'''
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):
OBJECT_STR_ATTRIBUTES = "str_obj_attr"
[docs]def apply(ocel: OCEL, parameters: Optional[Dict[Any, Any]] = None):
"""
One-hot-encoding of a given collection of string object attributes
(specified inside the "str_obj_attr" parameter)
Parameters
----------------
ocel
OCEL
parameters
Parameters of the algorithm:
- Parameters.OBJECT_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 = {}
data = []
feature_names = []
ordered_objects = list(ocel.objects[ocel.object_id_column])
object_str_attributes = exec_utils.get_param_value(Parameters.OBJECT_STR_ATTRIBUTES, parameters, None)
if object_str_attributes is not None:
dct_corr = {}
dct_corr_values = {}
for attr in object_str_attributes:
objects_attr_not_na = ocel.objects[[ocel.object_id_column, attr]].dropna(subset=[attr]).to_dict("records")
if objects_attr_not_na:
objects_attr_not_na = {x[ocel.object_id_column]: x[attr] for x in objects_attr_not_na}
dct_corr[attr] = objects_attr_not_na
dct_corr_values[attr] = list(set(objects_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("@@object_attr_value_"+attr+"_"+value)
for ev in ordered_objects:
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