Metadata-Version: 2.1
Name: graphgallery
Version: 0.5.0
Summary: Geometric Deep Learning Extension Library for TensorFlow and PyTorch
Home-page: https://github.com/EdisonLeeeee/GraphGallery
Author: Jintang Li
Author-email: cnljt@outlook.com
License: MIT LICENSE
Download-URL: https://github.com/EdisonLeeeee/GraphGallery/archive/0.5.0.tar.gz
Description: <p align="center">
          <img width = "600" height = "300" src="https://github.com/EdisonLeeeee/GraphGallery/blob/master/imgs/graphgallery.svg" alt="logo"/>
          <br/>
        </p>
        
        <p align="center"><strong><em>TensorFLow</em> or <em>PyTorch</em>? Both!</strong></p>
        <!-- <p align="center"><strong>A <em>gallery</em> of state-of-the-art Graph Neural Networks (GNNs) for TensorFlow and PyTorch</strong>.</p> -->
        
        <p align=center>
          <a href="https://www.python.org/downloads/release/python-370/">
            <img src="https://img.shields.io/badge/Python->=3.7-3776AB?logo=python" alt="Python">
          </a>    
          <a href="https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0">
            <img src="https://img.shields.io/badge/TensorFlow->=2.1.2-FF6F00?logo=tensorflow" alt="tensorflow">
          </a>      
          <a href="https://github.com/pytorch/pytorch">
            <img src="https://img.shields.io/badge/PyTorch->=1.5-FF6F00?logo=pytorch" alt="pytorch">
          </a>   
          <a href="https://pypi.org/project/graphgallery/">
            <img src="https://badge.fury.io/py/graphgallery.svg" alt="pypi">
          </a>       
          <img alt="stars" src="https://img.shields.io/github/stars/EdisonLeeeee/GraphGallery">
          <img alt="forks" src="https://img.shields.io/github/forks/EdisonLeeeee/GraphGallery">
          <img alt="issues" src="https://img.shields.io/github/issues/EdisonLeeeee/GraphGallery">    
          <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/LICENSE">
            <img src="https://img.shields.io/github/license/EdisonLeeeee/GraphGallery" alt="pypi">
          </a>       
        </p>
        
        - [GraphGallery](#graphgallery)
        - [👀 What's important](#whats-important)
        - [🚀 Installation](#installation)
        - [🤖 Implementations](#implementations)
          - [Semi-supervised models](#semi-supervised-models)
            - [General models](#general-models)
            - [Defense models](#defense-models)
          - [Unsupervised models](#unsupervised-models)
        - [⚡ Quick Start](#quick-start)
          - [Datasets](#datasets)
            - [Planetoid](#planetoid)
            - [NPZDataset](#npzdataset)
          - [Tensor](#tensor)
          - [Example of GCN model](#example-of-gcn-model)
          - [Customization](#customization)
          - [Visualization](#visualization)
          - [Using TensorFlow/PyTorch Backend](#using-tensorflowpytorch-backend)
            - [GCN using PyTorch backend](#gcn-using-pytorch-backend)
        - [❓ How to add your datasets](#how-to-add-your-datasets)
        - [❓ How to define your models](#how-to-define-your-models)
            - [GCN using PyG backend](#gcn-using-pyg-backend)
        - [😎 More Examples](#more-examples)
        - [⭐ Road Map](#road-map)
        - [😘 Acknowledgement](#acknowledgement)
        
        # GraphGallery
        GraphGallery is a gallery for benchmark graph neural networks with [TensorFlow 2.x](https://github.com/tensorflow/tensorflow) and [PyTorch](https://github.com/pytorch/pytorch) backend. GraphGallery 0.5.x is a total re-write from previous versions, and some things have changed. 
        
        # 👀 What's important
        Differences between GraphGallery and [Pytorch Geometric (PyG)](https://github.com/rusty1s/pytorch_geometric), [Deep Graph Library (DGL)](https://github.com/dmlc/dgl), etc...
        + PyG and DGL are just like **TensorFlow** while GraphGallery is more like **Keras**
        + GraphGallery is more extensible and user-friendly
        + GraphGallery has high scalaribility for researchers to use
        
        
        # 🚀 Installation
        + Build from source (latest version)
        ```bash
        git clone https://github.com/EdisonLeeeee/GraphGallery.git
        cd GraphGallery
        python setup.py install
        ```
        + Or using pip (stable version)
        ```bash
        pip install -U graphgallery
        ```
        # 🤖 Implementations
        In detail, the following methods are currently implemented:
        
