Merge pull request #70 from pyt-team/ninamiolane-apiiii

Fix API errors by fixing documentation errors in the codebase
geometric-intelligence · Jun 4, 2024 · 31f10a0 · 31f10a0
2 parents b8d866f + e0efde4
commit 31f10a0
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diff --git a/docs/api/index.rst b/docs/api/index.rst
@@ -15,7 +15,7 @@ The API reference gives an overview of `TopoBenchmarkX`, which consists of sever
 - `utils` implements utilities to handle the training process.
 
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 2
    :caption: Packages & Modules
 
    data/index

diff --git a/docs/api/nn/encoders/index.rst b/docs/api/nn/encoders/index.rst
@@ -5,5 +5,5 @@ Encoders
 .. automodule:: topobenchmarkx.nn.encoders.base
     :members:
 
-.. automodule:: topobenchmarkx.nn.encoders.all_cell_encoders
+.. automodule:: topobenchmarkx.nn.encoders.all_cell_encoder
     :members:
diff --git a/docs/api/transforms/data_manipulations/index.rst b/docs/api/transforms/data_manipulations/index.rst
@@ -5,7 +5,7 @@ Data Manipulations
 .. automodule:: topobenchmarkx.transforms.data_manipulations.calculate_simplicial_curvature
     :members:
 
-.. automodule:: topobenchmarkx.transforms.data_manipulations.equal_gauss_features
+.. automodule:: topobenchmarkx.transforms.data_manipulations.equal_gaus_features
     :members:
 
 .. automodule:: topobenchmarkx.transforms.data_manipulations.identity_transform
@@ -17,7 +17,7 @@ Data Manipulations
 .. automodule:: topobenchmarkx.transforms.data_manipulations.infere_radius_connectivity
     :members:
 
-.. automodule:: topobenchmarkx.transforms.data_manipulations.keep_only_connected_components
+.. automodule:: topobenchmarkx.transforms.data_manipulations.keep_only_connected_component
     :members:
 
 .. automodule:: topobenchmarkx.transforms.data_manipulations.keep_selected_data_fields

diff --git a/docs/api/transforms/data_transform/index.rst b/docs/api/transforms/data_transform/index.rst
@@ -0,0 +1,6 @@
+**************
+Data Transform
+**************
+
+.. automodule:: topobenchmarkx.transforms.data_transform
+    :members:
diff --git a/docs/api/transforms/data_transforms/index.rst b/docs/api/transforms/data_transforms/index.rst
diff --git a/docs/api/transforms/index.rst b/docs/api/transforms/index.rst
@@ -7,7 +7,7 @@ The `transforms` module of `TopoBenchmarkX`` consists of several submodules:
 1. :doc:`data_manipulations <data_manipulations/index>`
 2. :doc:`feature_liftings <feature_liftings/index>`
 3. :doc:`liftings <liftings/index>`
-4. :doc:`data_transforms <data_transforms/index>`
+4. :doc:`data_transform <data_transform/index>`
 
 .. toctree::
    :maxdepth: 2
@@ -16,4 +16,4 @@ The `transforms` module of `TopoBenchmarkX`` consists of several submodules:
    data_manipulations/index
    feature_liftings/index
    liftings/index
-   data_transforms/index
+   data_transform/index
diff --git a/docs/api/utils/index.rst b/docs/api/utils/index.rst
@@ -7,9 +7,6 @@ This module implements implements additional utilities to handle the training pr
 .. automodule:: topobenchmarkx.utils.config_resolvers
     :members:
 
-.. automodule:: topobenchmarkx.utils.dataset_statistics
-    :members:
-
 .. automodule:: topobenchmarkx.utils.instantiators
     :members:
 

diff --git a/topobenchmarkx/data/load/loaders.py b/topobenchmarkx/data/load/loaders.py
@@ -29,22 +29,26 @@ class GraphLoader(AbstractLoader):
     Parameters
     ----------
     parameters : DictConfig
-        Configuration parameters. The parameters must contain the following keys:
-        - data_dir (str): The directory where the dataset is stored.
-        - data_name (str): The name of the dataset.
-        - data_type (str): The type of the dataset.
-        - split_type (str): The type of split to be used. It can be "fixed", "random", or "k-fold".
-        If split_type is "random", the parameters must also contain the following keys:
-            - data_seed (int): The seed for the split.
-            - data_split_dir (str): The directory where the split is stored.
-            - train_prop (float): The proportion of the training set.
-        If split_type is "k-fold", the parameters must also contain the following keys:
-            - data_split_dir (str): The directory where the split is stored.
-            - k (int): The number of folds.
-            - data_seed (int): The seed for the split.
-        The parameters can be defined in a yaml file and then loaded using `omegaconf.OmegaConf.load('path/to/dataset/config.yaml')`.
+        Configuration parameters.
     **kwargs : dict
         Additional keyword arguments.
+
+    Notes
+    -----
+    The parameters must contain the following keys:
+    - data_dir (str): The directory where the dataset is stored.
+    - data_name (str): The name of the dataset.
+    - data_type (str): The type of the dataset.
+    - split_type (str): The type of split to be used. It can be "fixed", "random", or "k-fold".
+    If split_type is "random", the parameters must also contain the following keys:
+    - data_seed (int): The seed for the split.
+    - data_split_dir (str): The directory where the split is stored.
+    - train_prop (float): The proportion of the training set.
+    If split_type is "k-fold", the parameters must also contain the following keys:
+    - data_split_dir (str): The directory where the split is stored.
+    - k (int): The number of folds.
+    - data_seed (int): The seed for the split.
+    The parameters can be defined in a yaml file and then loaded using `omegaconf.OmegaConf.load('path/to/dataset/config.yaml')`.
     """
 
     def __init__(self, parameters: DictConfig, **kwargs):

diff --git a/topobenchmarkx/evaluator/evaluator.py b/topobenchmarkx/evaluator/evaluator.py
@@ -14,11 +14,11 @@ class TBXEvaluator(AbstractEvaluator):
         The task type. It can be either "classification" or "regression".
     **kwargs : dict
         Additional arguments for the class. The arguments depend on the task.
-        In "classification" scenario, the following arguments are expected:
-        - num_classes (int): The number of classes.
-        - classification_metrics (list[str]): A list of classification metrics to be computed.
-        In "regression" scenario, the following arguments are expected:
-        - regression_metrics (list[str]): A list of regression metrics to be computed.
+    In "classification" scenario, the following arguments are expected:
+    - num_classes (int): The number of classes.
+    - classification_metrics (list[str]): A list of classification metrics to be computed.
+    In "regression" scenario, the following arguments are expected:
+    - regression_metrics (list[str]): A list of regression metrics to be computed.
     """
 
     def __init__(self, task, **kwargs):
@@ -68,9 +68,9 @@ def update(self, model_out: dict):
         model_out : dict
             The model output. It should contain the following keys:
             - logits : torch.Tensor
-                The model predictions.
+            The model predictions.
             - labels : torch.Tensor
-                The ground truth labels.
+            The ground truth labels.
 
         Raises
         ------

diff --git a/topobenchmarkx/nn/backbones/cell/cin.py b/topobenchmarkx/nn/backbones/cell/cin.py
@@ -25,6 +25,13 @@ class CWN(torch.nn.Module):
         Dimension of hidden features.
     n_layers : int
         Number of CWN layers.
+
+    References
+    ----------
+    .. [1] Bodnar, et al.
+        Weisfeiler and Lehman go cellular: CW networks.
+        NeurIPS 2021.
+        https://arxiv.org/abs/2106.12575
     """
 
     def __init__(
@@ -135,6 +142,13 @@ class CWNLayer(nn.Module):
         A module that updates the aggregated representations of r-cells (default: None).
     eps : float, optional
         A learnable parameter that scales the input features before the first convolutional layer (default: 0.01).
+
+    References
+    ----------
+    .. [1] Bodnar, et al.
+        Weisfeiler and Lehman go cellular: CW networks.
+        NeurIPS 2021.
+        https://arxiv.org/abs/2106.12575
     """
 
     def __init__(