def estimate(self, X, y, **kwargs):
theta = kwargs.pop('theta', None)
if isinstance(theta, str):
theta = np.array(literal_eval(theta))
# remove warp string to prevent it being passed to the blr object
kwargs.pop('warp', 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
# (re-)initialize BLR object because parameters were not specified
self.blr = BLR(theta=theta,
X=self.Phi,
y=y,
var_groups=self.var_groups,
warp=self.warp,
**kwargs)
self.theta = self.blr.estimate(theta,
self.Phi,
y,
optimizer=self.optim_alg)
return self
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,
warp=self.warp)
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 predict(self, Xs, X=None, y=None, **kwargs):
theta = self.theta # always use the estimated coefficients
# remove from kwargs
kwargs.pop('theta', None)
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', 'powell')
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)
# configure variance groups (e.g. site specific variance)
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
# Configure warped likelihood
if 'warp' in kwargs:
warp_str = kwargs.pop('warp')
if warp_str is None:
self.warp = None
n_gamma = 0
else:
# set up warp
exec('self.warp =' + warp_str + '()')
n_gamma = self.warp.get_n_params()
else:
self.warp = None
n_gamma = 0
self._n_params = n_alpha + n_beta + n_gamma
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()