qtjXdZddlZddlmZmZddlmZmZmZm Z ddl m Z m Z m Z ddlZddlmZddlmZmZmZmZddlmZdd lmZmZmZmZdd lmZej e!Z"ed d d Z#edddZ$edddedddedddfZ% d5dZ&dZ' d6d!Z(ee)edgd"geeddd#$dgeeddd%$dgd"ge*edgd"gd"geed&dd%$geed'dd#$gd( d)ddd*dd e+d+d,e+d-d.fdd ddd( d/Z, d7d0Z-eehd1ge)edgd"geeddd#$dgd"ge*edgd"geed&dd%$geed'dd#$gd2 d)d3ddd*d e+d+d,e+d-d.fdddd2 d4Z.dS)8zLabeled Faces in the Wild (LFW) dataset This dataset is a collection of JPEG pictures of famous people collected over the internet, all details are available on the official website: http://vis-www.cs.umass.edu/lfw/ N)IntegralReal)PathLikelistdirmakedirsremove)existsisdirjoin)Memory)RemoteFileMetadata _fetch_remote get_data_home load_descr)Bunch)HiddenInterval StrOptionsvalidate_params)tarfile_extractallzlfw.tgzz.https://ndownloader.figshare.com/files/5976018@055f7d9c632d7370e6fb4afc7468d40f970c34a80d4c6f50ffec63f5a8d536c0)filenameurlchecksumzlfw-funneled.tgzz.https://ndownloader.figshare.com/files/5976015@b47c8422c8cded889dc5a13418c4bc2abbda121092b3533a83306f90d900100apairsDevTrain.txtz.https://ndownloader.figshare.com/files/5976012@1d454dada7dfeca0e7eab6f65dc4e97a6312d44cf142207be28d688be92aabfapairsDevTest.txtz.https://ndownloader.figshare.com/files/5976009@7cb06600ea8b2814ac26e946201cdb304296262aad67d046a16a7ec85d0ff87c pairs.txtz.https://ndownloader.figshare.com/files/5976006@ea42330c62c92989f9d7c03237ed5d591365e89b3e649747777b70e692dc1592T?ct|}t|d}t|st|tD]n}t||j}t|sH|r4t d|jt||||\td|zo|rt|d}t} nt|d}t} t|st|| j} t| sH|r4t d| jt| |||ntd| zd d l } t d || | d 5} t!| | d d d n #1swxYwYt#| ||fS)z0Helper function to download any missing LFW data) data_homelfw_homezDownloading LFW metadata: %s)dirname n_retriesdelayz %s is missing lfw_funneledlfwz!Downloading LFW data (~200MB): %srNz$Decompressing the data archive to %szr:gz)path)rr r rTARGETSrloggerinforrOSErrorFUNNELED_ARCHIVEARCHIVEtarfiledebugopenrr) r%funneleddownload_if_missingr(r)r&targettarget_filepathdata_folder_patharchive archive_pathr3fps S/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/datasets/_lfw.py_check_fetch_lfwr?RsJ  222IIz**H (   A Ax99o&& A" A :FJGGGH o?@@@ A.99"%00 " # #Hg&677 l## >" > ?MMMX%o <=== ;=MNNN \\, / / 22 r 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 | % %%sF44F8;F8c ddlm}n#t$rtdwxYwtddtddf}||}n't dt ||D}|\}}|j|jz |jpdz}|j|jz |jpdz} |3t|}t||z}t|| z} t|} |s$tj | || ftj} n$tj | || d ftj} t|D]\} } | d zdkrt d | dz| || 5}||j|j|j|jf}||| |f}tj|tj}dddn #1swxYwY|jdkrt/d | z|d z}|s|d}|| | df<| S)zInternally used to load imagesr)ImagezThe Python Imaging Library (PIL) is required to load data from jpeg files. Please refer to https://pillow.readthedocs.io/en/stable/installation.html for installing PIL.Nc3$K|] \}}|p|V dS)N).0sdss r> z_load_imgs..s*GG51bqwBGGGGGGdtyper"izLoading face #%05d / %05dzLFailed to read the image file %s, Please make sure that libjpeg is installedgo@)axis.)PILrA ImportErrorslicetuplezipstopstartstepfloatintlennpzerosfloat32 enumerater.r4r5cropresizeasarrayndim RuntimeErrormean) file_pathsslice_colorr_rA default_sliceh_slicew_slicehwn_facesfacesi file_pathpil_imgfaces r> _load_imgsrrs      "    1c]]E!SMM2M ~GGC ,F,FGGGGGGW  %7<+<1=A  %7<+<1=A v  OO  OO*ooG ?'1a ;;;'1a+2:>>>"*-- 9 t8q== LL4a!eW E E E ZZ " " 9gll w|W\JG!!..!Q00:gRZ888D  9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9>>=?HI    %99!9$$Daf Ls # A'G<<H H Fcgg}}tt|D]}t||ts"fdttD}t |} | |krD|dd}||g| z||t |} | dkrtd|ztj |} tj | |} t||||} tj | }tj d|| || |} } | | | fS)z~Perform the actual data loading for the lfw people dataset This operation is meant to be cached by a joblib wrapper. c0g|]}t|SrD)r )rEf folder_paths r> z%_fetch_lfw_people..s#LLL!k1%%LLLrI_ rz*min_faces_per_person=%d is too restrictive*)sortedrr r rYreplaceextend ValueErrorrZunique searchsortedrrarangerandom RandomStateshuffle)r:rerfr_min_faces_per_person person_namesrd person_namepaths n_picturesrl target_namesr8rmindicesrvs @r>_fetch_lfw_peoplers "2*Lg&67788 % % +[99 [!!  LLLLvgk6J6J/K/KLLLZZ - - -%--c377K    : ; ; ;   e $ $ $*ooG!