def test_predictions_constant_data(self):
with pm.Model():
x = pm.ConstantData("x", [1.0, 2.0, 3.0])
y = pm.MutableData("y", [1.0, 2.0, 3.0])
beta = pm.Normal("beta", 0, 1)
obs = pm.Normal("obs", x * beta, 1, observed=y) # pylint: disable=unused-variable
trace = pm.sample(100, tune=100, return_inferencedata=False)
inference_data = to_inference_data(trace)
test_dict = {"posterior": ["beta"], "observed_data": ["obs"], "constant_data": ["x"]}
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails
with pm.Model():
x = pm.MutableData("x", [1.0, 2.0])
y = pm.ConstantData("y", [1.0, 2.0])
beta = pm.Normal("beta", 0, 1)
obs = pm.Normal("obs", x * beta, 1, observed=y) # pylint: disable=unused-variable
predictive_trace = pm.sample_posterior_predictive(
inference_data, return_inferencedata=False
)
assert set(predictive_trace.keys()) == {"obs"}
# this should be four chains of 100 samples
# assert predictive_trace["obs"].shape == (400, 2)
# but the shape seems to vary between pymc versions
inference_data = predictions_to_inference_data(predictive_trace, posterior_trace=trace)
test_dict = {"posterior": ["beta"], "~observed_data": ""}
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails, "Posterior data not copied over as expected."
test_dict = {"predictions": ["obs"]}
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails, "Predictions not instantiated as expected."
test_dict = {"predictions_constant_data": ["x"]}
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails, "Predictions constant data not instantiated as expected."
def test_simultaneous_shape_and_dims(self, ellipsis_in):
with pm.Model() as pmodel:
x = pm.ConstantData("x", [1, 2, 3], dims="ddata")
if ellipsis_in == "none":
# The shape and dims tuples correspond to each other.
# Note: No checks are performed that implied shape (x), shape and dims actually match.
y = pm.Normal("y",
mu=x,
shape=(2, 3),
dims=("dshape", "ddata"))
assert pmodel.RV_dims["y"] == ("dshape", "ddata")
elif ellipsis_in == "shape":
y = pm.Normal("y",
mu=x,
shape=(2, ...),
dims=("dshape", "ddata"))
assert pmodel.RV_dims["y"] == ("dshape", "ddata")
elif ellipsis_in == "dims":
y = pm.Normal("y", mu=x, shape=(2, 3), dims=("dshape", ...))
assert pmodel.RV_dims["y"] == ("dshape", None)
elif ellipsis_in == "both":
y = pm.Normal("y", mu=x, shape=(2, ...), dims=("dshape", ...))
assert pmodel.RV_dims["y"] == ("dshape", None)
assert "dshape" in pmodel.dim_lengths
assert y.eval().shape == (2, 3)
def test_no_trace(self):
with pm.Model() as model:
x = pm.ConstantData("x", [1.0, 2.0, 3.0])
y = pm.MutableData("y", [1.0, 2.0, 3.0])
beta = pm.Normal("beta", 0, 1)
obs = pm.Normal("obs", x * beta, 1, observed=y) # pylint: disable=unused-variable
idata = pm.sample(100, tune=100)
prior = pm.sample_prior_predictive(return_inferencedata=False)
posterior_predictive = pm.sample_posterior_predictive(idata, return_inferencedata=False)
# Only prior
inference_data = to_inference_data(prior=prior, model=model)
test_dict = {"prior": ["beta"], "prior_predictive": ["obs"]}
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails
# Only posterior_predictive
inference_data = to_inference_data(posterior_predictive=posterior_predictive, model=model)
test_dict = {"posterior_predictive": ["obs"]}
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails
# Prior and posterior_predictive but no trace
inference_data = to_inference_data(
prior=prior, posterior_predictive=posterior_predictive, model=model
)
test_dict = {
"prior": ["beta"],
"prior_predictive": ["obs"],
"posterior_predictive": ["obs"],
}
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails
def test_ovewrite_model_coords_dims(self):
"""Check coords and dims from model object can be partially overwritten."""
