def run(max_flex, maxed_over, remove, chosen_dict, year, week):
solver = pywraplp.Solver('FD',
pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
all_players = []
if year == CUR_YEAR and week == CUR_WEEK:
with open('data/DKSalariesCurrent.csv', 'rb') as csvfile:
csvdata = csv.reader(csvfile, skipinitialspace=True)
for idx, row in enumerate(csvdata):
if idx > 0:
all_players.append(
Player(row[0], generate_pid(row[1], row[0]), row[1], row[2]))
else:
for i, row in df[df['Year'] == year][df['Week'] == week].iterrows():
if not np.isnan(row['Salary']) and row['Salary'] > 0:
all_players.append(
Player(row['Pos'], row['PID'], row['Name'], row['Salary']))
# give each a ranking
all_players = sorted(all_players, key=lambda x: x.cost, reverse=True)
for idx, x in enumerate(all_players):
x.cost_ranking = idx + 1
with open('data/%s-Week%s.csv' % (year, week), 'rb') as csvfile:
csvdata = csv.DictReader(csvfile)
worked = 0
for row in csvdata:
player = filter(lambda x: x.pid in row['playername'], all_players)
try:
player[0].proj = float(row['points'])
player[0].marked = 'Y'
except:
pass
#check_missing_players(all_players, args.sp, args.mp)
# remove previously optimize
all_players = filter(lambda x: x.pid not in remove, all_players)
variables, solution = run_solver(
solver, all_players, max_flex, chosen_dict)
if solution == solver.OPTIMAL:
roster = Roster()
for i, player in enumerate(all_players):
if variables[i].solution_value() == 1:
roster.add_player(player)
# print "Optimal roster for: %s" % maxed_over
# print roster
return roster
else:
raise Exception('No solution error')
def run(max_flex, maxed_over):
solver = pywraplp.Solver('FD',
pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
all_players = []
with open('data/dk-salaries-week-1.csv', 'rb') as csvfile:
csvdata = csv.reader(csvfile, skipinitialspace=True)
for idx, row in enumerate(csvdata):
if idx > 0:
all_players.append(Player(row[0], row[1], row[2]))
# give each a ranking
all_players = sorted(all_players, key=lambda x: x.cost, reverse=True)
for idx, x in enumerate(all_players):
x.cost_ranking = idx + 1
with open('data/ffa.csv', 'rb') as csvfile:
csvdata = csv.DictReader(csvfile)
for row in csvdata:
player = filter(lambda x: row['playername'] in x.name, all_players)
try:
player[0].proj = int(int(row['points'].split('.')[0]) / 16)
player[0].marked = 'Y'
except:
pass
check_missing_players(all_players, 100)
variables, solution = run_solver(solver, all_players, max_flex)
if solution == solver.OPTIMAL:
roster = Roster()
for i, player in enumerate(all_players):
if variables[i].solution_value() == 1:
roster.add_player(player)
print "Optimal roster for: %s" % maxed_over
print roster
else:
raise Exception('No solution error')
def run_solver(all_players, depth):
solver = pywraplp.Solver('FD',
pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
'''
Setup Binary Player variables
'''
variables = []
for player in all_players:
variables.append(solver.IntVar(0, 1, player.code))
'''
Setup Maximization over player point projections
'''
objective = solver.Objective()
objective.SetMaximization()
for i, player in enumerate(all_players):
objective.SetCoefficient(variables[i], player.proj)
'''
Add Salary Cap Constraint
'''
salary_cap = solver.Constraint(SALARY_CAP - 1000, SALARY_CAP)
for i, player in enumerate(all_players):
salary_cap.SetCoefficient(variables[i], player.cost)
'''
Add Player Position constraints including flex position
'''
flex_rb = solver.IntVar(0, 1, 'Flex_RB')
flex_wr = solver.IntVar(0, 1, 'Flex_WR')
flex_te = solver.IntVar(0, 1, 'Flex_TE')
solver.Add(flex_rb + flex_wr + flex_te == 1)
for position, limit in POSITION_LIMITS_FLEX:
ids, players_by_pos = zip(*filter(lambda (x, _): x.pos in position,
zip(all_players, variables)))
if position == 'WR':
solver.Add(solver.Sum(players_by_pos) == limit + flex_wr)
elif position == 'RB':
solver.Add(solver.Sum(players_by_pos) == limit + flex_rb)
elif position == 'TE':
solver.Add(solver.Sum(players_by_pos) == limit + flex_te)
else:
solver.Add(solver.Sum(players_by_pos) == limit)
'''
Add min number of different teams players must be drafted from constraint (draftkings == 2)
'''
team_names = set([o.team for o in all_players])
teams = []
for team in team_names:
teams.append(solver.IntVar(0, 1, team))
solver.Add(solver.Sum(teams) >= 2)
for idx, team in enumerate(team_names):
ids, players_by_team = zip(*filter(lambda (x, _): x.team in team,
zip(all_players, variables)))
solver.Add(teams[idx] <= solver.Sum(players_by_team))
'''
Add Defence cant play against any offense's player team constraint
'''
o_players = filter(lambda x: x.pos in ['QB', 'WR', 'RB', 'TE'],
all_players)
opps_team_names = set([o.opps_team for o in o_players])
teams_obj = filter(lambda x: x.pos == 'DST', all_players)
teams = set([o.team for o in teams_obj])
for opps_team in team_names:
if opps_team in teams:
ids, players_by_opps_team = zip(*filter(
lambda (x, _): x.pos in ['QB', 'WR', 'RB', 'TE'] and x.
