bagging.py 文件源码

def _parallel_predict_log_proba(estimators, estimators_features, X, n_classes):
    """Private function used to compute log probabilities within a job."""
    n_samples = X.shape[0]
    log_proba = np.empty((n_samples, n_classes))
    log_proba.fill(-np.inf)
    all_classes = np.arange(n_classes, dtype=np.int)

    for estimator, features in zip(estimators, estimators_features):
        log_proba_estimator = estimator.predict_log_proba(X[:, features])

        if n_classes == len(estimator.classes_):
            log_proba = np.logaddexp(log_proba, log_proba_estimator)

        else:
            log_proba[:, estimator.classes_] = np.logaddexp(
                log_proba[:, estimator.classes_],
                log_proba_estimator[:, range(len(estimator.classes_))])

            missing = np.setdiff1d(all_classes, estimator.classes_)
            log_proba[:, missing] = np.logaddexp(log_proba[:, missing],
                                                 -np.inf)

    return log_proba