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Skiing/new_method.py

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import sys
import operator
from copy import deepcopy
from os import listdir, system
import subprocess
import re
from collections import defaultdict
from random import randrange
from ale_py import ALEInterface, SDL_SUPPORT, Action
from PIL import Image
from matplotlib import pyplot as plt
import cv2
import pickle
import queue
from dataclasses import dataclass, field
from sklearn.cluster import KMeans, DBSCAN
from enum import Enum
from copy import deepcopy
import numpy as np
import logging
logger = logging.getLogger(__name__)
#import readchar
from sample_factory.algo.utils.tensor_dict import TensorDict
from query_sample_factory_checkpoint import SampleFactoryNNQueryWrapper
import time
tempest_binary = "/home/spranger/projects/tempest-devel/ranking_release/bin/storm"
rom_file = "/home/spranger/research/Skiing/env/lib/python3.10/site-packages/AutoROM/roms/skiing.bin"
def tic():
import time
global startTime_for_tictoc
startTime_for_tictoc = time.time()
def toc():
import time
if 'startTime_for_tictoc' in globals():
return time.time() - startTime_for_tictoc
class Verdict(Enum):
INCONCLUSIVE = 1
GOOD = 2
BAD = 3
verdict_to_color_map = {Verdict.BAD: "200,0,0", Verdict.INCONCLUSIVE: "40,40,200", Verdict.GOOD: "00,200,100"}
def convert(tuples):
return dict(tuples)
@dataclass(frozen=True)
class State:
x: int
y: int
ski_position: int
velocity: int
def default_value():
return {'action' : None, 'choiceValue' : None}
@dataclass(frozen=True)
class StateValue:
ranking: float
choices: dict = field(default_factory=default_value)
@dataclass(frozen=False)
class TestResult:
init_check_pes_min: float
init_check_pes_max: float
init_check_pes_avg: float
init_check_opt_min: float
init_check_opt_max: float
init_check_opt_avg: float
safe_states: int
unsafe_states: int
safe_cluster: int
unsafe_cluster: int
good_verdicts: int
bad_verdicts: int
policy_queries: int
def __str__(self):
return f"""Test Result:
init_check_pes_min: {self.init_check_pes_min}
init_check_pes_max: {self.init_check_pes_max}
init_check_pes_avg: {self.init_check_pes_avg}
init_check_opt_min: {self.init_check_opt_min}
init_check_opt_max: {self.init_check_opt_max}
init_check_opt_avg: {self.init_check_opt_avg}
"""
@staticmethod
def csv_header(ws=" "):
string = f"pesmin{ws}pesmax{ws}pesavg{ws}"
string += f"optmin{ws}optmax{ws}optavg{ws}"
string += f"sState{ws}uState{ws}"
string += f"sClust{ws}uClust{ws}"
string += f"gVerd{ws}bVerd{ws}queries"
return string
def csv(self):
ws = " "
string = f"{self.init_check_pes_min:0.04f}{ws}{self.init_check_pes_max:0.04f}{ws}{self.init_check_pes_avg:0.04f}{ws}"
string += f"{self.init_check_opt_min:0.04f}{ws}{self.init_check_opt_max:0.04f}{ws}{self.init_check_opt_avg:0.04f}{ws}"
ws = "\t"
string += f"{self.safe_states}{ws}{self.unsafe_states}{ws}"
string += f"{self.safe_cluster}{ws}{self.unsafe_cluster}{ws}"
string += f"{self.good_verdicts}{ws}{self.bad_verdicts}{ws}{self.policy_queries}"
