pm4py.algo.discovery.correlation_mining.variants 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.algo.discovery.correlation_mining.variants.classic 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.discovery.correlation_mining.variants.classic.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'#

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#

EXACT_TIME_MATCHING = 'exact_time_matching'#

INDEX_KEY = 'index_key'#

pm4py.algo.discovery.correlation_mining.variants.classic.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[str | Parameters, Any] | None = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]#

Apply the correlation miner to an event stream (other types of logs are converted to that)

The approach is described in: Pourmirza, Shaya, Remco Dijkman, and Paul Grefen. “Correlation miner: mining business process models and event correlations without case identifiers.” International Journal of Cooperative Information Systems 26.02 (2017): 1742002.

Parameters#

log: Log object
parameters: Parameters of the algorithm

Returns#

dfg: DFG
performance_dfg: Performance DFG (containing the estimated performance for the arcs)

pm4py.algo.discovery.correlation_mining.variants.classic.resolve_lp_get_dfg(PS_matrix, duration_matrix, activities, activities_counter)[source]#

Resolves a LP problem to get a DFG

Parameters#

PS_matrix: Precede-succeed matrix
duration_matrix: Duration matrix
activities: List of activities of the log
activities_counter: Counter of the activities

Returns#

dfg: DFG
performance_dfg: Performance DFG (containing the estimated performance for the arcs)

pm4py.algo.discovery.correlation_mining.variants.classic.get_PS_dur_matrix(activities_grouped, activities, parameters=None)[source]#

Combined methods to get the two matrixes

Parameters#

activities_grouped: Grouped activities
activities: List of activities of the log
parameters: Parameters of the algorithm

Returns#

PS_matrix: Precede-succeed matrix
duration_matrix: Duration matrix

pm4py.algo.discovery.correlation_mining.variants.classic.preprocess_log(log, activities=None, parameters=None)[source]#

Preprocess a log to enable correlation mining

Parameters#

log: Log object
activities: (if provided) list of activities of the log
parameters: Parameters of the algorithm

Returns#

transf_stream: Transformed stream
activities_grouped: Grouped activities
activities: List of activities of the log

pm4py.algo.discovery.correlation_mining.variants.classic.get_precede_succeed_matrix(activities, activities_grouped, timestamp_key, start_timestamp_key)[source]#

Calculates the precede succeed matrix

Parameters#

activities: Ordered list of activities of the log
activities_grouped: Grouped list of activities
timestamp_key: Timestamp key
start_timestamp_key: Start timestamp key (events start)

Returns#

precede_succeed_matrix: Precede succeed matrix

pm4py.algo.discovery.correlation_mining.variants.classic.get_duration_matrix(activities, activities_grouped, timestamp_key, start_timestamp_key, exact=False)[source]#

Calculates the duration matrix

Parameters#

activities: Ordered list of activities of the log
activities_grouped: Grouped list of activities
timestamp_key: Timestamp key
start_timestamp_key: Start timestamp key (events start)
exact: Performs an exact matching of the times (True/False)

Returns#

duration_matrix: Duration matrix

pm4py.algo.discovery.correlation_mining.variants.classic_split 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.discovery.correlation_mining.variants.classic_split.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'#

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#

SAMPLE_SIZE = 'sample_size'#

pm4py.algo.discovery.correlation_mining.variants.classic_split.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[str | Parameters, Any] | None = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]#

Applies the correlation miner (splits the log in smaller chunks)

Parameters#

log: Log object
parameters: Parameters of the algorithm

Returns#

dfg: Frequency DFG
performance_dfg: Performance DFG

pm4py.algo.discovery.correlation_mining.variants.trace_based 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.discovery.correlation_mining.variants.trace_based.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'#

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#

CASE_ID_KEY = 'pm4py:param:case_id_key'#

INDEX_KEY = 'index_key'#

pm4py.algo.discovery.correlation_mining.variants.trace_based.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[str | Parameters, Any] | None = None) → Tuple[Dict[Tuple[str, str], int], Dict[Tuple[str, str], float]][source]#

Novel approach of correlation mining, that creates the PS-matrix and the duration matrix using the order list of events of each trace of the log

Parameters#

log: Event log
parameters: Parameters

Returns#

dfg: DFG
performance_dfg: Performance DFG (containing the estimated performance for the arcs)

pm4py.algo.discovery.correlation_mining.variants.trace_based.resolve_lp_get_dfg(PS_matrix, duration_matrix, activities, activities_counter)[source]#

Resolves a LP problem to get a DFG

Parameters#

PS_matrix: Precede-succeed matrix
duration_matrix: Duration matrix
activities: List of activities of the log
activities_counter: Counter for the activities of the log

Returns#

dfg: Frequency DFG
performance_dfg: Performance DFG

pm4py.algo.discovery.correlation_mining.variants.trace_based.get_PS_duration_matrix(activities, trace_grouped_list, parameters=None)[source]#

Gets the precede-succeed matrix

Parameters#

activities: Activities
trace_grouped_list: Grouped list of simplified traces (per activity)
parameters: Parameters of the algorithm

Returns#

PS_matrix: precede-succeed matrix
duration_matrix: Duration matrix

pm4py.algo.discovery.correlation_mining.variants.trace_based.preprocess_log(log, activities=None, activities_counter=None, parameters=None)[source]#

Preprocess the log to get a grouped list of simplified traces (per activity)

Parameters#

log: Log object
activities: (if provided) activities of the log
activities_counter: (if provided) counter of the activities of the log
parameters: Parameters of the algorithm

Returns#

traces_list: List of simplified traces of the log
trace_grouped_list: Grouped list of simplified traces (per activity)
activities: Activities of the log
activities_counter: Activities counter

pm4py.algo.discovery.correlation_mining.variants.trace_based.get_precede_succeed_matrix(activities, trace_grouped_list, timestamp_key, start_timestamp_key)[source]#

Calculates the precede succeed matrix

Parameters#

activities: Sorted list of activities of the log
trace_grouped_list: A list of lists of lists, containing for each trace and each activity the events having such activity
timestamp_key: The key to be used as timestamp
start_timestamp_key: The key to be used as start timestamp

Returns#

mat: The precede succeed matrix

pm4py.algo.discovery.correlation_mining.variants.trace_based.get_duration_matrix(activities, trace_grouped_list, timestamp_key, start_timestamp_key)[source]#

Calculates the duration matrix

Parameters#

activities: Sorted list of activities of the log
trace_grouped_list: A list of lists of lists, containing for each trace and each activity the events having such activity
timestamp_key: The key to be used as timestamp
start_timestamp_key: The key to be used as start timestamp

Returns#

mat: The duration matrix