fe.py 文件源码

def onehot_gene(DB, tr, te):
    from utils.np_utils.encoder import onehot_encode
    if tr is None:
        train = DB.data['training_variants']
        if te=="stage1":
            test = DB.data['test_variants_filter']
        else:
            train = pd.concat([train,DB.data['test_variants_filter']],axis=0)
            test = DB.data['stage2_test_variants']
        lbl_encode(train,test)
        n = max(train['Gene'].max(),test['Gene'].max())
        gtr = onehot_encode(train['Gene'].values,n=n+1)
        gte = onehot_encode(test['Gene'].values)
        return gtr,gte
    else:
        data = DB.data['training_variants']
        lbl_encode(data,cols=['Gene'])
        gene = data['Gene'].values
        gene = onehot_encode(gene)
        return gene[tr],gene[te]