def select_arm(self):
#self.a = np.argmax(pymc.rbeta(1 + self.wins, 1 + self.k_n - self.wins))
ts_values = pymc.rbeta(1 + self.wins, 1 + self.k_n - self.wins)
max_p_t = np.max(ts_values)
max_idxs = np.argwhere(ts_values == max_p_t).flatten()
self.a = np.random.choice(max_idxs)
return
def generate_and_append_data(data,
data_type,
truth,
age_intervals,
gbd_region='Asia, Southeast',
country='Thailand',
year=2005,
sex='male'):
""" create simulated data"""
for a0, a1 in age_intervals:
d = {
'condition': 'type_2_diabetes',
'data_type': data_type,
'gbd_region': gbd_region,
'region': country,
'year_start': year,
'year_end': year,
'sex': sex,
'age_start': a0,
'age_end': a1,
'age_weights': list(np.ones(a1 + 1 - a0)),
'id': len(data)
}
p0 = dismod3.utils.rate_for_range(truth, range(a0, a1 + 1),
np.ones(a1 + 1 - a0))
p1 = mc.rbeta(p0 * dispersion, (1 - p0) * dispersion)
p2 = mc.rbinomial(n, p1) / n
d['value'] = p2
d['standard_error'] = np.sqrt(p2 * (1 - p2) / n)
data.append(d)
def sample(self, n=1):
score = np.zeros(n)
choices = np.zeros(n)
regret = np.zeros(n)
for k in range(n):
# sample from the bandits's priors, and select the largest sample
choice = np.argmax(
rbeta(1 + self.wins, 1 + self.trials - self.wins))
# sample the chosen bandit
result = self.bandits.pull(choice)
# update stats (which updates priors for next pull)
self.update_stats(k, choice, result)
score[k] = result
choices[k] = choice
regret[k] = self.bandits.p[
self.bandits.optimal] - self.bandits.p[choice]
self.score = np.r_[self.score, score]
self.choices = np.r_[self.choices, choices]
self.regret = np.r_[self.regret, regret]
return
def select_arm(self):
n_arms = len(self.counts)
for arm in range(n_arms):
if self.counts[arm] == 0:
return arm
val = np.array(self.values)
count = np.array(self.counts)
return np.argmax(rbeta(1 + val, 1 + count - val))
def select_arm(self):
ts_values = pymc.rbeta(1 + self.wins, 1 + self.k_n - self.wins)
# randomly break ties, np.argmax return the first occurence of maximum.
# get all occurences of the max and randomly select between them
max_p_t = np.max(ts_values)
max_idxs = np.argwhere(ts_values == max_p_t).flatten()
self.a = np.random.choice(max_idxs)
return
def thompsonExp():
for i in range(1, exp_count):
b = pymc.rbeta(1 + wins, 1 + trials - wins)
item = np.argmax(b)
trials[item] += 1
# Rewrite the judement code
result = np.random.binomial(n=1, p=arm_probs[item])
if result == 1:
wins[item] += 1
def run(mu1,mu2,mu3,N=10000):
bandits=[TSBandit(mu1),TSBandit(mu2),TSBandit(mu3)]
data=np.empty(N)#N個未初始化的值
n_arm=len(bandits)
for i in range(N):
j = np.argmax([pymc.rbeta(1 + b.wins, 1 +b.lose)for b in bandits])
x=bandits[j].pull()
bandits[j].update(x)
data[i]=x
cumul_average=np.cumsum(data)/(np.arange(N)+1)
return cumul_average
def test(self, function_g = lambda x: x, function_b = lambda x: 1,seed = 2):
'''function_g and function_b are the functions on number of goods or number of bads to add to
the beta draw. Default is H-test if GOOD, or 1 if BAD'''
# np.random.seed(seed)
# pm.numpy.random.seed(seed)
self.name = "Langsam Adaptive Bayesian"
l = 10
powers = np.arange(0,l,1)
# sizes = 2**powers
sizes = np.array([1,2,3,4,5,6,7,8,9,10,15,20,25,30])
l = len(sizes)
Gs = np.zeros(l)
Bs = np.zeros(l)
choice_history = []
f = open('./test.txt', 'w')
f2 = open('./draws.txt', 'w')
while self.queue.size > 0:
draw = rbeta(1 + np.log2(sizes) + Gs, 1 + Bs)
# f2.write("{}\n\n".format(draw))
draw = np.argmax(draw)
h_now = sizes[draw]
choice_history.append(h_now)
f.write ("{} ".format(h_now))
