Update docstrings and types.

l2gv2/anomaly_detection.py

@@ -8,11 +8,11 @@ def raw_anomaly_score_node_patch(aligned_patch_emb, emb, node) -> float:
     """TODO: docstring for `raw_anomaly_score_node_patch`
 
     Args:
-        aligned_patch_emb ():
+        aligned_patch_emb ([type]): [description]
 
-        emb ():
+        emb ([type]): [description]
 
-        node ():
+        node ([type]): [description]
 
     Returns:
         float: Raw anomaly score of the node in the patch.
@@ -25,10 +25,10 @@ def nodes_in_patches(patch_data: list[Patch]) -> list:
     """TODO: docstring for `nodes_in_patches`
 
     Args:
-        patch_data (list[Patch]):
+        patch_data (list[Patch]): [description]
 
     Returns:
-        list:
+        list: [description]
     """
 
     return [set(p.nodes.numpy()) for p in patch_data]
@@ -40,14 +40,14 @@ def normalized_anomaly(
     """TODO: docstring for `normalized_anomaly`
 
     Args:
-        patch_emb (list[Patch]):
+        patch_emb (list[Patch]): [description]
 
-        patch_data (list[Patch]):
+        patch_data (list[Patch]): [description]
 
-        emb (np.array):
+        emb (np.array): [description]
 
     Returns:
-        np.array:
+        np.array: [description]
     """
 
     nodes = nodes_in_patches(patch_data)
@@ -95,16 +95,16 @@ def get_outliers(
     """TODO: docstring for `get_outliers`
 
     Args:
-        patch_emb (list):
+        patch_emb (list): [description]
 
-        patch_data (list):
+        patch_data (list): [description]
 
-        emb (np.array):
+        emb (np.array): [description]
 
         k (float): Threshold for outliers as multiplier of the standard deviation.
 
     Returns:
-        list[int]:
+        list[int]: [description]
     """
 
     out = []

l2gv2/embedding/embeddings.py

@@ -33,11 +33,11 @@ def fit(
 
             data (torch_geometric.data.Data):
 
-            logger ():
+            logger ([type]): [description]
 
         Returns:
 
-            torch.nn.Module:
+            torch.nn.Module: [description]
         """
         optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)
         # optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
@@ -98,11 +98,11 @@ def loss_fun(self, data) -> torch.Tensor:
 
         Args:
 
-            data (torch_geometric.data.Data)
+            data (torch_geometric.data.Data): [description]
 
         Returns:
 
-            torch.Tensor:
+            torch.Tensor: [description]
         """
         return (
             self.model.recon_loss(self.model.encode(data), data.edge_index)

l2gv2/induced_subgraph.py

@@ -8,14 +8,14 @@ def induced_subgraph(data: tg.data.Data, nodes, extend_hops: int = 0) -> tg.data
     """TODO: docstring for `induced_subgraph`
 
     Args:
-        data (torch_geometric.data.Data):
+        data (torch_geometric.data.Data): [description]
 
-        nodes (int):
+        nodes (int): [description]
 
-        extend_hops (int, optional): default is 0.
+        extend_hops (int, optional): [description], default is 0.
 
     Returns:
-        torch_geometric.data.Data:
+        torch_geometric.data.Data: [description]
     """
 
     nodes = torch.as_tensor(nodes, dtype=torch.long)

l2gv2/manopt_optimization.py

@@ -19,10 +19,10 @@ def double_intersections_nodes(
     """TODO: docstring for `double_intersections_nodes`.
 
     Args:
-        patches (list[Patch]):
+        patches (list[Patch]): [description]
 
     Returns:
-        dict[tuple[int, int], list[int]]:
+        dict[tuple[int, int], list[int]]: [description]
     """
 
     double_intersections = {}
@@ -46,22 +46,22 @@ def anp_loss_nodes_consecutive_patches(
     """TODO: docstring for `anp_loss_nodes_consecutive_patches`.
 
     Args:
-        rotations ():
+        rotations ([type]): [description]
 
-        scales ():
+        scales ([type]): [description]
 
-        translations (int):
+        translations (int): [description]
 
-        patches ():
+        patches ([type]): [description]
 
-        nodes ():
+        nodes ([type]): [description]
 
-        dim (int):
+        dim (int): [description]
 
-        random_choice (bool, optional): default is True.
+        random_choice (bool, optional): [description] default is True.
 
     Returns:
-        float: loss function.
+        float: loss function value.
     """
 
     loss_function = 0
@@ -98,16 +98,16 @@ def optimization(
     """TODO: docstring for `optimization`.
 
     Args:
-        patches ():
+        patches ([type]): [description]
 
-        nodes ():
+        nodes ([type]): [description]
 
-        dim (int):
+        dim (int): [description]
 
     Returns:
-        result:
+        result: [description]
 
-        embedding:
+        embedding: [description]
     """
     n_patches = len(patches)
 

l2gv2/models.py

@@ -217,7 +217,7 @@ def node2vec_(
 
         data(torch_geometric.data.Data)
 
-        emb_dim (int):
+        emb_dim (int): [description]
 
         w_length (int, optional): The walk length, default is 20.
 
