SML - Standard Machine Learning Language
SML aims to proved a universal language agnostic framework to simplify the development of machine learning pipelines
Begin by cloning this repository with the following terminal command
git clone https://github.com/UI-DataScience/sml.git
Change directories into the main directory and run the following terminal command
sudo python3 setup.py develop
After running this command, the SML library will be accessible anywhere on your machine via Python.
Simple Code Example
import sml from sml import execute query1 = 'READ "data/auto.csv" (separator = "\s+", header = None) AND REPLACE ("?", "mode") AND \ SPLIT (train = .8, test = .2, validation = .0) AND \ REGRESS (predictors = [2,3,4,5,6,7,8], label = 1, algorithm = simple) AND \ SAVE "auto.sml"' execute(query2, verbose=True)
With verbose = true, a “pretty” output should be printed out
Sml Summary: ============================================= ============================================= Dataset: data/auto.csv Delimiter: \s+ Training Set Split: 80.00% Testing Set Split: 20.00% Predictiors: ['2', '3', '4', '5', '6', '7', '8'] Label: 1 Algorithm: simple ============================================= =============================================
Otherwise a general output summary will be printed out
Using simple Algorithm, the dataset is from: data/auto.csv. Currently using Predictors from column(s) ['2', '3', '4', '5', '6', '7', '8'] and Label(s) from column(s) 1.
For detailed tutorials of SML, take a look at the SML tutorials.
For more extensive documentation on the language, take a look at the documentation for the parser
For a more extensive documentation on the implementation details take a look at the SML documentation