gqlalchemy
scikit-learn
gqlalchemy | scikit-learn | |
---|---|---|
10 | 82 | |
207 | 58,200 | |
2.4% | 0.6% | |
7.1 | 9.9 | |
about 2 months ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
gqlalchemy
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Link Prediction With node2vec in Physics Collaboration Network
As already mentioned, link prediction refers to the task of predicting missing links or links that are likely to occur in the future. In this tutorial, we will make use the of MAGE spell called node2vec. Also, we will use Memgraph to store data, and gqlalchemy to connect from a Python application. The dataset will be similar to the one used in this paper: Graph Embedding Techniques, Applications, and Performance: A Survey.
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Importing Table Data Into a Graph Database With GQLAlchemy
For any other service provider, it is possible to implement your custom importer class, here's how. Don't forget that GQLAlchemy is an open source project, so you can submit your extended functionality on our GitHub repository.
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How to Become a GQLAlchemist?
If you think there is something crucial that is missing or are even willing the try out your expertise in Python and graphs, check out our GitHub repository and feel free to contribute.
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Monitoring a Dynamic Contact Network With Online Community Detection
gqlalchemy – a Python driver and object graph mapper (OGM)
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Neo4j vs Memgraph - How to choose a graph database?
There is a broad number of drivers in many different programming languages available for both solutions. While Memgraph only maintains a few in-house drivers that it develops and supports (C, C++, Python, Rust), most Neo4j drivers can also be used with Memgraph. This is due to the fact that both solutions use the Bolt protocol, labeled property graph model and Cypher query language.
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NetworkX Developers, Say Farewell to the Boilerplate Code
Memgraph natively has several methods of data import - import from files, MySQL or PostgreSQL, and data streams. Memgraph is also highly extendable, and with the help of its Python client, GQLAlchemy, you can import data from almost anywhere.
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Retrieve graph data with Python instead of writing Cypher queries
Source code for GQLAlchemy is available at GitHub repo.
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[D] Seeking Advice - For graph ML, Neo4j or nah?
I think building your graph database/structure can be quite an engineering and time-consuming challenge, as you mentioned, which I would personally avoid. I believe there are some solutions out there that may help you. There is one open source solution for the requirements and concerns you are mentioning. It checks out most of the things you need, functionality, efficiency, and custom low-level optimizations and it is not bulky as the Neo4j Java backend. In essence, we have built Memgraph an in-memory graph database written in C++. The distinctive key feature of DB is that all the data is stored in RAM for fast queries. There is some cool stuff with ML for graphs. Take a look at this blog post about node embedding and recommendation engines, it is native integration with Python and uses PyTorch. There is also the MAGE library for graph algorithms and ML, it is also open-sourced, which is great news for customization and expansions. I share your thoughts on OpenCypher, as being an issue. Memgraph has an object graph mapper (similar to ORM), called GQLAlchemy, and is in Python. There is also a learning curve, but not a different new skill as Cypher. The good thing is allowed various features for graphs manipulation via Python. There are also some other solutions such TigerGraph, Nebula, etc. But I am not very familiar with them. Feel free to explore. I hope this helps! 😁
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Twitch Streaming Graph Analysis - Part 3
Using gqlalchemy we are trying to connect to Memgraph, just like we have done before in our backend.
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Twitch Streaming Graph Analysis - Part 1
As expected, Flask is there, so it will be installed in our container. Next, we have pymgclient, Memgraph database adapter for Python language on top of which gqlalchemy is built. We will connect to the database with gqlalchemy and it will assist us in writing and running queries on Memgraph.
scikit-learn
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How to Build a Logistic Regression Model: A Spam-filter Tutorial
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!
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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
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!
- Contraction Clustering (RASTER): A fast clustering algorithm
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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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Transformers as Support Vector Machines
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...
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How to Build and Deploy a Machine Learning model using Docker
Scikit-learn Documentation
- Planning to get a laptop for ML/DL, is this good enough at the price point or are there better options at/below this price point?
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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.
What are some alternatives?
pymgclient - Python Memgraph Client
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
mgclient - C/C++ Memgraph Client
Surprise - A Python scikit for building and analyzing recommender systems
Memgraph - Open-source graph database, tuned for dynamic analytics environments. Easy to adopt, scale and own.
Keras - Deep Learning for humans
graphtage - A semantic diff utility and library for tree-like files such as JSON, JSON5, XML, HTML, YAML, and CSV.
tensorflow - An Open Source Machine Learning Framework for Everyone
twitch-analytics-demo - Visualization of Twitch analytics.
gensim - Topic Modelling for Humans
cugraph - cuGraph - RAPIDS Graph Analytics Library
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.