gpr done

surrogate/custom_calculators.py

+import numpy as np
+from scipy.spatial.distance import euclidean
+
+from ase.calculators.calculator import Calculator
+from time import time
+from ase.calculators.singlepoint import SinglePointCalculator
+
+
+
+class krr_calculator(Calculator):
+
+    implemented_properties = ['energy', 'forces']
+    default_parameters = {}
+
+    def __init__(self, MLmodel, label='MLmodel', kappa=None, **kwargs):
+        self.MLmodel = MLmodel
+        self.kappa = kappa
+        
+        Calculator.__init__(self, **kwargs)
+
+    def calculate(self, atoms=None, properties=['energy', 'forces'], system_changes=['positions']):
+
+        Calculator.calculate(self, atoms, properties, system_changes)
+
+        if 'energy' in properties:
+            if self.kappa is None:
+                E = self.MLmodel.predict_energy(atoms, return_error=False)
+            else:
+                energy, error, _ = self.MLmodel.predict_energy(atoms, return_error=True)
+                E = energy - self.kappa*error
+            self.results['energy'] = E
+
+        if 'forces' in properties:
+            if self.kappa is None:
+                F = self.MLmodel.predict_force(atoms).reshape((-1,3))
+            else:
+                F, F_error = self.MLmodel.predict_force(atoms, return_error=True)
+                F = (F + self.kappa*F_error).reshape((-1,3))
+            self.results['forces'] = F
+
+
+
+class doubleLJ_calculator(Calculator):
+
+    implemented_properties = ['energy', 'forces']
+    default_parameters = {}
+
+    def __init__(self, eps=1.8, r0=1.1, sigma=np.sqrt(0.02), label='doubleLJ', noZ=False, **kwargs):
+        self.eps = eps
+        self.r0 = r0
+        self.sigma = sigma
+
+        self.noZ = noZ
+        
+        Calculator.__init__(self, **kwargs)
+
+    def calculate(self, atoms=None, properties=['energy', 'forces'], system_changes=['positions']):
+        Calculator.calculate(self, atoms, properties, system_changes)
+
+        if 'energy' in properties:
+            self.results['energy'] = self.energy(atoms)
+        if 'forces' in properties:
+            F = self.forces(atoms)
+            if self.noZ:
+                F[:,-1] = 0
+            self.results['forces'] = F
+        
+            
+    def energy(self, a):
+        x = a.get_positions()
+        E = 0
+        for i, xi in enumerate(x):
+            for j, xj in enumerate(x):
+                if j > i:
+                    r = euclidean(xi, xj)
+                    E1 = 1/r**12 - 2/r**6
+                    E2 = -self.eps * np.exp(-(r - self.r0)**2 / (2*self.sigma**2))
+                    E += E1 + E2
+        return E
+
+    def forces(self, a):
+        x = a.get_positions()
+        Natoms, dim = x.shape
+        dE = np.zeros((Natoms, dim))
+        for i, xi in enumerate(x):
+            for j, xj in enumerate(x):
+                r = euclidean(xi,xj)
+                if j != i:
+                    rijVec = xi-xj
+
+                    dE1 = 12*rijVec*(-1 / r**14 + 1 / r**8)
+                    dE2 = self.eps*(r-self.r0)*rijVec / (r*self.sigma**2) * np.exp(-(r - self.r0)**2 / (2*self.sigma**2))
+                    
+                    dE[i] += dE1 + dE2
+        return -dE

surrogate/gpr.py

@@ -166,10 +166,11 @@ class gpr():
             K0 = self.kernel.kernel_value(x,x)
             g = K0 - np.dot(k.T, vk)
             assert g >= 0
-            F_std = -1/np.sqrt(g) * np.dot(k_ddr.T, vk)
-            return F, F_std
+            F_std = 1/np.sqrt(g) * np.dot(k_ddr.T, vk)
+            #F_std = -1/np.sqrt(g) * np.dot(k_ddr.T, vk)
+            return F.reshape((-1,3)), F_std.reshape(-1,3)
         else:
-            return F
+            return F.reshape(-1,3)
 
