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実験管理 #6

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matsuken92 opened this issue Jun 10, 2019 · 6 comments
Open

実験管理 #6

matsuken92 opened this issue Jun 10, 2019 · 6 comments

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@matsuken92
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matsuken92 commented Jun 10, 2019 •
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train_v003_057.py LB: -
⇒ yiemon feat 1j2j3j + pca_v002 CV : cv score: -
⇒ num_leaves 256, max_depth=-1

train_v003_059.py LB: -1.856
⇒ yiemon feat 1j2j3j HnJ CV : cv score: -1.5654
⇒ num_leaves 256, max_depth=-1

train_v003_064.py LB: -1.889
⇒ yiemon feat 1j2j3j HnJ train_v003_059のimportanceの高いものだけ
CV : cv score: -1.59941
⇒ num_leaves 256, max_depth=-1
nohup_v003_064.out.txt

train_v003_067.py LB: -1.897
⇒ train_v003_064にcircle featureを追加
CV : cv score: -1.6077

train_v003_072.py LB: -1.897
⇒ Objectiveをhuberに
CV : cv score: -1.63236

train_all_v003_074.py LB: -2.073 (25 seeds)
⇒ train_v003_072をベースに全データ学習

train_v003_075.py LB: xxx
⇒ train_v003_067をベースに下記を追加
seg_H2J_stats_feat2_train.pkl
seg_H3J_stats_feat2_train.pkl
seg_H2J_stats_feat2_test.pkl
seg_H3J_stats_feat2_test.pkl
fc CV mean score: -1.3420, std: 0.0048.
CV : cv score: -1.62411

train_v003_076.py LB: xxx
⇒ train_v003_075.py から追加特徴を上位importanceに絞る
fc CV mean score: -1.3426, std: 0.0046., CV : cv score: -1.62323

train_v003_077.py LB: xxx
⇒ train_v003_076.py からfcもtype別に

train_v003_078.py LB: -1.964
⇒ train_v003_076.py からseg_H1J_bond_extension1を追加
⇒ CV: -1.6617

train_v003_079.py LB: xxx
⇒ train_v003_078.py からseg_H1J_bond_extension1を重要度高いものに絞る
⇒ CV: -1.67293

train_v003_080.py LB: -1.977
⇒ train_v003_079.py からさらに全体の特徴からも重要度高いものに絞る
⇒ CV: -1.68498

train_v003_082.py LB: xxx
さらに特徴量を重要度で削減

train_v003_083.py LB: xxx
n_estimatorを増やす

train_v003_083.py LB: xxx
'max_bin': 64,

train_v003_085.py LB: xxx
y_fcをtarget(scalar_coupling_constant)にしてみる

train_v003_096.py LB:
⇒ train_v003_080.py をベースにfc無くしたらどうなるか確認

古い履歴
@matsuken92
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Log of v003_003

nohup.out_v003_003.txt

@matsuken92
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matsuken92 commented Jun 12, 2019 •
edited
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v003_006 score

STARTING : 2019-06-11 16:59:17
Mem. usage decreased to 1239.42 Mb (71.6% reduction)
Mem. usage decreased to 633.21 Mb (71.9% reduction)
Fold 1 started at Tue Jun 11 17:04:58 2019
[8000]	training's l1: 0.307622	valid_1's l1: 0.520485
Fold 2 started at Tue Jun 11 17:22:06 2019
[8000]	training's l1: 0.30656	valid_1's l1: 0.521003
Fold 3 started at Tue Jun 11 17:38:36 2019
[8000]	training's l1: 0.306582	valid_1's l1: 0.520394
Fold 4 started at Tue Jun 11 17:56:04 2019
[8000]	training's l1: 0.305736	valid_1's l1: 0.519107
Fold 5 started at Tue Jun 11 18:13:32 2019
[8000]	training's l1: 0.305946	valid_1's l1: 0.518649
CV mean score: -0.7760, std: 0.0025.
X['type'].unique(): [0 3 1 4 2 6 5 7]

Training of type 0
Fold 1 started at Tue Jun 11 18:31:23 2019
[15000]	training's l1: 0.0199649	valid_1's l1: 0.906052
Fold 2 started at Tue Jun 11 18:40:04 2019
[15000]	training's l1: 0.0198753	valid_1's l1: 0.913156
Fold 3 started at Tue Jun 11 18:48:49 2019
[15000]	training's l1: 0.0198629	valid_1's l1: 0.910464
Fold 4 started at Tue Jun 11 18:57:25 2019
[15000]	training's l1: 0.0197971	valid_1's l1: 0.91228
Fold 5 started at Tue Jun 11 19:06:04 2019
[15000]	training's l1: 0.0198354	valid_1's l1: 0.913914
CV mean score: -0.0930, std: 0.0031.

Training of type 3
Fold 1 started at Tue Jun 11 19:15:23 2019
[15000]	training's l1: 0.00119511	valid_1's l1: 0.229172
Fold 2 started at Tue Jun 11 19:23:32 2019
[15000]	training's l1: 0.0011875	valid_1's l1: 0.228336
Fold 3 started at Tue Jun 11 19:31:32 2019
[15000]	training's l1: 0.00119345	valid_1's l1: 0.228493
Fold 4 started at Tue Jun 11 19:39:29 2019
[15000]	training's l1: 0.00119267	valid_1's l1: 0.22976
Fold 5 started at Tue Jun 11 19:47:20 2019
[15000]	training's l1: 0.00119209	valid_1's l1: 0.229388
CV mean score: -1.4739, std: 0.0024.

Training of type 1
Fold 1 started at Tue Jun 11 19:56:15 2019
[4918]	training's l1: 0.00119977	valid_1's l1: 0.540004
Fold 2 started at Tue Jun 11 19:57:14 2019
[4502]	training's l1: 0.00130429	valid_1's l1: 0.54387
Fold 3 started at Tue Jun 11 19:58:10 2019
[6681]	training's l1: 0.000973862	valid_1's l1: 0.538346
Fold 4 started at Tue Jun 11 19:59:18 2019
[7275]	training's l1: 0.000925833	valid_1's l1: 0.544128
Fold 5 started at Tue Jun 11 20:00:30 2019
[5490]	training's l1: 0.00108372	valid_1's l1: 0.536281
CV mean score: -0.6152, std: 0.0057.

Training of type 4
Fold 1 started at Tue Jun 11 20:02:03 2019
[6334]	training's l1: 0.00123945	valid_1's l1: 0.238972
Fold 2 started at Tue Jun 11 20:04:03 2019
[12232]	training's l1: 0.000677554	valid_1's l1: 0.242251
Fold 3 started at Tue Jun 11 20:07:06 2019
[8054]	training's l1: 0.000869291	valid_1's l1: 0.244561
Fold 4 started at Tue Jun 11 20:09:30 2019
[12660]	training's l1: 0.00067459	valid_1's l1: 0.244021
Fold 5 started at Tue Jun 11 20:12:41 2019
[6786]	training's l1: 0.0010875	valid_1's l1: 0.24058
CV mean score: -1.4185, std: 0.0086.

Training of type 2
Fold 1 started at Tue Jun 11 20:15:20 2019
[15000]	training's l1: 0.024419	valid_1's l1: 0.376461
Fold 2 started at Tue Jun 11 20:27:02 2019
[15000]	training's l1: 0.0243986	valid_1's l1: 0.376453
Fold 3 started at Tue Jun 11 20:38:28 2019
[15000]	training's l1: 0.0242558	valid_1's l1: 0.376576
Fold 4 started at Tue Jun 11 20:49:43 2019
[15000]	training's l1: 0.0244827	valid_1's l1: 0.372675
Fold 5 started at Tue Jun 11 21:01:27 2019
[15000]	training's l1: 0.0243272	valid_1's l1: 0.376481
CV mean score: -0.9789, std: 0.0041.

