refactor: add deprecation warnings for deprecated build_posterior arguments

Author: abelaba

sbi/inference/trainers/fmpe/fmpe.py

@@ -16,6 +16,7 @@
 )
 from sbi.neural_nets import flowmatching_nn
 from sbi.neural_nets.estimators import ConditionalVectorFieldEstimator
+from sbi.utils.sbiutils import warn_if_deprecated
 
 
 class FMPE(VectorFieldTrainer):
@@ -98,6 +99,14 @@ def build_posterior(
             Posterior $p(\theta|x)$  with `.sample()` and `.log_prob()` methods.
         """
 
+        warn_if_deprecated(
+            self.build_posterior,
+            locals(),
+            {
+                "vectorfield_sampling_parameters",
+            },
+        )
+
         return super().build_posterior(
             estimator=vector_field_estimator,
             prior=prior,

sbi/inference/trainers/nle/mnle.py

@@ -15,7 +15,7 @@
 from sbi.inference.trainers.nle.nle_base import LikelihoodEstimatorTrainer
 from sbi.neural_nets.estimators import MixedDensityEstimator
 from sbi.sbi_types import TensorboardSummaryWriter
-from sbi.utils.sbiutils import del_entries
+from sbi.utils.sbiutils import del_entries, warn_if_deprecated
 
 
 class MNLE(LikelihoodEstimatorTrainer):
@@ -162,6 +162,20 @@ def build_posterior(
             Posterior $p(\theta|x)$  with `.sample()` and `.log_prob()` methods
             (the returned log-probability is unnormalized).
         """
+
+        warn_if_deprecated(
+            self.build_posterior,
+            locals(),
+            {
+                "mcmc_parameters",
+                "vi_parameters",
+                "rejection_sampling_parameters",
+                "importance_sampling_parameters",
+                "mcmc_method",
+                "vi_method",
+            },
+        )
+
         if density_estimator is not None:
             assert isinstance(
                 density_estimator, MixedDensityEstimator

sbi/inference/trainers/nle/nle_base.py

@@ -31,6 +31,7 @@
 )
 from sbi.sbi_types import TorchTransform
 from sbi.utils import check_estimator_arg, x_shape_from_simulation
+from sbi.utils.sbiutils import warn_if_deprecated
 from sbi.utils.torchutils import assert_all_finite
 
 
@@ -359,6 +360,19 @@ def build_posterior(
             (the returned log-probability is unnormalized).
         """
 
+        warn_if_deprecated(
+            self.build_posterior,
+            locals(),
+            {
+                "mcmc_parameters",
+                "vi_parameters",
+                "rejection_sampling_parameters",
+                "importance_sampling_parameters",
+                "mcmc_method",
+                "vi_method",
+            },
+        )
+
         return super().build_posterior(
             density_estimator,
             prior,

sbi/inference/trainers/npe/mnpe.py

@@ -16,7 +16,7 @@
 from sbi.inference.trainers.npe.npe_c import NPE_C
 from sbi.neural_nets.estimators import MixedDensityEstimator
 from sbi.sbi_types import TensorboardSummaryWriter
-from sbi.utils.sbiutils import del_entries
+from sbi.utils.sbiutils import del_entries, warn_if_deprecated
 
 
 class MNPE(NPE_C):
@@ -158,6 +158,21 @@ def build_posterior(
         Returns:
             Posterior $p(\theta|x)$ with `.sample()` and `.log_prob()` methods.
         """
+
+        warn_if_deprecated(
+            self.build_posterior,
+            locals(),
+            {
+                "direct_sampling_parameters",
+                "mcmc_parameters",
+                "vi_parameters",
+                "rejection_sampling_parameters",
+                "importance_sampling_parameters",
+                "mcmc_method",
+                "vi_method",
+            },
+        )
+
         if density_estimator is not None:
             assert isinstance(
                 density_estimator, MixedDensityEstimator

sbi/inference/trainers/npe/npe_base.py

@@ -46,7 +46,11 @@
     validate_theta_and_x,
     warn_if_zscoring_changes_data,
 )
-from sbi.utils.sbiutils import ImproperEmpirical, mask_sims_from_prior
+from sbi.utils.sbiutils import (
+    ImproperEmpirical,
+    mask_sims_from_prior,
+    warn_if_deprecated,
+)
 from sbi.utils.torchutils import assert_all_finite
 
