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Fix imports
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config/config.yaml

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# en/houses
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defaults:
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- language: da/huse
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- language: en/houses
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- override hydra/job_logging: custom
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- _self_
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model : o3-mini
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# Whether to generate new LLM responses or only evaluate existing ones when running evaluate.py
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generate_new_responses: False
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generate_new_responses: True
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# The folder where data is stored
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data_folder: data_backup
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data_folder: data
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# Number of puzzles to generate
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n_puzzles: 100
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n_objects: 2
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# Attributes e.g. pets
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n_attributes: 1
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n_attributes: 3
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# Weights for the clue types
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# The weights are used to determine the probability of generating each type of clue. The distribution of accepted clue types will be different, as redundant clues are removed.

src/scripts/build_dataset.py

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import hydra
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from omegaconf import DictConfig
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from zebra_puzzles.build_pipeline import build_dataset
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from zebra_puzzles.puzzle_creation.build_pipeline import build_dataset
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@hydra.main(config_path="../../config", config_name="config", version_base=None)

src/scripts/evaluate.py

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import hydra
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from omegaconf import DictConfig
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from zebra_puzzles.eval_pipeline import evaluate_all
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from zebra_puzzles.evaluation.eval_pipeline import evaluate_all
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@hydra.main(config_path="../../config", config_name="config", version_base=None)

src/scripts/plot_performance.py

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import hydra
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from omegaconf import DictConfig
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from zebra_puzzles.plot_pipeline import plot_results
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from zebra_puzzles.performance.plot_pipeline import plot_results
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@hydra.main(config_path="../../config", config_name="config", version_base=None)

src/zebra_puzzles/clue_removal.py

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import numpy as np
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from zebra_puzzles.zebra_solver import solver
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from zebra_puzzles.puzzle_creation.zebra_solver import solver
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def remove_redundant_clues_with_rules(

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