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ldacosta/line_optimizer.py

Created January 26, 2018 16:52
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line_optimizer.py
import itertools
from abc import abstractmethod
from typing import List, Set, Callable, Tuple, Dict
from enum import Enum, auto
class PlayerType(Enum):
DEFENSIVE = auto()
OFFENSIVE = auto()
NEUTRAL = auto()
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
class QValuesFetcher(object):
def lines_to_index(home_line: List[PlayerType], away_line: List[PlayerType]) -> str:
return ''.join(list(map(str, home_line + away_line)))
def __init__(self):
pass
@abstractmethod
def get(self, home_line: List[PlayerType], away_line: List[PlayerType]) -> float:
raise NotImplementedError
class QValuesFetcherFromDict(QValuesFetcher):
def __init__(self, a_dict: Dict):
QValuesFetcher.__init__(self)
self.dict = a_dict
@classmethod
def from_tuples(cls, q_value_tuples: List[Tuple[List[PlayerType], List[PlayerType], float]]):
q_value_dict = {}
for v in q_value_tuples:
home_line, away_line, q_value = v
for home_line_comb in list(set(itertools.permutations(home_line))):
for away_line_comb in list(set(itertools.permutations(away_line))):
q_value_dict[QValuesFetcher.lines_to_index(home_line_comb, away_line_comb)] = q_value
print("Q value dictionary has %d entries" % (len(q_value_dict)))
return cls(a_dict=q_value_dict)
def get(self, home_line: Set[PlayerType], away_line: Set[PlayerType]) -> float:
return self.dict[QValuesFetcher.lines_to_index(list(home_line), list(away_line))]
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
class CategoryFetcher(object):
def __init__(self):
pass
def category_of_player(self, player_id: int) -> PlayerType:
if player_id < 20:
return PlayerType.OFFENSIVE
else:
return PlayerType.NEUTRAL
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
class LineRecommender(object):
def __init__(self, player_category_fetcher: CategoryFetcher, q_values_fetcher: QValuesFetcher):
self.player_category_fetcher=player_category_fetcher
self.q_values_fetcher=q_values_fetcher
def recommend_lines(
self,
home_team_players_ids: Set[int],
away_team_lines: List[List[PlayerType]],
comb_q_values: Callable[[List[float]], float]) -> List[List[int]]:
"""
Builds optimal lines for home team.
Args:
home_team_players_ids: a list of id's for home team.
away_team_lines: 4 lines of away team. Each line is composed of 3 players, represented by their class (0 = defensive, 2 = offensive, 1 = neither)
q_values: a function that given 2 lines (represented by CLASSES of players) returns the q-value of the home team line.
comb_q_values: how to combine the q-values in order to obtain a "fitness" for a formation (larger is better).
Returns:
"""
import datetime
assert len(home_team_players_ids) == len(set(home_team_players_ids)), "There are repeated ids in the home team"
assert len(away_team_lines) == 4, "I need a formation (ie, 4 lines) for away team"
away_line_1, away_line_2, away_line_3, away_line_4 = away_team_lines
best_fitness = None
best_formation = None
how_many_first_lines_tried = 0
#
entry_timestamp = datetime.datetime.now().timestamp()
for home_line_1_ids in itertools.combinations(home_team_players_ids, 3):
best_formation_found = False
for home_line_2_ids in itertools.combinations(home_team_players_ids - set(home_line_1_ids), 3):
for home_line_3_ids in itertools.combinations(
home_team_players_ids - set(home_line_1_ids) - set(home_line_2_ids), 3):
for home_line_4_ids in itertools.combinations(
home_team_players_ids - set(home_line_1_ids) - set(home_line_2_ids) - set(home_line_3_ids), 3):
home_formation = [home_line_1_ids, home_line_2_ids, home_line_3_ids, home_line_4_ids]
# print(home_formation)
# get lines with CATEGORY of players
home_line_1 = list(map(self.player_category_fetcher.category_of_player, home_line_1_ids))
home_line_2 = list(map(self.player_category_fetcher.category_of_player, home_line_2_ids))
home_line_3 = list(map(self.player_category_fetcher.category_of_player, home_line_3_ids))
