def main():
parser = argparse.ArgumentParser()
parser.add_argument('--total_assets_path', required=True)
parser.add_argument('--intangible_assets_path', required=True)
parser.add_argument('--total_liabilities_path', required=True)
parser.add_argument('--prices_path', required=True)
parser.add_argument('--outstanding_shares_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
ta_map = utils.read_map(args.total_assets_path)
tl_map = utils.read_map(args.total_liabilities_path)
p_map = utils.read_map(args.prices_path)
s_map = utils.read_map(args.outstanding_shares_path)
tickers = ta_map.keys() & tl_map.keys() & p_map.keys() & s_map.keys()
# intangible assets are 0 by default
ia_map = dict()
for t in tickers:
ia_map[t] = 0.0
ia_part = utils.read_map(args.intangible_assets_path)
for k, v in ia_part.items():
ia_map[k] = v
with open(args.output_path, 'w') as fp:
for ticker in sorted(tickers):
output = ((ta_map[ticker] - ia_map[ticker] - tl_map[ticker])
/ s_map[ticker] / p_map[ticker])
print('%s %f' % (ticker, output), file=fp)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--operating_cashflow_path', required=True)
parser.add_argument('--preferred_dividend_path', required=True)
parser.add_argument('--prices_path', required=True)
parser.add_argument('--outstanding_shares_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
cf_map = utils.read_map(args.operating_cashflow_path)
p_map = utils.read_map(args.prices_path)
s_map = utils.read_map(args.outstanding_shares_path)
tickers = cf_map.keys() & p_map.keys() & s_map.keys()
# preferred dividend is default to 0
pd_map = dict()
for t in tickers:
pd_map[t] = 0.0
pd_part = utils.read_map(args.preferred_dividend_path)
for k, v in pd_part.items():
pd_map[k] = v
with open(args.output_path, 'w') as fp:
for ticker in sorted(tickers):
output = ((cf_map[ticker] - pd_map[ticker]) / s_map[ticker] /
p_map[ticker])
print('%s %f' % (ticker, output), file=fp)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--total_assets_path', required=True)
parser.add_argument('--intangible_assets_path', required=True)
parser.add_argument('--total_liabilities_path', required=True)
parser.add_argument('--prices_path', required=True)
parser.add_argument('--outstanding_shares_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
ta_map = utils.read_map(args.total_assets_path)
tl_map = utils.read_map(args.total_liabilities_path)
p_map = utils.read_map(args.prices_path)
s_map = utils.read_map(args.outstanding_shares_path)
tickers = ta_map.keys() & tl_map.keys() & p_map.keys() & s_map.keys()
# intangible assets are 0 by default
ia_map = dict()
for t in tickers:
ia_map[t] = 0.0
ia_part = utils.read_map(args.intangible_assets_path)
for k, v in ia_part.items():
ia_map[k] = v
with open(args.output_path, 'w') as fp:
for ticker in sorted(tickers):
output = ((ta_map[ticker] - ia_map[ticker] - tl_map[ticker]) /
s_map[ticker] / p_map[ticker])
print('%s %f' % (ticker, output), file=fp)
def test4(self):
'''
####### #######
#.E...# #.....#
#.#..G# #.#G..# G(200)
#.###.# --> #.###.#
#E#G#G# #.#.#.#
#...#G# #G.G#G# G(98), G(38), G(200)
####### #######
Combat ends after 54 full rounds
Goblins win with 536 total hit points left
Outcome: 54 * 536 = 28944
'''
initial_data = '''
#######
#.E...#
#.#..G#
#.###.#
#E#G#G#
#...#G#
#######
'''
m, p, w = read_map(initial_data.split('\n')[1:-1])
game = Game(m, w, p)
