From 1de0cb1f33ac3a72a286f8f67187162440001a4b Mon Sep 17 00:00:00 2001
From: David Luevano Alvarado <55825613+luevano@users.noreply.github.com>
Date: Sat, 7 Mar 2020 10:55:01 -0700
Subject: Rewrite kernel usage
---
ml_exp/do_ml.py | 71 ++++++++++++++++++----------------------------
ml_exp/kernels.py | 85 ++++++++++++++++---------------------------------------
2 files changed, 53 insertions(+), 103 deletions(-)
diff --git a/ml_exp/do_ml.py b/ml_exp/do_ml.py
index b30d73811..e1aece986 100644
--- a/ml_exp/do_ml.py
+++ b/ml_exp/do_ml.py
@@ -30,7 +30,7 @@ except ImportError:
print('Tensorflow couldn\'t be imported. Maybe it is not installed.')
TF_AV = False
from ml_exp.misc import printc
-from ml_exp.kernels import gaussian_kernel, laplacian_kernel
+from ml_exp.kernels import laplauss_kernel
from ml_exp.qm7db import qm7db
@@ -41,6 +41,7 @@ def simple_ml(descriptors,
sigma=1000.0,
opt=True,
identifier=None,
+ laplauss='gauss',
use_tf=True,
show_msgs=True):
"""
@@ -93,16 +94,11 @@ def simple_ml(descriptors,
with tf.device('GPU:0'):
X_tr = descriptors[:training_size]
Y_tr = energies[:training_size]
- if identifier == 'BOB':
- K_tr = laplacian_kernel(X_tr,
- X_tr,
- sigma,
- use_tf=use_tf)
- else:
- K_tr = gaussian_kernel(X_tr,
- X_tr,
- sigma,
- use_tf=use_tf)
+ K_tr = laplauss_kernel(X_tr,
+ X_tr,
+ sigma,
+ laplauss=laplauss,
+ use_tf=use_tf)
# Adding a small value on the diagonal for cho_solve.
dv = tf.linalg.tensor_diag(tf.constant(1e-8,
@@ -115,16 +111,11 @@ def simple_ml(descriptors,
X_te = descriptors[-test_size:]
Y_te = energies[-test_size:]
- if identifier == 'BOB':
- K_te = laplacian_kernel(X_te,
- X_tr,
- sigma,
- use_tf=use_tf)
- else:
- K_te = gaussian_kernel(X_te,
- X_tr,
- sigma,
- use_tf=use_tf)
+ K_te = laplauss_kernel(X_te,
+ X_tr,
+ sigma,
+ laplauss=laplauss,
+ use_tf=use_tf)
Y_te = tf.expand_dims(Y_te, 1)
Y_pr = tf.tensordot(K_te, alpha, 1)
@@ -133,16 +124,11 @@ def simple_ml(descriptors,
else:
X_tr = descriptors[:training_size]
Y_tr = energies[:training_size]
- if identifier == 'BOB':
- K_tr = laplacian_kernel(X_tr,
- X_tr,
- sigma,
- use_tf=use_tf)
- else:
- K_tr = gaussian_kernel(X_tr,
- X_tr,
- sigma,
- use_tf=use_tf)
+ K_tr = laplauss_kernel(X_tr,
+ X_tr,
+ sigma,
+ laplauss=laplauss,
+ use_tf=use_tf)
# Adding a small value on the diagonal for cho_solve.
K_tr[np.diag_indices_from(K_tr)] += 1e-8
@@ -151,16 +137,11 @@ def simple_ml(descriptors,
X_te = descriptors[-test_size:]
Y_te = energies[-test_size:]
- if identifier == 'BOB':
- K_te = laplacian_kernel(X_te,
- X_tr,
- sigma,
- use_tf=use_tf)
- else:
- K_te = gaussian_kernel(X_te,
- X_tr,
- sigma,
- use_tf=use_tf)
+ K_te = laplauss_kernel(X_te,
+ X_tr,
+ sigma,
+ laplauss=laplauss,
+ use_tf=use_tf)
Y_pr = np.dot(K_te, alpha)
mae = np.mean(np.abs(Y_pr - Y_te))
@@ -276,8 +257,8 @@ def do_ml(db_path='data',
cm_data = tf.convert_to_tensor(cm_data)
if 'LJM' in identifiers:
ljm_data = tf.convert_to_tensor(ljm_data)
- # if 'AM' in identifiers:
- # am_data = tf.convert_to_tensor(am_data)
+ if 'AM' in identifiers:
+ am_data = tf.convert_to_tensor(am_data)
if 'BOB' in identifiers:
bob_data = tf.convert_to_tensor(bob_data)
else:
@@ -296,6 +277,7 @@ def do_ml(db_path='data',
test_size=test_size,
sigma=sigma,
identifier='CM',
+ laplauss='gauss',
use_tf=use_tf,
show_msgs=show_msgs)
if 'LJM' in identifiers:
@@ -305,6 +287,7 @@ def do_ml(db_path='data',
test_size=test_size,
sigma=sigma,
identifier='LJM',
+ laplauss='gauss',
use_tf=use_tf,
show_msgs=show_msgs)
if 'AM' in identifiers:
@@ -314,6 +297,7 @@ def do_ml(db_path='data',
test_size=test_size,
sigma=sigma,
identifier='AM',
+ laplauss='gauss',
use_tf=use_tf,
show_msgs=show_msgs)
if 'BOB' in identifiers:
@@ -323,6 +307,7 @@ def do_ml(db_path='data',
test_size=test_size,
sigma=sigma,
identifier='BOB',
+ laplauss='laplace',
use_tf=use_tf,
show_msgs=show_msgs)
diff --git a/ml_exp/kernels.py b/ml_exp/kernels.py
index 488232392..11679460b 100644
--- a/ml_exp/kernels.py
+++ b/ml_exp/kernels.py
@@ -30,15 +30,17 @@ except ImportError:
TF_AV = False
-def gaussian_kernel(X1,
+def laplauss_kernel(X1,
X2,
sigma,
+ laplauss='gauss',
use_tf=True):
"""
- Calculates the Gaussian Kernel.