@@ -296,7 +296,7 @@ def node2vec_patch_embeddings(
 
         patch_data (list) torch_geometric.data.Data objects.
 
-        emb_dim (int):
+        emb_dim (int): [description]
 
         w_length (int, optional): The walk length, default is 20.
 

l2gv2/patch/lazy.py

@@ -246,7 +246,11 @@ def __array__(self, dtype=None):
         return self.as_array(out)
 
     def as_array(self, out=None):
-        """ TODO: docstring for `as_array` """
+        """ TODO: docstring for `as_array` 
+        
+        Args:            
+                out (Optional[type]): [description], defaults to None
+        """
         if out is None:
             out = np.zeros(self.shape)
         index = np.empty((self.nodes.max() + 1,), dtype=np.int64)
@@ -260,7 +264,14 @@ def as_array(self, out=None):
         return out
 
     def get_coordinates(self, nodes, out=None):
-        """ TODO: docstring for `get_coordinates` """
+        """ TODO: docstring for `get_coordinates` 
+        
+        Args:
+            
+                nodes ([type]): [description]
+            
+                out (Optional[type]): [description], default is None
+        """
         nodes = np.asanyarray(nodes)
         if out is None:
             out = np.zeros((len(nodes), self.dim))

l2gv2/patch/patches.py

@@ -2,6 +2,7 @@
 
 from random import choice
 from math import ceil
+from typing import List, Tuple, Optional, Literal
 from collections.abc import Iterable
 
 import torch
@@ -30,16 +31,16 @@ def geodesic_expand_overlap(
     """ Expand patch
 
     Args:
-        subgraph (): graph containing patch nodes and all target nodes for potential expansion
+        subgraph ([type]): graph containing patch nodes and all target nodes for potential expansion
         
-        seed_mask ():
+        seed_mask ([type]): [description]
         
-        min_overlap (): minimum overlap before stopping expansion
+        min_overlap ([type]): minimum overlap before stopping expansion
         
-        target_overlap (): maximum overlap 
+        target_overlap ([type]): maximum overlap 
             (if expansion step results in more overlap, the nodes added are sampled at random)
 
-        reseed_samples (): default is 10
+        reseed_samples ([type]): [description] default is 10
 
     Returns:
         index tensor of new nodes to add to patch
@@ -148,13 +149,13 @@ def create_overlapping_patches(
         
         partition_tensor (torch.LongTensor): partition of input graph
         
-        patch_graph (): graph where nodes are clusters of partition 
+        patch_graph ([type]): graph where nodes are clusters of partition 
             and an edge indicates that the corresponding patches 
             in the output should have at least ``min_overlap`` nodes in common
         
-        min_overlap (): minimum overlap for connected patches
+        min_overlap ([type]): minimum overlap for connected patches
         
-        target_overlap (): maximum overlap during expansion 
+        target_overlap ([type]): maximum overlap during expansion 
             for an edge (additional overlap may result from expansion of other edges)
 
     Returns:
@@ -232,40 +233,40 @@ def _patch_overlaps(
         )
     return patches
 
-
 def create_patch_data(
     graph: TGraph,
     partition_tensor: torch.LongTensor,
-    min_overlap,
-    target_overlap,
-    min_patch_size=None,
-    sparsify_method: str="resistance",
-    target_patch_degree: int=4,
-    gamma: int=0,
-    verbose: bool=False,
-):
+    min_overlap: int,
+    target_overlap: int,
+    min_patch_size: Optional[int] = None,
+    sparsify_method: Literal["resistance", "rmst", "none"] = "resistance",
+    target_patch_degree: int = 4,
+    gamma: int = 0,
+    verbose: bool = False,
+) -> Tuple[List, object]:
     """ Divide data into overlapping patches
 
     Args:
         graph (TGraph): input data
         
         partition_tensor (torch.LongTensor): starting partition for creating patches
         
-        min_overlap: minimum patch overlap for connected patches
+        min_overlap ([type]): minimum patch overlap for connected patches
         
-        target_overlap: maximum patch overlap during expansion of an edge of the patch graph
+        target_overlap ([type]): maximum patch overlap during expansion 
+            of an edge of the patch graph
         
-        min_patch_size (optional): minimum size of patches, defauls is None
+        min_patch_size (Optional[type]): minimum size of patches, defauls is None
         
-        sparsify_method (str): method for sparsifying patch graph 
+        sparsify_method (Optional[[str]): method for sparsifying patch graph 
             (one of ``'resistance'``, ``'rmst'``, ``'none'``), default is ``'resistance'``
 
-        target_patch_degree (optional): target patch degree for 
+        target_patch_degree (Optional[str]): target patch degree for 
             ``sparsify_method='resistance'``, default is 4
         
-        gamma (int): ``gamma`` value for use with ``sparsify_method='rmst'``, default is 0
+        gamma (Optional[int]): ``gamma`` value for use with ``sparsify_method='rmst'``, default is 0
         
-        verbose (bool): if true, print some info about created patches, default is False
+        verbose (Optional[bool]): if true, print some info about created patches, default is False
 
     Returns:
         list of patch data, patch graph