def load_h5(file_name="pyfeat_aus_to_landmarks.h5"):
"""Load the h5 PLS model for plotting.
Args:
file_name (str, optional): Specify model to load.. Defaults to 'blue.h5'.
Returns:
model: PLS model
"""
try:
hf = h5py.File(os.path.join(get_resource_path(), file_name), "r")
d1 = hf.get("coef")
d2 = hf.get("x_mean")
d3 = hf.get("y_mean")
d4 = hf.get("x_std")
model = PLSRegression(len(d1))
model.coef_ = np.array(d1)
if int(__version__.split(".")[1]) < 24:
model.x_mean_ = np.array(d2)
model.y_mean_ = np.array(d3)
model.x_std_ = np.array(d4)
else:
model._x_mean = np.array(d2)
model._y_mean = np.array(d3)
model._x_std = np.array(d4)
hf.close()
except Exception as e:
print("Unable to load data ", file_name, ":", e)
return model
def _load_model(cls, fh):
params = _parse_literal(fh)
coef_shape = _parse_literal(fh)
pls = PLSRegression().set_params(**params)
pls.x_mean_ = np.fromstring(fh.read(coef_shape[0] * 8))
pls.y_mean_ = np.fromstring(fh.read(coef_shape[1] * 8))
pls.x_std_ = np.ones(coef_shape[0])
pls.y_std_ = np.ones(coef_shape[1])
n = coef_shape[0] * coef_shape[1] * 8
pls.coef_ = np.fromstring(fh.read(n)).reshape(coef_shape)
return pls
def load_h5(file_name='blue.h5'):
"""Load the h5 PLS model for plotting.
Args:
file_name (str, optional): Specify model to load.. Defaults to 'blue.h5'.
Returns:
model: PLS model
"""
try:
hf = h5py.File(os.path.join(get_resource_path(), file_name), 'r')
d1 = hf.get('coef')
d2 = hf.get('x_mean')
d3 = hf.get('y_mean')
d4 = hf.get('x_std')
model = PLSRegression(len(d1))
model.coef_ = np.array(d1)
model.x_mean_ = np.array(d2)
model.y_mean_ = np.array(d3)
model.x_std_ = np.array(d4)
hf.close()
except Exception as e:
print('Unable to load data ', file_name, ':', e)
return model