pm4py.statistics.attributes.log package#

PM4Py – A Process Mining Library for Python

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

Submodules#

pm4py.statistics.attributes.log.get module#

PM4Py – A Process Mining Library for Python

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

class pm4py.statistics.attributes.log.get.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

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: Dict[str | Parameters, Any] | None = 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: Dict[str | Parameters, Any] | None = 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: Dict[str | Parameters, Any] | None = 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-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#

PM4Py – A Process Mining Library for Python

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

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: Set[str] | List[str]) → 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: Set[str] | List[str]) → 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