def RExt(dtype: DType,
rgen=None,
spec: SearchSpec = None,
depth: int = 1,
mode: str = None,
tracker: OpTracker = None,
arg_name: str = None,
identifier: str = None,
constraint: Callable[[Any], Any] = None,
**kwargs):
if constraint is None:
def constraint(x):
return True
if mode != 'training-data':
raise AutoPandasException("Unrecognized mode {} in RExt".format(mode))
pool: List[Optional[Value]] = []
for idx, val in enumerate(spec.inputs):
if not (dtype.hasinstance(val) and constraint(val)):
continue
pool.append(Fetcher(val=val, source='inps', idx=idx))
for idx, val in enumerate(spec.intermediates[:depth - 1]):
if not (dtype.hasinstance(val) and constraint(val)):
continue
pool.append(Fetcher(val=val, source='intermediates', idx=idx))
if rgen is not None:
pool.append(None)
random.shuffle(pool)
label = 'ext_' + arg_name + '_' + identifier
rlabel = 'rext_' + arg_name + '_' + identifier
for selection in pool:
tracker.record.pop(label, None)
tracker.record.pop(rlabel, None)
if selection is None:
# We've decided to create a new input altogether
val = next(rgen)
tracker.record[rlabel] = {'val': val, 'arg_name': arg_name}
yield NewInp(val)
else:
selection: Fetcher
tracker.record[label] = {
'source': selection.source,
'idx': selection.idx
}
yield selection
def Ext(dtype: DType,
spec: SearchSpec = None,
depth: int = 1,
mode: str = None,
tracker: OpTracker = None,
arg_name: str = None,
identifier: str = None,
constraint: Callable[[Any], Any] = None,
**kwargs):
if constraint is None:
def constraint(x):
return True
if mode == 'exhaustive' or mode == 'inference':
for idx, val in enumerate(reversed(spec.intermediates[:depth - 1])):
idx = depth - idx - 2
if not (dtype.hasinstance(val) and constraint(val)):
continue
yield Fetcher(val=val, source='intermediates', idx=idx)
for idx, val in enumerate(spec.inputs):
if not (dtype.hasinstance(val) and constraint(val)):
continue
yield Fetcher(val=val, source='inps', idx=idx)
elif mode == 'arguments-training-data':
label = 'ext_' + arg_name + '_' + identifier
if label not in tracker.record:
raise AutoPandasInversionFailedException(
"Could not find label {} in tracker".format(label))
record = tracker.record[label]
idx = record['idx']
if record['source'] == 'inps':
yield Fetcher(val=spec.inputs[idx], source='inps', idx=idx)
elif record['source'] == 'intermediates':
yield Fetcher(val=spec.intermediates[idx],
source='intermediates',
idx=idx)
return
elif mode == 'arguments-training-data-best-effort':
training_spec: ArgTrainingSpec = spec
label = 'ext_' + arg_name + '_' + identifier
for idx, val in enumerate(spec.inputs):
if not (dtype.hasinstance(val) and constraint(val)):
continue
if Checker.check(val, training_spec.args[arg_name]):
yield Fetcher(val=val, source='inps', idx=idx)
return
for idx, val in enumerate(spec.intermediates[:depth - 1]):
if not (dtype.hasinstance(val) and constraint(val)):
continue
if Checker.check(val, training_spec.args[arg_name]):
yield Fetcher(val=val, source='intermediates', idx=idx)
return
raise AutoPandasInversionFailedException(
"Could not invert generator for {} at {}".format(arg_name, label))
