Upload raw .npy continuous & decoded grid predictions, emb_train.npz, and updated rescoring Dataset Card

README.md

@@ -2,48 +2,84 @@
 license: apache-2.0
 tags:
 - diffusiongemma
+- gemma4
 - code-graphs
-- neural-code-representations
 - code-refactoring
 - latent-diffusion
-pretty_name: DiffusionGemma 2 Latent Code Graph Benchmarks
+- ast
+pretty_name: DiffusionGemma 2 Latent Code Graph Benchmarks & Raw Predictions
 size_categories:
-- 100MB-1GB
+- 1K<n<10K
 ---
 
-# DiffusionGemma 2: Latent Code Graph Benchmarks
+# DiffusionGemma 2: Latent Code-Graph Benchmarks & Raw Predictions
 
-Standardized empirical benchmark datasets, continuous latent embeddings, and linear evaluation probes for **DiffusionGemma 2: Latent Code Graphs & Embeddings**.
+Standardized AST code-graph benchmark packs, continuous symbol embeddings, pre-registration (`PREREG.json`), linear evaluation probes, and **raw `.npy` model prediction arrays** for independent CPU rescoring of ***Latent Roadmaps: DiffusionGemma State Transitions for Code-Graph Refactoring***.
 
-- **Author:** bumblebeers (GitHub: `@bumblebeers`)
-- **License:** Apache 2.0
-- **Repository:** [https://github.com/bumblebeers/dgg2-latent-code-graphs](https://github.com/bumblebeers/dgg2-latent-code-graphs)
+- **Author:** `bumblebeers` ([GitHub](https://github.com/bumblebeers))
+- **License:** Apache-2.0
+- **Code & Scoring Harness:** [bumblebeers/dgg2-latent-code-graphs](https://github.com/bumblebeers/dgg2-latent-code-graphs)
+- **Trained LoRA Adapters & Readout Heads:** [bumblebeer/dgg2-latent-code-graphs-adapters](https://huggingface.co/bumblebeer/dgg2-latent-code-graphs-adapters)
+- **Standalone Libraries:** [dgg-latent](https://github.com/bumblebeers/dgg-latent) | [dgg-codebook](https://github.com/bumblebeers/dgg-codebook) | [dgg-vit-decoder](https://github.com/bumblebeers/dgg-vit-decoder)
 
-## Contents
+---
+
+## Repository Contents
+
+### 1. Experiment A: 16×16 Visual Adjacency Grid Benchmark Packs (`data/gridA_*`)
+- **`data/gridA_train1608.npz`**: 1,608 16-symbol Python AST subgraphs extracted across 8 training repositories with 22 fixed candidate states and 3 open-ended edit families.
+- **`data/gridA_test360_httpx.npz`**: 360 held-out 16-symbol AST subgraphs from `encode/httpx` across seen, shuffled, unseen paraphrase, indirect intent, held-out single-pass, 2-step re-feeding, 3-step chain, and open-ended manifests.
+- **`data/gridA_test360_click.npz`**: 360 held-out 16-symbol AST subgraphs from `pallets/click` for zero-shot cross-repository transfer.
+- **`data/gridA_n32_*.npz` & `data/gridA_n48_*.npz`**: Multi-resolution 32×32 and 48×48 subgraph packs testing sub-patch packing limits (Gate A1).
+- **`data/*.nodes.json`**: Per-subgraph AST symbol and line metadata.
+- **`reader.npz`** (`outputs/m1/reader.npz`): Gate A1 closed-form Ridge probe mapping clean SigLIP latents $h_v \in \mathbb{R}^{256 \times 1152}$ to the 8 discrete palette classes (`1.000` clean cell accuracy across all 8 classes).
+
+### 2. Experiment B: 256-Symbol Continuous Embedding Packs (`data/nodeB_*`)
+- **`data/nodeB_train1608.json.gz`**: 1,608 256-symbol AST windows for single-site and multi-site symbol refactoring.
+- **`data/nodeB_test360_httpx.json.gz`** & **`data/nodeB_test360_click.json.gz`**: Held-out `httpx` ($N=360$) and zero-shot `click` ($N=360$) evaluation packs (`chance = 12.5%`, `mean-vector floor = 25.0%`).
+- **`data/nodeB_test360_httpx_unseenargs.json.gz`** & **`data/nodeB_test180_httpx_renameclass.json.gz`**: Unseen argument and class-rename splits.
+- **`data/nodeB_multi_test360_httpx_k2.json.gz`** & **`k4.json.gz`**: 2-site and 4-site simultaneous and re-fed symbol-editing splits.
+- **`emb.npz`** & **`emb_train.npz`**: Frozen `sentence-transformers/all-MiniLM-L6-v2` ($d=384$) symbol embeddings and train-only standardization statistics ($\mu, \sigma$).
+
+### 3. Raw Model Prediction Arrays (`predictions/` & `outputs/m2/decoded_grids_all_arms.npz`)
+To enable full independent verification without requiring a 26B GPU, this dataset hosts **both** continuous latent `.npy` outputs and Ridge-decoded discrete `(M, 16, 16)` `uint8` grid `.npy` files:
+- **`predictions/expA_hv/<arm>/preds_*.npy`**: Raw continuous predicted SigLIP latent tensors $\hat{h}_v \in \mathbb{R}^{360 \times 256 \times 1152}$ (`float32`) emitted by `stock_generate_with_readout` for `cg_lora_dg_seed42_ep3`, `cg_lora_dg_seed1337_ep3`, `cg_frozen_dg_ep3`, zero-shot `click`, and `NonDGTransformBaseline`.
+- **`predictions/expA_grids/<arm>/*.npy`** & **`outputs/m2/decoded_grids_all_arms.npz`**: Decoded `(360, 16, 16)` `uint8` palette grids (`0..7`) across all evaluated arms (including LoRA DG, Frozen DG, Non-DG Frozen/EncLoRA, and Gemma 4 AR baselines as completed) for instant lightweight CPU rescoring.
+- **`predictions/expB_vec/<arm>/*.npy`**: Raw standardized continuous symbol-vector predictions $\hat{z} \in \mathbb{R}^{M \times 256 \times 384}$ (`float32`) for Experiment B (`cg_expB_lora_dg_*` and `NonDGTransformBaseline`).
+
+---
+
+## Independent CPU Rescoring Quickstart
 
