Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261005T121346Z-scsemi-cv-budget-interim/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/scsemi_budget_20261002/fold4/summary.json

@@ -0,0 +1,573 @@
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+    "l1_median": 0.18194462988415472,
+    "l1_obs_loo_null_mean": 0.13719637503029242,
+    "l1_generated_loo_mean_vs_observed_mean": 0.17738381733777073,
+    "composition_top1": 0.14285714285714285,
+    "mean_fraction_generated": {
+     "T cell": 0.701016,
+     "B cell": 0.072571,
+     "Monocyte": 0.117968,
+     "NK cell": 0.054317,
+     "DC": 0.048317,
+     "Other": 0.00581
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.668001,
+     "B cell": 0.081749,
+     "Monocyte": 0.146645,
+     "NK cell": 0.07743,
+     "DC": 0.020687,
+     "Other": 0.005487
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5508,
+    "r_median": 0.6743108041222224,
+    "r_mean": 0.6679340776062811,
+    "top1": 0.14285714285714285,
+    "n_top1": 3,
+    "own_rank_median": 7.0,
+    "chance_top1": 0.047619047619047616
+   },
+   "precision_coverage": {
+    "precision": 0.7546031746031746,
+    "coverage": 0.1019047619047619,
+    "density": 0.23536507936507936,
+    "n_query": 3150,
+    "n_reference": 3150
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.9082539682539682,
+    "coverage": 0.4174603174603175,
+    "density": 0.8273650793650793,
+    "detected_median_before": {
+     "R1": 359.0,
+     "Q": 123.0
+    },
+    "detected_median_after": {
+     "R1": 123.0,
+     "Q": 123.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 11.796825396825396,
+    "observed_mean_over_donors": 14.664491120050386,
+    "generated_pooled_cells": 11.796825396825398
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1957.5,
+    "observed_pooled": 1973.0,
+    "generated_mean_of_donor_medians": 1957.5,
+    "observed_mean_of_donor_medians": 2012.3095238095239
+   },
+   "seconds": 7.5
+  }
+ },
+ "code_sha256": {
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py": "ec5f40b4cea6d5062d0138f3b934abb6465e34cbfeac869c8a0906924c033aec",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/ft_common.py": "4a6366b0f22208cd58714fc0261b8840c643b9d4569704d560cfd93534cbc747",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/umap/cell_immicro_5arm/tools/stage_b_embed.py": "d056cfdf38fb0e5f478756f370174e993b75d0c973c48e02e21749175a4ca2da",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py": "48b12d794782b840838853e93e0fc55fab00532c4b3698f4588e926b985d2881",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/immicro/tools/immicro_common.py": "c5c30e917148cc5e664e91ea30c11bbdff9b6d1f53a3e0a13e16b1917eff37e9",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/predict_scanvi.py": "482ee5dcf4d72b54bc36cbb6dcff62e5030bc7afc46cb5fb01eca73a82ee45a5",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/poisson_readout.py": "8bfa080942fbcdc89ae23b276524a1a83266fb8cbc56b9af279e50f82e6cde38"
+ },
+ "seconds": 30.7
+}

paper-releases/pbisc-diffusion/updates/20261005T121346Z-scsemi-cv-budget-interim/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/scsemi_budget_20261002/pooled/README.md

@@ -0,0 +1,60 @@
+# score_ft.py output: immicro, 107 scored donors
+
+Arms: scsemi_n5__raw, scsemi_n10__raw, scsemi_n20__raw, scsemi_n40__raw.  Every arm is scored on the same donors against the same observed reference.
+
+## Inputs and fixed contracts
+- Observed reference: `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/immicro/reference.npz` (sha c5e2547be3ec), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro;
+  observed cells sum exactly to the features pseudobulk of every donor. Observed labels: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/immicro/labels/observed_immicro/labels.parquet.
+- Generated cells: `generated_counts` read out once with Poisson(seed 20260916) over the **whole NPZ in row order**
+  (`poisson_readout.poisson_integerise`), then the scored donors' rows. These integer counts are used for labels, embedding,
+  depth and the contrast. Pseudobulk uses the summed `generated_counts` (rates), as `score_generated.py`.
