added documentation of constrain transform

Author: eodole

bayesflow/adapters/transforms/as_set.py

@@ -8,6 +8,13 @@ class AsSet(ElementwiseTransform):
     The `.as_set(["x", "y"])` transform indicates that both `x` and `y` are treated as sets. 
     That is, their values will be treated as *exchangable* such that they will imply the same inference regardless of the values' order. 
     This would be useful in a linear regression context where we can index the observations in arbitrary order and always get the same regression line.
+
+    Useage: 
+    
+    adapter = (
+        bf.Adapter()
+        .as_set(["x", "y"])
+        )
     """
 
     def forward(self, data: np.ndarray, **kwargs) -> np.ndarray:

bayesflow/adapters/transforms/constrain.py

@@ -15,6 +15,40 @@
 
 @serializable(package="bayesflow.adapters")
 class Constrain(ElementwiseTransform):
+    """
+    Constrains neural network predictions of a data variable to specificied bounds. 
+    
+    Parameters: 
+        String containing the name of the data variable to be transformed e.g. "sigma". See examples below. 
+
+    Named Parameters: 
+        lower: Lower bound for named data variable.
+        upper: Upper bound for named data variable.
+        method: Method by which to shrink the network predictions space to specified bounds. Choose from 
+            - Double bounded methods: sigmoid, expit, (default = sigmoid)
+            - Lower bound only methods: softplus, exp, (default = softplus)
+            - Upper bound only methods: softplus, exp, (default = softplus)
+        
+
+
+    Examples: 
+        Let sigma be the standard deviation of a normal distribution, then sigma should always be greater than zero. 
+
+        Useage: 
+        adapter = (
+            bf.Adapter()
+            .constrain("sigma", lower=0)
+            )
+
+        Suppose p is the parameter for a binomial distribution where p must be in [0,1] then we would constrain the neural network to estimate p in the following way
+
+        Usage: 
+        adapter = (
+            bf.Adapter()
+            .constrain("p", lower=0, upper=1, method = "sigmoid")
+            )
+    """
+
     def __init__(
         self, *, lower: int | float | np.ndarray = None, upper: int | float | np.ndarray = None, method: str = "default"
     ):