From 6d2a0a621f00af03ec276cf72ab258c4f482e8cb Mon Sep 17 00:00:00 2001
From: David Luevano Alvarado <55825613+luevano@users.noreply.github.com>
Date: Wed, 4 Mar 2020 00:06:53 -0700
Subject: Rewrite
---
ml_exp/do_ml.py | 88 ++++++++++++++++++++++++++++++---------------------------
1 file changed, 47 insertions(+), 41 deletions(-)
diff --git a/ml_exp/do_ml.py b/ml_exp/do_ml.py
index 57661578a..ffe9a2d85 100644
--- a/ml_exp/do_ml.py
+++ b/ml_exp/do_ml.py
@@ -80,50 +80,56 @@ def simple_ml(descriptors,
printc(f'\tSigma: {test_size}', 'CYAN')
if use_tf:
- X_training = descriptors[:training_size]
- Y_training = energies[:training_size]
- K_training = gaussian_kernel(X_training,
- X_training,
- sigma,
- use_tf=use_tf)
-
- # Y_training = tf.expand_dims(Y_training, 1)
- alpha = tf.linalg.cholesky_solve(tf.linalg.cholesky(K_training),
- Y_training)
-
- X_test = descriptors[-test_size:]
- Y_test = energies[-test_size:]
- K_test = gaussian_kernel(X_test,
- X_training,
- sigma,
- use_tf=use_tf)
-
- # Y_test = tf.expand_dims(Y_test, 1)
- Y_predicted = tf.tensordot(K_test, alpha, 1)
-
- mae = tf.reduce_mean(tf.abs(Y_predicted - Y_test))
+ if tf.config.experimental.list_physical_devices('GPU'):
+ with tf.device('GPU:0'):
+ X_tr = descriptors[:training_size]
+ Y_tr = energies[:training_size]
+ K_tr = gaussian_kernel(X_tr,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ dv = tf.linalg.tensor_diag(tf.constant(1e-8,
+ shape=(training_size),
+ dtype=tf.float64))
+ K_tr += dv
+ # Y_tr = tf.expand_dims(Y_tr, 1)
+ alpha = tf.linalg.cholesky_solve(tf.linalg.cholesky(K_tr),
+ Y_tr)
+
+ X_te = descriptors[-test_size:]
+ Y_te = energies[-test_size:]
+ K_te = gaussian_kernel(X_te,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+
+ # Y_te = tf.expand_dims(Y_te, 1)
+ Y_pr = tf.tensordot(K_te, alpha, 1)
+
+ mae = tf.reduce_mean(tf.abs(Y_pr - Y_te))
else:
- X_training = descriptors[:training_size]
- Y_training = energies[:training_size]
- K_training = gaussian_kernel(X_training,
- X_training,
- sigma,
- use_tf=use_tf)
+ X_tr = descriptors[:training_size]
+ Y_tr = energies[:training_size]
+ K_tr = gaussian_kernel(X_tr,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
# Adding a small value on the diagonal for cho_solve.
- K_training[np.diag_indices_from(K_training)] += 1e-8
- alpha = LA.cho_solve(LA.cho_factor(K_training),
- Y_training)
-
- X_test = descriptors[-test_size:]
- Y_test = energies[-test_size:]
- K_test = gaussian_kernel(X_test,
- X_training,
- sigma,
- use_tf=use_tf)
- Y_predicted = np.dot(K_test, alpha)
-
- mae = np.mean(np.abs(Y_predicted - Y_test))
+ K_tr[np.diag_indices_from(K_tr)] += 1e-8
+ alpha = LA.cho_solve(LA.cho_factor(K_tr),
+ Y_tr)
+
+ X_te = descriptors[-test_size:]
+ Y_te = energies[-test_size:]
+ K_te = gaussian_kernel(X_te,
+ X_tr,
+ sigma,
+ use_tf=use_tf)
+ Y_pr = np.dot(K_te, alpha)
+
+ mae = np.mean(np.abs(Y_pr - Y_te))
if show_msgs:
printc(f'\tMAE for {identifier}: {mae:.4f}', 'GREEN')
--
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