Imports

from datetime import datetime
import pandas as pd

from diive.core.io.files import load_parquet, save_parquet
from diive.pkgs.gapfilling.xgboost_ts import XGBoostTS

Load data

df = load_parquet(filepath="17.3_CH-CHA_meteo10_2005-2024.parquet")
df

Loaded .parquet file 17.3_CH-CHA_meteo10_2005-2024.parquet (0.355 seconds).
    --> Detected time resolution of <30 * Minutes> / 30min 

	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	...	TS_GF1_0.04_1_gfXG	FLAG_TS_GF1_0.04_1_gfXG_ISFILLED	TS_GF1_0.15_1_gfXG	FLAG_TS_GF1_0.15_1_gfXG_ISFILLED	TS_GF1_0.4_1_gfXG	FLAG_TS_GF1_0.4_1_gfXG_ISFILLED
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	NaN	978.100000	1.0	0.0	0	0.099893	...	1.014525	1	2.907254	1	4.007686	1
2005-01-01 00:45:00	NaN	977.933333	1.0	0.0	0	0.097606	...	1.029936	1	2.907254	1	4.007686	1
2005-01-01 01:15:00	NaN	977.900000	1.0	0.0	0	0.091683	...	1.003078	1	2.903765	1	4.077782	1
2005-01-01 01:45:00	NaN	977.833333	1.0	0.0	0	0.071157	...	1.056877	1	2.903765	1	4.077782	1
2005-01-01 02:15:00	NaN	977.833333	1.0	0.0	0	0.058333	...	0.963062	1	2.932330	1	3.979915	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	304.613900	983.370890	NaN	0.0	0	0.000011	...	3.474346	0	4.437078	0	5.528727	0
2024-12-31 22:15:00	303.039890	983.052160	NaN	0.0	0	0.000011	...	3.428224	0	4.440415	0	5.521962	0
2024-12-31 22:45:00	302.093633	982.851140	NaN	0.0	0	0.000011	...	3.384733	0	4.443751	0	5.523991	0
2024-12-31 23:15:00	302.217307	982.896827	NaN	0.0	0	0.000010	...	3.349179	0	4.439747	0	5.528050	0
2024-12-31 23:45:00	298.392973	982.856613	NaN	0.0	0	0.000010	...	3.316919	0	4.442417	0	5.523991	0

350640 rows × 29 columns

Gap-filling SWC_GF1_0.15_1 (most complete SWC variable)

df[['SWC_GF1_0.05_1', 'SWC_GF1_0.15_1', 'SWC_GF1_0.75_1']].resample('A').count()

C:\Users\nopan\AppData\Local\Temp\ipykernel_4164\320009507.py:1: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'YE' instead.
  df[['SWC_GF1_0.05_1', 'SWC_GF1_0.15_1', 'SWC_GF1_0.75_1']].resample('A').count()

	SWC_GF1_0.05_1	SWC_GF1_0.15_1	SWC_GF1_0.75_1
TIMESTAMP_MIDDLE
2005-12-31	3213	3213	3213
2006-12-31	17452	17428	17434
2007-12-31	17501	17503	17502
2008-12-31	17560	17554	17558
2009-12-31	17504	17505	17506
2010-12-31	10879	16539	16536
2011-12-31	17021	17018	17013
2012-12-31	17267	17268	17267
2013-12-31	17513	17512	17514
2014-12-31	17503	17507	17507
2015-12-31	17496	17496	17493
2016-12-31	16075	16101	16092
2017-12-31	17413	17413	17413
2018-12-31	17085	17085	17085
2019-12-31	17394	17394	17394
2020-12-31	16867	16867	16867
2021-12-31	14497	14497	17466
2022-12-31	17516	17517	17515
2023-12-31	17520	17520	17515
2024-12-31	17540	17540	17534

[print(c) for c in df if "SWC" in c];
[print(c) for c in df if "PREC" in c];

SWC_GF1_0.05_1
SWC_GF1_0.15_1
SWC_GF1_0.75_1
PREC_RAIN_TOT_GF1_0.5_1
FLAG_PREC_RAIN_TOT_GF1_0.5_1_ISFILLED
FLAG_PREC_RAIN_TOT_GF1_0.5_1_FLUXNET_ISFILLED
TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1

TARGET_COL = 'SWC_GF1_0.15_1'
TARGET_GAPFILLED_COL = f'{TARGET_COL}_gfXG'
FLAG_GAPFILLED_COL = f'FLAG_{TARGET_GAPFILLED_COL}_ISFILLED'

# Dataframe for gap-filling
_df = pd.DataFrame()
_df[TARGET_COL] = df[TARGET_COL].copy()
_df['PREC_RAIN_TOT_GF1_0.5_1'] = df['PREC_RAIN_TOT_GF1_0.5_1'].copy()
_df['TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1'] = df['TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1'].copy()

# XGBoost
xgb = XGBoostTS(
    input_df=_df,
    target_col=TARGET_COL,
    features_lag=[-5, -1],
    features_lag_exclude_cols=['TIMESINCE_PREC_RAIN_TOT_GF1_0.5_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
)
xgb.reduce_features()
xgb.report_feature_reduction()
xgb.trainmodel(showplot_scores=False, showplot_importance=False)
xgb.report_traintest()
xgb.fillgaps(showplot_scores=False, showplot_importance=False)
xgb.report_gapfilling()
results = xgb.gapfilling_df_

# Add results to main data
df = pd.concat([df, results[[TARGET_GAPFILLED_COL, FLAG_GAPFILLED_COL]]], axis=1)

# Plot
plotdf = df[[TARGET_COL, TARGET_GAPFILLED_COL, FLAG_GAPFILLED_COL]].copy()
plotdf.plot(x_compat=True, title=TARGET_COL, subplots=True, figsize=(20, 6));
locs = (plotdf.index.year == 2011) & (plotdf.index.month == 8)
plotdf[locs].plot(x_compat=True, title=TARGET_COL, subplots=True, figsize=(20, 6));

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.
(!)Some features are incomplete and have less than 350640 values:
    --> PREC_RAIN_TOT_GF1_0.5_1 (350627 values)
    --> .PREC_RAIN_TOT_GF1_0.5_1-5 (350622 values)
    --> .PREC_RAIN_TOT_GF1_0.5_1-4 (350623 values)
    --> .PREC_RAIN_TOT_GF1_0.5_1-3 (350624 values)
    --> .PREC_RAIN_TOT_GF1_0.5_1-2 (350625 values)
    --> .PREC_RAIN_TOT_GF1_0.5_1-1 (350626 values)
This means that not all target values can be predicted based on the full model.

