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README.rst

scikit-data

Scikits data is a library of datasets for empirical computer science. Lots of disciplines such as machine learning, natural language processing, and computer vision have datasets. This module makes the most popular and standard datasets (even the big awkward ones) easy to access from Python programs.

Goal

Consolidate several overlapping projects:

  • scikits.learn.datasets
  • pylearn.datasets
  • pythor.datasets

Standardize to some extent the representation of large datasets, so that it is possible to implement standard processing / learning algorithms for those datasets.

Design

Datasets are primarily lists of meta-data, in dictionaries. Usually one example from the dataset is one element of the list. The dictionary structures need not necessarily be homogeneous (same keys, same schema) but often it is natural to make them homogeneous when the examples of the dataset represent I.I.D. data. Datasets are typically implemented as classes, and this list of dictionaries is referred to as self.meta. Elements of self.meta should be built of simple data types and be JSON-encodable. This opens the door to using various tools to store and access meta-data.

Some datasets are for all intents and purposes infinite (i.e. dynamically generated). In such cases self.meta is implemented as lazily-evaluated list.

Sometimes there is meta-data that feels like it applies to an entire dataset rather than any particular example. That data goes into a dictionary in self.descr.

Sometimes there is meta-data that is constant across all examples (e.g. image size). Such data lives in self.meta_const. When such an attribute exists, then every element of self.meta will be consistent with it. In other words, self.meta[i] == self.meta[i].update(self.meta_const) is always true.

Datasets often have to download large files from the web. When they do, they store that bulky data in a dataset-specific subdirectory within $SCIKITS_DATA.

Dataset class instances should try to maintain a small footprint. Large collections of images for example do not live in the dataset instance directly. The locations of the images should be stored in the dataset, and the images themselves made available via a lazy-evaluation mechanism. This package provides the larray to help with that, but memory-mapped files and other techniques for working with large amounts of data are welcome as well.

Many datasets are meant to be used in the context of particular tasks. When that is the case, a dataset class will provide methods that filter self.meta and return the representation of the data that is most appropriate for the task. For example, a classification task method on a small dataset will return an X, y pair where X is numpy ndarray design matrix and y is a numpy array of integers. For a larger dataset, the design matrix might be provided by a lazily-evaluated proxy object.

Lazy-evaluation is important to the design of data processing pipelines for large datasets. It decouples the logic of providing access to data from the logic of efficient caching, which is often task specific.

More design and reference documentation will be written in doc/. This is already starting with doc/dataset.rst.

Development Status

Just starting.

Building on previous work in several projects (scikits.learn, PyLearn, PyLearn2, PyThor).

Contents

The library contains modules and programs.

Modules:

  • toy (Iris, Digits, Diabetes, Linnerud, Boston, SampleImages)

  • synthetic (Swiss Roll, S Curve, Friedman{1,2,3}, Madelon, Randlin, Blobs, SparseCodedSignal, LowRankMatrix, SparseUncorrelated)

  • mnist (MNIST: hand-drawn digit classification dataset)

  • lfw (Labeled Faces in the Wild: face recognition and verification)

  • pascal (PASCAL Visual Object Classes 2007-2011)

  • cifar10 (CIFAR-10 image classification dataset)

  • caltech (Caltech101 and Caltech256 Object datasets)

  • iicbu (IICBU 2008: Biomedical Image Classification)

  • larochelle_etal_2007 (MNIST Variations, Rectangles, and

    Convex: Image classification tasks with multiple factors of variation)

  • pubfig83

Programs:

  • datasets-fetch <dataset> download a dataset
  • datasets-show <dataset> visualize a dataset
  • datasets-erase <dataset> erase a downloaded dataset

Programs

datasets-fetch

Usage: dataset-fetch <dataset_name>

This program downloads the named dataset into the $SCIKITS_DATA directory. Typically this means downloading from original sources online.

How does this work? It works something like this:

exec "skdata.%s" % dataset_name
exec "skdata.%s.main_fetch()" % dataset_name

So every dataset module has to opt into this mechanism by implementing a global main_fetch function. To see more about how sub-modules use this mechanism, grep the code for main_fetch.

datasets-show

Usage: dataset-show <dataset_name>

This program downloads the named dataset if necessary into the $SCIKITS_DATA directory, loads it, and launches a simple GUI program to visualize the elements of the dataset. To see more about how sub-modules use this mechanism, grep the code for main_show.

datasets-erase

Usage: dataset-erase <dataset_name>

This program erases any data cached or downloaded in support of the named dataset. To see more about how sub-modules use this mechanism, grep the code for main_erase.