data = np.load(file)
print(file)
data_x = data['x'].astype(np.float32)
data_y = data['y'].astype(np.int32)
if np.unique(data_y).shape[0]<=16:
f = open("results/auctable.tex","a")
f.write("\\multirow{3}{*}{%s} " % (dataset_descriptions[file]))
fname = file[5:-4]
ftnet = ClassifierGenerator(128, 16, NETSIZE=384).cuda()
ftnet.load_state_dict(torch.load("models/classifier-generator-128-16-%s.pth" % fname))
if data_x.shape[0]>=20:
f.write("& 10 ")
results10 = np.array(compareMethodsOnSet(methods + [ lambda: NetworkSKL(ftnet) ], data_x, data_y, N=10, samples=800))
stdev = np.mean(results10[:,3])
maxval = np.max(results10[:,1])
f.write("& %.3g " % stdev)
for i in range(results10.shape[0]):
if abs(maxval-results10[i,1])<stdev:
f.write("& \\bf{%.3g} " % (results10[i,1]))
else:
f.write("& %.3g " % (results10[i,1]))
f.write("\\\\ \n")
avg10.append(results10)
if data_x.shape[0]>=60:
f.write("& 50 ")
results50 = np.array(compareMethodsOnSet(methods + [ lambda: NetworkSKL(ftnet) ], data_x, data_y, N=50, samples=800))
stdev = np.mean(results50[:,3])
tdy = []
for didx2 in range(len(data_names)):
if didx2 != didx:
if data_x[didx2].shape[0] >= 120:
tdx.append(data_x[didx2])
tdy.append(data_y[didx2])
ecol = 0
for i in range(500):
err = trainingStep(net, 100, 20, tdx, tdy)
ecol = ecol + err
if i % 10 == 9:
methods = [lambda: MAMLSKL(net)]
results1 = compareMethodsOnSet(methods,
echocardio['x'],
echocardio['y'].astype(np.int32),
samples=20)
auc1 = results1[0][1]
results2 = compareMethodsOnSet(methods,
bloodtransfusion['x'],
bloodtransfusion['y'].astype(
np.int32),
samples=20)
auc2 = results2[0][1]
results3 = compareMethodsOnSet(methods,
autism['x'],
autism['y'].astype(np.int32),
samples=20)
auc3 = results3[0][1]
f = open("finetuning-%s.txt" % data_names[didx], "a")
mamlnet.load_state_dict(torch.load("maml-%s.pth" % file[5:-4]))
methods = [lambda: MAMLSKL(mamlnet)]
data = np.load(file)
print(file)
data_x = data['x'].astype(np.float32)
data_y = data['y'].astype(np.int32)
if np.unique(data_y).shape[0] <= 16:
f = open("results/auctable_maml_ft.tex", "a")
f.write("\\multirow{3}{*}{%s} " % (dataset_descriptions[file]))
if data_x.shape[0] >= 20:
f.write("& 10 ")
results10 = np.array(
compareMethodsOnSet(methods, data_x, data_y, N=10,
samples=800))
stdev = np.mean(results10[:, 3])
maxval = np.max(results10[:, 1])
f.write("& %.3g " % stdev)
for i in range(results10.shape[0]):
if abs(maxval - results10[i, 1]) < stdev:
f.write("& \\bf{%.3g} " % (results10[i, 1]))
else:
f.write("& %.3g " % (results10[i, 1]))
f.write("\\\\ \n")
avg10.append(results10)
if data_x.shape[0] >= 60:
f.write("& 50 ")
results50 = np.array(
compareMethodsOnSet(methods, data_x, data_y, N=50,