def report(niter=50, **kwargs):
# these are all our null tests
fn_names = ['test_gaussian_pvals',
'test_logistic_pvals',
'test_data_carving_gaussian',
'test_data_carving_sqrt_lasso',
'test_data_carving_logistic',
'test_data_carving_poisson',
'test_data_carving_coxph'
]
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)
fig.savefig('algorithms_pvalues.pdf')
fig = reports.split_pvalue_plot(dfs)
fig.savefig('algorithms_split_pvalues.pdf')
def report(niter=50, **kwargs):
_report = goodness_of_fit_report = reports.reports['test_goodness_of_fit']
runs = reports.collect_multiple_runs(_report['test'], _report['columns'],
niter, reports.summarize_all,
**kwargs)
fig = reports.pvalue_plot(runs)
fig.savefig('sqrtlasso_goodness_of_fit.pdf')
def report(niter=50, **kwargs):
condition_report = reports.reports['test_condition']
runs = reports.collect_multiple_runs(condition_report['test'],
condition_report['columns'], niter,
reports.summarize_all, **kwargs)
fig = reports.pvalue_plot(runs)
fig.savefig('conditional_pivots.pdf')
def report(niter=50, **kwargs):
fixedX_report = reports.reports['test_fixedX']
runs = reports.collect_multiple_runs(fixedX_report['test'],
fixedX_report['columns'], niter,
reports.summarize_all, **kwargs)
fig = reports.pvalue_plot(runs)
fig.savefig(
'fixedX_pivots.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_cv_corrected_nonrandomized_lasso']
screened_results = reports.collect_multiple_runs(
intervals_report['test'], intervals_report['columns'], niter,
reports.summarize_all, **kwargs)
screened_results.to_pickle("cv_corrected_nonrandomized_lasso.pkl")
results = pd.read_pickle("cv_corrected_nonrandomized_lasso.pkl")
fig = reports.pvalue_plot(results, label='CV corrected')
fig.suptitle("CV corrected norandomized Lasso pivots", fontsize=20)
fig.savefig('cv_corrected_nonrandomized_lasso_pivots.pdf')
def report(niter=50, **kwargs):
# these are all our null tests
fn_names = ['test_threshold_score']
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.savefig(
'threshold_pvalues.pdf') # will have both bootstrap and CLT on plot
def report(niter=50, **kwargs):
# these are all our null tests
fn_names = ['test_parametric_covariance_small',
'test_multiple_queries_small',
'test_multiple_queries_individual_coeff_small']
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.savefig('randomization_to_zero_pvalues.pdf') # will have both bootstrap and CLT on plot
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