Archive 4 checkpoints (1-4)

.gitattributes

@@ -80,3 +80,4 @@ paper-releases/pbisc-diffusion/updates/20261002T022002Z-deck-time-to-apply-scsem
 paper-releases/pbisc-diffusion/updates/20261002T022002Z-deck-time-to-apply-scsemi-reps/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scsemi/work/ye2026_sepsis/fewshot_n5/draw0/scsemi_project/processed_bulkdata.h5ad filter=lfs diff=lfs merge=lfs -text
 paper-releases/pbisc-diffusion/updates/20261002T085943Z-deck-restructure-contrast-metrics-zeroshot-len/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_paper_summary_20260930.pdf filter=lfs diff=lfs merge=lfs -text
 paper-releases/pbisc-diffusion/updates/20261002T085943Z-deck-restructure-contrast-metrics-zeroshot-len/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_paper_summary_20260930.pptx filter=lfs diff=lfs merge=lfs -text
+paper-releases/pbisc-diffusion/updates/20261002T085943Z-deck-restructure-contrast-metrics-zeroshot-len/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/figures/sec01_slide_a.pdf filter=lfs diff=lfs merge=lfs -text

paper-releases/pbisc-diffusion/updates/20261002T085943Z-deck-restructure-contrast-metrics-zeroshot-len/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/figures/sec01_slide_a.pdf

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+oid sha256:491e541148abdb06be8fe765d86eaac15cc956f267101004d1e9f698ecd01c6f
+size 567053

paper-releases/pbisc-diffusion/updates/20261002T085943Z-deck-restructure-contrast-metrics-zeroshot-len/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/sec09_scsemi_representative.py