+- Labels: frozen f01common scANVI (`predict_scanvi.py`, CPU, zero query epochs), six-type gate ['T cell', 'B cell', 'Monocyte', 'NK cell', 'DC', 'Other']; query genes =
+  the 1,006-gene annotation list minus genes the dataset's sc assay did not measure in every sample (1004 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5508 genes).
+
+## Definitions
+1. **Composition**: fraction of each donor's cells per six-type label (all generated cells; all observed QC cells).
+   `comp_l1` = sum over the six labels of |generated - observed| (immicro_common.l1). `comp_l1_obs_loo_null` = L1 of the
+   donor's observed composition to the mean of the *other scored donors'* observed compositions. `composition_top1` =
+   fraction of donors whose generated composition is closest (L1) to their own observed composition.
+2. **Pseudobulk**: `pb_r` = Pearson r of CPM-log1p(summed generated_counts) vs CPM-log1p(observed pseudobulk) on the scoring
+   genes (evaluate_realbulk.cpm_log1p / paired_r). `pb_top1` = the donor's generated pseudobulk is closest (Pearson) to its own
+   observed pseudobulk among the 107 scored donors (immicro_common.retrieval = evaluate_realbulk.top1 arithmetic).
+3. **Precision / coverage / density** (stage_b_embed.py functions): per-cell CP10K over the scoring genes + log1p; PCA-50
+   (randomized, random_state 20260925) and k = 10 real-real k-NN balls fitted on **R1 of the scored donors only**.
+   Reference rule (independent of fold and arm): per donor, cells in canonical order (library_id+barcode), one
+   permutation from `default_rng([20260925, uint64(sha256(donor)[:8])])`; R1 = first n, R2 = next n,
+   n = min(150, n_cells // 2) (16050 R1 cells here). Generated query: per donor n cells (the donor's R1 size),
+   without replacement, `default_rng([20260926, same key])`. Precision = fraction of generated cells inside any R1 ball
+   (Kynkaanniemi 2019); coverage = fraction of R1 cells whose ball holds a generated cell; density = Naeem 2020.
+   Per-donor precision = that donor's generated cells; per-donor coverage = that donor's R1 cells (the pooled values are
+   their n-weighted means; plain means when every donor has 150 reference cells). R2 (held-out real cells of the same donors) is the
+   perfect-generator control: precision 0.9634, coverage 0.9999.
+   **Depth matched**: stage_b_embed depth matching run pairwise (R1 vs this arm): per donor and within-donor rank of detected
+   genes, both cells are thinned by UMI sampling without replacement to the smaller detected-gene count; PCA kept from
+   un-thinned R1, radii recomputed on thinned R1; fresh rng(20260929) per arm. R2 control: precision
+   0.9635, coverage 0.9997.
+4. **Monocyte %** = 100 x Monocyte fraction (all cells) per donor; **median UMI per cell** = median over cells of the
+   Poisson-read (generated) / observed counts summed over the scoring genes (per donor, and pooled).
+5. **Group contrast** (T, B, Monocyte, NK): not computed (dataset immicro has no group contrast (use --groups-csv / --case-groups)).
+   Genes per type = scoring genes detected (count > 0) in >= 1 % of the scored donors' observed
+   cells of that type. Primary statistic `mean_cp10k`: per-cell CP10K over the scoring genes; per donor the mean over its
+   cells of the type (donors with >= 1 such cell); group mean = mean over the group's donors (donor-balanced);
+   logFC = log2(case mean + 1) - log2(control mean + 1). Secondary `pseudobulk_log1p_cpm`: the SLE benchmark evaluator's
+   statistic (donor x type summed counts, log1p CPM over the scoring genes, mean case - mean control;
+   evaluate_generated_vs_truth.group_log_fold_change). Both are computed on the generated cells (their frozen labels) and on
+   the observed cells; `r_generated` = Pearson r of the two logFC vectors. Broadcast baseline `r_broadcast`: the same
+   observed vector against the logFC of the input bulk (condition NPZ `bulk_counts` of the arm, scoring genes, per-donor
+   CP10K resp. log1p CPM, group means). `partial_r_generated_given_broadcast`: r of the OLS residuals of observed and
+   generated logFC on the bulk logFC (residual_specificity.py).
