def test_base_case(self, mock_instance):
self._setup_data_labeler_mock(mock_instance)
data = pd.Series([], dtype=object)
profiler = DataLabelerColumn(data.name)
time_array = [float(i) for i in range(4, 0, -1)]
with mock.patch('time.time', side_effect=lambda: time_array.pop()):
profiler.update(data)
self.assertEqual(0, profiler.sample_size)
self.assertEqual(["a", "b"], profiler._possible_data_labels)
self.assertEqual(None, profiler.data_label)
self.assertEqual(None, profiler.avg_predictions)
six.assertCountEqual(self, [
"data_label", "avg_predictions", "data_label_representation",
"times"
], list(profiler.profile.keys()))
self.assertEqual(
{
"data_label": None,
"avg_predictions": None,
"data_label_representation": None,
"times": defaultdict()
},
profiler.profile,
)
def test_multi_labels(self, mock_instance):
mock_DataLabeler = mock_instance.return_value
mock_DataLabeler.label_mapping = {"a": 0, "b": 1, "c": 2, "d": 3}
mock_DataLabeler.reverse_label_mapping = \
{0: "a", 1: "b", 2: "c", 3: "d"}
mock_DataLabeler.model.num_labels = 4
mock_DataLabeler.model.requires_zero_mapping = False
def mock_low_predict(data, *args, **kwargs):
return {
'pred':
None,
'conf':
np.array([
[1, 0, 0, 0], # 4 repeated
[1, 0, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0], # 2 repeated
[0, 1, 0, 0],
[0, 0, 1, 0], # 3 repeated
[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 1], # 1 repeated
])
} # counts [4, 2, 3, 1] => [a, b, c, d]
mock_instance.return_value.predict.side_effect = mock_low_predict
data = pd.Series(['1'] * 10)
profiler = DataLabelerColumn(data.name)
profiler.update(data)
self.assertEqual("a|c|b", profiler.data_label)
def test_data_label_low_accuracy(self, mock_instance):
self._setup_data_labeler_mock(mock_instance)
def mock_low_predict(data, *args, **kwargs):
return {'pred': np.array([[0, 0]]), 'conf': np.array([[0.2, 0.2]])}
mock_instance.return_value.predict.side_effect = mock_low_predict
data = pd.Series(['1'])
profiler = DataLabelerColumn(data.name)
profiler.update(data)
self.assertEqual("could not determine", profiler.data_label)
def test_update(self, mock_instance):
self._setup_data_labeler_mock(mock_instance)
data = pd.Series(['1', '2', '3'])
profiler = DataLabelerColumn(data.name)
profiler.update(data)
self.assertEqual(3, profiler.sample_size)
self.assertEqual(["a", "b"], profiler._possible_data_labels)
self.assertEqual("a", profiler.data_label)
self.assertDictEqual(dict(a=2/3, b=1/3), profiler.avg_predictions)
self.assertDictEqual(dict(a=2, b=1), profiler.rank_distribution)
self.assertDictEqual(dict(a=2/3, b=1/3), profiler.label_representation)
def test_update_reserve_label_mapping(self, mock_instance):
self._setup_data_labeler_mock(mock_instance)
mock_DataLabeler = mock_instance.return_value
mock_DataLabeler.model.requires_zero_mapping = True
mock_DataLabeler.label_mapping = {"PAD": 0, "a": 1, "b": 2}
mock_DataLabeler.reverse_label_mapping = {0: "PAD", 1: "a", 2: "b"}
mock_DataLabeler.model.num_labels = 3
data = pd.Series(['1', '2', '3'])
profiler = DataLabelerColumn(data.name)
profiler.update(data)
self.assertEqual(["a", "b"], profiler._possible_data_labels)
self.assertEqual("a", profiler.data_label)
self.assertDictEqual(dict(a=2 / 3, b=1 / 3), profiler.avg_predictions)
self.assertDictEqual(dict(a=2, b=1), profiler.rank_distribution)
self.assertDictEqual(dict(a=2 / 3, b=1 / 3),
profiler.label_representation)
def test_label_match(self, mock_instance):
"""
Test label match between avg_prediction and data_label_representation
"""
mock_DataLabeler = mock_instance.return_value
