=filename_data_simple_2004_2024)
data_simple_2004_2024

Loaded .parquet file 12.1_METEO6_NOT-GAPFILLED_2004-2024.parquet (0.071 seconds).
    --> Detected time resolution of <30 * Minutes> / 30min 

	LW_IN_T1_47_1	PA_T1_47_1	PPFD_IN_T1_47_1	RH_T1_47_1	SW_IN_T1_47_1	TA_T1_47_1
TIMESTAMP_MIDDLE
2004-09-20 10:45:00	NaN	NaN	1070.543364	80.503701	NaN	13.300000
2004-09-20 11:15:00	NaN	NaN	881.543364	77.503701	NaN	13.390000
2004-09-20 11:45:00	NaN	NaN	1022.543364	75.803704	NaN	13.810000
2004-09-20 12:15:00	NaN	NaN	1284.543364	71.503701	NaN	14.470000
2004-09-20 12:45:00	NaN	NaN	742.543364	72.703705	NaN	13.980000
...	...	...	...	...	...	...
2024-12-31 21:45:00	232.595527	94.211806	0.000000	87.254008	0.0	-0.504794
2024-12-31 22:15:00	232.609777	94.189013	0.000000	87.430236	0.0	-0.296828
2024-12-31 22:45:00	232.345020	94.169525	0.000000	89.787920	0.0	-0.392922
2024-12-31 23:15:00	234.211100	94.168413	0.000000	81.809355	0.0	0.792661
2024-12-31 23:45:00	231.760533	94.170793	0.000000	88.311314	0.0	-0.422600

355563 rows × 6 columns

Gap-filling

SW_IN_T1_47_1

VAR = "SW_IN_T1_47_1"

_df = pd.DataFrame()
_df[VAR] = data_simple_2004_2024[[VAR]].copy()
_df['SW_IN_POT'] = potrad(timestamp_index=_df.index, lat=SITE_LAT, lon=SITE_LON, utc_offset=1)
_df

	SW_IN_T1_47_1	SW_IN_POT
TIMESTAMP_MIDDLE
2004-09-20 10:45:00	NaN	833.243912
2004-09-20 11:15:00	NaN	877.607230
2004-09-20 11:45:00	NaN	906.992208
2004-09-20 12:15:00	NaN	920.896059
2004-09-20 12:45:00	NaN	919.080885
...	...	...
2024-12-31 21:45:00	0.0	0.000000
2024-12-31 22:15:00	0.0	0.000000
2024-12-31 22:45:00	0.0	0.000000
2024-12-31 23:15:00	0.0	0.000000
2024-12-31 23:45:00	0.0	0.000000

355563 rows × 2 columns

xgb = XGBoostTS(
    input_df=_df,
    target_col=VAR,
    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 355563.