+6. **Top-50 DEG overlap** (contrast.csv rows `statistic = welch_top50_deg`; summary `deg_top50`): not computed (contrast off).
+
+## Files
+- `per_donor.csv`: one row per arm x donor (all per-donor quantities above).
+- `summary.json`: per arm the donor means / pooled values, provenance (NPZ sha, receipt check, generation metadata,
+  Poisson nnz of the whole NPZ, annotation contract), and the reference block (PCA, R2 control).
+- `contrast.csv`: per arm x cell type x statistic (`mean_cp10k`, `pseudobulk_log1p_cpm`: logFC r; `welch_top50_deg`:
+  top-50 DEG overlap, T / B / Monocyte / NK / DC; columns of the other kind are empty).
+- `arms/<arm>/labels/` (predict_scanvi.py labels + manifest), `arms/<arm>/generated_cells.parquet` (NPZ row, donor, label,
+  UMI on the scoring genes, P/C subsample flag), `arms/<arm>/deg_top50_lists.csv` (observed, generated and broadcast
+  top-50 gene lists, Ensembl ids, rank order).

paper-releases/pbisc-diffusion/updates/20261005T121346Z-scsemi-cv-budget-interim/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/scsemi_budget_20261002/pooled/summary.json

@@ -0,0 +1,659 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "immicro",
+ "written_utc": "2026-10-05T12:12:13.105582+00:00",
+ "command": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py",
+  "--dataset",
+  "immicro",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/scsemi_budget_20261002/pooled",
+  "--threads",
+  "8",
+  "--arm",
+  "scsemi_n5__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/pooled_scsemi_budget_20261002/scsemi_n5__raw.npz",
+  "--arm",
+  "scsemi_n10__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/pooled_scsemi_budget_20261002/scsemi_n10__raw.npz",
+  "--arm",
+  "scsemi_n20__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/pooled_scsemi_budget_20261002/scsemi_n20__raw.npz",
+  "--arm",
+  "scsemi_n40__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/pooled_scsemi_budget_20261002/scsemi_n40__raw.npz"
+ ],
+ "arms": [
+  "scsemi_n5__raw",
+  "scsemi_n10__raw",
+  "scsemi_n20__raw",
+  "scsemi_n40__raw"
+ ],
+ "donors": [
+  "HS1",
+  "HS2",
+  "HS3",
+  "HS4",
+  "HS6",
+  "HS7",
+  "HS8",
+  "HS9",
+  "HS10",
+  "HS11",
+  "HS12",
+  "HS13",
+  "HS14",
+  "HS15",
+  "HS16",
+  "HS17",
+  "HS18",
+  "HS19",
+  "HS20",
+  "HS21",
+  "HS23",
+  "HS24",
+  "HS25",
+  "HS26",
+  "HS27",
+  "HS28",
+  "HS29",
+  "HS30",
+  "HS31",
+  "HS32",
+  "HS34",
+  "HS35",
+  "HS36",
+  "HS37",
+  "HS38",
+  "HS39",
+  "HS40",
+  "HS41",
+  "HS42",
+  "HS43",
+  "HS44",
+  "HS45",
+  "HS46",
+  "HS47",
+  "HS48",
+  "HS49",
+  "HS50",
+  "HS51",
+  "HS52",
+  "HS53",
+  "HS54",
+  "HS55",
+  "HS56",
+  "HS57",
+  "HS58",
+  "HS59",
+  "HS60",
+  "HS61",
+  "HS62",
+  "HS63",
+  "HS64",
+  "HS65",
+  "HS66",
+  "HS67",
+  "HS68",
+  "HS69",
+  "HS70",
+  "HS71",
+  "HS72",
+  "HS73",
+  "HS74",
+  "HS75",
+  "HS76",
+  "HS77",
+  "HS78",
+  "HS79",
+  "HS80",
+  "HS81",
+  "HS82",
+  "HS83",
+  "HS84",
+  "HS85",
+  "HS86",
+  "HS87",
+  "HS88",
+  "HS89",
+  "HS90",
+  "HS91",
+  "HS92",
+  "HS93",
+  "HS94",
+  "HS95",
+  "HS96",
+  "HS97",
+  "HS98",
+  "HS100",
+  "HS101",
+  "HS102",
+  "HS103",
+  "HS104",
+  "HS105",
