Merge pull request #115 from stefanradev93/Development

Merge with dev

README.md

@@ -1,8 +1,10 @@
 # BayesFlow <img src="https://github.com/stefanradev93/BayesFlow/blob/master/img/bayesflow_hex.png?raw=true" align="right" width=20% height=20% />
 
 [![Actions Status](https://github.com/stefanradev93/bayesflow/workflows/Tests/badge.svg)](https://github.com/stefanradev93/bayesflow/actions)
-[![Licence](https://img.shields.io/github/license/stefanradev93/BayesFlow)](https://img.shields.io/github/license/stefanradev93/BayesFlow)
+[![License: MIT](https://img.shields.io/badge/License-MIT-red.svg)](https://opensource.org/licenses/MIT)
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.05702/status.svg)](https://doi.org/10.21105/joss.05702)
+[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/dwyl/esta/issues)
+
 
 Welcome to our BayesFlow library for efficient simulation-based Bayesian workflows! Our library enables users to create specialized neural networks for *amortized Bayesian inference*, which repay users with rapid statistical inference after a potentially longer simulation-based training phase.
 

bayesflow/computational_utilities.py

@@ -51,7 +51,7 @@ def posterior_calibration_error(
         The random draws from the approximate posteriors over ``num_datasets``
     prior_samples      : np.ndarray of shape (num_datasets, num_params)
         The corresponding ground-truth values sampled from the prior
-    alpha_resolution   : int, optional, default: 100
+    alpha_resolution   : int, optional, default: 20
         The number of credibility intervals (CIs) to consider
     aggregator_fun     : callable or None, optional, default: np.median
         The function used to aggregate the marginal calibration errors.
@@ -233,7 +233,7 @@ def mmd_kernel_unbiased(x, y, kernel):
     Returns
     -------
     loss   : tf.Tensor of shape (,)
-        The statistically unbiaserd squared maximum mean discrepancy (MMD) value.
+        The statistically unbiased squared maximum mean discrepancy (MMD) value.
     """
 
     m, n = x.shape[0], y.shape[0]

bayesflow/configuration.py

@@ -26,7 +26,7 @@
 
 
 class DefaultJointConfigurator:
-    """Fallback class for a generic configrator for joint posterior and likelihood approximation."""
+    """Fallback class for a generic configurator for joint posterior and likelihood approximation."""
 
     def __init__(self, default_float_type=np.float32):
         self.posterior_config = DefaultPosteriorConfigurator(default_float_type=default_float_type)

bayesflow/coupling_networks.py

@@ -313,7 +313,7 @@ def _calculate_spline(self, target, spline_params, inverse=False):
         target         : tf.Tensor of shape (batch_size, ..., dim_2)
             The target partition of the input vector to transform.
         spline_params  : tuple(tf.Tensor,...)
-            A tuple with tensors corresponding to the learnbale spline features:
+            A tuple with tensors corresponding to the learnable spline features:
             (left_edge, bottom_edge, widths, heights, derivatives)
         inverse        : bool, optional, default: False
             Flag indicating whether to run the block forward or backward.
@@ -516,7 +516,7 @@ def __init__(
             for the required entries.
         coupling_design       : str or callable, optional, default: 'affine'
             The type of internal coupling network to use. Must be in ['affine', 'spline'].
-            In general, spline couplings run slower than affine couplings, but require fewers coupling
+            In general, spline couplings run slower than affine couplings, but requires fewer coupling
             layers. Spline couplings may work best with complex (e.g., multimodal) low-dimensional
             problems. The difference will become less and less pronounced as we move to higher dimensions.
         permutation           : str or None, optional, default: 'fixed'
@@ -581,15 +581,15 @@ def __init__(
             self.act_norm = None
 
     def call(self, target_or_z, condition, inverse=False, **kwargs):
-        """Performs one pass through a the affine coupling layer (either inverse or forward).
+        """Performs one pass through the affine coupling layer (either inverse or forward).
 
         Parameters
         ----------
         target_or_z      : tf.Tensor
-            The estimation quantites of interest or latent representations z ~ p(z), shape (batch_size, ...)
+            The estimation quantities of interest or latent representations z ~ p(z), shape (batch_size, ...)
         condition        : tf.Tensor or None
             The conditioning data of interest, for instance, x = summary_fun(x), shape (batch_size, ...).
-            If `condition is None`, then the layer recuces to an unconditional ACL.
+            If `condition is None`, then the layer reduces to an unconditional ACL.
         inverse          : bool, optional, default: False
             Flag indicating whether to run the block forward or backward.
 

bayesflow/default_settings.py

@@ -199,3 +199,8 @@ def __init__(self, meta_dict: dict, mandatory_fields: list = []):
 
 
 MMD_BANDWIDTH_LIST = [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 5, 10, 15, 20, 25, 30, 35, 100, 1e3, 1e4, 1e5, 1e6]
+
+# Minimum time interval between tqdm status updates to reduce
+# load. Only respected when refresh=False in set_postfix
+# and set_postfix_str
+TQDM_MININTERVAL = 0.1  # in seconds