Training of type 6
Fold 1 started at Tue Jun 11 21:13:47 2019
[15000]	training's l1: 0.00282867	valid_1's l1: 0.201985
Fold 2 started at Tue Jun 11 21:22:23 2019
[15000]	training's l1: 0.0028309	valid_1's l1: 0.202628
Fold 3 started at Tue Jun 11 21:30:58 2019
[15000]	training's l1: 0.00281874	valid_1's l1: 0.203361
Fold 4 started at Tue Jun 11 21:39:33 2019
[15000]	training's l1: 0.00283694	valid_1's l1: 0.201092
Fold 5 started at Tue Jun 11 21:48:01 2019
[15000]	training's l1: 0.00280774	valid_1's l1: 0.202467
CV mean score: -1.5980, std: 0.0037.

Training of type 5
Fold 1 started at Tue Jun 11 21:57:21 2019
[15000]	training's l1: 0.0399831	valid_1's l1: 0.350548
Fold 2 started at Tue Jun 11 22:11:54 2019
[15000]	training's l1: 0.0398623	valid_1's l1: 0.351907
Fold 3 started at Tue Jun 11 22:26:34 2019
[15000]	training's l1: 0.0399994	valid_1's l1: 0.350007
Fold 4 started at Tue Jun 11 22:40:52 2019
[15000]	training's l1: 0.0400033	valid_1's l1: 0.34955
Fold 5 started at Tue Jun 11 22:54:56 2019
[15000]	training's l1: 0.0400072	valid_1's l1: 0.351625
CV mean score: -1.0477, std: 0.0026.

Training of type 7
Fold 1 started at Tue Jun 11 23:09:40 2019
[7199]	training's l1: 0.00118186	valid_1's l1: 0.168515
Fold 2 started at Tue Jun 11 23:12:22 2019
[13843]	training's l1: 0.000634087	valid_1's l1: 0.166695
Fold 3 started at Tue Jun 11 23:16:44 2019
[10761]	training's l1: 0.000736725	valid_1's l1: 0.171416
Fold 4 started at Tue Jun 11 23:20:27 2019
[13103]	training's l1: 0.000652251	valid_1's l1: 0.167788
Fold 5 started at Tue Jun 11 23:24:39 2019
[11342]	training's l1: 0.000711948	valid_1's l1: 0.169089
CV mean score: -1.7797, std: 0.0093.

