DALEX | moDel Agnostic Language for Exploration and eXplanation | Machine Learning library

by ModelOriented Python Version: 1.7.0 License: GPL-3.0

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

kandi X-RAY | DALEX Summary

DALEX is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. DALEX has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has medium support. However DALEX build file is not available. You can install using 'pip install DALEX' or download it from GitHub, PyPI.

Unverified black box model is the path to the failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. The DALEX package xrays any model and helps to explore and explain its behaviour, helps to understand how complex models are working. The main function explain() creates a wrapper around a predictive model. Wrapped models may then be explored and compared with a collection of local and global explainers. Recent developents from the area of Interpretable Machine Learning/eXplainable Artificial Intelligence. The philosophy behind DALEX explanations is described in the Explanatory Model Analysis e-book. The DALEX package is a part of DrWhy.AI universe. If you work with scikit-learn, keras, H2O, tidymodels, xgboost, mlr or mlr3, you may be interested in the DALEXtra package. It is an extension pack for DALEX with easy to use connectors to models created in these libraries.

Support

Quality

Security

License

Reuse

Support

DALEX has a medium active ecosystem.

It has 1209 star(s) with 164 fork(s). There are 46 watchers for this library.

There were 1 major release(s) in the last 12 months.

There are 17 open issues and 375 have been closed. On average issues are closed in 35 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DALEX is 1.7.0

Quality

DALEX has 0 bugs and 0 code smells.

Security

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

DALEX code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DALEX 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

DALEX releases are available to install and integrate.

Deployable package is available in PyPI.

DALEX has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

DALEX saves you 1384 person hours of effort in developing the same functionality from scratch.

It has 6718 lines of code, 396 functions and 94 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed DALEX and discovered the below as its top functions. This is intended to give you an instant insight into DALEX implemented functionality, and help decide if they suit your requirements.

Plot the aspect ratio
Return a text representation of a row
Plot the posterior distribution
Update plot
Raise a TypeError if ob is not a class
Check that object is of the same type
Plot the variable import
Plot the aspectance
Plot the Shapley results
Prepare data for plotting
Convert floats to strings
Plot the bar chart
Plot the performance check
Predict new observations
Generate model parts
Generate a set of model components
Plot residuals
Predict the aspect ratio
Start a web server
Add points to the plot
Plot a lightness check
Plot the model
Calculate the regression measures
Run the setup
Fit a model to an explanation
Plot the clustering dendrogram

Get all kandi verified functions for this library.

DALEX Key Features

No Key Features are available at this moment for DALEX.

DALEX Examples and Code Snippets

Champion-Challenger analysis,Funnel Plot

HTML

Lines of Code : 26

License : No License

Copy

 library("mlr")
 library("DALEXtra")
 task <- mlr::makeRegrTask(
   id = "R",
   data = apartments,
   target = "m2.price"
 )
 learner_lm <- mlr::makeLearner(
   "regr.lm"
 )
 model_lm <- mlr::train(learner_lm, task)
 explainer_lm <- expl

triplot ,Aspect importance for single instance,Select an instance

Lines of Code : 24

License : Strong Copyleft (GPL-3.0)

Copy

(johny_d <- titanic_imputed[2,])

##   gender age class    embarked  fare sibsp parch survived
## 2   male  13   3rd Southampton 20.05     0     2        0

predict(model_titanic_glm, johny_d, type = "response")

##         2 
## 0.1531932

explai

Creating explanations

HTML

Lines of Code : 22

License : No License

Copy

library(DALEX)
plot(model_performance(explainer))

library(ingredients)
plot(feature_importance(explainer))

describe(feature_importance(explainer))

## The number of important variables for scikitlearn_model's prediction is 3 out of 17. 
##  Variabl

Community Discussions

Trending Discussions on DALEX

SHAP Importance for Ranger in R

QUESTION

SHAP Importance for Ranger in R

Asked 2020-Dec-01 at 09:05

Having a binary Classification problem: how would be possible to get the Shap Contribution for variables for a Ranger model?

Sample data:

...

ANSWER

Answered 2020-Dec-01 at 09:05

Good Morning!, According to what I have found, you can use ranger() with fastshap() as following:

Source https://stackoverflow.com/questions/65005700

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

Vulnerabilities

No vulnerabilities reported

Install DALEX

The easiest way to get the R version of DALEX is to install it from CRAN. The Python version of dalex is available on PyPI.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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