_classif.py 文件源码

def _featinfo(self, clf, cv, da, grp=None, pbino=None, pperm=None):
        # Manage input arguments :
        dastd = np.round(100*da.std(axis=1))/100
        dam = da.mean(axis=1)
        if grp is None:
            grp = np.array([str(k) for k in range(len(dam))])
        if pbino is None:
            pbino = bino_da2p(self.y, dam)
        if pperm is None:
            pperm = np.ones((len(dam),))
        array = np.array([np.ravel(dam), np.ravel(dastd), np.ravel(pbino), np.ravel(pperm), np.ravel(grp)]).T

        # Create the dataframe:
        subcol = ['DA (%)', 'STD (+/-)', 'p-values (Binomial)', 'p-values (Permutations)', 'Group']
        str2repeat = clf.shStr+' / '+cv.shStr
        idxtuple = list(zip(*[[str2repeat]*len(subcol), subcol]))
        index = pd.MultiIndex.from_tuples(idxtuple, names=['Settings', 'Results'])
        return pd.DataFrame(array, columns=index)