.. Last Change: Mon Sep 17 04:00 PM 2007 J .. vim:syntax=rest Dataset for scipy: design proposal ================================== One of the thing numpy/scipy is missing now is a set of datasets, available for demo, courses, etc. For example, R has a set of dataset available at the core. The expected usage of the datasets are the following: - machine learning: eg the data contain also class information (discrete or continuous) - descriptive statistics - others ? That is, a dataset is not only data, but also some meta-data. The goal of this proposal is to propose common practices for organizing the data, in a way which is both straightforward, and does not prevent specific usage of the data. Organization ------------ A preliminary set of datasets is available at the following address: http://projects.scipy.org/scipy/scikits/browser/trunk/learn/scikits/learn/datasets Each dataset is a directory and defines a python package (e.g. has the __init__.py file). Each package is expected to define the function load, returning the corresponding data. For example, to access datasets data1, you should be able to do: >>> from datasets.data1 import load >>> d = load() # -> d contains the data. load can do whatever it wants: fetching data from a file (python script, csv file, etc...), from the internet, etc... Some special variables must be defined for each package, containing a python string: - COPYRIGHT: copyright informations - SOURCE: where the data are coming from - DESCHOSRT: short description - DESCLONG: long description - NOTE: some notes on the datasets. Format of the data ------------------ Here, I suggest a common practice for the returned value by the load function. Instead of using classes to provide meta-data, I propose to use a dictionnary of arrays, with some values mandatory. The key goals are: - for people who just want the data, there is no extra burden ("just give me the data !" MOTO). - for people who need more, they can easily extract what they need from the returned values. More high level abstractions can be built easily from this model. - all possible dataset should fit into this model. - In particular, I want to be able to be able to convert our dataset to Orange Dataset representation (or other machine learning tool), and vice-versa. For the datasets to be useful in the learn scikits, which is the project which initiated this datasets package, the data returned by load has to be a dict with the following conventions: - 'data': this value should be a record array containing the actual data. - 'label': this value should be a rank 1 array of integers, contains the label index for each sample, that is label[i] should be the label index of data[i]. If it contains float values, it is used for regression instead. - 'class': a record array such as class[i] is the class name. In other words, this makes the correspondance label name > label index. As an example, I use the famouse IRIS dataset: the dataset contains 3 classes of flowers, and for each flower, 4 measures (called attributes in machine learning vocabulary) are available (sepal width and length, petal width and length). In this case, the values returned by load would be: - 'data': a record array containing all the flowers' measurements. For descriptive statistics, that's all you may need. You can easily find the attributes from the dtype (a function to find the attributes is also available: it returns a list of the attributes). - 'labels': an array of integers (for class information) or float (for regression). each class is encoded as an integer, and labels[i] returns this integer for the sample i. - 'class': a record array, which returns the integer code for each class. For example, class['Iris-versicolor'] will return the integer used in label, and all samples i such as label[i] == class['Iris-versicolor'] are of the class 'Iris-versicolor'. This contains enough information to get all useful information through introspection and simple functions. I already implemented a small module to do basic things such as: - selecting only a subset of all samples. - selecting only a subset of the attributes (only sepal length and width, for example). - selecting only the samples of a given class. - small summary of the dataset. This is implemented in less than 100 lines, which tends to show that the above design is not too simplistic. Remaining problems: ------------------- I see mainly two big problems: - if the dataset is big and cannot fit into memory, what kind of API do we want to avoid loading all the data in memory ? Can we use memory mapped arrays ? - Missing data: I thought about subclassing both record arrays and masked arrays classes, but I don't know if this is feasable, or even makes sense. I have the feeling that some Data mining software use Nan (for example, weka seems to use float internally), but this prevents them from representing integer data. Current implementation ---------------------- An implementation following the above design is available in scikits.learn.datasets. If you installed scikits.learn, you can execute the file learn/utils/attrselect.py, which shows the information you can easily extract for now from this model. Also, once the above problems are solved, an arff converter will be available: arff is the format used by WEKA, and many datasets are available at this format: http://weka.sourceforge.net/wekadoc/index.php/en:ARFF_%283.5.4%29 http://www.cs.waikato.ac.nz/ml/weka/index_datasets.html Note ---- Although the datasets package emerged from the learn package, I try to keep it independent from everything else, that is once we agree on the remaining problems and where the package should go, it can easily be put elsewhere without too much trouble.