506 lines
17 KiB
Python
506 lines
17 KiB
Python
"""
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Base IO code for all datasets
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"""
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# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
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# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
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# 2010 Olivier Grisel <olivier.grisel@ensta.org>
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# License: Simplified BSD
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import os
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import csv
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import shutil
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from os import environ
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from os.path import dirname
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from os.path import join
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from os.path import exists
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from os.path import expanduser
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from os.path import isdir
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from os import listdir
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from os import makedirs
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import numpy as np
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from ..utils import check_random_state
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class Bunch(dict):
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"""Container object for datasets: dictionary-like object that
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exposes its keys as attributes."""
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def __init__(self, **kwargs):
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dict.__init__(self, kwargs)
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self.__dict__ = self
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def get_data_home(data_home=None):
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"""Return the path of the scikit-learn data dir.
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This folder is used by some large dataset loaders to avoid
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downloading the data several times.
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By default the data dir is set to a folder named 'scikit_learn_data'
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in the user home folder.
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Alternatively, it can be set by the 'SCIKIT_LEARN_DATA' environment
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variable or programatically by giving an explit folder path. The
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'~' symbol is expanded to the user home folder.
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If the folder does not already exist, it is automatically created.
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"""
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if data_home is None:
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data_home = environ.get('SCIKIT_LEARN_DATA',
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join('~', 'scikit_learn_data'))
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data_home = expanduser(data_home)
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if not exists(data_home):
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makedirs(data_home)
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return data_home
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def clear_data_home(data_home=None):
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"""Delete all the content of the data home cache."""
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data_home = get_data_home(data_home)
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shutil.rmtree(data_home)
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def load_files(container_path, description=None, categories=None,
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load_content=True, shuffle=True, charset=None,
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charse_error='strict', random_state=0):
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"""Load text files with categories as subfolder names.
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Individual samples are assumed to be files stored a two levels folder
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structure such as the following:
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container_folder/
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category_1_folder/
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file_1.txt
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file_2.txt
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...
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file_42.txt
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category_2_folder/
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file_43.txt
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file_44.txt
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...
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The folder names are used has supervised signal label names. The indivial
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file names are not important.
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This function does not try to extract features into a numpy array or
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scipy sparse matrix. In addition, if load_content is false it
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does not try to load the files in memory.
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To use utf-8 text files in a scikit-learn classification or clustering
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algorithm you will first need to use the `sklearn.features.text`
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module to build a feature extraction transformer that suits your
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problem.
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Similar feature extractors should be build for other kind of unstructured
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data input such as images, audio, video, ...
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Parameters
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----------
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container_path : string or unicode
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Path to the main folder holding one subfolder per category
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description: string or unicode, optional (default=None)
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A paragraph describing the characteristic of the dataset: its source,
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reference, etc.
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categories : A collection of strings or None, optional (default=None)
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If None (default), load all the categories.
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If not None, list of category names to load (other categories ignored).
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load_content : boolean, optional (default=True)
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Whether to load or not the content of the different files. If
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true a 'data' attribute containing the text information is present
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in the data structure returned. If not, a filenames attribute
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gives the path to the files.
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charset : string or None (default is None)
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If None, do not try to decode the content of the files (e.g. for
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images or other non-text content).
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If not None, charset to use to decode text files if load_content is
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True.
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charset_error: {'strict', 'ignore', 'replace'}
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Instruction on what to do if a byte sequence is given to analyze that
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contains characters not of the given `charset`. By default, it is
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'strict', meaning that a UnicodeDecodeError will be raised. Other
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values are 'ignore' and 'replace'.
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shuffle : bool, optional (default=True)
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Whether or not to shuffle the data: might be important for models that
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make the assumption that the samples are independent and identically
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distributed (i.i.d.), such as stochastic gradient descent.
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random_state : int, RandomState instance or None, optional (default=0)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Returns
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-------
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data : Bunch
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Dictionary-like object, the interesting attributes are: either
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data, the raw text data to learn, or 'filenames', the files
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holding it, 'target', the classification labels (integer index),
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'target_names', the meaning of the labels, and 'DESCR', the full
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description of the dataset.
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"""
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target = []
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target_names = []
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filenames = []
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folders = [f for f in sorted(listdir(container_path))
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if isdir(join(container_path, f))]
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if categories is not None:
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folders = [f for f in folders if f in categories]
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for label, folder in enumerate(folders):
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target_names.append(folder)
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folder_path = join(container_path, folder)
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documents = [join(folder_path, d)
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for d in sorted(listdir(folder_path))]
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target.extend(len(documents) * [label])
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filenames.extend(documents)
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# convert to array for fancy indexing
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filenames = np.array(filenames)
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target = np.array(target)
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if shuffle:
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random_state = check_random_state(random_state)
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indices = np.arange(filenames.shape[0])
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random_state.shuffle(indices)
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filenames = filenames[indices]
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target = target[indices]
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if load_content:
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data = [open(filename).read() for filename in filenames]
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if charset is not None:
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data = [d.decode(charset, charse_error) for d in data]
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return Bunch(data=data,
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filenames=filenames,
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target_names=target_names,
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target=target,
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DESCR=description)
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return Bunch(filenames=filenames,
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target_names=target_names,
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target=target,
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DESCR=description)
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def load_iris():
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"""Load and return the iris dataset (classification).
