def testXXX(self):
from analysis import load
from lib import expression
expr1 = expression.Leaf('fantasy_pts_per_game')
golden = expression.Leaf('fantasy_pts')
DF_15 = load.LoadDataForSeason(2015)
CheckVirtualFDGames(expr1, golden, DF_15)
def CheckVirtualFDGames(
predictions,
df,
requests=Position.FD_REQUEST,
print_selections=False,
df_filter=ENOUGH_DATA,
only_healthy=True,
expr_real_result=expression.Leaf('fantasy_pts'),
expr_salaries_from=expression.Leaf('fantasy_pts_per_game')):
_CheckFDGames(_VirtualGamesGenerator(df, expr_salaries_from),
predictions,
df,
requests,
print_selections=print_selections,
df_filter=df_filter,
only_healthy=only_healthy,
expr_real_result=expr_real_result)
def CheckAllFDGames(predictions,
df,
requests=Position.FD_REQUEST,
print_selections=False,
df_filter=ENOUGH_DATA,
only_healthy=True,
expr_real_result=expression.Leaf('fantasy_pts')):
_CheckFDGames(_FDGamesGenerator(FD_DIR),
predictions,
df,
requests,
print_selections=print_selections,
df_filter=df_filter,
only_healthy=only_healthy,
expr_real_result=expr_real_result)
def PredictDay(df,
prediction,
fd_date,
requests=Position.FD_REQUEST,
only_healthy=True,
banned_players=None,
included_players=None,
expr_real_result=expression.Leaf('fantasy_pts')):
banned_players = banned_players or []
included_players = included_players or []
prediction = expression.LeafOrExpr(prediction)
filepath = os.path.join(FD_DIR, '%s.html' % fd_date)
assert os.path.exists(filepath), filepath
date_need = fd_date.replace('_', '')
small_df = df[df['date_id'] == int(date_need)]
pid = small_df['player_id']
players_list = FDFromFile(filepath)
if only_healthy:
players_list = [p for p in players_list if p.status == PlayerStatus.OK]
pred_for_day = dict(itertools.izip(pid, prediction.Eval(small_df)))
results_for_day = dict(itertools.izip(pid,
expr_real_result.Eval(small_df)))
requests_adjusted = dict(requests)
salary_cap = 60000
preselected = []
for p in players_list:
if p.pid in included_players:
requests_adjusted[p.position] -= 1
salary_cap -= p.salary
preselected.append(p.Override(pts=-1))
players_list = [
p for p in players_list
if p.pid not in included_players and p.pid not in banned_players
]
result = Emulate(players_list,
pred_for_day,
results_for_day,
requests=requests_adjusted,
salary_cap=salary_cap)
print requests_adjusted
result = EmulationResult(score=result.score,
team=result.team + preselected)
for b in result.team:
print 'Scored:', '{:4.1f}'.format(results_for_day.get(b.pid, 0)), b
return result
def _CheckFDGames(fd_games_generator,
predictions,
df,
requests,
print_selections=False,
df_filter=ENOUGH_DATA,
only_healthy=True,
expr_real_result=expression.Leaf('fantasy_pts')):
df = df_filter.Filter(df)
for i, p in enumerate(predictions):
print i + 1, p
pred_series = [expression.LeafOrExpr(p).Eval(df) for p in predictions]
all_results = [[] for _ in predictions]
for date_need, players_list in fd_games_generator:
if only_healthy:
players_list = [
p for p in players_list if p.status == PlayerStatus.OK
]
flt = df['date_id'] == int(date_need)
pid = df['player_id'][flt]
pred_for_day = [
dict(itertools.izip(pid, ps[flt])) for ps in pred_series
]
results_for_day = dict(
itertools.izip(pid,
expr_real_result.Eval(df)[flt]))
results = [
Emulate(players_list, pred, results_for_day,
requests=requests).score for pred in pred_for_day
]
if all(r < 30 for r in results):
continue
for r, allr in zip(results, all_results):
allr.append(r)
print str(date_need).ljust(15), '\t', '\t'.join('%.1f' % r
for r in results)
PrintComparison(all_results)
PrintRRTable(all_results)
HistogramPlot(*all_results)
def _SeriesToPlayerMap(series, date):
res = {}
for index, value in series.iteritems():
pid, game_id = index.split(':')
assert game_id.startswith(date)
res[pid] = value
return res
def FDFromFile(filepath):
return ParseFDFile(filepath)[0]
ENOUGH_DATA = ((expression.Leaf('minutes') >= 10) &
(expression.Leaf('minutes_per_game') >= 10) &
(expression.Leaf('games_played') >= 10))
def _CheckFDGames(fd_games_generator,
predictions,
df,
requests,
print_selections=False,
df_filter=ENOUGH_DATA,
only_healthy=True,
expr_real_result=expression.Leaf('fantasy_pts')):
df = df_filter.Filter(df)
for i, p in enumerate(predictions):
print i + 1, p