def test_interactive():
S = MCMC(disaster_model)
S.isample(200,
100,
2,
out=open('testresults/interactive.log', 'w'),
progress_bar=0)
def test_interactive():
if 'sqlite' not in dir(pymc.database):
raise nose.SkipTest
M=MCMC(disaster_model,db='sqlite',
dbname=os.path.join(testdir, 'interactiveDisaster.sqlite'),
dbmode='w')
M.isample(10, out=open('testresults/interactivesqlite.log', 'w'), progress_bar=0)
def test_interactive():
if 'sqlite' not in dir(pymc.database):
raise nose.SkipTest
M = MCMC(DisasterModel,
db='sqlite',
dbname=os.path.join(testdir, 'interactiveDisaster.sqlite'),
dbmode='w')
M.isample(10, out=open('testresults/interactivesqlite.log', 'w'))
def test_interactive():
S = MCMC(disaster_model)
S.isample(
200,
100,
2,
out=open(
'testresults/interactive.log',
'w'),
progress_bar=0)
class LeagueModel(object):
"""MCMC model of a football league."""
def __init__(self, fname):
super(LeagueModel, self).__init__()
league = fuba.League(fname)
N = len(league.teams)
#dummy future games
future_games = [[league.teams["Werder Bremen"],league.teams["Dortmund"]]]
self.goal_rate = np.empty(N,dtype=object)
self.match_rate = np.empty(len(league.games)*2,dtype=object)
self.match_goals_future = np.empty(len(future_games)*2,dtype=object)
self.home_adv = Normal(name = 'home_adv',mu=0,tau=10.)
for t in league.teams.values():
print t.name,t.team_id
self.goal_rate[t.team_id] = Exponential('goal_rate_%i'%t.team_id,beta=1)
for game in range(len(league.games)):
self.match_rate[2*game] = Poisson('match_rate_%i'%(2*game),
mu=self.goal_rate[league.games[game].hometeam.team_id] + self.home_adv,
value=league.games[game].homescore, observed=True)
self.match_rate[2*game+1] = Poisson('match_rate_%i'%(2*game+1),
mu=self.goal_rate[league.games[game].hometeam.team_id],
value=league.games[game].homescore, observed=True)
for game in range(len(future_games)):
self.match_goals_future[2*game] = Poisson('match_goals_future_%i'%(2*game),
mu=self.goal_rate[future_games[game][0].team_id] + self.home_adv)
self.match_goals_future[2*game+1] = Poisson('match_goals_future_%i'%(2*game+1),
mu=self.goal_rate[future_games[game][1].team_id])
def run_mc(self,nsample = 10000,interactive=False):
"""run the model using mcmc"""
from pymc.Matplot import plot
from pymc import MCMC
self.M = MCMC(self)
if interactive:
self.M.isample(iter=nsample, burn=1000, thin=10)
else:
self.M.sample(iter=nsample, burn=1000, thin=10)
plot(self.M)
# Print comparison.
for molecule in typed_molecules:
# Get metadata.
name = OEGetSDData(molecule, 'name').strip()
try:
dg_exp = float(OEGetSDData(molecule, 'dG(exp)')) * units.kilocalories_per_mole
except Exception as exception:
# We couldn't find an experimental dG in the SDF file---ditch this molecule.
print "Couldn't find dG(exp) for molecule '%s'; discarding it." % name
typed_molecules.remove(molecule)
continue
# Form output.
outstring = "%48s %8.3f %8.3f" % (name, dg_exp / units.kilocalories_per_mole, energies[molecule] / units.kilocalories_per_mole)
print outstring
# Create MCMC model.
model = create_model(typed_molecules, parameters)
