def apply(self):
common_args = dict()
common_args["n_estimators"] = self.n_estimators
if self.use_max_features:
common_args["max_features"] = self.max_features
if self.use_random_state:
common_args["random_state"] = self.random_state
if self.use_max_depth:
common_args["max_depth"] = self.max_depth
if self.use_max_leaf_nodes:
common_args["max_leaf_nodes"] = self.max_leaf_nodes
learner = RandomForestLearner(
preprocessors=self.preprocessors,**common_args
)
learner.name = self.learner_name
classifier = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def apply(self):
common_args = dict()
common_args["n_estimators"] = self.n_estimators
if self.use_max_features:
common_args["max_features"] = self.max_features
if self.use_random_state:
common_args["random_state"] = self.random_state
if self.use_max_depth:
common_args["max_depth"] = self.max_depth
if self.use_max_leaf_nodes:
common_args["max_leaf_nodes"] = self.max_leaf_nodes
learner = RandomForestLearner(
preprocessors=self.preprocessors,**common_args
)
learner.name = self.learner_name
classifier = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
