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Fchl module #34

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Sep 22, 2017
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Fchl module #34

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3ff9c79
Adding initial code+restructuring for combined force+energy cost-func…
andersx Sep 18, 2017
b35f4c7
Added alphas for force+energy kernel
andersx Sep 19, 2017
9ad05e7
Working version of new force+energy cost function
andersx Sep 20, 2017
080f937
Removed some debug output
andersx Sep 20, 2017
be936c3
Updated example for FCHL force+energy learning
andersx Sep 21, 2017
59709f1
Updated to module and added screening function
andersx Sep 22, 2017
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183 changes: 32 additions & 151 deletions qml/fchl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,14 +23,12 @@
import numpy as np
import copy

from .ffchl_kernels import fget_kernels_fchl
from .ffchl_kernels import fget_symmetric_kernels_fchl
from .ffchl_kernels import fget_global_kernels_fchl
from .ffchl_kernels import fget_global_symmetric_kernels_fchl
from .ffchl_kernels import fget_atomic_kernels_fchl
from .ffchl_kernels import fget_atomic_symmetric_kernels_fchl
from .ffchl_kernels import fget_atomic_force_alphas_fchl
from .ffchl_kernels import fget_atomic_force_kernels_fchl
from .ffchl_module import fget_kernels_fchl
from .ffchl_module import fget_symmetric_kernels_fchl
from .ffchl_module import fget_global_kernels_fchl
from .ffchl_module import fget_global_symmetric_kernels_fchl
from .ffchl_module import fget_atomic_kernels_fchl
from .ffchl_module import fget_atomic_symmetric_kernels_fchl

from .alchemy import get_alchemy

Expand Down Expand Up @@ -102,7 +100,7 @@ def generate_fchl_representation(coordinates, nuclear_charges,


def get_local_kernels_fchl(A, B, sigmas, \
t_width=np.pi/1.0, d_width=0.2, cut_distance=5.0, \
t_width=np.pi/1.0, d_width=0.2, cut_start=1.0, cut_distance=5.0, \
r_width=1.0, order=1, c_width=0.5, scale_distance=1.0, scale_angular=0.1,
n_width = 1.0, m_width = 1.0, l_width = 1.0, s_width = 1.0, alchemy="periodic-table", two_body_power=6.0, three_body_power=3.0, elemental_vectors=None):
""" Calculates the Gaussian kernel matrix K, where :math:`K_{ij}`:
Expand All @@ -123,6 +121,8 @@ def get_local_kernels_fchl(A, B, sigmas, \
:type t_width: float
:param d_width: Gaussian width for the distance terms.
:type d_width: float
:param cut_start: The fraction of the cut-off radius at which cut-off damping start
:type cut_start: float
:param cut_distance: Cut-off radius.
:type cut_distance: float
:param r_width: Gaussian width along rows in the periodic table.
Expand Down Expand Up @@ -181,10 +181,10 @@ def get_local_kernels_fchl(A, B, sigmas, \
assert len(sigmas.shape) == 1, "Third argument (sigmas) is not a 1D list/numpy.array!"

return fget_kernels_fchl(A, B, N1, N2, neighbors1, neighbors2, sigmas, \
nm1, nm2, nsigmas, t_width, d_width, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)
nm1, nm2, nsigmas, t_width, d_width, cut_start, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)

def get_local_symmetric_kernels_fchl(A, sigmas, \
t_width=np.pi/1.0, d_width=0.2, cut_distance=5.0, \
t_width=np.pi/1.0, d_width=0.2, cut_start=1.0, cut_distance=5.0, \
r_width=1.0, order=1, c_width=0.5, scale_distance=1.0, scale_angular=0.1,
n_width = 1.0, m_width = 1.0, l_width = 1.0, s_width = 1.0, alchemy="periodic-table", two_body_power=6.0, three_body_power=3.0, elemental_vectors=None):
""" Calculates the Gaussian kernel matrix K, where :math:`K_{ij}`:
Expand All @@ -203,6 +203,8 @@ def get_local_symmetric_kernels_fchl(A, sigmas, \
:type t_width: float
:param d_width: Gaussian width for the distance terms.
:type d_width: float
:param cut_start: The fraction of the cut-off radius at which cut-off damping start
:type cut_start: float
:param cut_distance: Cut-off radius.
:type cut_distance: float
:param r_width: Gaussian width along rows in the periodic table.
Expand Down Expand Up @@ -246,10 +248,10 @@ def get_local_symmetric_kernels_fchl(A, sigmas, \
assert len(sigmas.shape) == 1, "Second argument (sigmas) is not a 1D list/numpy.array!"