# Sample models.
from pymc import MCMC
sampler = MCMC(model, db='txt')
sampler.isample(iter=10, burn=0, save_interval=1, verbose=True)
def test_interactive():
S = MCMC(DisasterModel)
S.isample(200,100,2, out=open('testresults/interactive.log', 'w'))
def r(s=s, e=e, l=l):
""" Concatenate Poisson means """
out = np.empty(len(disasters_array))
out[:s] = e
out[s:] = l
return out
from pymc.examples import DisasterModel
from pymc import MCMC
M = MCMC(DisasterModel)
M.isample(iter=10000, burn=1000, thin=10)
M.trace('s')[:]
#array([41, 40, 40, ..., 43, 44, 44])
from pylab import hist, show
hist(M.trace('l')[:])
#(array([ 8, 52, 565, 1624, 2563, 2105, 1292, 488, 258, 45]),
#array([ 0.52721865, 0.60788251, 0.68854637, 0.76921023, 0.84987409,
# 0.93053795, 1.01120181, 1.09186567, 1.17252953, 1.25319339]),
#<a list of 10 Patch objects>)
show()
elapsed_time = end_time - start_time
print "%.3f s elapsed" % elapsed_time
# Print comparison.
for molecule in typed_molecules:
# Get metadata.
name = OEGetSDData(molecule, 'name').strip()
try:
dg_exp = float(OEGetSDData(
molecule, 'dG(exp)')) * units.kilocalories_per_mole
except Exception as exception:
# We couldn't find an experimental dG in the SDF file---ditch this molecule.
print "Couldn't find dG(exp) for molecule '%s'; discarding it." % name
typed_molecules.remove(molecule)
continue
# Form output.
outstring = "%48s %8.3f %8.3f (%+8.3f)" % (
name, dg_exp / units.kilocalories_per_mole, energies[molecule] /
units.kilocalories_per_mole, total_charge(molecule))
print outstring
# Create MCMC model.
model = create_model(typed_molecules, parameters)
# Sample models.
from pymc import MCMC
sampler = MCMC(model, db='txt')
sampler.isample(iter=100, burn=0, save_interval=1, verbose=True)
# Print comparison.
signed_errors = numpy.zeros([len(typed_molecules)], numpy.float64)
for (i, molecule) in enumerate(typed_molecules):
# Get metadata.
name = OEGetSDData(molecule, 'name').strip()
try:
dg_exp = float(OEGetSDData(molecule, 'dG(exp)')) * units.kilocalories_per_mole
signed_errors[i] = energies[molecule] / units.kilocalories_per_mole - dg_exp / units.kilocalories_per_mole
except Exception as exception:
# We couldn't find an experimental dG in the SDF file---ditch this molecule.
print "Couldn't find dG(exp) for molecule '%s'; discarding it." % name
typed_molecules.remove(molecule)
continue
# Form output.
outstring = "%48s %8.3f %8.3f" % (name, dg_exp / units.kilocalories_per_mole, energies[molecule] / units.kilocalories_per_mole)
print outstring
print "Initial RMS error %8.3f kcal/mol" % (signed_errors.std())
# Create MCMC model.
model = create_model(typed_molecules, parameters)
# Sample models.
from pymc import MCMC
sampler = MCMC(model, db='txt', name=mcmcDbName)
sampler.isample(iter=mcmcIterations, burn=0, save_interval=1, verbose=True)
#sampler.sample(iter=mcmcIterations, burn=0, save_interval=1, verbose=True, progress_bar=True)
sampler.db.close()
def test_interactive():
if "sqlite" not in dir(pymc.database):
raise nose.SkipTest
M = MCMC(disaster_model, db="sqlite", dbname=os.path.join(testdir, "interactiveDisaster.sqlite"), dbmode="w")
M.isample(10, out=open("testresults/interactivesqlite.log", "w"), progress_bar=0)
@deterministic(plot=False)
def r(s=s, e=e, l=l):
""" Concatenate Poisson means """
out = np.empty(len(disasters_array))
out[:s] = e
out[s:] = l
return out
from pymc.examples import DisasterModel
from pymc import MCMC
M = MCMC(DisasterModel)
M.isample(iter=10000, burn=1000, thin=10)
M.trace('s')[:]
#array([41, 40, 40, ..., 43, 44, 44])
from pylab import hist, show
hist(M.trace('l')[:])
#(array([ 8, 52, 565, 1624, 2563, 2105, 1292, 488, 258, 45]),
#array([ 0.52721865, 0.60788251, 0.68854637, 0.76921023, 0.84987409,
# 0.93053795, 1.01120181, 1.09186567, 1.17252953, 1.25319339]),
#<a list of 10 Patch objects>)
show()
from pymc.Matplot import plot
plot(M)
class LeagueBasicModel(object):
"""MCMC model of a football league."""
