'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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
License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from enum import Enum
import numpy as np
from pm4py.util import constants, points_subset
from pm4py.util import exec_utils, pandas_utils
from pm4py.util import xes_constants as xes
from typing import Optional, Dict, Any, Union, List
import pandas as pd
[docs]
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
PARAMETER_SAMPLE_SIZE = "sample_size"
SORT_LOG_REQUIRED = "sort_log_required"
[docs]
def gen_patterns(pattern, length):
return [
"".join(pattern[i: i + length])
for i in range(len(pattern) - (length - 1))
]
[docs]
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 disconnected 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 = {}
case_id_glue = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.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 = 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
)
all_patterns = [
(
len(list_activities) - i,
gen_patterns(list_activities, len(list_activities) - i),
)
for i in range(len(list_activities) - 1)
]
def key(k, n):
return k + str(n)
def to_points(match, l):
return {
"case_id": match[key(case_id_glue, 0)],
"points": [
(match[key(activity_key, i)], match[key(timestamp_key, i)])
for i in range(l)
],
}
points = []
for l, patterns in all_patterns:
# concat shifted and suffixed dataframes to get a dataframe that allows
# to check for the patterns
dfs = [dataframe.add_suffix(str(i)).shift(-i) for i in range(l)]
df_merged = pandas_utils.concat(dfs, axis=1)
indices = [shift_index(dfs[i].index, i) for i in range(len(dfs))]
mindex = pd.MultiIndex.from_arrays(indices)
df_merged = df_merged.set_index(mindex)
for i in range(l - 1):
df_merged = df_merged[
df_merged[key(case_id_glue, i)]
== df_merged[key(case_id_glue, i + 1)]
]
column_list = [key(activity_key, i) for i in range(l)]
matches = df_merged[
np.isin(df_merged[column_list].sum(axis=1), patterns)
]
points.extend([to_points(m, l) for m in matches.to_dict("records")])
# drop rows of this match to not discover subsets of this match again
dataframe = dataframe.drop(
[int(i) for indices in matches.index for i in indices[:-1]]
)
pass
points = sorted(
points, key=lambda x: min(x["points"], key=lambda x: x[1])[1]
)
if len(points) > sample_size:
points = points_subset.pick_chosen_points_list(sample_size, points)
return points
[docs]
def shift_index(index, n):
if n == 0:
return list(index)
nones = [None for _ in range(n)]
return list(index[n:]) + nones
