'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from enum import Enum
from typing import Optional, Dict, Any, List, Tuple, Union
import pandas as pd
from pm4py.algo.discovery.batches.utils import detection
from pm4py.util import exec_utils, constants, xes_constants, pandas_utils
import numpy as np
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
RESOURCE_KEY = constants.PARAMETER_CONSTANT_RESOURCE_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
EVENT_ID_KEY = "event_id_key"
MERGE_DISTANCE = "merge_distance"
MIN_BATCH_SIZE = "min_batch_size"
[docs]
def apply(
log: pd.DataFrame,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> List[Tuple[Tuple[str, str], int, Dict[str, Any]]]:
"""
Provided a Pandas dataframe, returns
a list having as elements the activity-resources with the batches that are detected, divided in:
- Simultaneous (all the events in the batch have identical start and end timestamps)
- Batching at start (all the events in the batch have identical start timestamp)
- Batching at end (all the events in the batch have identical end timestamp)
- Sequential batching (for all the consecutive events, the end of the first is equal to the start of the second)
- Concurrent batching (for all the consecutive events that are not sequentially matched)
The approach has been described in the following paper:
Martin, N., Swennen, M., Depaire, B., Jans, M., Caris, A., & Vanhoof, K. (2015, December). Batch Processing:
Definition and Event Log Identification. In SIMPDA (pp. 137-140).
Parameters
-------------------
log
Dataframe
parameters
Parameters of the algorithm:
- ACTIVITY_KEY => the attribute that should be used as activity
- RESOURCE_KEY => the attribute that should be used as resource
- START_TIMESTAMP_KEY => the attribute that should be used as start timestamp
- TIMESTAMP_KEY => the attribute that should be used as timestamp
- CASE_ID_KEY => the attribute that should be used as case identifier
- MERGE_DISTANCE => the maximum time distance between non-overlapping intervals in order for them to be
considered belonging to the same batch (default: 15*60 15 minutes)
- MIN_BATCH_SIZE => the minimum number of events for a batch to be considered (default: 2)
Returns
------------------
list_batches
A (sorted) list containing tuples. Each tuple contain:
- Index 0: the activity-resource for which at least one batch has been detected
- Index 1: the number of batches for the given activity-resource
- Index 2: a list containing all the batches. Each batch is described by:
# The start timestamp of the batch
# The complete timestamp of the batch
# The list of events that are executed in the batch
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
)
resource_key = exec_utils.get_param_value(
Parameters.RESOURCE_KEY, parameters, xes_constants.DEFAULT_RESOURCE_KEY
)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
start_timestamp_key = exec_utils.get_param_value(
Parameters.START_TIMESTAMP_KEY, parameters, timestamp_key
)
case_id_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
)
event_id_key = exec_utils.get_param_value(
Parameters.EVENT_ID_KEY, parameters, constants.DEFAULT_INDEX_KEY
)
attributes_to_consider = {
activity_key,
resource_key,
start_timestamp_key,
timestamp_key,
case_id_key,
}
log_contains_evidkey = event_id_key in log
if log_contains_evidkey:
attributes_to_consider.add(event_id_key)
log = log[list(attributes_to_consider)]
# the timestamp columns are expressed in nanoseconds values
# here, we want them to have the second granularity, so we divide by 10**9
# for example 1001000000 nanoseconds (value stored in the column)
# is equivalent to 1,001 seconds.
log[timestamp_key] = pandas_utils.convert_to_seconds(log[timestamp_key])
if start_timestamp_key != timestamp_key:
# see the aforementioned explanation.
log[start_timestamp_key] = pandas_utils.convert_to_seconds(
log[start_timestamp_key]
)
actres_grouping0 = (
log.groupby([activity_key, resource_key]).agg(list).to_dict()
)
start_timestamps = actres_grouping0[start_timestamp_key]
complete_timestamps = actres_grouping0[timestamp_key]
cases = actres_grouping0[case_id_key]
if log_contains_evidkey:
events_ids = actres_grouping0[event_id_key]
actres_grouping = {}
for k in start_timestamps:
st = start_timestamps[k]
et = complete_timestamps[k]
c = cases[k]
if log_contains_evidkey:
eid = events_ids[k]
actres_grouping_k = []
for i in range(len(st)):
if log_contains_evidkey:
actres_grouping_k.append((st[i], et[i], c[i], eid[i]))
else:
actres_grouping_k.append((st[i], et[i], c[i]))
actres_grouping[k] = actres_grouping_k
return detection.detect(actres_grouping, parameters=parameters)