# Update histories
self.update(h_now)
q_now = self.q_history[-1]
items, self.queue = np.split(self.queue,[h_now])
if all(items):
self.G += items.size
# Gs[draw] += function_g(items.size)
Gs[draw] += (draw+1)/10
f.write ("SUCCESS ")
# Update
else:
f.write ("_______ ")
self.B += 1
self.g_test(items)
# Bs[draw] += function_b(items.size)
Bs[draw] += 1 / (1 + draw)
f.write ("({:.3f},{:.3f})\n".format((np.log2(sizes) + 1 + Gs)[draw], 1 + Bs[draw]))
f.close()
f2.close
# print(sizes,'\n',1 + Gs,'\n', 1 + Bs)
# print (np.histogram(choice_history, bins=sizes)[0])
# # print (choice_history)
# print()
self.choices = choice_history
def sample_bandits(self, n=1):
bb_score = np.zeros(n)
choices = np.zeros(n)
for k in range(n):
choice = np.argmax(rbeta(self.wins, self.trials - self.wins))
result = self.bandits.pull(choice)
self.wins[choice] += result
self.trials[choice] += 1
bb_score[k] = result
choices[k] = choice
self.N += 1
self.bb_score = np.r_[self.bb_score, bb_score]
self.choices = np.r_[self.choices, choices]
return
def choose(self):
"""
Make decision
"""
clicks = []
publishs = []
skips = []
for i in range(len(self.action_names)):
clicks.append(self.action_record[self.action_names[i]][KEY_CLICK])
publishs.append(
self.action_record[self.action_names[i]][KEY_PUBLISH])
clicks = np.array(clicks)
publishs = np.array(publishs)
skips = publishs - clicks
probs = []
for click, skip in zip(clicks, skips):
probs.append(pm.rbeta(click, skip))
probs = np.array(probs)
print(probs)
index = np.argmax(probs)
return self.action_names[index]
def plot_beta_binomial_funnel(alpha, beta):
pi_true = alpha/(alpha+beta)
pi = mc.rbeta(alpha, beta, size=10000)
n = pl.exp(mc.rnormal(10, 2**-2, size=10000))
k = mc.rbinomial(pl.array(n, dtype=int), pi)
r = k/n
pl.vlines([pi_true], .1*n.min(), 10*n.max(),
linewidth=2, linestyle='-', color='w', zorder=9)
pl.vlines([pi_true], .1*n.min(), 10*n.max(),
linewidth=1, linestyle='--', color='black', zorder=10)
pl.plot(r, n, 'ko',
mew=0, alpha=.25)
pl.semilogy(schiz['r'], schiz['n'], 'ks', mew=1, mec='white', ms=4,
label='Observed values')
pl.xlabel('Rate (per PY)')
pl.ylabel('Study size (PY)')
pl.xticks([0, .005, .01])
pl.axis([-.0001, .0101, 50., 1500000])
pl.title(r'$\alpha=%d$, $\beta=%d$' % (alpha, beta))
def sample_bandits(self, n=1):
bb_score = np.zeros(n)
choices = np.zeros(n)
for k in range(n):
# sample from the bandits's priors, and select the largest sample
choice = np.argmax(rbeta(1 + self.wins, 1 + self.trials - self.wins))
# sample the chosen bandit
result = self.bandits.pull(choice)
# update priors and score
self.wins[choice] += result
self.trials[choice] += 1
bb_score[k] = result
self.N += 1
choices[k] = choice
self.bb_score = np.r_[self.bb_score, bb_score]
self.choices = np.r_[self.choices, choices]
return
def sample_bandits( self, n=1 ):
bb_score = np.zeros( n )
choices = np.zeros( n )
for k in range(n):
#sample from the bandits's priors, and select the largest sample
choice = np.argmax( rbeta( 1 + self.wins, 1 + self.trials - self.wins) )
#sample the chosen bandit
result = self.bandits.pull( choice )
#update priors and score
self.wins[ choice ] += result
self.trials[ choice ] += 1
bb_score[ k ] = result
self.N += 1
choices[ k ] = choice
self.bb_score = np.r_[ self.bb_score, bb_score ]
self.choices = np.r_[ self.choices, choices ]
return
def generate_and_append_data(data, data_type, truth, age_intervals,
gbd_region='Asia, Southeast', country='Thailand', year=2005, sex='male'):
""" create simulated data"""
for a0, a1 in age_intervals:
