Merge remote-tracking branch 'origin/dev' into dev

Author: LarsKue

bayesflow/approximators/continuous_approximator.py

@@ -53,6 +53,7 @@ def build_adapter(
         inference_variables: Sequence[str],
         inference_conditions: Sequence[str] = None,
         summary_variables: Sequence[str] = None,
+        sample_weight: Sequence[str] = None,
     ) -> Adapter:
         """Create an :py:class:`~bayesflow.adapters.Adapter` suited for the approximator.
 
@@ -64,6 +65,8 @@ def build_adapter(
             Names of the inference conditions in the data
         summary_variables : Sequence of str, optional
             Names of the summary variables in the data
+        sample_weight : str, optional
+            Name of the sample weights
         """
         adapter = Adapter()
         adapter.to_array()
@@ -77,8 +80,11 @@ def build_adapter(
             adapter.as_set(summary_variables)
             adapter.concatenate(summary_variables, into="summary_variables")
 
-        adapter.keep(["inference_variables", "inference_conditions", "summary_variables"])
-        adapter.standardize()
+        if sample_weight is not None:
+            adapter = adapter.rename(sample_weight, "sample_weight")
+
+        adapter.keep(["inference_variables", "inference_conditions", "summary_variables", "sample_weight"])
+        adapter.standardize(exclude="sample_weight")
 
         return adapter
 
@@ -105,6 +111,7 @@ def compute_metrics(
         inference_variables: Tensor,
         inference_conditions: Tensor = None,
         summary_variables: Tensor = None,
+        sample_weight: Tensor = None,
         stage: str = "training",
     ) -> dict[str, Tensor]:
         if self.summary_network is None:
@@ -128,7 +135,7 @@ def compute_metrics(
         # Force a conversion to Tensor
         inference_variables = keras.tree.map_structure(keras.ops.convert_to_tensor, inference_variables)
         inference_metrics = self.inference_network.compute_metrics(
-            inference_variables, conditions=inference_conditions, stage=stage
+            inference_variables, conditions=inference_conditions, sample_weight=sample_weight, stage=stage
         )
 
         loss = inference_metrics.get("loss", keras.ops.zeros(())) + summary_metrics.get("loss", keras.ops.zeros(()))

bayesflow/experimental/free_form_flow/free_form_flow.py

@@ -214,8 +214,10 @@ def _sample_v(self, x):
                 raise ValueError(f"{self.hutchinson_sampling} is not a valid value for hutchinson_sampling.")
         return v
 
-    def compute_metrics(self, x: Tensor, conditions: Tensor = None, stage: str = "training") -> dict[str, Tensor]:
-        base_metrics = super().compute_metrics(x, conditions=conditions, stage=stage)
+    def compute_metrics(
+        self, x: Tensor, conditions: Tensor = None, sample_weight: Tensor = None, stage: str = "training"
+    ) -> dict[str, Tensor]:
+        base_metrics = super().compute_metrics(x, conditions=conditions, sample_weight=sample_weight, stage=stage)
         # sample random vector
         v = self._sample_v(x)
 
@@ -236,6 +238,8 @@ def decode(z):
         nll = -self.base_distribution.log_prob(z)
         maximum_likelihood_loss = nll - surrogate
         reconstruction_loss = ops.sum((x - x_pred) ** 2, axis=-1)
-        loss = ops.mean(maximum_likelihood_loss + self.beta * reconstruction_loss)
+
+        losses = maximum_likelihood_loss + self.beta * reconstruction_loss
+        loss = self.aggregate(losses, sample_weight)
 
         return base_metrics | {"loss": loss}

bayesflow/networks/coupling_flow/coupling_flow.py

@@ -155,10 +155,14 @@ def _inverse(
 
         return x
 
-    def compute_metrics(self, x: Tensor, conditions: Tensor = None, stage: str = "training") -> dict[str, Tensor]:
-        base_metrics = super().compute_metrics(x, conditions=conditions, stage=stage)
+    def compute_metrics(
+        self, x: Tensor, conditions: Tensor = None, sample_weight: Tensor = None, stage: str = "training"
+    ) -> dict[str, Tensor]:
+        if sample_weight is not None:
+            print(sample_weight)
+        base_metrics = super().compute_metrics(x, conditions=conditions, sample_weight=sample_weight, stage=stage)
 
         z, log_density = self(x, conditions=conditions, inverse=False, density=True)
-        loss = -keras.ops.mean(log_density)
+        loss = self.aggregate(-log_density, sample_weight)
 
         return base_metrics | {"loss": loss}

bayesflow/networks/flow_matching/flow_matching.py

@@ -225,7 +225,11 @@ def deltas(time, xz):
         return x
 
     def compute_metrics(
-        self, x: Tensor | Sequence[Tensor, ...], conditions: Tensor = None, stage: str = "training"
+        self,
+        x: Tensor | Sequence[Tensor, ...],
+        conditions: Tensor = None,
+        sample_weight: Tensor = None,
+        stage: str = "training",
     ) -> dict[str, Tensor]:
         if isinstance(x, Sequence):
             # already pre-configured
@@ -250,11 +254,11 @@ def compute_metrics(
             x = t * x1 + (1 - t) * x0
             target_velocity = x1 - x0
 
