def main():
oparser = argparse.ArgumentParser(
description="intelligent crawling with q-learning")
oparser.add_argument(
"--config-file",
dest="configFile",
required=True,
help="Path to config file (containing MySQL login etc.)")
options = oparser.parse_args()
np.random.seed()
np.set_printoptions(formatter={'float': lambda x: "{0:0.1f}".format(x)},
linewidth=666)
sqlconn = MySQL(options.configFile)
hostName = "http://vade-retro.fr/"
# hostName = "http://www.buchmann.ch/"
start = time.time()
env = Env(sqlconn, hostName)
end = time.time()
print('Time to build the graph took: ', end - start, ' seconds.')
# crawl_method1(sqlconn, env)
start = time.time()
crawl_method2(sqlconn, env)
end = time.time()
print('Time to crawl the graph took: ', end - start, ' seconds.')
def PickleDomain(url):
print("Pickling", url)
domain = extract(url).domain
if not os.path.exists('pickled_domains/' + domain):
sqlconn = MySQL('config.ini')
env = Env(sqlconn, url)
with open('pickled_domains/' + domain, 'wb') as f:
pickle.dump(env, f)
print("Done {}".format(domain))
def main():
global DEBUG
oparser = argparse.ArgumentParser(
description="intelligent crawling with q-learning")
oparser.add_argument(
"--config-file",
dest="configFile",
required=True,
help="Path to config file (containing MySQL login etc.)")
oparser.add_argument(
"--language-pair",
dest="langPair",
required=True,
help="The 2 language we're interested in, separated by ,")
options = oparser.parse_args()
np.random.seed()
np.set_printoptions(formatter={'float': lambda x: "{0:0.1f}".format(x)},
linewidth=666)
sqlconn = MySQL(options.configFile)
languages = Languages(sqlconn.mycursor)
langPairList = options.langPair.split(",")
assert (len(langPairList) == 2)
langIds = [
languages.GetLang(langPairList[0]),
languages.GetLang(langPairList[1])
]
#hostName = "http://vade-retro.fr/"
#hostName = "http://www.buchmann.ch/"
hostName = "http://www.visitbritain.com/"
env = Env(sqlconn, hostName)
#DEBUG = True
arrNaive = naive(sqlconn, env, len(env.nodes))
arrBalanced = balanced(sqlconn, env, len(env.nodes), langIds)
#print("arrNaive", arrNaive)
#print("arrBalanced", arrBalanced)
plt.plot(arrNaive, label="naive")
plt.plot(arrBalanced, label="balanced")
plt.legend(loc='upper left')
plt.show()
def main():
global TIMER
TIMER = Timer()
oparser = argparse.ArgumentParser(description="intelligent crawling with q-learning")
oparser.add_argument("--config-file", dest="configFile", required=True,
help="Path to config file (containing MySQL login etc.)")
oparser.add_argument("--language-pair", dest="langPair", required=True,
help="The 2 language we're interested in, separated by ,")
oparser.add_argument("--save-dir", dest="saveDir", default=".",
help="Directory that model WIP are saved to. If existing model exists then load it")
oparser.add_argument("--save-plots", dest="saveDirPlots", default="plots",
help="Directory ")
oparser.add_argument("--delete-duplicate-transitions", dest="deleteDuplicateTransitions",
default=False, help="If True then only unique transition are used in each batch")
oparser.add_argument("--n-hosts-train", dest="n_train", type=int,
default=1, help="If True then only unique transition are used in each batch")
oparser.add_argument("--m-hosts-test", dest="m_test", type=int,
default=1, help="If True then only unique transition are used in each batch")
options = oparser.parse_args()
np.random.seed(99)
np.set_printoptions(formatter={'float': lambda x: "{0:0.1f}".format(x)}, linewidth=666)
sqlconn = MySQL(options.configFile)
languages = Languages(sqlconn.mycursor)
#["http://vade-retro.fr/",] #
hostNames_train = ["http://vade-retro.fr/"]
hostNames_test = ["http://vade-retro.fr/"]
#hostNames_train = ["http://www.buchmann.ch/"]
#hostNames_test = ["http://www.visitbritain.com/"]
#hostNames_train = ["http://carta.ro/","http://www.bachelorstudies.fr/", "http://www.buchmann.ch/", "http://chopescollection.be/", "http://www.visitbritain.com/", "http://www.burnfateasy.info/"] #allhostNames[0:options.n_train]
#hostNames_test = ["http://www.lavery.ca/",] #allhostNames[options.n_train:options.n_train+options.m_test]
