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ftnext / atmaCup10-paintings-likes

https://www.guruguru.science/competitions/16/
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講義2で脳死で追加された特徴量に近づける [CV: 1.0581] [LB: 1.0319] #8

Closed ftnext closed 3 years ago

ftnext commented 3 years ago

ぱっと見きかなそうな特徴量は除いて洗い出し、必要なものは実装する

ftnext commented 3 years ago

OneHotEncoding

trainの最大カウントを()で示す

NaNが多くても特徴量として増やしてみる（LightGBMなら影響されにくいから）ということか

ftnext commented 3 years ago

dataset v1.5: OneHotEncodingが講義2と同じ [CV 1.0652]

$ python -i preprocess.py data/datasets/ data/preprocessed/v1.5
# train, testともに33MB

$ python training.py data/preprocessed/v1.5/ data/datasets/train.csv submissions/
train: (12026, 1066)
test: (12008, 1066)
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
[500]   valid_0's rmse: 1.07444
Early stopping, best iteration is:
[412]   valid_0's rmse: 1.07278
Fold 0 RMSLE: 1.0728
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
[500]   valid_0's rmse: 1.07319
Early stopping, best iteration is:
[485]   valid_0's rmse: 1.07228
Fold 1 RMSLE: 1.0723
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[356]   valid_0's rmse: 1.04132
Fold 2 RMSLE: 1.0413
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[378]   valid_0's rmse: 1.07961
Fold 3 RMSLE: 1.0796
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
[500]   valid_0's rmse: 1.0647
Early stopping, best iteration is:
[430]   valid_0's rmse: 1.05937
Fold 4 RMSLE: 1.0594
--------------------------------------------------
FINISHED | Whole RMSLE: 1.0652

20210312-135550_data_preprocessed_v1 5

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dating_year_late    8812.289369143546
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ftnext commented 3 years ago

CountEncoding

ftnext commented 3 years ago

文字列長

more_titleが落ちている（→落とさず残す考え）

ftnext commented 3 years ago

dataset v1.5.1: CountEncodingと文字列長を追加 [CV: 1.0581] [LB: 1.0319]

$ python preprocess.py data/datasets/ data/preprocessed/v1.5.1
train: (12026, 1082)
test: (12008, 1082)

$ python training.py data/preprocessed/v1.5.1/ data/datasets/train.csv submissions/
train: (12026, 1082)
test: (12008, 1082)
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
[500]   valid_0's rmse: 1.0618
Early stopping, best iteration is:
[425]   valid_0's rmse: 1.05996
Fold 0 RMSLE: 1.0600
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[279]   valid_0's rmse: 1.0677
Fold 1 RMSLE: 1.0677
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
[500]   valid_0's rmse: 1.03321
Early stopping, best iteration is:
[435]   valid_0's rmse: 1.03184
Fold 2 RMSLE: 1.0318
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[299]   valid_0's rmse: 1.08273
Fold 3 RMSLE: 1.0827
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
Training until validation scores don't improve for 100 rounds
[500]   valid_0's rmse: 1.04863
Early stopping, best iteration is:
[411]   valid_0's rmse: 1.0474
Fold 4 RMSLE: 1.0474
--------------------------------------------------
FINISHED | Whole RMSLE: 1.0581

20210313-012049_data_preprocessed_v1 5 1

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