return string
def exec(command,verbose=True):
if verbose: print(f"Executing {command}")
system(f"echo {command} >> list_of_exec")
return system(command)
num_tests_per_cluster = 50
#factor_tests_per_cluster = 0.2
num_ski_positions = 8
num_velocities = 5
def input_to_action(char):
if char == "0":
return Action.NOOP
if char == "1":
return Action.RIGHT
if char == "2":
return Action.LEFT
if char == "3":
return "reset"
if char == "4":
return "set_x"
if char == "5":
return "set_vel"
if char in ["w", "a", "s", "d"]:
return char
def saveObservations(observations, verdict, testDir):
testDir = f"images/testing_{experiment_id}/{verdict.name}_{testDir}_{len(observations)}"
if len(observations) < 20:
logger.warn(f"Potentially spurious test case for {testDir}")
testDir = f"{testDir}_pot_spurious"
exec(f"mkdir {testDir}", verbose=False)
for i, obs in enumerate(observations):
img = Image.fromarray(obs)
img.save(f"{testDir}/{i:003}.png")
ski_position_counter = {1: (Action.LEFT, 40), 2: (Action.LEFT, 35), 3: (Action.LEFT, 30), 4: (Action.LEFT, 10), 5: (Action.NOOP, 1), 6: (Action.RIGHT, 10), 7: (Action.RIGHT, 30), 8: (Action.RIGHT, 40) }
def run_single_test(ale, nn_wrapper, x,y,ski_position, velocity, duration=50):
#print(f"Running Test from x: {x:04}, y: {y:04}, ski_position: {ski_position}", end="")
testDir = f"{x}_{y}_{ski_position}_{velocity}"
try:
for i, r in enumerate(ramDICT[y]):
ale.setRAM(i,r)
ski_position_setting = ski_position_counter[ski_position]
for i in range(0,ski_position_setting[1]):
ale.act(ski_position_setting[0])
ale.setRAM(14,0)
ale.setRAM(25,x)
ale.setRAM(14,180) # TODO
except Exception as e:
print(e)
logger.warn(f"Could not run test for x: {x}, y: {y}, ski_position: {ski_position}, velocity: {velocity}")
return (Verdict.INCONCLUSIVE, 0)
num_queries = 0
all_obs = list()
speed_list = list()
resized_obs = cv2.resize(ale.getScreenGrayscale(), (84,84), interpolation=cv2.INTER_AREA)
for i in range(0,4):
all_obs.append(resized_obs)
for i in range(0,duration-4):
resized_obs = cv2.resize(ale.getScreenGrayscale(), (84,84), interpolation=cv2.INTER_AREA)
all_obs.append(resized_obs)
if i % 4 == 0:
stack_tensor = TensorDict({"obs": np.array(all_obs[-4:])})
action = nn_wrapper.query(stack_tensor)
num_queries += 1
ale.act(input_to_action(str(action)))
else:
ale.act(input_to_action(str(action)))
speed_list.append(ale.getRAM()[14])
if len(speed_list) > 15 and sum(speed_list[-6:-1]) == 0:
#saveObservations(all_obs, Verdict.BAD, testDir)
return (Verdict.BAD, num_queries)
#saveObservations(all_obs, Verdict.GOOD, testDir)
return (Verdict.GOOD, num_queries)
def skiPositionFormulaList(name):
formulas = list()
for i in range(1, num_ski_positions+1):
formulas.append(f"\"{name}_{i}\"")
return createBalancedDisjunction(formulas)
def computeStateRanking(mdp_file, iteration):
logger.info("Computing state ranking")
tic()
prop = f"filter(min, Pmin=? [ G !(\"Hit_Tree\" | \"Hit_Gate\" | {skiPositionFormulaList('Unsafe')}) ], (!\"S_Hit_Tree\" & !\"S_Hit_Gate\") | ({skiPositionFormulaList('Safe')} | {skiPositionFormulaList('Unsafe')}) );"
