This package represents a community effort to provide a native and generic Julia implementation for commonly used data access pattern in Machine Learning. Most notably it provides a number of pattern for shuffling, partitioning, and resampling data sets of various types and origin. At its core, the package was designed around the key requirement of allowing any user-defined type to serve as a custom data source and/or access pattern in a first class manner.
In contrast to other data-related Julia packages, the focus of MLDataPattern is specifically on functionality utilized in a machine learning context. As such, it is a part of the JuliaML ecosystem.
Where to begin?¶
If this is the first time you consider using MLDataPattern for your machine learning related experiments or packages, make sure to check out the “Getting Started” section. It will provide a very condensed overview of all the topics outlined below. If you are looking to perform some specific task, then take a look at “How to ...?”, which lists some of most common scenarios and links to the appropriate places that should guide you on how to approach these scenarios using the functionality provided by this or other packages.
Introduction and Motivation¶
If you are new to Machine Learning in Julia, or are simply interested in how and why this package works the way it works, feel free to take a look at the following documents. There we discuss the problem of data-partitioning itself and what challenges it entails. Further we will provide some insight on how this package approaches the task conceptually.
The main design principle behind this package is based on the
assumption that the data source a user is working with, is likely
of some user-specific custom type. That said, there was also a
lot of attention put into first class support for those types
that are most commonly employed to represent the data of
interest, such as
The first topic we will cover is about data containers. These represent a large subgroup of data sources, that all know how many observations they contain, as well as how to access specific observation(s). As such they are the most flexible kind of data sources and will thus be at the heart of most of the subsequent sections. To start off, we will discuss what makes some type a data container and what that term entails.
Once we understand what data containers are and how they can be interacted with, we can introduce more interesting behaviour on top of them. The most enabling of them all is the idea of a data subset. A data subset is in essence just a lazy representation of a specific sequence of observations from a data container, the sequence itself being another data container. What that means and why that is useful will be discussed in detail in the following section.
By this point we know what data containers and data subsets are. In particular, we discussed how we can split our data container into disjoint subsets. We have even seen how we can use tuples to link multiple data container together on a per-observation level. While we mentioned that this is particularly useful for labeled data, we did not really elaborate on what that means. In order to change that, we will spend the next section solely on working with data containers that have targets. This will put us into the realm of supervised learning. We will see how we can work with labeled data containers and what special functionality is available for them.
Now that we have covered all the basics, we can start to discuss some of the more advanced topics. A particularly important aspect of modern Machine Learning is what is known as model selection. Most of the time, this boils down to choosing appropriate hyper-parameters for the model one is working with. To avoid subtle problems in this selection process, and to reduce variance of the performance estimates, it is quite common to employ some kind of repartitioning strategy on the training data. Of course, the partitioning itself is just one part of such a model selection process, since we still have to somehow compute and compare the performance. However, it is an important step that is needed to make the most of the available data. So important in fact, that we will spend a whole section on it.
A different kind of partitioning-need arises from the fact that the interesting data sets are increasing in size as the scientific community continues to improve the state-of-the-art in Machine Learning. While “too much” data is a nice problem to have, bigger data sets also pose additional challenges in terms of computing resources. Luckily, there are popular techniques in place to deal with such constraints in a surprisingly effective manner. For example, there are a lot of empirical results that demonstrate the efficiency of optimization techniques that continuously update on small subsets of the data. As such, it has become a de facto standard for many algorithms to iterate over a given dataset in mini-batches, or even just one observation at a time.
The way this package approaches the topic of data iteration is complex enough that it deserves two parts. In the first part we will introduce a few special data iterators, which we call data views, that will allow us to perform such iteration-pattern conveniently for data containers. In fact, they are more than “just” data iterators; they are proper vectors. As such, they also serve as a tool to “view” a data container from a specific aspect: As a sequence of observations, or a sequences of batches. Thus these views know how many observations they contain, and how to query specific parts of the data.
While these data views are also data iterators, the inverse is not true. In the following section we will introduce a number of data iterators, that don’t make any other promises than, well, iteration. As such, they may not know how many observations they can provide, nor have the means to access specific observations. Consequently, these data iterators are not data containers. We will see how that is useful, and also how some of them are actually created using a data container as input.