Source code for skpro.ensemble

import numpy as np

from sklearn.ensemble import BaggingRegressor as BaseBaggingRegressor
from sklearn.utils.validation import check_is_fitted
from sklearn.utils import check_array

from .base import ProbabilisticEstimator

[docs]class BaggingRegressor(BaseBaggingRegressor, ProbabilisticEstimator):
[docs] class Distribution(ProbabilisticEstimator.Distribution): def __init__(self, estimator, X, distributions, n_estimators): super().__init__(estimator, X) self.distributions = distributions self.n_estimators = n_estimators def point(self): return NotImplemented def std(self): return NotImplemented def pdf(self, x): # Average the predicted PDFs arr = np.array([ d.pdf(x) for distribution in self.distributions for d in distribution ]) return np.mean(arr, axis=0)
[docs] def predict(self, X): """ Predict regression target for X. The predicted regression target of an input sample is computed as the averaged predicted distributions of the estimators in the ensemble. Parameters ---------- X : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- y : skpro.base.Distribution = [n_samples] The predicted bagged distributions. """ # Ensure estimator were being fitted check_is_fitted(self, "estimators_features_") # Check data X = check_array(X, accept_sparse=['csr', 'csc']) # Parallel loop from sklearn.ensemble.base import _partition_estimators n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators, self.n_jobs) def _parallel_predict_regression(estimators, estimators_features, X): """ Private function used to compute predictions within a job. """ return [ estimator.predict(X[:, features]) for estimator, features in zip(estimators, estimators_features) ] # Obtain predictions all_y_hat = [ _parallel_predict_regression( self.estimators_[starts[i]:starts[i + 1]], self.estimators_features_[starts[i]:starts[i + 1]], X ) for i in range(n_jobs) ] # Reduce return self._distribution()(self, X, all_y_hat, n_estimators)
def __str__(self, describer=str): return 'BaggingRegressor(' + describer(self.base_estimator) + ')' def __repr__(self): return self.__str__(repr)