def setUp(self):
config.StringParameter('test.param0', 'a param', default='foo')
config.StringParameter('test.param1', 'a param',
config.oneof('foo', 'bar'))
config.IntParameter('test.param2', 'a param', default=20)
config.IntParameter('test.param3', 'a param', config.atmost(100))
config.IntParameter('test.param4', 'a param', config.atleast(100))
config.IntParameter('test.param5', 'a param', config.between(10, 100))
config.IntParameter('test.param6', 'a param', lambda x: x == 50)
config.FloatParameter('test.param7', 'a param',
config.between(1.3, 2.7))
class EC2Controller(IPython1Controller):
_params = (config.StringParameter('ec2.config',
'EC2 config file',
default=''),
config.IntParameter(
'ec2.min_count',
'Minimum number of EC2 instances to create (default=1).',
default=1),
config.IntParameter(
'ec2.max_count',
"""Maximum number of EC2 instances to create
(default=0 means the same number as ec2.min_count).""",
default=0))
def __init__(self, **options):
config.setparams(self, options)
self.ec2 = ec2ipy1.EC2Cluster(self.config)
self.start()
def __del__(self):
self.stop()
def start(self):
self.ec2.create_instances(self.min_count, self.max_count)
print "Updating pebl on worker nodes"
self.ec2.remote_all(
"cd /usr/local/src/pebl; svn update; python setup.py install")
self.ec2.start_ipython1(engine_on_controller=True)
self.ipy1taskcontroller = IPython1Controller(
self.ec2.task_controller_url)
def stop(self):
self.ec2.terminate_instances()
def submit(self, tasks):
return self.ipy1taskcontroller.submit(tasks)
def retrieve(self, deferred_results):
return self.ipy1taskcontroller.retrieve(deferred_results)
def run(self, tasks):
return self.ipy1taskcontroller.run(tasks)
def test_configobj1(self):
expected = \
"""[test]
param1 = foo
param0 = foo
[test1]
param1 = 5
"""
config.IntParameter('test1.param1', 'a param', default=5)
config.set('test.param1', 'foo')
params = [
config._parameters.get(x)
for x in ('test.param0', 'test.param1', 'test1.param1')
]
tmpfile = NamedTemporaryFile(prefix="pebl.test")
config.configobj(params).write(tmpfile)
tmpfile.file.seek(0)
actual = tmpfile.read()
assert actual == expected
class LearnerResult:
"""Class for storing any and all output of a learner.
This is a mutable container for networks and scores. In the future, it will
also be the place to collect statistics related to the learning task.
"""
#
# Parameters
#
_params = (
config.StringParameter(
'result.filename',
'The name of the result output file',
default='result.pebl'
),
config.StringParameter(
'result.format',
'The format for the pebl result file (pickle or html)',
config.oneof('pickle', 'html'),
default='pickle'
),
config.StringParameter(
'result.outdir',
'Directory for html report.',
default='result'
),
config.IntParameter(
'result.size',
"""Number of top-scoring networks to save. Specify 0 to indicate that
all scored networks should be saved.""",
default=1000
)
)
def __init__(self, learner_=None, size=None):
self.data = learner_.data if learner_ else None
self.nodes = self.data.variables if self.data else None
self.size = size or config.get('result.size')
self.networks = []
self.nethashes = {}
self.runs = []
def start_run(self):
"""Indicates that the learner is starting a new run."""
self.runs.append(LearnerRunStats(time.time()))
def stop_run(self):
"""Indicates that the learner is stopping a run."""
self.runs[-1].end = time.time()
def add_network(self, net, score):
"""Add a network and score to the results."""
nets = self.networks
nethashes = self.nethashes
nethash = hash(net.edges)
if self.size == 0 or len(nets) < self.size:
if nethash not in nethashes:
snet = _ScoredNetwork(copy(net.edges), score)
insort(nets, snet)
nethashes[nethash] = 1
elif score > nets[0].score and nethash not in nethashes:
nethashes.pop(hash(nets[0].edges))
nets.remove(nets[0])
snet = _ScoredNetwork(copy(net.edges), score)
insort(nets, snet)
nethashes[nethash] = 1
def tofile(self, filename=None):
"""Save the result to a python pickle file.
The result can be later read using the result.fromfile function.
"""
filename = filename or config.get('result.filename')
with open(filename, 'w') as fp:
cPickle.dump(self, fp)
def tohtml(self, outdir=None):
"""Create a html report of the result.
outdir is a directory to create html files inside.
