def filter_variants(log, variants, retain=True):
"""
Filter a log on a specified set of variants
Parameters
---------------
log
Event log
variants
collection of variants to filter; A variant should be specified as a list of activity names, e.g., ['a','b','c']
retain
boolean; if True all traces conforming to the specified variants are retained; if False, all those traces are removed
Returns
--------------
filtered_log
Filtered log object
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.algo.filtering.pandas.variants import variants_filter
return variants_filter.apply(
log, [",".join(v) for v in variants],
parameters={variants_filter.Parameters.POSITIVE: retain})
else:
from pm4py.algo.filtering.log.variants import variants_filter
return variants_filter.apply(
log, [",".join(v) for v in variants],
parameters={variants_filter.Parameters.POSITIVE: retain})
def filter_paths(log, allowed_paths, positive=True):
"""
Filter a log_skeleton on a specified list of paths
Parameters
---------------
log
Log object
allowed_paths
Allowed/forbidden paths
positive
Parameter that says whether the paths
should be kept/removed
Returns
----------------
filtered_log
Filtered log_skeleton object
"""
if check_is_dataframe(log):
from pm4py.algo.filtering.pandas.paths import paths_filter
return paths_filter.apply(log, allowed_paths, parameters={paths_filter.Parameters.POSITIVE: positive})
else:
from pm4py.algo.filtering.log.paths import paths_filter
return paths_filter.apply(log, allowed_paths, parameters={paths_filter.Parameters.POSITIVE: positive})
def filter_directly_follows_relation(log, relations, retain=True):
"""
Retain traces that contain any of the specified 'directly follows' relations.
For example, if relations == [('a','b'),('a','c')] and log [<a,b,c>,<a,c,b>,<a,d,b>]
the resulting log will contain traces describing [<a,b,c>,<a,c,b>].
Parameters
---------------
log
Log object
relations
List of activity name pairs, which are allowed/forbidden paths
retain
Parameter that says whether the paths
should be kept/removed
Returns
----------------
filtered_log
Filtered log object
"""
if check_is_dataframe(log):
from pm4py.algo.filtering.pandas.paths import paths_filter
return paths_filter.apply(
log,
relations,
parameters={paths_filter.Parameters.POSITIVE: retain})
else:
from pm4py.algo.filtering.log.paths import paths_filter
return paths_filter.apply(
log,
relations,
parameters={paths_filter.Parameters.POSITIVE: retain})
def filter_trace_attribute_values(log: Union[EventLog, pd.DataFrame], attribute_key: str, values: List[str],
retain: bool = True) -> Union[EventLog, pd.DataFrame]:
"""
Filter a log on the values of a trace attribute
Parameters
--------------
log
Event log
attribute_key
Attribute to filter
values
Values to filter (list of)
retain
Boolean value (keep/discard matching traces)
Returns
--------------
filtered_log
Filtered event log
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.algo.filtering.pandas.attributes import attributes_filter
return attributes_filter.apply(log, values,
parameters={attributes_filter.Parameters.ATTRIBUTE_KEY: attribute_key,
attributes_filter.Parameters.POSITIVE: retain})
else:
from pm4py.algo.filtering.log.attributes import attributes_filter
return attributes_filter.apply_trace_attribute(log, values, parameters={
attributes_filter.Parameters.ATTRIBUTE_KEY: attribute_key, attributes_filter.Parameters.POSITIVE: retain})
def discover_dfg(log: Union[EventLog, pd.DataFrame]) -> Tuple[dict, dict, dict]:
"""
Discovers a DFG from a log
Parameters
--------------
log
Event log
Returns
--------------
dfg
DFG
start_activities
Start activities
end_activities
End activities
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.objects.dfg.retrieval.pandas import get_dfg_graph
dfg = get_dfg_graph(log)
from pm4py.statistics.start_activities.pandas import get as start_activities_module
from pm4py.statistics.end_activities.pandas import get as end_activities_module
start_activities = start_activities_module.get_start_activities(log)
end_activities = end_activities_module.get_end_activities(log)
else:
from pm4py.algo.discovery.dfg import algorithm as dfg_discovery
dfg = dfg_discovery.apply(log)
from pm4py.statistics.start_activities.log import get as start_activities_module
from pm4py.statistics.end_activities.log import get as end_activities_module
start_activities = start_activities_module.get_start_activities(log)
end_activities = end_activities_module.get_end_activities(log)
return dfg, start_activities, end_activities
def filter_trace_attribute(log, attribute, values, positive=True):
"""
Filter a log_skeleton on the values of a trace attribute
Parameters
--------------
log
Event log_skeleton
attribute
Attribute to filter
values
Values to filter (list of)
positive
Boolean value (keep/discard cases)
Returns
--------------
filtered_log
Filtered event log_skeleton
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.algo.filtering.pandas.attributes import attributes_filter
return attributes_filter.apply(log, values, parameters={attributes_filter.Parameters.ATTRIBUTE_KEY: attribute,
attributes_filter.Parameters.POSITIVE: positive})
else:
from pm4py.algo.filtering.log.attributes import attributes_filter
return attributes_filter.apply_trace_attribute(log, values, parameters={
attributes_filter.Parameters.ATTRIBUTE_KEY: attribute, attributes_filter.Parameters.POSITIVE: positive})
def get_variants(log: Union[EventLog, pd.DataFrame]) -> Dict[str, List[Trace]]:
"""
Gets the variants from the log
Parameters
--------------
log
Event log
Returns
--------------
variants
Dictionary of variants along with their count
"""
import pm4py
if pm4py.util.variants_util.VARIANT_SPECIFICATION == pm4py.util.variants_util.VariantsSpecifications.STRING:
import warnings
warnings.warn('pm4py.get_variants is deprecated. Please use pm4py.get_variants_as_tuples instead.')
