bugfixes

Author: stefanch

sgdml/__init__.py

@@ -22,7 +22,7 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-__version__ = '0.5.2.dev2'
+__version__ = '0.5.2.dev3'
 
 MAX_PRINT_WIDTH = 100
 LOG_LEVELNAME_WIDTH = 7  # do not modify

sgdml/cli.py

@@ -729,7 +729,7 @@ def create(  # noqa: C901
 
     if sigs is None:
         log.info(
-            'Kernel hyper-parameter sigma was automatically set to range \'10:10:100\'.'
+            'Kernel hyper-parameter sigma (length scale) was automatically set to range \'10:10:100\'.'
         )
         sigs = list(range(10, 100, 10))  # default range
 
@@ -919,14 +919,23 @@ def save_progr_callback(
     has_converged_once = False
 
     for i, task_file_name in enumerate(task_file_names):
-        if n_tasks > 1:
-            if i > 0:
-                print()
-            print(ui.color_str('Task {:d} of {:d}'.format(i + 1, n_tasks), bold=True))
 
         task_file_path = os.path.join(task_dir, task_file_name)
         with np.load(task_file_path, allow_pickle=True) as task:
 
+            if n_tasks > 1:
+                if i > 0:
+                    print()
+
+                n_train = len(task['idxs_train'])
+                n_valid = len(task['idxs_valid'])
+                ui.print_two_column_str(
+                    ui.color_str('Task {:d} of {:d}'.format(i + 1, n_tasks), bold=True),
+                    '{:,} + {:,} points (training + validation), sigma (length scale): {}'.format(
+                        n_train, n_valid, task['sig']
+                    ),
+                )
+
             model_file_name = io.model_file_name(task, is_extended=False)
             model_file_path = os.path.join(task_dir, model_file_name)
 
@@ -1625,9 +1634,16 @@ def test(
             e_rmse_pct = (e_rmse / e_err['rmse'] - 1.0) * 100
         f_rmse_pct = (f_rmse / f_err['rmse'] - 1.0) * 100
 
-        # if func_called_directly and n_models == 1:
         if is_test and n_models == 1:
-            print(ui.color_str('\nTest errors (MAE/RMSE)', bold=True))
+            n_train = len(model['idxs_train'])
+            n_valid = len(model['idxs_valid'])
+            print()
+            ui.print_two_column_str(
+                ui.color_str('Test errors (MAE/RMSE)', bold=True),
+                '{:,} + {:,} points (training + validation), sigma (length scale): {}'.format(
+                    n_train, n_valid, model['sig']
+                ),
+            )
 
             r_unit = 'unknown unit'
             e_unit = 'unknown unit'
@@ -1805,7 +1821,7 @@ def select(model_dir, overwrite, model_file=None, command=None, **kwargs):  # no
         sig_col = [row[0] for row in rows]
         if best_sig == min(sig_col) or best_sig == max(sig_col):
             log.warning(
-                'The optimal sigma lies on the boundary of the search grid.\n'
+                'The optimal sigma (length scale) lies on the boundary of the search grid.\n'
                 + 'Model performance might improve if the search grid is extended in direction sigma {} {:d}.'.format(
                     '<' if best_idx == 0 else '>', best_sig
                 )
@@ -1992,7 +2008,7 @@ def _add_argument_dir_with_file_type(parser, type, or_file=False):
             metavar=('<s1>', '<s2>'),
             dest='sigs',
             type=io.parse_list_or_range,
-            help='integer list and/or range <start>:[<step>:]<stop> for the kernel hyper-parameter sigma',
+            help='integer list and/or range <start>:[<step>:]<stop> for the kernel hyper-parameter sigma (length scale)',
             nargs='+',
         )
 

sgdml/train.py

@@ -144,7 +144,7 @@ def _assemble_kernel_mat_wkr(
 
     else:  # Sequential indexing
         K_j = j * dim_i if j < n_train else n_train * dim_i + (j % n_train)
-        blk_j = slice(K_j, K_j + dim_i) if j < n_train else slice(K_j, K_j+1) ######
+        blk_j = slice(K_j, K_j + dim_i) if j < n_train else slice(K_j, K_j + 1)
         keep_idxs_3n = slice(None)  # same as [:]
 
     # Note: The modulo-operator wraps around the index pointer on the training points when
@@ -179,7 +179,7 @@ def _assemble_kernel_mat_wkr(
         j < n_train
     ):  # This column only contrains second and first derivative constraints.
 
