fix: address comments

Author: jnsbck

sbi/neural_nets/estimators/__init__.py

@@ -1,13 +1,11 @@
 from sbi.neural_nets.estimators.base import ConditionalDensityEstimator
 from sbi.neural_nets.estimators.categorical_net import (
+    CategoricalMADE,
     CategoricalMassEstimator,
     CategoricalNet,
-    CategoricalMADE,
 )
 from sbi.neural_nets.estimators.flowmatching_estimator import FlowMatchingEstimator
-from sbi.neural_nets.estimators.mixed_density_estimator import (
-    MixedDensityEstimator,
-)
+from sbi.neural_nets.estimators.mixed_density_estimator import MixedDensityEstimator
 from sbi.neural_nets.estimators.nflows_flow import NFlowsFlow
 from sbi.neural_nets.estimators.score_estimator import ConditionalScoreEstimator
 from sbi.neural_nets.estimators.zuko_flow import ZukoFlow

sbi/neural_nets/estimators/categorical_net.py

@@ -1,7 +1,7 @@
 # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
 # under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
 
-from typing import Optional
+from typing import Callable, Optional
 
 import torch
 from nflows.nn.nde.made import MADE
@@ -15,29 +15,46 @@
 
 
 class CategoricalMADE(MADE):
+    """Conditional density (mass) estimation for a n-dim categorical random variable.
+
+    Takes as input parameters theta and learns the parameters p of a Categorical.
+
+    Defines log prob and sample functions.
+    """
+
     def __init__(
         self,
-        categories,  # Tensor[int]
-        hidden_features,
-        context_features=None,
-        num_blocks=2,
-        use_residual_blocks=True,
-        random_mask=False,
-        activation=F.relu,
-        dropout_probability=0.0,
-        use_batch_norm=False,
-        epsilon=1e-2,
-        custom_initialization=True,
+        num_categories: Tensor,  # Tensor[int]
+        hidden_features: int,
+        context_features: Optional[int] = None,
+        num_blocks: int = 2,
+        use_residual_blocks: bool = True,
+        random_mask: bool = False,
+        activation: Callable = F.relu,
+        dropout_probability: float = 0.0,
+        use_batch_norm: bool = False,
+        epsilon: float = 1e-2,
+        custom_initialization: bool = True,
         embedding_net: Optional[nn.Module] = nn.Identity(),
     ):
+        """Initialize the neural net.
+
+        Args:
+            num_categories: number of categories for each variable. len(categories)
+                defines the number of input units, i.e., dimensionality of the features.
+                max(categories) defines the number of output units, i.e., the largest
+                number of categories.
+            num_hidden: number of hidden units per layer.
+            num_layers: number of hidden layers.
+            embedding_net: emebedding net for input.
+        """
         if use_residual_blocks and random_mask:
             raise ValueError("Residual blocks can't be used with random masks.")
 
-        self.num_variables = len(categories)
-        self.num_categories = int(max(categories))
-        self.categories = categories
+        self.num_variables = len(num_categories)
+        self.num_categories = int(torch.max(num_categories))
         self.mask = torch.zeros(self.num_variables, self.num_categories)
-        for i, c in enumerate(categories):
+        for i, c in enumerate(num_categories):
             self.mask[i, :c] = 1
 
         super().__init__(
@@ -60,7 +77,18 @@ def __init__(
         if custom_initialization:
             self._initialize()
 
-    def forward(self, inputs, context=None):
+    def forward(self, inputs: Tensor, context: Optional[Tensor] = None) -> Tensor:
+        r"""Forward pass of the categorical density estimator network to compute the
+        conditional density at a given time.
+
+        Args:
+            input: Original data, x0. (batch_size, *input_shape)
+            condition: Conditioning variable. (batch_size, *condition_shape)
+
+        Returns:
+            Predicted categorical probabilities. (batch_size, *input_shape,
+                num_categories)
+        """
         embedded_context = self.embedding_net.forward(context)
         return super().forward(inputs, context=embedded_context)
 
@@ -69,8 +97,16 @@ def compute_probs(self, outputs):
         ps = ps / ps.sum(dim=-1, keepdim=True)
         return ps
 
-    # outputs (batch_size, num_variables, num_categories)
-    def log_prob(self, inputs, context=None):
+    def log_prob(self, inputs: Tensor, context: Optional[Tensor] = None) -> Tensor:
+        r"""Return log-probability of samples.
+
+        Args:
+            input: Input datapoints of shape `(batch_size, *input_shape)`.
+            context: Context of shape `(batch_size, *condition_shape)`.
+
+        Returns:
+            Log-probabilities of shape `(batch_size, num_variables, num_categories)`.
+        """
         outputs = self.forward(inputs, context=context)
         outputs = outputs.reshape(*inputs.shape, self.num_categories)
         ps = self.compute_probs(outputs)

sbi/neural_nets/estimators/mixed_density_estimator.py

@@ -147,8 +147,8 @@ def log_prob(self, input: Tensor, condition: Tensor) -> Tensor:
             f"{input_batch_dim} do not match."
         )
 
-        num_disc = self.discrete_net.net.num_variables
-        cont_input, disc_input = _separate_input(input, num_discrete_columns=num_disc)
+        num_discrete_variables = self.discrete_net.net.num_variables
+        cont_input, disc_input = _separate_input(input, num_discrete_variables)
         # Embed continuous condition
         embedded_condition = self.condition_embedding(condition)
         # expand and repeat to match batch of inputs.

sbi/neural_nets/net_builders/categorial.py

@@ -73,7 +73,7 @@ def build_autoregressive_categoricalmassestimator(
     z_score_y: Optional[str] = "independent",
     num_hidden: int = 20,
     num_layers: int = 2,
-    categories: Optional[Tensor] = None,
+    num_categories: Optional[Tensor] = None,
     embedding_net: nn.Module = nn.Identity(),
 ):
     """Returns a density estimator for a categorical random variable.
@@ -86,13 +86,14 @@ def build_autoregressive_categoricalmassestimator(
         num_hidden: Number of hidden units per layer.
         num_layers: Number of hidden layers.
         embedding_net: Embedding net for y.
+        num_categories: number of categories for each variable.
     """
 
     if z_score_x != "none":
         raise ValueError("Categorical input should not be z-scored.")
-    if categories is None:
+    if num_categories is None:
         warnings.warn(
-            "Inferring categories from batch_x. Ensure all categories are present.",
+            "Inferring num_categories from batch_x. Ensure all categories are present.",
             stacklevel=2,
         )
 
@@ -108,10 +109,12 @@ def build_autoregressive_categoricalmassestimator(
 
     batch_x_discrete = batch_x[:, _is_discrete(batch_x)]
     inferred_categories = tensor([unique(col).numel() for col in batch_x_discrete.T])
-    categories = categories if categories is not None else inferred_categories
+    num_categories = (
+        num_categories if num_categories is not None else inferred_categories
+    )
 
     categorical_net = CategoricalMADE(
-        categories=categories,
+        num_categories=num_categories,
         hidden_features=num_hidden,
         context_features=y_numel,
         num_blocks=num_layers,

sbi/neural_nets/net_builders/mnle.py

@@ -60,6 +60,7 @@ def build_mnle(
     z_score_y: Optional[str] = "independent",
     flow_model: str = "nsf",
     categorical_model: str = "mlp",
+    num_categorical_columns: Optional[Tensor] = None,
     embedding_net: nn.Module = nn.Identity(),
     combined_embedding_net: Optional[nn.Module] = None,
     num_transforms: int = 2,
@@ -108,6 +109,8 @@ def build_mnle(
             data.
         categorical_model: type of categorical net to use for the discrete part of
             the data. Can be "made" or "mlp".
+        num_categorical_columns: Number of categorical columns of each variable in the
+            input data. If None, the function will infer this from the data.
         embedding_net: Optional embedding network for y, required if y is > 1D.
         combined_embedding_net: Optional embedding for combining the discrete
             part of the input and the embedded condition into a joined
@@ -137,7 +140,10 @@ def build_mnle(
         stacklevel=2,
     )
     # Separate continuous and discrete data.
-    num_disc = int(torch.sum(_is_discrete(batch_x)))
+    if num_categorical_columns is None:
+        num_disc = int(torch.sum(_is_discrete(batch_x)))
+    else:
+        num_disc = len(num_categorical_columns)
     cont_x, disc_x = _separate_input(batch_x, num_discrete_columns=num_disc)
 
     # Set up y-embedding net with z-scoring.
@@ -160,6 +166,7 @@ def build_mnle(
             num_hidden=hidden_features,
             num_layers=hidden_layers,
             embedding_net=embedding_net,
+            num_categories=num_categorical_columns,
         )
     elif categorical_model == "mlp":
         assert num_disc == 1, "MLP only supports 1D input."