HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data
Authors: Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We highlight several examples of how low-dimensional projections may be used to understand the geometry of high-dimensional data in Figure 1. [...] The Hyper Tools toolbox (current version: 0.4.2) provides a powerful set of Python functions for projecting high dimensional data onto lower-dimensional spaces, aligning data of different types, and visualizing the results in publication-quality figures and movies. |
| Researcher Affiliation | Academia | Andrew C. Heusser EMAIL Kirsten Ziman EMAIL Lucy L. W. Owen EMAIL Jeremy R. Manning EMAIL Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA |
| Pseudocode | No | The paper provides examples of Python function calls (e.g., hyp.plot(...)) to demonstrate the toolbox's usage, but it does not contain structured pseudocode or algorithm blocks describing the underlying methods. |
| Open Source Code | Yes | Hyper Tools is open-source, installable from Git Hub or pip (pip install hypertools), and is distributed with the MIT License. |
| Open Datasets | Yes | Figure 1: Data visualization examples. [...] b. Topic modeling (Blei et al., 2003) of political Twitter data: ... (link to data). c. Changing temperatures across the Earth s surface from 1875 2013 (link to data). d. Brain/movie trajectories during movie viewing (link to data). |
| Dataset Splits | Yes | (Top left) Group-averaged trajectories of brain activity from the ventral visual cortex, split into two randomly-selected groups of subjects (group 1: n = 6, group 2: n = 5) watching Raiders of the Lost Ark (Haxby et al., 2011). |
| Hardware Specification | No | The paper describes a software toolbox and its functionalities but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running experiments or generating the results shown. |
| Software Dependencies | No | The toolbox depends on the following open-source software packages: Matplotlib (Hunter, 2007) for plotting functionality, Seaborn (Waskom et al., 2016) for plot styling, Scikit-learn (Pedregosa et al., 2011) for data analysis (dimensionality reduction, clustering, etc.), and PPCA for inferring missing data (Tipping and Bishop, 1999). |
| Experiment Setup | Yes | hyp.plot(list of arrays, reduce= TSNE , align= hyper , ndims=3) This example function call projects the high-dimensional data onto 3 dimensions using the t-SNE algorithm, aligns the data matrices in the given list of arrays into a common space using hyperalignment, and produces a 3D plot analogous to those shown in Figure 1. |