Initial commit

.gitignore

@@ -0,0 +1,64 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# Environment
+.env
+.venv
+env/
+venv/
+ENV/
+
+# VSCode
+.vscode/
+.idea/
+
+# OS specific
+.DS_Store 

CITATION.cff

@@ -0,0 +1,23 @@
+cff-version: 1.2.0
+message: "If you use this dataset, please cite it as below."
+title: "MOSTLY AI Prize Dataset"
+authors:
+  - family-names: "MOSTLY AI"
+    given-names: ""
+    website: "https://www.mostly.ai/"
+url: "https://www.mostlyaiprize.com/"
+repository-code: "https://huggingface.co/datasets/mostlyaiprize"
+abstract: >
+  Dataset for the MOSTLY AI Prize competition featuring two challenges:
+  The FLAT DATA Challenge and the SEQUENTIAL DATA Challenge.
+  The goal is to generate high-quality synthetic data that captures
+  statistical patterns of the original data without overfitting.
+keywords:
+  - synthetic data
+  - tabular data
+  - sequential data
+  - generative model
+  - data privacy
+license: Apache-2.0
+date-released: 2025-05-14
+year: 2025 

LICENSE

@@ -0,0 +1,201 @@
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+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
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README.md

@@ -1,3 +1,119 @@
 ---
 license: apache-2.0
 ---
+
+# MOSTLY AI Prize Dataset
+
+This repository contains the dataset used in the [MOSTLY AI Prize](https://www.mostlyaiprize.com/) competition.
+
+## About the Competition
+
+Generate the BEST tabular synthetic data and win 100,000 USD in cash.  
+Competition runs for 50 days: May 14 - July 3, 2025.
+
+This competition features two independent synthetic data challenges that you can join separately:
+
+1. The FLAT DATA Challenge
+2. The SEQUENTIAL DATA Challenge
+
+For each challenge, generate a dataset with the same size and structure as the original, capturing its statistical patterns — but without being significantly closer to the (released) original samples than to the (unreleased) holdout samples.
+
+Train a generative model that generalizes well, using any open-source tools (Synthetic Data SDK, synthcity, reprosyn, etc.) or your own solution. Submissions must be fully open-source, reproducible, and runnable within 6 hours on a standard machine.
+
+## Timeline
+
+- Submissions open: May 14, 2025, 15:30 UTC
+- Submission credits: 3 per calendar week (+bonus)
+- Submissions close: July 3, 2025, 23:59 UTC
+- Evaluation of Leaders: July 3 - July 9
+- Winners announced: on July 9 🏆
+
+## Dataset Description
+
+This dataset consists of two CSV files used in the MOSTLY AI Prize competition:
+
+### Flat Data
+- File: `flat-training.csv.gz` (7.4MB)
+- 100,000 records
+- 80 data columns: 60 numeric, 20 categorical
+
+### Sequential Data
+- File: `sequential-training.csv.gz` (1.3MB)
+- 20,000 groups
+- Each group contains 5-10 records
+- 10 data columns: 7 numeric, 3 categorical
+
+### Data Format
+
+The files are compressed using gzip. You can load them directly using pandas:
+
+```python
+import pandas as pd
+
+# Load flat data
+flat_df = pd.read_csv('data/flat-training.csv.gz', compression='gzip')
+
+# Load sequential data
+sequential_df = pd.read_csv('data/sequential-training.csv.gz', compression='gzip')
+```
+
+### Column Description
+
+Note: Detailed column descriptions are intentionally not provided as part of the competition challenge. The task is to generate synthetic data that preserves the statistical properties of the original data without needing to understand the semantic meaning of each column.
+
+### Notes on Holdout Data
+
+The competition evaluates submissions against a hidden holdout set that:
+- Has the same size as the training data
+- Does not overlap with the training data
+- Comes from the same source
+- Has the same structure and statistical properties
+
+Your synthetic data generation approach should generalize well to this unseen data.
+
+## Evaluation
+
+- CSV submissions are parsed using pandas.read_csv() and checked for expected structure & size
+- Evaluated using the [Synthetic Data Quality Assurance](https://github.com/mostly-ai/mostlyai-qa) toolkit
+- Compared against the released training set and a hidden holdout set (same size, non-overlapping, from the same source)
+
+## Usage with Hugging Face Datasets
+
+The dataset can be loaded using the Hugging Face Datasets library:
+
+```python
+from datasets import load_dataset
+
+# Load the flat dataset (default)
+flat_dataset = load_dataset("mostlyaiprize")
+# or explicitly specify the flat config
+flat_dataset = load_dataset("mostlyaiprize", "flat")
+
+# Load the sequential dataset
+sequential_dataset = load_dataset("mostlyaiprize", "sequential")
+
+# Access the data
+flat_data = flat_dataset["train"]
+sequential_data = sequential_dataset["train"]
+```
+
+## Dataset Schema
+
+The schema for each dataset is dynamically determined from the CSV headers. The datasets include various features relevant to the MOSTLY AI Prize competition task.
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```
+@dataset{mostlyaiprize,
+  author = {MOSTLY AI},
+  title = {MOSTLY AI Prize Dataset},
+  year = {2025},
+  url = {https://www.mostlyaiprize.com/},
+}
+```
+
+## License
+
+This dataset is provided under the Apache License 2.0. See the LICENSE file for full licensing information.