step, score = game.combat()
print(step, game)
game.display()
self.assertEqual(step, 54)
self.assertEqual(score, 28944)
def test3(self):
'''
####### #######
#E.G#.# #G.G#.# G(200), G(98)
#.#G..# #.#G..# G(200)
#G.#.G# --> #..#..#
#G..#.# #...#G# G(95)
#...E.# #...G.# G(200)
####### #######
Combat ends after 35 full rounds
Goblins win with 793 total hit points left
Outcome: 35 * 793 = 27755
'''
initial_data = '''
#######
#E.G#.#
#.#G..#
#G.#.G#
#G..#.#
#...E.#
#######
'''
m, p, w = read_map(initial_data.split('\n')[1:-1])
game = Game(m, w, p)
step, score = game.combat()
print(step, game)
game.display()
self.assertEqual(step, 35)
self.assertEqual(score, 27755)
def test2(self):
'''
####### #######
#E..EG# #.E.E.# E(164), E(197)
#.#G.E# #.#E..# E(200)
#E.##E# --> #E.##.# E(98)
#G..#.# #.E.#.# E(200)
#..E#.# #...#.#
####### #######
Combat ends after 46 full rounds
Elves win with 859 total hit points left
Outcome: 46 * 859 = 39514
'''
initial_data = '''
#######
#E..EG#
#.#G.E#
#E.##E#
#G..#.#
#..E#.#
#######
'''
m, p, w = read_map(initial_data.split('\n')[1:-1])
game = Game(m, w, p)
step, score = game.combat()
print(step, game)
game.display()
self.assertEqual(step, 46)
self.assertEqual(score, 39514)
def test1(self):
'''
####### #######
#G..#E# #...#E# E(200)
#E#E.E# #E#...# E(197)
#G.##.# --> #.E##.# E(185)
#...#E# #E..#E# E(200), E(200)
#...E.# #.....#
####### #######
Combat ends after 37 full rounds
Elves win with 982 total hit points left
Outcome: 37 * 982 = 36334
'''
initial_data = '''
#######
#G..#E#
#E#E.E#
#G.##.#
#...#E#
#...E.#
#######
'''
m, p, w = read_map(initial_data.split('\n')[1:-1])
game = Game(m, w, p)
step, score = game.combat()
print(step, game)
game.display()
self.assertEqual(step, 37)
self.assertEqual(score, 36334)
def test27(self):
'''
#######
#G....# G(200)
#.G...# G(131)
#.#.#G# G(119)
#...#E# E(119)
#...G.# G(200)
#######
'''
m, p, w = read_map(test_data_battle)
game = Game(m, w, p)
for step in range(27):
print(step)
game.step()
game.display()
print(game)
self.assertEqual(len(game.players), 5)
self.assertEqual(game.players[0].hit_point, 200)
self.assertEqual(game.players[1].hit_point, 131)
self.assertEqual(game.players[2].hit_point, 119)
self.assertEqual(game.players[3].hit_point, 119)
self.assertEqual(game.players[4].hit_point, 200)
self.assertFalse(game.done())
def test_find_in_range2(self):
"""
Targets: In range: Reachable: Nearest: Chosen:
####### ####### ####### ####### #######
#E..G.# #E.?G?# #E.@G.# #E.!G.# #E.+G.#
#...#.# --> #.?.#?# --> #.@.#.# --> #.!.#.# --> #...#.#
#.G.#G# #?G?#G# #@G@#G# #!G.#G# #.G.#G#
####### ####### ####### ####### #######
"""
map_, players, walls = read_map(test_data_chosen)
player = players[1]
player.find_in_range(walls, players)
self.assertEqual(len(player.in_range), 2)
self.assertListEqual(player.in_range, [(1, 3), (1, 5)])
player = players[2]
player.find_in_range(walls, players)
self.assertEqual(len(player.in_range), 3)
self.assertListEqual(player.in_range, [(2, 2), (3, 1), (3, 3)])
player = players[3]
player.find_in_range(walls, players)
self.assertEqual(len(player.in_range), 1)
self.assertListEqual(player.in_range, [(2, 5)])
def _parse_all_corpus(corpus_path: str, wn2bn: Dict[str, str]) -> None:
"""
A method to parse all available corpus
:param corpus_path: corpus folder path
:param wn2bn: map Wordnet to Babelnet
:return: None
"""
