def compare_models(true_model, trained_models_folder, training_data,
test_data):
ground_truth_slogl = true_model.slogl(test_data)
print("Ground truth loglik: " + str(ground_truth_slogl))
gbn_bic_folder = trained_models_folder + '/HillClimbing/GBN_BIC/'
all_models = sorted(glob.glob(gbn_bic_folder + '/*.pickle'))
final_model = all_models[-1]
gbn = load(final_model)
gbn.fit(training_data)
slogl = gbn.slogl(test_data)
print("GBN BIC results:")
print("Loglik: " + str(slogl))
print("SHD: " + str(experiments_helper.shd(gbn, true_model)))
print("Hamming: " + str(experiments_helper.hamming(gbn, true_model)))
print()
gbn_bge_folder = trained_models_folder + '/HillClimbing/GBN_BGe/'
all_models = sorted(glob.glob(gbn_bge_folder + '/*.pickle'))
final_model = all_models[-1]
gbn = load(final_model)
gbn.fit(training_data)
slogl = gbn.slogl(test_data)
print("GBN BGe results:")
print("Loglik: " + str(slogl))
print("SHD: " + str(experiments_helper.shd(gbn, true_model)))
print("Hamming: " + str(experiments_helper.hamming(gbn, true_model)))
print()
def compare_models(true_model, trained_models_folder, training_data,
test_data):
ground_truth_slogl = true_model.slogl(test_data)
print("Ground truth loglik: " + str(ground_truth_slogl))
gbn_bic_folder = trained_models_folder + '/PC/GBN/LinearCorrelation'
all_models = sorted(glob.glob(gbn_bic_folder + '/*.pickle'))
final_model = all_models[-1]
gbn = load(final_model)
gbn.fit(training_data)
slogl = gbn.slogl(test_data)
print("GBN LinearCorrelation results:")
print("Loglik: " + str(slogl))
print("SHD: " + str(experiments_helper.shd(gbn, true_model)))
print("Hamming: " + str(experiments_helper.hamming(gbn, true_model)))
print()
gbn_bge_folder = trained_models_folder + '/PC/GBN/RCoT'
all_models = sorted(glob.glob(gbn_bge_folder + '/*.pickle'))
final_model = all_models[-1]
gbn = load(final_model)
gbn.fit(training_data)
slogl = gbn.slogl(test_data)
print("GBN RCoT results:")
print("Loglik: " + str(slogl))
print("SHD: " + str(experiments_helper.shd(gbn, true_model)))
print("Hamming: " + str(experiments_helper.hamming(gbn, true_model)))
print()
def compare_models(true_model, trained_models_folder, training_data, test_data,
patience):
ground_truth_slogl = true_model.slogl(test_data)
print("Ground truth loglik: " + str(ground_truth_slogl))
print("SPBN results:")
for p in patience:
ckde_folder = trained_models_folder + '/HillClimbing/SPBN/' + str(p)
all_models = sorted(glob.glob(ckde_folder + '/*.pickle'))
final_model = all_models[-1]
spbn = load(final_model)
spbn.fit(training_data)
logl = spbn.slogl(test_data)
print("Loglik, p " + str(p) + ": " + str(logl))
print("SHD, p " + str(p) + ": " +
str(experiments_helper.shd(spbn, true_model)))
print("Hamming, p " + str(p) + ": " +
str(experiments_helper.hamming(spbn, true_model)))
print("Type Hamming, p " + str(p) + ": " +
str(experiments_helper.hamming_type(spbn)))
print()
def compare_models(true_model, trained_models_folder, training_data,
test_data):
ground_truth_slogl = true_model.slogl(test_data)
print("Ground truth loglik: " + str(ground_truth_slogl))
for p in experiments_helper.PATIENCE:
folder = trained_models_folder + '/PC/SPBN/LinearCorrelation/' + str(p)
all_models = sorted(glob.glob(folder + '/*.pickle'))
final_model = all_models[-1]
spbn = load(final_model)
spbn.fit(training_data)
slogl = spbn.slogl(test_data)
print("SPBN LinearCorrelation results:")
print("Loglik, p " + str(p) + ": " + str(slogl))
