Implement censored RVs logprob

aeppl/__init__.py

@@ -13,5 +13,6 @@
 # Add optimizations to the DBs
 import aeppl.mixture
 import aeppl.scan
+import aeppl.truncation
 
 # isort: on

aeppl/truncation.py

@@ -0,0 +1,125 @@
+import warnings
+from typing import List, Optional
+
+import aesara.tensor as at
+import numpy as np
+from aesara.assert_op import Assert
+from aesara.graph.basic import Node
+from aesara.graph.fg import FunctionGraph
+from aesara.graph.opt import local_optimizer
+from aesara.scalar.basic import Clip
+from aesara.scalar.basic import clip as scalar_clip
+from aesara.tensor.elemwise import Elemwise
+from aesara.tensor.var import TensorConstant
+
+from aeppl.abstract import MeasurableVariable, assign_custom_measurable_outputs
+from aeppl.logprob import _logcdf, _logprob
+from aeppl.opt import rv_sinking_db
+
+
+class CensoredRV(Elemwise):
+    """A placeholder used to specify a log-likelihood for a censored RV sub-graph."""
+
+
+MeasurableVariable.register(CensoredRV)
+
+
+@local_optimizer(tracks=[Elemwise])
+def find_censored_rvs(fgraph: FunctionGraph, node: Node) -> Optional[List[CensoredRV]]:
+    # TODO: Canonicalize x[x>ub] = ub -> clip(x, x, ub)
+
+    rv_map_feature = getattr(fgraph, "preserve_rv_mappings", None)
+    if rv_map_feature is None:
+        return None  # pragma: no cover
+
+    if isinstance(node.op, CensoredRV):
+        return None
+
+    if not (isinstance(node.op, Elemwise) and isinstance(node.op.scalar_op, Clip)):
+        return None
+
+    clipped_var = node.outputs[0]
+    if clipped_var not in rv_map_feature.rv_values:
+        return None
+
+    base_var, lower_bound, upper_bound = node.inputs
+
+    if not (base_var.owner and isinstance(base_var.owner.op, MeasurableVariable)):
+        return None
+
+    if base_var in rv_map_feature.rv_values:
+        warnings.warn(
+            f"Value variables were assigned to both the input ({base_var}) and "
+            f"output ({clipped_var}) of a censored random variable."
+        )
+        return None
+
+    # Replace bounds by `+-inf` if `y = clip(x, x, ?)` or `y=clip(x, ?, x)`
+    # This is used in `censor_logprob` to generate a more succint logprob graph
+    # for one-sided censored random variables
+    lower_bound = lower_bound if (lower_bound is not base_var) else at.constant(-np.inf)
+    upper_bound = upper_bound if (upper_bound is not base_var) else at.constant(np.inf)
+
+    censored_op = CensoredRV(scalar_clip)
+    # Make base_var unmeasurable
+    unmeasurable_base_var = assign_custom_measurable_outputs(base_var.owner)
+    censored_rv_node = censored_op.make_node(
+        unmeasurable_base_var, lower_bound, upper_bound
+    )
+    censored_rv = censored_rv_node.outputs[0]
+
+    censored_rv.name = clipped_var.name
+
+    return [censored_rv]
+
+
+rv_sinking_db.register("find_censored_rvs", find_censored_rvs, -5, "basic")
+
+
+@_logprob.register(CensoredRV)
+def censor_logprob(op, values, base_rv, lower_bound, upper_bound, **kwargs):
+    (value,) = values
+
+    base_rv_op = base_rv.owner.op
+    base_rv_inputs = base_rv.owner.inputs
+
+    logprob = _logprob(base_rv_op, (value,), *base_rv_inputs, **kwargs)
+    logcdf = _logcdf(base_rv_op, value, *base_rv_inputs, **kwargs)
+
+    if base_rv_op.name:
+        logprob.name = f"{base_rv_op}_logprob"
+        logcdf.name = f"{base_rv_op}_logcdf"
+
+    is_lower_bounded, is_upper_bounded = False, False
+    if not (
+        isinstance(upper_bound, TensorConstant) and np.all(upper_bound.value == np.inf)
+    ):
+        is_upper_bounded = True
+
+        logccdf = at.log(1 - at.exp(logcdf))
+        # For right censored discrete RVs, we need to add an extra term
+        # corresponding to the pmf at the upper bound
+        if base_rv_op.dtype == "int64":
+            logccdf = at.logaddexp(logccdf, logprob)
+
+        logprob = at.switch(
+            at.eq(value, upper_bound),
+            logccdf,
+            at.switch(at.gt(value, upper_bound), -np.inf, logprob),
+        )
+    if not (
+        isinstance(lower_bound, TensorConstant) and np.all(lower_bound.value == -np.inf)
+    ):
+        is_lower_bounded = True
+        logprob = at.switch(
+            at.eq(value, lower_bound),
+            logcdf,
+            at.switch(at.lt(value, lower_bound), -np.inf, logprob),
+        )
+
+    if is_lower_bounded and is_upper_bounded:
+        logprob = Assert("lower_bound <= upper_bound")(
+            logprob, at.all(at.le(lower_bound, upper_bound))
+        )
+
+    return logprob

