def plot(environment_name, storage: prism.state_storage.StateStorage, *, plot_type="lb", folder_path, file_name_save: str = None):
state_size = len(storage.root[0])
# states = unroll_methods.get_n_states(storage,horizon)
# shortest_path_abstract = nx.shortest_path(storage.graph, source=storage.root)
# layers = unroll_methods.get_layers(storage.graph,storage.root)
save_path = f"{folder_path}/{file_name_save}_{plot_type}.svg" if file_name_save is not None else None
if plot_type == "lb":
results = [(x, prob) for (x, action), prob in unroll_methods.get_property_at_timestep(storage, 1, ["lb"])]
# if state_size > 2:
# results = [(x, prob) for x, prob in results if x[3][0] <= 0 <= x[3][1] and x[2][0] <= 0 <= x[2][1]]
utils.show_heatmap(results, save_to=save_path, rounding=2) # , title="Heatmap of the lower bound measured at the initial state"
elif plot_type == "ub":
results = [(x, prob) for (x, action), prob in unroll_methods.get_property_at_timestep(storage, 1, ["ub"])]
if state_size > 2:
results = [(x, prob) for x, prob in results if x[3][0] <= 0 <= x[3][1] and x[2][0] <= 0 <= x[2][1]]
utils.show_heatmap(results, save_to=save_path, rounding=2) # , title="Heatmap of the upper bound measured at the initial state"
elif plot_type == "error":
results = [(x, ub - lb) for (x, action), lb, ub in unroll_methods.get_property_at_timestep(storage, 1, ["lb", "ub"])] # difference between boundaries
# if state_size > 2:
# results = [(x, prob) for x, prob in results if x[3][0] <= 0 <= x[3][1] and x[2][0] <= 0 <= x[2][1]]
utils.show_heatmap(results, save_to=save_path, rounding=2) # title="Heatmap of the probability error measured at the initial state"
elif plot_type == "safe_unsafe":
results = [(x, lb, ub) for (x, action), lb, ub in unroll_methods.get_property_at_timestep(storage, 1, ["lb", "ub"])]
safe = []
unsafe = []
undecidable = []
for x, lb, ub in results:
if lb >= 0.8:
unsafe.append(x)
elif ub <= 0.2:
safe.append(x)
else:
undecidable.append(x)
utils.show_plot(safe, unsafe, undecidable, legend=["Safe", "Unsafe", "Undecidable"])
elif plot_type == "p_chart":
results = [(x, prob) for (x, action), prob in unroll_methods.get_property_at_timestep(storage, 1, ["ub"])]
utils.p_chart(results, save_to=save_path, rounding=2) # , title="Barchart of volumes in the initial state grouped by the upper bound probability "
elif plot_type == "pca":
results = [(x, utils.centre_tuple(x), utils.area_tuple(x), action, prob) for (x, action), prob in unroll_methods.get_property_at_timestep(storage, 1, ["ub"])]
pca_map(results, save_path, state_size)
else:
raise Exception("Option for plot_type not recognised")
env_class,
n_workers,
explorer,
verification_model,
state_size,
horizon=horizon,
allow_assign_actions=True)
# if time.time() - time_from_last_save >= 60 * 5:
# storage.save_state(f"/home/edoardo/Development/SafeDRL/save/nx_graph_e{rounding}.p")
# rtree.save_to_file(f"/home/edoardo/Development/SafeDRL/save/union_states_total_e{rounding}.p")
# print("Graph Saved - Checkpoint")
# time_from_last_save = time.time()
if not split_performed or iterations == 1000:
# utils.save_graph_as_dot(storage.graph)
if not split_performed:
print("No more split performed")
break
iterations += 1
# %%
# storage.save_state(f"/home/edoardo/Development/SafeDRL/save/nx_graph_e{rounding}.p")
# rtree.save_to_file(f"/home/edoardo/Development/SafeDRL/save/union_states_total_e{rounding}.p")
# %%
# unroll_methods.remove_spurious_nodes(storage.graph)
# storage.remove_unreachable()
# storage.recreate_prism()
# utils.save_graph_as_dot(storage.graph)
utils.show_heatmap([
