Source code for pm4py.objects.log.util.activities_to_alphabet

'''
    PM4Py – A Process Mining Library for Python
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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
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but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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visit <https://www.gnu.org/licenses/>.

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'''

from enum import Enum
from pm4py.util import exec_utils, xes_constants
import pandas as pd
from typing import Optional, Dict, Any, Union, Tuple


[docs] class Parameters(Enum): ACTIVITY_KEY = "activity_key" RETURN_MAPPING = "return_mapping"
[docs] def apply( dataframe: pd.DataFrame, parameters: Optional[Dict[Any, Any]] = None ) -> Union[pd.DataFrame, Tuple[pd.DataFrame, Dict[str, str]]]: """ Remap the activities in a dataframe using an augmented alphabet to minimize the size of the encoding Running example: import pm4py from pm4py.objects.log.util import activities_to_alphabet from pm4py.util import constants dataframe = pm4py.read_xes("tests/input_data/running-example.xes") renamed_dataframe = activities_to_alphabet.apply(dataframe, parameters={constants.PARAMETER_CONSTANT_ACTIVITY_KEY: "concept:name"}) print(renamed_dataframe) Parameters -------------- dataframe Pandas dataframe parameters Parameters of the method, including: - Parameters.ACTIVITY_KEY => attribute to be used as activity - Parameters.RETURN_MAPPING => (boolean) enables the returning the mapping dictionary (so the original activities can be re-constructed) Returns -------------- ren_dataframe Pandas dataframe in which the activities have been remapped to the (augmented) alphabet inv_mapping (if required) Dictionary associating to every letter of the (augmented) alphabet the original activity """ if parameters is None: parameters = {} activity_key = exec_utils.get_param_value( Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY ) return_mapping = exec_utils.get_param_value( Parameters.RETURN_MAPPING, parameters, False ) activities_count = list(dataframe[activity_key].value_counts().to_dict()) remap_dict = {} for index, act in enumerate(activities_count): result = "" while index >= 0: result = chr((index % 26) + ord("A")) + result index = index // 26 - 1 remap_dict[act] = result dataframe[activity_key] = dataframe[activity_key].map(remap_dict) if return_mapping: inverse_dct = {y: x for x, y in remap_dict.items()} return dataframe, inverse_dct return dataframe