'''
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 pm4py.statistics.attributes.common import get as attributes_common
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.objects.log.obj import EventLog
from pm4py.util import xes_constants as xes
from pm4py.util.xes_constants import DEFAULT_TIMESTAMP_KEY
from pm4py.util import exec_utils
from pm4py.util import constants
from enum import Enum
from collections import Counter
from copy import copy
from typing import Optional, Dict, Any, Union, Tuple, List, Set
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.util.dt_parsing.variants import strpfromiso
[docs]
class Parameters(Enum):
ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
MAX_NO_POINTS_SAMPLE = "max_no_of_points_to_sample"
KEEP_ONCE_PER_CASE = "keep_once_per_case"
def __add_left_0(stri: str, target_length: int) -> str:
"""
Adds left 0s to the current string until the target length is reached
Parameters
----------------
stri
String
target_length
Target length
Returns
----------------
stri
Revised string
"""
while len(stri) < target_length:
stri = "0" + stri
return stri
[docs]
def get_events_distribution(
log: EventLog,
distr_type: str = "days_month",
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Tuple[List[str], List[int]]:
"""
Gets the distribution of the events in the specified dimension
Parameters
----------------
log
Event log
distr_type
Type of distribution:
- days_month => Gets the distribution of the events among the days of a month (from 1 to 31)
- months => Gets the distribution of the events among the months (from 1 to 12)
- years => Gets the distribution of the events among the years of the event log
- hours => Gets the distribution of the events among the hours of a day (from 0 to 23)
- days_week => Gets the distribution of the events among the days of a week (from Monday to Sunday)
parameters
Parameters of the algorithm, including:
- Parameters.TIMESTAMP_KEY
Returns
----------------
x
Points (of the X-axis)
y
Points (of the Y-axis)
"""
if parameters is None:
parameters = {}
log = log_converter.apply(
log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters
)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY, parameters, DEFAULT_TIMESTAMP_KEY
)
timestamp_values = []
for trace in log:
for event in trace:
timestamp_values.append(event[timestamp_key])
values = None
all_values = None
if distr_type == "days_month":
values = Counter(x.day for x in timestamp_values)
all_values = Counter({i: 0 for i in range(1, 32)})
elif distr_type == "months":
values = Counter(x.month for x in timestamp_values)
all_values = Counter({i: 0 for i in range(1, 13)})
elif distr_type == "years":
values = Counter(x.year for x in timestamp_values)
all_values = Counter(
{i: 0 for i in range(min(values), max(values) + 1)}
)
elif distr_type == "hours":
values = Counter(x.hour for x in timestamp_values)
all_values = Counter({i: 0 for i in range(0, 24)})
elif distr_type == "days_week":
values = Counter(x.weekday() for x in timestamp_values)
all_values = Counter({i: 0 for i in range(0, 7)})
elif distr_type == "weeks":
values = Counter(x.isocalendar().week for x in timestamp_values)
all_values = Counter({i: 0 for i in range(0, 53)})
# make sure that all the possible values appear
for v in all_values:
if v not in values:
values[v] = all_values[v]
values = sorted([(__add_left_0(str(x), 2), y) for x, y in values.items()])
if distr_type == "days_week":
mapping = {
"00": "Monday",
"01": "Tuesday",
"02": "Wednesday",
"03": "Thursday",
"04": "Friday",
"05": "Saturday",
"06": "Sunday",
}
values = [(mapping[x[0]], x[1]) for x in values]
return [x[0] for x in values], [x[1] for x in values]
[docs]
def get_all_trace_attributes_from_log(log: EventLog) -> Set[str]:
"""
Get all trace attributes from the log
Parameters
------------
log
Log
Returns
------------
all_attributes
All trace attributes from the log
"""
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG)
all_attributes = set()
for trace in log:
all_attributes = all_attributes.union(set(trace.attributes.keys()))
if xes.DEFAULT_TRACEID_KEY in all_attributes:
all_attributes.remove(xes.DEFAULT_TRACEID_KEY)
return all_attributes
[docs]
def get_all_event_attributes_from_log(log: EventLog) -> Set[str]:
"""
Get all events attributes from the log
Parameters
-------------
log
Log
Returns
-------------
all_attributes
All trace attributes from the log
"""
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG)
all_attributes = set()
for trace in log:
for event in trace:
all_attributes = all_attributes.union(set(event.keys()))
if xes.DEFAULT_TRANSITION_KEY in all_attributes:
all_attributes.remove(xes.DEFAULT_TRANSITION_KEY)
return all_attributes
[docs]
def get_attribute_values(
log: EventLog,
attribute_key: str,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[Any, int]:
"""
Get the attribute values of the log for the specified attribute along with their count
Parameters
----------
log
Log
attribute_key
Attribute for which we would like to know the values along with their count
parameters
Possible parameters of the algorithm
Returns
----------
attributes
Dictionary of attributes associated with their count
"""
log = log_converter.apply(
log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters
)
if parameters is None:
parameters = {}
keep_once_per_case = exec_utils.get_param_value(
Parameters.KEEP_ONCE_PER_CASE, parameters, False
)
attribute_values = {}
for trace in log:
trace_values = [x[attribute_key] for x in trace if attribute_key in x]
if keep_once_per_case:
trace_values = set(trace_values)
for val in trace_values:
if val not in attribute_values:
attribute_values[val] = 0
attribute_values[val] = attribute_values[val] + 1
return attribute_values
[docs]
def get_trace_attribute_values(
log: EventLog,
attribute_key: str,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[Any, int]:
"""
Get the attribute values of the log for the specified attribute along with their count
Parameters
------------
log
Log
attribute_key
Attribute for which we wish to get the values along with their count
parameters
Possible parameters of the algorithm
Returns
------------
attributes
Dictionary of attributes associated with their count
"""
if parameters is None:
parameters = {}
log = log_converter.apply(
log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters
)
attributes = {}
for trace in log:
if attribute_key in trace.attributes:
attribute = trace.attributes[attribute_key]
if attribute not in attributes:
attributes[attribute] = 0
attributes[attribute] = attributes[attribute] + 1
return attributes
[docs]
def get_kde_numeric_attribute(log, attribute, parameters=None):
"""
Gets the KDE estimation for the distribution of a numeric attribute values
Parameters
-------------
log
Event stream object (if log, is converted)
attribute
Numeric attribute to analyse
parameters
Possible parameters of the algorithm, including:
graph_points -> number of points to include in the graph
Returns
--------------
x
X-axis values to represent
y
Y-axis values to represent
"""
if parameters is None:
parameters = {}
this_parameters = copy(parameters)
this_parameters["deepcopy"] = False
this_parameters["include_case_attributes"] = False
event_log = log_conversion.apply(
log, variant=log_conversion.TO_EVENT_STREAM, parameters=this_parameters
)
values = [event[attribute] for event in event_log if attribute in event]
return attributes_common.get_kde_numeric_attribute(
values, parameters=parameters
)
[docs]
def get_kde_numeric_attribute_json(log, attribute, parameters=None):
"""
Gets the KDE estimation for the distribution of a numeric attribute values
(expressed as JSON)
Parameters
-------------
log
Event log object (if log, is converted)
attribute
Numeric attribute to analyse
parameters
Possible parameters of the algorithm, including:
graph_points -> number of points to include in the graph
Returns
--------------
x
X-axis values to represent
y
Y-axis values to represent
"""
if parameters is None:
parameters = {}
this_parameters = copy(parameters)
this_parameters["deepcopy"] = False
this_parameters["include_case_attributes"] = False
event_log = log_conversion.apply(
log, variant=log_conversion.TO_EVENT_STREAM, parameters=this_parameters
)
values = [event[attribute] for event in event_log if attribute in event]
return attributes_common.get_kde_numeric_attribute_json(
values, parameters=parameters
)
[docs]
def get_kde_date_attribute(
log, attribute=DEFAULT_TIMESTAMP_KEY, parameters=None
):
"""
Gets the KDE estimation for the distribution of a date attribute values
Parameters
-------------
log
Event stream object (if log, is converted)
attribute
Date attribute to analyse
parameters
Possible parameters of the algorithm, including:
graph_points -> number of points to include in the graph
Returns
--------------
x
X-axis values to represent
y
Y-axis values to represent
"""
if parameters is None:
parameters = {}
this_parameters = copy(parameters)
this_parameters["deepcopy"] = False
this_parameters["include_case_attributes"] = False
event_log = log_conversion.apply(
log, variant=log_conversion.TO_EVENT_STREAM, parameters=this_parameters
)
values = [
strpfromiso.fix_naivety(event[attribute])
for event in event_log
if attribute in event
]
return attributes_common.get_kde_date_attribute(
values, parameters=parameters
)
[docs]
def get_kde_date_attribute_json(
log, attribute=DEFAULT_TIMESTAMP_KEY, parameters=None
):
"""
Gets the KDE estimation for the distribution of a date attribute values
(expressed as JSON)
Parameters
-------------
log
Event stream object (if log, is converted)
attribute
Date attribute to analyse
parameters
Possible parameters of the algorithm, including:
graph_points -> number of points to include in the graph
Returns
--------------
x
X-axis values to represent
y
Y-axis values to represent
"""
if parameters is None:
parameters = {}
this_parameters = copy(parameters)
this_parameters["deepcopy"] = False
this_parameters["include_case_attributes"] = False
event_log = log_conversion.apply(
log, variant=log_conversion.TO_EVENT_STREAM, parameters=this_parameters
)
values = [
strpfromiso.fix_naivety(event[attribute])
for event in event_log
if attribute in event
]
return attributes_common.get_kde_date_attribute_json(
values, parameters=parameters
)
