def test_logistic_regression_categoric():
y = pd.Series(np.random.choice(["a", "b"], 50), dtype="category")
data = pd.DataFrame({"y": y, "x": np.random.normal(size=50)})
model = Model("y ~ x", data, family="bernoulli")
model.fit()
def test_empty_model(crossed_data):
model0 = Model("Y ~ 0", crossed_data)
model0.fit(tune=0, draws=1)
def test_cell_means_with_many_group_specific_effects(crossed_data):
# Group specific intercepts are added in different way, but the final result should be the same.
formula = "Y ~" + "+".join([
"0",
"threecats",
"(threecats|subj)",
"(1|subj)",
"(0 + continuous|item)",
"(dummy|item)",
"(0 + threecats|site)",
"(1|site)",
])
model0 = Model(formula, crossed_data)
model0.fit(tune=0, draws=1)
formula = "Y ~" + "+".join([
"0",
"threecats",
"(threecats|subj)",
"(continuous|item)",
"(dummy|item)",
"(threecats|site)",
])
model1 = Model(formula, crossed_data)
model1.fit(tune=0, draws=1)
# check that the group specific effects design matrices have the same shape
X0 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.group_specific_terms.values()
],
axis=1,
)
X1 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.group_specific_terms.values()
],
axis=1,
)
assert X0.shape == X1.shape
# check that the group specific effect design matrix contain the same columns,
# even if term names / columns names / order of columns is different
X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns)))
X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns)))
assert X0_set == X1_set
# check that common effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set([
tuple(t.data[:, lev]) for t in model0.common_terms.values()
for lev in range(len(t.levels))
])
X1 = set([
tuple(t.data[:, lev]) for t in model1.common_terms.values()
for lev in range(len(t.levels))
])
assert X0 == X1
# check that fit and add models have same priors for common effects
priors0 = {
x.name: x.prior.args
for x in model0.terms.values() if not x.group_specific
}
priors1 = {
x.name: x.prior.args
for x in model1.terms.values() if not x.group_specific
}
assert set(priors0) == set(priors1)
# check that fit and add models have same priors for group specific effects
priors0 = {
x.name: x.prior.args["sigma"].args
for x in model0.terms.values() if x.group_specific
}
priors1 = {
x.name: x.prior.args["sigma"].args
for x in model1.terms.values() if x.group_specific
}
assert set(priors0) == set(priors1)
def test_logistic_regression(crossed_data):
# Tests passing link="logit" is equivalent to using tt.nnet.sigmoid
model0 = Model("threecats['b'] ~ continuous + dummy",
crossed_data,
family="bernoulli",
link="logit")
fitted0 = model0.fit(
tune=0,
draws=1000,
)
# build model using fit, pymc3 and theano link function
model1 = Model(
"threecats['b'] ~ continuous + dummy",
crossed_data,
family="bernoulli",
link=tt.nnet.sigmoid,
)
fitted1 = model1.fit(
tune=0,
draws=1000,
)
# check that using a theano link function works
assert np.allclose(az.summary(fitted0)["mean"],
az.summary(fitted1)["mean"],
atol=0.2)
# check that term names agree
assert set(model0.term_names) == set(model1.term_names)
# check that common effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set([
tuple(t.data[:, lev]) for t in model0.common_terms.values()
for lev in range(len(t.levels))
])
X1 = set([
tuple(t.data[:, lev]) for t in model1.common_terms.values()
for lev in range(len(t.levels))
])
assert X0 == X1
# check that models have same priors for common effects
priors0 = {
x.name: x.prior.args
for x in model0.terms.values() if not x.group_specific
}
priors1 = {
x.name: x.prior.args
for x in model1.terms.values() if not x.group_specific
}
# check dictionary keys
assert set(priors0) == set(priors1)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
def test_many_common_many_group_specific(crossed_data):
# delete a few values to also test dropna=True functionality
crossed_data_missing = crossed_data.copy()
