Commit 816b3f9e authored by Volkan Kayatas's avatar Volkan Kayatas
Browse files

Init project

parent 27cde7c7
# Default ignored files
/shelf/
/workspace.xml
# Datasource local storage ignored files
/../../../../../../../../:\Users\Volkan\Desktop\Projects\HIWI\graph_drl\.idea/dataSources/
/dataSources.local.xml
# Editor-based HTTP Client requests
/httpRequests/
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<serverData>
<paths name="kayatas@home.l3s.uni-hannover.de:22 password">
<serverdata>
<mappings>
<mapping deploy="/home/kayatas/graph_drl" local="$PROJECT_DIR$" />
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</serverdata>
</paths>
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<option name="myAutoUpload" value="ALWAYS" />
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</project>
\ No newline at end of file
<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
<sourceFolder url="file://$MODULE_DIR$" isTestSource="false" />
<excludeFolder url="file://$MODULE_DIR$/venv" />
<excludeFolder url="file://$MODULE_DIR$/venvv" />
<excludeFolder url="file://$MODULE_DIR$/vvenv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.8 (graph_drl) (2)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
<orderEntry type="module" module-name="deepwalk" />
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<component name="ProjectModuleManager">
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<module fileurl="file://$PROJECT_DIR$/../deepwalk/.idea/deepwalk.iml" filepath="$PROJECT_DIR$/../deepwalk/.idea/deepwalk.iml" />
<module fileurl="file://$PROJECT_DIR$/.idea/graph_drl.iml" filepath="$PROJECT_DIR$/.idea/graph_drl.iml" />
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<?xml version="1.0" encoding="UTF-8"?>
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\ No newline at end of file
import itertools
import random
from collections import deque
import sys, os, time
import time, os
import argparse
import numpy as np
#import paho.mqtt.client as mqtt
from keras.layers import *
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.models import Sequential
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
......@@ -20,7 +20,7 @@ MAX_EPISODES = 6 #Paper= 60
EMBTOGGLE = 2
#DIMENSION = 30
#TARGET_LOC = 99
EMBEDFOLDER = "./Maze/Embedding/"
EMBEDFOLDER = "./Maze/Embedding/DeepWalk/"
RESULTFOLDER = "./Result/"
VALSFOLDER = RESULTFOLDER + "Vals/"
STPSFOLDER = RESULTFOLDER + "Steps/"
......@@ -202,7 +202,7 @@ class Environment:
self.num_states = row_x * col_y + 1
self.dimension = DIMENSION
self.cs = 0 #current node in maze
emb_path = EMBEDFOLDER + str(args.embedpath)
emb_path = os.path.join(EMBEDFOLDER, str(args.embedpath))
self.model1 = KeyedVectors.load_word2vec_format(
emb_path, binary=False)
# self.model2 = KeyedVectors.load_word2vec_format(
......
import os, argparse
from gensim.models import Word2Vec
from Graph import load_edgelist, generate_random_episodes
"""
Contains functions to generate graph embeddings or episode embeddings.
Graph embeddings are used in Vikram's code: Maze -> Graph -> Graph embedding -> DeepQSingle.py
Episode embeddings: Maze -> Episodes -> Episode Embedding -> DeepQSingle.py
"""
# helper function to automatically generate a shell script which generates graph embeddings for all episodes of all mazes
# Used to re-construct Vikram's findings
def generate_embedding_shell_scripts(prefix="C:/Users/Volkan/Desktop/Projects/HIWI/"):
#Static parameter
mazes = ["maze1", "maze2", "maze4", "maze4_1000", "maze5", "maze6", "maze6_1000", "maze6_2000", "maze6_3000", "maze7", "maze9", "maze10"]
embeddings = ["DeepWalk"]
maze_script_path, episode_script_path = "generate_maze_embeddings.sh","generate_episode_embeddings.sh"
# prefix = os.getcwd()
# generate maze script
with open(maze_script_path, "w") as maze_file:#, open(episode_script_path, "w") as episode_file:
header = "# !/usr/bin/env python3"
# add header
maze_file.write(header+"\n")
#episode_file.write(header + "\n")
for embedding in embeddings:
# add comment (for better reading)
maze_file.write("# "+embedding+"\n")
#episode_file.write("# " + embedding + "\n")
for maze in mazes:
# for better reading
#episode_file.write("# " + maze + "\n")