oof_log_mae: -1.1256088497006402
finished. : 2019-06-11 23:29:16

v003_006 importance LB: -1.327

  type_0 type_1 type_2 type_3 type_4 type_5 type_6 type_7 ave
oof_fc 2.7% 3.0% 3.0% 2.3% 2.9% 3.5% 2.4% 3.6% 2.9%
f006:dist_origin_mean 2.3% 3.4% 1.8% 2.6% 2.6% 2.0% 2.2% 2.8% 2.4%
abs_dist 2.5% 2.9% 2.0% 2.3% 2.3% 1.7% 1.8% 1.9% 2.2%
f006:dist_from_origin_0 2.2% 2.3% 2.1% 2.3% 2.2% 2.0% 2.0% 2.0% 2.1%
molecule_dist_mean 1.9% 2.7% 1.4% 2.2% 1.9% 1.6% 2.0% 2.0% 2.0%
molecule_dist_max 1.9% 2.8% 1.5% 2.2% 2.1% 1.2% 1.9% 2.0% 1.9%
f006:dist_from_origin_1 1.8% 1.9% 1.7% 2.4% 1.8% 1.7% 2.3% 1.7% 1.9%
molecule_atom_1_dist_mean 2.0% 2.4% 1.5% 2.1% 1.8% 1.5% 2.1% 1.7% 1.9%
molecule_atom_index_0_x_1_std 2.0% 2.0% 1.7% 1.8% 1.9% 1.6% 1.7% 1.7% 1.8%
z_0 1.8% 1.8% 1.6% 1.7% 2.1% 1.6% 1.7% 2.0% 1.8%
molecule_atom_1_dist_std_diff 2.4% 3.0% 1.5% 2.5% 1.7% 0.8% 1.2% 1.3% 1.8%
molecule_atom_index_0_z_1_std 2.0% 1.9% 1.6% 1.9% 1.8% 1.7% 1.7% 1.7% 1.8%
z_1 1.7% 1.9% 1.6% 1.9% 1.8% 1.7% 1.7% 1.8% 1.8%
f004:angle_abs 1.8% 2.0% 1.5% 1.6% 2.1% 1.3% 1.7% 1.9% 1.7%
dist_xy 1.8% 1.8% 1.6% 1.8% 1.9% 1.5% 1.6% 1.6% 1.7%
molecule_atom_index_0_y_1_std 1.8% 1.9% 1.5% 1.7% 1.7% 1.5% 1.5% 1.7% 1.7%
dist_xz 2.0% 2.3% 1.6% 1.6% 1.7% 1.4% 1.4% 1.4% 1.7%
molecule_atom_index_0_y_1_mean_div 1.8% 1.8% 1.6% 1.7% 1.7% 1.5% 1.6% 1.5% 1.6%
x_0 1.6% 1.7% 1.5% 1.7% 1.7% 1.5% 1.5% 1.8% 1.6%
dist_yz 1.8% 1.8% 1.5% 1.7% 1.7% 1.4% 1.5% 1.5% 1.6%
x_1 1.5% 1.8% 1.4% 1.8% 1.5% 1.5% 1.5% 1.6% 1.6%
gasteiger_1 1.8% 1.6% 1.8% 0.0% 1.7% 1.9% 1.9% 1.7% 1.5%
f004:angle 1.7% 1.8% 1.4% 1.5% 1.7% 1.3% 1.4% 1.5% 1.5%
gasteiger_0 1.5% 1.0% 1.8% 1.6% 1.1% 2.0% 1.5% 1.1% 1.5%
y_0 1.5% 1.5% 1.4% 1.7% 1.5% 1.4% 1.4% 1.5% 1.5%
molecule_atom_index_0_y_1_mean_diff 1.5% 1.4% 1.4% 1.5% 1.4% 1.2% 1.3% 1.2% 1.4%
molecule_couples 1.4% 1.6% 1.1% 1.3% 1.4% 1.0% 1.2% 1.3% 1.3%
molecule_atom_index_0_dist_max 1.1% 1.0% 1.5% 1.0% 1.2% 1.6% 1.4% 1.5% 1.3%
molecule_type_dist_mean_diff 1.8% 1.5% 1.2% 1.6% 1.5% 0.7% 0.9% 0.9% 1.3%
y_1 1.3% 1.3% 1.2% 1.6% 1.1% 1.3% 1.2% 1.3% 1.3%
molecule_type_0_dist_std_diff 1.0% 1.8% 1.1% 1.9% 1.4% 0.8% 1.1% 0.9% 1.3%
qtpie_0 1.1% 1.3% 1.2% 1.1% 1.3% 1.3% 1.2% 1.2% 1.2%
molecule_type_dist_min 0.6% 0.0% 1.6% 1.2% 0.8% 1.9% 1.9% 1.5% 1.2%
qeq_0 1.1% 1.4% 1.2% 1.2% 1.2% 1.2% 1.1% 1.2% 1.2%
molecule_atom_index_0_y_1_max_diff 1.4% 1.3% 1.2% 1.1% 1.2% 1.0% 1.2% 1.0% 1.2%
eem2015ha_0 1.3% 1.3% 1.0% 1.2% 1.3% 1.0% 1.2% 1.2% 1.2%
eem2015ha_1 1.2% 1.4% 1.1% 1.2% 1.1% 1.1% 1.2% 1.1% 1.2%
eem2015bm_0 1.3% 1.4% 1.0% 1.2% 1.2% 1.0% 1.1% 1.3% 1.2%
molecule_atom_index_0_dist_mean 1.0% 0.7% 1.3% 0.9% 1.1% 1.6% 1.3% 1.4% 1.2%
molecule_type_dist_std_diff 0.7% 0.9% 1.2% 1.5% 1.4% 0.8% 1.2% 1.5% 1.2%
molecule_atom_index_0_dist_std 1.1% 0.8% 1.1% 0.9% 1.1% 1.3% 1.2% 1.5% 1.1%
molecule_type_dist_max 1.1% 0.6% 1.5% 1.4% 0.9% 0.7% 1.4% 1.2% 1.1%
eem2015ba_0 1.2% 1.1% 1.0% 1.1% 1.2% 0.9% 1.1% 1.1% 1.1%
eem2015ba_1 1.2% 1.0% 1.1% 1.1% 0.9% 1.1% 1.1% 0.9% 1.0%
qtpie_1 0.9% 1.0% 1.0% 1.2% 0.9% 1.0% 1.3% 0.9% 1.0%
eem_0 1.2% 1.3% 0.9% 1.0% 1.0% 0.9% 0.9% 1.0% 1.0%
qeq_1 0.9% 1.0% 0.9% 1.2% 0.9% 0.9% 1.3% 0.9% 1.0%
eem2015hm_0 1.2% 1.3% 0.9% 1.0% 1.0% 0.8% 0.9% 1.0% 1.0%
molecule_dist_min 0.9% 0.4% 1.0% 1.5% 0.8% 1.1% 1.3% 0.8% 1.0%
molecule_atom_index_1_dist_std 0.9% 1.1% 0.9% 1.1% 0.8% 1.1% 0.9% 1.0% 1.0%
molecule_atom_index_1_dist_std_diff 0.8% 0.9% 1.0% 1.0% 0.9% 1.0% 1.1% 0.9% 0.9%
eem2015bm_1 0.9% 0.9% 0.8% 1.3% 0.7% 0.8% 1.2% 0.7% 0.9%
eem2015bn_0 1.0% 1.0% 0.8% 0.9% 1.1% 0.7% 0.8% 0.9% 0.9%
molecule_atom_index_1_dist_max 0.8% 0.6% 1.1% 0.6% 0.9% 1.4% 0.8% 0.8% 0.9%
molecule_atom_index_0_dist_std_div 0.6% 0.5% 0.9% 0.7% 0.8% 1.3% 0.9% 1.2% 0.9%
molecule_atom_index_0_dist_std_diff 0.9% 0.7% 1.1% 1.0% 0.8% 1.0% 0.8% 0.7% 0.9%
eem2015hn_0 1.0% 1.1% 0.7% 0.8% 1.0% 0.7% 0.8% 0.8% 0.9%
molecule_atom_index_0_dist_max_div 1.2% 1.2% 1.1% 0.9% 0.9% 0.5% 0.6% 0.3% 0.8%
eem2015hm_1 0.8% 1.0% 0.7% 1.1% 0.7% 0.7% 1.0% 0.7% 0.8%
molecule_atom_index_0_dist_mean_div 1.0% 0.8% 1.0% 0.7% 0.9% 0.9% 0.7% 0.8% 0.8%
molecule_atom_index_1_dist_mean 0.7% 0.5% 1.0% 0.5% 0.8% 1.2% 0.9% 0.9% 0.8%
molecule_atom_index_0_dist_max_diff 0.8% 0.8% 1.1% 0.8% 1.0% 0.7% 0.7% 0.4% 0.8%
molecule_atom_1_dist_min_diff 1.6% 0.7% 0.6% 1.4% 0.6% 0.3% 0.6% 0.5% 0.8%
eem2015bn_1 0.7% 0.9% 0.6% 0.9% 0.7% 0.6% 0.9% 0.7% 0.8%
eem_1 0.7% 0.8% 0.6% 1.1% 0.6% 0.6% 1.0% 0.6% 0.8%
molecule_atom_index_1_dist_std_div 0.5% 0.5% 0.7% 0.5% 0.7% 1.0% 0.8% 1.0% 0.7%
molecule_atom_index_0_dist_mean_diff 0.7% 0.5% 0.9% 0.6% 0.8% 0.8% 0.6% 0.7% 0.7%
molecule_atom_index_0_dist_min 0.5% 0.3% 0.9% 0.6% 0.6% 1.1% 0.7% 0.7% 0.7%
eem2015hn_1 0.6% 0.8% 0.5% 0.9% 0.6% 0.5% 0.8% 0.6% 0.7%
molecule_atom_index_1_dist_min 0.4% 0.2% 0.9% 0.5% 0.6% 1.1% 1.0% 0.7% 0.7%
cos2T 0.0% 0.0% 0.0% 0.0% 0.0% 1.9% 1.4% 1.5% 0.6%
molecule_atom_index_1_dist_max_diff 0.6% 0.5% 0.8% 0.3% 0.8% 0.7% 0.3% 0.5% 0.6%
molecule_atom_index_1_dist_min_diff 1.2% 0.4% 0.7% 0.4% 0.5% 0.6% 0.3% 0.4% 0.6%
molecule_atom_index_1_dist_mean_diff 0.5% 0.4% 0.7% 0.4% 0.6% 0.8% 0.4% 0.7% 0.6%
Torsion 0.0% 0.0% 0.0% 0.0% 0.0% 1.5% 1.3% 1.5% 0.6%
mmff94_1 0.5% 0.3% 1.0% 0.0% 0.5% 1.2% 0.1% 0.7% 0.5%
molecule_atom_index_1_dist_max_div 0.9% 0.6% 0.9% 0.1% 0.7% 0.5% 0.2% 0.3% 0.5%
atom_index_0 0.5% 0.7% 0.5% 0.2% 0.6% 0.5% 0.3% 0.6% 0.5%
molecule_atom_index_1_dist_mean_div 0.7% 0.4% 0.6% 0.1% 0.5% 0.8% 0.3% 0.6% 0.5%
Angle 0.0% 0.0% 1.8% 0.9% 1.3% 0.0% 0.0% 0.0% 0.5%
molecule_type_dist_mean_div 0.1% 0.0% 0.6% 0.2% 0.5% 0.6% 0.8% 0.8% 0.5%
molecule_atom_1_dist_min_div 0.2% 0.0% 0.7% 0.2% 0.5% 0.5% 0.7% 0.5% 0.4%
molecule_atom_index_0_dist_min_div 0.0% 0.0% 0.7% 0.4% 0.5% 0.6% 0.5% 0.6% 0.4%
atom_index_1 0.4% 0.2% 0.4% 0.3% 0.3% 0.5% 0.4% 0.3% 0.4%
molecule_atom_index_0_dist_min_diff 0.0% 0.0% 0.6% 0.8% 0.5% 0.4% 0.4% 0.3% 0.4%
cosT 0.0% 0.0% 0.0% 0.0% 0.0% 1.2% 0.8% 0.6% 0.3%
H 0.4% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%
molecule_atom_index_1_dist_min_div 0.0% 0.0% 0.6% 0.0% 0.3% 0.6% 0.2% 0.4% 0.3%
dist 0.0% 0.0% 0.5% 0.3% 0.3% 0.3% 0.4% 0.3% 0.3%
atom_1_couples_count 0.3% 0.1% 0.4% 0.0% 0.2% 0.4% 0.0% 0.3% 0.2%
N 0.2% 0.2% 0.2% 0.2% 0.1% 0.2% 0.2% 0.2% 0.2%
O 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2%
atom_0_couples_count 0.3% 0.1% 0.3% 0.1% 0.1% 0.3% 0.2% 0.1% 0.2%
C 0.2% 0.2% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%
mmff94_0 0.1% 0.0% 0.1% 0.0% 0.1% 0.1% 0.0% 0.0% 0.1%
F 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
sp 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
type 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

@matsuken92
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matsuken92 commented Jun 13, 2019 •
edited
Loading

v003_009 LB -1.416

STARTING : 2019-06-13 05:12:15

Fold 1 started at Thu Jun 13 05:20:21 2019
[8000]	training's l1: 0.275524	valid_1's l1: 0.469715
Fold 2 started at Thu Jun 13 05:45:24 2019
[8000]	training's l1: 0.275445	valid_1's l1: 0.47087
Fold 3 started at Thu Jun 13 06:10:52 2019
[8000]	training's l1: 0.27503	valid_1's l1: 0.469924
Fold 4 started at Thu Jun 13 06:34:39 2019
[8000]	training's l1: 0.275247	valid_1's l1: 0.470253
Fold 5 started at Thu Jun 13 07:00:13 2019
CV mean score: -0.8756, std: 0.0017.

