250 lines
11 KiB
Python
250 lines
11 KiB
Python
import aenum
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from typing import List, Tuple, Set
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import psycopg2.extras
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from hanabi import hanab_game
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import utils
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from database import conn_manager
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import games_db_interface
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from log_setup import logger
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class GameOutcome(aenum.Enum):
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_init_ = 'value string'
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win = 0, 'Win'
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discard_crit = 1, 'Discard Critical'
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bomb_crit = 2, 'Bomb Critical'
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strikeout = 3, 'Strikeout'
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bottom_deck = 4, 'Bottom Deck'
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vote_to_kill = 5, 'Vote to Kill'
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out_of_pace = 6, 'Out of Pace'
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class GameAnalysisResult:
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def __init__(self,
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outcomes: Set[GameOutcome],
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bdrs: List[Tuple[hanab_game.DeckCard, int]],
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lost_crits: List[hanab_game.DeckCard]
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):
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self.outcomes = outcomes
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self.bdrs = bdrs
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self.lost_crits = lost_crits
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def analyze_replay(instance: hanab_game.HanabiInstance, actions: List[hanab_game.Action]) -> GameAnalysisResult:
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# List of bdrs
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bdrs = []
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# This is the default value if we find no other reason why the game was lost (or won)
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outcomes = set()
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lost_crits = []
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game = hanab_game.GameState(instance)
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def handle_lost_card(card, game, play: bool):
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if not game.is_trash(card):
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if game.is_critical(card):
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outcomes.add(GameOutcome.bomb_crit if play else GameOutcome.discard_crit)
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lost_crits.append(card)
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elif card.rank != 1:
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if card in game.deck[game.progress:]:
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bdrs.append((card, game.draw_pile_size))
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else:
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if game.deck[game.progress:].count(card) == 2:
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bdrs.append((card, game.draw_pile_size))
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for action in actions:
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if action.type == hanab_game.ActionType.Discard:
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discarded_card = instance.deck[action.target]
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handle_lost_card(discarded_card, game, False)
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if action.type == hanab_game.ActionType.Play:
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played_card = instance.deck[action.target]
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if not game.is_playable(played_card) and not game.is_trash(played_card):
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bombed_card = instance.deck[action.target]
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handle_lost_card(bombed_card, game, True)
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game.make_action(action)
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if game.pace < 0:
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outcomes.add(GameOutcome.out_of_pace)
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if game.strikes == 3:
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outcomes.add(GameOutcome.strikeout)
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elif actions[-1].type in [hanab_game.ActionType.EndGame, hanab_game.ActionType.VoteTerminate]:
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outcomes.add(GameOutcome.vote_to_kill)
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if game.score == 5 * instance.num_suits:
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outcomes.add(GameOutcome.win)
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return GameAnalysisResult(outcomes, bdrs, lost_crits)
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def analyze_game_and_store_stats(game_id: int):
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logger.verbose("Analysing game {} for BDRs and lost crits".format(game_id))
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instance, actions = games_db_interface.load_game(game_id)
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analysis = analyze_replay(instance, actions)
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cur = conn_manager.get_new_cursor()
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cur.execute(
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"INSERT INTO game_statistics (game_id, num_bottom_deck_risks, num_crits_lost) "
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"VALUES (%s, %s, %s) "
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"ON CONFLICT (game_id) DO UPDATE "
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"SET (num_bottom_deck_risks, num_crits_lost) = (EXCLUDED.num_bottom_deck_risks, EXCLUDED.num_crits_lost)",
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(game_id, len(analysis.bdrs), len(analysis.lost_crits))
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)
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psycopg2.extras.execute_values(
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cur,
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"INSERT INTO game_outcomes (game_id, outcome) VALUES %s",
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((game_id, outcome.value) for outcome in analysis.outcomes)
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)
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conn_manager.get_connection().commit()
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def analyze_all_games():
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"""
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Runs analysis on replays of all games
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@warning: This assumes that detailed game data has been fetched from the server already
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"""
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logger.info("Analysing replays of all games.")
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cur = conn_manager.get_new_cursor()
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cur.execute(
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"SELECT id FROM games "
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"LEFT OUTER JOIN game_statistics "
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" ON games.id = game_statistics.game_id "
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"WHERE game_statistics.game_id IS NULL "
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"ORDER BY games.id"
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)
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for (game_id, ) in cur.fetchall():
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analyze_game_and_store_stats(game_id)
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def update_user_statistics():
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"""
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Update the cumulative user statistics for this user, assuming that the corresponding game statistics have
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been computed already.
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@param user_ids:
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@return:
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"""
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# Note that some of these statistics could be computed by updating them on each new game insertion.
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# However, it would be tedious to ensure that *every* new game triggers an update of these statistics.
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# Also, this would be error-prone, since doing a mistake once means that values will be off forever
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# (unless the DB is reset).
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# Since it is cheap to accumulate some values over the whole DB, we therefore recreate the statistics as a whole,
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# reusing only the individual results (that never change and therefore can only be missing, but never wrong)
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cur = conn_manager.get_new_cursor()
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# Update total number of moves
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for clue_starved in [True, False]:
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rating_type = utils.get_rating_type(clue_starved)
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# We insert 0 here to ensure that we have an entry for each player
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# Note that this will immediately be changed by the next query in case it is nonzero,
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# so the zero value never shows up in the database if it was nonzero before.