+  "HS106",
+  "HS107",
+  "HS108",
+  "HS109",
+  "HS110",
+  "HS111"
+ ],
+ "n_donors": 107,
+ "reference": {
+  "cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/immicro",
+  "reference_sha256": "c5e2547be3ec3b12cb0c0b404a49e14a046ae59d40be70427f98f083a76bce17",
+  "features_sha256": "735255066ce7ebbac13a1fdfd058e26f31edd60314cc02fd589b0a292dbbc838",
+  "n_r1_cells": 16050,
+  "pca": {
+   "n_components": 50,
+   "explained_variance_ratio_sum": 0.28717318177223206,
+   "n_fit_cells": 16050
+  },
+  "r2_control": {
+   "precision": 0.9634267912772586,
+   "coverage": 0.9999376947040498,
+   "density": 1.3859065420560748,
+   "n_query": 16050,
+   "depth_matched": {
+    "precision": 0.9634890965732087,
+    "coverage": 0.9996884735202493,
+    "density": 1.3669096573208723
+   }
+  },
+  "observed_l1_loo_null_mean": 0.1606372707722086
+ },
+ "contrast_definition": {
+  "enabled": false,
+  "reason": "dataset immicro has no group contrast (use --groups-csv / --case-groups)"
+ },
+ "deg_definition": {
+  "enabled": false,
+  "reason": "contrast off"
+ },
+ "headline": {
+  "scsemi_n5__raw": {
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+   "median_umi_gen": 1977.0,
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+   "seconds": 21.6
+  },
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+   "median_umi_gen": 1970.0,
+   "median_umi_obs": 1961.0,
+   "seconds": 21.2
+  }
+ },
+ "per_arm": {
+  "scsemi_n5__raw": {
+   "arm": "scsemi_n5__raw",
+   "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/pooled_scsemi_budget_20261002/scsemi_n5__raw.npz",
+   "npz_sha256": "c394b2318deeefa6c60fbba2394952654a7c4a6682942e8f1f9cd9a0df4082e6",
+   "receipt_verified": true,
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+    "input_condition_sha256": "0cf3f9750bb8b48a787720f344559c0f05bacddd14c68e314c7f9fda7f43820f",
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+   },
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+    "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
+    "zero_padded_model_genes": 1,
+    "label_counts": {
+     "T cell": 110615,
+     "Monocyte": 19578,
+     "B cell": 12239,
+     "NK cell": 9086,
+     "DC": 7982,
+     "Other": 1000
+    },
+    "mean_l2_max_prob": 0.9145451268082095,
+    "predict_seconds": 5.8
+   },
+   "n_donors": 107,
+   "n_generated_cells": 160500,
+   "composition": {
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+    "l1_median": 0.2072007020183026,
+    "l1_obs_loo_null_mean": 0.1606372707722086,
+    "l1_generated_loo_mean_vs_observed_mean": 0.2092393336546795,
+    "composition_top1": 0.028037383177570093,
+    "mean_fraction_generated": {
+     "T cell": 0.68919,
+     "B cell": 0.076255,
+     "Monocyte": 0.121981,
+     "NK cell": 0.056611,
+     "DC": 0.049732,
+     "Other": 0.006231
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.639048,
+     "B cell": 0.074796,
+     "Monocyte": 0.166538,
+     "NK cell": 0.091197,
+     "DC": 0.022818,
+     "Other": 0.005602
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5508,
+    "r_median": 0.6638991286907745,
+    "r_mean": 0.662854756735564,
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