From 1113483da7e054541c7ea17ca40b641ad8e24d88 Mon Sep 17 00:00:00 2001
From: David Luevano Alvarado <55825613+luevano@users.noreply.github.com>
Date: Thu, 26 Mar 2020 20:06:33 -0700
Subject: Reflect changes to representations and compound
---
ml_exp/krr.py | 195 ++++++++++++++++++++++++++++++++++++++++------------------
1 file changed, 134 insertions(+), 61 deletions(-)
diff --git a/ml_exp/krr.py b/ml_exp/krr.py
index 4a87884d4..358816a39 100644
--- a/ml_exp/krr.py
+++ b/ml_exp/krr.py
@@ -42,7 +42,7 @@ def krr(descriptors,
sigma=1000.0,
opt=True,
identifier=None,
- laplauss='gauss',
+ kernel='gaussian',
use_tf=True,
show_msgs=True):
"""
@@ -55,6 +55,7 @@ def krr(descriptors,
sigma: depth of the kernel.
opt: if the optimized algorithm should be used. For benchmarking purposes.
identifier: string with the name of the descriptor used.
+ kernel: which kernel to use.
use_tf: if tensorflow should be used.
show_msgs: if debug messages should be shown.
NOTE: identifier is just a string and is only for identification purposes.
@@ -98,11 +99,26 @@ def krr(descriptors,
with tf.device('GPU:0'):
X_tr = descriptors[:training_size]
Y_tr = labels[:training_size]
- K_tr = laplauss_kernel(X_tr,
- X_tr,
- sigma,
- laplauss=laplauss,
- use_tf=use_tf)
+ if kernel == 'gaussian':
+ K_tr = gaussian_kernel(X_tr,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ elif kernel == 'laplacian':
+ K_tr = laplacian_kernel(X_tr,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ elif kernel == 'wasserstein':
+ K_tr = wasserstein_kernel(X_tr,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ else:
+ raise TypeError(f'{kernel} kernel not found.')
# Adding a small value on the diagonal for cho_solve.
dv = tf.linalg.tensor_diag(tf.constant(1e-8,
@@ -115,11 +131,26 @@ def krr(descriptors,
X_te = descriptors[-test_size:]
Y_te = labels[-test_size:]
- K_te = laplauss_kernel(X_te,
- X_tr,
- sigma,
- laplauss=laplauss,
- use_tf=use_tf)
+ if kernel == 'gaussian':
+ K_te = gaussian_kernel(X_te,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ elif kernel == 'laplacian':
+ K_te = laplacian_kernel(X_te,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ elif kernel == 'wasserstein':
+ K_te = wasserstein_kernel(X_te,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ else:
+ raise TypeError(f'{kernel} kernel not found.')
Y_te = tf.expand_dims(Y_te, 1)
Y_pr = tf.tensordot(K_te, alpha, 1)
@@ -130,11 +161,26 @@ def krr(descriptors,
else:
X_tr = descriptors[:training_size]
Y_tr = labels[:training_size]
- K_tr = laplauss_kernel(X_tr,
- X_tr,
- sigma,
- laplauss=laplauss,
- use_tf=use_tf)
+ if kernel == 'gaussian':
+ K_tr = gaussian_kernel(X_tr,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ elif kernel == 'laplacian':
+ K_tr = laplacian_kernel(X_tr,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ elif kernel == 'wasserstein':
+ K_tr = wasserstein_kernel(X_tr,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ else:
+ raise TypeError(f'{kernel} kernel not found.')
# Adding a small value on the diagonal for cho_solve.
K_tr[np.diag_indices_from(K_tr)] += 1e-8
@@ -143,11 +189,26 @@ def krr(descriptors,
X_te = descriptors[-test_size:]
Y_te = labels[-test_size:]
- K_te = laplauss_kernel(X_te,
- X_tr,
- sigma,
- laplauss=laplauss,
- use_tf=use_tf)
+ if kernel == 'gaussian':
+ K_te = gaussian_kernel(X_te,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ elif kernel == 'laplacian':
+ K_te = laplacian_kernel(X_te,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ elif kernel == 'wasserstein':
+ K_te = wasserstein_kernel(X_te,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ else:
+ raise TypeError(f'{kernel} kernel not found.')
