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Finished the last tests
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@charnley
charnley committed Mar 17, 2024
1 parent ac28c34 commit 01a8496
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Showing 2 changed files with 37 additions and 105 deletions.
24 changes: 1 addition & 23 deletions tests/test_fchl_acsf_energy.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,5 @@
from pathlib import Path

import numpy as np
from conftest import ASSETS, shuffle_arrays
from conftest import ASSETS, get_energies, shuffle_arrays

from qmllib.kernels import get_local_kernel, get_local_symmetric_kernel
from qmllib.representations import generate_fchl_acsf
Expand All @@ -11,26 +9,6 @@
np.set_printoptions(linewidth=666)


def get_energies(filename: Path):
"""Returns a dictionary with heats of formation for each xyz-file."""

f = open(filename, "r")
lines = f.readlines()
f.close()

energies = dict()

for line in lines:
tokens = line.split()

xyz_name = tokens[0]
hof = float(tokens[1])

energies[xyz_name] = hof

return energies


def test_energy():

# Read the heat-of-formation energies
Expand Down
118 changes: 36 additions & 82 deletions tests/test_kernel_derivatives.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
from copy import deepcopy

import numpy as np
from conftest import ASSETS

from qmllib.kernels import (
get_atomic_local_gradient_kernel,
Expand All @@ -22,7 +23,7 @@

np.set_printoptions(linewidth=666, edgeitems=10)

CSV_FILE = "data/amons_small.csv"
CSV_FILE = ASSETS / "amons_small.csv"

TRAINING = 7
TEST = 5
Expand Down Expand Up @@ -56,7 +57,7 @@ def csv_to_molecular_reps(csv_filename):

coordinates = np.array(ast.literal_eval(row[2]))
nuclear_charges = ast.literal_eval(row[5])
atomtypes = ast.literal_eval(row[1])
# UNUSED atomtypes = ast.literal_eval(row[1])
force = np.array(ast.literal_eval(row[3]))
energy = float(row[6])

Expand Down Expand Up @@ -101,59 +102,25 @@ def test_global_kernel():
K1 = get_global_kernel(X, Xs, Q, Qs, SIGMA)
K2 = get_global_kernel(X, X, Q, Q, SIGMA)


def test_atomic_local_kernel():

Xall, dXall, Eall, Fall, dispXall, Nall, Qall = csv_to_molecular_reps(CSV_FILE)

X = Xall[:TRAINING]
dX = dXall[:TRAINING]
N = Nall[:TRAINING]
dispX = dispXall[:, : sum(N) * 3, :, :]
Q = Qall[:TRAINING]

Xs = Xall[-TEST:]
dXs = dXall[-TEST:]
Ns = Nall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
Qs = Qall[-TEST:]

K = get_atomic_local_kernel(X, Xs, Q, Qs, SIGMA)

K_numm = np.zeros(K.shape)

idx = 0

for i in range(TRAINING):
for n1 in range(N[i]):

for j in range(TEST):
for n2 in range(Ns[j]):

if Q[i][n1] == Qs[j][n2]:
d = np.linalg.norm(X[i, n1] - Xs[j, n2])
gauss = np.exp(-(d**2) / (2 * SIGMA**2))
K_numm[j, idx] += gauss

idx += 1

assert np.allclose(K, K_numm), "Error in get_local_kernel()"
# TODO No assertion
assert K1 is not None
assert K2 is not None


def test_local_kernel():

Xall, dXall, Eall, Fall, dispXall, Nall, Qall = csv_to_molecular_reps(CSV_FILE)

X = Xall[:TRAINING]
dX = dXall[:TRAINING]
# UNUSED dX = dXall[:TRAINING]
N = Nall[:TRAINING]
dispX = dispXall[:, : sum(N) * 3, :, :]
# UNUSED dispX = dispXall[:, : sum(N) * 3, :, :]
Q = Qall[:TRAINING]

Xs = Xall[-TEST:]
dXs = dXall[-TEST:]
# UNUSED dXs = dXall[-TEST:]
Ns = Nall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
# UNUSED dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
Qs = Qall[-TEST:]

K = get_local_kernel(X, X, Q, Q, SIGMA)
Expand Down Expand Up @@ -185,15 +152,15 @@ def test_atomic_local_kernel():
Xall, dXall, Eall, Fall, dispXall, Nall, Qall = csv_to_molecular_reps(CSV_FILE)

X = Xall[:TRAINING]
dX = dXall[:TRAINING]
# UNUSED dX = dXall[:TRAINING]
N = Nall[:TRAINING]
dispX = dispXall[:, : sum(N) * 3, :, :]
# UNUSED dispX = dispXall[:, : sum(N) * 3, :, :]
Q = Qall[:TRAINING]

Xs = Xall[-TEST:]
dXs = dXall[-TEST:]
# UNUSED dXs = dXall[-TEST:]
Ns = Nall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
# UNUSED dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
Qs = Qall[-TEST:]

