Transforms don't execute until the consumer pulls. There's no eager evaluation, no hidden buffering. Data flows on-demand from source, through transforms, to the consumer. If you stop iterating, processing stops.
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At first glance, the benchmarks and their construction looked good (i.e. no cheating) and are much faster than working with UMAP in Python. To further test, I asked the agents to implement additional different useful machine learning algorithms such as HDBSCAN as individual projects, with each repo starting with this 8 prompt plan in sequence:
Source: Computational Materials Science, Volume 267
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