prop += f"filter(max, Pmin=? [ G !(\"Hit_Tree\" | \"Hit_Gate\" | {skiPositionFormulaList('Unsafe')}) ], (!\"S_Hit_Tree\" & !\"S_Hit_Gate\") | ({skiPositionFormulaList('Safe')} | {skiPositionFormulaList('Unsafe')}) );"
prop += f"filter(avg, Pmin=? [ G !(\"Hit_Tree\" | \"Hit_Gate\" | {skiPositionFormulaList('Unsafe')}) ], (!\"S_Hit_Tree\" & !\"S_Hit_Gate\") | ({skiPositionFormulaList('Safe')} | {skiPositionFormulaList('Unsafe')}) );"
prop += f"filter(min, Pmax=? [ G !(\"Hit_Tree\" | \"Hit_Gate\" | {skiPositionFormulaList('Unsafe')}) ], (!\"S_Hit_Tree\" & !\"S_Hit_Gate\") | ({skiPositionFormulaList('Safe')} | {skiPositionFormulaList('Unsafe')}) );"
prop += f"filter(max, Pmax=? [ G !(\"Hit_Tree\" | \"Hit_Gate\" | {skiPositionFormulaList('Unsafe')}) ], (!\"S_Hit_Tree\" & !\"S_Hit_Gate\") | ({skiPositionFormulaList('Safe')} | {skiPositionFormulaList('Unsafe')}) );"
prop += f"filter(avg, Pmax=? [ G !(\"Hit_Tree\" | \"Hit_Gate\" | {skiPositionFormulaList('Unsafe')}) ], (!\"S_Hit_Tree\" & !\"S_Hit_Gate\") | ({skiPositionFormulaList('Safe')} | {skiPositionFormulaList('Unsafe')}) );"
prop += 'Rmax=? [C <= 200]'
results = list()
try:
command = f"{tempest_binary} --prism {mdp_file} --buildchoicelab --buildstateval --build-all-labels --prop '{prop}'"
output = subprocess.check_output(command, shell=True).decode("utf-8").split('\n')
num_states = 0
for line in output:
#print(line)
if "States:" in line:
num_states = int(line.split(" ")[-1])
if "Result" in line and not len(results) >= 6:
range_value = re.search(r"(.*:).*\[(-?\d+\.?\d*), (-?\d+\.?\d*)\].*", line)
if range_value:
results.append(float(range_value.group(2)))
results.append(float(range_value.group(3)))
else:
value = re.search(r"(.*:)(.*)", line)
results.append(float(value.group(2)))
exec(f"mv action_ranking action_ranking_{iteration:03}")
except subprocess.CalledProcessError as e:
# todo die gracefully if ranking is uniform
print(e.output)
logger.info(f"Computing state ranking - DONE: took {toc()} seconds")
return TestResult(*tuple(results),0,0,0,0,0,0,0), num_states
def fillStateRanking(file_name, match=""):
logger.info(f"Parsing state ranking, {file_name}")
tic()
state_ranking = dict()
try:
with open(file_name, "r") as f:
file_content = f.readlines()
for line in file_content:
if not "move=0" in line: continue
ranking_value = float(re.search(r"Value:([+-]?(\d*\.\d+)|\d+)", line)[0].replace("Value:",""))
if ranking_value <= 0.1:
continue
stateMapping = convert(re.findall(r"([a-zA-Z_]*[a-zA-Z])=(\d+)?", line))
choices = convert(re.findall(r"[a-zA-Z_]*(left|right|noop)[a-zA-Z_]*:(-?\d+\.?\d*)", line))
choices = {key:float(value) for (key,value) in choices.items()}
state = State(int(stateMapping["x"]), int(stateMapping["y"]), int(stateMapping["ski_position"]), int(stateMapping["velocity"])//2)
value = StateValue(ranking_value, choices)
state_ranking[state] = value
logger.info(f"Parsing state ranking - DONE: took {toc()} seconds")
return state_ranking
except EnvironmentError:
print("Ranking file not available. Exiting.")
