bench_lassolars.py

"""bench different LARS implementations"""

import numpy as np
from datetime import datetime


def bench_skl(X, y, T, valid):
#
#       .. scikits.learn ..
#
    from scikits.learn import linear_model
    start = datetime.now()
    skl_clf = linear_model.LassoLARS(alpha=0.)
    skl_clf.fit(X, y, normalize=False)
    pred = skl_clf.predict(T)
    delta = datetime.now() - start
    mse = np.linalg.norm(pred - valid, 2)**2
    return mse, delta


def bench_mlpy(X, y, T, valid):
#
#       .. MLPy ..
#
    from mlpy import Lasso
    start = datetime.now()
    mlpy_clf = Lasso(m=10 * X.shape[1]) # go till the end of the path
    mlpy_clf.learn(X, y)
    pred = mlpy_clf.pred(T)
    delta = datetime.now() - start
    mse = np.linalg.norm(pred - valid, 2)**2
    return mse, delta


def bench_pymvpa(X, y, T, valid):
#
#       .. PyMVPA ..
#
    from mvpa.datasets import dataset_wizard
    from mvpa.clfs import lars
    start = datetime.now()
    data = dataset_wizard(X, y)
    clf = lars.LARS(model_type="lasso")
    clf.train(data)
    pred = clf.predict(T)
    delta  = datetime.now() - start
    mse = np.linalg.norm(pred - valid, 2) ** 2
    return mse, delta


if __name__ == '__main__':
    import sys, misc

    # don't bother me with warnings
    import warnings; warnings.simplefilter('ignore')
    np.seterr(all='ignore')

    print __doc__ + '\n'
    if not len(sys.argv) == 2:
        print misc.USAGE % __file__
        sys.exit(-1)
    else:
        dataset = sys.argv[1]

    print 'Loading data ...'
    data = misc.load_data(dataset)

    print 'Done, %s samples with %s features loaded into ' \
      'memory\n' % data[0].shape

    score, res = misc.bench(bench_skl, data)
    misc.print_result("lassolars", dataset, "scikits.learn", score, res)

    score, res = misc.bench(bench_mlpy, data)
    misc.print_result("lassolars", dataset, "MLPy", score, res)

    score, res = misc.bench(bench_pymvpa, data)
    misc.print_result("lassolars", dataset, "PyMVPA", score, res)

    misc.save_results()