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Add ZeroInflatedPoisson distribution #2030

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2 changes: 2 additions & 0 deletions tensorflow_probability/python/distributions/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,6 +65,7 @@
from tensorflow_probability.python.distributions.independent import Independent
from tensorflow_probability.python.distributions.inflated import Inflated
from tensorflow_probability.python.distributions.inflated import ZeroInflatedNegativeBinomial
from tensorflow_probability.python.distributions.inflated import ZeroInflatedPoisson
from tensorflow_probability.python.distributions.inverse_gamma import InverseGamma
from tensorflow_probability.python.distributions.inverse_gaussian import InverseGaussian
from tensorflow_probability.python.distributions.johnson_su import JohnsonSU
Expand Down Expand Up @@ -302,5 +303,6 @@
'WishartLinearOperator',
'WishartTriL',
'ZeroInflatedNegativeBinomial',
'ZeroInflatedPoisson',
'Zipf',
]
9 changes: 8 additions & 1 deletion tensorflow_probability/python/distributions/inflated.py
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Expand Up @@ -25,6 +25,7 @@
from tensorflow_probability.python.distributions import distribution as distribution_lib
from tensorflow_probability.python.distributions import mixture
from tensorflow_probability.python.distributions import negative_binomial
from tensorflow_probability.python.distributions import poisson
from tensorflow_probability.python.internal import auto_composite_tensor
from tensorflow_probability.python.internal import dtype_util
from tensorflow_probability.python.internal import parameter_properties
Expand All @@ -33,7 +34,8 @@
from tensorflow_probability.python.internal import tensor_util
from tensorflow_probability.python.util.deferred_tensor import DeferredTensor

__all__ = ['Inflated', 'inflated_factory', 'ZeroInflatedNegativeBinomial']
__all__ = ['Inflated', 'inflated_factory', 'ZeroInflatedNegativeBinomial',
'ZeroInflatedPoisson']


def _safe_value_for_distribution(dist):
Expand Down Expand Up @@ -348,3 +350,8 @@ def another_property_getter(unused_self, value=v):
negative_binomial.NegativeBinomial,
0.0,
require_integer_total_count=False)

ZeroInflatedPoisson = inflated_factory(
'ZeroInflatedPoisson',
poisson.Poisson,
0.0)
31 changes: 31 additions & 0 deletions tensorflow_probability/python/distributions/inflated_test.py
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Expand Up @@ -21,6 +21,7 @@
from tensorflow_probability.python.distributions import lognormal
from tensorflow_probability.python.distributions import negative_binomial
from tensorflow_probability.python.distributions import normal
from tensorflow_probability.python.distributions import poisson
from tensorflow_probability.python.experimental.util import trainable
from tensorflow_probability.python.internal import test_util
from tensorflow_probability.python.math import gradient
Expand Down Expand Up @@ -89,6 +90,36 @@ def test_zero_inflated_negative_binomial(self):
inflated_loc_probs=0.2, probs=0.5, total_count=10.0)
self.assertEqual('ZeroInflatedNegativeBinomial', zinb.name)

def test_zero_inflated_poisson(self):
zip_ = inflated.ZeroInflatedPoisson(
inflated_loc_probs=0.3, rate=5.0)
self.assertEqual('ZeroInflatedPoisson', zip_.name)
samples = zip_.sample(sample_shape=100, seed=test_util.test_seed())
self.assertEqual((100,), samples.shape)
lprob = zip_.log_prob(0.0)
self.assertAllFinite(lprob)
lprob = zip_.log_prob(3.0)
self.assertAllFinite(lprob)

def test_zero_inflated_poisson_logits(self):
zip_ = inflated.ZeroInflatedPoisson(
inflated_loc_logits=0.5, rate=3.0)
self.assertEqual('ZeroInflatedPoisson', zip_.name)
samples = zip_.sample(seed=test_util.test_seed())
self.assertAllFinite(samples)

def test_zero_inflated_poisson_batched(self):
poisson_dist = poisson.Poisson(
rate=np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float32))
zip_ = inflated.ZeroInflatedPoisson(
poisson_dist,
inflated_loc_probs=np.array([0.1, 0.2, 0.3, 0.4, 0.5],
dtype=np.float32))
lprob = zip_.log_prob([0, 1, 2, 3, 4])
self.assertEqual((5,), lprob.shape)
samples = zip_.sample(seed=test_util.test_seed())
self.assertEqual((5,), samples.shape)

def test_zinb_is_trainable(self):
init_fn, apply_fn = trainable.make_trainable_stateless(
inflated.ZeroInflatedNegativeBinomial,
Expand Down