_lpso.py 文件源码

def _fit(x, y, train, test, self, n_jobs):
    """Sub fit function
    """
    nsuj, nfeat = x.shape
    iteract = product(range(nfeat), zip(train, test))
    ya = Parallel(n_jobs=n_jobs)(delayed(_subfit)(
            np.concatenate(tuple(x[i].iloc[k[0]])),
            np.concatenate(tuple(x[i].iloc[k[1]])),
            np.concatenate(tuple(y[0].iloc[k[0]])),
            np.concatenate(tuple(y[0].iloc[k[1]])),
            self) for i, k in iteract)
    # Re-arrange ypred and ytrue:
    ypred, ytrue = zip(*ya)
    ypred = [np.concatenate(tuple(k)) for k in np.split(np.array(ypred), nfeat)]
    ytrue = [np.concatenate(tuple(k)) for k in np.split(np.array(ytrue), nfeat)]
    da = np.ravel([100*accuracy_score(ytrue[k], ypred[k]) for k in range(nfeat)])
    return da, ytrue, ypred