# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""BlogCatalog Dataset"""
import os
import os.path as osp
import numpy as np
import pandas as pd
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from mindspore_gl.graph import MindHomoGraph, CsrAdj
from .base_dataset import BaseDataSet
#pylint: disable=W0223
[文档]class BlogCatalog(BaseDataSet):
"""
BlogCatalog Dataset, a source dataset for reading and parsing BlogCatalog dataset.
About BlogCatalog dataset:
This is the data from BlogCatalog. BlogCatalog is a social blog
directory website. This contains the friendship network and group memberships. For easier understanding,
all the contents are organized in CSV file format.
Statistics:
- Nodes: 10,312
- Edges: 333,983
- Number of Classes: 39
Dataset can be download here: `BlogCatalog <https://figshare.com/articles/dataset/BlogCatalog_dataset/11923611>`_ .
You can organize the dataset files into the following directory structure and read.
.. code-block::
.
└── ppi
├── edges.csv
├── group-edges.csv
├── groups.csv
└── nodes.csv
Args:
root(str): path to the root directory that contains blog_catalog.npz.
Raises:
TypeError: if `root` is not a str.
RuntimeError: if `root` does not contain data files.
Examples:
>>> from mindspore_gl.dataset.blog_catalog import BlogCatalog
>>> root = "path/to/blog_catalog"
>>> dataset = BlogCatalog(root)
"""
def __init__(self, root):
if not isinstance(root, str):
raise TypeError(f"For '{self.cls_name}', the 'root' should be a str, "
f"but got {type(root)}.")
self._root = root
self._path = osp.join(root, 'blog_catalog.npz')
self._csr_row = None
self._csr_col = None
self._nodes = None
self._vocab = None
self._node_label = None
self._npz_file = None
if os.path.exists(self._path) and os.path.isfile(self._path):
self._load()
elif os.path.exists(self._root):
self._preprocess()
self._load()
else:
raise Exception('data file does not exist')
def _preprocess(self):
"""Process data"""
nodes = pd.read_csv(osp.join(self._root, 'nodes.csv'), header=None)
nodes = list(nodes.values[:, 0])
node_num = len(nodes)
groups = pd.read_csv(osp.join(self._root, 'groups.csv'), header=None)
groups = list(groups.values[:, 0])
edges = pd.read_csv(osp.join(self._root, 'edges.csv'), header=None)
group_edges = pd.read_csv(osp.join(self._root, 'group-edges.csv'), header=None)
group_edges = group_edges.drop_duplicates(subset=[0])
vocab = group_edges.values[:, 0] - 1
label = group_edges.values[:, 1]
edges = edges.values
dir_row = edges[:, 0] - 1
dir_col = edges[:, 1] - 1
row = np.hstack((dir_row, dir_col))
col = np.hstack((dir_col, dir_row))
data = [1] * len(row)
coo = coo_matrix((data, (row, col)), shape=(node_num, node_num))
crs = coo.tocsr()
indptr = crs.indptr
indces = crs.indices
np.savez(self._path, num_classes=len(groups), adj_csr_indptr=indptr,
adj_csr_indices=indces, label=label, vocab=vocab)
def _load(self):
"""Load the saved npz dataset from files."""
self._npz_file = np.load(self._path)
self._csr_row = self._npz_file['adj_csr_indptr'].astype(np.int32)
self._csr_col = self._npz_file['adj_csr_indices'].astype(np.int32)
self._nodes = np.array(list(range(len(self._csr_row) - 1)))
@property
def num_classes(self):
"""
Number of label classes.
Returns:
- int, the number of classes.
Examples:
>>> #dataset is an instance object of Dataset
>>> num_classes = dataset.num_classes
"""
return int(self._npz_file["num_classes"])
@property
def node_count(self):
"""
Number of nodes， length of csr row.
Returns:
- int, the number of nodes.
Examples:
>>> #dataset is an instance object of Dataset
>>> node_count = dataset.node_count
"""
return len(self._csr_row) - 1
@property
def edge_count(self):
"""
Number of edges, length of csr col.
Returns:
- int, the number of edges.
Examples:
>>> #dataset is an instance object of Dataset
>>> edge_count = dataset.edge_count
"""
return len(self._csr_col)
@property
def node_label(self):
"""
Ground truth labels of each node.
Returns:
- numpy.ndarray, array of node label.
Examples:
>>> #dataset is an instance object of Dataset
>>> node_label = dataset.node_label
"""
if self._node_label is None:
self._node_label = self._npz_file["label"]
return self._node_label.astype(np.int32)
@property
def vocab(self):
"""
ID of each node.
Returns:
- numpy.ndarray, array of node ID.
Examples:
>>> #dataset is an instance object of Dataset
>>> node_label = dataset.vocab
"""
if self._vocab is None:
self._vocab = self._npz_file["vocab"]
return self._vocab.astype(np.int32)
@property
def adj_coo(self):
"""
Return the adjacency matrix of COO representation.
Returns:
- numpy.ndarray, array of COO matrix.
Examples:
>>> #dataset is an instance object of Dataset
>>> node_label = dataset.adj_coo
"""
return csr_matrix((np.ones(self._csr_col.shape), self._csr_col, self._csr_row)).tocoo(copy=False)
@property
def adj_csr(self):
"""
Return the adjacency matrix of CSR representation.
Returns:
- numpy.ndarray, array of CSR matrix.
Examples:
>>> #dataset is an instance object of Dataset
>>> node_label = dataset.adj_csr
"""
return csr_matrix((np.ones(self._csr_col.shape), self._csr_col, self._csr_row))
def __getitem__(self, idx):
if idx != 0:
raise ValueError("Blog Catalog only has one graph")
graph = MindHomoGraph()
node_dict = {idx: idx for idx in range(self.node_count)}
edge_ids = np.array(list(range(self.edge_count))).astype(np.int32)
graph.set_topo(CsrAdj(self._csr_row, self._csr_col), node_dict=node_dict, edge_ids=edge_ids)
return graph