def execute_script():
log = xes_importer.apply(os.path.join("..", "tests", "compressed_input_data", "09_a32f0n00.xes.gz"))
heu_net = heuristics_miner.apply_heu(log, parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH: 0.99})
gviz = hn_vis.apply(heu_net, parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "svg"})
hn_vis.view(gviz)
net, im, fm = heuristics_miner.apply(log, parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH: 0.99})
gviz2 = petri_vis.apply(net, im, fm, parameters={petri_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg"})
petri_vis.view(gviz2)
def view_heuristics_net(heu_net, format="png"):
"""
Views an heuristics net
Parameters
--------------
heu_net
Heuristics net
format
Format of the visualization (default: png)
"""
from pm4py.visualization.heuristics_net import visualizer as hn_visualizer
parameters = hn_visualizer.Variants.PYDOTPLUS.value.Parameters
gviz = hn_visualizer.apply(heu_net, parameters={parameters.FORMAT: format})
hn_visualizer.view(gviz)
def execute_script():
df = pm4py.read_csv("../tests/input_data/interval_event_log.csv")
log = pm4py.read_xes("../tests/input_data/interval_event_log.xes")
heu_net = plusplus.apply_heu(log, parameters={"heu_net_decoration": "performance"})
heu_net_2 = plusplus.apply_heu_pandas(df, parameters={"heu_net_decoration": "performance"})
gviz = visualizer.apply(heu_net, parameters={"format": "svg"})
visualizer.view(gviz)
gviz2 = visualizer.apply(heu_net_2, parameters={"format": "svg"})
visualizer.view(gviz2)
net1, im1, fm1 = plusplus.apply(log)
net2, im2, fm2 = plusplus.apply(log)
gviz3 = pn_visualizer.apply(net1, im1, fm1, parameters={"format": "svg"})
pn_visualizer.view(gviz3)
gviz4 = pn_visualizer.apply(net2, im2, fm2, parameters={"format": "svg"})
pn_visualizer.view(gviz4)
def execute_script():
# import csv & create log
dataframe = csv_import_adapter.import_dataframe_from_path(
datasourceMockdata(), sep=";")
dataframe = dataframe.rename(columns={
'coID': 'case:concept:name',
'Activity': 'concept:name'
})
log = conversion_factory.apply(dataframe)
# option 1: Directly-Follows Graph, represent frequency or performance
parameters = {constants.PARAMETER_CONSTANT_ACTIVITY_KEY: "concept:name"}
variant = 'frequency'
dfg = dfg_factory.apply(log, variant=variant, parameters=parameters)
gviz1 = dfg_vis_factory.apply(dfg,
log=log,
variant=variant,
parameters=parameters)
dfg_vis_factory.view(gviz1)
# option 2: Heuristics Miner, acts on the Directly-Follows Graph, find common structures, output: Heuristic Net (.svg)
heu_net = heuristics_miner.apply_heu(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.00
})
gviz2 = hn_vis.apply(
heu_net,
parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "svg"})
hn_vis.view(gviz2)
# option 3: Petri Net based on Heuristic Miner (.png)
net, im, fm = heuristics_miner.apply(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.00
})
gviz3 = petri_vis.apply(
net,
im,
fm,
parameters={
petri_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "png"
})
petri_vis.view(gviz3)
def create_graphs_errortypes(log, errortype):
"""
creates visualization for the error types: Heuristic Net
"""
path = "documentation_errortypes"
vis_type = "heuristicnet"
filename = f"{path}/{vis_type}_{errortype}.svg"
heu_net = heuristics_miner.apply_heu(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.00
})
gviz_error = hn_vis.apply(
heu_net,
parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "svg"})
hn_vis.view(gviz_error)
hn_vis.save(gviz_error, filename)
def execute_script():
dataframe2 = csv_import_adapter.import_dataframe_from_path(
datasourceMockdata(), sep=",")
dataframe2 = dataframe2.rename(
columns={
'timestamp': 'time:timestamp',
'source': 'case:concept:name',
'message': 'concept:name'
})
log2 = conversion_factory.apply(dataframe2)
# option 1: Heuristics Miner, acts on the Directly-Follows Graph, find common structures, output: Heuristic Net (.svg)
heu_net = heuristics_miner.apply_heu(
log2,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.99
