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98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
"""
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Ablation table: 4 mixings × 3 source distributions × {SIGReg, Whitening}.
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Reports R²(h→z). The winner per (mixing, α) is bolded when the difference
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is statistically significant (Welch's t-test, p < 0.05).
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Usage:
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python analysis/make_table_ablation.py --out figures/tab_ablation.tex
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"""
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import argparse, glob, json, os
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import numpy as np
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from scipy import stats
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from collections import defaultdict
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MIXINGS = [("spiral", "Spiral"), ("banana", "Banana"),
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("sinusoid", "Sinusoid"), ("nvp", "NVP")]
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ALPHAS = [
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(None, r"Gaussian ($\alpha = 2$)"),
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(0.25, r"Heavy tail / sparse ($\alpha = 1/4$)"),
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(16.0, r"Light tail / uniform ($\alpha = 16$)"),
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]
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# METRIC = "r2_hz" # marginal
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METRIC = "r2_hz_grid" # grid
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P_THRESH = 0.05
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def alpha_key(r):
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if r.get("source_dist") == "gennorm":
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return r.get("source_alpha")
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return None
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def fmt(vals, bold=False):
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if not vals:
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return r"$-$"
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s = f"{np.mean(vals):.3f} \\pm {np.std(vals):.3f}"
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return rf"$\mathbf{{{s}}}$" if bold else f"${s}$"
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def main():
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p = argparse.ArgumentParser()
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p.add_argument("--dirs", nargs="+",
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default=["results/2d",
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"results/ablation_alpha_0.25",
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"results/ablation_alpha_16"])
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p.add_argument("--out", default="figures/tab_ablation.tex")
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args = p.parse_args()
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groups = defaultdict(list)
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for d in args.dirs:
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for path in sorted(glob.glob(os.path.join(d, "*.json"))):
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with open(path) as f:
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r = json.load(f)
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if METRIC not in r:
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continue
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key = (r["mixing"], alpha_key(r), r["mode"])
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groups[key].append(r[METRIC])
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col_spec = "l" + " cc" * len(ALPHAS)
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multicol = " & ".join(rf"\multicolumn{{2}}{{c}}{{{lab}}}" for _, lab in ALPHAS)
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cmidrules = "".join(rf"\cmidrule(lr){{{2*i+2}-{2*i+3}}}" for i in range(len(ALPHAS)))
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method_hdr = " & ".join(["SIGReg & Whitening"] * len(ALPHAS))
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lines = [
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rf"\begin{{tabular}}{{{col_spec}}}",
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r"\toprule",
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rf" & {multicol} \\",
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cmidrules,
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rf"Mixing & {method_hdr} \\",
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r"\midrule",
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]
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for mix_key, mix_name in MIXINGS:
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row = [mix_name]
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for alpha_k, _ in ALPHAS:
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v_lej = groups.get((mix_key, alpha_k, "lejepa"), [])
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v_wht = groups.get((mix_key, alpha_k, "whiten"), [])
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bold_lej = bold_wht = False
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if len(v_lej) >= 2 and len(v_wht) >= 2:
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_, pval = stats.ttest_ind(v_lej, v_wht, equal_var=False)
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if pval < P_THRESH:
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if np.mean(v_lej) > np.mean(v_wht):
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bold_lej = True
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else:
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bold_wht = True
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row.append(fmt(v_lej, bold=bold_lej))
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row.append(fmt(v_wht, bold=bold_wht))
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lines.append(" & ".join(row) + r" \\")
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lines += [r"\bottomrule", r"\end{tabular}"]
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out = "\n".join(lines)
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print(out)
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os.makedirs(os.path.dirname(args.out) or ".", exist_ok=True)
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with open(args.out, "w") as f:
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f.write(out + "\n")
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print(f"\nSaved {args.out}")
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if __name__ == "__main__":
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main() |