+ Calculates the Lpalacian or Gaussian Kernel.
X1: first representations.
X2: second representations.
sigma: kernel width.
+ laplauss: which kernel to calculate.
use_tf: if tensorflow should be used.
"""
# If tf is to be used but couldn't be imported, don't try to use it.
@@ -47,62 +49,12 @@ def gaussian_kernel(X1,
X1_size = X1.shape[0]
X2_size = X2.shape[0]
- i_sigma = -0.5 / (sigma*sigma)
-
- if use_tf:
- if tf.config.experimental.list_physical_devices('GPU'):
- with tf.device('GPU:0'):
- X1 = tf.convert_to_tensor(X1)
- X2 = tf.convert_to_tensor(X2)
- X2r = tf.rank(X2)
-
- def cond(i, _):
- return tf.less(i, X1_size)
-
- def body(i, K):
- if X2r == 3:
- norm = tf.norm(X2 - X1[i], axis=(1, 2))
- else:
- norm = tf.norm(X2 - X1[i], axis=-1)
-
- return (i + 1,
- K.write(i, tf.exp(i_sigma * tf.square(norm))))
-
- K = tf.TensorArray(dtype=tf.float64,
- size=X1_size)
- i_state = (0, K)
- n, K = tf.while_loop(cond, body, i_state)
- K = K.stack()
+ if laplauss == 'gauss':
+ i_sigma = -0.5 / (sigma*sigma)
+ elif laplauss == 'laplace':
+ i_sigma = -0.5 / sigma
else:
- K = np.zeros((X1_size, X2_size), dtype=np.float64)
- # Faster way of calculating the kernel (no numba support).
- for i in range(X1_size):
- if X2.ndim == 3:
- norm = np.linalg.norm(X2 - X1[i], axis=(1, 2))
- else:
- norm = np.linalg.norm(X2 - X1[i], axis=-1)
- K[i, :] = np.exp(i_sigma * np.square(norm))
- return K
-
-
-def laplacian_kernel(X1,
- X2,
- sigma,
- use_tf=True):
- """
- Calculates the Laplacian Kernel.
- X1: first representations.
- X2: second representations.
- sigma: kernel width.
- use_tf: if tensorflow should be used.
- """
- # If tf is to be used but couldn't be imported, don't try to use it.
- if use_tf and not TF_AV:
- use_tf = False
-
- X1_size = X1.shape[0]
- X2_size = X2.shape[0]
- i_sigma = -0.5 / sigma
+ i_sigma = -0.5 / (sigma**2)
if use_tf:
if tf.config.experimental.list_physical_devices('GPU'):
@@ -120,8 +72,15 @@ def laplacian_kernel(X1,
else:
norm = tf.norm(X2 - X1[i], axis=-1)
- return (i + 1,
- K.write(i, tf.exp(i_sigma * norm)))
+ if laplauss == 'gauss':
+ return (i + 1,
+ K.write(i, tf.exp(i_sigma * tf.square(norm))))
+ elif laplauss == 'laplace':
+ return (i + 1,
+ K.write(i, tf.exp(i_sigma * norm)))
+ else:
+ return (i + 1,
+ K.write(i, tf.exp(i_sigma * tf.square(norm))))
K = tf.TensorArray(dtype=tf.float64,
size=X1_size)
@@ -136,5 +95,11 @@ def laplacian_kernel(X1,
norm = np.linalg.norm(X2 - X1[i], axis=(1, 2))
else:
norm = np.linalg.norm(X2 - X1[i], axis=-1)
- K[i, :] = np.exp(i_sigma * norm)
+ if laplauuss == 'gauss':
+ K[i, :] = np.exp(i_sigma * np.square(norm))
+ elif laplauce == 'laplace':
+ K[i, :] = np.exp(i_sigma * norm)
+ else:
+ K[i, :] = np.exp(i_sigma * np.square(norm))
+
return K
--
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