Showing 4 open source projects for "quality"

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    Deequ

    Deequ

    Deequ is a library built on top of Apache Spark

    ...It also includes a little domain-specific language called DQDL (Data Quality Definition Language) which allows declarative specification of quality rules. Users typically run Deequ before feeding data downstream (to ML pipelines, analytics, or production systems), enabling early detection and isolation of data errors. There is also a Python wrapper, PyDeequ, for users who prefer working from Python environments.
    Downloads: 4 This Week
    Last Update:
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  • 2
    Cats Effect

    Cats Effect

    The pure asynchronous runtime for Scala

    Cats-Effect is a high-quality functional programming library for Scala that provides a principled way to represent and manage side effects, particularly asynchronous and concurrent computations. It is part of the broader Typelevel ecosystem and builds on the abstractions from Cats (such as Functor, Monad, etc.). The core abstraction is the IO type (or effect types more generally), which encodes effectful computations in a pure, referentially transparent way.
    Downloads: 9 This Week
    Last Update:
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  • 3
    Monix

    Monix

    Asynchronous, Reactive Programming for Scala and Scala.js

    Monix is a high-performance, reactive, and asynchronous programming library for Scala and Scala.js. Built as a Typelevel project, it provides advanced abstractions like Task, Observable, Iterant, and Coeval, enabling compositional, back-pressure‑aware event-driven systems that integrate cleanly with Cats Effect and Reactive Streams.
    Downloads: 7 This Week
    Last Update:
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  • 4
    node2vec

    node2vec

    Learn continuous vector embeddings for nodes in a graph using biased R

    ...The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible.
    Downloads: 5 This Week
    Last Update:
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