def __init__(self, X=None, y=None, theta=None, model_order=3):
if X is None:
raise (ValueError, "Data matrix must be specified")
if len(X.shape) == 1:
self.D = 1
else:
self.D = X.shape[1]
# Force a default value and check datatype
if model_order is None:
model_order = 3
elif type(model_order) is not int:
model_order = int(model_order)
self._n_params = 1 + self.D * model_order
self._model_order = model_order
print("initialising BLR ( order", model_order, ")")
if (theta is None) or (len(theta) != self._n_params):
print("Using default hyperparameters")
self.theta0 = np.zeros(self._n_params)
else:
self.theta0 = theta
self.theta = self.theta0
if (theta is not None) and (y is not None):
self.Phi = create_poly_basis(X, self._model_order)
self.gpr = BLR(theta, self.Phi, y)
else:
self.gpr = BLR()
def predict(self, X, y, Xs, theta=None):
if theta is None:
theta = self.theta
Phis = create_poly_basis(Xs, self._model_order)
yhat, s2 = self.blr.predict(theta, self.Phi, y, Phis)
return yhat, s2
def estimate(self, X, y, theta=None):
if not hasattr(self, 'Phi'):
self.Phi = create_poly_basis(X, self._model_order)
if len(y.shape) > 1:
y = y.ravel()
if theta is None:
theta = self.theta0
self.blr = BLR(theta, self.Phi, y)
self.theta = self.blr.estimate(theta, self.Phi, y)
return self.theta
def estimate(self, X, y, **kwargs):
theta = kwargs.pop('theta', None)
if not hasattr(self, 'Phi'):
self.Phi = create_poly_basis(X, self._model_order)
if len(y.shape) > 1:
y = y.ravel()
if theta is None:
theta = self.theta0
self.blr = BLR(theta, self.Phi, y, var_groups=self.var_groups)
self.theta = self.blr.estimate(theta,
self.Phi,
y,
optimizer=self.optim_alg)
return self
def predict(self, Xs, X=None, y=None, **kwargs):
theta = kwargs.pop('theta', None)
if theta is None:
theta = self.theta
Phis = create_poly_basis(Xs, self._model_order)
if 'var_groups_test' in kwargs:
var_groups_test_file = kwargs.pop('var_groups_test')
if var_groups_test_file.endswith('.pkl'):
var_groups_te = pd.read_pickle(var_groups_test_file)
else:
var_groups_te = np.loadtxt(var_groups_test_file)
else:
var_groups_te = None
yhat, s2 = self.blr.predict(theta, self.Phi, y, Phis,
var_groups_test=var_groups_te)
return yhat, s2
def __init__(self, **kwargs): #X=None, y=None, theta=None,
X = kwargs.pop('X', None)
y = kwargs.pop('y', None)
theta = kwargs.pop('theta', None)
self.optim_alg = kwargs.pop('optimizer', 'cg')
if X is None:
raise (ValueError, "Data matrix must be specified")
if len(X.shape) == 1:
self.D = 1
else:
self.D = X.shape[1]
# Parse model order
if kwargs is None:
model_order = 1
elif 'configparam' in kwargs:
model_order = kwargs.pop('configparam')
elif 'model_order' in kwargs:
model_order = kwargs.pop('model_order')
else:
model_order = 1
# Force a default value and check datatype
if model_order is None:
model_order = 1
if type(model_order) is not int:
model_order = int(model_order)
if 'var_groups' in kwargs:
var_groups_file = kwargs.pop('var_groups')
if var_groups_file.endswith('.pkl'):
self.var_groups = pd.read_pickle(var_groups_file)
else:
self.var_groups = np.loadtxt(var_groups_file)
var_ids = set(self.var_groups)
var_ids = sorted(list(var_ids))
n_beta = len(var_ids)
else:
self.var_groups = None
n_beta = 1
# are we using ARD?
if 'use_ard' in kwargs:
self.use_ard = kwargs.pop('use_ard')
else:
self.use_ard = False
if self.use_ard:
n_alpha = self.D * model_order
else:
n_alpha = 1
self._n_params = n_alpha + n_beta
self._model_order = model_order
print("initialising BLR ( order", model_order, ")")
if (theta is None) or (len(theta) != self._n_params):
print("Using default hyperparameters")
self.theta0 = np.zeros(self._n_params)
else:
self.theta0 = theta
self.theta = self.theta0
if (theta is not None) and (y is not None):
self.Phi = create_poly_basis(X, self._model_order)
self.blr = BLR(theta, self.Phi, y)
else:
self.blr = BLR()