def test_update_fit_group_with_groupwise_dist_01(self):
""" test with generic distance metrics """
# load test data
user_ids, user_profile_df, user_connection_df = load_sample_test_data()
# definte test data
fit_group = {0: ['a', 'b', 'c'], 1: ['d', 'e']}
fit_pvals = {0: [1, 1, 1], 1: [1, 1]}
dist_metrics = {0: [1, 1, 1], 1: [1, 1, 1]}
dist_memory_container = [{}, {}]
new_fit_group, new_fit_pvals, unfit_group = _update_fit_group_with_groupwise_dist(
dist_metrics,
fit_group,
fit_pvals,
user_ids,
user_profile_df,
user_connection_df,
dist_memory_container,
is_directed=False,
ks_alpha=0.05)
print(
"--- update_fit_group_with_groupwise (with: generic distance metrics) ---"
)
print("new 1st fit_group: {}".format(new_fit_group[0]))
print("new 2nd fit_group: {}".format(new_fit_group[1]))
print("new 1st fit_pval: {}".format(new_fit_pvals[0]))
print("new 2nd fit_pval: {}".format(new_fit_pvals[1]))
is_ok = True
self.assertTrue(is_ok, True)
def test_update_fit_group_with_groupwise_dist_02(self):
""" test with generic distance metrics """
# load test data
user_ids, user_profile_df, user_connection_df = load_sample_test_data()
# definte test data
fit_group = {0: ['a', 'b', 'c'], 1: ['d', 'e']}
fit_pvals = {0: [1, 1, 1], 1: [1, 1]}
new_dist_metrics = {0: [0, 1, 0], 1: [0, 0, 1]}
new_fit_group, new_fit_pvals, unfit_group = _update_fit_group_with_groupwise_dist(new_dist_metrics,
fit_group,
fit_pvals,
user_ids,
user_profile_df,
user_connection_df,
ks_alpha=0.05)
print("--- update_fit_group_with_groupwise (with: learned distance metrics) ---")
print("new 1st fit_group: {}".format(new_fit_group[0]))
print("new 2nd fit_group: {}".format(new_fit_group[1]))
print("new 1st fit_pval: {}".format(new_fit_pvals[0]))
print("new 2nd fit_pval: {}".format(new_fit_pvals[1]))
is_ok = True
self.assertTrue(is_ok, True)
def test_update_fit_group_with_groupwise_dist_01(self):
""" test with generic distance metrics """
user_ids, user_profile_df, user_connection_df, true_group = load_simulated_test_data()
# define test data
n_feat = user_profile_df.shape[1]
#
a_group = [user_ids[i] for i, g in enumerate(true_group) if g == 0]
b_group = [user_ids[i] for i, g in enumerate(true_group) if g == 1]
fit_group = {0: a_group, 1: b_group}
fit_pvals = {0: [1]*len(a_group), 1: [1] * len(b_group)}
dist_metrics = {0: [0.49, 0.51, 0, 0, 0, 0], 1: [0, 0, 0, 0.88, 0.08, 0.04]}
new_fit_group, new_fit_pvals, unfit_group = _update_fit_group_with_groupwise_dist(dist_metrics,
fit_group,
fit_pvals,
user_ids,
user_profile_df,
user_connection_df,
ks_alpha=0.05)
print("--- update_fit_group_with_groupwise (with: correct distance metrics) ---")
print("new 1st fit_group: {}".format(new_fit_group[0]))
print("new 2nd fit_group: {}".format(new_fit_group[1]))
print("new 1st fit_pval: {}".format(new_fit_pvals[0]))
print("new 2nd fit_pval: {}".format(new_fit_pvals[1]))
is_ok = True
self.assertTrue(is_ok, True)
def test_update_fit_group_with_groupwise_dist_03(self):
""" test with generic distance metrics """
user_ids, user_profile_df, user_connection_df, true_group = load_simulated_test_data(
)
# define test data
n_feat = user_profile_df.shape[1]
#
a_group = [user_ids[i] for i, g in enumerate(true_group) if g == 0]
b_group = [user_ids[i] for i, g in enumerate(true_group) if g == 1]
fit_group = {0: a_group, 1: b_group}
fit_pvals = {0: [1] * len(a_group), 1: [1] * len(b_group)}
dist_metrics = {0: [1] * n_feat, 1: [1] * n_feat}
new_fit_group, new_fit_pvals, unfit_group = _update_fit_group_with_groupwise_dist(
dist_metrics,
fit_group,
fit_pvals,
user_ids,
user_profile_df,
user_connection_df,
ks_alpha=0.05)
print(
"--- update_fit_group_with_groupwise (with: generic distance metrics) ---"
)
print("new 1st fit_group: {}".format(new_fit_group[0]))
print("new 2nd fit_group: {}".format(new_fit_group[1]))
print("new 1st fit_pval: {}".format(new_fit_pvals[0]))
print("new 2nd fit_pval: {}".format(new_fit_pvals[1]))
is_ok = True
self.assertTrue(is_ok, True)