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Intelegentny_Pszczelarz/.venv/Lib/site-packages/sklearn/datasets/_base.py

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feature: "ANN commit 2"
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"""
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import csv
import hashlib
import gzip
import shutil
from collections import namedtuple
import os
from os import environ, listdir, makedirs
from os.path import expanduser, isdir, join, splitext
from importlib import resources
from pathlib import Path
from ..preprocessing import scale
from ..utils import Bunch
from ..utils import check_random_state
from ..utils import check_pandas_support
from ..utils.fixes import _open_binary, _open_text, _read_text
import numpy as np
from urllib.request import urlretrieve
DATA_MODULE = "sklearn.datasets.data"
DESCR_MODULE = "sklearn.datasets.descr"
IMAGES_MODULE = "sklearn.datasets.images"
RemoteFileMetadata = namedtuple("RemoteFileMetadata", ["filename", "url", "checksum"])
def get_data_home(data_home=None) -> str:
"""Return the path of the scikit-learn data directory.
This folder is used by some large dataset loaders to avoid downloading the
data several times.
By default the data directory is set to a folder named 'scikit_learn_data' in the
user home folder.
Alternatively, it can be set by the 'SCIKIT_LEARN_DATA' environment
variable or programmatically by giving an explicit folder path. The '~'
symbol is expanded to the user home folder.
If the folder does not already exist, it is automatically created.
Parameters
----------
data_home : str, default=None
The path to scikit-learn data directory. If `None`, the default path
is `~/sklearn_learn_data`.
Returns
-------
data_home: str
The path to scikit-learn data directory.
"""
if data_home is None:
data_home = environ.get("SCIKIT_LEARN_DATA", join("~", "scikit_learn_data"))
data_home = expanduser(data_home)
makedirs(data_home, exist_ok=True)
return data_home
def clear_data_home(data_home=None):
"""Delete all the content of the data home cache.
Parameters
----------
data_home : str, default=None
The path to scikit-learn data directory. If `None`, the default path
is `~/sklearn_learn_data`.
"""
data_home = get_data_home(data_home)
shutil.rmtree(data_home)
def _convert_data_dataframe(
caller_name, data, target, feature_names, target_names, sparse_data=False
):
pd = check_pandas_support("{} with as_frame=True".format(caller_name))
if not sparse_data:
data_df = pd.DataFrame(data, columns=feature_names)
else:
data_df = pd.DataFrame.sparse.from_spmatrix(data, columns=feature_names)
target_df = pd.DataFrame(target, columns=target_names)
combined_df = pd.concat([data_df, target_df], axis=1)
X = combined_df[feature_names]
y = combined_df[target_names]
if y.shape[1] == 1:
y = y.iloc[:, 0]
return combined_df, X, y
def load_files(
container_path,
*,
description=None,
categories=None,
load_content=True,
shuffle=True,
encoding=None,
decode_error="strict",
random_state=0,
allowed_extensions=None,
):
"""Load text files with categories as subfolder names.
Individual samples are assumed to be files stored a two levels folder
structure such as the following:
container_folder/
category_1_folder/
file_1.txt
file_2.txt
...
file_42.txt
category_2_folder/
file_43.txt
file_44.txt
...
The folder names are used as supervised signal label names. The individual
file names are not important.
This function does not try to extract features into a numpy array or scipy
sparse matrix. In addition, if load_content is false it does not try to
load the files in memory.
To use text files in a scikit-learn classification or clustering algorithm,
you will need to use the :mod`~sklearn.feature_extraction.text` module to
build a feature extraction transformer that suits your problem.
If you set load_content=True, you should also specify the encoding of the
text using the 'encoding' parameter. For many modern text files, 'utf-8'
will be the correct encoding. If you leave encoding equal to None, then the
content will be made of bytes instead of Unicode, and you will not be able
to use most functions in :mod:`~sklearn.feature_extraction.text`.
Similar feature extractors should be built for other kind of unstructured
data input such as images, audio, video, ...
If you want files with a specific file extension (e.g. `.txt`) then you
can pass a list of those file extensions to `allowed_extensions`.
