Add docstrings to transform_adapter.py

python/nutpie/transform_adapter.py

@@ -12,6 +12,42 @@ def make_transform_adapter(
     untransformed_dim=None,
     zero_init=True,
 ):
+    """
+    Configure the normalizing flows
+
+    Parameters
+    ----------
+    verbose: bool, default False
+        If True, print console messages about loss. Warning: This gets spammy fast with multiple chains
+    window_size: int
+
+    show_progress: bool, default False
+        Show a progress bar for each SGD optimization run
+    nn_depth: int, default 1
+        Number of hidden layers in each normalizing flow layer
+    nn_width: int, optional
+        Width of the neural network. Trades computation cost during optimizaiton for better normalizing
+        flows. Can lead to problems if the number of parameters grows too much -- you may need to increase
+        tuning steps to fit them.
+
+        If None, this is set to half the number of parameters being estimated in the posterior
+    num_layers: int, deafult 8
+        The number of normalizing flow layers. More layers leads to more numerical instability, but more
+        freedom in the functional approximation.
+    num_diag_windows
+    learning_rate: float, default 1e-3
+        Learning rate for the ADAM optimizer used in the normalizing flow optimization
+    scale_layer: False
+        Experimental, currently unused
+    untransformed_dim: int, optional
+        How many posterior parameters are passed unchanged in each flow layer.
+    zero_init: bool, default True
+        Experimental, if True it will initialize the network neurons with a small random value.
+
+    Returns
+    -------
+
+    """
     import traceback
     from functools import partial