#TODO: optimal Kelly Bettor
#TODO: refine model
#TODO: identify columns for autotesting
def __init__(self, fname, playedto=None):
super(LeagueBasicModel, self).__init__()
league = League(fname, playedto)
N = len(league.teams)
def outcome_eval(home=None, away=None):
if home > away:
return 1
if home < away:
return -1
if home == away:
return 0
def clip_rate(val):
if val > 0.2: return val
else: return 0.2
def linfun(x, c):
return 0. * x + c
self.goal_rate = np.empty(N, dtype=object)
self.match_rate = np.empty(len(league.games) * 2, dtype=object)
self.outcome_future = np.empty(len(league.games), dtype=object)
self.match_goals_future = np.empty(len(league.future_games) * 2,
dtype=object)
self.home_adv = Uniform(name='home_adv', lower=0., upper=2.0)
self.league = league
for t in league.teams.values():
self.goal_rate[t.team_id] = Exponential('goal_rate_%i' % t.team_id,
beta=1)
for game in range(len(league.games)):
self.match_rate[2 * game] = Poisson(
'match_rate_%i' % (2 * game),
mu=Deterministic(
eval=clip_rate,
parents={
'val':
self.goal_rate[league.games[game].hometeam.team_id] +
self.home_adv
},
doc='clipped goal rate',
name='clipped_h_%i' % game),
value=league.games[game].homescore,
observed=True)
self.match_rate[2 * game + 1] = Poisson(
'match_rate_%i' % (2 * game + 1),
mu=Deterministic(
eval=clip_rate,
parents={
'val':
self.goal_rate[league.games[game].awayteam.team_id]
},
doc='clipped goal rate',
name='clipped_a_%i' % game),
value=league.games[game].awayscore,
observed=True)
for game in range(len(league.future_games)):
self.match_goals_future[2 * game] = Poisson(
'match_goals_future_%i_home' % game,
mu=Deterministic(
eval=clip_rate,
parents={
'val':
self.goal_rate[league.future_games[game][0].team_id] +
self.home_adv
},
doc='clipped goal rate',
name='clipped_fut_h_%i' % game))
self.match_goals_future[2 * game + 1] = Poisson(
'match_goals_future_%i_away' % game,
mu=Deterministic(
eval=clip_rate,
parents={
'val':
self.goal_rate[league.future_games[game][1].team_id]
},
doc='clipped goal rate',
name='clipped_fut_a_%i' % game))
self.outcome_future[game] = Deterministic(
eval=outcome_eval,
parents={
'home': self.match_goals_future[2 * game],
'away': self.match_goals_future[2 * game + 1]
},
name='match_outcome_future_%i' % game,
dtype=int,
doc='The outcome of the match')
def run_mc(self,
nsample=10000,
interactive=False,
doplot=False,
verbose=0):
"""run the model using mcmc"""
from pymc import MCMC
self.M = MCMC(self)
if interactive:
self.M.isample(iter=nsample, burn=1000, thin=10, verbose=verbose)
else:
self.M.sample(iter=nsample, burn=1000, thin=10, verbose=verbose)
if doplot:
from pymc.Matplot import plot
plot(self.M)
class LeagueFullModel(object):
"""MCMC model of a football league."""
#TODO: optimal Kelly Bettor
#TODO: refine model
#TODO: identify columns for autotesting
def __init__(self, fname, playedto=None):
super(LeagueFullModel, self).__init__()
league = League(fname, playedto)
N = len(league.teams)
def outcome_eval(home=None, away=None):
if home > away:
return 1
if home < away:
return -1
if home == away:
return 0
def clip_rate(val):
if val > 0.2: return val
else: return 0.2
def linfun(x, c):
return 0. * x + c
# The covariance dtrm C is valued as a Covariance object.
#@pm.deterministic
#def C(eval_fun = gp.matern.euclidean, diff_degree=diff_degree, amp=amp, scale=scale):
# return gp.NearlyFullRankCovariance(eval_fun, diff_degree=diff_degree, amp=amp, scale=scale)
self.goal_rate = np.empty(N, dtype=object)
self.def_rate = np.empty(N, dtype=object)
self.goal_var = np.empty(N, dtype=object)
self.def_var = np.empty(N, dtype=object)
self.match_rate = np.empty(len(league.games) * 2, dtype=object)
self.outcome_future = np.empty(len(league.games), dtype=object)
self.match_goals_future = np.empty(len(league.future_games) * 2,
dtype=object)
self.home_adv = Uniform(name='home_adv', lower=0., upper=2.0)
self.league = league
fmesh = np.arange(0., league.n_days + 2.)
