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[DO NOT MERGE] Test statespace models in numba backend #477

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[DO NOT MERGE] Test statespace models in numba backend #477

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1 change: 0 additions & 1 deletion tests/statespace/test_coord_assignment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,6 @@ def make_model(index):
with pytest.warns(UserWarning, match="No time index found on the supplied data"):
ss_mod.build_statespace_graph(
a["A"],
mode="JAX",
)
return model

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20 changes: 11 additions & 9 deletions tests/statespace/test_statespace_JAX.py
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Expand Up @@ -22,8 +22,8 @@
)
from tests.statespace.utilities.test_helpers import load_nile_test_data

pytest.importorskip("jax")
pytest.importorskip("numpyro")
pytest.importorskip("numba")
# pytest.importorskip("numpyro")


floatX = pytensor.config.floatX
Expand All @@ -38,7 +38,7 @@ def pymc_mod(ss_mod):
zeta = pm.Deterministic("zeta", 1 - rho)

ss_mod.build_statespace_graph(
data=nile, mode="JAX", save_kalman_filter_outputs_in_idata=True
data=nile, mode="NUMBA", save_kalman_filter_outputs_in_idata=True
)
names = ["x0", "P0", "c", "d", "T", "Z", "R", "H", "Q"]
for name, matrix in zip(names, ss_mod.unpack_statespace()):
Expand All @@ -62,7 +62,7 @@ def exog_pymc_mod(exog_ss_mod, rng):
beta_exog = pm.Normal("beta_exog", dims=["exog_state"])

sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"])
exog_ss_mod.build_statespace_graph(y, mode="JAX")
exog_ss_mod.build_statespace_graph(y, mode="NUMBA")

return m

Expand All @@ -77,12 +77,13 @@ def idata(pymc_mod, rng):
tune=1,
chains=1,
random_seed=rng,
nuts_sampler="numpyro",
nuts_sampler="pymc",
compile_kwargs={"mode": "NUMBA"},
progressbar=False,
)
with freeze_dims_and_data(pymc_mod):
idata_prior = pm.sample_prior_predictive(
samples=10, random_seed=rng, compile_kwargs={"mode": "JAX"}
samples=10, random_seed=rng, compile_kwargs={"mode": "NUMBA"}
)

idata.extend(idata_prior)
Expand All @@ -100,12 +101,13 @@ def idata_exog(exog_pymc_mod, rng):
tune=1,
chains=1,
random_seed=rng,
nuts_sampler="numpyro",
nuts_sampler="pymc",
compile_kwargs={"mode": "NUMBA"},
progressbar=False,
)
with freeze_dims_and_data(pymc_mod):
idata_prior = pm.sample_prior_predictive(
samples=10, random_seed=rng, compile_kwargs={"mode": "JAX"}
samples=10, random_seed=rng, compile_kwargs={"mode": "NUMBA"}
)

idata.extend(idata_prior)
Expand All @@ -121,7 +123,7 @@ def test_no_nans_in_sampling_output(ss_mod, group, matrix, idata):
@pytest.mark.parametrize("group", ["prior", "posterior"])
@pytest.mark.parametrize("kind", ["conditional", "unconditional"])
def test_sampling_methods(group, kind, ss_mod, idata, rng):
assert ss_mod._fit_mode == "JAX"
assert ss_mod._fit_mode == "NUMBA"

f = getattr(ss_mod, f"sample_{kind}_{group}")
with pytest.warns(UserWarning, match="The RandomType SharedVariables"):
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