import pywikibot
import RevisionPuller as RP
import SearchEngine as SE
import PageProcessor as PP
engine = SE.SearchEngine()
processor = PP.PageProcessor()
def get_readable_text_of_old_revision(page_title: str, rev_id: int):
"""
Returns a string containing a "readable" version of a revision
:param page_title: A string of the page title
:param rev_id: The revision number of the desired revision
:return: A string of the revision's readable text
"""
page = engine.search(page_title, 1, "nearmatch")[0]
return processor.getReadableText(RP.get_text_of_old_revision(page, rev_id))
def get_revisions(page_title: str,
recent_to_oldest: bool = True,
num_revisions=None,
start_time: pywikibot.Timestamp = None,
end_time: pywikibot.Timestamp = None):
"""
Returns the last (num_revisions) revisions from a given Wikipedia page
If all revisions are desired use: get_latest_revisions(page)
:param page_title: A string containing the title of the desired page
:param recent_to_oldest: Set to false if we want the revisions in order of oldest to most recent
:param num_revisions: The number of revisions to be grabbed (set to an integer to set limit to number of revisions grabbed)
parser.add_argument("--mode", choices=["train","eval","repr_code","search"], default='train',
help="The mode to run. The `train` mode trains a model;"
" the `eval` mode evaluate models in a test set "
" The `repr_code/repr_desc` mode computes vectors"
" for a code snippet or a natural language description with a trained model.")
parser.add_argument("--gen", type=int, default='3', help="Number of GA generation")
parser.add_argument("--chunk_size", type=int, default='20', help="Number of inputs")
parser.add_argument("--mutation_rate", type=float, default='0.05', help="Mutation Rate")
parser.add_argument("--verbose",action="store_true", default=True, help="Be verbose")
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
config = getattr(configs, 'config_'+args.model)()
engine = SearchEngine.SearchEngine(args, config)
##### Define model ######
logger.info('Build Model')
#tf.compat.v1.global_variables_initializer()
model = getattr(models, args.model)(config) # initialize the model
model.build()
model.summary(export_path = "./output/{}/".format(args.model))
optimizer = config.get('training_params', dict()).get('optimizer', 'adam')
model.compile(optimizer=optimizer)
data_path = args.data_path+args.dataset+'/'
import sys
from PySide2.QtCore import *
from PySide2.QtGui import *
from PySide2.QtWidgets import *
from vmmpy import *
sys.path.append("RemoteMemoryScanner")
from SearchEngine import *
from UserInterface import *
if __name__ == "__main__":
app = QApplication(sys.argv)
search_engine = SearchEngine()
user_interface = UserInterface(search_engine)
user_interface.main_window.show()
sys.exit(app.exec_())
def test_steam_miss(test_list):
test_num = 200
auc_num = 0
alph = random.sample('qwertyuiopasdfghjklzxcvbnm', 1)
for ids in test_list.keys():
game_name = test_list[ids]
num = random.randint(0, len(game_name) - 1)
change = game_name[num]
new_name = game_name.replace(change, ''.join(alph), 1)
results = get_search_results(
"https://store.steampowered.com/search/?term=", new_name)
if results is None:
continue
if len(results) > 10:
results = results[:10]
for game in results:
if str(game['game_id']) == str(ids):
auc_num += 1
break
print('test_num:', test_num, 'accuracy', auc_num / test_num)
if __name__ == "__main__":
CACHE_DICT = open_cache()
PS = PorterStemmer()
search_engine = SearchEngine(config, word_tokenize, PS, isStemming=False)
# test_list = search_engine.test_auc_zh_steam()
# test_steam_miss(test_list)
app.run(debug=True)