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

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2004-09-20 18:45:00 and 2024-12-31 23:45:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:187.20808	validation_1-rmse:187.23916
[1]	validation_0-rmse:156.90019	validation_1-rmse:157.08066
[2]	validation_0-rmse:139.51174	validation_1-rmse:139.72723
[3]	validation_0-rmse:129.93945	validation_1-rmse:130.18473
[4]	validation_0-rmse:124.76174	validation_1-rmse:125.02259
[5]	validation_0-rmse:122.02444	validation_1-rmse:122.32108
[6]	validation_0-rmse:120.37094	validation_1-rmse:120.69973
[7]	validation_0-rmse:119.33020	validation_1-rmse:119.67993
[8]	validation_0-rmse:118.58894	validation_1-rmse:118.95800
[9]	validation_0-rmse:118.02906	validation_1-rmse:118.41308
[10]	validation_0-rmse:117.55467	validation_1-rmse:117.97495
[11]	validation_0-rmse:116.96120	validation_1-rmse:117.40361
[12]	validation_0-rmse:116.62434	validation_1-rmse:117.08419
[13]	validation_0-rmse:116.07044	validation_1-rmse:116.54447
[14]	validation_0-rmse:115.52410	validation_1-rmse:116.09288
[15]	validation_0-rmse:115.15085	validation_1-rmse:115.77416
[16]	validation_0-rmse:114.80006	validation_1-rmse:115.49234
[17]	validation_0-rmse:114.62852	validation_1-rmse:115.34056
[18]	validation_0-rmse:114.27683	validation_1-rmse:115.05659
[19]	validation_0-rmse:113.82306	validation_1-rmse:114.62143
[20]	validation_0-rmse:113.50995	validation_1-rmse:114.37709
[21]	validation_0-rmse:113.22396	validation_1-rmse:114.13386
[22]	validation_0-rmse:112.88746	validation_1-rmse:113.82672
[23]	validation_0-rmse:112.53725	validation_1-rmse:113.50201
[24]	validation_0-rmse:112.27783	validation_1-rmse:113.24324
[25]	validation_0-rmse:112.05377	validation_1-rmse:113.07457
[26]	validation_0-rmse:111.82167	validation_1-rmse:112.89027
[27]	validation_0-rmse:111.46769	validation_1-rmse:112.55713
[28]	validation_0-rmse:111.03551	validation_1-rmse:112.19603
[29]	validation_0-rmse:110.74377	validation_1-rmse:111.94683
[30]	validation_0-rmse:110.52400	validation_1-rmse:111.76191
[31]	validation_0-rmse:110.21928	validation_1-rmse:111.44986
[32]	validation_0-rmse:110.07326	validation_1-rmse:111.31851
[33]	validation_0-rmse:109.79818	validation_1-rmse:111.09192
[34]	validation_0-rmse:109.67988	validation_1-rmse:110.97092
[35]	validation_0-rmse:109.38008	validation_1-rmse:110.73183
[36]	validation_0-rmse:109.11979	validation_1-rmse:110.46075
[37]	validation_0-rmse:109.04421	validation_1-rmse:110.40443
[38]	validation_0-rmse:108.97116	validation_1-rmse:110.33866
[39]	validation_0-rmse:108.87827	validation_1-rmse:110.25254
[40]	validation_0-rmse:108.75669	validation_1-rmse:110.16561
[41]	validation_0-rmse:108.59914	validation_1-rmse:110.04096
[42]	validation_0-rmse:108.46806	validation_1-rmse:109.95885
[43]	validation_0-rmse:108.14619	validation_1-rmse:109.67275
[44]	validation_0-rmse:107.95396	validation_1-rmse:109.53198
[45]	validation_0-rmse:107.64891	validation_1-rmse:109.23491
[46]	validation_0-rmse:107.23572	validation_1-rmse:108.82915
[47]	validation_0-rmse:107.00071	validation_1-rmse:108.61879
[48]	validation_0-rmse:106.80306	validation_1-rmse:108.45449
[49]	validation_0-rmse:106.57463	validation_1-rmse:108.28821
[50]	validation_0-rmse:106.43239	validation_1-rmse:108.19327
[51]	validation_0-rmse:106.19825	validation_1-rmse:107.97274
[52]	validation_0-rmse:106.10320	validation_1-rmse:107.89154
[53]	validation_0-rmse:105.97261	validation_1-rmse:107.76333
[54]	validation_0-rmse:105.94579	validation_1-rmse:107.74210
[55]	validation_0-rmse:105.84272	validation_1-rmse:107.66205
[56]	validation_0-rmse:105.65574	validation_1-rmse:107.50947
[57]	validation_0-rmse:105.42560	validation_1-rmse:107.32045
[58]	validation_0-rmse:105.30728	validation_1-rmse:107.23071
[59]	validation_0-rmse:105.26893	validation_1-rmse:107.20531
[60]	validation_0-rmse:105.06125	validation_1-rmse:107.01790
[61]	validation_0-rmse:104.98064	validation_1-rmse:106.96975
[62]	validation_0-rmse:104.73280	validation_1-rmse:106.76827
[63]	validation_0-rmse:104.57090	validation_1-rmse:106.63433
[64]	validation_0-rmse:104.38331	validation_1-rmse:106.46427
[65]	validation_0-rmse:104.25631	validation_1-rmse:106.42511
[66]	validation_0-rmse:104.04334	validation_1-rmse:106.23896
[67]	validation_0-rmse:103.91220	validation_1-rmse:106.18280
[68]	validation_0-rmse:103.69310	validation_1-rmse:106.00418
[69]	validation_0-rmse:103.49270	validation_1-rmse:105.82318
[70]	validation_0-rmse:103.43398	validation_1-rmse:105.82372
[71]	validation_0-rmse:103.34981	validation_1-rmse:105.76086
[72]	validation_0-rmse:103.18878	validation_1-rmse:105.62448
[73]	validation_0-rmse:102.98876	validation_1-rmse:105.47420
[74]	validation_0-rmse:102.93389	validation_1-rmse:105.44977
[75]	validation_0-rmse:102.69544	validation_1-rmse:105.24760
[76]	validation_0-rmse:102.53092	validation_1-rmse:105.20341
[77]	validation_0-rmse:102.40332	validation_1-rmse:105.12861
[78]	validation_0-rmse:102.22716	validation_1-rmse:104.99025
[79]	validation_0-rmse:102.16419	validation_1-rmse:104.99765
[80]	validation_0-rmse:101.97898	validation_1-rmse:104.81021
[81]	validation_0-rmse:101.82344	validation_1-rmse:104.69048
[82]	validation_0-rmse:101.69187	validation_1-rmse:104.65241
[83]	validation_0-rmse:101.48684	validation_1-rmse:104.50425
[84]	validation_0-rmse:101.43725	validation_1-rmse:104.46544
[85]	validation_0-rmse:101.35245	validation_1-rmse:104.39464
[86]	validation_0-rmse:101.27299	validation_1-rmse:104.32698
[87]	validation_0-rmse:101.13174	validation_1-rmse:104.21133
[88]	validation_0-rmse:101.08919	validation_1-rmse:104.18103
[89]	validation_0-rmse:100.98603	validation_1-rmse:104.10308
[90]	validation_0-rmse:100.87216	validation_1-rmse:104.01029
[91]	validation_0-rmse:100.76537	validation_1-rmse:103.92708
[92]	validation_0-rmse:100.74310	validation_1-rmse:103.91768
[93]	validation_0-rmse:100.53307	validation_1-rmse:103.73192
[94]	validation_0-rmse:100.44160	validation_1-rmse:103.67888
[95]	validation_0-rmse:100.30800	validation_1-rmse:103.57683
[96]	validation_0-rmse:100.11744	validation_1-rmse:103.39849
[97]	validation_0-rmse:99.99062	validation_1-rmse:103.32677
[98]	validation_0-rmse:99.93723	validation_1-rmse:103.29040
[99]	validation_0-rmse:99.87767	validation_1-rmse:103.24235
[100]	validation_0-rmse:99.72076	validation_1-rmse:103.13869
[101]	validation_0-rmse:99.59646	validation_1-rmse:103.02239
[102]	validation_0-rmse:99.49361	validation_1-rmse:102.96886
[103]	validation_0-rmse:99.36160	validation_1-rmse:102.88705
[104]	validation_0-rmse:99.23805	validation_1-rmse:102.78927
[105]	validation_0-rmse:99.13300	validation_1-rmse:102.73236
[106]	validation_0-rmse:99.06059	validation_1-rmse:102.69030
[107]	validation_0-rmse:98.94676	validation_1-rmse:102.60845
[108]	validation_0-rmse:98.92777	validation_1-rmse:102.59498
[109]	validation_0-rmse:98.84709	validation_1-rmse:102.53660
[110]	validation_0-rmse:98.75332	validation_1-rmse:102.45211
[111]	validation_0-rmse:98.61288	validation_1-rmse:102.41977
[112]	validation_0-rmse:98.46512	validation_1-rmse:102.29729
[113]	validation_0-rmse:98.27671	validation_1-rmse:102.12303
[114]	validation_0-rmse:98.17273	validation_1-rmse:102.03571
[115]	validation_0-rmse:98.10073	validation_1-rmse:102.00025
[116]	validation_0-rmse:97.97915	validation_1-rmse:101.88485
[117]	validation_0-rmse:97.92291	validation_1-rmse:101.84915
[118]	validation_0-rmse:97.85672	validation_1-rmse:101.80623
[119]	validation_0-rmse:97.79847	validation_1-rmse:101.79331
[120]	validation_0-rmse:97.67926	validation_1-rmse:101.71664
[121]	validation_0-rmse:97.55969	validation_1-rmse:101.62452
[122]	validation_0-rmse:97.49399	validation_1-rmse:101.57573
[123]	validation_0-rmse:97.37530	validation_1-rmse:101.49532
[124]	validation_0-rmse:97.35472	validation_1-rmse:101.48729
[125]	validation_0-rmse:97.25165	validation_1-rmse:101.41307
[126]	validation_0-rmse:97.18878	validation_1-rmse:101.44775
[127]	validation_0-rmse:97.06431	validation_1-rmse:101.34713
[128]	validation_0-rmse:96.98775	validation_1-rmse:101.28087
[129]	validation_0-rmse:96.96799	validation_1-rmse:101.27207
[130]	validation_0-rmse:96.81669	validation_1-rmse:101.16019
[131]	validation_0-rmse:96.71855	validation_1-rmse:101.09294
[132]	validation_0-rmse:96.55317	validation_1-rmse:100.96927
[133]	validation_0-rmse:96.46609	validation_1-rmse:100.91050
[134]	validation_0-rmse:96.33967	validation_1-rmse:100.83343
[135]	validation_0-rmse:96.28086	validation_1-rmse:100.80036
[136]	validation_0-rmse:96.20478	validation_1-rmse:100.76076
[137]	validation_0-rmse:96.09626	validation_1-rmse:100.69068
[138]	validation_0-rmse:96.06777	validation_1-rmse:100.67768
[139]	validation_0-rmse:95.91871	validation_1-rmse:100.55413
[140]	validation_0-rmse:95.83862	validation_1-rmse:100.49325
[141]	validation_0-rmse:95.60352	validation_1-rmse:100.26360
[142]	validation_0-rmse:95.57660	validation_1-rmse:100.24306
[143]	validation_0-rmse:95.40424	validation_1-rmse:100.15896
[144]	validation_0-rmse:95.36488	validation_1-rmse:100.12205
[145]	validation_0-rmse:95.27933	validation_1-rmse:100.05262
[146]	validation_0-rmse:95.15342	validation_1-rmse:99.95083
[147]	validation_0-rmse:95.06264	validation_1-rmse:99.89008
[148]	validation_0-rmse:94.93020	validation_1-rmse:99.80720
[149]	validation_0-rmse:94.87562	validation_1-rmse:99.77342
[150]	validation_0-rmse:94.85442	validation_1-rmse:99.75808
[151]	validation_0-rmse:94.74059	validation_1-rmse:99.70434
[152]	validation_0-rmse:94.64908	validation_1-rmse:99.65003
[153]	validation_0-rmse:94.61869	validation_1-rmse:99.63181
[154]	validation_0-rmse:94.59146	validation_1-rmse:99.62413
[155]	validation_0-rmse:94.54658	validation_1-rmse:99.59679
[156]	validation_0-rmse:94.43685	validation_1-rmse:99.51678
[157]	validation_0-rmse:94.40070	validation_1-rmse:99.49553
[158]	validation_0-rmse:94.26018	validation_1-rmse:99.37488
[159]	validation_0-rmse:94.19465	validation_1-rmse:99.35188
[160]	validation_0-rmse:94.06006	validation_1-rmse:99.24296
[161]	validation_0-rmse:94.05081	validation_1-rmse:99.24012
[162]	validation_0-rmse:94.02245	validation_1-rmse:99.21661
[163]	validation_0-rmse:93.95370	validation_1-rmse:99.17244
[164]	validation_0-rmse:93.84325	validation_1-rmse:99.09129
[165]	validation_0-rmse:93.78975	validation_1-rmse:99.05351
[166]	validation_0-rmse:93.68780	validation_1-rmse:98.96878
[167]	validation_0-rmse:93.60012	validation_1-rmse:98.91875
[168]	validation_0-rmse:93.50224	validation_1-rmse:98.84189
[169]	validation_0-rmse:93.46637	validation_1-rmse:98.84493
[170]	validation_0-rmse:93.36703	validation_1-rmse:98.77638
[171]	validation_0-rmse:93.31984	validation_1-rmse:98.77036
[172]	validation_0-rmse:93.23654	validation_1-rmse:98.71976
[173]	validation_0-rmse:93.12107	validation_1-rmse:98.63846
[174]	validation_0-rmse:93.02395	validation_1-rmse:98.56334
[175]	validation_0-rmse:92.94886	validation_1-rmse:98.51579
[176]	validation_0-rmse:92.91005	validation_1-rmse:98.49293
[177]	validation_0-rmse:92.89098	validation_1-rmse:98.48429
[178]	validation_0-rmse:92.79628	validation_1-rmse:98.39819
[179]	validation_0-rmse:92.76414	validation_1-rmse:98.38765
[180]	validation_0-rmse:92.68541	validation_1-rmse:98.31268
[181]	validation_0-rmse:92.62764	validation_1-rmse:98.34100
[182]	validation_0-rmse:92.53640	validation_1-rmse:98.30049
[183]	validation_0-rmse:92.50998	validation_1-rmse:98.28496
[184]	validation_0-rmse:92.43173	validation_1-rmse:98.22494
[185]	validation_0-rmse:92.31408	validation_1-rmse:98.14310
[186]	validation_0-rmse:92.24695	validation_1-rmse:98.07893
[187]	validation_0-rmse:92.18067	validation_1-rmse:98.01137
[188]	validation_0-rmse:92.11952	validation_1-rmse:97.97305
[189]	validation_0-rmse:92.04357	validation_1-rmse:97.97348
[190]	validation_0-rmse:91.94057	validation_1-rmse:97.89356
[191]	validation_0-rmse:91.89008	validation_1-rmse:97.85236
[192]	validation_0-rmse:91.85493	validation_1-rmse:97.84245
[193]	validation_0-rmse:91.79088	validation_1-rmse:97.81386
[194]	validation_0-rmse:91.75359	validation_1-rmse:97.79473
[195]	validation_0-rmse:91.67880	validation_1-rmse:97.75050
[196]	validation_0-rmse:91.60835	validation_1-rmse:97.70398
[197]	validation_0-rmse:91.51683	validation_1-rmse:97.65241
[198]	validation_0-rmse:91.48368	validation_1-rmse:97.62222
[199]	validation_0-rmse:91.46981	validation_1-rmse:97.62081
[200]	validation_0-rmse:91.43415	validation_1-rmse:97.59283
[201]	validation_0-rmse:91.37711	validation_1-rmse:97.53268
[202]	validation_0-rmse:91.29786	validation_1-rmse:97.46591
[203]	validation_0-rmse:91.22267	validation_1-rmse:97.39965
[204]	validation_0-rmse:91.15595	validation_1-rmse:97.41198
[205]	validation_0-rmse:91.05036	validation_1-rmse:97.33658
[206]	validation_0-rmse:90.99011	validation_1-rmse:97.32590
[207]	validation_0-rmse:90.88545	validation_1-rmse:97.28830
[208]	validation_0-rmse:90.79315	validation_1-rmse:97.23522
[209]	validation_0-rmse:90.71134	validation_1-rmse:97.19767
[210]	validation_0-rmse:90.63978	validation_1-rmse:97.17382
[211]	validation_0-rmse:90.59356	validation_1-rmse:97.15715
[212]	validation_0-rmse:90.55531	validation_1-rmse:97.13458
[213]	validation_0-rmse:90.50097	validation_1-rmse:97.10573
[214]	validation_0-rmse:90.41194	validation_1-rmse:97.03111
[215]	validation_0-rmse:90.28963	validation_1-rmse:96.96968
[216]	validation_0-rmse:90.24259	validation_1-rmse:96.95896
[217]	validation_0-rmse:90.21063	validation_1-rmse:96.95930
[218]	validation_0-rmse:90.15703	validation_1-rmse:96.93745
[219]	validation_0-rmse:90.12079	validation_1-rmse:96.90752
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[775]	validation_0-rmse:72.06858	validation_1-rmse:88.33481
[776]	validation_0-rmse:72.04514	validation_1-rmse:88.34472
[777]	validation_0-rmse:72.04123	validation_1-rmse:88.34488
[778]	validation_0-rmse:72.01471	validation_1-rmse:88.35585
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[780]	validation_0-rmse:71.97265	validation_1-rmse:88.33704
[781]	validation_0-rmse:71.94240	validation_1-rmse:88.32507
[782]	validation_0-rmse:71.92321	validation_1-rmse:88.31932
[783]	validation_0-rmse:71.88114	validation_1-rmse:88.28801
[784]	validation_0-rmse:71.85201	validation_1-rmse:88.30221
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[792]	validation_0-rmse:71.69713	validation_1-rmse:88.27316
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[995]	validation_0-rmse:68.23710	validation_1-rmse:87.69354
[996]	validation_0-rmse:68.22447	validation_1-rmse:87.69892
[997]	validation_0-rmse:68.20041	validation_1-rmse:87.69487
[998]	validation_0-rmse:68.18655	validation_1-rmse:87.69013
[999]	validation_0-rmse:68.17333	validation_1-rmse:87.68055
>>> Using model to predict target SW_IN_T1_47_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_47_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_47_1
  > features: 11 ['SW_IN_POT', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
  > 355563 records (with missing)
  > 345624 available records for target and all features (no missing values)
  > training on 259218 records (75.0%) of 259218 features between 2004-09-20 18:45:00 and 2024-12-31 23:45:00
  > testing on 86406 unseen records (25.0%) of SW_IN_T1_47_1 between 2004-09-20 18:15:00 and 2024-12-31 22:45:00

## MODEL
  > the model was trained on training data (259218 records)
  > the model was tested on test data (86406 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,
             feature_weights=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, ...)
  > 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, 'feature_weights': 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:  ['SW_IN_POT', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of SW_IN_T1_47_1 (86406 records).
  > feature importances are showing permutation importances from 10 repeats

               PERM_IMPORTANCE   PERM_SD
SW_IN_POT             1.744640  0.008588
.DOY                  0.643600  0.007391
.RECORDNUMBER         0.476877  0.005382
.YEARDOY              0.431174  0.004414
.YEARWEEK             0.270051  0.003383
.WEEK                 0.266336  0.002739
.YEARMONTH            0.228096  0.003061
.MONTH                0.037190  0.000605
.YEAR                 0.032715  0.000527
.HOUR                 0.017898  0.000410
.SEASON               0.002638  0.000168


## MODEL SCORES
  All scores were calculated based on unseen test data (86406 records).
  > MAE:  45.01740741428343 (mean absolute error)
  > MedAE:  10.216288566589355 (median absolute error)
  > MSE:  7687.879400163811 (mean squared error)
  > RMSE:  87.68055314699954 (root mean squared error)
  > MAXE:  838.2482048025594 (max error)
  > MAPE:  13113046573230394.000 (mean absolute percentage error)
  > R2:  0.8629870451963979

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_47_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting SW_IN_T1_47_1 ...
>>> Predicting target SW_IN_T1_47_1 where all features are available ... predicted 355563 records.
>>> Collecting results for final model ...
>>> Filling 9939 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable SW_IN_T1_47_1_gfXG ...
>>> Restoring original timestamp in results ...

