Use approximate log_beta() in .fit(), .predict()

examples/sir_hmc.py

@@ -26,7 +26,8 @@
 import pyro.poutine as poutine
 from pyro.infer import MCMC, NUTS, config_enumerate, infer_discrete
 from pyro.infer.autoguide import init_to_value
-from pyro.ops.tensor_utils import convolve, safe_log
+from pyro.ops.special import safe_log
+from pyro.ops.tensor_utils import convolve
 from pyro.util import warn_if_nan
 
 logging.basicConfig(format='%(message)s', level=logging.INFO)

pyro/contrib/epidemiology/compartmental.py

@@ -281,6 +281,7 @@ def generate(self, fixed={}):
         self._concat_series(samples)
         return samples
 
+    @set_approx_log_prob_tol(0.1)
     def fit(self, **options):
         r"""
         Runs inference to generate posterior samples.
@@ -397,6 +398,7 @@ def heuristic():
         return mcmc  # E.g. so user can run mcmc.summary().
 
     @torch.no_grad()
+    @set_approx_log_prob_tol(0.1)
     @set_approx_sample_thresh(10000)
     def predict(self, forecast=0):
         """

pyro/contrib/epidemiology/distributions.py

@@ -54,7 +54,7 @@ def set_approx_log_prob_tol(tol):
     :type tol: int or float.
     """
     assert isinstance(tol, (float, int))
-    assert tol > 0
+    assert tol >= 0
     old1 = dist.Binomial.approx_log_prob_tol
     old2 = dist.BetaBinomial.approx_log_prob_tol
     try:

pyro/contrib/epidemiology/util.py

@@ -8,7 +8,7 @@
 import pyro.distributions as dist
 import pyro.poutine as poutine
 from pyro.distributions.util import broadcast_shape
-from pyro.ops.tensor_utils import safe_log
+from pyro.ops.special import safe_log
 
 
 def clamp(tensor, *, min=None, max=None):

pyro/distributions/coalescent.py

@@ -9,7 +9,7 @@
 from torch.distributions import constraints
 
 from pyro.distributions.util import broadcast_shape, is_validation_enabled
-from pyro.ops.tensor_utils import safe_log
+from pyro.ops.special import safe_log
 
 from .torch_distribution import TorchDistribution
 

pyro/distributions/conjugate.py

@@ -32,11 +32,14 @@ class BetaBinomial(TorchDistribution):
     is unknown and randomly drawn from a :class:`~pyro.distributions.Beta` distribution
     prior to a certain number of Bernoulli trials given by ``total_count``.
 
-    :param float or torch.Tensor concentration1: 1st concentration parameter (alpha) for the
+    :param concentration1: 1st concentration parameter (alpha) for the
         Beta distribution.
-    :param float or torch.Tensor concentration0: 2nd concentration parameter (beta) for the
+    :type concentration1: float or torch.Tensor
+    :param concentration0: 2nd concentration parameter (beta) for the
         Beta distribution.
-    :param int or torch.Tensor total_count: number of Bernoulli trials.
+    :type concentration0: float or torch.Tensor
+    :param total_count: Number of Bernoulli trials.
+    :type total_count: float or torch.Tensor
     """
     arg_constraints = {'concentration1': constraints.positive, 'concentration0': constraints.positive,
                        'total_count': constraints.nonnegative_integer}

pyro/distributions/extended.py

@@ -23,7 +23,7 @@ class ExtendedBinomial(Binomial):
 
     def log_prob(self, value):
         result = super().log_prob(value)
-        invalid = ~super().support.check(value)
+        invalid = (value < 0) | (value > self.total_count)
         return result.masked_fill(invalid, -math.inf)
 