for t in league.teams.values():
# Prior parameters of C
diff_degree_g = pm.Uniform('diff_degree_g_%i' % t.team_id, 1., 3)
amp_g = pm.Uniform('amp_g_%i' % t.team_id, .01, 2.)
scale_g = pm.Uniform('scale_g_%i' % t.team_id, 1., 10.)
diff_degree_d = pm.Uniform('diff_degree_d_%i' % t.team_id, 1., 3)
amp_d = pm.Uniform('amp_d_%i' % t.team_id, .01, 2.)
scale_d = pm.Uniform('scale_d_%i' % t.team_id, 1., 10.)
@pm.deterministic(name='C_d%i' % t.team_id)
def C_d(eval_fun=gp.matern.euclidean,
diff_degree=diff_degree_d,
amp=amp_d,
scale=scale_d):
return gp.NearlyFullRankCovariance(eval_fun,
diff_degree=diff_degree,
amp=amp,
scale=scale)
@pm.deterministic(name='C_g%i' % t.team_id)
def C_g(eval_fun=gp.matern.euclidean,
diff_degree=diff_degree_g,
amp=amp_g,
scale=scale_g):
return gp.NearlyFullRankCovariance(eval_fun,
diff_degree=diff_degree,
amp=amp,
scale=scale)
self.goal_rate[t.team_id] = Exponential('goal_rate_%i' % t.team_id,
beta=1)
self.def_rate[t.team_id] = Exponential('def_rate_%i' % t.team_id,
beta=1)
@pm.deterministic(name='M_d%i' % t.team_id)
def M_d(eval_fun=linfun, c=self.def_rate[t.team_id]):
return gp.Mean(eval_fun, c=c)
@pm.deterministic(name='M_g%i' % t.team_id)
def M_g(eval_fun=linfun, c=self.goal_rate[t.team_id]):
return gp.Mean(eval_fun, c=c)
self.def_var[t.team_id] = gp.GPSubmodel('smd_%i' % t.team_id, M_d,
C_d, fmesh)
self.goal_var[t.team_id] = gp.GPSubmodel('smg_%i' % t.team_id, M_g,
C_g, fmesh)
for game in range(len(league.games)):
gd = int(game / (league.n_teams / 2))
assert (gd < league.n_days)
self.match_rate[2 * game] = Poisson(
'match_rate_%i' % (2 * game),
mu=Deterministic(
eval=clip_rate,
parents={
'val':
self.goal_var[
league.games[game].hometeam.team_id].f_eval[gd] -
self.def_var[
league.games[game].awayteam.team_id].f_eval[gd] +
self.home_adv
},
doc='clipped goal rate',
name='clipped_h_%i' % game),
value=league.games[game].homescore,
observed=True)
self.match_rate[2 * game + 1] = Poisson(
'match_rate_%i' % (2 * game + 1),
mu=Deterministic(
eval=clip_rate,
parents={
'val':
self.goal_var[
league.games[game].awayteam.team_id].f_eval[gd] -
self.def_var[
league.games[game].hometeam.team_id].f_eval[gd]
},
doc='clipped goal rate',
name='clipped_a_%i' % game),
value=league.games[game].awayscore,
observed=True)
for game in range(len(league.future_games)):
gd = league.n_days
self.match_goals_future[2 * game] = Poisson(
'match_goals_future_%i_home' % game,
mu=Deterministic(
eval=clip_rate,
parents={
'val':
self.goal_var[league.future_games[game]
[0].team_id].f_eval[gd] -
self.def_var[league.future_games[game]
[1].team_id].f_eval[gd] + self.home_adv
},
doc='clipped goal rate',