})
gviz2 = hn_vis.apply(
heu_net,
parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "svg"})
hn_vis.view(gviz2)
# option 2: Petri Net based on Heuristic Miner (.png)
net, im, fm = heuristics_miner.apply(
log2,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.99
})
gviz3 = petri_vis.apply(
net,
im,
fm,
parameters={
petri_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "png"
})
petri_vis.view(gviz3)
# pt_visualizer.view(gviz)
net, initial_marking, final_marking = heuristics_miner.apply(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.99
})
heu_net = heuristics_miner.apply_heu(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.99
})
gviz = hn_visualizer.apply(heu_net)
hn_visualizer.view(gviz)
if (net and initial_marking and final_marking) is None:
raise Exception("This is for your safety. Check configuration")
#%%
gviz = pn_visualizer.apply(net, initial_marking, final_marking)
pn_visualizer.view(gviz)
#%%
# fit log against model
fitness_eval_TOKEN_BASED = replay_fitness_evaluator.apply(
log,
net,
initial_marking,
final_marking,
def create_graphs(without_error, log, approach):
"""
creates visualization: Directly-Follows-Graph and Heuristic Net
"""
# create dfg frequency
path = "common_path"
vis_type = "dfg_frequency"
naming_error = "with_error"
if without_error:
naming_error = "no_error"
file = f"{vis_type}_{approach}_{naming_error}.svg"
filename = f"{path}/{vis_type}_{approach}_{naming_error}.svg"
parameters = {
constants.PARAMETER_CONSTANT_ACTIVITY_KEY: "concept:name",
"format": "svg"
}
variant = 'frequency'
dfg = dfg_factory.apply(log, variant=variant, parameters=parameters)
gviz = dfg_vis_factory.apply(dfg,
log=log,
variant=variant,
parameters=parameters)
dfg_vis_factory.view(gviz)
dfg_vis_factory.save(gviz, filename)
log_info.info("DFG frequency has been stored in '%s' in file '%s'", path,
file)
# create dfg performance
vis_type = "dfg_performance"
file = f"{vis_type}_{approach}_{naming_error}.svg"
filename = f"{path}/{vis_type}_{approach}_{naming_error}.svg"
variant = 'performance'
dfg = dfg_factory.apply(log, variant=variant, parameters=parameters)
gviz = dfg_vis_factory.apply(dfg,
log=log,
variant=variant,
parameters=parameters)
dfg_vis_factory.view(gviz)
dfg_vis_factory.save(gviz, filename)
log_info.info("DFG performance has been stored in '%s' in file '%s'", path,
file)
# create heuristic net
vis_type = "heuristicnet"
file = f"{vis_type}_{approach}_{naming_error}.svg"
filename = f"{path}/{vis_type}_{approach}_{naming_error}.svg"
heu_net = heuristics_miner.apply_heu(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.60
})
gviz = hn_vis.apply(
heu_net,
parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "svg"})
hn_vis.view(gviz)
hn_vis.save(gviz, filename)
log_info.info("Heuristic Net has been stored in '%s' in file '%s'", path,
file)
# save heuristic net in plain-ext format
file = f"{vis_type}_{approach}_{naming_error}.plain-ext"
filename = f"{path}/{vis_type}_{approach}_{naming_error}.plain-ext"
gviz = hn_vis.apply(heu_net,
parameters={
hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT:
"plain-ext"
})
hn_vis.save(gviz, filename)
log_info.info(
"Heuristic Net as .plain-ext has been stored in '%s' "
"in file '%s'", path, file)
# save heuristic net in dot format
file = f"{vis_type}_{approach}_{naming_error}.dot"
filename = f"{path}/{vis_type}_{approach}_{naming_error}.dot"
gviz = hn_vis.apply(
heu_net,
parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "dot"})
hn_vis.save(gviz, filename)
log_info.info(
"Heuristic Net as .dot has been stored in '%s' "
"in file '%s'", path, file)
# save heuristic net in xdot format
file = f"{vis_type}_{approach}_{naming_error}.xdot"
filename = f"{path}/{vis_type}_{approach}_{naming_error}.xdot"
gviz = hn_vis.apply(
heu_net,
parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "xdot"})
hn_vis.save(gviz, filename)
log_info.info(
"Heuristic Net as .xdot has been stored in '%s' "
"in file '%s'", path, file)