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The iris dataset is a classic and very easy multi-class classification
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dataset.
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================= ==============
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Classes 3
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Samples per class 50
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Samples total 150
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Dimensionality 4
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Features real, positive
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================= ==============
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Returns
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-------
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data : Bunch
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Dictionary-like object, the interesting attributes are:
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'data', the data to learn, 'target', the classification labels,
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'target_names', the meaning of the labels, 'feature_names', the
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meaning of the features, and 'DESCR', the
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full description of the dataset.
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Examples
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--------
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Let's say you are interested in the samples 10, 25, and 50, and want to
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know their class name.
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>>> from sklearn.datasets import load_iris
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>>> data = load_iris()
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>>> data.target[[10, 25, 50]]
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array([0, 0, 1])
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>>> list(data.target_names)
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['setosa', 'versicolor', 'virginica']
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"""
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module_path = dirname(__file__)
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data_file = csv.reader(open(join(module_path, 'data', 'iris.csv')))
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fdescr = open(join(module_path, 'descr', 'iris.rst'))
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temp = next(data_file)
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n_samples = int(temp[0])
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n_features = int(temp[1])
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target_names = np.array(temp[2:])
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data = np.empty((n_samples, n_features))
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target = np.empty((n_samples,), dtype=np.int)
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for i, ir in enumerate(data_file):
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data[i] = np.asarray(ir[:-1], dtype=np.float)
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target[i] = np.asarray(ir[-1], dtype=np.int)
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return Bunch(data=data, target=target,
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target_names=target_names,
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DESCR=fdescr.read(),
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feature_names=['sepal length (cm)', 'sepal width (cm)',
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'petal length (cm)', 'petal width (cm)'])
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def load_digits(n_class=10):
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"""Load and return the digits dataset (classification).
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Each datapoint is a 8x8 image of a digit.
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================= ==============
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Classes 10
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Samples per class ~180
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Samples total 1797
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Dimensionality 64
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Features integers 0-16
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================= ==============
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Parameters
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----------
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n_class : integer, between 0 and 10, optional (default=10)
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The number of classes to return.
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Returns
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-------
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data : Bunch
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Dictionary-like object, the interesting attributes are:
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'data', the data to learn, 'images', the images corresponding
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to each sample, 'target', the classification labels for each
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sample, 'target_names', the meaning of the labels, and 'DESCR',
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the full description of the dataset.
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Examples
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--------
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To load the data and visualize the images::
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>>> from sklearn.datasets import load_digits
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>>> digits = load_digits()
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>>> print digits.data.shape
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(1797, 64)
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>>> import pylab as pl #doctest: +SKIP
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>>> pl.gray() #doctest: +SKIP
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>>> pl.matshow(digits.images[0]) #doctest: +SKIP
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>>> pl.show() #doctest: +SKIP
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"""
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module_path = dirname(__file__)
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data = np.loadtxt(join(module_path, 'data', 'digits.csv.gz'),
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delimiter=',')
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descr = open(join(module_path, 'descr', 'digits.rst')).read()
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target = data[:, -1]
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flat_data = data[:, :-1]
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images = flat_data.view()
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images.shape = (-1, 8, 8)
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if n_class < 10:
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idx = target < n_class
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flat_data, target = flat_data[idx], target[idx]
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images = images[idx]
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return Bunch(data=flat_data,
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target=target.astype(np.int),
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target_names=np.arange(10),
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images=images,
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DESCR=descr)
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def load_diabetes():
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"""Load and return the diabetes dataset (regression).
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============== ==================
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Samples total 442
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Dimensionality 10
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Features real, -.2 < x < .2
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Targets integer 25 - 346
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============== ==================
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Returns
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-------
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data : Bunch
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Dictionary-like object, the interesting attributes are:
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'data', the data to learn and 'target', the regression target for each
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sample.
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"""
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base_dir = join(dirname(__file__), 'data')
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data = np.loadtxt(join(base_dir, 'diabetes_data.csv.gz'))
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target = np.loadtxt(join(base_dir, 'diabetes_target.csv.gz'))
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return Bunch(data=data, target=target)
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def load_linnerud():
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"""Load and return the linnerud dataset (multivariate regression).
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Samples total: 20
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Dimensionality: 3 for both data and targets
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Features: integer
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Targets: integer
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Returns
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-------
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data : Bunch
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Dictionary-like object, the interesting attributes are: 'data' and
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'targets', the two multivariate datasets, with 'data' corresponding to
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the exercise and 'targets' corresponding to the physiological
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measurements, as well as 'feature_names' and 'target_names'.
