semvec/pypi-packages huggingface dataset:
gam 1.3.0
Global Explanations for Deep Neural Networks
License: Apache License 2.0
Requires Python: >=3.6

# GAM (Global Attribution Mapping)
> Global Explanations for Deep Neural Networks

GAM explains the landscape of neural network predictions across subpopulations. 

This implementation is based on "[Global Explanations for Neural Networks](https://arxiv.org/abs/1902.02384): Mapping the Landscape of Predictions" (AAAI/ACM AIES 2019).

## Installation
```sh
python3 -m pip install gam
```  
## Get Started
First generate local attributions using your favorite technique, then:

```Python
>>> from gam.gam import GAM
>>> # for a quick example use `attributions_path="tests/test_attributes.csv"`
>>> # Input/Output: csv (columns: features, rows: local/global attribution)
>>> gam = GAM(attributions_path="<path_to_your_attributes>.csv", distance="spearman", k=2)
>>> gam.generate()
>>> gam.explanations
[[('height', .6), ('weight', .3), ('hair color', .1)], 
 [('weight', .9), ('weight', .05), ('hair color', .05)]]
 
>>> gam.subpopulation_sizes
[90, 10]

>>> gam.subpopulations
# global explanation assignment
[0, 1, 0, 0,...]

>>> gam.plot()
# bar chart of feature importance with subpopulation size
```

### Tests
To run tests:
```bash
$ python -m pytest tests/
```

## Contributors

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“Project”). Any Contributor to the Project must accept and sign an
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and interest in and to Your Contributions; this Agreement does not
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## Code of Conduct

This project adheres to the [Open Code of Conduct](https://developer.capitalone.com/resources/code-of-conduct)
By participating, you are
expected to honor this code.
pytorch-kfp-components 0.1.0
PyTorch Kubeflow Pipeline
License: Apache License 2.0
Requires Python: >=3.6
Keywords: Kubeflow Pipelines,KFP,ML workflow,PyTorch

# PyTorch Kubeflow Pipeline Components

PyTorch Kubeflow Pipeline Components provides an SDK and a set of components that lets you build kubeflow pipelines using PyTorch. You can use the predefined components in this repository to build your pipeline using the Kubeflow Pipelines SDK.

## Installation
### Requirements
Python >= 3.6
Kubeflow cluster setuo (on-prem or in any of the Clouds)

### Install latest release
Use the following command to install PyTorch Pipeline Components from PyPI.

```
pip install -U pytorch-kfp-components
```

### Install from source
Use the following commands to install PyTorch Kubeflow Pipeline Components from GitHub.

```
git clone https://github.com/kubeflow/pipelines.git
pip install pipelines/components/PyTorch/pytorch_kfp_components/.
```

### Running the tests

Run the following command

```
pip install tox
cd ./components/PyTorch/pytorch-kfp-components/
tox -rvve py38
```

### Samples

For running the samples follow the instruction mentioned as below

[Samples README.md](../../../samples/contrib/pytorch-samples/README.md)