"""
if _can_create_html:
HtmlFormatter().htmlreport(
self,
outdir or config.get('result.outdir')
)
else:
print "Cannot create html reports because some dependencies are missing."
@property
def posterior(self):
"""Returns a posterior object for this result."""
return posterior.from_sorted_scored_networks(
self.nodes,
list(reversed(self.networks))
)
class SimulatedAnnealingLearner(Learner):
#
# Parameters
#
_params = (
config.FloatParameter(
'simanneal.start_temp',
"Starting temperature for a run.",
config.atleast(0.0),
default=100.0
),
config.FloatParameter(
'simanneal.delta_temp',
'Change in temp between steps.',
config.atleast(0.0),
default=0.5
),
config.IntParameter(
'simanneal.max_iters_at_temp',
'Max iterations at any temperature.',
config.atleast(0),
default=100
),
config.StringParameter(
'simanneal.seed',
'Starting network for a greedy search.',
default=''
)
)
def __init__(self, data_=None, prior_=None, **options):
"""Create a Simulated Aneaaling learner.
For more information about Simulated Annealing algorithms, consult:
1. http://en.wikipedia.org/wiki/Simulated_annealing
2. D. Heckerman. A Tutorial on Learning with Bayesian Networks.
Microsoft Technical Report MSR-TR-95-06, 1995. p.35-36.
Any config param for 'simanneal' can be passed in via options.
Use just the option part of the parameter name.
"""
super(SimulatedAnnealingLearner,self).__init__(data_, prior_)
config.setparams(self, options)
if not isinstance(self.seed, network.Network):
self.seed = network.Network(self.data.variables, self.seed)
def run(self):
"""Run the learner."""
self.stats = SALearnerStatistics(self.start_temp, self.delta_temp,
self.max_iters_at_temp)
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, self.seed, self.prior)
self.evaluator.score_network(self.seed.copy())
self.result.start_run()
curscore = self.evaluator.score_network()
# temperature decays exponentially, so we'll never get to 0.
# So, we continue until temp < 1
while self.stats.temp >= 1:
try:
newscore = self._alter_network_randomly_and_score()
except CannotAlterNetworkException:
return
self.result.add_network(self.evaluator.network, newscore)
if self._accept(newscore):
# set current score
self.stats.current_score = newscore
if self.stats.current_score > self.stats.best_score:
self.stats.best_score = self.stats.current_score
else:
# undo network alteration
self.evaluator.restore_network()
# temp not updated EVERY iteration. just whenever criteria met.
self.stats.update()
self.result.stop_run()
return self.result
def _accept(self, newscore):
oldscore = self.stats.current_score
if newscore >= oldscore:
return True
elif random.random() < exp((newscore - oldscore)/self.stats.temp):
return True
else:
return False
#
# Module parameters
#
_pfilename = config.StringParameter(
'data.filename',
'File to read data from.',
config.fileexists(),
)
_ptext = config.StringParameter(
'data.text', 'The text of a dataset included in config file.', default='')
_pdiscretize = config.IntParameter(
'data.discretize',
'Number of bins used to discretize data. Specify 0 to indicate that '+\
'data should not be discretized.',
default=0
)
#
# Exceptions
#
class ParsingError(Exception):
"""Error encountered while parsing an ill-formed datafile."""
pass
class IncorrectArityError(Exception):
"""Error encountered when the datafile speifies an incorrect variable arity.
class MissingDataNetworkEvaluator(SmartNetworkEvaluator):
#
# Parameters
#
_params = (config.IntParameter(
'gibbs.burnin',
"""Burn-in period for the gibbs sampler (specified as a multiple of
the number of missing values)""",
default=10),
config.StringParameter(
'gibbs.max_iterations',
"""Stopping criteria for the gibbs sampler.
The number of Gibb's sampler iterations to run. Should be a valid
python expression using the variable n (number of missing values).
Examples:
* n**2 (for n-squared iterations)
* 100 (for 100 iterations)
""",
default="n**2"))
def __init__(self,
data_,
network_,
prior_=None,
localscore_cache=None,
**options):
"""Create a network evaluator for use with missing values.
This evaluator uses a Gibb's sampler for sampling over the space of
possible completions for the missing values.
For more information about Gibb's sampling, consult:
1. http://en.wikipedia.org/wiki/Gibbs_sampling
2. D. Heckerman. A Tutorial on Learning with Bayesian Networks.
Microsoft Technical Report MSR-TR-95-06, 1995. p.21-22.
Any config param for 'gibbs' can be passed in via options.
Use just the option part of the parameter name.
