def run(self, cutoff, numsel, metric):
'''
Runs prediction tasks using internally defined methods
Most of these methods can be found at the stats folder
'''
# Load scaler and variable mask and preprocess the data
success, result = self.preprocess(self.X)
if not success:
self.conveyor.setError(result)
return
self.X = result
# instances space object
space = Space(self.param, self.conveyor)
# builds space from idata results
LOG.info('Starting space searching')
success, search_results = space.search(cutoff, numsel, metric)
if not success:
self.conveyor.setError(search_results)
return
self.conveyor.addVal(search_results, 'search_results',
'Search results', 'result', 'objs',
'Collection of similar compounds', 'main')
LOG.info('Space search finished successfully')
return
def run (self):
'''
Builds a chemical space
'''
# pre-process data
success, message = self.preprocess()
if not success:
self.conveyor.setError(message)
return
# instances space object
space = Space(self.param, self.conveyor)
# builds space from idata results
LOG.debug('Starting space building')
success, space_building_results = space.build()
if not success:
LOG.error('space_building_results')
self.conveyor.setError(space_building_results)
return
self.conveyor.addVal(
space_building_results,
'space_build_info',
'space build info',
'method',
'single',
'Information about the building of the chemical space')
# save model
try:
space.save_space()
except Exception as e:
LOG.error(f'Error saving space with exception {e}')
self.conveyor.setError(f'Error saving space with exception {e}')
return
LOG.info('Space building finished successfully')
return
def run(self, cutoff, numsel, metric):
'''
Runs prediction tasks using internally defined methods
Most of these methods can be found at the stats folder
'''
if self.param.getVal('input_type') != "smarts":
# Load scaler and variable mask and preprocess the data
success, result = self.preprocess(self.X)
if not success:
self.conveyor.setError(result)
return
self.X = result
# instances space object
space = Space(self.param, self.conveyor)
# check metric
if metric is None:
metric = space.defaultMetric
# builds space from idata results
LOG.info('Starting space searching')
success, search_results = space.search(cutoff, numsel, metric)
if not success:
self.conveyor.setError(search_results)
return
self.conveyor.addVal(space.metric, 'metric', 'metric', 'method',
'single', 'Final metric used in the search')
self.conveyor.addVal(search_results, 'search_results',
'Search results', 'result', 'objs',
'Collection of similar compounds', 'main')
LOG.info('Space search finished successfully')
return