def test_ipopt(df, intercept, slope):
true_params = [intercept, slope]
b_run = BinneyRun(col_success='success',
col_total='total',
covariates=['x1'],
df=df,
solver_method='ipopt')
b_run.fit()
rel_error = (b_run.params_opt - true_params) / true_params
assert all(rel_error < REL_TOL)
def test_splines(spline_df):
splines = {'x1': {'degree': 3, 'knots_num': 4, 'knots_type': 'frequency'}}
b_run = BinneyRun(col_success='success',
col_total='total',
df=spline_df,
splines=splines,
solver_method='ipopt')
b_run.fit()
predictions = b_run.predict()
np.testing.assert_array_almost_equal(x=predictions,
y=spline_df['p'].values,
decimal=1)
def test_fractional_outcomes():
p = np.repeat(np.exp(3) / (1 + np.exp(3)), repeats=1e3)
np.random.seed(100)
df = pd.DataFrame({
'success': np.random.binomial(n=1, size=len(p), p=p),
'total': np.repeat(1, repeats=len(p))
})
b_run = BinneyRun(col_success='success',
col_total='total',
df=df,
solver_method='scipy')
b_run.fit()
assert np.round(b_run.params_opt) == 3
def test_scipy_opt(df, intercept, slope):
true_params = [intercept, slope]
b_run = BinneyRun(col_success='success',
col_total='total',
covariates=['x1'],
df=df,
solver_method='scipy',
solver_options={'maxiter': 500})
b_run.fit()
rel_error = (b_run.params_opt - true_params) / true_params
assert all(rel_error < REL_TOL)
assert b_run.solver.predict().shape == (len(df), )
assert (0 <= b_run.solver.predict()).all()
assert (1 >= b_run.solver.predict()).all()
def test_unrecognized_solver(df):
with pytest.raises(RunException):
BinneyRun(col_success='success',
col_total='total',
covariates=['x1'],
df=df,
solver_method='my_random_solver')
def test_bootstrap_run(df, n):
np.random.seed(99)
b_run = BinneyRun(col_success='success',
col_total='total',
covariates=['x1'],
df=df,
solver_method='ipopt',
data_type='binomial')
b_run.fit()
b_run.make_uncertainty(n_boots=15)
assert np.vstack(b_run.bootstrap.parameters).shape == (15, 2)
np.testing.assert_array_almost_equal(b_run.bootstrap.parameters[0],
np.array([0.99940296, 1.99394325]))
np.testing.assert_array_almost_equal(b_run.bootstrap.parameters[5],
np.array([1.01366631, 1.99569274]))
np.testing.assert_array_almost_equal(np.vstack(
b_run.bootstrap.parameters).mean(axis=0),
b_run.params_opt,
decimal=1)
uis = np.quantile(np.vstack(b_run.bootstrap.parameters),
q=[0.025, 0.975],
axis=0)
assert all(b_run.params_opt > uis[0, :])
assert all(b_run.params_opt < uis[1, :])
draws = b_run.predict_draws(df=df)
assert draws.shape == (15, n)
assert all(draws.var(axis=1) > 0)
def test_spline_constraints_fail(spline_concave_df):
splines = {
'x1': {
'degree': 3,
'knots_num': 4,
'knots_type': 'frequency',
'convex': True
}
}
b_run = BinneyRun(col_success='success',
col_total='total',
df=spline_concave_df,
splines=splines,
solver_method='ipopt')
b_run.fit()
predictions = b_run.predict()
with pytest.raises(AssertionError):
np.testing.assert_array_almost_equal(x=predictions,
y=spline_concave_df['p'].values,
decimal=1)
def test_hierarchy_run(group_data):
b_run = BinneyRun(col_success='success',
col_total='total',
covariates=['x1'],
df=group_data,
solver_method='ipopt')
b_run.fit()
preds = b_run.predict(new_df=group_data)
b_run_grp = BinneyRun(col_success='success',
col_total='total',
covariates=['x1'],
df=group_data,
solver_method='ipopt',
col_group='g',
coefficient_prior_var=5.)
b_run_grp.fit()
preds_grp = b_run_grp.predict(new_df=group_data)
res = preds - group_data['p']
res_grp = preds_grp - group_data['p']
assert (np.abs(res) > np.abs(res_grp)).mean() >= 0.975
def test_new_data(df, new_df):
b_run = BinneyRun(col_success='success',
col_total='total',
covariates=['x1'],
df=df,
solver_method='ipopt')
b_run.fit()
predict_1 = b_run.predict()
predict_2 = b_run.predict(new_df=new_df)
assert predict_1.shape == predict_2.shape
assert all(predict_1 != predict_2)
def test_hierarchical_bootstrap(n, group_data):
np.random.seed(99)
n_boots = 30
b_run = BinneyRun(col_success='success',
col_total='total',
covariates=['x1'],
df=group_data,
col_group='g',
solver_method='scipy',
data_type='bernoulli')
b_run.fit()
b_run.make_uncertainty(n_boots=n_boots)
assert len(b_run.bootstrap.parameters) == n_boots
for param in b_run.bootstrap.parameters:
assert isinstance(param, Dict)
for group, item in param.items():
assert len(param[group]) == 2
draws = b_run.predict_draws(df=group_data)
assert draws.shape == (n_boots, n)
assert all(draws.var(axis=1) > 0)
assert (group_data['p'].values > np.quantile(draws, q=0.025, axis=0)).all()
assert (group_data['p'].values < np.quantile(draws, q=0.975, axis=0)).all()
def test_hierarchy_spline(group_data_2):
b_run_grp = BinneyRun(col_success='success',
col_total='total',
splines={
'x1': {
'degree': 3,
'knots_type': 'frequency',
'knots_num': 3
},
'x2': {
'degree': 3,
'knots_type': 'frequency',
'knots_num': 3
}
},
df=group_data_2,
solver_method='ipopt',
col_group='g',
coefficient_prior_var=5.)
b_run_grp.fit()
b_run_grp.predict(group_data_2)