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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
from __future__ import print_function
import os
import csv
import sys
import shutil
from collections import namedtuple
from os import environ, listdir, makedirs
from os.path import dirname, exists, expanduser, isdir, join, splitext
import hashlib
from ..utils import Bunch
from ..utils import check_random_state
import numpy as np
from sklearn.externals.six.moves.urllib.request import urlretrieve
RemoteFileMetadata = namedtuple('RemoteFileMetadata',
['filename', 'url', 'checksum'])
def get_data_home(data_home=None):
"""Return the path of the scikit-learn data dir.
This folder is used by some large dataset loaders to avoid downloading the
data several times.
By default the data dir 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 | None
The path to scikit-learn data dir.
"""
if data_home is None:
data_home = environ.get('SCIKIT_LEARN_DATA',
join('~', 'scikit_learn_data'))
data_home = expanduser(data_home)
if not exists(data_home):
makedirs(data_home)
return data_home
def clear_data_home(data_home=None):
"""Delete all the content of the data home cache.
Parameters
----------
data_home : str | None
The path to scikit-learn data dir.
"""
data_home = get_data_home(data_home)
shutil.rmtree(data_home)
def load_files(container_path, description=None, categories=None,
load_content=True, shuffle=True, encoding=None,
decode_error='strict', random_state=0):
"""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 `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 `sklearn.feature_extraction.text`.
Similar feature extractors should be built for other kind of unstructured
data input such as images, audio, video, ...
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
container_path : string or unicode
Path to the main folder holding one subfolder per category
description : string or unicode, optional (default=None)
A paragraph describing the characteristic of the dataset: its source,
reference, etc.
categories : A collection of strings or None, optional (default=None)
If None (default), load all the categories. If not None, list of
category names to load (other categories ignored).
load_content : boolean, optional (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, optional (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 : string or None (default is 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'}, optional
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, optional (default=0)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are: either
data, the raw text data to learn, or 'filenames', the files
holding it, 'target', the classification labels (integer index),
'target_names', the meaning of the labels, and 'DESCR', the full
description of 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]
for label, folder in enumerate(folders):
target_names.append(folder)
folder_path = join(container_path, folder)
documents = [join(folder_path, d)
for d in sorted(listdir(folder_path))]
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:
with open(filename, 'rb') as f:
data.append(f.read())
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_data(module_path, data_file_name):
"""Loads data from module_path/data/data_file_name.
Parameters
----------
data_file_name : String. Name of csv file to be loaded from
module_path/data/data_file_name. For example 'wine_data.csv'.
Returns
-------
data : Numpy Array
A 2D array with each row representing one sample and each column
representing the features of a given sample.
target : Numpy Array
A 1D array holding target variables for all the samples in `data.
For example target[0] is the target varible for data[0].
target_names : Numpy Array
A 1D array containing the names of the classifications. For example
target_names[0] is the name of the target[0] class.
"""
with open(join(module_path, 'data', 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=np.int)
for i, ir in enumerate(data_file):
data[i] = np.asarray(ir[:-1], dtype=np.float64)
target[i] = np.asarray(ir[-1], dtype=np.int)
return data, target, target_names
def load_wine(return_X_y=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
================= ==============
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
return_X_y : boolean, default=False.
If True, returns ``(data, target)`` instead of a Bunch object.
See below for more information about the `data` and `target` object.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are: 'data', the
data to learn, 'target', the classification labels, 'target_names', the
meaning of the labels, 'feature_names', the meaning of the features,
and 'DESCR', the full description of the dataset.
(data, target) : tuple if ``return_X_y`` is True
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
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']
"""
module_path = dirname(__file__)
data, target, target_names = load_data(module_path, 'wine_data.csv')
with open(join(module_path, 'descr', 'wine_data.rst')) as rst_file:
fdescr = rst_file.read()
if return_X_y:
return data, target
return Bunch(data=data, target=target,
target_names=target_names,
DESCR=fdescr,
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'])
def load_iris(return_X_y=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 <datasets>`.
Parameters
----------
return_X_y : boolean, 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
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the classification labels,
'target_names', the meaning of the labels, 'feature_names', the
meaning of the features, 'DESCR', the full description of
the dataset, 'filename', the physical location of
iris csv dataset (added in version `0.20`).
