"""
The :mod:`sklearn.datasets` module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.
"""
import textwrap

from ._base import load_breast_cancer
from ._base import load_diabetes
from ._base import load_digits
from ._base import load_files
from ._base import load_iris
from ._base import load_linnerud
from ._base import load_sample_images
from ._base import load_sample_image
from ._base import load_wine
from ._base import get_data_home
from ._base import clear_data_home
from ._covtype import fetch_covtype
from ._kddcup99 import fetch_kddcup99
from ._lfw import fetch_lfw_pairs
from ._lfw import fetch_lfw_people
from ._twenty_newsgroups import fetch_20newsgroups
from ._twenty_newsgroups import fetch_20newsgroups_vectorized
from ._openml import fetch_openml
from ._samples_generator import make_classification
from ._samples_generator import make_multilabel_classification
from ._samples_generator import make_hastie_10_2
from ._samples_generator import make_regression
from ._samples_generator import make_blobs
from ._samples_generator import make_moons
from ._samples_generator import make_circles
from ._samples_generator import make_friedman1
from ._samples_generator import make_friedman2
from ._samples_generator import make_friedman3
from ._samples_generator import make_low_rank_matrix
from ._samples_generator import make_sparse_coded_signal
from ._samples_generator import make_sparse_uncorrelated
from ._samples_generator import make_spd_matrix
from ._samples_generator import make_swiss_roll
from ._samples_generator import make_s_curve
from ._samples_generator import make_sparse_spd_matrix
from ._samples_generator import make_gaussian_quantiles
from ._samples_generator import make_biclusters
from ._samples_generator import make_checkerboard
from ._svmlight_format_io import load_svmlight_file
from ._svmlight_format_io import load_svmlight_files
from ._svmlight_format_io import dump_svmlight_file
from ._olivetti_faces import fetch_olivetti_faces
from ._species_distributions import fetch_species_distributions
from ._california_housing import fetch_california_housing
from ._rcv1 import fetch_rcv1


__all__ = [
    "clear_data_home",
    "dump_svmlight_file",
    "fetch_20newsgroups",
    "fetch_20newsgroups_vectorized",
    "fetch_lfw_pairs",
    "fetch_lfw_people",
    "fetch_olivetti_faces",
    "fetch_species_distributions",
    "fetch_california_housing",
    "fetch_covtype",
    "fetch_rcv1",
    "fetch_kddcup99",
    "fetch_openml",
    "get_data_home",
    "load_diabetes",
    "load_digits",
    "load_files",
    "load_iris",
    "load_breast_cancer",
    "load_linnerud",
    "load_sample_image",
    "load_sample_images",
    "load_svmlight_file",
    "load_svmlight_files",
    "load_wine",
    "make_biclusters",
    "make_blobs",
    "make_circles",
    "make_classification",
    "make_checkerboard",
    "make_friedman1",
    "make_friedman2",
    "make_friedman3",
    "make_gaussian_quantiles",
    "make_hastie_10_2",
    "make_low_rank_matrix",
    "make_moons",
    "make_multilabel_classification",
    "make_regression",
    "make_s_curve",
    "make_sparse_coded_signal",
    "make_sparse_spd_matrix",
    "make_sparse_uncorrelated",
    "make_spd_matrix",
    "make_swiss_roll",
]


def __getattr__(name):
    if name == "load_boston":
        msg = textwrap.dedent(
            """
            `load_boston` has been removed from scikit-learn since version 1.2.

            The Boston housing prices dataset has an ethical problem: as
            investigated in [1], the authors of this dataset engineered a
            non-invertible variable "B" assuming that racial self-segregation had a
            positive impact on house prices [2]. Furthermore the goal of the
            research that led to the creation of this dataset was to study the
            impact of air quality but it did not give adequate demonstration of the
            validity of this assumption.

            The scikit-learn maintainers therefore strongly discourage the use of
            this dataset unless the purpose of the code is to study and educate
            about ethical issues in data science and machine learning.

            In this special case, you can fetch the dataset from the original
            source::

                import pandas as pd
                import numpy as np

                data_url = "http://lib.stat.cmu.edu/datasets/boston"
                raw_df = pd.read_csv(data_url, sep="\\s+", skiprows=22, header=None)
                data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
                target = raw_df.values[1::2, 2]

            Alternative datasets include the California housing dataset and the
            Ames housing dataset. You can load the datasets as follows::

                from sklearn.datasets import fetch_california_housing
                housing = fetch_california_housing()

            for the California housing dataset and::

                from sklearn.datasets import fetch_openml
                housing = fetch_openml(name="house_prices", as_frame=True)

            for the Ames housing dataset.

            [1] M Carlisle.
            "Racist data destruction?"
            <https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>

            [2] Harrison Jr, David, and Daniel L. Rubinfeld.
            "Hedonic housing prices and the demand for clean air."
            Journal of environmental economics and management 5.1 (1978): 81-102.
            <https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>
            """
        )
        raise ImportError(msg)
    try:
        return globals()[name]
    except KeyError:
        # This is turned into the appropriate ImportError
        raise AttributeError