crossed_data_missing.loc[0, "Y"] = np.nan
crossed_data_missing.loc[1, "continuous"] = np.nan
crossed_data_missing.loc[2, "threecats"] = np.nan
# Here I'm comparing implicit/explicit intercepts for group specific effects workY the same way.
model0 = Model(
"Y ~ continuous + dummy + threecats + (threecats|subj) + (1|item) + (0+continuous|item) + (dummy|item) + (threecats|site)",
crossed_data_missing,
dropna=True,
)
model0.fit(
init=None,
tune=10,
draws=10,
chains=2,
)
model1 = Model(
"Y ~ continuous + dummy + threecats + (threecats|subj) + (continuous|item) + (dummy|item) + (threecats|site)",
crossed_data_missing,
dropna=True,
)
model1.fit(
tune=10,
draws=10,
chains=2,
)
# check that the group specific effects design matrices have the same shape
X0 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.group_specific_terms.values()
],
axis=1,
)
X1 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model1.group_specific_terms.values()
],
axis=1,
)
assert X0.shape == X1.shape
# check that the group specific effect design matrix contain the same columns,
# even if term names / columns names / order of columns is different
X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns)))
X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns)))
assert X0_set == X1_set
# check that common effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set([
tuple(t.data[:, lev]) for t in model0.common_terms.values()
for lev in range(len(t.levels))
])
X1 = set([
tuple(t.data[:, lev]) for t in model1.common_terms.values()
for lev in range(len(t.levels))
])
assert X0 == X1
# check that models have same priors for common effects
priors0 = {
x.name: x.prior.args
for x in model0.terms.values() if not x.group_specific
}
priors1 = {
x.name: x.prior.args
for x in model1.terms.values() if not x.group_specific
}
# check dictionary keys
assert set(priors0) == set(priors1)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
# check that fit and add models have same priors for group specific effects
priors0 = {
x.name: x.prior.args["sigma"].args
for x in model0.terms.values() if x.group_specific
}
priors1 = {
x.name: x.prior.args["sigma"].args
for x in model1.terms.values() if x.group_specific
}
# check dictionary keys
assert set(priors0) == set(priors1)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
def test_logistic_regression(crossed_data):
# build model using fit and pymc3
model0 = Model(crossed_data)
fitted = model0.fit('threecats[b] ~ continuous + dummy',
family='bernoulli',
link='logit',
backend='pymc3',
samples=100)
# model0.build()
# fitted = model0.fit()
# build model using add
model1 = Model(crossed_data)
model1.add('threecats[b] ~ 1', family='bernoulli', link='logit')
model1.add('continuous')
model1.add('dummy')
model1.build(backend='pymc3')
model1.fit(samples=1)
# build model using fit and stan
model2 = Model(crossed_data)
fitted2 = model2.fit('threecats[b] ~ continuous + dummy',
family='bernoulli',
link='logit',
backend='stan',
samples=100)
# check that term names agree
assert set(model0.term_names) == set(model1.term_names)
assert set(model0.term_names) == set(model2.term_names)
assert set(model1.term_names) == set(model2.term_names)
# check that fixed effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set([
tuple(t.data[:, lev]) for t in model0.fixed_terms.values()
for lev in range(len(t.levels))
])
X1 = set([
tuple(t.data[:, lev]) for t in model1.fixed_terms.values()
for lev in range(len(t.levels))
])
X2 = set([
tuple(t.data[:, lev]) for t in model2.fixed_terms.values()
for lev in range(len(t.levels))
])
assert X0 == X1
assert X0 == X2
assert X1 == X2
# check that models have same priors for fixed effects
priors0 = {
x.name: x.prior.args
for x in model0.terms.values() if not x.random
}
priors1 = {
x.name: x.prior.args
for x in model1.terms.values() if not x.random
}
priors2 = {
x.name: x.prior.args
for x in model2.terms.values() if not x.random
}
# check dictionary keys
assert set(priors0) == set(priors1)
assert set(priors0) == set(priors2)
assert set(priors1) == set(priors2)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
assert all([dicts_close(priors0[x], priors2[x]) for x in priors0.keys()])
assert all([dicts_close(priors1[x], priors2[x]) for x in priors0.keys()])
# test that summary reminds user which event is being modeled
fitted.summary(quantiles=.5)
# test that traceplot reminds user which event is being modeled
fitted.plot()
def test_logistic_regression_empty_index():
data = pd.DataFrame({"y": np.random.choice(["a", "b"], 50), "x": np.random.normal(size=50)})
model = Model("y ~ x", data, family="bernoulli")
model.fit()
def test_cell_means_parameterization(crossed_data):
model0 = Model("Y ~ 0 + threecats", crossed_data)
model0.fit(tune=0, draws=1, init=None)
def test_invalid_chars_in_random_effect(diabetes_data):
model = Model(diabetes_data)
with pytest.raises(ValueError):
model.fit(random=['1+BP|age_grp'])
def test_model_term_names_property(diabetes_data):
model = Model(diabetes_data)
model.fit("BMI ~ age_grp + BP + S1", run=False)