d = { 'condition': 'type_2_diabetes',
'data_type': data_type,
'gbd_region': gbd_region,
'region': country,
'year_start': year,
'year_end': year,
'sex': sex,
'age_start': a0,
'age_end': a1,
'age_weights': list(np.ones(a1 + 1 - a0)),
'id': len(data)}
p0 = dismod3.utils.rate_for_range(truth, range(a0, a1 + 1), np.ones(a1 + 1 - a0))
p1 = mc.rbeta(p0 * dispersion, (1 - p0) * dispersion)
p2 = mc.rbinomial(n, p1) / n
d['value'] = p2
d['standard_error'] = np.sqrt(p2 * (1 - p2) / n)
data.append(d)
def plot_beta_binomial_funnel(alpha, beta):
pi_true = alpha / (alpha + beta)
pi = mc.rbeta(alpha, beta, size=10000)
n = pl.exp(mc.rnormal(10, 2**-2, size=10000))
k = mc.rbinomial(pl.array(n, dtype=int), pi)
r = k / n
pl.vlines([pi_true],
.1 * n.min(),
10 * n.max(),
linewidth=2,
linestyle='-',
color='w',
zorder=9)
pl.vlines([pi_true],
.1 * n.min(),
10 * n.max(),
linewidth=1,
linestyle='--',
color='black',
zorder=10)
pl.plot(r, n, 'ko', mew=0, alpha=.25)
pl.semilogy(schiz['r'],
schiz['n'],
'ks',
mew=1,
mec='white',
ms=4,
label='Observed values')
pl.xlabel('Rate (per PY)')
pl.ylabel('Study size (PY)')
pl.xticks([0, .005, .01])
pl.axis([-.0001, .0101, 50., 1500000])
pl.title(r'$\alpha=%d$, $\beta=%d$' % (alpha, beta))
def f(sp_sub, x, a, b):
p = pm.invlogit(sp_sub(x))
h = pm.rbeta(a,b,size=len(p))
return g6pd.p_fem_def(p,h)
def f(sp_sub, a, b, n=n):
p = pm.invlogit(sp_sub)
h = pm.rbeta(a, b, size=len(sp_sub))
p_def = g6pd.p_fem_def(p, h)
return pm.rbinomial(n=n, p=p)
def policy(self, experiment, k):
return np.argmax(
rbeta(experiment.success + self.alpha,
experiment.failure + self.beta))
def bayesian_bandit_choice(self):
return np.argmax(rbeta(1 + self.wins, 1 + self.trials - self.wins))
def Thompson_sampling(estimated_beta_params):
totals = estimated_beta_params.sum(1) #The number of experiments per arm
successes = estimated_beta_params[:,0]
return np.argmax(pymc.rbeta(1 + successes, 1 + totals - successes))
def thompson_sample_desc(estimated_beta_params):
successes = estimated_beta_params[:, 0]
totals = estimated_beta_params.sum(1)
choice = numpy.argmin(pymc.rbeta(1 + successes, 1 + totals - successes))
return choice
def pred(alpha=alpha, beta=beta, phi=phi):
if pl.rand() < phi:
return 0
else:
return mc.rbinomial(n_pred, mc.rbeta(alpha, beta)) / float(n_pred)
import numpy as np import pymc """ Thompson sampling算法python实现 """ successes = 10 totals = 100 np.argmax(pymc.rbeta(1 + successes, 1 + totals - successes))
def fem_def(sp_sub, a, b):
h**o = male_def(sp_sub)
hetero = hw_hetero(sp_sub)
het_def = pm.rbeta(a,b)
hetero *= het_def
return hetero+h**o
def f(sp_sub, a, b, n=n):
p = pm.invlogit(sp_sub)
h = pm.rbeta(a,b,size=len(sp_sub))
p_def = g6pd.p_fem_def(p,h)
return pm.rbinomial(n=n, p=p)
def pred(alpha=alpha, beta=beta, phi=phi):
if pl.rand() < phi:
return 0
else:
return mc.rbinomial(n_pred, mc.rbeta(alpha, beta)) / float(n_pred)
def pred(alpha=alpha, beta=beta):
return mc.rbinomial(n_pred, mc.rbeta(alpha, beta)) / float(n_pred)
def bayesian_bandit_choice(self): return np.argmax( rbeta( 1 + self.wins, 1 + self.trials - self.wins) )
def bayesian_bandit(self):
alpha = 1 + self.wins
b = 1 + self.trials - self.wins # beta var and this is your losses
return np.argmax( rbeta( alpha, b) )
def f(sp_sub, x, a, b):
p = pm.invlogit(sp_sub(x))
h = pm.rbeta(a, b, size=len(p))
return g6pd.p_fem_def(p, h)
import numpy as np import pymc #wins 和 trials 是一个N维向量，N是赌博机的臂的个数，每个元素记录了 choice = np.argmax(pymc.rbeta(1 + wins, 1 + trials - wins)) wins[choice] += 1 trials += 1