Feature reduction based on permutation importance ...
[0]	validation_0-rmse:4.54191	validation_1-rmse:4.54191
[1]	validation_0-rmse:3.77102	validation_1-rmse:3.77102
[2]	validation_0-rmse:3.30914	validation_1-rmse:3.30914
[3]	validation_0-rmse:2.95928	validation_1-rmse:2.95928
[4]	validation_0-rmse:2.64368	validation_1-rmse:2.64368
[5]	validation_0-rmse:2.43508	validation_1-rmse:2.43508
[6]	validation_0-rmse:2.30312	validation_1-rmse:2.30312
[7]	validation_0-rmse:2.19572	validation_1-rmse:2.19572
[8]	validation_0-rmse:2.10559	validation_1-rmse:2.10559
[9]	validation_0-rmse:2.04759	validation_1-rmse:2.04759
[10]	validation_0-rmse:1.97234	validation_1-rmse:1.97234
[11]	validation_0-rmse:1.88055	validation_1-rmse:1.88055
[12]	validation_0-rmse:1.85110	validation_1-rmse:1.85110
[13]	validation_0-rmse:1.77355	validation_1-rmse:1.77355
[14]	validation_0-rmse:1.70463	validation_1-rmse:1.70463
[15]	validation_0-rmse:1.67661	validation_1-rmse:1.67661
[16]	validation_0-rmse:1.64142	validation_1-rmse:1.64142
[17]	validation_0-rmse:1.61673	validation_1-rmse:1.61673
[18]	validation_0-rmse:1.56290	validation_1-rmse:1.56290
[19]	validation_0-rmse:1.52226	validation_1-rmse:1.52226
[20]	validation_0-rmse:1.46564	validation_1-rmse:1.46564
[21]	validation_0-rmse:1.43841	validation_1-rmse:1.43841
[22]	validation_0-rmse:1.40729	validation_1-rmse:1.40729
[23]	validation_0-rmse:1.37681	validation_1-rmse:1.37681
[24]	validation_0-rmse:1.33206	validation_1-rmse:1.33206
[25]	validation_0-rmse:1.30011	validation_1-rmse:1.30011
[26]	validation_0-rmse:1.28720	validation_1-rmse:1.28720
[27]	validation_0-rmse:1.27806	validation_1-rmse:1.27806
[28]	validation_0-rmse:1.27145	validation_1-rmse:1.27145
[29]	validation_0-rmse:1.25743	validation_1-rmse:1.25743
[30]	validation_0-rmse:1.23439	validation_1-rmse:1.23439
[31]	validation_0-rmse:1.22680	validation_1-rmse:1.22680
[32]	validation_0-rmse:1.21538	validation_1-rmse:1.21538
[33]	validation_0-rmse:1.21236	validation_1-rmse:1.21236
[34]	validation_0-rmse:1.19438	validation_1-rmse:1.19438
[35]	validation_0-rmse:1.17827	validation_1-rmse:1.17827
[36]	validation_0-rmse:1.15777	validation_1-rmse:1.15777
[37]	validation_0-rmse:1.15016	validation_1-rmse:1.15016
[38]	validation_0-rmse:1.14816	validation_1-rmse:1.14816
[39]	validation_0-rmse:1.12810	validation_1-rmse:1.12810
[40]	validation_0-rmse:1.11597	validation_1-rmse:1.11597
[41]	validation_0-rmse:1.10523	validation_1-rmse:1.10523
[42]	validation_0-rmse:1.08901	validation_1-rmse:1.08901
[43]	validation_0-rmse:1.08464	validation_1-rmse:1.08464
[44]	validation_0-rmse:1.07845	validation_1-rmse:1.07845
[45]	validation_0-rmse:1.07026	validation_1-rmse:1.07026
[46]	validation_0-rmse:1.06759	validation_1-rmse:1.06759
[47]	validation_0-rmse:1.06628	validation_1-rmse:1.06628
[48]	validation_0-rmse:1.05599	validation_1-rmse:1.05599
[49]	validation_0-rmse:1.04634	validation_1-rmse:1.04634
[50]	validation_0-rmse:1.04329	validation_1-rmse:1.04329
[51]	validation_0-rmse:1.04111	validation_1-rmse:1.04111
[52]	validation_0-rmse:1.02423	validation_1-rmse:1.02423
[53]	validation_0-rmse:1.01922	validation_1-rmse:1.01922
[54]	validation_0-rmse:1.00826	validation_1-rmse:1.00826
[55]	validation_0-rmse:0.99720	validation_1-rmse:0.99720
[56]	validation_0-rmse:0.98454	validation_1-rmse:0.98454
[57]	validation_0-rmse:0.97267	validation_1-rmse:0.97267
[58]	validation_0-rmse:0.97163	validation_1-rmse:0.97163
[59]	validation_0-rmse:0.96897	validation_1-rmse:0.96897
[60]	validation_0-rmse:0.96667	validation_1-rmse:0.96667
[61]	validation_0-rmse:0.96552	validation_1-rmse:0.96552
[62]	validation_0-rmse:0.96117	validation_1-rmse:0.96117
[63]	validation_0-rmse:0.95305	validation_1-rmse:0.95305
[64]	validation_0-rmse:0.94821	validation_1-rmse:0.94821
[65]	validation_0-rmse:0.94543	validation_1-rmse:0.94543
[66]	validation_0-rmse:0.93590	validation_1-rmse:0.93590
[67]	validation_0-rmse:0.93232	validation_1-rmse:0.93232
[68]	validation_0-rmse:0.92798	validation_1-rmse:0.92798
[69]	validation_0-rmse:0.92215	validation_1-rmse:0.92215
[70]	validation_0-rmse:0.91413	validation_1-rmse:0.91413
[71]	validation_0-rmse:0.91186	validation_1-rmse:0.91186
[72]	validation_0-rmse:0.90390	validation_1-rmse:0.90390
[73]	validation_0-rmse:0.89828	validation_1-rmse:0.89828
[74]	validation_0-rmse:0.89294	validation_1-rmse:0.89294
[75]	validation_0-rmse:0.88053	validation_1-rmse:0.88053
[76]	validation_0-rmse:0.87719	validation_1-rmse:0.87719
[77]	validation_0-rmse:0.87024	validation_1-rmse:0.87024
[78]	validation_0-rmse:0.86591	validation_1-rmse:0.86591
[79]	validation_0-rmse:0.86432	validation_1-rmse:0.86432
[80]	validation_0-rmse:0.85474	validation_1-rmse:0.85474
[81]	validation_0-rmse:0.84843	validation_1-rmse:0.84843
[82]	validation_0-rmse:0.84496	validation_1-rmse:0.84496
[83]	validation_0-rmse:0.83981	validation_1-rmse:0.83981
[84]	validation_0-rmse:0.83712	validation_1-rmse:0.83712
[85]	validation_0-rmse:0.83530	validation_1-rmse:0.83530
[86]	validation_0-rmse:0.83116	validation_1-rmse:0.83116
[87]	validation_0-rmse:0.82871	validation_1-rmse:0.82871
[88]	validation_0-rmse:0.82465	validation_1-rmse:0.82465
[89]	validation_0-rmse:0.81968	validation_1-rmse:0.81968
[90]	validation_0-rmse:0.81835	validation_1-rmse:0.81835
[91]	validation_0-rmse:0.81679	validation_1-rmse:0.81679
[92]	validation_0-rmse:0.81306	validation_1-rmse:0.81306
[93]	validation_0-rmse:0.80386	validation_1-rmse:0.80386
[94]	validation_0-rmse:0.80039	validation_1-rmse:0.80039
[95]	validation_0-rmse:0.79599	validation_1-rmse:0.79599
[96]	validation_0-rmse:0.78885	validation_1-rmse:0.78885
[97]	validation_0-rmse:0.78832	validation_1-rmse:0.78832
[98]	validation_0-rmse:0.78637	validation_1-rmse:0.78637
[99]	validation_0-rmse:0.78353	validation_1-rmse:0.78353
[100]	validation_0-rmse:0.78296	validation_1-rmse:0.78296
[101]	validation_0-rmse:0.78202	validation_1-rmse:0.78202
[102]	validation_0-rmse:0.78012	validation_1-rmse:0.78012
[103]	validation_0-rmse:0.77911	validation_1-rmse:0.77911
[104]	validation_0-rmse:0.77662	validation_1-rmse:0.77662
[105]	validation_0-rmse:0.77455	validation_1-rmse:0.77455
[106]	validation_0-rmse:0.77275	validation_1-rmse:0.77275
[107]	validation_0-rmse:0.77092	validation_1-rmse:0.77092
[108]	validation_0-rmse:0.76721	validation_1-rmse:0.76721
[109]	validation_0-rmse:0.76112	validation_1-rmse:0.76112
[110]	validation_0-rmse:0.75323	validation_1-rmse:0.75323
[111]	validation_0-rmse:0.74810	validation_1-rmse:0.74810
[112]	validation_0-rmse:0.74749	validation_1-rmse:0.74749
[113]	validation_0-rmse:0.73905	validation_1-rmse:0.73905
[114]	validation_0-rmse:0.73768	validation_1-rmse:0.73768
[115]	validation_0-rmse:0.73448	validation_1-rmse:0.73448
[116]	validation_0-rmse:0.73325	validation_1-rmse:0.73325
[117]	validation_0-rmse:0.72796	validation_1-rmse:0.72796
[118]	validation_0-rmse:0.72727	validation_1-rmse:0.72727
[119]	validation_0-rmse:0.72601	validation_1-rmse:0.72601
[120]	validation_0-rmse:0.72489	validation_1-rmse:0.72489
[121]	validation_0-rmse:0.72425	validation_1-rmse:0.72425
[122]	validation_0-rmse:0.72173	validation_1-rmse:0.72173
[123]	validation_0-rmse:0.72061	validation_1-rmse:0.72061
[124]	validation_0-rmse:0.71803	validation_1-rmse:0.71803
[125]	validation_0-rmse:0.71450	validation_1-rmse:0.71450
[126]	validation_0-rmse:0.71141	validation_1-rmse:0.71141
[127]	validation_0-rmse:0.71073	validation_1-rmse:0.71073
[128]	validation_0-rmse:0.70919	validation_1-rmse:0.70919
[129]	validation_0-rmse:0.70771	validation_1-rmse:0.70771
[130]	validation_0-rmse:0.70619	validation_1-rmse:0.70619
[131]	validation_0-rmse:0.70518	validation_1-rmse:0.70518
[132]	validation_0-rmse:0.70209	validation_1-rmse:0.70209
[133]	validation_0-rmse:0.69700	validation_1-rmse:0.69700
[134]	validation_0-rmse:0.69404	validation_1-rmse:0.69404
[135]	validation_0-rmse:0.69237	validation_1-rmse:0.69237
[136]	validation_0-rmse:0.68869	validation_1-rmse:0.68869
[137]	validation_0-rmse:0.68573	validation_1-rmse:0.68573
[138]	validation_0-rmse:0.68527	validation_1-rmse:0.68527
[139]	validation_0-rmse:0.68351	validation_1-rmse:0.68351
[140]	validation_0-rmse:0.68153	validation_1-rmse:0.68153
[141]	validation_0-rmse:0.67886	validation_1-rmse:0.67886
[142]	validation_0-rmse:0.67817	validation_1-rmse:0.67817
[143]	validation_0-rmse:0.67724	validation_1-rmse:0.67724
[144]	validation_0-rmse:0.67687	validation_1-rmse:0.67687
[145]	validation_0-rmse:0.67551	validation_1-rmse:0.67551
[146]	validation_0-rmse:0.67436	validation_1-rmse:0.67436
[147]	validation_0-rmse:0.66767	validation_1-rmse:0.66767
[148]	validation_0-rmse:0.66612	validation_1-rmse:0.66612
[149]	validation_0-rmse:0.66301	validation_1-rmse:0.66301
[150]	validation_0-rmse:0.66232	validation_1-rmse:0.66232
[151]	validation_0-rmse:0.66101	validation_1-rmse:0.66101
[152]	validation_0-rmse:0.66002	validation_1-rmse:0.66002
[153]	validation_0-rmse:0.65940	validation_1-rmse:0.65940
[154]	validation_0-rmse:0.65584	validation_1-rmse:0.65584
[155]	validation_0-rmse:0.65344	validation_1-rmse:0.65344
[156]	validation_0-rmse:0.65165	validation_1-rmse:0.65165
[157]	validation_0-rmse:0.64996	validation_1-rmse:0.64996
[158]	validation_0-rmse:0.64877	validation_1-rmse:0.64877
[159]	validation_0-rmse:0.64774	validation_1-rmse:0.64774
[160]	validation_0-rmse:0.64488	validation_1-rmse:0.64488
[161]	validation_0-rmse:0.64423	validation_1-rmse:0.64423
[162]	validation_0-rmse:0.64361	validation_1-rmse:0.64361
[163]	validation_0-rmse:0.64200	validation_1-rmse:0.64200
[164]	validation_0-rmse:0.64057	validation_1-rmse:0.64057
[165]	validation_0-rmse:0.63981	validation_1-rmse:0.63981
[166]	validation_0-rmse:0.63937	validation_1-rmse:0.63937
[167]	validation_0-rmse:0.63912	validation_1-rmse:0.63912
[168]	validation_0-rmse:0.63724	validation_1-rmse:0.63724
[169]	validation_0-rmse:0.63550	validation_1-rmse:0.63550
[170]	validation_0-rmse:0.63473	validation_1-rmse:0.63473
[171]	validation_0-rmse:0.63450	validation_1-rmse:0.63450
[172]	validation_0-rmse:0.63427	validation_1-rmse:0.63427
[173]	validation_0-rmse:0.63328	validation_1-rmse:0.63328
[174]	validation_0-rmse:0.63202	validation_1-rmse:0.63202
[175]	validation_0-rmse:0.63137	validation_1-rmse:0.63137
[176]	validation_0-rmse:0.62865	validation_1-rmse:0.62865
[177]	validation_0-rmse:0.62675	validation_1-rmse:0.62675
[178]	validation_0-rmse:0.62468	validation_1-rmse:0.62468
[179]	validation_0-rmse:0.62264	validation_1-rmse:0.62264
[180]	validation_0-rmse:0.62217	validation_1-rmse:0.62217
[181]	validation_0-rmse:0.62174	validation_1-rmse:0.62174
[182]	validation_0-rmse:0.62110	validation_1-rmse:0.62110
[183]	validation_0-rmse:0.62001	validation_1-rmse:0.62001
[184]	validation_0-rmse:0.61877	validation_1-rmse:0.61877
[185]	validation_0-rmse:0.61806	validation_1-rmse:0.61806
[186]	validation_0-rmse:0.61611	validation_1-rmse:0.61611
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[744]	validation_0-rmse:0.33324	validation_1-rmse:0.33324
[745]	validation_0-rmse:0.33316	validation_1-rmse:0.33316
[746]	validation_0-rmse:0.33309	validation_1-rmse:0.33309
[747]	validation_0-rmse:0.33284	validation_1-rmse:0.33284
[748]	validation_0-rmse:0.33271	validation_1-rmse:0.33271
[749]	validation_0-rmse:0.33266	validation_1-rmse:0.33266
[750]	validation_0-rmse:0.33254	validation_1-rmse:0.33254
[751]	validation_0-rmse:0.33222	validation_1-rmse:0.33222
[752]	validation_0-rmse:0.33188	validation_1-rmse:0.33188
[753]	validation_0-rmse:0.33166	validation_1-rmse:0.33166
[754]	validation_0-rmse:0.33159	validation_1-rmse:0.33159
[755]	validation_0-rmse:0.33115	validation_1-rmse:0.33115
[756]	validation_0-rmse:0.33091	validation_1-rmse:0.33091
[757]	validation_0-rmse:0.33081	validation_1-rmse:0.33081
[758]	validation_0-rmse:0.33055	validation_1-rmse:0.33055
[759]	validation_0-rmse:0.33048	validation_1-rmse:0.33048
[760]	validation_0-rmse:0.33037	validation_1-rmse:0.33037
[761]	validation_0-rmse:0.33029	validation_1-rmse:0.33029
[762]	validation_0-rmse:0.33025	validation_1-rmse:0.33025
[763]	validation_0-rmse:0.33015	validation_1-rmse:0.33015
[764]	validation_0-rmse:0.32997	validation_1-rmse:0.32997
[765]	validation_0-rmse:0.32977	validation_1-rmse:0.32977
[766]	validation_0-rmse:0.32963	validation_1-rmse:0.32963
[767]	validation_0-rmse:0.32941	validation_1-rmse:0.32941
[768]	validation_0-rmse:0.32923	validation_1-rmse:0.32923
[769]	validation_0-rmse:0.32913	validation_1-rmse:0.32913
[770]	validation_0-rmse:0.32890	validation_1-rmse:0.32890
[771]	validation_0-rmse:0.32887	validation_1-rmse:0.32887
[772]	validation_0-rmse:0.32844	validation_1-rmse:0.32844
[773]	validation_0-rmse:0.32828	validation_1-rmse:0.32828
[774]	validation_0-rmse:0.32798	validation_1-rmse:0.32798
[775]	validation_0-rmse:0.32788	validation_1-rmse:0.32788
[776]	validation_0-rmse:0.32784	validation_1-rmse:0.32784
[777]	validation_0-rmse:0.32776	validation_1-rmse:0.32776
[778]	validation_0-rmse:0.32771	validation_1-rmse:0.32771
[779]	validation_0-rmse:0.32757	validation_1-rmse:0.32757
