outliertree | anomaly detection through decision tree conditioning | Predictive Analytics library

by david-cortes C++ Version: 1.9.0.post1 License: GPL-3.0

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | outliertree Summary

outliertree is a C++ library typically used in Analytics, Predictive Analytics applications. outliertree has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Explainable outlier/anomaly detection based on smart decision tree grouping, similar in spirit to the GritBot software developed by RuleQuest research. Written in C++ with interfaces for R and Python (additional Ruby wrapper can be found here). Supports columns of types numeric, categorical, binary/boolean, and ordinal, and can handle missing values in all of them. Ideal as a sanity checker in exploratory data analysis.

Support

Quality

Security

License

Reuse

Support

outliertree has a low active ecosystem.

It has 46 star(s) with 3 fork(s). There are 4 watchers for this library.

There were 3 major release(s) in the last 6 months.

There are 2 open issues and 4 have been closed. On average issues are closed in 0 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of outliertree is 1.9.0.post1

Quality

outliertree has 0 bugs and 0 code smells.

Security

outliertree has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

outliertree code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

outliertree is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

outliertree releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 703 lines of code, 24 functions and 3 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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outliertree Key Features

No Key Features are available at this moment for outliertree.

outliertree Examples and Code Snippets

Sample usage

C++

Lines of Code : 55

License : Strong Copyleft (GPL-3.0)

Copy

library(outliertree)

### random data frame with an obvious outlier
nrows = 100
set.seed(1)
df = data.frame(
    numeric_col1 = c(rnorm(nrows - 1), 1e6),
    numeric_col2 = rgamma(nrows, 1),
    categ_col    = sample(c('categA', 'categB', 'categC'),

Example outputs

C++

Lines of Code : 29

License : Strong Copyleft (GPL-3.0)

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row [1138] - suspicious column: [age] - suspicious value: [75.00]
	distribution: 95.122% <= 42.00 - [mean: 31.46] - [sd: 5.28] - [norm. obs: 39]
	given:
		[pregnant] = [TRUE]


row [2230] - suspicious column: [T3] - suspicious value: [10.60]
	dist

Installation

C++

Lines of Code : 5

License : Strong Copyleft (GPL-3.0)

Copy

install.packages("outliertree")

pip install outliertree

pip install --no-use-pep517 outliertree

export ENABLE_OMP=1
pip install outliertree

Community Discussions

Trending Discussions on outliertree

"Assertion '__builtin_expect(__n < this->size(), true)' failed" error in Visual Studio Code Python

QUESTION

"Assertion '__builtin_expect(__n < this->size(), true)' failed" error in Visual Studio Code Python

Asked 2021-Apr-01 at 04:23

I know there is already a lot on Assertion Failure. But none was useful for me. Hear me out.

This is the code:

...

ANSWER

Answered 2021-Mar-31 at 17:21

I don't know enough about OutlierTree to know how to fix the error. However, the error you see is in the function std::vector::operator[], which is std::vector's access by index function. The assertion error simply means that you tried to access an item at an index that is greater than the vector's length.

As to why you see this error only in VS Code, I assume that it's because assertions are usually checked only in debug mode. Google Collab is probably compiled in release mode without the assertions - that doesn't mean that you don't access out-of-bounds items, it just doesn't catch the error.

I suggest you use gdb or another debugger to understand which vector you try to access out-of-bound, then attaching a stacktrace to your question. Maybe file a bug report with OutlierTree - this seems something they could handle better.

Source https://stackoverflow.com/questions/66892158

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install outliertree

or if that fails:. (Package has only been tested in Python 3).
For R:
For Python:
For C++: package doesn't have a build system, nor a main function that can produce an executable, but can be built as a shared object and wrapped into other languages with any C++11-compliant compiler (std=c++11 in most compilers, /std:c++14 in MSVC). For parallelization, needs OpenMP linkage (-fopenmp in most compilers, /openmp in MSVC). Package should not be built with optimization higher than O3 (i.e. don't use -Ofast). Needs linkage to the math library, which should be enabled by default in most C++ compilers, but otherwise would require -lm argument. No external dependencies are required.
For Ruby: see external repository with wrapper.

Support

For R : documentation is built-in in the package (e.g. help(outliertree::outlier.tree)) - PDF can be downloaded in CRAN. For Python: documentation is available at ReadTheDocs (and it's also built-in in the package as docstrings, e.g. help(outliertree.OutlierTree.fit)). For Ruby: see the external repository and the [Python documentation)(http://outliertree.readthedocs.io/en/latest/). For C++: documentation is available in the source files (not in the header).

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