 
@@ -529,6 +533,20 @@ def build_posterior(
             (the returned log-probability is unnormalized).
         """
 
+        warn_if_deprecated(
+            self.build_posterior,
+            locals(),
+            {
+                "direct_sampling_parameters",
+                "mcmc_parameters",
+                "vi_parameters",
+                "rejection_sampling_parameters",
+                "importance_sampling_parameters",
+                "mcmc_method",
+                "vi_method",
+            },
+        )
+
         self._check_prior_for_rejection_sampling(
             prior, sample_with, posterior_parameters
         )

sbi/inference/trainers/npse/npse.py

@@ -14,6 +14,7 @@
 )
 from sbi.neural_nets.estimators import ConditionalVectorFieldEstimator
 from sbi.neural_nets.factory import posterior_score_nn
+from sbi.utils.sbiutils import warn_if_deprecated
 
 
 class NPSE(VectorFieldTrainer):
@@ -111,6 +112,15 @@ def build_posterior(
         Returns:
             Posterior $p(\theta|x)$  with `.sample()` and `.log_prob()` methods.
         """
+
+        warn_if_deprecated(
+            self.build_posterior,
+            locals(),
+            {
+                "vectorfield_sampling_parameters",
+            },
+        )
+
         return super().build_posterior(
             estimator=vector_field_estimator,
             prior=prior,

sbi/inference/trainers/nre/nre_base.py

@@ -31,6 +31,7 @@
     check_estimator_arg,
     clamp_and_warn,
 )
+from sbi.utils.sbiutils import warn_if_deprecated
 from sbi.utils.torchutils import repeat_rows
 
 
@@ -405,6 +406,20 @@ def build_posterior(
             Posterior $p(\theta|x)$  with `.sample()` and `.log_prob()` methods
             (the returned log-probability is unnormalized).
         """
+
+        warn_if_deprecated(
+            self.build_posterior,
+            locals(),
+            {
+                "mcmc_parameters",
+                "vi_parameters",
+                "rejection_sampling_parameters",
+                "importance_sampling_parameters",
+                "mcmc_method",
+                "vi_method",
+            },
+        )
+
         return super().build_posterior(
             density_estimator,
             prior,

sbi/utils/sbiutils.py

@@ -1,11 +1,23 @@
 # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
 # under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
 
+import inspect
 import logging
 import random
 import warnings
 from math import pi
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Type, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    Union,
+)
 
 import numpy as np
 import pyknos.nflows.transforms as nflows_tf
@@ -1006,3 +1018,43 @@ def seed_all_backends(seed: Optional[Union[int, Tensor]] = None) -> None:
     torch.cuda.manual_seed(seed)
     torch.backends.cudnn.deterministic = True  # type: ignore
     torch.backends.cudnn.benchmark = False  # type: ignore
+
+
+def warn_if_deprecated(
+    method: Callable, locals_dict: Dict[str, Any], deprecated_keys: Set
+) -> None:
+    """
+    Issues a warning if any deprecated parameters are used with non-default values.
+
+    This function compares the values of deprecated parameters (from `locals_dict`)
+    against their default values in the given `method` signature. If a deprecated
+    parameter is explicitly set to a non-default value, a `DeprecationWarning` is
+    raised.
+
+    Args:
+        method: The function whose parameters are checked.
+        locals_dict: The arguments of the function.
+        deprecated_keys: The names of the parameters that are deprecated.
+
+    """
+
+    # Get the signature of the function
+    method_signature = inspect.signature(method)
+
+    used = []
+    for key in deprecated_keys:
+        if key in locals_dict and key in method_signature.parameters:
+            default_value = method_signature.parameters[key].default
+
+            # Compare value to default
+            if locals_dict[key] != default_value:
+                used.append(key)
+
+    if used:
+        warnings.warn(
+            f"The following arguments are deprecated and"
+            " will be removed in a future version: "
+            f"{', '.join(used)}. Please use `posterior_parameters` instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )