getting the data right 1

.claude/settings.local.json

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+{
+  "permissions": {
+    "allow": [
+      "Bash(tree:*)",
+      "Bash(find:*)"
+    ],
+    "deny": [],
+    "ask": []
+  }
+}

.gitignore

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+.venv

.python-version

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+3.12

README.md

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 ---
 license: apache-2.0
 ---
+
+# ARC-AGI-V1 Dataset (A Take On Format)
+
+This dataset is a reorganized version of the [ARC-AGI v1](https://github.com/fchollet/ARC-AGI) (Abstraction and Reasoning Corpus) benchmark, formatted for HuggingFace Datasets.
+
+## Dataset Structure
+
+The original ARC-AGI dataset has been transformed from its file-based JSON structure into a standardized HuggingFace dataset with two splits:
+
+- **train** (400 examples): Tasks from the original `training` directory
+- **test** (400 examples): Tasks from the original `evaluation` directory
+
+### Original Structure
+
+The original ARC-AGI dataset consisted of:
+- A `training` directory with JSON files (one per task)
+- An `evaluation` directory with JSON files (one per task)
+- Each JSON file named with a task ID (e.g., `007bbfb7.json`)
+- Each file containing:
+  - `train`: Array of input/output example pairs for learning the pattern
+  - `test`: Array of input/output pairs representing the actual task to solve
+
+### Transformed Structure
+
+Each row in this dataset represents a single ARC-AGI task with the following schema:
+
+```
+{
+    "id": string,           // Task ID from the original filename
+    "list": [               // Combined training examples and test inputs
+        [                   // Training example inputs (from original 'train')
+            [[int]], [[int]], ...
+        ],
+        [                   // Training example outputs (from original 'train')
+            [[int]], [[int]], ...
+        ],
+        [                   // Test inputs (from original 'test')
+            [[int]], [[int]], ...
+        ]
+    ],
+    "label": [              // Test outputs (from original 'test')
+        [[int]], [[int]], ...
+    ]
+}
+```
+
+#### Field Descriptions
+
+- **`id`**: The unique task identifier from the original filename
+- **`list`**: A nested list containing three components in order:
+  1. **Example inputs** (`list[0]`): All input grids from the original `train` array
+  2. **Example outputs** (`list[1]`): All output grids from the original `train` array (paired with example inputs)
+  3. **Test inputs** (`list[2]`): All input grids from the original `test` array
+- **`label`**: The correct output grids for the test inputs (from original `test` array outputs)
+
+### Data Format
+
+Each grid is represented as a 2D array of integers (0-9), where:
+- Values range from 0 to 9 (representing different colors/states)
+- Grid dimensions vary from 1×1 to 30×30
+- Each integer represents a colored cell in the grid
+
+### Example
+
+```json
+{
+    "id": "007bbfb7",
+    "list": [
+        [
+            [[0, 7, 7],                            // Example input 1
+            [7, 7, 7],                             // 
+            [0, 7, 7]],                            // 
+            [[4, 0, 4], [0, 0, 0], [0, 4, 0]],     // Example input 2
+            [[0, 0, 0], [0, 0, 2], [2, 0, 2]]      // Example input 3
+        ],
+        [
+            [[0, 0, 0, 0, 7, 7, 0, 7, 7],          // Example output 1
+            [0, 0, 0, 7, 7, 7, 7, 7, 7],
+            [0, 0, 0, 0, 7, 7, 0, 7, 7],
+            [0, 7, 7, 0, 7, 7, 0, 7, 7],
+            [7, 7, 7, 7, 7, 7, 7, 7, 7],
+            [0, 7, 7, 0, 7, 7, 0, 7, 7],
+            [0, 0, 0, 0, 7, 7, 0, 7, 7],
+            [0, 0, 0, 7, 7, 7, 7, 7, 7],
+            [0, 0, 0, 0, 7, 7, 0, 7, 7]],
+            [[], [], [], [], [], [], [], [], []],  // etc..
+        ],
+        [
+            [[7, 0, 7], [7, 0, 7], [7, 7, 0]]      // Test input 1
+        ]
+    ],
+    "label": [
+        [[7, 0, 7, 0, 0, 0, 7, 0, 7],              // Test output 1 (ground truth)
+        [7, 0, 7, 0, 0, 0, 7, 0, 7],
+        [7, 7, 0, 0, 0, 0, 7, 7, 0],
+        [7, 0, 7, 0, 0, 0, 7, 0, 7],
+        [7, 0, 7, 0, 0, 0, 7, 0, 7],
+        [7, 7, 0, 0, 0, 0, 7, 7, 0],
+        [7, 0, 7, 7, 0, 7, 0, 0, 0],
+        [7, 0, 7, 7, 0, 7, 0, 0, 0],
+        [7, 7, 0, 7, 7, 0, 0, 0, 0]]
+    ]
+}
+```
+
+## Usage Philosophy
+
+pprint(dataset['train']['list'][0][0][0])
+pprint(dataset['train']['list'][0][1][0])
+print('')
+pprint(dataset['train']['list'][0][2][0])
+pprint(dataset['train']['label'][0][0])
+
+This ARC-AGI dataset format allows (me at least) to think about the tasks in this way:
+1. **Learn from examples**: Study the input/output pairs:
+   - input: `dataset['train']['list'][0][0][0]`
+   - output: `dataset['train']['list'][0][1][0]`
+   - input: `dataset['train']['list'][0][0][1]`
+   - output: `dataset['train']['list'][0][1][1]`
+   - where:
+     - 1st num: `task number`
+     - 2nd num: `either 0: example input || 1: example output`
+     - 3rd num: `which example?`
+2.  **Then 'Get the tests'**:
+    - `dataset['train']['list'][0][2][0]`
+3. **Apply the pattern**: Use the learned rule to make your two guesses
+4. **Evaluate performance**: Compare model predictions against the `label` field
+   - `dataset['train']['label'][0][0]`
+
+### Training Split
+- Contains all tasks from the original `training` directory
+- Intended for model training and development
+- Both example pairs and test solutions are provided
+
+### Test Split
+- Contains all tasks from the original `evaluation` directory
+- Intended for final model evaluation
+- In competition settings, test labels may be withheld
+
+## Dataset Features
+
+```python
+Features({
+    'id': Value('string'),
+    'list': List(List(List(List(Value('int64'))))),
+    'label': List(List(List(Value('int64'))))
+})
+```
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ardea/arc_agi_v1")
+
+# Access splits
+train_data = dataset['train']
+test_data = dataset['test']
+
+# Example: Get a single task
+task = train_data[0]
+task_id = task['id']
+example_inputs = task['list'][0]
+example_outputs = task['list'][1]
+test_inputs = task['list'][2]
+test_outputs = task['label']
+
+# Example: Get a task by id
+task = list(filter(lambda t: t['id'] == '007bbfb7', train_data))
+```
+
+## Transparency
+I've left the script I used on the original dataset here as `arc_to_my_hf.py`
+
+## Citation
+
+If you use this dataset, please cite the original ARC-AGI work:
+
+```bibtex
+@misc{chollet2019measure,
+    title={On the Measure of Intelligence},
+    author={François Chollet},
+    year={2019},
+    eprint={1911.01547},
+    archivePrefix={arXiv},
+    primaryClass={cs.AI}
+}
+```
+
+## License
+
+This dataset maintains the Apache 2.0 license from the original ARC-AGI corpus.

