chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
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+""" BitcoinOTC dataset for fraud detection """
+import datetime
+import gzip
+import os
+import shutil
+
+import numpy as np
+
+from .. import backend as F
+from ..convert import graph as dgl_graph
+from .dgl_dataset import DGLBuiltinDataset
+from .utils import check_sha1, download, load_graphs, makedirs, save_graphs
+
+
+class BitcoinOTCDataset(DGLBuiltinDataset):
+    r"""BitcoinOTC dataset for fraud detection
+
+    This is who-trusts-whom network of people who trade using Bitcoin on
+    a platform called Bitcoin OTC. Since Bitcoin users are anonymous,
+    there is a need to maintain a record of users' reputation to prevent
+    transactions with fraudulent and risky users.
+
+    Offical website: `<https://snap.stanford.edu/data/soc-sign-bitcoin-otc.html>`_
+
+    Bitcoin OTC dataset statistics:
+
+    - Nodes: 5,881
+    - Edges: 35,592
+    - Range of edge weight: -10 to +10
+    - Percentage of positive edges: 89%
+
+    Parameters
+    ----------
+    raw_dir : str
+        Raw file directory to download/contains the input data directory.
+        Default: ~/.dgl/
+    force_reload : bool
+        Whether to reload the dataset.
+        Default: False
+    verbose: bool
+        Whether to print out progress information.
+        Default: True.
+    transform : callable, optional
+        A transform that takes in a :class:`~dgl.DGLGraph` object and returns
+        a transformed version. The :class:`~dgl.DGLGraph` object will be
+        transformed before every access.
+
+    Attributes
+    ----------
+    graphs : list
+        A list of DGLGraph objects
+    is_temporal : bool
+        Indicate whether the graphs are temporal graphs
+
+    Raises
+    ------
+    UserWarning
+        If the raw data is changed in the remote server by the author.
+
+    Examples
+    --------
+    >>> dataset = BitcoinOTCDataset()
+    >>> len(dataset)
+    136
+    >>> for g in dataset:
+    ....    # get edge feature
+    ....    edge_weights = g.edata['h']
+    ....    # your code here
+    >>>
+    """
+
+    _url = "https://snap.stanford.edu/data/soc-sign-bitcoinotc.csv.gz"
+    _sha1_str = "c14281f9e252de0bd0b5f1c6e2bae03123938641"
+
+    def __init__(
+        self, raw_dir=None, force_reload=False, verbose=False, transform=None
+    ):
+        super(BitcoinOTCDataset, self).__init__(
+            name="bitcoinotc",
+            url=self._url,
+            raw_dir=raw_dir,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    def download(self):
+        gz_file_path = os.path.join(self.raw_dir, self.name + ".csv.gz")
+        download(self.url, path=gz_file_path)
+        if not check_sha1(gz_file_path, self._sha1_str):
+            raise UserWarning(
+                "File {} is downloaded but the content hash does not match."
+                "The repo may be outdated or download may be incomplete. "
+                "Otherwise you can create an issue for it.".format(
+                    self.name + ".csv.gz"
+                )
+            )
+        self._extract_gz(gz_file_path, self.raw_path)
+
+    def process(self):
+        filename = os.path.join(self.save_path, self.name + ".csv")
+        data = np.loadtxt(filename, delimiter=",").astype(np.int64)
+        data[:, 0:2] = data[:, 0:2] - data[:, 0:2].min()
+        delta = datetime.timedelta(days=14).total_seconds()
+        # The source code is not released, but the paper indicates there're
+        # totally 137 samples. The cutoff below has exactly 137 samples.
+        time_index = np.around((data[:, 3] - data[:, 3].min()) / delta).astype(
+            np.int64
+        )
+
+        self._graphs = []
+        for i in range(time_index.max()):
+            row_mask = time_index <= i
+            edges = data[row_mask][:, 0:2]
+            rate = data[row_mask][:, 2]
+            g = dgl_graph((edges[:, 0], edges[:, 1]))
+            g.edata["h"] = F.tensor(
+                rate.reshape(-1, 1), dtype=F.data_type_dict["int64"]
+            )
+            self._graphs.append(g)
+
+    @property
+    def graph_path(self):
+        return os.path.join(self.save_path, "dgl_graph.bin")
+
+    def has_cache(self):
+        return os.path.exists(self.graph_path)
+
+    def save(self):
+        save_graphs(self.graph_path, self.graphs)
+
+    def load(self):
+        self._graphs = load_graphs(self.graph_path)[0]
+
+    @property
+    def graphs(self):
+        return self._graphs
+
+    def __len__(self):
+        r"""Number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
+        return len(self.graphs)
+
+    def __getitem__(self, item):
+        r"""Get graph by index
+
+        Parameters
+        ----------
+        item : int
+            Item index
+
+        Returns
+        -------
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``edata['h']`` : edge weights
+        """
+        if self._transform is None:
+            return self.graphs[item]
+        else:
+            return self._transform(self.graphs[item])
+
+    @property
+    def is_temporal(self):
+        r"""Are the graphs temporal graphs
+
+        Returns
+        -------
+        bool
+        """
+        return True
+
+    def _extract_gz(self, file, target_dir, overwrite=False):
+        if os.path.exists(target_dir) and not overwrite:
+            return
+        print("Extracting file to {}".format(target_dir))
+        fname = os.path.basename(file)
+        makedirs(target_dir)
+        out_file_path = os.path.join(target_dir, fname[:-3])
+        with gzip.open(file, "rb") as f_in:
+            with open(out_file_path, "wb") as f_out:
+                shutil.copyfileobj(f_in, f_out)
+
+
+BitcoinOTC = BitcoinOTCDataset