def test_tangiblereachabilitygraph_calc(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
input_log = os.path.join(INPUT_DATA_DIR, "running-example.xes")
log = xes_importer.import_log(input_log)
net, initial_marking, final_marking = alpha_miner.apply(log)
s_map = stochastic_map.get_map_from_log_and_net(log, net, initial_marking, final_marking)
reachab_graph = construct_reachability_graph(net, initial_marking)
tang_reach_graph = tangible_reachability.get_tangible_reachability_from_reachability(reachab_graph,
s_map)
viz = ts_vis_factory.apply(tang_reach_graph, parameters={"format": "svg", "show_labels": True})
del viz
# gets the Q matrix assuming exponential distributions
q_matrix = ctmc.get_q_matrix_from_tangible_exponential(tang_reach_graph, s_map)
# pick a state to start from
states = sorted(list(tang_reach_graph.states), key=lambda x: x.name)
state = states[0]
transient_result = ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(tang_reach_graph, q_matrix,
state, 86400)
del transient_result
transient_result = ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(tang_reach_graph, q_matrix,
state, 864000)
del transient_result
steady_state = ctmc.steadystate_analysis_from_tangible_q_matrix(tang_reach_graph, q_matrix)
del steady_state
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "running-example.xes")
log = xes_importer.apply(log_path)
# obtain Petri net through Alpha Miner
net, initial_marking, final_marking = alpha_miner.apply(log)
# obtain stochastic information for transitions in the model
s_map = stochastic_map.get_map_from_log_and_net(
log,
net,
initial_marking,
final_marking,
force_distribution="EXPONENTIAL")
# export the current stochastic Petri net
petri_exporter.export_net(net,
initial_marking,
"example.pnml",
final_marking=final_marking,
stochastic_map=s_map)
# re-import the current stochastic Petri net from file
net, initial_marking, final_marking, s_map = petri_importer.import_net(
"example.pnml", return_stochastic_information=True)
# remove temporary file
os.remove("example.pnml")
# gets the reachability graph from the Petri net
reachab_graph = construct_reachability_graph(net, initial_marking)
# get the tangible reachability graph from the reachability graph and the stochastic map
tang_reach_graph = tangible_reachability.get_tangible_reachability_from_reachability(
reachab_graph, s_map)
# visualize the tangible reachability graph on the screen
viz = ts_vis_factory.apply(tang_reach_graph,
parameters={
"format": "svg",
"show_labels": True,
"show_names": True
})
ts_vis_factory.view(viz)
# gets the Q matrix assuming exponential distributions
q_matrix = ctmc.get_q_matrix_from_tangible_exponential(
tang_reach_graph, s_map)
# pick a state to start from
states = sorted(list(tang_reach_graph.states), key=lambda x: x.name)
state = states[0]
print("\n\nstarting from state = ", state.name)
# do transient analysis after 1 day
transient_result = ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(
tang_reach_graph, q_matrix, state, 86400)
print("\nprobability for each state after 1 day = ", transient_result)
# do transient analysis after 10 days
transient_result = ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(
tang_reach_graph, q_matrix, state, 864000)
print("\nprobability for each state after 10 days = ", transient_result)
# do transient analysis after 100 days
transient_result = ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(
tang_reach_graph, q_matrix, state, 8640000)
print("\nprobability for each state after 100 days = ", transient_result)
steady_state = ctmc.steadystate_analysis_from_tangible_q_matrix(
tang_reach_graph, q_matrix)
print("\nsteady state = ", steady_state)
def transient_analysis_from_petri_net_and_smap(net,
im,
s_map,
delay,
parameters=None):
"""
Gets the transient analysis from a Petri net, a stochastic map and a delay
Parameters
-------------
log
Event log
delay
Time delay
parameters
Parameters of the algorithm
Returns
-------------
transient_result
Transient analysis result
"""
if parameters is None:
parameters = {}
# gets the reachability graph from the Petri net
reachab_graph = construct_reachability_graph(net, im)
states_reachable_from_start = set()
for trans in reachab_graph.transitions:
if str(trans.from_state) == "start1":
states_reachable_from_start.add(trans.to_state)
# get the tangible reachability graph from the reachability graph and the stochastic map
tang_reach_graph = tangible_reachability.get_tangible_reachability_from_reachability(
reachab_graph, s_map)
# gets the Q matrix assuming exponential distributions
q_matrix = get_q_matrix_from_tangible_exponential(tang_reach_graph, s_map)
states = sorted(list(tang_reach_graph.states), key=lambda x: x.name)
states_vector = np.zeros((1, len(states)))
for state in states_reachable_from_start:
states_vector[
0, states.index(state)] = 1.0 / len(states_reachable_from_start)
probabilities = transient_analysis_from_tangible_q_matrix_and_states_vector(
tang_reach_graph, q_matrix, states_vector, delay)
color_dictionary = get_color_from_probabilities(probabilities)
return tang_reach_graph, probabilities, color_dictionary
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "running-example.csv")
dataframe = pandas_df_imp.import_dataframe_from_path(log_path)
activities_count = dict(dataframe.groupby("concept:name").size())
[dfg_frequency, dfg_performance] = df_statistics.get_dfg_graph(dataframe, measure="both",
perf_aggregation_key="mean")
net, initial_marking, final_marking = alpha_miner.apply_dfg(dfg_frequency)
spaths = get_shortest_paths(net)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(net, dfg_performance,
spaths,
activities_count,
variant="performance")
# obtain stochastic information for transitions in the model
s_map = stochastic_map.get_map_exponential_from_aggstatistics(aggregated_statistics)
# gets the reachability graph from the Petri net
reachab_graph = construct_reachability_graph(net, initial_marking)
# get the tangible reachability graph from the reachability graph and the stochastic map
tang_reach_graph = tangible_reachability.get_tangible_reachability_from_reachability(reachab_graph, s_map)
# visualize the tangible reachability graph on the screen
viz = ts_vis_factory.apply(tang_reach_graph, parameters={"format": "svg", "show_labels": True, "show_names": True})
ts_vis_factory.view(viz)
# gets the Q matrix assuming exponential distributions
q_matrix = ctmc.get_q_matrix_from_tangible_exponential(tang_reach_graph, s_map)
# pick a state to start from
states = sorted(list(tang_reach_graph.states), key=lambda x: x.name)
state = states[0]
print("\n\nstarting from state = ", state.name)
# do transient analysis after 1 day
transient_result = ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(tang_reach_graph, q_matrix,
state, 86400)
print("\nprobability for each state after 1 day = ", transient_result)
# do transient analysis after 10 days
transient_result = ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(tang_reach_graph, q_matrix,
state, 864000)
print("\nprobability for each state after 10 days = ", transient_result)
# do transient analysis after 100 days
transient_result = ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(tang_reach_graph, q_matrix,
state, 8640000)
print("\nprobability for each state after 100 days = ", transient_result)
steady_state = ctmc.steadystate_analysis_from_tangible_q_matrix(tang_reach_graph, q_matrix)
print("\nsteady state = ", steady_state)