Gap-filling 0 remaining missing records in SW_IN_T1_47_1_gfXG using fallback model ...
>>> Fallback model not necessary, all gaps were already filled.
>>> Combining predictions from full model and fallback model ...

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

Model scores and feature importances were calculated from high-quality predicted targets (9939 values, SW_IN_T1_47_1_gfXG where flag=1) in comparison to observed targets (345624 values, SW_IN_T1_47_1).

## TARGET
- first timestamp:  2004-09-20 10:45:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 355563 values)
- target column (observed):  SW_IN_T1_47_1
- missing records (observed):  9939 (cross-check from flag: 9939)
- target column (gap-filled):  SW_IN_T1_47_1_gfXG  (355563 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_SW_IN_T1_47_1_gfXG_ISFILLED
  > flag 0 ... observed targets (345624 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (9939 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

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

               PERM_IMPORTANCE   PERM_SD
SW_IN_POT             1.790828  0.004433
.DOY                  0.662968  0.002733
.RECORDNUMBER         0.485020  0.002599
.YEARDOY              0.438663  0.002028
.YEARWEEK             0.279463  0.001184
.WEEK                 0.271163  0.001778
.YEARMONTH            0.235167  0.001645
.YEAR                 0.038139  0.000264
.MONTH                0.037533  0.000283
.HOUR                 0.024933  0.000353
.SEASON               0.002768  0.000066

## 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,
             feature_weights=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, ...)
- 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, 'feature_weights': 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:  38.04020365935038 (mean absolute error)
- MedAE:  9.639815040606393 (median absolute error)
- MSE:  5407.671825295857 (mean squared error)
- RMSE:  73.53687391571563 (root mean squared error)
- MAXE:  838.2482048025594 (max error)
- MAPE:  13000074404434930.000 (mean absolute percentage error)
- R2:  0.90374263097933

observed = _df[VAR]
gapfilled = xgb.get_gapfilled_target()
data_simple_2004_2024[gapfilled.name] = gapfilled

TODO: SECOND GAPFILLING USING PPFD (more complete)

title=f"Short-wave incoming radiation {VAR}"
fig, ax = plt.subplots()
TimeSeries(series=gapfilled, ax=ax).plot(color='blue')
TimeSeries(series=observed, ax=ax).plot(color='orange')
fig.suptitle(f'{title}', fontsize=16)
# ax.set_ylim(-2000, 200)
fig.show()

Correction: Remove zero offset < 0 from SW_IN_T1_47_1_gfXG

• Additional correction needed because gap-filling introduced values < 0

_swin = data_simple_2004_2024['SW_IN_T1_47_1_gfXG'].copy()
_swin_corrected = remove_radiation_zero_offset(series=_swin, lat=SITE_LAT, lon=SITE_LON, utc_offset=1, showplot=True)
data_simple_2004_2024['SW_IN_T1_47_1_gfXG'] = np.nan
data_simple_2004_2024['SW_IN_T1_47_1_gfXG'] = _swin_corrected

[remove_radiation_zero_offset]  running remove_radiation_zero_offset ...

TA_T1_47_1

VAR = "TA_T1_47_1"

_df = pd.DataFrame()
_df['SW_IN_T1_47_1_gfXG'] = data_simple_2004_2024['SW_IN_T1_47_1_gfXG'].copy()
_df[VAR] = data_simple_2004_2024[VAR].copy()
_df['SW_IN_POT'] = potrad(timestamp_index=_df.index, lat=SITE_LAT, lon=SITE_LON, utc_offset=1)
_df

	SW_IN_T1_47_1_gfXG	TA_T1_47_1	SW_IN_POT
TIMESTAMP_MIDDLE
2004-09-20 10:45:00	602.874084	13.300000	833.243912
2004-09-20 11:15:00	703.762207	13.390000	877.607230
2004-09-20 11:45:00	537.947327	13.810000	906.992208
2004-09-20 12:15:00	634.747437	14.470000	920.896059
2004-09-20 12:45:00	634.747437	13.980000	919.080885
...	...	...	...
2024-12-31 21:45:00	0.000000	-0.504794	0.000000
2024-12-31 22:15:00	0.000000	-0.296828	0.000000
2024-12-31 22:45:00	0.000000	-0.392922	0.000000
2024-12-31 23:15:00	0.000000	0.792661	0.000000
2024-12-31 23:45:00	0.000000	-0.422600	0.000000

355563 rows × 3 columns

xgb = XGBoostTS(
    input_df=_df,
    target_col=VAR,
    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 355563.

