def report(niter=50, design="random", **kwargs):
if design == "fixed":
X, _, _, _, _ = gaussian_instance(**kwargs)
kwargs.update({'X': X})
kwargs.update({'cross_validation': True, 'condition_on_CVR': False})
intervals_report = reports.reports['test_naive']
screened_results = reports.collect_multiple_runs(
intervals_report['test'], intervals_report['columns'], niter,
reports.summarize_all, **kwargs)
screened_results.to_pickle("naive.pkl")
results = pd.read_pickle("naive.pkl")
fig = reports.naive_pvalue_plot(results)
#fig = reports.pvalue_plot(results, label="Naive p-values")
fig.suptitle("Naive p-values", fontsize=20)
fig.savefig('naive_pvalues.pdf')
def report(niter=50):
# these are all our null tests
fn_names = [
'test_overall_null_two_queries',
'test_one_inactive_coordinate_handcoded'
]
dfs = []
for fn in fn_names:
fn = reports.reports[fn]
dfs.append(
reports.collect_multiple_runs(fn['test'], fn['columns'], niter,
reports.summarize_all))
dfs = pd.concat(dfs)
fig = reports.pvalue_plot(dfs, colors=['r', 'g'])
fig = reports.naive_pvalue_plot(dfs, fig=fig, colors=['k', 'b'])
fig.savefig('Mest_pvalues.pdf') # will have both bootstrap and CLT on plot
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')