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+#!/usr/bin/env python3
+"""Section 9 slide of the 2026-09-30 paper-summary deck: scSemiProfiler's composition follows its representative.
+
+CPU only. Reads plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/
+{per_target.csv, summary.csv, kmeans_proxy.csv} (made by scsemi/tools/representative_dependence.py; rerun that first)
+and draws:
+  a  composition L1 per cohort: copying the representative's observed composition, scSemiProfiler-5, LoRA-5
+     (bar = mean over 5 draws of the per-draw mean, whiskers = SD, dots = draws)
+  b  Spearman, over test samples, of each method's L1 with the copy error
+The KMeans proxy (upstream's own representative choice, not a rerun) goes to the speaker notes only.
+
+Outputs (deck folder; DECK_FIGDIR / DECK_OUTDATA override the folders):
+  figures/sec09_scsemi_representative.{png,pdf}
+  data/sec09_scsemi_representative.csv              every plotted value with its source
+  data/sec09_scsemi_representative_slide_text.json  title, keywords, footnotes, quoted numbers (synced by
+                                                    tools/sync_sec09_text.py through text_source)
+"""
+from __future__ import annotations
+
+import os
+
+os.environ["CUDA_VISIBLE_DEVICES"] = ""  # plotting only
+
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+DECK = Path("/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/"
+            "2026-09-30--paper-summary--r00")
+sys.path.insert(0, str(DECK / "tools"))
+import sec09_paired_validation as S9  # noqa: E402  (shared section 9 names and colours; sets the deck rcParams)
+import style  # noqa: E402
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.patches import Patch  # noqa: E402
+
+A = S9.T / "scsemi" / "analysis" / "representative_dependence_20261002"
+FIGDIR = Path(os.environ.get("DECK_FIGDIR", str(DECK / "figures")))
+OUTDATA = Path(os.environ.get("DECK_OUTDATA", str(DECK / "data")))
+ORDER, NAME, ROWNAME = S9.ORDER, S9.NAME, S9.ROWNAME
+FS, SUB, EDGE = S9.FS, S9.SUB, S9.EDGE
+ARMS = [("copy", "copy_l1", style.ARM["broadcast"], "Copy the representative's composition"),
+        ("scsemi", "scsemi_l1", style.ARM["scsemi"], "scSemiProfiler, 5 donors"),
+        ("lora", "lora_l1", style.ARM["lora"], "LoRA, 5 donors")]
+
+
+def main() -> int:
+    per = pd.read_csv(A / "per_target.csv")
+    summ = pd.read_csv(A / "summary.csv").set_index("cohort")
+    prox = pd.read_csv(A / "kmeans_proxy.csv").set_index("cohort")
+    by_draw = per.groupby(["cohort", "draw"])[[c for _, c, _, _ in ARMS]].mean()
+    # the plotted pooled means must equal the analysis summary
+    for ds in ORDER:
+        for _, c, _, _ in ARMS:
+            if not np.isclose(per[per.cohort == ds][c].mean(), summ.loc[ds, c]):
+                raise SystemExit(f"{ds} {c}: per_target and summary disagree")
+    fig, (ax, bx) = plt.subplots(1, 2, figsize=(12.3, 4.6), gridspec_kw={"width_ratios": [2.0, 1.0], "wspace": 0.12})
+    h, gap = 0.27, 0.4
+    yt, rows = [], []
+    for i, ds in enumerate(ORDER):
+        y0 = i * (3 * h + gap)
+        for k, (key, col, colour, lab) in enumerate(ARMS):
+            v = by_draw.loc[ds][col].to_numpy()
+            y = y0 + k * h
+            ax.barh(y, v.mean(), h * 0.88, color=colour, edgecolor="none")
+            ax.errorbar(v.mean(), y, xerr=v.std(ddof=1), fmt="none", ecolor=EDGE, elinewidth=1, capsize=2.5)
+            ax.scatter(v, np.full(v.size, y), s=12, facecolor="white", edgecolor=EDGE, linewidth=0.8, zorder=3)
+            rows += [dict(panel="a", cohort=ds, series=key, draw=j, value=float(x),
+                          source=str(A / "per_target.csv")) for j, x in enumerate(v)]
+        yt.append(y0 + h)
+        p = summ.loc[ds, "scsemi_vs_copy_wilcoxon_p"]
+        diff = summ.loc[ds, "scsemi_minus_copy"]
+        verdict = "worse" if (p < 0.05 and diff > 0) else ("better" if (p < 0.05 and diff < 0) else "no difference")
+        ax.text(0.79, y0 + h, f"scSemi vs copy:\n{verdict}", va="center", ha="left", fontsize=FS - 3, color=SUB,
+                linespacing=1.1)
+    nt = per.groupby(["cohort", "draw"]).size().groupby(level=0).agg(["min", "max"])
+    ax.set_yticks(yt, [f"{ROWNAME[d]}\ntest {nt.loc[d, 'min']}" + (f"–{nt.loc[d, 'max']}" if nt.loc[d, 'max'] != nt.loc[d, 'min'] else "")
+                       + " per draw" for d in ORDER], fontsize=FS - 2)
+    ax.invert_yaxis()
+    ax.set_xlim(0, 1.02)
+    ax.set_xticks([0, 0.2, 0.4, 0.6])
+    ax.set_xlabel("Composition L1 ↓")
+    ax.tick_params(axis="y", length=0)
