def main():
rankings = read_ranks_file('qvist_rankings.txt')
ranker = Ranker([
#(win_margin, 1.287),
#(prob_win_margin, 1),
#(win_given_no_gain, -.05),
#(win_weighted_gain, 1.0),
#(frequency_purchased, .1),
#(frequency_weighted_win_margin, .01),
(win_given_any_gain, 2.5),
(log_odds_any_gained, .05),
(num_plus_actions, -.03),
(has_vp, -.1),
(is_reaction, .1),
])
rank_eval = RankEvaluator(ranker, rankings, ranking_accuracy)
learned_weights = scipy.optimize.fmin(rank_eval, rank_eval.ranker.weights)
ranker.weights = learned_weights
grouped_by_cost = collections.defaultdict(list)
for card in card_info.card_names():
grouped_by_cost[card_info.cost(card)].append(card)
for cost, card_list in grouped_by_cost.iteritems():
if len(card_list) >= 4:
card_list.sort(key=ranker.score)
print cost, ','.join(card_list)
def main():
rankings = read_ranks_file('qvist_rankings.txt')
ranker = Ranker([
#(win_margin, 1.287),
#(prob_win_margin, 1),
#(win_given_no_gain, -.05),
#(win_weighted_gain, 20),
#(frequency_purchased, .1),
#(frequency_weighted_win_margin, .01),
(win_given_any_gain, 30),
(log_odds_any_gained, 1.3),
(num_plus_actions, -1),
(has_vp, -1.5),
(is_reaction, 1.5),
])
# this should really be doing cross validation
rank_eval = RankEvaluator(ranker, rankings, ranking_log_loss)
learned_weights = [52.926, 1.358, -1.161, -1.712, 1.626]
ranker.weights = learned_weights
learned_weights = scipy.optimize.fmin_bfgs(rank_eval,
rank_eval.ranker.weights,
gtol=1e-3)
ranker.weights = learned_weights
print learned_weights
display_large_errors(ranker, rankings)
grouped_by_cost = collections.defaultdict(list)
for card in card_info.card_names():
grouped_by_cost[card_info.cost(card)].append(card)
for cost, card_list in grouped_by_cost.iteritems():
if len(card_list) >= 4:
card_list.sort(key=ranker.score)
def main():
rankings = read_ranks_file('qvist_rankings.txt')
ranker = Ranker([
#(win_margin, 1.287),
#(prob_win_margin, 1),
#(win_given_no_gain, -.05),
#(win_weighted_gain, 20),
#(frequency_purchased, .1),
#(frequency_weighted_win_margin, .01),
(win_given_any_gain, 30),
(log_odds_any_gained, 1.3),
(num_plus_actions, -1),
(has_vp, -1.5),
(is_reaction, 1.5),
])
# this should really be doing cross validation
rank_eval = RankEvaluator(ranker, rankings, ranking_log_loss)
learned_weights = [ 52.926, 1.358, -1.161, -1.712, 1.626]
ranker.weights = learned_weights
learned_weights = scipy.optimize.fmin_bfgs(
rank_eval, rank_eval.ranker.weights, gtol=1e-3)
ranker.weights = learned_weights
print learned_weights
display_large_errors(ranker, rankings)
grouped_by_cost = collections.defaultdict(list)
for card in card_info.card_names():
grouped_by_cost[card_info.cost(card)].append(card)
for cost, card_list in grouped_by_cost.iteritems():
if len(card_list) >= 4:
card_list.sort(key=ranker.score)
def GET(self):
web.header("Content-Type", "text/html; charset=utf-8")
query_dict = dict(urlparse.parse_qsl(web.ctx.env['QUERY_STRING']))
db = utils.get_mongo_database()
selected_card = ''
if 'card' in query_dict:
selected_card = query_dict['card']
results = db.trueskill_openings.find({'_id': {'$regex': '^open:'}})
openings = list(results)
card_list = card_info.OPENING_CARDS
def split_opening(o):
ret = o['_id'][len('open:'):].split('+')
if ret == ['']: return []
return ret
if selected_card not in ('All cards', ''):
openings = [o for o in openings if selected_card in
split_opening(o)]
openings = [o for o in openings if split_opening(o)]
for opening in openings:
floor = opening['mu'] - opening['sigma'] * 3
ceil = opening['mu'] + opening['sigma'] * 3
opening['level_key'] = make_level_key(floor, ceil)
opening['level_str'] = make_level_str(floor, ceil)
opening['skill_str'] = skill_str(opening['mu'], opening['sigma'])
opening['cards'] = split_opening(opening)
opening['cards'].sort()
opening['cards'].sort(key=lambda card: (card_info.cost(card)),
reverse=True)
costs = [str(card_info.cost(card)) for card in opening['cards']]
while len(costs) < 2:
costs.append('-')
opening['cost'] = '/'.join(costs)