# adds python run configurations in order (first only deepwalk, then only NERD, ..)
# TODO: When reusing this, change prefix at top & update graph embedding run path variable (e.g. location of deepwalk folder)
if embedding == "DeepWalk":
run_path = "../deepwalk/deepwalk/__main__.py"
command = "python " + run_path + " --input " +prefix+ "graph_drl/Maze/"+maze+".edgelist --output " +prefix+ "graph_drl/Maze/Embedding/DeepWalk/"+maze+".txt --walk-length 20 --representation-size 30"
maze_file.write(command+"\n")
# generate episode script
# for i in range(episodes):
# episode = "episode_{}".format(str(i + 1))
#check if saving folder exists
# folder = "/Episodes/{}/{}/{}/".format(maze, "Embedding", embedding)
# create folder if necessary
# if not os.path.isdir(os.getcwd() + folder):
# os.makedirs(os.getcwd() + folder)
# generate episode embedding run lines
# if embedding == "DeepWalk":
# command = "python ../deepwalk/deepwalk/__main__.py --input " + prefix + "graph_drl/Episodes/" + maze + "/"+ episode +".edgelist --output " + prefix + "graph_drl/Episodes/" + maze + "/Embedding/"+embedding+"/"+episode+".txt --walk-length 20 --representation-size 30"
# episode_file.write(command + "\n")
# generate embedding from episodes
def make_episodes_embedding(random_walks, output_path, dimension=30, window_size=5):
# episodes to embedding via gensim's Word2Vec, Parameter values from deepwalk paper
model = Word2Vec(random_walks, size=dimension, window=window_size, min_count=0, sg=1, hs=1)
model.wv.save_word2vec_format(output_path)
# end node is always bottom right state, selector needed since mazes with different grid dimensions used
def _get_end_node(maze_file):
end_dict = dict(maze1=99, maze2=99, maze4=99, maze4_1000=99, maze5=99, maze6=399, maze6_1000=399,
maze6_2000=399, maze6_3000=399, maze7=399, maze9=399, maze10=399)
return end_dict[maze_file]
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--maze_folder",default=os.path.join(os.getcwd(), "Maze"))
parser.add_argument("--episodes", help="Number of episodes to generate from a single maze." ,default=10)
parser.add_argument("--start", help="Start node of episode.", default=0)
parser.add_argument("--max_path_length", help="Force max length of random walk.", default=None)
args = parser.parse_args()
# generate shell script, which is run to generate graph embeddings (used to reproduce Vikram's Code more quickly)
# generate_embedding_shell_scripts(episodes=10)
# generate episodes for every maze in maze folder
for edgelist_file in os.listdir(args.maze_folder):
if edgelist_file.endswith(".edgelist"):
prefix = edgelist_file.replace(".edgelist", "")
# select target maze state
end = _get_end_node(prefix)
# generate episodes from a random start node to a specific end node..
edgelist_path = os.path.join(args.maze_folder, edgelist_file)
episodes = generate_random_episodes(edgelist_path, max_episodes=args.episodes,
start=args.start, end=end, max_path_length=args.max_path_length)
# generate embedding for single maze by using list of random episodes
output_path = args.maze_folder + "\\Embedding\\Episodes\\{}.txt".format(prefix)
make_episodes_embedding(episodes, output_path)
print("Embeddings generated.")
