num_cols = 32
num_rows = 400
num_views = 2
n_steps = 1
n_times = 5
n_chains = 3
n_test = 100
# generate some data
T, M_r, M_c, data_inverse_permutation_indices = du.gen_factorial_data_objects(
gen_seed, num_clusters, num_cols, num_rows, num_views,
max_mean=100, max_std=1, send_data_inverse_permutation_indices=True)
view_assignment_truth, X_D_truth = ctu.truth_from_permute_indices(
data_inverse_permutation_indices, num_rows, num_cols, num_views, num_clusters)
X_L_gen, X_D_gen = ttu.get_generative_clustering(M_c, M_r, T,
data_inverse_permutation_indices, num_clusters, num_views)
T_test = ctu.create_test_set(M_c, T, X_L_gen, X_D_gen, n_test, seed_seed=0)
#
generative_mean_test_log_likelihood = ctu.calc_mean_test_log_likelihood(M_c, T,
X_L_gen, X_D_gen, T_test)
# run some tests
engine = MultiprocessingEngine(seed=inf_seed)
# single state test
single_state_ARIs = []
single_state_mean_test_lls = []
X_L, X_D = engine.initialize(M_c, M_r, T, n_chains=1)
single_state_ARIs.append(ctu.get_column_ARI(X_L, view_assignment_truth))
single_state_mean_test_lls.append(
ctu.calc_mean_test_log_likelihood(M_c, T, X_L, X_D, T_test)
def convergence_analyze_helper(table_data, data_dict, command_dict):
gen_seed = data_dict['SEED']
num_clusters = data_dict['num_clusters']
num_cols = data_dict['num_cols']
num_rows = data_dict['num_rows']
num_views = data_dict['num_views']
max_mean = data_dict['max_mean']
n_test = data_dict['n_test']
num_transitions = data_dict['n_steps']
block_size = data_dict['block_size']
init_seed = data_dict['init_seed']
# generate some data
T, M_r, M_c, data_inverse_permutation_indices = \
du.gen_factorial_data_objects(gen_seed, num_clusters,
num_cols, num_rows, num_views,
max_mean=max_mean, max_std=1,
send_data_inverse_permutation_indices=True)
view_assignment_ground_truth = \
ctu.determine_synthetic_column_ground_truth_assignments(num_cols,
num_views)
X_L_gen, X_D_gen = ttu.get_generative_clustering(
M_c, M_r, T, data_inverse_permutation_indices, num_clusters, num_views)
T_test = ctu.create_test_set(M_c, T, X_L_gen, X_D_gen, n_test, seed_seed=0)
generative_mean_test_log_likelihood = \
ctu.calc_mean_test_log_likelihood(M_c, T, X_L_gen, X_D_gen, T_test)
# additional set up
engine = LE.LocalEngine(init_seed)
column_ari_list = []
mean_test_ll_list = []
elapsed_seconds_list = []
# get initial ARI, test_ll
with gu.Timer('initialize', verbose=False) as timer:
X_L, X_D = engine.initialize(M_c,
M_r,
T,
initialization='from_the_prior')
column_ari = ctu.get_column_ARI(X_L, view_assignment_ground_truth)
column_ari_list.append(column_ari)
mean_test_ll = ctu.calc_mean_test_log_likelihood(M_c, T, X_L, X_D, T_test)
mean_test_ll_list.append(mean_test_ll)
elapsed_seconds_list.append(timer.elapsed_secs)
# run blocks of transitions, recording ARI, test_ll progression
completed_transitions = 0
n_steps = min(block_size, num_transitions)
while (completed_transitions < num_transitions):
# We won't be limiting by time in the convergence runs
with gu.Timer('initialize', verbose=False) as timer:
X_L, X_D = engine.analyze(M_c,
T,
X_L,
X_D,
kernel_list=(),
n_steps=n_steps,
max_time=-1)
completed_transitions = completed_transitions + block_size
#
column_ari = ctu.get_column_ARI(X_L, view_assignment_ground_truth)