|| 8;O O   9\**L _\< 8 8F z65& 9 9Ei  GI"%%g...'NF7O6E &, &&rIbooleanneither)closedleftrJg) r%r6r_rrfrer7 return_X_yr(r))prefer_skip_nested_validationg?FNc ~t||||| \} } td| t| dd} | t } | | ||||\}}}|t|d}td}|r||fSt||||| S) a|Load the Labeled Faces in the Wild (LFW) people dataset (classification). Download it if necessary. ================= ======================= Classes 5749 Samples total 13233 Dimensionality 5828 Features real, between 0 and 255 ================= ======================= For a usage example of this dataset, see :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`. Read more in the :ref:`User Guide `. Parameters ---------- 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. funneled : bool, default=True Download and use the funneled variant of the dataset. resize : float or None, default=0.5 Ratio used to resize the each face picture. If `None`, no resizing is performed. min_faces_per_person : int, default=None The extracted dataset will only retain pictures of people that have at least `min_faces_per_person` different pictures. color : bool, default=False Keep the 3 RGB channels instead of averaging them to a single gray level channel. If color is True the shape of the data has one more dimension than the shape with color = False. slice_ : tuple of slice, default=(slice(70, 195), slice(78, 172)) Provide a custom 2D slice (height, width) to extract the 'interesting' part of the jpeg files and avoid use statistical correlation from the background. 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 ``(dataset.data, dataset.target)`` instead of a Bunch object. See below for more information about the `dataset.data` and `dataset.target` object. .. versionadded:: 0.20 n_retries : int, default=3 Number of retries when HTTP errors are encountered. .. versionadded:: 1.5 delay : float, default=1.0 Number of seconds between retries. .. versionadded:: 1.5 Returns ------- dataset : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. data : numpy array of shape (13233, 2914) Each row corresponds to a ravelled face image of original size 62 x 47 pixels. Changing the ``slice_`` or resize parameters will change the shape of the output. images : numpy array of shape (13233, 62, 47) Each row is a face image corresponding to one of the 5749 people in the dataset. Changing the ``slice_`` or resize parameters will change the shape of the output. target : numpy array of shape (13233,) Labels associated to each face image. Those labels range from 0-5748 and correspond to the person IDs. target_names : numpy array of shape (5749,) Names of all persons in the dataset. Position in array corresponds to the person ID in the target array. DESCR : str Description of the Labeled Faces in the Wild (LFW) dataset. (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 Examples -------- >>> from sklearn.datasets import fetch_lfw_people >>> lfw_people = fetch_lfw_people() >>> lfw_people.data.shape (13233, 2914) >>> lfw_people.target.shape (13233,) >>> for name in lfw_people.target_names[:5]: ... print(name) AJ Cook AJ Lamas Aaron Eckhart Aaron Guiel Aaron Patterson r%r6r7r(r)z Loading LFW people faces from %srlocationcompressverbose)r_rrfrelfw.rst)dataimagesr8rDESCR) r?r.r4r cacherreshaperYrr)r%r6r_rrfrer7rr(r)r&r:m load_funcrmr8rXfdescrs r>fetch_lfw_peoplersX"2/ """H LL3X>>> 1a888A)**I#,)1 ###E6<  c%jj"%%A  " "F&y  uV,f   rIc t|d5}d|D}dddn #1swxYwYd|D}t|}tj|t} t } t |D]{\} } t| dkrFd| | <| dt | ddz f| dt | d dz ff} npt| d krFd| | <| dt | ddz f| d t | ddz ff} ntd | dz| fzt | D]\}\}} t||}n.#t$r!t|t|d }YnwxYwt tt|}t|||}| |}t| |||}t |j}|d}|dd |d|d z||_|| tjd dgfS)z}Perform the actual data loading for the LFW pairs dataset This operation is meant to be cached by a joblib wrapper. rbcg|];}|drwz$_fetch_lfw_pairs..s:LLL2ryy{{((**0066LLLrINc8g|]}t|dk|S)rM)rY)rEsls r>rwz$_fetch_lfw_pairs..s#:::c"ggkk"kkkrIrKr"rJrrMzinvalid line %d: %rzUTF-8zDifferent personsz Same person)r5rYrZr[rXlistr]r~r TypeErrorstrr{rappendrrshapepopinsertarray)index_file_pathr:rerfr_ index_file split_lines pair_specsn_pairsr8rdrn componentspairjnameidx person_folder filenamesropairsrrls r>_fetch_lfw_pairsrs ot $ $M LLLLL MMMMMMMMMMMMMMM::{:::J*ooGXgS ) ) )FJ":..)) : z??a  F1IAJqM 2 2Q 