+    for i, ds in enumerate(ORDER):
+        y0 = i * (3 * h + gap)
+        for k, (key, col) in enumerate((("scsemi", "rho_scsemi_copy"), ("lora", "rho_lora_copy"))):
+            v = summ.loc[ds, col]
+            colour = style.ARM[key]
+            bx.barh(y0 + (k + 1) * h, v, h * 0.88, color=colour, edgecolor="none")
+            bx.text(v + 0.02, y0 + (k + 1) * h, f"{v:.2f}", va="center", fontsize=FS - 3, color=SUB)
+            rows.append(dict(panel="b", cohort=ds, series=key, draw=None, value=float(v), source=str(A / "summary.csv")))
+    bx.set_yticks(yt, [""] * len(yt))
+    bx.tick_params(axis="y", length=0)
+    bx.invert_yaxis()
+    bx.set_xlim(0, 1.08)
+    bx.set_xlabel("Spearman with the copy error")
+    fig.legend(handles=[Patch(color=c, label=l) for _, _, c, l in ARMS], loc="lower center", ncol=3,
+               bbox_to_anchor=(0.45, -0.08), fontsize=FS - 2, handlelength=1.2, columnspacing=1.6)
+    style.save(fig, "sec09_scsemi_representative", FIGDIR)
+    OUTDATA.mkdir(exist_ok=True)
+    pd.DataFrame(rows).to_csv(OUTDATA / "sec09_scsemi_representative.csv", index=False)
+
+    s = summ.loc[list(ORDER)]
+    worse = [d for d in ORDER if s.loc[d, "scsemi_vs_copy_wilcoxon_p"] < 0.05 and s.loc[d, "scsemi_minus_copy"] > 0]
+    better = [d for d in ORDER if s.loc[d, "scsemi_vs_copy_wilcoxon_p"] < 0.05 and s.loc[d, "scsemi_minus_copy"] < 0]
+    lora_below = [d for d in ORDER if s.loc[d, "lora_l1"] < s.loc[d, "copy_l1"]]
+    kw = [f"scSemi-5 never beats copying its representative: {len(ORDER) - len(better)}/{len(ORDER)}",
+          f"Its error tracks the representative's: rho {s.rho_scsemi_copy.min():.2f}–{s.rho_scsemi_copy.max():.2f}",
+          f"LoRA-5 below the copy error in {len(lora_below)}/{len(ORDER)} cohorts"]
+    numbers = [{"value": f"{NAME[d]}: copy {s.loc[d, 'copy_l1']:.3f}, scSemi-5 {s.loc[d, 'scsemi_l1']:.3f} "
+                         f"(Wilcoxon vs copy p {s.loc[d, 'scsemi_vs_copy_wilcoxon_p']:.2g}), LoRA-5 {s.loc[d, 'lora_l1']:.3f}; "
+                         f"rho scSemi {s.loc[d, 'rho_scsemi_copy']:.2f}, LoRA {s.loc[d, 'rho_lora_copy']:.2f}",
+                "source": str(A / "summary.csv")} for d in ORDER]
+    proxy = "; ".join(f"{NAME[d]} ({int(prox.loc[d, 'n_representative_samples'])} samples) "
+                      f"ours {prox.loc[d, 'copy_l1_our_draws']:.2f}, KMeans {prox.loc[d, 'copy_l1_kmeans']:.2f}, "
+                      f"random samples {prox.loc[d, 'copy_l1_random_samples']:.2f}" for d in ORDER)
+    numbers.append({"value": "KMeans proxy, copy error: " + proxy, "source": str(A / "kmeans_proxy.csv")})
+    full = ("Same 25 five-donor draws. scSemiProfiler gives every test sample exactly one representative, the nearest "
+            "training sample in its bulk PCA (assignment.json), and runs its encoder on that representative's cells. "
+            "Copy = composition L1 between the test sample's and its representative's observed cells (same frozen "
+            "scANVI labels as the scorer). Bars: mean over draws of the per-draw mean; dots = draws. scSemi vs copy: "
+            "paired Wilcoxon over test samples (" + ", ".join(f"{NAME[d]} p {s.loc[d, 'scsemi_vs_copy_wilcoxon_p']:.2g}"
+                                                             for d in ORDER) + "). "
+            "Upstream's own representative choice (KMeans on the same bulk PCA, the sample nearest each centre; proxy, "
+            "not a rerun) gives a lower copy error than our 5-donor draws in Imperial, Giroux and Ji, where our draws hold "
+            "all visits of 5 donors and KMeans spreads the same number of samples over as many donors; equal or higher "
+            "in ImMicro and sepsis. Copy error: " + proxy + ". "
+            "Source: plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/.")
+    txt = {"title": "scSemiProfiler copies its representative", "layout": "full",
+           "figures": [str(FIGDIR / "sec09_scsemi_representative.png")], "keywords": kw,
+           "footnote_short": "Copy = composition of the one training sample scSemiProfiler assigns to each test sample.",
+           "footnote": full, "numbers": numbers}
+    out = {"section": "09-scsemi-representative", "scripts": [str(Path(__file__).resolve())],
+           "generated_from": "tools/sec09_scsemi_representative.py (rerun to refresh; do not edit by hand)",
+           "slides": [txt]}
+    (OUTDATA / "sec09_scsemi_representative_slide_text.json").write_text(json.dumps(out, indent=1, ensure_ascii=False))
+    print("\n".join(kw))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())