def admin_fetchgames():
""" Return a JSON representation of all finished games """
q = GameModel.query(GameModel.over == True).order(GameModel.ts_last_move)
gamelist = []
for gm in q.fetch():
gamelist.append(dict(
id = gm.key.id(),
ts = Alphabet.format_timestamp(gm.timestamp),
lm = Alphabet.format_timestamp(gm.ts_last_move or gm.timestamp),
p0 = None if gm.player0 is None else gm.player0.id(),
p1 = None if gm.player1 is None else gm.player1.id(),
rl = gm.robot_level,
s0 = gm.score0,
s1 = gm.score1,
pr = gm.prefs
))
return jsonify(gamelist = gamelist)
def admin_fetchgames():
""" Return a JSON representation of all finished games """
# noinspection PyPep8
q = GameModel.query(GameModel.over == True).order(GameModel.ts_last_move)
gamelist = []
for gm in q.fetch():
gamelist.append(
dict(id=gm.key.id(),
ts=Alphabet.format_timestamp(gm.timestamp),
lm=Alphabet.format_timestamp(gm.ts_last_move or gm.timestamp),
p0=None if gm.player0 is None else gm.player0.id(),
p1=None if gm.player1 is None else gm.player1.id(),
rl=gm.robot_level,
s0=gm.score0,
s1=gm.score1,
pr=gm.prefs))
return jsonify(gamelist=gamelist)
def _run_stats(from_time, to_time):
""" Runs a process to update user statistics and Elo ratings """
logging.info(u"Generating stats from {0} to {1}".format(from_time, to_time))
if from_time is None or to_time is None:
# Time range must be specified
return
if from_time >= to_time:
# Null time range
return
# Iterate over all finished games within the time span in temporal order
q = GameModel.query(GameModel.over == True).order(GameModel.ts_last_move) \
.filter(GameModel.ts_last_move > from_time) \
.filter(GameModel.ts_last_move <= to_time)
# The accumulated user statistics
users = dict()
def _init_stat(user_id, robot_level):
""" Returns the newest StatsModel instance available for the given user """
return StatsModel.newest_before(from_time, user_id, robot_level)
cnt = 0
ts_last_processed = None
try:
# Use i as a progress counter
for i, gm in enumerate(q):
ts = Alphabet.format_timestamp(gm.timestamp)
lm = Alphabet.format_timestamp(gm.ts_last_move or gm.timestamp)
p0 = None if gm.player0 is None else gm.player0.id()
p1 = None if gm.player1 is None else gm.player1.id()
robot_game = (p0 is None) or (p1 is None)
if robot_game:
rl = gm.robot_level
else:
rl = 0
s0 = gm.score0
s1 = gm.score1
if (s0 == 0) and (s1 == 0):
# When a game ends by resigning immediately,
# make sure that the weaker player
# doesn't get Elo points for a draw; in fact,
# ignore such a game altogether in the statistics
continue
if p0 is None:
k0 = "robot-" + str(rl)
else:
k0 = p0
if p1 is None:
k1 = "robot-" + str(rl)
else:
k1 = p1
if k0 in users:
urec0 = users[k0]
else:
users[k0] = urec0 = _init_stat(p0, rl if p0 is None else 0)
if k1 in users:
urec1 = users[k1]
else:
users[k1] = urec1 = _init_stat(p1, rl if p1 is None else 0)
# Number of games played
urec0.games += 1
urec1.games += 1
if not robot_game:
urec0.human_games += 1
urec1.human_games += 1
# Total scores
urec0.score += s0
urec1.score += s1
urec0.score_against += s1
urec1.score_against += s0
if not robot_game:
urec0.human_score += s0
urec1.human_score += s1
urec0.human_score_against += s1
urec1.human_score_against += s0
# Wins and losses
if s0 > s1:
urec0.wins += 1
urec1.losses += 1
elif s1 > s0:
urec1.wins += 1
urec0.losses += 1
if not robot_game:
if s0 > s1:
urec0.human_wins += 1
