greppo VS scikit-learn

Hello HN!

We’re building Greppo (https://greppo.io/) for Data Scientists looking to prototype interactive Geospatial web-applications with Python. The library is a toolkit to quickly integrate data, algorithms, APIs and visualizations without any frontend code.

Why did we build Greppo? We wanted to build a web-app which could provide an interface to interact with data and ML code in Python. But we discovered that existing frameworks provide only visualizations or were tedious to set up. So we built Greppo as a quick and easy framework to build an interactive app that prioritizes Geospatial visualizations.

Here’s an example of an app that we built in a few minutes, and deployed with AWS Lightsail as a docker container service, https://greppo-demo-sfo.sm48tr5o7beei.us-west-2.cs.amazonlig... . We visualize the public amenities (toilets, atm etc) in San Francisco, and display the count within a selected area. You can interact with the app by modifying the pre-selected area loaded by default on the app or adding a new one. On update, the bar chart should reflect the new counts across the selected areas. This Greppo app could be used in a public policy setting to make decisions on what areas are underserved and why.

To get started simply install with pip, `pip install greppo`.

We are actively developing Greppo, and would love to hear your feedback! The github repo can be found at https://github.com/greppo-io/greppo

Feel free to reach out to us at [email protected] . We’re also on twitter, https://twitter.com/redbarchetta21

Online Courses: Coursera: "Machine Learning" by Andrew Ng edX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!

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).

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.

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!

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?

It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says

Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...

[1] https://en.wikipedia.org/wiki/Platt_scaling

[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...

Scikit-learn Documentation

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.