-io6SrSSKrSSKrSSKJrJr SSKJrJrJ r SSK J r SSK r SSK rSSKJr SSKJrJr S S KJr S S KJrJrJrJrJr \"S S SS9r\R:"\5r\"\ \S/S/S/S/\"\S SSS9/\"\SSSS9/S.SS9SSSSSSS.Sj5r!g)a9California housing dataset. The original database is available from StatLib http://lib.stat.cmu.edu/datasets/ The data contains 20,640 observations on 9 variables. This dataset contains the average house value as target variable and the following input variables (features): average income, housing average age, average rooms, average bedrooms, population, average occupation, latitude, and longitude in that order. References ---------- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33:291-297, 1997. N)IntegralReal)PathLikemakedirsremove)exists)Bunch)Intervalvalidate_params) get_data_home)RemoteFileMetadata_convert_data_dataframe _fetch_remote _pkl_filepath load_descrzcal_housing.tgzz.https://ndownloader.figshare.com/files/5976036@aaa5c9a6afe2225cc2aed2723682ae403280c4a3695a2ddda4ffb5d8215ea681)filenameurlchecksumbooleanleft)closedgneither) data_homedownload_if_missing return_X_yas_frame n_retriesdelayT)prefer_skip_nested_validationFg?c [US9n[U5(d [U5 [US5n[U5(dU(d [ S5e[ R SR[RU55 [[UUUS9n[R"SUS9n[R"URS5S S 9n /S Qn U S S 2U 4n [ R""XS S9 S S S 5 [%U5 O[ R&"U5n /SQn W S S 2S4U S S 2SS 24pU S S 2S4==U S S 2S4-ss'U S S 2S4==U S S 2S4-ss'U S S 2S4U S S 2S4- U S S 2S4'U S- n [)S5nU nU nS nS/nU(a[+SXU U5unnnU(aUU4$[-UUUUU US9$!,(df  N=f)a Load the California housing dataset (regression). ============== ============== Samples total 20640 Dimensionality 8 Features real Target real 0.15 - 5. ============== ============== 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. 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.data, data.target)`` instead of a Bunch object. .. versionadded:: 0.20 as_frame : bool, default=False If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target_columns. .. versionadded:: 0.23 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 : ndarray, shape (20640, 8) Each row corresponding to the 8 feature values in order. If ``as_frame`` is True, ``data`` is a pandas object. target : numpy array of shape (20640,) Each value corresponds to the average house value in units of 100,000. If ``as_frame`` is True, ``target`` is a pandas object. feature_names : list of length 8 Array of ordered feature names used in the dataset. DESCR : str Description of the California housing dataset. frame : pandas DataFrame Only present when `as_frame=True`. DataFrame with ``data`` and ``target``. .. versionadded:: 0.23 (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 Notes ----- This dataset consists of 20,640 samples and 9 features. Examples -------- >>> from sklearn.datasets import fetch_california_housing >>> housing = fetch_california_housing() >>> print(housing.data.shape, housing.target.shape) (20640, 8) (20640,) >>> print(housing.feature_names[0:6]) ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup'] )rzcal_housing.pkzz1Data not found and `download_if_missing` is Falsez&Downloading Cal. housing from {} to {})dirnamer r!zr:gz)modenamez"CaliforniaHousing/cal_housing.data,) delimiter) r r#r rNr.)compress)MedIncHouseAgeAveRooms AveBedrms PopulationAveOccupLatitude Longituderr r r-r#r,gj@zcalifornia_housing.rst MedHouseValfetch_california_housing)datatargetframe target_names feature_namesDESCR)rrrrOSErrorloggerinfoformatARCHIVErrtarfileopennploadtxt extractfilejoblibdumprloadrrr )rrrrr r!filepath archive_pathf cal_housing columns_indexr>r;r:descrXyr<r=s W/var/www/html/venv/lib/python3.13/site-packages/sklearn/datasets/_california_housing.pyr9r98sX 2I )  Y(9:H (  "MN N 4 ; ;GKK S %   \\vL 9Q** BCsK 8M%a&67K KK  :: |kk(+  Mq!t$k!QR%&8D AJ$q!t*J AJ$q!t*Jadd1a4j(DAJh F / 0E AA EL- &m\ q!!t  !#  s: 9s +AG G)"__doc__loggingrEnumbersrrosrrros.pathrrJnumpyrGutilsr utils._param_validationr r r_baserrrrrrD getLogger__name__rAstrr9rUres0")) ?  8 O    8 $8T* ){ kKxD@A4d9=> #'   u urd