def get_best_evaluated_split_suggestion(self,
criterion,
pre_split_dist,
att_idx,
class_idx=None):
best_suggestion = None
suggested_split_values = self.get_split_point_suggestions()
for expand_value in suggested_split_values:
post_split_dist = self.get_class_dists_from_binary_split(
expand_value)
if class_idx is not None:
criterion.class_idx = class_idx
merit = criterion.get_merit_of_split(pre_split_dist,
post_split_dist)
if best_suggestion is None or merit > best_suggestion.merit:
if criterion.best_idx == 0:
symbol = "<="
else:
symbol = ">"
best_suggestion = AttributeExpandSuggestion(
att_idx, expand_value, symbol, post_split_dist, merit)
return best_suggestion
def get_best_evaluated_split_suggestion(self,
criterion,
pre_split_dist,
att_idx,
class_idx=None):
best_suggestion = None
att_values = set([
att_val for class_val in self._att_val_dist_per_class.values()
for att_val in class_val
])
for att_val in att_values:
post_split_dist = self.get_class_dist_from_binary_split(att_val)
if class_idx is not None:
criterion.class_idx = class_idx
merit = criterion.get_merit_of_split(pre_split_dist,
post_split_dist)
if best_suggestion is None or merit > best_suggestion.merit:
if criterion.best_idx == 0:
symbol = "="
else:
symbol = "!="
best_suggestion = AttributeExpandSuggestion(
att_idx, att_val, symbol, post_split_dist, merit)
return best_suggestion