Metadata-Version: 2.1
Name: libra
Version: 1.1.1
Summary: Ergonomic machine learning
Home-page: https://github.com/Palashio/libra
Author: Palash Shah
Author-email: ps9cmk@virginia.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: colorama
Requires-Dist: transformers (==2.11.0)
Requires-Dist: tensorflow (==2.2.0)
Requires-Dist: keras (==2.4.3)
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Requires-Dist: tabulate
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Requires-Dist: seaborn
Requires-Dist: keras-tuner
Requires-Dist: spacy
Requires-Dist: jellyfish
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Requires-Dist: download

# Libra: Ergonomic Machine Learning


![Test Image 1](/tools/data/gh_images/new-logo.png)


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Libra automates the end-to-end machine learning process in just one line of code. It is built for both non-technical users and software professionals of all kinds.
*** 


Full documentation can be found at [libradocs.github.io](https://libradocs.github.io/)
## Installation

Install latest release version:

```
pip install -U libra
```

Install directory from github:

```
git clone https://github.com/Palashio/libra.git
cd libra
pip install .
```

Alternatively you can build and use the docker image locally with:

```
docker build . -f docker/libra-normal/Dockerfile -t libra
docker run -v /path/to/my/data:/data -it --rm libra
```

Or if you have [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) installed.

```
docker build . -f docker/libra-gpu/Dockerfile -t libra-gpu
docker run -v /path/to/my/data:/data --gpus all -it --rm libra-gpu
```
## Usage: the basics

The core functionality of libra works through the `client` object. A new client object should be created for every dataset that you want to produce results for. All information about the models that're built, the plots that are generated, and the metrics are created will be stored in the object.

You can then call different queries on that client object, and the dataset you passed to it will be used. 

```python
from libra import client

newClient = client('path/to/dataset') 
newClient.neural_network_query('please model the median number of households')
```
Now, calling 
```python
newClient.info()
```
will return a dictionary of all the information that was generated: 

```python
dict_keys(['id', 'model', 'num_classes', 'plots', 'target', 'preprocesser', 
          'interpreter', 'test_data', 'losses', 'accuracy'])
```

Other queries can also be called on the same object, and will be appended to the `models` dictionary.

```python
newClient.svm_query('predict the proximity to the ocean')
newClient.model().keys()

dict_keys(['regression_ANN', svm'])
```


