| Name | imbalanced-spdf JSON |
| Version |
0.0.18
JSON |
| download |
| home_page | None |
| Summary | A Python package for imbalanced learning with ensemble learning. |
| upload_time | 2025-01-31 12:37:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 Rahul Goswami
Permission is hereby granted, free of charge, to any person obtaining a copy
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| keywords |
imbalanced data
ensemble learning
surface to volume ratio
computational complexity
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> A rich documentation is available at [Read the Docs](https://imbalanced-spdf.readthedocs.io/en/latest/)
# Shape Penalized Decision Forests
Shape Penalized Decision Forests, for training ensemble classifiers tailored for imbalanced datasets. This package provides two primary implementations:
- **SPBoDF (Shape Penalized Boosting Decision Forest)**: A boosting ensemble method that builds multiple trees sequentially, adjusting the weights of samples to focus on harder-to-classify instances.
- **SPBaDF (Shape Penalized Bagging Decision Forest)**: A bagging ensemble method that builds multiple trees independently on bootstrap samples of the dataset, improving robustness and reducing overfitting.
Both implementations use the concept of **Surface-to-Volume Regularization (SVR)** to penalize irregular decision boundaries, thus improving generalization and addressing challenges associated with imbalanced datasets.
## Key Features
- **Boosting and Bagging**: Two ensemble approaches tailored for classification tasks.
- **Shape Penalization**: Incorporates a novel regularization technique to control decision boundary complexity.
- **Imbalanced Data Handling**: Designed with class imbalance in mind, using weighting and bootstrapping techniques.
- **Scikit-learn Compatible**: Implements `BaseEstimator` and `ClassifierMixin`, making it seamlessly integrable with the Scikit-learn ecosystem.
- **Customizability**: Hyperparameters such as the number of trees, shape penalty, and maximal leaves are configurable for fine-tuning.
---
## Installation
1. Downloading Locally and Installing
```bash
git clone https://www.github.com/yuvrajiro/imbalanced-spdf.git
cd imbalance-spdf
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Install the package:
```bash
python install -e .
```
2. Using pip install from GitHub
```bash
pip install git+https://www.github.com/yuvrajiro/imbalanced-spdf.git
```
3. Using pip install from PyPi
```bash
pip install imbalanced-spdf
```
---
## Usage
### 1. SPBoDF (Boosting)
#### Example
```python
import numpy as np
from imbalanced_spdf.ensemble import SPBoDF
# Generate synthetic data
X_train = np.random.rand(100, 5)
y_train = np.random.randint(0, 2, 100)
# Initialize and fit SPBoDF
boosting_model = SPBoDF(n_trees=50, weight=2, pen=1.0, random_state=42)
boosting_model.fit(X_train, y_train)
# Predict
X_test = np.random.rand(20, 5)
y_pred = boosting_model.predict(X_test)
print("Predictions:", y_pred)
```
### 2. SPBaDF (Bagging)
#### Example
```python
from imbalanced_spdf.ensemble import SPBaDF
# Generate synthetic data
X_train = np.random.rand(100, 5)
y_train = np.random.randint(0, 2, 100)
# Initialize and fit SPBaDF
bagging_model = SPBaDF(n_trees=50, weight=2, pen=1.0, random_state=42)
bagging_model.fit(X_train, y_train)
# Predict
X_test = np.random.rand(20, 5)
y_pred = bagging_model.predict(X_test)
print("Predictions:", y_pred)
```
---
## API Reference
### `SPBoDF`
A boosting ensemble classifier that uses SVR-regularized trees to handle imbalanced datasets.
#### Parameters:
- `n_trees` (int): Number of trees in the ensemble (default: 40).
- `weight` (float): Weight for the minority class to address imbalance (default: 1).
- `pen` (float): Regularization penalty controlling decision boundary complexity (default: 0).
- `maximal_leaves` (int or float, optional): Maximum leaves per tree. Defaults to `2 * sqrt(n_samples) * 0.3333`.
- `random_state` (int): Random seed for reproducibility (default: 23).
#### Methods:
- `fit(X, y)`: Fits the ensemble on the training data.
- `predict(X)`: Predicts the labels of the test data.
### `SPBaDF`
A bagging ensemble classifier that uses SVR-regularized trees to improve robustness.
