Populate MO-RELISH GraphArch dataset

.gitattributes

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 # Video files - compressed
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+full/train.jsonl filter=lfs diff=lfs merge=lfs -text
+full/validation.jsonl filter=lfs diff=lfs merge=lfs -text
+subset_10k/test.jsonl filter=lfs diff=lfs merge=lfs -text
+subset_10k/train.jsonl filter=lfs diff=lfs merge=lfs -text
+subset_10k/validation.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+pretty_name: Samsoup GraphArch
+configs:
+- config_name: full
+  data_files:
+  - split: train
+    path: full/train.jsonl
+  - split: validation
+    path: full/validation.jsonl
+  - split: test
+    path: full/test.jsonl
+- config_name: subset_10k
+  data_files:
+  - split: train
+    path: subset_10k/train.jsonl
+  - split: validation
+    path: subset_10k/validation.jsonl
+  - split: test
+    path: subset_10k/test.jsonl
+---
+
+# GraphArch
+
+This dataset is the MO-RELISH graph architecture regression collection. Each
+row contains serialized neural-network graph text and execution/benchmark
+measurements from neural architecture search spaces.
+
+## Configs And Splits
+
+| Config | Train | Validation | Test |
+| --- | ---: | ---: | ---: |
+| `full` | 475,257 | 10,000 | 10,000 |
+| `subset_10k` | 10,000 | 1,000 | 1,000 |
+
+The `full` config keeps all strict-clean rows, with 10,000 validation rows and
+10,000 test rows sampled deterministically and stratified by search space. The
+`subset_10k` config is a deterministic 10,000/1,000/1,000 sample from the full
+splits, also stratified by search space.
+
+## Columns
+
+Input columns:
+
+- `source_text`: task-level context.
+- `input_text`: serialized neural-network graph text.
+- `reference_outputs`: empty list; this is a regression benchmark.
+- `prompt_components.problem_context`: same task context as `source_text`.
+- `prompt_components.input_to_evaluate_column`: points to `input_text`.
+- `prompt_components.input_format`: serialized neural-network graph text.
+- `prompt_components.search_space`: NAS search-space name.
+
+Prediction targets:
+
+- `targets.val_accuracy`: Validation accuracy reported for the neural architecture.
+- `targets.flops`: Floating-point operation count for the architecture.
+
+Retained measurements:
+
+- `measurements.val_accuracy`: Validation accuracy reported for the neural architecture.
+- `measurements.flops`: Floating-point operation count for the architecture.
+- `measurements.params`: Number of trainable parameters in the architecture.
+
+`params` is retained for analysis but is not a default prediction target because
+it is usually deterministic from the architecture.
+
+The original zero-cost-proxy metadata is not included in rows because it may
+leak target values such as FLOPs, parameter count, or validation accuracy.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+full_ds = load_dataset("Samsoup/GraphArch", "full")
+small_ds = load_dataset("Samsoup/GraphArch", "subset_10k")
+```

