dsto-gan 0.1.8

Library for balancing tabular data with Deep SMOTE Optimize.

DSTO-GAN: Balancing Data with GAN

DSTO-GAN is a Python library that uses a Generative Adversarial Network (GAN) to generate synthetic samples and balance imbalanced datasets. It is beneficial for classification problems where classes are disproportionate.

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Features

1. Generating synthetic samples for class balancing.
2. Training a custom GAN for tabular data.
3. Saving the balanced dataset to a .csv file.

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Prerequisites

• Python 3.7 or higher.
• pip package manager.

Installation

You can install the library directly via pip:

pip install dsto-gan

Dependencies

The dependencies will be installed automatically during the installation. If you prefer to install manually, run:

pip install numpy torch pandas scikit-learn xgboost scikit-optimize

How to Use

1. Import and Initialization

First, import the DSTO_GAN class and initialize the object:

from dsto_gan import DSTO_GAN

Initialize DSTO-GAN

dsto_gan = DSTO_GAN(dim_h=64, n_z=10, lr=0.0002, epochs=100, batch_size=64)

2. Balancing Data

Use the fit_resample method to balance the data:

# Imbalanced data
X = ... # Features (numpy array or pandas DataFrame)
y = ... # Labels (numpy array or pandas Series)

# Balance the data
X_resampled, y_resampled = dsto_gan.fit_resample(X, y)

print(f"Shape of the balanced data: {X_resampled.shape}, {y_resampled.shape}")

3. Integration with Scikit-Learn

DSTO_GAN is compatible with Scikit-Learn pipelines. You can use it as part of a preprocessing pipeline:

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Split the data into training and testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a pipeline with DSTO-GAN and a classifier
pipeline = Pipeline([
('dsto_gan', DSTO_GAN()), # Balancing with DSTO-GAN
('classifier', RandomForestClassifier()) # Classifier
])

# Train the model
pipeline.fit(X_train, y_train)

# Evaluate the model
accuracy = pipeline.score(X_test, y_test)
print(f"Model accuracy: {accuracy:.2f}")

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## Usage Example

Here is a complete usage example of DSTO-GAN:

```bash
import pandas as pd
from sklearn.model_selection import train_test_split
from dsto_gan import DSTO_GAN

# 1. Load imbalanced data
file_path = "path/to/imbalanced.csv"
df = pd.read_csv(file_path)

# 2. Separate features (X) and labels (y)
X = df.iloc[:, :-1].values ​​# All columns except the last one
y = df.iloc[:, -1].values ​​# Last column is the class

# 3. Split data into training and testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 4. Balance training data with DSTO-GAN
dsto_gan = DSTO_GAN()
X_train_resampled, y_train_resampled = dsto_gan.fit_resample(X_train, y_train)

print(f"Shape of balanced data: {X_train_resampled.shape}, {y_train_resampled.shape}")

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Project Structure

dsto_gan/
│
├── dsto_gan/ # Main package
│ ├── __init__.py # Package initialization
│ ├── dsto_gan.py # Core code for data balancing
├── setup.py # Package configuration
├── README.md # Project documentation
└── LICENSE # Project license

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Contribution

Contributions are welcome! If you encounter problems or have suggestions for improvements, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the [LICENSE] file for more details.

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Contact

• Author: Erika Assis
• Email: [e-mail]
• Repository: GitHub

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Acknowledgements

This project was developed as part of research on data balancing using GANs. The open-source community provided the libraries used.