print("SHD, p " + str(p) + ": " +
str(experiments_helper.shd(spbn, true_model)))
print("Hamming, p " + str(p) + ": " +
str(experiments_helper.hamming(spbn, true_model)))
print("Type Hamming, p " + str(p) + ": " +
str(experiments_helper.hamming_type(spbn)))
print()
for p in experiments_helper.PATIENCE:
folder = trained_models_folder + '/PC/SPBN/RCoT/' + str(p)
all_models = sorted(glob.glob(folder + '/*.pickle'))
final_model = all_models[-1]
spbn = load(final_model)
spbn.fit(training_data)
slogl = spbn.slogl(test_data)
print("SPBN RCoT results:")
print("Loglik, p " + str(p) + ": " + str(slogl))
print("SHD, p " + str(p) + ": " +
str(experiments_helper.shd(spbn, true_model)))
print("Hamming, p " + str(p) + ": " +
str(experiments_helper.hamming(spbn, true_model)))
print("Type Hamming, p " + str(p) + ": " +
str(experiments_helper.hamming_type(spbn)))
print()
def test_spbn(df, model_folder, patience, dag_type):
print("Dag Type " + dag_type)
for p in patience:
result_folder = model_folder + '/PC/SPBN/' + dag_type + '/' + str(p)
pathlib.Path(result_folder).mkdir(parents=True, exist_ok=True)
all_models = sorted(glob.glob(result_folder + '/*.pickle'))
final_model = load(all_models[-1])
final_model.fit(df)
slogl = final_model.slogl(df_test)
print("Loglik, p " + str(p) + ": " + str(slogl))
print("SHD, p " + str(p) + ": " +
str(experiments_helper.shd(final_model, true_model)))
print("Hamming, p " + str(p) + ": " +
str(experiments_helper.hamming(final_model, true_model)))
print("Hamming type, p " + str(p) + ": " +
str(experiments_helper.hamming_type(final_model, true_model)))
print()
df_200 = pd.read_csv('synthetic_200.csv')
df_2000 = pd.read_csv('synthetic_2000.csv')
df_10000 = pd.read_csv('synthetic_10000.csv')
df_test = pd.read_csv('synthetic_test.csv')
print("True model logl: " + str(slogl_model(df_test)))
patience = experiments_helper.PATIENCE
for df, model_folder in [(df_200, 'models/200'), (df_2000, 'models/2000'), (df_10000, 'models/10000')]:
print("Folder " + model_folder)
for p in patience:
result_folder = model_folder + '/HillClimbing/SPBN_CKDE/' + str(p)
pathlib.Path(result_folder).mkdir(parents=True, exist_ok=True)
all_models = sorted(glob.glob(result_folder + '/*.pickle'))
final_model = load(all_models[-1])
final_model.fit(df)
slogl = final_model.slogl(df_test)
print("Loglik, p " + str(p) + ": " + str(slogl))
print("SHD, p " + str(p) + ": " + str(experiments_helper.shd(final_model, true_model)))
print("Hamming, p " + str(p) + ": " + str(experiments_helper.hamming(final_model, true_model)))
print("Hamming type, p " + str(p) + ": " + str(experiments_helper.hamming_type(final_model, true_model)))
print()
def extract_info(train_datasets, test_datasets, model_folders, true_models):
patience = experiments_helper.PATIENCE
tests = experiments_helper.TESTS
slogl_true = np.empty((len(train_datasets, )))
slogl_hc_gbn_bic = np.empty((len(train_datasets), len(train_datasets[0])))
slogl_hc_gbn_bge = np.empty((len(train_datasets), len(train_datasets[0])))
slogl_hc_spbn = np.empty(
(len(train_datasets), len(train_datasets[0]), len(patience)))
slogl_hc_spbn_ckde = np.empty(
(len(train_datasets), len(train_datasets[0]), len(patience)))
slogl_pc_gbn = np.empty(
(len(train_datasets), len(train_datasets[0]), len(tests)))
slogl_pc_spbn = np.empty((len(train_datasets), len(train_datasets[0]),
len(tests), len(patience)))
slogl_pc_spbn_ckde = np.empty((len(train_datasets), len(train_datasets[0]),