-        #for i in range(j if exploit_sym else 0, n_train):
+        # for i in range(j if exploit_sym else 0, n_train):
         for i in range(0, n_train):
 
             blk_i = slice(i * dim_i, (i + 1) * dim_i)
@@ -233,23 +233,23 @@ def _assemble_kernel_mat_wkr(
 
                 K_fe = -np.einsum('ik,jki -> j', K_fe, rj_d_desc_perms)
 
-                E_off_i = n_train * dim_i#, K.shape[1] - n_train
+                E_off_i = n_train * dim_i  # , K.shape[1] - n_train
                 K[E_off_i + i, blk_j] = K_fe
 
     else:
 
         if use_E_cstr:
 
-            #rj_d_desc = desc_func.d_desc_from_comp(R_d_desc[j % n_train, :, :])[0][
+            # rj_d_desc = desc_func.d_desc_from_comp(R_d_desc[j % n_train, :, :])[0][
             #    :, :
-            #]  # convert descriptor back to full representation
+            # ]  # convert descriptor back to full representation
 
-            #rj_d_desc_perms = np.reshape(
+            # rj_d_desc_perms = np.reshape(
             #    np.tile(rj_d_desc.T, n_perms)[:, tril_perms_lin], (-1, dim_d, n_perms)
-            #)
+            # )
 
-            E_off_i = n_train * dim_i # Account for 'alloc_extra_rows'!.
-            #blk_j_full = slice((j % n_train) * dim_i, ((j % n_train) + 1) * dim_i)
+            E_off_i = n_train * dim_i  # Account for 'alloc_extra_rows'!.
+            # blk_j_full = slice((j % n_train) * dim_i, ((j % n_train) + 1) * dim_i)
             # for i in range((j % n_train) if exploit_sym else 0, n_train):
             for i in range(0, n_train):
 
@@ -259,9 +259,12 @@ def _assemble_kernel_mat_wkr(
                     order='F',
                 )
 
-                ri_d_desc = desc_func.d_desc_from_comp(R_d_desc[i, :, :])[0] # convert descriptor back to full representation
+                ri_d_desc = desc_func.d_desc_from_comp(R_d_desc[i, :, :])[
+                    0
+                ]  # convert descriptor back to full representation
                 ri_d_desc_perms = np.reshape(
-                    np.tile(ri_d_desc.T, n_perms)[:, tril_perms_lin], (-1, dim_d, n_perms)
+                    np.tile(ri_d_desc.T, n_perms)[:, tril_perms_lin],
+                    (-1, dim_d, n_perms),
                 )
 
                 diff_ab_perms = R_desc[j % n_train, :] - ri_desc_perms
@@ -956,7 +959,7 @@ def train(  # noqa: C901
             self.log.debug('Iterative solver not installed.')
             use_analytic_solver = True
 
-        #use_analytic_solver = False  # remove me!
+        # use_analytic_solver = False  # remove me!
 
         if use_analytic_solver:
 
@@ -1063,10 +1066,10 @@ def train(  # noqa: C901
                 if E_train_mean is None
                 else E_train_mean
             )
-            #if c is None:
+            # if c is None:
             #    # Something does not seem right. Turn off energy predictions for this model, only output force predictions.
             #    model['use_E'] = False
-            #else:
+            # else:
             #    model['c'] = c
 
             model['c'] = c
@@ -1115,8 +1118,8 @@ def _recov_int_const(
                 If inconsistent/corrupted energy labels are detected
                 in the provided dataset.
             ValueError
-                If different scales in energy vs. force labels are
-                detected in the provided dataset.
+                If potentially inconsistent scales in energy vs.
+                force labels are detected in the provided dataset.
         """
 
         gdml_predict = GDMLPredict(
@@ -1156,16 +1159,22 @@ def _recov_int_const(
 
         if np.sign(e_fact) == -1:
             self.log.warning(
-                'The provided dataset contains gradients instead of force labels (flipped sign). Please correct!\n'
-                + ui.color_str('Note:', bold=True)
-                + 'Note: The energy prediction accuracy of the model will thus neither be validated nor tested in the following steps!'
+                'It looks like the provided dataset may contain gradients instead of force labels (flipped sign).\n\n'
+                + ui.color_str('Troubleshooting tips:\n', bold=True)
+                + ui.wrap_indent_str(
+                    '(1) ',
+                    'Verify the sign of your force labels.',
+                )
+                + '\n'
+                + ui.wrap_indent_str(
+                    '(2) ', 'This issue might very well just be a sympthom of using too few trainnig data and your labels are correct.'
+                )
             )
-            #return None
 
