def test_assert_crossingmatrix(self):
anz_sim = 3
# wealth and religion is unrelated nearliy
adj_m1 = adj_m
out_dict = ugd.graph_hyp_test(adj_m=adj_m,
var_dict=var_dict,
test_variable=('wealth', 'rich', 'poor'),
anz_sim=anz_sim,
show_polt=False,
controls=['wealth'])
partition = constr_partition(controls=['wealth'],
var_dict=var_dict,
adj_m=adj_m)
adj_m2 = out_dict['graph_list'][anz_sim - 1]
_, ins1, outs1 = adj_to_in_out_sequences(adj_m1)
_, ins2, outs2 = adj_to_in_out_sequences(adj_m2)
crossing_matrix1 = generate_graph(adj_m1, partition,
is_directed=False).crossing_matrix
crossing_matrix2 = generate_graph(adj_m2, partition,
is_directed=False).crossing_matrix
assert np.array_equal(crossing_matrix1,
crossing_matrix2) and np.array_equal(ins1, ins2)
def test_empty_graph(self):
adj_m = np.zeros((4, 4))
with pytest.raises(ValueError) as e_info:
_ = ugd.graph_hyp_test(adj_m=adj_m,
var_dict=var_dict_1,
test_variable=('gender', 'm', 'f'),
mixing_time=100,
anz_sim=100,
show_polt=False)
def test_graph_no_rstrc(self):
output_dict= ugd.graph_hyp_test(adj_m=adj_m_1, var_dict=var_dict_1, test_variable=('gender', 'm', 'f'), mixing_time=100, anz_sim=1000, show_polt=False)
mue = np.mean(output_dict['stat_list'])
true_mean = 0*1/3+ 2/3 * 2 # in this simple case the true mean can be calculated manually
true_normalized_numb_graphs_same_as_observed = 1/3
true_quantil_with_50_50_rule = 1/6
assert output_dict["info_dict"]["normalized_numb_graphs_same_as_observed"] > true_normalized_numb_graphs_same_as_observed - 0.15 and output_dict["info_dict"]["normalized_numb_graphs_same_as_observed"] < true_normalized_numb_graphs_same_as_observed + 0.15
assert output_dict["info_dict"]["quantile"] > true_quantil_with_50_50_rule - 0.15 and output_dict["info_dict"]["quantile"] < true_quantil_with_50_50_rule + 0.15
assert mue > true_mean - 0.15 and mue < true_mean + 0.15
def test_matrix_with_vardict(self):
_ = ugd.graph_hyp_test(adj_m_1, var_dict=var_dict_1, mixing_time=10)
assert True
def test_with_control_misspeled(self):
with pytest.raises(ValueError) as e_info:
_ = ugd.graph_hyp_test(adj_m=adj_m_1,
var_dict=var_dict_1,
controls=['genderr'],
mixing_time=10)
def test_controlls_in_vardict(self):
_ = ugd.graph_hyp_test(adj_m=adj_m_1,
var_dict=var_dict_1,
stat_f=my_stat,
mixing_time=10)
assert True
def test__with_only_stat_and_vardict(self):
_ = ugd.graph_hyp_test(adj_m=adj_m_1,
var_dict=var_dict_1,
stat_f=my_stat,
mixing_time=10)
assert True
def test__with_only_stat(self):
_ = ugd.graph_hyp_test(adj_m=adj_m_1, stat_f=my_stat, mixing_time=10)
assert True
def test_no_node_with_testvalue2(self):
with pytest.raises(ValueError) as e_info:
_ = ugd.graph_hyp_test(adj_m=adj_m_1,
var_dict=var_dict_1,
test_variable=('gender', 'm', 'female'))
def test_spelling_error(self):
with pytest.raises(ValueError) as e_info:
_ = ugd.graph_hyp_test(adj_m=adj_m_1,
var_dict=var_dict_1,
test_variable=('genderr', 'm', 'f'))
def test_only_matrix(self):
_ = ugd.graph_hyp_test(adj_m_1, mixing_time=10)
# should not rise an error
assert True