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24 -0.85481644 -0.19194707 -0.06080132 -1.1437984 -0.27260926 0.36392906 -0.655086 0.3060772 1.0001264 -0.5802457 0.37933332 -0.32962793 0.118812405 -0.1871417 -0.46751615 0.24470598 -0.1493399 0.38076738 -1.4675353 -0.22354846 0.20693798 0.696675 -0.05456343 -1.0962518 -0.7963015 -0.36548927 -0.38420564 -0.3713415 -0.8355279 -0.8719413
15 -0.9371756 -0.35123506 0.404451 -1.015721 -0.5936346 0.3139342 -0.38286608 0.41356277 1.0440915 -0.29476568 0.9442597 -0.11845907 -0.24974975 -0.46822703 -0.15456507 0.25040224 -0.39198068 0.22848369 -1.4950668 -0.09895133 -0.51229364 0.41521436 -0.07410068 -1.2242057 -0.52561826 -0.40823054 0.07346029 -0.104162686 -0.578672 -1.3506036
25 -0.9042254 -0.36507073 0.20860995 -1.0970913 -0.37376225 0.31667525 -0.60260177 0.41678604 1.1226743 -0.46170145 0.699083 -0.41640183 0.008152592 -0.32305744 -0.30848837 0.20110221 -0.2951708 0.2365306 -1.618845 -0.255571 -0.098575026 0.6456531 -0.06321746 -1.124849 -0.6453528 -0.45908612 -0.08264571 -0.19803514 -0.8258041 -1.0615513
10 -1.2784816 -0.7152079 -0.27774465 0.26237687 -1.7153908 0.16698612 0.028560193 -1.0093794 0.7223252 0.4464785 0.21252961 0.9950189 -0.26995054 -0.39269376 -1.0350683 0.045385372 0.40827683 0.61250395 -0.24386935 -0.12836765 -0.5008511 0.0069275186 0.46642417 -1.0444553 0.11823512 -0.69268537 0.61246413 0.17695881 -0.04202837 -0.20527655
83 -0.010851439 -0.5047385 -0.6313787 -0.7333902 0.6826183 -0.2861094 -1.1987377 -1.0522041 0.10680481 -0.84167737 1.7009319 -0.05221841 0.49556223 -0.40006787 -0.68910295 0.69687617 0.26099953 -0.7517951 -0.48403287 0.5089001 -0.018007867 0.33043846 0.036998432 -0.5189296 0.91317075 -0.018118877 0.010064827 -0.05044107 -0.20211534 0.16803132
32 -1.1367077 0.34356135 -0.19320148 -0.9084751 0.06109155 0.28002816 -0.5507295 -0.22848298 0.86490047 -1.3636045 -0.49163458 0.28438428 0.26908228 0.21080688 -0.26781756 0.25693774 0.48304653 0.58819306 -1.3947791 -0.24147439 0.47224694 0.69336385 -0.023869922 -0.7042411 -0.91889644 -0.30750975 -0.44947267 -0.31924477 -0.7300094 -0.13910316
61 -1.3585988 0.5983378 0.840193 -0.4356968 0.50026286 0.7571651 -0.6039777 -0.3138449 0.28282753 -0.9257782 -1.199678 0.6284003 0.115913354 -0.0657659 0.22828905 0.9168293 0.5289202 -0.17333528 -0.9246527 0.016633729 0.49977353 0.5993142 0.65981084 -0.40402 -0.45639792 -0.37364107 0.15002666 -0.07570682 0.095819734 0.4182153
51 -1.3026322 0.58912987 0.385411 -0.56603235 0.452299 0.6493865 -0.6599539 -0.28474447 0.52217954 -0.93153614 -1.2489569 0.68395627 0.051073182 0.18547359 -0.0121345045 0.8112262 0.5892363 0.28095102 -0.8922341 -0.06261242 0.5531326 0.586637 0.6707092 -0.7355254 -0.7465654 -0.28365362 -0.22102973 -0.23820475 -0.08407758 0.3414116
