Search Results for "python package" - Page 8

Showing 1011 open source projects for "python package"

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  • 1
    Causal ML

    Causal ML

    Uplift modeling and causal inference with machine learning algorithms

    Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows users to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data. Essentially, it estimates the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions...
    Downloads: 1 This Week
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  • 2
    PackageDev

    PackageDev

    Tools to ease the creation of snippets, syntax definitions, etc.

    PackageDev provides syntax highlighting and other helpful utility for Sublime Text resource files. Resource files are ways of configuring the Sublime Text text editor to various extends, including but not limited to: custom syntax definitions, context menus (and the main menu), and key bindings. Thus, this package is ideal for package developers, but even normal users of Sublime Text who want to configure it to their liking should find it very useful.
    Downloads: 1 This Week
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  • 3
    ClearML

    ClearML

    Streamline your ML workflow

    ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments...
    Downloads: 2 This Week
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  • 4
    GluonTS

    GluonTS

    Probabilistic time series modeling in Python

    GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models. GluonTS requires Python 3.6 or newer, and the easiest way to install it is via pip. We train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts...
    Downloads: 1 This Week
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  • 5
    Pipenv

    Pipenv

    Python Development Workflow for Humans

    Pipenv is a package manager that brings all the best of the packaging world together to the Python world. It's got everything you could need: bundler, composer, npm, cargo, yarn and more all in one convenient package so you can easily set up a working environment. Pipenv creates and manages a virtualenv automatically, and can add or remove packages from your Pipfile as you install/uninstall packages. It also produces the Pipfile.lock, which is essential for deterministic builds. Pipenv...
    Downloads: 1 This Week
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  • 6
    iCn3D

    iCn3D

    Web-based protein structure viewer and analysis tool

    "I see in 3D" (iCn3D) Structure Viewer is not only a web-based 3D viewer, but also a structure analysis tool interactively or in the batch mode using NodeJS scripts based on the npm package icn3d. iCn3D synchronizes the display of 3D structure, 2D interaction, and 1D sequences and annotations. Users' custom displays can be saved in a short URL or a PNG image. The complete package of iCn3D including Three.js and jQuery is in the directory "dist" after you get the source code with the "Code...
    Downloads: 2 This Week
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  • 7
    rmarkdown

    rmarkdown

    Dynamic Documents for R

    R Markdown is an R package for creating dynamic, reproducible documents that combine code (R, Python, SQL, etc.), results (figures, tables), and narrative text. Built on Knitr and Pandoc, it supports generating HTML, PDF, Word, slideshows, dashboards, and more. It’s widely used in data science and reproducible reporting workflows.
    Downloads: 2 This Week
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  • 8
    Graphs.jl

    Graphs.jl

    An optimized graphs package for the Julia programming language

    The goal of Graphs.jl is to offer a performant platform for network and graph analysis in Julia, following the example of libraries such as NetworkX in Python. Offers a set of simple, concrete graph implementations – SimpleGraph (for undirected graphs) and SimpleDiGraph (for directed graphs), an API for the development of more sophisticated graph implementations under the AbstractGraph type, and a large collection of graph algorithms with the same requirements as this API.
    Downloads: 2 This Week
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  • 9
    finvizfinance

    finvizfinance

    Finviz analysis python library

    finvizfinance is a package that collects financial information from FinViz website. Stock charts, fundamental & technical information, insider information and stock news. Forex charts and performance. Crypto charts and performance. Screener and Group provide data frames for comparing stocks according to different filters and trading signals. Getting information (fundament, description, outer rating, stock news, inside trader) of an individual stock.
    Downloads: 1 This Week
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  • 10
    Django friendly finite state machine

    Django friendly finite state machine

    Django friendly finite state machine support

    ... and template tags to integrate Django-fsm state transitions into the Django admin. FSM really helps to structure the code, especially when a new developer comes to the project. FSM is most effective when you use it for some sequential steps. Transition logging support could be achieved with help of django-fsm-log package.
    Downloads: 2 This Week
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  • 11
    tsai

    tsai

    Time series Timeseries Deep Learning Machine Learning Pytorch fastai

    tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, and imputation. Starting with tsai 0.3.0 tsai will only install hard dependencies. Other soft dependencies (which are only required for selected tasks) will not be installed by default (this is the recommended approach. If you require any of the dependencies that is not installed, tsai will ask you to install...
    Downloads: 2 This Week
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  • 12
    tsfresh

    tsfresh

    Automatic extraction of relevant features from time series

    tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python...
    Downloads: 2 This Week
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  • 13
    jsonschema

    jsonschema

    An implementation of the JSON Schema specification for Python

    jsonschema is an implementation of the JSON Schema specification for Python. Full support for Draft 2020-12, Draft 2019-09, Draft 7, Draft 6, Draft 4 and Draft 3. Lazy validation that can iteratively report all validation errors. Programmatic querying of which properties or items failed validation.
    Downloads: 1 This Week
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  • 14
    pyenv-virtualenv

    pyenv-virtualenv

    A pyenv plugin to manage virtualenv

    pyenv-virtualenv is a pyenv plugin that provides features to manage virtual environments and conda environments for Python on UNIX-like systems. If you installed pyenv into a non-standard directory, make sure that you clone this repo into the 'plugins' directory of wherever you installed into. macOS users can install pyenv-virtualenv with the Homebrew package manager. This will give you access to the pyenv-virtualenv command. If you have pyenv installed, you will also be able to use the pyenv...
    Downloads: 2 This Week
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  • 15
    PyCall.jl