home_line_4 = list(map(self.player_category_fetcher.category_of_player, home_line_4_ids))
# ok, then:
qs = [self.q_values_fetcher.get(home_line_1, away_line_1),
self.q_values_fetcher.get(home_line_2, away_line_2),
self.q_values_fetcher.get(home_line_3, away_line_3),
self.q_values_fetcher.get(home_line_4, away_line_4)]
fitness = comb_q_values(qs)
# print(qs)
if (best_fitness is None) or (fitness > best_fitness):
best_fitness = fitness
best_formation = home_formation
best_formation_found = True
print("Best fitness: %.2f by formation %s" % (best_fitness, best_formation))
how_many_first_lines_tried += 1
if best_formation_found:
time_it_took = datetime.datetime.now().timestamp() - entry_timestamp
time_per_cycle = time_it_took / how_many_first_lines_tried
print("=======> Took %.2f secs. to look at %d first-lines; I think we have around %.2f secs. to go" % (
time_it_took, how_many_first_lines_tried, (220 - how_many_first_lines_tried) * time_per_cycle))
print("ALL DONE!!!!!!")
print("================================")
print("Best fitness: %.2f by formation \n%s, to play against \n%s" % (best_fitness, best_formation, away_team_lines))
print("================================")
return best_formation
def recommend_lines_maximize_average(
self,
home_team_players_ids: Set[int],
away_team_lines: List[List[PlayerType]]) -> List[List[int]]:
return self.recommend_lines(home_team_players_ids, away_team_lines, comb_q_values=(lambda a_list: sum(a_list) / len(a_list)))
def recommend_lines_maximize_max(
self,
home_team_players_ids: Set[int],
away_team_lines: List[List[PlayerType]]) -> List[List[int]]:
return self.recommend_lines(home_team_players_ids, away_team_lines, comb_q_values=(lambda a_list: max(a_list)))
# ###########################################################################################
# ###########################################################################################
# ###########################################################################################
# ############################################################
# PUT ALL THE FOLLOWING INTO TESTING
q_value_tuples = [
([PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.OFFENSIVE],
[PlayerType.OFFENSIVE, PlayerType.DEFENSIVE, PlayerType.DEFENSIVE],
25),
([PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.OFFENSIVE],
[PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
12),
([PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
[PlayerType.OFFENSIVE, PlayerType.DEFENSIVE, PlayerType.DEFENSIVE],
20),
([PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
[PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
1),
]
line_rec = LineRecommender(
player_category_fetcher=CategoryFetcher(),
q_values_fetcher=QValuesFetcherFromDict.from_tuples(q_value_tuples))
# home_lines_rec = line_rec.recommend_lines(home_team_players_ids=set([1] + list(range(20, 31))),
# away_team_lines = [
# [PlayerType.OFFENSIVE, PlayerType.DEFENSIVE, PlayerType.DEFENSIVE],
# [PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
# [PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
# [PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
# ],
# comb_q_values=(lambda a_list: sum(a_list) / len(a_list)))
# print(home_lines_rec)
#
# ###
home_lines_rec = line_rec.recommend_lines_maximize_average(home_team_players_ids=set([1] + list(range(20, 31))),
away_team_lines = [
[PlayerType.OFFENSIVE, PlayerType.DEFENSIVE, PlayerType.DEFENSIVE],
[PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
[PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
[PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
])
print(home_lines_rec)
# ###
home_lines_rec = line_rec.recommend_lines_maximize_max(home_team_players_ids=set([1] + list(range(20, 31))),
away_team_lines = [
[PlayerType.OFFENSIVE, PlayerType.DEFENSIVE, PlayerType.DEFENSIVE],
[PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
[PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
[PlayerType.NEUTRAL, PlayerType.NEUTRAL, PlayerType.NEUTRAL],
])
print(home_lines_rec)
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