if not os.path.exists(options.saveDirPlots):
os.mkdir(options.saveDirPlots)
print("Training hosts are:")
for h in hostNames_train:
print(h)
print()
print("Testing hosts are:")
for h in hostNames_test:
print(h)
print()
with open('{}/hosts.info'.format(options.saveDirPlots), 'w') as f:
f.write('Training hosts are:\n')
for h in hostNames_train:
f.write(h+'\n')
f.write('\nTesting hosts are:\n')
for h in hostNames_test:
f.write(h+'\n')
params = LearningParams(languages, options.saveDir, options.saveDirPlots, options.deleteDuplicateTransitions, options.langPair)
env_train_dic = {hostName:Env(sqlconn, hostName) for hostName in hostNames_train}
env_test_dic = {hostName:Env(sqlconn, hostName) for hostName in hostNames_test}
for dic in [env_train_dic, env_test_dic]:
for hostName, env in env_test_dic.items():
env.maxLangId = MAX_LANG_ID
env.nodes[sys.maxsize].lang = languages.GetLang("None")
dic[hostName] = env
tf.reset_default_graph()
qns = Qnets(params, MAX_LANG_ID)
#qns_test = Qnets(params, env_test)
init = tf.global_variables_initializer()
saver = None #tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
totRewards, totDiscountedRewards = Train(params, sess, saver, env_train_dic, qns, env_test_dic)
def main():
global TIMER
TIMER = Timer()
oparser = argparse.ArgumentParser(description="intelligent crawling with q-learning")
oparser.add_argument("--config-file", dest="configFile", required=True,
help="Path to config file (containing MySQL login etc.)")
oparser.add_argument("--language-pair", dest="langPair", required=True,
help="The 2 language we're interested in, separated by ,")
oparser.add_argument("--save-dir", dest="saveDir", default=".",
help="Directory that model WIP are saved to. If existing model exists then load it")
oparser.add_argument("--save-plots", dest="saveDirPlots", default="plot",
help="Directory ")
oparser.add_argument("--delete-duplicate-transitions", dest="deleteDuplicateTransitions",
default=False, help="If True then only unique transition are used in each batch")
options = oparser.parse_args()
np.random.seed()
np.set_printoptions(formatter={'float': lambda x: "{0:0.1f}".format(x)}, linewidth=666)
sqlconn = MySQL(options.configFile)
languages = Languages(sqlconn.mycursor)
params = LearningParams(languages, options.saveDir, options.saveDirPlots, options.deleteDuplicateTransitions, options.langPair)
#hostName = "http://www.visitbritain.com/"
#
hostName = "http://www.buchmann.ch/"
#hostName = "http://vade-retro.fr/" # smallest domain for debugging
hostName_test = "http://www.visitbritain.com/"
#hostName_test = "http://www.buchmann.ch/"
#hostName_test = "http://vade-retro.fr/" # smallest domain for debugging
env = Env(sqlconn, hostName)
env_test = Env(sqlconn, hostName_test)
# change language of start node. 0 = stop
env.nodes[sys.maxsize].lang = languages.GetLang("None")
env_test.nodes[sys.maxsize].lang = languages.GetLang("None")
#for node in env.nodes.values():
# print(node.Debug())
max_env_maxLangId = max([env.maxLangId, env_test.maxLangId])
env.maxLangId = env_test.maxLangId = max_env_maxLangId
tf.reset_default_graph()
qns = Qnets(params, max_env_maxLangId)
init = tf.global_variables_initializer()
saver = None #tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
totRewards, totDiscountedRewards = Train(params, sess, saver, env, qns, env_test)
#params.debug = True
arrDumb = dumb(env, len(env.nodes), params)
arrRandom = randomCrawl(env, len(env.nodes), params)
arrBalanced = balanced(env, len(env.nodes), params)
arrRL = Walk(env, params, sess, qns)
#print("arrDumb", arrDumb)
#print("arrBalanced", arrBalanced)
plt.plot(arrDumb, label="dumb")
plt.plot(arrRandom, label="random")
plt.plot(arrBalanced, label="balanced")
plt.plot(arrRL, label="RL")
plt.legend(loc='upper left')
plt.xlabel('#crawled')
plt.ylabel('#found')
plt.show()
def Main():
print("Starting")
global TIMER
TIMER = Timer()
oparser = argparse.ArgumentParser(
description="intelligent crawling with q-learning")
oparser.add_argument(
"--config-file",
dest="configFile",
required=True,
help="Path to config file (containing MySQL login etc.")