arc_to_my_hf.py

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+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.12,<3.14"
+# dependencies = [
+#     "datasets",
+#     "pyarrow",
+# ]
+# ///
+
+import json
+from pathlib import Path
+from typing import Dict
+import argparse
+
+from datasets import Dataset, DatasetDict, load_dataset
+
+
+class ARCToHFConverter:
+    """Converts ARC-AGI task JSON files to HuggingFace Arrow format."""
+
+    def __init__(self, input_dir: Path):
+        self.input_dir = Path(input_dir)
+        self.output_dir = self.input_dir.parent / f"hf_{self.input_dir.name}"
+
+    def load_task(self, json_path: Path) -> Dict:
+        """Load single task JSON file."""
+        with open(json_path, 'r') as f:
+            return json.load(f)
+
+    def convert_task(self, task_data: Dict, task_id: str) -> Dict:
+        """Convert single task to HF schema.
+
+        Returns:
+            {
+                "id": str,
+                "list": [
+                    [grid, grid, ...],  # example inputs
+                    [grid, grid, ...],  # example outputs
+                    [grid, ...]         # test inputs
+                ],
+                "label": [grid, ...]    # test outputs
+            }
+        """
+        return {
+            "id": task_id,
+            "list": [
+                [ex["input"] for ex in task_data["train"]],   # index 0: example inputs
+                [ex["output"] for ex in task_data["train"]],  # index 1: example outputs
+                [ex["input"] for ex in task_data["test"]]     # index 2: test inputs
+            ],
+            "label": [ex["output"] for ex in task_data["test"]]  # test outputs
+        }
+
+    def convert_directory(self, subdir_name: str) -> Dataset:
+        """Convert all JSON files in a subdirectory to HF Dataset."""
+        subdir = self.input_dir / subdir_name
+        json_files = sorted(subdir.glob("*.json"))
+
+        print(f"Converting {subdir_name}/ directory ({len(json_files)} tasks)...")
+        tasks = []
+        for json_path in json_files:
+            task_id = json_path.stem  # filename without .json
+            task_data = self.load_task(json_path)
+            converted = self.convert_task(task_data, task_id)
+            tasks.append(converted)
+
+        return Dataset.from_list(tasks)
+
+    def convert_all(self) -> DatasetDict:
+        """Convert both training and evaluation subdirectories."""
+        train_dataset = self.convert_directory("training")
+        test_dataset = self.convert_directory("evaluation")
+
+        return DatasetDict({
+            "train": train_dataset,
+            "test": test_dataset
+        })
+
+    def save(self, dataset_dict: DatasetDict):
+        """Save dataset to disk in Parquet format for HuggingFace Hub."""
+        # Create output directory structure
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        data_dir = self.output_dir / "data"
+        data_dir.mkdir(exist_ok=True)
+
+        # Export to parquet files (HuggingFace Hub standard format)
+        print(f"Saving train split to {data_dir / 'train-00000-of-00001.parquet'}...")
+        dataset_dict['train'].to_parquet(data_dir / 'train-00000-of-00001.parquet')
+
+        print(f"Saving test split to {data_dir / 'test-00000-of-00001.parquet'}...")
+        dataset_dict['test'].to_parquet(data_dir / 'test-00000-of-00001.parquet')
+
+        print(f"\n✓ Dataset saved to {self.output_dir}")
+        print(f"  - Train: {len(dataset_dict['train'])} examples")
+        print(f"  - Test: {len(dataset_dict['test'])} examples")
+
+
+def look_at_data():
+    # Load the dataset from parquet files
+    print("Loading dataset from parquet files...")
+    dataset = load_dataset('parquet', data_files={
+        'train': 'data/train-00000-of-00001.parquet',
+        'test': 'data/test-00000-of-00001.parquet'
+    })
+
+    print("\nDataset loaded successfully!")
+    print(f"Splits: {list(dataset.keys())}")
+    print(f"Train size: {len(dataset['train'])}")
+    print(f"Test size: {len(dataset['test'])}")
+    print(f"\nFeatures: {dataset['train'].features}")
+    print(f"\nFirst example ID: {dataset['train'][0]['id']}")
+
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Convert ARC-AGI JSON tasks to HuggingFace dataset"
+    )
+    parser.add_argument(
+        "input_dir",
+        type=str,
+        help="Parent directory containing training/ and evaluation/ subdirectories"
+    )
+
+    args = parser.parse_args()
+
+    print(f"Input directory: {args.input_dir}")
+    converter = ARCToHFConverter(args.input_dir)
+    print(f"Output directory: {converter.output_dir}\n")
+
+    dataset_dict = converter.convert_all()
+    converter.save(dataset_dict)
+
+
+if __name__ == "__main__":
+    main()
+
+