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
container_path : str
Path to the main folder holding one subfolder per category.
description : str, default=None
A paragraph describing the characteristic of the dataset: its source,
reference, etc.
categories : list of str, default=None
If None (default), load all the categories. If not None, list of
category names to load (other categories ignored).
load_content : bool, default=True
Whether to load or not the content of the different files. If true a
'data' attribute containing the text information is present in the data
structure returned. If not, a filenames attribute gives the path to the
files.
shuffle : bool, default=True
Whether or not to shuffle the data: might be important for models that
make the assumption that the samples are independent and identically
distributed (i.i.d.), such as stochastic gradient descent.
encoding : str, default=None
If None, do not try to decode the content of the files (e.g. for images
or other non-text content). If not None, encoding to use to decode text
files to Unicode if load_content is True.
decode_error : {'strict', 'ignore', 'replace'}, default='strict'
Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given `encoding`. Passed as keyword
argument 'errors' to bytes.decode.
random_state : int, RandomState instance or None, default=0
Determines random number generation for dataset shuffling. Pass an int
for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
allowed_extensions : list of str, default=None
List of desired file extensions to filter the files to be loaded.
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : list of str
Only present when `load_content=True`.
The raw text data to learn.
target : ndarray
The target labels (integer index).
target_names : list
The names of target classes.
DESCR : str
The full description of the dataset.
filenames: ndarray
The filenames holding the dataset.
"""
target = []
target_names = []
filenames = []
folders = [
f for f in sorted(listdir(container_path)) if isdir(join(container_path, f))
]
if categories is not None:
folders = [f for f in folders if f in categories]
if allowed_extensions is not None:
allowed_extensions = frozenset(allowed_extensions)
for label, folder in enumerate(folders):
target_names.append(folder)
folder_path = join(container_path, folder)
files = sorted(listdir(folder_path))
if allowed_extensions is not None:
documents = [
join(folder_path, file)
for file in files
if os.path.splitext(file)[1] in allowed_extensions
]
else:
documents = [join(folder_path, file) for file in files]
target.extend(len(documents) * [label])
filenames.extend(documents)
# convert to array for fancy indexing
filenames = np.array(filenames)
target = np.array(target)
if shuffle:
random_state = check_random_state(random_state)
indices = np.arange(filenames.shape[0])
random_state.shuffle(indices)
filenames = filenames[indices]
target = target[indices]
if load_content:
data = []
for filename in filenames:
data.append(Path(filename).read_bytes())
if encoding is not None:
data = [d.decode(encoding, decode_error) for d in data]
return Bunch(
data=data,
filenames=filenames,
target_names=target_names,
target=target,
DESCR=description,
)
return Bunch(
filenames=filenames, target_names=target_names, target=target, DESCR=description
)
def load_csv_data(
data_file_name,
*,
data_module=DATA_MODULE,
descr_file_name=None,
descr_module=DESCR_MODULE,
):
"""Loads `data_file_name` from `data_module with `importlib.resources`.
Parameters
----------
data_file_name : str
Name of csv file to be loaded from `data_module/data_file_name`.
For example `'wine_data.csv'`.
data_module : str or module, default='sklearn.datasets.data'
Module where data lives. The default is `'sklearn.datasets.data'`.
descr_file_name : str, default=None
Name of rst file to be loaded from `descr_module/descr_file_name`.
For example `'wine_data.rst'`. See also :func:`load_descr`.
If not None, also returns the corresponding description of
the dataset.
descr_module : str or module, default='sklearn.datasets.descr'
Module where `descr_file_name` lives. See also :func:`load_descr`.
The default is `'sklearn.datasets.descr'`.
Returns
-------
data : ndarray of shape (n_samples, n_features)
A 2D array with each row representing one sample and each column
representing the features of a given sample.
target : ndarry of shape (n_samples,)
A 1D array holding target variables for all the samples in `data`.
For example target[0] is the target variable for data[0].
target_names : ndarry of shape (n_samples,)
A 1D array containing the names of the classifications. For example
target_names[0] is the name of the target[0] class.
descr : str, optional
Description of the dataset (the content of `descr_file_name`).
Only returned if `descr_file_name` is not None.