-        base_metrics = super().compute_metrics(x1, conditions, stage)
+        base_metrics = super().compute_metrics(x1, conditions, sample_weight, stage)
 
         predicted_velocity = self.velocity(x, time=t, conditions=conditions, training=stage == "training")
 
         loss = self.loss_fn(target_velocity, predicted_velocity)
-        loss = keras.ops.mean(loss)
+        loss = self.aggregate(loss, sample_weight)
 
         return base_metrics | {"loss": loss}

bayesflow/networks/inference_network.py

@@ -48,7 +48,9 @@ def log_prob(self, samples: Tensor, conditions: Tensor = None, **kwargs) -> Tens
         _, log_density = self(samples, conditions=conditions, inverse=False, density=True, **kwargs)
         return log_density
 
-    def compute_metrics(self, x: Tensor, conditions: Tensor = None, stage: str = "training") -> dict[str, Tensor]:
+    def compute_metrics(
+        self, x: Tensor, conditions: Tensor = None, sample_weight: Tensor = None, stage: str = "training"
+    ) -> dict[str, Tensor]:
         if not self.built:
             xz_shape = keras.ops.shape(x)
             conditions_shape = None if conditions is None else keras.ops.shape(conditions)
@@ -64,3 +66,10 @@ def compute_metrics(self, x: Tensor, conditions: Tensor = None, stage: str = "tr
                 metrics[metric.name] = metric(samples, x)
 
         return metrics
+
+    def aggregate(self, losses: Tensor, weights: Tensor = None):
+        if weights is not None:
+            weighted = losses * weights
+        else:
+            weighted = losses
+        return keras.ops.mean(weighted)

bayesflow/networks/point_inference_network.py

@@ -144,13 +144,15 @@ def call(
         }
         return output
 
-    def compute_metrics(self, x: Tensor, conditions: Tensor = None, stage: str = "training") -> dict[str, Tensor]:
+    def compute_metrics(
+        self, x: Tensor, conditions: Tensor = None, sample_weight: Tensor = None, stage: str = "training"
+    ) -> dict[str, Tensor]:
         output = self(x, conditions)
 
         metrics = {}
         # calculate negative score as mean over all scores
         for score_key, score in self.scores.items():
-            score_value = score.score(output[score_key], x)
+            score_value = score.score(output[score_key], x, sample_weight)
             metrics[score_key] = score_value
         neg_score = keras.ops.mean(list(metrics.values()))
 

tests/test_approximators/conftest.py

@@ -1,4 +1,5 @@
 import pytest
+from tests.utils import check_combination_simulator_adapter
 
 
 @pytest.fixture()
@@ -96,7 +97,7 @@ def approximator(request):
 
 
 @pytest.fixture()
-def adapter():
+def adapter_without_sample_weight():
     from bayesflow import ContinuousApproximator
 
     return ContinuousApproximator.build_adapter(
@@ -106,14 +107,48 @@ def adapter():
 
 
 @pytest.fixture()
-def simulator():
+def adapter_with_sample_weight():
+    from bayesflow import ContinuousApproximator
+
+    return ContinuousApproximator.build_adapter(
+        inference_variables=["mean", "std"],
+        inference_conditions=["x"],
+        sample_weight="weight",
+    )
+
+
+@pytest.fixture(params=["adapter_without_sample_weight", "adapter_with_sample_weight"])
+def adapter(request):
+    return request.getfixturevalue(request.param)
+
+
+@pytest.fixture()
+def normal_simulator():
     from tests.utils.normal_simulator import NormalSimulator
 
     return NormalSimulator()
 
 
+@pytest.fixture()
+def normal_simulator_with_sample_weight():
+    from tests.utils.normal_simulator import NormalSimulator
+    from bayesflow import make_simulator
+
+    def weight(mean):
+        return dict(weight=1.0)
+
+    return make_simulator([NormalSimulator(), weight])
+
+
+@pytest.fixture(params=["normal_simulator", "normal_simulator_with_sample_weight"])
+def simulator(request):
+    return request.getfixturevalue(request.param)
+
+
 @pytest.fixture()
 def train_dataset(batch_size, adapter, simulator):
+    check_combination_simulator_adapter(simulator, adapter)
+
     from bayesflow import OfflineDataset
 
     num_batches = 4

tests/test_approximators/test_estimate.py

@@ -1,8 +1,9 @@
 import keras
+from tests.utils import check_combination_simulator_adapter
 
 
 def test_approximator_estimate(approximator, simulator, batch_size, adapter):
-    approximator = approximator
+    check_combination_simulator_adapter(simulator, adapter)
 
     num_batches = 4
     data = simulator.sample((num_batches * batch_size,))

tests/test_approximators/test_fit.py

@@ -3,6 +3,7 @@
 import pytest
 import io
 from contextlib import redirect_stdout
+from tests.utils import check_approximator_multivariate_normal_score
 
 
 @pytest.mark.skip(reason="not implemented")
@@ -19,6 +20,9 @@ def test_fit(amortizer, dataset):
 
 
 def test_loss_progress(approximator, train_dataset, validation_dataset):
+    # as long as MultivariateNormalScore is unstable, skip fit progress test
+    check_approximator_multivariate_normal_score(approximator)
+
     approximator.compile(optimizer="AdamW")
     num_epochs = 3
 

tests/test_approximators/test_point_approximators/test_sample.py

@@ -1,9 +1,15 @@
 import keras
 import numpy as np
 from bayesflow.scores import ParametricDistributionScore
+from tests.utils import check_combination_simulator_adapter, check_approximator_multivariate_normal_score
 
 
 def test_approximator_sample(point_approximator, simulator, batch_size, num_samples, adapter):
+    check_combination_simulator_adapter(simulator, adapter)
+
+    # as long as MultivariateNormalScore is unstable, skip test
+    check_approximator_multivariate_normal_score(point_approximator)
+
     data = simulator.sample((batch_size,))
 
     batch = adapter(data)