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

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2004-09-20 11:15:00 and 2024-12-31 23:15:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:6.51439	validation_1-rmse:6.50729
[1]	validation_0-rmse:5.44212	validation_1-rmse:5.43619
[2]	validation_0-rmse:4.78323	validation_1-rmse:4.77965
[3]	validation_0-rmse:4.38358	validation_1-rmse:4.38169
[4]	validation_0-rmse:4.12475	validation_1-rmse:4.12273
[5]	validation_0-rmse:3.97601	validation_1-rmse:3.97627
[6]	validation_0-rmse:3.88080	validation_1-rmse:3.88344
[7]	validation_0-rmse:3.77624	validation_1-rmse:3.78136
[8]	validation_0-rmse:3.72931	validation_1-rmse:3.73688
[9]	validation_0-rmse:3.66637	validation_1-rmse:3.67507
[10]	validation_0-rmse:3.64036	validation_1-rmse:3.65014
[11]	validation_0-rmse:3.61965	validation_1-rmse:3.62953
[12]	validation_0-rmse:3.59049	validation_1-rmse:3.60058
[13]	validation_0-rmse:3.55664	validation_1-rmse:3.56586
[14]	validation_0-rmse:3.53167	validation_1-rmse:3.54136
[15]	validation_0-rmse:3.51426	validation_1-rmse:3.52550
[16]	validation_0-rmse:3.49217	validation_1-rmse:3.50386
[17]	validation_0-rmse:3.45070	validation_1-rmse:3.46290
[18]	validation_0-rmse:3.44208	validation_1-rmse:3.45497
[19]	validation_0-rmse:3.42344	validation_1-rmse:3.43457
[20]	validation_0-rmse:3.40456	validation_1-rmse:3.41438
[21]	validation_0-rmse:3.38228	validation_1-rmse:3.39265
[22]	validation_0-rmse:3.36085	validation_1-rmse:3.37214
[23]	validation_0-rmse:3.34742	validation_1-rmse:3.35881
[24]	validation_0-rmse:3.31689	validation_1-rmse:3.32762
[25]	validation_0-rmse:3.28994	validation_1-rmse:3.30132
[26]	validation_0-rmse:3.28064	validation_1-rmse:3.29333
[27]	validation_0-rmse:3.25066	validation_1-rmse:3.26326
[28]	validation_0-rmse:3.24243	validation_1-rmse:3.25548
[29]	validation_0-rmse:3.23612	validation_1-rmse:3.24995
[30]	validation_0-rmse:3.22373	validation_1-rmse:3.23742
[31]	validation_0-rmse:3.21636	validation_1-rmse:3.23009
[32]	validation_0-rmse:3.19004	validation_1-rmse:3.20356
[33]	validation_0-rmse:3.18226	validation_1-rmse:3.19567
[34]	validation_0-rmse:3.16147	validation_1-rmse:3.17434
[35]	validation_0-rmse:3.13243	validation_1-rmse:3.14714
[36]	validation_0-rmse:3.11821	validation_1-rmse:3.13334
[37]	validation_0-rmse:3.11585	validation_1-rmse:3.13114
[38]	validation_0-rmse:3.11256	validation_1-rmse:3.12808
[39]	validation_0-rmse:3.10776	validation_1-rmse:3.12324
[40]	validation_0-rmse:3.09544	validation_1-rmse:3.11072
[41]	validation_0-rmse:3.08333	validation_1-rmse:3.09863
[42]	validation_0-rmse:3.07441	validation_1-rmse:3.09010
[43]	validation_0-rmse:3.05548	validation_1-rmse:3.07008
[44]	validation_0-rmse:3.04528	validation_1-rmse:3.06042
[45]	validation_0-rmse:3.03061	validation_1-rmse:3.04613
[46]	validation_0-rmse:3.01749	validation_1-rmse:3.03334
[47]	validation_0-rmse:2.99651	validation_1-rmse:3.01208
[48]	validation_0-rmse:2.98360	validation_1-rmse:2.99915
[49]	validation_0-rmse:2.97247	validation_1-rmse:2.98860
[50]	validation_0-rmse:2.96257	validation_1-rmse:2.97833
[51]	validation_0-rmse:2.94966	validation_1-rmse:2.96562
[52]	validation_0-rmse:2.93693	validation_1-rmse:2.95278
[53]	validation_0-rmse:2.92239	validation_1-rmse:2.93814
[54]	validation_0-rmse:2.90595	validation_1-rmse:2.92204
[55]	validation_0-rmse:2.90115	validation_1-rmse:2.91780
[56]	validation_0-rmse:2.89779	validation_1-rmse:2.91467
[57]	validation_0-rmse:2.89379	validation_1-rmse:2.91135
[58]	validation_0-rmse:2.87340	validation_1-rmse:2.89025
[59]	validation_0-rmse:2.86688	validation_1-rmse:2.88375
[60]	validation_0-rmse:2.86189	validation_1-rmse:2.87874
[61]	validation_0-rmse:2.84950	validation_1-rmse:2.86624
[62]	validation_0-rmse:2.83549	validation_1-rmse:2.85246
[63]	validation_0-rmse:2.82492	validation_1-rmse:2.84113
[64]	validation_0-rmse:2.81482	validation_1-rmse:2.83124
[65]	validation_0-rmse:2.80926	validation_1-rmse:2.82581
[66]	validation_0-rmse:2.80289	validation_1-rmse:2.81936
[67]	validation_0-rmse:2.79410	validation_1-rmse:2.81110
[68]	validation_0-rmse:2.77916	validation_1-rmse:2.79600
[69]	validation_0-rmse:2.77312	validation_1-rmse:2.78932
[70]	validation_0-rmse:2.76071	validation_1-rmse:2.77735
[71]	validation_0-rmse:2.75745	validation_1-rmse:2.77446
[72]	validation_0-rmse:2.75188	validation_1-rmse:2.76890
[73]	validation_0-rmse:2.74922	validation_1-rmse:2.76630
[74]	validation_0-rmse:2.74466	validation_1-rmse:2.76160
[75]	validation_0-rmse:2.74127	validation_1-rmse:2.75863
[76]	validation_0-rmse:2.73390	validation_1-rmse:2.75175
[77]	validation_0-rmse:2.72932	validation_1-rmse:2.74726
[78]	validation_0-rmse:2.71247	validation_1-rmse:2.73020
[79]	validation_0-rmse:2.70781	validation_1-rmse:2.72613
[80]	validation_0-rmse:2.70442	validation_1-rmse:2.72294
[81]	validation_0-rmse:2.68478	validation_1-rmse:2.70385
[82]	validation_0-rmse:2.67295	validation_1-rmse:2.69253
[83]	validation_0-rmse:2.66675	validation_1-rmse:2.68671
[84]	validation_0-rmse:2.66116	validation_1-rmse:2.68117
[85]	validation_0-rmse:2.65345	validation_1-rmse:2.67379
[86]	validation_0-rmse:2.65110	validation_1-rmse:2.67194
[87]	validation_0-rmse:2.64206	validation_1-rmse:2.66246
[88]	validation_0-rmse:2.63333	validation_1-rmse:2.65426
[89]	validation_0-rmse:2.62273	validation_1-rmse:2.64430
[90]	validation_0-rmse:2.60869	validation_1-rmse:2.63104
[91]	validation_0-rmse:2.60449	validation_1-rmse:2.62727
[92]	validation_0-rmse:2.59186	validation_1-rmse:2.61573
[93]	validation_0-rmse:2.58922	validation_1-rmse:2.61390
[94]	validation_0-rmse:2.58544	validation_1-rmse:2.61052
[95]	validation_0-rmse:2.57463	validation_1-rmse:2.59979
[96]	validation_0-rmse:2.57175	validation_1-rmse:2.59711
[97]	validation_0-rmse:2.55956	validation_1-rmse:2.58451
[98]	validation_0-rmse:2.54847	validation_1-rmse:2.57430
[99]	validation_0-rmse:2.54220	validation_1-rmse:2.56801
[100]	validation_0-rmse:2.53349	validation_1-rmse:2.55976
[101]	validation_0-rmse:2.53133	validation_1-rmse:2.55801
[102]	validation_0-rmse:2.52103	validation_1-rmse:2.54756
[103]	validation_0-rmse:2.51853	validation_1-rmse:2.54547
[104]	validation_0-rmse:2.51121	validation_1-rmse:2.53737
[105]	validation_0-rmse:2.50764	validation_1-rmse:2.53411
[106]	validation_0-rmse:2.49934	validation_1-rmse:2.52674
[107]	validation_0-rmse:2.49257	validation_1-rmse:2.52014
[108]	validation_0-rmse:2.49007	validation_1-rmse:2.51794
[109]	validation_0-rmse:2.47947	validation_1-rmse:2.50725
[110]	validation_0-rmse:2.47557	validation_1-rmse:2.50321
[111]	validation_0-rmse:2.47347	validation_1-rmse:2.50169
[112]	validation_0-rmse:2.47012	validation_1-rmse:2.49835
[113]	validation_0-rmse:2.45869	validation_1-rmse:2.48689
[114]	validation_0-rmse:2.45682	validation_1-rmse:2.48538
[115]	validation_0-rmse:2.45074	validation_1-rmse:2.47955
[116]	validation_0-rmse:2.44637	validation_1-rmse:2.47534
[117]	validation_0-rmse:2.44385	validation_1-rmse:2.47297
[118]	validation_0-rmse:2.43498	validation_1-rmse:2.46445
[119]	validation_0-rmse:2.42655	validation_1-rmse:2.45652
[120]	validation_0-rmse:2.41737	validation_1-rmse:2.44705
[121]	validation_0-rmse:2.41550	validation_1-rmse:2.44549
[122]	validation_0-rmse:2.41321	validation_1-rmse:2.44334
[123]	validation_0-rmse:2.41212	validation_1-rmse:2.44256
[124]	validation_0-rmse:2.40471	validation_1-rmse:2.43540
[125]	validation_0-rmse:2.40250	validation_1-rmse:2.43349
[126]	validation_0-rmse:2.40161	validation_1-rmse:2.43266
[127]	validation_0-rmse:2.39600	validation_1-rmse:2.42747
[128]	validation_0-rmse:2.39119	validation_1-rmse:2.42244
[129]	validation_0-rmse:2.38404	validation_1-rmse:2.41543
[130]	validation_0-rmse:2.38223	validation_1-rmse:2.41393
[131]	validation_0-rmse:2.37190	validation_1-rmse:2.40485
[132]	validation_0-rmse:2.37003	validation_1-rmse:2.40305
[133]	validation_0-rmse:2.36219	validation_1-rmse:2.39608
[134]	validation_0-rmse:2.35345	validation_1-rmse:2.38728
[135]	validation_0-rmse:2.34430	validation_1-rmse:2.37884
[136]	validation_0-rmse:2.33953	validation_1-rmse:2.37433
[137]	validation_0-rmse:2.33757	validation_1-rmse:2.37269
[138]	validation_0-rmse:2.33316	validation_1-rmse:2.36823
[139]	validation_0-rmse:2.33074	validation_1-rmse:2.36613
[140]	validation_0-rmse:2.32655	validation_1-rmse:2.36229
[141]	validation_0-rmse:2.32458	validation_1-rmse:2.36125
[142]	validation_0-rmse:2.32035	validation_1-rmse:2.35722
[143]	validation_0-rmse:2.31503	validation_1-rmse:2.35205
[144]	validation_0-rmse:2.31126	validation_1-rmse:2.34834
[145]	validation_0-rmse:2.30752	validation_1-rmse:2.34460
[146]	validation_0-rmse:2.30463	validation_1-rmse:2.34169
[147]	validation_0-rmse:2.29989	validation_1-rmse:2.33681
[148]	validation_0-rmse:2.29312	validation_1-rmse:2.32986
[149]	validation_0-rmse:2.29181	validation_1-rmse:2.32858
[150]	validation_0-rmse:2.28623	validation_1-rmse:2.32354
[151]	validation_0-rmse:2.27900	validation_1-rmse:2.31647
[152]	validation_0-rmse:2.27624	validation_1-rmse:2.31396
[153]	validation_0-rmse:2.27068	validation_1-rmse:2.30818
[154]	validation_0-rmse:2.26920	validation_1-rmse:2.30674
[155]	validation_0-rmse:2.26771	validation_1-rmse:2.30550
[156]	validation_0-rmse:2.26088	validation_1-rmse:2.29845
[157]	validation_0-rmse:2.25798	validation_1-rmse:2.29557
[158]	validation_0-rmse:2.25226	validation_1-rmse:2.28941
[159]	validation_0-rmse:2.24870	validation_1-rmse:2.28609
[160]	validation_0-rmse:2.24132	validation_1-rmse:2.27895
[161]	validation_0-rmse:2.23994	validation_1-rmse:2.27789
[162]	validation_0-rmse:2.23817	validation_1-rmse:2.27710
[163]	validation_0-rmse:2.23166	validation_1-rmse:2.27077
[164]	validation_0-rmse:2.23082	validation_1-rmse:2.27028
[165]	validation_0-rmse:2.22669	validation_1-rmse:2.26599
[166]	validation_0-rmse:2.22434	validation_1-rmse:2.26316
[167]	validation_0-rmse:2.22059	validation_1-rmse:2.25953
[168]	validation_0-rmse:2.21480	validation_1-rmse:2.25427
[169]	validation_0-rmse:2.21276	validation_1-rmse:2.25278
[170]	validation_0-rmse:2.20763	validation_1-rmse:2.24757
[171]	validation_0-rmse:2.20004	validation_1-rmse:2.24007
[172]	validation_0-rmse:2.19899	validation_1-rmse:2.23945
[173]	validation_0-rmse:2.19512	validation_1-rmse:2.23599
[174]	validation_0-rmse:2.19348	validation_1-rmse:2.23453
[175]	validation_0-rmse:2.19069	validation_1-rmse:2.23178
[176]	validation_0-rmse:2.18448	validation_1-rmse:2.22604
[177]	validation_0-rmse:2.17771	validation_1-rmse:2.21873
[178]	validation_0-rmse:2.17381	validation_1-rmse:2.21481
[179]	validation_0-rmse:2.17187	validation_1-rmse:2.21321
[180]	validation_0-rmse:2.16780	validation_1-rmse:2.20935
[181]	validation_0-rmse:2.16471	validation_1-rmse:2.20641
[182]	validation_0-rmse:2.16216	validation_1-rmse:2.20397
[183]	validation_0-rmse:2.15947	validation_1-rmse:2.20166
[184]	validation_0-rmse:2.15480	validation_1-rmse:2.19698
[185]	validation_0-rmse:2.15355	validation_1-rmse:2.19598
[186]	validation_0-rmse:2.14845	validation_1-rmse:2.19047
[187]	validation_0-rmse:2.14263	validation_1-rmse:2.18505
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[743]	validation_0-rmse:1.44227	validation_1-rmse:1.57703
[744]	validation_0-rmse:1.44164	validation_1-rmse:1.57648
[745]	validation_0-rmse:1.44096	validation_1-rmse:1.57580
[746]	validation_0-rmse:1.44031	validation_1-rmse:1.57547
[747]	validation_0-rmse:1.43938	validation_1-rmse:1.57462
[748]	validation_0-rmse:1.43911	validation_1-rmse:1.57458
[749]	validation_0-rmse:1.43861	validation_1-rmse:1.57413
[750]	validation_0-rmse:1.43851	validation_1-rmse:1.57411
[751]	validation_0-rmse:1.43759	validation_1-rmse:1.57339
[752]	validation_0-rmse:1.43708	validation_1-rmse:1.57312
[753]	validation_0-rmse:1.43696	validation_1-rmse:1.57309
[754]	validation_0-rmse:1.43568	validation_1-rmse:1.57174
[755]	validation_0-rmse:1.43520	validation_1-rmse:1.57124