"""
with _open_text(data_module, data_file_name) as csv_file:
data_file = csv.reader(csv_file)
temp = next(data_file)
n_samples = int(temp[0])
n_features = int(temp[1])
target_names = np.array(temp[2:])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,), dtype=int)
for i, ir in enumerate(data_file):
data[i] = np.asarray(ir[:-1], dtype=np.float64)
target[i] = np.asarray(ir[-1], dtype=int)
if descr_file_name is None:
return data, target, target_names
else:
assert descr_module is not None
descr = load_descr(descr_module=descr_module, descr_file_name=descr_file_name)
return data, target, target_names, descr
def load_gzip_compressed_csv_data(
data_file_name,
*,
data_module=DATA_MODULE,
descr_file_name=None,
descr_module=DESCR_MODULE,
encoding="utf-8",
**kwargs,
):
"""Loads gzip-compressed with `importlib.resources`.
1) Open resource file with `importlib.resources.open_binary`
2) Decompress file obj with `gzip.open`
3) Load decompressed data with `np.loadtxt`
Parameters
----------
data_file_name : str
Name of gzip-compressed csv file (`'*.csv.gz'`) to be loaded from
`data_module/data_file_name`. For example `'diabetes_data.csv.gz'`.
data_module : str or module, default='sklearn.datasets.data'
Module where data lives. The default is `'sklearn.datasets.data'`.
descr_file_name : str, default=None
Name of rst file to be loaded from `descr_module/descr_file_name`.
For example `'wine_data.rst'`. See also :func:`load_descr`.
If not None, also returns the corresponding description of
the dataset.
descr_module : str or module, default='sklearn.datasets.descr'
Module where `descr_file_name` lives. See also :func:`load_descr`.
The default is `'sklearn.datasets.descr'`.
encoding : str, default="utf-8"
Name of the encoding that the gzip-decompressed file will be
decoded with. The default is 'utf-8'.
**kwargs : dict, optional
Keyword arguments to be passed to `np.loadtxt`;
e.g. delimiter=','.
Returns
-------
data : ndarray of shape (n_samples, n_features)
A 2D array with each row representing one sample and each column
representing the features and/or target of a given sample.
descr : str, optional
Description of the dataset (the content of `descr_file_name`).
Only returned if `descr_file_name` is not None.
"""
with _open_binary(data_module, data_file_name) as compressed_file:
compressed_file = gzip.open(compressed_file, mode="rt", encoding=encoding)
data = np.loadtxt(compressed_file, **kwargs)
if descr_file_name is None:
return data
else:
assert descr_module is not None
descr = load_descr(descr_module=descr_module, descr_file_name=descr_file_name)
return data, descr
def load_descr(descr_file_name, *, descr_module=DESCR_MODULE):
"""Load `descr_file_name` from `descr_module` with `importlib.resources`.
Parameters
----------
descr_file_name : str, default=None
Name of rst file to be loaded from `descr_module/descr_file_name`.
For example `'wine_data.rst'`. See also :func:`load_descr`.
If not None, also returns the corresponding description of
the dataset.
descr_module : str or module, default='sklearn.datasets.descr'
Module where `descr_file_name` lives. See also :func:`load_descr`.
The default is `'sklearn.datasets.descr'`.
Returns
-------
fdescr : str
Content of `descr_file_name`.
"""
fdescr = _read_text(descr_module, descr_file_name)
return fdescr
def load_wine(*, return_X_y=False, as_frame=False):
"""Load and return the wine dataset (classification).
.. versionadded:: 0.18
The wine dataset is a classic and very easy multi-class classification
dataset.
================= ==============
Classes 3
Samples per class [59,71,48]
Samples total 178
Dimensionality 13
Features real, positive
================= ==============
The copy of UCI ML Wine Data Set dataset is downloaded and modified to fit
standard format from:
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
Read more in the :ref:`User Guide <wine_dataset>`.
Parameters
----------
return_X_y : bool, default=False
If True, returns ``(data, target)`` instead of a Bunch object.
See below for more information about the `data` and `target` object.
as_frame : bool, default=False
If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric). The target is
a pandas DataFrame or Series depending on the number of target columns.
If `return_X_y` is True, then (`data`, `target`) will be pandas
DataFrames or Series as described below.
