Fix #9 (#10)

- Add `n_repeats` kwarg to `DiffPaSSModel.fit_bootstrap`
- Tidy up `fit_bootstrap` code

diffpass/base.py

@@ -514,6 +514,7 @@ def fit_bootstrap(
             int
         ] = None,  # If ``None``, the bootstrap will end when all pairs are fixed. Otherwise, the bootstrap will end when `n_end` pairs are fixed
         step_size: int = 1,  # Difference between the number of fixed pairings chosen at consecutive bootstrap iterations
+        n_repeats: int = 1,  # At each bootstrap iteration, `n_repeats` runs will be performed, and the run with the lowest loss will be chosen
         show_pbar: bool = True,  # If ``True``, show progress bar. Default: ``True``
         single_fit_cfg: Optional[
             dict
@@ -528,95 +529,187 @@ def fit_bootstrap(
         and fixed for the next run.
         The number of pairings fixed at each iteration ranges between `n_start` (default: 1) and `n_end` (default: total number of pairs), with a step size of `step_size`.
         """
-        self.prepare_fit(x, y)
-        if self.fixed_pairings is None:
-            initial_fixed_pairings = [[] for _ in self.group_sizes]
-        else:
-            initial_fixed_pairings = [list(fm) for fm in self.fixed_pairings]
-
-        _single_fit_cfg = deepcopy(self.single_fit_default_cfg)
-        if single_fit_cfg is not None:
-            _single_fit_cfg.update(single_fit_cfg)
-        single_fit_cfg = _single_fit_cfg
+        ########## Preparations ##########
 
-        # Initialize DiffPaSSResults object
-        results = self._init_results(
-            record_log_alphas=single_fit_cfg["record_log_alphas"],
-            record_soft_perms=single_fit_cfg["record_soft_perms"],
-            record_soft_losses=single_fit_cfg["record_soft_losses"],
-        )
-        available_fields = [
-            field.name
-            for field in fields(results)
-            if getattr(results, field.name) is not None
-        ]
-        field_to_length_so_far = {field_name: 0 for field_name in available_fields}
+        # Input validation
+        self.prepare_fit(x, y)
 
+        # Prepare variables for indexing
         n_samples = len(x)
         n_groups = len(self.group_sizes)
         cumsum_group_sizes = np.cumsum([0] + list(self.group_sizes))
         offsets = np.repeat(cumsum_group_sizes[:-1], repeats=self.group_sizes)
         group_idxs = np.repeat(np.arange(n_groups), repeats=self.group_sizes)
 
-        # First fit with initial fixed matchings
-        can_optimize = self._fit(x, y, results=results, **single_fit_cfg)
+        # Initially fixed pairings as derived from the `fixed_pairings` attribute
+        if self.fixed_pairings is None:
+            initially_fixed_pairings = [[] for _ in self.group_sizes]
+        else:
+            initially_fixed_pairings = [list(fm) for fm in self.fixed_pairings]
 
