Implementation

Note

Implementation details described here may change in the future, may already be outdated, incorrect, or incomplete. When in doubt, please refer to the actual source code.

If you feel something should be corrected, please raise an issue.

Training code flow

Interfaces

On the very top there are two kinds of public interfaces. One is for online training (partial_fit()) and the other is for training from scratch (fit()). These public interfaces exist as class methods for each of supported estimators. They are implemented as thin wrappers, which delegate further work to freestanding functions, called with estimator’s state as one of their arguments. Doing so enables us to take advantage of hiding implementation and departing from Object-oriented paradigm. These functions are called partial_fit_impl, and fit_impl, and are overloads for respective estimator state type.

What happens underneath partial_fit_impl, and fit_impl is driven by a need to converge into single invocation of either fit_classifier_online_impl or fit_regressor_online_impl.

hide empty description state "public interface" as public_interface { state "<font:courier><classifier>.fit()" as fit #palegreen state "<font:courier><classifier>.partial_fit()" as partial_fit #palegreen } state "<font:courier>fit_impl()" as fit_impl #yellow state "<font:courier>fit_classifier_online_with_input_check<>()" as fit_classifier_online_with_input_check state "<font:courier>fit_classifier_impl<>()" as fit_classifier_impl state "<font:courier>partial_fit_impl()" as partial_fit_impl #yellow state "<font:courier>fit_classifier_online_impl<>()" as fit_classifier_online_impl state "..." as ellipsis #skyblue state is_fitted <<choice>> fit --> fit_impl partial_fit --> partial_fit_impl partial_fit_impl --> is_fitted : is fitted? is_fitted -left-> fit_impl : [no] is_fitted --> fit_classifier_online_with_input_check : [yes] fit_classifier_online_with_input_check ---> fit_classifier_online_impl note bottom of fit_classifier_online_with_input_check ""check_X_y"" ""check_X"" ""check_labels"" end note fit_impl --> fit_classifier_impl fit_classifier_impl ---> fit_classifier_online_impl note bottom of fit_classifier_impl ""check_X_y"" ""check_labels"" end note fit_classifier_online_impl --> ellipsis

Portion of Classifier’s call trace

hide empty description state "public interface" as public_interface { state "<font:courier><regressor>.fit()" as fit #palegreen state "<font:courier><regressor>.partial_fit()" as partial_fit #palegreen } state "<font:courier>fit_impl()" as fit_impl #yellow state "<font:courier>fit_regressor_online_with_input_check<>()" as fit_regressor_online_with_input_check state "<font:courier>fit_regressor_impl<>()" as fit_regressor_impl state "<font:courier>partial_fit_impl()" as partial_fit_impl #yellow state "<font:courier>fit_regressor_online_impl<>()" as fit_regressor_online_impl state "..." as ellipsis #skyblue state is_fitted <<choice>> fit --> fit_impl partial_fit --> partial_fit_impl partial_fit_impl --> is_fitted : is fitted? is_fitted -left-> fit_impl : [no] is_fitted --> fit_regressor_online_with_input_check : [yes] fit_regressor_online_with_input_check ---> fit_regressor_online_impl note bottom of fit_regressor_online_with_input_check ""check_X_y"" ""check_X"" ""check_response_y"" end note fit_impl --> fit_regressor_impl fit_regressor_impl ---> fit_regressor_online_impl note bottom of fit_regressor_impl ""check_X_y"" ""check_response_y"" end note fit_regressor_online_impl --> ellipsis

Portion of Regressor’s call trace