"""Yelp Dataset"""
import json
import os
import numpy as np
import scipy.sparse as sp
from .. import backend as F
from ..convert import from_scipy
from ..transforms import reorder_graph
from .dgl_dataset import DGLBuiltinDataset
from .utils import _get_dgl_url, generate_mask_tensor, load_graphs, save_graphs
[docs]class YelpDataset(DGLBuiltinDataset):
r"""Yelp dataset for node classification from `GraphSAINT: Graph Sampling Based Inductive
Learning Method <https://arxiv.org/abs/1907.04931>`_
The task of this dataset is categorizing types of businesses based on customer reviewers and
friendship.
Yelp dataset statistics:
- Nodes: 716,847
- Edges: 13,954,819
- Number of classes: 100 (Multi-class)
- Node feature size: 300
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: False
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.
reorder : bool
Whether to reorder the graph using :func:`~dgl.reorder_graph`.
Default: False.
Attributes
----------
num_classes : int
Number of node classes
Examples
--------
>>> dataset = YelpDataset()
>>> dataset.num_classes
100
>>> g = dataset[0]
>>> # get node feature
>>> feat = g.ndata['feat']
>>> # get node labels
>>> labels = g.ndata['label']
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
"""
def __init__(
self,
raw_dir=None,
force_reload=False,
verbose=False,
transform=None,
reorder=False,
):
_url = _get_dgl_url("dataset/yelp.zip")
self._reorder = reorder
super(YelpDataset, self).__init__(
name="yelp",
raw_dir=raw_dir,
url=_url,
force_reload=force_reload,
verbose=verbose,
transform=transform,
)
def process(self):
"""process raw data to graph, labels and masks"""
coo_adj = sp.load_npz(os.path.join(self.raw_path, "adj_full.npz"))
g = from_scipy(coo_adj)
features = np.load(os.path.join(self.raw_path, "feats.npy"))
features = F.tensor(features, dtype=F.float32)
y = [-1] * features.shape[0]
with open(os.path.join(self.raw_path, "class_map.json")) as f:
class_map = json.load(f)
for key, item in class_map.items():
y[int(key)] = item
labels = F.tensor(np.array(y), dtype=F.int64)
with open(os.path.join(self.raw_path, "role.json")) as f:
role = json.load(f)
train_mask = np.zeros(features.shape[0], dtype=bool)
train_mask[role["tr"]] = True
val_mask = np.zeros(features.shape[0], dtype=bool)
val_mask[role["va"]] = True
test_mask = np.zeros(features.shape[0], dtype=bool)
test_mask[role["te"]] = True
g.ndata["feat"] = features
g.ndata["label"] = labels
g.ndata["train_mask"] = generate_mask_tensor(train_mask)
g.ndata["val_mask"] = generate_mask_tensor(val_mask)
g.ndata["test_mask"] = generate_mask_tensor(test_mask)
if self._reorder:
self._graph = reorder_graph(
g,
node_permute_algo="rcmk",
edge_permute_algo="dst",
store_ids=False,
)
else:
self._graph = g
def has_cache(self):
graph_path = os.path.join(self.save_path, "dgl_graph.bin")
return os.path.exists(graph_path)
def save(self):
graph_path = os.path.join(self.save_path, "dgl_graph.bin")
save_graphs(graph_path, self._graph)
def load(self):
graph_path = os.path.join(self.save_path, "dgl_graph.bin")
g, _ = load_graphs(graph_path)
self._graph = g[0]
@property
def num_classes(self):
return 100
[docs] def __len__(self):
r"""The number of graphs in the dataset."""
return 1
[docs] def __getitem__(self, idx):
r"""Get graph object
Parameters
----------
idx : int
Item index, FlickrDataset has only one graph object
Returns
-------
:class:`dgl.DGLGraph`
The graph contains:
- ``ndata['label']``: node label
- ``ndata['feat']``: node feature
- ``ndata['train_mask']``: mask for training node set
- ``ndata['val_mask']``: mask for validation node set
- ``ndata['test_mask']``: mask for test node set
"""
assert idx == 0, "This dataset has only one graph"
if self._transform is None:
return self._graph
else:
return self._transform(self._graph)