(data, target) : tuple if ``return_X_y`` is True
.. versionadded:: 0.18
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']
"""
module_path = dirname(__file__)
data, target, target_names = load_data(module_path, 'iris.csv')
iris_csv_filename = join(module_path, 'data', 'iris.csv')
with open(join(module_path, 'descr', 'iris.rst')) as rst_file:
fdescr = rst_file.read()
if return_X_y:
return data, target
return Bunch(data=data, target=target,
target_names=target_names,
DESCR=fdescr,
feature_names=['sepal length (cm)', 'sepal width (cm)',
'petal length (cm)', 'petal width (cm)'],
filename=iris_csv_filename)
def load_breast_cancer(return_X_y=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
================= ==============
Parameters
----------
return_X_y : boolean, 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
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the classification labels,
'target_names', the meaning of the labels, 'feature_names', the
meaning of the features, and 'DESCR', the full description of
the dataset, 'filename', the physical location of
breast cancer csv dataset (added in version `0.20`).
(data, target) : tuple if ``return_X_y`` is True
.. versionadded:: 0.18
The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
downloaded from:
https://goo.gl/U2Uwz2
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']
"""
module_path = dirname(__file__)
data, target, target_names = load_data(module_path, 'breast_cancer.csv')
csv_filename = join(module_path, 'data', 'breast_cancer.csv')
with open(join(module_path, 'descr', 'breast_cancer.rst')) as rst_file:
fdescr = rst_file.read()
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'])
if return_X_y:
return data, target
return Bunch(data=data, target=target,
target_names=target_names,
DESCR=fdescr,
feature_names=feature_names,
filename=csv_filename)
def load_digits(n_class=10, return_X_y=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
================= ==============
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
n_class : integer, between 0 and 10, optional (default=10)
The number of classes to return.
return_X_y : boolean, 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
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'images', the images corresponding
to each sample, 'target', the classification labels for each
sample, 'target_names', the meaning of the labels, and 'DESCR',
the full description of the dataset.
(data, target) : tuple if ``return_X_y`` is True
.. versionadded:: 0.18
This is a copy of the test set of the UCI ML hand-written digits datasets
http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
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 #doctest: +SKIP
>>> plt.gray() #doctest: +SKIP
>>> plt.matshow(digits.images[0]) #doctest: +SKIP
>>> plt.show() #doctest: +SKIP
"""
module_path = dirname(__file__)
data = np.loadtxt(join(module_path, 'data', 'digits.csv.gz'),
delimiter=',')
with open(join(module_path, 'descr', 'digits.rst')) as f:
descr = f.read()
target = data[:, -1].astype(np.int)
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]
if return_X_y:
return flat_data, target
return Bunch(data=flat_data,
target=target,
target_names=np.arange(10),
images=images,
DESCR=descr)
def load_diabetes(return_X_y=False):
"""Load and return the diabetes dataset (regression).
============== ==================
Samples total 442
Dimensionality 10
Features real, -.2 < x < .2
Targets integer 25 - 346
============== ==================
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
return_X_y : boolean, 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
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the regression target for each
sample, 'data_filename', the physical location
of diabetes data csv dataset, and 'target_filename', the physical
location of diabetes targets csv datataset (added in version `0.20`).
(data, target) : tuple if ``return_X_y`` is True
.. versionadded:: 0.18
"""
module_path = dirname(__file__)
base_dir = join(module_path, 'data')
data_filename = join(base_dir, 'diabetes_data.csv.gz')
data = np.loadtxt(data_filename)
target_filename = join(base_dir, 'diabetes_target.csv.gz')
target = np.loadtxt(target_filename)
with open(join(module_path, 'descr', 'diabetes.rst')) as rst_file:
fdescr = rst_file.read()
if return_X_y:
return data, target
return Bunch(data=data, target=target, DESCR=fdescr,
feature_names=['age', 'sex', 'bmi', 'bp',
's1', 's2', 's3', 's4', 's5', 's6'],
data_filename=data_filename,
target_filename=target_filename)
def load_linnerud(return_X_y=False):
"""Load and return the linnerud dataset (multivariate regression).
============== ============================
Samples total 20
Dimensionality 3 (for both data and target)
Features integer
Targets integer
============== ============================
Parameters
----------
return_X_y : boolean, 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
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are: 'data' and
'targets', the two multivariate datasets, with 'data' corresponding to
the exercise and 'targets' corresponding to the physiological
measurements, as well as 'feature_names' and 'target_names'.
In addition, you will also have access to 'data_filename',
the physical location of linnerud data csv dataset, and
'target_filename', the physical location of
linnerud targets csv datataset (added in version `0.20`).
