def evaluate(self,
output_resultset: ResultSetEntity,
evaluation_metric: Optional[str] = None):
performance = MetricsHelper.compute_accuracy(
output_resultset).get_performance()
logger.info(f"Computes performance of {performance}")
output_resultset.performance = performance
def evaluate(self,
output_result_set: ResultSetEntity,
evaluation_metric: Optional[str] = None):
if evaluation_metric is not None:
logger.warning(f'Requested to use {evaluation_metric} metric,'
'but parameter is ignored. Use accuracy instead.')
output_result_set.performance = MetricsHelper.compute_accuracy(
output_result_set).get_performance()
def evaluate(self,
output_resultset: ResultSetEntity,
evaluation_metric: Optional[str] = None):
"""
Evaluate the performance on a result set.
"""
f_measure_metrics = MetricsHelper.compute_f_measure(output_resultset)
output_resultset.performance = f_measure_metrics.get_performance()
logger.info("F-measure after evaluation: %d",
f_measure_metrics.f_measure.value)
def evaluate(self,
output_resultset: ResultSetEntity,
evaluation_metric: Optional[str] = None):
"""Evaluate the performance of the model.
Args:
output_resultset (ResultSetEntity): Result set storing ground truth and predicted dataset.
evaluation_metric (Optional[str], optional): Evaluation metric. Defaults to None.
"""
output_resultset.performance = MetricsHelper.compute_f_measure(
output_resultset).get_performance()
def test_resultset_entity(self):
"""
<b>Description:</b>
Check the ResultSetEntity can correctly return the value
<b>Input data:</b>
Mock data
<b>Expected results:</b>
Test passes if incoming data is processed correctly
<b>Steps</b>
1. Create dummy data
2. Check the processing of default values
3. Check the processing of changed values
"""
test_data = {
"model": None,
"ground_truth_dataset": None,
"prediction_dataset": None,
"purpose": None,
"performance": None,
"creation_date": None,
"id": None,
}
result_set = ResultSetEntity(**test_data)
for name, value in test_data.items():
set_attr_name = f"test_{name}"
if name in [
"model",
"ground_truth_dataset",
"prediction_dataset",
"purpose",
]:
assert getattr(result_set, name) == value
setattr(result_set, name, set_attr_name)
assert getattr(result_set, name) == set_attr_name
assert result_set.performance == NullPerformance()
assert type(result_set.creation_date) == datetime.datetime
assert result_set.id == ID()
assert result_set.has_score_metric() is False
result_set.performance = "test_performance"
assert result_set.performance != NullPerformance()
assert result_set.has_score_metric() is True
creation_date = self.creation_date
result_set.creation_date = creation_date
assert result_set.creation_date == creation_date
set_attr_id = ID(123456789)
result_set.id = set_attr_id
assert result_set.id == set_attr_id
test_result_set_repr = [
f"model={result_set.model}",
f"ground_truth_dataset={result_set.ground_truth_dataset}",
f"prediction_dataset={result_set.prediction_dataset}",
f"purpose={result_set.purpose}",
f"performance={result_set.performance}",
f"creation_date={result_set.creation_date}",
f"id={result_set.id}",
]
for i in test_result_set_repr:
assert i in repr(result_set)