[756]	validation_0-rmse:1.43497	validation_1-rmse:1.57121
[757]	validation_0-rmse:1.43429	validation_1-rmse:1.57064
[758]	validation_0-rmse:1.43402	validation_1-rmse:1.57051
[759]	validation_0-rmse:1.43379	validation_1-rmse:1.57051
[760]	validation_0-rmse:1.43316	validation_1-rmse:1.56988
[761]	validation_0-rmse:1.43247	validation_1-rmse:1.56920
[762]	validation_0-rmse:1.43177	validation_1-rmse:1.56847
[763]	validation_0-rmse:1.43067	validation_1-rmse:1.56735
[764]	validation_0-rmse:1.43036	validation_1-rmse:1.56710
[765]	validation_0-rmse:1.43011	validation_1-rmse:1.56701
[766]	validation_0-rmse:1.42998	validation_1-rmse:1.56701
[767]	validation_0-rmse:1.42942	validation_1-rmse:1.56671
[768]	validation_0-rmse:1.42911	validation_1-rmse:1.56656
[769]	validation_0-rmse:1.42861	validation_1-rmse:1.56609
[770]	validation_0-rmse:1.42824	validation_1-rmse:1.56578
[771]	validation_0-rmse:1.42779	validation_1-rmse:1.56535
[772]	validation_0-rmse:1.42740	validation_1-rmse:1.56510
[773]	validation_0-rmse:1.42708	validation_1-rmse:1.56503
[774]	validation_0-rmse:1.42621	validation_1-rmse:1.56429
[775]	validation_0-rmse:1.42568	validation_1-rmse:1.56399
[776]	validation_0-rmse:1.42530	validation_1-rmse:1.56372
[777]	validation_0-rmse:1.42453	validation_1-rmse:1.56293
[778]	validation_0-rmse:1.42436	validation_1-rmse:1.56282
[779]	validation_0-rmse:1.42385	validation_1-rmse:1.56235
[780]	validation_0-rmse:1.42335	validation_1-rmse:1.56198
[781]	validation_0-rmse:1.42275	validation_1-rmse:1.56144
[782]	validation_0-rmse:1.42241	validation_1-rmse:1.56134
[783]	validation_0-rmse:1.42186	validation_1-rmse:1.56078
[784]	validation_0-rmse:1.42150	validation_1-rmse:1.56051
[785]	validation_0-rmse:1.42131	validation_1-rmse:1.56046
[786]	validation_0-rmse:1.42060	validation_1-rmse:1.55988
[787]	validation_0-rmse:1.42042	validation_1-rmse:1.55993
[788]	validation_0-rmse:1.42025	validation_1-rmse:1.55979
[789]	validation_0-rmse:1.41975	validation_1-rmse:1.55938
[790]	validation_0-rmse:1.41896	validation_1-rmse:1.55863
[791]	validation_0-rmse:1.41875	validation_1-rmse:1.55850
[792]	validation_0-rmse:1.41859	validation_1-rmse:1.55841
[793]	validation_0-rmse:1.41795	validation_1-rmse:1.55789
[794]	validation_0-rmse:1.41769	validation_1-rmse:1.55785
[795]	validation_0-rmse:1.41756	validation_1-rmse:1.55782
[796]	validation_0-rmse:1.41715	validation_1-rmse:1.55757
[797]	validation_0-rmse:1.41692	validation_1-rmse:1.55741
[798]	validation_0-rmse:1.41665	validation_1-rmse:1.55722
[799]	validation_0-rmse:1.41626	validation_1-rmse:1.55683
[800]	validation_0-rmse:1.41553	validation_1-rmse:1.55615
[801]	validation_0-rmse:1.41525	validation_1-rmse:1.55611
[802]	validation_0-rmse:1.41478	validation_1-rmse:1.55571
[803]	validation_0-rmse:1.41458	validation_1-rmse:1.55573
[804]	validation_0-rmse:1.41407	validation_1-rmse:1.55545
[805]	validation_0-rmse:1.41382	validation_1-rmse:1.55541
[806]	validation_0-rmse:1.41280	validation_1-rmse:1.55443
[807]	validation_0-rmse:1.41194	validation_1-rmse:1.55367
[808]	validation_0-rmse:1.41174	validation_1-rmse:1.55363
[809]	validation_0-rmse:1.41144	validation_1-rmse:1.55336
[810]	validation_0-rmse:1.41132	validation_1-rmse:1.55330
[811]	validation_0-rmse:1.41061	validation_1-rmse:1.55268
[812]	validation_0-rmse:1.41040	validation_1-rmse:1.55265
[813]	validation_0-rmse:1.41020	validation_1-rmse:1.55250
[814]	validation_0-rmse:1.40989	validation_1-rmse:1.55250
[815]	validation_0-rmse:1.40925	validation_1-rmse:1.55202
[816]	validation_0-rmse:1.40868	validation_1-rmse:1.55162
[817]	validation_0-rmse:1.40838	validation_1-rmse:1.55151
[818]	validation_0-rmse:1.40807	validation_1-rmse:1.55148
[819]	validation_0-rmse:1.40740	validation_1-rmse:1.55106
[820]	validation_0-rmse:1.40715	validation_1-rmse:1.55110
[821]	validation_0-rmse:1.40706	validation_1-rmse:1.55100
[822]	validation_0-rmse:1.40654	validation_1-rmse:1.55074
[823]	validation_0-rmse:1.40595	validation_1-rmse:1.55025
[824]	validation_0-rmse:1.40470	validation_1-rmse:1.54914
[825]	validation_0-rmse:1.40415	validation_1-rmse:1.54876
[826]	validation_0-rmse:1.40371	validation_1-rmse:1.54846
[827]	validation_0-rmse:1.40349	validation_1-rmse:1.54844
[828]	validation_0-rmse:1.40307	validation_1-rmse:1.54815
[829]	validation_0-rmse:1.40286	validation_1-rmse:1.54797
[830]	validation_0-rmse:1.40224	validation_1-rmse:1.54751
[831]	validation_0-rmse:1.40193	validation_1-rmse:1.54722
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[833]	validation_0-rmse:1.40144	validation_1-rmse:1.54719
[834]	validation_0-rmse:1.40087	validation_1-rmse:1.54666
[835]	validation_0-rmse:1.40053	validation_1-rmse:1.54643
[836]	validation_0-rmse:1.40015	validation_1-rmse:1.54625
[837]	validation_0-rmse:1.39945	validation_1-rmse:1.54577
[838]	validation_0-rmse:1.39900	validation_1-rmse:1.54537
[839]	validation_0-rmse:1.39871	validation_1-rmse:1.54517
[840]	validation_0-rmse:1.39840	validation_1-rmse:1.54499
[841]	validation_0-rmse:1.39796	validation_1-rmse:1.54458
[842]	validation_0-rmse:1.39764	validation_1-rmse:1.54451
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[845]	validation_0-rmse:1.39677	validation_1-rmse:1.54414
[846]	validation_0-rmse:1.39615	validation_1-rmse:1.54352
[847]	validation_0-rmse:1.39599	validation_1-rmse:1.54349
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[851]	validation_0-rmse:1.39495	validation_1-rmse:1.54300
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[860]	validation_0-rmse:1.39136	validation_1-rmse:1.54065
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[862]	validation_0-rmse:1.38971	validation_1-rmse:1.53905
[863]	validation_0-rmse:1.38922	validation_1-rmse:1.53877
[864]	validation_0-rmse:1.38872	validation_1-rmse:1.53836
[865]	validation_0-rmse:1.38855	validation_1-rmse:1.53834
[866]	validation_0-rmse:1.38808	validation_1-rmse:1.53805
[867]	validation_0-rmse:1.38791	validation_1-rmse:1.53794
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[874]	validation_0-rmse:1.38478	validation_1-rmse:1.53609
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[880]	validation_0-rmse:1.38199	validation_1-rmse:1.53392
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[889]	validation_0-rmse:1.37856	validation_1-rmse:1.53172
[890]	validation_0-rmse:1.37797	validation_1-rmse:1.53116
[891]	validation_0-rmse:1.37752	validation_1-rmse:1.53078
[892]	validation_0-rmse:1.37711	validation_1-rmse:1.53053
[893]	validation_0-rmse:1.37691	validation_1-rmse:1.53042
[894]	validation_0-rmse:1.37667	validation_1-rmse:1.53031
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[896]	validation_0-rmse:1.37576	validation_1-rmse:1.52958
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[898]	validation_0-rmse:1.37448	validation_1-rmse:1.52853
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[900]	validation_0-rmse:1.37382	validation_1-rmse:1.52795
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[910]	validation_0-rmse:1.36976	validation_1-rmse:1.52474
[911]	validation_0-rmse:1.36898	validation_1-rmse:1.52428
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[915]	validation_0-rmse:1.36702	validation_1-rmse:1.52282
[916]	validation_0-rmse:1.36638	validation_1-rmse:1.52237
[917]	validation_0-rmse:1.36609	validation_1-rmse:1.52226
[918]	validation_0-rmse:1.36582	validation_1-rmse:1.52209
[919]	validation_0-rmse:1.36525	validation_1-rmse:1.52172
[920]	validation_0-rmse:1.36446	validation_1-rmse:1.52087
[921]	validation_0-rmse:1.36407	validation_1-rmse:1.52053
[922]	validation_0-rmse:1.36340	validation_1-rmse:1.52011
[923]	validation_0-rmse:1.36328	validation_1-rmse:1.52012
[924]	validation_0-rmse:1.36300	validation_1-rmse:1.51989
[925]	validation_0-rmse:1.36259	validation_1-rmse:1.51962
[926]	validation_0-rmse:1.36222	validation_1-rmse:1.51938
[927]	validation_0-rmse:1.36198	validation_1-rmse:1.51927
[928]	validation_0-rmse:1.36151	validation_1-rmse:1.51892
[929]	validation_0-rmse:1.36053	validation_1-rmse:1.51778
[930]	validation_0-rmse:1.36017	validation_1-rmse:1.51779
[931]	validation_0-rmse:1.35998	validation_1-rmse:1.51777
[932]	validation_0-rmse:1.35978	validation_1-rmse:1.51761
[933]	validation_0-rmse:1.35947	validation_1-rmse:1.51744
[934]	validation_0-rmse:1.35867	validation_1-rmse:1.51666
[935]	validation_0-rmse:1.35843	validation_1-rmse:1.51663
[936]	validation_0-rmse:1.35812	validation_1-rmse:1.51638
[937]	validation_0-rmse:1.35799	validation_1-rmse:1.51642
[938]	validation_0-rmse:1.35708	validation_1-rmse:1.51551
[939]	validation_0-rmse:1.35671	validation_1-rmse:1.51523
[940]	validation_0-rmse:1.35609	validation_1-rmse:1.51475
[941]	validation_0-rmse:1.35584	validation_1-rmse:1.51464
[942]	validation_0-rmse:1.35567	validation_1-rmse:1.51455
[943]	validation_0-rmse:1.35541	validation_1-rmse:1.51438
[944]	validation_0-rmse:1.35512	validation_1-rmse:1.51426
[945]	validation_0-rmse:1.35502	validation_1-rmse:1.51417
[946]	validation_0-rmse:1.35484	validation_1-rmse:1.51417
[947]	validation_0-rmse:1.35413	validation_1-rmse:1.51355
[948]	validation_0-rmse:1.35374	validation_1-rmse:1.51324
[949]	validation_0-rmse:1.35333	validation_1-rmse:1.51297
[950]	validation_0-rmse:1.35296	validation_1-rmse:1.51273
[951]	validation_0-rmse:1.35277	validation_1-rmse:1.51275
[952]	validation_0-rmse:1.35268	validation_1-rmse:1.51266
[953]	validation_0-rmse:1.35202	validation_1-rmse:1.51194
[954]	validation_0-rmse:1.35185	validation_1-rmse:1.51183
[955]	validation_0-rmse:1.35159	validation_1-rmse:1.51160
[956]	validation_0-rmse:1.35122	validation_1-rmse:1.51146
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[962]	validation_0-rmse:1.34908	validation_1-rmse:1.51017
[963]	validation_0-rmse:1.34855	validation_1-rmse:1.50994
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[967]	validation_0-rmse:1.34771	validation_1-rmse:1.50938
[968]	validation_0-rmse:1.34717	validation_1-rmse:1.50900
[969]	validation_0-rmse:1.34652	validation_1-rmse:1.50860
[970]	validation_0-rmse:1.34620	validation_1-rmse:1.50831
[971]	validation_0-rmse:1.34561	validation_1-rmse:1.50785
[972]	validation_0-rmse:1.34511	validation_1-rmse:1.50742
[973]	validation_0-rmse:1.34454	validation_1-rmse:1.50692
[974]	validation_0-rmse:1.34377	validation_1-rmse:1.50629
[975]	validation_0-rmse:1.34370	validation_1-rmse:1.50626
[976]	validation_0-rmse:1.34358	validation_1-rmse:1.50610
[977]	validation_0-rmse:1.34325	validation_1-rmse:1.50583
[978]	validation_0-rmse:1.34309	validation_1-rmse:1.50583
[979]	validation_0-rmse:1.34235	validation_1-rmse:1.50517
[980]	validation_0-rmse:1.34209	validation_1-rmse:1.50500
[981]	validation_0-rmse:1.34165	validation_1-rmse:1.50451
[982]	validation_0-rmse:1.34127	validation_1-rmse:1.50424
[983]	validation_0-rmse:1.34105	validation_1-rmse:1.50403
[984]	validation_0-rmse:1.34092	validation_1-rmse:1.50404
[985]	validation_0-rmse:1.34058	validation_1-rmse:1.50375
[986]	validation_0-rmse:1.34045	validation_1-rmse:1.50377
[987]	validation_0-rmse:1.33952	validation_1-rmse:1.50302
[988]	validation_0-rmse:1.33908	validation_1-rmse:1.50269
[989]	validation_0-rmse:1.33877	validation_1-rmse:1.50240
[990]	validation_0-rmse:1.33850	validation_1-rmse:1.50242
[991]	validation_0-rmse:1.33817	validation_1-rmse:1.50233
[992]	validation_0-rmse:1.33791	validation_1-rmse:1.50218
[993]	validation_0-rmse:1.33784	validation_1-rmse:1.50220
[994]	validation_0-rmse:1.33756	validation_1-rmse:1.50206
[995]	validation_0-rmse:1.33720	validation_1-rmse:1.50177
[996]	validation_0-rmse:1.33659	validation_1-rmse:1.50131
[997]	validation_0-rmse:1.33639	validation_1-rmse:1.50116
[998]	validation_0-rmse:1.33634	validation_1-rmse:1.50118
[999]	validation_0-rmse:1.33556	validation_1-rmse:1.50045
>>> Using model to predict target TA_T1_47_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_47_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