dim1 = ["a", "b"]
new_dim1 = ["c", "d"]
coords = {"dim1": dim1, "dim2": ["c1", "c2"]}
x_data = np.arange(4).reshape((2, 2))
y = x_data + np.random.normal(size=(2, 2))
with pm.Model(coords=coords):
x = pm.ConstantData("x", x_data, dims=("dim1", "dim2"))
beta = pm.Normal("beta", 0, 1, dims="dim1")
_ = pm.Normal("obs", x * beta, 1, observed=y, dims=("dim1", "dim2"))
trace = pm.sample(100, tune=100, return_inferencedata=False)
idata1 = to_inference_data(trace)
idata2 = to_inference_data(trace, coords={"dim1": new_dim1}, dims={"beta": ["dim2"]})
test_dict = {"posterior": ["beta"], "observed_data": ["obs"], "constant_data": ["x"]}
fails1 = check_multiple_attrs(test_dict, idata1)
assert not fails1
fails2 = check_multiple_attrs(test_dict, idata2)
assert not fails2
assert "dim1" in list(idata1.posterior.beta.dims)
assert "dim2" in list(idata2.posterior.beta.dims)
assert np.all(idata1.constant_data.x.dim1.values == np.array(dim1))
assert np.all(idata1.constant_data.x.dim2.values == np.array(["c1", "c2"]))
assert np.all(idata2.constant_data.x.dim1.values == np.array(new_dim1))
assert np.all(idata2.constant_data.x.dim2.values == np.array(["c1", "c2"]))
def model_with_dims():
with pm.Model(
coords={"city": ["Aachen", "Maastricht", "London", "Bergheim"]
}) as pmodel:
economics = pm.Uniform("economics", lower=-1, upper=1, shape=(1, ))
population = pm.HalfNormal("population", sigma=5, dims=("city"))
time = pm.ConstantData("time", [2014, 2015, 2016], dims="year")
n = pm.Deterministic("tax revenue",
economics * population[None, :] * time[:, None],
dims=("year", "city"))
yobs = pm.MutableData("observed", np.ones((3, 4)))
L = pm.Normal("L", n, observed=yobs)
compute_graph = {
"economics": set(),
"population": set(),
"time": set(),
"tax revenue": {"economics", "population", "time"},
"L": {"tax revenue"},
"observed": {"L"},
}
plates = {
"1": {"economics"},
"city (4)": {"population"},
"year (3)": {"time"},
"year (3) x city (4)": {"tax revenue"},
"3 x 4": {"L", "observed"},
}
return pmodel, compute_graph, plates
def test_data_defined_size_dimension_can_register_dimname(self):
with pm.Model() as pmodel:
x = pm.ConstantData("x", [[1, 2, 3, 4]], dims=("first", "second"))
assert "first" in pmodel.dim_lengths
assert "second" in pmodel.dim_lengths
# two dimensions are implied; a "third" dimension is created
y = pm.Normal("y", mu=x, size=2, dims=("third", "first", "second"))
assert "third" in pmodel.dim_lengths
assert y.eval().shape() == (2, 1, 4)
def test_data_naming():
"""
This is a test for issue #3793 -- `Data` objects in named models are
not given model-relative names.
"""
with pm.Model("named_model") as model:
x = pm.ConstantData("x", [1.0, 2.0, 3.0])
y = pm.Normal("y")
assert y.name == "named_model::y"
assert x.name == "named_model::x"
def test_coords_and_constantdata_create_immutable_dims():
"""
When created from `pm.Model(coords=...)` or `pm.ConstantData`
a dimension should be resizable.