31 -1.5971413 -0.68229413 -0.42772475 0.64554065 -1.2547711 0.27505073 -0.099893965 -0.6616151 0.90958524 0.6619048 -0.4359847 0.52206385 -0.38058567 -0.44141388 -1.0854976 -0.03929871 0.5002059 0.52570015 -0.0226867 -0.45881212 0.20443593 -0.02339149 0.71240675 -1.0368713 0.10323404 -0.8703759 0.78337467 0.3063976 -0.47752205 0.15416744
21 -1.7437681 -0.7254248 -0.34479585 0.66528 -1.1185234 0.3969617 -0.25774845 -0.55202574 0.9263506 0.7644581 -0.71931463 0.493802 -0.36949062 -0.45560032 -1.2029546 0.08852769 0.6058808 0.5012375 0.052287787 -0.5197438 0.45170623 0.09543122 0.8226553 -1.1429276 0.022157606 -0.9128334 0.75828296 0.26166588 -0.5127739 0.30637157
41 -1.1993077 0.4855467 0.15086631 -0.5904317 0.1536472 0.44995838 -0.6454485 -0.42679256 0.67760855 -1.0368241 -1.0486686 0.5254324 0.1430499 0.2485691 -0.119318426 0.5119683 0.6155097 0.4984197 -1.086376 -0.17467582 0.51057667 0.6654766 0.37156823 -0.7120868 -0.84660995 -0.38122883 -0.3005989 -0.21995719 -0.29785904 0.23354356
0 -0.9766628 -0.8224613 -0.112667836 -0.04152196 -1.7522762 0.20424315 -0.058139328 -0.94723445 0.47784352 0.36372617 0.583875 1.0368786 -0.15349405 -0.3354406 -1.0913731 0.23392367 0.18615398 0.5541285 -0.29558885 0.11404751 -0.71688366 0.22750314 0.15945418 -0.95624334 0.041856978 -0.63110816 0.29122204 0.080480896 0.16141479 -0.40999687
42 -1.2233332 0.45068333 -0.11550346 -0.80790013 0.060821682 0.4496697 -0.63165414 -0.19962771 0.8530611 -1.0909369 -0.8423632 0.5031225 0.13966028 0.3190529 -0.24801892 0.4204382 0.51691866 0.7057689 -1.2080182 -0.19454424 0.44793686 0.6797073 0.29316095 -0.86465466 -0.98019034 -0.37441644 -0.5075014 -0.31792167 -0.4301241 -0.060959786
13 -1.1419472 -0.40987274 0.45861074 -0.43102255 -0.8749249 0.04606861 0.25358018 0.080897555 0.58709407 -0.40571737 0.9542891 1.1098038 -0.16690402 -0.27513367 -0.3644401 0.45357013 -0.21383147 0.37787956 -0.66235036 0.164385 -0.97977227 0.27862942 -0.41530836 -0.49869847 -0.24816279 -0.6361519 0.22835366 0.28880075 0.15661676 -1.0786171
91 -1.5140032 0.67118806 0.93867075 -0.12755914 1.1445394 0.77626 -0.24329785 0.051431827 0.17327845 -0.76763594 -0.18495725 0.6144486 -0.26107067 -0.8877198 0.5661854 1.4449633 0.09323911 -0.9682655 -0.25849476 0.24214512 0.12391422 -0.051814627 0.8211954 -0.1887379 0.5193269 -0.5786555 0.8348205 0.32066017 0.29402092 -0.025635391
94 -0.002427511 -0.37738162 -0.39674312 -0.93557084 0.51134753 0.114157565 -1.2905548 -1.6345314 -0.03481207 -0.9326738 2.027867 -0.12786658 0.09648441 -0.90326846 -0.62917495 1.1399579 0.27325666 -0.94989926 -0.7235193 0.8316377 -0.111222774 0.1713098 0.19628823 -0.9915116 1.051945 0.16821414 0.0012642777 -0.18225536 -0.07567134 0.11963124