    PyCall.jl

    Package to call Python functions from the Julia language

    Package to call Python functions from the Julia language. This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them.
    Downloads: 0 This Week
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  • 16
    Guardrails

    Guardrails

    Adding guardrails to large language models

    Guardrails is a Python package that lets a user add structure, type and quality guarantees to the outputs of large language models (LLMs). At the heart of Guardrails is the rail spec. rail is intended to be a language-agnostic, human-readable format for specifying structure and type information, validators and corrective actions over LLM outputs. We create a RAIL spec to describe the expected structure and types of the LLM output, the quality criteria for the output to be considered valid...
    Downloads: 1 This Week
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  • 17
    aisuite

    aisuite

    Simple, unified interface to multiple Generative AI providers

    Simple, unified interface to multiple Generative AI providers. aisuite makes it easy for developers to use multiple LLM through a standardized interface. Using an interface similar to OpenAI's, aisuite makes it easy to interact with the most popular LLMs and compare the results. It is a thin wrapper around Python client libraries and allows creators to seamlessly swap out and test responses from different LLM providers without changing their code. Today, the library is primarily focused on chat...
    Downloads: 1 This Week
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  • 18
    latexcv

    latexcv

    A collection of cv and resume templates written in LaTeX

    A collection of user-friendly LaTeX CV and résumé templates (packaged within the R Markdown vitae ecosystem), offering simple themes and templates for creating professional CVs without heavy TeX coding. Supports multiple display themes such as classic, modern, sidebar layouts.
    Downloads: 1 This Week
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  • 19
    HDBSCAN

    HDBSCAN

    A high performance implementation of HDBSCAN clustering

    HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster...
    Downloads: 1 This Week
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  • 20
    classic.tplx

    classic.tplx

    A more accurate representation of jupyter notebooks

    A more accurate representation of Jupyter notebooks when converting to pdfs. This template was designed to make converted Jupyter notebooks look (almost) identical to the actual notebook. If something doesn't exist in the original notebook then it doesn't belong in the conversion. As of nbconvert 5.5.0, the majority of these improvements have been merged into nbconvert's default template. Version 3.x of this package will continue to support nbconvert 5.5.0 and lower, whereas in the future...
    Downloads: 1 This Week
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  • 21
    spacy-llm

    spacy-llm

    Integrating LLMs into structured NLP pipelines

    Large Language Models (LLMs) feature powerful natural language understanding capabilities. With only a few (and sometimes no) examples, an LLM can be prompted to perform custom NLP tasks such as text categorization, named entity recognition, coreference resolution, information extraction and more. This package integrates Large Language Models (LLMs) into spaCy, featuring a modular system for fast prototyping and prompting, and turning unstructured responses into robust outputs for various NLP...
    Downloads: 1 This Week
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  • 22
    UForm

    UForm

    Multi-Modal Neural Networks for Semantic Search, based on Mid-Fusion

    UForm is a Multi-Modal Modal Inference package, designed to encode Multi-Lingual Texts, Images, and, soon, Audio, Video, and Documents, into a shared vector space! It comes with a set of homonymous pre-trained networks available on HuggingFace portal and extends the transfromers package to support Mid-fusion Models. Late-fusion models encode each modality independently, but into one shared vector space. Due to independent encoding late-fusion models are good at capturing coarse-grained features...
    Downloads: 1 This Week
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  • 23
    OpenWPM

    OpenWPM

    A web privacy measurement framework

    ... officially support other platforms, conda is a cross-platform utility and the install script can be expected to work on OSX and other Linux distributions. OpenWPM does not support windows. The main pre-requisite for OpenWPM is conda, a cross-platform package management tool.
    Downloads: 1 This Week
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  • 24
    pep484 stubs for Django

    pep484 stubs for Django

    PEP-484 stubs for Django

    This package contains type stubs and a custom mypy plugin to provide more precise static types and type inference for Django framework. Django uses some Python "magic" that makes having precise types for some code patterns problematic. This is why we need this project. The final goal is to be able to get precise types for the most common patterns. We are independent from Django at the moment. There's a proposal to merge our project into the Django itself. You can show your support by liking...
    Downloads: 1 This Week
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  • 25
    PyTorch Geometric Temporal

    PyTorch Geometric Temporal

    Spatiotemporal Signal Processing with Neural Machine Learning Models

    ... management domains. Finally, you can also create your own datasets. The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy - see the accompanying tutorial. Head over to our documentation to find out more about installation, creation of datasets and a full list of implemented methods and available datasets.
    Downloads: 1 This Week
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