def std_experiments(dataset, prefix_length, models, splits,
classification_method, encoding_method):
models[dataset]['0-40_80-100'] = send_job_request(
payload=create_classification_payload(
split=splits[dataset]['0-40_80-100'],
encodings=[encoding_method],
encoding={
"padding": "zero_padding",
"generation_type": TaskGenerationTypes.ALL_IN_ONE.value,
"prefix_length": prefix_length,
"features": []
},
labeling={
"type": LabelTypes.ATTRIBUTE_STRING.value,
"attribute_name": "label",
"add_remaining_time": False,
"add_elapsed_time": False,
"add_executed_events": False,
"add_resources_used": False,
"add_new_traces": False
},
hyperparameter_optimization={
"type": HyperparameterOptimizationMethods.HYPEROPT.value,
"max_evaluations": 1000,
"performance_metric": HyperOptLosses.AUC.value,
"algorithm_type": HyperOptAlgorithms.TPE.value
},
classification=[classification_method]),
server_port='50401',
server_name='ashkin')[0]['id']
models[dataset]['0-80_80-100'] = send_job_request(
payload=create_classification_payload(
split=splits[dataset]['0-80_80-100'],
encodings=[encoding_method],
encoding={
"padding": "zero_padding",
"generation_type": TaskGenerationTypes.ALL_IN_ONE.value,
"prefix_length": prefix_length,
"features": []
},
labeling={
"type": LabelTypes.ATTRIBUTE_STRING.value,
"attribute_name": "label",
"add_remaining_time": False,
"add_elapsed_time": False,
"add_executed_events": False,
"add_resources_used": False,
"add_new_traces": False
},
hyperparameter_optimization={
"type": HyperparameterOptimizationMethods.HYPEROPT.value,
"max_evaluations": 1000,
"performance_metric": HyperOptLosses.AUC.value,
"algorithm_type": HyperOptAlgorithms.TPE.value
},
classification=[classification_method]),
server_port='50401',
server_name='ashkin')[0]['id']
def drift_size_experimentation(dataset, prefix_length, models, splits,
classification_method, encoding_method):
if classification_method != "randomForest":
models[dataset]['40-55_80-100'] = send_job_request(
payload=create_classification_payload(
split=splits[dataset]['40-55_80-100'],
encodings=[encoding_method],
encoding={
"padding": "zero_padding",
"generation_type": TaskGenerationTypes.ALL_IN_ONE.value,
"prefix_length": prefix_length,
"features": []
},
labeling={
"type": LabelTypes.ATTRIBUTE_STRING.value,
"attribute_name": "label",
"add_remaining_time": False,
"add_elapsed_time": False,
"add_executed_events": False,
"add_resources_used": False,
"add_new_traces": False
},
classification=[classification_method],
hyperparameter_optimization={
"type": HyperparameterOptimizationMethods.NONE.value
},
incremental_train=[
get_pretrained_model_id(config=retrieve_job(
config={
'type': JobTypes.PREDICTION.value,
# 'status': JobStatuses.COMPLETED.value, # TODO sometimes some jobs hang in running while they are actually finished
'create_models': True,
'split': splits[dataset]['0-40_80-100'],
'encoding': {
"value_encoding":
encoding_method,
"padding":
True,
"task_generation_type":
TaskGenerationTypes.ALL_IN_ONE.value,
"prefix_length":
prefix_length
},
'labelling': {
"type": LabelTypes.ATTRIBUTE_STRING.value,
"attribute_name": "label",
"add_remaining_time": False,
"add_elapsed_time": False,
"add_executed_events": False,
"add_resources_used": False,
"add_new_traces": False
},
'hyperparameter_optimization': {
"optimization_method":
HyperparameterOptimizationMethods.HYPEROPT.
value
},
# "max_evaluations": 1000, #TODO not yet supported
# "performance_metric": HyperOptLosses.AUC.value,
# "algorithm_type": HyperOptAlgorithms.TPE.value},
'predictive_model': {
'predictive_model': 'classification',
'prediction_method': classification_method
},
'clustering': {
'clustering_method':
ClusteringMethods.NO_CLUSTER.value
}
},
server_name='ashkin',
server_port='50401'))
]),
server_port='50401',
server_name='ashkin')[0]['id']
models[dataset]['0-55_80-100'] = send_job_request(
payload=create_classification_payload(
split=splits[dataset]['0-55_80-100'],
encodings=[encoding_method],
encoding={
"padding": "zero_padding",
"generation_type": TaskGenerationTypes.ALL_IN_ONE.value,
"prefix_length": prefix_length,
"features": []
},
labeling={
"type": LabelTypes.ATTRIBUTE_STRING.value,
"attribute_name": "label",
"add_remaining_time": False,
"add_elapsed_time": False,
"add_executed_events": False,
"add_resources_used": False,
"add_new_traces": False
},
classification=[classification_method],
hyperparameter_optimization={
"type": HyperparameterOptimizationMethods.HYPEROPT.value,
"max_evaluations": 1000,
"performance_metric": HyperOptLosses.AUC.value,
"algorithm_type": HyperOptAlgorithms.TPE.value
},
),
server_port='50401',
server_name='ashkin')[0]['id']