mock_DataLabeler.label_mapping = \
{"a": 0, "b": 1, "c": 1, "d": 2, "e": 2, "f": 3}
mock_DataLabeler.reverse_label_mapping = \
{0: "a", 1: "c", 2: "e", 3: "f"}
mock_DataLabeler.model.num_labels = 4
data = pd.Series(['1', '2', '3', '4', '5', '6'])
profiler = DataLabelerColumn(data.name)
profiler.sample_size = 1
self.assertEqual(["a", "c", "e", "f"], profiler._possible_data_labels)
self.assertDictEqual(dict(a=0, c=0, e=0, f=0), profiler.label_representation)
self.assertDictEqual(dict(a=0, c=0, e=0, f=0), profiler.avg_predictions)
def test_profile(self, mock_instance):
self._setup_data_labeler_mock(mock_instance)
data = pd.Series(['1', '2', '3'])
profiler = DataLabelerColumn(data.name)
expected_profile = {
"data_label": 'a',
"avg_predictions": dict(a=2 / 3, b=1 / 3),
"data_label_representation": dict(a=2 / 3, b=1 / 3),
"times": defaultdict(float, {'data_labeler_predict': 1.0})
}
time_array = [float(i) for i in range(4, 0, -1)]
with mock.patch('time.time', side_effect=lambda: time_array.pop()):
# Validate that the times dictionary is empty
self.assertEqual(defaultdict(float), profiler.profile['times'])
profiler.update(data)
# Validate the time in the DataLabeler class has the expected time.
profile = profiler.profile
self.assertDictEqual(expected_profile, profile)
# Validate time in datetime class has expected time after second update
profiler.update(data)
expected = defaultdict(float, {'data_labeler_predict': 2.0})
self.assertEqual(expected, profiler.profile['times'])
def test_diff(self, mock_instance):
self._setup_data_labeler_mock(mock_instance)
profiler1 = DataLabelerColumn("")
profiler2 = DataLabelerColumn("")
# Mock out the data_label, avg_predictions, and label_representation
# properties
with mock.patch("dataprofiler.profilers.data_labeler_column_profile"
".DataLabelerColumn.data_label"), \
mock.patch("dataprofiler.profilers.data_labeler_column_profile."
"DataLabelerColumn.avg_predictions"),\
mock.patch("dataprofiler.profilers.data_labeler_column_profile."
"DataLabelerColumn.label_representation"):
profiler1.data_label = "a|b|c"
profiler1.avg_predictions = {"a": 0.25, "b": 0.0, "c": 0.75}
profiler1.label_representation = {"a": 0.15, "b": 0.01, "c": 0.84}
profiler2.data_label = "b|c|d"
profiler2.avg_predictions = {"a": 0.25, "b": 0.70, "c": 0.05}
profiler2.label_representation = {"a": 0.99, "b": 0.01, "c": 0.0}
diff = profiler1.diff(profiler2)
expected_diff = {
"data_label":
utils.find_diff_of_lists_and_sets(['a', 'b', 'c'],
['b', 'c', 'd']),
"avg_predictions": {
"a": "unchanged",
"b": -0.70,
"c": 0.70
},
"label_representation": {
"a": -0.84,
"b": "unchanged",
"c": 0.84
}
}
self.maxDiff = None
self.assertDictEqual(expected_diff, diff)
def test_profile_merge_with_different_options(self, mock_instance):
self._setup_data_labeler_mock(mock_instance)
# Different max_sample_size values
data = pd.Series(['1', '2', '3', '11'])
data2 = pd.Series(['4', '5', '6', '7', '9', '10', '12'])
options = DataLabelerOptions()
options.max_sample_size = 20
profiler = DataLabelerColumn(data.name, options=options)
profiler.update(data)
options2 = DataLabelerOptions()
options2.max_sample_size = 15
profiler2 = DataLabelerColumn(data2.name, options=options2)
profiler2.update(data2)
with self.assertRaisesRegex(
AttributeError,
"Cannot merge. The data labeler and/or the max "
"sample size are not the same for both column "
"profiles."):
profiler3 = profiler + profiler2
# Different labelers
profiler = DataLabelerColumn(data.name)
profiler.data_labeler = mock.MagicMock()
profiler2 = DataLabelerColumn(data2.name)
with self.assertRaisesRegex(