[780]	validation_0-rmse:0.32746	validation_1-rmse:0.32746
[781]	validation_0-rmse:0.32730	validation_1-rmse:0.32730
[782]	validation_0-rmse:0.32727	validation_1-rmse:0.32727
[783]	validation_0-rmse:0.32712	validation_1-rmse:0.32712
[784]	validation_0-rmse:0.32680	validation_1-rmse:0.32680
[785]	validation_0-rmse:0.32658	validation_1-rmse:0.32658
[786]	validation_0-rmse:0.32653	validation_1-rmse:0.32653
[787]	validation_0-rmse:0.32644	validation_1-rmse:0.32644
[788]	validation_0-rmse:0.32641	validation_1-rmse:0.32641
[789]	validation_0-rmse:0.32603	validation_1-rmse:0.32603
[790]	validation_0-rmse:0.32590	validation_1-rmse:0.32590
[791]	validation_0-rmse:0.32573	validation_1-rmse:0.32573
[792]	validation_0-rmse:0.32562	validation_1-rmse:0.32562
[793]	validation_0-rmse:0.32555	validation_1-rmse:0.32555
[794]	validation_0-rmse:0.32546	validation_1-rmse:0.32546
[795]	validation_0-rmse:0.32539	validation_1-rmse:0.32539
[796]	validation_0-rmse:0.32512	validation_1-rmse:0.32512
[797]	validation_0-rmse:0.32508	validation_1-rmse:0.32508
[798]	validation_0-rmse:0.32482	validation_1-rmse:0.32482
[799]	validation_0-rmse:0.32455	validation_1-rmse:0.32455
[800]	validation_0-rmse:0.32433	validation_1-rmse:0.32433
[801]	validation_0-rmse:0.32416	validation_1-rmse:0.32416
[802]	validation_0-rmse:0.32400	validation_1-rmse:0.32400
[803]	validation_0-rmse:0.32381	validation_1-rmse:0.32381
[804]	validation_0-rmse:0.32370	validation_1-rmse:0.32370
[805]	validation_0-rmse:0.32361	validation_1-rmse:0.32361
[806]	validation_0-rmse:0.32277	validation_1-rmse:0.32277
[807]	validation_0-rmse:0.32267	validation_1-rmse:0.32267
[808]	validation_0-rmse:0.32265	validation_1-rmse:0.32265
[809]	validation_0-rmse:0.32239	validation_1-rmse:0.32239
[810]	validation_0-rmse:0.32213	validation_1-rmse:0.32213
[811]	validation_0-rmse:0.32203	validation_1-rmse:0.32203
[812]	validation_0-rmse:0.32194	validation_1-rmse:0.32194
[813]	validation_0-rmse:0.32168	validation_1-rmse:0.32168
[814]	validation_0-rmse:0.32134	validation_1-rmse:0.32134
[815]	validation_0-rmse:0.32120	validation_1-rmse:0.32120
[816]	validation_0-rmse:0.32080	validation_1-rmse:0.32080
[817]	validation_0-rmse:0.32059	validation_1-rmse:0.32059
[818]	validation_0-rmse:0.32052	validation_1-rmse:0.32052
[819]	validation_0-rmse:0.31988	validation_1-rmse:0.31988
[820]	validation_0-rmse:0.31981	validation_1-rmse:0.31981
[821]	validation_0-rmse:0.31968	validation_1-rmse:0.31968
[822]	validation_0-rmse:0.31944	validation_1-rmse:0.31944
[823]	validation_0-rmse:0.31915	validation_1-rmse:0.31915
[824]	validation_0-rmse:0.31907	validation_1-rmse:0.31907
[825]	validation_0-rmse:0.31885	validation_1-rmse:0.31885
[826]	validation_0-rmse:0.31864	validation_1-rmse:0.31864
[827]	validation_0-rmse:0.31849	validation_1-rmse:0.31849
[828]	validation_0-rmse:0.31832	validation_1-rmse:0.31832
[829]	validation_0-rmse:0.31817	validation_1-rmse:0.31817
[830]	validation_0-rmse:0.31798	validation_1-rmse:0.31798
[831]	validation_0-rmse:0.31777	validation_1-rmse:0.31777
[832]	validation_0-rmse:0.31771	validation_1-rmse:0.31771
[833]	validation_0-rmse:0.31747	validation_1-rmse:0.31747
[834]	validation_0-rmse:0.31717	validation_1-rmse:0.31717
[835]	validation_0-rmse:0.31699	validation_1-rmse:0.31699
[836]	validation_0-rmse:0.31686	validation_1-rmse:0.31686
[837]	validation_0-rmse:0.31673	validation_1-rmse:0.31673
[838]	validation_0-rmse:0.31659	validation_1-rmse:0.31659
[839]	validation_0-rmse:0.31644	validation_1-rmse:0.31644
[840]	validation_0-rmse:0.31628	validation_1-rmse:0.31628
[841]	validation_0-rmse:0.31593	validation_1-rmse:0.31593
[842]	validation_0-rmse:0.31580	validation_1-rmse:0.31580
[843]	validation_0-rmse:0.31576	validation_1-rmse:0.31576
[844]	validation_0-rmse:0.31570	validation_1-rmse:0.31570
[845]	validation_0-rmse:0.31551	validation_1-rmse:0.31551
[846]	validation_0-rmse:0.31517	validation_1-rmse:0.31517
[847]	validation_0-rmse:0.31495	validation_1-rmse:0.31495
[848]	validation_0-rmse:0.31484	validation_1-rmse:0.31484
[849]	validation_0-rmse:0.31474	validation_1-rmse:0.31474
[850]	validation_0-rmse:0.31462	validation_1-rmse:0.31462
[851]	validation_0-rmse:0.31445	validation_1-rmse:0.31445
[852]	validation_0-rmse:0.31389	validation_1-rmse:0.31389
[853]	validation_0-rmse:0.31372	validation_1-rmse:0.31372
[854]	validation_0-rmse:0.31350	validation_1-rmse:0.31350
[855]	validation_0-rmse:0.31322	validation_1-rmse:0.31322
[856]	validation_0-rmse:0.31316	validation_1-rmse:0.31316
[857]	validation_0-rmse:0.31309	validation_1-rmse:0.31309
[858]	validation_0-rmse:0.31282	validation_1-rmse:0.31282
[859]	validation_0-rmse:0.31272	validation_1-rmse:0.31272
[860]	validation_0-rmse:0.31245	validation_1-rmse:0.31245
[861]	validation_0-rmse:0.31214	validation_1-rmse:0.31214
[862]	validation_0-rmse:0.31198	validation_1-rmse:0.31198
[863]	validation_0-rmse:0.31193	validation_1-rmse:0.31193
[864]	validation_0-rmse:0.31177	validation_1-rmse:0.31177
[865]	validation_0-rmse:0.31131	validation_1-rmse:0.31131
[866]	validation_0-rmse:0.31113	validation_1-rmse:0.31113
[867]	validation_0-rmse:0.31096	validation_1-rmse:0.31096
[868]	validation_0-rmse:0.31074	validation_1-rmse:0.31074
[869]	validation_0-rmse:0.31030	validation_1-rmse:0.31030
[870]	validation_0-rmse:0.31028	validation_1-rmse:0.31028
[871]	validation_0-rmse:0.31022	validation_1-rmse:0.31022
[872]	validation_0-rmse:0.31003	validation_1-rmse:0.31003
[873]	validation_0-rmse:0.30983	validation_1-rmse:0.30983
[874]	validation_0-rmse:0.30967	validation_1-rmse:0.30967
[875]	validation_0-rmse:0.30955	validation_1-rmse:0.30955
[876]	validation_0-rmse:0.30944	validation_1-rmse:0.30944
[877]	validation_0-rmse:0.30920	validation_1-rmse:0.30920
[878]	validation_0-rmse:0.30883	validation_1-rmse:0.30883
[879]	validation_0-rmse:0.30856	validation_1-rmse:0.30856
[880]	validation_0-rmse:0.30839	validation_1-rmse:0.30839
[881]	validation_0-rmse:0.30809	validation_1-rmse:0.30809
[882]	validation_0-rmse:0.30805	validation_1-rmse:0.30805
[883]	validation_0-rmse:0.30795	validation_1-rmse:0.30795
[884]	validation_0-rmse:0.30785	validation_1-rmse:0.30785
[885]	validation_0-rmse:0.30773	validation_1-rmse:0.30773
[886]	validation_0-rmse:0.30751	validation_1-rmse:0.30751
[887]	validation_0-rmse:0.30745	validation_1-rmse:0.30745
[888]	validation_0-rmse:0.30710	validation_1-rmse:0.30710
[889]	validation_0-rmse:0.30695	validation_1-rmse:0.30695
[890]	validation_0-rmse:0.30661	validation_1-rmse:0.30661
[891]	validation_0-rmse:0.30657	validation_1-rmse:0.30657
[892]	validation_0-rmse:0.30651	validation_1-rmse:0.30651
[893]	validation_0-rmse:0.30624	validation_1-rmse:0.30624
[894]	validation_0-rmse:0.30611	validation_1-rmse:0.30611
[895]	validation_0-rmse:0.30603	validation_1-rmse:0.30603
[896]	validation_0-rmse:0.30598	validation_1-rmse:0.30598
[897]	validation_0-rmse:0.30594	validation_1-rmse:0.30594
[898]	validation_0-rmse:0.30590	validation_1-rmse:0.30590
[899]	validation_0-rmse:0.30581	validation_1-rmse:0.30581
[900]	validation_0-rmse:0.30577	validation_1-rmse:0.30577
[901]	validation_0-rmse:0.30565	validation_1-rmse:0.30565
[902]	validation_0-rmse:0.30547	validation_1-rmse:0.30547
[903]	validation_0-rmse:0.30519	validation_1-rmse:0.30519
[904]	validation_0-rmse:0.30515	validation_1-rmse:0.30515
[905]	validation_0-rmse:0.30503	validation_1-rmse:0.30503
[906]	validation_0-rmse:0.30497	validation_1-rmse:0.30497
[907]	validation_0-rmse:0.30490	validation_1-rmse:0.30490
[908]	validation_0-rmse:0.30485	validation_1-rmse:0.30485
[909]	validation_0-rmse:0.30481	validation_1-rmse:0.30481
[910]	validation_0-rmse:0.30447	validation_1-rmse:0.30447
[911]	validation_0-rmse:0.30398	validation_1-rmse:0.30398
[912]	validation_0-rmse:0.30336	validation_1-rmse:0.30336
[913]	validation_0-rmse:0.30323	validation_1-rmse:0.30323
[914]	validation_0-rmse:0.30302	validation_1-rmse:0.30302
[915]	validation_0-rmse:0.30292	validation_1-rmse:0.30292
[916]	validation_0-rmse:0.30287	validation_1-rmse:0.30287
[917]	validation_0-rmse:0.30280	validation_1-rmse:0.30280
[918]	validation_0-rmse:0.30259	validation_1-rmse:0.30259
[919]	validation_0-rmse:0.30246	validation_1-rmse:0.30246
[920]	validation_0-rmse:0.30233	validation_1-rmse:0.30233
[921]	validation_0-rmse:0.30180	validation_1-rmse:0.30180
[922]	validation_0-rmse:0.30168	validation_1-rmse:0.30168
[923]	validation_0-rmse:0.30164	validation_1-rmse:0.30164
[924]	validation_0-rmse:0.30150	validation_1-rmse:0.30150
[925]	validation_0-rmse:0.30129	validation_1-rmse:0.30129
[926]	validation_0-rmse:0.30118	validation_1-rmse:0.30118
[927]	validation_0-rmse:0.30105	validation_1-rmse:0.30105
[928]	validation_0-rmse:0.30092	validation_1-rmse:0.30092
[929]	validation_0-rmse:0.30063	validation_1-rmse:0.30063
[930]	validation_0-rmse:0.30026	validation_1-rmse:0.30026
[931]	validation_0-rmse:0.30006	validation_1-rmse:0.30006
[932]	validation_0-rmse:0.29986	validation_1-rmse:0.29986
[933]	validation_0-rmse:0.29967	validation_1-rmse:0.29967
[934]	validation_0-rmse:0.29960	validation_1-rmse:0.29960
[935]	validation_0-rmse:0.29954	validation_1-rmse:0.29954
[936]	validation_0-rmse:0.29918	validation_1-rmse:0.29918
[937]	validation_0-rmse:0.29910	validation_1-rmse:0.29910
[938]	validation_0-rmse:0.29905	validation_1-rmse:0.29905
[939]	validation_0-rmse:0.29897	validation_1-rmse:0.29897
[940]	validation_0-rmse:0.29883	validation_1-rmse:0.29883
[941]	validation_0-rmse:0.29853	validation_1-rmse:0.29853
[942]	validation_0-rmse:0.29841	validation_1-rmse:0.29841
[943]	validation_0-rmse:0.29836	validation_1-rmse:0.29836
[944]	validation_0-rmse:0.29833	validation_1-rmse:0.29833
[945]	validation_0-rmse:0.29821	validation_1-rmse:0.29821
[946]	validation_0-rmse:0.29776	validation_1-rmse:0.29776
[947]	validation_0-rmse:0.29768	validation_1-rmse:0.29768
[948]	validation_0-rmse:0.29765	validation_1-rmse:0.29765
[949]	validation_0-rmse:0.29753	validation_1-rmse:0.29753
[950]	validation_0-rmse:0.29736	validation_1-rmse:0.29736
[951]	validation_0-rmse:0.29730	validation_1-rmse:0.29730
[952]	validation_0-rmse:0.29710	validation_1-rmse:0.29710
[953]	validation_0-rmse:0.29698	validation_1-rmse:0.29698
[954]	validation_0-rmse:0.29690	validation_1-rmse:0.29690
[955]	validation_0-rmse:0.29680	validation_1-rmse:0.29680
[956]	validation_0-rmse:0.29668	validation_1-rmse:0.29668
[957]	validation_0-rmse:0.29659	validation_1-rmse:0.29659
[958]	validation_0-rmse:0.29642	validation_1-rmse:0.29642
[959]	validation_0-rmse:0.29634	validation_1-rmse:0.29634
[960]	validation_0-rmse:0.29621	validation_1-rmse:0.29621
[961]	validation_0-rmse:0.29606	validation_1-rmse:0.29606
[962]	validation_0-rmse:0.29550	validation_1-rmse:0.29550
[963]	validation_0-rmse:0.29543	validation_1-rmse:0.29543
[964]	validation_0-rmse:0.29510	validation_1-rmse:0.29510
[965]	validation_0-rmse:0.29508	validation_1-rmse:0.29508
[966]	validation_0-rmse:0.29496	validation_1-rmse:0.29496
[967]	validation_0-rmse:0.29482	validation_1-rmse:0.29482
[968]	validation_0-rmse:0.29470	validation_1-rmse:0.29470
[969]	validation_0-rmse:0.29455	validation_1-rmse:0.29455
[970]	validation_0-rmse:0.29444	validation_1-rmse:0.29444
[971]	validation_0-rmse:0.29391	validation_1-rmse:0.29391
[972]	validation_0-rmse:0.29389	validation_1-rmse:0.29389
[973]	validation_0-rmse:0.29384	validation_1-rmse:0.29384
[974]	validation_0-rmse:0.29374	validation_1-rmse:0.29374
[975]	validation_0-rmse:0.29352	validation_1-rmse:0.29352
[976]	validation_0-rmse:0.29297	validation_1-rmse:0.29297
[977]	validation_0-rmse:0.29289	validation_1-rmse:0.29289
[978]	validation_0-rmse:0.29276	validation_1-rmse:0.29276
[979]	validation_0-rmse:0.29251	validation_1-rmse:0.29251
[980]	validation_0-rmse:0.29242	validation_1-rmse:0.29242
[981]	validation_0-rmse:0.29223	validation_1-rmse:0.29223
[982]	validation_0-rmse:0.29214	validation_1-rmse:0.29214
[983]	validation_0-rmse:0.29205	validation_1-rmse:0.29205
[984]	validation_0-rmse:0.29192	validation_1-rmse:0.29192
[985]	validation_0-rmse:0.29162	validation_1-rmse:0.29162
[986]	validation_0-rmse:0.29144	validation_1-rmse:0.29144
[987]	validation_0-rmse:0.29128	validation_1-rmse:0.29128
[988]	validation_0-rmse:0.29109	validation_1-rmse:0.29109
[989]	validation_0-rmse:0.29104	validation_1-rmse:0.29104
[990]	validation_0-rmse:0.29092	validation_1-rmse:0.29092
[991]	validation_0-rmse:0.29089	validation_1-rmse:0.29089
[992]	validation_0-rmse:0.29082	validation_1-rmse:0.29082
[993]	validation_0-rmse:0.29067	validation_1-rmse:0.29067
[994]	validation_0-rmse:0.29048	validation_1-rmse:0.29048
[995]	validation_0-rmse:0.29037	validation_1-rmse:0.29037
[996]	validation_0-rmse:0.29019	validation_1-rmse:0.29019
[997]	validation_0-rmse:0.29015	validation_1-rmse:0.29015
[998]	validation_0-rmse:0.29011	validation_1-rmse:0.29011
[999]	validation_0-rmse:0.29000	validation_1-rmse:0.29000
>>> Calculating feature importances (permutation importance, 10 repeats) ...
>>> Setting threshold for feature rejection to 0.0007770172196579605.
>>> Removing rejected features from model data ...