for subdir, dirs, files in os.walk(str(corpus_path)):
data_path, gold_path, parsed_path = "", "", ""
for file in files:
if file.endswith("data.xml"):
data_path = os.path.join(subdir, file)
elif file.endswith("gold.key.txt"):
gold_path = os.path.join(subdir, file)
# if the corpus is not parsed yet
parsed_path = os.path.join(
config.SENTENCES, file.split(".")[0] + "_sentences.txt"
)
if not os.path.isfile(parsed_path) and all(
(path != "") for path in [data_path, gold_path]
):
key_map = utils.read_map(gold_path, delimiter=" ")
utils.write_sentences_and_labels(
parsed_path, parser_raganato_format(data_path, key_map, wn2bn)
)
def test5(self):
'''
######### #########
#G......# #.G.....# G(137)
#.E.#...# #G.G#...# G(200), G(200)
#..##..G# #.G##...# G(200)
#...##..# --> #...##..#
#...#...# #.G.#...# G(200)
#.G...G.# #.......#
#.....G.# #.......#
######### #########
Combat ends after 20 full rounds
Goblins win with 937 total hit points left
Outcome: 20 * 937 = 18740
'''
initial_data = '''
#########
#G......#
#.E.#...#
#..##..G#
#...##..#
#...#...#
#.G...G.#
#.....G.#
#########
'''
m, p, w = read_map(initial_data.split('\n')[1:-1])
game = Game(m, w, p)
step, score = game.combat()
print(step, game)
game.display()
self.assertEqual(step, 20)
self.assertEqual(score, 18740)
def preprocessing_domain(
train_y: List[str],
dev_y: List[str],
dom_path: str,
to_write: str,
vocab: Dict[str, int],
num_vocab: int,
min_count: int,
reverse: bool = False,
bn: bool = True,
) -> Tuple[List[str], List[str], Dict[str, int], FreqDist]:
"""
This method is used to preprocess the labels.
:param train_y: the labels to preprocess
:param dev_y: the dev labels to preprocess
:param dom_path: the path which from read the conversion map
:param to_write: the path to write the vocabulary
:param vocab: training vocabulary
:param num_vocab: maximum number of vocab
:param min_count: min number of word occurrences
:param reverse: if True, it reads domain2bn. False otherwise
:param bn: if True is done the Babelnet preprocess. False coarse-grained
:return:
train_y = train_y preprocessed
dev_y = dev_y preprocessed
out_vocab = the sense-inventory
frequency = word frequency
"""
bn2domain = utils.read_map(dom_path, reverse=False)
if not bn:
train_y, dev_y = utils.domains_converters([train_y, dev_y], bn2domain)
bn2domain = utils.read_map(dom_path, reverse=reverse)
senses = utils.retrieve_senses(train_y, bn2domain, is_bn=bn)
out_vocab, frequency = preprocesser.compute_vocabulary(senses,
min_count=min_count,
num_vocab=num_vocab,
vocab=vocab)
train_y, dev_y = preprocesser.text2id_corpus(corpus=[train_y, dev_y],
vocab=out_vocab)
utils.write_vocabulary(to_write, out_vocab)
return train_y, dev_y, out_vocab, frequency
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--net_income_path', required=True)
parser.add_argument('--total_equity_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
ni_map = utils.read_map(args.net_income_path)
e_map = utils.read_map(args.total_equity_path)
tickers = ni_map.keys() & e_map.keys()
with open(args.output_path, 'w') as fp:
for ticker in sorted(tickers):
output = ni_map[ticker] / e_map[ticker]
print('%s %f' % (ticker, output), file=fp)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--net_income_path', required=True)
parser.add_argument('--total_equity_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
ni_map = utils.read_map(args.net_income_path)
