PyImbalReg 0.0.3

Pre-processing technics for imbalanced datasets in regression modelling

PyImbalReg

Pre-processing technics for imbalanced datasets in regression modelling

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Dealing with imbalanced datasets for regression modelling

Your trained regression model has heteroskedasticity problem? Your model can't predict the extreme values very well? Consider using these pre-processing technics for solving these issues probably caused by your imbalanced dataset.

• RandomOversampling (RO)
• GaussianNoise and Undersampling (GN)
• WEighted Relevance-based Combination Strategy (WERCS)

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How to use? (2 Minutes read)

1. Pass your data as a pandas dataframe to any of these technics
2. Define a releavnce function that maps the output variable to [0, 1] (The higher this value the rarer the samples)
3. Set a threshold for flagging the samples: rare and normal
4. Set model-specific parameters
5. get() a new dataset with new samples

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Installation

## Pypi version
pip install PyImbalReg

## GitHub version
pip install git+https://github.com/vd1371/PyImbalReg.git

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Example

# importing PyImbalReg
import PyImbalReg as pir

# importing the data
from seaborn import load_dataset
data = load_dataset('dots')

ro = pir.RandomOversampling(data,           # Passing the data
			rel_func = None,    # Default relevance function will be used
			threshold = 0.7,    # Set the threshold
			o_percentage = 5    # ( o_percentage - 1 ) x n_rare_samples will be added 
			)
new_data = ro.get()

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Requirements

1. SciPy
2. Pandas
3. Numpy

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Other examples

1. RandomOversampling
2. GaussianNoise
3. WERCS

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Contributions

Please share your issues, new technics and your contributions with us. Your help is much appreciated in advance.

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If you are using this repository

Please cite the below reference(s)

Branco, P., Torgo, L. and Ribeiro, R.P., 2019. Pre-processing approaches for imbalanced distributions in regression. Neurocomputing, 343, pp.76-99.

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License

© Vahid Asghari, 2020. Licensed under the General Public License v3.0 (GPLv3).

P.S. Some parts of the readme and codes were inspired from https://github.com/nickkunz/smogn

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