data/flat-training.csv.gz

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+oid sha256:4d1a8f9b8b4e7d211269f37a95283e96c77145165a09bc892a9b178c9f1f8060
+size 7737713

data/sequential-training.csv.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e6c61106dd5a706ed88f646c52bcd010abd105bf617ba46e7de3579cc1bb28e
+size 1374441

dataset_infos.json

@@ -0,0 +1,54 @@
+{
+  "flat": {
+    "description": "This dataset contains the data used in the MOSTLY AI Prize competition.\nThe competition focuses on synthetic data generation and evaluation.\nIt contains two datasets:\n- flat-training.csv.gz: A flat (non-sequential) dataset\n- sequential-training.csv.gz: A sequential dataset",
+    "citation": "@dataset{mostlyaiprize,\n  author = {MOSTLY AI},\n  title = {MOSTLY AI Prize Dataset},\n  year = {2023},\n  url = {https://www.mostlyaiprize.com/},\n}\n",
+    "homepage": "https://www.mostlyaiprize.com/",
+    "license": "Apache License 2.0",
+    "features": {
+      "__dynamic__": true
+    },
+    "builder_name": "mostlyaiprize",
+    "config_name": "flat",
+    "version": {
+      "version_str": "1.0.0",
+      "description": null,
+      "major": 1,
+      "minor": 0,
+      "patch": 0
+    },
+    "splits": {
+      "train": {
+        "name": "train",
+        "num_bytes": 7864320,
+        "num_examples": null,
+        "dataset_name": "mostlyaiprize"
+      }
+    }
+  },
+  "sequential": {
+    "description": "This dataset contains the data used in the MOSTLY AI Prize competition.\nThe competition focuses on synthetic data generation and evaluation.\nIt contains two datasets:\n- flat-training.csv.gz: A flat (non-sequential) dataset\n- sequential-training.csv.gz: A sequential dataset",
+    "citation": "@dataset{mostlyaiprize,\n  author = {MOSTLY AI},\n  title = {MOSTLY AI Prize Dataset},\n  year = {2023},\n  url = {https://www.mostlyaiprize.com/},\n}\n",
+    "homepage": "https://www.mostlyaiprize.com/",
+    "license": "Apache License 2.0",
+    "features": {
+      "__dynamic__": true
+    },
+    "builder_name": "mostlyaiprize",
+    "config_name": "sequential",
+    "version": {
+      "version_str": "1.0.0",
+      "description": null,
+      "major": 1,
+      "minor": 0,
+      "patch": 0
+    },
+    "splits": {
+      "train": {
+        "name": "train",
+        "num_bytes": 1363149,
+        "num_examples": null,
+        "dataset_name": "mostlyaiprize"
+      }
+    }
+  }
+} 