93 -0.8616029 0.36093214 -0.047116455 0.0026044087 0.947997 0.29303184 -0.9408825 -0.5439051 -0.13605872 -0.08831196 0.29225662 1.2719519 -0.21999447 -0.22854042 -0.38934392 1.5333027 0.34407592 -0.60930294 0.5963242 0.7522499 -0.03548465 -0.060388446 1.358128 -0.6263315 0.7455542 -0.06567146 -0.028952425 -0.09522565 1.001701 0.37650216
97 -1.4864519 -0.9252356 -0.5564122 0.41685283 0.8414117 -0.77079886 0.30208117 0.48095033 0.18443803 0.2177 0.45209828 1.1401116 0.057987124 -0.30788937 -1.449738 0.7588793 0.5354427 -0.2995708 0.95105845 -0.4197973 0.7457344 -0.13026768 -0.009422422 -0.46703583 0.46114126 0.12837239 0.6776422 0.22745758 -0.6197237 0.22018112
40 -1.6890047 -0.567907 -0.40292957 0.58334255 -1.1966026 0.21496613 0.11225667 -0.62317014 0.9519087 0.43006027 -0.3632715 0.5147439 -0.31140527 -0.5014983 -0.94053864 -0.12572819 0.47555408 0.5421132 -0.1795821 -0.5250928 0.20165178 -0.06355296 0.6348135 -0.90409964 0.13684827 -0.89936626 0.8843013 0.31711593 -0.5775788 0.070499055
96 -0.88643163 -1.2084938 -0.24993831 -0.17298527 0.6428824 -0.5930257 -0.14885376 0.22715217 0.49324164 -0.24626179 1.4978831 -0.14668025 0.78371316 -0.684834 -1.0734477 0.6029572 0.013049377 -0.59452087 -0.18258108 -0.29789403 0.5379652 0.5399972 -0.6793437 0.30785295 0.86281794 -0.6519982 0.75141484 0.5157266 -0.692091 0.13920343
6 -1.1289624 -0.36714244 0.5872365 -0.7930078 -0.477925 0.14691988 -0.0076468885 0.49956456 0.8903407 -0.31656817 0.99562734 0.34848157 -0.35026035 -0.4092984 -0.06834243 0.42182076 -0.41202894 0.21878804 -1.1438894 -0.041942105 -0.7488316 0.27403286 -0.26111728 -0.99381125 -0.43840376 -0.34623876 0.19769624 0.06664647 -0.2927009 -1.3702141
50 -1.4392267 0.58007663 1.1702312 -0.5119186 0.6879347 0.7896147 -0.5561853 -0.05070661 0.16802213 -1.0626175 -0.96291023 0.789295 0.22082262 -0.16248204 0.3737868 1.0963056 0.4155258 -0.5028226 -0.9633579 0.13497794 0.36196354 0.69384617 0.61512935 -0.1778599 -0.31239894 -0.3923321 0.2793363 -0.0006150583 0.23591723 0.32065153
71 -1.2707413 0.6724431 0.90813786 -0.3494936 0.74172145 0.8210419 -0.655624 -0.01931834 0.21129942 -0.67441165 -0.6985235 0.65732354 -0.0996307 -0.3085806 0.49253032 1.049729 0.2905977 -0.65243024 -0.5999838 0.2978435 0.044727553 0.19590916 1.0598812 -0.374657 0.113128625 -0.31795776 0.52530843 0.013648447 0.34876955 0.10312018
43 -1.0547308 0.19151874 -0.5348739 -1.1167026 0.23646323 0.17534852 -0.6398799 0.11602897 1.0245107 -1.2468245 -0.12712373 0.12773225 0.2683048 0.21225527 -0.48917302 0.2460574 0.32438123 0.6200245 -1.3350862 -0.28173536 0.55283755 0.63318074 -0.10461258 -0.8805453 -0.93807036 -0.16414775 -0.62872833 -0.42493826 -0.9385609 -0.37411317
