def field_importance_data(self):
"""Computes field importance based on the field importance information
of the individual models in the ensemble.
"""
field_importance = {}
field_names = {}
if self.importance:
field_importance = self.importance
field_names = {field_id: {'name': self.fields[field_id]["name"]} \
for field_id in field_importance.keys()}
return [list(importance) for importance in \
sorted(field_importance.items(), key=lambda x: x[1],
reverse=True)], field_names
if (self.distributions is not None
and isinstance(self.distributions, list)
and all('importance' in item for item in self.distributions)):
# Extracts importance from ensemble information
importances = [
model_info['importance'] for model_info in self.distributions
]
for index in range(0, len(importances)):
model_info = importances[index]
for field_info in model_info:
field_id = field_info[0]
if field_id not in field_importance:
field_importance[field_id] = 0.0
name = self.fields[field_id]['name']
field_names[field_id] = {'name': name}
field_importance[field_id] += field_info[1]
else:
# Old ensembles, extracts importance from model information
for model_id in self.model_ids:
local_model = BaseModel(model_id, api=self.api)
for field_info in local_model.field_importance:
field_id = field_info[0]
if field_info[0] not in field_importance:
field_importance[field_id] = 0.0
name = self.fields[field_id]['name']
field_names[field_id] = {'name': name}
field_importance[field_id] += field_info[1]
number_of_models = len(self.model_ids)
for field_id in field_importance:
field_importance[field_id] /= number_of_models
return [list(importance) for importance in \
sorted(field_importance.items(), key=lambda x: x[1],
reverse=True)], field_names