e_map = utils.read_map(args.total_equity_path)
tickers = ni_map.keys() & e_map.keys()
with open(args.output_path, 'w') as fp:
for ticker in sorted(tickers):
output = ni_map[ticker] / e_map[ticker]
print('%s %f' % (ticker, output), file=fp)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--prices_path', required=True)
parser.add_argument('--outstanding_shares_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
p_map = utils.read_map(args.prices_path)
s_map = utils.read_map(args.outstanding_shares_path)
tickers = p_map.keys() & s_map.keys()
with open(args.output_path, 'w') as fp:
for ticker in sorted(tickers):
output = p_map[ticker] * s_map[ticker]
print('%s %f' % (ticker, output), file=fp)
def parse_all():
"""
A simple method used to handle both the corpus and the test sets
:return: None
"""
wn2bn = utils.read_map(config.BABELNET2WORDNET_TR, reverse=True)
_parse_all_corpus(config.TEST_SETS, wn2bn)
_parse_all_corpus(config.TRAINING_SETS, wn2bn)
def test_find_in_range(self):
map_, players, walls = read_map(test_data)
player = players[1]
player.find_in_range(walls, players)
self.assertListEqual(player.in_range, [(1, 3), (1, 5), (2, 4)])
player = players[3]
player.find_in_range(walls, players)
self.assertListEqual(player.in_range, [(1, 3), (2, 2), (2, 4), (3, 3)])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--trading_volumes_path', required=True)
parser.add_argument('--prices_path', required=True)
parser.add_argument('--outstanding_shares_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
tv_map = utils.read_map(args.trading_volumes_path)
p_map = utils.read_map(args.prices_path)
s_map = utils.read_map(args.outstanding_shares_path)
tickers = tv_map.keys() & p_map.keys() & s_map.keys()
with open(args.output_path, 'w') as fp:
for ticker in sorted(tickers):
output = tv_map[ticker] / (p_map[ticker] * s_map[ticker])
print('%s %f' % (ticker, output), file=fp)
def test_adjacent(self):
"""
all players always have 4 adjacent cells which doesn't me they are reachable
"""
map_, players, walls = read_map(test_data)
player = players[1]
print(player)
self.assertEqual(len(player.adjacent), 4)
player = players[3]
self.assertEqual(len(player.adjacent), 4)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--scores_path', required=True)
parser.add_argument('--prices_path', required=True)
parser.add_argument('--mc_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
s_map = utils.read_map(args.scores_path)
p_map = utils.read_map(args.prices_path)
mc_map = utils.read_map(args.mc_path)
tickers = s_map.keys() & p_map.keys() & mc_map.keys()
with open(args.output_path, 'w') as fp:
for ticker in sorted(tickers):
if p_map[ticker] < MIN_PRICE: continue
if mc_map[ticker] < MIN_MC: continue
print('%s %f' % (ticker, s_map[ticker]), file=fp)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--er1_path', required=True)
parser.add_argument('--er12_path', required=True)
parser.add_argument('--tv2mc_path', required=True)
parser.add_argument('--er2_path', required=True)
parser.add_argument('--e2p_path', required=True)
parser.add_argument('--roe_path', required=True)
parser.add_argument('--b2p_path', required=True)
parser.add_argument('--er6_path', required=True)
parser.add_argument('--cf2p_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
er1_map = utils.read_map(args.er1_path)
er12_map = utils.read_map(args.er12_path)