train/data-00000-of-00001.arrow → data/test-00000-of-00001.parquet

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+size 204509

test/data-00000-of-00001.arrow → data/train-00000-of-00001.parquet

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+size 129834

dataset_dict.json

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-{"splits": ["train", "test"]}

test/dataset_info.json

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-{
-  "citation": "",
-  "description": "",
-  "features": {
-    "id": {
-      "dtype": "string",
-      "_type": "Value"
-    },
-    "list": {
-      "feature": {
-        "feature": {
-          "feature": {
-            "feature": {
-              "dtype": "int64",
-              "_type": "Value"
-            },
-            "_type": "List"
-          },
-          "_type": "List"
-        },
-        "_type": "List"
-      },
-      "_type": "List"
-    },
-    "label": {
-      "feature": {
-        "feature": {
-          "feature": {
-            "dtype": "int64",
-            "_type": "Value"
-          },
-          "_type": "List"
-        },
-        "_type": "List"
-      },
-      "_type": "List"
-    }
-  },
-  "homepage": "",
-  "license": ""
-}

test/state.json

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-{
-  "_data_files": [
-    {
-      "filename": "data-00000-of-00001.arrow"
-    }
-  ],
-  "_fingerprint": "8a2c8ef92930ac7c",
-  "_format_columns": null,
-  "_format_kwargs": {},
-  "_format_type": null,
-  "_output_all_columns": false,
-  "_split": null
-}

train/dataset_info.json

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-{
-  "citation": "",
-  "description": "",
-  "features": {
-    "id": {
-      "dtype": "string",
-      "_type": "Value"
-    },
-    "list": {
-      "feature": {
-        "feature": {
-          "feature": {
-            "feature": {
-              "dtype": "int64",
-              "_type": "Value"
-            },
-            "_type": "List"
-          },
-          "_type": "List"
-        },
-        "_type": "List"
-      },
-      "_type": "List"
-    },
-    "label": {
-      "feature": {
-        "feature": {
-          "feature": {
-            "dtype": "int64",
-            "_type": "Value"
-          },
-          "_type": "List"
-        },
-        "_type": "List"
-      },
-      "_type": "List"
-    }
-  },
-  "homepage": "",
-  "license": ""
-}

train/state.json

@@ -1,13 +0,0 @@
-{
-  "_data_files": [
-    {
-      "filename": "data-00000-of-00001.arrow"
-    }
-  ],
-  "_fingerprint": "9bdcf8a374134b57",
-  "_format_columns": null,
-  "_format_kwargs": {},
-  "_format_type": null,
-  "_output_all_columns": false,
-  "_split": null
-}