def test_p_value_union():
"""Test if the p-value is calculated correctly."""
data = Data()
data.generate_mute_data(100, 5)
path = os.path.join(os.path.dirname(__file__), 'data/')
res_0 = pickle.load(open(path + 'mute_results_0.p', 'rb'))
res_1 = pickle.load(open(path + 'mute_results_1.p', 'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'n_perm_comp': 6,
'alpha_comp': 0.2,
'tail_comp': 'one_bigger',
'stats_type': 'independent'
}
comp = NetworkComparison()
comp.compare_within(comp_settings, res_0, res_1, data, data)
# Replace the surrogate CMI by all zeros for source 0 and all ones for
# source 1. Set the CMI difference to 0.5 for both sources. Check if this
# results in one significant and one non-significant result with the
# correct p-values.
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
comp._calculate_cmi_diff_within(data, data)
comp._create_surrogate_distribution_within(data, data)
target = 1
source = 0
comp.cmi_surr[target] = np.zeros((1, comp_settings['n_perm_comp']))
comp.cmi_diff[target] = np.array([0.5])
comp._p_value_union()
p = comp.pvalue
s = comp.significance
assert s[target][source], (
'The significance was not determined correctly: {0}'.format(s[target]))
assert p[target][source] == 1 / comp_settings['n_perm_comp'], (
'The p-value was not calculated correctly: {0}'.format(p[target]))
comp.cmi_surr[target] = np.ones((1, comp_settings['n_perm_comp']))
comp.cmi_diff[target] = np.array([0.5])
comp._p_value_union()
p = comp.pvalue
s = comp.significance
assert not s[target][source], (
'The significance was not determined correctly: {0}'.format(s[target]))
assert p[target][source] == 1.0, (
'The p-value was not calculated correctly: {0}'.format(p[target]))
def test_p_value_union():
"""Test if the p-value is calculated correctly."""
data = Data()
data.generate_mute_data(100, 5)
path = os.path.join(os.path.dirname(__file__), 'data/')
res_0 = pickle.load(open(path + 'mute_results_0.p', 'rb'))
res_1 = pickle.load(open(path + 'mute_results_1.p', 'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'n_perm_comp': 6,
'alpha_comp': 0.2,
'tail_comp': 'one_bigger',
'stats_type': 'independent'
}
comp = NetworkComparison()
comp.compare_within(comp_settings, res_0, res_1, data, data)
# Replace the surrogate CMI by all zeros for source 0 and all ones for
# source 1. Set the CMI difference to 0.5 for both sources. Check if this
# results in one significant and one non-significant result with the
# correct p-values.
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
comp._calculate_cmi_diff_within(data, data)
comp._create_surrogate_distribution_within(data, data)
target = 1
source = 0
comp.cmi_surr[target] = np.zeros((1, comp_settings['n_perm_comp']))
comp.cmi_diff[target] = np.array([0.5])
comp._p_value_union()
p = comp.pvalue
s = comp.significance
assert s[target][source], (
'The significance was not determined correctly: {0}'.format(s[target]))
assert p[target][source] == 1 / comp_settings['n_perm_comp'], (
'The p-value was not calculated correctly: {0}'.format(p[target]))
comp.cmi_surr[target] = np.ones((1, comp_settings['n_perm_comp']))
comp.cmi_diff[target] = np.array([0.5])
comp._p_value_union()
p = comp.pvalue
s = comp.significance
assert not s[target][source], (
'The significance was not determined correctly: {0}'.format(s[target]))
assert p[target][source] == 1.0, (
'The p-value was not calculated correctly: {0}'.format(p[target]))
def test_p_value_union():
"""Test if the p-value is calculated correctly."""
dat = Data()
dat.generate_mute_data(100, 5)
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_0.pkl'))
res_1 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_1.pkl'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'n_perm_comp': 6,
'alpha_comp': 0.2,
'tail_comp': 'one_bigger',
'stats_type': 'independent'
}
comp = NetworkComparison()
res_comp = comp.compare_within(comp_settings, res_0, res_1, dat, dat)
# Replace the surrogate CMI by all zeros for source 0 and all ones for
# source 1. Set the CMI difference to 0.5 for both sources. Check if this
# results in one significant and one non-significant result with the
# correct p-values.
comp._initialise(comp_settings)
comp._create_union(res_0, res_1)
comp._calculate_cmi_diff_within(dat, dat)
comp._create_surrogate_distribution_within(dat, dat)
target = 1
for p in range(comp_settings['n_perm_comp']):
comp.cmi_surr[p][target] = np.array([0, 1])
comp.cmi_diff[target] = np.array([0.5, 0.5])
[p, s] = comp._p_value_union()
assert (s[target] == np.array([True, False])).all(), (
'The significance was not determined '
'correctly: {0}'.format(s[target]))
p_1 = 1 / comp_settings['n_perm_comp']
p_2 = 1.0
print(p[target])
assert (p[target] == np.array([p_1, p_2])).all(), (
'The p-value was not calculated correctly: {0}'
.format(p[target]))
