# update matrix
embedding_copy = np.zeros((1, 2))
for z, each_element in enumerate(mean_input):
if each_element > x_test[each_t][z].item():
embedding_copy[0][z] = 0
else:
embedding_copy[0][z] = 1
index = pass_in_embedding_out_state_ID(states, embedding_copy[0])
action = y_test[each_t]
current_schedule_matrix[index][int(action)] += 1
per_schedule_test_accs.append(np.mean(test_accs))
sensitivity, specificity = compute_sensitivity(preds,
actual), compute_specificity(
preds, actual)
print('Loss: {}, Accuracy: {}'.format(np.mean(test_losses),
np.mean(test_accs)))
print('per sched accuracy: ', np.mean(per_schedule_test_accs))
print('mean sensitivity: ', sensitivity, ', mean specificity: ', specificity)
# Compute sensitivity and specificity (ideally these should be very high)
file = open('heterogeneous_toy_env_results.txt', 'a')
file.write('gmm -> NN: mean: ' + str(np.mean(per_schedule_test_accs)) +
', std: ' + str(np.std(per_schedule_test_accs)) +
', sensitivity: ' + str(sensitivity) + ', specificity: ' +
str(specificity) + ', Distribution of Class: 0: ' +
str(percent_of_zeros) + ', 1: ' + str(1 - percent_of_zeros) + '\n')
file.close()
def test(ddt):
x_data_test, y_test, percent_of_zeros = create_simple_classification_dataset(
50, get_percent_of_zeros=True)
schedule_starts = np.linspace(0, int(50 * 20 - 20), num=50)
x_test = []
for each_ele in x_data_test:
x_test.append(each_ele[2:])
x_test = torch.Tensor(x_test).reshape(-1, 1, 2)
y_test = torch.Tensor(y_test).reshape((-1, 1))
test_losses, test_accs = [], []
per_schedule_test_losses, per_schedule_test_accs = [], []
preds, actual = [[] for _ in range(50)], [[] for _ in range(50)]
test_distributions = [np.ones(2) * 1 / 2 for _ in range(50)]
total_acc = []
for i in range(50):
chosen_schedule_start = int(schedule_starts[i])
schedule_num = int(chosen_schedule_start / 20)
embedding_given_dis, count = get_embedding_given_dist(
test_distributions[schedule_num])
prod = [.5, .5]
acc = 0
ddt.set_bayesian_embedding(embedding_given_dis)
for each_t in range(chosen_schedule_start, chosen_schedule_start + 20):
# at each timestep you what to resample the embedding
x_t = x_test[each_t]
output = ddt.forward(x_t).reshape(1, 2)
label = y_test[each_t]
label = torch.Tensor([label]).reshape(1)
label = label.long()
print('output is ',
torch.argmax(output).item(), ' label is ', label.item())
if torch.argmax(output).item() == label.item():
acc += 1
tally = output[0][int(label.item())].item()
second_tally = output[0][int(not label.item())].item()
prod[count] = tally * test_distributions[i][count]
prod[int(not count
)] *= second_tally * test_distributions[i][int(not count)]
preds[i].append(torch.argmax(output).item())
actual[i].append(label.item())
normalization_factor = sum(prod)
prod = [k / normalization_factor for k in prod]
test_distributions[schedule_num][0] = prod[0]
test_distributions[schedule_num][1] = prod[1]
normalization_factor_for_dist = sum(
test_distributions[schedule_num])
test_distributions[
schedule_num] /= normalization_factor_for_dist # [i/normalization_factor_for_dist for i in distributions[schedule_num]]
print('distribution at time ', each_t, ' is',
test_distributions[schedule_num])
if each_t % 20 < 5:
embedding_given_dis, count = get_embedding_given_dist(
test_distributions[schedule_num])
else:
embedding_given_dis = get_most_likely_embedding_given_dist(
test_distributions[schedule_num])
ddt.set_bayesian_embedding(embedding_given_dis)
per_schedule_test_accs.append(acc / 20)
# print('Loss: {}, Accuracy: {}'.format(0, np.mean(per_schedule_test_accs)))
print('per sched accuracy: ', np.mean(per_schedule_test_accs))
sensitivity, specificity = compute_sensitivity(
preds, actual), compute_specificity(preds, actual)
print('mean sensitivity: ', sensitivity, ', mean specificity: ',
specificity)
file = open('heterogeneous_toy_env_results.txt', 'a')
file.write('DDT w/ bimodal embedding: mean: ' +
str(np.mean(per_schedule_test_accs)) + ', std: ' +
str(np.std(per_schedule_test_accs)) + ', sensitivity: ' +
str(sensitivity) + ', specificity: ' + str(specificity) +
', Distribution of Class: 0: ' + str(percent_of_zeros) +
', 1: ' + str(1 - percent_of_zeros) + '\n')
file.close()
def DTtest(tree):
"""
Pr[omega_i | game_g] = Pr[omega = omega_i] * \Prod_{data points j in game g} Pr[y_j | omega_i , x_j]
