[ML]² : Machine Learning for Machine Learning
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
- Create a Virtual Environment(optional)
virtualenv ~/venv source ~/venv/bin/activate
- Install
mlsquare
packagepip install mlsquare
- Import
dope
function frommlsquare
and pass thesklearn
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