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

- Target variable: SWC_GF1_0.15_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
.YEARDOY                                  0.582189  0.001232
.RECORDNUMBER                             0.439615  0.001299
.YEARMONTH                                0.252445  0.000971
.DOY                                      0.249388  0.000458
.YEARWEEK                                 0.205115  0.000858
.YEAR                                     0.075219  0.000367
.WEEK                                     0.064169  0.000195
TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1         0.032977  0.000159
.MONTH                                    0.009337  0.000037
.SEASON                                   0.007960  0.000027
.HOUR                                     0.003205  0.000070
.PREC_RAIN_TOT_GF1_0.5_1-5                0.002094  0.000014
PREC_RAIN_TOT_GF1_0.5_1                   0.001381  0.000018
.PREC_RAIN_TOT_GF1_0.5_1-3                0.000912  0.000009
.PREC_RAIN_TOT_GF1_0.5_1-4                0.000861  0.000011
.PREC_RAIN_TOT_GF1_0.5_1-1                0.000806  0.000006
.PREC_RAIN_TOT_GF1_0.5_1-2                0.000789  0.000007
.RANDOM                                   0.000777  0.000009

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

--> 17 original input features (before feature reduction): ['PREC_RAIN_TOT_GF1_0.5_1', 'TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1', '.PREC_RAIN_TOT_GF1_0.5_1-5', '.PREC_RAIN_TOT_GF1_0.5_1-4', '.PREC_RAIN_TOT_GF1_0.5_1-3', '.PREC_RAIN_TOT_GF1_0.5_1-2', '.PREC_RAIN_TOT_GF1_0.5_1-1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
--> 5 rejected features (during feature reduction): None.
--> 17 accepted features (after feature reduction): ['.YEARDOY', '.RECORDNUMBER', '.YEARMONTH', '.DOY', '.YEARWEEK', '.YEAR', '.WEEK', 'TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1', '.MONTH', '.SEASON', '.HOUR', '.PREC_RAIN_TOT_GF1_0.5_1-5', 'PREC_RAIN_TOT_GF1_0.5_1', '.PREC_RAIN_TOT_GF1_0.5_1-3', '.PREC_RAIN_TOT_GF1_0.5_1-4', '.PREC_RAIN_TOT_GF1_0.5_1-1', '.PREC_RAIN_TOT_GF1_0.5_1-2']


Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2005-09-09 10:15:00 and 2024-12-31 23:15:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:4.54191	validation_1-rmse:4.54946
[1]	validation_0-rmse:3.77102	validation_1-rmse:3.78538
[2]	validation_0-rmse:3.30914	validation_1-rmse:3.32352
[3]	validation_0-rmse:2.95928	validation_1-rmse:2.97931
[4]	validation_0-rmse:2.64368	validation_1-rmse:2.65810
[5]	validation_0-rmse:2.43508	validation_1-rmse:2.44714
[6]	validation_0-rmse:2.30312	validation_1-rmse:2.31557
[7]	validation_0-rmse:2.19572	validation_1-rmse:2.20634
[8]	validation_0-rmse:2.10559	validation_1-rmse:2.11666
[9]	validation_0-rmse:2.04759	validation_1-rmse:2.05936
[10]	validation_0-rmse:1.97234	validation_1-rmse:1.98113
[11]	validation_0-rmse:1.88055	validation_1-rmse:1.89267
[12]	validation_0-rmse:1.85110	validation_1-rmse:1.86436
[13]	validation_0-rmse:1.77355	validation_1-rmse:1.78692
[14]	validation_0-rmse:1.70463	validation_1-rmse:1.71909
[15]	validation_0-rmse:1.67661	validation_1-rmse:1.69195
[16]	validation_0-rmse:1.64144	validation_1-rmse:1.65778
[17]	validation_0-rmse:1.61687	validation_1-rmse:1.63428
[18]	validation_0-rmse:1.56305	validation_1-rmse:1.57685
[19]	validation_0-rmse:1.52238	validation_1-rmse:1.53566
[20]	validation_0-rmse:1.46578	validation_1-rmse:1.47991
[21]	validation_0-rmse:1.43858	validation_1-rmse:1.45284
[22]	validation_0-rmse:1.40746	validation_1-rmse:1.42138
[23]	validation_0-rmse:1.37697	validation_1-rmse:1.39040
[24]	validation_0-rmse:1.33222	validation_1-rmse:1.34529
[25]	validation_0-rmse:1.30025	validation_1-rmse:1.31552
[26]	validation_0-rmse:1.28762	validation_1-rmse:1.30378
[27]	validation_0-rmse:1.27833	validation_1-rmse:1.29495
[28]	validation_0-rmse:1.26574	validation_1-rmse:1.28225
[29]	validation_0-rmse:1.24295	validation_1-rmse:1.25838
[30]	validation_0-rmse:1.23303	validation_1-rmse:1.24881
[31]	validation_0-rmse:1.22045	validation_1-rmse:1.23622
[32]	validation_0-rmse:1.21526	validation_1-rmse:1.23127
[33]	validation_0-rmse:1.20657	validation_1-rmse:1.22282
[34]	validation_0-rmse:1.18897	validation_1-rmse:1.20455
[35]	validation_0-rmse:1.18110	validation_1-rmse:1.19629
[36]	validation_0-rmse:1.15476	validation_1-rmse:1.17105
[37]	validation_0-rmse:1.15092	validation_1-rmse:1.16777
[38]	validation_0-rmse:1.14026	validation_1-rmse:1.15696
[39]	validation_0-rmse:1.11975	validation_1-rmse:1.13533
[40]	validation_0-rmse:1.10966	validation_1-rmse:1.12533
[41]	validation_0-rmse:1.10369	validation_1-rmse:1.11996
[42]	validation_0-rmse:1.08613	validation_1-rmse:1.10283
[43]	validation_0-rmse:1.07616	validation_1-rmse:1.09333
[44]	validation_0-rmse:1.07379	validation_1-rmse:1.09143
[45]	validation_0-rmse:1.07056	validation_1-rmse:1.08868
[46]	validation_0-rmse:1.06905	validation_1-rmse:1.08801
[47]	validation_0-rmse:1.06577	validation_1-rmse:1.08497
[48]	validation_0-rmse:1.05418	validation_1-rmse:1.07371
[49]	validation_0-rmse:1.04946	validation_1-rmse:1.06919
[50]	validation_0-rmse:1.03864	validation_1-rmse:1.05804
[51]	validation_0-rmse:1.03271	validation_1-rmse:1.05249
[52]	validation_0-rmse:1.03070	validation_1-rmse:1.05091
[53]	validation_0-rmse:1.01963	validation_1-rmse:1.03889
[54]	validation_0-rmse:1.01440	validation_1-rmse:1.03395
[55]	validation_0-rmse:0.99553	validation_1-rmse:1.01519
[56]	validation_0-rmse:0.98669	validation_1-rmse:1.00675
[57]	validation_0-rmse:0.98292	validation_1-rmse:1.00318
[58]	validation_0-rmse:0.97871	validation_1-rmse:0.99918
[59]	validation_0-rmse:0.96988	validation_1-rmse:0.99032
[60]	validation_0-rmse:0.96210	validation_1-rmse:0.98288
[61]	validation_0-rmse:0.95805	validation_1-rmse:0.97991
[62]	validation_0-rmse:0.95462	validation_1-rmse:0.97645
[63]	validation_0-rmse:0.94855	validation_1-rmse:0.97072
[64]	validation_0-rmse:0.94460	validation_1-rmse:0.96673
[65]	validation_0-rmse:0.94092	validation_1-rmse:0.96317
[66]	validation_0-rmse:0.93790	validation_1-rmse:0.96029
[67]	validation_0-rmse:0.93544	validation_1-rmse:0.95808
[68]	validation_0-rmse:0.93372	validation_1-rmse:0.95651
[69]	validation_0-rmse:0.93249	validation_1-rmse:0.95602
[70]	validation_0-rmse:0.92586	validation_1-rmse:0.94927
[71]	validation_0-rmse:0.91380	validation_1-rmse:0.93756
[72]	validation_0-rmse:0.90740	validation_1-rmse:0.93125
[73]	validation_0-rmse:0.90654	validation_1-rmse:0.93089
[74]	validation_0-rmse:0.90280	validation_1-rmse:0.92770
[75]	validation_0-rmse:0.89675	validation_1-rmse:0.92248
[76]	validation_0-rmse:0.88398	validation_1-rmse:0.90913
[77]	validation_0-rmse:0.88086	validation_1-rmse:0.90583
[78]	validation_0-rmse:0.87637	validation_1-rmse:0.90177
[79]	validation_0-rmse:0.86878	validation_1-rmse:0.89557
[80]	validation_0-rmse:0.86165	validation_1-rmse:0.88938
[81]	validation_0-rmse:0.85960	validation_1-rmse:0.88848
[82]	validation_0-rmse:0.85803	validation_1-rmse:0.88716
[83]	validation_0-rmse:0.85145	validation_1-rmse:0.88042
[84]	validation_0-rmse:0.84953	validation_1-rmse:0.87898
[85]	validation_0-rmse:0.84150	validation_1-rmse:0.87115
[86]	validation_0-rmse:0.83951	validation_1-rmse:0.86921
[87]	validation_0-rmse:0.83669	validation_1-rmse:0.86666
[88]	validation_0-rmse:0.83620	validation_1-rmse:0.86653
[89]	validation_0-rmse:0.82976	validation_1-rmse:0.85962
[90]	validation_0-rmse:0.82266	validation_1-rmse:0.85282
[91]	validation_0-rmse:0.82082	validation_1-rmse:0.85163
[92]	validation_0-rmse:0.81544	validation_1-rmse:0.84589
[93]	validation_0-rmse:0.80843	validation_1-rmse:0.83892
[94]	validation_0-rmse:0.80616	validation_1-rmse:0.83729
[95]	validation_0-rmse:0.80415	validation_1-rmse:0.83548
[96]	validation_0-rmse:0.80076	validation_1-rmse:0.83221
[97]	validation_0-rmse:0.79601	validation_1-rmse:0.82745
[98]	validation_0-rmse:0.79464	validation_1-rmse:0.82669
[99]	validation_0-rmse:0.79406	validation_1-rmse:0.82662
[100]	validation_0-rmse:0.79089	validation_1-rmse:0.82329
[101]	validation_0-rmse:0.78691	validation_1-rmse:0.81943
[102]	validation_0-rmse:0.78149	validation_1-rmse:0.81425
[103]	validation_0-rmse:0.77743	validation_1-rmse:0.81024
[104]	validation_0-rmse:0.76947	validation_1-rmse:0.80271
[105]	validation_0-rmse:0.76808	validation_1-rmse:0.80173
[106]	validation_0-rmse:0.76629	validation_1-rmse:0.80083
[107]	validation_0-rmse:0.76391	validation_1-rmse:0.79846
[108]	validation_0-rmse:0.76304	validation_1-rmse:0.79788
[109]	validation_0-rmse:0.76102	validation_1-rmse:0.79615
[110]	validation_0-rmse:0.75299	validation_1-rmse:0.78759
[111]	validation_0-rmse:0.75113	validation_1-rmse:0.78586
[112]	validation_0-rmse:0.74879	validation_1-rmse:0.78385
[113]	validation_0-rmse:0.74678	validation_1-rmse:0.78197
[114]	validation_0-rmse:0.74481	validation_1-rmse:0.78022
[115]	validation_0-rmse:0.74415	validation_1-rmse:0.77964
[116]	validation_0-rmse:0.73359	validation_1-rmse:0.76944
[117]	validation_0-rmse:0.73004	validation_1-rmse:0.76615
[118]	validation_0-rmse:0.72928	validation_1-rmse:0.76612
[119]	validation_0-rmse:0.72802	validation_1-rmse:0.76490
[120]	validation_0-rmse:0.72524	validation_1-rmse:0.76206
[121]	validation_0-rmse:0.72231	validation_1-rmse:0.75895
[122]	validation_0-rmse:0.71954	validation_1-rmse:0.75587
[123]	validation_0-rmse:0.71842	validation_1-rmse:0.75558
[124]	validation_0-rmse:0.71668	validation_1-rmse:0.75374
[125]	validation_0-rmse:0.71397	validation_1-rmse:0.75110
[126]	validation_0-rmse:0.71234	validation_1-rmse:0.74965
[127]	validation_0-rmse:0.70923	validation_1-rmse:0.74636
[128]	validation_0-rmse:0.70753	validation_1-rmse:0.74504
[129]	validation_0-rmse:0.70608	validation_1-rmse:0.74423
[130]	validation_0-rmse:0.70553	validation_1-rmse:0.74429
[131]	validation_0-rmse:0.70472	validation_1-rmse:0.74357
[132]	validation_0-rmse:0.70350	validation_1-rmse:0.74273
[133]	validation_0-rmse:0.70122	validation_1-rmse:0.74010
[134]	validation_0-rmse:0.69984	validation_1-rmse:0.73893
[135]	validation_0-rmse:0.69763	validation_1-rmse:0.73693
[136]	validation_0-rmse:0.69482	validation_1-rmse:0.73452
[137]	validation_0-rmse:0.69322	validation_1-rmse:0.73297
[138]	validation_0-rmse:0.68823	validation_1-rmse:0.72811
[139]	validation_0-rmse:0.68606	validation_1-rmse:0.72621
[140]	validation_0-rmse:0.68464	validation_1-rmse:0.72486
[141]	validation_0-rmse:0.68327	validation_1-rmse:0.72416
[142]	validation_0-rmse:0.67999	validation_1-rmse:0.72145
[143]	validation_0-rmse:0.67655	validation_1-rmse:0.71819
[144]	validation_0-rmse:0.67386	validation_1-rmse:0.71542
[145]	validation_0-rmse:0.67243	validation_1-rmse:0.71416
[146]	validation_0-rmse:0.67024	validation_1-rmse:0.71268
[147]	validation_0-rmse:0.66725	validation_1-rmse:0.70966
[148]	validation_0-rmse:0.66609	validation_1-rmse:0.70887
[149]	validation_0-rmse:0.66511	validation_1-rmse:0.70815
[150]	validation_0-rmse:0.66459	validation_1-rmse:0.70774
[151]	validation_0-rmse:0.66286	validation_1-rmse:0.70614
[152]	validation_0-rmse:0.66025	validation_1-rmse:0.70374
[153]	validation_0-rmse:0.65957	validation_1-rmse:0.70339
[154]	validation_0-rmse:0.65661	validation_1-rmse:0.70056
[155]	validation_0-rmse:0.65494	validation_1-rmse:0.69907
[156]	validation_0-rmse:0.65423	validation_1-rmse:0.69906
[157]	validation_0-rmse:0.65392	validation_1-rmse:0.69910
[158]	validation_0-rmse:0.65260	validation_1-rmse:0.69805
[159]	validation_0-rmse:0.65079	validation_1-rmse:0.69675
[160]	validation_0-rmse:0.64842	validation_1-rmse:0.69495
[161]	validation_0-rmse:0.64772	validation_1-rmse:0.69433
[162]	validation_0-rmse:0.64691	validation_1-rmse:0.69381
[163]	validation_0-rmse:0.64642	validation_1-rmse:0.69344
[164]	validation_0-rmse:0.64611	validation_1-rmse:0.69321
[165]	validation_0-rmse:0.64117	validation_1-rmse:0.68885
[166]	validation_0-rmse:0.64067	validation_1-rmse:0.68837
[167]	validation_0-rmse:0.63977	validation_1-rmse:0.68760
[168]	validation_0-rmse:0.63853	validation_1-rmse:0.68650
[169]	validation_0-rmse:0.63775	validation_1-rmse:0.68623
[170]	validation_0-rmse:0.63211	validation_1-rmse:0.68042
[171]	validation_0-rmse:0.62863	validation_1-rmse:0.67701
[172]	validation_0-rmse:0.62736	validation_1-rmse:0.67565
[173]	validation_0-rmse:0.62557	validation_1-rmse:0.67391
[174]	validation_0-rmse:0.62274	validation_1-rmse:0.67143
[175]	validation_0-rmse:0.62059	validation_1-rmse:0.66945
[176]	validation_0-rmse:0.61948	validation_1-rmse:0.66917
[177]	validation_0-rmse:0.61835	validation_1-rmse:0.66817
[178]	validation_0-rmse:0.61771	validation_1-rmse:0.66752
[179]	validation_0-rmse:0.61531	validation_1-rmse:0.66475
[180]	validation_0-rmse:0.61413	validation_1-rmse:0.66354
[181]	validation_0-rmse:0.61097	validation_1-rmse:0.66063
[182]	validation_0-rmse:0.61016	validation_1-rmse:0.66046
[183]	validation_0-rmse:0.60891	validation_1-rmse:0.65936
[184]	validation_0-rmse:0.60775	validation_1-rmse:0.65873
[185]	validation_0-rmse:0.60580	validation_1-rmse:0.65693
[186]	validation_0-rmse:0.60523	validation_1-rmse:0.65642
[187]	validation_0-rmse:0.60381	validation_1-rmse:0.65521
[188]	validation_0-rmse:0.60285	validation_1-rmse:0.65451
[189]	validation_0-rmse:0.60027	validation_1-rmse:0.65215
[190]	validation_0-rmse:0.59981	validation_1-rmse:0.65224
[191]	validation_0-rmse:0.59902	validation_1-rmse:0.65206
[192]	validation_0-rmse:0.59760	validation_1-rmse:0.65084
[193]	validation_0-rmse:0.59688	validation_1-rmse:0.65035
[194]	validation_0-rmse:0.59564	validation_1-rmse:0.64911
[195]	validation_0-rmse:0.59468	validation_1-rmse:0.64824
[196]	validation_0-rmse:0.59237	validation_1-rmse:0.64650
[197]	validation_0-rmse:0.59169	validation_1-rmse:0.64594
[198]	validation_0-rmse:0.59076	validation_1-rmse:0.64504
[199]	validation_0-rmse:0.59019	validation_1-rmse:0.64439
[200]	validation_0-rmse:0.58899	validation_1-rmse:0.64325
[201]	validation_0-rmse:0.58761	validation_1-rmse:0.64185
[202]	validation_0-rmse:0.58615	validation_1-rmse:0.64059
[203]	validation_0-rmse:0.58404	validation_1-rmse:0.63863
[204]	validation_0-rmse:0.58291	validation_1-rmse:0.63799
[205]	validation_0-rmse:0.58244	validation_1-rmse:0.63822
[206]	validation_0-rmse:0.58174	validation_1-rmse:0.63755
[207]	validation_0-rmse:0.58087	validation_1-rmse:0.63681
[208]	validation_0-rmse:0.57858	validation_1-rmse:0.63517
[209]	validation_0-rmse:0.57806	validation_1-rmse:0.63513
[210]	validation_0-rmse:0.57602	validation_1-rmse:0.63340
[211]	validation_0-rmse:0.57368	validation_1-rmse:0.63099
[212]	validation_0-rmse:0.57317	validation_1-rmse:0.63080
[213]	validation_0-rmse:0.57162	validation_1-rmse:0.62920
[214]	validation_0-rmse:0.56963	validation_1-rmse:0.62737
[215]	validation_0-rmse:0.56880	validation_1-rmse:0.62677
[216]	validation_0-rmse:0.56698	validation_1-rmse:0.62493
[217]	validation_0-rmse:0.56579	validation_1-rmse:0.62376
[218]	validation_0-rmse:0.56535	validation_1-rmse:0.62336
[219]	validation_0-rmse:0.56390	validation_1-rmse:0.62216
[220]	validation_0-rmse:0.56314	validation_1-rmse:0.62144
[221]	validation_0-rmse:0.56238	validation_1-rmse:0.62072
[222]	validation_0-rmse:0.56149	validation_1-rmse:0.61999
