def test_add_metric_to_batch_no_modification(self):
eval_metrics_array, aggregator_value, target_value, metrics_value, observations, doc = \
self._preperate_dummy_data_terms()
# Create expected result
observations["target"] = [target_value]
observations["aggregator"] = [aggregator_value]
expected_eval_terms = defaultdict()
expected_eval_terms[aggregator_value] = defaultdict(list)
expected_eval_terms[aggregator_value]["metrics"] = [metrics_value]
expected_eval_terms[aggregator_value]["observations"] = [observations]
expected_eval_terms[aggregator_value]["raw_docs"] = [doc]
result = MetricsAnalyzer.add_metric_to_batch(eval_metrics_array,
aggregator_value,
target_value,
metrics_value,
observations, doc)
self.assertEqual(result, expected_eval_terms)
def test_evaluate_batch_for_outliers_fetch_remain_metrics(self):
self.test_settings.change_configuration_path(
"/app/tests/unit_tests/files/metrics_test_01.conf")
analyzer = AnalyzerFactory.create(
"/app/tests/unit_tests/files/use_cases/metrics/metrics_dummy_test.conf"
)
eval_metrics_array, aggregator_value, target_value, metrics_value, observations = \
self._preperate_data_terms_with_doc()
doc = DummyDocumentsGenerate().generate_document()
metrics = MetricsAnalyzer.add_metric_to_batch(eval_metrics_array,
aggregator_value,
target_value,
metrics_value,
observations, doc)
result = analyzer._evaluate_batch_for_outliers(metrics, False)
# outliers, not_enough_value, document_need_to_be_recompute
self.assertEqual(result, ([], metrics))
def test_evaluate_batch_for_outliers_add_outlier(self):
self.test_settings.change_configuration_path(
"/app/tests/unit_tests/files/metrics_test_02.conf")
analyzer = AnalyzerFactory.create(
"/app/tests/unit_tests/files/use_cases/metrics/metrics_dummy_test_2.conf"
)
eval_metrics_array, aggregator_value, target_value, metrics_value, observations = \
self._preperate_data_terms_with_doc(metrics_value=12)
doc_without_outlier = copy.deepcopy(doc_without_outlier_test_file)
self.test_es.add_doc(doc_without_outlier)
metrics = MetricsAnalyzer.add_metric_to_batch(
eval_metrics_array, aggregator_value, target_value, metrics_value,
observations, doc_without_outlier)
outliers, remaining_metrics = analyzer._evaluate_batch_for_outliers(
metrics, True)
analyzer.process_outlier(outliers[0])
result = [elem for elem in es._scan()][0]
doc_with_outlier = copy.deepcopy(doc_with_outlier_test_file)
self.maxDiff = None
self.assertEqual(result, doc_with_outlier)
def test_add_metric_to_batch_empty(self):
eval_metrics_array = defaultdict()
aggregator_value = ""
target_value = ""
metrics_value = ""
observations = {}
doc = {}
# Create expected result
observations["target"] = [target_value]
observations["aggregator"] = [aggregator_value]
expected_eval_terms = defaultdict()
expected_eval_terms[aggregator_value] = defaultdict(list)
expected_eval_terms[aggregator_value]["metrics"] = [metrics_value]
expected_eval_terms[aggregator_value]["observations"] = [observations]
expected_eval_terms[aggregator_value]["raw_docs"] = [doc]
result = MetricsAnalyzer.add_metric_to_batch(eval_metrics_array,
aggregator_value,
target_value,
metrics_value,
observations, doc)
self.assertEqual(result, expected_eval_terms)
def test_remove_metric_from_batch_simple_value(self):
eval_metrics_array = defaultdict()
aggregator_value = "agg"
target_value = "dummy_target"
metrics_value = "dummy_metric"
observations = {}
dummy_doc_gen = DummyDocumentsGenerate()
doc = dummy_doc_gen.generate_document()
batch = MetricsAnalyzer.add_metric_to_batch(eval_metrics_array,
aggregator_value,
target_value,
metrics_value,
observations, doc)
result = MetricsAnalyzer.remove_metric_from_batch(
batch[aggregator_value], 0)
expected_aggregator_value = defaultdict(list)
expected_aggregator_value["metrics"] = []
expected_aggregator_value["observations"] = []
expected_aggregator_value["raw_docs"] = []
self.assertEqual(result, expected_aggregator_value)