def set_params(self, **parameters): for parameter, value in parameters.items(): setattr(self, parameter, value) hp_string = self._build_hyperparameters_string() self._ea = GPGOMEA(hp_string) return self
def __init__(self,
time=60, generations=-1, evaluations=-1,
prob='symbreg', linearscaling=True, functions='+_*_-_aq', erc=True,
classweights = False,
gomea=True, gomfos='LT',
subcross=0.5, submut=0.5, reproduction=0.0,
sblibtype=False, sbrdo=0.0, sbagx=0.0,
unifdepthvar=True, tournament=4, elitism=1,
ims='5_1', syntuniqinit=1000, popsize=500,
initmaxtreeheight=4, maxtreeheight=17, maxsize=1000,
seed = -1, parallel=1, caching=False, silent=True):
args, _, _, values = inspect.getargvalues(inspect.currentframe())
values.pop("self")
for arg, val in values.items():
setattr(self, arg, val)
hp_string = self._build_hyperparameters_string()
self._ea = GPGOMEA(hp_string)
class GPGOMEARegressor(BaseEstimator, RegressorMixin):
''' GP-GOMEA as scikit-learn regressor '''
def __init__(self,
time=60, generations=-1, evaluations=-1,
prob='symbreg', multiobj=False, linearscaling=True, functions='+_*_-_aq', erc=True,
classweights = False,
gomea=True, gomfos='LT',
subcross=0.5, submut=0.5, reproduction=0.0,
sblibtype=False, sbrdo=0.0, sbagx=0.0,
unifdepthvar=True, tournament=4, elitism=1,
ims='5_1', syntuniqinit=1000, popsize=500,
initmaxtreeheight=4, maxtreeheight=17, maxsize=1000,
validation=False,
seed = -1, parallel=1, caching=False,
silent=True, logtofile=False):
args, _, _, values = inspect.getargvalues(inspect.currentframe())
values.pop("self")
for arg, val in values.items():
setattr(self, arg, val)
hp_string = self._build_hyperparameters_string()
self._ea = GPGOMEA(hp_string)
def __del__(self):
del self._ea
def get_params(self, deep=True):
attributes = inspect.getmembers(self, lambda a:not(inspect.isroutine(a)))
attributes = [a for a in attributes if '_' not in a[0]]
dic = {}
for a in attributes:
dic[a[0]] = a[1]
return dic
def set_params(self, **parameters):
for parameter, value in parameters.items():
setattr(self, parameter, value)
hp_string = self._build_hyperparameters_string()
self._ea = GPGOMEA(hp_string)
return self
def _build_hyperparameters_string(self):
result = ''
attributes = inspect.getmembers(self, lambda a:not(inspect.isroutine(a)))
attributes = [a for a in attributes if '_' not in a[0]]
for a in attributes:
# special cases
if a[0] == 'seed' and a[1] < 0:
continue
if type(a[1]) == bool:
if a[1] == True:
result = result + '--'+a[0]+' '
else:
result = result + '--'+a[0]+' '+str(a[1])+' '
return result
def fit(self, X, y):
self._ea.run(X, y.reshape((-1,1)))
return self
def predict(self, X, y=None):
prediction = self._ea.predict(X)
return prediction
def score(self, X, y=None):
if y is None:
raise ValueError('The ground truth y was not set.')
sc = self._ea.score(X, y.reshape((-1,1)))
return sc
def get_model(self):
return self._ea.get_model()
def get_final_population(self, X):
return self._ea.get_final_population(X)
def get_n_nodes(self):
return self._ea.get_n_nodes()
def get_evaluations(self):
return self._ea.get_evaluations()