 
@@ -40,6 +40,18 @@ class ExtendedBetaBinomial(BetaBinomial):
     support = constraints.integer
 
     def log_prob(self, value):
-        result = super().log_prob(value)
-        invalid = ~super().support.check(value)
+        if self._validate_args:
+            self._validate_sample(value)
+
+        total_count = self.total_count
+        invalid = (value < 0) | (value > total_count)
+        n = total_count.clamp(min=0)
+        k = value.masked_fill(invalid, 0)
+
+        try:
+            self.total_count = n
+            result = super().log_prob(k)
+        finally:
+            self.total_count = total_count
+
         return result.masked_fill(invalid, -math.inf)

pyro/distributions/hmm.py

@@ -10,7 +10,8 @@
 from pyro.ops.gamma_gaussian import (GammaGaussian, gamma_and_mvn_to_gamma_gaussian, gamma_gaussian_tensordot,
                                      matrix_and_mvn_to_gamma_gaussian)
 from pyro.ops.gaussian import Gaussian, gaussian_tensordot, matrix_and_mvn_to_gaussian, mvn_to_gaussian
-from pyro.ops.tensor_utils import cholesky, cholesky_solve, safe_log
+from pyro.ops.special import safe_log
+from pyro.ops.tensor_utils import cholesky, cholesky_solve
 
 
 @torch.jit.script

pyro/ops/einsum/torch_log.py

@@ -4,7 +4,7 @@
 import torch
 
 from pyro.ops.einsum.util import Tensordot
-from pyro.ops.tensor_utils import safe_log
+from pyro.ops.special import safe_log
 
 
 def transpose(a, axes):

pyro/ops/special.py

@@ -8,6 +8,26 @@
 import torch
 
 
+class _SafeLog(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return x.log()
+
+    @staticmethod
+    def backward(ctx, grad):
+        x, = ctx.saved_tensors
+        return grad / x.clamp(min=torch.finfo(x.dtype).eps)
+
+
+def safe_log(x):
+    """
+    Like :func:`torch.log` but avoids infinite gradients at log(0)
+    by clamping them to at most ``1 / finfo.eps``.
+    """
+    return _SafeLog.apply(x)
+
+
 def log_beta(x, y, tol=0.):
     """
     Computes log Beta function.

pyro/ops/tensor_utils.py

@@ -9,26 +9,6 @@
 _ROOT_TWO_INVERSE = 1.0 / math.sqrt(2.0)
 
 
-class _SafeLog(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        ctx.save_for_backward(x)
-        return x.log()
-
-    @staticmethod
-    def backward(ctx, grad):
-        x, = ctx.saved_tensors
-        return grad / x.clamp(min=torch.finfo(x.dtype).eps)
-
-
-def safe_log(x):
-    """
-    Like :func:`torch.log` but avoids infinite gradients at log(0)
-    by clamping them to at most ``1 / finfo.eps``.
-    """
-    return _SafeLog.apply(x)
-
-
 def block_diag_embed(mat):
     """
     Takes a tensor of shape (..., B, M, N) and returns a block diagonal tensor

tests/distributions/test_extended.py

@@ -5,73 +5,92 @@
 
 import pytest
 import torch
+from torch.autograd import grad
 
 import pyro.distributions as dist
+from pyro.contrib.epidemiology.distributions import set_approx_log_prob_tol
 from tests.common import assert_equal
 