[223]	validation_0-rmse:0.56090	validation_1-rmse:0.61944
[224]	validation_0-rmse:0.56012	validation_1-rmse:0.61896
[225]	validation_0-rmse:0.55969	validation_1-rmse:0.61895
[226]	validation_0-rmse:0.55927	validation_1-rmse:0.61887
[227]	validation_0-rmse:0.55904	validation_1-rmse:0.61869
[228]	validation_0-rmse:0.55868	validation_1-rmse:0.61843
[229]	validation_0-rmse:0.55673	validation_1-rmse:0.61629
[230]	validation_0-rmse:0.55600	validation_1-rmse:0.61552
[231]	validation_0-rmse:0.55584	validation_1-rmse:0.61540
[232]	validation_0-rmse:0.55501	validation_1-rmse:0.61463
[233]	validation_0-rmse:0.55459	validation_1-rmse:0.61459
[234]	validation_0-rmse:0.55312	validation_1-rmse:0.61322
[235]	validation_0-rmse:0.55263	validation_1-rmse:0.61291
[236]	validation_0-rmse:0.55231	validation_1-rmse:0.61264
[237]	validation_0-rmse:0.55128	validation_1-rmse:0.61151
[238]	validation_0-rmse:0.54973	validation_1-rmse:0.60988
[239]	validation_0-rmse:0.54915	validation_1-rmse:0.60938
[240]	validation_0-rmse:0.54821	validation_1-rmse:0.60853
[241]	validation_0-rmse:0.54644	validation_1-rmse:0.60694
[242]	validation_0-rmse:0.54587	validation_1-rmse:0.60668
[243]	validation_0-rmse:0.54529	validation_1-rmse:0.60617
[244]	validation_0-rmse:0.54453	validation_1-rmse:0.60557
[245]	validation_0-rmse:0.54405	validation_1-rmse:0.60551
[246]	validation_0-rmse:0.54374	validation_1-rmse:0.60561
[247]	validation_0-rmse:0.54239	validation_1-rmse:0.60444
[248]	validation_0-rmse:0.54190	validation_1-rmse:0.60400
[249]	validation_0-rmse:0.54124	validation_1-rmse:0.60344
[250]	validation_0-rmse:0.54086	validation_1-rmse:0.60305
[251]	validation_0-rmse:0.54066	validation_1-rmse:0.60293
[252]	validation_0-rmse:0.54036	validation_1-rmse:0.60269
[253]	validation_0-rmse:0.53950	validation_1-rmse:0.60197
[254]	validation_0-rmse:0.53897	validation_1-rmse:0.60144
[255]	validation_0-rmse:0.53860	validation_1-rmse:0.60156
[256]	validation_0-rmse:0.53813	validation_1-rmse:0.60177
[257]	validation_0-rmse:0.53570	validation_1-rmse:0.59936
[258]	validation_0-rmse:0.53393	validation_1-rmse:0.59770
[259]	validation_0-rmse:0.53247	validation_1-rmse:0.59624
[260]	validation_0-rmse:0.53220	validation_1-rmse:0.59620
[261]	validation_0-rmse:0.53076	validation_1-rmse:0.59488
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[819]	validation_0-rmse:0.31651	validation_1-rmse:0.45366
[820]	validation_0-rmse:0.31639	validation_1-rmse:0.45361
[821]	validation_0-rmse:0.31609	validation_1-rmse:0.45330
[822]	validation_0-rmse:0.31600	validation_1-rmse:0.45321
[823]	validation_0-rmse:0.31595	validation_1-rmse:0.45326
[824]	validation_0-rmse:0.31570	validation_1-rmse:0.45309
[825]	validation_0-rmse:0.31552	validation_1-rmse:0.45304
[826]	validation_0-rmse:0.31530	validation_1-rmse:0.45289
[827]	validation_0-rmse:0.31525	validation_1-rmse:0.45287
[828]	validation_0-rmse:0.31490	validation_1-rmse:0.45260
[829]	validation_0-rmse:0.31474	validation_1-rmse:0.45248
[830]	validation_0-rmse:0.31440	validation_1-rmse:0.45229
[831]	validation_0-rmse:0.31411	validation_1-rmse:0.45209
[832]	validation_0-rmse:0.31381	validation_1-rmse:0.45187
[833]	validation_0-rmse:0.31361	validation_1-rmse:0.45199
[834]	validation_0-rmse:0.31346	validation_1-rmse:0.45194
[835]	validation_0-rmse:0.31330	validation_1-rmse:0.45181
[836]	validation_0-rmse:0.31304	validation_1-rmse:0.45161
[837]	validation_0-rmse:0.31300	validation_1-rmse:0.45160
[838]	validation_0-rmse:0.31295	validation_1-rmse:0.45157
[839]	validation_0-rmse:0.31273	validation_1-rmse:0.45140
[840]	validation_0-rmse:0.31233	validation_1-rmse:0.45124
[841]	validation_0-rmse:0.31215	validation_1-rmse:0.45115
[842]	validation_0-rmse:0.31196	validation_1-rmse:0.45105
[843]	validation_0-rmse:0.31177	validation_1-rmse:0.45095
[844]	validation_0-rmse:0.31157	validation_1-rmse:0.45085
[845]	validation_0-rmse:0.31141	validation_1-rmse:0.45081
[846]	validation_0-rmse:0.31105	validation_1-rmse:0.45063
[847]	validation_0-rmse:0.31085	validation_1-rmse:0.45050
[848]	validation_0-rmse:0.31074	validation_1-rmse:0.45054
[849]	validation_0-rmse:0.31071	validation_1-rmse:0.45054
[850]	validation_0-rmse:0.31069	validation_1-rmse:0.45053
[851]	validation_0-rmse:0.31058	validation_1-rmse:0.45049
[852]	validation_0-rmse:0.31040	validation_1-rmse:0.45038
[853]	validation_0-rmse:0.31021	validation_1-rmse:0.45024
[854]	validation_0-rmse:0.30990	validation_1-rmse:0.45001
[855]	validation_0-rmse:0.30959	validation_1-rmse:0.44969
[856]	validation_0-rmse:0.30937	validation_1-rmse:0.44967
[857]	validation_0-rmse:0.30928	validation_1-rmse:0.44968
[858]	validation_0-rmse:0.30914	validation_1-rmse:0.44964
[859]	validation_0-rmse:0.30885	validation_1-rmse:0.44949
[860]	validation_0-rmse:0.30875	validation_1-rmse:0.44949
[861]	validation_0-rmse:0.30860	validation_1-rmse:0.44928
[862]	validation_0-rmse:0.30818	validation_1-rmse:0.44911
[863]	validation_0-rmse:0.30788	validation_1-rmse:0.44893
[864]	validation_0-rmse:0.30775	validation_1-rmse:0.44886
[865]	validation_0-rmse:0.30743	validation_1-rmse:0.44864
[866]	validation_0-rmse:0.30727	validation_1-rmse:0.44852
[867]	validation_0-rmse:0.30719	validation_1-rmse:0.44849
[868]	validation_0-rmse:0.30706	validation_1-rmse:0.44842
[869]	validation_0-rmse:0.30694	validation_1-rmse:0.44834
[870]	validation_0-rmse:0.30659	validation_1-rmse:0.44813
[871]	validation_0-rmse:0.30652	validation_1-rmse:0.44812
[872]	validation_0-rmse:0.30646	validation_1-rmse:0.44818
[873]	validation_0-rmse:0.30624	validation_1-rmse:0.44803
[874]	validation_0-rmse:0.30618	validation_1-rmse:0.44800
[875]	validation_0-rmse:0.30605	validation_1-rmse:0.44789
[876]	validation_0-rmse:0.30600	validation_1-rmse:0.44787
[877]	validation_0-rmse:0.30591	validation_1-rmse:0.44787
[878]	validation_0-rmse:0.30582	validation_1-rmse:0.44786
[879]	validation_0-rmse:0.30578	validation_1-rmse:0.44784
[880]	validation_0-rmse:0.30565	validation_1-rmse:0.44770
[881]	validation_0-rmse:0.30546	validation_1-rmse:0.44756
[882]	validation_0-rmse:0.30525	validation_1-rmse:0.44750
[883]	validation_0-rmse:0.30518	validation_1-rmse:0.44756
[884]	validation_0-rmse:0.30516	validation_1-rmse:0.44755
[885]	validation_0-rmse:0.30472	validation_1-rmse:0.44712
[886]	validation_0-rmse:0.30455	validation_1-rmse:0.44700
[887]	validation_0-rmse:0.30444	validation_1-rmse:0.44708
[888]	validation_0-rmse:0.30438	validation_1-rmse:0.44704
[889]	validation_0-rmse:0.30389	validation_1-rmse:0.44666
[890]	validation_0-rmse:0.30368	validation_1-rmse:0.44651
[891]	validation_0-rmse:0.30355	validation_1-rmse:0.44643
[892]	validation_0-rmse:0.30331	validation_1-rmse:0.44631
[893]	validation_0-rmse:0.30320	validation_1-rmse:0.44626
[894]	validation_0-rmse:0.30297	validation_1-rmse:0.44618
[895]	validation_0-rmse:0.30290	validation_1-rmse:0.44614
[896]	validation_0-rmse:0.30242	validation_1-rmse:0.44563
[897]	validation_0-rmse:0.30229	validation_1-rmse:0.44556
[898]	validation_0-rmse:0.30200	validation_1-rmse:0.44537
[899]	validation_0-rmse:0.30185	validation_1-rmse:0.44524
[900]	validation_0-rmse:0.30178	validation_1-rmse:0.44514
[901]	validation_0-rmse:0.30170	validation_1-rmse:0.44509
[902]	validation_0-rmse:0.30167	validation_1-rmse:0.44507
[903]	validation_0-rmse:0.30158	validation_1-rmse:0.44501
[904]	validation_0-rmse:0.30143	validation_1-rmse:0.44491
[905]	validation_0-rmse:0.30137	validation_1-rmse:0.44493
[906]	validation_0-rmse:0.30129	validation_1-rmse:0.44492
[907]	validation_0-rmse:0.30122	validation_1-rmse:0.44495
[908]	validation_0-rmse:0.30115	validation_1-rmse:0.44495
[909]	validation_0-rmse:0.30112	validation_1-rmse:0.44498
[910]	validation_0-rmse:0.30084	validation_1-rmse:0.44486
[911]	validation_0-rmse:0.30081	validation_1-rmse:0.44486
[912]	validation_0-rmse:0.30024	validation_1-rmse:0.44466
[913]	validation_0-rmse:0.30019	validation_1-rmse:0.44464
[914]	validation_0-rmse:0.30017	validation_1-rmse:0.44462
[915]	validation_0-rmse:0.30008	validation_1-rmse:0.44457
[916]	validation_0-rmse:0.30004	validation_1-rmse:0.44455
[917]	validation_0-rmse:0.29996	validation_1-rmse:0.44452
[918]	validation_0-rmse:0.29991	validation_1-rmse:0.44450
[919]	validation_0-rmse:0.29969	validation_1-rmse:0.44435
[920]	validation_0-rmse:0.29967	validation_1-rmse:0.44433
[921]	validation_0-rmse:0.29963	validation_1-rmse:0.44432
[922]	validation_0-rmse:0.29958	validation_1-rmse:0.44428
[923]	validation_0-rmse:0.29929	validation_1-rmse:0.44409
[924]	validation_0-rmse:0.29922	validation_1-rmse:0.44412
[925]	validation_0-rmse:0.29898	validation_1-rmse:0.44402
[926]	validation_0-rmse:0.29870	validation_1-rmse:0.44387
[927]	validation_0-rmse:0.29857	validation_1-rmse:0.44387
[928]	validation_0-rmse:0.29849	validation_1-rmse:0.44382
[929]	validation_0-rmse:0.29835	validation_1-rmse:0.44372
[930]	validation_0-rmse:0.29805	validation_1-rmse:0.44370
[931]	validation_0-rmse:0.29767	validation_1-rmse:0.44343
[932]	validation_0-rmse:0.29752	validation_1-rmse:0.44346
[933]	validation_0-rmse:0.29700	validation_1-rmse:0.44350
[934]	validation_0-rmse:0.29675	validation_1-rmse:0.44333
[935]	validation_0-rmse:0.29668	validation_1-rmse:0.44334
[936]	validation_0-rmse:0.29665	validation_1-rmse:0.44333
[937]	validation_0-rmse:0.29636	validation_1-rmse:0.44310
[938]	validation_0-rmse:0.29599	validation_1-rmse:0.44281
[939]	validation_0-rmse:0.29574	validation_1-rmse:0.44262
[940]	validation_0-rmse:0.29570	validation_1-rmse:0.44265
[941]	validation_0-rmse:0.29546	validation_1-rmse:0.44248
[942]	validation_0-rmse:0.29532	validation_1-rmse:0.44244
[943]	validation_0-rmse:0.29528	validation_1-rmse:0.44244
[944]	validation_0-rmse:0.29522	validation_1-rmse:0.44245
[945]	validation_0-rmse:0.29475	validation_1-rmse:0.44225
[946]	validation_0-rmse:0.29464	validation_1-rmse:0.44219
[947]	validation_0-rmse:0.29449	validation_1-rmse:0.44220
[948]	validation_0-rmse:0.29441	validation_1-rmse:0.44224
[949]	validation_0-rmse:0.29387	validation_1-rmse:0.44187
[950]	validation_0-rmse:0.29364	validation_1-rmse:0.44184
[951]	validation_0-rmse:0.29343	validation_1-rmse:0.44178
[952]	validation_0-rmse:0.29335	validation_1-rmse:0.44178
[953]	validation_0-rmse:0.29322	validation_1-rmse:0.44170
[954]	validation_0-rmse:0.29316	validation_1-rmse:0.44168
[955]	validation_0-rmse:0.29307	validation_1-rmse:0.44165
[956]	validation_0-rmse:0.29269	validation_1-rmse:0.44151
[957]	validation_0-rmse:0.29258	validation_1-rmse:0.44153
[958]	validation_0-rmse:0.29237	validation_1-rmse:0.44129
[959]	validation_0-rmse:0.29226	validation_1-rmse:0.44144
[960]	validation_0-rmse:0.29206	validation_1-rmse:0.44133
[961]	validation_0-rmse:0.29191	validation_1-rmse:0.44121
[962]	validation_0-rmse:0.29175	validation_1-rmse:0.44111
[963]	validation_0-rmse:0.29159	validation_1-rmse:0.44100
[964]	validation_0-rmse:0.29119	validation_1-rmse:0.44076
[965]	validation_0-rmse:0.29081	validation_1-rmse:0.44046
[966]	validation_0-rmse:0.29043	validation_1-rmse:0.44033
[967]	validation_0-rmse:0.29027	validation_1-rmse:0.44023
[968]	validation_0-rmse:0.29018	validation_1-rmse:0.44019
[969]	validation_0-rmse:0.28992	validation_1-rmse:0.44005
[970]	validation_0-rmse:0.28978	validation_1-rmse:0.43993
[971]	validation_0-rmse:0.28970	validation_1-rmse:0.43994
[972]	validation_0-rmse:0.28958	validation_1-rmse:0.43994
[973]	validation_0-rmse:0.28936	validation_1-rmse:0.43971
[974]	validation_0-rmse:0.28923	validation_1-rmse:0.43971
[975]	validation_0-rmse:0.28909	validation_1-rmse:0.43963
[976]	validation_0-rmse:0.28888	validation_1-rmse:0.43947
[977]	validation_0-rmse:0.28871	validation_1-rmse:0.43935
[978]	validation_0-rmse:0.28869	validation_1-rmse:0.43934
[979]	validation_0-rmse:0.28854	validation_1-rmse:0.43922
[980]	validation_0-rmse:0.28840	validation_1-rmse:0.43911
[981]	validation_0-rmse:0.28820	validation_1-rmse:0.43894
[982]	validation_0-rmse:0.28805	validation_1-rmse:0.43887
[983]	validation_0-rmse:0.28787	validation_1-rmse:0.43870
[984]	validation_0-rmse:0.28768	validation_1-rmse:0.43857
[985]	validation_0-rmse:0.28760	validation_1-rmse:0.43852
[986]	validation_0-rmse:0.28743	validation_1-rmse:0.43839
[987]	validation_0-rmse:0.28733	validation_1-rmse:0.43832
[988]	validation_0-rmse:0.28716	validation_1-rmse:0.43825
[989]	validation_0-rmse:0.28712	validation_1-rmse:0.43822
[990]	validation_0-rmse:0.28707	validation_1-rmse:0.43827
[991]	validation_0-rmse:0.28675	validation_1-rmse:0.43806
[992]	validation_0-rmse:0.28656	validation_1-rmse:0.43796
[993]	validation_0-rmse:0.28627	validation_1-rmse:0.43785
[994]	validation_0-rmse:0.28615	validation_1-rmse:0.43787
[995]	validation_0-rmse:0.28594	validation_1-rmse:0.43763
[996]	validation_0-rmse:0.28580	validation_1-rmse:0.43760
[997]	validation_0-rmse:0.28576	validation_1-rmse:0.43762
[998]	validation_0-rmse:0.28570	validation_1-rmse:0.43760
[999]	validation_0-rmse:0.28561	validation_1-rmse:0.43754
>>> Using model to predict target SWC_GF1_0.15_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 SWC_GF1_0.15_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