#### Parameters:
- `n_trees` (int): Number of trees in the ensemble (default: 40).
- `weight` (float): Weight for the minority class to address imbalance (default: 1).
- `pen` (float): Regularization penalty controlling decision boundary complexity (default: 0).
- `maximal_leaves` (int or float, optional): Maximum leaves per tree. Defaults to `2 * sqrt(n_samples) * 0.3333`.
- `random_state` (int): Random seed for reproducibility (default: 23).
#### Methods:
- `fit(X, y)`: Fits the ensemble on the training data.
- `predict(X)`: Predicts the labels of the test data.
---
## How It Works
1. **SPBoDF (Boosting)**:
- Trees are built sequentially, with sample weights updated after each iteration to focus on misclassified samples.
- Regularization (SVR) penalizes irregular decision boundaries to avoid overfitting.
2. **SPBaDF (Bagging)**:
- Trees are built independently on bootstrap samples of the training data.
- Each tree focuses on non-constant feature subsets, improving robustness and generalization.
## Dataset Details
| Dataset | Available at | Comments (if any) |
|---------------------| --- |------------------------------------------------------------------------|
| Appendicitis | https://github.com/ZixiaoShen/Datasets/blob/master/UCI/C2_F7_S106_Appendicitis/Appendicitis.csv | ----------- |
| Data User Modelling | http://archive.ics.uci.edu/ml/machine-learning-databases/00257/Data_User_Modeling_Dataset_Hamdi%20Tolga%20KAHRAMAN.xls | ------------- |
| Ecoli | https://raw.githubusercontent.com/jbrownlee/Datasets/master/ecoli.csv | 'pp' is considered as class 1 and cp, im, om, omL, imL, imU as class 0 | ------------------ |
| Ecoli-0-6-7-vs-5 | https://github.com/w4k2/umce/blob/master/datasets/imb_IRhigherThan9p2/ecoli-0-6-7_vs_5/ecoli-0-6-7_vs_5.dat | ------ |
| Estate | https://github.com/MKLab-ITI/Posterior-Rebalancing/blob/1a0b561e6418e9df25a75006206598bff2babe2c/data/hddt/imbalanced/estate.data#L4 | ------ |
| Fertility Diagonosis | https://archive.ics.uci.edu/ml/machine-learning-databases/00244/fertility_Diagnosis.txt | ------ |
| Imbalance-scale | https://archive.ics.uci.edu/ml/machine-learning-databases/balance-scale/balance-scale.data | 'B' is conisdered as 1, otherwise 0 |
| Oil | https://github.com/MKLab-ITI/Posterior-Rebalancing/blob/1a0b561e6418e9df25a75006206598bff2babe2c/data/hddt/imbalanced/oil.data | ------ |
| Page-blocks0 | https://github.com/w4k2/DSE/blob/ac0e824d3a7507fe9d57356150cef0def5c4a36d/streams/real/page-blocks0.arff#L4 | ------ |
| Winequality-red | https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv | ------- |
| Yeast-0-3-5-9-vs-7-8 | https://github.com/w4k2/DSE/blob/ac0e824d3a7507fe9d57356150cef0def5c4a36d/streams/real/yeast-0-3-5-9_vs_7-8.arff | ------ |
| car-vgood | https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data | 0 if 'negative' otherwise 1 |
| cleveland_0_vs_4 | https://github.com/Jaga7/Metody-Sztucznej-Inteligencji/blob/d46ae0c897b5524d5e0c9b9b800e190b9727fd52/PROJEKT/datasets/cleveland_0_vs_4.csv#L4 | ------ |
| haberman | https://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data | |
| led7digit-0-2-4-5-6-7-8-9_vs_1 | https://github.com/ikurek/PWr-Uczenie-Maszyn/blob/f9561a959c49229f22e489b17ccb23b52e99d2a2/data/led7digit-0-2-4-5-6-7-8-9_vs_1.dat#L4 | ------ |
| new-thyroid1 | https://github.com/jamesrobertlloyd/dataset-space/blob/d195fd8748ba8def627ae2e727395aee608952ec/data/class/raw/keel/new-thyroid1.dat#L4 | ------ |
