MLSquare

MLSquare is an open source developer-friendly Python library, designed to make use of Deep Learning for Machine Learning developers.

Note

mlsquare python library is developed and maintained by MLSquare Foundation

In the first version we have developed Dope. Dope is aimed to provide every Machine Learning Algorithm with an equivalent DNN Implementation. The dope feature available in the mlsquare framework is aimed at making interoperability between machine learning models easier.

Getting Started!

Setting up mlsquare is simple and easy

  1. Create a Virtual Environment
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)

Note

For a comprehensive tutorial please do checkout this link

Indices and tables