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

## DATA
  > target: SWC_GF1_0.15_1
  > features: 17 ['.YEARDOY', '.RECORDNUMBER', '.YEARMONTH', '.DOY', '.YEARWEEK', '.YEAR', '.WEEK', 'TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1', '.MONTH', '.SEASON', '.HOUR', '.PREC_RAIN_TOT_GF1_0.5_1-5', 'PREC_RAIN_TOT_GF1_0.5_1', '.PREC_RAIN_TOT_GF1_0.5_1-3', '.PREC_RAIN_TOT_GF1_0.5_1-4', '.PREC_RAIN_TOT_GF1_0.5_1-1', '.PREC_RAIN_TOT_GF1_0.5_1-2']
  > 350640 records (with missing)
  > 328467 available records for target and all features (no missing values)
  > training on 246350 records (75.0%) of 246350 features between 2005-09-09 10:15:00 and 2024-12-31 23:15:00
  > testing on 82117 unseen records (25.0%) of SWC_GF1_0.15_1 between 2005-09-09 09:45:00 and 2024-12-31 23:45:00

## MODEL
  > the model was trained on training data (246350 records)
  > the model was tested on test data (82117 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:  17
  > names of features used in model:  ['.YEARDOY', '.RECORDNUMBER', '.YEARMONTH', '.DOY', '.YEARWEEK', '.YEAR', '.WEEK', 'TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1', '.MONTH', '.SEASON', '.HOUR', '.PREC_RAIN_TOT_GF1_0.5_1-5', 'PREC_RAIN_TOT_GF1_0.5_1', '.PREC_RAIN_TOT_GF1_0.5_1-3', '.PREC_RAIN_TOT_GF1_0.5_1-4', '.PREC_RAIN_TOT_GF1_0.5_1-1', '.PREC_RAIN_TOT_GF1_0.5_1-2']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of SWC_GF1_0.15_1 (82117 records).
  > feature importances are showing permutation importances from 10 repeats

                                   PERM_IMPORTANCE   PERM_SD
.RECORDNUMBER                             0.567251  0.002313
.YEARDOY                                  0.539671  0.002455
.DOY                                      0.290328  0.001874
.YEARMONTH                                0.261595  0.001834
.YEARWEEK                                 0.240673  0.001311
.WEEK                                     0.046804  0.000215
TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1         0.034528  0.000295
.SEASON                                   0.008375  0.000047
.MONTH                                    0.006224  0.000078
PREC_RAIN_TOT_GF1_0.5_1                   0.003517  0.000036
.HOUR                                     0.002949  0.000112
.PREC_RAIN_TOT_GF1_0.5_1-5                0.001206  0.000017
.PREC_RAIN_TOT_GF1_0.5_1-1                0.000527  0.000024
.PREC_RAIN_TOT_GF1_0.5_1-4                0.000487  0.000013
.PREC_RAIN_TOT_GF1_0.5_1-3                0.000382  0.000019
.PREC_RAIN_TOT_GF1_0.5_1-2                0.000319  0.000015
.YEAR                                     0.000228  0.000007


## MODEL SCORES
  All scores were calculated based on unseen test data (82117 records).
  > MAE:  0.21656241131859102 (mean absolute error)
  > MedAE:  0.105941769150391 (median absolute error)
  > MSE:  0.19143775028767251 (mean squared error)
  > RMSE:  0.43753599884772054 (root mean squared error)
  > MAXE:  9.835889818583983 (max error)
  > MAPE:  0.005 (mean absolute percentage error)
  > R2:  0.9942305598752307


Gap-filling using final model ...
>>> Using final model on all data to predict target SWC_GF1_0.15_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting SWC_GF1_0.15_1 ...
>>> Predicting target SWC_GF1_0.15_1 where all features are available ... predicted 350612 records.
>>> Collecting results for final model ...
>>> Filling 22145 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable SWC_GF1_0.15_1_gfXG ...
>>> Restoring original timestamp in results ...

