BREAKING CHANGE: Remove deprecated behavior of Poisson.{rate,log_rate}.

We now return None when the `rate` or `log_rate` parameter was not specified when building the model -- e.g.: `Poisson(rate=[...]).log_rate` is now None and `Poisson(log_rate=[...]).rate` is now None.  If the previous behavior is needed, use methods `rate_parameter()` and `log_rate_parameter()`.

PiperOrigin-RevId: 287219881

tensorflow_probability/python/distributions/poisson.py

@@ -26,7 +26,7 @@
 from tensorflow_probability.python.internal import dtype_util
 from tensorflow_probability.python.internal import reparameterization
 from tensorflow_probability.python.internal import tensor_util
-from tensorflow.python.util import deprecation  # pylint: disable=g-direct-tensorflow-import
+
 
 __all__ = [
     'Poisson',
@@ -118,15 +118,11 @@ def _params_event_ndims(cls):
   @property
   def rate(self):
     """Rate parameter."""
-    if self._rate is None:
-      return self._rate_deprecated_behavior()
     return self._rate
 
   @property
   def log_rate(self):
     """Log rate parameter."""
-    if self._log_rate is None:
-      return self._log_rate_deprecated_behavior()
     return self._log_rate
 
   @property
@@ -219,22 +215,6 @@ def _log_rate_parameter_no_checks(self):
       return tf.math.log(self._rate)
     return tf.identity(self._log_rate)
 
-  @deprecation.deprecated(
-      '2019-10-01',
-      'The `rate` property will return `None` when the distribution is '
-      'parameterized with `rate=None`. Use `rate_parameter()` instead.',
-      warn_once=True)
-  def _rate_deprecated_behavior(self):
-    return self.rate_parameter()
-
-  @deprecation.deprecated(
-      '2019-10-01',
-      'The `log_rate` property will return `None` when the distribution is '
-      'parameterized with `log_rate=None`. Use `log_rate_parameter()` instead.',
-      warn_once=True)
-  def _log_rate_deprecated_behavior(self):
-    return self.log_rate_parameter()
-
   def _default_event_space_bijector(self):
     return
 

tensorflow_probability/python/distributions/poisson_lognormal.py

@@ -371,7 +371,7 @@ def _sample_n(self, n, seed=None):
         delta=self._quadrature_size,
         dtype=ids.dtype)
     ids = ids + offset
-    rate = tf.gather(tf.reshape(dist.rate, shape=[-1]), ids)
+    rate = tf.gather(tf.reshape(dist.rate_parameter(), shape=[-1]), ids)
     rate = tf.reshape(
         rate, shape=concat_vectors([n], self._batch_shape_tensor(
             distributions=distributions)))

tensorflow_probability/python/distributions/poisson_test.py

@@ -78,7 +78,8 @@ def testPoissonLogPmfContinuousRelaxation(self):
         rate=lam, interpolate_nondiscrete=True, validate_args=False)
 
     expected_continuous_log_pmf = (
-        x * poisson.log_rate - tf.math.lgamma(1. + x) - poisson.rate)
+        x * poisson.log_rate_parameter()
+        - tf.math.lgamma(1. + x) - poisson.rate_parameter())
     expected_continuous_log_pmf = tf.where(
         x >= 0., expected_continuous_log_pmf,
         dtype_util.as_numpy_dtype(