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 ddlZddlZddlmZ ddlmZmZmZmZmZ ddlmZ dd	lmZmZ  ed
dd¬¦  «        Z ej        e¦  «        Z eeedgdgdgdg eeddd¬¦  «        g eeddd¬¦  «        gdœd¬¦  «        dddddddœd„¦   «         Z dS )a8  California 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 median 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.

é    N)ÚIntegralÚReal)ÚPathLikeÚremove)Úexists)Úget_data_home)ÚRemoteFileMetadataÚ_convert_data_dataframeÚ_fetch_remoteÚ_pkl_filepathÚ
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f         }	t          j        |	|d¬¦  «         ddd¦  «         n# 1 swxY w Y   t#          |¦  «         nt          j        |¦  «        }	g d¢}|	dd…df         |	dd…dd…f         }}|dd…dfxx         |dd…df         z  cc<   |dd…dfxx         |dd…df         z  cc<   |dd…df         |dd…df         z  |dd…df<   |dz  }t'          d¦  «        }|}|}d}dg}|rt)          d||||¦  «        \  }}}|r||fS t+          ||||||¬¦  «        S )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 <california_housing_dataset>`.

    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 median
            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']
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