def test_round_trip_smoke(self):
original = GroupMetricSet()
original.model_type = GroupMetricSet.BINARY_CLASSIFICATION
original.y_true = [0, 1, 0, 0]
original.y_pred = [1, 1, 1, 0]
original.groups = [0, 1, 2, 0]
original.group_title = 123
# Some wholly synthetic metrics
firstMetric = GroupMetricResult()
firstMetric.overall = 0.2
firstMetric.by_group[0] = 0.25
firstMetric.by_group[1] = 0.5
firstMetric.by_group[2] = 0.2
secondMetric = GroupMetricResult()
secondMetric.overall = 0.6
secondMetric.by_group[0] = 0.75
secondMetric.by_group[1] = 0.25
secondMetric.by_group[2] = 0.25
metric_dict = {
GroupMetricSet.GROUP_ACCURACY_SCORE: firstMetric,
GroupMetricSet.GROUP_MISS_RATE: secondMetric
}
original.metrics = metric_dict
original.group_names = ['First', 'Second', 'Something else']
intermediate_dict = original.to_dict()
result = GroupMetricSet.from_dict(intermediate_dict)
assert original == result
def test_group_names_do_not_match_groups(self):
target = GroupMetricSet()
target.model_type = GroupMetricSet.BINARY_CLASSIFICATION
target.y_true = [0, 1, 0, 0]
target.y_pred = [1, 1, 1, 0]
target.groups = [0, 1, 1, 0]
# Some wholly synthetic metrics
firstMetric = GroupMetricResult()
firstMetric.overall = 0.2
firstMetric.by_group[0] = 0.3
firstMetric.by_group[1] = 0.4
secondMetric = GroupMetricResult()
secondMetric.overall = 0.6
secondMetric.by_group[0] = 0.7
secondMetric.by_group[1] = 0.8
metric_dict = {
GroupMetricSet.GROUP_ACCURACY_SCORE: firstMetric,
GroupMetricSet.GROUP_MISS_RATE: secondMetric
}
target.metrics = metric_dict
target.group_names = ['First']
target.group_title = "Some string"
with pytest.raises(ValueError) as exception_context:
target.check_consistency()
expected = "Count of group_names not the same as the number of unique groups"
assert exception_context.value.args[0] == expected
def test_length_mismatch_groups(self):
target = GroupMetricSet()
target.y_true = [0, 1, 0, 1]
target.y_pred = [0, 1, 1, 0]
target.groups = [0, 1, 1]
with pytest.raises(ValueError) as exception_context:
target.check_consistency()
assert exception_context.value.args[
0] == "Lengths of y_true, y_pred and groups must match"
def test_metric_has_bad_groups(self):
target = GroupMetricSet()
target.y_true = [0, 1, 1, 1, 0]
target.y_pred = [1, 1, 1, 0, 0]
target.groups = [0, 1, 0, 1, 1]
bad_metric = GroupMetricResult()
bad_metric.by_group[0] = 0.1
metric_dict = {'bad_metric': bad_metric}
target.metrics = metric_dict
with pytest.raises(ValueError) as exception_context:
target.check_consistency()
expected = "The groups for metric bad_metric do not match the groups property"
assert exception_context.value.args[0] == expected
def test_to_dict_smoke(self):
target = GroupMetricSet()
target.model_type = GroupMetricSet.BINARY_CLASSIFICATION
target.y_true = [0, 1, 0, 0]
target.y_pred = [1, 1, 1, 0]
target.groups = [0, 1, 1, 0]
# Some wholly synthetic metrics
firstMetric = GroupMetricResult()
firstMetric.overall = 0.2
firstMetric.by_group[0] = 0.3
firstMetric.by_group[1] = 0.4
secondMetric = GroupMetricResult()
secondMetric.overall = 0.6
secondMetric.by_group[0] = 0.7
secondMetric.by_group[1] = 0.8
metric_dict = {
GroupMetricSet.GROUP_ACCURACY_SCORE: firstMetric,
GroupMetricSet.GROUP_MISS_RATE: secondMetric
}
target.metrics = metric_dict
target.group_names = ['First', 'Second']
target.group_title = "Some string"
result = target.to_dict()
assert result['predictionType'] == 'binaryClassification'
assert np.array_equal(target.y_true, result['trueY'])
assert len(result['predictedYs']) == 1
assert np.array_equal(result['predictedYs'][0], target.y_pred)
assert len(result['precomputedMetrics']) == 1
assert len(result['precomputedMetrics'][0]) == 1
rmd = result['precomputedMetrics'][0][0]
assert len(rmd) == 2
assert rmd['accuracy_score']['global'] == 0.2
assert rmd['accuracy_score']['bins'][0] == 0.3
assert rmd['accuracy_score']['bins'][1] == 0.4
assert rmd['miss_rate']['global'] == 0.6
assert rmd['miss_rate']['bins'][0] == 0.7
assert rmd['miss_rate']['bins'][1] == 0.8
assert result['precomputedBins'][0]['featureBinName'] == "Some string"
assert np.array_equal(result['precomputedBins'][0]['binLabels'],
['First', 'Second'])
def test_y_pred(self):
target = GroupMetricSet()
target.y_pred = [1, 2]
assert isinstance(target.y_pred, np.ndarray)
assert np.array_equal(target.y_pred, [1, 2])