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started working on clustering via kmeans

add_velocity_into_framework
sp 8 months ago
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eb99c79712
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e7b5f8344b
1 changed files with 133 additions and 25 deletions
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  1. 158
      rom_evaluate.py

158
rom_evaluate.py
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@ -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")
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