APLRRegressor

APLRRegressor#

Link to Algorithm description: Automatic Piecewise Linear Regression

The below API reference explains the methods added by the light wrapper in InterpretML. The API reference for the remaining members and methods can be found here.

class interpret.glassbox.APLRRegressor(**kwargs)#

APLR Regressor.

Initializes class.

Parameters:: **kwargs – Kwargs passed to APLRRegressor at initialization time.

clear_cv_results()#: Clears the stored cross-validation results (predictions, y, etc.) to free up memory.

explain_global(name=None)#

Provides global explanation for model.

Parameters:: name – User-defined explanation name.
Returns:: An explanation object, visualizing feature-value pairs as horizontal bar chart.

explain_local(X, y=None, name=None)#

Provides local explanations for provided instances.

Parameters:

X – Numpy array for X to explain.
y – Numpy vector for y to explain.
name – User-defined explanation name.

Returns:

An explanation object, visualizing feature-value pairs for each instance as horizontal bar charts.

fit(X, y, **kwargs)#: Fits model.

get_cv_sample_weight(fold_index)#

Gets the validation sample weights for a specific cross-validation fold.

Parameters:: fold_index – The index of the fold.
Returns:: A numpy array containing the validation sample weights.

get_cv_validation_indexes(fold_index)#

Gets the original indexes of the validation observations for a specific cross-validation fold.

Parameters:: fold_index – The index of the fold.
Returns:: A numpy array containing the original indexes.

get_cv_validation_predictions(fold_index)#

Gets the validation predictions for a specific cross-validation fold.

Note that these predictions may be conservative, as the final model is an ensemble of the models from all cross-validation folds, which has a variance-reducing effect similar to bagging.

Parameters:: fold_index – The index of the fold.
Returns:: A numpy array containing the validation predictions.

get_cv_y(fold_index)#

Gets the validation response values (y) for a specific cross-validation fold.

Parameters:: fold_index – The index of the fold.
Returns:: A numpy array containing the validation response values.

get_num_cv_folds()#

Gets the number of cross-validation folds used during training.

Returns:: The number of folds.

plot_affiliation_shape(affiliation, plot=True, save=False, path='')#

Plots or saves the shape of a given unique term affiliation.

For main effects, it produces a line plot. For two-way interactions, it produces a heatmap. Plotting for higher-order interactions is not supported.

Parameters:

affiliation – A string specifying which unique_term_affiliation to use.
plot – If True, displays the plot.
save – If True, saves the plot to a file.
path – The file path to save the plot. If empty and save is True, a default path will be used.

score(X, y, sample_weight=None)#

Return coefficient of determination on test data.

The coefficient of determination, \(R^2\), is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – \(R^2\) of self.predict(X) w.r.t. y.

Return type:

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).