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}",