X_train = data[0]

K = data[1]

start_time = time.time()

mixture = BayesMixture(X_train, K=K, alpha=50, beta=0.5, delta=0.8, seed=int(time.time()))

z_new, a_k, gamma, mixing_props = mixture.gibbs_sample(max_iterations=30, hard_cluster=False, verbose=False)

duration = time.time() - start_time

X_train = data[0]

K = data[1]

start_time = time.time()

mixture = BernoulliMixtureEM(K=K, D=784, X=X_train)

a_k, pi = mixture.train(max_iterations=80, verbose=False, display_interval=50, save_interval=None)

duration = time.time() - start_time

X_train, y_train, X_test, y_test = get_digits(0.5)

cores = 6

pool = Pool(processes=cores)

for k in [10]:

for digit_estimated in [3, 4, 5, 6, 7, 8, 9]:

condition = (y_train == digit_estimated)

selected_digits = X_train[np.where(condition)]

selected_labels = y_train[np.where(condition)]

h5 = h5py.File(f"./datasets/{digit_estimated}_estimates_{k}.h5", "a")

remaining_digits = 250 - len(h5.keys())

durations = []

idx = 0

for estimate, duration in pool.imap_unordered(threaded_estimates,

get_data(selected_digits, n=remaining_digits, K=k)):

h5.create_dataset(f"{idx}{time.time()}", data=estimate)

durations.append(duration)

if idx % 20 == 0:

print(

f"{digit_estimated} - Done with {idx}/{remaining_digits}. Avg {np.mean(durations) / cores:.3f}s per estimate.")

idx += 1

X_train, y_train, X_test, y_test = get_digits(0.5)

bayes_ests = []

em_ests = []

for i in range(1000):

condition = (y_train == 3)

selected_digits = X_train[np.where(condition)]

mixture = BayesMixture(selected_digits, K=20, alpha=50, beta=0.5, delta=0.8, seed=int(time.time()))

z_new, a_k, gamma, mixing_props, best_ak = mixture.gibbs_sample(max_iterations=50, hard_cluster=False,

verbose=False)

bayes_ests.append({"best_ak": best_ak,

"all_ak": a_k,

"all_z": z_new})

if i % 10 == 0:

print(f"Done {i} out of 1000 for Bayes")

if i % 100 == 0:

with open(f"D:\\bayes_ests_{time.time()}.bin", "wb") as f:

pickle.dump(bayes_ests, f)

exit()

X_train, y_train, X_test, y_test = get_digits(0.5)

bayes_ests = {}

with open(f"ests_k_1565828825.666521.bin", "rb") as f:

bayes_ests = pickle.load(f)

for digit in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]:

print(f"Doing digit {digit}")

if digit not in bayes_ests:

bayes_ests[digit] = {}

for k in [10, 20, 30, 50]:

print(f"Doing k= {k}")

if k in bayes_ests[digit]:

continue

condition = (y_train == digit)

selected_digits = X_train[np.where(condition)]

local_est = []

for est in range(10):

mixture = BayesMixture(selected_digits, K=20, alpha=50, beta=0.5, delta=0.8, seed=int(time.time()))

z_new, a_k, gamma, mixing_props = mixture.gibbs_sample(max_iterations=50, hard_cluster=False,

verbose=False)

local_est.append(a_k[:, :, -1])

bayes_ests[digit][k] = average_of_estimates(local_est, 10)

with open(f"ests_k_{time.time()}.bin", "wb") as f:

pickle.dump(bayes_ests, f)

X_train, y_train, X_test, y_test = get_digits(0.5)

h5 = h5py.File(f"./datasets/EM_estimates_{10}.h5", "a")

for digit in [0, 1, 2, 3]:

condition = (y_train == digit)

selected_digits = X_train[np.where(condition)]

mixture = BernoulliMixtureEM(K=10, D=784, X=selected_digits)

a_k, pi = mixture.train(max_iterations=100, display_interval=20, save_interval=None, verbose=False)

h5.create_dataset(f"{digit}", data=a_k.T)

X_train, y_train, X_test, y_test = get_digits(0.5)

cores = 4

pool = Pool(processes=cores)

estimates = {}

for k in [20, 30]:

estimates[k] = {}

for digit_estimated in [0, 1, 2, 3]:

condition = (y_train == digit_estimated)

selected_digits = X_train[np.where(condition)]

durations = []

local_estimates = []

if mode == "bayes":

est_fun = threaded_estimates

else:

est_fun = threaded_estimates_EM

for estimate, duration in pool.imap_unordered(est_fun, get_data(selected_digits, n=10, K=k)):

local_estimates.append(estimate)

estimates[k][digit_estimated] = average_of_estimates(local_estimates, 10)

print(f"Done k={k}, digit #{digit_estimated}")

with open(f"{mode}_auroc_allk.bin", "wb") as f:

X_train, y_train, X_test, y_test = get_digits(0.5)

with open(f"bayes_auroc_allk.bin", "rb") as f:

bayes_data = pickle.load(f)

for k in [20, 30]:

for digit_estimated in [0, 1, 2, 3]:

avg_est = bayes_data[k][digit_estimated][0]

predictions = find_clusters(avg_est, X_test, partial=True)

auroc = calculate_auroc(avg_est, X_test, predictions)

X_train, y_train, X_test, y_test = get_digits(0.5)

with h5py.File(f"./datasets/EM_estimates_10.h5", "r") as h5:

bernoulli_estimates = {key: h5[key][()] for key in h5.keys()}

for digit in [0, 1, 2, 3]:

print(f"Doing digit {digit}")

with h5py.File(f"./datasets/{digit}_estimates_10.h5", "r") as h5:

bayes_estimates = [h5[key][()] for key in h5.keys()]

avg_bayes_set = average_of_estimates(bayes_estimates, size=10)[0:10]

bayes_auroc = []

for idx, estimate in enumerate(avg_bayes_set):

predictions = find_clusters(estimate, X_test, partial=True)

auroc = calculate_auroc(estimate, X_test, predictions)

bayes_auroc.append(auroc)

print("Done calculating Bayes AUROC")

predictions = find_clusters(bernoulli_estimates[f"{digit}"].T, X_test, partial=True)

auroc = calculate_auroc(bernoulli_estimates[f"{digit}"].T, X_test, predictions)

print(f"Bayes best estimate: {np.min(bayes_auroc):.4f}")