hp3 dZddlZddlZddlZddlmZddlmZmZddl Z ddl Z ddl m Z mZddl mZddlmZmZd d lmZd d lmZmZmZmZed d dZedddZejeZeehddge ej!dgdgdgdgdgdgdgddddddddddddZ"ddZ#dZ$dS)zKDDCUP 99 dataset. A classic dataset for anomaly detection. The dataset page is available from UCI Machine Learning Repository https://archive.ics.uci.edu/ml/machine-learning-databases/kddcup99-mld/kddcup.data.gz N)GzipFile)existsjoin)Bunchcheck_random_state)shuffle) StrOptionsvalidate_params) get_data_home)RemoteFileMetadata_convert_data_dataframe _fetch_remote load_descr kddcup99_dataz.https://ndownloader.figshare.com/files/5976045@3b6c942aa0356c0ca35b7b595a26c89d343652c9db428893e7494f837b274292)filenameurlchecksumkddcup99_10_dataz.https://ndownloader.figshare.com/files/5976042@8045aca0d84e70e622d1148d7df782496f6333bf6eb979a1b0837c42a9fd9561>SASFhttpsmtpboolean random_state)subset data_homer r percent10download_if_missing return_X_yas_frameT)prefer_skip_nested_validationFc|t|}t|||}|j} |j} |j} |j} |dkr| dk} t j| }| | ddf}| | }| |ddf}| |}|jd}t|}| d|d}||}||}t j ||f} t j ||f} |dks |d ks|d kr]| ddd fd k} t j | | dd f| | d dff} | dd | d dz} | | } t j | dddfdztd| dddf<t j | dddfdztd| dddf<t j | dddfdztd| dddf<|d krj| dddfdk} | | } | | } t j | dddf| dddf| dddff} | d| d| dg} |d krj| dddfdk} | | } | | } t j | dddf| dddf| dddff} | d| d| dg} |dkr\t j | dddf| dddf| dddf| dddff} | d| d| d| dg} |rt!| | |\} } t#d}d}|rt%d| | | | \}} } |r| | fSt'| | || | |S)a Load the kddcup99 dataset (classification). Download it if necessary. ================= ==================================== Classes 23 Samples total 4898431 Dimensionality 41 Features discrete (int) or continuous (float) ================= ==================================== Read more in the :ref:`User Guide `. .. versionadded:: 0.18 Parameters ---------- subset : {'SA', 'SF', 'http', 'smtp'}, default=None To return the corresponding classical subsets of kddcup 99. If None, return the entire kddcup 99 dataset. data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. .. versionadded:: 0.19 shuffle : bool, default=False Whether to shuffle dataset. random_state : int, RandomState instance or None, default=None Determines random number generation for dataset shuffling and for selection of abnormal samples if `subset='SA'`. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. percent10 : bool, default=True Whether to load only 10 percent of the data. download_if_missing : bool, default=True If False, raise an OSError if the data is not locally available instead of trying to download the data from the source site. return_X_y : bool, default=False If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object. .. versionadded:: 0.20 as_frame : bool, default=False If `True`, returns a pandas Dataframe for the ``data`` and ``target`` objects in the `Bunch` returned object; `Bunch` return object will also have a ``frame`` member. .. versionadded:: 0.24 Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. data : {ndarray, dataframe} of shape (494021, 41) The data matrix to learn. If `as_frame=True`, `data` will be a pandas DataFrame. target : {ndarray, series} of shape (494021,) The regression target for each sample. If `as_frame=True`, `target` will be a pandas Series. frame : dataframe of shape (494021, 42) Only present when `as_frame=True`. Contains `data` and `target`. DESCR : str The full description of the dataset. feature_names : list The names of the dataset columns target_names: list The names of the target columns (data, target) : tuple if ``return_X_y`` is True A tuple of two ndarray. The first containing a 2D array of shape (n_samples, n_features) with each row representing one sample and each column representing the features. The second ndarray of shape (n_samples,) containing the target samples. .. versionadded:: 0.20 r )r r!r"rsnormal.Nri1 rrr r g?F)copyrshttpssmtp)rz kddcup99.rstfetch_kddcup99)datatargetframe target_names feature_namesDESCR)r _fetch_brute_kddcup99r.r/r2r1np logical_notshaperrandintr_c_logastypefloatshuffle_methodrrr)rr r rr!r"r#r$kddcup99r.r/r2r1stnormal_samplesnormal_targetsabnormal_samplesabnormal_targetsn_samples_abnormalrfdescrr0s b/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/sklearn/datasets/_kddcup99.pyr-r-2soX 222I$/H =D _F*M(L ~~ j  N1  ad111:!!9-3A6),77  $6 = =+A.