toc()
sys.exit(-1)
except:
toc()
def createDisjunction(formulas):
return " | ".join(formulas)
def statesFormulaTrimmed(states, name):
#states = [(s[0].x,s[0].y, s[0].ski_position) for s in cluster]
skiPositionGroup = defaultdict(list)
for item in states:
skiPositionGroup[item[2]].append(item)
formulas = list()
for skiPosition, skiPos_group in skiPositionGroup.items():
formula = f"formula {name}_{skiPosition} = ( ski_position={skiPosition} & "
#print(f"{name} ski_pos:{skiPosition}")
velocityGroup = defaultdict(list)
velocityFormulas = list()
for item in skiPos_group:
velocityGroup[item[3]].append(item)
for velocity, velocity_group in velocityGroup.items():
#print(f"\tvel:{velocity}")
formulasPerSkiPosition = list()
yPosGroup = defaultdict(list)
yFormulas = list()
for item in velocity_group:
yPosGroup[item[1]].append(item)
for y, y_group in yPosGroup.items():
#print(f"\t\ty:{y}")
sorted_y_group = sorted(y_group, key=lambda s: s[0])
current_x_min = sorted_y_group[0][0]
current_x = sorted_y_group[0][0]
x_ranges = list()
for state in sorted_y_group[1:-1]:
if state[0] - current_x == 1:
current_x = state[0]
else:
x_ranges.append(f" ({current_x_min}<=x&x<={current_x})")
current_x_min = state[0]
current_x = state[0]
x_ranges.append(f" {current_x_min}<=x&x<={sorted_y_group[-1][0]}")
yFormulas.append(f" (y={y} & {createBalancedDisjunction(x_ranges)})")
#x_ranges.clear()
#velocityFormulas.append(f"(velocity={velocity} & {createBalancedDisjunction(yFormulas)})")
velocityFormulas.append(f"({createBalancedDisjunction(yFormulas)})")
#yFormulas.clear()
formula += createBalancedDisjunction(velocityFormulas) + ");"
#velocityFormulas.clear()
formulas.append(formula)
for i in range(1, num_ski_positions+1):
if i in skiPositionGroup:
continue
formulas.append(f"formula {name}_{i} = false;")
return "\n".join(formulas) + "\n"
# https://stackoverflow.com/questions/5389507/iterating-over-every-two-elements-in-a-list
def pairwise(iterable):
"s -> (s0, s1), (s2, s3), (s4, s5), ..."
a = iter(iterable)
return zip(a, a)
def createBalancedDisjunction(formulas):
if len(formulas) == 0:
return "false"
while len(formulas) > 1:
formulas_tmp = [f"({f} | {g})" for f,g in pairwise(formulas)]
if len(formulas) % 2 == 1:
formulas_tmp.append(formulas[-1])
formulas = formulas_tmp
return " ".join(formulas)
def updatePrismFile(newFile, iteration, safeStates, unsafeStates):
logger.info("Creating next prism file")
tic()
initFile = f"{newFile}_no_formulas.prism"
newFile = f"{newFile}_{iteration:03}.prism"
exec(f"cp {initFile} {newFile}", verbose=False)
with open(newFile, "a") as prism:
prism.write(statesFormulaTrimmed(safeStates, "Safe"))
prism.write(statesFormulaTrimmed(unsafeStates, "Unsafe"))
for i in range(1,num_ski_positions+1):
prism.write(f"label \"Safe_{i}\" = Safe_{i};\n")
prism.write(f"label \"Unsafe_{i}\" = Unsafe_{i};\n")
logger.info(f"Creating next prism file - DONE: took {toc()} seconds")
ale = ALEInterface()
#if SDL_SUPPORT:
# ale.setBool("sound", True)
# ale.setBool("display_screen", True)
# Load the ROM file
ale.loadROM(rom_file)
with open('all_positions_v2.pickle', 'rb') as handle:
ramDICT = pickle.load(handle)
y_ram_setting = 60
x = 70
nn_wrapper = SampleFactoryNNQueryWrapper()
experiment_id = int(time.time())
init_mdp = "velocity_safety"
exec(f"mkdir -p images/testing_{experiment_id}", verbose=False)
imagesDir = f"images/testing_{experiment_id}"
def drawOntoSkiPosImage(states, color, target_prefix="cluster_", alpha_factor=1.0, markerSize=1, drawCircle=False):
#markerList = {ski_position:list() for ski_position in range(1,num_ski_positions + 1)}