"""
with pm.Model(coords={"group": ["A", "B"]}) as m:
x = pm.ConstantData("x", [0], dims="feature")
y = pm.Normal("y", x, 1, dims=("group", "feature"))
assert isinstance(m._dim_lengths["feature"], TensorConstant)
assert isinstance(m._dim_lengths["group"], TensorConstant)
assert x.eval().shape == (1,)
assert y.eval().shape == (2, 1)
def test_autodetect_coords_from_model(self, use_context):
pd = pytest.importorskip("pandas")
df_data = pd.DataFrame(columns=["date"]).set_index("date")
dates = pd.date_range(start="2020-05-01", end="2020-05-20")
for city, mu in {"Berlin": 15, "San Marino": 18, "Paris": 16}.items():
df_data[city] = np.random.normal(loc=mu, size=len(dates))
df_data.index = dates
df_data.index.name = "date"
coords = {"date": df_data.index, "city": df_data.columns}
with pm.Model(coords=coords) as model:
europe_mean = pm.Normal("europe_mean_temp", mu=15.0, sigma=3.0)
city_offset = pm.Normal("city_offset",
mu=0.0,
sigma=3.0,
dims="city")
city_temperature = pm.Deterministic("city_temperature",
europe_mean + city_offset,
dims="city")
data_dims = ("date", "city")
data = pm.ConstantData("data", df_data, dims=data_dims)
_ = pm.Normal("likelihood",
mu=city_temperature,
sigma=0.5,
observed=data,
dims=data_dims)
trace = pm.sample(
return_inferencedata=False,
compute_convergence_checks=False,
cores=1,
chains=1,
tune=20,
draws=30,
step=pm.Metropolis(),
)
if use_context:
idata = to_inference_data(trace=trace)
if not use_context:
idata = to_inference_data(trace=trace, model=model)
assert "city" in list(idata.posterior.dims)
assert "city" in list(idata.observed_data.dims)
assert "date" in list(idata.observed_data.dims)
np.testing.assert_array_equal(idata.posterior.coords["city"],
coords["city"])
np.testing.assert_array_equal(idata.observed_data.coords["date"],
coords["date"])
np.testing.assert_array_equal(idata.observed_data.coords["city"],
coords["city"])
def test_set_coords_through_pmdata(self):
with pm.Model() as pmodel:
pm.ConstantData("population", [100, 200],
dims="city",
coords={"city": ["Tinyvil", "Minitown"]})
pm.MutableData(
"temperature",
[[15, 20, 22, 17], [18, 22, 21, 12]],
dims=("city", "season"),
coords={"season": ["winter", "spring", "summer", "fall"]},
)
assert "city" in pmodel.coords
assert "season" in pmodel.coords
assert pmodel.coords["city"] == ("Tinyvil", "Minitown")
assert pmodel.coords["season"] == ("winter", "spring", "summer",
"fall")
def test_simultaneous_size_and_dims(self, with_dims_ellipsis):
with pm.Model() as pmodel:
x = pm.ConstantData("x", [1, 2, 3], dims="ddata")
assert "ddata" in pmodel.dim_lengths
# Size does not include support dims, so this test must use a dist with support dims.
kwargs = dict(name="y", size=2, mu=at.ones((3, 4)), cov=at.eye(4))
if with_dims_ellipsis:
y = pm.MvNormal(**kwargs, dims=("dsize", ...))
assert pmodel.RV_dims["y"] == ("dsize", None, None)
else:
y = pm.MvNormal(**kwargs, dims=("dsize", "ddata", "dsupport"))
assert pmodel.RV_dims["y"] == ("dsize", "ddata", "dsupport")
assert "dsize" in pmodel.dim_lengths
assert y.eval().shape == (2, 3, 4)
def test_constant_data(self, use_context):
"""Test constant_data group behaviour."""
with pm.Model() as model:
x = pm.ConstantData("x", [1.0, 2.0, 3.0])
y = pm.MutableData("y", [1.0, 2.0, 3.0])
beta = pm.Normal("beta", 0, 1)
obs = pm.Normal("obs", x * beta, 1, observed=y) # pylint: disable=unused-variable
trace = pm.sample(100, chains=2, tune=100, return_inferencedata=False)
if use_context:
inference_data = to_inference_data(trace=trace)
if not use_context:
inference_data = to_inference_data(trace=trace, model=model)
test_dict = {"posterior": ["beta"], "observed_data": ["obs"], "constant_data": ["x"]}
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails
assert inference_data.log_likelihood["obs"].shape == (2, 100, 3)
def model_with_different_descendants():
"""
Model proposed by Michael to test variable selection functionality
From here: https://github.com/pymc-devs/pymc/pull/5634#pullrequestreview-916297509
"""
with pm.Model() as pmodel2:
a = pm.Normal("a")
b = pm.Normal("b")
pm.Normal("c", a * b)
intermediate = pm.Deterministic("intermediate", a + b)
pred = pm.Deterministic("pred", intermediate * 3)
obs = pm.ConstantData("obs", 1.75)
L = pm.Normal("L", mu=1 + 0.5 * pred, observed=obs)
return pmodel2
def test_implicit_coords_series(self):
ser_sales = pd.Series(
data=np.random.randint(low=0, high=30, size=22),
index=pd.date_range(start="2020-05-01",
periods=22,
freq="24H",
name="date"),
name="sales",
)
with pm.Model() as pmodel:
pm.ConstantData("sales",
ser_sales,
dims="date",
export_index_as_coords=True)
assert "date" in pmodel.coords
assert len(pmodel.coords["date"]) == 22
assert pmodel.RV_dims == {"sales": ("date", )}
def test_implicit_coords_dataframe(self):
N_rows = 5
N_cols = 7
df_data = pd.DataFrame()
for c in range(N_cols):
df_data[f"Column {c+1}"] = np.random.normal(size=(N_rows, ))
df_data.index.name = "rows"
df_data.columns.name = "columns"
# infer coordinates from index and columns of the DataFrame
with pm.Model() as pmodel:
pm.ConstantData("observations",
df_data,
dims=("rows", "columns"),
export_index_as_coords=True)
assert "rows" in pmodel.coords
assert "columns" in pmodel.coords
assert pmodel.RV_dims == {"observations": ("rows", "columns")}
def test_priors_separation(self, use_context):
"""Test model is enough to get prior, prior predictive and observed_data."""