| page-blocks-1-3_vs_4 | https://github.com/ikurek/PWr-Uczenie-Maszyn/blob/f9561a959c49229f22e489b17ccb23b52e99d2a2/data/page-blocks-1-3_vs_4.dat | ------ |
| shuttle-c0-vs-c4 | https://github.com/ikurek/PWr-Uczenie-Maszyn/blob/f9561a959c49229f22e489b17ccb23b52e99d2a2/data/shuttle-c0-vs-c4.dat | ------ |
|vehicle3 | https://github.com/jamesrobertlloyd/dataset-space/blob/d195fd8748ba8def627ae2e727395aee608952ec/data/class/raw/keel/vehicle3.dat | ------ |
| yeast-2_vs_8 | https://github.com/jamesrobertlloyd/dataset-space/blob/d195fd8748ba8def627ae2e727395aee608952ec/data/class/raw/keel/yeast-2_vs_8.dat | ------ |
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Citation
If you are using this package in your research, please consider citing the following paper:
```bibtex
Shape Penalized Decision Forests for Imbalanced Data Classification : Rahul Goswami, Aindrila Garai, Payel Sadhukhan, Palash Ghosh, Tanujit Chakraborty
```
---
## References
- Zhu, Y., Li, C., & Dunson, D. B. (2023). "Classification Trees for Imbalanced Data: Surface-to-Volume Regularization." Journal of the American Statistical Association.
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"description": "\r\n\r\n\r\n\r\n\r\n\r\n\r\n> A rich documentation is available at [Read the Docs](https://imbalanced-spdf.readthedocs.io/en/latest/)\r\n\r\n\r\n# Shape Penalized Decision Forests\r\n\r\nShape Penalized Decision Forests, for training ensemble classifiers tailored for imbalanced datasets. This package provides two primary implementations:\r\n\r\n- **SPBoDF (Shape Penalized Boosting Decision Forest)**: A boosting ensemble method that builds multiple trees sequentially, adjusting the weights of samples to focus on harder-to-classify instances.\r\n\r\n- **SPBaDF (Shape Penalized Bagging Decision Forest)**: A bagging ensemble method that builds multiple trees independently on bootstrap samples of the dataset, improving robustness and reducing overfitting.\r\n\r\nBoth implementations use the concept of **Surface-to-Volume Regularization (SVR)** to penalize irregular decision boundaries, thus improving generalization and addressing challenges associated with imbalanced datasets.\r\n\r\n## Key Features\r\n\r\n- **Boosting and Bagging**: Two ensemble approaches tailored for classification tasks.\r\n- **Shape Penalization**: Incorporates a novel regularization technique to control decision boundary complexity.\r\n- **Imbalanced Data Handling**: Designed with class imbalance in mind, using weighting and bootstrapping techniques.\r\n- **Scikit-learn Compatible**: Implements `BaseEstimator` and `ClassifierMixin`, making it seamlessly integrable with the Scikit-learn ecosystem.\r\n- **Customizability**: Hyperparameters such as the number of trees, shape penalty, and maximal leaves are configurable for fine-tuning.\r\n\r\n---\r\n\r\n## Installation\r\n\r\n1. Downloading Locally and Installing\r\n\r\n ```bash\r\n git clone https://www.github.com/yuvrajiro/imbalanced-spdf.git\r\n cd imbalance-spdf\r\n ```\r\n\r\n2. Install dependencies:\r\n\r\n ```bash\r\n pip install -r requirements.txt\r\n ```\r\n3. Install the package:\r\n\r\n ```bash\r\n python install -e .