for j in range(4):
plt.scatter(x[0, 0, 0, yl == j],
x[0, 0, 1, yl == j],
c=colors[j],
edgecolors='k',
lw=1,
s=10)
plt.xticks([])
plt.yticks([])
plt.xlim(-3, 3)
plt.ylim(-3, 3)
net2_4_400_1 = ClassifierGenerator(2, 4, 384).cuda()
net2_4_400_1.load_state_dict(
torch.load("models/classifier-generator-2-4-base.pth"))
net2_4_20_1 = ClassifierGenerator(2, 4, 384).cuda()
net2_4_20_1.load_state_dict(
torch.load("models/classifier-generator-2-4-N20.pth"))
net2_4_100_1 = ClassifierGenerator(2, 4, 384).cuda()
net2_4_100_1.load_state_dict(
torch.load("models/classifier-generator-2-4-N100.pth"))
net2_4_100_4 = ClassifierGenerator(2, 4, 384).cuda()
net2_4_100_4.load_state_dict(
torch.load("models/classifier-generator-2-4-diff4.pth"))
net2_4_gen = ClassifierGenerator(2, 4, 384).cuda()
net2_4_gen.load_state_dict(
data_names = []
data_x = []
data_y = []
for file in glob.glob("data/*.npz"):
data = np.load(file)
if np.unique(data['y']).shape[0] <= 16:
data_names.append(file[5:-4])
data_x.append(data['x'].copy())
data_y.append(data['y'].copy().astype(np.int32))
for didx in range(len(data_names)):
net = ClassifierGenerator(FEATURES=128, CLASSES=16, NETSIZE=384).cuda()
net.load_state_dict(torch.load("models/classifier-generator-128-16.pth"))
tdx = []
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])
for i in range(20):
err = trainingStep(net, 100, 20, tdx, tdy)
f = open("training_curves/finetuning-%s.txt" % data_names[didx], "a")
f.write("%d %.6g\n" % (i, err))
f.close()
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import roc_auc_score
import xgboost as xgb
import warnings
def fxn():
warnings.warn("deprecated", DeprecationWarning)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
net128_16 = ClassifierGenerator(128, 16, NETSIZE=384).cuda()
net128_16.load_state_dict(torch.load("models/classifier-generator-128-16.pth"))
net32_16 = ClassifierGenerator(32, 16, NETSIZE=384).cuda()
net32_16.load_state_dict(torch.load("models/classifier-generator-32-16.pth"))
dataset_descriptions = {
"data/immunotherapy.npz": "Immunotherapy\\cite{khozeimeh2017expert, khozeimeh2017intralesional}",
"data/foresttype.npz": "Forest type\\cite{johnson2012using}",
"data/winetype.npz" : "Wine type\\cite{forina1990parvus}",
"data/cryotherapy.npz" : "Cryotherapy\\cite{khozeimeh2017expert, khozeimeh2017intralesional}",
"data/chronic-kidney.npz" : "Chronic kidney\\cite{chronickidney}",
"data/echocardiogram.npz" : "Echocardiogram\\cite{echocardiogram}",
"data/haberman.npz" : "Haberman\\cite{haberman1976generalized}",
"data/iris.npz" : "Iris\\cite{fisher1936use}",
"data/hcc-survival.npz" : "HCC Survival\\cite{santos2015new}",
"data/horse-colic.npz" : "Horse Colic\\cite{horsecolic}",
"data/lung-cancer.npz" : "Lung cancer\\cite{hong1991optimal}",