-        # Find effective initial fixed matchings
-        effective_initial_fixed_idxs = []
+        # *Effective* initially fixed pairings as global indices (not relative to group)
+        # Used to exclude these pairs from the random sampling of new fixed pairings
+        # and to determine when the bootstrap will end
+        effective_initially_fixed_idxs = []
         for s, efmz in zip(
             cumsum_group_sizes, self.permutation._effective_fixed_pairings_zip
         ):
             if efmz:
-                effective_initial_fixed_idxs += [
-                    (s + efmz_fixed) for efmz_fixed in efmz[1]
+                effective_initially_fixed_idxs += [
+                    s + efmz_fixed for efmz_fixed in efmz[1]
                 ]
-        effective_initial_fixed_idxs = np.asarray(effective_initial_fixed_idxs)
-        nonfixed_idxs = np.setdiff1d(np.arange(n_samples), effective_initial_fixed_idxs)
-        n_effective_initial_fixed_pairings = len(effective_initial_fixed_idxs)
-
+        non_initially_fixed_idxs = np.setdiff1d(
+            np.arange(n_samples), effective_initially_fixed_idxs
+        )
         if n_end is None:
-            n_end = n_samples - n_effective_initial_fixed_pairings - 1
-
-        # Subsequent fits: at a given iteration we use fixed matchings chosen uniformly at
-        # random from the results of the previous iteration (excluding the effective initial
-        # fixed matchings)
-        pbar = list(range(n_start, n_end, step_size))
+            n_end = n_samples - len(effective_initially_fixed_idxs) - 1
+        # Bootstrap range and progress bar
+        pbar = range(n_start, n_end, step_size)
         pbar = tqdm(pbar) if show_pbar else pbar
-        n_iters_with_optimization = int(can_optimize)
-        for N in pbar:
-            latest_hard_perms = results.hard_perms[-1]
-            mapped_idxs = offsets + np.concatenate(latest_hard_perms)
-            rand_fixed_idxs = np.random.permutation(nonfixed_idxs)[:N]
+
+        ########## End preparations ##########
+
+        ########## Closures ##########
+
+        def make_new_fixed_pairings(
+            mapped_idxs: np.ndarray, N: int
+        ) -> IndexPairsInGroups:
+            """Subroutine for randomly sampling new fixed pairings for the next bootstrap iteration."""
+            rand_fixed_idxs = np.random.permutation(non_initially_fixed_idxs)[:N]
             rand_fixed_idxs = np.sort(rand_fixed_idxs)
             rand_mapped_idxs = mapped_idxs[rand_fixed_idxs]
             rand_group_idxs = group_idxs[rand_fixed_idxs]
             rand_fixed_rel_idxs = rand_fixed_idxs - offsets[rand_fixed_idxs]
             rand_mapped_rel_idxs = rand_mapped_idxs - offsets[rand_mapped_idxs]
 
-            # Update fixed matchings
+            # Update fixed pairings
             fixed_pairings = [[] for _ in range(n_groups)]
             for rand_group_idx, mapped_rel_idx, fixed_rel_idx in zip(
                 rand_group_idxs, rand_mapped_rel_idxs, rand_fixed_rel_idxs
             ):
                 pair = (mapped_rel_idx, fixed_rel_idx)
                 fixed_pairings[rand_group_idx].append(pair)
             fixed_pairings = [
-                initial_fixed_pairings[k] + fixed_pairings[k] for k in range(n_groups)
+                initially_fixed_pairings[k] + fixed_pairings[k] for k in range(n_groups)
             ]
-            self.permutation.init_fixed_pairings_and_log_alphas(
-                fixed_pairings, device=x.device
+
+            return fixed_pairings
+
+        def init_diffpassresults() -> DiffPaSSResults:
+            return self._init_results(
+                record_log_alphas=single_fit_cfg["record_log_alphas"],
+                record_soft_perms=single_fit_cfg["record_soft_perms"],
+                record_soft_losses=single_fit_cfg["record_soft_losses"],
             )
 
+        def extend_results_with_lowest_loss_repeat(
+            results_this_iter: DiffPaSSResults,
+            results: DiffPaSSResults,
+            can_optimize: bool,
+        ) -> None:
+            """Extend the global optimization object `results` with the portion of
+            `results_this_iter` (from the latest bootstrap iteration) corresponding to
+            the repeat with the lowest hard loss."""
+            if can_optimize:
+                # Select run with lowest hard loss, discard the rest
+                reshaped_hard_losses_this_repeat = np.asarray(
+                    results_this_iter.hard_losses
+                ).reshape(n_repeats, -1)
+                min_loss_idx = np.argmin(reshaped_hard_losses_this_repeat[:, -1])
+                size_each_repeat = reshaped_hard_losses_this_repeat.shape[1]
+                # Record complete results of the run with the lowest loss
+                slice_to_append = slice(
+                    min_loss_idx * size_each_repeat,
+                    (min_loss_idx + 1) * size_each_repeat,
+                )
+            else:
+                slice_to_append = slice(None)
+            [
+                getattr(results, field_name).extend(
+                    getattr(results_this_iter, field_name)[slice_to_append]
+                )
+                for field_name in available_fields
+            ]
+
+        postprocess_results_after_repeats = (
+            extend_results_with_lowest_loss_repeat
+            if n_repeats > 1
+            else lambda *args: None
+        )
+
+        ########## End closures ##########
+
+        # Configuration for each gradient descent run
+        _single_fit_cfg = deepcopy(self.single_fit_default_cfg)
+        if single_fit_cfg is not None:
+            _single_fit_cfg.update(single_fit_cfg)
+        single_fit_cfg = _single_fit_cfg
+
+        # Initialize DiffPaSSResults object
+        results = init_diffpassresults()
+        available_fields = [
+            field.name
+            for field in fields(results)
+            if getattr(results, field.name) is not None
+        ]
+        field_to_length_so_far = {field_name: 0 for field_name in available_fields}
+
+        ########## Optimization ##########
+
+        # First fit with initially fixed pairings
+        can_optimize = self._fit(x, y, results=results, **single_fit_cfg)
+        n_iters_with_optimization = int(can_optimize)
+
+        # DiffPaSSResults object for each bootstrap iteration:
+        # new object if `n_repeats` > 1, else the existing `results`
+        get_results_to_use_in_each_bootstrap_iter = (
+            init_diffpassresults if n_repeats > 1 else lambda: results
+        )
+
+        # Subsequent bootstrap fits: at a given iteration we use fixed pairings chosen uniformly at
+        # random from the results of the previous iteration (excluding the effective initially
+        # fixed pairings)
+        for N in pbar:
+            latest_hard_perms = results.hard_perms[-1]
+            mapped_idxs = offsets + np.concatenate(latest_hard_perms)
+
             field_to_length_so_far = {
                 field_name: len(getattr(results, field_name))
                 for field_name in available_fields
             }
-            can_optimize = self._fit(x, y, results=results, **single_fit_cfg)
+
+            results_this_iter = (
+                get_results_to_use_in_each_bootstrap_iter()
+            )  # `results` alias if `n_repeats` == 1
+            for _ in range(n_repeats):
+                # Randomly sample N fixed pairings
+                fixed_pairings = make_new_fixed_pairings(mapped_idxs, N)
+                # Reinitialize permutation module with new fixed pairings
+                self.permutation.init_fixed_pairings_and_log_alphas(
+                    fixed_pairings, device=x.device
+                )
+                # Fit with gradient descent
+                can_optimize = self._fit(
+                    x, y, results=results_this_iter, **single_fit_cfg
+                )
+                if not can_optimize:
+                    # If we can't fit, we break the "repeats" loop
+                    break
+
+            postprocess_results_after_repeats(
+                results_this_iter, results, can_optimize
+            )  # Does nothing if `n_repeats` == 1
+
             if can_optimize:
                 n_iters_with_optimization += 1
             else:
+                # If we could not fit, terminate the bootstrap
                 break
 
+        ########## End optimization ##########
+
+        ########## Post-processing ##########
+
         # Reshape results according to number of iterations performed
         reshaped_fields = {}
         for field_name in available_fields:
@@ -644,4 +737,6 @@ def fit_bootstrap(
             )
         results = replace(results, **reshaped_fields)
 
+        ########## End post-processing ##########
+
         return results

diffpass/model.py

@@ -194,7 +194,7 @@ def hard_(self) -> None:
         self.mode = "hard"
 
     def _impl_fixed_pairings(self, func: callable) -> callable:
-        """Include fixed matchings in the Gumbel-Sinkhorn or Gumbel-matching operators."""
+        """Include fixed pairings in the Gumbel-Sinkhorn or Gumbel-matching operators."""
 
         def wrapper(gen: Iterator[torch.Tensor]) -> Iterator[torch.Tensor]:
             for s, mat, (row_group, col_group), mask in zip(