len(tests), len(patience)))
hmd_hc_gbn_bic = np.empty((len(train_datasets), len(train_datasets[0])))
hmd_hc_gbn_bge = np.empty((len(train_datasets), len(train_datasets[0])))
hmd_hc_spbn = np.empty(
(len(train_datasets), len(train_datasets[0]), len(patience)))
hmd_hc_spbn_ckde = np.empty(
(len(train_datasets), len(train_datasets[0]), len(patience)))
hmd_pc = np.empty(
(len(train_datasets), len(train_datasets[0]), len(tests)))
shd_hc_gbn_bic = np.empty((len(train_datasets), len(train_datasets[0])))
shd_hc_gbn_bge = np.empty((len(train_datasets), len(train_datasets[0])))
shd_hc_spbn = np.empty(
(len(train_datasets), len(train_datasets[0]), len(patience)))
shd_hc_spbn_ckde = np.empty(
(len(train_datasets), len(train_datasets[0]), len(patience)))
shd_pc = np.empty(
(len(train_datasets), len(train_datasets[0]), len(tests)))
thd_hc_spbn = np.empty(
(len(train_datasets), len(train_datasets[0]), len(patience)))
thd_hc_spbn_ckde = np.empty(
(len(train_datasets), len(train_datasets[0]), len(patience)))
thd_pc_spbn = np.empty((len(train_datasets), len(train_datasets[0]),
len(tests), len(patience)))
thd_pc_spbn_ckde = np.empty((len(train_datasets), len(train_datasets[0]),
len(tests), len(patience)))
for idx_dataset, (instance_datasets, test_data, dataset_folders,
true_model) in enumerate(
zip(train_datasets, test_datasets, model_folders,
true_models)):
for idx_instances, (training_data, folder) in enumerate(
zip(instance_datasets, dataset_folders)):
slogl_true[idx_dataset] = true_model.slogl(test_data)
###########################
# GBN BIC
###########################
gbn_bic_folder = folder + '/HillClimbing/GBN_BIC/'
all_models = sorted(glob.glob(gbn_bic_folder + '/*.pickle'))
final_model = all_models[-1]
bic = load(final_model)
bic.fit(training_data)
slogl_hc_gbn_bic[idx_dataset, idx_instances] = bic.slogl(test_data)
hmd_hc_gbn_bic[idx_dataset,
idx_instances] = experiments_helper.hamming(
bic, true_model)
shd_hc_gbn_bic[idx_dataset,
idx_instances] = experiments_helper.shd(
bic, true_model)
###########################
# GBN BGe
###########################
gbn_bge_folder = folder + '/HillClimbing/GBN_BGe/'
all_models = sorted(glob.glob(gbn_bge_folder + '/*.pickle'))
final_model = all_models[-1]
bge = load(final_model)
bge.fit(training_data)
slogl_hc_gbn_bge[idx_dataset, idx_instances] = bge.slogl(test_data)
hmd_hc_gbn_bge[idx_dataset,
idx_instances] = experiments_helper.hamming(
bge, true_model)
shd_hc_gbn_bge[idx_dataset,
idx_instances] = experiments_helper.shd(
bge, true_model)
###########################
# HC SPBN
###########################
for idx_p, p in enumerate(patience):
spbn_hc_folder = folder + '/HillClimbing/SPBN/' + str(p)
all_models = sorted(glob.glob(spbn_hc_folder + '/*.pickle'))
final_model = all_models[-1]
spbn = load(final_model)
spbn.fit(training_data)
slogl_hc_spbn[idx_dataset, idx_instances,
idx_p] = spbn.slogl(test_data)
hmd_hc_spbn[idx_dataset, idx_instances,
idx_p] = experiments_helper.hamming(
spbn, true_model)
shd_hc_spbn[idx_dataset, idx_instances,
idx_p] = experiments_helper.shd(spbn, true_model)
thd_hc_spbn[idx_dataset, idx_instances,
idx_p] = experiments_helper.hamming_type(spbn)
###########################
# HC SPBN CKDE
###########################
for idx_p, p in enumerate(patience):
spbn_ckde_hc_folder = folder + '/HillClimbing/SPBN_CKDE/' + str(
p)
all_models = sorted(
glob.glob(spbn_ckde_hc_folder + '/*.pickle'))
final_model = all_models[-1]
spbn_ckde = load(final_model)