\r\n ```\r\n2. Using pip install from GitHub\r\n\r\n ```bash\r\n pip install git+https://www.github.com/yuvrajiro/imbalanced-spdf.git\r\n ```\r\n3. Using pip install from PyPi\r\n\r\n ```bash\r\n pip install imbalanced-spdf\r\n ```\r\n \r\n\r\n---\r\n\r\n## Usage\r\n\r\n### 1. SPBoDF (Boosting)\r\n\r\n#### Example\r\n\r\n```python\r\nimport numpy as np\r\nfrom imbalanced_spdf.ensemble import SPBoDF\r\n\r\n# Generate synthetic data\r\nX_train = np.random.rand(100, 5)\r\ny_train = np.random.randint(0, 2, 100)\r\n\r\n# Initialize and fit SPBoDF\r\nboosting_model = SPBoDF(n_trees=50, weight=2, pen=1.0, random_state=42)\r\nboosting_model.fit(X_train, y_train)\r\n\r\n# Predict\r\nX_test = np.random.rand(20, 5)\r\ny_pred = boosting_model.predict(X_test)\r\nprint(\"Predictions:\", y_pred)\r\n```\r\n\r\n### 2. SPBaDF (Bagging)\r\n\r\n#### Example\r\n\r\n```python\r\nfrom imbalanced_spdf.ensemble import SPBaDF\r\n\r\n# Generate synthetic data\r\nX_train = np.random.rand(100, 5)\r\ny_train = np.random.randint(0, 2, 100)\r\n\r\n# Initialize and fit SPBaDF\r\nbagging_model = SPBaDF(n_trees=50, weight=2, pen=1.0, random_state=42)\r\nbagging_model.fit(X_train, y_train)\r\n\r\n# Predict\r\nX_test = np.random.rand(20, 5)\r\ny_pred = bagging_model.predict(X_test)\r\nprint(\"Predictions:\", y_pred)\r\n```\r\n\r\n---\r\n\r\n## API Reference\r\n\r\n### `SPBoDF`\r\n\r\nA boosting ensemble classifier that uses SVR-regularized trees to handle imbalanced datasets.\r\n\r\n#### Parameters:\r\n\r\n- `n_trees` (int): Number of trees in the ensemble (default: 40).\r\n- `weight` (float): Weight for the minority class to address imbalance (default: 1).\r\n- `pen` (float): Regularization penalty controlling decision boundary complexity (default: 0).\r\n- `maximal_leaves` (int or float, optional): Maximum leaves per tree. Defaults to `2 * sqrt(n_samples) * 0.3333`.\r\n- `random_state` (int): Random seed for reproducibility (default: 23).\r\n\r\n#### Methods:\r\n\r\n- `fit(X, y)`: Fits the ensemble on the training data.\r\n- `predict(X)`: Predicts the labels of the test data.\r\n\r\n### `SPBaDF`\r\n\r\nA bagging ensemble classifier that uses SVR-regularized trees to improve robustness.\r\n\r\n#### Parameters:\r\n\r\n- `n_trees` (int): Number of trees in the ensemble (default: 40).\r\n- `weight` (float): Weight for the minority class to address imbalance (default: 1).\r\n- `pen` (float): Regularization penalty controlling decision boundary complexity (default: 0).\r\n- `maximal_leaves` (int or float, optional): Maximum leaves per tree. Defaults to `2 * sqrt(n_samples) * 0.3333`.\r\n- `random_state` (int): Random seed for reproducibility (default: 23).\r\n\r\n#### Methods:\r\n\r\n- `fit(X, y)`: Fits the ensemble on the training data.\r\n- `predict(X)`: Predicts the labels of the test data.\r\n\r\n---\r\n\r\n## How It Works\r\n\r\n1. **SPBoDF (Boosting)**:\r\n - Trees are built sequentially, with sample weights updated after each iteration to focus on misclassified samples.\r\n - Regularization (SVR) penalizes irregular decision boundaries to avoid overfitting.\r\n\r\n2. **SPBaDF (Bagging)**:\r\n - Trees are built independently on bootstrap samples of the training data.\r\n - Each tree focuses on non-constant feature subsets, improving robustness and generalization.