urec1.human_losses += 1
elif s1 > s0:
urec1.human_wins += 1
urec0.human_losses += 1
# Find out whether players are established or beginners
est0 = urec0.games > ESTABLISHED_MARK
est1 = urec1.games > ESTABLISHED_MARK
# Save the Elo point state used in the calculation
gm.elo0, gm.elo1 = urec0.elo, urec1.elo
# Compute the Elo points of both players
adj = _compute_elo((urec0.elo, urec1.elo), s0, s1, est0, est1)
# When an established player is playing a beginning (provisional) player,
# leave the Elo score of the established player unchanged
# Adjust player 0
if est0 and not est1:
adj = (0, adj[1])
gm.elo0_adj = adj[0]
urec0.elo += adj[0]
# Adjust player 1
if est1 and not est0:
adj = (adj[0], 0)
gm.elo1_adj = adj[1]
urec1.elo += adj[1]
# If not a robot game, compute the human-only Elo
if not robot_game:
gm.human_elo0, gm.human_elo1 = urec0.human_elo, urec1.human_elo
adj = _compute_elo((urec0.human_elo, urec1.human_elo), s0, s1, est0, est1)
# Adjust player 0
if est0 and not est1:
adj = (0, adj[1])
gm.human_elo0_adj = adj[0]
urec0.human_elo += adj[0]
# Adjust player 1
if est1 and not est0:
adj = (adj[0], 0)
gm.human_elo1_adj = adj[1]
urec1.human_elo += adj[1]
# Save the game object with the new Elo adjustment statistics
gm.put()
# Save the last processed timestamp
ts_last_processed = lm
cnt += 1
# Report on our progress
if (i + 1) % 1000 == 0:
logging.info(u"Processed {0} games".format(i + 1))
except DeadlineExceededError as ex:
# Hit deadline: save the stuff we already have and
# defer a new task to continue where we left off
logging.info(u"Deadline exceeded in stats loop after {0} games and {1} users"
.format(cnt, len(users)))
logging.info(u"Resuming from timestamp {0}".format(ts_last_processed))
if ts_last_processed is not None:
_write_stats(ts_last_processed, users)
deferred.defer(deferred_stats,
from_time = ts_last_processed or from_time, to_time = to_time)
# Normal return prevents this task from being run again
return
except Exception as ex:
logging.info(u"Exception in stats loop: {0}".format(ex))
# Avoid having the task retried
raise deferred.PermanentTaskFailure()
# Completed without incident
logging.info(u"Normal completion of stats for {1} games and {0} users".format(len(users), cnt))
_write_stats(to_time, users)
def _run_stats(from_time, to_time):
""" Runs a process to update user statistics and Elo ratings """
logging.info(u"Generating stats from {0} to {1}".format(
from_time, to_time))
if from_time is None or to_time is None:
# Time range must be specified
return
if from_time >= to_time:
# Null time range
return
# Iterate over all finished games within the time span in temporal order
q = GameModel.query(ndb.AND(GameModel.ts_last_move > from_time, GameModel.ts_last_move <= to_time)) \
.order(GameModel.ts_last_move) \
.filter(GameModel.over == True)
# The accumulated user statistics
users = dict()
def _init_stat(user_id, robot_level):
""" Returns the newest StatsModel instance available for the given user """
return StatsModel.newest_before(from_time, user_id, robot_level)
cnt = 0
ts_last_processed = None
try:
# Use i as a progress counter
i = 0
for gm in iter_q(q, chunk_size=250):
i += 1
lm = Alphabet.format_timestamp(gm.ts_last_move or gm.timestamp)
p0 = None if gm.player0 is None else gm.player0.id()
p1 = None if gm.player1 is None else gm.player1.id()
robot_game = (p0 is None) or (p1 is None)