        ## Semi-supervised models
        ### General models
        
        <!-- 1 -->
        <details>
        <summary>
        <b>ChebyNet</b> from <i>Michaël Defferrard et al</i>,
        <a href="https://arxiv.org/abs/1606.09375"> 📝<i>Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (NeurIPS'16)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_ChebyNet.ipynb"> :octocat:TensorFLow Example</a>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_ChebyNet.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_ChebyNet.ipynb"> [🔥PyG Example] </a>
        
        </details>
        
        <!-- 2 -->
        
        <details>
        <summary>
        <b>GCN</b> from <i>Thomas N. Kipf et al</i>,
        <a href="https://arxiv.org/abs/1609.02907"> 📝<i>Semi-Supervised Classification with Graph Convolutional Networks (ICLR'17)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GCN.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_GCN.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_GCN.ipynb"> [🔥PyG Example] </a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/DGL-PyTorch/test_GCN.ipynb"> [🔥DGL-PyTorch Example] </a>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/DGL-TensorFlow/test_GCN.ipynb"> [:octocat:DGL-TensorFlow Example] </a>
        </details>
        
        <!-- 3 -->
        <details>
        <summary>
        <b>GraphSAGE</b> from <i>William L. Hamilton et al</i>,
        <a href="https://arxiv.org/abs/1706.02216"> 📝<i>Inductive Representation Learning on Large Graphs (NeurIPS'17)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GraphSAGE.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_GraphSAGE.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_GraphSAGE.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 4 -->
        <details>
        <summary>
        <b>FastGCN</b> from <i>Jie Chen et al</i>,
        <a href="https://arxiv.org/abs/1801.10247"> 📝<i>FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling (ICLR'18)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_FastGCN.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_FastGCN.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_FastGCN.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 5 -->
        <details>
        <summary>
        <b>LGCN</b> from <i>Hongyang Gao et al</i>,
        <a href="https://arxiv.org/abs/1808.03965"> 📝<i>Large-Scale Learnable Graph Convolutional Networks (KDD'18)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_LGCN.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_LGCN.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_LGCN.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 6 -->
        <details>
        <summary>
        <b>GAT</b> from <i>Petar Veličković et al</i>,
        <a href="https://arxiv.org/abs/1710.10903"> 📝<i>Graph Attention Networks (ICLR'18)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GAT.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_GAT.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_GAT.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 7 -->
        <details>
        <summary>
        <b>SGC</b> from <i>Felix Wu et al</i>,
        <a href="https://arxiv.org/abs/1902.07153"> 📝<i>Simplifying Graph Convolutional Networks (ICLR'19)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_SGC.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_SGC.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_SGC.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 8 -->
        <details>
        <summary>
        <b>GWNN</b> from <i>Bingbing Xu et al</i>,
        <a href="https://arxiv.org/abs/1904.07785"> 📝<i>Graph Wavelet Neural Network (ICLR'19)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GWNN.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_GWNN.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_GWNN.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 69 -->
        <details>
        <summary>
        <b>GMNN</b> from <i>Meng Qu et al</i>,
        <a href="https://arxiv.org/abs/1905.06214"> 📝<i>Graph Attention Networks (ICLR'19)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GMNN.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_GMNN.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_GMNN.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 10 -->
        <details>
        <summary>
        <b>ClusterGCN</b> from <i>Wei-Lin Chiang et al</i>,
        <a href="https://arxiv.org/abs/1905.07953"> 📝<i>Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (KDD'19)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_ClusterGCN.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_ClusterGCN.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_ClusterGCN.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 11 -->
        <details>
        <summary>
        <b>DAGNN</b> from <i>Meng Liu et al</i>,
        <a href="https://arxiv.org/abs/2007.09296"> 📝<i>Towards Deeper Graph Neural Networks (KDD'20)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_DAGNN.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_DAGNN.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_DAGNN.ipynb"> [🔥PyG Example] </a>
        </details>
        
        ### Defense models
        
        <!-- 1 -->
        <details>
        <summary>
        <b>RobustGCN</b> from <i>Petar Veličković et al</i>,
        <a href="https://dl.acm.org/doi/10.1145/3292500.3330851"> 📝<i>Robust Graph Convolutional Networks Against Adversarial Attacks (KDD'19)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_RobustGCN.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_RobustGCN.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_RobustGCN.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 2 -->
        <details>
        <summary>
        <b>SBVAT</b> from <i>Zhijie Deng et al</i>,
        <a href="https://arxiv.org/abs/1902.09192"> 📝<i>Batch Virtual Adversarial Training for Graph Convolutional Networks (ICML'19)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_SBVAT.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_SBVAT.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_SBVAT.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 3 -->
        <details>
        <summary>
        <b>OBVAT</b> from <i>Zhijie Deng et al</i>,
        <a href="https://arxiv.org/abs/1902.09192"> 📝<i>Batch Virtual Adversarial Training for Graph Convolutional Networks (ICML'19)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_OBVAT.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_OBVAT.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_OBVAT.ipynb"> [🔥PyG Example] </a>
        </details>
        
