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inverse_gaussian.py
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inverse_gaussian.py
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# Copyright 2018 The TensorFlow Probability Authors.
#
# 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.
# ============================================================================
"""The InverseGaussian distribution class."""
# Dependency imports
import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow_probability.python.bijectors import chain as chain_bijector
from tensorflow_probability.python.bijectors import exp as exp_bijector
from tensorflow_probability.python.bijectors import scale as scale_bijector
from tensorflow_probability.python.bijectors import softplus as softplus_bijector
from tensorflow_probability.python.distributions import distribution
from tensorflow_probability.python.distributions import normal
from tensorflow_probability.python.internal import assert_util
from tensorflow_probability.python.internal import custom_gradient as tfp_custom_gradient
from tensorflow_probability.python.internal import distribution_util
from tensorflow_probability.python.internal import dtype_util
from tensorflow_probability.python.internal import parameter_properties
from tensorflow_probability.python.internal import prefer_static as ps
from tensorflow_probability.python.internal import reparameterization
from tensorflow_probability.python.internal import samplers
from tensorflow_probability.python.internal import special_math
from tensorflow_probability.python.internal import tensor_util
from tensorflow_probability.python.internal import tensorshape_util
from tensorflow_probability.python.math import generic
__all__ = [
'InverseGaussian',
]
class InverseGaussian(distribution.AutoCompositeTensorDistribution):
"""Inverse Gaussian distribution.
The [inverse Gaussian distribution]
(https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution)
is parameterized by a `loc` and a `concentration` parameter. It's also known
as the Wald distribution. Some, e.g., the Python scipy package, refer to the
special case when `loc` is 1 as the Wald distribution.
The "inverse" in the name does not refer to the distribution associated to
the multiplicative inverse of a random variable. Rather, the cumulant
generating function of this distribution is the inverse to that of a Gaussian
random variable.
#### Mathematical Details
The probability density function (pdf) is,
```none
pdf(x; mu, lambda) = [lambda / (2 pi x ** 3)] ** 0.5
exp{-lambda(x - mu) ** 2 / (2 mu ** 2 x)}
```
where
* `loc = mu`
* `concentration = lambda`.
The support of the distribution is defined on `(0, infinity)`.
Mapping to R and Python scipy's parameterization:
* R: statmod::invgauss
- mean = loc
- shape = concentration
- dispersion = 1 / concentration. Used only if shape is NULL.
* Python: scipy.stats.invgauss
- mu = loc / concentration
- scale = concentration
"""
def __init__(self,
loc,
concentration,
validate_args=False,
allow_nan_stats=True,
name='InverseGaussian'):
"""Constructs inverse Gaussian distribution with `loc` and `concentration`.
Args:
loc: Floating-point `Tensor`, the loc params. Must contain only positive
values.
concentration: Floating-point `Tensor`, the concentration params.
Must contain only positive values.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
Default value: `False` (i.e. do not validate args).
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
Default value: `True`.
name: Python `str` name prefixed to Ops created by this class.
Default value: 'InverseGaussian'.
"""
parameters = dict(locals())
with tf.name_scope(name):
dtype = dtype_util.common_dtype([loc, concentration],
dtype_hint=tf.float32)
self._concentration = tensor_util.convert_nonref_to_tensor(
concentration, dtype=dtype, name='concentration')
self._loc = tensor_util.convert_nonref_to_tensor(
loc, dtype=dtype, name='loc')
super(InverseGaussian, self).__init__(
dtype=self._loc.dtype,
reparameterization_type=reparameterization.FULLY_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
name=name)
@classmethod
def _parameter_properties(cls, dtype, num_classes=None):
# pylint: disable=g-long-lambda
return dict(
loc=parameter_properties.ParameterProperties(
default_constraining_bijector_fn=(
lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype)))),
concentration=parameter_properties.ParameterProperties(
default_constraining_bijector_fn=(
lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype)))))
# pylint: enable=g-long-lambda
@property
def loc(self):
"""Location parameter."""
return self._loc
@property
def concentration(self):
"""Concentration parameter."""
