modeva.models.MoScoredRegressor#

class modeva.models.MoScoredRegressor(dataset, prediction_name: str = None, name: str = None)#

A wrapper for a scored regression model that provides predictions without holding the model object itself.

Parameters:

dataset (object) – The dataset to be used for predictions.
prediction_name (str) – The prediction column name in dataset. If None, will use the prediction_proba_name attribute in dataset.
name (str, default=None) – The name of the model.

calibrate_interval(X, y, alpha=0.1, max_depth: int = 5)#

Fit a conformal prediction model to the given data.

This method computes the model’s prediction interval calibrated to the given data.

If the model is a regressor, splits the data with 50% for fitting lower (alpha / 5) and upper (1 - alpha / 2) gradient boosting trees-based quantile regression to the model’s residual; and 50% for calibration.

If the model is a binary classifiers, it computes the calibration quantile based on predicted probabilities for the positive class.

Parameters:

X (X : np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.
y (array-like of shape (n_samples, )) – Target values.
alpha (float, default=0.1) – Expected miscoverage for the conformal prediction.
max_depth (int, default=5) – Maximum depth of the gradient boosting trees for regression tasks. Only used when task_type is REGRESSION.

Raises:

ValueError – If the model is neither a regressor nor a classifier.:

get_params(deep=True)#

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

load(file_name: str)#

Load the model into memory from file system.

Parameters:: file_name (str) – The path and name of the file.
Return type:: estimator object

predict(X)#

Model predictions, calling the child class’s ‘_predict’ method.

Parameters:: X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.
Returns:: np.ndarray
Return type:: The (calibrated) final prediction

predict_interval(X)#

Predict the prediction interval for the given data based on the conformal prediction model.

It splits the data with 50% for fitting lower (alpha / 5) and upper (1 - alpha / 2) gradient boosting trees-based quantile regression to the model’s residual; and 50% for calibration.

Parameters:: X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.
Returns:: np.ndarray – in the format [n_samples, 2] for regressors or a flattened array for classifiers.
Return type:: The lower and upper bounds of the prediction intervals for each sample
Raises:: ValueError – If fit_conformal has not been called to fit the conformal prediction model: before calling this method.

save(file_name: str)#

Save the model into file system.

Parameters:: file_name (str) – The path and name of the file.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance