pm4py.statistics.attributes.log package#

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/>.

Submodules#

pm4py.statistics.attributes.log.get module#

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/>.

class pm4py.statistics.attributes.log.get.Parameters(value)[source]#

Bases: Enum

An enumeration.

ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
MAX_NO_POINTS_SAMPLE = 'max_no_of_points_to_sample'#
KEEP_ONCE_PER_CASE = 'keep_once_per_case'#
pm4py.statistics.attributes.log.get.get_events_distribution(log: EventLog, distr_type: str = 'days_month', parameters: Optional[Dict[Union[str, Parameters], Any]] = None) Tuple[List[str], List[int]][source]#

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)

pm4py.statistics.attributes.log.get.get_all_trace_attributes_from_log(log: EventLog) Set[str][source]#

Get all trace attributes from the log

Parameters#

log

Log

Returns#

all_attributes

All trace attributes from the log

pm4py.statistics.attributes.log.get.get_all_event_attributes_from_log(log: EventLog) Set[str][source]#

Get all events attributes from the log

Parameters#

log

Log

Returns#

all_attributes

All trace attributes from the log

pm4py.statistics.attributes.log.get.get_attribute_values(log: EventLog, attribute_key: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) Dict[Any, int][source]#

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

pm4py.statistics.attributes.log.get.get_trace_attribute_values(log: EventLog, attribute_key: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) Dict[Any, int][source]#

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

pm4py.statistics.attributes.log.get.get_kde_numeric_attribute(log, attribute, parameters=None)[source]#

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

pm4py.statistics.attributes.log.get.get_kde_numeric_attribute_json(log, attribute, parameters=None)[source]#

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

pm4py.statistics.attributes.log.get.get_kde_date_attribute(log, attribute='time:timestamp', parameters=None)[source]#

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

pm4py.statistics.attributes.log.get.get_kde_date_attribute_json(log, attribute='time:timestamp', parameters=None)[source]#

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

pm4py.statistics.attributes.log.select module#

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/>.

pm4py.statistics.attributes.log.select.select_attributes_from_log_for_tree(log: EventLog, max_cases_for_attr_selection=50, max_diff_occ=12.5)[source]#

Select attributes from log for tree

Parameters#

log

Log

max_cases_for_attr_selection

Maximum number of cases to consider for attribute selection

max_diff_occ

Maximum number of different occurrences

Returns#

pm4py.statistics.attributes.log.select.check_trace_attributes_presence(log: EventLog, attributes_set: Union[Set[str], List[str]]) Union[Set[str], List[str]][source]#

Check trace attributes presence in all the traces of the log

Parameters#

log

Log

attributes_set

Set of attributes

Returns#

filtered_set

Filtered set of attributes

pm4py.statistics.attributes.log.select.check_event_attributes_presence(log: EventLog, attributes_set: Union[Set[str], List[str]]) Union[Set[str], List[str]][source]#

Check event attributes presence in all the traces of the log

Parameters#

log

Log

attributes_set

Set of attributes

Returns#

filtered_set

Filtered set of attributes

pm4py.statistics.attributes.log.select.verify_if_event_attribute_is_in_each_trace(log: EventLog, attribute: str) bool[source]#

Verify if the event attribute is in each trace

Parameters#

log

Log

attribute

Attribute

Returns#

boolean

Boolean value that is aiming to check if the event attribute is in each trace

pm4py.statistics.attributes.log.select.verify_if_trace_attribute_is_in_each_trace(log: EventLog, attribute: str) bool[source]#

Verify if the trace attribute is in each trace

Parameters#

log

Log

attribute

Attribute

Returns#

boolean

Boolean value that is aiming to check if the trace attribute is in each trace