name='clipped_fut_h_%i' % game))
self.match_goals_future[2 * game + 1] = Poisson(
'match_goals_future_%i_away' % game,
mu=Deterministic(eval=clip_rate,
parents={
'val':
self.goal_var[league.future_games[game]
[1].team_id].f_eval[gd] -
self.def_var[league.future_games[game]
[0].team_id].f_eval[gd]
},
doc='clipped goal rate',
name='clipped_fut_a_%i' % game))
self.outcome_future[game] = Deterministic(
eval=outcome_eval,
parents={
'home': self.match_goals_future[2 * game],
'away': self.match_goals_future[2 * game + 1]
},
name='match_outcome_future_%i' % game,
dtype=int,
doc='The outcome of the match')
def run_mc(self,
nsample=30000,
interactive=False,
doplot=False,
verbose=0):
"""run the model using mcmc"""
from pymc import MCMC
self.M = MCMC(self)
if interactive:
self.M.isample(iter=nsample, burn=1000, thin=30, verbose=verbose)
else:
self.M.sample(iter=nsample, burn=1000, thin=30, verbose=verbose)
if doplot:
from pymc.Matplot import plot
plot(self.M)
name = OEGetSDData(molecule, 'name').strip()
try:
dg_exp = float(OEGetSDData(
molecule, 'dG(exp)')) * units.kilocalories_per_mole
signed_errors[i] = energies[
molecule] / units.kilocalories_per_mole - dg_exp / units.kilocalories_per_mole
except Exception as exception:
# We couldn't find an experimental dG in the SDF file---ditch this molecule.
print "Couldn't find dG(exp) for molecule '%s'; discarding it." % name
typed_molecules.remove(molecule)
continue
# Form output.
outstring = "%48s %8.3f %8.3f" % (
name, dg_exp / units.kilocalories_per_mole,
energies[molecule] / units.kilocalories_per_mole)
print outstring
print "Initial RMS error %8.3f kcal/mol" % (signed_errors.std())
# Create MCMC model.
model = create_model(typed_molecules, parameters)
# Sample models.
from pymc import MCMC
sampler = MCMC(model, db='txt', name=mcmcDbName)
sampler.isample(iter=mcmcIterations, burn=0, save_interval=1, verbose=True)
#sampler.sample(iter=mcmcIterations, burn=0, save_interval=1, verbose=True, progress_bar=True)
sampler.db.close()
# PyMC implementation of Panel 6.4 from Royle & Dorazio (2008) pp. 217
# MA MacNeil - 04.03.14
import Mbht
import sys
import os
import pdb
from pymc import MCMC, BinaryMetropolis, Metropolis, AdaptiveMetropolis
from pymc import Matplot as mp
import pdb
M = MCMC(Mbht)
#M = MCMC(models,db='sqlite',dbname='xx_dbase')
xex = 6
M.isample(10**xex, 10**xex-10**(xex-1), thin=10**(xex-4), verbose=2)
#M.isample(100000, 80000, thin=10, verbose=2)
try:
os.mkdir('Outputs')
except OSError:
pass
os.chdir('Outputs')
M.write_csv("zz_results.csv")
mp.plot(M)
class LeagueBasicModel(object):
"""MCMC model of a football league."""
