The CrossPy package (short for Cross-validation in Python) is a package for performing cross-validation in Python.

• Nazli Ozum Kafaee / @nazliozum
• Daniel Raff / @raffrica
• Shun Chi / @ShunChi100

Cross-validation is an important technique used in model selection and hyper-parameter optimization. Scores from cross-validation are a good estimation of test score of a predictive model in test data or new data as long as the IID assumption approximately holds in data. This package aims to provide a standardized pipeline for performing cross-validation for different modeling functions in Python. In addition, summary statistics of the cross-validation results are provided for users.

Three main functions in CrossPy:

• train_test_split(): This function split data according to input train/all ratio returns the split data. A random shuffling option is provided.

• cross_validation(): This function performs k-fold cross validation using the partitioned data and a selected model. It returns the scores of each validation. Additional methods for cross validation will be implemented (such as "Leave-One-Out" if time allows).

• summary_cv(): This function outputs summary statistics(mean, standard deviation, mode, median) of cross-validation scores.

To install the package:

pip install git+https://github.com/UBCMDS/CrossPy.git

Given X (features) and y (targets) as pandas dataframes, one can split the data with default test size 0.25 as the following:

from CrossPy import *
X_train, X_test, y_train, y_test = train_test_split(X, y)

To do cross-validation on X, y using the sklearn LinearRegression() model:

lm = LinearRegression()
scores = cross_validation(lm, X, y)

To see the summary of scores:

summary_cv(scores)

For more options of these functions, please see the documentation and source codes.

The scikit-learn library in Python implements the first two functions we propose in sklearn.model_selection.train_test_split and sklearn.cross_validation.

MIT License

This is an open source project. So feedback, suggestions and contributions are very welcome. For feedback and suggestions, please open an issue in this repo. If you are willing to contribute this package, please refer Contributing guidelines for details.