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Add coords argument to pymc.set_data #5588

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Merged
merged 5 commits into from
Apr 24, 2022
Merged

Add coords argument to pymc.set_data #5588

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e9bf9f9
Aadd coords argument to pymc.set_data
soma2000-lang Mar 12, 2022
7a1c71d
Adding
soma2000-lang Mar 12, 2022
e8fe4a0
Update model.py
soma2000-lang Mar 17, 2022
855f55c
Update model.py
soma2000-lang Mar 17, 2022
654418b
add test and run black
OriolAbril Apr 23, 2022
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4 changes: 2 additions & 2 deletions pymc/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1734,7 +1734,7 @@ def point_logps(self, point=None, round_vals=2):
Model._context_class = Model


def set_data(new_data, model=None):
def set_data(new_data, model=None, *, coords=None):
"""Sets the value of one or more data container variables.

Parameters
Expand Down Expand Up @@ -1771,7 +1771,7 @@ def set_data(new_data, model=None):
model = modelcontext(model)

for variable_name, new_value in new_data.items():
model.set_data(variable_name, new_value)
model.set_data(variable_name, new_value, coords=coords)


def compile_fn(outs, mode=None, point_fn=True, model=None, **kwargs):
Expand Down
25 changes: 25 additions & 0 deletions pymc/tests/test_data_container.py
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Expand Up @@ -94,6 +94,31 @@ def test_sample_posterior_predictive_after_set_data(self):
x_test, y_test.posterior_predictive["obs"].mean(("chain", "draw")), atol=1e-1
)

def test_sample_posterior_predictive_after_set_data_with_coords(self):
y = np.array([1.0, 2.0, 3.0])
with pm.Model() as model:
x = pm.MutableData("x", [1.0, 2.0, 3.0], dims="obs_id")
beta = pm.Normal("beta", 0, 10.0)
pm.Normal("obs", beta * x, np.sqrt(1e-2), observed=y, dims="obs_id")
idata = pm.sample(
10,
tune=100,
chains=1,
return_inferencedata=True,
compute_convergence_checks=False,
)
# Predict on new data.
with model:
x_test = [5, 6]
pm.set_data(new_data={"x": x_test}, coords={"obs_id": ["a", "b"]})
pm.sample_posterior_predictive(idata, extend_inferencedata=True, predictions=True)

assert idata.predictions["obs"].shape == (1, 10, 2)
assert np.all(idata.predictions["obs_id"].values == np.array(["a", "b"]))
np.testing.assert_allclose(
x_test, idata.predictions["obs"].mean(("chain", "draw")), atol=1e-1
)

def test_sample_after_set_data(self):
with pm.Model() as model:
x = pm.MutableData("x", [1.0, 2.0, 3.0])
Expand Down