code: def fetch_kddcup99(
      subset=None,
      shuffle=False,
      random_state=None,
      percent10=True,
      download_if_missing=True,
  ):
      """Load and return the kddcup 99 dataset (classification).
  
      The KDD Cup '99 dataset was created by processing the tcpdump portions
      of the 1998 DARPA Intrusion Detection System (IDS) Evaluation dataset,
      created by MIT Lincoln Lab [1] . The artificial data was generated using
      a closed network and hand-injected attacks to produce a large number of
      different types of attack with normal activity in the background.
      As the initial goal was to produce a large training set for supervised
      learning algorithms, there is a large proportion (80.1%) of abnormal
      data which is unrealistic in real world, and inappropriate for unsupervised
      anomaly detection which aims at detecting 'abnormal' data, ie
  
      1) qualitatively different from normal data.
  
      2) in large minority among the observations.
  
      We thus transform the KDD Data set into two different data sets: SA and SF.
  
      - SA is obtained by simply selecting all the normal data, and a small
        proportion of abnormal data to gives an anomaly proportion of 1%.
  
      - SF is obtained as in [2]
        by simply picking up the data whose attribute logged_in is positive, thus
        focusing on the intrusion attack, which gives a proportion of 0.3% of
        attack.
  
      - http and smtp are two subsets of SF corresponding with third feature
        equal to 'http' (resp. to 'smtp')
  
  
      General KDD structure :
  
      ================      ==========================================
      Samples total         4898431
      Dimensionality        41
      Features              discrete (int) or continuous (float)
      Targets               str, 'normal.' or name of the anomaly type
      ================      ==========================================
  
      SA structure :
  
      ================      ==========================================
      Samples total         976158
      Dimensionality        41
      Features              discrete (int) or continuous (float)
      Targets               str, 'normal.' or name of the anomaly type
      ================      ==========================================
  
      SF structure :
  
      ================      ==========================================
      Samples total         699691
      Dimensionality        4
      Features              discrete (int) or continuous (float)
      Targets               str, 'normal.' or name of the anomaly type
      ================      ==========================================
  
      http structure :
  
      ================      ==========================================
      Samples total         619052
      Dimensionality        3
      Features              discrete (int) or continuous (float)
      Targets               str, 'normal.' or name of the anomaly type
      ================      ==========================================
  
      smtp structure :
  
      ================      ==========================================
      Samples total         95373
      Dimensionality        3
      Features              discrete (int) or continuous (float)
      Targets               str, 'normal.' or name of the anomaly type
      ================      ==========================================
  
      .. versionadded:: 0.18
  
      Parameters
      ----------
      subset : None, 'SA', 'SF', 'http', 'smtp'
          To return the corresponding classical subsets of kddcup 99.
          If None, return the entire kddcup 99 dataset.
  
      random_state : int, RandomState instance or None, optional (default=None)
          Random state for shuffling the dataset.
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      shuffle : bool, default=False
          Whether to shuffle dataset.
  
      percent10 : bool, default=False
          Whether to load only 10 percent of the data.
  
      download_if_missing : bool, default=True
          If False, raise a IOError if the data is not locally available
          instead of trying to download the data from the source site.
  
      Returns
      -------
      data : Bunch
          Dictionary-like object, the interesting attributes are:
          'data', the data to learn and 'target', the regression target for each
          sample.
  
  
      References
      ----------
      .. [1] Analysis and Results of the 1999 DARPA Off-Line Intrusion
             Detection Evaluation Richard Lippmann, Joshua W. Haines,
             David J. Fried, Jonathan Korba, Kumar Das
  
      .. [2] A Geometric Framework for Unsupervised Anomaly Detection: Detecting
             Intrusions in Unlabeled Data (2002) by Eleazar Eskin, Andrew Arnold,
             Michael Prerau, Leonid Portnoy, Sal Stolfo
  
      """
      kddcup99 = _fetch_brute_kddcup99(
          shuffle=shuffle,
          percent10=percent10,
          download_if_missing=download_if_missing,
      )
  
      data = kddcup99.data
      target = kddcup99.target
  
      if subset == "SA":
          s = target == b"normal."
          t = np.logical_not(s)
          normal_samples = data[s, :]
          normal_targets = target[s]
          abnormal_samples = data[t, :]
          abnormal_targets = target[t]
  
          n_samples_abnormal = abnormal_samples.shape[0]
          # selected abnormal samples:
          random_state = check_random_state(random_state)
          r = random_state.randint(0, n_samples_abnormal, 3377)
          abnormal_samples = abnormal_samples[r]
          abnormal_targets = abnormal_targets[r]
  
          data = np.r_[normal_samples, abnormal_samples]
          target = np.r_[normal_targets, abnormal_targets]
  
      if subset == "SF" or subset == "http" or subset == "smtp":
          # select all samples with positive logged_in attribute:
          s = data[:, 11] == 1
          data = np.c_[data[s, :11], data[s, 12:]]
          target = target[s]
  
          data[:, 0] = np.log((data[:, 0] + 0.1).astype(float))
          data[:, 4] = np.log((data[:, 4] + 0.1).astype(float))
          data[:, 5] = np.log((data[:, 5] + 0.1).astype(float))
  
          if subset == "http":
              s = data[:, 2] == b"http"
              data = data[s]
              target = target[s]
              data = np.c_[data[:, 0], data[:, 4], data[:, 5]]
  
          if subset == "smtp":
              s = data[:, 2] == b"smtp"
              data = data[s]
              target = target[s]
              data = np.c_[data[:, 0], data[:, 4], data[:, 5]]
  
          if subset == "SF":
              data = np.c_[
                  data[:, 0], data[:, 2], data[:, 4], data[:, 5]
              ]
  
      return Bunch(data=data, target=target)
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/datasets/kddcup99.py
imports: import sys
  import errno
  from gzip import GzipFile
  from io import BytesIO
  import logging
  import os
  from os.path import exists, join
  import numpy
  from .base import get_data_home
  from .base import Bunch
  from ..externals import joblib, six
  from ..utils import check_random_state
  from ..utils import shuffle
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_kddcup99(subset, shuffle, random_state, percent10, download_if_missing) and returns: Bunch