NIID-Bench-modified/add.py at master · SkyTu/NIID-Bench-modified · GitHub

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import numpy as np

init_dict = {"fednova":
{"mnist":{1:[0.0892, 0.0892, 0.0892], 2:[0.0892, 0.0892, 0.0892], 3:[0.0892, 0.0892, 0.0892]},
"cifar10":{1:[0.0997, 0.0997, 0.0997], 2:[0.0997, 0.0997, 0.0997], 3:[0.0997, 0.0997, 0.0997]},
"fmnist":{1:[0.1, 0.1, 0.1], 2:[0.1, 0.1, 0.1], 3:[0.1, 0.1, 0.1]},
"a9a":{1:[0.3749155457281494, 0.3749155457281494, 0.3749155457281494]},
"rcv1":{1:[0.48745307251531317, 0.48745307251531317, 0.48745307251531317]},
"covtype":{1:[0.5119894253474971, 0.5119894253474971, 0.5119894253474971]}},
"fedprox": {"mnist":{1:[0.0997, 0.0997, 0.0997], 2:[0.0997, 0.0997, 0.0997], 3:[0.0997, 0.0997, 0.0997]},
"cifar10":{1:[0.0997, 0.1, 0.0997], 2:[0.0997, 0.0997, 0.0997], 3:[0.0997, 0.0997, 0.0997]},
"fmnist":{1:[0.1, 0.1, 0.1], 2:[0.1, 0.1, 0.1], 3:[0.1, 0.1, 0.1]},
"a9a":{1:[0.3749155457281494, 0.7392666298138935, 0.31908359437380995]},
"rcv1":{1:[0.48745307251531317, 0.48745307251531317, 0.48745307251531317]},
"covtype":{1:[0.5119894253474971, 0.5119894253474971, 0.5119894253474971]}},
"fedavg":  {"mnist":{1:[0.0892, 0.1009, 0.0892], 2:[0.1009, 0.0892, 0.0892], 3:[0.0892, 0.1009, 0.0892]},
"cifar10":{1:[0.0997, 0.1, 0.0997], 2:[0.1, 0.1, 0.0997], 3:[0.0997, 0.1, 0.1]},
"fmnist":{1:[0.1, 0.1, 0.1], 2:[0.1, 0.1, 0.1], 3:[0.1, 0.1, 0.1]},
"a9a":{1:[0.3749155457281494, 0.7392666298138935, 0.31908359437380995]},
"rcv1":{1:[0.48745307251531317, 0.5107686228018178, 0.48745307251531317]},
"covtype":{1:[0.5119894253474971, 0.5118517345596992, 0.5119894253474971]}},
"scaffold":{"mnist":{1:[0.0892, 0.0892, 0.1009], 2:[0.0892, 0.0892, 0.0892], 3:[0.0892, 0.0892, 0.0892]},
"cifar10":{1:[0.0997, 0.1, 0.0997], 2:[0.1, 0.0997, 0.0997], 3:[0.0997, 0.1, 0.0997]},
"fmnist":{1:[0.1, 0.1, 0.1], 2:[0.1, 0.1, 0.1], 3:[0.1, 0.1, 0.1]},
"a9a":{1:[0.3749155457281494, 0.7392666298138935, 0.31908359437380995]},
"rcv1":{1:[0.48745307251531317, 0.5107686228018178, 0.48863860897055916]},
"covtype":{1:[0.5119894253474971, 0.5118517345596992, 0.5129326072439123]}}}

for m in  ("fedavg", "fednova", "fedprox", "scaffold"):
    for d in ("mnist", "cifar10", "fmnist", "a9a"):#, "rcv1", "covtype"):
        for t in range(1, 4):
            if d in ("mnist", "cifar10", "fmnist"):
                for c in range(1,4):
                    if t == 1:
                        data = np.load("/home/txy/NIID-Bench/acc_npy/" + d + "/10 party/" + m + "_noniid-#label"+ str(c) +"_test_acc.npy")
                    else:
                        data = np.load("/home/txy/NIID-Bench/acc_npy/" + d + "/10 party/"+str(t) + "_"+ m +"_noniid-#label"+ str(c) +"_test_acc.npy")
                    print(init_dict[m][d][c][t-1])
                    data = np.insert(data, 0, init_dict[m][d][c][t-1])
                    np.save("/home/txy/NIID-Bench/acc_npy/" + d + "/10/" + str(m) + "/" + str(c) + "/" + str(t) + ".npy", data)
            else:
                if t == 1:
                        data = np.load("/home/txy/NIID-Bench/acc_npy/" + d + "/10 party/" + m + "_noniid-#label1_test_acc.npy")
                else:
                    data = np.load("/home/txy/NIID-Bench/acc_npy/" + d + "/10 party/"+str(t) + "_"+ m +"_noniid-#label1_test_acc.npy")
                np.insert(data, 0, init_dict[m][d][1][t-1])
                np.save("/home/txy/NIID-Bench/acc_npy/" + d + "/10/" + str(m) + "/" + str(c) + "/" + str(t) + ".npy", data)