AttributeError,
"Cannot merge. The data labeler and/or the max "
"sample size are not the same for both column "
"profiles."):
profiler3 = profiler + profiler2
def test_data_labeler_column_with_wrong_options(self, *mocks):
with self.assertRaisesRegex(
ValueError, "DataLabelerColumn parameter 'options' must "
"be of type DataLabelerOptions."):
profiler = DataLabelerColumn("Data_Labeler",
options="wrong_data_type")
def test_profile_merge(self, mock_instance):
self._setup_data_labeler_mock(mock_instance)
data = pd.Series(['1', '2', '3', '11'])
data2 = pd.Series(['4', '5', '6', '7', '9', '10', '12'])
expected_profile = {
"data_label": "a|b",
"avg_predictions": dict(a=54 / 99, b=45 / 99),
"data_label_representation": dict(a=54 / 99, b=45 / 99),
"times": defaultdict(float, {'data_labeler_predict': 2.0})
}
expected_sum_predictions = [6, 5]
expected_rank_distribution = {'a': 6, 'b': 5}
time_array = [float(i) for i in range(4, 0, -1)]
with mock.patch('time.time', side_effect=lambda: time_array.pop()):
profiler = DataLabelerColumn(data.name)
profiler.update(data)
profiler2 = DataLabelerColumn(data2.name)
profiler2.update(data2)
profiler3 = profiler + profiler2
# Assert correct values
self.assertEqual(expected_profile, profiler3.profile)
self.assertEqual(expected_sum_predictions,
profiler3._sum_predictions.tolist())
self.assertEqual(expected_rank_distribution,
profiler3.rank_distribution)
self.assertEqual(expected_profile, profiler3.profile)
self.assertEqual(profiler.data_labeler, profiler3.data_labeler)
self.assertEqual(profiler._possible_data_labels,
profiler3._possible_data_labels)
self.assertEqual(profiler._top_k_voting, profiler3._top_k_voting)
self.assertEqual(profiler._min_voting_prob,
profiler3._min_voting_prob)
self.assertEqual(profiler._min_prob_differential,
profiler3._min_prob_differential)
self.assertEqual(profiler._top_k_labels, profiler3._top_k_labels)
self.assertEqual(profiler._min_top_label_prob,
profiler3._min_top_label_prob)
self.assertEqual(profiler._max_sample_size,
profiler3._max_sample_size)
self.assertEqual(profiler._top_k_voting, profiler3._top_k_voting)
# Check adding even more profiles together
profiler3 = profiler + profiler3
expected_profile = {
"data_label": "a|b",
"avg_predictions": dict(a=8 / 15, b=7 / 15),
"data_label_representation": dict(a=8 / 15, b=7 / 15),
"times": defaultdict(float, {'data_labeler_predict': 3.0})
}
expected_sum_predictions = [8, 7]
expected_rank_distribution = {'a': 8, 'b': 7}
# Assert only the proper changes have been made
self.assertEqual(expected_profile, profiler3.profile)
self.assertEqual(expected_sum_predictions,
profiler3._sum_predictions.tolist())
self.assertEqual(expected_rank_distribution,
profiler3.rank_distribution)
self.assertEqual(expected_profile, profiler3.profile)
self.assertEqual(profiler.data_labeler, profiler3.data_labeler)
self.assertEqual(profiler._possible_data_labels,
profiler3._possible_data_labels)
self.assertEqual(profiler._top_k_voting, profiler3._top_k_voting)
self.assertEqual(profiler._min_voting_prob,
profiler3._min_voting_prob)
self.assertEqual(profiler._min_prob_differential,
profiler3._min_prob_differential)
self.assertEqual(profiler._top_k_labels, profiler3._top_k_labels)
self.assertEqual(profiler._min_top_label_prob,
profiler3._min_top_label_prob)
self.assertEqual(profiler._max_sample_size,
profiler3._max_sample_size)
self.assertEqual(profiler._top_k_voting, profiler3._top_k_voting)
# Check that error is thrown if profiles are unequal
with self.assertRaises(ValueError):
profiler._top_k_voting = 13
test = profiler + profiler2