tv2mc_map = utils.read_map(args.tv2mc_path)
er2_map = utils.read_map(args.er2_path)
e2p_map = utils.read_map(args.e2p_path)
roe_map = utils.read_map(args.roe_path)
b2p_map = utils.read_map(args.b2p_path)
er6_map = utils.read_map(args.er6_path)
cf2p_map = utils.read_map(args.cf2p_path)
tickers = (er1_map.keys() & er12_map.keys() & tv2mc_map.keys()
& er2_map.keys() & e2p_map.keys() & roe_map.keys()
& b2p_map.keys() & er6_map.keys() & cf2p_map.keys())
logging.info('%d tickers' % len(tickers))
logging.info('total weight: %f' %
(ER1 + ER12 + TV2MC + ER2 + E2P + ROE + B2P + ER6 + CF2P))
with open(args.output_path, 'w') as fp:
for t in sorted(tickers):
score = (er1_map[t] * ER1
+ er12_map[t] * ER12
+ tv2mc_map[t] * TV2MC
+ er2_map[t] * ER2
+ e2p_map[t] * E2P
+ roe_map[t] * ROE
+ b2p_map[t] * B2P
+ er6_map[t] * ER6
+ cf2p_map[t] * CF2P) / 100 # accounting for %
print('%s %f' % (t, score), file=fp)
def test_distance(self):
"""
Targets: In range: Reachable: Nearest: Chosen:
####### ####### ####### ####### #######
#E..G.# #E.?G?# #E.@G.# #E.!G.# #E.+G.#
#...#.# --> #.?.#?# --> #.@.#.# --> #.!.#.# --> #...#.#
#.G.#G# #?G?#G# #@G@#G# #!G.#G# #.G.#G#
####### ####### ####### ####### #######
"""
map_, players, walls = read_map(test_data_chosen)
player = players[0]
distance = player.calculate_reachable_bfs(map_, walls, players)
print(distance)
def test_step(self):
"""
In range: Nearest: Chosen: Distance: Step:
####### ####### ####### ####### #######
#.E...# #.E...# #.E...# #4E212# #..E..#
#...?.# --> #...!.# --> #...+.# --> #32101# --> #.....#
#..?G?# #..!G.# #...G.# #432G2# #...G.#
####### ####### ####### ####### #######
"""
map_, players, walls = read_map(test_data_step)
player = players[0]
distances = player.step(map_, walls, players)
print(distances)
self.assertEqual(player.position, (1, 3))
def test_chosen(self):
"""
Targets: In range: Reachable: Nearest: Chosen:
####### ####### ####### ####### #######
#E..G.# #E.?G?# #E.@G.# #E.!G.# #E.+G.#
#...#.# --> #.?.#?# --> #.@.#.# --> #.!.#.# --> #...#.#
#.G.#G# #?G?#G# #@G@#G# #!G.#G# #.G.#G#
####### ####### ####### ####### #######
"""
map_, players, walls = read_map(test_data_chosen)
player = players[0]
distance, chosen = player.chosen(map_, walls, players)
self.assertEqual(distance, 2)
self.assertEqual(chosen, (1, 3))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--er1_path', required=True)
parser.add_argument('--er12_path', required=True)
parser.add_argument('--tv2mc_path', required=True)
parser.add_argument('--er2_path', required=True)
parser.add_argument('--e2p_path', required=True)
parser.add_argument('--roe_path', required=True)
parser.add_argument('--b2p_path', required=True)
parser.add_argument('--er6_path', required=True)
parser.add_argument('--cf2p_path', required=True)
parser.add_argument('--output_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
er1_map = utils.read_map(args.er1_path)
er12_map = utils.read_map(args.er12_path)
tv2mc_map = utils.read_map(args.tv2mc_path)
er2_map = utils.read_map(args.er2_path)
e2p_map = utils.read_map(args.e2p_path)
roe_map = utils.read_map(args.roe_path)
b2p_map = utils.read_map(args.b2p_path)
er6_map = utils.read_map(args.er6_path)
cf2p_map = utils.read_map(args.cf2p_path)
tickers = (er1_map.keys() & er12_map.keys() & tv2mc_map.keys()
& er2_map.keys() & e2p_map.keys() & roe_map.keys()
& b2p_map.keys() & er6_map.keys() & cf2p_map.keys())
