249 lines
7.1 KiB
Python
249 lines
7.1 KiB
Python
"""California housing dataset.
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The original database is available from StatLib
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http://lib.stat.cmu.edu/datasets/
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The data contains 20,640 observations on 9 variables.
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This dataset contains the average house value as target variable
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and the following input variables (features): average income,
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housing average age, average rooms, average bedrooms, population,
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average occupation, latitude, and longitude in that order.
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References
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----------
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Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
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Statistics and Probability Letters, 33 (1997) 291-297.
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"""
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# Authors: Peter Prettenhofer
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# License: BSD 3 clause
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import logging
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import tarfile
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from numbers import Integral, Real
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from os import PathLike, makedirs, remove
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from os.path import exists
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import joblib
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import numpy as np
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from ..utils import Bunch
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from ..utils._param_validation import Interval, validate_params
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from . import get_data_home
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from ._base import (
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RemoteFileMetadata,
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_convert_data_dataframe,
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_fetch_remote,
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_pkl_filepath,
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load_descr,
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)
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# The original data can be found at:
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# https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz
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ARCHIVE = RemoteFileMetadata(
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filename="cal_housing.tgz",
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url="https://ndownloader.figshare.com/files/5976036",
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checksum="aaa5c9a6afe2225cc2aed2723682ae403280c4a3695a2ddda4ffb5d8215ea681",
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)
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logger = logging.getLogger(__name__)
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@validate_params(
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{
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"data_home": [str, PathLike, None],
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"download_if_missing": ["boolean"],
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"return_X_y": ["boolean"],
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"as_frame": ["boolean"],
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"n_retries": [Interval(Integral, 1, None, closed="left")],
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"delay": [Interval(Real, 0.0, None, closed="neither")],
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},
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prefer_skip_nested_validation=True,
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)
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def fetch_california_housing(
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*,
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data_home=None,
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download_if_missing=True,
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return_X_y=False,
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as_frame=False,
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n_retries=3,
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delay=1.0,
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):
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"""Load the California housing dataset (regression).
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============== ==============
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Samples total 20640
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Dimensionality 8
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Features real
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Target real 0.15 - 5.
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============== ==============
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Read more in the :ref:`User Guide <california_housing_dataset>`.
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Parameters
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----------
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data_home : str or path-like, default=None
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Specify another download and cache folder for the datasets. By default
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all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
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download_if_missing : bool, default=True
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If False, raise an OSError if the data is not locally available
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instead of trying to download the data from the source site.
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return_X_y : bool, default=False
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If True, returns ``(data.data, data.target)`` instead of a Bunch
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object.
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.. versionadded:: 0.20
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as_frame : bool, default=False
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If True, the data is a pandas DataFrame including columns with
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appropriate dtypes (numeric, string or categorical). The target is
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a pandas DataFrame or Series depending on the number of target_columns.
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.. versionadded:: 0.23
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n_retries : int, default=3
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Number of retries when HTTP errors are encountered.
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.. versionadded:: 1.5
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delay : float, default=1.0
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Number of seconds between retries.
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.. versionadded:: 1.5
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Returns
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-------
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dataset : :class:`~sklearn.utils.Bunch`
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Dictionary-like object, with the following attributes.
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data : ndarray, shape (20640, 8)
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Each row corresponding to the 8 feature values in order.
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If ``as_frame`` is True, ``data`` is a pandas object.
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target : numpy array of shape (20640,)
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Each value corresponds to the average
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house value in units of 100,000.
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If ``as_frame`` is True, ``target`` is a pandas object.
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feature_names : list of length 8
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Array of ordered feature names used in the dataset.
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DESCR : str
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Description of the California housing dataset.
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frame : pandas DataFrame
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Only present when `as_frame=True`. DataFrame with ``data`` and
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``target``.
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.. versionadded:: 0.23
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(data, target) : tuple if ``return_X_y`` is True
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A tuple of two ndarray. The first containing a 2D array of
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shape (n_samples, n_features) with each row representing one
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sample and each column representing the features. The second
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ndarray of shape (n_samples,) containing the target samples.
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.. versionadded:: 0.20
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Notes
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-----
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This dataset consists of 20,640 samples and 9 features.
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Examples
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--------
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>>> from sklearn.datasets import fetch_california_housing
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>>> housing = fetch_california_housing()
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>>> print(housing.data.shape, housing.target.shape)
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(20640, 8) (20640,)
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>>> print(housing.feature_names[0:6])
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['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup']
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"""
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data_home = get_data_home(data_home=data_home)
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if not exists(data_home):
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makedirs(data_home)
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filepath = _pkl_filepath(data_home, "cal_housing.pkz")
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if not exists(filepath):
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if not download_if_missing:
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raise OSError("Data not found and `download_if_missing` is False")
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logger.info(
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"Downloading Cal. housing from {} to {}".format(ARCHIVE.url, data_home)
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)
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archive_path = _fetch_remote(
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ARCHIVE,
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dirname=data_home,
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n_retries=n_retries,
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delay=delay,
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)
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with tarfile.open(mode="r:gz", name=archive_path) as f:
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cal_housing = np.loadtxt(
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f.extractfile("CaliforniaHousing/cal_housing.data"), delimiter=","
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)
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# Columns are not in the same order compared to the previous
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# URL resource on lib.stat.cmu.edu
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columns_index = [8, 7, 2, 3, 4, 5, 6, 1, 0]
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cal_housing = cal_housing[:, columns_index]
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joblib.dump(cal_housing, filepath, compress=6)
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remove(archive_path)
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else:
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cal_housing = joblib.load(filepath)
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feature_names = [
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"MedInc",
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"HouseAge",
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"AveRooms",
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"AveBedrms",
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"Population",
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"AveOccup",
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"Latitude",
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"Longitude",
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]
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target, data = cal_housing[:, 0], cal_housing[:, 1:]
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# avg rooms = total rooms / households
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data[:, 2] /= data[:, 5]
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# avg bed rooms = total bed rooms / households
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data[:, 3] /= data[:, 5]
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# avg occupancy = population / households
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data[:, 5] = data[:, 4] / data[:, 5]
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# target in units of 100,000
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target = target / 100000.0
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descr = load_descr("california_housing.rst")
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X = data
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y = target
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frame = None
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target_names = [
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"MedHouseVal",
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]
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if as_frame:
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frame, X, y = _convert_data_dataframe(
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"fetch_california_housing", data, target, feature_names, target_names
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)
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if return_X_y:
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return X, y
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return Bunch(
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data=X,
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target=y,
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frame=frame,
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target_names=target_names,
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feature_names=feature_names,
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DESCR=descr,
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)
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