Data-science-utils

Added

- **GitHub Actions**: Implemented as the primary CI/CD infrastructure, replacing TravisCI.
- **ROC and Precision-Recall Curves**: Introduced new functions to generate ROC and Precision-Recall curves with
threshold annotations using Plotly figures.

Changed

- **Testing Infrastructure**: Switched from `nose` to `pytest-mpl` for testing plots, ensuring better compatibility
and features.
- **Codebase, Tests, Readme, and Documentation**: Refactored using Claude 3.5 Sonnet to improve readability,
maintainability, and overall quality.

Fixed

- **Coveralls Integration**: Restored Coveralls integration to track code coverage and ensure high-quality code.
- **xai::generate_decision_paths**: Deprecated the method and recommended using `sklearn.tree.export_text` as a more
suitable alternative.
- minor changes

Changed
- Updated Matplotlib version to accommodate the change of xticks now returning a list of a numpy's ndarry.
Fixed
- Tests
- minor changes

Changed
- update packages and supported python version
Fixed
- minor changes

Added
- xai::plot_features_importance method that visualize into bar chart the feature importance.
- a new module named `unsupervised` was added. The module contains methods that calculate and/or visualize evaluation
performance of an unsupervised model.
- unsupervised::plot_cluster_cardinality method that plots the number of points per cluster as a bar chart.
- unsupervised::plot_cluster_magnitude method that plots the Total Point-to-Centroid Distance per cluster as a bar
chart.
- unsupervised::plot_magnitude_vs_cardinality method plots the cardinality vs. magnitude as a scatter plot.
- unsupervised::plot_loss_vs_cluster_number method that plots the graph which helps to find the optimum parameter ``k``
for KMeans.
Changed
- deprecated xai::draw_tree. Use sklearn.tree.plot_tree instead.
- requirements dependencies.
Fixed
- minor changes

Added
- code examples to README.md
Changed
- visualization_aids module was merged into the preprocess module.
Fixed
- avoid FutureWarning due to sklearn version upgrade (Pass labels=[1, 0], pos_label=0, average=binary, sample_weight=None as keyword args. From version 0.25 passing these as positional arguments will result in an error).
- fixed docs
- minor changes

Added
- visualization_aids::visualize_feature method that visualize one feature distribution.
- metrics::visualize_accuracy_grouped_by_probability method that visualize accuracy stacked by probability.
Changed
- visualization_aids::visualize_features was deprecated.
Fixed
- Ravel y_train in metrics::plot_metric_growth_per_labeled_instances if the shape is (n_sample, 1) to avoid DataConversionWarning (A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().)
- minor changes