Add SHAP beeswarm plot script for RBF optimization objective

This commit introduces a new script, `rbf_objective_shap_beeswarm.py`, which generates a SHAP beeswarm plot for the RBF optimization objective function. The script includes functionality to load RBF models, prepare background data, compute SHAP values, and save the results in various formats. It also implements a uniform design for sampling and applies a specific styling for the plots.

Add SHAP beeswarm plot script for RBF optimization objective
7abfa26f · Joaquín Irazábal González · 99f20325 · 7abfa26f · 7abfa26f · 7abfa26f
Commit 7abfa26f authored May 20, 2026 by Joaquín Irazábal González
19 changed files
--- a/Code/reports/width_optimization/2W/rbf_shap/it2/objective_values_2W_B29_H30_TFD_W90_it2.csv
+++ b/Code/reports/width_optimization/2W/rbf_shap/it2/objective_values_2W_B29_H30_TFD_W90_it2.csv
+tw1,tw2,objective_score,shap_baseline,shap_sum_plus_baseline
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--- a/Code/reports/width_optimization/5W/rbf_shap/it2/shap_importance_objective_5W_B34_H60_TFD_W90_it2.csv
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--- a/Code/reports/width_optimization/5W/rbf_shap/it2/shap_values_objective_5W_B34_H60_TFD_W90_it2.csv
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+1468.9185070517685,-5265.470603998576,11428.529079388298,-1471.5013814352653,-232.29016577522907
+499.660217976946,-924.2844504384191,-6350.334218782959,-986.5988246789148,-39.758670233182784
+-314.408727529777,-2082.8655727181704,14934.138934696897,-1084.1548603412275,-2439.9479790071405
--- a/Code/src/width_optimization/rbf_objective_shap_beeswarm.py
+++ b/Code/src/width_optimization/rbf_objective_shap_beeswarm.py
+"""
+SHAP beeswarm plot for the RBF optimization objective.
+This script explains the scalar objective used in the RBF Differential
+Evolution workflow. The explained model is the complete objective function,
+including Exy contribution and TFM penalties, not an individual surrogate
+output.
+"""
+import argparse
+import os
+import sys
+from pathlib import Path
+os.environ.setdefault("MPLCONFIGDIR", "/tmp/matplotlib")
+import joblib
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+BASE_DIR = Path(__file__).resolve().parent
+REPO_CODE_DIR = BASE_DIR.parents[1]
+if str(BASE_DIR) not in sys.path:
+    sys.path.insert(0, str(BASE_DIR))
+from rbf_optimization_de import objective  # noqa: E402
+SHAP_CMAP = sns.color_palette("vlag", as_cmap=True)
+plt.rcParams.update(
+    {
+        "font.family": "serif",
+        "font.serif": ["Times New Roman", "DejaVu Serif"],
+        "mathtext.fontset": "stix",
+        "font.size": 12,
+        "axes.labelsize": 12,
+        "axes.titlesize": 12,
+        "xtick.labelsize": 11,
+        "ytick.labelsize": 11,
+        "axes.linewidth": 0.7,
+        "axes.spines.top": False,
+        "axes.spines.right": False,
+        "figure.dpi": 300,
+        "savefig.dpi": 300,
+    }
+)
+def infer_h_value(w_val):
+    if w_val == 2:
+        return 30
+    if w_val == 3:
+        return 45
+    if w_val == 5:
+        return 60
+    raise ValueError(f"No H value is defined for W={w_val}.")
+def get_bounds(w_val):
+    if w_val == 2:
+        return [(10.0, 20.0), (10.0, 20.0)]
+    if w_val == 3:
+        return [(5.0, 14.0), (5.0, 14.0), (5.0, 14.0)]
+    if w_val == 5:
+        return [(5.0, 12.0)] * 5
+    return [(10.0, 20.0)] * w_val
+def load_rbf_models(w_val, b_val, h_val, it_val):
+    models_dir = (
+        REPO_CODE_DIR
+        / "models"
+        / "width_optimization"
+        / f"{w_val}W"
+        / "rbf_models"
+        / f"per_output_models_B{b_val}_H{h_val}"
+        / f"it{it_val}"
+    )
+    if not models_dir.exists():
+        raise FileNotFoundError(f"RBF model directory not found: {models_dir}")
+    models_exy = []
+    models_tfm = []
+    missing = []
+    for window_idx in range(w_val):
+        exy_path = models_dir / f"rbf_exymax_tw{window_idx + 1}.joblib"
+        tfm_path = models_dir / f"rbf_tfmmax_tw{window_idx + 1}.joblib"
+        if not exy_path.exists():
+            missing.append(exy_path.name)
+        if not tfm_path.exists():
+            missing.append(tfm_path.name)
+        if exy_path.exists() and tfm_path.exists():
+            models_exy.append(joblib.load(exy_path))
+            models_tfm.append(joblib.load(tfm_path))
+    frame_path = models_dir / "rbf_tfmmax_frame.joblib"
+    if not frame_path.exists():
+        missing.append(frame_path.name)
+    if missing:
+        raise FileNotFoundError(
+            f"Missing required model files in {models_dir}: {', '.join(missing)}"
+        )
+    return models_exy, models_tfm, joblib.load(frame_path)
+def load_training_background(w_val, b_val, h_val, it_val):
+    data_path = (
+        REPO_CODE_DIR
+        / "data"
+        / "width_optimization"
+        / f"{w_val}W"
+        / f"rbf_FEMdata_B{b_val}_H{h_val}.csv"
+    )
+    if not data_path.exists():
+        raise FileNotFoundError(f"FEM data file not found: {data_path}")
+    start_rows = {2: 8, 3: 16, 5: 64}.get(w_val, 10)
+    n_curr = start_rows + it_val
+    df = pd.read_csv(data_path)
+    df.columns = [c.strip().lower() for c in df.columns]
+    features = [f"tw{i + 1}" for i in range(w_val)]
+    if n_curr > len(df):
+        raise ValueError(
+            f"Iteration {it_val} requires {n_curr} rows, but {data_path} has "
+            f"only {len(df)} rows."
