def iter_func_seqs(self) -> Generator[List[BaseGenerator], None, None]:
generators: Dict[str, BaseGenerator] = load_generators()
if self.model_store is None or 'function-model' not in self.model_store:
model = ModelStore({'function-model': self.model_path})
else:
model = self.model_store
if self.use_old_featurization:
from autopandas_v2.ml.featurization_old.featurizer import RelationGraph
from autopandas_v2.ml.featurization_old.options import GraphOptions
else:
from autopandas_v2.ml.featurization.featurizer import RelationGraph
from autopandas_v2.ml.featurization.options import GraphOptions
options = GraphOptions()
graph: RelationGraph = RelationGraph(options)
graph.from_input_output(self.iospec.inputs, self.iospec.output)
encoding = graph.get_encoding(get_mapping=False)
str_seqs, probs = list(
zip(*model.predict_graphs('function-model', [encoding],
top_k=self.top_k)[0]))
str_seqs = [i.split(':') for i in str_seqs]
model.close()
for str_seq in str_seqs:
result = [generators[i] for i in str_seq]
if self.typecheck(result, self.iospec.output):
logger.info(str_seq)
yield result
else:
logger.warn("Skipping", str_seq)
def iter_func_seqs(self) -> Generator[List[BaseGenerator], None, None]:
generators: Dict[str, BaseGenerator] = load_generators()
if self.use_spec_funcs and self.iospec.funcs is not None:
generators = {k: v for k, v in generators.items() if k in self.iospec.funcs}
if self.use_spec_seqs and self.iospec.seqs is not None:
for seq in self.iospec.seqs:
yield [generators[self.iospec.funcs[i]] for i in seq]
return
raise NotImplementedError("Iteration of function sequences not defined yet")
def iter_func_seqs(self) -> Generator[List[BaseGenerator], None, None]:
generators: Dict[str, BaseGenerator] = load_generators()
model = RelGraphInterface.from_model_dir(self.model_path)
if self.use_old_featurization:
from autopandas_v2.ml.featurization_old.featurizer import RelationGraph
from autopandas_v2.ml.featurization_old.options import GraphOptions
else:
from autopandas_v2.ml.featurization.featurizer import RelationGraph
from autopandas_v2.ml.featurization.options import GraphOptions
options = GraphOptions()
graph: RelationGraph = RelationGraph(options)
graph.from_input_output(self.iospec.inputs, self.iospec.output)
encoding = graph.get_encoding(get_mapping=False)
str_seqs, probs = list(zip(*model.predict_graphs([encoding], top_k=self.top_k)[0]))
str_seqs = [i.split(':') for i in str_seqs]
model.close()
for str_seq in str_seqs:
yield [generators[i] for i in str_seq]
def init(cls, args: ArgNamespace):
cls.args = args
cls.generators = load_generators()
if cls.args.debug:
logger.info("Loaded {} generators in process {}".format(
len(cls.generators), os.getpid()))