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 })