rpotter6298
62 lines
1.5 KiB
Python
62 lines
1.5 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import Iterable, Optional
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import pandas as pd
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from classes.v2.data_bundle import DataBundle
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from classes.v2.papila_builders import build_papila_data
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@dataclass
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class PapilaData:
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"""
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V2-friendly wrapper around the DataBundle pipeline.
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Keeps all formatting/normalization behavior from build_papila_clinical,
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but exposes a minimal surface area for the V2 engine.
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"""
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clinical: DataBundle
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patient_col: str = "Patient ID"
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@property
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def df(self) -> pd.DataFrame:
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return self.clinical.df
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@property
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def label_col(self) -> str:
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return self.clinical.label_col
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@property
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def feature_dim(self) -> int:
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return self.clinical.feature_dim
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def get_image_path(self, row: pd.Series):
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return self.clinical.get_image_path(row)
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def vectorize_row(self, row: pd.Series):
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return self.clinical.vectorize_row(row)
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@classmethod
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def from_dirs(
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cls,
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*,
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image_dir: str,
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clinical_dir: str,
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label_col: str,
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cat_cols: Iterable[str],
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n_splits: int = 5,
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random_seed: int = 42,
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) -> "PapilaData":
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clinical = build_papila_data(
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image_dir=image_dir,
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clinical_dir=clinical_dir,
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label_col=label_col,
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cat_cols=list(cat_cols),
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n_splits=n_splits,
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random_seed=random_seed,
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)
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return cls(clinical=clinical)
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