     def set_bias(self):
         self.bias = np.mean(self.memory.energies - self.memory.prior_values)
@@ -232,7 +233,7 @@ class gpr():
         lml = -0.5 * np.dot(self.Y, alpha)
         lml -= np.sum(np.log(np.diag(L)))
         lml -= K.shape[0]/2 * np.log(2*np.pi)
-
+        
         if eval_gradient:
             # Equation (5.9) in GPML
             K_inv = cho_solve((L, True), np.eye(K.shape[0]))
@@ -252,6 +253,7 @@ class gpr():
                           options={'gtol': 1e-2, 'ftol': 1e-4})
         if not result.success:
             warnings.warn(f"L_BFGS_B terminated with state: {result.message}")
+        print('lml:', result.fun)
         return result.x, result.fun
 
     def numerical_neg_lml(self, dx=1e-4):
@@ -265,13 +267,41 @@ class gpr():
             theta_up[i] += 0.5*dx
             theta_down[i] -= 0.5*dx
 
-            d_theta = np.exp(theta_up[i])-np.exp(theta_down[i])
-            
             lml_up = self.neg_log_marginal_likelihood(theta_up, eval_gradient=False)
             lml_down = self.neg_log_marginal_likelihood(theta_down, eval_gradient=False)
-            #lml_ddTheta[i] = (lml_up - lml_down)/dx
-            lml_ddTheta[i] = (lml_up - lml_down)/d_theta
+            lml_ddTheta[i] = (lml_up - lml_down)/dx
         return lml_ddTheta
+
+    def numerical_forces(self, a, dx=1e-4, eval_std=False):
+        Na, Nd = a.positions.shape
+        if not eval_std:
+            F = np.zeros((Na,Nd))
+            for ia in range(Na):
+                for idim in range(Nd):
+                    a_up = a.copy()
+                    a_down = a.copy()
+                    a_up.positions[ia,idim] += 0.5*dx
+                    a_down.positions[ia,idim] -= 0.5*dx
+                    
+                    E_up = self.predict_energy(a_up)
+                    E_down = self.predict_energy(a_down)
+                    F[ia,idim] = -(E_up - E_down)/dx
+            return F
+        else:
+            F = np.zeros((Na,Nd))
+            Fstd = np.zeros((Na,Nd))
+            for ia in range(Na):
+                for idim in range(Nd):
+                    a_up = a.copy()
+                    a_down = a.copy()
+                    a_up.positions[ia,idim] += 0.5*dx
+                    a_down.positions[ia,idim] -= 0.5*dx
+                    
+                    E_up, Estd_up = self.predict_energy(a_up, eval_std=True)
+                    E_down, Estd_down = self.predict_energy(a_down, eval_std=True)
+                    F[ia,idim] = -(E_up - E_down)/dx
+                    Fstd[ia,idim] = (Estd_up - Estd_down)/dx
+            return F, Fstd
     
     def get_calculator():
         pass

surrogate/kernel.py

@@ -54,6 +54,9 @@ class kernel(ABC):
         return f_jac
 
     def numerical_hyperparameter_gradient(self,X, dx=1.e-5):
+        """Calculates the numerical derivative of the kernel with respect to the
+        log transformed hyperparameters.
+        """
         N_data = X.shape[0]
         theta = np.copy(self.theta)
         N_hyper = len(theta)
@@ -63,6 +66,12 @@ class kernel(ABC):
             theta_down = np.copy(theta)
             theta_up[i] += 0.5*dx
             theta_down[i] -= 0.5*dx
+
+            #theta_up[i] = np.log(np.exp(theta_up[i]) + 0.5*dx)
+            #theta_down[i] = np.log(np.exp(theta_down[i]) - 0.5*dx)
+            #dTheta = theta_up[i] - theta_down[i]
+            #dTheta = np.log(np.exp(theta_up[i]) + 0.5*dx) - np.log(np.exp(theta_down[i]) - 0.5*dx)
+            #print('dTheta:', dTheta)
             
             self.theta = theta_up
             K_up = self(X, eval_gradient=False)
@@ -76,12 +85,24 @@ class gauss_kernel(kernel):
     def __init__(self, amplitude=10.0, length_scale=10.0, amplitude_bounds=(1e0, 1e3), length_scale_bounds=(1e-1, 1e1)):
         self.amplitude = amplitude
         self.length_scale = length_scale
+
         self.amplitude_bounds = amplitude_bounds
         self.length_scale_bounds = length_scale_bounds
-        self._theta_bounds = [amplitude_bounds, length_scale_bounds]
 
