def test_superior_models(self):
adj_models = self.models - linspace(-0.4, 0.4, self.k)
stepm = StepM(self.benchmark, adj_models, reps=120)
stepm.compute()
superior_models = stepm.superior_models
spa = SPA(self.benchmark, adj_models, reps=120)
spa.compute()
spa.pvalues
spa.critical_values(0.05)
spa.better_models(0.05)
adj_models = self.models_df - linspace(-3.0, 3.0, self.k)
stepm = StepM(self.benchmark_series, adj_models, reps=120)
stepm.compute()
superior_models = stepm.superior_models
def test_superior_models(self):
adj_models = self.models - linspace(-0.4, 0.4, self.k)
stepm = StepM(self.benchmark, adj_models, reps=120)
stepm.compute()
superior_models = stepm.superior_models
spa = SPA(self.benchmark, adj_models, reps=120)
spa.compute()
spa.pvalues
spa.critical_values(0.05)
spa.better_models(0.05)
adj_models = self.models_df - linspace(-3.0, 3.0, self.k)
stepm = StepM(self.benchmark_series, adj_models, reps=120)
stepm.compute()
superior_models = stepm.superior_models
def test_errors(self):
spa = SPA(self.benchmark, self.models, reps=100)
with pytest.raises(RuntimeError):
spa.pvalues
with pytest.raises(RuntimeError):
spa.critical_values()
with pytest.raises(RuntimeError):
spa.better_models()
with pytest.raises(ValueError):
SPA(self.benchmark, self.models, bootstrap='unknown')
spa.compute()
with pytest.raises(ValueError):
spa.better_models(pvalue_type='unknown')
with pytest.raises(ValueError):
spa.critical_values(pvalue=1.0)
def test_pvalues_and_critvals(self):
spa = SPA(self.benchmark, self.models, reps=100)
spa.compute()
spa.seed(23456)
simulated_vals = spa._simulated_vals
max_stats = np.max(simulated_vals, 0)
max_loss_diff = np.max(spa._loss_diff.mean(0), 0)
pvalues = np.mean(max_loss_diff <= max_stats, 0)
pvalues = pd.Series(pvalues, index=['lower', 'consistent', 'upper'])
assert_series_equal(pvalues, spa.pvalues)
crit_vals = np.percentile(max_stats, 90.0, axis=0)
crit_vals = pd.Series(crit_vals, index=['lower', 'consistent', 'upper'])
assert_series_equal(spa.critical_values(0.10), crit_vals)
def test_pvalues_and_critvals(self):
spa = SPA(self.benchmark, self.models, reps=100)
spa.compute()
spa.seed(23456)
simulated_vals = spa._simulated_vals
max_stats = np.max(simulated_vals, 0)
max_loss_diff = np.max(spa._loss_diff.mean(0), 0)
pvalues = np.mean(max_loss_diff <= max_stats, 0)
pvalues = pd.Series(pvalues, index=['lower', 'consistent', 'upper'])
assert_series_equal(pvalues, spa.pvalues)
crit_vals = np.percentile(max_stats, 90.0, axis=0)
crit_vals = pd.Series(crit_vals,
index=['lower', 'consistent', 'upper'])
assert_series_equal(spa.critical_values(0.10), crit_vals)