+        )
+    return df.loc[: n_curr - 1, features].copy()
+def uniform_design(bounds, n_samples, rng, feature_names):
+    lower = np.array([b[0] for b in bounds], dtype=float)
+    upper = np.array([b[1] for b in bounds], dtype=float)
+    values = rng.uniform(lower, upper, size=(n_samples, len(bounds)))
+    return pd.DataFrame(values, columns=feature_names)
+def make_objective_predictor(
+    models_exy, models_tfm, model_frame, w_val, b_val, tfd_w_val
+):
+    def predict(x_values):
+        x_arr = np.asarray(x_values, dtype=float)
+        if x_arr.ndim == 1:
+            x_arr = x_arr.reshape(1, -1)
+        return np.array(
+            [
+                objective(
+                    row,
+                    models_exy,
+                    models_tfm,
+                    model_frame,
+                    w_val,
+                    b_val,
+                    tfd_w_val,
+                )
+                for row in x_arr
+            ],
+            dtype=float,
+        )
+    return predict
+def style_axis(ax, ylabel):
+    """Apply the manuscript SHAP beeswarm style used in the 2024 script."""
+    ax.set_title("")
+    ax.set_ylabel(ylabel, fontsize=12, fontfamily="Times New Roman", fontweight="bold")
+    ax.set_xlabel("SHAP value", fontsize=12, fontfamily="Times New Roman")
+    ax.tick_params(
+        axis="x", which="major", direction="in", labelsize=11, length=4, width=0.8
+    )
+    ax.tick_params(
+        axis="y", which="major", direction="in", labelsize=11, length=0, width=0.8
+    )
+    plt.setp(ax.get_xticklabels(), fontname="Times New Roman", fontsize=11)
+    plt.setp(
+        ax.get_yticklabels(),
+        fontname="Times New Roman",
+        fontsize=11,
+        rotation=45,
+        ha="right",
+        rotation_mode="anchor",
+    )
+def save_figure(fig, out_base):
+    """Save a figure in the same raster/vector formats as the reference script."""
+    fig.tight_layout(rect=(0.0, 0.0, 1.0, 0.98))
+    for extension in ("png", "pdf", "svg"):
+        fig.savefig(f"{out_base}.{extension}", dpi=300, bbox_inches="tight")
+    plt.close(fig)
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Create a SHAP beeswarm plot for the RBF objective function."
+    )
+    parser.add_argument("--W", type=int, required=True, help="Number of windows.")
+    parser.add_argument("--B", type=int, required=True, help="B configuration.")
+    parser.add_argument("--TFD_W", type=int, required=True, help="Window TFD limit.")
+    parser.add_argument("--it", type=int, required=True, help="RBF iteration.")
+    parser.add_argument(
+        "--background-samples",
+        type=int,
+        default=50,
+        help="Number of background samples for KernelExplainer.",
+    )
+    parser.add_argument(
+        "--explain-samples",
+        type=int,
+        default=200,
+        help="Number of design points shown in the beeswarm.",
+    )
+    parser.add_argument(
+        "--nsamples",
+        default="auto",
+        help="Kernel SHAP nsamples argument. Use 'auto' or an integer.",
+    )
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument(
+        "--output-dir",
+        default=None,
+        help="Optional output directory. Defaults to reports/width_optimization/.../rbf_shap.",
+    )
+    return parser.parse_args()
+def main():
+    args = parse_args()
+    try:
+        import shap
+    except ImportError as exc:
+        raise SystemExit(
+            "The 'shap' package is required. Install it in the active environment "
+            "before running this script."
