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Fix optimization tests for new cmdstan #855

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Merged
WardBrian merged 2 commits into from
May 20, 2026
+17 −17
Merged

Fix optimization tests for new cmdstan #855

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5ef72db
Fix optimization tests for new cmdstan
WardBrian May 19, 2026
f08c698
Also update gq tests
WardBrian May 19, 2026
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8 changes: 4 additions & 4 deletions test/test_generate_quantities.py
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Original file line number Diff line number Diff line change
Expand Up @@ -565,7 +565,7 @@ def test_from_optimization() -> None:
assert os.path.exists(csv_file)

assert bern_gqs.draws().shape == (1, 1, 10)
assert bern_gqs.draws(inc_sample=True).shape == (1, 1, 12)
assert bern_gqs.draws(inc_sample=True).shape == (1, 1, 13)

# draws_pd()
assert bern_gqs.draws_pd().shape == (1, 13)
Expand Down Expand Up @@ -611,18 +611,18 @@ def test_opt_save_iterations(caplog: pytest.LogCaptureFixture) -> None:
assert bern_gqs.draws(inc_warmup=True, inc_sample=True).shape == (
iters,
1,
12,
13,
)

assert bern_gqs.draws(concat_chains=True).shape == (1, 10)
assert bern_gqs.draws(concat_chains=True, inc_sample=True).shape == (1, 12)
assert bern_gqs.draws(concat_chains=True, inc_sample=True).shape == (1, 13)
assert bern_gqs.draws(concat_chains=True, inc_warmup=True).shape == (
iters,
10,
)
assert bern_gqs.draws(
concat_chains=True, inc_warmup=True, inc_sample=True
).shape == (iters, 12)
).shape == (iters, 13)

# stan_variable
theta = bern_gqs.stan_variable(var='theta')
Expand Down
26 changes: 13 additions & 13 deletions test/test_optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,13 +95,13 @@ def test_rosenbrock(caplog: pytest.LogCaptureFixture) -> None:
assert 'CmdStanMLE: model=rosenbrock' in repr(mle)
assert 'method=optimize' in repr(mle)
assert mle.converged
assert mle.column_names == ('lp__', 'x', 'y')
assert mle.column_names == ('lp__', 'converged__', 'x', 'y')
np.testing.assert_almost_equal(mle.stan_variable('x'), 1, decimal=3)
np.testing.assert_almost_equal(mle.stan_variable('y'), 1, decimal=3)
np.testing.assert_almost_equal(
mle.optimized_params_pd['x'][0], 1, decimal=3
)
np.testing.assert_almost_equal(mle.optimized_params_np[1], 1, decimal=3)
np.testing.assert_almost_equal(mle.optimized_params_np[2], 1, decimal=3)
np.testing.assert_almost_equal(mle.optimized_params_dict['x'], 1, decimal=3)
with caplog.at_level(logging.WARNING):
assert mle.optimized_iterations_np is None
Expand Down Expand Up @@ -131,8 +131,8 @@ def test_rosenbrock(caplog: pytest.LogCaptureFixture) -> None:
np.testing.assert_almost_equal(mle.stan_variable('x'), 1, decimal=3)
np.testing.assert_almost_equal(mle.stan_variable('y'), 1, decimal=3)

assert mle.optimized_params_np.shape == (3,)
np.testing.assert_almost_equal(mle.optimized_params_np[1], 1, decimal=3)
assert mle.optimized_params_np.shape == (4,)
np.testing.assert_almost_equal(mle.optimized_params_np[2], 1, decimal=3)
np.testing.assert_almost_equal(
mle.optimized_params_pd['x'][0], 1, decimal=3
)
Expand All @@ -143,7 +143,7 @@ def test_rosenbrock(caplog: pytest.LogCaptureFixture) -> None:
mle.optimized_iterations_np[0, 1]
!= mle.optimized_iterations_np[last_iter, 1]
)
for i in range(3):
for i in range(4):
assert (
mle.optimized_params_np[i]
== mle.optimized_iterations_np[last_iter, i]
Expand All @@ -162,7 +162,7 @@ def test_eight_schools(caplog: pytest.LogCaptureFixture) -> None:
assert 'method=optimize' in repr(mle)
assert not mle.converged
with caplog.at_level(logging.WARNING):
assert mle.optimized_params_pd.shape == (1, 11)
assert mle.optimized_params_pd.shape == (1, 12)
check_present(
caplog,
(
Expand Down Expand Up @@ -294,15 +294,15 @@ def test_optimize_good() -> None:
# test numpy output
assert isinstance(mle.optimized_params_np, np.ndarray)
np.testing.assert_almost_equal(mle.optimized_params_np[0], -5, decimal=2)
np.testing.assert_almost_equal(mle.optimized_params_np[1], 0.2, decimal=3)
np.testing.assert_almost_equal(mle.optimized_params_np[2], 0.2, decimal=3)

# test pandas output
assert mle.optimized_params_np[0] == mle.optimized_params_pd['lp__'][0]
assert mle.optimized_params_np[1] == mle.optimized_params_pd['theta'][0]
assert mle.optimized_params_np[2] == mle.optimized_params_pd['theta'][0]

# test dict output
assert mle.optimized_params_np[0] == mle.optimized_params_dict['lp__']
assert mle.optimized_params_np[1] == mle.optimized_params_dict['theta']
assert mle.optimized_params_np[2] == mle.optimized_params_dict['theta']


def test_negative_parameter_values() -> None:
Expand Down Expand Up @@ -530,14 +530,14 @@ def test_optimize_good_dict() -> None:
)
# test numpy output
np.testing.assert_almost_equal(mle.optimized_params_np[0], -5, decimal=2)
np.testing.assert_almost_equal(mle.optimized_params_np[1], 0.2, decimal=3)
np.testing.assert_almost_equal(mle.optimized_params_np[2], 0.2, decimal=3)


def test_optimize_rosenbrock() -> None:
stan = os.path.join(DATAFILES_PATH, 'optimize', 'rosenbrock.stan')
rose_model = CmdStanModel(stan_file=stan)
mle = rose_model.optimize(seed=1239812093, inits=None, algorithm='BFGS')
assert mle.column_names == ('lp__', 'x', 'y')
assert mle.column_names == ('lp__', 'converged__', 'x', 'y')
np.testing.assert_almost_equal(mle.optimized_params_dict['x'], 1, decimal=3)
np.testing.assert_almost_equal(mle.optimized_params_dict['y'], 1, decimal=3)

Expand All @@ -546,7 +546,7 @@ def test_optimize_no_data() -> None:
stan = os.path.join(DATAFILES_PATH, 'optimize', 'no_data.stan')
rose_model = CmdStanModel(stan_file=stan)
mle = rose_model.optimize(seed=1239812093)
assert mle.column_names == ('lp__', 'a')
assert mle.column_names == ('lp__', 'converged__', 'a')
np.testing.assert_almost_equal(mle.optimized_params_dict['a'], 0, decimal=3)


Expand Down Expand Up @@ -599,7 +599,7 @@ def test_exe_only() -> None:
jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
mle = bern2_model.optimize(data=jdata)
assert mle.optimized_params_np[0] == mle.optimized_params_dict['lp__']
assert mle.optimized_params_np[1] == mle.optimized_params_dict['theta']
assert mle.optimized_params_np[2] == mle.optimized_params_dict['theta']


def test_complex_output() -> None:
Expand Down
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