def test_next_activity_kmeans(self):
self.max_diff = None
job = create_test_job(
clustering=create_test_clustering(
clustering_type=ClusteringMethods.KMEANS.value),
split=repair_example(),
encoding=create_test_encoding(prefix_length=8, padding=True),
labelling=create_test_labelling(
label_type=LabelTypes.NEXT_ACTIVITY.value),
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value,
prediction_method=ClassificationMethods.RANDOM_FOREST.value))
result, _ = calculate(job)
del result['elapsed_time']
self.assertDictEqual(
result, {
'f1score': 0.54239884582595577,
'acc': 0.80995475113122173,
'true_positive': '--',
'true_negative': '--',
'false_negative': '--',
'false_positive': '--',
'precision': 0.62344720496894401,
'recall': 0.5224945442336747,
'auc': 0.4730604801339352
})
def test_class_no_cluster(self):
self.max_diff = None
job = create_test_job(
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value),
split=repair_example(),
encoding=create_test_encoding(prefix_length=5,
padding=True,
add_elapsed_time=True),
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value,
prediction_method=ClassificationMethods.RANDOM_FOREST.value))
result, _ = calculate(job)
del result['elapsed_time']
self.assertDictEqual(
result, {
'f1score': 1.0,
'acc': 1.0,
'true_positive': '--',
'true_negative': '--',
'false_negative': '--',
'false_positive': '--',
'precision': 1.0,
'recall': 1.0,
'auc': 0.0
})
def test_regression_no_cluster(self):
self.max_diff = None
job = create_test_job(
clustering=create_test_clustering(clustering_type=ClusteringMethods.NO_CLUSTER.value),
split=repair_example(),
encoding=create_test_encoding(prefix_length=5, padding=True),
labelling=create_test_labelling(label_type=LabelTypes.DURATION.value),
predictive_model=create_test_predictive_model(predictive_model=PredictiveModels.REGRESSION.value,
prediction_method=RegressionMethods.RANDOM_FOREST.value)
)
result, _ = calculate(job)
self.assertAlmostEqual(result['rmse'], 0.4868515876868242)
self.assertAlmostEqual(result['mae'], 0.44340838774645464)
self.assertAlmostEqual(result['rscore'], 0.02142755175443678)
self.assertAlmostEqual(result['mape'], -1)
def test_regression_kmeans(self):
self.max_diff = None
job = create_test_job(
clustering=create_test_clustering(clustering_type=ClusteringMethods.KMEANS.value),
split=repair_example(),
encoding=create_test_encoding(prefix_length=5, padding=True),
labelling=create_test_labelling(label_type=LabelTypes.DURATION.value),
predictive_model=create_test_predictive_model(predictive_model=PredictiveModels.REGRESSION.value,
prediction_method=RegressionMethods.RANDOM_FOREST.value)
)
result, _ = calculate(job)
self.assertAlmostEqual(result['rmse'], 0.48841552839653984)
self.assertAlmostEqual(result['mae'], 0.44282462605873457)
self.assertAlmostEqual(result['rscore'], 0.015130407121517586)
self.assertAlmostEqual(result['mape'], -1)
def test_next_activity_no_cluster(self):
self.max_diff = None
job = create_test_job(
clustering=create_test_clustering(clustering_type=ClusteringMethods.NO_CLUSTER.value),
split=repair_example(),
encoding=create_test_encoding(prefix_length=8, padding=True),
labelling=create_test_labelling(label_type=LabelTypes.NEXT_ACTIVITY.value),
predictive_model=create_test_predictive_model(predictive_model=PredictiveModels.CLASSIFICATION.value,
prediction_method=ClassificationMethods.RANDOM_FOREST.value)
)
result, _ = calculate(job)
self.assertAlmostEqual(result['f1score'], 0.542398845)
self.assertAlmostEqual(result['acc'], 0.809954751)
self.assertAlmostEqual(result['precision'], 0.623447204)
self.assertAlmostEqual(result['recall'], 0.52249454423)
self.assertAlmostEqual(result['auc'], 0)
def get_job():
return create_test_job(split=repair_example(),
encoding=create_test_encoding(prefix_length=5,
padding=True),
job_type=JobTypes.LABELLING.value)