markerList = {(ski_position, velocity):list() for velocity in range(0, num_velocities) for ski_position in range(1,num_ski_positions + 1)}
images = dict()
mergedImages = dict()
for ski_position in range(1, num_ski_positions + 1):
for velocity in range(0,num_velocities):
images[(ski_position, velocity)] = cv2.imread(f"{imagesDir}/{target_prefix}_{ski_position:02}_{velocity:02}_individual.png")
mergedImages[ski_position] = cv2.imread(f"{imagesDir}/{target_prefix}_{ski_position:02}_individual.png")
for state in states:
s = state[0]
marker = [color, alpha_factor * state[1].ranking, (s.x-markerSize, s.y-markerSize), (s.x+markerSize, s.y+markerSize)]
markerList[(s.ski_position, s.velocity)].append(marker)
for (pos, vel), marker in markerList.items():
if len(marker) == 0: continue
if drawCircle:
for m in marker:
images[(pos,vel)] = cv2.circle(images[(pos,vel)], m[2], 1, m[0], thickness=-1)
mergedImages[pos] = cv2.circle(mergedImages[pos], m[2], 1, m[0], thickness=-1)
else:
for m in marker:
images[(pos,vel)] = cv2.rectangle(images[(pos,vel)], m[2], m[3], m[0], cv2.FILLED)
mergedImages[pos] = cv2.rectangle(mergedImages[pos], m[2], m[3], m[0], cv2.FILLED)
for (ski_position, velocity), image in images.items():
cv2.imwrite(f"{imagesDir}/{target_prefix}_{ski_position:02}_{velocity:02}_individual.png", image)
for ski_position, image in mergedImages.items():
cv2.imwrite(f"{imagesDir}/{target_prefix}_{ski_position:02}_individual.png", image)
def concatImages(prefix, iteration):
logger.info(f"Concatenating images")
images = [f"{imagesDir}/{prefix}_{pos:02}_{vel:02}_individual.png" for vel in range(0,num_velocities) for pos in range(1,num_ski_positions+1)]
mergedImages = [f"{imagesDir}/{prefix}_{pos:02}_individual.png" for pos in range(1,num_ski_positions+1)]
for vel in range(0, num_velocities):
for pos in range(1, num_ski_positions + 1):
command = f"convert {imagesDir}/{prefix}_{pos:02}_{vel:02}_individual.png "
command += f"-pointsize 10 -gravity NorthEast -annotate +8+0 'p{pos:02}v{vel:02}' "
command += f"{imagesDir}/{prefix}_{pos:02}_{vel:02}_individual.png"
exec(command, verbose=False)
exec(f"montage {' '.join(images)} -geometry +0+0 -tile 8x9 {imagesDir}/{prefix}_{iteration:03}.png", verbose=False)
exec(f"montage {' '.join(mergedImages)} -geometry +0+0 -tile 8x9 {imagesDir}/{prefix}_{iteration:03}_merged.png", verbose=False)
#exec(f"sxiv {imagesDir}/{prefix}_{iteration}.png&", verbose=False)
logger.info(f"Concatenating images - DONE")
def drawStatesOntoTiledImage(states, color, target, source="images/1_full_scaled_down.png", alpha_factor=1.0):
"""
Useful to draw a set of states, e.g. a single cluster
TODO
markerList = {1: list(), 2:list(), 3:list(), 4:list(), 5:list(), 6:list(), 7:list(), 8:list()}
logger.info(f"Drawing {len(states)} states onto {target}")
tic()
for state in states:
s = state[0]
marker = f"-fill 'rgba({color}, {alpha_factor * state[1].ranking})' -draw 'rectangle {s.x-markerSize},{s.y-markerSize} {s.x+markerSize},{s.y+markerSize} '"
markerList[s.ski_position].append(marker)
for pos, marker in markerList.items():
command = f"convert {source} {' '.join(marker)} {imagesDir}/{target}_{pos:02}_individual.png"
exec(command, verbose=False)
exec(f"montage {imagesDir}/{target}_*_individual.png -geometry +0+0 -tile x1 {imagesDir}/{target}.png", verbose=False)
logger.info(f"Drawing {len(states)} states onto {target} - Done: took {toc()} seconds")
"""
def drawClusters(clusterDict, target, iteration, alpha_factor=1.0):
logger.info(f"Drawing {len(clusterDict)} clusters")