#TODO: optimal Kelly Bettor
#TODO: refine model
#TODO: identify columns for autotesting
def __init__(self, fname, playedto=None):
super(LeagueBasicModel, self).__init__()
league = League(fname,playedto)
N = len(league.teams)
def outcome_eval(home=None,away=None):
if home > away:
return 1
if home < away:
return -1
if home == away:
return 0
def clip_rate(val):
if val>0.2:return val
else: return 0.2
def linfun(x, c):
return 0.*x+ c
self.goal_rate = np.empty(N,dtype=object)
self.match_rate = np.empty(len(league.games)*2,dtype=object)
self.outcome_future = np.empty(len(league.games),dtype=object)
self.match_goals_future = np.empty(len(league.future_games)*2,dtype=object)
self.home_adv = Uniform(name = 'home_adv',lower=0.,upper=2.0)
self.league = league
for t in league.teams.values():
self.goal_rate[t.team_id] = Exponential('goal_rate_%i'%t.team_id,beta=1)
for game in range(len(league.games)):
self.match_rate[2*game] = Poisson('match_rate_%i'%(2*game),
mu=Deterministic(eval=clip_rate,
parents={'val':
self.goal_rate[league.games[game].hometeam.team_id] + self.home_adv},
doc='clipped goal rate',name='clipped_h_%i'%game),
value=league.games[game].homescore, observed=True)
self.match_rate[2*game+1] = Poisson('match_rate_%i'%(2*game+1),
mu=Deterministic(eval=clip_rate,
parents={'val':
self.goal_rate[league.games[game].awayteam.team_id]},
doc='clipped goal rate',name='clipped_a_%i'%game),
value=league.games[game].awayscore, observed=True)
for game in range(len(league.future_games)):
self.match_goals_future[2*game] = Poisson('match_goals_future_%i_home'%game,
mu=Deterministic(eval=clip_rate,
parents={'val':
self.goal_rate[league.future_games[game][0].team_id] + self.home_adv},
doc='clipped goal rate',name='clipped_fut_h_%i'%game))
self.match_goals_future[2*game+1] = Poisson('match_goals_future_%i_away'%game,
mu=Deterministic(eval=clip_rate,
parents={'val':
self.goal_rate[league.future_games[game][1].team_id]},
doc='clipped goal rate',name='clipped_fut_a_%i'%game))
self.outcome_future[game] = Deterministic(eval=outcome_eval,parents={
'home':self.match_goals_future[2*game],
'away':self.match_goals_future[2*game+1]},name='match_outcome_future_%i'%game,
dtype=int,doc='The outcome of the match'
)
def run_mc(self,nsample = 10000,interactive=False,doplot=False,verbose=0):
"""run the model using mcmc"""
from pymc import MCMC
self.M = MCMC(self)
if interactive:
self.M.isample(iter=nsample, burn=1000, thin=10,verbose=verbose)
else:
self.M.sample(iter=nsample, burn=1000, thin=10,verbose=verbose)
if doplot:
from pymc.Matplot import plot
plot(self.M)
class LeagueFullModel(object):
"""MCMC model of a football league."""
#TODO: optimal Kelly Bettor
#TODO: refine model
#TODO: identify columns for autotesting
def __init__(self, fname, playedto=None):
super(LeagueFullModel, self).__init__()
league = League(fname,playedto)
N = len(league.teams)
def outcome_eval(home=None,away=None):
if home > away:
return 1
if home < away:
return -1
if home == away:
return 0
def clip_rate(val):
if val>0.2:return val
else: return 0.2
def linfun(x, c):
return 0.*x+ c
# The covariance dtrm C is valued as a Covariance object.
#@pm.deterministic
#def C(eval_fun = gp.matern.euclidean, diff_degree=diff_degree, amp=amp, scale=scale):
# return gp.NearlyFullRankCovariance(eval_fun, diff_degree=diff_degree, amp=amp, scale=scale)
self.goal_rate = np.empty(N,dtype=object)
self.def_rate = np.empty(N,dtype=object)
self.goal_var = np.empty(N,dtype=object)
self.def_var = np.empty(N,dtype=object)
self.match_rate = np.empty(len(league.games)*2,dtype=object)
self.outcome_future = np.empty(len(league.games),dtype=object)
self.match_goals_future = np.empty(len(league.future_games)*2,dtype=object)
self.home_adv = Uniform(name = 'home_adv',lower=0.,upper=2.0)
self.league = league
fmesh = np.arange(0.,league.n_days+2.)
for t in league.teams.values():
# Prior parameters of C
diff_degree_g = pm.Uniform('diff_degree_g_%i'%t.team_id, 1., 3)
amp_g = pm.Uniform('amp_g_%i'%t.team_id, .01, 2.)
scale_g = pm.Uniform('scale_g_%i'%t.team_id, 1., 10.)
diff_degree_d = pm.Uniform('diff_degree_d_%i'%t.team_id, 1., 3)
amp_d = pm.Uniform('amp_d_%i'%t.team_id, .01, 2.)
scale_d = pm.Uniform('scale_d_%i'%t.team_id, 1., 10.)
