def fromconfig(data_=None, prior_=None):
learnertype = config.get('learner.type')
if ':' in learnertype:
CustomLearner(
data_ or data.fromconfig(),
prior_ or prior.fromconfig(),
learnerurl=learnertype
)
else:
learnermodule,learnerclass = learnertype.split('.')
mymod = __import__("pebl.learner.%s" % learnermodule, fromlist=['pebl.learner'])
mylearner = getattr(mymod, learnerclass)
return mylearner(data_ or data.fromconfig(), prior_ or prior.fromconfig())
def run(self):
# re-create the custom learner
tempdir = tempfile.mkdtemp()
with file(os.path.join(tempdir, self.learner_filename), 'w') as f:
f.write(self.learner_source)
sys.path.insert(0, tempdir)
modname = self.learner_filename.split('.')[0]
mod = __import__(modname, fromlist=['*'])
reload(mod) # to load the latest if an older version exists
custlearner = getattr(mod, self.learner_class)
# run the custom learner
clearn = custlearner(
self.data or data.fromconfig(),
self.prior or prior.fromconfig(),
**self.kw
)
self.result = clearn.run()
# cleanup
sys.path.remove(tempdir)
shutil.rmtree(tempdir)
return self.result
def __init__(self, data_=None, prior_=None, whitelist=tuple(), blacklist=tuple(), **kw):
self.data = data_ or data.fromconfig()
self.prior = prior_ or prior.fromconfig()
self.black_edges = kw.pop('blacklist', ())
self.white_edges = kw.pop('whitelist', ())
self.__dict__.update(kw)
# parameters
self.numtasks = config.get('learner.numtasks')
# stats
self.reverse = 0
self.add = 0
self.remove = 0
def fromconfig(data_=None, network_=None, prior_=None):
"""Create an evaluator based on configuration parameters.
This function will return the correct evaluator based on the relevant
configuration parameters.
"""
data_ = data_ or data.fromconfig()
network_ = network_ or network.fromdata(data_)
prior_ = prior_ or prior.fromconfig()
if data_.missing.any():
e = _missingdata_evaluators[config.get('evaluator.missingdata_evaluator')]
return e(data_, network_, prior_)
else:
return SmartNetworkEvaluator(data_, network_, prior_)