-    def __call__(self, X,Y, eval_gradient=False):
-        pass
+        self.theta_bounds = np.array([amplitude_bounds, length_scale_bounds])
+
+    def __call__(self, X, eval_gradient=False):
+        if np.ndim(X) == 1:
+            X = X.reshape((1,-1))
+        d = cdist(X / self.length_scale,
+                  X / self.length_scale, metric='sqeuclidean')
+        K = self.amplitude * np.exp(-0.5 * d)
+        
+        if eval_gradient:
+            K_gradient = self.kernel_hyperparameter_gradient(X)
+            return K, K_gradient
+        else:
+            return K
         
     def kernel(self, X,Y):
         if np.ndim(X) == 1:
@@ -92,17 +113,13 @@ class gauss_kernel(kernel):
                   Y / self.length_scale, metric='sqeuclidean')
         K = self.amplitude * np.exp(-0.5 * d)
         return K
-        
-    def kernel_value(self, x,y):
-        d = cdist(x.reshape(1,-1) / self.length_scale,
-                  y.reshape(1,-1) / self.length_scale, metric='sqeuclidean')
-        K = self.amplitude * np.exp(-0.5 * d)
-        return K
 
+    def kernel_value(self, x,y):
+        K = self.kernel(x,y)
+        return np.asscalar(K)
+    
     def kernel_vector(self, x,Y):
-        d = cdist(x.reshape(1,-1) / self.length_scale,
-                  Y / self.length_scale, metric='sqeuclidean')
-        K = self.amplitude * np.exp(-0.5 * d)
+        K = self.kernel(x,Y).reshape(-1)
         return K
 
     def kernel_matrix(self, X,Y=None):
@@ -127,21 +144,29 @@ class gauss_kernel(kernel):
 
     @property
     def theta(self):
-        self._theta = [self.amplitude, self.length_scale]
-        return self._theta
+        """Returns the log-transformed hyperparameters of the kernel.
+        """
+        self._theta = np.array([self.amplitude, self.length_scale])
+        return np.log(self._theta)
+        #return self._theta
 
     @theta.setter
     def theta(self, theta):
-        self._theta = theta
+        """Sets the hyperparameters of the kernel.
+
+        theta: log-transformed hyperparameters
+        """
+        self._theta = np.exp(theta)
+        #self._theta = theta
         self.amplitude = self._theta[0]
         self.length_scale = self._theta[1]
-
+    
     def dK_da(self, X):
         if np.ndim(X) == 1:
             X = X.reshape((1,-1))
         d = cdist(X / self.length_scale,
                   X / self.length_scale, metric='sqeuclidean')
-        dK_da = np.exp(-0.5 * d)
+        dK_da = self.amplitude * np.exp(-0.5 * d)
         return dK_da
         
     def dK_dl(self, X):
@@ -149,10 +174,13 @@ class gauss_kernel(kernel):
             X = X.reshape((1,-1))
         d = cdist(X / self.length_scale,
                   X / self.length_scale, metric='sqeuclidean')
-        dK_dl = self.amplitude * d/self.length_scale * np.exp(-0.5 * d)
+        dK_dl = self.amplitude * d * np.exp(-0.5 * d)
         return dK_dl
 
     def kernel_hyperparameter_gradient(self, X):
+        """Calculates the derivative of the kernel with respect to the
+        log transformed hyperparameters.
+        """
         return np.array([self.dK_da(X), self.dK_dl(X)])
     