opps_team in opps_team, zip(all_players, variables)))
idxs, defense = zip(
*filter(lambda (x, _): x.pos == 'DST' and x.team in opps_team,
zip(all_players, variables)))
for player in players_by_opps_team:
solver.Add(player <= 1 - defense[0])
'''
Add QB stacking (at least 1 wr on same team as QB) constraint
'''
offense_team_names = set([o.team for o in o_players])
for o_team in offense_team_names:
ids, players_by_team = zip(
*filter(lambda (x, _): x.pos in ['WR'] and x.team == o_team,
zip(all_players, variables)))
idxs, qb = zip(
*filter(lambda (x, _): x.pos == 'QB' and x.team == o_team,
zip(all_players, variables)))
solver.Add(solver.Sum(players_by_team) >= solver.Sum(qb))
'''
Add Max of 1 qb wr te or rb per team constraint, NOT TO BE USED WITH QB STACKING
'''
# for team in list(team_names):
# ids, players_by_team = zip(*filter(lambda (x,_): x.team in team and x.pos in ['WR','TE','RB','QB'], zip(all_players, variables)))
# solver.Add(solver.Sum(players_by_team)<=1)
'''
Add Max of 2 qb wr te or rb per game constraint, spread risk
'''
#for team in list(team_names):
# team_players = filter(lambda x: x.team in team, all_players)
# ids, players_by_game = zip(*filter(lambda (x,_): x.team in team or x.team in team_players[0].opps_team and x.pos in ['WR','TE','RB','QB'], zip(all_players, variables)))
# solver.Add(solver.Sum(players_by_game)<=2)
'''
Add remove previous solutions constraint and loop to generate X rosters
'''
rosters = []
for x in xrange(depth):
if rosters:
ids, players_from_roster = zip(
*filter(lambda (x, _): x in rosters[-1].sorted_players(),
zip(all_players, variables)))
ids, players_not_from_roster = zip(
*filter(lambda (x, _): x not in rosters[-1].sorted_players(),
zip(all_players, variables)))
solver.Add(
solver.Sum(players_not_from_roster) +
solver.Sum(1 - x for x in players_from_roster) >= 9)
solution = solver.Solve()
if solution == solver.OPTIMAL:
roster = Roster()
for i, player in enumerate(all_players):
if variables[i].solution_value() == 1:
roster.add_player(player)
rosters.append(roster)
print "Optimal roster: %s" % x
print roster
else:
raise Exception('No solution error')
return rosters
def run_solver(all_players, depth):
solver = pywraplp.Solver("FD", pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
"""
Setup Binary Player variables
"""
variables = []
for player in all_players:
variables.append(solver.IntVar(0, 1, player.code))
"""
Setup Maximization over player point projections
"""
objective = solver.Objective()
objective.SetMaximization()
for i, player in enumerate(all_players):
objective.SetCoefficient(variables[i], player.proj)
"""
Add Salary Cap Constraint
"""
salary_cap = solver.Constraint(SALARY_CAP - 1000, SALARY_CAP)
for i, player in enumerate(all_players):
salary_cap.SetCoefficient(variables[i], player.cost)
"""
Add Player Position constraints including flex position
"""
flex_rb = solver.IntVar(0, 1, "Flex_RB")
flex_wr = solver.IntVar(0, 1, "Flex_WR")
flex_te = solver.IntVar(0, 1, "Flex_TE")
solver.Add(flex_rb + flex_wr + flex_te == 1)
for position, limit in POSITION_LIMITS_FLEX:
ids, players_by_pos = zip(*filter(lambda (x, _): x.pos in position, zip(all_players, variables)))
if position == "WR":
solver.Add(solver.Sum(players_by_pos) == limit + flex_wr)
elif position == "RB":
