def perform_alignments(log, petri_string, parameters=None):
"""
Perform alignments
Parameters
------------
log
Log
net
Petri net
parameters
Parameters of the algorithm
Returns
-------------
petri
SVG of the decorated Petri
table
SVG of the decorated table
"""
if parameters is None:
parameters = {}
net, im, fm = pnml.import_petri_from_string(petri_string,
parameters=parameters)
parameters_align = copy(parameters)
parameters_align["ret_tuple_as_trans_desc"] = True
alignments = align_factory.apply(log,
net,
im,
fm,
parameters=parameters_align)
decorations = alignments_decoration.get_alignments_decoration(
net, im, fm, aligned_traces=alignments, parameters=parameters)
gviz_on_petri = pn_vis_factory.apply(net,
im,
fm,
aggregated_statistics=decorations,
variant="alignments",
parameters={"format": "svg"})
svg_on_petri = get_base64_from_gviz(gviz_on_petri)
parameters_table = copy(parameters)
parameters_table["format"] = "svg"
gviz_table = align_table_factory.apply(log,
alignments,
parameters=parameters_table)
svg_table = get_base64_from_gviz(gviz_table)
return svg_on_petri, svg_table
def apply(df, delay, parameters=None):
"""
Perform CTMC simulation on a dataframe
Parameters
-------------
df
Dataframe
delay
Delay
parameters
Possible parameters of the algorithm
Returns
-------------
graph
Case duration graph
"""
if parameters is None:
parameters = {}
tang_reach_graph, transient_analysis, fillcolors = ctmc.transient_analysis_from_dataframe(
df, delay, parameters=parameters)
viz = ts_vis_factory.apply(tang_reach_graph,
parameters={
"format": "svg",
"force_names": transient_analysis,
"fillcolors": fillcolors
})
gviz_base64 = base64.b64encode(str(viz).encode('utf-8'))
return get_base64_from_gviz(viz), gviz_base64
def apply(log, parameters=None):
"""
Gets the frequency DFG
Parameters
------------
log
Log
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
log = attributes_filter.filter_log_on_max_no_activities(
log,
max_no_activities=constants.MAX_NO_ACTIVITIES,
parameters=parameters)
filtered_log = auto_filter.apply_auto_filter(log, parameters=parameters)
activities_count = attributes_filter.get_attribute_values(
filtered_log, activity_key)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
filtered_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(filtered_log,
parameters=parameters).keys())
dfg = dfg_factory.apply(filtered_log, parameters=parameters)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
parameters["format"] = "svg"
parameters["start_activities"] = start_activities
parameters["end_activities"] = end_activities
gviz = dfg_vis_factory.apply(dfg,
log=filtered_log,
variant="frequency",
parameters=parameters)
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret_graph = get_graph.get_graph_from_dfg(dfg, start_activities,
end_activities)
net, im, fm = dfg_conv_factory.apply(dfg,
parameters={
"start_activities":
start_activities,
"end_activities": end_activities
})
return get_base64_from_gviz(gviz), export_petri_as_string(
net, im, fm
), ".pnml", "xes", activities, start_activities, end_activities, gviz_base64, ret_graph, "dfg", "freq", None, "", activity_key
def apply(dataframe, parameters=None):
"""
Gets the performance DFG
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=constants.MAX_NO_ACTIVITIES)
dataframe, end_activities = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
end_activities = list(end_activities.keys())
[dfg, dfg_perf
] = df_statistics.get_dfg_graph(dataframe,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
sort_caseid_required=False,
sort_timestamp_along_case_id=False,
measure="both")
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
activities = list(activities_count.keys())
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
dfg_perf = {x: y for x, y in dfg_perf.items() if x in dfg}
start_activities = list(
start_activities_filter.get_start_activities(
dataframe, parameters=parameters).keys())
gviz = dfg_vis_factory.apply(dfg_perf,
activities_count=activities_count,
variant="performance",
parameters={
"format": "svg",
"start_activities": start_activities,
"end_activities": end_activities
})
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret_graph = get_graph.get_graph_from_dfg(dfg, start_activities,
end_activities)
net, im, fm = dfg_conv_factory.apply(dfg,
parameters={
"start_activities":
start_activities,
"end_activities": end_activities
})
return get_base64_from_gviz(gviz), export_petri_as_string(
net, im, fm
), ".pnml", "parquet", activities, start_activities, end_activities, gviz_base64, ret_graph, "dfg", "perf", None, "", activity_key
def apply(dataframe, parameters=None):
"""
Gets the Petri net through Inductive Miner, decorated by frequency metric
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=constants.MAX_NO_ACTIVITIES)
