Merge branch 'main' of github.com:iMoonLab/DeepHypergraph

Author: yifanfeng97

README.md

@@ -5,7 +5,7 @@
 ![Release version](https://img.shields.io/github/v/release/iMoonLab/DeepHypergraph)
 [![PyPI version](https://img.shields.io/pypi/v/dhg?color=purple)](https://pypi.org/project/dhg/)
 [![Website Build Status](https://github.com/yifanfeng97/dhg-page-source/actions/workflows/website.yml/badge.svg)](https://deephypergraph.com/)
-[![Documentation Status](https://readthedocs.org/projects/deephypergraph/badge/?version=latest)](https://deephypergraph.readthedocs.io/en/latest/?badge=latest)
+[![Documentation Status](https://readthedocs.org/projects/deephypergraph/badge/?version=latest)](https://deephypergraph.readthedocs.io/)
 [![Downloads](https://pepy.tech/badge/dhg)](https://pepy.tech/project/dhg)
 [![Visits Badge](https://visitor-badge.glitch.me/badge?page_id=iMoonLab.DeepHypergraph)](https://visitor-badge.glitch.me/)
 [![license](https://img.shields.io/github/license/imoonlab/DeepHypergraph)](LICENSE)

dhg/__init__.py

@@ -14,7 +14,7 @@
 
 from ._global import AUTHOR_EMAIL, CACHE_ROOT, DATASETS_ROOT, REMOTE_DATASETS_ROOT
 
-__version__ = "0.9.2"
+__version__ = "0.9.3"
 
 __all__ = {
     "AUTHOR_EMAIL",

dhg/metrics/recommender.py

@@ -112,7 +112,9 @@ def recall(
     assert y_true.max() == 1, "The input y_true must be binary."
     pred_seq = y_true.gather(1, torch.argsort(y_pred, dim=-1, descending=True))[:, :k]
     num_true = y_true.sum(dim=1)
-    res_list = (pred_seq.sum(dim=1) / num_true).detach().cpu()
+    res_list = (pred_seq.sum(dim=1) / num_true).cpu()
+    res_list[torch.isinf(res_list)] = 0
+    res_list[torch.isnan(res_list)] = 0
     if ret_batch:
         return [res.item() for res in res_list]
     else:
@@ -169,6 +171,7 @@ def ndcg(
 
     res_list = (pred_dcg / ideal_dcg).detach().cpu()
     res_list[torch.isinf(res_list)] = 0
+    res_list[torch.isnan(res_list)] = 0
     if ret_batch:
         return [res.item() for res in res_list]
     else:

dhg/metrics/retrieval.py

@@ -104,7 +104,9 @@ def recall(
     assert y_true.max() == 1, "The input y_true must be binary."
     pred_seq = y_true.gather(1, torch.argsort(y_pred, dim=-1, descending=True))[:, :k]
     num_true = y_true.sum(dim=1)
-    res_list = (pred_seq.sum(dim=1) / num_true).detach().cpu()
+    res_list = (pred_seq.sum(dim=1) / num_true).cpu()
+    res_list[torch.isinf(res_list)] = 0
+    res_list[torch.isnan(res_list)] = 0
     if ret_batch:
         return res_list
     else:
@@ -252,6 +254,7 @@ def ndcg(
 
     res_list = pred_dcg / ideal_dcg
     res_list[torch.isinf(res_list)] = 0
+    res_list[torch.isnan(res_list)] = 0
     if ret_batch:
         return res_list
     else:

dhg/structure/hypergraphs/hypergraph.py

@@ -96,7 +96,7 @@ def draw(
         pull_center_strength: float = 1.0,
     ):
         r"""Draw the hypergraph structure.
-        
+
         Args:
             ``e_style`` (``str``): The style of hyperedges. The available styles are only ``'circle'``. Defaults to ``'circle'``.
             ``v_label`` (``list``): The labels of vertices. Defaults to ``None``.
@@ -201,7 +201,7 @@ def from_feature_kNN(features: torch.Tensor, k: int, device: torch.device = torc
 
     @staticmethod
     def from_graph(graph: "Graph", device: torch.device = torch.device("cpu")) -> "Hypergraph":
-        r"""Construct the hypergraph from the graph. Each edge in the graph is treated as a hyperedge in the constructed hypergraph. 
+        r"""Construct the hypergraph from the graph. Each edge in the graph is treated as a hyperedge in the constructed hypergraph.
 
