from enum import Enum
import json
[docs]class Goal(Enum):
"""An enumeration representing the different types of goals a Criteria can have."""
NONE = 0
MAXIMIZE = 1
MINIMIZE = 2
TARGET = 3
IN_RANGE = 4
EQUAL_TO = 5
CPK = 6
def __str__(self):
return self.name
[docs]class Optimizer:
"""The Optimizer class is used to set criteria on one or more Analyses,
then use those criteria to find the optimal parameters.
"""
def __init__(self, client):
self._client = client
result = self._client.send_payload({
"method": "GET",
"uri": "optimizer",
})
self._criteria = []
self._solutions = tuple()
def __str__(self):
out = ''
for c in self._criteria:
if c.goal != Goal.NONE:
out += '{}\n'.format(c)
out += "Found {} solutions:".format(len(self._solutions))
for s in self._solutions:
out += '{}\n'.format(s)
return out
def have_criteria(self):
for c in self._criteria:
if c._factor and c.goal == Goal.NONE:
return False
return True
def add_criteria(self, criteria):
if criteria._factor and criteria.goal==Goal.NONE:
raise ValueError("Can't add criteria - no goal specified for '{}'.".format(criteria.name))
if criteria.restrict_discrete==True:
if not criteria._factor:
raise ValueError("Can't add criteria - It is not a discrete factor. For response/analysis '{}', restrict_discrete must be False.".format(criteria.name))
if (criteria._factor.type!="Numeric") or (criteria._factor.type=="Numeric" and criteria._factor.subtype!="Discrete"):
raise ValueError("Can't add criteria - It is not a discrete factor. For factor '{}', restrict_discrete must be False.".format(criteria.name))
self._criteria.append(criteria)
@property
def solutions(self):
return self._solutions
[docs] def optimize(self):
""" Runs the optimization routine. Must have one or more Criteria specified. """
if not self.have_criteria():
raise ValueError("Can't run optimization - no criteria specified!")
result = self._client.send_payload({
"method": "POST",
"uri": "optimizer",
"criteria": [ c.to_json() for c in self._criteria ],
})
solutions = []
for solution in result['payload']['solutions']:
solutions.append(json.loads(solution))
self._solutions = tuple(solutions)
[docs]class Criteria:
"""The Criteria class is used by the optimizer to calculate a desirability score
for a given point in the design space, which is then used to search for an optimal point.
Each Analysis and Factor can have a Criteria (e.g. you might maximize the output of an Analysis,
and target a certain value of a Factor).
"""
def __init__(self, factor=None, analysis=None):
""" Create a Criteria for a Factor or Analysis. """
if (not analysis and not factor) or (analysis and factor):
raise ValueError("You must pass in either an analysis or factor.")
self._analysis = analysis
self._factor = factor
self._name = ''
self._goal = Goal.NONE
self._target = 0
self._lower_limit = 0
self._upper_limit = 0
self._lower_weight = 1
self._upper_weight = 1
self._importance = 0
self._restrict_discrete = False
def __str__(self):
name = ''
if self._analysis:
name += self._analysis.name
if self._factor:
name += self._factor.name
return "Criteria for {} -> Goal: {} Target: {} Lower Limit: {} Upper Limit: {} Lower Weight: {} Upper Weight: {} Importance: {} Restrict discrete: {}".format(
name,
self._goal,
self._target,
self._lower_limit,
self. _upper_limit,
self._lower_weight,
self._upper_weight,
self._importance,
self._restrict_discrete,
)
if self._factor:
result += " " + "Restrict discrete: "+ str(self._restrict_discrete)
return result
@property
def name(self):
""" The name of this Criteria (analysis/response or factor name). """
if self._analysis:
self._name = self._analysis.name
elif self._factor:
self._name = self._factor.name
else:
raise ValueError("You must pass in either an analysis or factor.")
return self._name
@property
def goal(self):
""" The goal of this Criteria (e.g. Goal.MAXIMIZE). """
return self._goal
@goal.setter
def goal(self, goal):
self._goal = goal
@property
def target(self):
""" The target for this Criteria, if using Goal.EQUAL_TO or Goal.TARGET """
return self._target
@target.setter
def target(self, target):
self._target = target
@property
def lower_limit(self):
""" The lower limit for this Criteria. """
return self._lower_limit
@lower_limit.setter
def lower_limit(self, lower_limit):
self._lower_limit = lower_limit
@property
def upper_limit(self):
""" The upper limit for this Criteria. """
return self._upper_limit
@upper_limit.setter
def upper_limit(self, upper_limit):
self._upper_limit = upper_limit
@property
def lower_weight(self):
""" The lower weight for this Criteria. """
return self._lower_weight
@lower_weight.setter
def lower_weight(self, lower_weight):
self._lower_weight = lower_weight
@property
def upper_weight(self):
""" The upper weight for this Criteria. """
return self._upper_weight
@upper_weight.setter
def upper_weight(self, upper_weight):
self._upper_weight = upper_weight
@property
def importance(self):
""" The importance of this Criteria, relative to other Criteria. """
return self._importance
@importance.setter
def importance(self, importance):
self._importance = importance
@property
def restrict_discrete(self):
""" The restrict discrete for this Criteria. """
return self._restrict_discrete
@restrict_discrete.setter
def restrict_discrete(self, restrict_discrete):
self._restrict_discrete = restrict_discrete
def to_json(self):
out_dict = {
'analysis': self._analysis.name if self._analysis else None,
'factor': self._factor.name if self._factor else None,
'goal': str(self._goal),
}
# the rest of the members are primitives
for k, v in self.__dict__.items():
key = k[1:] # strip off leading underscore
if key in out_dict.keys():
continue
out_dict[key] = v
return out_dict