Pr[assignment_g^{t+1} = i] = Pr[assignment_g^t = i] * Pr[omega_i | game g]
:param tree:
:param num_schedules:
:param data:
:param labels:
:return:
"""
num_schedules = 50
x_data_test, y_test, percent_of_zeros = create_simple_classification_dataset(
50, get_percent_of_zeros=True)
schedule_starts = np.linspace(0, int(50 * 20 - 20), num=50)
x_test = []
preds, actual = [[] for _ in range(50)], [[] for _ in range(50)]
for each_ele in x_data_test:
x_test.append(each_ele[2:])
data = torch.Tensor(x_test).reshape(-1, 2)
y_test = torch.Tensor(y_test).reshape((-1, 1))
distributions = [np.ones(2) * 1 / 2 for _ in range(50)]
total_acc = []
for i in range(num_schedules):
# choose a schedule
chosen_schedule_start = int(schedule_starts[i])
schedule_num = int(chosen_schedule_start / 20)
embedding_given_dis, count = get_embedding_given_dist(
distributions[schedule_num])
prod = [.5, .5]
acc = 0
tally = [1, 1]
for each_t in range(chosen_schedule_start, chosen_schedule_start + 20):
# at each timestep you what to resample the embedding
if each_t == 92:
print('hi')
x = data[each_t]
x = list(np.array(torch.cat([x, embedding_given_dis])))
y_pred = tree.predict_proba(np.array(x).reshape(1, -1))
label = y_test[each_t]
print('output is ', y_pred[0], ' label is ', label)
if np.argmax(y_pred[0]) == label:
acc += 1
preds[i].append(np.argmax(y_pred[0]))
actual[i].append(label.item())
tally[count] *= y_pred[0][int(label[0])]
tally[int(not count)] *= y_pred[0][int(not label[0])]
prod[count] = tally[count] * distributions[schedule_num][count]
prod[int(not count)] = tally[int(
not count)] * distributions[schedule_num][int(not count)]
normalization_factor = sum(prod)
prod = [k / normalization_factor for k in prod]
distributions[schedule_num][0] = prod[0]
distributions[schedule_num][1] = prod[1]
normalization_factor_for_dist = sum(distributions[schedule_num])
distributions[
schedule_num] /= normalization_factor_for_dist # [i/normalization_factor_for_dist for i in distributions[schedule_num]]
print('distribution at time ', each_t, ' is',
distributions[schedule_num])
if each_t % 20 < 5:
embedding_given_dis, count = get_embedding_given_dist(
distributions[schedule_num])
else:
embedding_given_dis = get_most_likely_embedding_given_dist(
distributions[schedule_num])
count = np.argmax(distributions[schedule_num])
total_acc.append(acc / 20)
print('mean is ', np.mean(total_acc))
print('finite')
sensitivity, specificity = compute_sensitivity(
preds, actual), compute_specificity(preds, actual)
print('per sched accuracy: ', np.mean(total_acc))
print('mean sensitivity: ', sensitivity, ', mean specificity: ',
specificity)
file = open('heterogeneous_toy_env_results.txt', 'a')
file.write('DT w/ bimodal embedding: mean: ' + str(np.mean(total_acc)) +
', std: ' + str(np.std(total_acc)) + ', sensitivity: ' +
str(sensitivity) + ', specificity: ' + str(specificity) +
', Distribution of Class: 0: ' + str(percent_of_zeros) +
', 1: ' + str(1 - percent_of_zeros) + '\n')
file.close()
pred = ddt(x_test[each_t]).reshape(1, 2)
loss = F.cross_entropy(pred, y_test[each_t].long())
preds[i].append(pred.argmax(dim=-1).item())
actual[i].append(y_test[each_t].item())
# print(pred.argmax(dim=-1), y_test[each_t])
acc = (pred.argmax(dim=-1) == y_test[each_t].item()).to(
torch.float32).mean()
test_losses.append(loss.item())
test_accs.append(acc.mean().item())
per_schedule_test_accs.append(np.mean(test_accs))
print('Loss: {}, Accuracy: {}'.format(np.mean(test_losses),
np.mean(test_accs)))
print('per sched accuracy: ', np.mean(per_schedule_test_accs))
fuzzy_sensitivity, fuzzy_specificity = compute_sensitivity(
preds, actual), compute_specificity(preds, actual)
fuzzy_accuracy = np.mean(per_schedule_test_accs)
ddt = convert_to_crisp(ddt, None)
print(test_distributions)
test_losses, test_accs = [], []
per_schedule_test_losses, per_schedule_test_accs = [], []
preds, actual = [[] for _ in range(50)], [[] for _ in range(50)]
for i in range(50):
chosen_schedule_start = int(schedule_starts[i])
schedule_num = int(chosen_schedule_start / 20)
ddt.set_bayesian_embedding(list(test_distributions[schedule_num]))
for each_t in range(chosen_schedule_start, chosen_schedule_start + 20):
pred = ddt(x_test[each_t]).reshape(1, 2)
loss = F.cross_entropy(pred, y_test[each_t].long())