        ## Unsupervised models
        
        <!-- 1 -->
        <details>
        <summary>
        <b>Deepwalk</b> from <i>Zhijie Deng et al</i>,
        <a href="https://arxiv.org/abs/1403.6652"> 📝<i>DeepWalk: Online Learning of Social Representations (KDD'14)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_Deepwalk.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_Deepwalk.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_Deepwalk.ipynb"> [🔥PyG Example] </a>
        </details>
        
        <!-- 2 -->
        <details>
        <summary>
        <b>Node2vec</b> from <i>Zhijie Deng et al</i>,
        <a href="https://arxiv.org/abs/1607.00653"> 📝<i>node2vec: Scalable Feature Learning for Networks (KDD'16)</i> </a>
        </summary>
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_Node2vec.ipynb"> [:octocat:TensorFLow Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_Node2vec.ipynb"> [🔥PyTorch Example]</a>,
        <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyG/test_Node2vec.ipynb"> [🔥PyG Example] </a>
        </details>
        
        # ⚡ Quick Start
        ## Datasets
        more details please refer to [GraphData](https://github.com/EdisonLeeeee/GraphData).
        ### Planetoid
        fixed datasets
        ```python
        from graphgallery.data import Planetoid
        # set `verbose=False` to avoid additional outputs 
        data = Planetoid('cora', verbose=False)
        graph = data.graph
        idx_train, idx_val, idx_test = data.split_nodes()
        # idx_train:  training indices: 1D Numpy array
        # idx_val:  validation indices: 1D Numpy array
        # idx_test:  testing indices: 1D Numpy array
        >>> graph
        Graph(adj_matrix(2708, 2708), attr_matrix(2708, 2708), labels(2708,))
        ```
        currently the supported datasets are:
        ```python
        >>> data.supported_datasets
        ('citeseer', 'cora', 'pubmed')
        ```
        ### NPZDataset
        more scalable datasets (stored with `.npz`)
        ```python
        from graphgallery.data import NPZDataset;
        # set `verbose=False` to avoid additional outputs
        data = NPZDataset('cora', verbose=False, standardize=False)
        graph = data.graph
        idx_train, idx_val, idx_test = data.split_nodes(random_state=42)
        >>> graph
        Graph(adj_matrix(2708, 2708), attr_matrix(2708, 2708), labels(2708,))
        ```
        currently the supported datasets are:
        ```python
        >>> data.supported_datasets
        ('citeseer','citeseer_full','cora','cora_ml','cora_full',
         'amazon_cs','amazon_photo','coauthor_cs','coauthor_phy', 
         'polblogs', 'pubmed', 'flickr','blogcatalog','dblp')
        ```
        
        ## Tensor
        + Strided (dense) Tensor 
        ```python
        >>> backend()
        TensorFlow 2.1.2 Backend
        
        >>> from graphgallery import functional as F
        >>> arr = [1, 2, 3]
        >>> F.astensor(arr)
        <tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 2, 3], dtype=int32)>
        
        ```
        
        + Sparse Tensor
        
        ```python
        >>> import scipy.sparse as sp
        >>> sp_matrix = sp.eye(3)
        >>> F.astensor(sp_matrix)
        <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7f1bbc205dd8>
        ```
        
        + also works for PyTorch, just like
        
        ```python
        >>> from graphgallery import set_backend
        >>> set_backend('torch') # torch, pytorch or th
        PyTorch 1.6.0+cu101 Backend
        
        >>> F.astensor(arr)
        tensor([1, 2, 3])
        
        >>> F.astensor(sp_matrix)
        tensor(indices=tensor([[0, 1, 2],
                               [0, 1, 2]]),
               values=tensor([1., 1., 1.]),
               size=(3, 3), nnz=3, layout=torch.sparse_coo)
        ```
        
        + To Numpy or Scipy sparse matrix
        ```python
        >>> tensor = F.astensor(arr)
        >>> F.tensoras(tensor)
        array([1, 2, 3])
        