67AJqM 2 2Q 67DD__ ! !F1IAJqM 2 2Q 67AJqM 2 2Q 67DD 2a!eZ5HHII I'oo ) )NA{c K $%5t < <  K K K $%5s47I7I J J  KVGM$:$:;;<testtrain10_folds) subsetr%r6r_rfrer7r(r)rc 2t|||||\} } td|| t| dd} | t } dddd } || vrAt d |d tt| t| | |}| || ||| \}}}td }t| t|d||||S)awLoad the Labeled Faces in the Wild (LFW) pairs dataset (classification). Download it if necessary. ================= ======================= Classes 2 Samples total 13233 Dimensionality 5828 Features real, between 0 and 255 ================= ======================= In the `original paper `_ the "pairs" version corresponds to the "restricted task", where the experimenter should not use the name of a person to infer the equivalence or non-equivalence of two face images that are not explicitly given in the training set. The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 47. Read more in the :ref:`User Guide `. Parameters ---------- subset : {'train', 'test', '10_folds'}, default='train' Select the dataset to load: 'train' for the development training set, 'test' for the development test set, and '10_folds' for the official evaluation set that is meant to be used with a 10-folds cross validation. 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. funneled : bool, default=True Download and use the funneled variant of the dataset. resize : float, default=0.5 Ratio used to resize the each face picture. color : bool, default=False Keep the 3 RGB channels instead of averaging them to a single gray level channel. If color is True the shape of the data has one more dimension than the shape with color = False. slice_ : tuple of slice, default=(slice(70, 195), slice(78, 172)) Provide a custom 2D slice (height, width) to extract the 'interesting' part of the jpeg files and avoid use statistical correlation from the background. 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. n_retries : int, default=3 Number of retries when HTTP errors are encountered. .. versionadded:: 1.5 delay : float, default=1.0 Number of seconds between retries. .. versionadded:: 1.5 Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. data : ndarray of shape (2200, 5828). Shape depends on ``subset``. Each row corresponds to 2 ravel'd face images of original size 62 x 47 pixels. Changing the ``slice_``, ``resize`` or ``subset`` parameters will change the shape of the output. pairs : ndarray of shape (2200, 2, 62, 47). Shape depends on ``subset`` Each row has 2 face images corresponding to same or different person from the dataset containing 5749 people. Changing the ``slice_``, ``resize`` or ``subset`` parameters will change the shape of the output. target : numpy array of shape (2200,). Shape depends on ``subset``. Labels associated to each pair of images. The two label values being different persons or the same person. target_names : numpy array of shape (2,) Explains the target values of the target array. 0 corresponds to "Different person", 1 corresponds to "same person". DESCR : str Description of the Labeled Faces in the Wild (LFW) dataset. Examples -------- >>> from sklearn.datasets import fetch_lfw_pairs >>> lfw_pairs_train = fetch_lfw_pairs(subset='train') >>> list(lfw_pairs_train.target_names) [np.str_('Different persons'), np.str_('Same person')] >>> lfw_pairs_train.pairs.shape (2200, 2, 62, 47) >>> lfw_pairs_train.data.shape (2200, 5828) >>> lfw_pairs_train.target.shape (2200,) rzLoading %s LFW pairs from %srrrrrr )rrrzsubset='z' is invalid: should be one of )r_rfrerr)rrr8rr)r?r.r4r rrr~rr{keysr rrrrY)rr%r6r_rfrer7r(r)r&r:rrlabel_filenamesrrr8rrs r>fetch_lfw_pairsrs`B"2/ """H LL/BBB 1a888A())I%"O _$$jvvtF?#7#7#9#9::;;; =   8_V%<==O#,))&f###E6< " "F  ]]3u::r * *!    rI)NTTr"r#)NFNr)NFN)/__doc__loggingnumbersrrosrrrros.pathr r r numpyrZjoblibr sklearn.datasets._baser rrr sklearn.utilsrsklearn.utils._param_validationrrrrsklearn.utils.fixesr getLogger__name__r.r2r1r-r?rrrrrRrQrrrrDrIr>rs""""""""222222222222''''''''''   322222  8 $ $   8 O   &% 8 O$ <S # <S  <S 4QT0&0&0&0&fCCCXST('('('('V8T*K8D!T)<<  #'"    E"cNNEE"cNN + ^^^^^NIM1I1I1I1Ih:;;;<<=8T*K8D!T)<<  #'        E"cNNEE"cNN + \\\\  \\\rI