         .. note::
             The construsted hypergraph is a 2-uniform hypergraph, and has the same number of vertices and edges/hyperedges as the graph.
@@ -263,13 +263,13 @@ def from_graph_kHop(
 
     @staticmethod
     def _e_list_from_bigraph(bigraph: "BiGraph", U_as_vertex: bool = True) -> List[tuple]:
-        r"""Construct hyperedges from the bipartite graph. 
+        r"""Construct hyperedges from the bipartite graph.
 
         Args:
             ``bigraph`` (``BiGraph``): The bipartite graph to construct the hypergraph.
-            ``U_as_vertex`` (``bool``, optional): If set to ``True``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}` 
-             will be treated as vertices and hyperedges in the constructed hypergraph, respectively. 
-             If set to ``False``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}` 
+            ``U_as_vertex`` (``bool``, optional): If set to ``True``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}`
+             will be treated as vertices and hyperedges in the constructed hypergraph, respectively.
+             If set to ``False``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}`
              will be treated as hyperedges and vertices in the constructed hypergraph, respectively. Defaults to ``True``.
         """
         e_list = []
@@ -289,13 +289,13 @@ def _e_list_from_bigraph(bigraph: "BiGraph", U_as_vertex: bool = True) -> List[t
     def from_bigraph(
         bigraph: "BiGraph", U_as_vertex: bool = True, device: torch.device = torch.device("cpu")
     ) -> "Hypergraph":
-        r"""Construct the hypergraph from the bipartite graph. 
+        r"""Construct the hypergraph from the bipartite graph.
 
         Args:
             ``bigraph`` (``BiGraph``): The bipartite graph to construct the hypergraph.
-            ``U_as_vertex`` (``bool``, optional): If set to ``True``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}` 
-             will be treated as vertices and hyperedges in the constructed hypergraph, respectively. 
-             If set to ``False``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}` 
+            ``U_as_vertex`` (``bool``, optional): If set to ``True``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}`
+             will be treated as vertices and hyperedges in the constructed hypergraph, respectively.
+             If set to ``False``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}`
              will be treated as hyperedges and vertices in the constructed hypergraph, respectively. Defaults to ``True``.
             ``device`` (``torch.device``, optional): The device to store the hypergraph. Defaults to ``torch.device('cpu')``.
         """
@@ -356,7 +356,7 @@ def add_hyperedges_from_feature_kNN(self, feature: torch.Tensor, k: int, group_n
         self.add_hyperedges(e_list, group_name=group_name)
 
     def add_hyperedges_from_graph(self, graph: "Graph", group_name: str = "main"):
-        r"""Add hyperedges from edges in the graph. Each edge in the graph is treated as a hyperedge. 
+        r"""Add hyperedges from edges in the graph. Each edge in the graph is treated as a hyperedge.
 
         Args:
             ``graph`` (``Graph``): The graph to join the hypergraph.
@@ -385,13 +385,13 @@ def add_hyperedges_from_graph_kHop(
         self.add_hyperedges(e_list, group_name=group_name)
 
     def add_hyperedges_from_bigraph(self, bigraph: "BiGraph", U_as_vertex: bool = False, group_name: str = "main"):
-        r"""Add hyperedges from the bipartite graph. 
+        r"""Add hyperedges from the bipartite graph.
 