\r\n\r\n\r\n## Dataset Details\r\n\r\n\r\n| Dataset | Available at | Comments (if any) |\r\n|---------------------| --- |------------------------------------------------------------------------|\r\n| Appendicitis | https://github.com/ZixiaoShen/Datasets/blob/master/UCI/C2_F7_S106_Appendicitis/Appendicitis.csv | ----------- |\r\n| Data User Modelling | http://archive.ics.uci.edu/ml/machine-learning-databases/00257/Data_User_Modeling_Dataset_Hamdi%20Tolga%20KAHRAMAN.xls | ------------- |\r\n| Ecoli | https://raw.githubusercontent.com/jbrownlee/Datasets/master/ecoli.csv | 'pp' is considered as class 1 and cp, im, om, omL, imL, imU as class 0 | ------------------ |\r\n| Ecoli-0-6-7-vs-5 | https://github.com/w4k2/umce/blob/master/datasets/imb_IRhigherThan9p2/ecoli-0-6-7_vs_5/ecoli-0-6-7_vs_5.dat | ------ |\r\n| Estate | https://github.com/MKLab-ITI/Posterior-Rebalancing/blob/1a0b561e6418e9df25a75006206598bff2babe2c/data/hddt/imbalanced/estate.data#L4 | ------ |\r\n| Fertility Diagonosis | https://archive.ics.uci.edu/ml/machine-learning-databases/00244/fertility_Diagnosis.txt | ------ |\r\n| Imbalance-scale | https://archive.ics.uci.edu/ml/machine-learning-databases/balance-scale/balance-scale.data | 'B' is conisdered as 1, otherwise 0 |\r\n| Oil | https://github.com/MKLab-ITI/Posterior-Rebalancing/blob/1a0b561e6418e9df25a75006206598bff2babe2c/data/hddt/imbalanced/oil.data | ------ |\r\n| Page-blocks0 | https://github.com/w4k2/DSE/blob/ac0e824d3a7507fe9d57356150cef0def5c4a36d/streams/real/page-blocks0.arff#L4 | ------ |\r\n| Winequality-red | https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv | ------- |\r\n| Yeast-0-3-5-9-vs-7-8 | https://github.com/w4k2/DSE/blob/ac0e824d3a7507fe9d57356150cef0def5c4a36d/streams/real/yeast-0-3-5-9_vs_7-8.arff | ------ |\r\n| car-vgood | https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data | 0 if 'negative' otherwise 1 |\r\n| cleveland_0_vs_4 | https://github.com/Jaga7/Metody-Sztucznej-Inteligencji/blob/d46ae0c897b5524d5e0c9b9b800e190b9727fd52/PROJEKT/datasets/cleveland_0_vs_4.csv#L4 | ------ |\r\n| haberman | https://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data | |\r\n| led7digit-0-2-4-5-6-7-8-9_vs_1 | https://github.com/ikurek/PWr-Uczenie-Maszyn/blob/f9561a959c49229f22e489b17ccb23b52e99d2a2/data/led7digit-0-2-4-5-6-7-8-9_vs_1.dat#L4 | ------ |\r\n| new-thyroid1 | https://github.com/jamesrobertlloyd/dataset-space/blob/d195fd8748ba8def627ae2e727395aee608952ec/data/class/raw/keel/new-thyroid1.dat#L4 | ------ |\r\n| page-blocks-1-3_vs_4 | https://github.com/ikurek/PWr-Uczenie-Maszyn/blob/f9561a959c49229f22e489b17ccb23b52e99d2a2/data/page-blocks-1-3_vs_4.dat | ------ |\r\n| shuttle-c0-vs-c4 | https://github.com/ikurek/PWr-Uczenie-Maszyn/blob/f9561a959c49229f22e489b17ccb23b52e99d2a2/data/shuttle-c0-vs-c4.dat | ------ |\r\n|vehicle3 | https://github.com/jamesrobertlloyd/dataset-space/blob/d195fd8748ba8def627ae2e727395aee608952ec/data/class/raw/keel/vehicle3.dat | ------ |\r\n| yeast-2_vs_8 | https://github.com/jamesrobertlloyd/dataset-space/blob/d195fd8748ba8def627ae2e727395aee608952ec/data/class/raw/keel/yeast-2_vs_8.dat | ------ |\r\n\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License. See the `LICENSE` file for details.\r\n\r\n## Citation\r\n\r\nIf you are using this package in your research, please consider citing the following paper:\r\n\r\n```bibtex\r\nShape Penalized Decision Forests for Imbalanced Data Classification : Rahul Goswami, Aindrila Garai, Payel Sadhukhan, Palash Ghosh, Tanujit Chakraborty\r\n```\r\n\r\n---\r\n\r\n## References\r\n\r\n- Zhu, Y., Li, C., & Dunson, D. B. (2023). \"Classification Trees for Imbalanced Data: Surface-to-Volume Regularization.\" Journal of the American Statistical Association.\r\n\r\n\r\n\r\n",
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