+        ) from exc
+    w_val = args.W
+    b_val = args.B
+    h_val = infer_h_value(w_val)
+    feature_names = [f"tw{i + 1}" for i in range(w_val)]
+    bounds = get_bounds(w_val)
+    rng = np.random.default_rng(args.seed)
+    models_exy, models_tfm, model_frame = load_rbf_models(
+        w_val, b_val, h_val, args.it
+    )
+    objective_predictor = make_objective_predictor(
+        models_exy, models_tfm, model_frame, w_val, b_val, args.TFD_W
+    )
+    fem_background = load_training_background(w_val, b_val, h_val, args.it)
+    n_background = min(args.background_samples, len(fem_background))
+    background = fem_background.sample(
+        n=n_background, random_state=args.seed
+    ).reset_index(drop=True)
+    explain_points = uniform_design(bounds, args.explain_samples, rng, feature_names)
+    output_dir = Path(args.output_dir) if args.output_dir else (
+        REPO_CODE_DIR
+        / "reports"
+        / "width_optimization"
+        / f"{w_val}W"
+        / "rbf_shap"
+        / f"it{args.it}"
+    )
+    output_dir.mkdir(parents=True, exist_ok=True)
+    print("Creating Kernel SHAP explainer...")
+    explainer = shap.KernelExplainer(objective_predictor, background)
+    expected_value = float(np.asarray(explainer.expected_value).reshape(-1)[0])
+    objective_values = objective_predictor(explain_points)
+    nsamples = args.nsamples if args.nsamples == "auto" else int(args.nsamples)
+    print(
+        f"Computing SHAP values for {len(explain_points)} samples "
+        f"with {len(background)} background points..."
+    )
+    shap_values = explainer.shap_values(explain_points, nsamples=nsamples)
+    shap_values = np.asarray(shap_values, dtype=float)
+    shap_csv = output_dir / (
+        f"shap_values_objective_{w_val}W_B{b_val}_H{h_val}"
+        f"_TFD_W{args.TFD_W}_it{args.it}.csv"
+    )
+    pd.DataFrame(shap_values, columns=feature_names).to_csv(shap_csv, index=False)
+    objective_csv = output_dir / (
+        f"objective_values_{w_val}W_B{b_val}_H{h_val}"
+        f"_TFD_W{args.TFD_W}_it{args.it}.csv"
+    )
+    objective_df = explain_points.copy()
+    objective_df["objective_score"] = objective_values
+    objective_df["shap_baseline"] = expected_value
+    objective_df["shap_sum_plus_baseline"] = expected_value + shap_values.sum(axis=1)
+    objective_df.to_csv(objective_csv, index=False)
+    importance = (
+        pd.DataFrame(
+            {
+                "feature": feature_names,
+                "mean_abs_shap": np.abs(shap_values).mean(axis=0),
+            }
+        )
+        .sort_values("mean_abs_shap", ascending=False)
+        .reset_index(drop=True)
+    )
+    importance_csv = output_dir / (
+        f"shap_importance_objective_{w_val}W_B{b_val}_H{h_val}"
+        f"_TFD_W{args.TFD_W}_it{args.it}.csv"
+    )
+    importance.to_csv(importance_csv, index=False)
+    title = f"RBF objective | {w_val}W B{b_val} H{h_val} | TFD_W={args.TFD_W} | it={args.it}"
+    fig, ax = plt.subplots(1, 1, figsize=(4.2, 5.2))
+    plt.sca(ax)
+    shap.summary_plot(
+        shap_values,
+        explain_points,
+        feature_names=feature_names,
+        plot_type="dot",
+        show=False,
+        sort=False,
+        max_display=w_val,
+        plot_size=None,
+        cmap=SHAP_CMAP,
+    )
+    max_abs_shap = float(np.max(np.abs(shap_values)))
+    x_limit = max_abs_shap * 1.05 if max_abs_shap > 0.0 else 1.0
+    ax.set_xlim(-x_limit, x_limit)
+    style_axis(ax, ylabel="Objective")
+    fig.suptitle(title, fontsize=13, fontweight="bold", y=1.02)
+    out_base = output_dir / (
+        f"shap_beeswarm_objective_{w_val}W_B{b_val}_H{h_val}"
+        f"_TFD_W{args.TFD_W}_it{args.it}"
+    )
+    save_figure(fig, out_base)
+    png_path = out_base.with_suffix(".png")
+    pdf_path = out_base.with_suffix(".pdf")
+    svg_path = out_base.with_suffix(".svg")
+    print(f"Saved beeswarm PNG: {png_path}")
+    print(f"Saved beeswarm PDF: {pdf_path}")
+    print(f"Saved beeswarm SVG: {svg_path}")
+    print(f"Saved SHAP values: {shap_csv}")
+    print(f"Saved objective values: {objective_csv}")
+    print(f"Saved SHAP importance: {importance_csv}")
+    print(f"SHAP baseline E[objective(background)] = {expected_value:.6g}")
+    print(
+        "Explained objective range = "
+        f"{objective_values.min():.6g} to {objective_values.max():.6g}"
+    )
+if __name__ == "__main__":
+    main()