if pm4py.util.variants_util.VARIANT_SPECIFICATION == pm4py.util.variants_util.VariantsSpecifications.LIST:
raise Exception('Please use pm4py.get_variants_as_tuples')
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.variants.pandas import get
return get.get_variants_count(log)
else:
from pm4py.statistics.variants.log import get
return get.get_variants(log)
def get_variants_as_tuples(log: Union[EventLog, pd.DataFrame]) -> Dict[Tuple[str], List[Trace]]:
"""
Gets the variants from the log
(where the keys are tuples and not strings)
Parameters
--------------
log
Event log
Returns
--------------
variants
Dictionary of variants along with their count
"""
import pm4py
# the behavior of PM4Py is changed to allow this to work
pm4py.util.variants_util.VARIANT_SPECIFICATION = pm4py.util.variants_util.VariantsSpecifications.LIST
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.variants.pandas import get
return get.get_variants_count(log)
else:
from pm4py.statistics.variants.log import get
return get.get_variants(log)
def discover_handover_of_work_network(log: Union[EventLog, pd.DataFrame], beta=0):
"""
Calculates the handover of work network of the event log.
The handover of work network is essentially the DFG of the event log, however, using the
resource as a node of the graph, instead of the activity.
As such, to use this, resource information should be present in the event log.
Parameters
---------------
log
Event log or Pandas dataframe
beta
beta parameter for Handover metric
Returns
---------------
metric_values
Values of the metric
"""
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_dataframe(log):
check_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.HANDOVER_PANDAS, parameters={"beta": beta})
else:
return sna.apply(log, variant=sna.Variants.HANDOVER_LOG, parameters={"beta": beta})
def filter_end_activities(log, activities, retain=True):
"""
Filter cases having an end activity in the provided list
Parameters
---------------
log
Log object
activities
List of admitted end activities
retain
if True, we retain the traces containing the given activities, if false, we drop the traces
Returns
---------------
filtered_log
Filtered log object
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.algo.filtering.pandas.end_activities import end_activities_filter
return end_activities_filter.apply(
log,
activities,
parameters={end_activities_filter.Parameters.POSITIVE: retain})
else:
from pm4py.algo.filtering.log.end_activities import end_activities_filter
return end_activities_filter.apply(
log,
activities,
parameters={end_activities_filter.Parameters.POSITIVE: retain})
def filter_variants_percentage(
log: Union[EventLog, pd.DataFrame],
threshold: float = 0.8) -> Union[EventLog, pd.DataFrame]:
"""
Filter a log on the percentage of variants
Parameters
---------------
log
Event log
threshold
Percentage (scale 0.1) of admitted variants
Returns
--------------
filtered_log
Filtered log object
"""
if check_is_dataframe(log):
raise Exception(
"filtering variants percentage on Pandas dataframe is currently not available! please convert the dataframe to event log with the method: log = pm4py.convert_to_event_log(df)"
)
else:
from pm4py.algo.filtering.log.variants import variants_filter
return variants_filter.filter_log_variants_percentage(
log, percentage=threshold)
def filter_start_activities(
log: Union[EventLog, pd.DataFrame],
activities: List[str],
retain: bool = True) -> Union[EventLog, pd.DataFrame]:
"""
Filter cases having a start activity in the provided list
Parameters
--------------
log
Log object
activities
List start activities
retain
if True, we retain the traces containing the given activities, if false, we drop the traces
Returns
--------------
filtered_log
Filtered log object
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.algo.filtering.pandas.start_activities import start_activities_filter
return start_activities_filter.apply(
log,
activities,
parameters={start_activities_filter.Parameters.POSITIVE: retain})
else:
from pm4py.algo.filtering.log.start_activities import start_activities_filter
return start_activities_filter.apply(
log,
activities,
parameters={start_activities_filter.Parameters.POSITIVE: retain})
def filter_eventually_follows_relation(log: Union[EventLog, pd.DataFrame], relations: List[str], retain: bool = True) -> \
Union[EventLog, pd.DataFrame]:
"""
Retain traces that contain any of the specified 'eventually follows' relations.
