refactor: improve warning clarity and formatting

Author: janfb

sbi/diagnostics/sbc.py

@@ -56,14 +56,14 @@ def run_sbc(
 
     if num_sbc_samples < 100:
         warnings.warn(
-            """Number of SBC samples should be on the order of 100s to give realiable
-            results.""",
+            "Number of SBC samples should be on the order of 100s to give realiable "
+            "results.",
             stacklevel=2,
         )
     if num_posterior_samples < 100:
         warnings.warn(
-            """Number of posterior samples for ranking should be on the order
-            of 100s to give reliable SBC results.""",
+            "Number of posterior samples for ranking should be on the order "
+            "of 100s to give reliable SBC results.",
             stacklevel=2,
         )
 
@@ -73,8 +73,8 @@ def run_sbc(
 
     if "sbc_batch_size" in kwargs:
         warnings.warn(
-            """`sbc_batch_size` is deprecated and will be removed in future versions.
-            Use `num_workers` instead.""",
+            "`sbc_batch_size` is deprecated and will be removed in future versions."
+            " Use `num_workers` instead.",
             DeprecationWarning,
             stacklevel=2,
         )
@@ -182,8 +182,8 @@ def get_nltp(thetas: Tensor, xs: Tensor, posterior: NeuralPosterior) -> Tensor:
 
     if unnormalized_log_prob:
         warnings.warn(
-            """Note that log probs of the true parameters under the posteriors
-        are not normalized because the posterior used is likelihood-based.""",
+            "Note that log probs of the true parameters under the posteriors are not "
+            "normalized because the posterior used is likelihood-based.",
             stacklevel=2,
         )
 
@@ -216,9 +216,9 @@ def check_sbc(
     """
     if ranks.shape[0] < 100:
         warnings.warn(
-            """You are computing SBC checks with less than 100 samples. These checks
-            should be based on a large number of test samples theta_o, x_o. We
-            recommend using at least 100.""",
+            "You are computing SBC checks with less than 100 samples. These checks"
+            " should be based on a large number of test samples theta_o, x_o. We"
+            " recommend using at least 100.",
             stacklevel=2,
         )
 
@@ -315,9 +315,8 @@ def check_uniformity_c2st(
     c2st_std = c2st_scores.std(0, correction=0 if num_repetitions == 1 else 1)
     if (c2st_std > 0.05).any():
         warnings.warn(
-            f"""C2ST score variability is larger than {0.05}: std={c2st_scores.std(0)},
-            result may be unreliable. Consider increasing the number of samples.
-            """,
+            f"C2ST score variability is larger than {0.05}: std={c2st_scores.std(0)}, "
+            "result may be unreliable. Consider increasing the number of samples.",
             stacklevel=2,
         )
 

