Wine Quality Classification Baseline Source Code

Challenge participants: Build robust models to classify wine quality based on its chemical properties.

Quick Start
Introduction
Requirements
Project Structure
Detailed Tutorial
Submission Guidelines
License

Quick Start

Clone the repository:

git clone [email protected]:AIOZAI/wine_quality_baseline.git

or, download directly: Wine Quality Baseline

Then, navigate into the project directory and follow the steps below:

# 1. Install dependencies (Do not add any other libraries to requirements.txt file)
pip install -r requirements.txt

# 2. Start developing your solution. Follow the tutorial below to implement your AI model

# 3. Run the demo to test your implementation
python demo.py

# 4. Verify Your Submission
python -m my_ai_lib.predict_submission

Note: You need to implement your Wine Quality classification solution following the tutorial guide.

Introduction

In this challenge, you will build a machine learning model to classify wine quality based on its chemical properties. Participants will work with physicochemical data from red and white wines, each labeled with an expert-rated quality score.

The goal is to build a model that classifies the wine quality score (ranging from 0 to 10).

In this challenge, participants will be provided with:

Model Access: Wine Quality Baseline
- Code baseline to develop solutions.
- Predefined libraries and tools in requirements.txt (Do not add any other libraries to requirements.txt file).
Dataset Access: Wine Quality Dataset
- Training dataset to train your AI models.
- Testing dataset to predict labels for generating the submission file.

Goal: Build a model that predicts the quality score (0–10) of each wine sample.

Requirements

System Requirements

Python 3.10+

Dependencies

Install all required packages:

pip install -r requirements.txt

Project Structure

Your AI library should follow this structure:

repository/
├── my_ai_lib/                   # Your AI library
│   ├── __init__.py              # Required: Library initialization
│   ├── run.py                   # Required: Main workflow function
│   ├── predict_submission.py    # Required: Submission function
│   └── [your_modules]/          # Your custom modules
├── models/                      # Model weights directory
├── demo.py                      # Demo script
├── requirements.txt             # Dependencies
└── README.md                    # Documentation

Description

The input and output of the AI model are defined in my_ai_lib/run.py.
The AI model is designed to process a single input item.
In my_ai_lib/predict_submission.py, we process all items in the test dataset and save the results to result.csv in the required submission format.
Please refer to the instructions below for more details.

Key Components

Component	Description	Status
`my_ai_lib/`	Core AI library directory	Required
`my_ai_lib/__init__.py`	Library initialization	Required
`my_ai_lib/predict_submission.py`	Generate predictions for challenge submission.	Required
`my_ai_lib/run.py`	Main AI workflow	Required
`demo.py`	Demo and testing script	Required

Detailed Tutorial

Step 1: Initialize Your AI Library

1.1 Define `my_ai_lib/init.py`

from .run import run

This file exposes the run() function from run.py as an attribute of my_ai_lib, so you can call it as my_ai_lib.run().

1.2 Define Input/Output Objects in `my_ai_lib/run.py`

Create your custom input and output classes:

from pathlib import Path
from typing import Any, Union, Literal
from aioz_ainode_adapter.schemas import InputObject, OutputObject, FileObject

class WineQualityInput(InputObject):
    # input_text is a comma-separated string representation of wine features
    # (e.g., "red,7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4")
    input_text: str

class WineQualityOutput(OutputObject):
    # quality is the predicted score (0-10)
    quality: int

Step 2: Understanding AIOZ Schema Objects

The aioz_ainode_adapter library defines 3 core object types based on pydantic.BaseModel:

🔸 InputObject

Define the format for input when the AIOZ-AI-Node system sends to your AI library.

Default Parameters:

Parameter	Type	Description
`device`	Choice	Device for your model: `["cuda", "cpu", "gpu"]`
`model_storage_directory`	String	Directory containing model weights

Important: Always use model_storage_directory for model weight paths, as AIOZ-AI-Node will specify this location.

🔸 OutputObject

Define the format for output when your AI library sends to the AIOZ-AI-Node system.

🔸 FileObject

Define the format for the file, if your output has a file. This object has two fields:

Field	Type	Description
`data`	Choice	File data: `io.BufferedReader`, `Path`, or `URL`
`name`	String	File name

Example FileObject creation:

output_file = FileObject(data=open("file/path.csv", "rb"), name="output.csv")

Note:

Input files must be local file paths or URLs

Output files must be FileObject instances

Step 3: Implement the Main Workflow

3.1 Define Your AI Task Function

def do_ai_task(
        input_text: str,
        model_storage_directory: Union[str, Path],
        device: Literal["cpu", "cuda", "gpu"] = "cpu",
        *args, **kwargs) -> int:
    """
    Predict wine quality score based on its chemical properties.

    Args:
        input_text: Comma-separated feature string
        model_storage_directory: Path to directory containing trained model weights
        device: Device to run inference on ("cpu", "cuda", or "gpu")

    Returns:
        quality: Predicted wine quality score (0-10)
    """
    # 1. Parse features from input string
    # features = input_text.split(",")

    # 2. Run prediction using your trained model
    # quality = model.predict([features])[0]
    quality = 5
    return quality

3.2 Implement the Required `run()` Function

def run(input_obj: InputObject) -> OutputObject:
    """
    Main entry point for the Wine Quality Classification library.

