sp
/
Skiing
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

started working on clustering via kmeans

add_velocity_into_framework
sp 7 months ago
parent
eb99c79712
commit
e7b5f8344b
1 changed files with 133 additions and 25 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 158
      rom_evaluate.py

158
rom_evaluate.py
View File

@ -1,9 +1,10 @@
import sys
import operator
from os import listdir, system
import re
from random import randrange
from ale_py import ALEInterface, SDL_SUPPORT, Action
from colors import *
#from colors import *
from PIL import Image
from matplotlib import pyplot as plt
import cv2
@ -11,13 +12,18 @@ import pickle
import queue
from dataclasses import dataclass, field
from sklearn.cluster import KMeans
from enum import Enum
from copy import deepcopy
import numpy as np
import readchar
import logging
logger = logging.getLogger(__name__)
#import readchar
from sample_factory.algo.utils.tensor_dict import TensorDict
from query_sample_factory_checkpoint import SampleFactoryNNQueryWrapper
@ -25,9 +31,17 @@ 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/python3.8/site-packages/AutoROM/roms/skiing.bin"
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
@ -56,6 +70,7 @@ def exec(command,verbose=True):
system(f"echo {command} >> list_of_exec")
return system(command)
num_ski_positions = 8
def model_to_actual(ski_position):
if ski_position == 1:
return 1
@ -140,31 +155,42 @@ 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 --prop 'Rmax=? [C <= 1000]'"
exec(command)
logger.info(f"Computing state ranking - DONE: took {toc()} seconds")
def fillStateRanking(file_name, match=""):
logger.info("Parsing state ranking")
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))
#print("stateMapping", stateMapping)
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()}
#print("choices", choices)
ranking_value = float(re.search(r"Value:([+-]?(\d*\.\d+)|\d+)", line)[0].replace("Value:",""))
#print("ranking_value", ranking_value)
state = State(int(stateMapping["x"]), int(stateMapping["y"]), int(stateMapping["ski_position"]))
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()
fixed_left_states = list()
@ -226,26 +252,108 @@ exec(f"cp 1_full_scaled_down.png images/testing_{id}/testing_0000.png")
exec(f"cp {init_mdp}.prism {init_mdp}_000.prism")
markerSize = 1
markerList = {1: list(), 2:list(), 3:list(), 4:list(), 5:list(), 6:list(), 7:list(), 8:list()}
while True:
computeStateRanking(f"{init_mdp}_{iteration:03}.prism")
ranking = fillStateRanking("action_ranking")
sorted_ranking = sorted(ranking.items(), key=lambda x: x[1].ranking)
for important_state in sorted_ranking[-100:-1]:
optimal_choice = optimalAction(important_state[1].choices)
#print(important_state[1].choices, f"\t\tOptimal: {optimal_choice}")
x = important_state[0].x
y = important_state[0].y
ski_pos = model_to_actual(important_state[0].ski_position)
result = run_single_test(ale,nn_wrapper,x,y,ski_pos, duration=50)
#print(f".... {result}")
marker = f"-fill 'rgba({verdict_to_color_map[result[0]],0.7})' -draw 'rectangle {x-markerSize},{y-markerSize} {x+markerSize},{y+markerSize} '"
markerList[ski_pos].append(marker)
populate_fixed_actions(important_state[0], result[1])
#markerList = {1: list(), 2:list(), 3:list(), 4:list(), 5:list(), 6:list(), 7:list(), 8:list()}
def f(n):
if n >= 1.0:
return True
return False
def drawOntoSkiPosImage(states, color, target_prefix="cluster_", alpha_factor=1.0):
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} '"
markerList[s.ski_position].append(marker)
for pos, marker in markerList.items():
command = f"convert images/testing_{id}/{target_prefix}_{pos:02}.png {' '.join(marker)} images/testing_{id}/{target_prefix}_{pos:02}.png"
exec(command, verbose=False)
def concatImages(prefix):
exec(f"montage images/testing_{id}/{prefix}_*png -geometry +0+0 -tile x1 images/testing_{id}/{prefix}.png", verbose=False)
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
"""
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 images/testing_{id}/testing_0000.png {' '.join(marker)} images/testing_{id}/testing_{iteration+1:03}_{pos:02}.png"
command = f"convert {source} {' '.join(marker)} images/testing_{id}/{target}_{pos:02}.png"
exec(command, verbose=False)
exec(f"montage images/testing_{id}/testing_{iteration+1:03}_*png -geometry +0+0 -tile x1 images/testing_{id}/{iteration+1:03}.png", verbose=False)
iteration += 1
update_prism_file(f"{init_mdp}_{iteration-1:03}.prism", f"{init_mdp}_{iteration:03}.prism")
exec(f"montage images/testing_{id}/{target}_*png -geometry +0+0 -tile x1 images/testing_{id}/{target}.png", verbose=False)
logger.info(f"Drawing {len(states)} states onto {target} - Done: took {toc()} seconds")
def drawClusters(clusterDict, target, alpha_factor=1.0):
for ski_position in range(1, num_ski_positions + 1):
source = "images/1_full_scaled_down.png"
exec(f"cp {source} images/testing_{id}/{target}_{ski_position:02}.png")
for _, clusterStates in clusterDict.items():
color = f"{np.random.choice(range(256))}, {np.random.choice(range(256))}, {np.random.choice(range(256))}"
drawOntoSkiPosImage(clusterStates, color, f"clusters")
concatImages("clusters")
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, n_clusters=10):
logger.info(f"Starting to cluster {len(ranking)} states into {n_clusters} cluster")
tic()
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)
logger.info(f"Starting to cluster {len(ranking)} states into {n_clusters} cluster - DONE: took {toc()} seconds")
clusterDict = {i : list() for i in range(0,n_clusters)}
for i, state in enumerate(ranking):
clusterDict[kmeans.labels_[i]].append(state)
drawClusters(clusterDict, f"clusters")
return clusterDict
if __name__ == '__main__':
_init_logger()
logger = logging.getLogger('main')
logger.info("Starting")
while True:
#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)
print(type(sorted_ranking))
clusters = clusterImportantStates(sorted_ranking)
sys.exit(1)
#for i, state in enumerate(sorted_ranking):
# print(state)
# if i % 10 == 0:
# input("")
#print(len(sorted_ranking))
"""
for important_state in ranking[-100:-1]:
optimal_choice = optimalAction(important_state[1].choices)
#print(important_state[1].choices, f"\t\tOptimal: {optimal_choice}")
x = important_state[0].x
y = important_state[0].y
ski_pos = model_to_actual(important_state[0].ski_position)
result = run_single_test(ale,nn_wrapper,x,y,ski_pos, duration=50)
#print(f".... {result}")
marker = f"-fill 'rgba({verdict_to_color_map[result[0]],0.7})' -draw 'rectangle {x-markerSize},{y-markerSize} {x+markerSize},{y+markerSize} '"
markerList[ski_pos].append(marker)
populate_fixed_actions(important_state[0], result[1])
for pos, marker in markerList.items():
command = f"convert images/testing_{id}/testing_0000.png {' '.join(marker)} images/testing_{id}/testing_{iteration+1:03}_{pos:02}.png"
exec(command, verbose=False)
exec(f"montage images/testing_{id}/testing_{iteration+1:03}_*png -geometry +0+0 -tile x1 images/testing_{id}/{iteration+1:03}.png", verbose=False)
iteration += 1
"""
update_prism_file(f"{init_mdp}_{iteration-1:03}.prism", f"{init_mdp}_{iteration:03}.prism")
Write Preview
Loading…
Cancel
Save