return fget_symmetric_kernels_fchl(A, N1, neighbors1, sigmas, \
nm1, nsigmas, t_width, d_width, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)
nm1, nsigmas, t_width, d_width, cut_start, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)

def get_global_symmetric_kernels_fchl(A, sigmas, \
t_width=np.pi/1.0, d_width=0.2, cut_distance=5.0, \
t_width=np.pi/1.0, d_width=0.2, cut_start=1.0, cut_distance=5.0, \
r_width=1.0, order=1, c_width=0.5, scale_distance=1.0, scale_angular=0.1,
n_width = 1.0, m_width = 1.0, l_width = 1.0, s_width = 1.0, alchemy="periodic-table", two_body_power=6.0, three_body_power=3.0, elemental_vectors=None):
""" Calculates the Gaussian kernel matrix K, where :math:`K_{ij}`:
Expand All @@ -268,6 +270,8 @@ def get_global_symmetric_kernels_fchl(A, sigmas, \
:type t_width: float
:param d_width: Gaussian width for the distance terms.
:type d_width: float
:param cut_start: The fraction of the cut-off radius at which cut-off damping start
:type cut_start: float
:param cut_distance: Cut-off radius.
:type cut_distance: float
:param r_width: Gaussian width along rows in the periodic table.
Expand Down Expand Up @@ -311,11 +315,11 @@ def get_global_symmetric_kernels_fchl(A, sigmas, \
assert len(sigmas.shape) == 1, "Second argument (sigmas) is not a 1D list/numpy.array!"

return fget_global_symmetric_kernels_fchl(A, N1, neighbors1, sigmas, \
nm1, nsigmas, t_width, d_width, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)
nm1, nsigmas, t_width, d_width, cut_start, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)


def get_global_kernels_fchl(A, B, sigmas, \
t_width=np.pi/1.0, d_width=0.2, cut_distance=5.0, \
t_width=np.pi/1.0, d_width=0.2, cut_start=1.0, cut_distance=5.0, \
r_width=1.0, order=1, c_width=0.5, scale_distance=1.0, scale_angular=0.1,
n_width = 1.0, m_width = 1.0, l_width = 1.0, s_width = 1.0, alchemy="periodic-table", two_body_power=6.0, three_body_power=3.0, elemental_vectors=None):
""" Calculates the Gaussian kernel matrix K, where :math:`K_{ij}`:
Expand All @@ -336,6 +340,10 @@ def get_global_kernels_fchl(A, B, sigmas, \
:type t_width: float
:param d_width: Gaussian width for the distance terms.
:type d_width: float
:param cut_start: The fraction of the cut-off radius at which cut-off damping start
:type cut_start: float
:param cut_start: The fraction of the cut-off radius at which cut-off damping start
:type cut_start: float
:param cut_distance: Cut-off radius.
:type cut_distance: float
:param r_width: Gaussian width along rows in the periodic table.
Expand Down Expand Up @@ -394,11 +402,11 @@ def get_global_kernels_fchl(A, B, sigmas, \
assert len(sigmas.shape) == 1, "Third argument (sigmas) is not a 1D list/numpy.array!"