.. versionadded:: 0.23
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : {ndarray, dataframe} of shape (178, 13)
The data matrix. If `as_frame=True`, `data` will be a pandas
DataFrame.
target: {ndarray, Series} of shape (178,)
The classification target. If `as_frame=True`, `target` will be
a pandas Series.
feature_names: list
The names of the dataset columns.
target_names: list
The names of target classes.
frame: DataFrame of shape (178, 14)
Only present when `as_frame=True`. DataFrame with `data` and
`target`.
.. versionadded:: 0.23
DESCR: str
The full description of the dataset.
(data, target) : tuple if ``return_X_y`` is True
A tuple of two ndarrays by default. The first contains a 2D array of shape
(178, 13) with each row representing one sample and each column representing
the features. The second array of shape (178,) contains the target samples.
Examples
--------
Let's say you are interested in the samples 10, 80, and 140, and want to
know their class name.
>>> from sklearn.datasets import load_wine
>>> data = load_wine()
>>> data.target[[10, 80, 140]]
array([0, 1, 2])
>>> list(data.target_names)
['class_0', 'class_1', 'class_2']
"""
data, target, target_names, fdescr = load_csv_data(
data_file_name="wine_data.csv", descr_file_name="wine_data.rst"
)
feature_names = [
"alcohol",
"malic_acid",
"ash",
"alcalinity_of_ash",
"magnesium",
"total_phenols",
"flavanoids",
"nonflavanoid_phenols",
"proanthocyanins",
"color_intensity",
"hue",
"od280/od315_of_diluted_wines",
"proline",
]
frame = None
target_columns = [
"target",
]
if as_frame:
frame, data, target = _convert_data_dataframe(
"load_wine", data, target, feature_names, target_columns
)
if return_X_y:
return data, target
return Bunch(
data=data,
target=target,
frame=frame,
target_names=target_names,
DESCR=fdescr,
feature_names=feature_names,
)
def load_iris(*, return_X_y=False, as_frame=False):
"""Load and return the iris dataset (classification).
The iris dataset is a classic and very easy multi-class classification
dataset.
================= ==============
Classes 3
Samples per class 50
Samples total 150
Dimensionality 4
Features real, positive
================= ==============
Read more in the :ref:`User Guide <iris_dataset>`.
Parameters
----------
return_X_y : bool, default=False
If True, returns ``(data, target)`` instead of a Bunch object. See
below for more information about the `data` and `target` object.
.. versionadded:: 0.18
as_frame : bool, default=False
If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric). The target is
a pandas DataFrame or Series depending on the number of target columns.
If `return_X_y` is True, then (`data`, `target`) will be pandas
DataFrames or Series as described below.
.. versionadded:: 0.23
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : {ndarray, dataframe} of shape (150, 4)
The data matrix. If `as_frame=True`, `data` will be a pandas
DataFrame.
target: {ndarray, Series} of shape (150,)
The classification target. If `as_frame=True`, `target` will be
a pandas Series.
feature_names: list
The names of the dataset columns.
target_names: list
The names of target classes.
frame: DataFrame of shape (150, 5)
Only present when `as_frame=True`. DataFrame with `data` and
`target`.
.. versionadded:: 0.23
DESCR: str
The full description of the dataset.
filename: str
The path to the location of the data.
.. versionadded:: 0.20
(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:: 0.18
Notes
-----
.. versionchanged:: 0.20
Fixed two wrong data points according to Fisher's paper.
The new version is the same as in R, but not as in the UCI
Machine Learning Repository.
Examples
--------
Let's say you are interested in the samples 10, 25, and 50, and want to
know their class name.
>>> from sklearn.datasets import load_iris
>>> data = load_iris()
>>> data.target[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
['setosa', 'versicolor', 'virginica']
"""
data_file_name = "iris.csv"
data, target, target_names, fdescr = load_csv_data(
data_file_name=data_file_name, descr_file_name="iris.rst"
)
feature_names = [
"sepal length (cm)",
"sepal width (cm)",
"petal length (cm)",
"petal width (cm)",
]
frame = None
target_columns = [
"target",
]
if as_frame:
frame, data, target = _convert_data_dataframe(
"load_iris", data, target, feature_names, target_columns
)
if return_X_y:
return data, target
return Bunch(
data=data,
target=target,
frame=frame,
target_names=target_names,
DESCR=fdescr,
feature_names=feature_names,
filename=data_file_name,
data_module=DATA_MODULE,
)
def load_breast_cancer(*, return_X_y=False, as_frame=False):
"""Load and return the breast cancer wisconsin dataset (classification).