Training of type 0
X_t.shape: (709416, 147), X_test_t.shape: (380609, 147)
Fold 1 started at Thu Jun 13 07:25:46 2019
[15000]	training's l1: 0.019366	valid_1's l1: 0.866321
Fold 2 started at Thu Jun 13 07:37:30 2019
[15000]	training's l1: 0.0191301	valid_1's l1: 0.870355
Fold 3 started at Thu Jun 13 07:49:18 2019
[15000]	training's l1: 0.0191912	valid_1's l1: 0.867929
Fold 4 started at Thu Jun 13 08:00:56 2019
[15000]	training's l1: 0.0193442	valid_1's l1: 0.869011
Fold 5 started at Thu Jun 13 08:12:20 2019
CV mean score: -0.1408, std: 0.0017.

Training of type 3
X_t.shape: (378036, 147), X_test_t.shape: (203126, 147)
Fold 1 started at Thu Jun 13 08:24:57 2019
[15000]	training's l1: 0.00115351	valid_1's l1: 0.212802
Fold 2 started at Thu Jun 13 08:34:11 2019
[15000]	training's l1: 0.00114644	valid_1's l1: 0.213157
Fold 3 started at Thu Jun 13 08:43:30 2019
[15000]	training's l1: 0.00114995	valid_1's l1: 0.213095
Fold 4 started at Thu Jun 13 08:52:51 2019
[15000]	training's l1: 0.00115019	valid_1's l1: 0.212825
Fold 5 started at Thu Jun 13 09:02:01 2019
CV mean score: -1.5465, std: 0.0007.

Training of type 1
X_t.shape: (43363, 147), X_test_t.shape: (24195, 147)
Fold 1 started at Thu Jun 13 09:12:25 2019
[6562]	training's l1: 0.000972868	valid_1's l1: 0.513828
Fold 2 started at Thu Jun 13 09:13:43 2019
[5606]	training's l1: 0.00110532	valid_1's l1: 0.520146
Fold 3 started at Thu Jun 13 09:14:51 2019
[7601]	training's l1: 0.000906742	valid_1's l1: 0.518258
Fold 4 started at Thu Jun 13 09:16:10 2019
[6771]	training's l1: 0.000962967	valid_1's l1: 0.517405
Fold 5 started at Thu Jun 13 09:17:28 2019
CV mean score: -0.6598, std: 0.0044.

Training of type 4
X_t.shape: (119253, 147), X_test_t.shape: (64424, 147)
Fold 1 started at Thu Jun 13 09:19:17 2019
[8205]	training's l1: 0.000852979	valid_1's l1: 0.222738
Fold 2 started at Thu Jun 13 09:22:16 2019
[7345]	training's l1: 0.000927949	valid_1's l1: 0.223598
Fold 3 started at Thu Jun 13 09:25:05 2019
[11940]	training's l1: 0.000677343	valid_1's l1: 0.224487
Fold 4 started at Thu Jun 13 09:28:57 2019
[8405]	training's l1: 0.000839033	valid_1's l1: 0.224196
Fold 5 started at Thu Jun 13 09:31:59 2019
CV mean score: -1.4962, std: 0.0033.

Training of type 2
X_t.shape: (1140674, 147), X_test_t.shape: (613138, 147)
Fold 1 started at Thu Jun 13 09:36:14 2019
[15000]	training's l1: 0.0221611	valid_1's l1: 0.335969
Fold 2 started at Thu Jun 13 09:51:03 2019
[15000]	training's l1: 0.0221469	valid_1's l1: 0.336252
Fold 3 started at Thu Jun 13 10:06:04 2019
[15000]	training's l1: 0.0221432	valid_1's l1: 0.33623
Fold 4 started at Thu Jun 13 10:21:02 2019
[15000]	training's l1: 0.0221748	valid_1's l1: 0.333912
Fold 5 started at Thu Jun 13 10:36:10 2019
CV mean score: -1.0918, std: 0.0026.

Training of type 6
X_t.shape: (590611, 147), X_test_t.shape: (317435, 147)
Fold 1 started at Thu Jun 13 10:51:54 2019
[15000]	training's l1: 0.00264541	valid_1's l1: 0.184185
Fold 2 started at Thu Jun 13 11:03:48 2019
[15000]	training's l1: 0.0026492	valid_1's l1: 0.184293
Fold 3 started at Thu Jun 13 11:15:30 2019
[15000]	training's l1: 0.00263448	valid_1's l1: 0.1845
Fold 4 started at Thu Jun 13 11:27:25 2019
[15000]	training's l1: 0.00264616	valid_1's l1: 0.183882
Fold 5 started at Thu Jun 13 11:39:24 2019
CV mean score: -1.6915, std: 0.0011.

Training of type 5
X_t.shape: (1510379, 147), X_test_t.shape: (811999, 147)
Fold 1 started at Thu Jun 13 11:52:21 2019
[15000]	training's l1: 0.0356988	valid_1's l1: 0.313844
Fold 2 started at Thu Jun 13 12:12:15 2019
[15000]	training's l1: 0.0357428	valid_1's l1: 0.314336
Fold 3 started at Thu Jun 13 12:32:27 2019
[15000]	training's l1: 0.0356953	valid_1's l1: 0.313332
Fold 4 started at Thu Jun 13 12:52:32 2019
[15000]	training's l1: 0.0357354	valid_1's l1: 0.31366
Fold 5 started at Thu Jun 13 13:12:25 2019
CV mean score: -1.1589, std: 0.0010.

Training of type 7
X_t.shape: (166415, 147), X_test_t.shape: (90616, 147)
Fold 1 started at Thu Jun 13 13:33:23 2019
[14587]	training's l1: 0.000611207	valid_1's l1: 0.154428
Fold 2 started at Thu Jun 13 13:39:00 2019
[14369]	training's l1: 0.000615051	valid_1's l1: 0.152621
Fold 3 started at Thu Jun 13 13:44:20 2019
[12589]	training's l1: 0.000661184	valid_1's l1: 0.154007
Fold 4 started at Thu Jun 13 13:49:12 2019
[13861]	training's l1: 0.000626634	valid_1's l1: 0.152488
Fold 5 started at Thu Jun 13 13:54:33 2019
[10924]	training's l1: 0.000720345	valid_1's l1: 0.154056
CV mean score: -1.8739, std: 0.0052.

oof_log_mae: -1.2074376400054934

finished. : 2019-06-13 13:59:58

nohup_v003_009.out.txt

  type_0 type_1 type_2 type_3 type_4 type_5 type_6 type_7 ave
oof_fc 2.6% 2.8% 2.9% 2.2% 2.8% 3.3% 2.4% 3.5% 2.8%
f006:dist_origin_mean 2.3% 3.3% 1.6% 2.5% 2.5% 1.8% 2.1% 2.7% 2.3%
abs_dist 2.4% 2.9% 1.9% 2.3% 2.3% 1.6% 1.7% 1.8% 2.1%
f006:dist_from_origin_0 2.2% 2.3% 2.0% 2.2% 2.2% 1.9% 1.8% 2.0% 2.1%
molecule_dist_mean 1.9% 2.7% 1.3% 2.1% 1.9% 1.5% 1.9% 1.9% 1.9%
molecule_dist_max 1.9% 2.8% 1.4% 2.1% 2.1% 1.1% 1.8% 1.9% 1.9%
molecule_atom_1_dist_mean 2.0% 2.3% 1.4% 2.0% 1.7% 1.5% 2.0% 1.6% 1.8%
f006:dist_from_origin_1 1.7% 1.9% 1.5% 2.4% 1.6% 1.5% 2.2% 1.5% 1.8%
molecule_atom_1_dist_std_diff 2.4% 3.0% 1.4% 2.4% 1.6% 0.7% 1.1% 1.3% 1.7%
molecule_atom_index_0_x_1_std 1.9% 2.0% 1.6% 1.7% 1.7% 1.5% 1.5% 1.6% 1.7%
molecule_atom_index_0_z_1_std 1.9% 1.9% 1.5% 1.8% 1.6% 1.5% 1.5% 1.5% 1.7%
f004:angle_abs 1.7% 2.0% 1.4% 1.5% 2.0% 1.3% 1.6% 1.8% 1.7%
dist_xy 1.8% 1.8% 1.5% 1.8% 1.9% 1.3% 1.5% 1.5% 1.6%
dist_xz 2.0% 2.2% 1.5% 1.5% 1.7% 1.3% 1.4% 1.3% 1.6%
molecule_atom_index_0_y_1_std 1.8% 1.8% 1.4% 1.6% 1.6% 1.4% 1.4% 1.6% 1.6%
molecule_atom_index_0_y_1_mean_div 1.7% 1.7% 1.5% 1.6% 1.6% 1.4% 1.4% 1.3% 1.5%
dist_yz 1.7% 1.8% 1.4% 1.6% 1.6% 1.3% 1.4% 1.4% 1.5%
f004:angle 1.6% 1.8% 1.3% 1.4% 1.5% 1.2% 1.3% 1.4% 1.4%
gasteiger_1 1.7% 1.5% 1.7% 0.0% 1.6% 1.7% 1.7% 1.6% 1.4%
z_1 1.5% 1.7% 1.2% 1.6% 1.4% 1.3% 1.3% 1.4% 1.4%
z_0 1.5% 1.4% 1.3% 1.4% 1.6% 1.2% 1.3% 1.5% 1.4%
gasteiger_0 1.4% 1.0% 1.7% 1.5% 1.0% 1.8% 1.4% 1.0% 1.4%
molecule_atom_index_0_y_1_mean_diff 1.5% 1.4% 1.3% 1.4% 1.3% 1.1% 1.2% 1.1% 1.3%
x_0 1.3% 1.3% 1.1% 1.4% 1.3% 1.1% 1.2% 1.4% 1.3%
molecule_couples 1.4% 1.6% 1.0% 1.3% 1.3% 1.0% 1.2% 1.3% 1.3%
molecule_type_dist_mean_diff 1.8% 1.5% 1.1% 1.6% 1.5% 0.7% 0.9% 0.9% 1.2%
molecule_atom_index_0_dist_max 1.1% 0.9% 1.4% 0.9% 1.2% 1.5% 1.3% 1.4% 1.2%
x_1 1.3% 1.5% 1.1% 1.5% 1.1% 1.1% 1.1% 1.1% 1.2%
molecule_type_0_dist_std_diff 0.9% 1.8% 1.1% 1.9% 1.4% 0.8% 1.1% 0.8% 1.2%
y_0 1.2% 1.1% 1.1% 1.4% 1.2% 1.1% 1.1% 1.2% 1.2%
qtpie_0 1.0% 1.3% 1.1% 1.1% 1.2% 1.2% 1.1% 1.2% 1.2%
qeq_0 1.1% 1.3% 1.1% 1.1% 1.2% 1.1% 1.1% 1.2% 1.1%
eem2015ha_0 1.3% 1.2% 1.0% 1.1% 1.3% 0.9% 1.1% 1.1% 1.1%
molecule_type_dist_min 0.6% 0.0% 1.5% 1.1% 0.8% 1.9% 1.9% 1.4% 1.1%
eem2015bm_0 1.3% 1.3% 0.9% 1.2% 1.1% 0.9% 1.1% 1.3% 1.1%
eem2015ha_1 1.2% 1.3% 1.0% 1.2% 1.0% 1.0% 1.1% 1.1% 1.1%
molecule_atom_index_0_dist_mean 1.0% 0.6% 1.2% 0.8% 1.1% 1.4% 1.2% 1.4% 1.1%
molecule_type_dist_std_diff 0.6% 0.8% 1.1% 1.4% 1.3% 0.8% 1.2% 1.5% 1.1%
z_a1_nb 1.2% 1.1% 1.3% 0.0% 1.3% 1.3% 1.3% 1.3% 1.1%
molecule_atom_index_0_dist_std 1.0% 0.8% 1.0% 0.9% 1.1% 1.2% 1.1% 1.4% 1.1%
molecule_atom_index_0_y_1_max_diff 1.3% 1.3% 1.1% 1.0% 1.1% 0.9% 1.1% 0.8% 1.1%
eem2015ba_0 1.2% 1.1% 0.9% 1.1% 1.2% 0.9% 1.1% 1.1% 1.1%
molecule_type_dist_max 1.1% 0.6% 1.4% 1.4% 0.8% 0.6% 1.3% 1.1% 1.0%
z_a0_nb 0.0% 0.0% 1.1% 1.5% 1.4% 1.3% 1.3% 1.5% 1.0%
y_1 1.1% 1.1% 0.9% 1.3% 0.8% 1.0% 0.9% 0.9% 1.0%
x_a1_nb 1.1% 1.1% 1.2% 0.0% 1.1% 1.2% 1.0% 1.2% 1.0%
eem2015ba_1 1.1% 1.0% 1.0% 1.1% 0.8% 1.0% 1.1% 0.8% 1.0%
eem_0 1.2% 1.3% 0.8% 1.0% 0.9% 0.8% 0.9% 1.0% 1.0%
eem2015hm_0 1.2% 1.2% 0.8% 1.0% 0.9% 0.8% 0.9% 1.0% 1.0%
qtpie_1 0.9% 0.9% 0.9% 1.2% 0.8% 0.9% 1.3% 0.8% 1.0%
qeq_1 0.9% 0.9% 0.9% 1.2% 0.8% 0.9% 1.2% 0.9% 1.0%
molecule_dist_min 0.9% 0.4% 1.0% 1.4% 0.7% 1.0% 1.2% 0.7% 0.9%
molecule_atom_index_1_dist_std 0.9% 1.1% 0.8% 1.1% 0.7% 1.0% 0.9% 0.9% 0.9%
y_a1_nb 1.0% 0.9% 1.0% 0.0% 1.0% 1.2% 0.9% 1.1% 0.9%
molecule_atom_index_1_dist_std_diff 0.8% 0.9% 1.0% 0.9% 0.8% 1.0% 1.0% 0.9% 0.9%
eem2015bn_0 1.0% 1.0% 0.7% 0.8% 1.0% 0.7% 0.8% 0.9% 0.9%
x_a0_nb 0.0% 0.0% 1.0% 1.4% 1.0% 1.1% 1.1% 1.3% 0.9%
eem2015bm_1 0.9% 0.9% 0.7% 1.2% 0.6% 0.8% 1.1% 0.6% 0.9%
molecule_atom_index_0_dist_std_div 0.6% 0.5% 0.9% 0.7% 0.8% 1.2% 0.9% 1.2% 0.8%
eem2015hn_0 1.0% 1.0% 0.7% 0.8% 0.9% 0.6% 0.8% 0.8% 0.8%
molecule_atom_index_0_dist_std_diff 0.9% 0.7% 1.0% 1.0% 0.8% 0.9% 0.7% 0.7% 0.8%
molecule_atom_index_1_dist_max 0.8% 0.6% 1.0% 0.6% 0.8% 1.3% 0.8% 0.7% 0.8%
molecule_atom_index_0_dist_max_div 1.1% 1.1% 1.1% 0.9% 0.9% 0.5% 0.6% 0.3% 0.8%
eem2015hm_1 0.8% 0.9% 0.7% 1.0% 0.6% 0.7% 0.9% 0.6% 0.8%
molecule_atom_index_0_dist_mean_div 1.0% 0.7% 0.9% 0.7% 0.8% 0.8% 0.6% 0.7% 0.8%
molecule_atom_index_0_dist_max_diff 0.8% 0.8% 1.0% 0.8% 0.9% 0.7% 0.7% 0.4% 0.8%
molecule_atom_index_1_dist_mean 0.7% 0.5% 0.9% 0.5% 0.7% 1.2% 0.8% 0.8% 0.8%
molecule_atom_1_dist_min_diff 1.6% 0.7% 0.5% 1.4% 0.6% 0.3% 0.5% 0.4% 0.7%
eem_1 0.7% 0.8% 0.6% 1.0% 0.5% 0.5% 1.0% 0.6% 0.7%
eem2015bn_1 0.7% 0.9% 0.5% 0.9% 0.7% 0.6% 0.8% 0.6% 0.7%
y_a0_nb 0.0% 0.0% 0.8% 1.2% 0.8% 1.0% 0.9% 0.9% 0.7%
molecule_atom_index_1_dist_std_div 0.5% 0.5% 0.7% 0.5% 0.7% 0.9% 0.8% 0.9% 0.7%
molecule_atom_index_0_dist_mean_diff 0.7% 0.5% 0.9% 0.6% 0.8% 0.7% 0.6% 0.7% 0.7%
eem2015hn_1 0.6% 0.7% 0.5% 0.9% 0.6% 0.5% 0.8% 0.5% 0.6%
molecule_atom_index_1_dist_min 0.3% 0.2% 0.8% 0.5% 0.5% 0.9% 1.0% 0.6% 0.6%
molecule_atom_index_0_dist_min 0.3% 0.2% 0.8% 0.5% 0.6% 1.0% 0.7% 0.6% 0.6%
cos2T 0.0% 0.0% 0.0% 0.0% 0.0% 1.8% 1.4% 1.4% 0.6%
molecule_atom_index_1_dist_max_diff 0.6% 0.5% 0.8% 0.3% 0.8% 0.7% 0.3% 0.5% 0.6%
molecule_atom_index_1_dist_min_diff 1.2% 0.5% 0.7% 0.4% 0.4% 0.5% 0.3% 0.3% 0.5%
molecule_atom_index_1_dist_mean_diff 0.5% 0.4% 0.7% 0.4% 0.5% 0.8% 0.4% 0.6% 0.5%
Torsion 0.0% 0.0% 0.0% 0.0% 0.0% 1.5% 1.3% 1.4% 0.5%
molecule_atom_index_1_dist_max_div 0.9% 0.6% 0.8% 0.1% 0.7% 0.5% 0.2% 0.3% 0.5%
molecule_atom_index_1_dist_mean_div 0.6% 0.4% 0.6% 0.1% 0.5% 0.7% 0.3% 0.6% 0.5%
atom_index_0 0.5% 0.8% 0.5% 0.2% 0.5% 0.5% 0.3% 0.5% 0.5%
Angle 0.0% 0.0% 1.6% 0.9% 1.2% 0.0% 0.0% 0.0% 0.5%
mmff94_1 0.5% 0.2% 0.9% 0.0% 0.5% 1.0% 0.1% 0.6% 0.5%
molecule_type_dist_mean_div 0.1% 0.0% 0.6% 0.2% 0.5% 0.6% 0.7% 0.8% 0.4%
molecule_atom_1_dist_min_div 0.2% 0.0% 0.7% 0.2% 0.5% 0.5% 0.7% 0.5% 0.4%
molecule_atom_index_0_dist_min_div 0.0% 0.0% 0.7% 0.4% 0.5% 0.6% 0.5% 0.5% 0.4%
molecule_atom_index_0_dist_min_diff 0.0% 0.0% 0.5% 0.7% 0.5% 0.3% 0.3% 0.3% 0.3%
atom_index_1 0.4% 0.2% 0.4% 0.3% 0.2% 0.5% 0.3% 0.2% 0.3%
dist_to_type_mean 0.5% 0.5% 0.3% 0.3% 0.3% 0.2% 0.2% 0.2% 0.3%
cosT 0.0% 0.0% 0.0% 0.0% 0.0% 1.1% 0.8% 0.5% 0.3%
H 0.4% 0.3% 0.3% 0.3% 0.3% 0.2% 0.3% 0.2% 0.3%
molecule_atom_index_1_dist_min_div 0.0% 0.0% 0.6% 0.0% 0.2% 0.6% 0.2% 0.3% 0.3%
atom_1_couples_count 0.3% 0.1% 0.4% 0.0% 0.2% 0.4% 0.0% 0.2% 0.2%
N 0.2% 0.2% 0.2% 0.2% 0.1% 0.2% 0.2% 0.1% 0.2%
O 0.2% 0.2% 0.2% 0.2% 0.1% 0.2% 0.2% 0.1% 0.2%
dist 0.0% 0.0% 0.3% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2%
atom_0_couples_count 0.2% 0.1% 0.3% 0.1% 0.1% 0.3% 0.2% 0.1% 0.2%
C 0.2% 0.2% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%
a0_nb_nb_c 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.1% 0.0% 0.0%
mmff94_0 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a0_nb_inring5 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_inring4 0.1% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_inring5 0.1% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a0_nb_nb_n 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a0_nb_inring4 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_nb_nb_c 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0%
a1_inring3 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_nb_nb_h 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0%
a1_nb_c 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a0_nb_nb_h 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_nb_n 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a0_nb_inring6 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_inring6 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a0_nb_nb_o 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_nb_nb_n 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0%
a1_nb_nb_o 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_o 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_nb_inring5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0%
a0_nb_inring 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_inring4 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0%
a1_nb_degree 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_inring7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a0_nb_inring3 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%
a1_degree 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_inring 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a0_nb_inring7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_inring3 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_inring6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_h 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
F 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_hybridization 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_hybridization 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a0_nb_hybridization 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_inring8 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_inring 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a0_nb_inring8 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_inring7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a0_nb_degree 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_inring8 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_na 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
sp 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a1_nb_nb_na 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
type 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
a0_nb_nb_na 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

resultv003_009

@matsuken92
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matsuken92 commented Jun 24, 2019 •
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holdout v003_025

[500] training's l1: 0.566029 valid_1's l1: 0.589641
[1000] training's l1: 0.478477 valid_1's l1: 0.517248
[1500] training's l1: 0.429449 valid_1's l1: 0.480525
[2000] training's l1: 0.394687 valid_1's l1: 0.456913
[2500] training's l1: 0.368058 valid_1's l1: 0.440431
[3000] training's l1: 0.345617 valid_1's l1: 0.427161
[3500] training's l1: 0.32731 valid_1's l1: 0.417293
[4000] training's l1: 0.311112 valid_1's l1: 0.408887
[4500] training's l1: 0.296723 valid_1's l1: 0.401715
[5000] training's l1: 0.283922 valid_1's l1: 0.395972
[5500] training's l1: 0.272583 valid_1's l1: 0.391227
[6000] training's l1: 0.262024 valid_1's l1: 0.386568
[6500] training's l1: 0.252317 valid_1's l1: 0.382687
[7000] training's l1: 0.243352 valid_1's l1: 0.379186
[7500] training's l1: 0.235024 valid_1's l1: 0.376095
[8000] training's l1: 0.227153 valid_1's l1: 0.373319
Did not meet early stopping. Best iteration is:
[8000] training's l1: 0.227153 valid_1's l1: 0.373319

v003_025 HOLD_OUT score: -1.0758

holdout v003_026

add "sum_radius", "tda_cocycles_shape", "tda_max_radius",
"tda_mean_radius", "tda_min_radius", "tda_num_circle"