solver.Add(solver.Sum(players_by_pos) == limit + flex_rb)
elif position == "TE":
solver.Add(solver.Sum(players_by_pos) == limit + flex_te)
else:
solver.Add(solver.Sum(players_by_pos) == limit)
"""
Add min number of different teams players must be drafted from constraint (draftkings == 2)
"""
team_names = set([o.team for o in all_players])
teams = []
for team in team_names:
teams.append(solver.IntVar(0, 1, team))
solver.Add(solver.Sum(teams) >= 2)
for idx, team in enumerate(team_names):
ids, players_by_team = zip(*filter(lambda (x, _): x.team in team, zip(all_players, variables)))
solver.Add(teams[idx] <= solver.Sum(players_by_team))
"""
Add Defence cant play against any offense's player team constraint
"""
o_players = filter(lambda x: x.pos in ["QB", "WR", "RB", "TE"], all_players)
# opps_team_names= set([o.opps_team for o in o_players])
# teams_obj = filter(lambda x: x.pos == 'DST' , all_players)
# teams = set([o.team for o in teams_obj])
# for opps_team in team_names:
# if opps_team in teams :
# ids, players_by_opps_team = zip(*filter(lambda (x,_): x.pos in ['QB','WR','RB','TE'] and x.opps_team in opps_team, zip(all_players, variables)))
# idxs, defense = zip(*filter(lambda (x,_): x.pos == 'DST' and x.team in opps_team, zip(all_players, variables)))
# solver.Add(solver.Sum(1-x for x in players_by_opps_team)+solver.Sum(1-x for x in defense)>=1)
"""
Add QB stacking (at least 1 wr or te on same team as QB) constraint
"""
offense_team_names = set([o.team for o in o_players])
for o_team in offense_team_names:
ids, players_by_team = zip(
*filter(lambda (x, _): x.pos in ["WR", "TE"] and x.team == o_team, zip(all_players, variables))
)
idxs, qb = zip(*filter(lambda (x, _): x.pos == "QB" and x.team == o_team, zip(all_players, variables)))
solver.Add(solver.Sum(players_by_team) >= solver.Sum(qb))
"""
Add Max of 1 qb wr te or rb per team constraint, NOT TO BE USED WITH QB STACKING
"""
# for team in list(team_names):
# ids, players_by_team = zip(*filter(lambda (x,_): x.team in team and x.pos in ['WR','TE','RB','QB'], zip(all_players, variables)))
# solver.Add(solver.Sum(players_by_team)<=1)
"""
Add Max of 2 qb wr te or rb per game constraint
"""
for team in list(team_names)[: len(team_names) / 2]:
team_players = filter(lambda x: x.team in team, all_players)
ids, players_by_game = zip(
*filter(
lambda (x, _): x.team in team
or x.team in team_players[0].opps_team
and x.pos in ["WR", "TE", "RB", "QB"],
zip(all_players, variables),
)
)
solver.Add(solver.Sum(players_by_game) <= 2)
"""
Add remove previous solutions constraint and loop to generate X rosters
"""
rosters = []
for x in xrange(depth):
if rosters:
ids, players_from_roster = zip(
*filter(lambda (x, _): x in rosters[-1].sorted_players(), zip(all_players, variables))
)
ids, players_not_from_roster = zip(
*filter(lambda (x, _): x not in rosters[-1].sorted_players(), zip(all_players, variables))
)
solver.Add(solver.Sum(players_not_from_roster) + solver.Sum(1 - x for x in players_from_roster) >= 1)
solution = solver.Solve()
if solution == solver.OPTIMAL:
roster = Roster()
for i, player in enumerate(all_players):
if variables[i].solution_value() == 1:
roster.add_player(player)
rosters.append(roster)
print "Optimal roster iterations: %s" % x
print roster
else:
raise Exception("No solution error")
return rosters