def test_recon_error(mix_model, model, model_path, data_path,
save=True, save_path=""):
M = mix_model.load_checkpoint(model, model_path)
models = [M.model1, M.model2, M.model3, M.model4]
test_data = Data.load(data_path)["data"]
with torch.no_grad():
recon_error_matrix = np.zeros((4,4))
pairs = list(combinations_with_replacement(np.arange(len(test_data)), r=2))
for (i, j) in pairs:
for x1, y1 in test_data[i]:
for x2, y2 in test_data[j]:
h1 = models[i].encode(x1)
h2 = models[j].encode(x2)
out11 = models[i].decode(h1)
out12 = models[i].decode(h2)
out22 = models[j].decode(h2)
out21 = models[j].decode(h1)
recon_loss_xx = Loss.mse_loss(out11, y1)
recon_loss_yy = Loss.mse_loss(out22, y2)
recon_loss_xy = Loss.mse_loss(out12, y1)
recon_loss_yx = Loss.mse_loss(out21, y2)
recon_error_matrix[i,i] = recon_loss_xx
recon_error_matrix[i,j] = recon_loss_xy
recon_error_matrix[j,j] = recon_loss_yy
recon_error_matrix[j,i] = recon_loss_yx
if save:
if not os.path.exists(save_path): os.mkdir(save_path)
names = ["M1", "M2", "M3", "M4"]
recon_df = pd.DataFrame(data=recon_error_matrix, index=names, columns=names)
recon_df.to_csv(M.name + "_recon_error.csv")
def test_recon_error_withLabel_withProb(mix_model, model, model_path, data_path,
save=True, save_path=""):
M = mix_model.load_checkpoint(model, model_path)
models = [M.model1, M.model2, M.model3, M.model4]
test_data = Data.load(data_path)["data"]
with torch.no_grad():
recon_error_matrix = np.zeros((4,4))
latent_error_matrix = np.zeros((4,4))
kl_error_matrix = np.zeros((4,4))
error_matrix = np.zeros((4,4))
pairs = list(combinations_with_replacement(np.arange(len(test_data)), r=2))
for (i, j) in pairs:
for x1, y1 in test_data[i]:
for x2, y2 in test_data[j]:
h1, l1, mu1, logvar1 = models[i].encode(x1)
h2, l2, mu2, logvar2 = models[j].encode(x2)
out11, _, _, _ = models[i].decode(h1, l1, mu1, logvar1)
out12, _, _, _ = models[i].decode(h2, l1, mu2, logvar2)
out22, _, _, _ = models[j].decode(h2, l2, mu2, logvar2)
out21, _, _, _ = models[j].decode(h1, l2, mu1, logvar1)
loss_xx, kl_loss_xx, recon_loss_xx = Loss.kl_loss(out11, y1, mu1, logvar1)
loss_yy, kl_loss_yy, recon_loss_yy = Loss.kl_loss(out22, y2, mu2, logvar2)
loss_xy, kl_loss_xy, recon_loss_xy = Loss.kl_loss(out12, y1, mu1, logvar1)
loss_yx, kl_loss_yx, recon_loss_yx = Loss.kl_loss(out21, y2, mu2, logvar2)
latent_loss = Loss.mse_loss(h1, h2)
recon_error_matrix[i,i] = recon_loss_xx
recon_error_matrix[i,j] = recon_loss_xy
recon_error_matrix[j,j] = recon_loss_yy
recon_error_matrix[j,i] = recon_loss_yx
error_matrix[i, i] = loss_xx
error_matrix[i, j] = loss_xy
error_matrix[j, j] = loss_yy
error_matrix[j, i] = loss_yx
latent_error_matrix[i,j] = latent_error_matrix[j,i] = latent_loss
kl_error_matrix[i,j] = kl_loss_xy
kl_error_matrix[j, i] = kl_loss_yx
if save:
if not os.path.exists(save_path): os.mkdir(save_path)
names = ["M1", "M2", "M3", "M4"]
recon_df = pd.DataFrame(data=recon_error_matrix, index=names, columns=names)
latent_df = pd.DataFrame(data=latent_error_matrix, index=names, columns=names)
error_df = pd.DataFrame(data=error_matrix, index=names, columns=names)
kl_df = pd.DataFrame(data=kl_error_matrix, index=names, columns=names)
recon_df.to_csv(M.name + "_recon_error.csv")
latent_df.to_csv(M.name + "_latent_error.csv")
error_df.to_csv(M.name + "_error.csv")
kl_df.to_csv(M.name + "_kl_error.csv")
def test_single_model(model, model_path, data_path, save=True, save_path=""):
model = model.load_checkpoint(model_path)
test_data = Data.load(data_path)["data"][0]
N = len(test_data)
print(model, N)
with torch.no_grad():
recon_errors = np.zeros(N)
for i, (x, y) in enumerate(test_data):
h, l = model.encode(x)
out = model.decode(h, l)
recon_loss = Loss.mse_loss(out, y)
recon_errors[i] = recon_loss
if save:
if not os.path.exists(save_path): os.mkdir(save_path)
recon_df = pd.DataFrame(data=recon_errors, index=np.arange(N))
recon_df.to_csv(model.name + "_recon_error.csv")
def test_single_model(model, model_path, data_path,
save=True, save_path=""):
model = model.load_checkpoint(model_path)
test_data = Data.load(data_path)["data"][0]
N = len(test_data)
print(model, N)
with torch.no_grad():
recon_errors = np.zeros(N)
for i, (x, y) in enumerate(test_data):
# models[j].to("cpu")
# h1, l1, mu1, logvar1 = models[i].encode(x1)
# start_time = time.time()
# h2, l2, mu2, logvar2 = models[j].encode(x2)
# time1 = time.time() - start_time
#
# # out11, _, _, _ = models[i].decode(h1, l1, mu1, logvar1)
# # out12, _, _, _ = models[i].decode(h2, l1, mu2, logvar2)
# start_time = time.time()
# out22, _, _, _ = models[j].decode(h2, l2, mu2, logvar2)
# time2 = time.time() - start_time
#
# print(models[j])
# print("time 1: ", time1)
# print("time 2:", time2)
# return
# out21, _, _, _ = models[j].decode(h1, l2, mu1, logvar1)
h, l = model.encode(x)
out = model.decode(h, l)
recon_loss = Loss.mse_loss(out,y)
recon_errors[i] = recon_loss
if save:
if not os.path.exists(save_path): os.mkdir(save_path)
recon_df = pd.DataFrame(data=recon_errors, index=np.arange(N))
recon_df.to_csv(model.name + "_recon_error.csv")