         Args:
             ``bigraph`` (``BiGraph``): The bigraph to join the hypergraph.
-            ``U_as_vertex`` (``bool``): If set to ``True``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}` 
-             will be treated as vertices and hyperedges in the constructed hypergraph, respectively. 
-             If set to ``False``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}` 
+            ``U_as_vertex`` (``bool``): If set to ``True``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}`
+             will be treated as vertices and hyperedges in the constructed hypergraph, respectively.
+             If set to ``False``, vertices in set :math:`\mathcal{U}` and set :math:`\mathcal{V}`
              will be treated as hyperedges and vertices in the constructed hypergraph, respectively. Defaults to ``True``.
             ``group_name`` (``str``, optional): The target hyperedge group to add these hyperedges. Defaults to the ``main`` hyperedge group.
         """
@@ -624,17 +624,17 @@ def group_names(self) -> List[str]:
     # properties for deep learning
     @property
     def vars_for_DL(self) -> List[str]:
-        r"""Return a name list of available variables for deep learning in the hypergraph including 
+        r"""Return a name list of available variables for deep learning in the hypergraph including
 
         Sparse Matrices:
-        
+
         .. math::
             \mathbf{H}, \mathbf{H}^\top, \mathcal{L}_{sym}, \mathcal{L}_{rw} \mathcal{L}_{HGNN},
-        
+
         Sparse Diagnal Matrices:
 
         .. math::
-            \mathbf{W}_e, \mathbf{D}_v, \mathbf{D}_v^{-1}, \mathbf{D}_v^{-\frac{1}{2}}, \mathbf{D}_e, \mathbf{D}_e^{-1}, 
+            \mathbf{W}_e, \mathbf{D}_v, \mathbf{D}_v^{-1}, \mathbf{D}_v^{-\frac{1}{2}}, \mathbf{D}_e, \mathbf{D}_e^{-1},
 
         Vectors:
 
@@ -670,7 +670,7 @@ def v2e_src(self) -> torch.Tensor:
 
     def v2e_src_of_group(self, group_name: str) -> torch.Tensor:
         r"""Return the source vertex index vector :math:`\overrightarrow{v2e}_{src}` of the connections (vertices point to hyperedges) in the specified hyperedge group.
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -685,7 +685,7 @@ def v2e_dst(self) -> torch.Tensor:
 
     def v2e_dst_of_group(self, group_name: str) -> torch.Tensor:
         r"""Return the destination hyperedge index vector :math:`\overrightarrow{v2e}_{dst}` of the connections (vertices point to hyperedges) in the specified hyperedge group.
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -700,7 +700,7 @@ def v2e_weight(self) -> torch.Tensor:
 
     def v2e_weight_of_group(self, group_name: str) -> torch.Tensor:
         r"""Return the weight vector :math:`\overrightarrow{v2e}_{weight}` of the connections (vertices point to hyperedges) in the specified hyperedge group.
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -715,7 +715,7 @@ def e2v_src(self) -> torch.Tensor:
 
     def e2v_src_of_group(self, group_name: str) -> torch.Tensor:
         r"""Return the source hyperedge index vector :math:`\overrightarrow{e2v}_{src}` of the connections (hyperedges point to vertices) in the specified hyperedge group.
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -730,7 +730,7 @@ def e2v_dst(self) -> torch.Tensor:
 
     def e2v_dst_of_group(self, group_name: str) -> torch.Tensor:
         r"""Return the destination vertex index vector :math:`\overrightarrow{e2v}_{dst}` of the connections (hyperedges point to vertices) in the specified hyperedge group.
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -745,7 +745,7 @@ def e2v_weight(self) -> torch.Tensor:
 
     def e2v_weight_of_group(self, group_name: str) -> torch.Tensor:
         r"""Return the weight vector :math:`\overrightarrow{e2v}_{weight}` of the connections (hyperedges point to vertices) in the specified hyperedge group.
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -1010,7 +1010,7 @@ def N_e_of_group(self, v_idx: int, group_name: str) -> torch.Tensor:
         return e_indices.clone()
 
     def N_v(self, e_idx: int) -> torch.Tensor:
-        r"""Return the neighbor vertices of the specified hyperedge with ``torch.Tensor`` format. 
+        r"""Return the neighbor vertices of the specified hyperedge with ``torch.Tensor`` format.
 