if robot_game:
rl = gm.robot_level
else:
rl = 0
s0 = gm.score0
s1 = gm.score1
if (s0 == 0) and (s1 == 0):
# When a game ends by resigning immediately,
# make sure that the weaker player
# doesn't get Elo points for a draw; in fact,
# ignore such a game altogether in the statistics
continue
if p0 is None:
k0 = "robot-" + str(rl)
else:
k0 = p0
if p1 is None:
k1 = "robot-" + str(rl)
else:
k1 = p1
if k0 in users:
urec0 = users[k0]
else:
users[k0] = urec0 = _init_stat(p0, rl if p0 is None else 0)
if k1 in users:
urec1 = users[k1]
else:
users[k1] = urec1 = _init_stat(p1, rl if p1 is None else 0)
# Number of games played
urec0.games += 1
urec1.games += 1
if not robot_game:
urec0.human_games += 1
urec1.human_games += 1
# Total scores
urec0.score += s0
urec1.score += s1
urec0.score_against += s1
urec1.score_against += s0
if not robot_game:
urec0.human_score += s0
urec1.human_score += s1
urec0.human_score_against += s1
urec1.human_score_against += s0
# Wins and losses
if s0 > s1:
urec0.wins += 1
urec1.losses += 1
elif s1 > s0:
urec1.wins += 1
urec0.losses += 1
if not robot_game:
if s0 > s1:
urec0.human_wins += 1
urec1.human_losses += 1
elif s1 > s0:
urec1.human_wins += 1
urec0.human_losses += 1
# Find out whether players are established or beginners
est0 = urec0.games > ESTABLISHED_MARK
est1 = urec1.games > ESTABLISHED_MARK
# Save the Elo point state used in the calculation
gm.elo0, gm.elo1 = urec0.elo, urec1.elo
# Compute the Elo points of both players
adj = _compute_elo((urec0.elo, urec1.elo), s0, s1, est0, est1)
# When an established player is playing a beginning (provisional) player,
# leave the Elo score of the established player unchanged
# Adjust player 0
if est0 and not est1:
adj = (0, adj[1])
gm.elo0_adj = adj[0]
urec0.elo += adj[0]
# Adjust player 1
if est1 and not est0:
adj = (adj[0], 0)
gm.elo1_adj = adj[1]
urec1.elo += adj[1]
# If not a robot game, compute the human-only Elo
if not robot_game:
gm.human_elo0, gm.human_elo1 = urec0.human_elo, urec1.human_elo
adj = _compute_elo((urec0.human_elo, urec1.human_elo), s0, s1,
est0, est1)
# Adjust player 0
if est0 and not est1:
adj = (0, adj[1])
gm.human_elo0_adj = adj[0]
urec0.human_elo += adj[0]
# Adjust player 1
if est1 and not est0:
adj = (adj[0], 0)
gm.human_elo1_adj = adj[1]
urec1.human_elo += adj[1]
# Save the game object with the new Elo adjustment statistics
gm.put()
# Save the last processed timestamp
ts_last_processed = lm
cnt += 1
# Report on our progress
if i % 1000 == 0:
logging.info(u"Processed {0} games".format(i))
except DeadlineExceededError as ex:
# Hit deadline: save the stuff we already have and
# defer a new task to continue where we left off
logging.info(
u"Deadline exceeded in stats loop after {0} games and {1} users".
format(cnt, len(users)))
logging.info(u"Resuming from timestamp {0}".format(ts_last_processed))
if ts_last_processed is not None:
_write_stats(ts_last_processed, users)
deferred.defer(deferred_stats,
from_time=ts_last_processed or from_time,
to_time=to_time)
# Normal return prevents this task from being run again
return
except Exception as ex:
logging.info(u"Exception in stats loop: {0}".format(ex))
# Avoid having the task retried
raise deferred.PermanentTaskFailure()
# Completed without incident
logging.info(
u"Normal completion of stats for {1} games and {0} users".format(
len(users), cnt))
_write_stats(to_time, users)