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

## DATA
  > target: TA_T1_47_1
  > features: 12 ['SW_IN_T1_47_1_gfXG', 'SW_IN_POT', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
  > 355563 records (with missing)
  > 352760 available records for target and all features (no missing values)
  > training on 264570 records (75.0%) of 264570 features between 2004-09-20 11:15:00 and 2024-12-31 23:15:00
  > testing on 88190 unseen records (25.0%) of TA_T1_47_1 between 2004-09-20 10:45:00 and 2024-12-31 23:45:00

## MODEL
  > the model was trained on training data (264570 records)
  > the model was tested on test data (88190 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,
             feature_weights=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, ...)
  > 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, 'feature_weights': 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:  ['SW_IN_T1_47_1_gfXG', 'SW_IN_POT', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of TA_T1_47_1 (88190 records).
  > feature importances are showing permutation importances from 10 repeats

                    PERM_IMPORTANCE   PERM_SD
.DOY                       0.742518  0.002396
.SEASON                    0.333028  0.002007
.YEARDOY                   0.264844  0.001089
.YEARWEEK                  0.245494  0.001338
.YEARMONTH                 0.216788  0.001340
.RECORDNUMBER              0.216698  0.000982
SW_IN_T1_47_1_gfXG         0.139915  0.000701
.WEEK                      0.138766  0.000669
.HOUR                      0.082271  0.000341
.YEAR                      0.043126  0.000147
SW_IN_POT                  0.037201  0.000330
.MONTH                     0.020968  0.000138


## MODEL SCORES
  All scores were calculated based on unseen test data (88190 records).
  > MAE:  1.1185057061025927 (mean absolute error)
  > MedAE:  0.8504686623535154 (median absolute error)
  > MSE:  2.251352171611588 (mean squared error)
  > RMSE:  1.5004506561735338 (root mean squared error)
  > MAXE:  9.514570236206055 (max error)
  > MAPE:  312396304077.647 (mean absolute percentage error)
  > R2:  0.966197689684095

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_47_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting TA_T1_47_1 ...
>>> Predicting target TA_T1_47_1 where all features are available ... predicted 355563 records.
>>> Collecting results for final model ...
>>> Filling 2803 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable TA_T1_47_1_gfXG ...
>>> Restoring original timestamp in results ...

Gap-filling 0 remaining missing records in TA_T1_47_1_gfXG using fallback model ...
>>> Fallback model not necessary, all gaps were already filled.
>>> Combining predictions from full model and fallback model ...

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

Model scores and feature importances were calculated from high-quality predicted targets (2803 values, TA_T1_47_1_gfXG where flag=1) in comparison to observed targets (352760 values, TA_T1_47_1).

## TARGET
- first timestamp:  2004-09-20 10:45:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 355563 values)
- target column (observed):  TA_T1_47_1
- missing records (observed):  2803 (cross-check from flag: 2803)
- target column (gap-filled):  TA_T1_47_1_gfXG  (355563 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_TA_T1_47_1_gfXG_ISFILLED
  > flag 0 ... observed targets (352760 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (2803 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['.DOY', '.SEASON', '.YEARDOY', '.YEARWEEK', '.YEARMONTH', '.RECORDNUMBER', 'SW_IN_T1_47_1_gfXG', '.WEEK', '.HOUR', '.YEAR', 'SW_IN_POT', '.MONTH']
- number of features used in model:  12
- permutation importances were calculated from 10 repeats.

                    PERM_IMPORTANCE   PERM_SD
.DOY                       0.743867  0.001589
.SEASON                    0.333421  0.000901
.YEARDOY                   0.265450  0.000477
.YEARWEEK                  0.244946  0.000648
.YEARMONTH                 0.216878  0.000345
.RECORDNUMBER              0.216536  0.000392
SW_IN_T1_47_1_gfXG         0.142530  0.000422
.WEEK                      0.139645  0.000186
.HOUR                      0.084286  0.000305
.YEAR                      0.043269  0.000133
SW_IN_POT                  0.038834  0.000130
.MONTH                     0.021271  0.000059

## 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,
             feature_weights=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, ...)
- 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, 'feature_weights': 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:  1.020740021008993 (mean absolute error)
- MedAE:  0.7665551081299782 (median absolute error)
- MSE:  1.9006196807945184 (mean squared error)
- RMSE:  1.3786296387335208 (root mean squared error)
- MAXE:  9.514570236206055 (max error)
- MAPE:  105159731189.152 (mean absolute percentage error)
- R2:  0.9715286200815227

observed = _df[VAR]
gapfilled = xgb.get_gapfilled_target()
data_simple_2004_2024[gapfilled.name] = gapfilled

title=f"Air temperature {VAR}"
fig, ax = plt.subplots()
TimeSeries(series=gapfilled, ax=ax).plot(color='blue')
TimeSeries(series=observed, ax=ax).plot(color='orange')
fig.suptitle(f'{title}', fontsize=16)
# ax.set_ylim(-2000, 200)
fig.show()

PPFD_IN_T1_47_1

VAR = "PPFD_IN_T1_47_1"

_df = pd.DataFrame()
_df[VAR] = data_simple_2004_2024[[VAR]].copy()
_df['SW_IN_T1_47_1_gfXG'] = data_simple_2004_2024[['SW_IN_T1_47_1_gfXG']].copy()
_df['SW_IN_POT'] = potrad(timestamp_index=_df.index, lat=SITE_LAT, lon=SITE_LON, utc_offset=1)
_df

	PPFD_IN_T1_47_1	SW_IN_T1_47_1_gfXG	SW_IN_POT
TIMESTAMP_MIDDLE
2004-09-20 10:45:00	1070.543364	602.874084	833.243912
2004-09-20 11:15:00	881.543364	703.762207	877.607230
2004-09-20 11:45:00	1022.543364	537.947327	906.992208
2004-09-20 12:15:00	1284.543364	634.747437	920.896059
2004-09-20 12:45:00	742.543364	634.747437	919.080885
...	...	...	...
2024-12-31 21:45:00	0.000000	0.000000	0.000000
2024-12-31 22:15:00	0.000000	0.000000	0.000000
2024-12-31 22:45:00	0.000000	0.000000	0.000000
2024-12-31 23:15:00	0.000000	0.000000	0.000000
2024-12-31 23:45:00	0.000000	0.000000	0.000000

355563 rows × 3 columns

xgb = XGBoostTS(
    input_df=_df,
    target_col=VAR,
    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 355563.

