Fast Machine Learning in Data Science with a Comprehensive Data Summarization

Machine learning algorithms must be able to handle large volume in big data. Nowadays, data science languages such as Python and R, are widely popular to compute machine learning models. Unfortunately, model computation can be slow, especially when the data set does not fit in the main memory or it needs to be iteratively analyzed. With these motivations in mind, we present theory and algorithms to produce a multidimensional data set summary. We show our data summaries preserves essential statistical properties of the data set and it can be computed with an accelerated Gramian matrix multiplication. That is, our data summaries represents a lossless compression and we accelerate the expensive Gramian matrix multiplication in Python with C++ code. Our solution also works for a subset of the original data set obtained by variable selection without much loss on accuracy and without recomputing all intermediate matrices. We also consider parallel processing aspects leveraging our recently introduced low-cost parallel architecture. Our experimental evaluation shows that our Gramian matrix multiplication mechanism is superior to Python and it can work beyond RAM limitations. On the other hand, our computation of the machine learning model is competitive with the Python and R on a single machine, but it outperforms Spark in parallel machines.