assert model.term_names == ["Intercept", "age_grp", "BP", "S1"]
def test_logistic_regression(crossed_data):
# build model using fit and pymc3
model0 = Model(crossed_data)
fitted0 = model0.fit(
"threecats[b] ~ continuous + dummy",
family="bernoulli",
link="logit",
backend="pymc3",
tune=0,
samples=1000,
)
# model0.build()
# fitted0 = model0.fit()
# build model using add
model1 = Model(crossed_data)
model1.add("threecats[b] ~ 1", family="bernoulli", link="logit")
model1.add("continuous")
model1.add("dummy")
model1.build(backend="pymc3")
model1.fit(tune=0, samples=1)
# build model using fit and stan
model2 = Model(crossed_data)
fitted2 = model2.fit(
"threecats[b] ~ continuous + dummy",
family="bernoulli",
link="logit",
backend="stan",
samples=100,
)
# build model using fit, pymc3 and theano link function
model3 = Model(crossed_data)
fitted3 = model3.fit(
"threecats[b] ~ continuous + dummy",
family="bernoulli",
link=tt.nnet.sigmoid,
backend="pymc3",
tune=0,
samples=1000,
)
# check that using a theano link function works
assert np.allclose(az.summary(fitted0)["mean"],
az.summary(fitted3)["mean"],
atol=0.2)
# check that term names agree
assert set(model0.term_names) == set(model1.term_names)
assert set(model0.term_names) == set(model2.term_names)
assert set(model1.term_names) == set(model2.term_names)
# check that fixed effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set([
tuple(t.data[:, lev]) for t in model0.fixed_terms.values()
for lev in range(len(t.levels))
])
X1 = set([
tuple(t.data[:, lev]) for t in model1.fixed_terms.values()
for lev in range(len(t.levels))
])
X2 = set([
tuple(t.data[:, lev]) for t in model2.fixed_terms.values()
for lev in range(len(t.levels))
])
assert X0 == X1
assert X0 == X2
assert X1 == X2
# check that models have same priors for fixed effects
priors0 = {
x.name: x.prior.args
for x in model0.terms.values() if not x.random
}
priors1 = {
x.name: x.prior.args
for x in model1.terms.values() if not x.random
}
priors2 = {
x.name: x.prior.args
for x in model2.terms.values() if not x.random
}
# check dictionary keys
assert set(priors0) == set(priors1)
assert set(priors0) == set(priors2)
assert set(priors1) == set(priors2)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
assert all([dicts_close(priors0[x], priors2[x]) for x in priors0.keys()])
assert all([dicts_close(priors1[x], priors2[x]) for x in priors0.keys()])
def test_cell_means_with_many_random_effects(crossed_data):
# build model using fit
model0 = Model(crossed_data)
model0.fit(
"Y ~ 0 + threecats",
random=[
"0+threecats|subj", "continuous|item", "dummy|item",
"threecats|site"
],
run=False,
)
model0.build(backend="pymc3")
# model0.fit(tune=0, samples=1)
# build model using add(append=True)
model1 = Model(crossed_data)
model1.add("Y ~ 0")
model1.add("0 + threecats")
model1.add(random="0+threecats|subj")
model1.add(random="1|item")
model1.add(random="0+continuous|item")
model1.add(random="dummy|item")
model1.add(random="threecats|site")
model1.build(backend="pymc3")
# model1.fit(tune=0, samples=1)
# check that the random effects design matrices have the same shape
X0 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.random_terms.values()
],
axis=1,
)
X1 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.random_terms.values()
],
axis=1,
)
assert X0.shape == X1.shape
# check that the random effect design matrix contain the same columns,
# even if term names / columns names / order of columns is different
X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns)))
X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns)))
assert X0_set == X1_set
# check that fixed effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set([
tuple(t.data[:, lev]) for t in model0.fixed_terms.values()
for lev in range(len(t.levels))
])
X1 = set([
tuple(t.data[:, lev]) for t in model1.fixed_terms.values()
for lev in range(len(t.levels))
])
assert X0 == X1
# check that fit and add models have same priors for fixed
# effects
priors0 = {
x.name: x.prior.args
for x in model0.terms.values() if not x.random
}
priors1 = {
x.name: x.prior.args
for x in model1.terms.values() if not x.random
}
assert set(priors0) == set(priors1)
# check that fit and add models have same priors for random
# effects
priors0 = {
x.name: x.prior.args["sd"].args
for x in model0.terms.values() if x.random
}
priors1 = {
x.name: x.prior.args["sd"].args
for x in model1.terms.values() if x.random
}
assert set(priors0) == set(priors1)
def test_many_fixed_many_random(crossed_data):
# delete a few values to also test dropna=True functionality
crossed_data_missing = crossed_data.copy()
crossed_data_missing.loc[0, "Y"] = np.nan
crossed_data_missing.loc[1, "continuous"] = np.nan
crossed_data_missing.loc[2, "threecats"] = np.nan
# build model using fit
model0 = Model(crossed_data_missing, dropna=True)
fitted = model0.fit(
"Y ~ continuous + dummy + threecats",
random=[
"0+threecats|subj", "1|item", "0+continuous|item", "dummy|item",
"threecats|site"
],
backend="pymc3",
init=None,
tune=10,
samples=10,
chains=2,
)
# model0.build(backend='pymc3')
# model0.fit(tune=0, samples=1)
# build model using add(append=True)
model1 = Model(crossed_data_missing, dropna=True)
model1.add("Y ~ 1")
model1.add("continuous")
model1.add("dummy")
model1.add("threecats")
model1.add(random="0+threecats|subj")
model1.add(random="1|item")
model1.add(random="0+continuous|item")
model1.add(random="dummy|item")
model1.add(random="threecats|site")
model1.build(backend="pymc3")
# model1.fit(tune=0, samples=1)
# build model using stan backend
model2 = Model(crossed_data_missing, dropna=True)
fitted2 = model2.fit(
"Y ~ continuous + dummy + threecats",
random=[
"0+threecats|subj", "1|item", "0+continuous|item", "dummy|item",
"threecats|site"
],
backend="stan",
samples=100,
chains=2,
)
# check that the random effects design matrices have the same shape
X0 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.random_terms.values()
],
axis=1,
)
X1 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model1.random_terms.values()
],
axis=1,
)
X2 = pd.concat(
[
pd.DataFrame(t.data) if not isinstance(t.data, dict) else
pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model2.random_terms.values()
],
axis=1,
)
assert X0.shape == X1.shape
assert X0.shape == X2.shape
assert X1.shape == X2.shape
# check that the random effect design matrix contain the same columns,
# even if term names / columns names / order of columns is different
X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns)))
X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns)))
X2_set = set(tuple(X1.iloc[:, i]) for i in range(len(X2.columns)))
assert X0_set == X1_set
assert X0_set == X2_set
assert X1_set == X2_set
# check that fixed effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set([
tuple(t.data[:, lev]) for t in model0.fixed_terms.values()
for lev in range(len(t.levels))
])
X1 = set([
tuple(t.data[:, lev]) for t in model1.fixed_terms.values()
for lev in range(len(t.levels))
])
X2 = set([
tuple(t.data[:, lev]) for t in model2.fixed_terms.values()
for lev in range(len(t.levels))
])
assert X0 == X1
assert X0 == X2
assert X1 == X2
# check that models have same priors for fixed effects
priors0 = {
x.name: x.prior.args
for x in model0.terms.values() if not x.random
}
priors1 = {
x.name: x.prior.args
for x in model1.terms.values() if not x.random
}
priors2 = {
x.name: x.prior.args
for x in model2.terms.values() if not x.random
}
# check dictionary keys
assert set(priors0) == set(priors1)
assert set(priors0) == set(priors2)
assert set(priors1) == set(priors2)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
assert all([dicts_close(priors0[x], priors2[x]) for x in priors0.keys()])
assert all([dicts_close(priors1[x], priors2[x]) for x in priors0.keys()])
# check that fit and add models have same priors for random effects
priors0 = {
x.name: x.prior.args["sd"].args
for x in model0.terms.values() if x.random
}
priors1 = {
x.name: x.prior.args["sd"].args
for x in model1.terms.values() if x.random
}
priors2 = {
x.name: x.prior.args["sd"].args
for x in model2.terms.values() if x.random
}
# check dictionary keys
assert set(priors0) == set(priors1)
assert set(priors0) == set(priors2)
assert set(priors1) == set(priors2)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
assert all([dicts_close(priors0[x], priors2[x]) for x in priors0.keys()])
assert all([dicts_close(priors1[x], priors2[x]) for x in priors0.keys()])
def test_intercept_only_model(crossed_data):
model0 = Model("Y ~ 1", crossed_data)
model0.fit(tune=0, draws=1, init=None)
def test_logistic_regression_good_numeric():
data = pd.DataFrame({"y": np.random.choice([1, 0], 50), "x": np.random.normal(size=50)})
model = Model("y ~ x", data, family="bernoulli")
model.fit()
def test_slope_only_model(crossed_data):
model0 = Model("Y ~ 0 + continuous", crossed_data)
model0.fit(tune=0, draws=1, init=None)
def test_logistic_regression_bad_numeric():
data = pd.DataFrame({"y": np.random.choice([1, 2], 50), "x": np.random.normal(size=50)})
with pytest.raises(ValueError):
model = Model("y ~ x", data, family="bernoulli")
model.fit()
def test_cell_means_with_covariate(crossed_data):
model0 = Model("Y ~ 0 + threecats + continuous", crossed_data)
model0.fit(tune=0, draws=1, init=None)
# check that threecats priors have finite variance
assert not np.isinf(model0.terms["threecats"].prior.args["sigma"])
def test_one_shot_formula_fit(diabetes_data):
model = Model("S3 ~ S1 + S2", diabetes_data)
model.fit(draws=50)
named_vars = model.backend.model.named_vars
targets = ["S3", "S1", "Intercept"]
assert len(set(named_vars.keys()) & set(targets)) == 3
def test_poisson_regression(crossed_data):
# build model using fit and pymc3
crossed_data["count"] = (crossed_data["Y"] - crossed_data["Y"].min()).round()
model0 = Model(crossed_data)
fitted = model0.fit(
"count ~ threecats + continuous + dummy",
family="poisson",
backend="pymc3",
tune=0,
samples=1,
init=None,
)
# model0.build()
# model0.fit()
# build model using add
model1 = Model(crossed_data)
model1.add("count ~ 1", family="poisson")
model1.add("threecats")
model1.add("continuous")
model1.add("dummy")
