def test_keys_and_weights(self, transform_y_t, transform_y_p,
transform_gid, transform_s_w):
a = "ABC"
b = "DEF"
c = "GHI"
z = "something_longer"
y_t = transform_y_t([0, 1, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([a, z, a, b, b, c, c, c])
s_w = transform_s_w([1, 1, 1, 5, 5, 7, 7, 7])
grouped_metric_func = metrics.make_metric_group_summary(
mock_func_weight)
result = grouped_metric_func(y_t,
y_p,
sensitive_features=gid,
sample_weight=s_w)
assert result.overall == 28
assert len(result.by_group) == 4
assert result.by_group[a] == 1
assert result.by_group[b] == 5
assert result.by_group[c] == 21
assert result.by_group[z] == 1
assert metrics.group_min_from_summary(result) == 1
assert metrics.group_max_from_summary(result) == 21
assert metrics.difference_from_summary(result) == 20
assert metrics.ratio_from_summary(result) == pytest.approx(1.0 / 21.0)
def test_smoke(self, transform_y_t, transform_y_p, transform_gid):
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([0, 0, 0, 0, 1, 1, 1, 1])
result = metrics.group_summary(mock_func, y_t, y_p, sensitive_features=gid)
assert result.overall == 5
assert len(result.by_group) == 2
assert result.by_group[0] == 2
assert result.by_group[1] == 3
assert metrics.group_min_from_summary(result) == 2
assert metrics.group_max_from_summary(result) == 3
assert metrics.difference_from_summary(result) == 1
assert metrics.ratio_from_summary(result) == pytest.approx(0.6666666667)
def test_smoke(self):
y_t = [0, 0, 1, 1, 0, 1, 1, 1]
y_p = [0, 1, 1, 1, 1, 0, 0, 1]
gid = [0, 0, 0, 0, 1, 1, 1, 1]
grouped_metric_func = metrics.make_metric_group_summary(mock_func)
result = grouped_metric_func(y_t, y_p, sensitive_features=gid)
assert result.overall == 5
assert len(result.by_group) == 2
assert result.by_group[0] == 2
assert result.by_group[1] == 3
assert metrics.group_min_from_summary(result) == 2
assert metrics.group_max_from_summary(result) == 3
assert metrics.difference_from_summary(result) == 1
assert metrics.ratio_from_summary(result) == pytest.approx(0.66666666667)
def test_groups_only_one_element(self):
y_t = [1, 2]
y_p = [1, 2]
gid = [0, 1]
def sum_lengths(y_true, y_pred):
return len(y_true) + len(y_pred)
result = metrics.group_summary(sum_lengths, y_t, y_p, sensitive_features=gid)
assert result.overall == 4
assert result.by_group[0] == 2
assert result.by_group[1] == 2
assert metrics.group_min_from_summary(result) == 2
assert metrics.group_max_from_summary(result) == 2
assert metrics.difference_from_summary(result) == 0
assert metrics.ratio_from_summary(result) == 1
def test_single_element_input(self):
y_t = [0]
y_p = [0]
gid = [0]
s_w = [0]
def sum_lengths(y_true, y_pred, sample_weight):
return len(y_true) + len(y_pred) + len(sample_weight)
result = metrics.group_summary(
sum_lengths, y_t, y_p, sensitive_features=gid, sample_weight=s_w)
assert result.overall == 3
assert result.by_group[0] == 3
assert metrics.group_min_from_summary(result) == 3
assert metrics.group_max_from_summary(result) == 3
assert metrics.difference_from_summary(result) == 0
assert metrics.ratio_from_summary(result) == 1
def test_negative_results(self):
y_t = [0, 0, 1, 1, 0, 1, 1, 1]
y_p = [0, 1, 1, 1, 1, 0, 0, 1]
gid = [0, 0, 0, 0, 0, 1, 1, 1]
def negative_results(y_true, y_pred):
return -(len(y_true) + len(y_pred))
result = metrics.group_summary(negative_results, y_t, y_p, sensitive_features=gid)
assert result.overall == -16
assert result.by_group[0] == -10
assert result.by_group[1] == -6
assert metrics.group_min_from_summary(result) == -10
assert metrics.group_max_from_summary(result) == -6
assert metrics.difference_from_summary(result) == 4
assert np.isnan(metrics.ratio_from_summary(result))
def test_with_weights(self, transform_y_t, transform_y_p, transform_gid, transform_s_w):
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([0, 0, 0, 0, 1, 1, 2, 2])
s_w = transform_s_w([1, 1, 1, 1, 2, 2, 3, 3])
result = metrics.group_summary(
mock_func_weight, y_t, y_p, sensitive_features=gid, sample_weight=s_w)
assert result.overall == 10
assert len(result.by_group) == 3
assert result.by_group[0] == 2
assert result.by_group[1] == 2
assert result.by_group[2] == 6
assert metrics.group_min_from_summary(result) == 2
assert metrics.group_max_from_summary(result) == 6
assert metrics.difference_from_summary(result) == 4
assert metrics.ratio_from_summary(result) == pytest.approx(0.33333333333333)
def test_string_groups(self, transform_y_t, transform_y_p, transform_gid):
a = "ABC"
b = "DEF"
c = "GHI"
y_t = transform_y_t([0, 0, 1, 1, 0, 1, 1, 1])
y_p = transform_y_p([0, 1, 1, 1, 1, 0, 0, 1])
gid = transform_gid([a, a, a, b, b, c, c, c])
result = metrics.group_summary(mock_func, y_t, y_p, sensitive_features=gid)
assert result.overall == 5
assert len(result.by_group) == 3
assert result.by_group[a] == 1
assert result.by_group[b] == 1
assert result.by_group[c] == 3
assert metrics.group_min_from_summary(result) == 1
assert metrics.group_max_from_summary(result) == 3
assert metrics.difference_from_summary(result) == 2
assert metrics.ratio_from_summary(result) == pytest.approx(0.33333333333333)
def test_metric_results_zero(self):
y_t = [0, 0, 1, 1, 0, 1, 1, 1]
y_p = [0, 1, 1, 1, 1, 0, 0, 1]
gid = [0, 0, 0, 0, 0, 1, 1, 1]
def zero_results(y_true, y_pred):
# Arrays will always be same length
return len(y_true)-len(y_pred)
result = metrics.group_summary(zero_results, y_t, y_p, sensitive_features=gid)
assert result.overall == 0
assert result.by_group[0] == 0
assert result.by_group[1] == 0
assert metrics.group_min_from_summary(result) == 0
assert metrics.group_max_from_summary(result) == 0
assert metrics.difference_from_summary(result) == 0
# Following is special case
assert metrics.ratio_from_summary(result) == 1
def test_matrix_metric_other_properties(self):
a = "ABC"
b = "DEF"
c = "GHI"
y_t = [0, 0, 1, 1, 0, 1, 1, 1]
y_p = [0, 1, 1, 1, 1, 0, 0, 1]
gid = [a, a, a, b, b, c, c, c]
result = metrics.group_summary(mock_func_matrix_return, y_t, y_p, sensitive_features=gid)
# Other fields should fail
with pytest.raises(ValueError):
_ = metrics.group_min_from_summary(result)
with pytest.raises(ValueError):
_ = metrics.group_max_from_summary(result)
with pytest.raises(ValueError):
_ = metrics.difference_from_summary(result)
with pytest.raises(ValueError):
_ = metrics.ratio_from_summary(result)