    Args:
        input_obj: Input object containing parameterized features

    Returns:
        output_obj: Object containing the predicted quality score
    """
    try:
        # Validate and parse input
        wine_input = WineQualityInput.model_validate(input_obj.model_dump())

        # Execute AI task
        quality = do_ai_task(
            input_text=wine_input.input_text,
            model_storage_directory=wine_input.model_storage_directory,
            device=wine_input.device
        )
        # Create output object
        output_obj = WineQualityOutput(quality=quality)
    except Exception as e:
        raise Exception(e)

    return output_obj

Critical: The run() function name is mandatory and cannot be changed. The do_ai_task() function can be renamed and customized.

Step 4: Create Demo Script

Create demo.py to test your implementation:

import my_ai_lib
from aioz_ainode_adapter.schemas import InputObject

def main():
    """Demo function to test the Wine Quality Classification library."""
    # Example features:
    # type,fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,pH,sulphates,alcohol
    features = "red,7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4"

    input_obj = InputObject(
        input_text=features
    )
    output_obj = my_ai_lib.run(input_obj)
    print(f"Output: {output_obj}")

if __name__ == '__main__':
    main()

The my_ai_lib.run() function receives an InputObject and returns an OutputObject.

Run this command to test your implementation:

python demo.py

Expected console output:

Input: device='cpu' model_storage_directory='models' input_text='red,7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4'
Output: quality=5

Step 5: Add Model Weights

Place your trained model files in the models/ directory:

models/
├── model.pkl          # Your trained model
├── config.json        # Model configuration
└── etc.

Step 6: Create Prediction Script (For Submission)

Implement the predict_submission() function in my_ai_lib/predict_submission.py

Requirements:

Function accepting test data folder path (string)
Load your trained model
Process test dataset (test.csv in test data folder)
Generate predictions
Save results as ./result.csv

Implementation Template:

from aioz_ainode_adapter.schemas import InputObject
import my_ai_lib
import os
import csv

def predict_submission(test_data_folder: str):
    """
    Generate predictions for challenge submission.

    Args:
        test_data_folder: Path to test data directory
    """
    # 1. Find test data (test.csv) in test data folder (using os.walk)
    test_csv_path = os.path.join(test_data_folder, "test.csv")
    if not os.path.exists(test_csv_path):
        for root, _, files in os.walk(test_data_folder):
            if "test.csv" in files:
                test_csv_path = os.path.join(root, "test.csv")
                break

    results = []

    # 2. Load model and predict on test dataset
    with open(test_csv_path, "r", encoding="utf-8") as f:
        reader = csv.DictReader(f)
        for row in reader:
            # Prepare feature string from row columns
            feature_cols = ['type', 'fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density', 'pH', 'sulphates', 'alcohol']
            features = ",".join([str(row.get(col, "")) for col in feature_cols])

            output_obj = my_ai_lib.run(InputObject(input_text=features))
            results.append({"id": row["id"], "quality": output_obj.quality})

    # 3. Save to ./result.csv
    with open("./result.csv", "w", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=["id", "quality"])
        writer.writeheader()
        writer.writerows(results)

    print(f"Saved {len(results)} predictions to ./result.csv")

def main():
    """Main function for testing submission."""
    predict_submission("path/to/test/data")

if __name__ == '__main__':
    main()

Important: The result.csv must match the challenge's sample submission format.

Verify Your Submission:

python -m my_ai_lib.predict_submission

Submission Guidelines

Submission Format

The submission file has two fields:

id: The unique identifier for each wine sample.
quality: The predicted wine quality score (0-10).

Example: result.csv

id,quality
1001,3
1002,10
1003,7

License

This repository is licensed under the MIT License.

All Challenges

Wine Quality Classification Challenge

Wine Quality Classification Baseline Source Code

Table of Contents

Quick Start

Introduction

Requirements

System Requirements

Dependencies

Project Structure

Description

Key Components

Detailed Tutorial

Step 1: Initialize Your AI Library

1.1 Define `my_ai_lib/init.py`

1.2 Define Input/Output Objects in `my_ai_lib/run.py`

Step 2: Understanding AIOZ Schema Objects

🔸 InputObject

🔸 OutputObject

🔸 FileObject

Step 3: Implement the Main Workflow

3.1 Define Your AI Task Function

3.2 Implement the Required `run()` Function

Step 4: Create Demo Script

Step 5: Add Model Weights

Step 6: Create Prediction Script (For Submission)

Requirements:

Implementation Template:

Verify Your Submission:

Submission Guidelines

Submission Format

License

AIOZAI

Wine Quality Classification Challenge

All Challenges

Wine Quality Classification Challenge

Wine Quality Classification Baseline Source Code

Table of Contents

Quick Start

Introduction

Requirements

System Requirements

Dependencies

Project Structure

Description

Key Components

Detailed Tutorial

Step 1: Initialize Your AI Library

1.1 Define my_ai_lib/__init__.py

1.2 Define Input/Output Objects in my_ai_lib/run.py

Step 2: Understanding AIOZ Schema Objects

🔸 InputObject

🔸 OutputObject

🔸 FileObject

Step 3: Implement the Main Workflow

3.1 Define Your AI Task Function

3.2 Implement the Required run() Function

Step 4: Create Demo Script

Step 5: Add Model Weights

Step 6: Create Prediction Script (For Submission)

Requirements:

Implementation Template:

Verify Your Submission:

Submission Guidelines

Submission Format

License

AIOZAI

Wine Quality Classification Challenge

1.1 Define `my_ai_lib/init.py`

1.2 Define Input/Output Objects in `my_ai_lib/run.py`

3.2 Implement the Required `run()` Function