Source code for pm4py.algo.transformation.ocel.features.events.event_str_attributes

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py 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 General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
from pm4py.objects.ocel.obj import OCEL
from typing import Optional, Dict, Any
from enum import Enum
from pm4py.util import exec_utils


[docs]class Parameters(Enum): EVENT_STR_ATTRIBUTES = "str_ev_attr"
[docs]def apply(ocel: OCEL, parameters: Optional[Dict[Any, Any]] = None): """ One-hot-encoding of a given collection of string event attributes (specified inside the "str_ev_attr" parameter) Parameters ---------------- ocel OCEL parameters Parameters of the algorithm: - Parameters.EVENT_STR_ATTRIBUTES => collection of string attributes to consider for feature extraction Returns ---------------- data Extracted feature values feature_names Feature names """ if parameters is None: parameters = {} ordered_events = list(ocel.events[ocel.event_id_column]) data = [] feature_names = [] event_str_attributes = exec_utils.get_param_value(Parameters.EVENT_STR_ATTRIBUTES, parameters, None) if event_str_attributes is not None: dct_corr = {} dct_corr_values = {} for attr in event_str_attributes: events_attr_not_na = ocel.events[[ocel.event_id_column, attr]].dropna(subset=[attr]).to_dict("records") if events_attr_not_na: events_attr_not_na = {x[ocel.event_id_column]: str(x[attr]) for x in events_attr_not_na} dct_corr[attr] = events_attr_not_na dct_corr_values[attr] = list(set(events_attr_not_na.values())) dct_corr_list = list(dct_corr) for attr in dct_corr_list: for value in dct_corr_values[attr]: feature_names.append("@@event_attr_value_"+attr+"_"+value) for ev in ordered_events: data.append([0] * len(feature_names)) count = 0 for attr in dct_corr_list: if ev in dct_corr[attr]: value = dct_corr[attr][ev] idx = count + dct_corr_values[attr].index(value) data[-1][idx] = 1 count += len(dct_corr_values[attr]) return data, feature_names