optimization fix for bs-disp, bug fixes

Cargo.toml

@@ -26,6 +26,8 @@ default = ["extension-module"]
 
 [profile.dev]
 opt-level = 3
+debug = false
 
 [profile.release]
-opt-level = 3
+opt-level = 3
+debug = false

esat/error/bootstrap.py

@@ -389,10 +389,10 @@ def _train(self,
         #TODO: Implement parallelization
         for i in tqdm(range(1, self.bootstrap_n+1), desc="Bootstrap resampling, training and mapping"):
             sample_seed = self.rng.integers(low=0, high=1e10, size=1)
-            _V =  copy.deepcopy(self.data)
-            _U =  copy.deepcopy(self.uncertainty)
-            _W =  copy.deepcopy(self.base_W)
-            _H =  copy.deepcopy(self.base_H)
+            _V = copy.deepcopy(self.data)
+            _U = copy.deepcopy(self.uncertainty)
+            _W = copy.deepcopy(self.base_W)
+            _H = copy.deepcopy(self.base_H)
             train_seed = sample_seed
             if block:
                 bs_data, bs_uncertainty, bs_W, bs_index = self._block_resample(data=_V,

esat/error/bs_disp.py

@@ -149,17 +149,20 @@ def run(self,
         if parallel:
             cpus = mp.cpu_count()
             cpus = cpus - 1 if cpus > 1 else 1
-            with mp.Pool(processes=cpus) as pool:
-                p_args = []
-                for i, bs_key in enumerate(bs_keys):
-                    i_model = self.bootstrap.bs_results[bs_key]["model"]
-                    i_args = (bs_key, i_model, self.feature_labels, self.model_selected, self.threshold_dQ,
-                              self.max_search, self.features, self.dQmax)
-                    p_args.append(i_args)
-
-                for result in pool.starmap(BSDISP._parallel_disp, p_args, chunksize=10):
-                    i, i_disp = result
-                    self.disp_results[i] = i_disp
+            pool = mp.Pool(processes=cpus)
+            p_args = []
+            for i, bs_key in enumerate(bs_keys):
+                i_model = self.bootstrap.bs_results[bs_key]["model"]
+                i_args = (bs_key, i_model, self.feature_labels, self.model_selected, self.threshold_dQ,
+                          self.max_search, self.features, self.dQmax)
+                p_args.append(i_args)
+            results = pool.starmap(BSDISP._parallel_disp, p_args)
+            pool.close()
+            pool.join()
+            # for result in pool.starmap(BSDISP._parallel_disp, p_args, chunksize=10):
+            for result in results:
+                i, i_disp = result
+                self.disp_results[i] = i_disp
         else:
             for bs_key in tqdm(bs_keys, desc="BS-DISP - Displacement Stage", position=0, leave=True):
                 bs_result = self.bootstrap.bs_results[bs_key]

esat/model/batch_sa.py

@@ -274,8 +274,8 @@ def _train_task(self, sa, model_i) -> (int, SA):
         t1 = time.time()
         if self.verbose:
             logger.info(f"Model: {model_i}, Seed: {sa.seed}, "
-                        f"Q(true): {round(sa.Qtrue, 4)}, MSE(true): {round(sa.Qtrue/sa.V.shape[0], 4)}, "
-                        f"Q(robust): {round(sa.Qrobust, 4)}, MSE(robust): {round(sa.Qrobust/sa.V.shape[0], 4)},"
+                        f"Q(true): {round(sa.Qtrue, 4)}, MSE(true): {round(sa.Qtrue/sa.V.size, 4)}, "
+                        f"Q(robust): {round(sa.Qrobust, 4)}, MSE(robust): {round(sa.Qrobust/sa.V.size, 4)}, "
                         f"Steps: {sa.converge_steps}/{self.max_iter}, Converged: {sa.converged}, "
                         f"Runtime: {round(t1 - t0, 2)} sec")
         return model_i, sa