tests/test_truncation.py

@@ -0,0 +1,191 @@
+import aesara
+import aesara.tensor as at
+import numpy as np
+import pytest
+import scipy as sp
+import scipy.stats as st
+
+from aeppl import joint_logprob
+from aeppl.transforms import LogTransform, TransformValuesOpt
+from tests.utils import assert_no_rvs
+
+
+@aesara.config.change_flags(compute_test_value="raise")
+def test_continuous_rv_censoring():
+    x_rv = at.random.normal(0.5, 1)
+    cens_x_rv = at.clip(x_rv, -2, 2)
+
+    cens_x_vv = cens_x_rv.clone()
+    cens_x_vv.tag.test_value = 0
+
+    logp = joint_logprob({cens_x_rv: cens_x_vv})
+    assert_no_rvs(logp)
+
+    logp_fn = aesara.function([cens_x_vv], logp)
+    ref_scipy = st.norm(0.5, 1)
+
+    assert logp_fn(-3) == -np.inf
+    assert logp_fn(3) == -np.inf
+
+    assert np.isclose(logp_fn(-2), ref_scipy.logcdf(-2))
+    assert np.isclose(logp_fn(2), ref_scipy.logsf(2))
+    assert np.isclose(logp_fn(0), ref_scipy.logpdf(0))
+
+
+def test_discrete_rv_censoring():
+    x_rv = at.random.poisson(2)
+    cens_x_rv = at.clip(x_rv, 1, 4)
+
+    cens_x_vv = cens_x_rv.clone()
+
+    logp = joint_logprob({cens_x_rv: cens_x_vv})
+    assert_no_rvs(logp)
+
+    logp_fn = aesara.function([cens_x_vv], logp)
+    ref_scipy = st.poisson(2)
+
+    assert logp_fn(0) == -np.inf
+    assert logp_fn(5) == -np.inf
+
+    assert np.isclose(logp_fn(1), ref_scipy.logcdf(1))
+    assert np.isclose(logp_fn(4), np.logaddexp(ref_scipy.logsf(4), ref_scipy.logpmf(4)))
+    assert np.isclose(logp_fn(2), ref_scipy.logpmf(2))
+
+
+def test_one_sided_censoring():
+    x_rv = at.random.normal(0, 1)
+    lb_cens_x_rv = at.clip(x_rv, -1, x_rv)
+    ub_cens_x_rv = at.clip(x_rv, x_rv, 1)
+
+    lb_cens_x_vv = lb_cens_x_rv.clone()
+    ub_cens_x_vv = ub_cens_x_rv.clone()
+
+    lb_logp = joint_logprob({lb_cens_x_rv: lb_cens_x_vv})
+    ub_logp = joint_logprob({ub_cens_x_rv: ub_cens_x_vv})
+    assert_no_rvs(lb_logp)
+    assert_no_rvs(ub_logp)
+
+    logp_fn = aesara.function([lb_cens_x_vv, ub_cens_x_vv], [lb_logp, ub_logp])
+    ref_scipy = st.norm(0, 1)
+
+    assert np.all(np.array(logp_fn(-2, 2)) == -np.inf)
+    assert np.all(np.array(logp_fn(2, -2)) != -np.inf)
+    np.testing.assert_almost_equal(logp_fn(-1, 1), ref_scipy.logcdf(-1))
+    np.testing.assert_almost_equal(logp_fn(1, -1), ref_scipy.logpdf(-1))
+
+
+def test_useless_censoring():
+    x_rv = at.random.normal(0.5, 1, size=3)
+    cens_x_rv = at.clip(x_rv, x_rv, x_rv)
+
+    cens_x_vv = cens_x_rv.clone()
+
+    logp = joint_logprob({cens_x_rv: cens_x_vv}, sum=False)
+    assert_no_rvs(logp)
+
+    logp_fn = aesara.function([cens_x_vv], logp)
+    ref_scipy = st.norm(0.5, 1)
+
+    np.testing.assert_allclose(logp_fn([-2, 0, 2]), ref_scipy.logpdf([-2, 0, 2]))
+
+
+def test_random_censoring():
+    lb_rv = at.random.normal(0, 1, size=2)
+    x_rv = at.random.normal(0, 2)
+    cens_x_rv = at.clip(x_rv, lb_rv, [1, 1])
+
+    lb_vv = lb_rv.clone()