return self._concentration
def _event_shape(self):
return tf.TensorShape([])
def _sample_n(self, n, seed=None):
seed = samplers.sanitize_seed(seed, salt='inverse_gaussian')
loc = tf.convert_to_tensor(self.loc)
concentration = tf.convert_to_tensor(self.concentration)
total_shape = ps.concat(
[ps.convert_to_shape_tensor([n]),
self._batch_shape_tensor(loc=loc, concentration=concentration)],
axis=0)
return _random_inverse_gaussian_gradient(
total_shape, loc, concentration, seed)
def _log_prob(self, x):
concentration = tf.convert_to_tensor(self.concentration)
loc = tf.convert_to_tensor(self.loc)
return (0.5 * (tf.math.log(concentration) - np.log(2. * np.pi) -
3. * tf.math.log(x)) +
(-concentration * tf.math.squared_difference(x, loc)) /
(2. * tf.square(loc) * x))
def _cdf(self, x):
concentration = tf.convert_to_tensor(self.concentration)
loc = tf.convert_to_tensor(self.loc)
return (
special_math.ndtr((tf.math.rsqrt(x / concentration) * (x / loc - 1.))) +
tf.exp(2. * concentration / loc) *
special_math.ndtr(-tf.math.rsqrt(x / concentration) * (x / loc + 1)))
@distribution_util.AppendDocstring(
"""The mean of inverse Gaussian is the `loc` parameter.""")
def _mean(self):
# Shape is broadcasted with + tf.zeros_like().
return self.loc + tf.zeros_like(self.concentration)
@distribution_util.AppendDocstring(
"""The variance of inverse Gaussian is `loc` ** 3 / `concentration`.""")
def _variance(self):
return self.loc ** 3 / self.concentration
def _default_event_space_bijector(self):
return chain_bijector.Chain([
softplus_bijector.Softplus(validate_args=self.validate_args),
scale_bijector.Scale(scale=-1., validate_args=self.validate_args),
exp_bijector.Log(validate_args=self.validate_args),
softplus_bijector.Softplus(validate_args=self.validate_args)
], validate_args=self.validate_args)
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.append(assert_util.assert_non_negative(
x, message='Sample must be non-negative.'))
return assertions
def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
return []
assertions = []
if is_init != tensor_util.is_ref(self.concentration):
assertions.append(assert_util.assert_positive(
self.concentration,
message='Argument `concentration` must be positive.'))
if is_init != tensor_util.is_ref(self.loc):
assertions.append(assert_util.assert_positive(
self.loc,
message='Argument `loc` must be positive.'))
return assertions
def _random_inverse_gaussian_no_gradient(shape, loc, concentration, seed):
"""Sample from Inverse Gaussian distribution."""
# See https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution or
# https://www.jstor.org/stable/2683801
dtype = dtype_util.common_dtype([loc, concentration], tf.float32)
concentration = tf.convert_to_tensor(concentration)
loc = tf.convert_to_tensor(loc)
chi2_seed, unif_seed = samplers.split_seed(seed, salt='inverse_gaussian')
sampled_chi2 = tf.square(samplers.normal(shape, seed=chi2_seed, dtype=dtype))
sampled_uniform = samplers.uniform(shape, seed=unif_seed, dtype=dtype)
# Wikipedia defines an intermediate x with the formula
# x = loc + loc ** 2 * y / (2 * conc)
# - loc / (2 * conc) * sqrt(4 * loc * conc * y + loc ** 2 * y ** 2)
# where y ~ N(0, 1)**2 (sampled_chi2 above) and conc is the concentration.
# Let us write
# w = loc * y / (2 * conc)
# Then we can extract the common factor in the last two terms to obtain
# x = loc + loc * w * (1 - sqrt(2 / w + 1))
# Now we see that the Wikipedia formula suffers from catastrphic
# cancellation for large w (e.g., if conc << loc).
#
# Fortunately, we can fix this by multiplying both sides
# by 1 + sqrt(2 / w + 1). We get
# x * (1 + sqrt(2 / w + 1)) =
# = loc * (1 + sqrt(2 / w + 1)) + loc * w * (1 - (2 / w + 1))
# = loc * (sqrt(2 / w + 1) - 1)
# The term sqrt(2 / w + 1) + 1 no longer presents numerical
# difficulties for large w, and sqrt(2 / w + 1) - 1 is just
# sqrt1pm1(2 / w), which we know how to compute accurately.
# This just leaves the matter of small w, where 2 / w may
# overflow. In the limit a w -> 0, x -> loc, so we just mask
# that case.
sqrt1pm1_arg = 4 * concentration / (loc * sampled_chi2) # 2 / w above
safe_sqrt1pm1_arg = tf.where(sqrt1pm1_arg < np.inf, sqrt1pm1_arg, 1.0)
denominator = 1.0 + tf.sqrt(safe_sqrt1pm1_arg + 1.0)
ratio = generic.sqrt1pm1(safe_sqrt1pm1_arg) / denominator
sampled = loc * tf.where(sqrt1pm1_arg < np.inf, ratio, 1.0) # x above
return tf.where(sampled_uniform <= loc / (loc + sampled),
sampled, tf.square(loc) / sampled)
def _random_inverse_gaussian_fwd(shape, loc, concentration, seed):
"""Compute output, aux (collaborates with _random_inverse_gaussian_bwd)."""