(data, target) : tuple if ``return_X_y`` is True
.. versionadded:: 0.18
"""
base_dir = join(dirname(__file__), 'data/')
data_filename = join(base_dir, 'linnerud_exercise.csv')
target_filename = join(base_dir, 'linnerud_physiological.csv')
# Read data
data_exercise = np.loadtxt(data_filename, skiprows=1)
data_physiological = np.loadtxt(target_filename, skiprows=1)
# Read header
with open(data_filename) as f:
header_exercise = f.readline().split()
with open(target_filename) as f:
header_physiological = f.readline().split()
with open(dirname(__file__) + '/descr/linnerud.rst') as f:
descr = f.read()
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,
DESCR=descr,
data_filename=data_filename,
target_filename=target_filename)
def load_boston(return_X_y=False):
"""Load and return the boston house-prices dataset (regression).
============== ==============
Samples total 506
Dimensionality 13
Features real, positive
Targets real 5. - 50.
============== ==============
Parameters
----------
return_X_y : boolean, 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
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the regression targets,
'DESCR', the full description of the dataset,
and 'filename', the physical location of boston
csv dataset (added in version `0.20`).
(data, target) : tuple if ``return_X_y`` is True
.. versionadded:: 0.18
Examples
--------
>>> from sklearn.datasets import load_boston
>>> boston = load_boston()
>>> print(boston.data.shape)
(506, 13)
"""
module_path = dirname(__file__)
fdescr_name = join(module_path, 'descr', 'boston_house_prices.rst')
with open(fdescr_name) as f:
descr_text = f.read()
data_file_name = join(module_path, 'data', 'boston_house_prices.csv')
with open(data_file_name) as f:
data_file = csv.reader(f)
temp = next(data_file)
n_samples = int(temp[0])
n_features = int(temp[1])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,))
temp = next(data_file) # names of features
feature_names = np.array(temp)
for i, d in enumerate(data_file):
data[i] = np.asarray(d[:-1], dtype=np.float64)
target[i] = np.asarray(d[-1], dtype=np.float64)
if return_X_y:
return data, target
return Bunch(data=data,
target=target,
# last column is target value
feature_names=feature_names[:-1],
DESCR=descr_text,
filename=data_file_name)
def load_sample_images():
"""Load sample images for image manipulation.
Loads both, ``china`` and ``flower``.
Returns
-------
data : Bunch
Dictionary-like object with the following attributes : 'images', the
two sample images, 'filenames', the file names for the images, and
'DESCR' 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 to import imread from scipy. We do this lazily here to prevent
# this module from depending on PIL.
try:
try:
from scipy.misc import imread
except ImportError:
from scipy.misc.pilutil import imread
except ImportError:
raise ImportError("The Python Imaging Library (PIL) "
"is required to load data from jpeg files")
module_path = join(dirname(__file__), "images")
with open(join(module_path, 'README.txt')) as f:
descr = f.read()
filenames = [join(module_path, filename)
for filename in os.listdir(module_path)
if filename.endswith(".jpg")]
# Load image data for each image in the source folder.
images = [imread(filename) for filename in filenames]
return Bunch(images=images,
filenames=filenames,
DESCR=descr)
def load_sample_image(image_name):
"""Load the numpy array of a single sample image
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):
"""Ensure different filenames for Python 2 and Python 3 pickles
An object pickled under Python 3 cannot be loaded under Python 2. An object
pickled under Python 2 can sometimes not be loaded correctly under Python 3
because some Python 2 strings are decoded as Python 3 strings which can be
problematic for objects that use Python 2 strings as byte buffers for
numerical data instead of "real" strings.
Therefore, dataset loaders in scikit-learn use different files for pickles
manages by Python 2 and Python 3 in the same SCIKIT_LEARN_DATA folder so as
to avoid conflicts.
args[-1] is expected to be the ".pkl" filename. Under Python 3, a suffix is
inserted before the extension to s
_pkl_filepath('/path/to/folder', 'filename.pkl') returns:
- /path/to/folder/filename.pkl under Python 2
- /path/to/folder/filename_py3.pkl under Python 3+
"""
py3_suffix = kwargs.get("py3_suffix", "_py3")
basename, ext = splitext(args[-1])
if sys.version_info[0] >= 3:
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 : string
Directory to save the file to.
Returns
-------
file_path: string
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