Source code for pm4py.algo.discovery.batches.variants.pandas

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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

[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) log = log[list({activity_key, resource_key, start_timestamp_key, timestamp_key, case_id_key})] events = log.to_dict('records') actres_grouping = {} for ev in events: case = ev[case_id_key] activity = ev[activity_key] resource = ev[resource_key] st = ev[start_timestamp_key].timestamp() et = ev[timestamp_key].timestamp() if (activity, resource) not in actres_grouping: actres_grouping[(activity, resource)] = [] actres_grouping[(activity, resource)].append((st, et, case)) return detection.detect(actres_grouping, parameters=parameters)