+A.u^%556~'778 ~~6))Vv-=-= BK1 uT!SbS&\4233</0%crc*]233-?? VT!!!Q$Z#-55e%5HHIIQQQT VT!!!Q$Z#-55e%5HHIIQQQT VT!!!Q$Z#-55e%5HHIIQQQT V  QQQT g%A7DAYF5aaadT!!!Q$Zaaad;>5aaadT!!!Q$ZaaadT!!!Q$ZGHDa a a a MO%dFNNN f  ' 'F E 5 dFM<  tVV|  !#    ct|}d}|rt|d|z}t}nt|d|z}t}t|d}t|d}t |}gdt fdd d d t fd t fd t fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fdt fd t fd!t fd"t fd#t fd$t fd%t fd&t fd't fd(t fd)t fd*t fd+t fd,t fd-t fd.t fd/t fd0} d1| D} | d2} | d3d2} |r^ tj|} tj|}n(#t$r&}td4t|d5|d3}~wwxYw|rt|td6|jzt!||7t#j| }td8t||j}t+|d9:}g}|D]R}|}||d;d<d=S|td>t9j|t#j|t>?}tAd@D].}|d3d3|f!|||d3d3|f</|d3d3d3d2f} |d3d3d2f}tj"| |dABtj"||dABntdCtG| || | gDS)EaLoad the kddcup99 dataset, downloading it if necessary. Parameters ---------- data_home : str, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. download_if_missing : bool, default=True If False, raise an OSError if the data is not locally available instead of trying to download the data from the source site. percent10 : bool, default=True Whether to load only 10 percent of the data. Returns ------- dataset : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. data : ndarray of shape (494021, 41) Each row corresponds to the 41 features in the dataset. target : ndarray of shape (494021,) Each value corresponds to one of the 21 attack types or to the label 'normal.'. feature_names : list The names of the dataset columns target_names: list The names of the target columns DESCR : str Description of the kddcup99 dataset. r'z-py3 kddcup99_10r?samplestargetsduration) protocol_typeS4)serviceS11)flagS6 src_bytes dst_byteslandwrong_fragmenturgenthotnum_failed_logins logged_innum_compromised root_shell su_attemptednum_rootnum_file_creations num_shellsnum_access_filesnum_outbound_cmds is_host_loginis_guest_logincount srv_count serror_ratesrv_serror_rate rerror_ratesrv_rerror_rate same_srv_rate diff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rate)labelsS16cg|] }|d S)r).0cs rI z)_fetch_brute_kddcup99..Ts%%%QAaD%%%rJNz7The cache for fetch_kddcup99 is invalid, please delete z! and run the fetch_kddcup99 againzDownloading %s)dirnamezextracting archiverG)rmode ,zextraction done)dtype*r)compressz1Data not found and `download_if_missing` is False)r.r/r2r1)$r rARCHIVE_10_PERCENTARCHIVErintr=joblibload ExceptionOSErrorstr_mkdirploggerinforrr5rdebugrr readlinesdecodeappendreplacesplitcloseosremoveasarrayobjectranger<dumpr)r r"r! dir_suffix kddcup_dirarchive samples_path targets_path availabledt column_namesr1r2XyeDT archive_pathfile_Xylinejs rIr4r4sF 222IJ)]Z%?@@ $)Z*%<==  I..L I..L|$$I+  S+ +  +   +  c +  c +   +  3+  3+   +  c"+  c+  C +  s+  +  S!+ " s##+ $ s%+ & S!'+ ( c")+ * #++ , 3-+ . #/+ 0 c1+ 2 3+ 4 E"5+ 6 7+ 8 E"9+ : % ;+ < % =+ > u%?+ @ 3A+ B s#C+ D "5)E+ F "5)G+ H '.I+ J '.K+ L 'M+ N $U+O+ P 'Q+ R $U+S+ T U+ BZ&%"%%%L#L "%M'K  L))A L))AA   Fz??FFF   K  $w{2333gz2222 Xb\\ )***J(899 ,S999 OO%% 9 9D;;==D IIdll4,,22377 8 8 8 8  &''' , Z& ) ) )r . .A!!!Q$xr!u--Bqqq!tHH qqq#2#vJ qqq"uI  A|a0000 A|a00000IJJJ  #"^    s:(H$$ I.!IIc tj|dS#t$r!}|jtjkrYd}~dSd}~wwxYw)zgEnsure directory d exists (like mkdir -p on Unix) No guarantee that the directory is writable. N)rmakedirsrerrnoEEXIST)drs rIrrs] A  7el " "  # " " " " "s A>A)NTT)%__doc__rloggingrgzipros.pathrrrnumpyr5utilsrrr r>utils._param_validationr r rr _baserrrrrr getLogger__name__rrPathLiker-r4rr~rJrIrs2   --------------AAAAAAAA   8 O   (' 8 O  8 $ $::::;;TB2;-;'([ ){ kK  #'      tttt  tnPPPPfrJ