mtl_bench.py

"""
Benchmark of MultiTaskLasso
"""
import gc
from itertools import product
from time import time
import numpy as np
import pandas as pd

from sklearn.datasets import make_regression
from sklearn.linear_model import MultiTaskLasso


def compute_bench(alpha, n_samples, n_features, n_tasks):
    results = []
    n_bench = len(n_samples) * len(n_features) * len(n_tasks)

    for it, (ns, nf, nt) in enumerate(product(n_samples, n_features, n_tasks)):
        print('==================')
        print('Iteration %s of %s' % (it, n_bench))
        print('==================')
        n_informative = nf // 10
        X, Y, coef_ = make_regression(n_samples=ns, n_features=nf,
                                      n_informative=n_informative,
                                      n_targets=nt,
                                      noise=0.1, coef=True)

        X /= np.sqrt(np.sum(X ** 2, axis=0))  # Normalize data

        gc.collect()
        clf = MultiTaskLasso(alpha=alpha, fit_intercept=False)
        tstart = time()
        clf.fit(X, Y)
        results.append(
            dict(n_samples=ns, n_features=nf, n_tasks=nt, time=time() - tstart)
        )

    return pd.DataFrame(results)


def compare_results():
    results_new = pd.read_csv('mlt_new.csv').set_index(['n_samples', 'n_features', 'n_tasks'])
    results_old = pd.read_csv('mlt_old.csv').set_index(['n_samples', 'n_features', 'n_tasks'])
    results_ratio = (results_old / results_new)
    results_ratio.columns = ['time (old) / time (new)']
    print(results_new)
    print(results_old)
    print(results_ratio)


if __name__ == '__main__':
    import matplotlib.pyplot as plt

    alpha = 0.01  # regularization parameter

    list_n_features = [300, 1000, 4000]
    list_n_samples = [100, 500]
    list_n_tasks = [2, 10, 20, 50]
    results = compute_bench(alpha, list_n_samples,
                            list_n_features, list_n_tasks)

    # results.to_csv('mlt_old.csv', index=False)
    results.to_csv('mlt_new.csv', index=False)

    compare_results()