logging.info('%d tickers' % len(tickers))
logging.info('total weight: %f' %
(ER1 + ER12 + TV2MC + ER2 + E2P + ROE + B2P + ER6 + CF2P))
with open(args.output_path, 'w') as fp:
for t in sorted(tickers):
score = (er1_map[t] * ER1 + er12_map[t] * ER12 +
tv2mc_map[t] * TV2MC + er2_map[t] * ER2 + e2p_map[t] * E2P
+ roe_map[t] * ROE + b2p_map[t] * B2P + er6_map[t] * ER6 +
cf2p_map[t] * CF2P) / 100 # accounting for %
print('%s %f' % (t, score), file=fp)
def test1(self):
test_data = '''
#######
#.G.E.#
#E.G.E#
#.G.E.#
#######
'''
test_data = test_data.split('\n')[1:-1]
map_, players, walls = read_map(test_data)
self.assertEqual(map_[0][0], '#')
self.assertEqual(map_[1][2], 'G')
self.assertEqual(map_[2][5], 'E')
self.assertEqual(len(players), 7)
self.assertEqual(players[5].type_, 'G')
self.assertEqual(players[5].position, (3,2))
self.assertEqual(players[1].type_, 'E')
self.assertEqual(players[1].position, (1,4))
def test2(self):
'''
#######
#...G.# G(200)
#..GEG# G(200), E(188), G(194)
#.#.#G# G(194)
#...#E# E(194)
#.....#
#######
'''
m, p, w = read_map(test_data_battle)
game = Game(m, w, p)
for _ in range(2):
game.step()
game.display()
print(game)
self.assertEqual(game.players[0].hit_point, 200)
self.assertEqual(game.players[1].hit_point, 200)
self.assertEqual(game.players[2].hit_point, 188)
self.assertEqual(game.players[3].hit_point, 194)
self.assertEqual(game.players[4].hit_point, 194)
self.assertEqual(game.players[5].hit_point, 194)
def predict_babelnet(input_path: str, output_path: str,
resources_path: str) -> None:
"""
DO NOT MODIFY THE SIGNATURE!
This is the skeleton of the prediction function.
The predict function will build your model, load the weights from the checkpoint and write a new file (output_path)
with your predictions in the "<id> <BABELSynset>" format (e.g. "d000.s000.t000 bn:01234567n").
The resources folder should contain everything you need to make the predictions. It is the "resources" folder in your submission.
N.B. DO NOT HARD CODE PATHS IN HERE. Use resource_path instead, otherwise we will not be able to run the code.
If you don't know what HARD CODING means see: https://en.wikipedia.org/wiki/Hard_coding
:param input_path: the path of the input file to predict in the same format as Raganato's framework (XML files you downloaded).
:param output_path: the path of the output file (where you save your predictions)
:param resources_path: the path of the resources folder containing your model and stuff you might need.
:return: None
"""
print("Predicting Babelnet...")
out_vocab = utils.read_vocabulary(
os.path.join(resources_path, config.OUT_VOCAB_BN))
bn2wn = utils.read_map(os.path.join(resources_path,
config.BABELNET2WORDNET),
reverse=False)
dense_layer = 0
is_bn = True
_prediction(
input_path,
output_path,
resources_path,
out_vocab,
dense_layer,
is_bn,
bn2domain=bn2wn,
)
def test23(self):
'''
After 23 rounds:
#######
#...G.# G(200)
#..G.G# G(200), G(131)
#.#.#G# G(131)
#...#E# E(131)
#.....#
#######
'''
m, p, w = read_map(test_data_battle)
game = Game(m, w, p)
for _ in range(23):
game.step()
game.display()
print(game)
self.assertEqual(len(game.players), 5)
self.assertEqual(game.players[0].hit_point, 200)
self.assertEqual(game.players[1].hit_point, 200)
self.assertEqual(game.players[2].hit_point, 131)
self.assertEqual(game.players[3].hit_point, 131)
self.assertEqual(game.players[4].hit_point, 131)
