def evaluate(self, ind, **kwargs):
"""
Note that math functions used in the solutions are imported from either
utilities.fitness.math_functions or called from numpy.
:param ind: An individual to be evaluated.
:param kwargs: An optional parameter for problems with training/test
data. Specifies the distribution (i.e. training or test) upon which
evaluation is to be performed.
:return: The fitness of the evaluated individual.
"""
dist = kwargs.get('dist', 'training')
if dist == "training":
# Set training datasets.
x = self.training_in
y = self.training_exp
elif dist == "test":
# Set test datasets.
x = self.test_in
y = self.test_exp
else:
raise ValueError("Unknown dist: " + dist)
if params['OPTIMIZE_CONSTANTS']:
# if we are training, then optimize the constants by
# gradient descent and save the resulting phenotype
# string as ind.phenotype_with_c0123 (eg x[0] +
# c[0] * x[1]**c[1]) and values for constants as
# ind.opt_consts (eg (0.5, 0.7). Later, when testing,
# use the saved string and constants to evaluate.
if dist == "training":
return optimize_constants(x, y, ind)
else:
# this string has been created during training
phen = ind.phenotype_consec_consts
c = ind.opt_consts
# phen will refer to x (ie test_in), and possibly to c
yhat = eval(phen)
assert np.isrealobj(yhat)
# let's always call the error function with the
# true values first, the estimate second
return params['ERROR_METRIC'](y, yhat)
else:
# phenotype won't refer to C
yhat = eval(ind.phenotype)
assert np.isrealobj(yhat)
# let's always call the error function with the true
# values first, the estimate second
return params['ERROR_METRIC'](y, yhat)
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