model1.build(backend="pymc3")
model1.fit(tune=0, samples=1, init=None)
# build model using fit and stan
model2 = Model(crossed_data)
fitted2 = model2.fit(
"count ~ threecats + continuous + dummy", family="poisson", backend="stan", samples=1
)
# check that term names agree
assert set(model0.term_names) == set(model1.term_names)
assert set(model0.term_names) == set(model2.term_names)
assert set(model1.term_names) == set(model2.term_names)
# check that fixed effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set(
[tuple(t.data[:, lev]) for t in model0.fixed_terms.values() for lev in range(len(t.levels))]
)
X1 = set(
[tuple(t.data[:, lev]) for t in model1.fixed_terms.values() for lev in range(len(t.levels))]
)
X2 = set(
[tuple(t.data[:, lev]) for t in model2.fixed_terms.values() for lev in range(len(t.levels))]
)
assert X0 == X1
assert X0 == X2
assert X1 == X2
# check that models have same priors for fixed effects
priors0 = {x.name: x.prior.args for x in model0.terms.values() if not x.random}
priors1 = {x.name: x.prior.args for x in model1.terms.values() if not x.random}
priors2 = {x.name: x.prior.args for x in model2.terms.values() if not x.random}
# check dictionary keys
assert set(priors0) == set(priors1)
assert set(priors0) == set(priors2)
assert set(priors1) == set(priors2)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
assert all([dicts_close(priors0[x], priors2[x]) for x in priors0.keys()])
assert all([dicts_close(priors1[x], priors2[x]) for x in priors0.keys()])
def test_many_fixed_many_random(crossed_data):
# delete a few values to also test dropna=True functionality
crossed_data_missing = crossed_data.copy()
crossed_data_missing.loc[0, 'Y'] = np.nan
crossed_data_missing.loc[1, 'continuous'] = np.nan
crossed_data_missing.loc[2, 'threecats'] = np.nan
# build model using fit
model0 = Model(crossed_data_missing, dropna=True)
fitted = model0.fit('Y ~ continuous + dummy + threecats',
random=[
'0+threecats|subj', '1|item', '0+continuous|item',
'dummy|item', 'threecats|site'
],
backend='pymc3',
samples=10,
chains=2)
# model0.build(backend='pymc3')
# model0.fit(samples=1)
# build model using add(append=True)
model1 = Model(crossed_data_missing, dropna=True)
model1.add('Y ~ 1')
model1.add('continuous')
model1.add('dummy')
model1.add('threecats')
model1.add(random='0+threecats|subj')
model1.add(random='1|item')
model1.add(random='0+continuous|item')
model1.add(random='dummy|item')
model1.add(random='threecats|site')
model1.build(backend='pymc3')
# model1.fit(samples=1)
# build model using stan backend
model2 = Model(crossed_data_missing, dropna=True)
fitted2 = model2.fit('Y ~ continuous + dummy + threecats',
random=[
'0+threecats|subj', '1|item', '0+continuous|item',
'dummy|item', 'threecats|site'
],
backend='stan',
samples=100,
chains=2)
# check that the random effects design matrices have the same shape
X0 = pd.concat([
pd.DataFrame(t.data) if not isinstance(t.data, dict) else pd.concat(
[pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model0.random_terms.values()
],
axis=1)
X1 = pd.concat([
pd.DataFrame(t.data) if not isinstance(t.data, dict) else pd.concat(
[pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model1.random_terms.values()
],
axis=1)
X2 = pd.concat([
pd.DataFrame(t.data) if not isinstance(t.data, dict) else pd.concat(
[pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1)
for t in model2.random_terms.values()
],
axis=1)
assert X0.shape == X1.shape
assert X0.shape == X2.shape
assert X1.shape == X2.shape
# check that the random effect design matrix contain the same columns,
# even if term names / columns names / order of columns is different
X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns)))
X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns)))
X2_set = set(tuple(X1.iloc[:, i]) for i in range(len(X2.columns)))
assert X0_set == X1_set
assert X0_set == X2_set
assert X1_set == X2_set
# check that fixed effect design matrices are the same,
# even if term names / level names / order of columns is different
X0 = set([
tuple(t.data[:, lev]) for t in model0.fixed_terms.values()
for lev in range(len(t.levels))
])
X1 = set([
tuple(t.data[:, lev]) for t in model1.fixed_terms.values()
for lev in range(len(t.levels))
])
X2 = set([
tuple(t.data[:, lev]) for t in model2.fixed_terms.values()
for lev in range(len(t.levels))
])
assert X0 == X1
assert X0 == X2
assert X1 == X2
# check that models have same priors for fixed effects
priors0 = {
x.name: x.prior.args
for x in model0.terms.values() if not x.random
}
priors1 = {
x.name: x.prior.args
for x in model1.terms.values() if not x.random
}
priors2 = {
x.name: x.prior.args
for x in model2.terms.values() if not x.random
}
# check dictionary keys
assert set(priors0) == set(priors1)
assert set(priors0) == set(priors2)
assert set(priors1) == set(priors2)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
assert all([dicts_close(priors0[x], priors2[x]) for x in priors0.keys()])
assert all([dicts_close(priors1[x], priors2[x]) for x in priors0.keys()])