K = get_atomic_local_kernel(X, Xs, Q, Qs, SIGMA)
Expand Down Expand Up @@ -228,11 +195,11 @@ def test_atomic_local_gradient():
dispX = dispXall[:, : sum(N) * 3, :, :]
Q = Qall[:TRAINING]

Xs = Xall[-TEST:]
dXs = dXall[-TEST:]
Xs = Xall[-TEST:] # noqa:F841
dXs = dXall[-TEST:] # noqa:F841
Ns = Nall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
Qs = Qall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :] # noqa:F841
Qs = Qall[-TEST:] # noqa:F841

Kt_gradient = get_atomic_local_gradient_kernel(X, X, dX, Q, Q, SIGMA)

Expand Down Expand Up @@ -272,11 +239,11 @@ def test_local_gradient():
dispX = dispXall[:, : sum(N) * 3, :, :]
Q = Qall[:TRAINING]

Xs = Xall[-TEST:]
dXs = dXall[-TEST:]
Xs = Xall[-TEST:] # noqa:F841
dXs = dXall[-TEST:] # noqa:F841
Ns = Nall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
Qs = Qall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :] # noqa:F841
Qs = Qall[-TEST:] # noqa:F841

Kt_gradient = get_local_gradient_kernel(X, X, dX, Q, Q, SIGMA)

Expand Down Expand Up @@ -383,14 +350,14 @@ def test_symmetric_gdml_kernel():
X = Xall[:TRAINING]
dX = dXall[:TRAINING]
N = Nall[:TRAINING]
dispX = dispXall[:, : sum(N) * 3, :, :]
dispX = dispXall[:, : sum(N) * 3, :, :] # noqa:F841
Q = Qall[:TRAINING]

Xs = Xall[-TEST:]
dXs = dXall[-TEST:]
Xs = Xall[-TEST:] # noqa:F841
dXs = dXall[-TEST:] # noqa:F841
Ns = Nall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
Qs = Qall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :] # noqa:F841
Qs = Qall[-TEST:] # noqa:F841

K = get_gdml_kernel(X, X, dX, dX, Q, Q, SIGMA)
K_symm = get_symmetric_gdml_kernel(X, dX, Q, SIGMA)
Expand All @@ -405,13 +372,13 @@ def test_gp_kernel():
X = Xall[:TRAINING]
dX = dXall[:TRAINING]
N = Nall[:TRAINING]
dispX = dispXall[:, : sum(N) * 3, :, :]
dispX = dispXall[:, : sum(N) * 3, :, :] # noqa:F841
Q = Qall[:TRAINING]

Xs = Xall[-TEST:]
dXs = dXall[-TEST:]
Ns = Nall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :] # noqa:F841
Qs = Qall[-TEST:]

K = get_gp_kernel(X, Xs, dX, dXs, Q, Qs, SIGMA)
Expand Down Expand Up @@ -439,26 +406,26 @@ def test_local_kernels():
Xall, dXall, Eall, Fall, dispXall, Nall, Qall = csv_to_molecular_reps(CSV_FILE)

X = Xall[:TRAINING]
dX = dXall[:TRAINING]
dX = dXall[:TRAINING] # noqa:F841
N = Nall[:TRAINING]
dispX = dispXall[:, : sum(N) * 3, :, :]
dispX = dispXall[:, : sum(N) * 3, :, :] # noqa:F841
Q = Qall[:TRAINING]

Xs = Xall[-TEST:]
dXs = dXall[-TEST:]
dXs = dXall[-TEST:] # noqa:F841
Ns = Nall[-TEST:]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :]
dispXs = dispXall[:, -sum(Ns) * 3 :, :, :] # noqa:F841
Qs = Qall[-TEST:]

sigma1 = 2.5
sigma2 = 25.0

K1 = get_local_kernel(X, Xs, Q, Qs, sigma1)
K2 = get_local_kernel(X, Xs, Q, Qs, sigma2)
K3 = get_local_kernel(X, Xs, Q, Qs, 10.0)
K4 = get_local_kernel(X, Xs, Q, Qs, 2.0)
K3 = get_local_kernel(X, Xs, Q, Qs, 10.0) # noqa:F841
K4 = get_local_kernel(X, Xs, Q, Qs, 2.0) # noqa:F841

K5 = get_local_kernel(X, Xs, Q, Qs, 3.0)
K5 = get_local_kernel(X, Xs, Q, Qs, 3.0) # noqa:F841

K = get_local_kernels(X, Xs, Q, Qs, [sigma1, sigma2, 10.0, 2.0, 3.0])

Expand All @@ -472,16 +439,3 @@ def test_local_kernels():

assert np.allclose(K1, K[0]), "Error in get_local_symmetric_kernels() 1"
assert np.allclose(K2, K[1]), "Error in get_local_symmetric_kernels() 2"


if __name__ == "__main__":

test_local_kernel()
test_atomic_local_kernel()
test_atomic_local_gradient()
test_local_gradient()
test_gdml_kernel()
test_symmetric_gdml_kernel()
test_gp_kernel()
test_global_kernel()
test_local_kernels()

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