Gap-filling 28 remaining missing records in SWC_GF1_0.15_1_gfXG using fallback model ...
>>> Fallback model is trained on SWC_GF1_0.15_1_gfXG and timestamp info ...
++ Added new columns with timestamp info: ['.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK'] (ONLY FALLBACK)
[0]	validation_0-rmse:4.49298	validation_1-rmse:4.49298
[1]	validation_0-rmse:3.72983	validation_1-rmse:3.72983
[2]	validation_0-rmse:3.15432	validation_1-rmse:3.15432
[3]	validation_0-rmse:2.75873	validation_1-rmse:2.75873
[4]	validation_0-rmse:2.49513	validation_1-rmse:2.49513
[5]	validation_0-rmse:2.31990	validation_1-rmse:2.31990
[6]	validation_0-rmse:2.16132	validation_1-rmse:2.16132
[7]	validation_0-rmse:2.07469	validation_1-rmse:2.07469
[8]	validation_0-rmse:2.00812	validation_1-rmse:2.00812
[9]	validation_0-rmse:1.93900	validation_1-rmse:1.93900
[10]	validation_0-rmse:1.87427	validation_1-rmse:1.87427
[11]	validation_0-rmse:1.77350	validation_1-rmse:1.77350
[12]	validation_0-rmse:1.71488	validation_1-rmse:1.71488
[13]	validation_0-rmse:1.65352	validation_1-rmse:1.65352
[14]	validation_0-rmse:1.60621	validation_1-rmse:1.60621
[15]	validation_0-rmse:1.57409	validation_1-rmse:1.57409
[16]	validation_0-rmse:1.50295	validation_1-rmse:1.50295
[17]	validation_0-rmse:1.46077	validation_1-rmse:1.46077
[18]	validation_0-rmse:1.42713	validation_1-rmse:1.42713
[19]	validation_0-rmse:1.39244	validation_1-rmse:1.39244
[20]	validation_0-rmse:1.37992	validation_1-rmse:1.37992
[21]	validation_0-rmse:1.35761	validation_1-rmse:1.35761
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[855]	validation_0-rmse:0.45812	validation_1-rmse:0.45812
[856]	validation_0-rmse:0.45801	validation_1-rmse:0.45801
[857]	validation_0-rmse:0.45798	validation_1-rmse:0.45798
[858]	validation_0-rmse:0.45794	validation_1-rmse:0.45794
[859]	validation_0-rmse:0.45792	validation_1-rmse:0.45792
[860]	validation_0-rmse:0.45785	validation_1-rmse:0.45785
[861]	validation_0-rmse:0.45780	validation_1-rmse:0.45780
[862]	validation_0-rmse:0.45777	validation_1-rmse:0.45777
[863]	validation_0-rmse:0.45766	validation_1-rmse:0.45766
[864]	validation_0-rmse:0.45760	validation_1-rmse:0.45760
[865]	validation_0-rmse:0.45755	validation_1-rmse:0.45755
[866]	validation_0-rmse:0.45751	validation_1-rmse:0.45751
[867]	validation_0-rmse:0.45750	validation_1-rmse:0.45750
[868]	validation_0-rmse:0.45743	validation_1-rmse:0.45743
[869]	validation_0-rmse:0.45743	validation_1-rmse:0.45743
[870]	validation_0-rmse:0.45738	validation_1-rmse:0.45738
[871]	validation_0-rmse:0.45732	validation_1-rmse:0.45732
[872]	validation_0-rmse:0.45731	validation_1-rmse:0.45731
[873]	validation_0-rmse:0.45727	validation_1-rmse:0.45727
[874]	validation_0-rmse:0.45720	validation_1-rmse:0.45720
[875]	validation_0-rmse:0.45709	validation_1-rmse:0.45709
[876]	validation_0-rmse:0.45700	validation_1-rmse:0.45700
[877]	validation_0-rmse:0.45699	validation_1-rmse:0.45699
[878]	validation_0-rmse:0.45698	validation_1-rmse:0.45698
[879]	validation_0-rmse:0.45684	validation_1-rmse:0.45684
[880]	validation_0-rmse:0.45650	validation_1-rmse:0.45650
[881]	validation_0-rmse:0.45646	validation_1-rmse:0.45646
[882]	validation_0-rmse:0.45639	validation_1-rmse:0.45639
[883]	validation_0-rmse:0.45628	validation_1-rmse:0.45628
[884]	validation_0-rmse:0.45617	validation_1-rmse:0.45617
[885]	validation_0-rmse:0.45605	validation_1-rmse:0.45605
[886]	validation_0-rmse:0.45599	validation_1-rmse:0.45599
[887]	validation_0-rmse:0.45595	validation_1-rmse:0.45595
[888]	validation_0-rmse:0.45590	validation_1-rmse:0.45590
[889]	validation_0-rmse:0.45582	validation_1-rmse:0.45582
[890]	validation_0-rmse:0.45579	validation_1-rmse:0.45579
[891]	validation_0-rmse:0.45546	validation_1-rmse:0.45546
[892]	validation_0-rmse:0.45541	validation_1-rmse:0.45541
[893]	validation_0-rmse:0.45531	validation_1-rmse:0.45531
[894]	validation_0-rmse:0.45527	validation_1-rmse:0.45527
[895]	validation_0-rmse:0.45515	validation_1-rmse:0.45515
[896]	validation_0-rmse:0.45508	validation_1-rmse:0.45508
[897]	validation_0-rmse:0.45505	validation_1-rmse:0.45505
[898]	validation_0-rmse:0.45501	validation_1-rmse:0.45501
[899]	validation_0-rmse:0.45497	validation_1-rmse:0.45497
[900]	validation_0-rmse:0.45492	validation_1-rmse:0.45492
[901]	validation_0-rmse:0.45483	validation_1-rmse:0.45483
[902]	validation_0-rmse:0.45468	validation_1-rmse:0.45468
[903]	validation_0-rmse:0.45458	validation_1-rmse:0.45458
[904]	validation_0-rmse:0.45440	validation_1-rmse:0.45440
[905]	validation_0-rmse:0.45436	validation_1-rmse:0.45436
[906]	validation_0-rmse:0.45429	validation_1-rmse:0.45429
[907]	validation_0-rmse:0.45425	validation_1-rmse:0.45425
[908]	validation_0-rmse:0.45420	validation_1-rmse:0.45420
[909]	validation_0-rmse:0.45417	validation_1-rmse:0.45417
[910]	validation_0-rmse:0.45411	validation_1-rmse:0.45411
[911]	validation_0-rmse:0.45400	validation_1-rmse:0.45400
[912]	validation_0-rmse:0.45398	validation_1-rmse:0.45398
[913]	validation_0-rmse:0.45386	validation_1-rmse:0.45386
[914]	validation_0-rmse:0.45364	validation_1-rmse:0.45364
[915]	validation_0-rmse:0.45360	validation_1-rmse:0.45360
[916]	validation_0-rmse:0.45351	validation_1-rmse:0.45351
[917]	validation_0-rmse:0.45347	validation_1-rmse:0.45347
[918]	validation_0-rmse:0.45339	validation_1-rmse:0.45339
[919]	validation_0-rmse:0.45336	validation_1-rmse:0.45336
[920]	validation_0-rmse:0.45320	validation_1-rmse:0.45320
[921]	validation_0-rmse:0.45319	validation_1-rmse:0.45319
[922]	validation_0-rmse:0.45317	validation_1-rmse:0.45317
[923]	validation_0-rmse:0.45307	validation_1-rmse:0.45307
[924]	validation_0-rmse:0.45301	validation_1-rmse:0.45301
[925]	validation_0-rmse:0.45297	validation_1-rmse:0.45297
[926]	validation_0-rmse:0.45289	validation_1-rmse:0.45289
[927]	validation_0-rmse:0.45287	validation_1-rmse:0.45287
[928]	validation_0-rmse:0.45278	validation_1-rmse:0.45278
[929]	validation_0-rmse:0.45274	validation_1-rmse:0.45274
[930]	validation_0-rmse:0.45273	validation_1-rmse:0.45273
[931]	validation_0-rmse:0.45264	validation_1-rmse:0.45264
[932]	validation_0-rmse:0.45260	validation_1-rmse:0.45260
[933]	validation_0-rmse:0.45253	validation_1-rmse:0.45253
[934]	validation_0-rmse:0.45251	validation_1-rmse:0.45251
[935]	validation_0-rmse:0.45245	validation_1-rmse:0.45245
[936]	validation_0-rmse:0.45223	validation_1-rmse:0.45223
[937]	validation_0-rmse:0.45219	validation_1-rmse:0.45219
[938]	validation_0-rmse:0.45212	validation_1-rmse:0.45212
[939]	validation_0-rmse:0.45210	validation_1-rmse:0.45210
[940]	validation_0-rmse:0.45205	validation_1-rmse:0.45205
[941]	validation_0-rmse:0.45198	validation_1-rmse:0.45198
[942]	validation_0-rmse:0.45189	validation_1-rmse:0.45189
[943]	validation_0-rmse:0.45185	validation_1-rmse:0.45185
[944]	validation_0-rmse:0.45177	validation_1-rmse:0.45177
[945]	validation_0-rmse:0.45169	validation_1-rmse:0.45169
[946]	validation_0-rmse:0.45162	validation_1-rmse:0.45162
[947]	validation_0-rmse:0.45152	validation_1-rmse:0.45152
[948]	validation_0-rmse:0.45149	validation_1-rmse:0.45149
[949]	validation_0-rmse:0.45142	validation_1-rmse:0.45142
[950]	validation_0-rmse:0.45138	validation_1-rmse:0.45138
[951]	validation_0-rmse:0.45131	validation_1-rmse:0.45131
[952]	validation_0-rmse:0.45125	validation_1-rmse:0.45125
[953]	validation_0-rmse:0.45119	validation_1-rmse:0.45119
[954]	validation_0-rmse:0.45116	validation_1-rmse:0.45116
[955]	validation_0-rmse:0.45115	validation_1-rmse:0.45115
[956]	validation_0-rmse:0.45113	validation_1-rmse:0.45113
[957]	validation_0-rmse:0.45091	validation_1-rmse:0.45091
[958]	validation_0-rmse:0.45087	validation_1-rmse:0.45087
[959]	validation_0-rmse:0.45085	validation_1-rmse:0.45085
[960]	validation_0-rmse:0.45074	validation_1-rmse:0.45074
[961]	validation_0-rmse:0.45064	validation_1-rmse:0.45064
[962]	validation_0-rmse:0.45053	validation_1-rmse:0.45053
[963]	validation_0-rmse:0.45049	validation_1-rmse:0.45049
[964]	validation_0-rmse:0.45045	validation_1-rmse:0.45045
[965]	validation_0-rmse:0.45038	validation_1-rmse:0.45038
[966]	validation_0-rmse:0.45034	validation_1-rmse:0.45034
[967]	validation_0-rmse:0.45033	validation_1-rmse:0.45033
[968]	validation_0-rmse:0.45027	validation_1-rmse:0.45027
[969]	validation_0-rmse:0.45023	validation_1-rmse:0.45023
[970]	validation_0-rmse:0.45017	validation_1-rmse:0.45017
[971]	validation_0-rmse:0.45012	validation_1-rmse:0.45012
[972]	validation_0-rmse:0.45009	validation_1-rmse:0.45009
[973]	validation_0-rmse:0.45006	validation_1-rmse:0.45006
[974]	validation_0-rmse:0.45000	validation_1-rmse:0.45000
[975]	validation_0-rmse:0.44996	validation_1-rmse:0.44996
[976]	validation_0-rmse:0.44988	validation_1-rmse:0.44988
[977]	validation_0-rmse:0.44985	validation_1-rmse:0.44985
[978]	validation_0-rmse:0.44978	validation_1-rmse:0.44978
[979]	validation_0-rmse:0.44975	validation_1-rmse:0.44975
[980]	validation_0-rmse:0.44970	validation_1-rmse:0.44970
[981]	validation_0-rmse:0.44968	validation_1-rmse:0.44968
[982]	validation_0-rmse:0.44966	validation_1-rmse:0.44966
[983]	validation_0-rmse:0.44960	validation_1-rmse:0.44960
[984]	validation_0-rmse:0.44958	validation_1-rmse:0.44958
[985]	validation_0-rmse:0.44949	validation_1-rmse:0.44949
[986]	validation_0-rmse:0.44942	validation_1-rmse:0.44942
[987]	validation_0-rmse:0.44938	validation_1-rmse:0.44938
[988]	validation_0-rmse:0.44929	validation_1-rmse:0.44929
[989]	validation_0-rmse:0.44922	validation_1-rmse:0.44922
[990]	validation_0-rmse:0.44921	validation_1-rmse:0.44921
[991]	validation_0-rmse:0.44919	validation_1-rmse:0.44919
[992]	validation_0-rmse:0.44912	validation_1-rmse:0.44912
[993]	validation_0-rmse:0.44904	validation_1-rmse:0.44904
[994]	validation_0-rmse:0.44897	validation_1-rmse:0.44897
[995]	validation_0-rmse:0.44893	validation_1-rmse:0.44893
[996]	validation_0-rmse:0.44890	validation_1-rmse:0.44890
[997]	validation_0-rmse:0.44888	validation_1-rmse:0.44888
[998]	validation_0-rmse:0.44887	validation_1-rmse:0.44887
[999]	validation_0-rmse:0.44881	validation_1-rmse:0.44881
>>> Predicting target SWC_GF1_0.15_1_gfXG using fallback model ...
>>> Combining predictions from full model and fallback model ...

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

Model scores and feature importances were calculated from high-quality predicted targets (22145 values, SWC_GF1_0.15_1_gfXG where flag=1) in comparison to observed targets (328467 values, SWC_GF1_0.15_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):  SWC_GF1_0.15_1
- missing records (observed):  22173 (cross-check from flag: 22173)
- target column (gap-filled):  SWC_GF1_0.15_1_gfXG  (350640 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_SWC_GF1_0.15_1_gfXG_ISFILLED
  > flag 0 ... observed targets (328467 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (22145 values)
  > flag 2 ... targets gap-filled with fallback (28 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['.RECORDNUMBER', '.YEARDOY', '.DOY', '.YEARMONTH', '.YEARWEEK', '.WEEK', 'TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1', '.SEASON', '.MONTH', 'PREC_RAIN_TOT_GF1_0.5_1', '.HOUR', '.PREC_RAIN_TOT_GF1_0.5_1-5', '.PREC_RAIN_TOT_GF1_0.5_1-3', '.PREC_RAIN_TOT_GF1_0.5_1-4', '.PREC_RAIN_TOT_GF1_0.5_1-1', '.PREC_RAIN_TOT_GF1_0.5_1-2', '.YEAR']
- number of features used in model:  17
- permutation importances were calculated from 10 repeats.

                                   PERM_IMPORTANCE   PERM_SD
.RECORDNUMBER                             0.569959  0.001116
.YEARDOY                                  0.540536  0.001854
.DOY                                      0.290236  0.000615
.YEARMONTH                                0.261259  0.000668
.YEARWEEK                                 0.241586  0.000843
.WEEK                                     0.046470  0.000144
TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1         0.034402  0.000138
.SEASON                                   0.008311  0.000031
.MONTH                                    0.006219  0.000020
PREC_RAIN_TOT_GF1_0.5_1                   0.003880  0.000016
.HOUR                                     0.003531  0.000058
.PREC_RAIN_TOT_GF1_0.5_1-5                0.001743  0.000008
.PREC_RAIN_TOT_GF1_0.5_1-3                0.000824  0.000005
.PREC_RAIN_TOT_GF1_0.5_1-4                0.000806  0.000007
.PREC_RAIN_TOT_GF1_0.5_1-1                0.000785  0.000008
.PREC_RAIN_TOT_GF1_0.5_1-2                0.000682  0.000005
.YEAR                                     0.000217  0.000002

## 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:  0.17563355080639423 (mean absolute error)
- MedAE:  0.09637703613281445 (median absolute error)
- MSE:  0.10903946131018935 (mean squared error)
- RMSE:  0.3302112374074955 (root mean squared error)
- MAXE:  9.835889818583983 (max error)
- MAPE:  0.004 (mean absolute percentage error)
- R2:  0.9967097676559689

Sort column order

df = df.reindex(sorted(df.columns), axis=1)  # Sort column order

Plot

df.plot(x_compat=True, subplots=True, figsize=(20, 14));

Save to file

OUTNAME = "17.5_CH-CHA_meteo10_2005-2024"
filepath = save_parquet(filename=OUTNAME, data=df)
# df.to_csv(f"{OUTNAME}.csv")

Saved file 17.5_CH-CHA_meteo10_2005-2024.parquet (0.436 seconds).

End of notebook.

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

Finished. 2025-01-21 10:04:50