# check that fit and add models have same priors for random effects
priors0 = {
x.name: x.prior.args['sd'].args
for x in model0.terms.values() if x.random
}
priors1 = {
x.name: x.prior.args['sd'].args
for x in model1.terms.values() if x.random
}
priors2 = {
x.name: x.prior.args['sd'].args
for x in model2.terms.values() if x.random
}
# check dictionary keys
assert set(priors0) == set(priors1)
assert set(priors0) == set(priors2)
assert set(priors1) == set(priors2)
# check dictionary values
def dicts_close(a, b):
if set(a) != set(b):
return False
else:
return [
np.allclose(a[x], b[x], atol=0, rtol=.01) for x in a.keys()
]
assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()])
assert all([dicts_close(priors0[x], priors2[x]) for x in priors0.keys()])
assert all([dicts_close(priors1[x], priors2[x]) for x in priors0.keys()])
# test consistency between summary and to_df for pymc3
assert len(set(fitted.to_df().columns)) == 15
assert set(fitted.to_df().columns) == set(fitted.summary().index)
# test consistency between summary and to_df for stan
assert len(set(fitted2.to_df().columns)) == 15
assert set(fitted2.to_df().columns) == set(fitted2.summary().index)
# test consistenct between pymc3 and stan
assert set(fitted.to_df().columns) == set(fitted2.to_df().columns)
assert set(fitted.summary().index) == set(fitted2.summary().index)
# check hide_transformed for pymc3
# it looks like some versions of pymc3 add a trailing '_' to transformed
# vars and some dont. so here for consistency we strip out any trailing '_'
# that we find
full = fitted.summary(ranefs=True, transformed=True).index
full = set([re.sub(r'_$', r'', x) for x in full])
test_set = fitted.summary(ranefs=True, transformed=False).index
test_set = set([re.sub(r'_$', r'', x) for x in test_set])
answer = {
'1|item_offset[0]', '1|item_offset[10]', '1|item_offset[11]',
'1|item_offset[1]', '1|item_offset[2]', '1|item_offset[3]',
'1|item_offset[4]', '1|item_offset[5]', '1|item_offset[6]',
'1|item_offset[7]', '1|item_offset[8]', '1|item_offset[9]',
'1|item_sd_log', '1|site_offset[0]', '1|site_offset[1]',
'1|site_offset[2]', '1|site_offset[3]', '1|site_offset[4]',
'1|site_sd_log', 'Y_sd_interval', 'continuous|item_offset[0]',
'continuous|item_offset[10]', 'continuous|item_offset[11]',
'continuous|item_offset[1]', 'continuous|item_offset[2]',
'continuous|item_offset[3]', 'continuous|item_offset[4]',
'continuous|item_offset[5]', 'continuous|item_offset[6]',
'continuous|item_offset[7]', 'continuous|item_offset[8]',
'continuous|item_offset[9]', 'continuous|item_sd_log',
'dummy|item_offset[0]', 'dummy|item_offset[10]',
'dummy|item_offset[11]', 'dummy|item_offset[1]',
'dummy|item_offset[2]', 'dummy|item_offset[3]', 'dummy|item_offset[4]',
'dummy|item_offset[5]', 'dummy|item_offset[6]', 'dummy|item_offset[7]',
'dummy|item_offset[8]', 'dummy|item_offset[9]', 'dummy|item_sd_log',
'threecats[T.b]|site_offset[0]', 'threecats[T.b]|site_offset[1]',
'threecats[T.b]|site_offset[2]', 'threecats[T.b]|site_offset[3]',
'threecats[T.b]|site_offset[4]', 'threecats[T.b]|site_sd_log',
'threecats[T.c]|site_offset[0]', 'threecats[T.c]|site_offset[1]',
'threecats[T.c]|site_offset[2]', 'threecats[T.c]|site_offset[3]',
'threecats[T.c]|site_offset[4]', 'threecats[T.c]|site_sd_log',
'threecats[a]|subj_offset[0]', 'threecats[a]|subj_offset[1]',
'threecats[a]|subj_offset[2]', 'threecats[a]|subj_offset[3]',
'threecats[a]|subj_offset[4]', 'threecats[a]|subj_offset[5]',
'threecats[a]|subj_offset[6]', 'threecats[a]|subj_offset[7]',
'threecats[a]|subj_offset[8]', 'threecats[a]|subj_offset[9]',
'threecats[a]|subj_sd_log', 'threecats[b]|subj_offset[0]',
'threecats[b]|subj_offset[1]', 'threecats[b]|subj_offset[2]',
'threecats[b]|subj_offset[3]', 'threecats[b]|subj_offset[4]',
'threecats[b]|subj_offset[5]', 'threecats[b]|subj_offset[6]',
'threecats[b]|subj_offset[7]', 'threecats[b]|subj_offset[8]',
'threecats[b]|subj_offset[9]', 'threecats[b]|subj_sd_log',
'threecats[c]|subj_offset[0]', 'threecats[c]|subj_offset[1]',
'threecats[c]|subj_offset[2]', 'threecats[c]|subj_offset[3]',
'threecats[c]|subj_offset[4]', 'threecats[c]|subj_offset[5]',
'threecats[c]|subj_offset[6]', 'threecats[c]|subj_offset[7]',
'threecats[c]|subj_offset[8]', 'threecats[c]|subj_offset[9]',
'threecats[c]|subj_sd_log'
}
assert full.difference(test_set) == answer
# check hide_transformed for stan
full2 = fitted2.summary(ranefs=True, transformed=True)
full2 = set(full2.index)
test_set2 = fitted2.summary(ranefs=True, transformed=False)
test_set2 = set(test_set2.index)
answer = set(['lp__'] \
+ ['yhat[{}]'.format(i) for i in range(len(crossed_data.index)-3)] \
+ ['1|item_offset[0]','1|item_offset[10]','1|item_offset[11]',
'1|item_offset[1]','1|item_offset[2]','1|item_offset[3]',
'1|item_offset[4]','1|item_offset[5]','1|item_offset[6]',
'1|item_offset[7]','1|item_offset[8]','1|item_offset[9]',
'1|site_offset[0]','1|site_offset[1]',
'1|site_offset[2]','1|site_offset[3]','1|site_offset[4]',
'continuous|item_offset[0]',
'continuous|item_offset[10]','continuous|item_offset[11]',
'continuous|item_offset[1]','continuous|item_offset[2]',