-This benchmark package contains pre-processed dataset packs and evaluation assets for testing latent space diffusion on neural code graphs:
+Clone the scoring harness from [bumblebeers/dgg2-latent-code-graphs](https://github.com/bumblebeers/dgg2-latent-code-graphs) and download this dataset (or individual `.npy` files):
 
-### 1. Experiment A: Adjacency Grid Packs (`data/gridA_*`)
-- **`gridA_train1608.npz`**: 1,608 training Python repositories with extracted 16x16 adjacency grids and 23 target states.
-- **`gridA_test360_httpx.npz`**: 360 held-out Python repositories from `encode/httpx` with 33 target states across 16 refactoring manifests.
-- **`gridA_test360_click.npz`**: Held-out transfer repository (`pallets/click`).
-- **`gridA_n32_*.npz` & `gridA_n48_*.npz`**: Multi-resolution grid benchmarks (32x32 and 48x48 nodes) for testing sub-patch resolution limits.
-- Associated symbol metadata files: `*.nodes.json`.
+```bash
+git clone https://github.com/bumblebeers/dgg2-latent-code-graphs.git
+cd dgg2-latent-code-graphs
 
-### 2. Experiment B: Continuous Symbol Vector Packs (`data/nodeB_*`)
-- **`nodeB_train1608.json.gz`**: 1,608 training repositories with symbol text representations and 22-state target vectors.
-- **`nodeB_test360_httpx.json.gz`**: Held-out test set ($N=360$) for single-step and compositional symbol refactoring.
-- **`nodeB_test360_click.json.gz`**: Transfer benchmark on `click`.
-- **`nodeB_test360_httpx_unseenargs.json.gz`**: Parameter signature mutation generalization split.
-- **`nodeB_test180_httpx_renameclass.json.gz`**: Class hierarchy rename split.
-- **`nodeB_multi_test360_httpx_k2.json.gz` & `k4`**: Multi-site simultaneous code refactoring benchmarks (2-site and 4-site edits).
+# Rescore Experiment A directly from raw continuous h_v (.npy, shape 360x256x1152) + Ridge probe:
+python3 cg/cg_score_preds.py \\
+  --pack data/gridA_test360_httpx.npz \\
+  --reader reader.npz \\
+  --manifest seen_bal360 \\
+  --pred predictions/expA_hv/cg_lora_dg_seed42_ep3/preds_cg_lora_dg_seed42_ep3_seen_bal360.npy
 
-### 3. Latent Probes & Text Embeddings
-- **`reader.npz`** / **`outputs/m1/reader.npz`**: Ridge regression cell reader probe weights mapping 1152-D continuous SigLIP latent vectors $h_v$ to discrete 8-class palette assignments with 100.0% accuracy (Gate A1).
-- **`emb.npz`**: 54,624 pre-computed 384-D symbol text embeddings (`sentence-transformers/all-MiniLM-L6-v2`) with train-only standardization statistics ($\mu, \sigma$) and zero sequence truncation.
-- **`PREREG.json`**: Formal pre-registration contract defining all hypotheses, gates, candidate states, and evaluation protocols.
+# Or rescore instantly from the decoded (360, 16, 16) uint8 palette grid (.npy):
+python3 cg/cg_score_preds.py \\
+  --pack data/gridA_test360_httpx.npz \\
+  --manifest refeed_heldout_comp_bal360 \\
+  --grids predictions/expA_grids/cg_lora_dg_seed42_ep3/preds_cg_lora_dg_seed42_ep3_refeed_heldout_comp_bal360.npy
 
-### 4. Standalone Tool Wheels (`outputs/m4/wheels/` and `wheels/`)
-- `dgg_latent-0.1.0-py3-none-any.whl`
-- `dgg_codebook-0.1.0-py3-none-any.whl`
-- `dgg_vit_decoder-0.1.0-py3-none-any.whl`
+# Rescore Experiment B raw (360, 256, 384) symbol vectors (.npy):
+python3 -c "
+import gzip, json, numpy as np, sys
+sys.path.insert(0, 'cg')
+from cg_node_gate import Emb
+from cg_node_metrics import Evaluator, default_items
+ds = json.load(gzip.open('data/nodeB_test360_httpx.json.gz', 'rt'))
+ev = Evaluator(ds, Emb.load('emb.npz'), Emb.load('emb_train.npz').vec)
+pred = np.load('predictions/expB_vec/cg_expB_lora_dg_seed42_ep3/preds_cg_expB_lora_dg_seed42_ep3_instr_seen.npy')
+res = ev.score(pred, default_items(ds))
+print({k: round(res[k], 4) for k in ('site_hit', 'all_sites_hit', 'top_is_site', 'drift')})
+"
+```

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