Added option to ignore failed bags

Author: sichinaga

pydmd/bopdmd.py

@@ -76,8 +76,10 @@ class BOPDMDOperator(DMDOperator):
         modes at every iteration of variable projection routine.
     :type mode_prox: function
     :param bag_maxfail: Number of consecutive non-converged trials of BOP-DMD
-        at which to terminate the fit. Default is -1, no stopping condition.
-    :type bag_maxfail: int
+        at which to terminate the fit. Set this parameter to infinity for no
+        stopping condition. Set to a non-positive value to simply use the
+        results of the non-converged trials. This is the default behavior.
+    :type bag_maxfail: int or float
     :param init_lambda: Initial value used for the regularization parameter in
         the Levenberg method. Default is 1.0.
         Note: Larger lambda values make the method more like gradient descent.
@@ -764,13 +766,22 @@ def compute_operator(self, H, t):
             self._A = A_0
             return b_0
 
-        # Otherwise, perform BOP-DMD.
+        # Otherwise, perform BOP-DMD:
         verbose = self._varpro_opts[-1]
         if verbose:
             num_trial_print = 5
             msg = "\nDisplaying the results of the next {} trials...\n"
             print(msg.format(num_trial_print))
 
+        # We'll consider non-converged trials successful if the user didn't
+        # request a positive amount of bagging trials at which to terminate.
+        use_bad_bags = self._bag_maxfail <= 0.0
+        if verbose:
+            if use_bad_bags:
+                print("Using all bag trial results...\n")
+            else:
+                print("Non-converged trial results will be removed...\n")
+
         # Initialize storage for values needed for stat computations.
         w_sum = np.zeros(w_0.shape, dtype="complex")
         e_sum = np.zeros(e_0.shape, dtype="complex")
@@ -796,9 +807,8 @@ def compute_operator(self, H, t):
                 verbose = num_trial_print > 0
                 self._varpro_opts[-1] = verbose
 
-            # Incorporate results into the running average
-            # ONLY IF the trial successfully converged.
-            if converged:
+            # Incorporate trial results into the running average if successful.
+            if converged or use_bad_bags:
                 sorted_inds = self._argsort_eigenvalues(e_i)
 
                 # Add to iterative sums.
@@ -815,13 +825,12 @@ def compute_operator(self, H, t):
                 # and reset the consecutive fails counter.
                 num_successful_trials += 1
                 num_consecutive_fails = 0
+
+            # Trial did not converge, and we are throwing away bad bags.
             else:
                 num_consecutive_fails += 1
 
-            if (
-                not runtime_warning_given
-                and num_consecutive_fails == 100
-            ):
+            if not runtime_warning_given and num_consecutive_fails == 100:
                 msg = (
                     "100 trials without convergence. "
                     "Consider loosening the tol requirements "
@@ -830,7 +839,7 @@ def compute_operator(self, H, t):
                 print(msg)
                 runtime_warning_given = True
 
-            elif num_consecutive_fails == self._bag_maxfail:
+            if num_consecutive_fails >= self._bag_maxfail and not use_bad_bags:
                 msg = (
                     "Terminating the bagging routine due to "
                     "{} many trials without convergence."
@@ -933,8 +942,10 @@ class BOPDMD(DMDBase):
         modes at every iteration of variable projection routine.
     :type mode_prox: function
     :param bag_maxfail: Number of consecutive non-converged trials of BOP-DMD
-        at which to terminate the fit. Default is -1, no stopping condition.
-    :type bag_maxfail: int
+        at which to terminate the fit. Set this parameter to infinity for no
+        stopping condition. Set to a non-positive value to simply use the
+        results of the non-converged trials. This is the default behavior.
+    :type bag_maxfail: int or float
     :param varpro_opts_dict: Dictionary containing the desired parameter values
         for variable projection. The following parameters may be specified:
         `init_lambda`, `maxlam`, `lamup`, `use_levmarq`, `maxiter`, `tol`,
@@ -957,7 +968,7 @@ def __init__(
         eig_sort="auto",
         eig_constraints=None,
         mode_prox=None,
-        bag_maxfail=-1,
+        bag_maxfail=0,
         varpro_opts_dict=None,
     ):
         self._svd_rank = svd_rank
@@ -969,15 +980,6 @@ def __init__(
         self._trial_size = trial_size
         self._eig_sort = eig_sort
 
-        if not isinstance(bag_maxfail, int):
-            msg = (
-                "bag_maxfail must be an integer. "
-                "Please use a non-positive integer if no warning "
-                "or stopping condition is desired."
-            )
-            raise TypeError(msg)
-        self._bag_maxfail = bag_maxfail
-
         if varpro_opts_dict is None:
             self._varpro_opts_dict = {}
         elif not isinstance(varpro_opts_dict, dict):
@@ -994,6 +996,7 @@ def __init__(
         self._check_eig_constraints(eig_constraints)
         self._eig_constraints = eig_constraints
         self._mode_prox = mode_prox
+        self._bag_maxfail = bag_maxfail
 
         self._snapshots_holder = None
         self._time = None