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Dataset loading utilities

.. currentmodule:: sklearn.datasets

The sklearn.datasets package embeds some small toy datasets as introduced in the "Getting Started" section.

To evaluate the impact of the scale of the dataset (n_samples and n_features) while controlling the statistical properties of the data (typically the correlation and informativeness of the features), it is also possible to generate synthetic data.

This package also features helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithm on data that comes from the 'real world'.

Toy datasets

scikit-learn comes with a few small standard datasets that do not require to download any file from some external website.

.. autosummary::

   :toctree: generated/
   :template: function.rst

   load_boston
   load_iris
   load_diabetes
   load_digits
   load_linnerud

These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in the scikit. They are however often too small to be representative of real world machine learning tasks.

Sample images

The scikit also embed a couple of sample JPEG images published under Creative Commons license by their authors. Those image can be useful to test algorithms and pipeline on 2D data.

.. autosummary::

   load_sample_images
   load_sample_image

Note

The default coding of images is based on the uint8 dtype to spare memory. Often machine learning algorithms work best if the input is converted to a floating point representation first. Also, if you plan to use pylab.imshow don't forget to scale to the range 0 - 1 as done in the following example.

Sample generators

In addition, scikit-learn includes various random sample generators that can be used to build artifical datasets of controled size and complexity.

.. autosummary::

   :toctree: generated/
   :template: function.rst

   make_classification
   make_regression
   make_blobs
   make_friedman1
   make_friedman2
   make_friedman3
   make_low_rank_matrix
   make_sparse_coded_signal
   make_sparse_uncorrelated
   make_spd_matrix
   make_swiss_roll
   make_s_curve

Datasets in svmlight / libsvm format

scikit-learn includes a fast utility function, load_svmlight_format, to load datasets in the svmlight / libsvm format. In this format, each line takes the form <label> <feature-id>:<feature-value> <feature-id>:<feature-value> .... This format is especially suitable for sparse datasets. Scipy sparse CSR matrices are used for X and numpy arrays are used for y.

You may load a dataset like this:

>>> from sklearn.datasets import load_svmlight_file
>>> X_train, y_train = load_svmlight_file("/path/to/train_dataset.txt")
...                                                         # doctest: +SKIP

You may also load two datasets at once:

>>> X_train, y_train, X_test, y_test = load_svmlight_file(
...     "/path/to/train_dataset.txt",
...     "/path/to/test_dataset.txt")                        # doctest: +SKIP

In this case, X_train and X_test are guaranteed to have the same number of features. Another way to achieve the same result is to fix the number of features:

>>> X_test, y_test = load_svmlight_file(
...     "/path/to/test_dataset.txt", n_features=X_train.shape[1])
...                                                         # doctest: +SKIP

Public datasets:

Public datasets in svmlight / libsvm format: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/