Source code for pm4py.algo.transformation.ocel.features.events.event_num_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_NUM_ATTRIBUTES = "num_ev_attr"
[docs]def apply(ocel: OCEL, parameters: Optional[Dict[Any, Any]] = None): """ Enables the extraction of a given collection of numeric event attributes in the feature table (specified inside the "num_ev_attr" parameter). Parameters ---------------- ocel OCEL parameters Parameters of the algorithm: - Parameters.EVENT_NUM_ATTRIBUTES => collection of numeric 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_num_attributes = exec_utils.get_param_value(Parameters.EVENT_NUM_ATTRIBUTES, parameters, None) if event_num_attributes: feature_names = feature_names + ["@@event_num_"+x for x in event_num_attributes] attr_values = {} for attr in event_num_attributes: values = ocel.events[[ocel.event_id_column, attr]].dropna(subset=[attr]).to_dict("records") values = {x[ocel.event_id_column]: x[attr] for x in values} attr_values[attr] = values for ev in ordered_events: data.append([]) for attr in event_num_attributes: data[-1].append(float(attr_values[attr][ev]) if ev in attr_values[attr] else 0.0) return data, feature_names