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data-maker
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Author SHA1 Message Date
Steve Nyemba d529ad9f96 bug fix: 2024-02-26 19:00:29 -06:00
2 changed files with 5 additions and 3 deletions
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1
data/__init__.py
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@ -4,3 +4,4 @@ import transport
from multiprocessing import Process, Queue from multiprocessing import Process, Queue
from data.maker import prepare from data.maker import prepare
from data.maker import state from data.maker import state

7
data/maker/__init__.py
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@ -76,6 +76,7 @@ class Learner(Process):
# sel.max_epoc # sel.max_epoc
self.logger = None self.logger = None
if 'logger' in self.store : if 'logger' in self.store :
# self.store['logger']['context'] = 'write'
self.logger = transport.factory.instance(**self.store['logger']) self.logger = transport.factory.instance(**self.store['logger'])
self.autopilot = False #-- to be set by caller self.autopilot = False #-- to be set by caller
self._initStateSpace() self._initStateSpace()
@ -243,7 +244,7 @@ class Trainer(Learner):
# #
# @TODO Log that the dataset was empty or not statistically relevant # @TODO Log that the dataset was empty or not statistically relevant
return return
_space,_matrix = self._encoder.convert() _space,_matrix = self._encoder._convert()
_args = self.network_args _args = self.network_args
if self.gpu : if self.gpu :
@ -341,7 +342,7 @@ class Generator (Learner):
# The values will be returned because we have provided _map information from the constructor # The values will be returned because we have provided _map information from the constructor
# #
values,_matrix = self._encoder.convert() values,_matrix = self._encoder._convert()
_args = self.network_args _args = self.network_args
_args['map'] = self._map _args['map'] = self._map
_args['values'] = np.array(values) _args['values'] = np.array(values)
@ -353,7 +354,7 @@ class Generator (Learner):
gHandler = gan.Predict(**_args) gHandler = gan.Predict(**_args)
gHandler.load_meta(columns=None) gHandler.load_meta(columns=None)
_iomatrix = gHandler.apply() _iomatrix = gHandler.apply()
_candidates= [ self._encoder.revert(matrix=_item) for _item in _iomatrix] _candidates= [ self._encoder._revert(matrix=_item) for _item in _iomatrix]
_size = np.sum([len(_item) for _item in _iomatrix]) _size = np.sum([len(_item) for _item in _iomatrix])
_log = {'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}} _log = {'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}}