oparser.add_argument(
"--save-dir",
dest="saveDir",
default=".",
help=
"Directory that model WIP are saved to. If existing model exists then load it"
)
oparser.add_argument(
"--delete-duplicate-transitions",
dest="deleteDuplicateTransitions",
default=False,
help="If True then only unique transition are used in each batch")
oparser.add_argument(
"--language-pair",
dest="langPair",
required=True,
help="The 2 language we're interested in, separated by ,")
options = oparser.parse_args()
np.random.seed()
np.set_printoptions(formatter={'float': lambda x: "{0:0.1f}".format(x)},
linewidth=666)
sqlconn = MySQL(options.configFile)
hostName = "http://vade-retro.fr/"
#hostName = "http://www.buchmann.ch/"
#hostName = "http://www.visitbritain.com/"
#pickleName = hostName + ".pickle"
env = Env(sqlconn, hostName)
# if os.path.exists(pickleName):
# with open(pickleName, 'rb') as f:
# print("unpickling")
# env = pickle.load(f)
# else:
# env = Env(sqlconn, hostName)
# with open(pickleName, 'wb') as f:
# print("pickling")
# pickle.dump(env,f)
languages = Languages(sqlconn.mycursor)
params = LearningParams(languages, options.saveDir,
options.deleteDuplicateTransitions,
options.langPair)
tf.reset_default_graph()
qns = Qnets(params, env)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
#with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
sess.run(init)
qns.q[0].PrintAllQ(params, env, sess)
#WalkAll(env, params, sess, qns.q[0])
print()
TIMER.Start("Train")
totRewards, totDiscountedRewards = Train(params, sess, saver, env, qns)
TIMER.Pause("Train")
#qn.PrintAllQ(params, env, sess)
#env.WalkAll(params, sess, qn)
Walk(env, sys.maxsize, params, sess, qns.q[0], True)
del TIMER
plt.plot(totRewards)
plt.plot(totDiscountedRewards)
plt.show()
plt.plot(qns.q[0].corpus.losses)
plt.plot(qns.q[1].corpus.losses)
plt.show()
plt.plot(qns.q[0].corpus.sumWeights)
plt.plot(qns.q[1].corpus.sumWeights)
plt.show()
print("Finished")
def main():
global TIMER
TIMER = Timer()
oparser = argparse.ArgumentParser(
description="intelligent crawling with q-learning")
oparser.add_argument(
"--config-file",
dest="configFile",
required=True,
help="Path to config file (containing MySQL login etc.)")
