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@ -165,34 +165,4 @@ ### 5. Custom Baselines
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To run additional baselines:
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- Each baseline must produce `dev_predictions.csv` and `test_predictions.csv` with a `target` column.
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- Use the `evaluate_score` function for evaluation.
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---
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## MLE-Bench
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**Note:** MLE-Bench requires Python 3.11 or higher.
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#### Setup
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Clone the repository and install:
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```bash
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git clone https://github.com/openai/mle-bench.git
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cd mle-bench
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pip install -e .
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```
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Prepare the data:
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```bash
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mlebench prepare -c <competition-id> --data-dir <dataset-dir-save-path>
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```
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#### Run the MLE-Bench Experiment
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Run the following command to execute the experiment:
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```bash
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python run_experiment.py --exp_mode mcts --custom_dataset_dir <dataset-dir-save-path/prepared/public> --rollouts 10 --from_scratch --role_timeout 3600
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```
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- Use the `evaluate_score` function for evaluation.
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