 
-def test_extended_binomial():
-    total_count = torch.tensor([1., 2., 10.])
-    probs = torch.tensor([0.5, 0.4, 0.2])
-
-    d1 = dist.Binomial(total_count, probs)
-    d2 = dist.ExtendedBinomial(total_count, probs)
-
-    # Check on good data.
-    data = d1.sample((100,))
-    assert_equal(d1.log_prob(data), d2.log_prob(data))
-
-    # Check on extended data.
-    data = torch.arange(-10., 20.).unsqueeze(-1)
-    with pytest.raises(ValueError):
-        d1.log_prob(data)
-    log_prob = d2.log_prob(data)
-    valid = d1.support.check(data)
-    assert ((log_prob > -math.inf) == valid).all()
-
-    # Check on shape error.
-    with pytest.raises(ValueError):
-        d2.log_prob(torch.tensor([0., 0.]))
-
-    # Check on value error.
-    with pytest.raises(ValueError):
-        d2.log_prob(torch.tensor(0.5))
-
-    # Check on negative total_count.
-    total_count = torch.arange(-10, 0.)
-    d = dist.ExtendedBinomial(total_count, 0.5)
-    assert (d.log_prob(data) == -math.inf).all()
-
-
-def test_extended_beta_binomial():
-    concentration1 = torch.tensor([1.0, 2.0, 1.0])
-    concentration0 = torch.tensor([0.5, 1.0, 2.0])
-    total_count = torch.tensor([1., 2., 10.])
-
-    d1 = dist.BetaBinomial(concentration1, concentration0, total_count)
-    d2 = dist.ExtendedBetaBinomial(concentration1, concentration0, total_count)
-
-    # Check on good data.
-    data = d1.sample((100,))
-    assert_equal(d1.log_prob(data), d2.log_prob(data))
-
-    # Check on extended data.
-    data = torch.arange(-10., 20.).unsqueeze(-1)
-    with pytest.raises(ValueError):
-        d1.log_prob(data)
-    log_prob = d2.log_prob(data)
-    valid = d1.support.check(data)
-    assert ((log_prob > -math.inf) == valid).all()
-
-    # Check on shape error.
-    with pytest.raises(ValueError):
-        d2.log_prob(torch.tensor([0., 0.]))
-
-    # Check on value error.
-    with pytest.raises(ValueError):
-        d2.log_prob(torch.tensor(0.5))
-
-    # Check on negative total_count.
-    total_count = torch.arange(-10, 0.)
-    d = dist.ExtendedBetaBinomial(1.5, 1.5, total_count)
-    assert (d.log_prob(data) == -math.inf).all()
+def check_grad(value, *params):
+    grads = grad(value.sum(), params, create_graph=True)
+    assert all(torch.isfinite(g).all() for g in grads)
+
+
+@pytest.mark.parametrize("tol", [0., 0.02, 0.05, 0.1])
+def test_extended_binomial(tol):
+    with set_approx_log_prob_tol(tol):
+        total_count = torch.tensor([0., 1., 2., 10.])
+        probs = torch.tensor([0.5, 0.5, 0.4, 0.2]).requires_grad_()
+
+        d1 = dist.Binomial(total_count, probs)
+        d2 = dist.ExtendedBinomial(total_count, probs)
+        # Check on good data.
+        data = d1.sample((100,))
+        assert_equal(d1.log_prob(data), d2.log_prob(data))
+
+        # Check on extended data.
+        data = torch.arange(-10., 20.).unsqueeze(-1)
+        with pytest.raises(ValueError):
+            d1.log_prob(data)
+        log_prob = d2.log_prob(data)
+        valid = d1.support.check(data)
+        assert ((log_prob > -math.inf) == valid).all()
+        check_grad(log_prob, probs)
+
+        # Check on shape error.
+        with pytest.raises(ValueError):
+            d2.log_prob(torch.tensor([0., 0.]))
+
+        # Check on value error.
+        with pytest.raises(ValueError):
+            d2.log_prob(torch.tensor(0.5))
+
+        # Check on negative total_count.
+        total_count = torch.arange(-10, 0.)
+        probs = torch.tensor(0.5).requires_grad_()
+        d = dist.ExtendedBinomial(total_count, probs)
+        log_prob = d.log_prob(data)
+        assert (log_prob == -math.inf).all()
+        check_grad(log_prob, probs)
+
+
+@pytest.mark.parametrize("tol", [0., 0.02, 0.05, 0.1])
+def test_extended_beta_binomial(tol):
+    with set_approx_log_prob_tol(tol):
+        concentration1 = torch.tensor([0.2, 1.0, 2.0, 1.0]).requires_grad_()
+        concentration0 = torch.tensor([0.2, 0.5, 1.0, 2.0]).requires_grad_()
+        total_count = torch.tensor([0., 1., 2., 10.])
+
+        d1 = dist.BetaBinomial(concentration1, concentration0, total_count)
+        d2 = dist.ExtendedBetaBinomial(concentration1, concentration0, total_count)
+
+        # Check on good data.
+        data = d1.sample((100,))
+        assert_equal(d1.log_prob(data), d2.log_prob(data))
+
+        # Check on extended data.
+        data = torch.arange(-10., 20.).unsqueeze(-1)
+        with pytest.raises(ValueError):
+            d1.log_prob(data)
+        log_prob = d2.log_prob(data)
+        valid = d1.support.check(data)
+        assert ((log_prob > -math.inf) == valid).all()
+        check_grad(log_prob, concentration1, concentration0)
+
+        # Check on shape error.
+        with pytest.raises(ValueError):
+            d2.log_prob(torch.tensor([0., 0.]))
+
+        # Check on value error.
+        with pytest.raises(ValueError):
+            d2.log_prob(torch.tensor(0.5))
+
+        # Check on negative total_count.
+        concentration1 = torch.tensor(1.5).requires_grad_()
+        concentration0 = torch.tensor(1.5).requires_grad_()
+        total_count = torch.arange(-10, 0.)
+        d = dist.ExtendedBetaBinomial(concentration1, concentration0, total_count)
+        log_prob = d.log_prob(data)
+        assert (log_prob == -math.inf).all()
+        check_grad(log_prob, concentration1, concentration0)

