AIX360 | Interpretability and explainability of data and machine learning models | Machine Learning library
kandi X-RAY | AIX360 Summary
kandi X-RAY | AIX360 Summary
AIX360 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. AIX360 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install AIX360' or download it from GitHub, PyPI.
The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. To help, we have created some guidance material and a chart that can be consulted.
The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. To help, we have created some guidance material and a chart that can be consulted.
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AIX360 has a medium active ecosystem.
It has 1320 star(s) with 284 fork(s). There are 52 watchers for this library.
It had no major release in the last 12 months.
There are 43 open issues and 29 have been closed. On average issues are closed in 32 days. There are 11 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of AIX360 is 0.3.0
Quality
AIX360 has 0 bugs and 0 code smells.
Security
AIX360 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
AIX360 code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
AIX360 is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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AIX360 releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
AIX360 saves you 2083 person hours of effort in developing the same functionality from scratch.
It has 5177 lines of code, 283 functions and 93 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed AIX360 and discovered the below as its top functions. This is intended to give you an instant insight into AIX360 implemented functionality, and help decide if they suit your requirements.
- Compute the feature indicator matrix
- Perform beam search
- Compute the lambda function
- Evaluate singular values
- Probe the probe model for the training
- A fully connected layer
- Learn the rule
- Beam search
- Resnet model
- Resnet layer
- Fit the model
- Plots the latent traversal transformation
- Generate matplotlib plot
- An explanation of the DnF rule
- Add label and explanation to dataset
- Generate random features
- Calculates the accuracy of a simple model
- Plots the latent traversal traversal
- Predict for the given data
- Forward the layer
- Plot the reconstruction of the trained network
- Downloads the cifar - 10 dataset
- Return an example for the given example id
- Predict for each feature
- FeatureBinarizer
- Uses FFT
- Computes confusion matrix
- Return the explanation of X
Get all kandi verified functions for this library.
AIX360 Key Features
No Key Features are available at this moment for AIX360.
AIX360 Examples and Code Snippets
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def to_rgb(x):
x_rgb = np.zeros((x.shape[0], 28, 28, 3))
for i in range(3):
x_rgb[..., i] = x[..., 0]
return x_rgb
train_rgb = to_rgb(train)
test_rgb = to_rgb(test)
limeExplainer.explain_instan
Community Discussions
Trending Discussions on AIX360
QUESTION
Keras, AIX360(LIME) - ValueError: the input array must be have a shape == (.., ..,[ ..,] 3)), got (28, 28, 1)
Asked 2020-Aug-03 at 21:11
I am trying to make some program using a model from Keras and then explain it with Lime explainer from AIX360 (which is just wrapper for the original LIME). All the data are MNIST grayscale digits. But in my case, I am not able to explain the instance because I can't figure out what to feed to the explainers.
My code:
...ANSWER
Answered 2020-Aug-03 at 19:26I added this conversion and trained on RGB images:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install AIX360
Clone the latest version of this repository:. If you'd like to run the examples and tutorial notebooks, download the datasets now and place them in their respective folders as described in aix360/data/README.md.
If you would like to quickly start using the AI explainability 360 toolkit without cloning this repository, then you can install the aix360 pypi package as follows. If you follow this approach, you may need to download the notebooks in the examples folder separately.
If you would like to quickly start using the AI explainability 360 toolkit without cloning this repository, then you can install the aix360 pypi package as follows. If you follow this approach, you may need to download the notebooks in the examples folder separately.
Support
Faithfulness (Alvarez-Melis and Jaakkola, 2018)Monotonicity (Luss et al., 2019)
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