def test_exact_ties(self):
adj_models = self.models_df - 100.0
adj_models.iloc[:, :2] -= adj_models.iloc[:, :2].mean()
adj_models.iloc[:, :2] += self.benchmark_df.mean().iloc[0]
stepm = StepM(self.benchmark_df, adj_models, size=0.10)
stepm.compute()
assert_equal(len(stepm.superior_models), self.models.shape[1] - 2)
def test_exact_ties(self):
adj_models = self.models_df - 100.0
adj_models.iloc[:, :2] -= adj_models.iloc[:,:2].mean()
adj_models.iloc[:, :2] += self.benchmark_df.mean().iloc[0]
stepm = StepM(self.benchmark_df, adj_models, size=0.10)
stepm.compute()
assert_equal(len(stepm.superior_models), self.models.shape[1] - 2)
def test_equivalence(self):
adj_models = self.models - linspace(-2.0, 2.0, self.k)
stepm = StepM(self.benchmark, adj_models, size=0.20, reps=200)
stepm.seed(23456)
stepm.compute()
adj_models = self.models_df - linspace(-2.0, 2.0, self.k)
stepm_pandas = StepM(self.benchmark_series, adj_models, size=0.20, reps=200)
stepm_pandas.seed(23456)
stepm_pandas.compute()
stepm_pandas.superior_models
numeric_locs = np.argwhere(adj_models.columns.isin(stepm_pandas.superior_models)).squeeze()
numeric_locs.sort()
assert_equal(np.array(stepm.superior_models), numeric_locs)
def test_str_repr(self):
stepm = StepM(self.benchmark_series, self.models, size=0.10)
expected = 'StepM(FWER (size): 0.10, studentization: asymptotic, bootstrap: ' + str(stepm.spa.bootstrap) + ')'
assert_equal(str(stepm), expected)
expected = expected[:-1] + ', ID: ' + hex(id(stepm)) + ')'
assert_equal(stepm.__repr__(), expected)
expected = ('<strong>StepM</strong>('
'<strong>FWER (size)</strong>: 0.10, '
'<strong>studentization</strong>: asymptotic, '
'<strong>bootstrap</strong>: ' + str(stepm.spa.bootstrap) + ', ' +
'<strong>ID</strong>: ' + hex(id(stepm)) + ')')
assert_equal(stepm._repr_html_(), expected)
stepm = StepM(self.benchmark_series, self.models, size=0.05, studentize=False)
expected = 'StepM(FWER (size): 0.05, studentization: none, bootstrap: ' + str(stepm.spa.bootstrap) + ')'
assert_equal(expected, str(stepm))
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_str_repr(self):
stepm = StepM(self.benchmark_series, self.models, size=0.10)
expected = 'StepM(FWER (size): 0.10, studentization: asymptotic, bootstrap: ' + str(
stepm.spa.bootstrap) + ')'
assert_equal(str(stepm), expected)
expected = expected[:-1] + ', ID: ' + hex(id(stepm)) + ')'
assert_equal(stepm.__repr__(), expected)
expected = ('<strong>StepM</strong>('
'<strong>FWER (size)</strong>: 0.10, '
'<strong>studentization</strong>: asymptotic, '
'<strong>bootstrap</strong>: ' + str(stepm.spa.bootstrap) +
', ' + '<strong>ID</strong>: ' + hex(id(stepm)) + ')')
assert_equal(stepm._repr_html_(), expected)
stepm = StepM(self.benchmark_series,
self.models,
size=0.05,
studentize=False)
expected = 'StepM(FWER (size): 0.05, studentization: none, bootstrap: ' + str(
stepm.spa.bootstrap) + ')'
assert_equal(expected, str(stepm))
def test_all_superior(self):
adj_models = self.models - 100.0
stepm = StepM(self.benchmark, adj_models, size=0.10)
stepm.compute()
assert_equal(len(stepm.superior_models), self.models.shape[1])
def test_single_model(self):
stepm = StepM(self.benchmark, self.models[:,0], size=0.10)
stepm.compute()
stepm = StepM(self.benchmark_series, self.models_df.iloc[:,0])
stepm.compute()
def test_errors(self):
stepm = StepM(self.benchmark, self.models, size=0.10)
with pytest.raises(RuntimeError):
stepm.superior_models
def test_all_superior(self):
adj_models = self.models - 100.0
stepm = StepM(self.benchmark, adj_models, size=0.10)
stepm.compute()
assert_equal(len(stepm.superior_models), self.models.shape[1])
def test_single_model(self):
stepm = StepM(self.benchmark, self.models[:, 0], size=0.10)
stepm.compute()
stepm = StepM(self.benchmark_series, self.models_df.iloc[:, 0])
stepm.compute()
def test_equivalence(self):
adj_models = self.models - linspace(-2.0, 2.0, self.k)
stepm = StepM(self.benchmark, adj_models, size=0.20, reps=200)
stepm.seed(23456)
stepm.compute()
adj_models = self.models_df - linspace(-2.0, 2.0, self.k)
stepm_pandas = StepM(self.benchmark_series,
adj_models,
size=0.20,
reps=200)
stepm_pandas.seed(23456)
stepm_pandas.compute()
stepm_pandas.superior_models
numeric_locs = np.argwhere(
adj_models.columns.isin(stepm_pandas.superior_models)).squeeze()
numeric_locs.sort()
assert_equal(np.array(stepm.superior_models), numeric_locs)
