GraKeL: A Graph Kernel Library in Python Machine Learning

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and is build on top of scikit-learn. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at:

How to Learn Pandas – Dunder Data – Medium


In this post, I will outline a strategy to'learn pandas'. For those who are unaware, pandas is the most popular library in the scientific Python ecosystem for doing data analysis.

Positive Definite Kernels in Machine Learning Machine Learning

This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions $\{k(x,\cdot),x\in\mathcal{X}\}$ associated with a kernel $k$ defined on a space $\mathcal{X}$. We discuss at length the construction of kernel functions that take advantage of well-known statistical models. We provide an overview of numerous data-analysis methods which take advantage of reproducing kernel Hilbert spaces and discuss the idea of combining several kernels to improve the performance on certain tasks. We also provide a short cookbook of different kernels which are particularly useful for certain data-types such as images, graphs or speech segments.

Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling Machine Learning

Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis. The core of the library is an optimization module providing model computational classes, solvers and proximal operators for regularization. tick relies on a C++ implementation and state-of-the-art optimization algorithms to provide very fast computations in a single node multi-core setting. Source code and documentation can be downloaded from

A Randomized String Kernel and Its Application to RNA Interference

AAAI Conferences

Since they better capture complex traits in the sequences, string kernels often achieve better prediction performance. RNA interference is an important biological mechanism with many therapeutical applications, where strings can be used to represent target messenger RNAs and initiating short RNAs and string kernels can be applied for learning and prediction. However, existing string kernels are not particularly developed for RNA applications. Moreover, most existing string kernels are n-gram based and suffer from high dimensionality and inability of preserving subsequence orderings. We propose a randomized string kernel for use with support vector regression with a purpose of better predicting silencing efficacy scores for the candidate sequences and eventually improving the efficiency of biological experiments. We show the positive definiteness of this kernel and give an analysis of randomization error rates. Empirical results on biological data demonstrate that the proposed kernel performed better than existing string kernels and achieved significant improvements over kernels computed from numerical descriptors extracted according to structural and thermodynamic rules. In addition, it is computationally more efficient.