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import logging
import math
import numpy as np
import stormpy
import stormpy.examples.files
from stormpy.core import ExplicitQualitativeCheckResult
from neus_v.model_checking.proposition import process_proposition_set
from neus_v.model_checking.video_state import VideoState
class StormModelChecker:
"""Model Checker using Stormpy for verifying properties."""
def __init__(
self,
proposition_set: list[str],
ltl_formula: str,
) -> None:
"""Initialize the StormModelChecker.
Args:
proposition_set: List of propositions.
ltl_formula: LTL formula to check.
verbose: Enable verbose output.
is_filter: Apply filtering to results.
"""
self.proposition_set = process_proposition_set(proposition_set)
self.ltl_formula = ltl_formula
def create_model(
self,
transitions: list[tuple[int, int, float]],
states: list[VideoState],
model_type: str = "sparse_ma",
) -> any:
"""Create model.
Args:
transitions (list[tuple[int, int, float]]): List of transitions.
states (list[VideoState]): List of states.
model_type (str): Type of model to create ("sparse_ma" or "dtmc").
verbose (bool): Whether to print verbose output.
"""
state_labeling = self._build_label_func(states, self.proposition_set)
if model_type in ["sparse_ma", "mdp"]:
transition_matrix = self._build_trans_matrix(
transitions=transitions,
states=states,
model_type="nondeterministic",
)
else:
transition_matrix = self._build_trans_matrix(
transitions=transitions,
states=states,
model_type="deterministic",
)
components = stormpy.SparseModelComponents(
transition_matrix=transition_matrix,
state_labeling=state_labeling,
)
if model_type == "sparse_ma":
markovian_states = stormpy.BitVector(len(states), list(range(len(states))))
components.markovian_states = markovian_states
components.exit_rates = [1.0 for _ in range(len(states))]
model = stormpy.SparseMA(components)
elif model_type == "dtmc":
model = stormpy.storage.SparseDtmc(components)
elif model_type == "mdp":
model = stormpy.storage.SparseMdp(components)
else:
msg = f"Unsupported model type: {model_type}"
raise ValueError(msg)
return model
def check_automaton(
self,
transitions: list[tuple[int, int, float]],
states: list[VideoState],
model_type: str = "sparse_ma",
use_filter: bool = False,
) -> any:
"""Check automaton.
Args:
transitions: List of transitions.
states: List of states.
verbose: Enable verbose output.
use_filter: Apply filtering to results.
"""
model = self.create_model(
transitions=transitions,
states=states,
model_type=model_type,
)
# Check the model
# Initialize Prism Program
path = stormpy.examples.files.prism_dtmc_die # prism_mdp_maze
prism_program = stormpy.parse_prism_program(path)
# Define Properties
properties = stormpy.parse_properties(self.ltl_formula, prism_program)
# Get Result and Filter it
result = stormpy.model_checking(model, properties[0])
if use_filter:
# The final result will only consider paths starting from the initial states of the automaton. # noqa: E501
filtered_result = stormpy.create_filter_initial_states_sparse(model)
result.filter(filtered_result)
return result
def qualitative_result_eval(self, verification_result: ExplicitQualitativeCheckResult) -> bool:
if isinstance(verification_result, ExplicitQualitativeCheckResult):
# string result is "true" when is absolutely true
# but it returns "true, false" when we have some true and false
verification_result_str = str(verification_result)
string_result = verification_result_str.split("{")[-1].split("}")[0]
if len(string_result) == 4:
if string_result[0] == "t": # 0,6
result = True
elif len(string_result) > 5:
# "true, false" -> some true and some false
result = True
else:
result = False
return result
msg = "Model Checking is not qualitative"
raise ValueError(msg)
def _build_trans_matrix(
self,
transitions: list[tuple[int, int, float]],
states: list[VideoState],
model_type: str = "nondeterministic",
) -> stormpy.storage.SparseMatrix:
"""Build transition matrix.
Args:
transitions: List of transitions.
states: List of states.
model_type: Type of model ("nondeterministic" or "deterministic").
"""
if model_type not in ["nondeterministic", "deterministic"]:
msg = "Invalid model_type. Must be 'nondeterministic' or 'deterministic'" # noqa: E501
raise ValueError(msg)
if model_type == "nondeterministic":
matrix = np.zeros((len(states), len(states)))
for t in transitions:
matrix[int(t[0]), int(t[1])] = float(t[2])
trans_matrix = stormpy.build_sparse_matrix(matrix, list(range(len(states))))
elif model_type == "deterministic":
num_states = len(states)
builder = stormpy.SparseMatrixBuilder(
rows=num_states,
columns=num_states,
entries=len(transitions),
force_dimensions=False,
)
states_with_transitions = set(src for src, _, _ in transitions)
outgoing_probs = {i: 0.0 for i in range(num_states)}
for src, dest, prob in transitions:
builder.add_next_value(src, dest, prob)
outgoing_probs[src] += prob
for state in range(num_states):
if state not in states_with_transitions:
builder.add_next_value(state, state, 1.0)
outgoing_probs[state] = 1.0
# Check probabilities
for state, prob_sum in outgoing_probs.items():
# if not math.isclose(prob_sum, 1.0, rel_tol=1e-9):
if not math.isclose(prob_sum, 1.0, abs_tol=1e-2):
logging.warning(f"State {state} has outgoing probability sum of {prob_sum}, not 1.0")
# ... (existing logging code) ...
trans_matrix = builder.build()
return trans_matrix
def _build_label_func(
self,
states: list[VideoState],
props: list[str],
model_type: str = "nondeterministic",
) -> stormpy.storage.StateLabeling:
"""Build label function.
Args:
states (list[State]): List of states.
props (list[str]): List of propositions.
model_type (str): Type of model
("nondeterministic" or "deterministic").
Returns:
stormpy.storage.StateLabeling: State labeling.
"""
state_labeling = stormpy.storage.StateLabeling(len(states))
state_labeling.add_label("init")
state_labeling.add_label("terminal")
for label in props:
state_labeling.add_label(label)
if model_type == "nondeterministic":
for state in states:
for label in state.descriptive_label:
state_labeling.add_label_to_state(label, state.state_index)
else:
for i, state in enumerate(states):
for prop in state.props:
if prop in props:
state_labeling.add_label_to_state(prop, i)
return state_labeling
def validate_tl_specification(self, ltl_formula: str) -> bool:
"""Validate LTL specification.
Args:
ltl_formula: LTL formula to validate.
"""
path = stormpy.examples.files.prism_dtmc_die # prism_mdp_maze
prism_program = stormpy.parse_prism_program(path)
# Define Properties
try:
stormpy.parse_properties(ltl_formula, prism_program)
except Exception as e:
msg = f"Error validating LTL specification: {e}"
logging.exception(msg)
return False
else:
return True
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