def fem_def(sp_sub, a, b):
h**o = male_def(sp_sub)
hetero = hw_hetero(sp_sub)
het_def = pm.rbeta(a, b)
hetero *= het_def
return hetero + h**o
def DoSamplingIteration(self, y):
# Setup
try:
xLast = self.xSeq[-1]
varLast = self.varianceSeq[-1]
except:
raise NameError('Cannot access xLast and/or varLast')
# n0 = #{ i : xLast[i] == 0} whereas n1 = ||xLast||_0
n0, n1 = NumericalHelper.CalculateNumZerosNonzeros(xLast, self.Eps)
# Sample to get w|xLast and a|xLast,alpha
wSample = pymc.rbeta(1 + n1, 1 + n0)
assert (wSample >= 0) and (wSample <= 1), __file__ + ': wSample is out of bounds'
logging.info(" Samp. Iter. {0}, generating wSample ~ Beta({1},{2}) ... {3:.5f}".format(self._samplerIter, 1 + n1, 1 + n0, wSample))
igShapeForA = n1 + self.hyperparameterPriorDict['alpha0']
xLastL1Norm = np.sum(np.abs(xLast))
igScaleForA = xLastL1Norm + self.hyperparameterPriorDict['alpha1']
assert (igShapeForA > 0) and (igScaleForA > 0), "IG shape and scale aren't strictly positive"
bSampleGenerated = False
aSample = None
for tryInd in range(5, 0, -1):
try:
aSample = pymc.rinverse_gamma(igShapeForA, igScaleForA)
bSampleGenerated = True
except (ZeroDivisionError, OverflowError) as e:
logging.error("Couldn't generate aSample ~ IG({0},{1}) using n0={2}, n1={3}: {4}".format(igShapeForA, igScaleForA, n0, n1, e.message))
# Only raise the exception if repeated attempts failed
if tryInd == 0:
raise
if bSampleGenerated is True:
break
logging.info(" Samp. Iter. {0}, generating aSample ~ IG({1:.4f},{2:.4f}) ... {3:.4e}".format(self._samplerIter, igShapeForA, igScaleForA, aSample))
self.hyperparameterSeq.append({'w' : wSample, 'a' : aSample})
# Sample to get x_i, 1 <= i <= M. The method DoSamplingXConditionedAll updates self.xSeq and self._mappedX
xNext, hxNext = self.DoSamplingXConditionedAll(y, wSample, aSample, varLast, xLast, self.hx, self.nVerbose > 0)
# Sample to get variance
yErr = y - hxNext[:, 0]
igShapeForVariance = y.size / 2
igScaleForVariance = np.sum(yErr * yErr) / 2
varianceSample = pymc.rinverse_gamma(igShapeForVariance, igScaleForVariance)
logging.info(" Samp. Iter. {0}, generating varianceSample ~ IG({1:.4f},{2:.4f}) ... {3:.4e}".format(
self._samplerIter,
igShapeForVariance,
igScaleForVariance,
varianceSample
))
self.varianceSeq.append(varianceSample)
self.iterObserver.UpdateState({
McmcIterationEvaluator.STATE_KEY_COUNT_ITER: self._samplerIter,
McmcIterationEvaluator.STATE_KEY_X_ITER: xNext,
McmcIterationEvaluator.STATE_KEY_HX_ITER: hxNext,
McmcIterationEvaluator.STATE_KEY_W_ITER: wSample,
McmcIterationEvaluator.STATE_KEY_A_ITER: aSample,
McmcIterationEvaluator.STATE_KEY_NOISEVAR_ITER: varianceSample
})
self.xSeq.append(xNext)
assert len(self.xSeq) == len(self.varianceSeq), __file__ + ': DoSamplingIteration: seq length mismatch #1'
assert len(self.xSeq) == (len(self.hyperparameterSeq) + 1), __file__ + ': DoSamplingIteration: seq length mismatch #2'
self.hx = hxNext
@contact: [email protected] @file: beta.py @time: 2018/1/22 下午7:27 @desc: beta分布图像 """ import sys import pymc from scipy.stats import beta import matplotlib.pyplot as plt import numpy as np reload(sys) sys.setdefaultencoding('utf8') a, b = 32, 199 print pymc.rbeta(1 + a, b) print pymc.rbeta(1 + a, b) print pymc.rbeta(1 + a, b) print pymc.rbeta(1 + a, b + 1) print pymc.rbeta(1 + a, b + 1) print pymc.rbeta(1 + a, b + 1) mean, var, skew, kurt = beta.stats(a, b, moments='mvsk') print beta.pdf(0.1, a, b) x = np.linspace(0, 1, 100) plt.plot(x, beta.pdf(x, a, b), 'r-', lw=5, alpha=0.6, label='beta pdf') plt.show()
