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redid querying

add_velocity_into_framework
sp 6 months ago
parent
4d6733b6d8
commit
019ea0ad1e
1 changed files with 53 additions and 35 deletions
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  1. 88
      rom_evaluate.py

88
rom_evaluate.py
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@ -1,6 +1,7 @@
import sys
import operator
from os import listdir, system
import subprocess
import re
from collections import defaultdict
@ -104,6 +105,7 @@ def saveObservations(observations, verdict, testDir):
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):
def run_single_test(ale, nn_wrapper, x,y,ski_position, duration=50):
#print(f"Running Test from x: {x:04}, y: {y:04}, ski_position: {ski_position}", end="")
@ -119,19 +121,18 @@ def run_single_test(ale, nn_wrapper, x,y,ski_position, duration=50):
all_obs = list()
speed_list = list()
first_action_set = False
first_action = 0
for i in range(0,duration):
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)
if len(all_obs) >= 4:
all_obs.append(resized_obs)
if i % 4 == 0:
stack_tensor = TensorDict({"obs": np.array(all_obs[-4:])})
action = nn_wrapper.query(stack_tensor)
if not first_action_set:
first_action_set = True
first_action = input_to_action(str(action))
ale.act(input_to_action(str(action)))
else:
ale.act(Action.NOOP)
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)
@ -139,14 +140,16 @@ def run_single_test(ale, nn_wrapper, x,y,ski_position, duration=50):
saveObservations(all_obs, Verdict.GOOD, testDir)
return Verdict.GOOD
def optimalAction(choices):
return max(choices.items(), key=operator.itemgetter(1))[0]
def computeStateRanking(mdp_file):
logger.info("Computing state ranking")
tic()
command = f"{tempest_binary} --prism {mdp_file} --buildchoicelab --buildstateval --build-all-labels --prop 'Rmax=? [C <= 1000]'"
exec(command)
try:
command = f"{tempest_binary} --prism {mdp_file} --buildchoicelab --buildstateval --build-all-labels --prop 'Rmax=? [C <= 1000]'"
result = subprocess.run(command, shell=True, check=True)
print(result)
except Exception as e:
print(e)
sys.exit(-1)
logger.info(f"Computing state ranking - DONE: took {toc()} seconds")
def fillStateRanking(file_name, match=""):
@ -221,26 +224,37 @@ def clusterFormula(cluster):
formula += ")"
return formula
def createBalancedDisjunction(indices, name):
#logger.info(f"Creating balanced disjunction for {len(indices)} ({indices}) formulas")
if len(indices) == 0:
return f"formula {name} = false;\n"
else:
while len(indices) > 1:
indices_tmp = [f"({indices[i]} | {indices[i+1]})" for i in range(0,len(indices)//2)]
if len(indices) % 2 == 1:
indices_tmp.append(indices[-1])
indices = indices_tmp
disjunction = f"formula {name} = " + " ".join(indices) + ";\n"
return disjunction
def createUnsafeFormula(clusters):
label = "label \"Unsafe\" = Unsafe;\n"
formulas = ""
disjunction = "formula Unsafe = false"
indices = list()
for i, cluster in enumerate(clusters):
formulas += f"formula Unsafe_{i} = {clusterFormula(cluster)};\n"
clusterFormula(cluster)
disjunction += f" | Unsafe_{i}"
disjunction += ";\n"
label = "label \"Unsafe\" = Unsafe;\n"
return formulas + "\n" + disjunction + label
indices.append(f"Unsafe_{i}")
return formulas + "\n" + createBalancedDisjunction(indices, "Unsafe") + label
def createSafeFormula(clusters):
label = "label \"Safe\" = Safe;\n"
formulas = ""
disjunction = "formula Safe = false"
indices = list()
for i, cluster in enumerate(clusters):
formulas += f"formula Safe_{i} = {clusterFormula(cluster)};\n"
disjunction += f" | Safe_{i}"
disjunction += ";\n"
label = "label \"Safe\" = Safe;\n"
return formulas + "\n" + disjunction + label
indices.append(f"Safe_{i}")
return formulas + "\n" + createBalancedDisjunction(indices, "Safe") + label
def updatePrismFile(newFile, iteration, safeStates, unsafeStates):
logger.info("Creating next prism file")
@ -285,8 +299,8 @@ def drawOntoSkiPosImage(states, color, target_prefix="cluster_", alpha_factor=1.
markerList = {ski_position:list() for ski_position in range(1,num_ski_positions + 1)}
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} '"
marker = f"-fill 'rgba({color}, {alpha_factor * state[1].ranking})' -draw 'point {s.x},{s.y} '"
marker = f"-fill 'rgba({color}, {alpha_factor * state[1].ranking})' -draw 'rectangle {s.x-markerSize},{s.y-markerSize} {s.x+markerSize},{s.y+markerSize} '"
#marker = f"-fill 'rgba({color}, {alpha_factor * state[1].ranking})' -draw 'point {s.x},{s.y} '"
markerList[s.ski_position].append(marker)
for pos, marker in markerList.items():
command = f"convert {imagesDir}/{target_prefix}_{pos:02}_individual.png {' '.join(marker)} {imagesDir}/{target_prefix}_{pos:02}_individual.png"
@ -344,17 +358,21 @@ def _init_logger():
handler.setFormatter(formatter)
logger.addHandler(handler)
def clusterImportantStates(ranking, iteration, n_clusters=40):
logger.info(f"Starting to cluster {len(ranking)} states into {n_clusters} cluster")
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 * 10, s[0].velocity * 10, s[1].ranking] for s in ranking]
states = [[s[0].x,s[0].y, s[0].ski_position * 10, s[1].ranking] for s in ranking]
kmeans = KMeans(n_clusters, random_state=0, n_init="auto").fit(states)
#dbscan = DBSCAN().fit(states)
logger.info(f"Starting to cluster {len(ranking)} states into {n_clusters} cluster - DONE: took {toc()} seconds")
states = [[s[0].x,s[0].y, s[0].ski_position * 30, s[1].ranking] for s in ranking]
#kmeans = KMeans(n_clusters, random_state=0, n_init="auto").fit(states)
dbscan = DBSCAN(eps=15).fit(states)
labels = dbscan.labels_
print(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)}
for i, state in enumerate(ranking):
clusterDict[kmeans.labels_[i]].append(state)
if labels[i] == -1: continue
clusterDict[labels[i]].append(state)
drawClusters(clusterDict, f"clusters", iteration)
return clusterDict
@ -373,7 +391,7 @@ if __name__ == '__main__':
computeStateRanking(f"{init_mdp}_{iteration:03}.prism")
ranking = fillStateRanking("action_ranking")
sorted_ranking = sorted( (x for x in ranking.items() if x[1].ranking > 0.1), key=lambda x: x[1].ranking)
clusters = clusterImportantStates(sorted_ranking, iteration, n_clusters)
clusters = clusterImportantStates(sorted_ranking, iteration)
if testAll: failingPerCluster = {i: list() for i in range(0, n_clusters)}
clusterResult = dict()
@ -401,7 +419,7 @@ if __name__ == '__main__':
if verdictGood:
clusterResult[id] = (cluster, Verdict.GOOD)
safeStates.append(cluster)
if testAll: drawClusters(failingPerCluster, f"failing")
if testAll: drawClusters(failingPerCluster, f"failing", iteration)
drawResult(clusterResult, "result", iteration)
iteration += 1

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