"""
super(MissingDataNetworkEvaluator,
self).__init__(data_, network_, prior_)
self._localscore = None # no cache w/ missing data
config.setparams(self, options)
def _init_state(self):
parents = self.network.edges.parents
self.cpds = [self._cpd(n, parents(n)) for n in self.datavars]
self.localscores = N.array([cpd.loglikelihood() for cpd in self.cpds],
dtype=float)
self.data_dirtynodes = set(self.datavars)
def _update_dirtynodes(self, add, remove):
# With hidden nodes:
# 1. dirtynode calculation is more expensive (need to look beyond
# markov blanket).
# 2. time spent rescoring observed nodes is insignificant compared
# to scoring hidden/missing nodes.
self.dirtynodes = set(self.datavars)
def _score_network_with_tempdata(self):
# update localscore for data_dirtynodes, then calculate globalscore.
for n in self.data_dirtynodes:
self.localscores[n] = self.cpds[n].loglikelihood()
self.data_dirtynodes = set()
self.score = self._globalscore(self.localscores)
return self.score
def _alter_data(self, row, col, value):
oldrow = self.data.observations[row].copy()
self.data.observations[row, col] = value
# update data_dirtynodes
affected_nodes = set(self.network.edges.children(col) + [col])
self.data_dirtynodes.update(affected_nodes)
# update cpds
for node in affected_nodes:
datacols = [node] + self.network.edges.parents(node)
if not self.data.interventions[row, node]:
self.cpds[node].replace_data(
oldrow[datacols], self.data.observations[row][datacols])
def _alter_data_and_score(self, row, col, value):
self._alter_data(row, col, value)
return self._score_network_with_tempdata()
def _calculate_score(self, chosenscores, gibbs_state):
# discard the burnin period scores and average the rest
burnin_period = self.burnin * \
self.data.missing[self.data.missing==True].size
if gibbs_state:
# resuming from a previous gibbs run. so, no burnin required.
scoresum = logsum(
N.concatenate((chosenscores, [gibbs_state.scoresum])))
numscores = len(chosenscores) + gibbs_state.numscores
elif len(chosenscores) > burnin_period:
# remove scores from burnin period.
nonburn_scores = chosenscores[burnin_period:]
scoresum = logsum(nonburn_scores)
numscores = len(nonburn_scores)
else:
# this occurs when gibbs iterations were less than burnin period.
scoresum = chosenscores[-1]
numscores = 1
score = scoresum - log(numscores)
return score, numscores
def _assign_missingvals(self, indices, gibbs_state):
if gibbs_state:
assignedvals = gibbs_state.assignedvals
else:
arities = [v.arity for v in self.data.variables]
assignedvals = [
random.randint(0, arities[col] - 1) for row, col in indices
]
self.data.observations[unzip(indices)] = assignedvals
def score_network(self, net=None, gibbs_state=None):
"""Score a network.
If net is provided, scores that. Otherwise, score network previously
set.
The default stopping criteria is to run for n**2 iterations.
gibbs_state is the state of a previous run of the Gibb's sampler. With
this, one can do the following::
myeval = evaluator.MissingDataNetworkEvaluator(...)
myeval.score_network(...)
gibbs_state = myeval.gibbs_state
cPickle.dump(gibbs_state, 'gibbs_state.txt')
# look at results, do other analysis, etc
# If we decide that we need further Gibb's sampler iterations, we
# don't need to restart
gibbs_state = cPickle.load(open('gibbs_state.txt'))
myeval = evaluator.MissingDataNetworkEvaluator(...)
# continue with the previous run of the Gibb's sampler
myeval.score_network(
gibbs_state=gibbs_state,
stopping_criteria=lambda i,N: i>200*N**2
)
"""
self.gibbs_state = gibbs_state
return super(MissingDataNetworkEvaluator, self).score_network(net)
def _score_network_core(self):
# create some useful lists and local variables
missing_indices = unzip(N.where(self.data.missing == True))
num_missingvals = len(missing_indices)
n = num_missingvals
max_iterations = eval(self.max_iterations)
arities = [v.arity for v in self.data.variables]
chosenscores = []
self._assign_missingvals(missing_indices, self.gibbs_state)
self._init_state()
# Gibbs Sampling:
# For each missing value:
# 1) score net with each possible value (based on node's arity)
# 2) using a probability wheel, sample a value from the possible values
iters = 0
while iters < max_iterations:
for row, col in missing_indices:
scores = [self._alter_data_and_score(row, col, val) \
for val in xrange(arities[col])]
chosenval = logscale_probwheel(range(len(scores)), scores)
self._alter_data(row, col, chosenval)
chosenscores.append(scores[chosenval])
iters += num_missingvals
self.chosenscores = N.array(chosenscores)
self.score, numscores = self._calculate_score(self.chosenscores,
self.gibbs_state)
# save state of gibbs sampler
self.gibbs_state = GibbsSamplerState(
avgscore=self.score,
numscores=numscores,
assignedvals=self.data.observations[unzip(
missing_indices)].tolist())
return self.score
class LocalscoreCache(object):
""" A LRU cache for local scores.