examples/README.md

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+# MOSTLY AI Prize Dataset Examples
+
+This directory contains example scripts for working with the MOSTLY AI Prize dataset.
+
+## Contents
+
+- `basic_usage.py`: A script showing how to load the dataset, train a generative model, and create synthetic data using the MOSTLY AI SDK
+
+## Requirements
+
+To run the example scripts, you'll need the following packages:
+
+```
+pip install mostlyai[local] pandas matplotlib seaborn
+```
+
+## Usage
+
+You can run the example script using:
+
+```bash
+python basic_usage.py
+```
+
+The script demonstrates:
+1. Loading data directly from CSV files
+2. Training a generative model using the MOSTLY AI SDK in local mode
+3. Generating synthetic data with the same structure as the original
+4. Saving the synthetic data for submission
+
+## Additional Resources
+
+- [MOSTLY AI Prize Competition](https://www.mostlyaiprize.com/)
+- [Synthetic Data Quality Assurance Toolkit](https://github.com/mostly-ai/mostlyai-qa) 

examples/basic_usage.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+"""
+MOSTLY AI Prize Dataset - Basic Usage Example
+
+This script demonstrates how to load the MOSTLY AI Prize dataset,
+train a generative model, and create synthetic data using the MOSTLY AI SDK.
+"""
+
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def main():
+    """Main function to demonstrate dataset usage"""
+    print("MOSTLY AI Prize Dataset - Basic Usage Example")
+    print("=" * 50)
+    
+    # Install the MOSTLY AI SDK
+    print("Installing MOSTLY AI SDK...")
+    import subprocess
+    subprocess.run(["pip", "install", "mostlyai[local]"])
+    
+    # Load the flat training data
+    print("Loading data...")
+    data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data")
+    flat_file = os.path.join(data_dir, "flat-training.csv.gz")
+    trn = pd.read_csv(flat_file)
+    
+    print(f"Loaded data with shape: {trn.shape}")
+    print(f"Columns: {', '.join(trn.columns[:5])}...")
+    
+    # Train a generative model using MOSTLY AI SDK
+    print("\nTraining a generative model...")
+    from mostlyai.sdk import MostlyAI
+    
+    # Instantiate SDK in LOCAL mode
+    mostly = MostlyAI(local=True)
+    
+    # Train a generator (limiting training time to 1 minute for this example)
+    g = mostly.train(config={'tables': [{
+        'name': 'flat',
+        'data': trn,  # your training data
+        'tabular_model_configuration': {
+            'max_training_time': 1,  # limit training to 1 minute
+        }
+    }]})
+    
+    # Generate synthetic data
+    print("\nGenerating synthetic data...")
+    sd = mostly.generate(g)
+    syn = sd.data()
+    
+    # Save the synthetic dataset
+    output_file = os.path.join(data_dir, "flat-synthetic.csv.gz")
+    syn.to_csv(output_file, index=False)
+    print(f"Synthetic data saved to: {output_file}")
+    
+    # Compare original and synthetic data
+    print("\nComparing first 5 rows of original data:")
+    print(trn.head())
+    
+    print("\nComparing first 5 rows of synthetic data:")
+    print(syn.head())
+    
+    print("\n--- Next Steps ---")
+    print("1. Adjust model parameters to improve synthetic data quality")
+    print("2. Use the Synthetic Data Quality Assurance toolkit to evaluate your results:")
+    print("   https://github.com/mostly-ai/mostlyai-qa")
+    print("3. Submit your synthetic data for the MOSTLY AI Prize competition")
+    print("\nFor more information, visit: https://www.mostlyaiprize.com/")
+
+if __name__ == "__main__":
+    main()