For example, if relations == [('a','b'),('a','c')] and log [<a,b,c>,<a,c,b>,<a,d,b>]
the resulting log will contain traces describing [<a,b,c>,<a,c,b>,<a,d,b>].
Parameters
---------------
log
Log object
relations
List of activity name pairs, which are allowed/forbidden paths
retain
Parameter that says whether the paths
should be kept/removed
Returns
----------------
filtered_log
Filtered log object
"""
if check_is_dataframe(log):
from pm4py.algo.filtering.pandas.ltl import ltl_checker
if retain:
cases = set()
else:
cases = set(log[constants.CASE_CONCEPT_NAME])
for path in relations:
filt_log = ltl_checker.A_eventually_B(log, path[0], path[1],
parameters={ltl_checker.Parameters.POSITIVE: retain})
this_traces = set(filt_log[constants.CASE_CONCEPT_NAME])
if retain:
cases = cases.union(this_traces)
else:
cases = cases.intersection(this_traces)
return log[log[constants.CASE_CONCEPT_NAME].isin(cases)]
else:
from pm4py.objects.log.log import EventLog
from pm4py.algo.filtering.log.ltl import ltl_checker
if retain:
cases = set()
else:
cases = set(id(trace) for trace in log)
for path in relations:
filt_log = ltl_checker.A_eventually_B(log, path[0], path[1],
parameters={ltl_checker.Parameters.POSITIVE: retain})
this_traces = set(id(trace) for trace in filt_log)
if retain:
cases = cases.union(this_traces)
else:
cases = cases.intersection(this_traces)
filtered_log = EventLog(attributes=log.attributes, extensions=log.extensions, omni_present=log.omni_present,
classifiers=log.classifiers)
for trace in log:
if id(trace) in cases:
filtered_log.append(trace)
return filtered_log
def filter_time_range(log: Union[EventLog, pd.DataFrame],
dt1: str,
dt2: str,
mode="events") -> Union[EventLog, pd.DataFrame]:
"""
Filter a log on a time interval
Parameters
----------------
log
Log object
dt1
Left extreme of the interval
dt2
Right extreme of the interval
mode
Modality of filtering (events, traces_contained, traces_intersecting)
events: any event that fits the time frame is retained
traces_contained: any trace completely contained in the timeframe is retained
traces_intersecting: any trace intersecting with the time-frame is retained.
Returns
----------------
filtered_log
Filtered log
"""
if check_is_dataframe(log):
from pm4py.algo.filtering.pandas.timestamp import timestamp_filter
if mode == "events":
return timestamp_filter.apply_events(log, dt1, dt2)
elif mode == "traces_contained":
return timestamp_filter.filter_traces_contained(log, dt1, dt2)
elif mode == "traces_intersecting":
return timestamp_filter.filter_traces_intersecting(log, dt1, dt2)
else:
warnings.warn('mode provided: ' + mode +
' is not recognized; original log returned!')
return log
else:
from pm4py.algo.filtering.log.timestamp import timestamp_filter
if mode == "events":
return timestamp_filter.apply_events(log, dt1, dt2)
elif mode == "traces_contained":
return timestamp_filter.filter_traces_contained(log, dt1, dt2)
elif mode == "traces_intersecting":
return timestamp_filter.filter_traces_intersecting(log, dt1, dt2)
else:
warnings.warn('mode provided: ' + mode +
' is not recognized; original log returned!')
