pm4py.algo.conformance.alignments.edit_distance.variants package#

PM4Py â€“ A Process Mining Library for Python

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

Submodules#

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

Bases: Enum

PERFORM_ANTI_ALIGNMENT = 'perform_anti_alignment'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

pm4py.algo.conformance.alignments.edit_distance.variants.edit_distance.apply(log1: EventLog, log2: EventLog, parameters: Dict[str | Parameters, Any] | None = None) → List[Dict[str, Any]][source]#

Aligns each trace of the first log against the second log, minimizing the edit distance

Parameters#

log1: First log
log2: Second log
parameters: Parameters of the algorithm

Returns#

aligned_traces: List that contains, for each trace of the first log, the corresponding alignment

pm4py.algo.conformance.alignments.edit_distance.variants.edit_distance.align_trace(trace: Trace, list_encodings: List[str], set_encodings: Set[str], mapping: Dict[str, str], cache_align: Dict[Any, Any] | None = None, parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, Any][source]#

Aligns a trace against a list of traces, minimizing the edit distance

Parameters#

trace: Trace
list_encodings: List of encoded traces (the same as set_encodings, but as a list)
set_encodings: Set of encoded traces (the same as list_encodings, but as a set), useful to quickly check if the provided trace is contained in the traces of the other log
mapping: Mapping (of activities to characters)
cache_align: Cache of the alignments
parameters: Parameters of the algorithm

Returns#

aligned_trace: Aligned trace

pm4py.algo.conformance.alignments.edit_distance.variants.edit_distance.project_log_on_variant(log: EventLog | DataFrame, variant: List[str], parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Projects the traces of an event log to the specified variant, in order to assess the conformance of the different directly-follows relationships and their performance (as the timestamps are recorded). The result is a event log where each ‘projected’ trace can be replayed on the given variant. Each event of a ‘projected’ trace has the ‘@@is_conforming’ attribute set to: - True when the activity is mimicked by the original trace (sync move) - False when the activity is not reflected in the original trace (move-on-model) Move-on-log (activities of the trace that are not mimicked by the variant) are skipped altogether.

Minimum Viable Example:

import pm4py from pm4py.algo.conformance.alignments.edit_distance.variants import edit_distance

log = pm4py.read_xes(“tests/input_data/receipt.xes”, return_legacy_log_object=True) variant = (‘Confirmation of receipt’, ‘T02 Check confirmation of receipt’, ‘T04 Determine confirmation of receipt’,

‘T05 Print and send confirmation of receipt’, ‘T06 Determine necessity of stop advice’, ‘T10 Determine necessity to stop indication’)

projected_log = edit_distance.project_log_on_variant(log, variant) pm4py.write_xes(projected_log, “projected_log2.xes”)

Parameters#

log

Event log

variant

Considered variant

parameters

Parameters of the method, including:

Parameters.ACTIVITY_KEY => the attribute of the event log to be used as activity
Parameters.TIMESTAMP_KEY => the attribute of the event log to be used as timestamp

Returns#

projected_log: Projected event log with the aforementioned features