DAU Undersampling (Density-Aware Undersampling)

DAU (Density-Aware Undersampling) is a Python package to handle imbalanced datasets by reducing the majority class without losing important information.

Instead of random undersampling, DAU keeps:

• Sparse points (outliers / rare cases) → retained fully
• Dense clusters → represented by a few points (using DBSCAN)
• Noise points → kept separately

This leads to smarter undersampling and better ML performance compared to random undersampling.

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Installation

From PyPI:

pip install dau-undersampler

(Optionally, for testing on TestPyPI):

pip install -i https://test.pypi.org/simple/ dau-undersampling

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⚡ Quickstart

import pandas as pd
from sklearn.datasets import make_classification
from dau_undersampling import DAU

# 1. Create an imbalanced dataset
X, y = make_classification(
    n_samples=1000, n_features=10,
    n_classes=2, weights=[0.9, 0.1],
    random_state=42
)

X = pd.DataFrame(X)
y = pd.Series(y)

# 2. Apply DAU undersampling
dau = DAU(n_neighbors=5, min_samples=3, eps=0.5, percentile=25)
X_resampled, y_resampled = dau.fit_transform(X, y)

print("Original dataset shape:", y.value_counts().to_dict())
print("Resampled dataset shape:", y_resampled.value_counts().to_dict())

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🛠 Usage & Parameters

Class: DAU

DAU(n_neighbors=3, min_samples=5, eps=0.05, percentile=25)

Parameters:

• n_neighbors (int, default=3) Number of neighbors for KNN distance calculation.
• min_samples (int, default=5) Minimum samples per cluster (DBSCAN).
• eps (float, default=0.05) Maximum neighborhood radius (DBSCAN).
• percentile (int, default=25) Threshold to split sparse vs dense points.

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Method: fit_transform(X, y)

Performs density-aware undersampling.

Arguments:

• X: pd.DataFrame → features of majority class or dataset.
• y: pd.Series → labels (binary classification).

Returns:

• X_resampled: Reduced features after undersampling.
• y_resampled: Reduced labels aligned with features.

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Example in Pipeline

You can also integrate DAU into an ML pipeline (with imblearn):

from imblearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

pipeline = Pipeline([
    ('undersample', DAU(n_neighbors=7, min_samples=5, eps=0.4, percentile=30)),
    ('clf', LogisticRegression())
])

pipeline.fit(X, y)

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Why DAU vs Other Methods?

Method	Behavior
Random undersampling	Drops samples randomly (risk of losing rare but important cases).
NearMiss / Tomek Links	Works with distances but may remove outliers or boundary points.
DAU (this package)	Preserves outliers + keeps 1 representative per dense cluster (balanced).

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Contributing

1. Fork this repo
2. Create a new branch (git checkout -b feature-xyz)
3. Commit changes (git commit -m "Added xyz")
4. Push (git push origin feature-xyz)
5. Open a Pull Request

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License

This project is licensed under the MIT License – see the LICENSE file for details.