        >>> sp_tensor = F.astensor(sp_matrix)
        >>> F.tensoras(sp_tensor)
        <3x3 sparse matrix of type '<class 'numpy.float32'>'
            with 3 stored elements in Compressed Sparse Row format>
        ```
        
        + Or even convert one Tensor to another one
        ```python
        >>> tensor = F.astensor(arr, backend="tensorflow") # or "tf" in short
        >>> tensor
        <tf.Tensor: shape=(3,), dtype=int64, numpy=array([1, 2, 3])>
        >>> F.tensor2tensor(tensor)
        tensor([1, 2, 3])
        
        >>> sp_tensor = F.astensor(sp_matrix, backend="tensorflow") # set backend="tensorflow" to convert to tensorflow tensor
        >>> sp_tensor
        <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7efb6836a898>
        >>> F.tensor2tensor(sp_tensor)
        tensor(indices=tensor([[0, 1, 2],
                               [0, 1, 2]]),
               values=tensor([1., 1., 1.]),
               size=(3, 3), nnz=3, layout=torch.sparse_coo)
        
        ```
        
        ## Example of GCN model
        ```python
        from graphgallery.nn.gallery import GCN
        
        model = GCN(graph, attr_transform="normalize_attr", device="CPU", seed=123)
        # build your GCN model with default hyper-parameters
        model.build()
        # train your model. here idx_train and idx_val are numpy arrays
        # verbose takes 0, 1, 2, 3, 4
        his = model.train(idx_train, idx_val, verbose=1, epochs=100)
        # test your model
        # verbose takes 0, 1, 2
        loss, accuracy = model.test(idx_test, verbose=1)
        print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
        ```
        On `Cora` dataset:
        ```
        Training...
        100/100 [==============================] - 1s 14ms/step - loss: 1.0161 - acc: 0.9500 - val_loss: 1.4101 - val_acc: 0.7740 - time: 1.4180
        Testing...
        1/1 [==============================] - 0s 62ms/step - test_loss: 1.4123 - test_acc: 0.8120 - time: 0.0620
        Test loss 1.4123, Test accuracy 81.20%
        ```
        ## Customization
        + Build your model
        you can use the following statement to build your model
        ```python
        # one hidden layer with hidden units 32 and activation function RELU
        >>> model.build(hiddens=32, activations='relu')
        
        # two hidden layer with hidden units 32, 64 and all activation functions are RELU
        >>> model.build(hiddens=[32, 64], activations='relu')
        
        # two hidden layer with hidden units 32, 64 and activation functions RELU and ELU
        >>> model.build(hiddens=[32, 64], activations=['relu', 'elu'])
        
        ```
        + Train your model
        ```python
        # train with validation
        >>> his = model.train(idx_train, idx_val, verbose=1, epochs=100)
        # train without validation
        >>> his = model.train(idx_train, verbose=1, epochs=100)
        ```
        here `his` is a tensorflow `History` instance.
        
        + Test you model
        ```python
        >>> loss, accuracy = model.test(idx_test, verbose=1)
        Testing...
        1/1 [==============================] - 0s 62ms/step - test_loss: 1.4123 - test_acc: 0.8120 - time: 0.0620
        >>> print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
        Test loss 1.4123, Test accuracy 81.20%
        ```
        
        ## Visualization
        NOTE: you must install [SciencePlots](https://github.com/garrettj403/SciencePlots) package for a better preview.
        
        ```python
        import matplotlib.pyplot as plt
        with plt.style.context(['science', 'no-latex']):
            fig, axes = plt.subplots(1, 2, figsize=(15, 5))
            axes[0].plot(his.history['acc'], label='Training accuracy', linewidth=3)
            axes[0].plot(his.history['val_acc'], label='Validation accuracy', linewidth=3)
            axes[0].legend(fontsize=20)
            axes[0].set_title('Accuracy', fontsize=20)
            axes[0].set_xlabel('Epochs', fontsize=20)
            axes[0].set_ylabel('Accuracy', fontsize=20)
        
            axes[1].plot(his.history['loss'], label='Training loss', linewidth=3)
            axes[1].plot(his.history['val_loss'], label='Validation loss', linewidth=3)
            axes[1].legend(fontsize=20)
            axes[1].set_title('Loss', fontsize=20)
            axes[1].set_xlabel('Epochs', fontsize=20)
            axes[1].set_ylabel('Loss', fontsize=20)
            
            plt.autoscale(tight=True)
            plt.show()        
        ```
        ![visualization](https://github.com/EdisonLeeeee/GraphGallery/blob/master/imgs/history.png)
        
