README.md

---
language: 
  - en
pretty_name: "COGITAO"
tags:
  - COGITAO
  - compositionality
  - generalization
  - visual reasoning
license: "cc-by-4.0"
task_categories:
  - text2text-generation
  - image-to-image
annotations_creators:
  - machine-generated
source_datasets:
  - original
size_categories:
  - 10K<n<100K
author: "Yassine Taoudi Benchekroun"
---

# Before ARC Dataset

This dataset contains .parquet files organized in nested subfolders under `COGITAO`, split into two main categories: `generalization` and `compositionality`. Each category contains data for different experiment settings and experiments, with JSON files for training, validation, and testing splits. The nested structure is intentional for clarity.

## Dataset Structure
- `COGITAO/`: Root folder
  - `generalization/`: Data for generalization experiments
    - `experiment_settings[1-5]/`: Five settings (e.g., different conditions or parameters)
      - `experiment[1-5]/`: Four experiments per setting
        - `train.parquet`: Training data
        - `train_val.parquet`: Training validation data
        - `test_val.parquet`: Test validation data
        - `test.parquet`: Test data
  - `compositionality/`: Data for compositionality experiments
    - `experiment_settings[1-5]/`: Five settings (e.g. different combination of transformations)
      - `experiment[N]/`: N experiments per setting (N changes per experiment setting)
        - `train.parquet`: Training data
        - `train_val.parquet`: Training validation data
        - `test_val.parquet`: Test validation data
        - `test.parquet`: Test data

We provide instruction on how to read the JSON file on the open_data.ipynb notebook, as well as in the original repo which was used to create this dataset.

## Content 

Each .parquet file is a dict containing the following keys: `'input'`, `'output'`, `'transformation_suite'`, `'task_key'`. The `input` is the input grid, while the `output` is the output grid subject to the `transformation_suite`. the `task_key` is simply an identifier for the task instance. *NOTE*: In the compositionality study, we provide an additional `demo_input` and `demo_output` for demonstration examples of the task. This is in case the user would like to pass a demonstration example (in-context learning style) as opposed to simply the `transformation_suite` to specify which transformation the model should apply. 

## Usage
Load the dataset using the `datasets` library:

```python
from datasets import load_dataset
gen_exps3_exp2_test = load_dataset("yassinetb/COGITAO", data_files={"data": "generalization/exp_setting_3/experiment_2/test.parquet"})
print(dataset["data"][0].keys()) # Prints the keys of the first sample from the chosen dataset. Should output: dict_keys(['input', 'output', 'transformation_suite', 'task_key'])