pm4py.algo.decision_mining package#

PM4Py â€“ 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.

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Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

pm4py.algo.decision_mining.algorithm 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.algo.decision_mining.algorithm.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#

LABELS = 'labels'#

pm4py.algo.decision_mining.algorithm.create_data_petri_nets_with_decisions(log: EventLog | DataFrame, net: PetriNet, initial_marking: Marking, final_marking: Marking) → Tuple[PetriNet, Marking, Marking][source]#

Given a Petri net, create a data Petri net with the decisions given for each place by the decision mining algorithm

Parameters#

log: Event log
net: Petri net
initial_marking: Initial marking
final_marking: Final marking

Returns#

data_petri_net: Data petri net
initial_marking: Initial marking (unchanged)
final_marking: Final marking (unchanged)

pm4py.algo.decision_mining.algorithm.get_decision_tree(log: EventLog | DataFrame, net: PetriNet, initial_marking: Marking, final_marking: Marking, decision_point=None, attributes=None, parameters: Dict[str | Parameters, Any] | None = None) → Any[source]#

Gets a decision tree classifier on a specific point of the model

Parameters#

log: Event log
net: Petri net
initial_marking: Initial marking
final_marking: Final marking
decision_point: Point of the process in which a decision happens: - if not specified, the method crashes, but provides a list of possible decision points - if specified, the method goes on and produce the decision tree
attributes: Attributes of the log. If not specified, then an automatic attribute selection is performed.
parameters: Parameters of the algorithm

Returns#

clf: Decision tree
feature_names: The names of the features
classes: The classes

pm4py.algo.decision_mining.algorithm.apply(log: EventLog | DataFrame, net: PetriNet, initial_marking: Marking, final_marking: Marking, decision_point=None, attributes=None, parameters: Dict[str | Parameters, Any] | None = None) → Any[source]#

Gets the essential information (features, target class and names of the target class) in order to learn a classifier

Parameters#

log: Event log
net: Petri net
initial_marking: Initial marking
final_marking: Final marking
decision_point: Point of the process in which a decision happens: - if not specified, the method crashes, but provides a list of possible decision points - if specified, the method goes on and produce the decision tree
attributes: Attributes of the log. If not specified, then an automatic attribute selection is performed.
parameters: Parameters of the algorithm

Returns#

X: features
y: Target class
class_name: Target class names

pm4py.algo.decision_mining.algorithm.get_decisions_table(log0, net, initial_marking, final_marking, attributes=None, use_trace_attributes=False, k=1, pre_decision_points=None, trace_attributes=None, parameters=None)[source]#

Gets a decision table out of a log and an accepting Petri net

Parameters#

log0: Event log
net: Petri net
initial_marking: Initial marking
final_marking: Final marking
attributes: List of attributes which are considered (if not provided, all the attributes are considered)
use_trace_attributes: Include trace attributes in the decision table
k: Number that determines the number of last activities to take into account
pre_decision_points: List of Strings of place Names that have to be considered as decision points. If not provided, the decision points are inferred from the Petri net
trace_attributes: List of trace attributes to consider
parameters: Possible parameters of the algorithm

Returns#

I: decision table
decision_points: The decision points as places of the Petri net, which are the keys of a dictionary having as values the list of transitions that are target

pm4py.algo.decision_mining.algorithm.prepare_event_log(log)[source]#: If trace attributes are considered, it is possible that trace attributes have the same name as event attributes. To tackle this issue, the attributes get renamed. For trace attributes, we add “t_” at the beginning of the dictionary keys. For event attributes, we add “e_” at the beginning of the dict keys. :param log: :return:

pm4py.algo.decision_mining.algorithm.prepare_attributes(attributes)[source]#: Method that “e_” in front of every attribute if trace attributes are considered. :param attributes: List of event attributes that the user wants to consider. :return: list of edited attribute names

pm4py.algo.decision_mining.algorithm.get_decision_points(net, labels=False, pre_decision_points=None, parameters=None)[source]#: The goal is to get all decision places. These are places where there are at least two outgoing arcs. :type labels: bool :param net: Petri Net where decision points are discovered (places with at least two outgoing arcs) :param labels: If someone wants to get the labels of the transitions after a decision point and not the “ID” :return:

pm4py.algo.decision_mining.algorithm.simplify_token_replay(replay)[source]#

pm4py.algo.decision_mining.algorithm.get_attributes(log, decision_points, attributes, use_trace_attributes, trace_attributes, k, net, initial_marking, final_marking, decision_points_names, parameters=None)[source]#

This method aims to construct for each decision place a table where for each decision place a list if given with the: label of the later decision and as value the given attributes

Parameters:

log – Log on which the method is applied
alignments – Computed alignments for a log and a model
decision_points – Places that have multiple outgoing arcs
attributes – Attributes that are considered
use_trace_attributes – If trace attributes have to be considered or not
trace_attributes – List of trace attributes that are considered
k – Taking k last activities into account

Returns:

Dictionary that has as keys the decision places. The value for this key is a list.

The content of these lists are tuples. The first element of these tuples is information regrading the attributes, the second element of these tuples is the transition which chosen in a decision.

pm4py.algo.decision_mining.algorithm.encode_target(df, target_column)[source]#: Add column to df with integers for the target. Method taken from: http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html Args —- df – pandas DataFrame. target_column – column to map to int, producing

new Target column.

Returns#

df_mod – modified DataFrame. targets – list of target names.