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add moment for BART distribution #5211

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Merged
merged 2 commits into from
Nov 20, 2021
Merged

add moment for BART distribution #5211

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0e994bb
add moment
aloctavodia Nov 19, 2021
eb77473
simplify
aloctavodia Nov 20, 2021
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11 changes: 10 additions & 1 deletion pymc/bart/bart.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
from aesara.tensor.random.op import RandomVariable, default_shape_from_params
from pandas import DataFrame, Series

from pymc.distributions.distribution import NoDistribution
from pymc.distributions.distribution import NoDistribution, _get_moment

__all__ = ["BART"]

Expand Down Expand Up @@ -110,6 +110,10 @@ def __new__(

NoDistribution.register(BARTRV)

@_get_moment.register(BARTRV)
def get_moment(rv, size, *rv_inputs):
return cls.get_moment(rv, size, *rv_inputs)

cls.rv_op = bart_op
params = [X, Y, m, alpha, k]
return super().__new__(cls, name, *params, **kwargs)
Expand All @@ -132,6 +136,11 @@ def logp(x, *inputs):
"""
return at.zeros_like(x)

@classmethod
def get_moment(cls, rv, size, *rv_inputs):
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Could BART shape be determined by the shape of the rv_inputs? Or does the mean already integrate this info?

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The shape of BART is always the shape of observed data Y.

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And do you do anything with the size variable or is that branch below unnecessary?

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everything is a lie :-) Initially I tough about having a size argument for future extension. But probably that will not be very useful.

mean = at.fill(size, rv.Y.mean())
return mean


def preprocess_XY(X, Y):
if isinstance(Y, (Series, DataFrame)):
Expand Down
16 changes: 16 additions & 0 deletions pymc/tests/test_bart.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@

import pymc as pm

from pymc.tests.test_distributions_moments import assert_moment_is_expected


def test_split_node():
split_node = pm.bart.tree.SplitNode(index=5, idx_split_variable=2, split_value=3.0)
Expand Down Expand Up @@ -97,3 +99,17 @@ def test_predict(self):
)
def test_pdp(self, kwargs):
pm.bart.utils.plot_dependence(self.idata, X=self.X, Y=self.Y, **kwargs)


@pytest.mark.parametrize(
"size, expected",
[
(None, np.zeros(50)),
],
)
def test_bart_moment(size, expected):
X = np.zeros((50, 2))
Y = np.zeros(50)
with pm.Model() as model:
pm.BART("x", X=X, Y=Y, size=size)
assert_moment_is_expected(model, expected)