Visualizing Correlations Among Dow 30 Stocks Via NetworkX

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Using daily adjusted close data from 20201118 to 20201218 for Dow 30 stocks, we compute correlation coefficients, apply a threshold of 0.8 to find similar stocks, and produce two types of graphs with NetworkX.

Paper: XPySom: High-Performance Self-Organizing Maps – Mancini et al 2020

In this paper, we introduce XPySom, a new open-source Python implementation of the well-known Self-Organizing Maps (SOM) technique. It is designed to achieve high performance on a single node, exploiting widely available Python libraries for vector processing on multi-core CPUs and GP-GPUs. We present results from an extensive experimental evaluation of XPySom in comparison to widely used open-source SOM implementations, showing that it outperforms the other available alternatives.

Finding Similar Stocks Via Fast GPU Based Nearest Neighbors with Faiss

There are many ways to find stocks with similar behavior based on how one defines similarity and the data used. In this article we use a 12 period channel where, for each period, we have (current adjusted close price – minimum value)/(maximum value – minimum value). Maximum and minimum values are computed for the adjusted close prices for the past 21 trading days (representing a trading month), then 42 days, …, 252 days. Our channel will then be normalized so that all values are in the interval [0, 1]. We use the Euclidean distance measure as our similarity.

After transforming our data into normalized channels, our task then becomes finding the K nearest neighbors. We will use the Faiss Python library.

Fast GPU Based Nearest Neighbors with Faiss

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research.

Hyperparameter Search With GPyOpt: Part 3 – Keras (CNN) Classification and Ensembling

GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield. It is based on GPy, a Python framework for Gaussian process modelling.

In this article, we demonstrate how to use this package to perform hyperparameter search for a classification problem with Keras.

Paper: A Deep Learning Approach to Antibiotic Discovery – Stokes et al 2020

This is a very interesting paper in which the authors use a message passing neural network on a carefully selected data set to predict antibacterial activity against E. coli. Then they apply their model to other data sets, while also prioritizing molecules that are different (via minimum Tanimoto similarity) from existing antibiotics, to find candidates for new antibiotics.

Paper: DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures – O’Boyle and Dalke 2018

SMILES (Simplified Molecular Input Line Entry System) representations of molecules have found many uses in machine learning algorithms, especially those derived from natural language processing techniques. However, they were not designed for machine learning and thus suffer from various syntax issues that can hamper machine learning methods, especially generative methods. DeepSMILES is a modification of SMILES explicitly designed to address these issues.

Paper: Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space – Nigam et al 2020

In this paper, the authors use a genetic algorithm operating on the SELFIES (SELF-referencIng Embedded Strings) representation of molecules to explore the vast space of small molecules. A neural network is used to guide the exploration process. Also, fitness functions are constructed to generate molecules with specific properties.

At no cost to you, Machine Learning Applied earns a commission from qualified purchases when you click on the links below.

Pin It on Pinterest