samples = _random_inverse_gaussian_no_gradient(
shape, loc, concentration, seed)
return samples, (samples, loc, concentration)
def _compute_partials(samples, loc, concentration):
"""Compute the Implicit Gradients for the samples."""
# The implicit gradient here is derived in Appendix H.1 of [1].
# Note that [1] uses the parametermization (1 / loc, concentration), and the
# formula below are modified accordingly.
# References
# [1] W. Lin, M. Schmidt, M. Khan. Handling the Positive-Definite Constraint
# in the Bayesian Learning Rule. https://arxiv.org/abs/2002.10060v8
dtype = dtype_util.common_dtype([samples, loc, concentration], tf.float32)
numpy_dtype = dtype_util.as_numpy_dtype(dtype)
norm = normal.Normal(numpy_dtype(0.), numpy_dtype(1.))
z = -tf.math.sqrt(concentration / samples) * (samples / loc + 1.)
log_mills_ratio = norm.log_cdf(z) - norm.log_prob(z)
partial_c = (
numpy_dtype(np.log(2.)) - tf.math.log(loc) -
0.5 * tf.math.log(concentration) + 1.5 * tf.math.log(samples) +
log_mills_ratio)
partial_c = samples / concentration - tf.math.exp(partial_c)
partial_l = (
numpy_dtype(np.log(2.)) + 0.5 * tf.math.log(concentration) +
1.5 * tf.math.log(samples) + log_mills_ratio)
# We divide by -loc**2, since we need to take in to account that [1] is
# parameterized by 1 / loc (hence by the chain rule we incur a - 1 / loc**2
# factor, where the negative sign is cancelled out.
partial_l = tf.math.exp(partial_l) / tf.math.square(loc)
return partial_c, partial_l
def _random_inverse_gaussian_bwd(shape, aux, g):
"""The gradient of the inverse gaussian samples."""
samples, loc, concentration = aux
partial_concentration, partial_loc = _compute_partials(
samples, loc, concentration)
dsamples = g
# These will need to be shifted by the extra dimensions added from
# `sample_shape`.
reduce_dims = tf.range(
tf.size(shape) - tf.maximum(tf.rank(concentration), tf.rank(loc)))
grad_concentration = tf.math.reduce_sum(
dsamples * partial_concentration, axis=reduce_dims)
grad_loc = tf.math.reduce_sum(dsamples * partial_loc, axis=reduce_dims)
if (tensorshape_util.is_fully_defined(concentration.shape) and
tensorshape_util.is_fully_defined(loc.shape) and
concentration.shape == loc.shape):
return grad_concentration, grad_loc, None # seed=None
grad_loc, grad_concentration = generic.fix_gradient_for_broadcasting(
[loc, concentration],
[grad_loc, grad_concentration])
return grad_loc, grad_concentration, None # seed=None
def _random_inverse_gaussian_jvp(shape, primals, tangents):
"""Computes JVP for inverse_gaussian sample (supports JAX custom derivative)."""
loc, concentration, seed = primals
dloc, dconcentration, dseed = tangents
del dseed
# TODO(https://github.com/google/jax/issues/3768): eliminate broadcast_to?
dconcentration = tf.broadcast_to(dconcentration, shape)
dloc = tf.broadcast_to(dloc, shape)
samples = _random_inverse_gaussian_no_gradient(
shape, loc, concentration, seed)
partial_concentration, partial_loc = _compute_partials(
samples, loc, concentration)
dsamples = (partial_concentration * dconcentration + partial_loc * dloc)
return samples, dsamples
@tfp_custom_gradient.custom_gradient(
vjp_fwd=_random_inverse_gaussian_fwd,
vjp_bwd=_random_inverse_gaussian_bwd,
jvp_fn=_random_inverse_gaussian_jvp,
nondiff_argnums=(0,))
def _random_inverse_gaussian_gradient(
shape, loc, concentration, seed):
return _random_inverse_gaussian_no_gradient(shape, loc, concentration, seed)