return fget_global_kernels_fchl(A, B, N1, N2, neighbors1, neighbors2, sigmas, \
nm1, nm2, nsigmas, t_width, d_width, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)
nm1, nm2, nsigmas, t_width, d_width, cut_start, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)


def get_atomic_kernels_fchl(A, B, sigmas, \
t_width=np.pi/1.0, d_width=0.2, cut_distance=5.0, \
t_width=np.pi/1.0, d_width=0.2, cut_start=1.0, cut_distance=5.0, \
r_width=1.0, order=1, c_width=0.5, scale_distance=1.0, scale_angular=0.1,
n_width = 1.0, m_width = 1.0, l_width = 1.0, s_width = 1.0, alchemy="periodic-table", two_body_power=6.0, three_body_power=3.0, elemental_vectors=None):
""" Calculates the Gaussian kernel matrix K, where :math:`K_{ij}`:
Expand All @@ -419,6 +427,8 @@ def get_atomic_kernels_fchl(A, B, sigmas, \
:type t_width: float
:param d_width: Gaussian width for the distance terms.
:type d_width: float
:param cut_start: The fraction of the cut-off radius at which cut-off damping start
:type cut_start: float
:param cut_distance: Cut-off radius.
:type cut_distance: float
:param r_width: Gaussian width along rows in the periodic table.
Expand Down Expand Up @@ -464,11 +474,11 @@ def get_atomic_kernels_fchl(A, B, sigmas, \
assert len(sigmas.shape) == 1

return fget_atomic_kernels_fchl(A, B, neighbors1, neighbors2, sigmas, \
na1, na2, nsigmas, t_width, d_width, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)
na1, na2, nsigmas, t_width, d_width, cut_start, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)


def get_atomic_symmetric_kernels_fchl(A, sigmas, \
t_width=np.pi/1.0, d_width=0.2, cut_distance=5.0, \
t_width=np.pi/1.0, d_width=0.2, cut_start=1.0, cut_distance=5.0, \
r_width=1.0, order=1, c_width=0.5, scale_distance=1.0, scale_angular=0.1,
n_width = 1.0, m_width = 1.0, l_width = 1.0, s_width = 1.0, alchemy="periodic-table",
two_body_power=6.0, three_body_power=3.0, elemental_vectors=None):
Expand All @@ -488,6 +498,8 @@ def get_atomic_symmetric_kernels_fchl(A, sigmas, \
:type t_width: float
:param d_width: Gaussian width for the distance terms.
:type d_width: float
:param cut_start: The fraction of the cut-off radius at which cut-off damping start
:type cut_start: float
:param cut_distance: Cut-off radius.
:type cut_distance: float
:param r_width: Gaussian width along rows in the periodic table.
Expand Down Expand Up @@ -524,135 +536,4 @@ def get_atomic_symmetric_kernels_fchl(A, sigmas, \
assert len(sigmas.shape) == 1, "Second argument (sigmas) is not a 1D list/numpy.array!"

return fget_atomic_symmetric_kernels_fchl(A, neighbors1, sigmas, \
na1, nsigmas, t_width, d_width, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)


def get_atomic_force_alphas_fchl(A, F, sigmas, llambda=1e-7, \
t_width=np.pi/1.0, d_width=0.2, cut_distance=5.0, \
r_width=1.0, order=1, c_width=0.5, scale_distance=1.0, scale_angular=0.1,
n_width = 1.0, m_width = 1.0, l_width = 1.0, s_width = 1.0, alchemy="periodic-table",
two_body_power=6.0, three_body_power=3.0, elemental_vectors=None):
""" Calculates the Gaussian kernel matrix K, where :math:`K_{ij}`:

:math:`K_{ij} = \\exp \\big( -\\frac{\\|A_i - B_j\\|_2^2}{2\sigma^2} \\big)`

Where :math:`A_{i}` and :math:`B_{j}` are FCHL representation vectors.
K is calculated analytically using an OpenMP parallel Fortran routine.
Note, that this kernel will ONLY work with FCHL representations as input.

:param A: Array of FCHL representation - shape=(N, maxsize, 5, size).
:type A: numpy array
:param sigma: List of kernel-widths.
:type sigma: list
:param t_width: Gaussian width for the angular (theta) terms.
:type t_width: float
:param d_width: Gaussian width for the distance terms.
:type d_width: float
:param cut_distance: Cut-off radius.
:type cut_distance: float
:param r_width: Gaussian width along rows in the periodic table.
:type r_width: float
:param c_width: Gaussian width along columns in the periodic table.
:type c_width: float
:param order: Fourier-expansion truncation order.
:type order: integer
:param scale_distance: Weight for distance-dependent terms.
:type scale_distance: float
:param scale_angular: Weight for angle-dependent terms.
:type scale_angular: float

