""" Base IO code for all datasets """ # Copyright (c) 2007 David Cournapeau # 2010 Fabian Pedregosa # 2010 Olivier Grisel # 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 `. 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 `. 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 `. 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. (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') 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)']) 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. (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') 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) 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 `. 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 `. 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 and 'target', the regression target for each sample. (data, target) : tuple if ``return_X_y`` is True .. versionadded:: 0.18 """ module_path = dirname(__file__) base_dir = join(module_path, 'data') data = np.loadtxt(join(base_dir, 'diabetes_data.csv.gz')) target = np.loadtxt(join(base_dir, 'diabetes_target.csv.gz')) 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']) 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'. (data, target) : tuple if ``return_X_y`` is True .. versionadded:: 0.18 """ base_dir = join(dirname(__file__), 'data/') # Read data data_exercise = np.loadtxt(base_dir + 'linnerud_exercise.csv', skiprows=1) data_physiological = np.loadtxt(base_dir + 'linnerud_physiological.csv', skiprows=1) # Read header with open(base_dir + 'linnerud_exercise.csv') as f: header_exercise = f.readline().split() with open(base_dir + 'linnerud_physiological.csv') 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) 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, and 'DESCR', the full description of the dataset. (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) 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