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cur.execute(
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"INSERT INTO user_statistics"
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" (user_id, variant_type, total_game_moves, games_played, games_won, current_streak, maximum_streak, total_bdr, total_crits_lots)"
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" ("
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" SELECT id, %s, 0, 0, 0, 0, 0, 0, 0 FROM users"
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" )"
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"ON CONFLICT (user_id, variant_type) DO UPDATE "
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"SET"
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" (total_game_moves, games_played, games_won, current_streak, maximum_streak, total_bdr, total_crits_lots)"
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" ="
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" (EXCLUDED.total_game_moves, EXCLUDED.games_played, EXCLUDED.games_won, EXCLUDED.current_streak, EXCLUDED.maximum_streak, EXCLUDED.total_bdr, EXCLUDED.total_crits_lots)",
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(rating_type,)
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)
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# Most of the statistics are very easy to compute: We just have to accumulate data from other tables
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cur.execute(
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"INSERT INTO user_statistics (user_id, variant_type, total_game_moves, games_played, games_won, total_bdr, total_crits_lots)"
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" ("
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" SELECT"
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" users.id,"
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" rating_type,"
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" SUM(games.num_turns),"
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" COUNT(*)," # This counts the number of rows (per user id), so the number of played game
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" COUNT(*) FILTER ( WHERE variants.num_suits * 5 = games.score )," # Same, but only count wins now
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" SUM (game_statistics.num_bottom_deck_risks)," # Simple accumulation of the game stats
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" SUM (game_statistics.num_crits_lost)"
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"FROM users"
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" INNER JOIN game_participants "
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" ON game_participants.user_id = users.id "
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" INNER JOIN games "
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" ON game_participants.game_id = games.id "
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" INNER JOIN variants"
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" ON variants.id = games.variant_id "
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" LEFT OUTER JOIN game_statistics"
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" ON games.id = game_statistics.game_id"
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" GROUP BY users.id, rating_type "
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" ) "
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"ON CONFLICT (user_id, variant_type) DO UPDATE "
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"SET"
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" (total_game_moves, games_played, games_won, total_bdr, total_crits_lots)"
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" ="
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" (EXCLUDED.total_game_moves, EXCLUDED.games_played, EXCLUDED.games_won, EXCLUDED.total_bdr, EXCLUDED.total_crits_lots)",
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(utils.get_rating_type(True), utils.get_rating_type(False))
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)
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# This computes the maximum streak lengths, it's quite complicated.
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# First (in the innermost select clause), we build up an auxiliary table, which consists of some joined data that
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# we are interested in, but most importantly each row gets a 'group_id' entry in such a way that games belonging
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# to the same streak will have the same group_id:
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# In ascending league_id order, this entry counts the number of *losses* up until this point: Therefore, the number
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# does not increase during win streaks, but increases for each loss.
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# Additionally, we subtract 1 from this sum for lost games, so that losses always have the same group id as the last
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# win immediately before them. Therefore, each group (= entries with the same group id) now consists of
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# some consecutive wins, optionally followed by a loss
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# In the second query, we can now use these group ids to add a 'streak_length' to each row by numbering the rows
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# inside their corresponding group (his is what the OVER (PARTITION BY ..., group_id) does.
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# Now, in a third select statement, it is now easy to calculate the maximum streak by taking the maximum of this
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# row ranging over all games, where we grouped by user id and rating type (Clue Starved/Non-CS currently)
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# Finally, we just wrap the computed data into an insert statement to directly store it in the statistics table
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cur.execute(
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"INSERT INTO user_statistics (user_id, variant_type, maximum_streak, current_streak, maximum_streak_last_game)"
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" ("
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" SELECT"
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" user_id,"
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" rating_type,"
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" COALESCE(MAX(streak_length), 0) AS maximum_streak,"
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" COALESCE((ARRAY_AGG(streak_length ORDER BY league_id DESC))[1], 0) AS current_streak,"
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" ("
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" ARRAY_AGG(league_id ORDER BY streak_length DESC, league_id ASC)"
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" FILTER ( WHERE streak_length IS NOT NULL)"
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" )[1] AS maximum_streak_last_game"
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" FROM"
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" ("
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" SELECT"
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" *,"
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# Note that here we have the extra distinction to only add a streak_length to wins, not losses.
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# Otherwise, a streak of n games would result in a loss that has 'streak' n + 1, which is not what we want.
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" CASE"
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" WHEN num_suits * 5 = score"
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" THEN"
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" COUNT(*)"
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" OVER (PARTITION BY user_id, rating_type, group_id ORDER BY league_id ASC)"
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" END"
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" AS streak_length "
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" FROM"
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" ("
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" SELECT"
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" users.id AS user_id,"
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" variants.num_suits,"
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" variants.rating_type,"
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" games.score,"
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" games.league_id,"
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# This count function is the tricky part that labels each game with the group_id of the streak it belongs to
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" COUNT(*) "
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" FILTER (WHERE variants.num_suits * 5 != games.score)"
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" OVER (PARTITION BY users.id, variants.clue_starved ORDER BY games.league_id)"
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" - CASE WHEN variants.num_suits * 5 != games.score THEN 1 ELSE 0 END"
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" AS group_id"
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" FROM users "
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" INNER JOIN game_participants "
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" ON game_participants.user_id = users.id "
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" INNER JOIN games "
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" ON game_participants.game_id = games.id "
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" INNER JOIN variants "
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" ON variants.id = games.variant_id "
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" ) AS games_grouped_by_streak "
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" ) AS games_with_streaks "
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" GROUP BY user_id, rating_type"
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" )"
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"ON CONFLICT (user_id, variant_type) DO UPDATE "
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"SET (maximum_streak, current_streak, maximum_streak_last_game) = (EXCLUDED.maximum_streak, EXCLUDED.current_streak, EXCLUDED.maximum_streak_last_game)",
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(utils.get_rating_type(True), utils.get_rating_type(False))
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)
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conn_manager.get_connection().commit()
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