matrics_calculator

A package providing functions to calculate key regression metrics: R-squared, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE).

Python Ecosystem

matrics_calculator provides a lightweight and easy-to-use alternative for calculating key regression metrics, complementing existing libraries like scikit-learn. While scikit-learn offers a full suite of machine learning tools, matrics_calculator focuses solely on evaluation metrics, making it a useful option for quick analysis, custom workflows, or educational purposes. Its simplicity makes it accessible for users who need essential regression metrics without the overhead of a larger machine learning framework.

Features

This package consists of four functions:

• r2:
  • This function calculates the R-squared of the model, which measures how well the model explains the variation in the data.
• MAE:
  • This function finds the average difference between predicted and actual values.
• MSE:
  • This fuction calculates the average of the squared differences between predictions and actual values.
• MAPE:
  • This function shows prediction error as a percentage, making it easy to understand.

matrics_calculator in the Python Ecosystem

matrics_calculator works alongside Python libraries like scikit-learn by providing simple implementations of regression metrics. Unlike scikit-learn’s full toolkit for modeling and evaluation, this package focuses only on metrics, making it easy to use for quick analysis or custom workflows.

Installation

To install the package, navigate to the root directory of the project and run:

$ pip install matrics_calculator

matrics_calculator requires Python version 3.10 or later.

Dependencies

This package requires the following dependencies:

• numpy
• pandas
• scikit-learn
• matplotlib
• vega-datasets

These dependencies are installed automatically when you install the package using:

pip install matrics_calculator

Usage

Here’s how to use the functions in this package:

1. Import the Package

from matrics_calculator.r2 import r2_score
from matrics_calculator.MAE import mean_absolute_error
from matrics_calculator.MSE import mean_squared_error
from matrics_calculator.MAPE import mean_absolute_percentage_error

2. Prepare Your Data Ensure you have two arrays:

   y_true: The actual target values.

   y_pred: The predicted values from your regression model.

   Example:

y_true = [100, 200, 300]
y_pred = [110, 190, 290]

3. Calculate Metrics Use the functions to evaluate your model:

# Calculate MAPE
mape = mean_absolute_percentage_error(y_true, y_pred)
print(f"MAPE: {mape:.2f}%")

# Calculate MAE
mae = mean_absolute_error(y_true, y_pred)
print(f"MAE: {mae:.2f}")

# Calculate MSE
mse = mean_squared_error(y_true, y_pred)
print(f"MSE: {mse:.2f}")

# Calculate R-squared
r2 = r2_score(y_true, y_pred)
print(f"R-squared: {r2:.2f}")

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

matrics_calculator was created by Celine Habashy, Jay Mangat, Yajing Liu, Zhiwei Zhang. It is licensed under the terms of the MIT license.

Credits

matrics_calculator was created with cookiecutter and the py-pkgs-cookiecutter template.

Constributors

• Celine Habashy
• Jay Mangat
• Yajing Liu
• Zhiwei Zhang