From 487bf8840846b5d4d694b38985268c308aadb36e Mon Sep 17 00:00:00 2001
From: David Luevano <55825613+luevano@users.noreply.github.com>
Date: Wed, 18 Dec 2019 07:21:35 -0700
Subject: Refactor files

---
 lj_matrix/do_ml.py | 108 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 108 insertions(+)
 create mode 100644 lj_matrix/do_ml.py

(limited to 'lj_matrix/do_ml.py')

diff --git a/lj_matrix/do_ml.py b/lj_matrix/do_ml.py
new file mode 100644
index 000000000..acf5455f4
--- /dev/null
+++ b/lj_matrix/do_ml.py
@@ -0,0 +1,108 @@
+"""MIT License
+
+Copyright (c) 2019 David Luevano Alvarado
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+import time
+from misc import printc
+import numpy as np
+from lj_matrix.gauss_kernel import gauss_kernel
+from lj_matrix.cholesky_solve import cholesky_solve
+
+
+def do_ml(desc_data,
+          energy_data,
+          training_size,
+          desc_type=None,
+          pipe=None,
+          test_size=None,
+          sigma=1000.0,
+          show_msgs=True):
+    """
+    Does the ML methodology.
+    desc_data: descriptor (or representation) data.
+    energy_data: energy data associated with desc_data.
+    training_size: size of the training set to use.
+    desc_type: string with the name of the descriptor used.
+    pipe: for multiprocessing purposes. Sends the data calculated
+        through a pipe.
+    test_size: size of the test set to use. If no size is given,
+        the last remaining molecules are used.
+    sigma: depth of the kernel.
+    show_msgs: Show debug messages or not.
+    NOTE: desc_type is just a string and is only for identification purposes.
+    Also, training is done with the first part of the data and
+    testing with the ending part of the data.
+    """
+    # Initial calculations for later use.
+    d_len = len(desc_data)
+    e_len = len(energy_data)
+
+    if not desc_type:
+        desc_type = 'NOT SPECIFIED'
+
+    if d_len != e_len:
+        printc(''.join(['ERROR. Descriptor data size different ',
+                        'than energy data size.']), 'RED')
+        return None
+
+    if training_size >= d_len:
+        printc('ERROR. Training size greater or equal than data size.', 'RED')
+        return None
+
+    if not test_size:
+        test_size = d_len - training_size
+        if test_size > 1500:
+            test_size = 1500
+
+    tic = time.perf_counter()
+    if show_msgs:
+        printc('{} ML started.'.format(desc_type), 'GREEN')
+        printc('\tTraining size: {}'.format(training_size), 'CYAN')
+        printc('\tTest size: {}'.format(test_size), 'CYAN')
+        printc('\tSigma: {}'.format(sigma), 'CYAN')
+
+    Xcm_training = desc_data[:training_size]
+    Ycm_training = energy_data[:training_size]
+    Kcm_training = gauss_kernel(Xcm_training, Xcm_training, sigma)
+    alpha_cm = cholesky_solve(Kcm_training, Ycm_training)
+
+    Xcm_test = desc_data[-test_size:]
+    Ycm_test = energy_data[-test_size:]
+    Kcm_test = gauss_kernel(Xcm_test, Xcm_training, sigma)
+    Ycm_predicted = np.dot(Kcm_test, alpha_cm)
+
+    mae = np.mean(np.abs(Ycm_predicted - Ycm_test))
+    if show_msgs:
+        printc('\tMAE for {}: {:.4f}'.format(desc_type, mae), 'GREEN')
+
+    toc = time.perf_counter()
+    tictoc = toc - tic
+    if show_msgs:
+        printc('\t{} ML took {:.4f} seconds.'.format(desc_type, tictoc),
+               'GREEN')
+        printc('\t\tTraining size: {}'.format(training_size), 'CYAN')
+        printc('\t\tTest size: {}'.format(test_size), 'CYAN')
+        printc('\t\tSigma: {}'.format(sigma), 'CYAN')
+
+    if pipe:
+        pipe.send([desc_type, training_size, test_size, sigma, mae, tictoc])
+
+    return mae, tictoc
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