/
constraint_set.py
135 lines (114 loc) · 4.86 KB
/
constraint_set.py
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"""Fit constraint set"""
from numpy import amax, array, hstack
from gpkit import ConstraintSet
from gpkit import Variable, NomialArray, NamedVariables, VectorVariable
from gpkit.small_scripts import initsolwarning, appendsolwarning
# pylint: disable=too-many-instance-attributes, too-many-locals,
# pylint: disable=too-many-branches, no-member, import-error
# pylint: disable=too-many-arguments
class FitConstraintSet(ConstraintSet):
"""
Constraint set for fitted functions
Arguments
---------
fitdata : dict
dictionary of fit parameters
ivar : gpkit Variable, Monomial, or NomialArray
independent variable
dvars : list of gpkit Variables, Monomials, or NomialArrays
dependent variables
err_margin : string, either "max" or "rms"
flag to set margin factor using RMS or max error
"""
def __init__(self, fitdata, ivar=None, dvars=None, name="", err_margin=None):
self.fitdata = fitdata
if ivar is None:
with NamedVariables("fit"):
dvars = VectorVariable(fitdata["d"], "u")
ivar = Variable("w")
self.dvars = dvars
self.ivar = ivar
self.rms_err = fitdata["rms_err"]
self.max_err = fitdata["max_err"]
monos = [
fitdata["c%d" % k]
*NomialArray(
array(dvars).T
**array([fitdata["e%d%d" % (k, i)] for i in range(fitdata["d"])])
).prod(NomialArray(dvars).ndim - 1)
for k in range(fitdata["K"])
]
with NamedVariables(name):
self.mfac = Variable("mfac", 1.0, "-", "fit factor")
if err_margin == "max":
self.mfac.key.descr["value"] = 1 + self.max_err
elif err_margin == "rms":
self.mfac.key.descr["value"] = 1 + self.rms_err
if fitdata["ftype"] == "ISMA":
# constraint of the form 1 >= c1*u1^exp1*u2^exp2*w^(-alpha) + ....
alpha = array([fitdata["a%d" % k] for k in range(fitdata["K"])])
lhs, rhs = 1, NomialArray(monos/(ivar/self.mfac)**alpha).sum(0)
elif fitdata["ftype"] == "SMA":
# constraint of the form w^alpha >= c1*u1^exp1 + c2*u2^exp2 +....
alpha = fitdata["a1"]
lhs, rhs = (ivar/self.mfac)**alpha, NomialArray(monos).sum(0)
elif fitdata["ftype"] == "MA":
# constraint of the form w >= c1*u1^exp1, w >= c2*u2^exp2, ....
lhs, rhs = (ivar/self.mfac), NomialArray(monos).T
if fitdata["K"] == 1:
# when possible, return an equality constraint
if hasattr(rhs, "shape"):
if rhs.ndim > 1:
self.constraint = [(lh == rh) for lh, rh in zip(lhs, rhs)]
else:
self.constraint = lhs == rhs
else:
self.constraint = lhs == rhs
else:
if hasattr(rhs, "shape"):
if rhs.ndim > 1:
self.constraint = [(lh >= rh) for lh, rh in zip(lhs, rhs)]
else:
self.constraint = lhs >= rhs
else:
self.constraint = lhs >= rhs
self.bounds = {}
for i, dvar in enumerate(self.dvars):
self.bounds[dvar] = [fitdata["lb%d" % i], fitdata["ub%d" % i]]
ConstraintSet.__init__(self, [self.constraint])
def get_dataframe(self):
"return a pandas DataFrame of fit parameters"
import pandas as pd
df = pd.DataFrame(list(self.fitdata.values())).transpose()
df.columns = list(self.fitdata.keys())
return df
def process_result(self, result):
"""
make sure fit result is within bounds of fitted data
"""
super(FitConstraintSet, self).process_result(result)
initsolwarning(result, "Fit Out-of-Bounds")
if self.mfac not in result["sensitivities"]["constants"]:
return
if amax([abs(result["sensitivities"]["constants"][self.mfac])]) < 1e-5:
return
for dvar in self.dvars:
if isinstance(dvar, NomialArray):
num = [result(x) for x in dvar]
else:
num = result(dvar)
direct = None
if any(x < self.bounds[dvar][0] for x in hstack([num])):
direct, state = "lower", "below"
bnd = self.bounds[dvar][0]
if any(x > self.bounds[dvar][1] for x in hstack([num])):
direct, state = "upper", "above"
bnd = self.bounds[dvar][1]
if direct:
msg = (
"Variable %.100s could cause inaccurate result"
" because it is %s" % (dvar, state)
+ " %s bound. Solution is %.4f but"
" bound is %.4f" % (direct, amax([num]), bnd)
)
appendsolwarning(msg, self, result, "Fit Out-of-Bounds")