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

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2004-09-20 11:15:00 and 2024-12-31 23:45:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:345.02626	validation_1-rmse:344.00697
[1]	validation_0-rmse:246.73296	validation_1-rmse:246.29028
[2]	validation_0-rmse:179.59376	validation_1-rmse:179.74638
[3]	validation_0-rmse:134.65078	validation_1-rmse:135.46387
[4]	validation_0-rmse:105.47418	validation_1-rmse:106.77603
[5]	validation_0-rmse:87.35770	validation_1-rmse:89.15519
[6]	validation_0-rmse:76.08535	validation_1-rmse:78.44043
[7]	validation_0-rmse:69.66377	validation_1-rmse:72.43137
[8]	validation_0-rmse:65.70490	validation_1-rmse:68.62900
[9]	validation_0-rmse:63.02954	validation_1-rmse:66.05020
[10]	validation_0-rmse:61.06255	validation_1-rmse:64.20536
[11]	validation_0-rmse:59.74582	validation_1-rmse:63.01348
[12]	validation_0-rmse:58.47759	validation_1-rmse:62.10080
[13]	validation_0-rmse:57.21922	validation_1-rmse:60.94420
[14]	validation_0-rmse:56.38522	validation_1-rmse:60.22034
[15]	validation_0-rmse:55.75317	validation_1-rmse:59.87149
[16]	validation_0-rmse:55.43550	validation_1-rmse:59.70493
[17]	validation_0-rmse:55.13051	validation_1-rmse:59.57689
[18]	validation_0-rmse:54.94301	validation_1-rmse:59.41459
[19]	validation_0-rmse:54.65417	validation_1-rmse:59.33104
[20]	validation_0-rmse:53.96736	validation_1-rmse:58.65397
[21]	validation_0-rmse:53.87984	validation_1-rmse:58.57265
[22]	validation_0-rmse:52.73182	validation_1-rmse:57.61891
[23]	validation_0-rmse:52.59277	validation_1-rmse:57.64690
[24]	validation_0-rmse:52.34813	validation_1-rmse:57.52821
[25]	validation_0-rmse:51.85126	validation_1-rmse:57.25236
[26]	validation_0-rmse:51.67608	validation_1-rmse:57.11152
[27]	validation_0-rmse:51.12792	validation_1-rmse:56.74801
[28]	validation_0-rmse:50.53346	validation_1-rmse:56.33365
[29]	validation_0-rmse:50.05007	validation_1-rmse:55.91090
[30]	validation_0-rmse:49.83705	validation_1-rmse:55.74623
[31]	validation_0-rmse:49.71664	validation_1-rmse:55.71856
[32]	validation_0-rmse:49.50926	validation_1-rmse:55.65689
[33]	validation_0-rmse:49.06808	validation_1-rmse:55.34899
[34]	validation_0-rmse:48.83062	validation_1-rmse:55.30314
[35]	validation_0-rmse:48.67843	validation_1-rmse:55.33807
[36]	validation_0-rmse:48.40345	validation_1-rmse:55.07801
[37]	validation_0-rmse:48.12925	validation_1-rmse:54.85482
[38]	validation_0-rmse:47.92910	validation_1-rmse:54.78730
[39]	validation_0-rmse:47.84584	validation_1-rmse:54.73876
[40]	validation_0-rmse:47.68689	validation_1-rmse:54.63083
[41]	validation_0-rmse:47.49961	validation_1-rmse:54.48791
[42]	validation_0-rmse:47.20620	validation_1-rmse:54.21836
[43]	validation_0-rmse:46.77933	validation_1-rmse:54.00141
[44]	validation_0-rmse:46.71896	validation_1-rmse:53.95038
[45]	validation_0-rmse:46.51116	validation_1-rmse:53.90092
[46]	validation_0-rmse:46.45740	validation_1-rmse:53.91227
[47]	validation_0-rmse:46.31197	validation_1-rmse:53.82235
[48]	validation_0-rmse:46.24166	validation_1-rmse:53.78486
[49]	validation_0-rmse:46.00910	validation_1-rmse:53.68474
[50]	validation_0-rmse:45.93529	validation_1-rmse:53.64677
[51]	validation_0-rmse:45.90554	validation_1-rmse:53.62565
[52]	validation_0-rmse:45.75843	validation_1-rmse:53.53241
[53]	validation_0-rmse:45.54709	validation_1-rmse:53.38656
[54]	validation_0-rmse:45.43003	validation_1-rmse:53.39279
[55]	validation_0-rmse:45.36603	validation_1-rmse:53.39969
[56]	validation_0-rmse:45.23261	validation_1-rmse:53.48526
[57]	validation_0-rmse:45.09664	validation_1-rmse:53.49819
[58]	validation_0-rmse:45.05641	validation_1-rmse:53.50598
[59]	validation_0-rmse:44.98474	validation_1-rmse:53.47484
[60]	validation_0-rmse:44.87090	validation_1-rmse:53.45712
[61]	validation_0-rmse:44.56788	validation_1-rmse:53.23201
[62]	validation_0-rmse:44.46200	validation_1-rmse:53.23777
[63]	validation_0-rmse:44.42492	validation_1-rmse:53.23651
[64]	validation_0-rmse:44.31855	validation_1-rmse:53.15397
[65]	validation_0-rmse:44.27119	validation_1-rmse:53.12485
[66]	validation_0-rmse:44.11087	validation_1-rmse:53.02572
[67]	validation_0-rmse:43.88094	validation_1-rmse:52.97415
[68]	validation_0-rmse:43.71863	validation_1-rmse:52.88112
[69]	validation_0-rmse:43.55819	validation_1-rmse:52.88232
[70]	validation_0-rmse:43.51547	validation_1-rmse:52.89075
[71]	validation_0-rmse:43.42595	validation_1-rmse:52.83475
[72]	validation_0-rmse:43.30082	validation_1-rmse:52.83024
[73]	validation_0-rmse:42.89616	validation_1-rmse:52.61248
[74]	validation_0-rmse:42.57619	validation_1-rmse:52.44232
[75]	validation_0-rmse:42.52957	validation_1-rmse:52.44156
[76]	validation_0-rmse:42.36026	validation_1-rmse:52.45530
[77]	validation_0-rmse:42.06564	validation_1-rmse:52.18803
[78]	validation_0-rmse:42.00446	validation_1-rmse:52.23584
[79]	validation_0-rmse:41.93038	validation_1-rmse:52.17908
[80]	validation_0-rmse:41.85547	validation_1-rmse:52.15032
[81]	validation_0-rmse:41.70422	validation_1-rmse:52.07948
[82]	validation_0-rmse:41.42601	validation_1-rmse:51.94037
[83]	validation_0-rmse:41.40305	validation_1-rmse:51.91213
[84]	validation_0-rmse:41.25705	validation_1-rmse:51.87166
[85]	validation_0-rmse:41.22996	validation_1-rmse:51.86439
[86]	validation_0-rmse:41.15973	validation_1-rmse:51.85123
[87]	validation_0-rmse:41.11550	validation_1-rmse:51.83025
[88]	validation_0-rmse:41.05363	validation_1-rmse:51.80492
[89]	validation_0-rmse:40.85506	validation_1-rmse:51.71098
[90]	validation_0-rmse:40.71617	validation_1-rmse:51.68527
[91]	validation_0-rmse:40.49551	validation_1-rmse:51.69049
[92]	validation_0-rmse:40.23548	validation_1-rmse:51.51964
[93]	validation_0-rmse:40.18137	validation_1-rmse:51.49157
[94]	validation_0-rmse:39.96668	validation_1-rmse:51.38905
[95]	validation_0-rmse:39.84164	validation_1-rmse:51.30373
[96]	validation_0-rmse:39.81565	validation_1-rmse:51.29528
[97]	validation_0-rmse:39.72632	validation_1-rmse:51.23547
[98]	validation_0-rmse:39.45788	validation_1-rmse:51.00901
[99]	validation_0-rmse:39.35448	validation_1-rmse:50.98372
[100]	validation_0-rmse:39.33905	validation_1-rmse:50.97601
[101]	validation_0-rmse:39.21889	validation_1-rmse:50.94378
[102]	validation_0-rmse:39.16570	validation_1-rmse:50.95861
[103]	validation_0-rmse:39.14472	validation_1-rmse:50.94352
[104]	validation_0-rmse:38.96918	validation_1-rmse:50.95608
[105]	validation_0-rmse:38.94145	validation_1-rmse:50.93262
[106]	validation_0-rmse:38.86391	validation_1-rmse:50.97274
[107]	validation_0-rmse:38.82606	validation_1-rmse:50.97069
[108]	validation_0-rmse:38.70210	validation_1-rmse:51.02364
[109]	validation_0-rmse:38.64186	validation_1-rmse:50.99778
[110]	validation_0-rmse:38.59211	validation_1-rmse:50.96337
[111]	validation_0-rmse:38.47447	validation_1-rmse:50.96566
[112]	validation_0-rmse:38.41205	validation_1-rmse:50.92760
[113]	validation_0-rmse:38.36680	validation_1-rmse:50.90260
[114]	validation_0-rmse:38.32314	validation_1-rmse:50.89171
[115]	validation_0-rmse:38.29775	validation_1-rmse:50.89386
[116]	validation_0-rmse:38.16698	validation_1-rmse:50.95731
[117]	validation_0-rmse:38.13489	validation_1-rmse:50.93807
[118]	validation_0-rmse:38.05960	validation_1-rmse:50.86977
[119]	validation_0-rmse:37.94131	validation_1-rmse:50.83351
[120]	validation_0-rmse:37.79453	validation_1-rmse:50.78211
[121]	validation_0-rmse:37.66642	validation_1-rmse:50.75449
[122]	validation_0-rmse:37.61733	validation_1-rmse:50.73669
[123]	validation_0-rmse:37.53620	validation_1-rmse:50.68573
[124]	validation_0-rmse:37.50243	validation_1-rmse:50.66582
[125]	validation_0-rmse:37.38659	validation_1-rmse:50.71398
[126]	validation_0-rmse:37.29729	validation_1-rmse:50.67625
[127]	validation_0-rmse:37.24932	validation_1-rmse:50.65254
[128]	validation_0-rmse:37.17825	validation_1-rmse:50.64111
[129]	validation_0-rmse:37.10586	validation_1-rmse:50.60028
[130]	validation_0-rmse:37.07240	validation_1-rmse:50.58481
[131]	validation_0-rmse:37.00299	validation_1-rmse:50.56951
[132]	validation_0-rmse:36.90409	validation_1-rmse:50.56458
[133]	validation_0-rmse:36.84972	validation_1-rmse:50.54170
[134]	validation_0-rmse:36.82043	validation_1-rmse:50.51697
[135]	validation_0-rmse:36.71436	validation_1-rmse:50.41933
[136]	validation_0-rmse:36.66688	validation_1-rmse:50.41981
[137]	validation_0-rmse:36.61543	validation_1-rmse:50.49643
[138]	validation_0-rmse:36.58198	validation_1-rmse:50.48730
[139]	validation_0-rmse:36.54685	validation_1-rmse:50.51434
[140]	validation_0-rmse:36.44943	validation_1-rmse:50.47740
[141]	validation_0-rmse:36.41189	validation_1-rmse:50.45551
[142]	validation_0-rmse:36.39447	validation_1-rmse:50.45447
[143]	validation_0-rmse:36.38364	validation_1-rmse:50.45312
[144]	validation_0-rmse:36.33043	validation_1-rmse:50.44695
[145]	validation_0-rmse:36.24285	validation_1-rmse:50.37349
[146]	validation_0-rmse:36.17669	validation_1-rmse:50.32736
[147]	validation_0-rmse:35.98145	validation_1-rmse:50.28557
[148]	validation_0-rmse:35.91780	validation_1-rmse:50.29815
[149]	validation_0-rmse:35.87520	validation_1-rmse:50.27422
[150]	validation_0-rmse:35.84735	validation_1-rmse:50.26231
[151]	validation_0-rmse:35.71860	validation_1-rmse:50.23489
[152]	validation_0-rmse:35.59464	validation_1-rmse:50.17273
[153]	validation_0-rmse:35.52700	validation_1-rmse:50.16156
[154]	validation_0-rmse:35.48309	validation_1-rmse:50.15360
[155]	validation_0-rmse:35.41736	validation_1-rmse:50.16660
[156]	validation_0-rmse:35.38656	validation_1-rmse:50.17255
[157]	validation_0-rmse:35.33317	validation_1-rmse:50.16813
[158]	validation_0-rmse:35.27879	validation_1-rmse:50.16872
[159]	validation_0-rmse:35.25102	validation_1-rmse:50.16066
[160]	validation_0-rmse:35.19176	validation_1-rmse:50.15509
[161]	validation_0-rmse:35.13863	validation_1-rmse:50.12891
[162]	validation_0-rmse:35.09363	validation_1-rmse:50.11429
[163]	validation_0-rmse:34.96990	validation_1-rmse:50.11864
[164]	validation_0-rmse:34.94851	validation_1-rmse:50.12259
[165]	validation_0-rmse:34.81430	validation_1-rmse:50.13192
[166]	validation_0-rmse:34.76802	validation_1-rmse:50.11445
[167]	validation_0-rmse:34.73800	validation_1-rmse:50.09759
[168]	validation_0-rmse:34.71349	validation_1-rmse:50.08893
[169]	validation_0-rmse:34.68805	validation_1-rmse:50.07341
[170]	validation_0-rmse:34.64112	validation_1-rmse:50.08224
[171]	validation_0-rmse:34.56388	validation_1-rmse:50.04746
[172]	validation_0-rmse:34.49348	validation_1-rmse:50.02953
[173]	validation_0-rmse:34.39926	validation_1-rmse:49.95363
[174]	validation_0-rmse:34.35934	validation_1-rmse:49.95926
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[176]	validation_0-rmse:34.16332	validation_1-rmse:49.94363
[177]	validation_0-rmse:34.12579	validation_1-rmse:49.95518
[178]	validation_0-rmse:34.07432	validation_1-rmse:49.94384
[179]	validation_0-rmse:33.89741	validation_1-rmse:49.86168
[180]	validation_0-rmse:33.84283	validation_1-rmse:49.85915
[181]	validation_0-rmse:33.78051	validation_1-rmse:49.82753
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[183]	validation_0-rmse:33.70796	validation_1-rmse:49.81808
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[203]	validation_0-rmse:32.96793	validation_1-rmse:49.64435
[204]	validation_0-rmse:32.90078	validation_1-rmse:49.60185
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[221]	validation_0-rmse:31.97154	validation_1-rmse:49.47914
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[257]	validation_0-rmse:30.59900	validation_1-rmse:49.41727
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[303]	validation_0-rmse:28.96492	validation_1-rmse:49.22618
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[379]	validation_0-rmse:26.99112	validation_1-rmse:49.07109
[380]	validation_0-rmse:26.93824	validation_1-rmse:49.08765
[381]	validation_0-rmse:26.90552	validation_1-rmse:49.08924
[382]	validation_0-rmse:26.88516	validation_1-rmse:49.09105
[383]	validation_0-rmse:26.87157	validation_1-rmse:49.09800
[384]	validation_0-rmse:26.84993	validation_1-rmse:49.08344
[385]	validation_0-rmse:26.78844	validation_1-rmse:49.10942
[386]	validation_0-rmse:26.75532	validation_1-rmse:49.09781
[387]	validation_0-rmse:26.71844	validation_1-rmse:49.10703
[388]	validation_0-rmse:26.69626	validation_1-rmse:49.09909
[389]	validation_0-rmse:26.68371	validation_1-rmse:49.09059
[390]	validation_0-rmse:26.67660	validation_1-rmse:49.09150
[391]	validation_0-rmse:26.66289	validation_1-rmse:49.09207
[392]	validation_0-rmse:26.63517	validation_1-rmse:49.09926
[393]	validation_0-rmse:26.62462	validation_1-rmse:49.09660
[394]	validation_0-rmse:26.58023	validation_1-rmse:49.10607
[395]	validation_0-rmse:26.57111	validation_1-rmse:49.11308
>>> Using model to predict target PPFD_IN_T1_47_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 PPFD_IN_T1_47_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

