_classification.py 文件源码

def _fit(x, y, clf, cv, mf, grp, center, n_jobs):
    """Sub function for fitting
    """
    # Check the inputs size :
    x, y = checkXY(x, y, mf, grp, center)
    rep, nfeat = len(cv), len(x)

    # Tricks : construct a list of tuple containing the index of
    # (repetitions,features) & loop on it. Optimal for parallel computing :
    claIdx, listRep, listFeat = list2index(rep, nfeat)

    # Run the classification :
    cvs = Parallel(n_jobs=n_jobs)(delayed(_cvscore)(
        x[k[1]], y, clone(clf), cv[k[0]]) for k in claIdx)
    da, y_true, y_pred = zip(*cvs)

    # Reconstruct elements :
    da = np.array(groupInList(da, listFeat))
    y_true = groupInList(y_true, listFeat)
    y_pred = groupInList(y_pred, listFeat)

    return da, x, y, y_true, y_pred