def test_conditional_stochastic_warning(self):
data = pd.DataFrame()
data['A'] = [1]*50 + [0]*50
data['L'] = [1]*25 + [0]*25 + [1]*40 + [0]*10
data['Y'] = [1]*25 + [0]*25 + [1]*25 + [0]*25
g = TimeFixedGFormula(data, exposure='A', outcome='Y')
g.outcome_model(model='A + L + A:L', print_results=False)
with pytest.warns(UserWarning):
g.fit_stochastic(p=[1.0, 1.0], conditional=["(g['L']==1) | (g['L']==0)", "g['L']==0"])
def test_weight_stochastic_between_marginal(self, data):
g = TimeFixedGFormula(data, exposure='A', outcome='Y', weights='w')
g.outcome_model(model='A + L + A:L', print_results=False)
g.fit(treatment='all')
r1 = g.marginal_outcome
g.fit(treatment='none')
r0 = g.marginal_outcome
g.fit_stochastic(p=0.5)
r_star = g.marginal_outcome
assert r0 < r_star < r1
def test_conditional_weight_stochastic_matches_marginal(self, data):
g = TimeFixedGFormula(data, exposure='A', outcome='Y', weights='w')
g.outcome_model(model='A + L + A:L', print_results=False)
g.fit(treatment='all')
rm = g.marginal_outcome
g.fit_stochastic(p=[1.0, 1.0], conditional=["g['L']==1", "g['L']==0"])
rs = g.marginal_outcome
npt.assert_allclose(rm, rs, rtol=1e-7)
g.fit(treatment='none')
rn = g.marginal_outcome
g.fit_stochastic(p=[0.0, 0.0], conditional=["g['L']==1", "g['L']==0"])
rs = g.marginal_outcome
npt.assert_allclose(rn, rs, rtol=1e-7)
def test_weight_stochastic_matches_marginal(self, data):
g = TimeFixedGFormula(data, exposure='A', outcome='Y', weights='w')
g.outcome_model(model='A + L + A:L', print_results=False)
g.fit(treatment='all')
rm = g.marginal_outcome
g.fit_stochastic(p=1.0)
rs = g.marginal_outcome
npt.assert_allclose(rm, rs, rtol=1e-7)
g.fit(treatment='none')
rm = g.marginal_outcome
g.fit_stochastic(p=0.0)
rs = g.marginal_outcome
npt.assert_allclose(rm, rs, rtol=1e-7)
def test_stochastic_conditional_probability(self, data):
g = TimeFixedGFormula(data, exposure='A', outcome='Y')
g.outcome_model(model='A + L + A:L', print_results=False)
with pytest.raises(ValueError):
g.fit_stochastic(p=[0.0], conditional=["g['L']==1", "g['L']==0"])