scikit-learn
OSQuery
scikit-learn | OSQuery | |
---|---|---|
81 | 44 | |
58,130 | 21,361 | |
0.5% | 0.4% | |
9.9 | 8.8 | |
5 days ago | 1 day ago | |
Python | C++ | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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
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.
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WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole.
OSQuery
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Ask HN: SQLite in Production?
Perhaps the OP means OsQuery: https://github.com/osquery/osquery
OsQuery is an SQLite extension consisting of hundreds of virtual tables
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Osquery: An sqlite3 virtual table exposing operating system data to SQL
There's at least one open data quality issue for `process_open_sockets` on macOS[1]. It's a few years old however and, if you aren't seeing that casting error, you probably aren't hitting it. But that's a good example of the kind of debt that's been built up over time.
(In terms of general purpose/flexible tooling, I'm not aware of a close replacement for osquery.)
[1]: https://github.com/osquery/osquery/issues/6319
- SQLite virtual table to query operating system data via SQL
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Show HN: Natural Language to SQL "Text-to-SQL" API by Dataherald
The largest we have successfully deployed is on the OSQuery schema https://osquery.io/ which is 277 tables and lots of business context (malwares, vulnerabilities, Windows registry keys, etc).
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Alternative to Endpoint Protector?
From a self hosted standpoint OSQuery or Wazuh are your best bets for monitoring USB devices. Windows makes blocking really challenging and I’m not aware of any “free” solutions that attempt it.
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Firewall rules beyond "deny incoming, enable only the ports that you need"
Configure auditd to monitor host activity: https://izyknows.medium.com/linux-auditd-for-threat-detection-d06c8b941505 or osquery: https://osquery.io/ (or similar software: filebeat for example).
- Craziest thing I ever used SQLite for: partial file deduplication
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Best Websites For Coders
OS Query : Easily ask questions about your Linux, Windows, and macOS infrastructure
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Tool that let you know see EXE file on multiple PC?
Osquery + Fleet. https://osquery.io/ https://fleetdm.com/, using the two allows you to build a query to answer what ever questions you (or an auditor) might have about your environment.
- Osquery: SQL powered operating system instrumentation
What are some alternatives?
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Wazuh - Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
Surprise - A Python scikit for building and analyzing recommender systems
OSSEC - OSSEC is an Open Source Host-based Intrusion Detection System that performs log analysis, file integrity checking, policy monitoring, rootkit detection, real-time alerting and active response.
Keras - Deep Learning for humans
falco - Cloud Native Runtime Security
tensorflow - An Open Source Machine Learning Framework for Everyone
lynis - Lynis - Security auditing tool for Linux, macOS, and UNIX-based systems. Assists with compliance testing (HIPAA/ISO27001/PCI DSS) and system hardening. Agentless, and installation optional.
gensim - Topic Modelling for Humans
Suricata - Suricata is a network Intrusion Detection System, Intrusion Prevention System and Network Security Monitoring engine developed by the OISF and the Suricata community.
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.
SaltStack - Software to automate the management and configuration of any infrastructure or application at scale. Get access to the Salt software package repository here: