Open victor-von-pooh opened 2 months ago
Commit ID: https://github.com/victor-von-pooh/Clairvoyant/tree/512dfd681b98a6a2c95fa8129b2691f96205ddee
VAE モデルをデフォルト config で走らせると次のような結果が出る.
2024-06-19 19:22:29,458 - experiment_tools.start_logging - INFO - commit id: 512dfd681b98a6a2c95fa8129b2691f96205ddee 2024-06-19 19:22:29,459 - experiment_tools.start_logging - INFO - username: victor-von-pooh 2024-06-19 19:22:29,474 - experiment_tools.start_logging - INFO - OS infomation: OS: Darwin 21.2.0 Processor: i386 Machine: x86_64 Node: akitayaacBookea Python Version: 3.12.3 2024-06-19 19:22:31,347 - experiment_tools.start_logging - INFO - pip3 list: Package Version ----------------- ----------- contourpy 1.2.1 cycler 0.12.1 filelock 3.15.1 fonttools 4.53.0 fsspec 2024.6.0 Jinja2 3.1.4 joblib 1.4.2 kiwisolver 1.4.5 MarkupSafe 2.1.5 matplotlib 3.9.0 mpmath 1.3.0 networkx 3.3 numpy 1.26.4 packaging 24.1 pandas 2.2.2 pillow 10.3.0 pip 24.0 pyparsing 3.1.2 python-dateutil 2.9.0.post0 pytz 2024.1 scikit-learn 1.5.0 scipy 1.13.1 six 1.16.0 sympy 1.12.1 threadpoolctl 3.5.0 torch 2.2.2 tqdm 4.66.4 typing_extensions 4.12.2 tzdata 2024.1 2024-06-19 19:22:31,349 - experiment_tools.start_logging - INFO - diff: 2024-06-19 19:22:31,349 - experiment_tools.start_logging - INFO - device: cpu 2024-06-19 19:22:31,423 - experiment_tools.start_logging - INFO - original data records: 1000 2024-06-19 19:22:31,423 - experiment_tools.start_logging - INFO - train data records: 20 2024-06-19 19:22:31,423 - experiment_tools.start_logging - INFO - valid data records: 20 2024-06-19 19:22:31,452 - experiment_tools.start_logging - INFO - train raw data: Feature_1 Feature_2 Feature_3 Feature_4 Feature_5 Feature_6 Feature_7 Feature_8 Feature_9 Feature_10 0 24 7.4 152000 0.6 5 0.5 62 75 376.57 4 1 53 2.3 198000 -0.5 5 9.4 44 40 408.97 -2 2 52 2.5 103000 0.3 3 3.4 89 0 331.10 4 3 27 6.2 154000 1.0 1 9.2 93 35 414.87 0 4 23 1.6 79000 0.7 2 9.7 51 25 337.64 -1 5 21 7.1 148000 0.4 1 0.4 56 0 225.61 0 6 60 9.0 126000 0.1 5 3.9 87 60 479.19 -1 7 58 5.8 93000 1.0 5 9.2 81 30 456.96 -5 8 58 6.9 7000 0.4 1 3.8 31 85 150.67 -4 9 45 5.7 29000 0.0 4 8.6 27 15 461.72 -2 10 54 3.4 73000 0.9 1 5.0 54 65 155.00 -4 11 16 7.4 154000 -0.3 3 0.6 46 25 460.68 4 12 39 5.9 198000 -0.2 3 3.4 60 90 326.58 4 13 21 0.0 71000 0.7 2 4.9 1 85 222.56 1 14 19 6.8 32000 0.1 2 1.9 59 35 285.05 5 15 45 7.0 122000 0.2 1 4.2 83 45 215.72 5 16 51 3.0 42000 0.2 4 0.6 2 40 298.42 -2 17 64 8.5 174000 -0.1 1 0.7 35 25 343.95 -4 18 46 9.6 38000 1.0 4 6.7 97 0 192.51 -3 19 48 5.7 87000 1.0 5 8.1 0 60 480.40 -3 2024-06-19 19:22:31,465 - experiment_tools.start_logging - INFO - model architecture: VAE( (encoder): Sequential( (0): Linear(in_features=10, out_features=32, bias=True) (1): ReLU() (2): Linear(in_features=32, out_features=32, bias=True) (3): ReLU() ) (mu_layer): Linear(in_features=32, out_features=2, bias=True) (logvar_layer): Linear(in_features=32, out_features=2, bias=True) (decoder): Sequential( (0): Linear(in_features=2, out_features=32, bias=True) (1): ReLU() (2): Linear(in_features=32, out_features=32, bias=True) (3): ReLU() (4): Linear(in_features=32, out_features=10, bias=True) (5): Sigmoid() ) ) 2024-06-19 19:22:41,849 - experiment_tools.start_logging - INFO - predicted data: Feature_1 Feature_2 Feature_3 Feature_4 Feature_5 Feature_6 Feature_7 Feature_8 Feature_9 Feature_10 0 42.600281 5.866580 110218.570312 0.428658 3.074170 5.217875 56.132259 45.128628 339.997162 0.174258 1 44.787125 6.183745 117730.406250 0.485122 3.248748 5.630626 59.975510 48.895264 353.625854 0.605274 2 45.071800 6.217615 119443.656250 0.485645 3.268175 5.723142 60.748035 48.700741 354.879028 0.636057 3 43.502167 6.011938 113321.429688 0.453804 3.157583 5.398370 57.841728 46.829391 345.888794 0.365702 4 45.548992 6.275405 120603.921875 0.500292 3.299255 5.768045 61.295090 50.105072 358.839111 0.710487 ... ... ... ... ... ... ... ... ... ... ... 1995 45.214622 6.245523 119857.984375 0.489996 3.279915 5.749880 60.960617 48.972870 356.172760 0.658550 1996 44.223518 6.108051 116994.445312 0.474607 3.205203 5.567621 58.759975 47.651127 351.326385 0.418155 1997 43.676949 6.028704 115397.468750 0.461031 3.162693 5.467710 57.822735 46.772301 347.503662 0.302553 1998 44.052334 6.062654 115601.054688 0.469944 3.174276 5.521293 58.182262 47.544823 349.079742 0.389820 1999 44.294186 6.092695 116055.710938 0.473566 3.200169 5.534237 58.943115 48.118080 350.378876 0.481435 [2000 rows x 10 columns] 2024-06-19 19:22:41,864 - experiment_tools.start_logging - INFO - KL divergence: 0.3953488966053363
KL ダイバージェンスの値は 0.3953488966053363 とだいぶ大きい. 実際に生成されたデータを見ても, Feature_1 では 42〜46 の辺りにまとまっており, 他の特徴量についても学習データに比べてスケールが圧倒的に小さい. これを改善できるように実験 config の値をいくつか変更して, 試してみる. ただし, データは変更しないものとする.
0.3953488966053363
Feature_1
42〜46
loss_curve.png
リンク $\rightarrow$ Commit ID: https://github.com/victor-von-pooh/Clairvoyant/tree/bdcf4cce40f83c14f93061a310f75bf0751bb7db
VAE の KL ダイバージェンスでの評価実験の結果
Commit ID: https://github.com/victor-von-pooh/Clairvoyant/tree/512dfd681b98a6a2c95fa8129b2691f96205ddee
VAE モデルをデフォルト config で走らせると次のような結果が出る.
KL ダイバージェンスの値は
0.3953488966053363
とだいぶ大きい. 実際に生成されたデータを見ても,Feature_1
では42〜46
の辺りにまとまっており, 他の特徴量についても学習データに比べてスケールが圧倒的に小さい.これを改善できるように実験 config の値をいくつか変更して, 試してみる. ただし, データは変更しないものとする.
参考資料
loss_curve.png