/
conditional_transformed_distribution.py
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/
conditional_transformed_distribution.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A Conditional Transformed Distribution class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.distributions.python.ops import conditional_distribution
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.distributions import transformed_distribution
from tensorflow.python.ops.distributions import util as distribution_util
# pylint: disable=protected-access
_concat_vectors = transformed_distribution._concat_vectors
# pylint: enable=protected-access
__all__ = [
"ConditionalTransformedDistribution",
]
_condition_kwargs_dict = {
"bijector_kwargs": ("Python dictionary of arg names/values "
"forwarded to the bijector."),
"distribution_kwargs": ("Python dictionary of arg names/values "
"forwarded to the distribution."),
}
class ConditionalTransformedDistribution(
conditional_distribution.ConditionalDistribution,
transformed_distribution.TransformedDistribution):
"""A TransformedDistribution that allows intrinsic conditioning."""
@distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict)
def _sample_n(self, n, seed=None,
bijector_kwargs=None,
distribution_kwargs=None):
sample_shape = _concat_vectors(
distribution_util.pick_vector(self._needs_rotation, self._empty, [n]),
self._override_batch_shape,
self._override_event_shape,
distribution_util.pick_vector(self._needs_rotation, [n], self._empty))
distribution_kwargs = distribution_kwargs or {}
x = self.distribution.sample(sample_shape=sample_shape,
seed=seed,
**distribution_kwargs)
x = self._maybe_rotate_dims(x)
# We'll apply the bijector in the `_call_sample_n` function.
return x
def _call_sample_n(self, sample_shape, seed, name,
bijector_kwargs=None,
distribution_kwargs=None):
# We override `_call_sample_n` rather than `_sample_n` so we can ensure that
# the result of `self.bijector.forward` is not modified (and thus caching
# works).
with self._name_scope(name, values=[sample_shape]):
sample_shape = ops.convert_to_tensor(
sample_shape, dtype=dtypes.int32, name="sample_shape")
sample_shape, n = self._expand_sample_shape_to_vector(
sample_shape, "sample_shape")
# First, generate samples. We will possibly generate extra samples in the
# event that we need to reinterpret the samples as part of the
# event_shape.
x = self._sample_n(n, seed, bijector_kwargs, distribution_kwargs)
# Next, we reshape `x` into its final form. We do this prior to the call
# to the bijector to ensure that the bijector caching works.
batch_event_shape = array_ops.shape(x)[1:]
final_shape = array_ops.concat([sample_shape, batch_event_shape], 0)
x = array_ops.reshape(x, final_shape)
# Finally, we apply the bijector's forward transformation. For caching to
# work, it is imperative that this is the last modification to the
# returned result.
bijector_kwargs = bijector_kwargs or {}
y = self.bijector.forward(x, **bijector_kwargs)
y = self._set_sample_static_shape(y, sample_shape)
return y
@distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict)
def _log_prob(self, y, bijector_kwargs=None, distribution_kwargs=None):
# For caching to work, it is imperative that the bijector is the first to
# modify the input.
bijector_kwargs = bijector_kwargs or {}
distribution_kwargs = distribution_kwargs or {}
x = self.bijector.inverse(y, **bijector_kwargs)
event_ndims = self._maybe_get_static_event_ndims()
ildj = self.bijector.inverse_log_det_jacobian(
y, event_ndims=event_ndims, **bijector_kwargs)
if self.bijector._is_injective: # pylint: disable=protected-access
return self._finish_log_prob_for_one_fiber(y, x, ildj,
distribution_kwargs)
lp_on_fibers = [
self._finish_log_prob_for_one_fiber(y, x_i, ildj_i, distribution_kwargs)
for x_i, ildj_i in zip(x, ildj)]
return math_ops.reduce_logsumexp(array_ops.stack(lp_on_fibers), axis=0)
def _finish_log_prob_for_one_fiber(self, y, x, ildj, distribution_kwargs):
"""Finish computation of log_prob on one element of the inverse image."""