oparser.add_argument(
"--language-pair",
dest="langPair",
required=True,
help="The 2 language we're interested in, separated by ,")
oparser.add_argument(
"--save-dir",
dest="saveDir",
default=".",
help=
"Directory that model WIP are saved to. If existing model exists then load it"
)
oparser.add_argument("--save-plots",
dest="saveDirPlots",
default="",
help="Directory ")
oparser.add_argument(
"--delete-duplicate-transitions",
dest="deleteDuplicateTransitions",
default=False,
help="If True then only unique transition are used in each batch")
oparser.add_argument(
"--n-hosts-train",
dest="n_train",
type=int,
default=1,
help="If True then only unique transition are used in each batch")
oparser.add_argument(
"--m-hosts-test",
dest="m_test",
type=int,
default=1,
help="If True then only unique transition are used in each batch")
options = oparser.parse_args()
np.random.seed(99)
np.set_printoptions(formatter={'float': lambda x: "{0:0.1f}".format(x)},
linewidth=666)
sqlconn = MySQL(options.configFile)
languages = Languages(sqlconn.mycursor)
"""
allhostNames = ["http://www.buchmann.ch/",
"http://vade-retro.fr/",
"http://www.visitbritain.com/",
"http://www.lespressesdureel.com/",
"http://www.otc-cta.gc.ca/",
"http://tagar.es/",
"http://lacor.es/",
"http://telasmos.org/",
"http://www.haitilibre.com/",
"http://legisquebec.gouv.qc.ca",
"http://hobby-france.com/",
"http://www.al-fann.net/",
"http://www.antique-prints.de/",
"http://www.gamersyde.com/",
"http://inter-pix.com/",
"http://www.acklandsgrainger.com/",
"http://www.predialparque.pt/",
"http://carta.ro/",
"http://www.restopages.be/",
"http://www.burnfateasy.info/",
"http://www.bedandbreakfast.eu/",
"http://ghc.freeguppy.org/",
"http://www.bachelorstudies.fr/",
"http://chopescollection.be/",
"http://www.lavery.ca/",
"http://www.thecanadianencyclopedia.ca/",
"http://www.vistastamps.com/",
"http://www.linker-kassel.com/",
"http://www.enterprise.fr/"]
shuffle(allhostNames)
"""
# assert len(allhostNames) >= options.n_train + options.m_test
hostNames_train = [
"http://www.buchmann.ch/", "http://www.lespressesdureel.com/",
"http://www.enterprise.fr/", "http://tagar.es/"
]
hostNames_test = ["http://www.visitbritain.com/"]
# Test on small domain!
# hostNames_train = ["http://vade-retro.fr/"]
# hostNames_test = ["http://vade-retro.fr/"]
if options.saveDirPlots:
save_plots = 'plot'
if not os.path.exists(save_plots):
os.mkdir(save_plots)
else:
par_d = 'train{}test{}'.format(options.n_train, options.m_test)
if not os.path.exists(par_d):
os.mkdir(par_d)
new_run = max(
[int(run.replace('run', ''))
for run in os.listdir(par_d)] + [0]) + 1
save_plots = '{}/run{}'.format(par_d, new_run)
os.mkdir(save_plots)
os.mkdir('{}/{}'.format(save_plots, 'train'))
os.mkdir('{}/{}'.format(save_plots, 'test'))
for hostName in hostNames_train:
os.mkdir('{}/{}/{}'.format(save_plots, 'train',
extract(hostName).domain))
for hostName in hostNames_test:
os.mkdir('{}/{}/{}'.format(save_plots, 'test',
extract(hostName).domain))
print("Training hosts are:")
for h in hostNames_train:
print(h)
print()
print("Testing hosts are:")
for h in hostNames_test:
print(h)
print()
with open('{}/hosts.info'.format(save_plots), 'w') as f:
f.write('Training hosts are:\n')
for h in hostNames_train:
f.write(h + '\n')
f.write('\nTesting hosts are:\n')
for h in hostNames_test:
f.write(h + '\n')
params = LearningParams(languages, options.saveDir, save_plots,
options.deleteDuplicateTransitions,
options.langPair)
env_train_dic = {
hostName: Env(sqlconn, hostName)
for hostName in hostNames_train
}
env_test_dic = {
hostName: Env(sqlconn, hostName)
for hostName in hostNames_test
}
for dic in [env_train_dic, env_test_dic]:
for hostName, env in dic.items():
env.maxLangId = MAX_LANG_ID
env.nodes[sys.maxsize].lang = languages.GetLang("None")
dic[hostName] = env
tf.reset_default_graph()
qns = Qnets(params, MAX_LANG_ID)
#qns_test = Qnets(params, env_test)
init = tf.global_variables_initializer()
saver = None #tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
totRewards, totDiscountedRewards = Train(params, sess, saver,
env_train_dic, qns,
env_test_dic)