RegscorePy
A python package that does model comparison between different regression models.
Installation
pip install git+https://github.com/UBC-MDS/RegscorePy.git
#or
pip install RegscorePyFunction Description And Usage
AIC
AIC stands for Akaike’s Information Criterion. It estimates the quality of a model, relative to each of other models. The lower AIC score is, the better the model is. Therefore, a model with lowest AIC - in comparison to others, is chosen.
AIC = n*log(residual sum of squares/n) + 2K
where: - n: number of observations - K: number of parameters (including intercept)
Function
aic(y, y_pred, p)
Parameters:
y: array-like of shape = (n_samples) or (n_samples, n_outputs)
True target variable(s)
y_pred: array-like of shape = (n_samples) or (n_samples, n_outputs)
Fitted target variable(s) obtained from your regression model
p: int
Number of predictive variable(s) used in the model
Return: * aic_score: int * AIC score of the model
BIC
BIC stands for Bayesian Information Criterion. Like AIC, it also estimates the quality of a model. When fitting models, it is possible to increase model fitness by adding more parameters. Doing this may result in model overfit. Both AIC and BIC help to resolve this problem by using a penalty term for the number of parameters in the model. This term is bigger in BIC than in AIC.
BIC = n*log(residual sum of squares/n) + K*log(n)
where: - n: number of observations - K: number of parameters (including intercept)
Function
bic(y, y_pred, p)
Parameters: * y: array-like of shape = (n_samples) or (n_samples, n_outputs) * True target variable(s)
y_pred: array-like of shape = (n_samples) or (n_samples, n_outputs)
Fitted target variable(s) obtained from your regression model
p: int
Number of predictive variable(s) used in the model
Return: * bic_score: int * BIC score of the model
Mallow’s C_p
Introduction
Mallow’s C_p is named for Colin Lingwood Mallows. It is used to assess the fit of regression model, finding the best model involving a subset of predictive variables available for predicting some outcome.
C_p = (SSE_p/MSE) - (n - 2p)
where: - SSE_k: residual sum of squares for the subset model containing p explanatory variables counting the intercept. - MSE: mean squared error for the full model (model containing all k explanatory variables of interest) - n: number of observations - p: number of subset explanatory variables
Function
mallow(y, y_pred, y_sub, k, p)
Parameters:
y: array-like of shape = (n_samples) or (n_samples, n_outputs)
True target variable(s)
y_pred: array-like of shape = (n_samples) or (n_samples, n_outputs)
Fitted target variable(s) obtained from your regression model
y_sub: array-like of shape = (n_samples) or (n_samples, n_outputs)
Fitted target variable(s) obtained from your subset regression model
k: int
Number of predictive variable(s) used in the model
p: int
Number of predictive variable(s) used in the subset model
Return:
mallow_score: int
Mallow’s C_p score of the subset model
Usage
>> from RegscorePy import * >> y = [1,2,3,4] >> y_pred = [5,6,7,8] >> p = 3 >> aic.aic(y, y_pred, p) 17.090354888959126 >> >> >> bic.bic(y, y_pred, p) 15.249237972318795 >> >> >> y_sub = [1,2,3,5] >> k = 3 >> p = 2 >> mallow.mallow(y, y_pred, y_sub, k, p) >> 0.015625
This usage apply to python3. If you use python2, please run from __future__ import division before run the function.
How to run tests
>From root directory, run all test files in terminal:
python -m pytest
You also have the option to run individual test files by referencing its path. For example, if you want to test aic function, you can use the command below:
python -m pytest RegscorePy/test/test_aic.py
License
Contributing
This is an open source project. Please follow the guidelines below for contribution. - Open an issue for any feedback and suggestions. - For contributing to the project, please refer to Contributing for details.
1 maintainer
Metadata
1 maintainer
Release files for RegscorePy 1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| RegscorePy-1.1-py3-none-any.whl | Python 3 | none | any | Details |
Release files / RegscorePy-1.1-py3-none-any.whl
| Download URL | RegscorePy-1.1-py3-none-any.whl |
|---|---|
| Size | 11.4 kB |
| Tags | Python 3 |
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