11 -1.7432708 -0.751267 -0.49312583 0.68381655 -1.0744693 0.43491974 -0.40427107 -0.5693705 1.0232407 0.7857204 -0.8435384 0.33916253 -0.34387112 -0.4308648 -1.2621448 0.08035328 0.6340925 0.5130021 0.0029673744 -0.6068901 0.6063888 0.1566977 0.8739845 -1.1944765 -0.01486431 -0.9535703 0.6901443 0.25709778 -0.6361248 0.40392026
99 -1.5992862 -0.85081315 -0.5431014 0.47425553 0.86088777 -0.76430297 0.37400338 0.5182211 0.16403006 0.18949968 0.3556071 1.3811848 0.051925983 -0.27105847 -1.4676903 0.8661972 0.5485334 -0.26566765 1.0471338 -0.38673797 0.7104196 -0.17134662 0.05487773 -0.41400698 0.46261865 0.08906333 0.6727973 0.24970984 -0.5215999 0.22440979
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0 10
10 0
0 10
10 11
11 10
10 11
11 10
10 0
0 10
10 0
0 10
10 20
20 10
10 20
20 21
21 20
20 10
10 11
11 10
10 11
11 10
10 0
0 10
10 0
0 1
1 11
11 21
21 11
11 21
21 11
11 10
10 11
11 10
10 0
0 1
1 2
2 12
12 2
2 12
12 11
11 1
1 0
0 1
1 2
2 3
3 4
4 5
5 6
6 5
5 15
15 5
5 4
4 5
5 4
4 3
3 2
2 12
12 22
22 23
23 24
24 14
14 15
15 5
5 4
4 5
5 15
15 25
25 15
15 25
25 24
24 14
14 15
15 25
25 35
35 34
34 24
24 34
34 44
44 54
54 53
53 54
54 44
44 34
34 44
44 54
54 53
53 52
52 53
53 43
43 33
33 34
34 44
44 54
54 44
44 54
54 55
55 65
65 66
66 67
67 77
77 87
87 88
88 78
78 79
79 78
78 79
79 78
78 79
79 89
89 79
79 89
89 88
88 98
98 97
97 96
96 97
97 96
96 86
86 87
87 88
88 89
89 88
88 87
87 77
77 78
78 68
68 67
67 77
77 76
76 66
66 76
76 86
86 76
76 75
75 74
74 64
64 65
65 55
55 54
54 55
55 65
65 66
66 65
65 66
66 76
76 77
77 67
67 77
77 67
67 66
66 56
56 46
46 56
56 55
55 56
56 66
66 67
67 57
57 47
47 46
46 56
56 57
57 56
56 66
66 76
76 86
86 76
76 66
66 56
56 55
55 65
65 75
75 85
85 86
86 87
87 86
86 76
76 75
75 65
65 66
66 67
67 66
66 67
67 68
68 67
67 66
66 76
76 86
86 87
87 97
97 98
98 88
88 87
87 97
97 87
87 77
77 67
67 66
66 76
76 66
66 76
76 75
75 65
65 55
55 65
65 75
75 76
76 75
75 76
76 75
75 74
74 75
75 76
76 75
75 65
65 55
55 56
56 55
55 56
56 57
57 47
47 37
37 36
36 26
26 16
16 26
26 36
36 26
26 25
25 35
35 25
25 24
24 23
23 13
13 14
14 24
24 25
25 24
24 23
23 24
24 14
14 4
4 3
3 2
2 3
3 4
4 14
14 24
24 14
14 4
4 5
5 15
15 14
14 4
4 14
14 4
4 5
5 15
15 5
5 15
15 14
14 13
13 14
14 24
24 23
23 24
24 23
23 24
24 34
34 24
24 34
34 35
35 25
25 35
35 25
25 26
26 25
25 15
15 25
25 35
35 25
25 15
15 25
25 26
26 27
27 26
26 25
25 24
24 25
25 15
15 5
5 4
4 3
3 4
4 14
14 24
24 23
23 13
13 23
23 24
24 23
23 13
13 12
12 13
13 23
23 24
24 25
25 26
26 36
36 26
26 25
25 15
15 25
25 35
35 36
36 35
35 45
45 35
35 45
45 46
46 45
45 35
35 34
34 24
24 14
14 24
24 14
14 13
13 23
23 33
33 32
32 22
22 21
21 11
11 12