add all trained checkpoints: main splits + norman perturbation ablations

.gitattributes

@@ -33,3 +33,26 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 models/replogle_k562/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/comp_map/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/comp_map_semi/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/comp_map_tan/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/default/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/frac_0.1/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/frac_0.25/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/frac_0.5/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/frac_0.75/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/match_batch_celltype/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/match_celltype/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/match_nearest/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/match_ot/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/match_random/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/rep_gene_only/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/rep_gene_pathway_op/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/rep_op_only/model.pt filter=lfs diff=lfs merge=lfs -text
+models/ablations/norman_perturbation/rep_random_id/model.pt filter=lfs diff=lfs merge=lfs -text
+models/norman/cell/model.pt filter=lfs diff=lfs merge=lfs -text
+models/norman/combination/model.pt filter=lfs diff=lfs merge=lfs -text
+models/norman/perturbation/model.pt filter=lfs diff=lfs merge=lfs -text
+models/replogle_k562/cell/model.pt filter=lfs diff=lfs merge=lfs -text
+models/replogle_k562/gene/model.pt filter=lfs diff=lfs merge=lfs -text
+models/replogle_k562/perturbation/model.pt filter=lfs diff=lfs merge=lfs -text

README.md

@@ -64,10 +64,13 @@ conda run -n pivot_gears python scripts/gears_ranking.py
 
 ## Models
 
-Trained PIVOT checkpoints live under `models/`:
+Every model behind the paper is checkpointed under `models/`, and `scripts/train_all.py`
+retrains them from scratch with the same configs and seed, so the numbers reproduce.
 
-- `models/norman/` - trained on Norman 2019 (CRISPRa K562)
-- `models/replogle_k562/` - trained on Replogle 2022 (CRISPRi K562)
+- `models/norman/{cell,perturbation,combination}/` - Norman 2019 (CRISPRa K562), one per split
+- `models/replogle_k562/{cell,perturbation,gene}/` - Replogle 2022 (CRISPRi K562), one per split
+- `models/ablations/norman_perturbation/` - the ablation variants (loss components, perturbation
+  representation, training-data fraction, control-matching strategy), each a separate folder
 
 each folder has `model.pt` (a plain torch state dict), `config.json` (the training config),
 and `train_info.json` (history + run info). loading needs the matching preprocessed dataset,
@@ -78,13 +81,19 @@ import json, torch
 from src.data.perturb_data import load_dataset
 from src.training.train import TrainConfig, make_model
 
-cfg = TrainConfig(**json.load(open("models/norman/config.json")))
+cfg = TrainConfig(**json.load(open("models/norman/perturbation/config.json")))
 data = load_dataset(cfg.dataset)
 model = make_model(data, cfg, device="cpu")
-model.load_state_dict(torch.load("models/norman/model.pt", map_location="cpu"))
+model.load_state_dict(torch.load("models/norman/perturbation/model.pt", map_location="cpu"))
 model.eval()
 ```
 
+To retrain everything:
+
+```bash
+python scripts/train_all.py
+```
+
 ## License
 
 MIT, Bryan Cheng 2026. See `LICENSE`.

models/ablations/norman_perturbation/comp_map/config.json

@@ -0,0 +1,28 @@
+{
+  "dataset": "norman",
+  "embedding": "pca",
+  "split": "perturbation",
+  "rep_mode": "gene_op",
+  "match": "batch",
+  "d_pert": 64,
+  "hidden": 512,
+  "depth": 4,
+  "dropout": 0.0,
+  "lr": 0.001,
+  "weight_decay": 1e-05,
+  "batch_size": 1024,
+  "epochs": 45,
+  "lam_tan": 1.0,
+  "lam_semi": 0.5,
+  "lam_reg": 0.0001,
+  "grad_clip": 5.0,
+  "train_frac": 1.0,
+  "lam_dist": 0.0,
+  "n_dist_perts": 4,
+  "dist_n": 64,
+  "seed": 0,
+  "device_index": 3,
+  "components": [
+    "map"
+  ]
+}

models/ablations/norman_perturbation/comp_map/model.pt

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+size 3575254

models/ablations/norman_perturbation/comp_map/train_info.json

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+}

models/ablations/norman_perturbation/comp_map_semi/config.json

@@ -0,0 +1,29 @@
+{
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+  "seed": 0,
+  "device_index": 3,
+  "components": [
+    "map",
+    "semi"
+  ]
+}

models/ablations/norman_perturbation/comp_map_semi/model.pt

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models/ablations/norman_perturbation/match_celltype/config.json

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models/ablations/norman_perturbation/match_nearest/config.json

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models/ablations/norman_perturbation/match_random/config.json

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models/ablations/norman_perturbation/rep_gene_only/config.json

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