 
@@ -265,30 +293,33 @@ class double_gauss_kernel(kernel):
                    X / self.length_scale1, metric='sqeuclidean')
         d2 = cdist(X / self.length_scale2,
                    X / self.length_scale2, metric='sqeuclidean')
-        dK_da = np.exp(-0.5 * d1) + self.weight*np.exp(-0.5 * d2) + self.noise*np.eye(X.shape[0])
+        dK_da = self.amplitude * (np.exp(-0.5 * d1) + self.weight*np.exp(-0.5 * d2) + self.noise*np.eye(X.shape[0]))
         return dK_da
         
     def dK_dl1(self, X):
         d1 = cdist(X / self.length_scale1,
                    X / self.length_scale1, metric='sqeuclidean')
-        dK_dl1 = self.amplitude*d1/self.length_scale1*np.exp(-0.5 * d1)
+        dK_dl1 = self.amplitude*d1 * np.exp(-0.5 * d1)
         return dK_dl1
 
     def dK_dl2(self, X):
         d2 = cdist(X / self.length_scale2,
                    X / self.length_scale2, metric='sqeuclidean')
-        dK_dl2 = self.amplitude*self.weight*d2/self.length_scale2*np.exp(-0.5 * d2)
+        dK_dl2 = self.amplitude*self.weight*d2 * np.exp(-0.5 * d2)
         return dK_dl2
 
     def dK_dw(self, X):
         d2 = cdist(X / self.length_scale2,
                    X / self.length_scale2, metric='sqeuclidean')
-        dK_dl2 = self.amplitude*np.exp(-0.5 * d2)
+        dK_dl2 = self.amplitude*self.weight*np.exp(-0.5 * d2)
         return dK_dl2
 
     def dK_dn(self, X):
-        dK_dn = self.amplitude * np.eye(X.shape[0])
+        dK_dn = self.amplitude * self.noise * np.eye(X.shape[0])
         return dK_dn
 
     def kernel_hyperparameter_gradient(self, X):
+        """Calculates the derivative of the kernel with respect to the
+        log transformed hyperparameters.
+        """
         return np.array([self.dK_da(X), self.dK_dl1(X), self.dK_dl2(X), self.dK_dw(X), self.dK_dn(X)])

surrogate/test_descriptor.py

+import numpy as np
+import matplotlib.pyplot as plt
+import unittest
+from gpr import gpr
+
+from ase.io import read
+
+# old gpr
+from kernels import RBF, ConstantKernel as C, WhiteKernel
+from descriptor.fingerprint import Fingerprint
+from delta_functions_multi.delta import delta as deltaFunc
+from GPR import GPR
+
+
+def finite_diff(descriptor, a, dx=1e-5):
+    Nf = descriptor.get_feature(a).shape[0]
+    Natoms, dim = a.positions.shape
+    f_ddr = np.zeros((Natoms, dim, Nf))
+    for ia in range(Natoms):
+        for idim in range(dim):
+            a_up = a.copy()
+            a_down = a.copy()
+            a_up.positions[ia,idim] += dx/2
+            a_down.positions[ia,idim] -= dx/2
+            
+            
+            f_up = descriptor.get_feature(a_up)
+            f_down = descriptor.get_feature(a_down)
+            f_ddr[ia,idim,:] = (f_up - f_down)/dx
+    return f_ddr.reshape((-1,Nf))
+
+def get_E_with_std(traj, gpr):
+    E = []
+    F = []
+    for a in traj:
+        e = gpr.predict_energy(a, )
+
+class test_gpr(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        print('setupClass')
+
+    @classmethod
+    def tearDownClass(cls):
+        print('teardownClass')
+
+    def setUp(self):
+        print('setUp')
+        ### Set up feature ###
+
+        # Initialize feature
+        self.descriptor = Fingerprint()
+
+    def tearDown(self):
+        print('tearDown\n')
+    
+    def test_forces(self):
+        a = read('structures.traj', index='0')
+
+        f_ddr = self.descriptor.get_featureGradient(a)
+        f_ddr_num = finite_diff(self.descriptor, a)
+        Na, Nf = f_ddr.shape
+        x = np.arange(Nf)
+        fig, ax = plt.subplots(1,1)
+        ax.plot(x, f_ddr.T)
+        ax.plot(x, f_ddr_num.T, 'k:')
+        plt.show()
+        print(f_ddr - f_ddr_num)
+        np.testing.assert_almost_equal(f_ddr, f_ddr_num)
+
+if __name__ == '__main__':
+    unittest.main()

surrogate/test_gpr.py

@@ -6,7 +6,7 @@ from ase.io import read
 
 # old gpr
 from kernels import RBF, ConstantKernel as C, WhiteKernel
-from featureCalculators_multi.angular_fingerprintFeature_cy import Angular_Fingerprint
+from descriptor.fingerprint import Fingerprint
 from delta_functions_multi.delta import delta as deltaFunc
 from GPR import GPR
 