'continuous|item_offset[3]','continuous|item_offset[4]',
'continuous|item_offset[5]','continuous|item_offset[6]',
'continuous|item_offset[7]','continuous|item_offset[8]',
'continuous|item_offset[9]',
'dummy|item_offset[0]','dummy|item_offset[10]','dummy|item_offset[11]',
'dummy|item_offset[1]','dummy|item_offset[2]','dummy|item_offset[3]',
'dummy|item_offset[4]','dummy|item_offset[5]','dummy|item_offset[6]',
'dummy|item_offset[7]','dummy|item_offset[8]','dummy|item_offset[9]',
'threecats[T.b]|site_offset[0]',
'threecats[T.b]|site_offset[1]','threecats[T.b]|site_offset[2]',
'threecats[T.b]|site_offset[3]','threecats[T.b]|site_offset[4]',
'threecats[T.c]|site_offset[0]',
'threecats[T.c]|site_offset[1]','threecats[T.c]|site_offset[2]',
'threecats[T.c]|site_offset[3]','threecats[T.c]|site_offset[4]',
'threecats[a]|subj_offset[0]',
'threecats[a]|subj_offset[1]','threecats[a]|subj_offset[2]',
'threecats[a]|subj_offset[3]','threecats[a]|subj_offset[4]',
'threecats[a]|subj_offset[5]','threecats[a]|subj_offset[6]',
'threecats[a]|subj_offset[7]','threecats[a]|subj_offset[8]',
'threecats[a]|subj_offset[9]',
'threecats[b]|subj_offset[0]','threecats[b]|subj_offset[1]',
'threecats[b]|subj_offset[2]','threecats[b]|subj_offset[3]',
'threecats[b]|subj_offset[4]','threecats[b]|subj_offset[5]',
'threecats[b]|subj_offset[6]','threecats[b]|subj_offset[7]',
'threecats[b]|subj_offset[8]','threecats[b]|subj_offset[9]',
'threecats[c]|subj_offset[0]',
'threecats[c]|subj_offset[1]','threecats[c]|subj_offset[2]',
'threecats[c]|subj_offset[3]','threecats[c]|subj_offset[4]',
'threecats[c]|subj_offset[5]','threecats[c]|subj_offset[6]',
'threecats[c]|subj_offset[7]','threecats[c]|subj_offset[8]',
'threecats[c]|subj_offset[9]'])
assert full2.difference(test_set2) == answer
# check for consistency in parameter names between pymc3 and stan
assert test_set == test_set2
# check exclude_ranefs for pymc3
test_set = fitted.summary(ranefs=False, transformed=True).index
test_set = set([re.sub(r'_$', r'', x) for x in test_set])
answer = {
'1|item[0]', '1|item[10]', '1|item[11]', '1|item[1]', '1|item[2]',
'1|item[3]', '1|item[4]', '1|item[5]', '1|item[6]', '1|item[7]',
'1|item[8]', '1|item[9]', '1|item_offset[0]', '1|item_offset[10]',
'1|item_offset[11]', '1|item_offset[1]', '1|item_offset[2]',
'1|item_offset[3]', '1|item_offset[4]', '1|item_offset[5]',
'1|item_offset[6]', '1|item_offset[7]', '1|item_offset[8]',
'1|item_offset[9]', '1|site[0]', '1|site[1]', '1|site[2]', '1|site[3]',
'1|site[4]', '1|site_offset[0]', '1|site_offset[1]',
'1|site_offset[2]', '1|site_offset[3]', '1|site_offset[4]',
'continuous|item[0]', 'continuous|item[10]', 'continuous|item[11]',
'continuous|item[1]', 'continuous|item[2]', 'continuous|item[3]',
'continuous|item[4]', 'continuous|item[5]', 'continuous|item[6]',
'continuous|item[7]', 'continuous|item[8]', 'continuous|item[9]',
'continuous|item_offset[0]', 'continuous|item_offset[10]',
'continuous|item_offset[11]', 'continuous|item_offset[1]',
'continuous|item_offset[2]', 'continuous|item_offset[3]',
'continuous|item_offset[4]', 'continuous|item_offset[5]',
'continuous|item_offset[6]', 'continuous|item_offset[7]',
'continuous|item_offset[8]', 'continuous|item_offset[9]',
'dummy|item[0]', 'dummy|item[10]', 'dummy|item[11]', 'dummy|item[1]',
'dummy|item[2]', 'dummy|item[3]', 'dummy|item[4]', 'dummy|item[5]',
'dummy|item[6]', 'dummy|item[7]', 'dummy|item[8]', 'dummy|item[9]',
'dummy|item_offset[0]', 'dummy|item_offset[10]',
'dummy|item_offset[11]', 'dummy|item_offset[1]',
'dummy|item_offset[2]', 'dummy|item_offset[3]', 'dummy|item_offset[4]',
'dummy|item_offset[5]', 'dummy|item_offset[6]', 'dummy|item_offset[7]',
'dummy|item_offset[8]', 'dummy|item_offset[9]',
'threecats[T.b]|site[0]', 'threecats[T.b]|site[1]',
'threecats[T.b]|site[2]', 'threecats[T.b]|site[3]',
'threecats[T.b]|site[4]', 'threecats[T.b]|site_offset[0]',
'threecats[T.b]|site_offset[1]', 'threecats[T.b]|site_offset[2]',
'threecats[T.b]|site_offset[3]', 'threecats[T.b]|site_offset[4]',
'threecats[T.c]|site[0]', 'threecats[T.c]|site[1]',
'threecats[T.c]|site[2]', 'threecats[T.c]|site[3]',
'threecats[T.c]|site[4]', 'threecats[T.c]|site_offset[0]',
'threecats[T.c]|site_offset[1]', 'threecats[T.c]|site_offset[2]',
'threecats[T.c]|site_offset[3]', 'threecats[T.c]|site_offset[4]',
'threecats[a]|subj[0]', 'threecats[a]|subj[1]', 'threecats[a]|subj[2]',
'threecats[a]|subj[3]', 'threecats[a]|subj[4]', 'threecats[a]|subj[5]',
'threecats[a]|subj[6]', 'threecats[a]|subj[7]', 'threecats[a]|subj[8]',
'threecats[a]|subj[9]', 'threecats[a]|subj_offset[0]',
'threecats[a]|subj_offset[1]', 'threecats[a]|subj_offset[2]',
'threecats[a]|subj_offset[3]', 'threecats[a]|subj_offset[4]',
'threecats[a]|subj_offset[5]', 'threecats[a]|subj_offset[6]',
'threecats[a]|subj_offset[7]', 'threecats[a]|subj_offset[8]',
'threecats[a]|subj_offset[9]', 'threecats[b]|subj[0]',
'threecats[b]|subj[1]', 'threecats[b]|subj[2]', 'threecats[b]|subj[3]',
'threecats[b]|subj[4]', 'threecats[b]|subj[5]', 'threecats[b]|subj[6]',
'threecats[b]|subj[7]', 'threecats[b]|subj[8]', 'threecats[b]|subj[9]',
