class Build:
def __init__(self, model, param_file=None, output_format=None):
LOG.debug('Starting build...')
self.model = model
self.param = Parameters()
self.conveyor = Conveyor()
# load parameters
if param_file is not None:
# use the param_file to update existing parameters at the model
# directory and save changes to make them persistent
success, message = self.param.delta(model, 0, param_file)
else:
# load parameter file at the model directory
success, message = self.param.loadYaml(model, 0)
# being unable to load parameters is a critical error
if not success:
LOG.critical(
f'Unable to load model parameters. "{message}" Aborting...')
sys.exit(1)
# add additional output formats included in the constructor
# this is requiered to add JSON format as output when the object is
# instantiated from a web service call, requiring this output
if output_format is not None:
if output_format not in self.param.getVal('output_format'):
self.param.appVal('output_format', output_format)
def get_model_set(self):
''' Returns a Boolean indicating if the model uses external input
sources and a list with these sources '''
return self.param.getModelSet()
def set_single_CPU(self) -> None:
''' Forces the use of a single CPU '''
LOG.debug('parameter "numCPUs" forced to be 1')
self.param.setVal('numCPUs', 1)
def run(self, input_source):
''' Executes a default predicton workflow '''
# path to endpoint
epd = utils.model_path(self.model, 0)
if not os.path.isdir(epd):
self.conveyor.setError(f'Unable to find model {self.model}')
#LOG.error(f'Unable to find model {self.model}')
# import ichild classes
if not self.conveyor.getError():
# uses the child classes within the 'model' folder,
# to allow customization of the processing applied to each model
modpath = utils.module_path(self.model, 0)
idata_child = importlib.import_module(modpath + ".idata_child")
learn_child = importlib.import_module(modpath + ".learn_child")
odata_child = importlib.import_module(modpath + ".odata_child")
# run idata object, in charge of generate model data from input
try:
idata = idata_child.IdataChild(self.param, self.conveyor,
input_source)
except:
LOG.warning(
'Idata child architecture mismatch, defaulting to Idata parent'
)
idata = Idata(self.param, self.conveyor, input_source)
idata.run()
LOG.debug(f'idata child {type(idata).__name__} completed `run()`')
if not self.conveyor.getError():
# check there is a suitable X and Y
if not self.conveyor.isKey('xmatrix'):
self.conveyor.setError(f'Failed to compute MDs')
if not self.conveyor.isKey('ymatrix'):
self.conveyor.setError(
f'No activity data (Y) found in training series')
if not self.conveyor.getError():
# instantiate learn (build a model from idata) and run it
learn = learn_child.LearnChild(self.param, self.conveyor)
learn.run()
try:
learn = learn_child.LearnChild(self.param, self.conveyor)
except:
LOG.warning(
'Learn child architecture mismatch, defaulting to Learn parent'
)
learn = Learn(self.param, self.conveyor)
LOG.debug(f'learn child {type(learn).__name__} completed `run()`')
# run odata object, in charge of formatting the prediction results
# note that if any of the above steps failed, an error has been inserted in the
# conveyor and odata will take case of showing an error message
try:
odata = odata_child.OdataChild(self.param, self.conveyor)
except:
LOG.warning(
'Odata child architecture mismatch, defaulting to Odata parent'
)
odata = Odata(self.param, self.conveyor)
return odata.run()
class Predict:
def __init__(self, model, version, output_format=None):
LOG.debug('Starting predict...')
self.model = model
self.version = version
self.param = Parameters()
self.conveyor = Conveyor()
if not self.param.loadYaml(model, version):
LOG.critical('Unable to load model parameters. Aborting...')
sys.exit()
# add additional output formats included in the constructor
# this is requiered to add JSON format as output when the object is
# instantiated from a web service call, requiring this output
if output_format != None:
if output_format not in self.param.getVal('output_format'):
self.param.appVal('output_format',output_format)
return
def get_model_set(self):
''' Returns a Boolean indicating if the model uses external input
sources and a list with these sources '''
return self.param.getModelSet()
def set_single_CPU(self) -> None:
''' Forces the use of a single CPU '''
LOG.debug('parameter "numCPUs" forced to be 1')
self.param.setVal('numCPUs',1)
def run(self, input_source):
''' Executes a default predicton workflow '''
# path to endpoint
# path to endpoint
endpoint = utils.model_path(self.model, self.version)
if not os.path.isdir(endpoint):
self.conveyor.setError(f'Unable to find model {self.model}, version {self.version}')
#LOG.error(f'Unable to find model {self.model}')
if not self.conveyor.getError():
# uses the child classes within the 'model' folder,
# to allow customization of
# the processing applied to each model
modpath = utils.module_path(self.model, self.version)
idata_child = importlib.import_module(modpath+".idata_child")
apply_child = importlib.import_module(modpath+".apply_child")
odata_child = importlib.import_module(modpath+".odata_child")
# run idata object, in charge of generate model data from input
try:
idata = idata_child.IdataChild(self.param, self.conveyor, input_source)
except:
LOG.warning ('Idata child architecture mismatch, defaulting to Idata parent')
idata = Idata(self.param, self.conveyor, input_source)
idata.run()
LOG.debug(f'idata child {type(idata).__name__} completed `run()`')
if not self.conveyor.getError():
# make sure there is X data
if not self.conveyor.isKey('xmatrix'):
LOG.debug(f'Failed to compute MDs')
self.conveyor.setError(f'Failed to compute MDs')
if not self.conveyor.getError():
# run apply object, in charge of generate a prediction from idata
try:
apply = apply_child.ApplyChild(self.param, self.conveyor)
except:
LOG.warning ('Apply child architecture mismatch, defaulting to Apply parent')
apply = Apply(self.param, self.conveyor)
apply.run()
LOG.debug(f'apply child {type(apply).__name__} completed `run()`')
# run odata object, in charge of formatting the prediction results
# note that if any of the above steps failed, an error has been inserted in the
# conveyor and odata will take case of showing an error message
try:
odata = odata_child.OdataChild(self.param, self.conveyor)
except:
LOG.warning ('Odata child architecture mismatch, defaulting to Odata parent')
odata = Odata(self.param, self.conveyor)
return odata.run()