tic()
for _, clusterStates in clusterDict.items():
color = (np.random.choice(range(256)), np.random.choice(range(256)), np.random.choice(range(256)))
color = (int(color[0]), int(color[1]), int(color[2]))
drawOntoSkiPosImage(clusterStates, color, target, alpha_factor=alpha_factor)
concatImages(target, iteration)
logger.info(f"Drawing {len(clusterDict)} clusters - DONE: took {toc()} seconds")
def drawResult(clusterDict, target, iteration, drawnCluster=set()):
logger.info(f"Drawing {len(clusterDict)} results")
tic()
for id, (clusterStates, result) in clusterDict.items():
if id in drawnCluster: continue
# opencv wants BGR
color = (100,100,100)
if result == Verdict.GOOD:
color = (0,200,0)
elif result == Verdict.BAD:
color = (0,0,200)
drawOntoSkiPosImage(clusterStates, color, target, alpha_factor=0.7)
logger.info(f"Drawing {len(clusterDict)} results - DONE: took {toc()} seconds")
def _init_logger():
logger = logging.getLogger('main')
logger.setLevel(logging.INFO)
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter( '[%(levelname)s] %(module)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
def clusterImportantStates(ranking, iteration):
logger.info(f"Starting to cluster {len(ranking)} states into clusters")
tic()
states = [[s[0].x,s[0].y, s[0].ski_position * 20, s[0].velocity * 20, s[1].ranking] for s in ranking]
#states = [[s[0].x,s[0].y, s[0].ski_position * 30, s[1].ranking] for s in ranking]
kmeans = KMeans(len(states) // 15, random_state=0, n_init="auto").fit(states)
#dbscan = DBSCAN(eps=5).fit(states)
#labels = dbscan.labels_
labels = kmeans.labels_
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
logger.info(f"Starting to cluster {len(ranking)} states into clusters - DONE: took {toc()} seconds with {n_clusters} cluster")
clusterDict = {i : list() for i in range(0,n_clusters)}
strayStates = list()
for i, state in enumerate(ranking):
if labels[i] == -1:
clusterDict[n_clusters + len(strayStates) + 1] = list()
clusterDict[n_clusters + len(strayStates) + 1].append(state)
strayStates.append(state)
continue
clusterDict[labels[i]].append(state)
if len(strayStates) > 0: logger.warning(f"{len(strayStates)} stray states with label -1")
#drawClusters(clusterDict, f"clusters", iteration)
return clusterDict
def run_experiment(factor_tests_per_cluster):
logger.info("Starting")
num_queries = 0
source = "images/1_full_scaled_down.png"
for ski_position in range(1, num_ski_positions + 1):
for velocity in range(0,num_velocities):
exec(f"cp {source} {imagesDir}/clusters_{ski_position:02}_{velocity:02}_individual.png", verbose=False)
exec(f"cp {source} {imagesDir}/result_{ski_position:02}_{velocity:02}_individual.png", verbose=False)
exec(f"cp {source} {imagesDir}/clusters_{ski_position:02}_individual.png", verbose=False)
exec(f"cp {source} {imagesDir}/result_{ski_position:02}_individual.png", verbose=False)
goodVerdicts = 0
badVerdicts = 0
goodVerdictTestCases = list()
badVerdictTestCases = list()
safeClusters = 0
unsafeClusters = 0
safeStates = set()
unsafeStates = set()
iteration = 0
results = list()
eps = 0.1
updatePrismFile(init_mdp, iteration, set(), set())
#modelCheckingResult, numStates = TestResult(0,0,0,0,0,0,0,0,0,0,0,0,0), 10
modelCheckingResult, numStates = computeStateRanking(f"{init_mdp}_000.prism", iteration)
results.append(modelCheckingResult)
ranking = fillStateRanking(f"action_ranking_000")
sorted_ranking = sorted( (x for x in ranking.items() if x[1].ranking > 0.1), key=lambda x: x[1].ranking)
try:
clusters = clusterImportantStates(sorted_ranking, iteration)