         .. note::
             The ``e_idx`` must be in the range of [0, :attr:`num_e`).
@@ -1048,7 +1048,7 @@ def smoothing(self, X: torch.Tensor, L: torch.Tensor, lamb: float) -> torch.Tens
     @property
     def L_sym(self) -> torch.Tensor:
         r"""Return the symmetric Laplacian matrix :math:`\mathcal{L}_{sym}` of the hypergraph with ``torch.sparse_coo_tensor`` format.
-        
+
         .. math::
             \mathcal{L}_{sym} = \mathbf{I} - \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}}
         """
@@ -1064,10 +1064,10 @@ def L_sym(self) -> torch.Tensor:
 
     def L_sym_of_group(self, group_name: str) -> torch.Tensor:
         r"""Return the symmetric Laplacian matrix :math:`\mathcal{L}_{sym}` of the specified hyperedge group with ``torch.sparse_coo_tensor`` format.
-        
+
         .. math::
             \mathcal{L}_{sym} = \mathbf{I} - \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}}
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -1085,7 +1085,7 @@ def L_sym_of_group(self, group_name: str) -> torch.Tensor:
     @property
     def L_rw(self) -> torch.Tensor:
         r"""Return the random walk Laplacian matrix :math:`\mathcal{L}_{rw}` of the hypergraph with ``torch.sparse_coo_tensor`` format.
-        
+
         .. math::
             \mathcal{L}_{rw} = \mathbf{I} - \mathbf{D}_v^{-1} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top
         """
@@ -1105,10 +1105,10 @@ def L_rw(self) -> torch.Tensor:
 
     def L_rw_of_group(self, group_name: str) -> torch.Tensor:
         r"""Return the random walk Laplacian matrix :math:`\mathcal{L}_{rw}` of the specified hyperedge group with ``torch.sparse_coo_tensor`` format.
-        
+
         .. math::
             \mathcal{L}_{rw} = \mathbf{I} - \mathbf{D}_v^{-1} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -1151,7 +1151,7 @@ def L_HGNN_of_group(self, group_name: str) -> torch.Tensor:
 
         .. math::
             \mathcal{L}_{HGNN} = \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}}
-        
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
         """
@@ -1172,8 +1172,8 @@ def smoothing_with_HGNN(self, X: torch.Tensor, drop_rate: float = 0.0) -> torch.
         r"""Return the smoothed feature matrix with the HGNN Laplacian matrix :math:`\mathcal{L}_{HGNN}`.
 
             .. math::
-                \mathbf{X} = \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}} \mathbf{X} 
-            
+                \mathbf{X} = \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}} \mathbf{X}
+
         Args:
             ``X`` (``torch.Tensor``): The feature matrix. Size :math:`(|\mathcal{V}|, C)`.
             ``drop_rate`` (``float``): Dropout rate. Randomly dropout the connections in incidence matrix with probability ``drop_rate``. Default: ``0.0``.
@@ -1190,8 +1190,8 @@ def smoothing_with_HGNN_of_group(self, group_name: str, X: torch.Tensor, drop_ra
         r"""Return the smoothed feature matrix with the HGNN Laplacian matrix :math:`\mathcal{L}_{HGNN}`.
 