Based on code from http://code.activestate.com/recipes/498245/
"""
_params = (config.IntParameter(
'localscore_cache.maxsize',
"Max number of localscores to cache. Default=-1 means unlimited size.",
default=-1))
def __init__(self, evaluator, cachesize=None):
self._cache = {}
self._queue = deque()
self._refcount = {}
self.cachesize = cachesize or config.get('localscore_cache.maxsize')
self.neteval = evaluator
self.hits = 0
self.misses = 0
def __call__(self, node, parents):
# make variables local
_len = len
_queue = self._queue
_refcount = self._refcount
_cache = self._cache
_maxsize = self.cachesize
index = tuple([node] + parents)
# get from cache or compute
try:
score = _cache[index]
self.hits += 1
except KeyError:
score = _cache[index] = self.neteval._cpd(node,
parents).loglikelihood()
self.misses += 1
# if using LRU cache (maxsize != -1)
if _maxsize > 0:
# record that key was accessed
_queue.append(index)
_refcount[index] = _refcount.get(index, 0) + 1
# purge LRU entry
while _len(_cache) > _maxsize:
k = _queue.popleft()
_refcount[k] -= 1
if not _refcount[k]:
del _cache[k]
del _refcount[k]
# Periodically compact the queue by duplicate keys
if _len(_queue) > _maxsize * 4:
for i in xrange(_len(_queue)):
k = _queue.popleft()
if _refcount[k] == 1:
_queue.append(k)
else:
_refcount[k] -= 1
return score
#
_plearnertype = config.StringParameter(
'learner.type',
"""Type of learner to use.
The following learners are included with pebl:
* greedy.GreedyLearner
* simanneal.SimulatedAnnealingLearner
* exhaustive.ListLearner
""",
default = 'greedy.GreedyLearner'
)
_ptasks = config.IntParameter(
'learner.numtasks',
"Number of learner tasks to run.",
config.atleast(0),
default=1
)
class Learner(Task):
def __init__(self, data_=None, prior_=None, **kw):
self.data = data_ or data.fromconfig()
self.prior = prior_ or prior.fromconfig()
self.__dict__.update(kw)
# parameters
self.numtasks = config.get('learner.numtasks')
# stats
self.reverse = 0
class MultiProcessController(_BaseController):
#
# Parameters
#
_params = (
config.IntParameter(
'multiprocess.poolsize',
'Number of processes to run concurrently (0 means no limit)',
default=0
)
)
def __init__(self, poolsize=None):
"""Creates a task controller that runs taks on multiple processes.
This task controller uses a pool of processes rather than spawning all
processes concurrently. poolsize is the size of this pool and by
default it is big enough to run all processes concurrently.
"""
self.poolsize = poolsize or config.get('multiprocess.poolsize')
def run(self, tasks):
"""Run tasks by creating multiple processes.
If poolsize was specified when creating this controller, additional
tasks will be queued.
"""
tasks = copy(tasks) # because we do tasks.pop() below..
numtasks = len(tasks)
poolsize = self.poolsize or numtasks
running = {}
done = []
opjoin = os.path.join
while len(done) < numtasks:
# submit tasks (if below poolsize and tasks remain)
for i in xrange(min(poolsize-len(running), len(tasks))):
task = tasks.pop()
task.cwd = tempfile.mkdtemp()
cPickle.dump(task, open(opjoin(task.cwd, 'task.pebl'), 'w'))
pid = os.spawnlp(os.P_NOWAIT, PEBL, PEBL, "runtask",
opjoin(task.cwd, "task.pebl"))
running[pid] = task
# wait for any child process to finish
pid,status = os.wait()
done.append(running.pop(pid, None))
results = [result.fromfile(opjoin(t.cwd, 'result.pebl')) for t in done]
# to make the results look like deferred results
for r in results:
r.taskid = 0
# clean up
for t in done:
shutil.rmtree(t.cwd)
return results
