[Feature] add two fraud datasets (#2908)

* add two fraud dataset

* add comments and modify to heterogenous graph

* modify the node name based on datasets

* add assertion message
Co-authored-by: zhjwy9343 <6593865@qq.com>

python/dgl/data/fraud.py

+"""Fraud Dataset
+"""
+import torch
+import os
+from scipy import io
+from sklearn.model_selection import train_test_split
+
+from .utils import save_graphs, load_graphs, _get_dgl_url
+from ..convert import heterograph
+from ..utils import graphdata2tensors
+from .dgl_dataset import DGLBuiltinDataset
+
+
+class FraudDataset(DGLBuiltinDataset):
+    r"""Fraud node prediction dataset.
+
+    The dataset includes two multi-relational graphs extracted from Yelp and Amazon
+    where nodes represent fraudulent reviews or fraudulent reviewers.
+
+    It was first proposed in a CIKM'20 paper <https://arxiv.org/pdf/2008.08692.pdf> and
+    has been used by a recent WWW'21 paper <https://ponderly.github.io/pub/PCGNN_WWW2021.pdf>
+    as a benchmark. Another paper <https://arxiv.org/pdf/2104.01404.pdf> also takes
+    the dataset as an example to study the non-homophilous graphs. This dataset is built
+    upon industrial data and has rich relational information and unique properties like
+    class-imbalance and feature inconsistency, which makes the dataset be a good instance
+    to investigate how GNNs perform on real-world noisy graphs. These graphs are bidirected
+    and not self connected.
+
+    Reference: <https://github.com/YingtongDou/CARE-GNN>
+
+    Parameters
+    ----------
+    name : str
+        Name of the dataset
+    raw_dir : str
+        Specifying the directory that will store the
+        downloaded data or the directory that
+        already stores the input data.
+        Default: ~/.dgl/
+    random_seed : int
+        Specifying the random seed in splitting the dataset.
+        Default: 2
+    train_size : float
+        training set size of the dataset.
+        Default: 0.7
+    val_size : float
+        validation set size of the dataset, and the
+        size of testing set is (1 - train_size - val_size)
+        Default: 0.1
+
+    Attributes
+    ----------
+    num_classes : int
+        Number of label classes
+    graph : dgl.heterograph.DGLHeteroGraph
+        Graph structure, etc.
+    seed : int
+        Random seed in splitting the dataset.
+    train_size : float
+        Training set size of the dataset.
+    val_size : float
+        Validation set size of the dataset
+
+    Examples
+    --------
+    >>> dataset = FraudDataset('yelp')
+    >>> graph = dataset[0]
+    >>> num_classes = dataset.num_classes
+    >>> feat = dataset.ndata['feature']
+    >>> label = dataset.ndata['label']
+    """
+    file_urls = {
+        'yelp': 'dataset/FraudYelp.zip',
+        'amazon': 'dataset/FraudAmazon.zip'
+    }
+    relations = {
+        'yelp': ['net_rsr', 'net_rtr', 'net_rur'],
+        'amazon': ['net_upu', 'net_usu', 'net_uvu']
+    }
+    file_names = {
+        'yelp': 'YelpChi.mat',
+        'amazon': 'Amazon.mat'
+    }
+    node_name = {
+        'yelp': 'user',
+        'amazon': 'review'
+    }
+
+    def __init__(self, name, raw_dir=None, random_seed=2, train_size=0.7, val_size=0.1):
+        assert name in ['yelp', 'amazon'], "only supports 'yelp', or 'amazon'"
+        url = _get_dgl_url(self.file_urls[name])
+        self.seed = random_seed
+        self.train_size = train_size
+        self.val_size = val_size
+        super(FraudDataset, self).__init__(name=name,
+                                           url=url,
+                                           raw_dir=raw_dir)
+
+    def process(self):
+        """process raw data to graph, labels, splitting masks"""
+        file_path = os.path.join(self.raw_path, self.file_names[self.name])
+
+        data = io.loadmat(file_path)
+        node_features = torch.from_numpy(data['features'].todense())
+        node_labels = torch.from_numpy(data['label'])
+        node_labels = node_labels.transpose(0, 1)
+
+        graph_data = {}
+        for relation in self.relations[self.name]:
+            u, v, _, _ = graphdata2tensors(data[relation])
+            graph_data[(self.node_name[self.name], relation, self.node_name[self.name])] = (u, v)
