fix the bug of the visualization

README.md

@@ -23,13 +23,13 @@
 - 2022-12-28 -> **v0.9.3** 正式发布！ 包含更多数据集和超图操作！
 - 2022-09-25 -> **v0.9.2** is now available! More datasets, SOTA models, and visualizations are included!
 - 2022-09-25 -> **v0.9.2** 正式发布！ 包含更多数据集、最新模型和可视化功能！
-- 2022-08-25 -> DHG's first version **v0.9.1** is now available! 
+- 2022-08-25 -> DHG's first version **v0.9.1** is now available!
 - 2022-08-25 -> DHG的第一个版本 **v0.9.1** 正式发布！
 
 
 **DHG** *(DeepHypergraph)* is a deep learning library built upon [PyTorch](https://pytorch.org) for learning with both Graph Neural Networks and Hypergraph Neural Networks. It is a general framework that supports both low-order and high-order message passing like **from vertex to vertex**, **from vertex in one domain to vertex in another domain**, **from vertex to hyperedge**, **from hyperedge to vertex**, **from vertex set to vertex set**.
 
-It supports a wide variety of structures like low-order structures (graph, directed graph, bipartite graph, etc.), high-order structures (hypergraph, etc.). Various spectral-based operations (like Laplacian-based smoothing) and spatial-based operations (like message psssing from domain to domain) are integrated inside different structures. It provides multiple common metrics for performance evaluation on different tasks. Many state-of-the-art models are implemented and can be easily used for research. We also provide various visualization tools for both low-order structures and high-order structures. 
+It supports a wide variety of structures like low-order structures (graph, directed graph, bipartite graph, etc.), high-order structures (hypergraph, etc.). Various spectral-based operations (like Laplacian-based smoothing) and spatial-based operations (like message psssing from domain to domain) are integrated inside different structures. It provides multiple common metrics for performance evaluation on different tasks. Many state-of-the-art models are implemented and can be easily used for research. We also provide various visualization tools for both low-order structures and high-order structures.
 
 In addition, DHG's [dhg.experiments](https://deephypergraph.readthedocs.io/en/latest/api/experiments.html) module (that implements **Auto-ML** upon [Optuna](https://optuna.org)) can help you automatically tune the hyper-parameters of your models in training and easily outperforms the state-of-the-art models.
 
@@ -39,6 +39,7 @@ In addition, DHG's [dhg.experiments](https://deephypergraph.readthedocs.io/en/la
 
 * [Hightlights](#highlights)
 * [Installation](#installation)
+* [Dependencies](#dependencies)
 * [Quick Start](#quick-start)
 * [Examples](#examples)
 * [Datasets](#datasets)
@@ -50,7 +51,7 @@ In addition, DHG's [dhg.experiments](https://deephypergraph.readthedocs.io/en/la
 
 ## Highlights
 
-- **Support High-Order Message Passing on Structure**: 
+- **Support High-Order Message Passing on Structure**:
 DHG supports pair-wise message passing on the graph structure and beyond-pair-wise message passing on the hypergraph structure.
 
