1st Place Winning Solution - Mechanisms of Action (MoA) Prediction

This documentation outlines how to reproduce the 1st place solution by the Hungry for gold🥇🥇 team for the Mechanisms of Action (MoA) Prediction competition on Kaggle.

Winning Solution Writeup on Kaggle

Winning Solution Writeup in PDF

How to Reproduce Winning Solution Locally

Archive Contents

• In the final folder: All of the scripts used for the final submissions
  • Best CV: A set of model scripts used in our first submission, best CV score blending
  • Best LB: A set of model scripts used in our second submission, best public LB score blending
    • Training: Includes Jupyter notebooks for each single models to preprocess the input data and save trained model weights. to be run in kaggle GPU notebook environment.
    • Inference: Includes Python scripts for each single models to preprocess the input data and make inferences using pre-trained weights. Note that for the 2-StageNN+TabNet model, we were running it as a notebooks due to unknown Kaggle environment errors to the UMAP dependency library "numba.core".
    • Submission: Includes predicted labels on public test data.
    • A notebook to blend single model predictions

Hardware

Most of our single models were using Kaggle Notebook instances with GPU enabled to run all data preprocessing, model training, blending weight search and inference.

https://www.kaggle.com/docs/notebooks

For DeepInsight CNNs, such as EfficientNet B3 NS and ResNeSt, they were trained on a local machine with 64GB RAM and two Nividia 2080-Ti GPUs. Each of them took about 12-25 hours to train for 10-folds.

Software

We used Kaggle GPU notebooks to run all our inference scripts.

Below are the packages used in addition to the ones included in the default Kaggle Docker environment for Python. All packages were installed via uploaded kaggle dataset.

Package Name	Repository	Kaggle Dataset
pytorch-lightning=1.0.2	https://github.com/PyTorchLightning/pytorch-lightning	https://www.kaggle.com/markpeng/pytorch-lightning
pytorch-optimizer=0.0.1a17	https://github.com/jettify/pytorch-optimizer	https://www.kaggle.com/markpeng/pytorch-optimizer
pytorch-ranger=0.1.1	https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer	https://www.kaggle.com/markpeng/pytorch-ranger
pytorch-tabnet=2.0.0	https://github.com/dreamquark-ai/tabnet	https://www.kaggle.com/ryati131457/pytorchtabnet
ResNeSt=0.0.6	https://github.com/zhanghang1989/ResNeSt	https://www.kaggle.com/markpeng/resnest
umap-learn=0.4.6	https://github.com/lmcinnes/umap	https://www.kaggle.com/kozistr/umaplearn
iterative-stratification=0.1.6	https://github.com/trent-b/iterative-stratification	https://www.kaggle.com/yasufuminakama/iterative-stratification
gen-efficientnet-pytorch	https://github.com/rwightman/gen-efficientnet-pytorch	https://www.kaggle.com/markpeng/gen-efficientnet-pytorch

Data Setup

Please add https://www.kaggle.com/c/lish-moa/data as the input dataset.

Model Build

Models Summary

Best LB Blend

Model Name	CV	Public LB	Private LB	Training Notebook	Inference Script
3-stage NN	0.01561	0.01823	0.01618	3-stagenn-train.ipynb	3stage-nn-inference.py
2-stage NN + TabNet	0.01615	0.01837	0.01625	2stagenn-tabnet-train.ipynb	2stage-nn-tabnet-inference.ipynb
Simple NN (old CV)	0.01585	0.01833	0.01626	simple-nn-using-old-cv-train.ipynb	simple-nn-old-split-inference.py
Simple NN (new CV)	0.01564	0.01830	0.01622	simple-nn-new-split-train.ipynb	simple-nn-new-split-inference.py
2-heads ResNet	0.01589	0.01836	0.01624	2heads-ResNest-train.ipynb	2heads-ResNest-inference.py
EfficientNet B3 Noisy Student	0.01602	0.01850	0.01634	deepinsight-efficientnet-lightning-v7-b3-train.ipynb	deepinsight-efficientnet-lightning-v7-b3-inference.py
ResNeSt V2	0.01576	0.01854	0.01636	deepinsight-resnest-lightning-v2-train.ipynb	deepinsight-resnest-lightning-v2-inference.py

• Original Submission Notebook: ./final/Best LB/fork-of-blending-with-6-models-5old-1new.ipynb
• Cleaned Submission Notebook: ./final/Best LB/final-best-lb-cleaned.ipynb

Best CV Blend

Model Name	CV	Public LB	Private LB	Training Notebook	Inference Script
3-stage NN	0.01561	0.01823	0.01618	3stagenn-10folds-train.ipynb	3stagenn-10folds-inference.py
2-heads ResNet V2	0.01566	0.01844	0.01623	2heads-resnest-train.ipynb	2heads-resnest-inference.py
EfficientNet B3 Noisy Student	0.01602	0.01850	0.01634	deepinsight-efficientnet-lightning-v7-b3-train.ipynb	deepinsight-efficientnet-lightning-v7-b3-inference.py
ResNeSt V1	0.01582	0.01853	0.01636	deepinsight-resnest-lightning-v1-train.ipynb	deepinsight-resnest-lightning-v1-inference.py
ResNeSt V2	0.01576	0.01854	0.01636	deepinsight-resnest-lightning-v2-train.ipynb	deepinsight-resnest-lightning-v2-inference.py

• Original Submission Notebook: ./final/Best CV/blend-search-optuna-v7.ipynb
• Cleaned Submission Notebook: ./final/Best CV/final-best-CV-cleaned.ipynb

Training

All of the training can be done by running notebooks above as a kaggle notebook. This generates pickled preprocessing modules, model weights used by inference scripts, and predictions used in blend weights search.

Blend Weights Search

Once oof predictions are generated, run the blend weight search notebooks to determine good blending weights for the set of models. The weights in the submission notebooks need to be updated manually.

Inference

The submission notebooks make inference by running each single-model inference scripts and blending the predictions. All of the training notebooks must be added as dataset to load preprocessing class instances and model weights.

To make predictions on a new dataset, you just need to replace test_features.csv in input dataset.

How to Reproduce Winning Solution Locally

Please refer to this documentation.

License

All of our solution code are open-sourced under the Apache 2.0 license.