return log
def filter_event_attribute_values(log: Union[EventLog, pd.DataFrame], attribute_key: str, values: List[str],
level: str = "case", retain: bool = True) -> Union[EventLog, pd.DataFrame]:
"""
Filter a log object on the values of some event attribute
Parameters
--------------
log
Log object
attribute_key
Attribute to filter
values
Admitted (or forbidden) values
level
Specifies how the filter should be applied ('case' filters the cases where at least one occurrence happens,
'event' filter the events eventually trimming the cases)
retain
Specified if the values should be kept or removed
Returns
--------------
filtered_log
Filtered log object
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.algo.filtering.pandas.attributes import attributes_filter
if level == "event":
return attributes_filter.apply_events(log, values,
parameters={constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute_key,
attributes_filter.Parameters.POSITIVE: retain})
elif level == "case":
return attributes_filter.apply(log, values, parameters={
constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute_key,
attributes_filter.Parameters.POSITIVE: retain})
else:
from pm4py.algo.filtering.log.attributes import attributes_filter
if level == "event":
return attributes_filter.apply_events(log, values,
parameters={constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute_key,
attributes_filter.Parameters.POSITIVE: retain})
elif level == "case":
return attributes_filter.apply(log, values, parameters={
constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute_key,
attributes_filter.Parameters.POSITIVE: retain})
def filter_attribute_values(log, attribute, values, how="cases", positive=True):
"""
Filter a log_skeleton object on the values of some attribute
Parameters
--------------
log
Log object
attribute
Attribute to filter
values
Admitted (or forbidden) values
how
Specifies how the filter should be applied (cases filters the cases where at least one occurrence happens,
events filter the events eventually trimming the cases)
positive
Specified if the values should be kept or removed
Returns
--------------
filtered_log
Filtered log_skeleton object
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.algo.filtering.pandas.attributes import attributes_filter
if how == "events":
return attributes_filter.apply_events(log, values,
parameters={constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute,
attributes_filter.Parameters.POSITIVE: positive})
elif how == "cases":
return attributes_filter.apply(log, values, parameters={
constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute, attributes_filter.Parameters.POSITIVE: positive})
else:
from pm4py.algo.filtering.log.attributes import attributes_filter
if how == "events":
return attributes_filter.apply_events(log, values,
parameters={constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute,
attributes_filter.Parameters.POSITIVE: positive})
else:
return attributes_filter.apply(log, values, parameters={
constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute, attributes_filter.Parameters.POSITIVE: positive})
def get_attributes(log):
"""
Returns the attributes at the event level of the log
Parameters
---------------
log
Log object
Returns
---------------
attributes_list
List of attributes contained in the log
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
return list(log.columns)
else:
from pm4py.statistics.attributes.log import get
return list(get.get_all_event_attributes_from_log(log))
def discover_activity_based_resource_similarity(log: Union[EventLog, pd.DataFrame]):
"""
Calculates similarity between the resources in the event log, based on their activity profiles.
Parameters
---------------
log
Event log or Pandas dataframe
Returns
---------------
metric_values
Values of the metric
"""
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_dataframe(log):
check_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.JOINTACTIVITIES_PANDAS)
else:
return sna.apply(log, variant=sna.Variants.JOINTACTIVITIES_LOG)
def discover_eventually_follows_graph(log: Union[EventLog, pd.DataFrame]) -> Dict[Tuple[str, str], int]:
"""
Gets the eventually follows graph from a log object
Parameters
---------------
log
Log object
Returns
---------------
eventually_follows_graph
Dictionary of tuples of activities that eventually follows each other; along with the number of occurrences
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.eventually_follows.pandas import get
return get.apply(log)
else:
from pm4py.statistics.eventually_follows.log import get
return get.apply(log)
def discover_working_together_network(log: Union[EventLog, pd.DataFrame]):
"""
Calculates the working together network of the process.
Two nodes resources are connected in the graph if the resources collaborate on an instance of the process.
