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'''
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 enum import Enum
from pm4py.util import constants
from pm4py.util import exec_utils, pandas_utils
from pm4py.util import xes_constants as xes
from pm4py.util.constants import CASE_CONCEPT_NAME
from typing import Optional, Dict, Any, Union, List
import pandas as pd
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY
PARAMETER_SAMPLE_SIZE = "sample_size"
SORT_LOG_REQUIRED = "sort_log_required"
def apply(dataframe: pd.DataFrame, list_activities: List[str], sample_size: int, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]:
"""
Finds the performance spectrum provided a dataframe
and a list of activities
Parameters
-------------
dataframe
Dataframe
list_activities
List of activities interesting for the performance spectrum (at least two)
sample_size
Size of the sample
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY
- Parameters.TIMESTAMP_KEY
- Parameters.CASE_ID_KEY
Returns
-------------
points
Points of the performance spectrum
"""
if parameters is None:
parameters = {}
import pandas as pd
import numpy as np
case_id_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, CASE_CONCEPT_NAME)
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes.DEFAULT_TIMESTAMP_KEY)
sort_log_required = exec_utils.get_param_value(Parameters.SORT_LOG_REQUIRED, parameters, True)
dataframe = dataframe[[case_id_glue, activity_key, timestamp_key]]
dataframe[activity_key] = dataframe[activity_key].astype("string")
dataframe = dataframe[dataframe[activity_key].isin(list_activities)]
dataframe = pandas_utils.insert_index(dataframe, constants.DEFAULT_EVENT_INDEX_KEY)
if sort_log_required:
dataframe = dataframe.sort_values([case_id_glue, timestamp_key, constants.DEFAULT_EVENT_INDEX_KEY])
dataframe[timestamp_key] = dataframe[timestamp_key].astype(np.int64) / 10 ** 9
def key(k, n):
return k + str(n)
# create a dataframe with all needed columns to check for the activities pattern
dfs = [dataframe.add_suffix(str(i)).shift(-i) for i in range(len(list_activities))]
dataframe = pandas_utils.concat(dfs, axis=1)
# keep only rows that belong to exactly one case
for i in range(len(list_activities) - 1):
dataframe = dataframe[dataframe[key(case_id_glue, i)] == dataframe[key(case_id_glue, i + 1)]]
column_list = [key(activity_key, i) for i in range(len(list_activities))]
pattern = "".join(list_activities)
# keep only rows that have the desired activities pattern
matches = dataframe[np.equal(dataframe[column_list].agg(''.join, axis=1), pattern)]
if len(matches) > sample_size:
matches = matches.sample(n=sample_size)
filt_col_names = [timestamp_key + str(i) for i in range(len(list_activities))]
points = pandas_utils.to_dict_records(matches)
points = [[p[tk] for tk in filt_col_names] for p in points]
points = sorted(points, key=lambda x: x[0])
return points
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