import os
from collections import OrderedDict
from cave.analyzer.parameter_importance.base_parameter_importance import BaseParameterImportance
[docs]class ForwardSelection(BaseParameterImportance):
"""
Forward Selection is a generic method to obtain a subset of parameters to achieve the same prediction error as
with the full parameter set. Each parameter is scored by how much the out-of-bag-error of an empirical
performance model based on a random forest is decreased.
"""
def __init__(self,
runscontainer,
marginal_threshold=0.05):
super().__init__(runscontainer)
self.marginal_threshold = marginal_threshold
self.parameter_importance("forward-selection")
[docs] def get_name(self):
return "Forward Selection"
[docs] def postprocess(self, pimp, output_dir):
return OrderedDict([
('figure', [os.path.join(output_dir, fn) for fn in ["forward-selection-barplot.png",
"forward-selection-chng.png"]])
])