README.md

Sparse LR Examples

This example is based on scikit-learn example: l1 penalty and sparsity in logistic regression, which classifies 8x8 images of digits into two classes: 0-4 against 5-9, and visualize the coefficients of the model for different penalty methods(l1 or l2) and C.

The algorithm is defined in function sparse_lr_plot from model.py. We use the decorator hp.param to declare hyper-parameters for our function:

import hyperparameter as hp

@hp.param
def sparse_lr_plot(X, y, learning_rate=0.01, penalty='l1', C=0.01, tol=0.01):
    print({'C': C, 'penalty': penalty, 'tol': tol})
    ...

Four keyword arguments are defined for sparse_lr_plot: learning_rate, penalty, C and tol. hp.param will convert these arguments into hyper-parameters.

There are two ways to control the hyper-parameters:

1. parameter scope (see detail in example_1.py):

with hp.scope('model.sparse_lr_train.C=0.1'):
    sparse_lr_plot(X, y)

2. command line arguments (see detail in example_2.py):

def run(args):
    # run the lr model with parameter from cmdline
    with hp.scope(*args.define):  # set parameters according to cmd line
        sparse_lr_plot(X, y)
        ...


if __name__ == '__main__':
    # create cmd line arguments parser
    import argparse
    parser = argparse.ArgumentParser('example')
    parser.add_argument('-D', '--define', nargs='*', default=[])
    args = parser.parse_args()

    run(args)