[no ci] docs: formatting in docstrings

Author: vpratz

bayesflow/approximators/approximator.py

@@ -74,6 +74,7 @@ def fit(self, *, dataset: keras.utils.PyDataset = None, simulator: Simulator = N
             A simulator used to generate a dataset. If provided, `dataset` must be None.
         **kwargs : dict
             Additional keyword arguments passed to `keras.Model.fit()`, including (see also `build_dataset`):
+
             batch_size : int or None, default='auto'
                 Number of samples per gradient update. Do not specify if `dataset` is provided as a
                 `keras.utils.PyDataset`, `tf.data.Dataset`, `torch.utils.data.DataLoader`, or a generator function.

bayesflow/approximators/continuous_approximator.py

@@ -124,6 +124,7 @@ def fit(self, *args, **kwargs):
             A simulator used to generate a dataset. If provided, `dataset` must be None.
         **kwargs : dict
             Additional keyword arguments passed to `keras.Model.fit()`, including (see also `build_dataset`):
+
             batch_size : int or None, default='auto'
                 Number of samples per gradient update. Do not specify if `dataset` is provided as a
                 `keras.utils.PyDataset`, `tf.data.Dataset`, `torch.utils.data.DataLoader`, or a generator function.

bayesflow/diagnostics/metrics/calibration_error.py

@@ -46,6 +46,7 @@ def calibration_error(
     -------
     result : dict
         Dictionary containing:
+
         - "values" : float or np.ndarray
             The aggregated calibration error per variable
         - "metric_name" : str

bayesflow/diagnostics/metrics/posterior_contraction.py

@@ -34,6 +34,7 @@ def posterior_contraction(
     -------
     result : dict
         Dictionary containing:
+
         - "values" : float or np.ndarray
             The aggregated posterior contraction per variable
         - "metric_name" : str

bayesflow/diagnostics/metrics/root_mean_squared_error.py

@@ -42,6 +42,7 @@ def root_mean_squared_error(
     -------
     result : dict
         Dictionary containing:
+
         - "values" : np.ndarray
             The aggregated (N)RMSE for each variable.
         - "metric_name" : str

bayesflow/diagnostics/plots/pairs_posterior.py

@@ -30,6 +30,8 @@ def pairs_posterior(
 ) -> sns.PairGrid:
     """Generates a bivariate pair plot given posterior draws and optional prior or prior draws.
 
+    Parameters
+    ----------
     estimates   : np.ndarray of shape (n_post_draws, n_params)
         The posterior draws obtained for a SINGLE observed data set.
     targets       : np.ndarray of shape (n_params,) or None, optional, default: None

bayesflow/networks/consistency_models/consistency_model.py

@@ -59,8 +59,7 @@ def __init__(
             instantiated using subnet_kwargs.
         max_time : int or float, optional, default: 200.0
             The maximum time of the diffusion
-        sigma2      : float or Tensor of dimension (input_dim, 1),
-                      optional, default: 1.0
+        sigma2      : float or Tensor of dimension (input_dim, 1), optional, default: 1.0
             Controls the shape of the skip-function
         eps         : float, optional, default: 0.001
             The minimum time

bayesflow/utils/jacobian/jacobian.py

@@ -12,19 +12,22 @@ def jacobian(f: Callable[[Tensor], Tensor], x: Tensor, return_output: bool = Fal
     """
     Compute the Jacobian matrix of f with respect to x.
 
-    :param f: The function to be differentiated.
-
-    :param x: Tensor of shape (..., D_in)
+    Parameters
+    ----------
+    f : callable
+        The function to be differentiated.
+    x : Tensor of shape (..., D_in)
         The input tensor to f.
-
-    :param return_output: bool
+    return_output : bool, optional
         Whether to return the output of f(x) along with the Jacobian matrix.
         Default: False
 
-    :return: Tensor of shape (..., D_out, D_in)
+    Returns
+    -------
+    Tensor of shape (..., D_out, D_in)
         The Jacobian matrix of f with respect to x.
 
-    :return: 2-tuple of tensors:
+    2-tuple of tensors
         1. The output of f(x) (if return_output is True)
         2. Tensor of shape (..., D_out, D_in)
             The Jacobian matrix of f with respect to x.

bayesflow/utils/jacobian/jacobian_trace.py

@@ -16,25 +16,27 @@ def jacobian_trace(
 ):
     """Compute or estimate the trace of the Jacobian matrix of f.
 
-    :param f: The function to be differentiated.
-
-    :param x: Tensor of shape (n, ..., d)
+    Parameters
+    ----------
+    f : callable
+        The function to be differentiated.
+    x :  Tensor of shape (n, ..., d)
         The input tensor to f.
-
-    :param max_steps: The maximum number of steps to use for the estimate.
+    max_steps : int, optional
+        The maximum number of steps to use for the estimate.
         If this does not exceed the dimensionality of f(x), use Hutchinson's algorithm to
         return an unbiased estimate of the Jacobian trace.
         Otherwise, perform an exact computation.
         Default: None
-
-    :param return_output: bool
+    return_output : bool, optional
         Whether to return the output of f(x) along with the trace of the Jacobian.
         Default: False
-
-    :param seed: int or keras SeedGenerator
+    seed : int or keras SeedGenerator, optional
         The seed to use for hutchinson trace estimation. Only has an effect when max_steps < d.
 
-    :return: 2-tuple of tensors:
+    Returns
+    -------
+    2-tuple of tensors:
         1. The output of f(x) (if return_output is True)
         2. Tensor of shape (n,)
             An unbiased estimate or the exact trace of the Jacobian of f.

bayesflow/utils/plot_utils.py

@@ -27,6 +27,7 @@ def prepare_plot_data(
     ----------
     estimates           : dict[str, ndarray] or ndarray
         The model-generated predictions or estimates, which can take the following forms:
+
         - ndarray of shape (num_datasets, num_variables)
             Point estimates for each dataset, where `num_datasets` is the number of datasets
             and `num_variables` is the number of variables per dataset.