             .. math::
-                \mathbf{X} = \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}} \mathbf{X} 
-            
+                \mathbf{X} = \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}} \mathbf{X}
+
         Args:
             ``group_name`` (``str``): The name of the specified hyperedge group.
             ``X`` (``torch.Tensor``): The feature matrix. Size :math:`(|\mathcal{V}|, C)`.

dhg/utils/structure.py

@@ -65,6 +65,7 @@ def remap_edge_list(
         ``bipartite_graph`` (``bool``): Whether the structure is bipartite graph. Defaults to ``False``.
         ``ret_map`` (``bool``): Whether to return the map dictionary of raw marker to new index. Defaults to ``False``.
     """
+    e_list = [[str(v) for v in e] for e in e_list]
     if bipartite_graph:
         u_set, v_set = set(), set()
         for u, v in e_list:
@@ -87,7 +88,7 @@ def remap_edge_list(
                 v_set.add(v)
         v_list = sorted(v_set)
         v_map = {raw_v: new_v for new_v, raw_v in enumerate(v_list)}
-        e_list = [set([v_map[v] for v in e]) for e in e_list]
+        e_list = [tuple([v_map[v] for v in e]) for e in e_list]
         if ret_map:
             return e_list, v_map
         else:
@@ -107,6 +108,7 @@ def remap_edge_lists(
         ``bipartite_graph`` (``bool``): Whether the structure is bipartite graph. Defaults to ``False``.
         ``ret_map`` (``bool``): Whether to return the map dictionary of raw marker to new index. Defaults to ``False``.
     """
+    e_lists = [[[str(v) for v in e] for e in e_list] for e_list in e_lists]
     if bipartite_graph:
         u_set, v_set = set(), set()
         for e_list in e_lists:
@@ -131,7 +133,7 @@ def remap_edge_lists(
                     v_set.add(v)
         v_list = sorted(v_set)
         v_map = {raw_v: new_v for new_v, raw_v in enumerate(v_list)}
-        e_list = [[set([v_map[v] for v in e]) for e in e_list] for e_list in e_lists]
+        e_list = [[tuple([v_map[v] for v in e]) for e in e_list] for e_list in e_lists]
         if ret_map:
             return e_list, v_map
         else:
@@ -151,6 +153,7 @@ def remap_adj_list(
         ``bipartite_graph`` (``bool``): Whether the structure is bipartite graph. Defaults to ``False``.
         ``ret_map`` (``bool``): Whether to return the map dictionary of raw marker to new index. Defaults to ``False``.
     """
+    adj_list = [[str(v) for v in line] for line in adj_list]
     if bipartite_graph:
         u_set, v_set = set(), set()
         for line in adj_list:
@@ -202,6 +205,7 @@ def remap_adj_lists(
         ``bipartite_graph`` (``bool``): Whether the structure is bipartite graph. Defaults to ``False``.
         ``ret_map`` (``bool``): Whether to return the map dictionary of raw marker to new index. Defaults to ``False``.
     """
+    adj_lists = [[[str(v) for v in line] for line in adj_list] for adj_list in adj_lists]
     if bipartite_graph:
         u_set, v_set = set(), set()
         for adj_list in adj_lists:

dhg/visualization/structure/defaults.py

@@ -67,7 +67,7 @@ def default_size(
     v_size: Union[float, list] = 1.0,
     v_line_width: Union[float, list] = 1.0,
     e_line_width: Union[float, list] = 1.0,
-    font_size: int = 1.0,
+    font_size: float = 1.0,
 ):
     _v_size = 1 / np.sqrt(num_v + 10) * 0.1
     _v_line_width = 1 * np.exp(-num_v / 50)
@@ -91,8 +91,8 @@ def default_bipartite_size(
     v_size: Union[float, list] = 1.0,
     v_line_width: Union[float, list] = 1.0,
     e_line_width: Union[float, list] = 1.0,
-    u_font_size: int = 1.0,
-    v_font_size: int = 1.0,
+    u_font_size: float = 1.0,
+    v_font_size: float = 1.0,
 ):
     _u_size = 1 / np.sqrt(num_u + 12) * 0.08
     _u_line_width = 1 * np.exp(-num_u / 50)