Parameters
---------------
log
Event log or Pandas dataframe
Returns
---------------
metric_values
Values of the metric
"""
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_dataframe(log):
check_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.WORKING_TOGETHER_PANDAS)
else:
return sna.apply(log, variant=sna.Variants.WORKING_TOGETHER_LOG)
def get_trace_attributes(log):
"""
Gets the attributes at the trace level of a log object
Parameters
----------------
log
Log object
Returns
---------------
trace_attributes_list
List of attributes at the trace level
"""
from pm4py.util import constants
if check_is_dataframe(log):
check_dataframe_columns(log)
return [x for x in list(log.columns) if x.startswith(constants.CASE_ATTRIBUTE_PREFIX)]
else:
from pm4py.statistics.attributes.log import get
return list(get.get_all_trace_attributes_from_log(log))
def get_start_activities(log):
"""
Returns the start activities from a log object
Parameters
---------------
log
Log object
Returns
---------------
start_activities
Dictionary of start activities along with their count
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.start_activities.pandas import get
return get.get_start_activities(log)
else:
from pm4py.statistics.start_activities.log import get
return get.get_start_activities(log)
def get_case_arrival_average(log: Union[EventLog, pd.DataFrame]) -> float:
"""
Gets the average difference between the start times of two consecutive cases
Parameters
---------------
log
Log object
Returns
---------------
case_arrival_average
Average difference between the start times of two consecutive cases
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.traces.pandas import case_arrival
return case_arrival.get_case_arrival_avg(log)
else:
from pm4py.statistics.traces.log import case_arrival
return case_arrival.get_case_arrival_avg(log)
def get_end_activities(log):
"""
Returns the end activities of a log
Parameters
---------------
log
Lob object
Returns
---------------
end_activities
Dictionary of end activities along with their count
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.end_activities.pandas import get
return get.get_end_activities(log)
else:
from pm4py.statistics.end_activities.log import get
return get.get_end_activities(log)
def get_variants(log):
"""
Gets the variants from the log
Parameters
--------------
log
Event log
Returns
--------------
variants
Dictionary of variants along with their count
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.variants.pandas import get
return get.get_variants_count(log)
else:
from pm4py.statistics.variants.log import get
return get.get_variants(log)
def discover_subcontracting_network(log: Union[EventLog, pd.DataFrame], n=2):
"""
Calculates the subcontracting network of the process.
Parameters
---------------
log
Event log or Pandas dataframe
n
n parameter for Subcontracting metric
Returns
---------------
metric_values
Values of the metric
"""
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_dataframe(log):
check_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.SUBCONTRACTING_PANDAS, parameters={"n": n})
else:
return sna.apply(log, variant=sna.Variants.SUBCONTRACTING_LOG, parameters={"n": n})
def view_events_per_time_graph(log: Union[EventLog, pd.DataFrame], format: str = "png"):
"""
Visualizes the events per time graph
Parameters
-----------------
log
Log object
format
Format of the visualization (png, svg, ...)
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.attributes.pandas import get as attributes_get
graph = attributes_get.get_kde_date_attribute(log)
else:
from pm4py.statistics.attributes.log import get as attributes_get
graph = attributes_get.get_kde_date_attribute(log)
from pm4py.visualization.graphs import visualizer as graphs_visualizer
graph_vis = graphs_visualizer.apply(graph[0], graph[1], variant=graphs_visualizer.Variants.DATES,
parameters={"format": format})
graphs_visualizer.view(graph_vis)
def view_case_duration_graph(log: Union[EventLog, pd.DataFrame], format: str = "png"):
"""
Visualizes the case duration graph
Parameters
-----------------
log
Log object
format
Format of the visualization (png, svg, ...)
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.traces.pandas import case_statistics
graph = case_statistics.get_kde_caseduration(log)
else:
from pm4py.statistics.traces.log import case_statistics
graph = case_statistics.get_kde_caseduration(log)
from pm4py.visualization.graphs import visualizer as graphs_visualizer
graph_vis = graphs_visualizer.apply(graph[0], graph[1], variant=graphs_visualizer.Variants.CASES,
parameters={"format": format})
graphs_visualizer.view(graph_vis)
def filter_variants_percentage(log, percentage=0.8):
"""
Filter a log_skeleton on the percentage of variants
Parameters
---------------
log
Event log_skeleton
percentage
Percentage of admitted variants
Returns
--------------
filtered_log
Filtered log_skeleton object
"""
if check_is_dataframe(log):
raise Exception(
"filtering variants percentage on Pandas dataframe is currently not available! please convert the dataframe to event log_skeleton with the method: log_skeleton = pm4py.convert_to_event_log(df)")
else:
from pm4py.algo.filtering.log.variants import variants_filter
return variants_filter.filter_log_variants_percentage(log, percentage=percentage)
def get_attribute_values(log, attribute):
"""
Returns the values for a specified attribute
Parameters
---------------
log
Log object
attribute
Attribute
Returns
---------------
attribute_values
Dictionary of values along with their count
"""
if check_is_dataframe(log):
check_dataframe_columns(log)
from pm4py.statistics.attributes.pandas import get
return get.get_attribute_values(log, attribute)
else:
from pm4py.statistics.attributes.log import get
return get.get_attribute_values(log, attribute)