openings.sort(key=lambda opening: opening['level_key'])
openings.reverse()
if selected_card == '':
openings = [op for op in openings
if op['level_key'][0] != 0
or op['_id'] == ['Silver', 'Silver']]
render = web.template.render('')
return render.openings_template(openings, card_list, selected_card)
def GET(self):
web.header("Content-Type", "text/html; charset=utf-8")
query_dict = dict(urlparse.parse_qsl(web.ctx.env['QUERY_STRING']))
db = utils.get_mongo_database()
selected_card = ''
if 'card' in query_dict:
selected_card = query_dict['card']
results = db.trueskill_openings.find({'_id': {'$regex': '^open:'}})
openings = list(results)
card_list = card_info.OPENING_CARDS
def split_opening(o):
ret = o['_id'][len('open:'):].split('+')
if ret == ['']: return []
return ret
if selected_card not in ('All cards', ''):
openings = [o for o in openings if selected_card in
split_opening(o)]
openings = [o for o in openings if split_opening(o)]
for opening in openings:
floor = opening['mu'] - opening['sigma'] * 3
ceil = opening['mu'] + opening['sigma'] * 3
opening['level_key'] = make_level_key(floor, ceil)
opening['level_str'] = make_level_str(floor, ceil)
opening['skill_str'] = skill_str(opening['mu'], opening['sigma'])
opening['cards'] = split_opening(opening)
opening['cards'].sort()
opening['cards'].sort(key=lambda card: (card_info.cost(card)),
reverse=True)
costs = [str(card_info.cost(card)) for card in opening['cards']]
while len(costs) < 2:
costs.append('-')
opening['cost'] = '/'.join(costs)
openings.sort(key=lambda opening: opening['level_key'])
openings.reverse()
if selected_card == '':
openings = [op for op in openings
if op['level_key'][0] != 0
or op['_id'] == ['Silver', 'Silver']]
render = web.template.render('')
return render.openings_template(openings, card_list, selected_card)
def main():
ARCH = 'Archivist'
card_data = json.load(open('card_conditional_data.json'))
card_names = card_info.card_names()
card_names.remove(ARCH)
card_inds = {}
for ind, card_name in enumerate(card_names):
card_inds[card_name] = ind
N = len(card_inds)
# cluster based on gain prob, win rate given any gained,
# avg gained per game, and win rate per gain
M = 4
grouped_data = np.zeros((N, M, N))
for card_row in card_data:
card_name = card_row['card_name']
condition = card_row['condition'][0]
if card_name == ARCH or condition == ARCH:
continue
assert len(card_row['condition']) == 1
if card_name == condition:
continue
i = card_inds[card_name]
j = card_inds[condition]
stats = card_row['stats']
def parse(key):
ret = MeanVarStat()
ret.from_primitive_object(stats[key])
return ret
wgag = parse('win_given_any_gain')
wgng = parse('win_given_no_gain')
wwg = parse('win_weighted_gain')
total_games = wgag.frequency() + wgng.frequency()
grouped_data[i][0][j] = wgag.frequency() / total_games
grouped_data[i][1][j] = wgag.mean()
#grouped_data[i][2][j] = wwg.frequency() / total_games
# grouped_data[i][3][j] = wwg.mean()
for i in range(N):
for j in range(M):
s = sum(grouped_data[i][j])
# make the self data == avg
grouped_data[i][j][i] = s / (N - 1)
for i in range(N):
for j in range(M):
grouped_data[i][j] = preprocessing.scale(grouped_data[i][j])
flattened_normed_data = np.zeros((N,
2 * N * M + len(bonus_feature_funcs)))
for i in range(N):
bonus_vec = get_bonus_vec(card_names[i])
v1, v2 = [], []
for j in range(M):
for k in range(N):
v1.append(grouped_data[i][j][k])
v2.append(grouped_data[k][j][i])
v1, v2 = np.array(v1), np.array(v2)
catted = np.concatenate((v1, v1, bonus_vec))
flattened_normed_data[i] = catted
flattened_normed_data, card_names = trim(
lambda x: not (card_info.cost(x)[0] >= '5' or
card_info.cost(x)[0] == '1' or
card_info.cost(x)[0] == 'P') and not (
x in card_info.EVERY_SET_CARDS or
card_info.cost(x)[0:2] == '*0'),
flattened_normed_data, card_names)
z = scipy.cluster.hierarchy.ward(flattened_normed_data)
scipy.cluster.hierarchy.dendrogram(z, labels=card_names,
orientation='left', leaf_font_size=4.5,
)
pylab.savefig('expensive_group_win_prob.png',
dpi=len(card_names) * 2.5, bbox_inches='tight')