@pm.deterministic(name='C_d%i'%t.team_id)
def C_d(eval_fun = gp.matern.euclidean, diff_degree=diff_degree_d, amp=amp_d, scale=scale_d):
return gp.NearlyFullRankCovariance(eval_fun, diff_degree=diff_degree, amp=amp, scale=scale)
@pm.deterministic(name='C_g%i'%t.team_id)
def C_g(eval_fun = gp.matern.euclidean, diff_degree=diff_degree_g, amp=amp_g, scale=scale_g):
return gp.NearlyFullRankCovariance(eval_fun, diff_degree=diff_degree, amp=amp, scale=scale)
self.goal_rate[t.team_id] = Exponential('goal_rate_%i'%t.team_id,beta=1)
self.def_rate[t.team_id] = Exponential('def_rate_%i'%t.team_id,beta=1)
@pm.deterministic(name='M_d%i'%t.team_id)
def M_d(eval_fun = linfun, c=self.def_rate[t.team_id]):
return gp.Mean(eval_fun, c=c)
@pm.deterministic(name='M_g%i'%t.team_id)
def M_g(eval_fun = linfun, c=self.goal_rate[t.team_id]):
return gp.Mean(eval_fun, c=c)
self.def_var[t.team_id] = gp.GPSubmodel('smd_%i'%t.team_id,M_d,C_d,fmesh)
self.goal_var[t.team_id] = gp.GPSubmodel('smg_%i'%t.team_id,M_g,C_g,fmesh)
for game in range(len(league.games)):
gd = int(game/(league.n_teams/2))
assert(gd<league.n_days)
self.match_rate[2*game] = Poisson('match_rate_%i'%(2*game),
mu=Deterministic(eval=clip_rate,
parents={'val':
self.goal_var[league.games[game].hometeam.team_id].f_eval[gd] -
self.def_var[league.games[game].awayteam.team_id].f_eval[gd] + self.home_adv},
doc='clipped goal rate',name='clipped_h_%i'%game),
value=league.games[game].homescore, observed=True)
self.match_rate[2*game+1] = Poisson('match_rate_%i'%(2*game+1),
mu=Deterministic(eval=clip_rate,
parents={'val':
self.goal_var[league.games[game].awayteam.team_id].f_eval[gd] -
self.def_var[league.games[game].hometeam.team_id].f_eval[gd]},
doc='clipped goal rate',name='clipped_a_%i'%game),
value=league.games[game].awayscore, observed=True)
for game in range(len(league.future_games)):
gd = league.n_days
self.match_goals_future[2*game] = Poisson('match_goals_future_%i_home'%game,
mu=Deterministic(eval=clip_rate,
parents={'val':
self.goal_var[league.future_games[game][0].team_id].f_eval[gd] -
self.def_var[league.future_games[game][1].team_id].f_eval[gd] + self.home_adv},
doc='clipped goal rate',name='clipped_fut_h_%i'%game))
self.match_goals_future[2*game+1] = Poisson('match_goals_future_%i_away'%game,
mu=Deterministic(eval=clip_rate,
parents={'val':
self.goal_var[league.future_games[game][1].team_id].f_eval[gd] -
self.def_var[league.future_games[game][0].team_id].f_eval[gd]},
doc='clipped goal rate',name='clipped_fut_a_%i'%game))
self.outcome_future[game] = Deterministic(eval=outcome_eval,parents={
'home':self.match_goals_future[2*game],
'away':self.match_goals_future[2*game+1]},name='match_outcome_future_%i'%game,
dtype=int,doc='The outcome of the match'
)
def run_mc(self,nsample = 30000,interactive=False,doplot=False,verbose=0):
"""run the model using mcmc"""
from pymc import MCMC
self.M = MCMC(self)
if interactive:
self.M.isample(iter=nsample, burn=1000, thin=30,verbose=verbose)
else:
self.M.sample(iter=nsample, burn=1000, thin=30,verbose=verbose)
if doplot:
from pymc.Matplot import plot
plot(self.M)
def bayesian_model():
model = MCMC(disastermodel)
model.isample(iter=10000, burn=1000, thin=10)
print model.trace('l')[:]
plot(model)
return model