tests/ops/test_special.py

@@ -3,8 +3,24 @@
 
 import pytest
 import torch
+from torch.autograd import grad
 
-from pyro.ops.special import log_beta, log_binomial
+from pyro.ops.special import log_beta, log_binomial, safe_log
+from tests.common import assert_equal
+
+
+def test_safe_log():
+    # Test values.
+    x = torch.randn(1000).exp().requires_grad_()
+    expected = x.log()
+    actual = safe_log(x)
+    assert_equal(actual, expected)
+    assert_equal(grad(actual.sum(), [x])[0], grad(expected.sum(), [x])[0])
+
+    # Test gradients.
+    x = torch.tensor(0., requires_grad=True)
+    assert not torch.isfinite(grad(x.log(), [x])[0])
+    assert torch.isfinite(grad(safe_log(x), [x])[0])
 
 
 @pytest.mark.parametrize("tol", [

tests/ops/test_tensor_utils.py

@@ -7,31 +7,16 @@
 import pytest
 import scipy.fftpack as fftpack
 import torch
-from torch.autograd import grad
 
 import pyro
 from pyro.ops.tensor_utils import (block_diag_embed, block_diagonal, convolve, dct, idct, next_fast_len,
                                    periodic_cumsum, periodic_features, periodic_repeat, precision_to_scale_tril,
-                                   repeated_matmul, safe_log)
+                                   repeated_matmul)
 from tests.common import assert_close, assert_equal
 
 pytestmark = pytest.mark.stage('unit')
 
 
-def test_safe_log():
-    # Test values.
-    x = torch.randn(1000).exp().requires_grad_()
-    expected = x.log()
-    actual = safe_log(x)
-    assert_equal(actual, expected)
-    assert_equal(grad(actual.sum(), [x])[0], grad(expected.sum(), [x])[0])
-
-    # Test gradients.
-    x = torch.tensor(0., requires_grad=True)
-    assert not torch.isfinite(grad(x.log(), [x])[0])
-    assert torch.isfinite(grad(safe_log(x), [x])[0])
-
-
 @pytest.mark.parametrize('batch_size', [1, 2, 3])
 @pytest.mark.parametrize('block_size', [torch.Size([2, 2]), torch.Size([3, 1]), torch.Size([4, 2])])
 def test_block_diag_embed(batch_size, block_size):