x = self._maybe_rotate_dims(x, rotate_right=True)
log_prob = self.distribution.log_prob(x, **distribution_kwargs)
if self._is_maybe_event_override:
log_prob = math_ops.reduce_sum(log_prob, self._reduce_event_indices)
return math_ops.cast(ildj, log_prob.dtype) + log_prob
@distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict)
def _prob(self, y, bijector_kwargs=None, distribution_kwargs=None):
bijector_kwargs = bijector_kwargs or {}
distribution_kwargs = distribution_kwargs or {}
x = self.bijector.inverse(y, **bijector_kwargs)
event_ndims = self._maybe_get_static_event_ndims()
ildj = self.bijector.inverse_log_det_jacobian(
y, event_ndims=event_ndims, **bijector_kwargs)
if self.bijector._is_injective: # pylint: disable=protected-access
return self._finish_prob_for_one_fiber(y, x, ildj, distribution_kwargs)
prob_on_fibers = [
self._finish_prob_for_one_fiber(y, x_i, ildj_i, distribution_kwargs)
for x_i, ildj_i in zip(x, ildj)]
return sum(prob_on_fibers)
def _finish_prob_for_one_fiber(self, y, x, ildj, distribution_kwargs):
"""Finish computation of prob on one element of the inverse image."""
x = self._maybe_rotate_dims(x, rotate_right=True)
prob = self.distribution.prob(x, **distribution_kwargs)
if self._is_maybe_event_override:
prob = math_ops.reduce_prod(prob, self._reduce_event_indices)
return math_ops.exp(math_ops.cast(ildj, prob.dtype)) * prob
@distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict)
def _log_cdf(self, y, bijector_kwargs=None, distribution_kwargs=None):
if self._is_maybe_event_override:
raise NotImplementedError("log_cdf is not implemented when overriding "
"event_shape")
if not self.bijector._is_injective: # pylint: disable=protected-access
raise NotImplementedError("log_cdf is not implemented when "
"bijector is not injective.")
bijector_kwargs = bijector_kwargs or {}
distribution_kwargs = distribution_kwargs or {}
x = self.bijector.inverse(y, **bijector_kwargs)
return self.distribution.log_cdf(x, **distribution_kwargs)
@distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict)
def _cdf(self, y, bijector_kwargs=None, distribution_kwargs=None):
if self._is_maybe_event_override:
raise NotImplementedError("cdf is not implemented when overriding "
"event_shape")
if not self.bijector._is_injective: # pylint: disable=protected-access
raise NotImplementedError("cdf is not implemented when "
"bijector is not injective.")
bijector_kwargs = bijector_kwargs or {}
distribution_kwargs = distribution_kwargs or {}
x = self.bijector.inverse(y, **bijector_kwargs)
return self.distribution.cdf(x, **distribution_kwargs)
@distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict)
def _log_survival_function(self, y,
bijector_kwargs=None, distribution_kwargs=None):
if self._is_maybe_event_override:
raise NotImplementedError("log_survival_function is not implemented when "
"overriding event_shape")
if not self.bijector._is_injective: # pylint: disable=protected-access
raise NotImplementedError("log_survival_function is not implemented when "
"bijector is not injective.")
bijector_kwargs = bijector_kwargs or {}
distribution_kwargs = distribution_kwargs or {}
x = self.bijector.inverse(y, **bijector_kwargs)
return self.distribution.log_survival_function(x, **distribution_kwargs)
@distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict)
def _survival_function(self, y,
bijector_kwargs=None, distribution_kwargs=None):
if self._is_maybe_event_override:
raise NotImplementedError("survival_function is not implemented when "
"overriding event_shape")
if not self.bijector._is_injective: # pylint: disable=protected-access
raise NotImplementedError("survival_function is not implemented when "
"bijector is not injective.")
bijector_kwargs = bijector_kwargs or {}
distribution_kwargs = distribution_kwargs or {}
x = self.bijector.inverse(y, **bijector_kwargs)
return self.distribution.survival_function(x, **distribution_kwargs)
@distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict)
def _quantile(self, value, bijector_kwargs=None, distribution_kwargs=None):
if self._is_maybe_event_override:
raise NotImplementedError("quantile is not implemented when overriding "
"event_shape")
if not self.bijector._is_injective: # pylint: disable=protected-access
raise NotImplementedError("quantile is not implemented when "
"bijector is not injective.")
bijector_kwargs = bijector_kwargs or {}
distribution_kwargs = distribution_kwargs or {}
# x_q is the "qth quantile" of X iff q = P[X <= x_q]. Now, since X =
# g^{-1}(Y), q = P[X <= x_q] = P[g^{-1}(Y) <= x_q] = P[Y <= g(x_q)],
# implies the qth quantile of Y is g(x_q).
inv_cdf = self.distribution.quantile(value, **distribution_kwargs)
return self.bijector.forward(inv_cdf, **bijector_kwargs)
def _maybe_get_static_event_ndims(self):
if self.event_shape.ndims is not None:
return self.event_shape.ndims
event_ndims = array_ops.size(self.event_shape_tensor())
event_ndims_ = distribution_util.maybe_get_static_value(event_ndims)
if event_ndims_ is not None:
return event_ndims_
return event_ndims