The breast cancer dataset is a classic and very easy binary classification
dataset.
================= ==============
Classes 2
Samples per class 212(M),357(B)
Samples total 569
Dimensionality 30
Features real, positive
================= ==============
The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
downloaded from:
https://goo.gl/U2Uwz2
Read more in the :ref:`User Guide <breast_cancer_dataset>`.
Parameters
----------
return_X_y : bool, default=False
If True, returns ``(data, target)`` instead of a Bunch object.
See below for more information about the `data` and `target` object.
.. versionadded:: 0.18
as_frame : bool, default=False
If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric). The target is
a pandas DataFrame or Series depending on the number of target columns.
If `return_X_y` is True, then (`data`, `target`) will be pandas
DataFrames or Series as described below.
.. versionadded:: 0.23
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : {ndarray, dataframe} of shape (569, 30)
The data matrix. If `as_frame=True`, `data` will be a pandas
DataFrame.
target : {ndarray, Series} of shape (569,)
The classification target. If `as_frame=True`, `target` will be
a pandas Series.
feature_names : list
The names of the dataset columns.
target_names : list
The names of target classes.
frame : DataFrame of shape (569, 31)
Only present when `as_frame=True`. DataFrame with `data` and
`target`.
.. versionadded:: 0.23
DESCR : str
The full description of the dataset.
filename : str
The path to the location of the data.
.. versionadded:: 0.20
(data, target) : tuple if ``return_X_y`` is True
A tuple of two ndarrays by default. The first contains a 2D ndarray of
shape (569, 30) with each row representing one sample and each column
representing the features. The second ndarray of shape (569,) contains
the target samples. If `as_frame=True`, both arrays are pandas objects,
i.e. `X` a dataframe and `y` a series.
.. versionadded:: 0.18
Examples
--------
Let's say you are interested in the samples 10, 50, and 85, and want to
know their class name.
>>> from sklearn.datasets import load_breast_cancer
>>> data = load_breast_cancer()
>>> data.target[[10, 50, 85]]
array([0, 1, 0])
>>> list(data.target_names)
['malignant', 'benign']
"""
data_file_name = "breast_cancer.csv"
data, target, target_names, fdescr = load_csv_data(
data_file_name=data_file_name, descr_file_name="breast_cancer.rst"
)
feature_names = np.array(
[
"mean radius",
"mean texture",
"mean perimeter",
"mean area",
"mean smoothness",
"mean compactness",
"mean concavity",
"mean concave points",
"mean symmetry",
"mean fractal dimension",
"radius error",
"texture error",
"perimeter error",
"area error",
"smoothness error",
"compactness error",
"concavity error",
"concave points error",
"symmetry error",
"fractal dimension error",
"worst radius",
"worst texture",
"worst perimeter",
"worst area",
"worst smoothness",
"worst compactness",
"worst concavity",
"worst concave points",
"worst symmetry",
"worst fractal dimension",
]
)
frame = None
target_columns = [
"target",
]
if as_frame:
frame, data, target = _convert_data_dataframe(
"load_breast_cancer", data, target, feature_names, target_columns
)
if return_X_y:
return data, target
return Bunch(
data=data,
target=target,
frame=frame,
target_names=target_names,
DESCR=fdescr,
feature_names=feature_names,
filename=data_file_name,
data_module=DATA_MODULE,
)
def load_digits(*, n_class=10, return_X_y=False, as_frame=False):
"""Load and return the digits dataset (classification).
Each datapoint is a 8x8 image of a digit.
================= ==============
Classes 10
Samples per class ~180
Samples total 1797
Dimensionality 64
Features integers 0-16
================= ==============
This is a copy of the test set of the UCI ML hand-written digits datasets
https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
Read more in the :ref:`User Guide <digits_dataset>`.
Parameters
----------
n_class : int, default=10
The number of classes to return. Between 0 and 10.
return_X_y : bool, default=False
If True, returns ``(data, target)`` instead of a Bunch object.