(x, prob) for (x, action), prob in unroll_methods.get_property_at_timestep(
storage, 1, ["lb"])
])
state = np.array((params['param1'], params['param2']))
current_intervals.append(torch.tensor(state))
tensor_intervals = torch.stack(current_intervals)
# ix = Symbolic_interval(lower=torch.tensor([[0, 0]], dtype=torch.float64, requires_grad=False), upper=torch.tensor([[0.01, 0.01]], dtype=torch.float64, requires_grad=False))
ix2 = Symbolic_interval(lower=tensor_intervals,
upper=tensor_intervals + np.array([delta_x, delta_y]))
# verif.get_boundaries(ix, 0)
inet = Interval_network(sequential_nn.double(), None)
result_interval = inet(ix2)
upper_bound = result_interval.u
lower_bound = result_interval.l
upper_bound_rectangles = []
for x, y in zip(tensor_intervals, upper_bound[:, 0]):
x_numpy = x.numpy()
x_numpy = np.stack([x_numpy, x_numpy + np.array([delta_x, delta_y])])
from_numpy = HyperRectangle.from_numpy(x_numpy)
upper_bound_rectangles.append((from_numpy, y.item()))
lower_bound_rectangles = []
for x, y in zip(tensor_intervals, lower_bound[:, 0]):
x_numpy = x.numpy()
x_numpy = np.stack([x_numpy, x_numpy + np.array([delta_x, delta_y])])
from_numpy = HyperRectangle.from_numpy(x_numpy)
lower_bound_rectangles.append((from_numpy, y.item()))
utils.show_heatmap(upper_bound_rectangles,
title="Upper bound probability of action=0",
rounding=3)
utils.show_heatmap(lower_bound_rectangles,
title="Lower bound probability of action=0",
rounding=3)
print("Finished")
next_state2, reward2, done2, _ = env.step(action)
next_state = next_state.round(rounding)
next_state2 = next_state2.round(rounding)
new_states.append(next_state)
new_states.append(next_state2)
storage.store_sticky_successors(tuple(next_state),
tuple(next_state2),
tuple(current_interval))
if done:
storage.mark_as_fail([tuple(next_state)])
if done2:
storage.mark_as_fail([tuple(next_state2)])
current_intervals = new_states
print("Finished")
storage.save_state(
f"/home/edoardo/Development/SafeDRL/save/nx_graph_concrete_e{rounding}.p"
)
storage.recreate_prism(horizon * 2)
storage.save_state(
f"/home/edoardo/Development/SafeDRL/save/nx_graph_concrete_e{rounding}.p"
)
# %%
# utils.show_heatmap(unroll_methods.get_property_at_timestep(storage, 1, ["lb"]), rounding=2, concrete=True)
storage.recreate_prism(horizon * 2)
utils.show_heatmap(unroll_methods.get_property_at_timestep(storage, 1, ["ub"]),
rounding=3,
concrete=True,
title=f"MC concrete horizon:{horizon}")
# utils.show_heatmap(unroll_methods.get_property_at_timestep(storage, 1, ["lb"]), rounding=3, concrete=True, title=f"LB concrete horizon:{horizon}")
rtree.save_to_file(
f"/home/edoardo/Development/SafeDRL/save/union_states_total_e{rounding}.p"
)
print("Graph Saved - Checkpoint")
time_from_last_save = time.time()
if not split_performed or iterations == 50:
# utils.save_graph_as_dot(storage.graph)
if not split_performed:
print("No more split performed")
break
iterations += 1
# %%
if allow_save:
storage.save_state(
f"/home/edoardo/Development/SafeDRL/save/nx_graph_e{rounding}.p")
rtree.save_to_file(
f"/home/edoardo/Development/SafeDRL/save/union_states_total_e{rounding}.p"
)
# %%
# unroll_methods.remove_spurious_nodes(storage.graph)
# storage.remove_unreachable()
# storage.recreate_prism()
# utils.save_graph_as_dot(storage.graph)
storage.recreate_prism(horizon * 2)
utils.show_heatmap(unroll_methods.get_property_at_timestep(storage, 1, ["lb"]),
rounding=2,
title=f"LB abstract horizon:{horizon}")
utils.show_heatmap(unroll_methods.get_property_at_timestep(storage, 1, ["ub"]),
rounding=2,
title=f"UB abstract horizon:{horizon}")