def test_linreg_various_values(n, d):
m = 64
A_tag = get_varying_d_data(d, n)
n, d_tag = A_tag.shape
d = d_tag - 1
linreg_mes = measure_method(scipy.linalg.lstsq, A_tag[:, :d], A_tag[:, d])
linreg_boost_mes = measure_method(linreg_boost, A_tag, m,
get_optimal_k_value(d))
with open('LINREG_PERFORMANCE_TEST_D_VALUES_very_large.txt', 'a+') as f:
f.write("linreg,{n},{d},{duration}\n".format(
n=n, d=d, duration=linreg_mes.duration))
f.write("linreg_boost,{n},{d},{duration}\n".format(
n=n, d=d, duration=linreg_boost_mes.duration))
def test_size_time_for_various_alphas(n, alpha_count):
d = 7
m = 3
alphas = np.ones(alpha_count)
A_tag = get_varying_d_data(d=d, n=n)
ridgecv_mes = measure_method(
RidgeCV(alphas=alphas, cv=m).fit, A_tag[:, :d], A_tag[:, d])
ridge_boost_mes = measure_method(ridgecv_boost, A_tag, alphas, m,
get_optimal_k_value(d))
lassocv_mes = measure_method(
LassoCV(alphas=alphas, cv=m).fit, A_tag[:, :d], A_tag[:, d])
lasso_boost_mes = measure_method(lassocv_boost, A_tag, alphas, m,
get_optimal_k_value(d))
elasticcv_mes = measure_method(
ElasticNetCV(alphas=alphas, cv=m, l1_ratio=RHO).fit, A_tag[:, :d],
A_tag[:, d])
elastic_boost_mes = measure_method(elasticcv_boost, A_tag, m, alphas, RHO,
get_optimal_k_value(d))
with open('SECOND_PERFORMANCE_TEST_RESULTS.txt', 'a+') as f:
f.write("ridge,{n},{alpha_count},{duration}\n".format(
n=n, alpha_count=alpha_count, duration=ridgecv_mes.duration))
f.write("ridge_boost,{n},{alpha_count},{duration}\n".format(
n=n, alpha_count=alpha_count, duration=ridge_boost_mes.duration))
f.write("lasso,{n},{alpha_count},{duration}\n".format(
n=n, alpha_count=alpha_count, duration=lassocv_mes.duration))
f.write("lasso_boost,{n},{alpha_count},{duration}\n".format(
n=n, alpha_count=alpha_count, duration=lasso_boost_mes.duration))
f.write("elastic,{n},{alpha_count},{duration}\n".format(
n=n, alpha_count=alpha_count, duration=elasticcv_mes.duration))
f.write("elastic_boost,{n},{alpha_count},{duration}\n".format(
n=n, alpha_count=alpha_count, duration=elastic_boost_mes.duration))
def test_time_for_increasing_alphas(dataset, dataset_name, alpha_count):
m = 3
alphas = np.ones(alpha_count)
A_tag = get_dataset(dataset)
n, d_tag = A_tag.shape
d = d_tag - 1
ridgecv_mes = measure_method(
RidgeCV(alphas=alphas, cv=m).fit, A_tag[:, :d], A_tag[:, d])
ridge_boost_mes = measure_method(ridgecv_boost, A_tag, alphas, m,
get_optimal_k_value(d))
lassocv_mes = measure_method(
LassoCV(alphas=alphas, cv=m).fit, A_tag[:, :d], A_tag[:, d])
lasso_boost_mes = measure_method(lassocv_boost, A_tag, alphas, m,
get_optimal_k_value(d))
elasticcv_mes = measure_method(
ElasticNetCV(alphas=alphas, cv=m, l1_ratio=RHO).fit, A_tag[:, :d],
A_tag[:, d])
elastic_boost_mes = measure_method(elasticcv_boost, A_tag, m, alphas, RHO,
get_optimal_k_value(d))
with open('THIRD_PERFORMANCE_TEST_RESULTS.txt', 'a+') as f:
f.write("ridge,{dataset_name},{alpha_count},{duration}\n".format(
dataset_name=dataset_name,
alpha_count=alpha_count,
duration=ridgecv_mes.duration))
f.write("ridge_boost,{dataset_name},{alpha_count},{duration}\n".format(
dataset_name=dataset_name,
alpha_count=alpha_count,
duration=ridge_boost_mes.duration))
f.write("lasso,{dataset_name},{alpha_count},{duration}\n".format(
dataset_name=dataset_name,
alpha_count=alpha_count,
duration=lassocv_mes.duration))
f.write("lasso_boost,{dataset_name},{alpha_count},{duration}\n".format(
dataset_name=dataset_name,
alpha_count=alpha_count,
duration=lasso_boost_mes.duration))
f.write("elastic,{dataset_name},{alpha_count},{duration}\n".format(
dataset_name=dataset_name,
alpha_count=alpha_count,
duration=elasticcv_mes.duration))
f.write(
"elastic_boost,{dataset_name},{alpha_count},{duration}\n".format(
dataset_name=dataset_name,
alpha_count=alpha_count,
duration=elastic_boost_mes.duration))
def test_measurement_function(seconds_to_sleep):
# 0.01 seconds room for error
err = 1e-2
assert approx(measure_method(time.sleep, seconds_to_sleep).duration,
abs=err) == seconds_to_sleep