API_REFERENCE_FOR_APLR_TUNER.md

APLRTuner

class aplr.APLRTuner(parameters: Dict[str, List[Any]] = {"max_interaction_level": [0, 1], "min_observations_in_split": [0.1, 0.3, 0.5, 0.6, 0.7, 0.8]}, is_regressor: bool = True, sequential_tuning: bool = False)

Constructor parameters

parameters (default = {"max_interaction_level": [0, 1], "min_observations_in_split": [0.1, 0.3, 0.5, 0.6, 0.7, 0.8]})

A dictionary where keys are parameter names and values are lists of parameter settings to try.

is_regressor (default = True)

Whether you want to use APLRRegressor (True) or APLRClassifier (False).

sequential_tuning (default = False)

If True, hyperparameters are tuned sequentially instead of performing a full grid search. The tuning order is determined by the key order in the parameters dictionary. This can be much faster but may not find the global optimum.

Method: fit(X: Union[pd.DataFrame, FloatMatrix], y: FloatVector, **kwargs)

This method tunes the model to data.

Parameters

X

A numpy matrix or pandas DataFrame with predictor values.

y

A numpy vector with response values.

kwargs

Optional parameters sent to the fit methods in the underlying APLRRegressor or APLRClassifier models.

Method: predict(X: Union[pd.DataFrame, FloatMatrix], **kwargs)

Returns the predictions of the best tuned model as a numpy array if regression or as a list of strings if classification.

Parameters

X

A numpy matrix or pandas DataFrame with predictor values.

kwargs

Optional parameters sent to the predict method in the best tuned model.

Method: predict_class_probabilities(X: Union[pd.DataFrame, FloatMatrix], **kwargs)

This method returns predicted class probabilities of the best tuned model as a numpy matrix.

Parameters

X

A numpy matrix or pandas DataFrame with predictor values.

kwargs

Optional parameters sent to the predict_class_probabilities method in the best tuned model.

Method: predict_proba(X: Union[pd.DataFrame, FloatMatrix], **kwargs)

This method returns predicted class probabilities of the best tuned model as a numpy matrix. Similar to the predict_class_probabilities method but the name predict_proba is compatible with scikit-learn.

Parameters

X

A numpy matrix or pandas DataFrame with predictor values.

kwargs

Optional parameters sent to the predict_class_probabilities method in the best tuned model.

Method: get_best_estimator()

Returns the best tuned model. This is an APLRRegressor or APLRClassifier object.

Method: get_cv_results()

Returns the cv results from the tuning as a list of dictionaries, List[Dict[str, Any]].