scikit-learn
Robot Framework
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scikit-learn | Robot Framework | |
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81 | 52 | |
57,985 | 9,011 | |
0.9% | 2.1% | |
9.9 | 9.7 | |
4 days ago | 6 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
scikit-learn
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AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:
- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...
- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.
There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).
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Polars
sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.
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[D] Major bug in Scikit-Learn's implementation of F-1 score
Here is the (as of posting time) still un-merged fix to the bug in question. Note that this bug also affects sklearn.metrics.classification_report. I think you you can temporarily get around this by using sklearn version 1.2.2. Anyway, if you use Scikit-Learn's metrics for evaluation, go double check your scores!
Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!
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Ask HN: Learning new coding patterns – how to start?
I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.
I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.
You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.
As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?
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How to Build and Deploy a Machine Learning model using Docker
Scikit-learn Documentation
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Link Prediction With node2vec in Physics Collaboration Network
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.
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List of AI-Models
Click to Learn more...
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PSA: You don't need fancy stuff to do good work.
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive documentation and community support, making it easy to learn and apply new techniques without needing specialized training or expensive software licenses.
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
1. Scikit-learn Scikit-learn is a must-know Python library for any data scientist. It offers a wide range of machine learning algorithms, data preprocessing tools, and model evaluation metrics that are easy to use and highly efficient. Whether you’re working on regression, classification, or clustering tasks, Scikit-learn has got you covered.
Robot Framework
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Deep Dive into API Testing - An introduction to RESTful APIs
Robot Framework
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Robot Framework VS vedro - a user suggested alternative
2 projects | 16 Jul 2023
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How is Python used in test automation in embedded systems?
In the industry I've seen the framework "Robot framework" https://robotframework.org/ used a lot for test automation.
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Gherkin and Robot Framework
Greetings! They say all good things must come to an end, and with this post, so it is with my series of posts covering Robot Framework.
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Arista Network Validation
Hi , Arista Network Validation is a wrapper on top ofrobot framework. it resides on the software downloads on the extension parts. Is this the same with https://github.com/aristanetworks/robotframework-aristalibrary or do I need a contract to download this ?
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Web Testing With Robot Framework
This is part 3 in a series of blog posts meant to get you started with automated testing using Robot Framework. If you haven't checked out the other posts in the series, please do. This post builds on what I've discussed previously.
- Robot Framework, generic open source automation framework
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Open source sustainment and the future of Gitea
The development of Robot Framework* was originally paid by Nokia, but then things happened and Nokia decided not to continue doing that anymore.
The main developer of Robot Framework and few companies using RF heavily understood that something had to be done, so Robot Framework Foundation* was formed. RF Foundation has membership fees, it arranges RF conferences etc. which allows RF Foundation to pay for the development of RF.
I think that is a really good way to fund OSS development. Those companies which benefit the most from it, pay membership fees and gets to vote on the direction of the product.
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When webscraping dating websites how do you access chat replies?
You might want to take a look at https://robotframework.org/ and more specifically https://rpaframework.org/ which you can access from python (see eg. https://robocorp.com/docs/development-guide/python/python-robot) but also allows you to describe crawlers using their own simpler language for which RPA Framework provides, among other things, a browser library that wraps Selenium intended for use in scraping.
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Can someone recommend technologies for testing automation for API application?
If you need automated end to end treats(e.g from outside your API gateway or similar) you can try something like this: https://robotframework.org/
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
Behave - BDD, Python style.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Surprise - A Python scikit for building and analyzing recommender systems
Keras - Deep Learning for humans
tensorflow - An Open Source Machine Learning Framework for Everyone
gensim - Topic Modelling for Humans
Selenium Wire - Extends Selenium's Python bindings to give you the ability to inspect requests made by the browser.
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
PyBrain
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
Slash - The Slash testing infrastructure