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https://github.com/magnusax/automl

The project aims to develop a customized ML framework on top of existing libraries
https://github.com/magnusax/automl

data-science machine-learning machine-learning-algorithms machine-learning-library python scikit-learn

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The project aims to develop a customized ML framework on top of existing libraries

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README 

        

# Gazer



> "Standing on the shoulders of giants, gazing at the stars"



The project aims to develop a customized "automl" framework, leveraging the power of existing popular libraries. 

As time passes the intention of the author is to add non-standard capabilities such as advanced forms of ensemble

building and Markov Chain Monte Carlo (MCMC) like optimization algorithms (see further below for a brief overview of ongoing efforts).

The project is still in its infancy, and fairly basic functionality has been implemented; but it is currently 

under active development (conditional on time and energy).



Install is now available through the python package index. Run `pip install gazer==0.1.dev1` to install development version.

Currently tested for python versions `>=3.4` only. 



### Using the library

The entrance to functionality is the **GazerMetaLearner** object;

    

```python

from gazer import GazerMetaLearner

learner = GazerMetaLearner()

```

	

Set parameter `method='all'` to initialize all available algorithms. Default is to initialize 3 algorithms randomly.



Post initialization, (meta) algorithms are available in the `.clf` variable of the initialized object. To be more specific, the variable 

returns a dictionary of (name, MetaClassifier) tuples, where the **MetaClassifier** object is a wrapper around a "sklearn-like"

classifier. The current version of the library also implements *xgboost* and *keras* classifiers.



```python    	

learner.clf = {name1: MetaClassifier1, name2: MetaClassifier2, ... , nameN: MetaClassifierN}

```

   	

To inspect loaded algorithms, simply call `learner.names`.



Assuming a standard supervised learning scenario with data available in the form of `X, y`, you can easily fit 

available algorithms using default (i.e. reasonble) hyperparameter settings:



```python	

learner.fit(X, y)

```



This method trains all initialized algorithms. Moreover, Random, Grid search, and Bayesian optimization methods

are implemented and may be directly called from the `learner` instance. To see parameters, simply call:

```python

learner.clf['name'].cv_params



```

This returns a list containing one or more parameter dictionaries that may be edited in place.



### Roadmap

Some ideas are currently being implemented, e.g.

* Markov Chain Monte Carlo scheme for optimization of (very-) large ensemble models 

for large data sets where training times are relatively long.

* Model perturbation and greedy adaptation

* Advanced ensembling techniques

  - See [these two papers from Caruana et al.](http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf)



ML libraries which will be heavily relied upon are first and foremost:

* (Done) Scikit-learn

* (Done) Xgboost

* (Done) Keras 

* (Coming) Pytorch (`\ge 0.4`)



TODO: add direct links to each github project mentioned on this page, 

to ensure that credit and recognition goes to developers



### Comments

As this project is in its initial development phase, the code has not been tested properly yet. It thus follows

that it has not yet been uploaded to the PyPi repository yet. Project developer's contact info is available in the setup.py file.