162 lines
5.8 KiB
Python
162 lines
5.8 KiB
Python
"""
|
|
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
|