rg = re.findall(r"init(\d+)?", f)
if len(rg) > 0:
init = np.array(rg[0]).astype(int)
else:
init = None
if init is not None:
if init > 10:
continue
else:
continue
metrics = pickle.load(open(SAVE_DIR + FOLDERS + met_files[j], 'rb'))
args = pickle.load(open(SAVE_DIR + FOLDERS + arg_files[j], 'rb'))
net = students.MultiGLM(
students.Feedforward([100, N, N], ['ReLU', 'ReLU']),
students.Feedforward([N, N_out], [None]), students.GausId(N_out))
net.load(SAVE_DIR + FOLDERS + f)
# if metrics['test_perf'][-1,...].min() > maxmin:
# maxmin = metrics['test_perf'][-1,...].min()
# best_net = model
# this_arg = args
for key, val in metrics.items():
if len(val) == 1000:
continue
if key not in all_metrics.keys():
shp = (num, ) + np.squeeze(np.array(val)).shape
all_metrics[key] = np.zeros(shp) * np.nan
# for i in np.unique(these_conds):
# c = [int(i), int(np.mod(i+1,U.shape[0]))]
# ax.plot(U[c,0],U[c,1],U[c,2],'k')
util.set_axes_equal(ax)
# plt.title('PCA dimension: %.2f'%((np.sum(S**2)**2)/np.sum(S**4)))
#%%
N = 100
# net = students.Feedforward([inputs.shape[1],100,2],['ReLU',None])
# net = students.MultiGLM(students.Feedforward([inputs.shape[1], N], ['ReLU']),
# students.Feedforward([N, targets.shape[1]], [None]),
# students.GausId(targets.shape[1]))
net = students.MultiGLM(
students.Feedforward([dim_inp * num_var, N], ['ReLU']),
students.Feedforward([N, output_task.dim_output], [None]),
students.Bernoulli(output_task.dim_output))
# net = students.MultiGLM(students.Feedforward([inputs.shape[1],N], ['ReLU']),
# students.Feedforward([N, p], [None]),
# students.Categorical(p))
n_trn = int(0.8 * num_data)
trn = np.random.choice(num_data, n_trn, replace=False)
tst = np.setdiff1d(range(num_data), trn)
optimizer = optim.Adam(net.enc.parameters(), lr=1e-4)
dset = torch.utils.data.TensorDataset(inputs[trn, :].float(),
outputs[trn, :].float())
dl = torch.utils.data.DataLoader(dset, batch_size=64, shuffle=True)
n_compute = np.min([len(tst), 1000])
samps = np.concatenate([np.random.choice(np.where(abstract_conds==c)[0],n) \
for c,n in zip(cond_set,succ_counts)])
unscramble = np.argsort(np.argsort(succ_conds))
successor_idx = samps[unscramble]
targets = output_states[successor_idx,:]
# targets = output_state
#%%
N = 100
# net = students.Feedforward([inputs.shape[1],100,2],['ReLU',None])
net = students.MultiGLM(students.Feedforward([inputs.shape[1], N], ['ReLU']),
students.Feedforward([N, targets.shape[1]], [None]),
students.GausId(targets.shape[1]))
# net = students.MultiGLM(students.Feedforward([inputs.shape[1],N], ['ReLU']),
# students.Feedforward([N,targets.shape[1]], [None]),
# students.Bernoulli(targets.shape[1]))
# net = students.MultiGLM(students.Feedforward([inputs.shape[1],N], ['ReLU']),
# students.Feedforward([N, p], [None]),
# students.Categorical(p))
n_trn = int(0.5*targets.shape[0])
trn = np.random.choice(targets.shape[0],n_trn,replace=False)
tst = np.random.choice(np.setdiff1d(range(targets.shape[0]),trn), int(0.5*n_trn), replace=False)
optimizer = optim.Adam(net.parameters(), lr=1e-4)
dset = torch.utils.data.TensorDataset(torch.tensor(inputs[trn,:]).float(),
targets[trn,:].float())
# for i in np.unique(these_conds):
# c = [int(i), int(np.mod(i+1,U.shape[0]))]
# ax.plot(U[c,0],U[c,1],U[c,2],'k')
util.set_axes_equal(ax)
# plt.title('PCA dimension: %.2f'%((np.sum(S**2)**2)/np.sum(S**4)))
#%%
N = 200
# net = students.Feedforward([inputs.shape[1],100,2],['ReLU',None])
# net = students.MultiGLM(students.Feedforward([inputs.shape[1], N], ['ReLU']),
# students.Feedforward([N, targets.shape[1]], [None]),
# students.GausId(targets.shape[1]))
net = students.MultiGLM(
students.Feedforward([dim_inp * 4, N, N], ['ReLU', 'ReLU']),
students.Feedforward([N, 2], [None]), students.Bernoulli(2))
# net = students.MultiGLM(students.Feedforward([inputs.shape[1],N], ['ReLU']),
# students.Feedforward([N, p], [None]),
# students.Categorical(p))
n_trn = int(0.8 * num_data)
trn = np.random.choice(num_data, n_trn, replace=False)
tst = np.setdiff1d(range(num_data), trn)
optimizer = optim.Adam(net.enc.parameters(), lr=1e-4)
dset = torch.utils.data.TensorDataset(inputs[trn, :].float(),
outputs[trn, :].float())
dl = torch.utils.data.DataLoader(dset, batch_size=128, shuffle=True)
n_compute = np.min([len(tst), 1000])