validate_dataset.py

"""Validate synthetic asbestos exposure & mesothelioma dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "former_mining_community": "asbestos_mining_community.csv",
    "urban_construction": "asbestos_urban_construction.csv",
    "rural_asbestos_roofing": "asbestos_rural_roofing.csv",
}

COLORS = {"former_mining_community": "#e6550d", "urban_construction": "#756bb1", "rural_asbestos_roofing": "#31a354"}


def load_data() -> pd.DataFrame:
    frames = []
    for scenario, filename in SCENARIO_FILES.items():
        df = pd.read_csv(Path("data") / filename)
        frames.append(df)
    return pd.concat(frames, ignore_index=True)


def plot_validation(df: pd.DataFrame, output_path: Path) -> None:
    fig, axes = plt.subplots(4, 2, figsize=(14, 16))
    axes = axes.flatten()

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[0].hist(subset["cumulative_exposure"], bins=40, alpha=0.5, color=COLORS[s], label=s, range=(0, 100))
    axes[0].set_title("Cumulative Exposure Distribution (f/mL·yr)")
    axes[0].legend(fontsize=7)

    disease_cols = ["mesothelioma", "asbestosis", "lung_cancer", "pleural_plaques"]
    dis = df.groupby("scenario")[disease_cols].mean() * 100
    dis.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Asbestos-Related Disease (%)")
    axes[1].legend(fontsize=6)

    ft = df.groupby(["scenario", "fibre_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    ft.unstack().plot(kind="bar", stacked=True, ax=axes[2])
    axes[2].set_title("Fibre Type Distribution")
    axes[2].legend(fontsize=7)

    exp = df.groupby(["scenario", "exposure_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    exp.unstack().plot(kind="bar", stacked=True, ax=axes[3])
    axes[3].set_title("Exposure Type Distribution")
    axes[3].legend(fontsize=5)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[4].scatter(subset["cumulative_exposure"], subset["any_asbestos_disease"],
                        s=4, alpha=0.05, color=COLORS[s], label=s)
    axes[4].set_title("Cumulative Exposure vs Disease")
    axes[4].legend(fontsize=7)

    resp_cols = ["dyspnoea", "cough_chronic", "reduced_fvc"]
    resp = df.groupby("scenario")[resp_cols].mean() * 100
    resp.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Respiratory Symptoms (%)")
    axes[5].legend(fontsize=7)

    reg_cols = ["ban_in_place", "medical_surveillance", "ppe_use", "chest_xray_done"]
    reg = df.groupby("scenario")[reg_cols].mean() * 100
    reg.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Regulation & Surveillance (%)")
    axes[6].legend(fontsize=6)

    mort_cols = ["died", "hiv_positive", "compensation_claimed"]
    mort = df.groupby("scenario")[mort_cols].mean() * 100
    mort.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Mortality, HIV & Compensation (%)")
    axes[7].legend(fontsize=7)

    plt.tight_layout()
    fig.savefig(output_path, dpi=200)
    plt.close(fig)


def main() -> None:
    df = load_data()
    plot_validation(df, Path("validation_report.png"))
    print("Saved validation_report.png")


if __name__ == "__main__":
    main()