        ## Using TensorFlow/PyTorch Backend
        ```python
        >>> import graphgallery
        >>> graphgallery.backend()
        TensorFlow 2.1.0 Backend
        
        >>> graphgallery.set_backend("pytorch")
        PyTorch 1.6.0+cu101 Backend
        ```
        ###  GCN using PyTorch backend
        
        ```python
        
        # The following codes are the same with TensorFlow Backend
        >>> from graphgallery.nn.gallery import GCN
        >>> model = GCN(graph, attr_transform="normalize_attr", device="GPU", seed=123);
        >>> model.build()
        >>> his = model.train(idx_train, idx_val, verbose=1, epochs=100)
        Training...
        100/100 [==============================] - 0s 5ms/step - loss: 0.6813 - acc: 0.9214 - val_loss: 1.0506 - val_acc: 0.7820 - time: 0.4734
        >>> loss, accuracy = model.test(idx_test, verbose=1)
        Testing...
        1/1 [==============================] - 0s 1ms/step - test_loss: 1.0131 - test_acc: 0.8220 - time: 0.0013
        >>> print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
        Test loss 1.0131, Test accuracy 82.20%
        
        ```
        
        # ❓ How to add your datasets
        This is motivated by [gnn-benchmark](https://github.com/shchur/gnn-benchmark/)
        ```python
        from graphgallery.data import Graph
        
        # Load the adjacency matrix A, attribute matrix X and labels vector y
        # A - scipy.sparse.csr_matrix of shape [n_nodes, n_nodes]
        # X - scipy.sparse.csr_matrix or np.ndarray of shape [n_nodes, n_atts]
        # y - np.ndarray of shape [n_nodes]
        
        mydataset = Graph(adj_matrix=A, attr_matrix=X, labels=y)
        # save dataset
        mydataset.to_npz('path/to/mydataset.npz')
        # load dataset
        mydataset = Graph.from_npz('path/to/mydataset.npz')
        ```
        
        # ❓ How to define your models
        
        You can follow the codes in the folder `graphgallery.nn.gallery` and write you models based on:
        
        + TensorFlow
        + PyTorch
        + PyTorch Geometric (PyG)
        + Deep Graph Library (DGL)
        
        NOTE: [PyG](https://github.com/rusty1s/pytorch_geometric) backend and [DGL](https://github.com/dmlc/dgl) backend now are supported in GraphGallery!
        
        ```python
        >>> import graphgallery
        >>> graphgallery.set_backend("pyg")
        PyTorch Geometric 1.6.1 (PyTorch 1.6.0+cu101) Backend
        ```
        
        ### GCN using PyG backend
        
        ```python
        # The following codes are the same with TensorFlow or PyTorch Backend
        >>> from graphgallery.nn.gallery import GCN
        >>> model = GCN(graph, attr_transform="normalize_attr", device="GPU", seed=123);
        >>> model.build()
        >>> his = model.train(idx_train, idx_val, verbose=1, epochs=100)
        Training...
        100/100 [==============================] - 0s 3ms/step - loss: 0.5325 - acc: 0.9643 - val_loss: 1.0034 - val_acc: 0.7980 - time: 0.3101
        >>> loss, accuracy = model.test(idx_test, verbose=1)
        Testing...
        1/1 [==============================] - 0s 834us/step - test_loss: 0.9733 - test_acc: 0.8130 - time: 8.2737e-04
        >>> print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
        Test loss 0.97332, Test accuracy 81.30%
        ```
        
        
        
        # 😎 More Examples
        Please refer to the [examples](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples) directory.
        
        # ⭐ Road Map
        - [x] Add PyTorch models support
        - [x] Add other frameworks (PyG and DGL) support
        - [ ] Add more GNN models (TF and Torch backend)
        - [ ] Support for more tasks, e.g., `graph Classification` and `link prediction`
        - [ ] Support for more types of graphs, e.g., Heterogeneous graph
        - [ ] Add Docstrings and Documentation (Building)
        
        
        # 😘 Acknowledgement
        This project is motivated by [Pytorch Geometric](https://github.com/rusty1s/pytorch_geometric), [Tensorflow Geometric](https://github.com/CrawlScript/tf_geometric), [Stellargraph](https://github.com/stellargraph/stellargraph) and [DGL](https://github.com/dmlc/dgl), etc., and the original implementations of the authors, thanks for their excellent works!
Keywords: tensorflow,pytorch,geometric-deep-learning,graph-neural-networks
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
