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README.rst

[ML]² : Machine Learning for Machine Learning

https://circleci.com/gh/mlsquare/mlsquare/tree/dev.svg?style=svg https://api.codacy.com/project/badge/Coverage/5b23c72bf17246e6b3df610a798f8935

ML Square is a python library that utilises deep learning techniques to

  • Enable interoperability between existing standard machine learning frameworks.
  • Provide explainability as a first-class function.
  • Make ML self learnable.

Getting Started!

Setting up mlsquare is simple and easy

  1. Create a Virtual Environment(optional)
virtualenv ~/venv
source ~/venv/bin/activate
  1. Install mlsquare package
pip install mlsquare
  1. Import dope function from mlsquare and pass the sklearn model object
>>> from mlsquare import dope
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.model_selection import train_test_split
>>> import pandas as pd
>>> from sklearn.datasets import load_diabetes

>>> model = LinearRegression()
>>> diabetes = load_diabetes()

>>> X = diabetes.data
>>> sc = StandardScaler()
>>> X = sc.fit_transform(X)
>>> Y = diabetes.target
>>> x_train, x_test, y_train, y_test =
    train_test_split(X, Y, test_size=0.60, random_state=0)

>>> m = dope(model)

>>> # All sklearn operations can be performed on m, except that the underlying implementation uses DNN
>>> m.fit(x_train, y_train)
>>> m.score(x_test, y_test)

Tutorial

For a comprehensive tutorial please do checkout this link

Contribute

To get started with contributing, refer our devoloper guide here

For detailed documentation refer documentation

We would love to hear your feedback. Drop us a mail at info@mlsquare.org

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