 - **Shared Ecosystem with Pytorch Framework**:
@@ -89,9 +90,24 @@ You can also try the nightly version (0.9.5) of **DHG** library with ``pip`` as
 pip install git+https://github.com/iMoonLab/DeepHypergraph.git
 ```
 
-Nightly version is the development version of **DHG**. It may include the lastest SOTA methods and datasets, but it can also be unstable and not fully tested. 
+Nightly version is the development version of **DHG**. It may include the lastest SOTA methods and datasets, but it can also be unstable and not fully tested.
 If you find any bugs, please report it to us in [GitHub Issues](https://github.com/iMoonLab/DeepHypergraph/issues).
 
+### Dependencies
+
+**DHG** requires the following dependencies:
+
+- Python >= 3.8
+- PyTorch >= 1.12.1, < 2.0
+- scipy >= 1.8
+- matplotlib >= 3.7.0
+- numpy
+- scikit-learn
+- optuna
+- requests
+
+For visualization features, matplotlib 3.7.0 or higher is required to properly render 3D plots.
+
 ## Quick Start
 
 ### Visualization
@@ -227,7 +243,7 @@ class GCNConv(nn.Module):
         self.reset_parameters()
 
     def forward(self, X: torch.Tensor, g: dhg.Graph) -> torch.Tensor:
-        # apply the trainable parameters ``theta`` to the input ``X``  
+        # apply the trainable parameters ``theta`` to the input ``X``
         X = self.theta(X)
         # smooth the input ``X`` with the GCN's Laplacian
         X = g.smoothing_with_GCN(X)
@@ -253,7 +269,7 @@ class GATConv(nn.Module):
         e_atten_score = x_for_src[g.e_src] + x_for_dst[g.e_dst]
         e_atten_score = F.leaky_relu(e_atten_score).squeeze()
         # apply ``e_atten_score`` to each edge in the graph ``g``, aggragete neighbor messages
-        #  with ``softmax_then_sum``, and perform vertex->vertex message passing in graph 
+        #  with ``softmax_then_sum``, and perform vertex->vertex message passing in graph
         #  with message passing function ``v2v()``
         X = g.v2v(X, aggr="softmax_then_sum", e_weight=e_atten_score)
         X = F.elu(X)
@@ -474,4 +490,4 @@ DHG is developed by DHG's core team including [Yifan Feng](http://fengyifan.site
 
 ## License
 
-DHG uses Apache License 2.0.
+DHG uses Apache License 2.0.

dhg/visualization/feature/utils.py

@@ -16,8 +16,8 @@ def make_animation(embeddings: np.ndarray, colors: Union[np.ndarray, str], cmap=
     r"""Make an animation of embeddings.
 
     Args:
-        ``embeddings`` (``np.ndarray``): The embedding matrix. Size :math:`(N, 3)`. 
-        ``colors`` (``Union[np.ndarray, str]``): The color matrix. ``str`` or Size :math:`(N, )`. 
+        ``embeddings`` (``np.ndarray``): The embedding matrix. Size :math:`(N, 3)`.
+        ``colors`` (``Union[np.ndarray, str]``): The color matrix. ``str`` or Size :math:`(N, )`.
         ``cmap`` (``str``, optional): The `color map <https://matplotlib.org/stable/tutorials/colors/colormaps.html>`_. Defaults to ``"viridis"``.
     """
     embeddings = normalize(embeddings)
@@ -29,7 +29,7 @@ def init():
         if colors is not None:
             ax.scatter(x, y, z, c=colors, cmap=cmap)
         else:
-            ax.scatter(x, y, z, cmap=cmap)
+            ax.scatter(x, y, z)
         return fig
 
     def animate(i):
@@ -41,7 +41,7 @@ def animate(i):
 
 def plot_2d_embedding(embeddings: np.ndarray, label: Optional[np.ndarray] = None, cmap="viridis"):
     r"""Plot the embedding in 2D.
-    
+
     Args:
         ``embeddings`` (``np.ndarray``): The embedding matrix. Size :math:`(N, 2)`.
         ``label`` (``np.ndarray``, optional): The label matrix.
@@ -52,16 +52,15 @@ def plot_2d_embedding(embeddings: np.ndarray, label: Optional[np.ndarray] = None
     if label is not None:
         plt.scatter(embeddings[:, 0], embeddings[:, 1], c=label, cmap=cmap)
     else:
-        plt.scatter(embeddings[:, 0], embeddings[:, 1], cmap=cmap)
-
-    plt.xlim((0, 1.0))
-    plt.ylim((0, 1.0))
+        plt.scatter(embeddings[:, 0], embeddings[:, 1])
+    plt.xlim(0, 1.0)
+    plt.ylim(0, 1.0)
     fig.tight_layout()
 
 
 def plot_3d_embedding(embeddings: np.ndarray, label: Optional[np.ndarray] = None, cmap="viridis"):
     r"""Plot the embedding in 3D.
-    
+
     Args:
         ``embeddings`` (``np.ndarray``): The embedding matrix. Size :math:`(N, 3)`.
         ``label`` (``np.ndarray``, optional): The label matrix.
@@ -70,11 +69,11 @@ def plot_3d_embedding(embeddings: np.ndarray, label: Optional[np.ndarray] = None
     embeddings = normalize(embeddings)
     x, y, z = embeddings[:, 0], embeddings[:, 1], embeddings[:, 2]
     fig = plt.figure(figsize=(8, 8))
-    ax = fig.gca(projection="3d")
+    ax = fig.add_subplot(111, projection="3d")
     if label is not None:
         ax.scatter(x, y, z, c=label, cmap=cmap)
     else:
-        ax.scatter(x, y, z, cmap=cmap)
+        ax.scatter(x, y, z)
 
     ax.set_xlim3d(0, 1.0)
     ax.set_ylim3d(0, 1.0)
@@ -90,7 +89,7 @@ def normalize(coor):
 # for poincare_ball
 def tanh(x, clamp=15):
     r"""Calculate the tanh value of the matrix x.
-    
+
     Args:
         ``x`` (``np.ndarray``): The feature matrix. Size :math:`(N, C)`.
         ``clap`` (``int``): Boundary value.

docs/requirements.txt

@@ -5,5 +5,5 @@ sphinxcontrib-bibtex
 numpy
 optuna
 torch>=1.11.0
-matplotlib
+matplotlib>=3.7.0
 scikit-learn

pyproject.toml

@@ -38,7 +38,7 @@ optuna = "*"
 numpy = "*"
 scikit-learn = "*"
 requests = "*"
-matplotlib = "*"
+matplotlib = ">= 3.7.0"
 
 [tool.poetry.dev-dependencies]
 

tests/random/test_random_hypergraph.py

@@ -24,7 +24,7 @@ def test_uniform_hypergraph_Gnp():
     assert all(map(lambda e: len(e) == k, edges))
 
     max_n_e = C(n_v, k)
-    assert pytest.approx(g.num_e / max_n_e, 1) == prob
+    assert pytest.approx(g.num_e / max_n_e, rel=0.5) == prob
 
 
 def test_uniform_hypergraph_Gnm():