def main():
#read list
li = utils.read_arg_list_all(sys.argv[1])
#read map
map = utils.read_map(sys.argv[2])
#read check_match
match = utils.read_check_match(sys.argv[3])
if(sys.argv[4] == "truseq"):
print 'gene\torg\ttruseq_f\ttruseq_r\texpected_length'
else:
print 'gene\torg\tconventional_pcr_f\tconventional_pcr_r\texpected_length'
for x in li:
gene = x[0]
if(len(x) == 2):
target1 = x[1]
show_list(gene,target1,map,match)
else:
target1 = x[1]
target2 = x[2]
show_list(gene,target1,map,match)
show_list(gene,target2,map,match)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--current_prices_path', required=True)
parser.add_argument('--future_prices_path', required=True)
parser.add_argument('--scores_path', required=True)
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
utils.setup_logging(args.verbose)
cp_map = utils.read_map(args.current_prices_path)
fp_map = utils.read_map(args.future_prices_path)
s_map = utils.read_map(args.scores_path)
tickers = cp_map.keys() & fp_map.keys() & s_map.keys()
logging.info('%d tickers in this test' % len(tickers))
scores = s_map.values()
maxs, mins, means = max(scores), min(scores), sum(scores)/len(scores)
logging.info('max score = %f, min score = %f, mean score = %f' %
(maxs, mins, means))
r_map = dict()
for t in tickers:
r_map[t] = (fp_map[t] - cp_map[t]) / (cp_map[t] + 0.01) # avoid div-by-0
ups, rups, corrects = 0, 0, 0
for t in tickers:
if (s_map[t] > 0) == (r_map[t] > 0):
corrects += 1
if s_map[t] > 0:
ups += 1
if r_map[t] > 0:
rups += 1
logging.info('%d of %d are predicted to go up' % (ups, len(tickers)))
logging.info('%d of %d did go up' % (rups, len(tickers)))
logging.info('%d of %d correct signs (%.2f%%)'
% (corrects, len(tickers), 100.0 * corrects / len(tickers)))
rs = [(t, s) for t, s in s_map.items()]
rs.sort(key=lambda x: x[1])
bucket_size = int(len(tickers) / K)
for i in range(K):
f = i * bucket_size
t = f + bucket_size
if i == K - 1: t = len(tickers)
meanr, count = 0.0, 0
for j in range(f, t):
r = r_map[rs[j][0]]
meanr += r
count += 1
meanr /= count
logging.info('decile %d: %d stocks, mean return = %.2f%%'
% (i+1, count, meanr * 100))
tr = compute_return(rs, r_map, T)
br = compute_return(rs, r_map, B)
logging.info('Mean return for top %s is %s' % (T, tr))
logging.info('Mean return for bot %s is %s' % (B, br))
rss = [(p[0], p[1], r_map[p[0]]) for p in rs]
rss.sort(key=lambda x: x[2])
logging.info('Top 10 gains: %s, average: %f'
% ([x[2] for x in rss[-10:]], sum([x[2] for x in rss[-10:]])/10))
logging.info('Top 10 scores: %s, average: %f'
% ([x[1] for x in rss[-10:]], sum([x[1] for x in rss[-10:]])/10))
logging.info('Bot 10 gains: %s, average: %f'
% ([x[2] for x in rss[:10]], sum([x[2] for x in rss[:10]])/10))
logging.info('Bot 10 scores: %s, average: %f'
% ([x[1] for x in rss[:10]], sum([x[1] for x in rss[:10]])/10))
def _prediction(
input_path: str,
output_path: str,
resources_path: str,
out_vocab: Dict,
i_dense: int,
is_bn: bool,
bn2domain: Dict = None,
) -> None:
"""
This method is used to handle the prediction of a task
:param input_path: the path of the input file to predict in the same format as Raganato's framework (XML files you downloaded).
:param output_path: the path of the output file (where you save your predictions)
:param resources_path: the path of the resources folder containing your model and stuff you might need.