[500] training's l1: 0.563407 valid_1's l1: 0.587388
[1000] training's l1: 0.477219 valid_1's l1: 0.516854
[1500] training's l1: 0.427973 valid_1's l1: 0.480503
[2000] training's l1: 0.393202 valid_1's l1: 0.456683
[2500] training's l1: 0.366525 valid_1's l1: 0.440105
[3000] training's l1: 0.344808 valid_1's l1: 0.427501
[3500] training's l1: 0.326128 valid_1's l1: 0.417173
[4000] training's l1: 0.309906 valid_1's l1: 0.408808
[4500] training's l1: 0.295743 valid_1's l1: 0.402032
[5000] training's l1: 0.282954 valid_1's l1: 0.396243
[5500] training's l1: 0.271474 valid_1's l1: 0.391291
[6000] training's l1: 0.260916 valid_1's l1: 0.386974
[6500] training's l1: 0.251339 valid_1's l1: 0.383271
[7000] training's l1: 0.242284 valid_1's l1: 0.379728
[7500] training's l1: 0.233908 valid_1's l1: 0.376693
[8000] training's l1: 0.225893 valid_1's l1: 0.373774
Did not meet early stopping. Best iteration is:
[8000] training's l1: 0.225893 valid_1's l1: 0.373774
v003_026HOLD_OUT score: -1.0781 .

holdout v003_028

[500] training's l1: 0.564218 valid_1's l1: 0.587363
[1000] training's l1: 0.476771 valid_1's l1: 0.515268
[1500] training's l1: 0.427689 valid_1's l1: 0.479324
[2000] training's l1: 0.393346 valid_1's l1: 0.456341
[2500] training's l1: 0.366496 valid_1's l1: 0.439514
[3000] training's l1: 0.344709 valid_1's l1: 0.426899
[3500] training's l1: 0.325971 valid_1's l1: 0.41646
[4000] training's l1: 0.309585 valid_1's l1: 0.40771
[4500] training's l1: 0.295316 valid_1's l1: 0.400746
[5000] training's l1: 0.28251 valid_1's l1: 0.394671
[5500] training's l1: 0.271071 valid_1's l1: 0.389632
[6000] training's l1: 0.260586 valid_1's l1: 0.385247
[6500] training's l1: 0.250897 valid_1's l1: 0.381341
[7000] training's l1: 0.241896 valid_1's l1: 0.37798
[7500] training's l1: 0.233551 valid_1's l1: 0.37485
[8000] training's l1: 0.225663 valid_1's l1: 0.371988
Did not meet early stopping. Best iteration is:
[8000] training's l1: 0.225663 valid_1's l1: 0.371988
v003_028HOLD_OUT score: -1.0816 .

holdout v003_029

[500] training's l1: 0.544537 valid_1's l1: 0.567271
[1000] training's l1: 0.460407 valid_1's l1: 0.49808
[1500] training's l1: 0.411908 valid_1's l1: 0.461823
[2000] training's l1: 0.37895 valid_1's l1: 0.440118
[2500] training's l1: 0.353092 valid_1's l1: 0.424013
[3000] training's l1: 0.331885 valid_1's l1: 0.411772
[3500] training's l1: 0.31368 valid_1's l1: 0.401764
[4000] training's l1: 0.298216 valid_1's l1: 0.3939
[4500] training's l1: 0.284504 valid_1's l1: 0.387557
[5000] training's l1: 0.272024 valid_1's l1: 0.381773
[5500] training's l1: 0.260994 valid_1's l1: 0.37712
[6000] training's l1: 0.250798 valid_1's l1: 0.372839
[6500] training's l1: 0.241187 valid_1's l1: 0.369066
[7000] training's l1: 0.232459 valid_1's l1: 0.365779
[7500] training's l1: 0.224269 valid_1's l1: 0.362654
[8000] training's l1: 0.216631 valid_1's l1: 0.359984
Did not meet early stopping. Best iteration is:
[8000] training's l1: 0.216631 valid_1's l1: 0.359984
v003_029HOLD_OUT score: -1.1120 .

holdout v003_030

[LightGBM] [Warning] num_threads is set=-1, n_jobs=-1 will be ignored. Current value: num_threads=-1
Training until validation scores don't improve for 200 rounds.
[500] training's l1: 0.565497 valid_1's l1: 0.590105
[1000] training's l1: 0.479003 valid_1's l1: 0.518853
[1500] training's l1: 0.429585 valid_1's l1: 0.482372
[2000] training's l1: 0.394412 valid_1's l1: 0.458456
[2500] training's l1: 0.367211 valid_1's l1: 0.44135
[3000] training's l1: 0.345316 valid_1's l1: 0.429041
[3500] training's l1: 0.326603 valid_1's l1: 0.418916
[4000] training's l1: 0.310245 valid_1's l1: 0.410607
[4500] training's l1: 0.295665 valid_1's l1: 0.403374
[5000] training's l1: 0.282766 valid_1's l1: 0.397506
[5500] training's l1: 0.271163 valid_1's l1: 0.392556
[6000] training's l1: 0.26057 valid_1's l1: 0.388205
[6500] training's l1: 0.250659 valid_1's l1: 0.384267
[7000] training's l1: 0.241611 valid_1's l1: 0.380885
[7500] training's l1: 0.233248 valid_1's l1: 0.377946
[8000] training's l1: 0.225284 valid_1's l1: 0.375133
Did not meet early stopping. Best iteration is:
[8000] training's l1: 0.225284 valid_1's l1: 0.375133
v003_030HOLD_OUT score: -1.0682 .

@matsuken92
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matsuken92 commented Jun 29, 2019 •
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  • train_v003_003.py : LB -1.056

  • train_v003_004.py
    ⇒ n_fold = 5 → n_fold = 3
    ⇒ 1st layer: n_estimators=8000 → n_estimators=3000
    ⇒ 2nd layer: n_estimators=15000 → n_estimators=5000

  • train_v004_001.py
    ⇒ train data augmentation with train2 : processed/v004

  • train_v004_002.py : LB 0.6 惨敗・・・
    train data augmentation with train2
    ⇒ n_fold = 3 → n_fold = 5
    ⇒ 1st layer: n_estimators=3000 → n_estimators=10000
    ⇒ 2nd layer: n_estimators=5000 → n_estimators=20000

train_v003_005.py
⇒ train_v003_004.py に babel_train、babel_testと諸々特徴量追加

train_v003_006.py
⇒ train_v003_005.pyにob_charges.csvの追加

train_v003_007.py
⇒ train_v003_006.py にたいして2ndモデルをxgboost化

train_v003_008.py
⇒ train_v003_006.py に rdkit_train、rdkit_test 追加

train_v003_009.py
⇒ train_v003_008.py はrdkit_colsをgood_colsに入れ忘れるというミスをしたのでやりなおし

train_v003_010.py
⇒ train_v003_009.pyにtrain augmentation(augmented_train_v001.py)を適用
⇒ GroupKFoldしないとLeakしていた・・・

