def profile_cwpath_robust_graphnet():
# get training data and constants
Data = np.load("Data.npz")
X = Data['X'][0:1000,:]
print "Data matrix size:",X.shape
Y = Data['Y'][0:1000]
nx = np.sqrt(X.shape[1])
ny = np.sqrt(X.shape[1])
A = construct_adjacency_list(nx,ny,1)
lam_max = get_lambda_max(X,Y)
tol = 1e-6
initial=None
# choose penalty grid
l1 = np.linspace(4*lam_max, 0.2*lam_max, num=100)
l2 = 100.
l3 = 1000.
delta = 1.0
# setup problem
problemtype = graphnet.RobustGraphNet
problemkey = 'RobustGraphNet'
print "Robust GraphNet with penalties (l1, l2, l3, delta)", l1, l2, l3, delta
l = cwpath.CoordWise((X, Y, A), problemtype, initial_coefs=initial) #,initial_coefs=np.array([14.]*10))
l.problem.assign_penalty(path_key='l1',l1=l1,l2=l2,l3=l3,delta=delta)
coefficients, residuals = l.fit(tol=tol, initial=initial)
def profile_cwpath_robust_graphnet():
# get training data and constants
Data = np.load("Data.npz")
X = Data["X"][0:1000, :]
print "Data matrix size:", X.shape
Y = Data["Y"][0:1000]
nx = np.sqrt(X.shape[1])
ny = np.sqrt(X.shape[1])
A = construct_adjacency_list(nx, ny, 1)
lam_max = get_lambda_max(X, Y)
tol = 1e-6
initial = None
# choose penalty grid
l1 = np.linspace(4 * lam_max, 0.2 * lam_max, num=100)
l2 = 100.0
l3 = 1000.0
delta = 1.0
# setup problem
problemtype = graphnet.RobustGraphNet
problemkey = "RobustGraphNet"
print "Robust GraphNet with penalties (l1, l2, l3, delta)", l1, l2, l3, delta
l = cwpath.CoordWise((X, Y, A), problemtype, initial_coefs=initial) # ,initial_coefs=np.array([14.]*10))
l.problem.assign_penalty(path_key="l1", l1=l1, l2=l2, l3=l3, delta=delta)
coefficients, residuals = l.fit(tol=tol, initial=initial)
def profile_test_graphnet():
# get training data and constants
Data = np.load("Data.npz")
X = Data['X'][0:1000,:]
Y = Data['Y'][0:1000]
G = None
lam_max = get_lambda_max(X,Y)
cwpathtol = 1e-6
# penalty grid values
l1vec = np.linspace(0.95*lam_max, 0.0001*lam_max, num=100).tolist()
results, problemkey = test_graphnet(X, Y, G, l1vec, 1e4, 1e6, 0.1, -999.0,initial=None,tol=cwpathtol,scipy_compare=False)
def profile_test_graphnet():
# get training data and constants
Data = np.load("Data.npz")
X = Data["X"][0:1000, :]
Y = Data["Y"][0:1000]
G = None
lam_max = get_lambda_max(X, Y)
cwpathtol = 1e-6
# penalty grid values
l1vec = np.linspace(0.95 * lam_max, 0.0001 * lam_max, num=100).tolist()
results, problemkey = test_graphnet(
X, Y, G, l1vec, 1e4, 1e6, 0.1, -999.0, initial=None, tol=cwpathtol, scipy_compare=False
)