grakn VS scikit-learn

TypeDB : A Strongly-typed Database

How does it compare to, say grakn (renamed https://vaticle.com/, I think?), or draph (https://dgraph.io/), or Ontotext's GraphDB (https://www.ontotext.com/products/graphdb/), or Datomic?

Edge database looks pretty freakin awesome. It basically seems to re-write SQL and Graph databases together to create some new-ish programming language. It takes care of all the problems GraphQL has, and seems to be built separately but on top of postgres. It is really something unique, beautiful, and powerful. They don't have a security layer yet or a cloud hosting environment, but both are in the works. However, postgres still suffers from the scalable problems we all know. If you like unique fetching and strong typing, also check out TypeDB. It doesn't make its own list number because there is not cloud version, middleware, etc. However, worth checking out.

If you find yourself limited by triplestores, there's also a new growing area of development in knowledge engines, which allow edges-of-edges, entailed relations, hypergraph relations, and more of the power you'd get from full logic programming. TypeDB (recently renamed from Grakn) is an example of that type of database.

In the story to follow, David presents how his team at Roche was able to identify potential novel targets that were not identified by Open Targets as highly ranked. This was made possible with TypeDB, which his team used to store the relevant data and then find underlying biological evidence for those new targets.

{ stdenv, lib, openjdk,typedbHome ? "~/.typedb_home", fetchzip}: let typedbVersion = "2.1.1"; typedbDirLinux = "typedb-all-linux-${typedbVersion}"; typedbDirMac = "typedb-all-mac-${typedbVersion}"; typedbDirWindows = "typedb-all-windows-${typedbVersion}"; typedbDir = if stdenv.hostPlatform.isWindows then typedbDirWindows else if stdenv.isDarwin then typedbDirMac else typedbDirLinux; linuxSrc = builtins.fetchTarball { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-linux-2.1.1.tar.gz"; sha256 = "15nwm2dr68p67c2xcqigs66gd679j1zr72gqv7qxgvflwyvyz8fb"; }; windowsSrc = fetchzip { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-windows-2.1.1.zip"; sha256 = "0vd66gfshkg697z07nhy957mwqzlli4r4pmn67hx58n9mkg024kq"; }; macSrc = fetchzip { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-mac-2.1.1.zip"; sha256 = "16hlfy6kh2rnvcralz206q13mghb0rv8wazpg6q3h324p5rdys54"; }; srcFolder = if stdenv.hostPlatform.isWindows then windowsSrc else if stdenv.isDarwin then macSrc else linuxSrc ; javaPatch = '' 20c20 < JAVA_BIN=java --- > JAVA_BIN=${openjdk}/bin/java ''; in stdenv.mkDerivation rec { pname = "typedb"; version = typedbVersion; src = srcFolder; phases = [ "installPhase" ]; buildDepends = [ openjdk ]; installPhase = '' echo "here" # added for debugging ls -lah # " echo "--" # " #patch before install echo "${javaPatch}" > typedb_java.patch patch ./${typedbDir}/typedb typedb_java.patch mkdir $out cp -r ./${typedbDir} $out # add a wrapper script to $out that will move typedb to $typedb # this is necessary because typedb needs a writable environment echo " # on the first start copy everything to typedbHome if [ ! -f ${typedbHome}/typedb ]; then mkdir -p ${typedbHome}; cp -r $out/${typedbDir}/* ${typedbHome}; # correct permissions so that typedb and the user can write there chmod -R u+rw ${typedbHome} chmod u+x ${typedbHome}/typedb fi; ${typedbHome}/typedb \$@; " > $out/typedb chmod +x $out/typedb ''; doCheck = true; meta = with lib; { description = "TypeDB is a distributed knowledge graph: a logical database to organise large and complex networks of data as one body of knowledge."; longDescription = '' TypeDB is a distributed knowledge graph: a logical database to organise large and complex networks of data as one body of knowledge. TypeDB provides the knowledge engineering tools for developers to easily leverage the power of Knowledge Representation and Automated Reasoning when building complex systems. Ultimately, TypeDB serves as the knowledge-base foundation for intelligent systems. ''; homepage = "https://www.grakn.ai/"; license = licenses.gpl3Plus; platforms = platforms.all; maintainers = [ maintainers.haskie ]; }; }

grakn.ai : The Database for AI

Give the open-source Grakn a look as well (full transparency: I work there), it is an abstraction over a built in-house hypergraph storage engine and persisted layer using RocksDB. It's a logical database with a reasoning engine at the database level. https://github.com/graknlabs/grakn

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.

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.