Y_pr = np.dot(K_te, alpha)
mae = np.mean(np.abs(Y_pr - Y_te))
@@ -168,8 +229,10 @@ def multi_krr(db_path='data',
lj_sigma=1.0,
lj_epsilon=1.0,
use_forces=False,
- stuff='bonds',
+ acount={'C':7, 'H':16, 'N':3, 'O':3, 'S':1},
size=23,
+ sort=False,
+ flatten=True,
as_eig=True,
bohr_ru=False,
training_size=1500,
@@ -187,8 +250,10 @@ def multi_krr(db_path='data',
lj_sigma: sigma value.
lj_epsilon: epsilon value.
use_forces: if the use of forces instead of k_cx should be used.
- stuff: elements of the bag, by default the known bag of bonds.
+ acount: atom count for the compound, defaults to qm7 sizes.
size: compound size.
+ sort: if the representation should be sorted row-norm or bag-wise.
+ flatten: if the representation should be 1D.
as_eig: if the representation should be as the eigenvalues.
bohr_ru: if radius units should be in bohr's radius units.
training_size: size of the training set to use.
@@ -224,6 +289,8 @@ def multi_krr(db_path='data',
for compound in compounds:
if 'CM' in identifiers:
compound.gen_cm(size=size,
+ sort=sort,
+ flatten=flatten,
as_eig=as_eig,
bohr_ru=bohr_ru)
if 'LJM' in identifiers:
@@ -231,15 +298,21 @@ def multi_krr(db_path='data',
sigma=lj_sigma,
epsilon=lj_epsilon,
size=size,
+ sort=sort,
+ flatten=flatten,
as_eig=as_eig,
bohr_ru=bohr_ru)
if 'AM' in identifiers:
compound.gen_hd(size=size,
bohr_ru=bohr_ru)
compound.gen_am(use_forces=use_forces,
- size=size)
+ size=size,
+ sort=sort,
+ flatten=flatten)
if 'BOB' in identifiers:
- compound.gen_bob(size=size)
+ compound.gen_bob(size=size,
+ sort=sort,
+ acount=acount)
# Create a numpy array (or tensorflow tensor) for the descriptors.
if 'CM' in identifiers:
@@ -272,45 +345,45 @@ def multi_krr(db_path='data',
# ML calculation.
if 'CM' in identifiers:
- cm_mae, cm_tictoc = simple_ml(cm_data,
- energy_pbe0,
- training_size=training_size,
- test_size=test_size,
- sigma=sigma,
- identifier='CM',
- laplauss='gauss',
- use_tf=use_tf,
- show_msgs=show_msgs)
+ cm_mae, cm_tictoc = krr(cm_data,
+ energy_pbe0,
+ training_size=training_size,
+ test_size=test_size,
+ sigma=sigma,
+ identifier='CM',
+ kernel='gaussian',
+ use_tf=use_tf,
+ show_msgs=show_msgs)
if 'LJM' in identifiers:
- ljm_mae, ljm_tictoc = simple_ml(ljm_data,
- energy_pbe0,
- training_size=training_size,
- test_size=test_size,
- sigma=sigma,
- identifier='LJM',
- laplauss='gauss',
- use_tf=use_tf,
- show_msgs=show_msgs)
+ ljm_mae, ljm_tictoc = krr(ljm_data,
+ energy_pbe0,
+ training_size=training_size,
+ test_size=test_size,
+ sigma=sigma,
+ identifier='LJM',
+ laplauss='gaussian',
+ use_tf=use_tf,
+ show_msgs=show_msgs)
if 'AM' in identifiers:
- am_mae, am_tictoc = simple_ml(am_data,
- energy_pbe0,
- training_size=training_size,
- test_size=test_size,
- sigma=sigma,
- identifier='AM',
- laplauss='gauss',
- use_tf=use_tf,
- show_msgs=show_msgs)
+ am_mae, am_tictoc = krr(am_data,
+ energy_pbe0,
+ training_size=training_size,
+ test_size=test_size,
+ sigma=sigma,
+ identifier='AM',
+ laplauss='gaussian',
+ use_tf=use_tf,
+ show_msgs=show_msgs)
if 'BOB' in identifiers:
- bob_mae, bob_tictoc = simple_ml(bob_data,
- energy_pbe0,
- training_size=training_size,
- test_size=test_size,
- sigma=sigma,
- identifier='BOB',
- laplauss='laplace',
- use_tf=use_tf,
- show_msgs=show_msgs)
+ bob_mae, bob_tictoc = krr(bob_data,
+ energy_pbe0,
+ training_size=training_size,
+ test_size=test_size,
+ sigma=sigma,
+ identifier='BOB',
+ laplauss='laplacian',
+ use_tf=use_tf,
+ show_msgs=show_msgs)
# End of program
end_time = time.perf_counter()
--
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