train_v003_011.py
⇒ train_v003_010.pyのGroupKFold版

train_v003_017.py
⇒ train_v003_009.py にcoulomb_featを追加
CV : -1.5089, LB -1.757

train_v003_018.py
⇒ bond_calc_featの採用

train_v003_019.py
⇒ train_v003_018.py を GroupKFoldに
CV: -1.468 LB: -1.728

train_v003_021.py
⇒ all data LB: -1.659

train_v003_022.py
⇒ all data seed ave 7
LB : -1.864

train_v003_023.py
⇒ tda_feat
⇒ CV -1.5139, LB: -1.766

train_v003_024
⇒ train_v003_022.py all data に対して tda_feat を適用して seed ave

train_valid_v003_025.py CV: 1.0758
⇒ train_v003_024を特徴量選択用にhold outでscore計算できるようにしたもの

train_valid_v003_026.py CV:1.0781
⇒ tda featを追加
"sum_radius", "tda_cocycles_shape", "tda_max_radius",
"tda_mean_radius", "tda_min_radius", "tda_num_circle"

train_valid_v003_027.py
⇒ adversarial validation

train_valid_v003_028.py CV:1.0816
⇒ train_valid_v003_026.pyのtda featをmin, maxだけにしたもの

holdout v003_029 CV: 1.1120
⇒ reduce mem usageを修正

holdout v003_030 CV: 1.0682(もうちょっと良いはず)
⇒ pca_feat導入

train_v003_031
⇒ pca導入、reduce mem usage修正を入れて ALL TRAIN & seed ave

train_valid_v003_032.py CV: -1.1074
⇒ permutation importanceで効果のないcolumnを除外してみる

train_valid_v003_033.py CV: -1.1183
⇒ permutation importanceで効果のないcolumnを除外してみる その２

train_valid_v003_034.py CV: -1.1426
⇒ permutation importanceで効果のないcolumnを除外してみる その3

train_valid_v003_035.py CV: -1.1628 .
⇒ permutation importanceで効果のないcolumnを除外してみる その4

train_v003_036
⇒ permutation importanceで効果のないcolumnを除外 ALL TRAIN & seed ave

train_valid_v003_037
⇒ validをtype別に分けるbase line確認

train_valid_v003_038
⇒ validをtype別depth, num_leavesを変えることのsearch

train_valid_v003_039.py
⇒ MLPお試し ⇒ CV全然ダメ・・・。途中でやめた。

holdout v003_029 CV: 1.1120
⇒ reduce mem usageを修正

holdout v003_030 CV: 1.0682(もうちょっと良いはず)
⇒ pca_feat導入

train_v003_031
⇒ pca導入、reduce mem usage修正を入れて ALL TRAIN & seed ave

train_valid_v003_032.py CV: -1.1074
⇒ permutation importanceで効果のないcolumnを除外してみる

train_valid_v003_033.py CV: -1.1183
⇒ permutation importanceで効果のないcolumnを除外してみる その２

train_valid_v003_034.py CV: -1.1426
⇒ permutation importanceで効果のないcolumnを除外してみる その3

train_valid_v003_035.py CV: -1.1628 .
⇒ permutation importanceで効果のないcolumnを除外してみる その4

train_v003_036
⇒ permutation importanceで効果のないcolumnを除外 ALL TRAIN & seed ave

train_valid_v003_037
⇒ validをtype別に分けるbase line確認

train_valid_v003_038
⇒ validをtype別depth, num_leavesを変えることのsearch

train_valid_v003_039.py
⇒ MLPお試し ⇒ CV全然ダメ・・・。途中でやめた。

train_valid_v003_041.py CV: -1.2896
⇒ lgb type別num_leaves効果チェック
⇒ いい感じにnum_leavesをtype別にすると-1.5149
#6 (comment)

train_valid_v003_042.py　Holdout: -1.1342
⇒ type splitなしの実験

train_valid_v003_043.py CV: -1.5149
⇒ typeごとにnum_leavesを指定
importance_all_V003_043.xlsx
nohup_v003_043.out.txt

train_v003_044
⇒ oof_fcの算出(type別) ⇒ all data(type別) ⇒ めっちゃ時間かかる・・・

train_valid_v003_045.py CV: -1.3925719
⇒ typeごとにnum_leavesを指定 w/yiemon feat
importance_all_v003_045.xlsx
nohup_v003_045.out.txt

train_valid_v003_046.py CV: 測定中
⇒ yemon faet imporatnce上位のみ利用
atm_dist_min_all_of_nn
2J2nd_AverageBondAngle
2J2nd_SmallestBondAngle
d_ratio_0_all_comb_min
inM_atm0_atm_d_var
d_diff_0_all_comb_min
gasteiger_max_min_ratio

train_valid_v003_048.py
⇒ 2段階 base line

train_valid_v003_049.py
⇒ 2段階 mullkan1, mullkan2, fc → holdout
⇒ type:0 以外は精度向上

type v003_049 type0 no mullken
0 -0.4923 -0.6281023
1 -1.2356 -1.2356
2 -1.5185 -1.5185
3 -2.1063 -2.1063
4 -1.9051 -1.9051
5 -1.4799 -1.4799
6 -2.0131 -2.0131
7 -2.1672 -2.1672
ave -1.61475 -1.6317253
importance_all.xlsx

train_v003_050.py
⇒ 2段階 mullkan1, mullkan2, fc → all data (type 0 : no mullken)

train_v003_052.py
⇒ yiemon feat 1j2j3j hold out

train_v003_053.py
⇒ no yiemon feat hold out for compare

train_v003_054.py
⇒ yiemon feat 1j2j3j hold out
⇒ num_leaves 256, max_depth=-1

train_v003_055.py LB: -1.874
⇒ yiemon feat 1j2j3j CV : cv score: -1.5841730
⇒ num_leaves 256, max_depth=-1

train_v003_056.py LB: -1.813
⇒ without yiemon feat 1j2j3j CV : cv score: -1.5310483
⇒ num_leaves 256, max_depth=-1

train_v003_057.py LB: xxxx
⇒ yiemon feat 1j2j3j CV : cv score: -1.58087
⇒ add mullken oof from train_v003_055.py

train_v004_001.py
⇒ basic feature for baseline of augment CV: -0.523706
⇒ num_leaves 256, max_depth=-1

train_v004_002.py
⇒ z axis augment CV: -0.5349
⇒ num_leaves 256, max_depth=-1

train_v004_003.py
⇒ y axis augment CV: xxx
⇒ num_leaves 256, max_depth=-1

@matsuken92
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matsuken92 commented Jul 2, 2019 •
edited
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num_leavesチェック

スクリーンショット 2019-07-03 2 54 48

result_analysis.xlsx

type num_leaves score
0 8 -0.6261
1 8 -1.1679
2 16 -1.3788
3 8 -1.9578
4 8 -1.6824
5 16 -1.3118
6 8 -1.9195
7 16 -2.0746
    -1.5149

v003_042 without type split : Holdout: -1.1342

STARTING : 2019-07-02 18:00:54
X.shape: (4658147, 95), X_test.shape: (2505542, 95)
LGBMRegressor(bagging_seed=72, boosting_type='gbdt', class_weight=None,
       colsample_bytree=1.0, feature_fraction_seed=73,
       importance_type='gain', learning_rate=0.2, max_depth=9,
       metric='mae', min_child_samples=79, min_child_weight=0.001,
       min_split_gain=0.0, n_estimators=128000, n_jobs=-1, num_leaves=8,
       num_threads=-1, objective='regression', random_state=None,
       reg_alpha=0.1, reg_lambda=0.3, seed=71, silent=True, subsample=0.9,
       subsample_for_bin=200000, subsample_freq=1, verbosity=-1)
[LightGBM] [Warning] num_threads is set=-1, n_jobs=-1 will be ignored. Current value: num_threads=-1
Did not meet early stopping. Best iteration is:
[128000]	training's l1: 0.255779	valid_1's l1: 0.34963
v003_042HOLD_OUT score: -1.1342 .

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