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manifest.json

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+{
+  "clean_rows": 495257,
+  "configs": {
+    "full": {
+      "config_name": "full",
+      "files": {
+        "test": "full/test.jsonl",
+        "train": "full/train.jsonl",
+        "validation": "full/validation.jsonl"
+      },
+      "measurements_retained": [
+        "val_accuracy",
+        "flops",
+        "params"
+      ],
+      "split_counts": {
+        "test": 10000,
+        "train": 475257,
+        "validation": 10000
+      },
+      "split_space_counts": {
+        "test": {
+          "Amoeba": 101,
+          "DARTS": 101,
+          "DARTS_fix-w-d": 101,
+          "DARTS_lr-wd": 94,
+          "ENAS": 100,
+          "ENAS_fix-w-d": 100,
+          "Hiaml": 93,
+          "Inception": 12,
+          "NASBench101": 8554,
+          "NASBench201": 315,
+          "NASNet": 98,
+          "PNAS": 100,
+          "PNAS_fix-w-d": 92,
+          "TwoPath": 139
+        },
+        "train": {
+          "Amoeba": 4775,
+          "DARTS": 4790,
+          "DARTS_fix-w-d": 4797,
+          "DARTS_lr-wd": 4467,
+          "ENAS": 4750,
+          "ENAS_fix-w-d": 4775,
+          "Hiaml": 4443,
+          "Inception": 556,
+          "NASBench101": 406516,
+          "NASBench201": 14995,
+          "NASNet": 4641,
+          "PNAS": 4775,
+          "PNAS_fix-w-d": 4365,
+          "TwoPath": 6612
+        },
+        "validation": {
+          "Amoeba": 101,
+          "DARTS": 101,
+          "DARTS_fix-w-d": 101,
+          "DARTS_lr-wd": 94,
+          "ENAS": 100,
+          "ENAS_fix-w-d": 100,
+          "Hiaml": 93,
+          "Inception": 12,
+          "NASBench101": 8554,
+          "NASBench201": 315,
+          "NASNet": 98,
+          "PNAS": 100,
+          "PNAS_fix-w-d": 92,
+          "TwoPath": 139
+        }
+      },
+      "targets_predicted": [
+        "val_accuracy",
+        "flops"
+      ]
+    },
+    "subset_10k": {
+      "config_name": "subset_10k",
+      "files": {
+        "test": "subset_10k/test.jsonl",
+        "train": "subset_10k/train.jsonl",
+        "validation": "subset_10k/validation.jsonl"
+      },
+      "measurements_retained": [
+        "val_accuracy",
+        "flops",
+        "params"
+      ],
+      "split_counts": {
+        "test": 1000,
+        "train": 10000,
+        "validation": 1000
+      },
+      "split_space_counts": {
+        "test": {
+          "Amoeba": 10,
+          "DARTS": 10,
+          "DARTS_fix-w-d": 10,
+          "DARTS_lr-wd": 10,
+          "ENAS": 10,
+          "ENAS_fix-w-d": 10,
+          "Hiaml": 9,
+          "Inception": 1,
+          "NASBench101": 855,
+          "NASBench201": 32,
+          "NASNet": 10,
+          "PNAS": 10,
+          "PNAS_fix-w-d": 9,
+          "TwoPath": 14
+        },
+        "train": {
+          "Amoeba": 100,
+          "DARTS": 101,
+          "DARTS_fix-w-d": 101,
+          "DARTS_lr-wd": 94,
+          "ENAS": 100,
+          "ENAS_fix-w-d": 100,
+          "Hiaml": 93,
+          "Inception": 12,
+          "NASBench101": 8554,
+          "NASBench201": 316,
+          "NASNet": 98,
+          "PNAS": 100,
+          "PNAS_fix-w-d": 92,
+          "TwoPath": 139
+        },
+        "validation": {
+          "Amoeba": 10,
+          "DARTS": 10,
+          "DARTS_fix-w-d": 10,
+          "DARTS_lr-wd": 10,
+          "ENAS": 10,
+          "ENAS_fix-w-d": 10,
+          "Hiaml": 9,
+          "Inception": 1,
+          "NASBench101": 855,
+          "NASBench201": 32,
+          "NASNet": 10,
+          "PNAS": 10,
+          "PNAS_fix-w-d": 9,
+          "TwoPath": 14
+        }
+      },
+      "targets_predicted": [
+        "val_accuracy",
+        "flops"
+      ]
+    }
+  },
+  "dataset_family": "GraphArch",
+  "host": "huggingface",
+  "measurement_descriptions": {
+    "flops": "Floating-point operation count for the architecture.",
+    "params": "Number of trainable parameters in the architecture.",
+    "val_accuracy": "Validation accuracy reported for the neural architecture."
+  },
+  "measurements_retained": [
+    "val_accuracy",
+    "flops",
+    "params"
+  ],
+  "metadata_policy": "Original zero-cost-proxy metadata is excluded from staged rows to avoid target leakage.",
+  "namespace": "Samsoup",
+  "repo_name": "GraphArch",
+  "source_file": "/home/samsoup/Work/phd-writing-hub/projects/MO-RELISH/data/grapharch_regression/data.parquet",
+  "split_names": [
+    "train",
+    "validation",
+    "test"
+  ],
+  "split_policy": "Strict-clean rows only; full config uses 10,000 validation and 10,000 test rows stratified by search space, with all remaining rows assigned to training.",
+  "split_seed": "mo-relish-grapharch-v1",
+  "target_descriptions": {
+    "flops": "Floating-point operation count for the architecture.",
+    "val_accuracy": "Validation accuracy reported for the neural architecture."
+  },
+  "targets_predicted": [
+    "val_accuracy",
+    "flops"
+  ]
+}

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