+        g = heterograph(graph_data)
+
+        g.ndata['feature'] = node_features
+        g.ndata['label'] = node_labels
+        self.graph = g
+
+        self._random_split(g.ndata['feature'], g.ndata['label'], self.seed, self.train_size, self.val_size)
+
+    def __getitem__(self, idx):
+        r""" Get graph object
+
+        Parameters
+        ----------
+        idx : int
+            Item index
+
+        Returns
+        -------
+        :class:`dgl.heterograph.DGLHeteroGraph`
+            graph structure, node features, node labels and masks
+
+            - ``ndata['feature']``: node features
+            - ``ndata['label']``: node labels
+            - ``ndata['train_mask']``: mask of training set
+            - ``ndata['val_mask']``: mask of validation set
+            - ``ndata['test_mask']``: mask of testing set
+        """
+        assert idx == 0, "This dataset has only one graph"
+        return self.graph
+
+    def __len__(self):
+        """number of data examples"""
+        return len(self.graph)
+
+    @property
+    def num_classes(self):
+        """Number of classes.
+
+        Return
+        -------
+        int
+        """
+        return 2
+
+    def save(self):
+        """save processed data to directory `self.save_path`"""
+        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph.bin')
+        save_graphs(str(graph_path), self.graph)
+
+    def load(self):
+        """load processed data from directory `self.save_path`"""
+        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph.bin')
+        graph_list, _ = load_graphs(str(graph_path))
+        g = graph_list[0]
+        self.graph = g
+
+    def has_cache(self):
+        """check whether there are processed data in `self.save_path`"""
+        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph.bin')
+        return os.path.exists(graph_path)
+
+    def _random_split(self, x, node_labels, seed=2, train_size=0.7, val_size=0.1):
+        """split the dataset into training set, validation set and testing set"""
+        N = x.shape[0]
+        index = list(range(N))
+        train_idx, test_idx, _, y = train_test_split(index,
+                                                     node_labels,
+                                                     stratify=node_labels,
+                                                     train_size=train_size,
+                                                     random_state=seed,
+                                                     shuffle=True)
+
+        if self.name == 'amazon':
+            # 0-3304 are unlabeled nodes
+            index = list(range(3305, N))
+            train_idx, test_idx, _, y = train_test_split(index,
+                                                         node_labels[3305:],
+                                                         stratify=node_labels[3305:],
+                                                         test_size=train_size,
+                                                         random_state=seed,
+                                                         shuffle=True)
+
+        val_idx, test_idx, _, _ = train_test_split(test_idx,
+                                                   y,
+                                                   stratify=y,
+                                                   train_size=val_size / (1 - train_size),
+                                                   random_state=seed,
+                                                   shuffle=True)
+        train_mask = torch.zeros(N, dtype=torch.bool)
+        val_mask = torch.zeros(N, dtype=torch.bool)
+        test_mask = torch.zeros(N, dtype=torch.bool)
+        train_mask[train_idx] = True
+        val_mask[val_idx] = True
+        test_mask[test_idx] = True
+        self.graph.ndata['train_mask'] = train_mask
+        self.graph.ndata['val_mask'] = val_mask
+        self.graph.ndata['test_mask'] = test_mask
+
+
+class FraudYelpDataset(FraudDataset):
+    r""" Fraud Yelp Dataset
+
+    The Yelp dataset includes hotel and restaurant reviews filtered (spam) and recommended
+    (legitimate) by Yelp. A spam review detection task can be conducted, which is a binary
+    classification task. 32 handcrafted features from
