From ec9f69ad7521c189b5d6808c05031cf9d40723de Mon Sep 17 00:00:00 2001
From: David Luevano <55825613+luevano@users.noreply.github.com>
Date: Thu, 12 Dec 2019 03:31:29 -0700
Subject: Add features

---
 do_ml.py | 30 ++++++++++++++++++++++--------
 1 file changed, 22 insertions(+), 8 deletions(-)

(limited to 'do_ml.py')

diff --git a/do_ml.py b/do_ml.py
index b92b15a99..561b33904 100644
--- a/do_ml.py
+++ b/do_ml.py
@@ -36,25 +36,34 @@ def printc(text, color):
 
 
 def do_ml(desc_data,
-          desc_type,
           energy_data,
           training_size,
           test_size,
-          sigma=1000.0):
+          sigma=1000.0,
+          desc_type=None,
+          show_msgs=True):
     """
     Does the ML methodology.
     desc_data: descriptor (or representation) data.
-    desc_type: string with the name of the descriptor used.
     energy_data: energy data associated with desc_data.
     training_size: size of the training set to use.
     test_size: size of the test set to use.
     sigma: depth of the kernel.
+    desc_type: string with the name of the descriptor used.
+    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.
     """
+    if not desc_type:
+        desc_type = 'NOT SPECIFIED'
+
     tic = time.perf_counter()
-    printc('{} ML started.'.format(desc_type), Fore.CYAN)
+    if show_msgs:
+        printc('{} ML started, with parameters:'.format(desc_type), Fore.CYAN)
+        printc('\tTraining size: {}'.format(training_size), Fore.BLUE)
+        printc('\tTest size: {}'.format(test_size), Fore.BLUE)
+        printc('\tSigma: {}'.format(sigma), Fore.BLUE)
 
     Xcm_training = desc_data[:training_size]
     Ycm_training = energy_data[:training_size]
@@ -66,9 +75,14 @@ def do_ml(desc_data,
     Kcm_test = gauss_kernel(Xcm_test, Xcm_training, sigma)
     Ycm_predicted = np.dot(Kcm_test, alpha_cm)
 
-    print('\tMAE for {}: {}'.format(desc_type,
-                                    np.mean(np.abs(Ycm_predicted - Ycm_test))))
+    mae = np.mean(np.abs(Ycm_predicted - Ycm_test))
+    if show_msgs:
+        print('\tMAE for {}: {}'.format(desc_type, mae))
 
     toc = time.perf_counter()
-    printc('\t{} ML took {:.4f} seconds.'.format(desc_type, toc-tic),
-           Fore.GREEN)
+    tictoc = toc - tic
+    if show_msgs:
+        printc('\t{} ML took {:.4f} seconds.'.format(desc_type, tictoc),
+               Fore.GREEN)
+
+    return mae, tictoc
-- 
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