In addition to bringing efficient array computing and standard mathematical tools to Python, the NumPy/SciPy libraries provide an ecosystem where multiple libraries can coexist and interact. This talk describes a success story where we integrate several libraries, developed by different groups, to solve our research problems. A brief description of our research and how we use these components follows.
Our research focuses on using Reinforcement Learning (RL) to gather information in domains described by an underlying linked dataset. For instance, we are interested in problems such as the following: given a Wikipedia article as a seed, finding other articles that are interesting relative to the starting point. Of particular interest is to find articles that are more than one-click away from the seed, since these articles are in general harder to find by a human.
In addition to the staples of scientific Python computing NumPy, SciPy, Matplotlib, and IPython, we use the libraries RL-Glue/RL-Library, NetworkX, Gensim, and scikit-learn.
Reinforcement Learning considers the interaction between a given environment and an agent. The objective is to design an agent able to learn a policy that allows it to maximize its total expected reward. We use the RL-Glue/RL-Library libraries for our RL experiments. This libraries provide the infrastructure to connect an environment and an agent, each one described by an independent Python program.
We represent the linked datasets we work with as graphs. For this we use NetworkX, which provides data structures to efficiently represent graphs together with implementations of many classic graph algorithms. We use NetworkX graphs to describe the environments implemented in RL-Glue/RL- Library. We also use these graphs to create, analyze and visualize graphs built from unstructured data.
One of the contributions of our research is the idea of representing the items in the datasets as vectors belonging to a linear space. To this end, we build a Latent Semantic Analysis (LSA) model to project documents onto a vector space. This allows us, in addition to being able to compute similarities between documents, to leverage a variety of RL techniques that require a vector representation. We use the Gensim library to build the LSA model. This library provides all the machinery to build, among other options, the LSA model. One place where Gensim shines is in its capability to handle big data sets, like the entire Wikipedia, that do not fit in memory. We also combine the vector representation of the items as property of the NetworkX nodes.
Finally, we also use the manifold learning capabilities of sckit-learn, like the ISOMAP algorithm, to perform some exploratory data analysis. By reducing the dimensionality of the LSA vectors obtained using Gensim from 400 to 3, we are able to visualize the relative position of the vectors together with their connections.
In summary, this talk shows, by combining a variety of libraries to solve our research problems, that the NumPy/SciPy ecosystem has become the lingua-franca of scientific Python computing.