CrbnsCat10n
02e488cd0d
new file: checkpoints_tree/task1_tr_tree/model_selection.csv new file: evaluation_outputs/task1_tr_tree/evaluation_train_all_samples.csv new file: evaluation_outputs/task1_tr_tree/evaluation_train_curve.png new file: evaluation_outputs/task1_tr_tree/evaluation_val_all_samples.csv new file: evaluation_outputs/task1_tr_tree/evaluation_val_curve.png new file: evaluation_outputs/task1_tr_tree/harmonic_5mm_1.55Hz_prediction.png new file: scripts_tree/__pycache__/config.cpython-310.pyc new file: scripts_tree/config.py new file: scripts_tree/evaluate.py new file: scripts_tree/predict_single.py new file: scripts_tree/train.py
51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, field
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from pathlib import Path
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import sys
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from scripts.config import CORE_FEATURE_NAMES, DataConfig
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@dataclass
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class TreeModelConfig:
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candidate_models: tuple[str, ...] = ("extra_trees", "random_forest", "gradient_boosting")
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random_state: int = 42
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@dataclass
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class TrainConfig:
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checkpoint_dir: str = "checkpoints_tree"
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summary_name: str = "model_selection.csv"
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best_model_name: str = "best_tr_tree.pkl"
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@dataclass
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class ExperimentConfig:
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data: DataConfig = field(default_factory=DataConfig)
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model: TreeModelConfig = field(default_factory=TreeModelConfig)
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train: TrainConfig = field(default_factory=TrainConfig)
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def make_experiment_config() -> ExperimentConfig:
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return ExperimentConfig()
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def checkpoint_dir(config: ExperimentConfig) -> Path:
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path = config.data.project_root / config.train.checkpoint_dir / "task1_tr_tree"
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path.mkdir(parents=True, exist_ok=True)
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return path
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def evaluation_dir(config: ExperimentConfig) -> Path:
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path = config.data.project_root / "evaluation_outputs" / "task1_tr_tree"
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path.mkdir(parents=True, exist_ok=True)
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return path
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FEATURE_NAMES = CORE_FEATURE_NAMES
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