'threecats[b]|subj_offset[0]', 'threecats[b]|subj_offset[1]',
'threecats[b]|subj_offset[2]', 'threecats[b]|subj_offset[3]',
'threecats[b]|subj_offset[4]', 'threecats[b]|subj_offset[5]',
'threecats[b]|subj_offset[6]', 'threecats[b]|subj_offset[7]',
'threecats[b]|subj_offset[8]', 'threecats[b]|subj_offset[9]',
'threecats[c]|subj[0]', 'threecats[c]|subj[1]', 'threecats[c]|subj[2]',
'threecats[c]|subj[3]', 'threecats[c]|subj[4]', 'threecats[c]|subj[5]',
'threecats[c]|subj[6]', 'threecats[c]|subj[7]', 'threecats[c]|subj[8]',
'threecats[c]|subj[9]', 'threecats[c]|subj_offset[0]',
'threecats[c]|subj_offset[1]', 'threecats[c]|subj_offset[2]',
'threecats[c]|subj_offset[3]', 'threecats[c]|subj_offset[4]',
'threecats[c]|subj_offset[5]', 'threecats[c]|subj_offset[6]',
'threecats[c]|subj_offset[7]', 'threecats[c]|subj_offset[8]',
'threecats[c]|subj_offset[9]'
}
assert full.difference(test_set) == answer
# check exclude_ranefs for stan
test_set2 = set(fitted2.summary(ranefs=False, transformed=True).index)
answer = set(['1|item[0]','1|item[10]','1|item[11]','1|item[1]','1|item[2]',
'1|item[3]','1|item[4]','1|item[5]','1|item[6]','1|item[7]','1|item[8]',
'1|item[9]','1|site[0]','1|site[1]','1|site[2]','1|site[3]','1|site[4]',
'continuous|item[0]','continuous|item[10]','continuous|item[11]',
'continuous|item[1]','continuous|item[2]','continuous|item[3]',
'continuous|item[4]','continuous|item[5]','continuous|item[6]',
'continuous|item[7]','continuous|item[8]','continuous|item[9]',
'dummy|item[0]','dummy|item[10]','dummy|item[11]','dummy|item[1]',
'dummy|item[2]','dummy|item[3]','dummy|item[4]','dummy|item[5]',
'dummy|item[6]','dummy|item[7]','dummy|item[8]','dummy|item[9]',
'threecats[T.b]|site[0]','threecats[T.b]|site[1]',
'threecats[T.b]|site[2]','threecats[T.b]|site[3]',
'threecats[T.b]|site[4]','threecats[T.c]|site[0]',
'threecats[T.c]|site[1]','threecats[T.c]|site[2]',
'threecats[T.c]|site[3]','threecats[T.c]|site[4]',
'threecats[a]|subj[0]','threecats[a]|subj[1]','threecats[a]|subj[2]',
'threecats[a]|subj[3]','threecats[a]|subj[4]','threecats[a]|subj[5]',
'threecats[a]|subj[6]','threecats[a]|subj[7]','threecats[a]|subj[8]',
'threecats[a]|subj[9]','threecats[b]|subj[0]','threecats[b]|subj[1]',
'threecats[b]|subj[2]','threecats[b]|subj[3]','threecats[b]|subj[4]',
'threecats[b]|subj[5]','threecats[b]|subj[6]','threecats[b]|subj[7]',
'threecats[b]|subj[8]','threecats[b]|subj[9]','threecats[c]|subj[0]',
'threecats[c]|subj[1]','threecats[c]|subj[2]','threecats[c]|subj[3]',
'threecats[c]|subj[4]','threecats[c]|subj[5]','threecats[c]|subj[6]',
'threecats[c]|subj[7]','threecats[c]|subj[8]','threecats[c]|subj[9]'] \
+ ['1|item_offset[0]','1|item_offset[10]','1|item_offset[11]',
'1|item_offset[1]','1|item_offset[2]','1|item_offset[3]',
'1|item_offset[4]','1|item_offset[5]','1|item_offset[6]',
'1|item_offset[7]','1|item_offset[8]','1|item_offset[9]',
'1|site_offset[0]','1|site_offset[1]',
'1|site_offset[2]','1|site_offset[3]','1|site_offset[4]',
'continuous|item_offset[0]',
'continuous|item_offset[10]','continuous|item_offset[11]',
'continuous|item_offset[1]','continuous|item_offset[2]',
'continuous|item_offset[3]','continuous|item_offset[4]',
'continuous|item_offset[5]','continuous|item_offset[6]',
'continuous|item_offset[7]','continuous|item_offset[8]',
'continuous|item_offset[9]',
'dummy|item_offset[0]','dummy|item_offset[10]','dummy|item_offset[11]',
'dummy|item_offset[1]','dummy|item_offset[2]','dummy|item_offset[3]',
'dummy|item_offset[4]','dummy|item_offset[5]','dummy|item_offset[6]',
'dummy|item_offset[7]','dummy|item_offset[8]','dummy|item_offset[9]',
'threecats[T.b]|site_offset[0]',
'threecats[T.b]|site_offset[1]','threecats[T.b]|site_offset[2]',
'threecats[T.b]|site_offset[3]','threecats[T.b]|site_offset[4]',
'threecats[T.c]|site_offset[0]',
'threecats[T.c]|site_offset[1]','threecats[T.c]|site_offset[2]',
'threecats[T.c]|site_offset[3]','threecats[T.c]|site_offset[4]',
'threecats[a]|subj_offset[0]',
'threecats[a]|subj_offset[1]','threecats[a]|subj_offset[2]',
'threecats[a]|subj_offset[3]','threecats[a]|subj_offset[4]',
'threecats[a]|subj_offset[5]','threecats[a]|subj_offset[6]',
'threecats[a]|subj_offset[7]','threecats[a]|subj_offset[8]',
'threecats[a]|subj_offset[9]',
'threecats[b]|subj_offset[0]','threecats[b]|subj_offset[1]',
'threecats[b]|subj_offset[2]','threecats[b]|subj_offset[3]',
'threecats[b]|subj_offset[4]','threecats[b]|subj_offset[5]',
'threecats[b]|subj_offset[6]','threecats[b]|subj_offset[7]',
'threecats[b]|subj_offset[8]','threecats[b]|subj_offset[9]',
'threecats[c]|subj_offset[0]',
'threecats[c]|subj_offset[1]','threecats[c]|subj_offset[2]',
'threecats[c]|subj_offset[3]','threecats[c]|subj_offset[4]',
'threecats[c]|subj_offset[5]','threecats[c]|subj_offset[6]',
'threecats[c]|subj_offset[7]','threecats[c]|subj_offset[8]',
'threecats[c]|subj_offset[9]'])
assert full2.difference(test_set2) == answer
# test plots for pymc3
fitted.plot(kind='priors')
fitted.plot()
# test plots for stan
fitted2.plot(kind='priors')
fitted2.plot()