sbi/inference/posteriors/mcmc_posterior.py

@@ -136,9 +136,9 @@ def __init__(
 
         if init_strategy_num_candidates is not None:
             warn(
-                """Passing `init_strategy_num_candidates` is deprecated as of sbi
-                v0.19.0. Instead, use e.g.,
-                `init_strategy_parameters={"num_candidate_samples": 1000}`""",
+                "Passing `init_strategy_num_candidates` is deprecated as of sbi "
+                "v0.19.0. Instead, use e.g., `init_strategy_parameters "
+                f"={'num_candidate_samples': 1000}`",
                 stacklevel=2,
             )
             self.init_strategy_parameters["num_candidate_samples"] = (
@@ -194,9 +194,8 @@ def log_prob(
             `len($\theta$)`-shaped log-probability.
         """
         warn(
-            """`.log_prob()` is deprecated for methods that can only evaluate the
-            log-probability up to a normalizing constant. Use `.potential()`
-            instead.""",
+            "`.log_prob()` is deprecated for methods that can only evaluate the "
+            "log-probability up to a normalizing constant. Use `.potential()` instead.",
             stacklevel=2,
         )
         warn("The log-probability is unnormalized!", stacklevel=2)
@@ -264,9 +263,9 @@ def sample(
         )
         if init_strategy_num_candidates is not None:
             warn(
-                """Passing `init_strategy_num_candidates` is deprecated as of sbi
-                v0.19.0. Instead, use e.g.,
-                `init_strategy_parameters={"num_candidate_samples": 1000}`""",
+                "Passing `init_strategy_num_candidates` is deprecated as of sbi"
+                "v0.19.0. Instead, use e.g.,"
+                f"`init_strategy_parameters={"num_candidate_samples": 1000}`",
                 stacklevel=2,
             )
             self.init_strategy_parameters["num_candidate_samples"] = (
@@ -275,7 +274,7 @@ def sample(
         if sample_with is not None:
             raise ValueError(
                 f"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting "
-                f"`sample_with` is no longer supported. You have to rerun "
+                "`sample_with` is no longer supported. You have to rerun "
                 f"`.build_posterior(sample_with={sample_with}).`"
             )
         if mcmc_method is not None:
@@ -426,9 +425,9 @@ def sample_batched(
         # warn if num_chains is larger than num requested samples
         if num_chains > torch.Size(sample_shape).numel():
             warnings.warn(
-                f"""Passed num_chains {num_chains} is larger than the number of
-                requested samples {torch.Size(sample_shape).numel()}, resetting
-                it to {torch.Size(sample_shape).numel()}.""",
+                "The passed number of MCMC chains is larger than the number of "
+                f"requested samples: {num_chains} > {torch.Size(sample_shape).numel()},"
+                f" resetting it to {torch.Size(sample_shape).numel()}.",
                 stacklevel=2,
             )
             num_chains = torch.Size(sample_shape).numel()
@@ -453,12 +452,11 @@ def sample_batched(
         num_chains_extended = batch_size * num_chains
         if num_chains_extended > 100:
             warnings.warn(
-                f"""Note that for batched sampling, we use {num_chains} for each
-                 x in the batch. With the given settings, this results in a
-                 large number of chains ({num_chains_extended}),  This can be
-                 large number of chains ({num_chains_extended}), which can be
-                 slow and memory-intensive. Consider reducing the number of
-                 chains.""",
+                "Note that for batched sampling, we use num_chains many chains for each"
+                " x in the batch. With the given settings, this results in a large "
+                f"number large number of chains ({num_chains_extended}), which can be "
+                "slow and memory-intensive for vectorized MCMC. Consider reducing the "
+                "number of chains.",
                 stacklevel=2,
             )
         init_strategy_parameters["num_return_samples"] = num_chains_extended
@@ -905,8 +903,8 @@ def _prepare_potential(self, method: str) -> Callable:
         else:
             if "hmc" in method or "nuts" in method:
                 warn(
-                    """The kwargs "hmc" and "nuts" are deprecated. Use "hmc_pyro",
-                    "nuts_pyro", "hmc_pymc", or "nuts_pymc" instead.""",
+                    "The kwargs 'hmc' and 'nuts' are deprecated. Use 'hmc_pyro',"
+                    "'nuts_pyro', 'hmc_pymc', or 'nuts_pymc' instead.",
                     DeprecationWarning,
                     stacklevel=2,
                 )

sbi/inference/posteriors/rejection_posterior.py

@@ -84,9 +84,8 @@ def log_prob(
             `len($\theta$)`-shaped log-probability.
         """
         warn(
-            """`.log_prob()` is deprecated for methods that can only evaluate the
-            log-probability up to a normalizing constant. Use `.potential()`
-            instead.""",
+            "`.log_prob()` is deprecated for methods that can only evaluate the "
+            "log-probability up to a normalizing constant. Use `.potential()` instead.",
             stacklevel=2,
         )
         warn("The log-probability is unnormalized!", stacklevel=2)

sbi/neural_nets/mnle.py

@@ -124,10 +124,10 @@ def build_mnle(
     check_data_device(batch_x, batch_y)
 
     warnings.warn(
-        """The mixed neural likelihood estimator assumes that x contains
-        continuous data in the first n-1 columns (e.g., reaction times) and
-        categorical data in the last column (e.g., corresponding choices). If
-        this is not the case for the passed `x` do not use this function.""",
+        "The mixed neural likelihood estimator assumes that x contains "
+        "continuous data in the first n-1 columns (e.g., reaction times) and "
+        "categorical data in the last column (e.g., corresponding choices). If "
+        "this is not the case for the passed `x` do not use this function.",
         stacklevel=2,
     )
     # Separate continuous and discrete data.

sbi/samplers/mcmc/slice_numpy.py

@@ -394,8 +394,8 @@ def __init__(
         # TODO: implement parallelization across batches of chains.
         if num_workers > 1:
             warn(
-                """Parallelization of vectorized slice sampling not implement, running
-                serially.""",
+                "Parallelization of vectorized slice sampling not implement, running "
+                "serially.",
                 stacklevel=2,
             )
         self._reset()

sbi/utils/sbiutils.py

@@ -43,9 +43,9 @@ def warn_if_zscoring_changes_data(x: Tensor, duplicate_tolerance: float = 0.1) -
     # Check we do have different data in the batch
     if num_unique == 1:
         warnings.warn(
-            """Beware that there is only a single unique element in the simulated data.
-            If this is intended, make sure to set `z_score_x='none'` as z-scoring would
-            result in NaNs""",
+            "Beware that there is only a single unique element in the simulated data. "
+            "If this is intended, make sure to set `z_score_x='none'` as z-scoring "
+            "would result in NaNs",
             UserWarning,
             stacklevel=2,
         )
@@ -61,13 +61,14 @@ def warn_if_zscoring_changes_data(x: Tensor, duplicate_tolerance: float = 0.1) -
 
