def report(niter=50, **kwargs):
condition_report = reports.reports['test_marginalize']
runs = reports.collect_multiple_runs(condition_report['test'],
condition_report['columns'], niter,
reports.summarize_all, **kwargs)
fig = reports.pivot_plot_plus_naive(runs)
#fig = reports.pivot_plot_2in1(runs,color='b', label='marginalized subgradient')
fig.suptitle('Randomized Lasso marginalized subgradient')
fig.savefig('marginalized_subgrad_pivots.pdf')
def report(niter=200, **kwargs):
kwargs = {'s': 0, 'n': 200, 'p': 20, 'snr': 7, 'loss': 'gaussian', 'randomizer': 'gaussian'}
split_report = reports.reports['test_approximate_ci']
screened_results = reports.collect_multiple_runs(split_report['test'],
split_report['columns'],
niter,
reports.summarize_all,
**kwargs)
fig = reports.pivot_plot_plus_naive(screened_results)
fig.savefig('approx_pivots_threshold.pdf')
def report(niter=50, **kwargs):
kwargs = {
's': 0,
'n': 600,
'p': 100,
'signal': 7,
'bootstrap': False,
'randomizer': 'gaussian',
'loss': 'gaussian',
'intervals': 'old'
}
intervals_report = reports.reports['test_intervals']
runs = reports.collect_multiple_runs(intervals_report['test'],
intervals_report['columns'], niter,
reports.summarize_all, **kwargs)
fig = reports.pivot_plot_plus_naive(runs)
fig.suptitle('Selective vs naive p-values after group Lasso')
fig.savefig('Group_lasso.pdf')
def report(niter=1, **kwargs):
condition_report = reports.reports['test_without_screening']
runs = reports.collect_multiple_runs(condition_report['test'],
condition_report['columns'], niter,
reports.summarize_all, **kwargs)
pkl_label = ''.join(["test_without_screening.pkl", "_", kwargs['loss'],"_",\
kwargs['randomizer'], ".pkl"])
pdf_label = ''.join(["test_without_screening.pkl", "_", kwargs['loss'], "_", \
kwargs['randomizer'], ".pdf"])
runs.to_pickle(pkl_label)
runs_read = pd.read_pickle(pkl_label)
fig = reports.pivot_plot_plus_naive(runs_read,
color='b',
label='no screening')
fig.suptitle('Testing without screening', fontsize=20)
fig.savefig(pdf_label)
def report(niter=50, **kwargs):
np.random.seed(500)
intervals_report = reports.reports['test_cv']
runs = reports.collect_multiple_runs(intervals_report['test'],
intervals_report['columns'], niter,
reports.summarize_all, **kwargs)
pkl_label = ''.join([
kwargs['loss'], "_",
str(kwargs['condition_on_CVR']), "_", "test_cv.pkl"
])
pdf_label = ''.join([
kwargs['loss'], "_",
str(kwargs['condition_on_CVR']), "_", "test_cv.pdf"
])
runs.to_pickle(pkl_label)
runs_read = pd.read_pickle(pkl_label)
fig = reports.pivot_plot_plus_naive(runs_read)
fig.suptitle("CV pivots", fontsize=20)
fig.savefig(pdf_label)
def report(niter=100, design="random", **kwargs):
if design == "fixed":
X, _, _, _, _ = gaussian_instance(**kwargs)
kwargs.update({'X': X})
intervals_report = reports.reports['test_lee_et_al']
screened_results = reports.collect_multiple_runs(
intervals_report['test'], intervals_report['columns'], niter,
reports.summarize_all, **kwargs)
screened_results.to_pickle("lee_et_al_pivots.pkl")
results = pd.read_pickle("lee_et_al_pivots.pkl")
#naive plus lee et al.
fig = reports.pivot_plot_plus_naive(results)
fig.suptitle("Lee et al. and naive p-values", fontsize=20)
fig.savefig('lee_et_al_pivots.pdf')
# naive only
fig1 = reports.naive_pvalue_plot(results)
fig1.suptitle("Naive p-values", fontsize=20)
fig1.savefig('naive_pvalues.pdf')