column_ari_list.append(column_ari)
mean_test_ll = ctu.calc_mean_test_log_likelihood(
M_c, T, X_L, X_D, T_test)
mean_test_ll_list.append(mean_test_ll)
elapsed_seconds_list.append(timer.elapsed_secs)
ret_dict = dict(
num_rows=num_rows,
num_cols=num_cols,
num_views=num_views,
num_clusters=num_clusters,
max_mean=max_mean,
column_ari_list=column_ari_list,
mean_test_ll_list=mean_test_ll_list,
generative_mean_test_log_likelihood=generative_mean_test_log_likelihood,
elapsed_seconds_list=elapsed_seconds_list,
n_steps=num_transitions,
block_size=block_size,
)
return ret_dict
def convergence_analyze_helper(table_data, data_dict, command_dict):
gen_seed = data_dict['SEED']
num_clusters = data_dict['num_clusters']
num_cols = data_dict['num_cols']
num_rows = data_dict['num_rows']
num_views = data_dict['num_views']
max_mean = data_dict['max_mean']
n_test = data_dict['n_test']
num_transitions = data_dict['n_steps']
block_size = data_dict['block_size']
init_seed = data_dict['init_seed']
# generate some data
T, M_r, M_c, data_inverse_permutation_indices = \
du.gen_factorial_data_objects(gen_seed, num_clusters,
num_cols, num_rows, num_views,
max_mean=max_mean, max_std=1,
send_data_inverse_permutation_indices=True)
view_assignment_ground_truth = \
ctu.determine_synthetic_column_ground_truth_assignments(num_cols,
num_views)
X_L_gen, X_D_gen = ttu.get_generative_clustering(M_c, M_r, T,
data_inverse_permutation_indices, num_clusters, num_views)
T_test = ctu.create_test_set(M_c, T, X_L_gen, X_D_gen, n_test, seed_seed=0)
generative_mean_test_log_likelihood = \
ctu.calc_mean_test_log_likelihood(M_c, T, X_L_gen, X_D_gen, T_test)
# additional set up
engine=LE.LocalEngine(init_seed)
column_ari_list = []
mean_test_ll_list = []
elapsed_seconds_list = []
# get initial ARI, test_ll
with gu.Timer('initialize', verbose=False) as timer:
X_L, X_D = engine.initialize(M_c, M_r, T, initialization='from_the_prior')
column_ari = ctu.get_column_ARI(X_L, view_assignment_ground_truth)
column_ari_list.append(column_ari)
mean_test_ll = ctu.calc_mean_test_log_likelihood(M_c, T, X_L, X_D,
T_test)
mean_test_ll_list.append(mean_test_ll)
elapsed_seconds_list.append(timer.elapsed_secs)
# run blocks of transitions, recording ARI, test_ll progression
completed_transitions = 0
n_steps = min(block_size, num_transitions)
while (completed_transitions < num_transitions):
# We won't be limiting by time in the convergence runs
with gu.Timer('initialize', verbose=False) as timer:
X_L, X_D = engine.analyze(M_c, T, X_L, X_D, kernel_list=(),
n_steps=n_steps, max_time=-1)
completed_transitions = completed_transitions + block_size
#
column_ari = ctu.get_column_ARI(X_L, view_assignment_ground_truth)
column_ari_list.append(column_ari)
mean_test_ll = ctu.calc_mean_test_log_likelihood(M_c, T, X_L, X_D,
T_test)
mean_test_ll_list.append(mean_test_ll)
elapsed_seconds_list.append(timer.elapsed_secs)
ret_dict = dict(
num_rows=num_rows,
num_cols=num_cols,
num_views=num_views,
num_clusters=num_clusters,
max_mean=max_mean,
column_ari_list=column_ari_list,
mean_test_ll_list=mean_test_ll_list,
generative_mean_test_log_likelihood=generative_mean_test_log_likelihood,
elapsed_seconds_list=elapsed_seconds_list,
n_steps=num_transitions,
block_size=block_size,
)
return ret_dict