spbn_ckde.fit(training_data)
slogl_hc_spbn_ckde[idx_dataset, idx_instances,
idx_p] = spbn_ckde.slogl(test_data)
hmd_hc_spbn_ckde[idx_dataset, idx_instances,
idx_p] = experiments_helper.hamming(
spbn_ckde, true_model)
shd_hc_spbn_ckde[idx_dataset, idx_instances,
idx_p] = experiments_helper.shd(
spbn_ckde, true_model)
thd_hc_spbn_ckde[idx_dataset, idx_instances,
idx_p] = experiments_helper.hamming_type(
spbn_ckde)
###########################
# PC GBN and PC Graph
###########################
for idx_t, test in enumerate(tests):
gbn_pc_folder = folder + '/PC/GBN/' + test
all_models = sorted(glob.glob(gbn_pc_folder + '/*.pickle'))
final_model = all_models[-1]
gbn_pc = load(final_model)
gbn_pc.fit(training_data)
slogl_pc_gbn[idx_dataset, idx_instances,
idx_t] = gbn_pc.slogl(test_data)
hmd_pc[idx_dataset, idx_instances,
idx_t] = experiments_helper.hamming(gbn_pc, true_model)
shd_pc[idx_dataset, idx_instances,
idx_t] = experiments_helper.shd(gbn_pc, true_model)
###########################
# PC SPBN
###########################
for idx_t, test in enumerate(tests):
for idx_p, p in enumerate(patience):
spbn_pc_folder = folder + '/PC/SPBN/' + test + '/' + str(p)
all_models = sorted(glob.glob(spbn_pc_folder +
'/*.pickle'))
final_model = all_models[-1]
spbn_pc = load(final_model)
spbn_pc.fit(training_data)
slogl_pc_spbn[idx_dataset, idx_instances, idx_t,
idx_p] = spbn_pc.slogl(test_data)
thd_pc_spbn[idx_dataset, idx_instances, idx_t,
idx_p] = experiments_helper.hamming_type(
spbn_pc)
###########################
# PC SPBN CKDE
###########################
for idx_t, test in enumerate(tests):
for idx_p, p in enumerate(patience):
spbn_ckde_pc_folder = folder + '/PC/SPBN_CKDE/' + test + '/' + str(
p)
all_models = sorted(
glob.glob(spbn_ckde_pc_folder + '/*.pickle'))
final_model = all_models[-1]
spbn_ckde_pc = load(final_model)
spbn_ckde_pc.fit(training_data)
slogl_pc_spbn_ckde[idx_dataset, idx_instances, idx_t,
idx_p] = spbn_ckde_pc.slogl(test_data)
thd_pc_spbn_ckde[idx_dataset, idx_instances, idx_t,
idx_p] = experiments_helper.hamming_type(
spbn_ckde_pc)
return (slogl_true, slogl_hc_gbn_bic, slogl_hc_gbn_bge, slogl_hc_spbn, slogl_hc_spbn_ckde, slogl_pc_gbn, slogl_pc_spbn, slogl_pc_spbn_ckde), \
(hmd_hc_gbn_bic, hmd_hc_gbn_bge, hmd_hc_spbn, hmd_hc_spbn_ckde, hmd_pc), \
(shd_hc_gbn_bic, shd_hc_gbn_bge, shd_hc_spbn, shd_hc_spbn_ckde, shd_pc),\
(thd_hc_spbn, thd_hc_spbn_ckde, thd_pc_spbn, thd_pc_spbn_ckde)
df_2000 = pd.read_csv('synthetic_2000.csv')
df_10000 = pd.read_csv('synthetic_10000.csv')
df_test = pd.read_csv('synthetic_test.csv')
print("True model logl: " + str(slogl_model(df_test)))
patience = experiments_helper.PATIENCE
for df, model_folder in [(df_200, 'models/200'), (df_2000, 'models/2000'),
(df_10000, 'models/10000')]:
print("Folder " + model_folder)
for p in patience:
result_folder = model_folder + '/HillClimbing/SPBN/' + str(p)
pathlib.Path(result_folder).mkdir(parents=True, exist_ok=True)
all_models = sorted(glob.glob(result_folder + '/*.pickle'))
final_model = load(all_models[-1])
final_model.fit(df)
slogl = final_model.slogl(df_test)
print("Loglik, p " + str(p) + ": " + str(slogl))
print("SHD, p " + str(p) + ": " +
str(experiments_helper.shd(final_model, true_model)))
print("Hamming, p " + str(p) + ": " +
str(experiments_helper.hamming(final_model, true_model)))
print("Hamming type, p " + str(p) + ": " +
str(experiments_helper.hamming_type(final_model, true_model)))
print()