with pm.Model() as model:
x = pm.MutableData("x", [1.0, 2.0, 3.0])
y = pm.ConstantData("y", [1.0, 2.0, 3.0])
beta = pm.Normal("beta", 0, 1)
obs = pm.Normal("obs", x * beta, 1, observed=y) # pylint: disable=unused-variable
prior = pm.sample_prior_predictive(return_inferencedata=False)
test_dict = {
"prior": ["beta", "~obs"],
"observed_data": ["obs"],
"prior_predictive": ["obs"],
}
if use_context:
with model:
inference_data = to_inference_data(prior=prior)
else:
inference_data = to_inference_data(prior=prior, model=model)
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails
def test_model_to_graphviz_for_model_with_data_container(self):
with pm.Model() as model:
x = pm.ConstantData("x", [1.0, 2.0, 3.0])
y = pm.MutableData("y", [1.0, 2.0, 3.0])
beta = pm.Normal("beta", 0, 10.0)
obs_sigma = floatX(np.sqrt(1e-2))
pm.Normal("obs", beta * x, obs_sigma, observed=y)
pm.sample(
1000,
init=None,
tune=1000,
chains=1,
compute_convergence_checks=False,
)
for formatting in {"latex", "latex_with_params"}:
with pytest.raises(ValueError, match="Unsupported formatting"):
pm.model_to_graphviz(model, formatting=formatting)
exp_without = [
'x [label="x\n~\nConstantData" shape=box style="rounded, filled"]',
'y [label="x\n~\nMutableData" shape=box style="rounded, filled"]',
'beta [label="beta\n~\nNormal"]',
'obs [label="obs\n~\nNormal" style=filled]',
]
exp_with = [
'x [label="x\n~\nConstantData" shape=box style="rounded, filled"]',
'y [label="x\n~\nMutableData" shape=box style="rounded, filled"]',
'beta [label="beta\n~\nNormal(mu=0.0, sigma=10.0)"]',
f'obs [label="obs\n~\nNormal(mu=f(f(beta), x), sigma={obs_sigma})" style=filled]',
]
for formatting, expected_substrings in [
("plain", exp_without),
("plain_with_params", exp_with),
]:
g = pm.model_to_graphviz(model, formatting=formatting)
# check formatting of RV nodes
for expected in expected_substrings:
assert expected in g.source
def test_sample_posterior_predictive_after_set_data(self):
with pm.Model() as model:
x = pm.MutableData("x", [1.0, 2.0, 3.0])
y = pm.ConstantData("y", [1.0, 2.0, 3.0])
beta = pm.Normal("beta", 0, 10.0)
pm.Normal("obs", beta * x, np.sqrt(1e-2), observed=y)
trace = pm.sample(
1000,
tune=1000,
chains=1,
return_inferencedata=False,
compute_convergence_checks=False,
)
# Predict on new data.
with model:
x_test = [5, 6, 9]
pm.set_data(new_data={"x": x_test})
y_test = pm.sample_posterior_predictive(trace)
assert y_test.posterior_predictive["obs"].shape == (1, 1000, 3)
np.testing.assert_allclose(x_test,
y_test.posterior_predictive["obs"].mean(
("chain", "draw")),
atol=1e-1)
def test_creation_of_data_outside_model_context(self):
with pytest.raises((IndexError, TypeError)) as error:
pm.ConstantData("data", [1.1, 2.2, 3.3])
error.match("No model on context stack")