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

## DATA
  > target: PPFD_IN_T1_47_1
  > features: 12 ['SW_IN_T1_47_1_gfXG', 'SW_IN_POT', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
  > 355563 records (with missing)
  > 354281 available records for target and all features (no missing values)
  > training on 265710 records (75.0%) of 265710 features between 2004-09-20 11:15:00 and 2024-12-31 23:45:00
  > testing on 88571 unseen records (25.0%) of PPFD_IN_T1_47_1 between 2004-09-20 10:45:00 and 2024-12-31 19:45:00

## MODEL
  > the model was trained on training data (265710 records)
  > the model was tested on test data (88571 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,
             feature_weights=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, ...)
  > 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, 'feature_weights': 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:  ['SW_IN_T1_47_1_gfXG', 'SW_IN_POT', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of PPFD_IN_T1_47_1 (88571 records).
  > feature importances are showing permutation importances from 10 repeats

                    PERM_IMPORTANCE   PERM_SD
SW_IN_T1_47_1_gfXG         1.392596  0.003978
SW_IN_POT                  0.114338  0.001528
.DOY                       0.034188  0.000801
.RECORDNUMBER              0.015165  0.000336
.WEEK                      0.013024  0.000294
.YEARDOY                   0.012365  0.000277
.YEARWEEK                  0.008423  0.000366
.YEARMONTH                 0.008275  0.000121
.HOUR                      0.006043  0.000282
.YEAR                      0.004018  0.000122
.SEASON                    0.001070  0.000041
.MONTH                     0.000426  0.000032


## MODEL SCORES
  All scores were calculated based on unseen test data (88571 records).
  > MAE:  13.185209143063895 (mean absolute error)
  > MedAE:  1.950302004814148 (median absolute error)
  > MSE:  2405.4096941836588 (mean squared error)
  > RMSE:  49.044976237976286 (root mean squared error)
  > MAXE:  1549.4255015990313 (max error)
  > MAPE:  2910133814358730.000 (mean absolute percentage error)
  > R2:  0.9897961919378194

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_47_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting PPFD_IN_T1_47_1 ...
>>> Predicting target PPFD_IN_T1_47_1 where all features are available ... predicted 355563 records.
>>> Collecting results for final model ...
>>> Filling 1282 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable PPFD_IN_T1_47_1_gfXG ...
>>> Restoring original timestamp in results ...

Gap-filling 0 remaining missing records in PPFD_IN_T1_47_1_gfXG using fallback model ...
>>> Fallback model not necessary, all gaps were already filled.
>>> Combining predictions from full model and fallback model ...

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

Model scores and feature importances were calculated from high-quality predicted targets (1282 values, PPFD_IN_T1_47_1_gfXG where flag=1) in comparison to observed targets (354281 values, PPFD_IN_T1_47_1).

## TARGET
- first timestamp:  2004-09-20 10:45:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 355563 values)
- target column (observed):  PPFD_IN_T1_47_1
- missing records (observed):  1282 (cross-check from flag: 1282)
- target column (gap-filled):  PPFD_IN_T1_47_1_gfXG  (355563 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_PPFD_IN_T1_47_1_gfXG_ISFILLED
  > flag 0 ... observed targets (354281 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (1282 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['SW_IN_T1_47_1_gfXG', 'SW_IN_POT', '.DOY', '.RECORDNUMBER', '.WEEK', '.YEARDOY', '.YEARMONTH', '.YEARWEEK', '.HOUR', '.YEAR', '.SEASON', '.MONTH']
- number of features used in model:  12
- permutation importances were calculated from 10 repeats.

                    PERM_IMPORTANCE   PERM_SD
SW_IN_T1_47_1_gfXG         1.395373  0.003196
SW_IN_POT                  0.117094  0.000773
.DOY                       0.036045  0.000241
.RECORDNUMBER              0.016664  0.000260
.WEEK                      0.012968  0.000105
.YEARDOY                   0.012818  0.000075
.YEARMONTH                 0.009414  0.000076
.YEARWEEK                  0.009007  0.000138
.HOUR                      0.006959  0.000111
.YEAR                      0.004512  0.000093
.SEASON                    0.001023  0.000015
.MONTH                     0.000485  0.000016

## 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,
             feature_weights=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, ...)
- 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, 'feature_weights': 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:  10.680220133481352 (mean absolute error)
- MedAE:  1.9261380434036255 (median absolute error)
- MSE:  1177.5472772592204 (mean squared error)
- RMSE:  34.31540874387511 (root mean squared error)
- MAXE:  1549.4255015990313 (max error)
- MAPE:  2886368350023944.500 (mean absolute percentage error)
- R2:  0.9950322406937908

observed = _df[VAR]
gapfilled = xgb.get_gapfilled_target()
data_simple_2004_2024[gapfilled.name] = gapfilled

title=f"{VAR}"
fig, ax = plt.subplots()
TimeSeries(series=gapfilled, ax=ax).plot(color='blue')
TimeSeries(series=observed, ax=ax).plot(color='orange')
fig.suptitle(f'{title}', fontsize=16)
# ax.set_ylim(-2000, 200)
fig.show()

Plots

_plot_df = data_simple_2004_2024.copy()
_plot_df = _plot_df.replace(-9999, np.nan)

Time series plot

_plot_df.plot(subplots=True, figsize=(20, 9), title="Gap-filled (some) meteo 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()    

Stats

data_simple_2004_2024.describe()

	LW_IN_T1_47_1	PA_T1_47_1	PPFD_IN_T1_47_1	RH_T1_47_1	SW_IN_T1_47_1	TA_T1_47_1	SW_IN_T1_47_1_gfXG	TA_T1_47_1_gfXG	PPFD_IN_T1_47_1_gfXG
count	334651.000000	335887.000000	354281.000000	352774.000000	345624.000000	352760.000000	355563.000000	355563.000000	355563.000000
mean	310.191939	93.234546	288.553030	79.283494	140.243968	8.898763	143.758864	8.890540	289.405280
std	45.718410	0.743782	486.866078	18.817820	237.022077	8.170412	237.382005	8.173191	487.050354
min	135.445129	89.446007	0.000000	16.934985	0.000000	-17.200001	0.000000	-17.200001	-2.392997
25%	282.017899	92.851006	0.000000	65.473702	0.000000	2.581000	0.000000	2.560453	0.000000
50%	315.560983	93.309998	0.000000	82.847867	0.000000	8.822186	0.000000	8.790000	0.000000
75%	343.077050	93.703298	369.686569	98.043015	182.744032	14.940000	196.250065	14.947231	372.034233
max	441.779694	95.543335	2270.612673	100.000000	1118.665920	33.558765	1118.665920	33.558765	2270.612673

Save to file

OUTNAME = "12.3_METEO6_GAPFILLED_2004-2024"
OUTPATH = r""
filepath = save_parquet(filename=OUTNAME, data=data_simple_2004_2024, outpath=OUTPATH)
data_simple_2004_2024.to_csv(Path(OUTPATH) / f"{OUTNAME}.csv")

Saved file 12.3_METEO6_GAPFILLED_2004-2024.parquet (0.190 seconds).

End of notebook.

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

Finished. 2025-06-12 00:37:47