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OpenStack Study: weights.py**** CubicPower OpenStack Study ****
# Copyright (c) 2011-2012 OpenStack Foundation # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """ Pluggable Weighing support """ import abc import six from nova import loadables
**** CubicPower OpenStack Study **** def normalize(weight_list, minval=None, maxval=None): """Normalize the values in a list between 0 and 1.0. The normalization is made regarding the lower and upper values present in weight_list. If the minval and/or maxval parameters are set, these values will be used instead of the minimum and maximum from the list. If all the values are equal, they are normalized to 0. """ if not weight_list: return () if maxval is None: maxval = max(weight_list) if minval is None: minval = min(weight_list) maxval = float(maxval) minval = float(minval) if minval == maxval: return [0] * len(weight_list) range_ = maxval - minval return ((i - minval) / range_ for i in weight_list)
**** CubicPower OpenStack Study **** class WeighedObject(object): """Object with weight information."""
**** CubicPower OpenStack Study **** def __init__(self, obj, weight): self.obj = obj self.weight = weight
**** CubicPower OpenStack Study **** def __repr__(self): return " @six.add_metaclass(abc.ABCMeta)
**** CubicPower OpenStack Study **** class BaseWeigher(object): """Base class for pluggable weighers. The attributes maxval and minval can be specified to set up the maximum and minimum values for the weighed objects. These values will then be taken into account in the normalization step, instead of taking the values from the calculated weights. """ minval = None maxval = None
**** CubicPower OpenStack Study **** def weight_multiplier(self): """How weighted this weigher should be. Override this method in a subclass, so that the returned value is read from a configuration option to permit operators specify a multiplier for the weigher. """ return 1.0 @abc.abstractmethod
**** CubicPower OpenStack Study **** def _weigh_object(self, obj, weight_properties): """Weigh an specific object."""
**** CubicPower OpenStack Study **** def weigh_objects(self, weighed_obj_list, weight_properties): """Weigh multiple objects. Override in a subclass if you need access to all objects in order to calculate weights. Do not modify the weight of an object here, just return a list of weights. """ # Calculate the weights weights = [] for obj in weighed_obj_list: weight = self._weigh_object(obj.obj, weight_properties) # Record the min and max values if they are None. If they anything # but none we assume that the weigher has set them if self.minval is None: self.minval = weight if self.maxval is None: self.maxval = weight if weight < self.minval: self.minval = weight elif weight > self.maxval: self.maxval = weight weights.append(weight) return weights
**** CubicPower OpenStack Study **** class BaseWeightHandler(loadables.BaseLoader): object_class = WeighedObject
**** CubicPower OpenStack Study **** def get_weighed_objects(self, weigher_classes, obj_list, weighing_properties): """Return a sorted (descending), normalized list of WeighedObjects.""" if not obj_list: return [] weighed_objs = [self.object_class(obj, 0.0) for obj in obj_list] for weigher_cls in weigher_classes: weigher = weigher_cls() weights = weigher.weigh_objects(weighed_objs, weighing_properties) # Normalize the weights weights = normalize(weights, minval=weigher.minval, maxval=weigher.maxval) for i, weight in enumerate(weights): obj = weighed_objs[i] obj.weight += weigher.weight_multiplier() * weight return sorted(weighed_objs, key=lambda x: x.weight, reverse=True)
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