:return: Array of FCHL kernel matrices matrix - shape=(n_sigmas, N, M),
:rtype: numpy array
"""

assert len(A.shape) == 3

na1 = A.shape[0]

neighbors1 = np.zeros((na1), dtype=np.int32)

for i, x in enumerate(A):
neighbors1[i] = len(np.where(x[0]< cut_distance)[0])

nsigmas = len(sigmas)

doalchemy, pd = get_alchemy(alchemy, emax=100, r_width=r_width, c_width=c_width,
n_width = n_width, m_width = m_width, l_width = l_width, s_width = s_width,
elemental_vectors=elemental_vectors)

sigmas = np.array(sigmas)
assert len(sigmas.shape) == 1, "Second argument (sigmas) is not a 1D list/numpy.array!"

return fget_atomic_force_alphas_fchl(A, F, neighbors1, sigmas, llambda, \
na1, nsigmas, t_width, d_width, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)


def get_atomic_force_kernels_fchl(A, B, sigmas,
t_width=np.pi/1.0, d_width=0.2, cut_distance=5.0,
r_width=1.0, order=1, c_width=0.5, scale_distance=1.0, scale_angular=0.1,
n_width = 1.0, m_width = 1.0, l_width = 1.0, s_width = 1.0, alchemy="periodic-table",
two_body_power=6.0, three_body_power=3.0, elemental_vectors=None):
""" Calculates the Gaussian kernel matrix K, where :math:`K_{ij}`:

:math:`K_{ij} = \\exp \\big( -\\frac{\\|A_i - B_j\\|_2^2}{2\sigma^2} \\big)`

Where :math:`A_{i}` and :math:`B_{j}` are FCHL representation vectors.
K is calculated analytically using an OpenMP parallel Fortran routine.
Note, that this kernel will ONLY work with FCHL representations as input.

:param A: Array of FCHL representation - shape=(N, maxsize, 5, size).
:type A: numpy array
:param B: Array of FCHL representation - shape=(M, maxsize, 5, size).
:type B: numpy array
:param sigma: List of kernel-widths.
:type sigma: list
:param t_width: Gaussian width for the angular (theta) terms.
:type t_width: float
:param d_width: Gaussian width for the distance terms.
:type d_width: float
:param cut_distance: Cut-off radius.
:type cut_distance: float
:param r_width: Gaussian width along rows in the periodic table.
:type r_width: float
:param c_width: Gaussian width along columns in the periodic table.
:type c_width: float
:param order: Fourier-expansion truncation order.
:type order: integer
:param scale_distance: Weight for distance-dependent terms.
:type scale_distance: float
:param scale_angular: Weight for angle-dependent terms.
:type scale_angular: float

:return: Array of FCHL kernel matrices matrix - shape=(n_sigmas, N, M),
:rtype: numpy array
"""

assert len(A.shape) == 3
assert len(B.shape) == 3

# assert B.shape[1] == atoms_max, "ERROR: Check FCHL representation sizes! code = 2"
# assert B.shape[3] == neighbors_max, "ERROR: Check FCHL representation sizes! code = 3"

na1 = A.shape[0]
na2 = B.shape[0]

neighbors1 = np.zeros((na1), dtype=np.int32)
neighbors2 = np.zeros((na2), dtype=np.int32)

for i, x in enumerate(A):
neighbors1[i] = len(np.where(x[0]< cut_distance)[0])

for i, x in enumerate(B):
neighbors2[i] = len(np.where(x[0]< cut_distance)[0])

nsigmas = len(sigmas)

doalchemy, pd = get_alchemy(alchemy, emax=100, r_width=r_width, c_width=c_width,
n_width = n_width, m_width = m_width, l_width = l_width, s_width = s_width,
elemental_vectors=elemental_vectors)

sigmas = np.array(sigmas)
assert len(sigmas.shape) == 1

return fget_atomic_force_kernels_fchl(A, B, neighbors1, neighbors2, sigmas, \
na1, na2, nsigmas, t_width, d_width, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)
na1, nsigmas, t_width, d_width, cut_start, cut_distance, order, pd, scale_distance, scale_angular, doalchemy, two_body_power, three_body_power)
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