class_docstr: Class to read benchmark datasets for the community detection or node embedding task.
  
  Args:
      dataset (str): Dataset of interest, one of:
          (:obj:`"facebook"`, :obj:`"twitch"`, :obj:`"wikipedia"`, :obj:`"github"`, :obj:`"lastfm"`, :obj:`"deezer"`). Default is 'wikipedia'.
class_docstr_tok: ["Class","to","read","benchmark","datasets","for","the","community","detection","or","node","embedding","task",".","Args",":","dataset","(","str",")",":","Dataset","of","interest",",","one","of",":","(",":","obj",":","`","``","facebook","''","`",",",":","obj",":","`","``","twitch","''","`",",",":","obj",":","`","``","wikipedia","''","`",",",":","obj",":","`","``","github","''","`",",",":","obj",":","`","``","lastfm","''","`",",",":","obj",":","`","``","deezer","''","`",")",".","Default","is","'wikipedia","'","."]
docstr: Getting the node features Scipy matrix.
  
  Return types:
      * **features** *(COO Scipy array)* - Node feature matrix.
docstr_tok: ["Getting","the","node","features","Scipy","matrix",".","Return","types",":","*","*","*","features","*","*","*","(","COO","Scipy","array",")","*","-","Node","feature","matrix","."]
code: def get_features(self) -> coo_matrix:
      r"""Getting the node features Scipy matrix.
  
      Return types:
          * **features** *(COO Scipy array)* - Node feature matrix.
      """
      data = self._dataset_reader("features.csv")
      row = np.array(data["node_id"])
      col = np.array(data["feature_id"])
      values = np.array(data["value"])
      node_count = max(row) + 1
      feature_count = max(col) + 1
      shape = (node_count, feature_count)
      features = coo_matrix((values, (row, col)), shape=shape)
      return features
  
imports: import io
  import os
  import json
  import numpy
  import pandas
  import networkx
  from typing import List
  from six.moves import urllib
  from scipy.sparse import coo_matrix
label_desc: Use image node_id 5 for calling the GraphReader obj's underlying member method code with example usage: obj.get_features() and returns: features