Scikit-learn pipelines provide a really simple way to chain together the preprocessing steps with the model fitting stages in machine learning development. With pipelines, you can embed these steps so that in one line of code the model will perform all necessary preprocessing steps at the same time as either fitting the model or calling predict. There are many benefits to this besides reducing the lines of code in your project. Using the standard pipeline layouts means that it is very easy for a colleague, or your future self, to quickly understand your workflow. This in turns means that your work is more reproducible.
Ecopetrol, which owns the pipeline via its subsidiary Cenit, did not attribute the attack to a specific armed group. However, according to military sources, the pipeline has been attacked nearly 60 times this year by the National Liberation Army (ELN), the country's remaining active guerrilla group.
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.
U.S. President Donald Trump signs an executive order to advance construction of the Keystone XL pipeline at the White House in Washington, D.C. REUTERS/Kevin Lamarque President Donald Trump signed executive actions Tuesday allowing construction on the Keystone XL and Dakota Access oil pipelines to move forward. Trump said he plans to "renegotiate some of the terms" of the controversial pipelines but did not answer further questions on how he plans to advance the project, according to the Associated Press. President Barack Obama halted work on Keystone XL, which would run across the Canada U.S. border and, therefore, required presidential approval, in 2015 after an outcry from environmental groups. Those opposed to the project said it would boost extraction from Canada's oil sands, a process that emits 14 percent more greenhouse gases than other forms of oil production. They also argued constructing a new oil pipeline would diminish America's role as a global leader of climate change.
MongoDB is best known for creating a document database that Web and mobile developers love to use. But developers and analysts alike may be interested in a little-known MongoDB feature called the aggregation pipeline. What's more, the aggregation pipeline just got easier to use with MongoDB 4.0. The aggregation pipeline presents a powerful abstraction for working with and analyzing data stored in the MongoDB database. According to MongoDB CTO and co-founder Eliot Horowitz, the composability of the aggregation pipeline is one of the keys to its power.