See below for more information about the `data` and `target` object.
.. versionadded:: 0.18
as_frame : bool, default=False
If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric). The target is
a pandas DataFrame or Series depending on the number of target columns.
If `return_X_y` is True, then (`data`, `target`) will be pandas
DataFrames or Series as described below.
.. versionadded:: 0.23
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : {ndarray, dataframe} of shape (1797, 64)
The flattened data matrix. If `as_frame=True`, `data` will be
a pandas DataFrame.
target: {ndarray, Series} of shape (1797,)
The classification target. If `as_frame=True`, `target` will be
a pandas Series.
feature_names: list
The names of the dataset columns.
target_names: list
The names of target classes.
.. versionadded:: 0.20
frame: DataFrame of shape (1797, 65)
Only present when `as_frame=True`. DataFrame with `data` and
`target`.
.. versionadded:: 0.23
images: {ndarray} of shape (1797, 8, 8)
The raw image data.
DESCR: str
The full description of the dataset.
(data, target) : tuple if ``return_X_y`` is True
A tuple of two ndarrays by default. The first contains a 2D ndarray of
shape (1797, 64) with each row representing one sample and each column
representing the features. The second ndarray of shape (1797) contains
the target samples. If `as_frame=True`, both arrays are pandas objects,
i.e. `X` a dataframe and `y` a series.
.. versionadded:: 0.18
Examples
--------
To load the data and visualize the images::
>>> from sklearn.datasets import load_digits
>>> digits = load_digits()
>>> print(digits.data.shape)
(1797, 64)
>>> import matplotlib.pyplot as plt
>>> plt.gray()
>>> plt.matshow(digits.images[0])
<...>
>>> plt.show()
"""
data, fdescr = load_gzip_compressed_csv_data(
data_file_name="digits.csv.gz", descr_file_name="digits.rst", delimiter=","
)
target = data[:, -1].astype(int, copy=False)
flat_data = data[:, :-1]
images = flat_data.view()
images.shape = (-1, 8, 8)
if n_class < 10:
idx = target < n_class
flat_data, target = flat_data[idx], target[idx]
images = images[idx]
feature_names = [
"pixel_{}_{}".format(row_idx, col_idx)
for row_idx in range(8)
for col_idx in range(8)
]
frame = None
target_columns = [
"target",
]
if as_frame:
frame, flat_data, target = _convert_data_dataframe(
"load_digits", flat_data, target, feature_names, target_columns
)
if return_X_y:
return flat_data, target
return Bunch(
data=flat_data,
target=target,
frame=frame,
feature_names=feature_names,
target_names=np.arange(10),
images=images,
DESCR=fdescr,
)
def load_diabetes(*, return_X_y=False, as_frame=False, scaled=True):
"""Load and return the diabetes dataset (regression).
============== ==================
Samples total 442
Dimensionality 10
Features real, -.2 < x < .2
Targets integer 25 - 346
============== ==================
.. note::
The meaning of each feature (i.e. `feature_names`) might be unclear
(especially for `ltg`) as the documentation of the original dataset is
not explicit. We provide information that seems correct in regard with
the scientific literature in this field of research.
Read more in the :ref:`User Guide <diabetes_dataset>`.
Parameters
----------
return_X_y : bool, default=False
If True, returns ``(data, target)`` instead of a Bunch object.
See below for more information about the `data` and `target` object.
.. versionadded:: 0.18
as_frame : bool, default=False
If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric). The target is
a pandas DataFrame or Series depending on the number of target columns.
If `return_X_y` is True, then (`data`, `target`) will be pandas
DataFrames or Series as described below.
.. versionadded:: 0.23
scaled : bool, default=True
If True, the feature variables are mean centered and scaled by the
standard deviation times the square root of `n_samples`.
If False, raw data is returned for the feature variables.
.. versionadded:: 1.1
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : {ndarray, dataframe} of shape (442, 10)
The data matrix. If `as_frame=True`, `data` will be a pandas
DataFrame.
target: {ndarray, Series} of shape (442,)
The regression target. If `as_frame=True`, `target` will be
a pandas Series.
feature_names: list
The names of the dataset columns.
frame: DataFrame of shape (442, 11)
Only present when `as_frame=True`. DataFrame with `data` and
`target`.
