instrumentum 0.8.12

General utilities for data science projects

instrumentum

General utilities for data science projects.

The goal of this repository is to consolidate functionalities that are tipically not found in other packages, and can facilitate some steps during a data science project.

The classes created in instrumentum -tipically- inherit from sklearn, which makes them easier to work with, and reuse some code that has been extensively battle-tested. Classes use parallelism whenever possible.

1. Feature Generation
2. Model Tuning
3. Feature Selection
4. Dashboards & Plots

Feature Generation

Class Interactions offers an easy way to create combinatiors of existing features. It is a lightweight class that can be extended with minimum effort.

This simple example showcase how this class can be used with a small DataFrame. The degree indicates how the different columns will be combined (careful, it grows exponentially)

arr = np.array([[5, 2, 3], [5, 2, 3], [1, 2, 3]])
arr = pd.DataFrame(arr, columns=["a", "b", "c"])

interactions = Interactions(operations=["sum", "prod"], degree=(2, 3), verbose=logging.DEBUG)
interactions.fit(arr)


pd.DataFrame(interactions.transform(arr), columns=interactions.get_feature_names_out())

Depending on the verbosity, the output can provide a large degree of information

Model Tuning

Class OptunaSearchCV implements a sklearn wrapper for the great Optuna class. It provides a set of distribution parameters that can be easily extended. In this example it makes use of the dispatcher by fetching a decision tree (which is named after the Sklearn class)

search_function = optuna_param_disp[DecisionTreeClassifier.__name__]
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=2)

os = OptunaSearchCV(
    estimator=DecisionTreeClassifier(),
    scoring="roc_auc",
    cv=cv,
    search_space=search_function,
    n_iter=5,
)
os.fit(X_train, y_train)

The output presents all the details depending on the verbosity

Usage

• TODO

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

instrumentum was created by Federico Montanana. It is licensed under the terms of the MIT license.

Credits

instrumentum uses:

• Optbining for bining the visuals: https://github.com/guillermo-navas-palencia/optbinning