:param out_vocab: sense inventory
:param i_dense: i-esime fully connected layer:
0 -> Babelnet
1 -> Wordnet domains
2 -> Lexicographer
:param is_bn: if True, predicts Babelnet
:param bn2domain: a map Babelnet to domain
:return: None
"""
config_tf = tf.ConfigProto()
config_tf.gpu_options.allow_growth = True
tf.keras.backend.set_session(tf.Session(config=config_tf))
test_set = parser.parser_test_set(input_path)
vocab = utils.read_vocabulary(os.path.join(resources_path, config.VOCAB))
wn2bn = utils.read_map(os.path.join(resources_path,
config.BABELNET2WORDNET),
reverse=True)
out_vocab_bn = utils.read_vocabulary(
os.path.join(resources_path, config.OUT_VOCAB_BN))
out_vocab_wnd = utils.read_vocabulary(
os.path.join(resources_path, config.OUT_VOCAB_WND))
out_vocab_lex = utils.read_vocabulary(
os.path.join(resources_path, config.OUT_VOCAB_LEX))
out_vocab_pos = utils.read_vocabulary(
os.path.join(resources_path, config.POS_VOCAB))
pre_trained = gensim.models.KeyedVectors.load_word2vec_format(os.path.join(
resources_path, config.SQUEEZED_EMB),
binary=True)
print("Downloading ELMo...")
model = models.build_model(
vocab_size=len(vocab),
out_size_bn=len(out_vocab_bn),
out_size_wnd=len(out_vocab_wnd),
out_size_lex=len(out_vocab_lex),
out_size_pos=len(out_vocab_pos),
word2vec=pre_trained,
is_elmo=config.IS_ELMO,
attention=config.ATTENTION,
is_sense_emb=config.SENSE_EMB,
)
reversed_vocab = utils.reverse_vocab(out_vocab_bn)
model.load_weights(str(os.path.join(resources_path, config.MODEL_WEIGHTS)))
with open(str(output_path), mode="w") as file:
for row in tqdm(test_set):
if not config.IS_ELMO:
tmp = preprocesser.text2id([row[0]], vocab)
else:
tmp = np.array([row[0]])
tmp_row = list(row[2])
inp = tmp
if config.SENSE_EMB:
sens_emb = np.ones((1, len(inp[0].split())), dtype=int)
inp_pos = np.array([row[1]])
inp = [inp, sens_emb, inp_pos]
predictions = model.predict(inp, verbose=0)[i_dense]
for senses in tmp_row:
sense_position = utils.senses_position_from_vocab(
senses["lemma"], out_vocab_bn, bn2domain)
synsets = [reversed_vocab[x] for x in sense_position]
if not is_bn:
synsets = [
bn2domain.get(syn.split("_")[-1]) for syn in synsets
]
sense_position = [out_vocab[syn] for syn in synsets]
to_compute = np.array([
predictions[0][senses["position"]][sen_pos]
for sen_pos in sense_position
])
if len(to_compute) != 0:
file.write(senses["id"] + " " +
synsets[to_compute.argmax()].split("_")[-1] +
"\n")
else:
file.write(senses["id"] + " " + utils.most_frequent_sense(
senses["lemma"],
senses["pos"],
wn2bn,
bn2domain=bn2domain,
is_bn=is_bn,
) + "\n")
#this script match map file and blast result
#output: organism, locus, ARG name
#python match_map_blast.py mapfile blastfile > output.txt
import sys
import utils
map = utils.read_map(sys.argv[1])
for line in open(sys.argv[2],'r'):
spl = line.strip().split('\t')
arg = spl[1].split('|')
ar = arg[len(arg)-1]
if(map.has_key(spl[0])):
result = [ map[spl[0]], spl[0], ar]
print '\t'.join(result)
#!/usr/bin/env python
from __future__ import print_function
from utils import read_map
from game import Game
if __name__ == '__main__':
with open('data.txt', 'r') as f:
m, p, w = read_map(f)
game = Game(m, w, p)
step, score = game.combat(5000)
print(score)
# 208413 too low
# 210843 too low