         if corrcoef < 0.95:
             self.log.warning(
-                'Inconsistent energy labels detected!\n'
-                + 'The predicted energies for the training data are only weakly correlated with the reference labels (correlation coefficient {:.2f}) which indicates that the issue is most likely NOT just a unit conversion error.\n\n'.format(
+                'Potentially inconsistent energy labels detected!\n'
+                + 'The predicted energies for the training data are only weakly correlated with the reference labels (correlation coefficient {:.2f}). Note that correlation is independent of scale, which indicates that the issue is most likely not just a unit conversion error.\n\n'.format(
                     corrcoef
                 )
                 + ui.color_str('Troubleshooting tips:\n', bold=True)
@@ -1175,37 +1184,37 @@ def _recov_int_const(
                 )
                 + '\n'
                 + ui.wrap_indent_str(
-                    '(2) ', 'Verify the consistency between energy and force labels.'
+                    '(2) ', 'This issue might very well just be a sympthom of using too few trainnig data and your labels are correct.'
                 )
                 + '\n'
-                + ui.wrap_indent_str('    - ', 'Correspondence correct?')
+                + ui.wrap_indent_str(
+                    '(3) ', 'Verify the consistency between energy and force labels.'
+                )
                 + '\n'
-                + ui.wrap_indent_str('    - ', 'Same level of theory?')
+                + ui.wrap_indent_str('    - ', 'Correspondence between force and energy labels correct?')
                 + '\n'
-                + ui.wrap_indent_str('    - ', 'Accuracy of forces?')
+                + ui.wrap_indent_str('    - ', 'Accuracy of forces (convergence of your ab-initio calculations)?')
+                + '\n'
+                + ui.wrap_indent_str('    - ', 'Was the same level of theory used to compute forces and energies?')
                 + '\n'
                 + ui.wrap_indent_str(
-                    '(3) ',
+                    '(4) ',
                     'Is the training data spread too broadly (i.e. weakly sampled transitions between example clusters)?',
                 )
                 + '\n'
                 + ui.wrap_indent_str(
-                    '(4) ', 'Are there duplicate geometries in the training data?'
+                    '(5) ', 'Are there duplicate geometries in the training data?'
                 )
                 + '\n'
                 + ui.wrap_indent_str(
-                    '(5) ', 'Are there any corrupted data points (e.g. parsing errors)?'
+                    '(6) ', 'Are there any corrupted data points (e.g. parsing errors)?'
                 )
-                + '\n\n'
-                + ui.color_str('Note:', bold=True)
-                + ' The energy prediction accuracy of the model will thus neither be validated nor tested in the following steps!'
             )
-            #return None
 
         if np.abs(e_fact - 1) > 1e-1:
             self.log.warning(
-                'Different scales in energy vs. force labels detected!\n'
-                + 'The integrated forces differ from the energy labels by factor ~{:.2f}, meaning that the trained model will likely fail to predict energies accurately.\n\n'.format(
+                'Potentially inconsistent scales in energy vs. force labels detected!\n'
+                + 'The integrated force predictions differ from the reference energy labels by factor ~{:.2f} (for the training data), meaning that this model will likely fail to predict energies accurately in real-world use.\n\n'.format(
                     e_fact
                 )
                 + ui.color_str('Troubleshooting tips:\n', bold=True)
@@ -1214,14 +1223,14 @@ def _recov_int_const(
                 )
                 + '\n'
                 + ui.wrap_indent_str(
-                    '(2) ',
+                    '(2) ', 'This issue might very well just be a sympthom of using too few trainnig data and your labels are correct.'
+                )
+                + '\n'
+                + ui.wrap_indent_str(
+                    '(3) ',
                     'Is the training data spread too broadly (i.e. weakly sampled transitions between example clusters)?',
                 )
-                + '\n\n'
-                + ui.color_str('Note:', bold=True)
-                + ' The energy prediction accuracy of the model will thus neither be validated nor tested in the following steps!'
             )
-            #return None
 
         # Least squares estimate for integration constant.
         return np.sum(E_ref - E_pred) / E_ref.shape[0]