validate_dataset.py

"""Validate synthetic visual impairment & low vision services dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "urban_eye_clinic": "vision_urban.csv",
    "district_outreach": "vision_district.csv",
    "rural_community": "vision_rural.csv",
}

COLORS = {"urban_eye_clinic": "#e6550d", "district_outreach": "#756bb1", "rural_community": "#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()

    acc_cols = ["screened", "referred", "referral_completed", "ophthalmologist_seen"]
    acc = df.groupby("scenario")[acc_cols].mean() * 100
    acc.plot(kind="bar", ax=axes[0])
    axes[0].set_title("Service Access Cascade (%)")
    axes[0].legend(fontsize=6)

    cau = df.groupby(["scenario", "cause"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    cau.unstack().plot(kind="bar", stacked=True, ax=axes[1])
    axes[1].set_title("Cause of Visual Impairment")
    axes[1].legend(fontsize=5)

    int_cols = ["spectacles_received", "cataract_surgery_done", "low_vision_device"]
    intv = df.groupby("scenario")[int_cols].mean() * 100
    intv.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Interventions Received (%)")
    axes[2].legend(fontsize=7)

    bar_cols = ["cost_barrier", "distance_barrier", "awareness_barrier"]
    bar = df.groupby("scenario")[bar_cols].mean() * 100
    bar.plot(kind="bar", ax=axes[3])
    axes[3].set_title("Barriers (%)")
    axes[3].legend(fontsize=7)

    sev = df.groupby(["scenario", "severity"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    sev.unstack().plot(kind="bar", stacked=True, ax=axes[4])
    axes[4].set_title("Severity Distribution")
    axes[4].legend(fontsize=7)

    out_cols = ["vision_improved", "quality_of_life_improved", "employment_impacted"]
    out = df.groupby("scenario")[out_cols].mean() * 100
    out.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Outcomes (%)")
    axes[5].legend(fontsize=7)

    need_cols = ["spectacles_needed", "cataract_surgery_needed", "unmet_need"]
    need = df.groupby("scenario")[need_cols].mean() * 100
    need.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Need & Unmet Need (%)")
    axes[6].legend(fontsize=7)

    avd = df.groupby("scenario")["avoidable"].mean() * 100
    avd.plot(kind="bar", ax=axes[7], color="#e6550d")
    axes[7].set_title("Avoidable Blindness (%)")

    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()