class_docstr: Malicious URLs dataset.
  
  This dataset contains features about URLs that are classified as malicious or not.
  
  References
  ----------
  [^1]: [Detecting Malicious URLs](http://www.sysnet.ucsd.edu/projects/url/)
  [^2]: [Identifying Suspicious URLs: An Application of Large-Scale Online Learning](http://cseweb.ucsd.edu/~jtma/papers/url-icml2009.pdf)
code: def _iter(self):
      files = list(self.path.glob("Day*.svm"))
      files.sort(
          key=lambda x: int(os.path.basename(x).split(".")[0][3:])
      )
  
      def parse_libsvm_feature(f):
          k, v = f.split(":")
          return int(k), float(v)
  
      # There are 150 files with each one corresponding to a day
      for file in files:
          with open(file) as f:
              for line in f:
                  # Each file has the libsvm format
                  elements = line.rstrip().split(" ")
                  y = elements.pop(0) == "+1"
                  x = dict(parse_libsvm_feature(f) for f in elements)
                  yield x, y
  
imports: from __future__ import annotations
  import os
  from .None import base
label_desc: Use image node_id 2 for calling the MaliciousURL obj's underlying member method code with example usage: obj._iter() and returns: int,  float