         if num_unique_z < num_unique * (1 - duplicate_tolerance):
             warnings.warn(
-                """Z-scoring these simulation outputs resulted in {num_unique_z} unique
-                datapoints. Before z-scoring, it had been {num_unique}. This can occur
-                due to numerical inaccuracies when the data covers a large range of
-                values. Consider either setting `z_score_x=False` (but beware that this
-                can be problematic for training the NN) or exclude outliers from your
-                dataset. Note: if you have already set `z_score_x=False`, this warning
-                will still be displayed, but you can ignore it.""",
+                "Z-scoring these simulation outputs resulted in {num_unique_z} unique "
+                "datapoints. Before z-scoring, it had been {num_unique}. This can "
+                "occur due to numerical inaccuracies when the data covers a large "
+                "range of values. Consider either setting `z_score_x=False` (but "
+                "beware that this can be problematic for training the NN) or exclude "
+                "outliers from your dataset. Note: if you have already set "
+                "`z_score_x=False`, this warning will still be displayed, but you can"
+                " ignore it.",
                 UserWarning,
                 stacklevel=2,
             )
@@ -406,11 +407,11 @@ def warn_on_batched_x(batch_size):
     if batch_size > 1:
         warnings.warn(
             f"An x with a batch size of {batch_size} was passed. "
-            + """Unless you are using `sample_batched` or `log_prob_batched`, this will
-            be interpreted as a batch of independent and identically distributed data
-            X={x_1, ..., x_n}, i.e., data generated based on the same underlying
-            (unknown) parameter. The resulting posterior will be with respect to entire
-            batch, i.e,. p(theta | X).""",
+            "Unless you are using `sample_batched` or `log_prob_batched`, this will"
+            "be interpreted as a batch of independent and identically distributed data"
+            "X={x_1, ..., x_n}, i.e., data generated based on the same underlying"
+            "(unknown) parameter. The resulting posterior will be with respect to"
+            " the entire batch, i.e,. p(theta | X).",
             stacklevel=2,
         )
 
@@ -714,9 +715,9 @@ def mcmc_transform(
             # does not implement support.
             # AttributeError -> Custom distribution that has no support attribute.
             warnings.warn(
-                """The passed prior has no support property, transform will be
-                constructed from mean and std. If the passed prior is supposed to be
-                bounded consider implementing the prior.support property.""",
+                "The passed prior has no support property, transform will be "
+                "constructed from mean and std. If the passed prior is supposed to be "
+                "bounded consider implementing the prior.support property.",
                 stacklevel=2,
             )
             has_support = False
@@ -749,9 +750,8 @@ def mcmc_transform(
                 # does not implement mean, e.g., TransformedDistribution.
                 # AttributeError -> Custom distribution that has no mean/std attribute.
                 warnings.warn(
-                    """The passed prior has no mean or stddev attribute, estimating
-                    them from samples to build affimed standardizing
-                    transform.""",
+                    "The passed prior has no mean or stddev attribute, estimating "
+                    "them from samples to build affimed standardizing transform.",
                     stacklevel=2,
                 )
                 theta = prior.sample(torch.Size((num_prior_samples_for_zscoring,)))

sbi/utils/user_input_checks.py

@@ -71,9 +71,9 @@ def process_prior(
     # If prior is a sequence, assume independent components and check as PyTorch prior.
     if isinstance(prior, Sequence):
         warnings.warn(
-            f"""Prior was provided as a sequence of {len(prior)} priors. They will be
-            interpreted as independent of each other and matched in order to the
-            components of the parameter.""",
+            f"Prior was provided as a sequence of {len(prior)} priors. They will be "
+            "interpreted as independent of each other and matched in order to the "
+            "components of the parameter.",
             stacklevel=2,
         )
         # process individual priors

sbi/utils/user_input_checks_utils.py

@@ -77,8 +77,8 @@ def _set_mean_and_variance(self):
                 ** 2
             )
             warnings.warn(
-                """Prior is lacking variance attribute, estimating prior variance from
-                samples...""",
+                "Prior is lacking variance attribute, estimating prior variance from "
+                "samples.",
                 UserWarning,
                 stacklevel=2,
             )
@@ -333,8 +333,8 @@ def build_support(
     if lower_bound is None and upper_bound is None:
         support = constraints.real
         warnings.warn(
-            """No prior bounds were passed, consider passing lower_bound
-            and / or upper_bound if your prior has bounded support.""",
+            "No prior bounds were passed, consider passing lower_bound "
+            "and / or upper_bound if your prior has bounded support.",
             stacklevel=2,
         )
     # Only lower bound is specified.