Score-based density estimators for SBI

sbi/analysis/tensorboard_output.py

@@ -61,7 +61,7 @@ def plot_summary(
     logger = logging.getLogger(__name__)
 
     if tags is None:
-        tags = ["validation_log_probs"]
+        tags = ["validation_loss"]
 
     size_guidance = deepcopy(DEFAULT_SIZE_GUIDANCE)
     size_guidance.update(scalars=tensorboard_scalar_limit)

sbi/inference/__init__.py

@@ -6,6 +6,7 @@
     simulate_for_sbi,
 )
 from sbi.inference.fmpe import FMPE
+from sbi.inference.npse.npse import NPSE
 from sbi.inference.snle import MNLE, SNLE_A
 from sbi.inference.snpe import SNPE_A, SNPE_B, SNPE_C  # noqa: F401
 from sbi.inference.snre import BNRE, SNRE, SNRE_A, SNRE_B, SNRE_C  # noqa: F401

sbi/inference/base.py

@@ -176,7 +176,7 @@
         self._data_round_index = []
 
         self._round = 0
-        self._val_log_prob = float("-Inf")
+        self._val_loss = float("Inf")
 
         # XXX We could instantiate here the Posterior for all children. Two problems:
         #     1. We must dispatch to right PotentialProvider for mcmc based on name
@@ -190,9 +190,9 @@
         # Logging during training (by SummaryWriter).
         self._summary = dict(
             epochs_trained=[],
-            best_validation_log_prob=[],
-            validation_log_probs=[],
-            training_log_probs=[],
+            best_validation_loss=[],
+            validation_loss=[],
+            training_loss=[],
             epoch_durations_sec=[],
         )
 
@@ -393,8 +393,8 @@
         neural_net = self._neural_net
 
         # (Re)-start the epoch count with the first epoch or any improvement.
-        if epoch == 0 or self._val_log_prob > self._best_val_log_prob:
-            self._best_val_log_prob = self._val_log_prob
+        if epoch == 0 or self._val_loss < self._best_val_loss:
+            self._best_val_loss = self._val_loss
             self._epochs_since_last_improvement = 0
             self._best_model_state_dict = deepcopy(neural_net.state_dict())
         else:
@@ -419,14 +419,14 @@
     @staticmethod
     def _describe_round(round_: int, summary: Dict[str, list]) -> str:
         epochs = summary["epochs_trained"][-1]
-        best_validation_log_prob = summary["best_validation_log_prob"][-1]
+        best_validation_loss = summary["best_validation_loss"][-1]
 
         description = f"""
         -------------------------
         ||||| ROUND {round_ + 1} STATS |||||:
         -------------------------
         Epochs trained: {epochs}
-        Best validation performance: {best_validation_log_prob:.4f}
+        Best validation performance: {best_validation_loss:.4f}
         -------------------------
         """
 
@@ -472,11 +472,11 @@
         Scalar tags:
             - epochs_trained:
                 number of epochs trained
-            - best_validation_log_prob:
-                best validation log prob (for each round).
-            - validation_log_probs:
-                validation log probs for every epoch (for each round).
-            - training_log_probs
+            - best_validation_loss:
+                best validation loss (for each round).
+            - validation_loss:
+                validation loss for every epoch (for each round).
+            - training_loss
                 training log probs for every epoch (for each round).
             - epoch_durations_sec
                 epoch duration for every epoch (for each round)
@@ -491,28 +491,28 @@
         )
 
         self._summary_writer.add_scalar(
-            tag="best_validation_log_prob",
-            scalar_value=self._summary["best_validation_log_prob"][-1],
+            tag="best_validation_loss",
+            scalar_value=self._summary["best_validation_loss"][-1],
             global_step=round_ + 1,
         )
 
-        # Add validation log prob for every epoch.
+        # Add validation loss for every epoch.
         # Offset with all previous epochs.
         offset = (
             torch.tensor(self._summary["epochs_trained"][:-1], dtype=torch.int)
             .sum()
             .item()
         )
-        for i, vlp in enumerate(self._summary["validation_log_probs"][offset:]):
+        for i, vlp in enumerate(self._summary["validation_loss"][offset:]):
             self._summary_writer.add_scalar(
-                tag="validation_log_probs",
+                tag="validation_loss",
                 scalar_value=vlp,
                 global_step=offset + i,
             )
 
-        for i, tlp in enumerate(self._summary["training_log_probs"][offset:]):
+        for i, tlp in enumerate(self._summary["training_loss"][offset:]):
             self._summary_writer.add_scalar(
-                tag="training_log_probs",
+                tag="training_loss",
                 scalar_value=tlp,
                 global_step=offset + i,
             )

sbi/inference/fmpe/fmpe_base.py

@@ -241,8 +241,7 @@ def train(
             self.epoch += 1
 
             train_loss_average = train_loss_sum / len(train_loader)  # type: ignore
-            # TODO: rename to loss once renaming is done in base class.
-            self._summary["training_log_probs"].append(-train_loss_average)
+            self._summary["training_loss"].append(train_loss_average)
 
             # Calculate validation performance.
             self._neural_net.eval()
@@ -262,11 +261,8 @@ def train(
             self._val_loss = val_loss_sum / (
                 len(val_loader) * val_loader.batch_size  # type: ignore
             )
-            # TODO: remove this once renaming to loss in base class is done.
-            self._val_log_prob = -self._val_loss
-            # Log validation log prob for every epoch.
-            # TODO: rename to loss and fix sign once renaming in base is done.
-            self._summary["validation_log_probs"].append(-self._val_loss)
+            # Log validation loss for every epoch.
+            self._summary["validation_loss"].append(self._val_loss)
             self._summary["epoch_durations_sec"].append(time.time() - epoch_start_time)
 
             self._maybe_show_progress(self._show_progress_bars, self.epoch)
@@ -275,8 +271,7 @@ def train(
 
         # Update summary.
         self._summary["epochs_trained"].append(self.epoch)
-        # TODO: rename to loss once renaming is done in base class.
-        self._summary["best_validation_log_prob"].append(self._best_val_log_prob)
+        self._summary["best_validation_loss"].append(self._best_val_loss)
 
         # Update tensorboard and summary dict.
         self._summarize(round_=self._round)

sbi/inference/npse/__init__.py

@@ -0,0 +1 @@
+from sbi.inference.npse.npse import NPSE