This is the Preferred Wave's solution for G2Net Detecting Continuous Gravitational Waves.
We have a single file input/test_real.csv that lists the test data with real noise, which we detected in a similar way as a public notebook.
Please add the competition dataset under input/.
$ ls -F input
sample_submission.csv test/ test_real.csv train/ train_labels.csv
Our solution does not require any training. You can make predictions for the test data by simply running the following one command.
python predict.py --data_name test --config_path config/default.yaml --seed 0 --out_dir result/seed0
It saves the results under result/seed0/. You can use pred.csv as a prediction. You can see the parameters of --topk (100 by default) highest scores for each data in score.csv.
By specifying --data_name train, you can run validation on train data.
python predict.py --data_name test --config_path config/default.yaml --seed 0 --out_dir result/seed0
It took around 20 seconds to predict single data on NVIDIA V100 (=around 3 GPU hours and 2 GPU days for the execution of all train data and test data, respectively).
The prediction by the above command scores around 0.825 in the private leaderboard. Averaging the results of 2 seeds raises the score to around 0.828, which is enough to win 2nd place. You can increase the score to 0.832 by averaging more seeds (~5) and even to 0.836 by ensembling different configurations (config/freq4.yaml and config/freq6.yaml).
- For an overview of our key ideas and detailed explanation, please also refer to 2nd Place Solution: GPU-Accelerated Random Search in Kaggle discussion.