.. versionadded:: 0.23
DESCR: str
The full description of the dataset.
data_filename: str
The path to the location of the data.
target_filename: str
The path to the location of the target.
(data, target) : tuple if ``return_X_y`` is True
Returns a tuple of two ndarray of shape (n_samples, n_features)
A 2D array with each row representing one sample and each column
representing the features and/or target of a given sample.
.. versionadded:: 0.18
"""
data_filename = "diabetes_data_raw.csv.gz"
target_filename = "diabetes_target.csv.gz"
data = load_gzip_compressed_csv_data(data_filename)
target = load_gzip_compressed_csv_data(target_filename)
if scaled:
data = scale(data, copy=False)
data /= data.shape[0] ** 0.5
fdescr = load_descr("diabetes.rst")
feature_names = ["age", "sex", "bmi", "bp", "s1", "s2", "s3", "s4", "s5", "s6"]
frame = None
target_columns = [
"target",
]
if as_frame:
frame, data, target = _convert_data_dataframe(
"load_diabetes", data, target, feature_names, target_columns
)
if return_X_y:
return data, target
return Bunch(
data=data,
target=target,
frame=frame,
DESCR=fdescr,
feature_names=feature_names,
data_filename=data_filename,
target_filename=target_filename,
data_module=DATA_MODULE,
)
def load_linnerud(*, return_X_y=False, as_frame=False):
"""Load and return the physical exercise Linnerud dataset.
This dataset is suitable for multi-output regression tasks.
============== ============================
Samples total 20
Dimensionality 3 (for both data and target)
Features integer
Targets integer
============== ============================
Read more in the :ref:`User Guide <linnerrud_dataset>`.
Parameters
----------
return_X_y : bool, default=False
If True, returns ``(data, target)`` instead of a Bunch object.
See below for more information about the `data` and `target` object.
.. versionadded:: 0.18
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.
If `return_X_y` is True, then (`data`, `target`) will be pandas
DataFrames or Series as described below.
.. versionadded:: 0.23
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : {ndarray, dataframe} of shape (20, 3)
The data matrix. If `as_frame=True`, `data` will be a pandas
DataFrame.
target: {ndarray, dataframe} of shape (20, 3)
The regression targets. If `as_frame=True`, `target` will be
a pandas DataFrame.
feature_names: list
The names of the dataset columns.
target_names: list
The names of the target columns.
frame: DataFrame of shape (20, 6)
Only present when `as_frame=True`. DataFrame with `data` and
`target`.
.. versionadded:: 0.23
DESCR: str
The full description of the dataset.
data_filename: str
The path to the location of the data.
target_filename: str
The path to the location of the target.
.. versionadded:: 0.20
(data, target) : tuple if ``return_X_y`` is True
Returns a tuple of two ndarrays or dataframe of shape
`(20, 3)`. Each row represents one sample and each column represents the
features in `X` and a target in `y` of a given sample.
.. versionadded:: 0.18
"""
data_filename = "linnerud_exercise.csv"
target_filename = "linnerud_physiological.csv"
# Read header and data
with _open_text(DATA_MODULE, data_filename) as f:
header_exercise = f.readline().split()
f.seek(0) # reset file obj
data_exercise = np.loadtxt(f, skiprows=1)
with _open_text(DATA_MODULE, target_filename) as f:
header_physiological = f.readline().split()
f.seek(0) # reset file obj
data_physiological = np.loadtxt(f, skiprows=1)
fdescr = load_descr("linnerud.rst")
frame = None
if as_frame:
(frame, data_exercise, data_physiological) = _convert_data_dataframe(
"load_linnerud",
data_exercise,
data_physiological,
header_exercise,
header_physiological,
)
if return_X_y:
return data_exercise, data_physiological
return Bunch(
data=data_exercise,
feature_names=header_exercise,
target=data_physiological,
target_names=header_physiological,
frame=frame,
DESCR=fdescr,
data_filename=data_filename,
target_filename=target_filename,
data_module=DATA_MODULE,
)
def load_sample_images():
"""Load sample images for image manipulation.