+    <http://dx.doi.org/10.1145/2783258.2783370> are taken as the raw node features. Reviews
+    are nodes in the graph, and three relations are:
+        1. R-U-R: it connects reviews posted by the same user
+        2. R-S-R: it connects reviews under the same product with the same star rating (1-5 stars)
+        3. R-T-R: it connects two reviews under the same product posted in the same month.
+
+    Statistics:
+
+    - Nodes: 45954
+    - Edges:
+        R-U-R: 49,315
+        R-T-R: 573,616
+        R-S-R: 3,402,743
+        ALL: 3,846,979
+    - Number of Classes: 2
+    - Node feature size: 32
+
+    Parameters
+    ----------
+    raw_dir : str
+        Specifying the directory that will store the
+        downloaded data or the directory that
+        already stores the input data.
+        Default: ~/.dgl/
+    random_seed : int
+        Specifying the random seed in splitting the
+        dataset.
+        Default: 2
+    train_size : float
+        training set size of the dataset.
+        Default: 0.7
+    val_size : float
+        validation set size of the dataset, and the
+        size of testing set is (1 - train_size - val_size)
+        Default: 0.1
+
+    Examples
+    --------
+    >>> dataset = FraudYelpDataset()
+    >>> graph = dataset[0]
+    >>> num_classes = dataset.num_classes
+    >>> feat = dataset.ndata['feature']
+    >>> label = dataset.ndata['label']
+    """
+
+    def __init__(self, raw_dir=None, random_seed=2, train_size=0.7, val_size=0.1):
+        super(FraudYelpDataset, self).__init__(name='yelp',
+                                               raw_dir=raw_dir,
+                                               random_seed=random_seed,
+                                               train_size=train_size,
+                                               val_size=val_size)
+
+
+class FraudAmazonDataset(FraudDataset):
+    r""" Fraud Amazon Dataset
+
+    The Amazon dataset includes product reviews under the Musical Instruments category.
+    Users with more than 80% helpful votes are labelled as benign entities and users with
+    less than 20% helpful votes are labelled as fraudulent entities. A fraudulent user
+    detection task can be conducted on the Amazon dataset, which is a binary classification
+    task. 25 handcrafted features from <https://arxiv.org/pdf/2005.10150.pdf> are taken as
+    the raw node features .
+
+    Users are nodes in the graph, and three relations are:
+        1. U-P-U : it connects users reviewing at least one same product
+        2. U-S-U : it connects users having at least one same star rating within one week
+        3. U-V-U : it connects users with top 5% mutual review text similarities (measured by
+                   TF-IDF) among all users.
+
+    Statistics:
+
+    - Nodes: 11944
+    - Edges:
+        U-P-U: 175,608
+        U-S-U: 3,566,479
+        U-V-U: 1,036,737
+        ALL: 4,398,392
+    - Number of Classes: 2
+    - Node feature size: 25
+
+    Parameters
+    ----------
+    raw_dir : str
+        Specifying the directory that will store the
+        downloaded data or the directory that
+        already stores the input data.
+        Default: ~/.dgl/
+    random_seed : int
+        Specifying the random seed in splitting the
+        dataset.
+        Default: 2
+    train_size : float
+        training set size of the dataset.
+        Default: 0.7
+    val_size : float
+        validation set size of the dataset, and the
+        size of testing set is (1 - train_size - val_size)
+        Default: 0.1
+
+    Examples
+    --------
+    >>> dataset = FraudAmazonDataset()
+    >>> graph = dataset[0]
+    >>> num_classes = dataset.num_classes
+    >>> feat = dataset.ndata['feature']
+    >>> label = dataset.ndata['label']
+    """
+
+    def __init__(self, raw_dir=None, random_seed=2, train_size=0.7, val_size=0.1):
+        super(FraudAmazonDataset, self).__init__(name='amazon',
+                                                 raw_dir=raw_dir,
+                                                 random_seed=random_seed,
+                                                 train_size=train_size,
+                                                 val_size=val_size)