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 come up with Interoperable Machine Learning [IMLY]. IMLY is aimed to provide every Machine Learning Algorithm with an equivalent DNN Implementation.

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.imly 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

>>> model = LinearRegression()
>>> data = pd.read_csv('./datasets/diabetes.csv', delimiter=",",
               header=None, index_col=False)
>>> sc = StandardScaler()
>>> data = sc.fit_transform(data)
>>> data = pd.DataFrame(data)

>>> X = data.iloc[:, :-1]
>>> Y = data.iloc[:, -1]
>>> 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

Contents

Installation Guide

This guide describes how to install mlsquare

On this page

Setting up a virtual environment

The recommended way to install mlsquare is to use a virtual environment created by virtualenv. Setup and activate a new virtual environment like this:

$ virtualenv envname
$ source envname/bin/activate

If you use the virtualenvwrapper scripts, type this instead:

$ mkvirtualenv envname

Installing the mlsquare package

The next step is to install mlsquare. The easiest way is to use pip to fetch the package from the Python Package Index (PyPI). This will also install the dependencies for Python.

(envname) $ pip install mlsquare

Note

Installation via pip installs the stable version in your environment. To install the developer version checkout the package source from GitHub and run python setup.py install from the directory root. Note that developer version is not stable and there are chances that code will break. If you are not sure about it, we suggest you use the stable version.

Testing the installation

Verify that the packages are installed correctly:

(envname) $ python -c 'import mlsquare'

If you don’t see any errors, the installation was successful. Congratulations!

Next steps

Now that you successfully installed HappyBase on your machine, continue with the User Guide to learn how to use it.

User Guide

This user guide explores the MLSquare API and should provide you with enough information to get you started. Note that this user guide is intended as an introduction to MLSquare, not to Keras or SkLearn or any other packages in general. Readers should already have a basic understanding of the packages they were using and its API.

While the user guide does cover most features, it is not a complete reference guide. More information about the MLSquare API is available from the API documentation.

On this page

Importing the mlsquare module

To start using the package, we need to import the module into the python enviroment.

>>> import mlsquare

If the above command doesn’t result in any errors, then the import is successful

Note

To use mlsquare you need Python 3.6 or higher

Load dope() method into the enviroment

dope() is the base function, that returns an implementation of a given model to its DNN implementation. Once a model is dope’d, users will be able to use the same work flow as their initial model on the dope’d object.

>>> from mlsquare.imly import dope

Transpiling an existing model using dope

To demonstrate dope(), we will transpile sklearn LinearRegression and use the sklearn operations on the transpiled model.

>>> from sklearn.linear_model import LinearRegression
>>> model = LinearRegression()
>>> m = dope(model)

# Dope maintains the same interface as the base model package
>>> m.fit(x_train, y_train)
>>> m.score(x_test, y_test)

Note

dope() function doesn’t support all the packages and the models in the package. A list of supported packages and models is available at the Supported Modules and Models

Developer Guide

mlsquare is open source. Developers can contribute to the module by making contributions.

  1. To contribute to the development, you can create a branch by checkout the source from GitHub.
  2. Include proper test cases for the feature.
  3. Raise a pull request against the master branch

How to raise a pull request

Bug Fix
  1. Description about the bug that was been fixed
    This pull request fixes #issue_number
  2. How it was fixed
    Problem & Solution
  3. How to verify it
    Steps or Code to verify the fix
New Feature
  1. Description about the feature that was been fixed
    The pull request adds the functionality
  2. How it was done
    Description about the solution
  3. How to verify it
    Steps or Code to verify the feature

API Reference

mlsquare.imly.dope(model, **kwargs)[source]

Transpiles a given model to it’s DNN equivalent.

Parameters:
  • model (class) – The primal model passed by the user that needs to be transpiled.
  • using (str) – Choice of type of “model transpilation” you want your model to undergo.
  • accepts None and 'dnn' as values. (Currently) –
    1. None: Returns the model as it is.
    2. dnn (default): Converts the model to it’s DNN equivalent.
  • best (bool) – Whether to optmize the model or not.
  • **kwargs (dict) – Dictionary of parameters mapped to their keras params.
Returns:

The transpiled model.
Return type:

model (class)

License

The MIT License (MIT)

Copyright (c) 2018 MLSquare

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Contributors

We hear your feedback!

If you notice any issues during the usage the package mlsquare, please create an issue on GitHub. Before creating any issue, please check if the same issue was already created by any other user.

Creating a Issue

  1. To create a new issue, navigate to the github page of the project and create an issue from the issues column
  1. Include a short title
  2. Include the error generate
  3. Include the steps to reproduce it.

Changelog

Version 0.1

  • Support for Linear & Logistic Regression from Sklearn

Supported Modules and Models

As of the current release, mlsquare supports the following models from the below modules

  • sklearn
    • LinearRegression
    • LogisticRegression

We are working supporting more models and modules, however if you would like us to add any module, please write to us at info[at]mlsquare.org

Indices and tables