+    cens_x_vv = cens_x_rv.clone()
+    logp = joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv}, sum=False)
+    assert_no_rvs(logp)
+
+    logp_fn = aesara.function([lb_vv, cens_x_vv], logp)
+    res = logp_fn([0, -1], [-1, -1])
+    assert res[0] == -np.inf
+    assert res[1] != -np.inf
+
+
+def test_broadcasted_censoring_constant():
+    lb_rv = at.random.uniform(0, 1)
+    x_rv = at.random.normal(0, 2)
+    cens_x_rv = at.clip(x_rv, lb_rv, [1, 1])
+
+    lb_vv = lb_rv.clone()
+    cens_x_vv = cens_x_rv.clone()
+
+    logp = joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv})
+    assert_no_rvs(logp)
+
+
+def test_broadcasted_censoring_random():
+    lb_rv = at.random.normal(0, 1)
+    x_rv = at.random.normal(0, 2, size=2)
+    cens_x_rv = at.clip(x_rv, lb_rv, 1)
+
+    lb_vv = lb_rv.clone()
+    cens_x_vv = cens_x_rv.clone()
+
+    logp = joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv})
+    assert_no_rvs(logp)
+
+
+def test_fail_base_and_censored_have_values():
+    """Test failure when both base_rv and clipped_rv are given value vars"""
+    x_rv = at.random.normal(0, 1)
+    cens_x_rv = at.clip(x_rv, x_rv, 1)
+    cens_x_rv.name = "cens_x"
+
+    x_vv = x_rv.clone()
+    cens_x_vv = cens_x_rv.clone()
+    with pytest.raises(RuntimeError, match="{cens_x}"):
+        joint_logprob({cens_x_rv: cens_x_vv, x_rv: x_vv})
+
+
+def test_fail_multiple_censored_single_base():
+    """Test failure when multiple clipped_rvs share a single base_rv"""
+    base_rv = at.random.normal(0, 1)
+    cens_rv1 = at.clip(base_rv, -1, 1)
+    cens_rv1.name = "cens1"
+    cens_rv2 = at.clip(base_rv, -1, 1)
+    cens_rv2.name = "cens2"
+
+    cens_vv1 = cens_rv1.clone()
+    cens_vv2 = cens_rv2.clone()
+    with pytest.raises(RuntimeError, match="{cens2}"):
+        joint_logprob({cens_rv1: cens_vv1, cens_rv2: cens_vv2})
+
+
+def test_deterministic_clipping():
+    x_rv = at.random.normal(0, 1)
+    clip = at.clip(x_rv, 0, 0)
+    y_rv = at.random.normal(clip, 1)
+
+    x_vv = x_rv.clone()
+    y_vv = y_rv.clone()
+    logp = joint_logprob({x_rv: x_vv, y_rv: y_vv})
+    assert_no_rvs(logp)
+
+    logp_fn = aesara.function([x_vv, y_vv], logp)
+    assert np.isclose(
+        logp_fn(-1, 1),
+        st.norm(0, 1).logpdf(-1) + st.norm(0, 1).logpdf(1),
+    )
+
+
+@pytest.mark.xfail(reason="Transform does not work with Elemwise ops, see #60")
+def test_censored_transform():
+    x_rv = at.random.normal(0.5, 1)
+    cens_x_rv = at.clip(x_rv, 0, x_rv)
+
+    cens_x_vv = cens_x_rv.clone()
+
+    transform = TransformValuesOpt({cens_x_vv: LogTransform()})
+    logp = joint_logprob({cens_x_rv: cens_x_vv}, extra_rewrites=transform)
+
+    cens_x_vv_testval = -1
+    obs_logp = logp.eval({cens_x_vv: cens_x_vv_testval})
+    exp_logp = (
+        sp.stats.norm(0.5, 1).logpdf(np.exp(cens_x_vv_testval)) + cens_x_vv_testval
+    )
+
+    assert np.isclose(obs_logp, exp_logp)