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""" Compute correlations between when/how many of a card is gained and wins."""
from __future__ import division
import os
import card_info
from stats import MeanVarStat
from game import Game
import incremental_scanner
import pymongo
import collections
import simplejson as json
from primitive_util import PrimitiveConversion, ConvertibleDefaultDict
import utils
class CardStatistic(PrimitiveConversion):
""" Per card statistics.
win_weighted_accum_turn: Dictionary keyed by turn that correlates
winning and gaining/buying the card on a given turn. It is
weighted in the sense that if a card is bought twice in a given
turn, it counts twice for that turn.
win_diff_accum: Dictionary keyed by relative card advantage that
correlates winning and have an advantage/disadvantage in the
number of a given card. Card advantages are rounded relative
to the average number gained/bought by other players through the
def __init__(self):
self.available = 0
self.win_any_accum = MeanVarStat()
self.win_weighted_accum = MeanVarStat()
self.win_weighted_accum_turn = ConvertibleDefaultDict(MeanVarStat, int)
self.win_diff_accum = ConvertibleDefaultDict(MeanVarStat, int)
class GamesAnalysis(PrimitiveConversion):
""" A collection of CardStatistics for every card in the deck. """
def __init__(self):
self.card_stats = ConvertibleDefaultDict(CardStatistic)
self.num_games = 0
self.max_game_id = ''
def analyze_game(self, game):
""" Aggregate information about game into this object.
game: game.Game object to analyze.
self.num_games += 1
seen_cards_players = set()
self.max_game_id = max(self.max_game_id, game.get_id())
for card in game.get_supply() + card_info.EVERY_SET_CARDS:
self.card_stats[card].available += len(game.get_player_decks())
accumed_by_player = collections.defaultdict(lambda : collections.defaultdict(int))
for turn in game.get_turns():
deck = turn.get_player()
turnno = turn.get_turn_no()
for card in turn.player_accumulates():
per_card_stat = self.card_stats[card]
if (deck, card) not in seen_cards_players:
seen_cards_players.add((deck, card))
accumed_by_player[deck][card] += 1
for deck, card_accum_dict in accumed_by_player.iteritems():
total_other_decks = collections.defaultdict(int)
odeck_count = 0
for other_deck, other_accum in accumed_by_player.iteritems():
if other_deck != deck:
odeck_count += 1
for card in other_accum:
total_other_decks[card] += other_accum[card]
assert odeck_count != 0, game.isotropic_url()
for card in set(card_accum_dict.keys() + total_other_decks.keys()):
per_card_stat = self.card_stats[card]
other_avg_freq = total_other_decks[card] / odeck_count
card_diff_index = int(card_accum_dict[card] - other_avg_freq)
def main():
""" Update analysis statistics. By default, do so incrementally, unless
--noincremental argument is given."""
parser = utils.incremental_max_parser()
parser.add_argument('--output_collection_name', default='analysis')
args = parser.parse_args()
conn = pymongo.Connection()
database = conn.test
games =
output_collection_name = args.output_collection_name
output_collection = database[output_collection_name]
game_analysis = GamesAnalysis()
scanner = incremental_scanner.IncrementalScanner(output_collection_name,
if args.incremental:
utils.read_object_from_db(game_analysis, output_collection, '')
output_file_name = 'static/output/all_games_card_stats.js'
if not os.path.exists('static/output'):
print scanner.status_msg()
for idx, raw_game in enumerate(scanner.scan(games, {})):
if idx % 1000 == 0:
print idx
if idx == args.max_games:
except int, exception:
print Game(raw_game).isotropic_url()
print exception
print raw_game
game_analysis.max_game_id = scanner.get_max_game_id()
game_analysis.num_games = scanner.get_num_games()
utils.write_object_to_db(game_analysis, output_collection, '')
output_file = open(output_file_name, 'w')
output_file.write('var all_card_data = ')
json.dump(game_analysis.to_primitive_object(), output_file)
print scanner.status_msg()
if __name__ == '__main__':