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_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_hyperopt(self):
job = Job.objects.create(
split=create_test_split(split_type=SplitTypes.SPLIT_SINGLE.value,
original_log=create_test_log(
log_name=general_example_filename,
log_path=general_example_filepath)),
encoding=create_test_encoding(
value_encoding=ValueEncodings.SIMPLE_INDEX.value,
prefix_length=3,
padding=False),
labelling=create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.KMEANS.value),
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value,
prediction_method=ClassificationMethods.RANDOM_FOREST.value),
hyperparameter_optimizer=create_test_hyperparameter_optimizer(
hyperoptim_type=HyperparameterOptimizationMethods.HYPEROPT.
value,
performance_metric=HyperOptLosses.ACC.value,
max_evals=2))
prediction_task(job.pk)
job = Job.objects.get(pk=1)
self.assertFalse(classification_random_forest(
) == job.predictive_model.classification.__getattribute__(
ClassificationMethods.RANDOM_FOREST.value.lower()).to_dict())
def test_prediction_task_save_model_clustering(self):
job = create_test_job(
create_models=True,
clustering=create_test_clustering(
clustering_type=ClusteringMethods.KMEANS.value))
prediction_task(job.id)
job.refresh_from_db()
self.assertEqual('completed', job.status)
self.assertIsNotNone(job.predictive_model.model_path)
self.assertIsNotNone(job.clustering.model_path)
def test_next_activity_DecisionTree(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
prediction_method=ClassificationMethods.DECISION_TREE.value),
labelling=create_test_labelling(
label_type=LabelTypes.NEXT_ACTIVITY.value),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
self.assertDictEqual(result, self.results3())
def test_class_randomForest(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
prediction_method=ClassificationMethods.RANDOM_FOREST.value),
labelling=create_test_labelling(
label_type=LabelTypes.ATTRIBUTE_STRING.value,
attribute_name='label'),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
self.assertDictEqual(result, self.results2())
def test_tsp_gru(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.TIME_SERIES_PREDICTION.value,
prediction_method=TimeSeriesPredictionMethods.RNN.value,
configuration={'rnn_type': 'gru'}),
labelling=create_test_labelling(),
encoding=create_test_encoding(prefix_length=2, padding=True),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
del result['elapsed_time']
self.assertDictEqual(result, {'nlevenshtein': 0.6})
def test_regression_nn(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.REGRESSION.value,
prediction_method=RegressionMethods.NN.value),
labelling=create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
del result['elapsed_time']
print(result)
self.assertAlmostEqual(result['mae'], 0.0001388888888888889)
self.assertAlmostEqual(result['mape'], -1)
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),
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.REGRESSION.value,
prediction_method=RegressionMethods.RANDOM_FOREST.value))
result, _ = calculate(job)
self.assertAlmostEqual(result['rmse'], 0.03263757)
self.assertAlmostEqual(result['mae'], 0.00011685)
self.assertAlmostEqual(result['rscore'], 0.13776124)
self.assertAlmostEqual(result['mape'], float('inf'))
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),
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.REGRESSION.value,
prediction_method=RegressionMethods.RANDOM_FOREST.value))
result, _ = calculate(job)
self.assertAlmostEqual(result['rmse'], 0.0325738)
self.assertAlmostEqual(result['mae'], 0.00014269)
self.assertAlmostEqual(result['rscore'], -0.11336870)
self.assertAlmostEqual(result['mape'], float('inf'))
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),
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.REGRESSION.value,
prediction_method=RegressionMethods.RANDOM_FOREST.value))
result, _ = calculate(job)
self.assertAlmostEqual(result['rmse'], 0.036930128)
self.assertAlmostEqual(result['mae'], 0.023046561975)
self.assertAlmostEqual(result['rscore'], 0.99830687)
self.assertAlmostEqual(result['mape'], 0.5761640)
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_update_nb(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
prediction_method=ClassificationMethods.HOEFFDING_TREE.value),
labelling=create_test_labelling(
label_type=LabelTypes.ATTRIBUTE_STRING.value,
attribute_name='concept:name'),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value),
create_models=True)
result1, _ = calculate(job)
job = create_test_job(predictive_model=create_test_predictive_model(
prediction_method=ClassificationMethods.HOEFFDING_TREE.value),
encoding=job.encoding,
labelling=create_test_labelling(
label_type=LabelTypes.ATTRIBUTE_STRING.value,
attribute_name='concept:name'),
clustering=job.clustering,
incremental_train=job)
result2, _ = calculate(job)
del result1['elapsed_time']
del result2['elapsed_time']
self.assertDictEqual(
result1, {
'f1score': 0.0,
'acc': 0.0,
'precision': 0.0,
'recall': 0.0,
'true_positive': 0,
'true_negative': 0,
'false_negative': 2,
'false_positive': 0,
'auc': 0.0
})
self.assertDictEqual(
result2, {
'f1score': 0.0,
'acc': 0.0,
'precision': 0.0,
'recall': 0.0,
'true_positive': 0,
'true_negative': 0,
'false_negative': 2,
'false_positive': 0,
'auc': 0.0
})
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 test_regression_xgboost(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.REGRESSION.value,
prediction_method=RegressionMethods.XGBOOST.value),
labelling=create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
del result['elapsed_time']
print(result)
self.assertDictEqual(
result, {
'mae': 0.00011968612670898438,
'mape': -1,
'rmse': 0.00011968612670898438,
'rscore': 0.0
})
def test_regression_lasso(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.REGRESSION.value,
prediction_method=RegressionMethods.LASSO.value),
labelling=create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
del result['elapsed_time']
print(result)
self.assertDictEqual(
result, {
'mae': 0.0001388888888888889,
'mape': -1,
'rmse': 0.0001552824984374854,
'rscore': -0.25
})
def test_class_nn(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
prediction_method=ClassificationMethods.NN.value),
labelling=create_test_labelling(
label_type=LabelTypes.ATTRIBUTE_STRING.value,
attribute_name='concept:name'),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
del result['elapsed_time']
self.assertIn('f1score', result)
self.assertIn('acc', result)
self.assertIn('precision', result)
self.assertIn('recall', result)
self.assertIn('true_positive', result)
self.assertIn('true_negative', result)
self.assertIn('false_negative', result)
self.assertIn('false_positive', result)
self.assertIn('auc', result)
def test_class_perc(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
prediction_method=ClassificationMethods.PERCEPTRON.value),
labelling=create_test_labelling(
label_type=LabelTypes.ATTRIBUTE_STRING.value,
attribute_name='concept:name'),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
del result['elapsed_time']
self.assertDictEqual(
result, {
'f1score': 0.0,
'acc': 0.0,
'precision': 0.0,
'recall': 0.0,
'true_positive': 0,
'true_negative': 0,
'false_negative': 2,
'false_positive': 0,
'auc': 0.0
})
def test_class_nn_binary(self):
job = create_test_job(
predictive_model=create_test_predictive_model(
prediction_method=ClassificationMethods.NN.value),
labelling=create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value,
threshold_type=ThresholdTypes.THRESHOLD_MEAN.value),
clustering=create_test_clustering(
clustering_type=ClusteringMethods.NO_CLUSTER.value))
result, _ = calculate(job)
del result['elapsed_time']
self.assertDictEqual(
result, {
'f1score': 0.0,
'acc': 0.0,
'precision': 0.0,
'recall': 0.0,
'true_positive': 0,
'true_negative': 0,
'false_negative': 2,
'false_positive': 0,
'auc': 0.0
})