@@ -29,9 +29,10 @@ def initialize_old_gpr(atoms):
     use_angular = True
     
     # Initialize feature
-    featureCalculator = Angular_Fingerprint(atoms, Rc1=Rc1, Rc2=Rc2, binwidth1=binwidth1, Nbins2=Nbins2, sigma1=sigma1, sigma2=sigma2, gamma=gamma, eta=eta, use_angular=use_angular)
+    featureCalculator = Fingerprint(Rc1=Rc1, Rc2=Rc2, binwidth1=binwidth1, Nbins2=Nbins2, sigma1=sigma1, sigma2=sigma2, gamma=gamma, eta=eta, use_angular=use_angular)
 
-    kernel = C(10, (1e1, 1e6)) * (C(1, (1, 1)) * RBF(10, (1,1000)) + C(0.01, (0.01, 0.01)) * RBF(10, (1,1000)) + WhiteKernel(1e-5, (1e-6,1e-2)))
+    kernel = C(10, (1e1, 1e6)) * RBF(10, (1,1000))
+    #kernel = C(10, (1e1, 1e6)) * (RBF(10, (1,1000)) + C(0.01, (0.01, 0.01)) * RBF(10, (1,1000)) + WhiteKernel(1e-5, (1e-6,1e-2)))
     delta = deltaFunc(atoms=atoms, rcut=6)
     gpr = GPR(kernel=kernel,
           featureCalculator=featureCalculator,
@@ -63,7 +64,8 @@ class test_gpr(unittest.TestCase):
         #self.kernel = gauss_kernel()
         a = read('structures.traj', index='0')
         self.gpr_old = initialize_old_gpr(a)
-        self.gpr = gpr()
+        #self.gpr = gpr()
+        self.gpr = gpr(kernel='single')
 
     def tearDown(self):
         print('tearDown\n')
@@ -82,11 +84,11 @@ class test_gpr(unittest.TestCase):
         E = np.array([self.gpr.predict_energy(a, eval_std=True) for a in traj_predict])
         np.testing.assert_almost_equal(E, E_old)
 
-        F_old = np.array([self.gpr_old.predict_force(a) for a in traj_predict])
+        F_old = np.array([self.gpr_old.predict_force(a).reshape((-1,3)) for a in traj_predict])
         F = np.array([self.gpr.predict_forces(a) for a in traj_predict])
         np.testing.assert_almost_equal(F, F_old)
         
-        Fstd_old = np.array([self.gpr_old.predict_force(a, return_error=True)[1] for a in traj_predict])
+        Fstd_old = np.array([self.gpr_old.predict_force(a, return_error=True)[1].reshape((-1,3)) for a in traj_predict])
         Fstd = np.array([self.gpr.predict_forces(a, eval_std=True)[1] for a in traj_predict])
         np.testing.assert_almost_equal(Fstd, Fstd_old)
 
@@ -113,7 +115,7 @@ class test_gpr(unittest.TestCase):
 
         _, lml_ddTheta_old = self.gpr_old.log_marginal_likelihood(self.gpr_old.kernel.theta, eval_gradient=True)
         _, lml_ddTheta = self.gpr.neg_log_marginal_likelihood(eval_gradient=True)
-        np.testing.assert_almost_equal(lml_ddTheta, lml_ddTheta_old)
+        np.testing.assert_almost_equal(-lml_ddTheta, lml_ddTheta_old)
 
     def test_lml_gradient(self):
         traj = read('structures.traj', index=':50')
@@ -128,7 +130,157 @@ class test_gpr(unittest.TestCase):
         np.testing.assert_almost_equal(lml_ddTheta, lml_ddTheta_numeric)
     
     def test_forces(self):
-        pass
+        traj = read('structures.traj', index=':50')
+        traj_train = traj[:40]
+        traj_predict = traj[40:]
+
+        self.gpr_old.train(traj_train)
+        self.gpr.train(traj_train)
+
+        a = traj_predict[0]
+        F = self.gpr.predict_forces(a)