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"""
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base_dir = join(dirname(__file__), 'data/')
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# Read data
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data_exercise = np.loadtxt(base_dir + 'linnerud_exercise.csv', skiprows=1)
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data_physiological = np.loadtxt(base_dir + 'linnerud_physiological.csv',
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skiprows=1)
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# Read header
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with open(base_dir + 'linnerud_exercise.csv') as f:
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header_exercise = f.readline().split()
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with open(base_dir + 'linnerud_physiological.csv') as f:
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header_physiological = f.readline().split()
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with open(dirname(__file__) + '/descr/linnerud.rst') as f:
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descr = f.read()
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return Bunch(data=data_exercise, feature_names=header_exercise,
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target=data_physiological,
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target_names=header_physiological,
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DESCR=descr)
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def load_boston():
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"""Load and return the boston house-prices dataset (regression).
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============== ==============
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Samples total 506
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Dimensionality 13
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Features real, positive
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Targets real 5. - 50.
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============== ==============
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Returns
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-------
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data : Bunch
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Dictionary-like object, the interesting attributes are:
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'data', the data to learn, 'target', the regression targets,
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'target_names', the meaning of the labels, and 'DESCR', the
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full description of the dataset.
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Examples
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--------
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>>> from sklearn.datasets import load_boston
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>>> boston = load_boston()
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>>> print boston.data.shape
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(506, 13)
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"""
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module_path = dirname(__file__)
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data_file = csv.reader(open(join(module_path, 'data',
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'boston_house_prices.csv')))
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fdescr = open(join(module_path, 'descr', 'boston_house_prices.rst'))
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temp = data_file.next()
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n_samples = int(temp[0])
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n_features = int(temp[1])
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data = np.empty((n_samples, n_features))
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target = np.empty((n_samples,))
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temp = data_file.next() # names of features
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feature_names = np.array(temp)
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for i, d in enumerate(data_file):
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data[i] = np.asarray(d[:-1], dtype=np.float)
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target[i] = np.asarray(d[-1], dtype=np.float)
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return Bunch(data=data,
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target=target,
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feature_names=feature_names,
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DESCR=fdescr.read())
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def load_sample_images():
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"""Load sample images for image manipulation.
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Loads both, ``china`` and ``flower``.
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Returns
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-------
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data : Bunch
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Dictionary-like object with the following attributes :
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'images', the two sample images, 'filenames', the file
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names for the images, and 'DESCR'
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the full description of the dataset.
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Examples
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--------
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To load the data and visualize the images:
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>>> from sklearn.datasets import load_sample_images
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>>> dataset = load_sample_images() #doctest: +SKIP
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>>> len(dataset.images) #doctest: +SKIP
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2
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>>> first_img_data = dataset.images[0] #doctest: +SKIP
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>>> first_img_data.shape #doctest: +SKIP
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(427, 640, 3)
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>>> first_img_data.dtype #doctest: +SKIP
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dtype('uint8')
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"""
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# Try to import imread from scipy. We do this lazily here to prevent
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# this module from depending on PIL.
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try:
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try:
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from scipy.misc import imread
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except ImportError:
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from scipy.misc.pilutil import imread
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except ImportError:
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raise ImportError("The Python Imaging Library (PIL) "
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"is required to load data from jpeg files")
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module_path = join(dirname(__file__), "images")
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with open(join(module_path, 'README.txt')) as f:
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descr = f.read()
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filenames = [join(module_path, filename)
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for filename in os.listdir(module_path)
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if filename.endswith(".jpg")]
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# Load image data for each image in the source folder.
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images = [imread(filename) for filename in filenames]
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return Bunch(images=images,
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filenames=filenames,
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DESCR=descr)
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def load_sample_image(image_name):
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"""Load the numpy array of a single sample image
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Parameters
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-----------
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image_name: {`china.jpg`, `flower.jpg`}
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The name of the sample image loaded
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Returns
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-------
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img: 3D array
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The image as a numpy array: height x width x color
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Examples
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---------
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>>> from sklearn.datasets import load_sample_image
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>>> china = load_sample_image('china.jpg') # doctest: +SKIP
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>>> china.dtype # doctest: +SKIP
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dtype('uint8')
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>>> china.shape # doctest: +SKIP
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(427, 640, 3)
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>>> flower = load_sample_image('flower.jpg') # doctest: +SKIP
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>>> flower.dtype # doctest: +SKIP
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dtype('uint8')
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>>> flower.shape # doctest: +SKIP
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(427, 640, 3)
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"""
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images = load_sample_images()
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index = None
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for i, filename in enumerate(images.filenames):
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if filename.endswith(image_name):
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index = i
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break
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if index is None:
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raise AttributeError("Cannot find sample image: %s" % image_name)
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return images.images[index]
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