ICP In Practice

This post explores the iterative constrained pathways rule ensemble (ICPRE) method introduced in an earlier post using the Titanic dataset popularized by Kaggle [1]. The purpose of the text is to introduce the features and explore the behavior of the library.

Some of the code snippets in this post are shortened for brevity sake. To obtain the full source and data, please see the ICPExamples GitHub page [2].
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The Iterative Constrained Pathways Optimizer

Many optimization methods seek an optimal parameter set with regard to error or likelihood. Such a solution is most desirable in many regards. However, when the broader context of a problem is included, the indisputable superiority of the optimum frequently becomes less clear. This context often includes other guidelines and restrictions that may limit the usefulness of solutions lacking certain properties. Unfortunately, typical loss criteria can rarely take these into account.

This blog post presents a method that abandons the quest for optimality and instead focuses on better satisfying the broader context of a problem. It describes a method that does not attempt to find the minimum, but instead simply tries to get closer to it while respecting imposed constraints. This blog post describes the iterative constrained pathways optimizer.
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Simulated Genetic Drift in Populations

This video shows a simulation of genetic drift in a synthetically generated population as a result of sexual reproduction. A fixed population size is divided into different distinct sub-populations with differing allele frequencies. Starting with an initial population, 80 simulated epochs are passed in which each population member is replaced via sexual reproduction from two randomly selected parents. Pairs of parents are chosen such that the probability they come from the same sub-population is higher than the probability that they from come different sub-populations.

Figure 1: Simulated Genetic Drift in Populations

In the animation, the high-dimensional allele features for each population member are represented in two dimensions using PCA. The size and color of each marker encodes information about sub-population makeup and may be used to help distinguish highly mixed samples.

Although the data is entirely synthetic, it demonstrates the middling affect that cross-breeding has on the genetic makeup of the overall population. As the number of sub-populations grows and the overall population size remains fixed, the impact of cross-breeding is intensified and the population as a whole converges more rapidly.

CoV-2 Genome Explorer

At the time of this writing, NCBI Virus has over 40,000 complete CoV-2 sequences available. I made an interactive webpage to help visualize a random subset of these genomes using principal component analysis.

Figure 1: CoV-2 Explorer Interface

The x, y, size, and color axes respectively represent the first four principal axes. The plot markers identify viruses with unique nucleotide sequences in the GU280_gp02 (spike glycoprotein) gene region. The range sliders can be used to visualize shift in the viral genomes over time. The bar chart in the bottom right counts the frequency of SNP mutations in the sequences present in the specified date range.

Ancestry Determination Part 3: openSNP Data Evaluation

The website openSNP allows users to share and discuss genetic information [1]. The purpose of the site is perhaps best summarized by its bio on Twitter: “crowdsourcing genome wide association studies” [2]. This post uses the machine learning and statistical techniques developed in previous blog entries to analyze the ancestry of openSNP members who have genotyping data associated to their accounts. By comparing the derived results with the self-reported answers from openSNP users, the accuracy of these methods on real-world data is assessed.
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Analysis of the 2016 US General Election

Counties within the United States vary substantially along a number of demographic and socioeconomic axes. These factors explain much of the variation observed throughout the US with respect to election and polling trends. This post attempts to untangle some of this complexity to give insight into the factors that influence voter behavior and other broader national trends.
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