Loads both, ``china`` and ``flower``.
Read more in the :ref:`User Guide <sample_images>`.
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
images : list of ndarray of shape (427, 640, 3)
The two sample image.
filenames : list
The filenames for the images.
DESCR : str
The full description of the dataset.
Examples
--------
To load the data and visualize the images:
>>> from sklearn.datasets import load_sample_images
>>> dataset = load_sample_images() #doctest: +SKIP
>>> len(dataset.images) #doctest: +SKIP
2
>>> first_img_data = dataset.images[0] #doctest: +SKIP
>>> first_img_data.shape #doctest: +SKIP
(427, 640, 3)
>>> first_img_data.dtype #doctest: +SKIP
dtype('uint8')
"""
try:
from PIL import Image
except ImportError:
raise ImportError(
"The Python Imaging Library (PIL) is required to load data "
"from jpeg files. Please refer to "
"https://pillow.readthedocs.io/en/stable/installation.html "
"for installing PIL."
)
descr = load_descr("README.txt", descr_module=IMAGES_MODULE)
filenames, images = [], []
for filename in sorted(resources.contents(IMAGES_MODULE)):
if filename.endswith(".jpg"):
filenames.append(filename)
with _open_binary(IMAGES_MODULE, filename) as image_file:
pil_image = Image.open(image_file)
image = np.asarray(pil_image)
images.append(image)
return Bunch(images=images, filenames=filenames, DESCR=descr)
def load_sample_image(image_name):
"""Load the numpy array of a single sample image.
Read more in the :ref:`User Guide <sample_images>`.
Parameters
----------
image_name : {`china.jpg`, `flower.jpg`}
The name of the sample image loaded.
Returns
-------
img : 3D array
The image as a numpy array: height x width x color.
Examples
--------
>>> from sklearn.datasets import load_sample_image
>>> china = load_sample_image('china.jpg') # doctest: +SKIP
>>> china.dtype # doctest: +SKIP
dtype('uint8')
>>> china.shape # doctest: +SKIP
(427, 640, 3)
>>> flower = load_sample_image('flower.jpg') # doctest: +SKIP
>>> flower.dtype # doctest: +SKIP
dtype('uint8')
>>> flower.shape # doctest: +SKIP
(427, 640, 3)
"""
images = load_sample_images()
index = None
for i, filename in enumerate(images.filenames):
if filename.endswith(image_name):
index = i
break
if index is None:
raise AttributeError("Cannot find sample image: %s" % image_name)
return images.images[index]
def _pkl_filepath(*args, **kwargs):
"""Return filename for Python 3 pickles
args[-1] is expected to be the ".pkl" filename. For compatibility with
older scikit-learn versions, a suffix is inserted before the extension.
_pkl_filepath('/path/to/folder', 'filename.pkl') returns
'/path/to/folder/filename_py3.pkl'
"""
py3_suffix = kwargs.get("py3_suffix", "_py3")
basename, ext = splitext(args[-1])
basename += py3_suffix
new_args = args[:-1] + (basename + ext,)
return join(*new_args)
def _sha256(path):
"""Calculate the sha256 hash of the file at path."""
sha256hash = hashlib.sha256()
chunk_size = 8192
with open(path, "rb") as f:
while True:
buffer = f.read(chunk_size)
if not buffer:
break
sha256hash.update(buffer)
return sha256hash.hexdigest()
def _fetch_remote(remote, dirname=None):
"""Helper function to download a remote dataset into path
Fetch a dataset pointed by remote's url, save into path using remote's
filename and ensure its integrity based on the SHA256 Checksum of the
downloaded file.
Parameters
----------
remote : RemoteFileMetadata
Named tuple containing remote dataset meta information: url, filename
and checksum
dirname : str
Directory to save the file to.
Returns
-------
file_path: str
Full path of the created file.
"""
file_path = remote.filename if dirname is None else join(dirname, remote.filename)
urlretrieve(remote.url, file_path)
checksum = _sha256(file_path)
if remote.checksum != checksum:
raise IOError(
"{} has an SHA256 checksum ({}) "
"differing from expected ({}), "
"file may be corrupted.".format(file_path, checksum, remote.checksum)
)
return file_path
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