dataframe, end_activities = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
end_activities = list(end_activities.keys())
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
dataframe, parameters=parameters).keys())
dfg = df_statistics.get_dfg_graph(dataframe,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
sort_caseid_required=False,
sort_timestamp_along_case_id=False)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
net, im, fm = inductive_miner.apply_dfg(dfg,
parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
spaths = get_shortest_paths(net)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(
net, dfg, spaths, activities_count, variant="frequency")
gviz = pn_vis_factory.apply(net,
im,
fm,
parameters={"format": "svg"},
variant="frequency",
aggregated_statistics=aggregated_statistics)
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret_graph = get_graph.get_graph_from_petri(net, im, fm)
return get_base64_from_gviz(gviz), export_petri_as_string(
net, im, fm
), ".pnml", "parquet", activities, start_activities, end_activities, gviz_base64, ret_graph, "inductive", "freq", None, "", activity_key
def apply(log, parameters=None):
"""
Gets the process tree using Inductive Miner Directly-Follows
Parameters
------------
log
Log
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
log = attributes_filter.filter_log_on_max_no_activities(
log,
max_no_activities=constants.MAX_NO_ACTIVITIES,
parameters=parameters)
filtered_log = auto_filter.apply_auto_filter(log, parameters=parameters)
activities_count = attributes_filter.get_attribute_values(
filtered_log, activity_key)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
filtered_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(filtered_log,
parameters=parameters).keys())
dfg = dfg_factory.apply(filtered_log, parameters=parameters)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
tree = inductive_miner.apply_tree_dfg(dfg,
parameters=parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
parameters["format"] = "svg"
gviz = pt_vis_factory.apply(tree, parameters=parameters)
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
return get_base64_from_gviz(gviz), None, "", "xes", activities, start_activities, end_activities, gviz_base64, [], "tree", "freq", None, "", activity_key
def apply(log, parameters=None):
"""
Gets the Petri net through Inductive Miner, decorated by frequency metric
Parameters
------------
log
Log
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
# reduce the depth of the search done by token-based replay
token_replay.MAX_REC_DEPTH = 1
token_replay.MAX_IT_FINAL1 = 1
token_replay.MAX_IT_FINAL2 = 1
token_replay.MAX_REC_DEPTH_HIDTRANSENABL = 1
log = attributes_filter.filter_log_on_max_no_activities(
log,
max_no_activities=constants.MAX_NO_ACTIVITIES,
parameters=parameters)
filtered_log = auto_filter.apply_auto_filter(log, parameters=parameters)
activities_count = attributes_filter.get_attribute_values(
filtered_log, activity_key)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
filtered_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(filtered_log,
parameters=parameters).keys())
dfg = dfg_factory.apply(filtered_log, parameters=parameters)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
net, im, fm = inductive_miner.apply_dfg(dfg,
parameters=parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
parameters["format"] = "svg"
gviz = pn_vis_factory.apply(net,
im,
fm,
log=filtered_log,
variant="frequency",
parameters=parameters)
svg = get_base64_from_gviz(gviz)
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret_graph = get_graph.get_graph_from_petri(net, im, fm)
return svg, export_petri_as_string(
net, im, fm
), ".pnml", "xes", activities, start_activities, end_activities, gviz_base64, ret_graph, "inductive", "freq", None, "", activity_key
def apply(dataframe, parameters=None):
"""
Gets the process tree using Inductive Miner Directly-Follows
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=constants.MAX_NO_ACTIVITIES)
dataframe, end_activities = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
end_activities = list(end_activities.keys())
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
dataframe, parameters=parameters).keys())
dfg = df_statistics.get_dfg_graph(dataframe,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
sort_caseid_required=False,
sort_timestamp_along_case_id=False)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
tree = inductive_miner.apply_tree_dfg(dfg,
parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
gviz = pt_vis_factory.apply(tree, parameters={"format": "svg"})
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
return get_base64_from_gviz(gviz), None, "", "parquet", activities, start_activities, end_activities, gviz_base64, [], "tree", "freq", None, "", activity_key