except Exception as e:
print(e)
sys.exit(-1)
clusterResult = dict()
logger.info(f"Running tests")
tic()
num_cluster_tested = 0
iteration = 0
drawnCluster = set()
for id, cluster in clusters.items():
num_tests = int(factor_tests_per_cluster * len(cluster))
if num_tests == 0: num_tests = 1
logger.info(f"Testing {num_tests} states (from {len(cluster)} states) from cluster {id}")
randomStates = np.random.choice(len(cluster), num_tests, replace=False)
randomStates = [cluster[i] for i in randomStates]
verdictGood = True
for state in randomStates:
x = state[0].x
y = state[0].y
ski_pos = state[0].ski_position
velocity = state[0].velocity
result, num_queries_this_test_case = run_single_test(ale,nn_wrapper,x,y,ski_pos, velocity, duration=50)
num_queries += num_queries_this_test_case
if result == Verdict.BAD:
clusterResult[id] = (cluster, Verdict.BAD)
verdictGood = False
unsafeStates.update([(s[0].x,s[0].y, s[0].ski_position, s[0].velocity) for s in cluster])
badVerdicts += 1
badVerdictTestCases.append(state)
elif result == Verdict.GOOD:
goodVerdicts += 1
goodVerdictTestCases.append(state)
if verdictGood:
clusterResult[id] = (cluster, Verdict.GOOD)
safeClusters += 1
safeStates.update([(s[0].x,s[0].y, s[0].ski_position, s[0].velocity) for s in cluster])
else:
unsafeClusters += 1
results[-1].safe_states = len(safeStates)
results[-1].unsafe_states = len(unsafeStates)
results[-1].policy_queries = num_queries
results[-1].safe_cluster = safeClusters
results[-1].unsafe_cluster = unsafeClusters
results[-1].good_verdicts = goodVerdicts
results[-1].bad_verdicts = badVerdicts
num_cluster_tested += 1
if num_cluster_tested % (len(clusters)//20) == 0:
iteration += 1
logger.info(f"Tested Cluster: {num_cluster_tested:03}\tSafe Cluster States : {len(safeStates)}({safeClusters}/{len(clusters)})\tUnsafe Cluster States:{len(unsafeStates)}({unsafeClusters}/{len(clusters)})\tGood Test Cases:{goodVerdicts}\tFailing Test Cases:{badVerdicts}\t{len(safeStates)/len(unsafeStates)} - {goodVerdicts/badVerdicts}")
drawResult(clusterResult, "result", iteration, drawnCluster)
drawOntoSkiPosImage(goodVerdictTestCases, (10,255,50), "result", alpha_factor=0.7, markerSize=0, drawCircle=True)
drawOntoSkiPosImage(badVerdictTestCases, (0,0,0), "result", alpha_factor=0.7, markerSize=0, drawCircle=True)
concatImages("result", iteration)
drawnCluster.update(clusterResult.keys())
#updatePrismFile(init_mdp, iteration, safeStates, unsafeStates)
#modelCheckingResult, numStates = computeStateRanking(f"{init_mdp}_{iteration:03}.prism", iteration)
results.append(deepcopy(modelCheckingResult))
logger.info(f"Model Checking Result: {modelCheckingResult}")
# Account for self-loop states after first iteration
if iteration > 0:
results[-1].init_check_pes_avg = 1/(numStates+len(safeStates)+len(unsafeStates)) * (results[-1].init_check_pes_avg*numStates + 1.0*results[-2].unsafe_states + 0.0*results[-2].safe_states)
results[-1].init_check_opt_avg = 1/(numStates+len(safeStates)+len(unsafeStates)) * (results[-1].init_check_opt_avg*numStates + 0.0*results[-2].unsafe_states + 1.0*results[-2].safe_states)
print(TestResult.csv_header())
for result in results[:-1]:
print(result.csv())
with open(f"data_new_method_{factor_tests_per_cluster}", "w") as f:
f.write(TestResult.csv_header() + "\n")
for result in results[:-1]:
f.write(result.csv() + "\n")
_init_logger()
logger = logging.getLogger('main')
if __name__ == '__main__':
for factor_tests_per_cluster in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
run_experiment(factor_tests_per_cluster)