/
vector_sinh_arcsinh_diag.py
274 lines (237 loc) · 11.4 KB
/
vector_sinh_arcsinh_diag.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
# Copyright 2017 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.
# ==============================================================================
"""Multi-dimensional (Vector) SinhArcsinh transformation of a distribution."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.distributions.python.ops import bijectors
from tensorflow.contrib.distributions.python.ops import distribution_util
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops.distributions import normal
from tensorflow.python.ops.distributions import transformed_distribution
from tensorflow.python.util import deprecation
__all__ = [
"VectorSinhArcsinhDiag",
]
class VectorSinhArcsinhDiag(transformed_distribution.TransformedDistribution):
"""The (diagonal) SinhArcsinh transformation of a distribution on `R^k`.
This distribution models a random vector `Y = (Y1,...,Yk)`, making use of
a `SinhArcsinh` transformation (which has adjustable tailweight and skew),
a rescaling, and a shift.
The `SinhArcsinh` transformation of the Normal is described in great depth in
[Sinh-arcsinh distributions](https://www.jstor.org/stable/27798865).
Here we use a slightly different parameterization, in terms of `tailweight`
and `skewness`. Additionally we allow for distributions other than Normal,
and control over `scale` as well as a "shift" parameter `loc`.
#### Mathematical Details
Given iid random vector `Z = (Z1,...,Zk)`, we define the VectorSinhArcsinhDiag
transformation of `Z`, `Y`, parameterized by
`(loc, scale, skewness, tailweight)`, via the relation (with `@` denoting
matrix multiplication):
```
Y := loc + scale @ F(Z) * (2 / F_0(2))
F(Z) := Sinh( (Arcsinh(Z) + skewness) * tailweight )
F_0(Z) := Sinh( Arcsinh(Z) * tailweight )
```
This distribution is similar to the location-scale transformation
`L(Z) := loc + scale @ Z` in the following ways:
* If `skewness = 0` and `tailweight = 1` (the defaults), `F(Z) = Z`, and then
`Y = L(Z)` exactly.
* `loc` is used in both to shift the result by a constant factor.
* The multiplication of `scale` by `2 / F_0(2)` ensures that if `skewness = 0`
`P[Y - loc <= 2 * scale] = P[L(Z) - loc <= 2 * scale]`.
Thus it can be said that the weights in the tails of `Y` and `L(Z)` beyond
`loc + 2 * scale` are the same.
This distribution is different than `loc + scale @ Z` due to the
reshaping done by `F`:
* Positive (negative) `skewness` leads to positive (negative) skew.
* positive skew means, the mode of `F(Z)` is "tilted" to the right.
* positive skew means positive values of `F(Z)` become more likely, and
negative values become less likely.
* Larger (smaller) `tailweight` leads to fatter (thinner) tails.
* Fatter tails mean larger values of `|F(Z)|` become more likely.
* `tailweight < 1` leads to a distribution that is "flat" around `Y = loc`,
and a very steep drop-off in the tails.
* `tailweight > 1` leads to a distribution more peaked at the mode with
heavier tails.
To see the argument about the tails, note that for `|Z| >> 1` and
`|Z| >> (|skewness| * tailweight)**tailweight`, we have
`Y approx 0.5 Z**tailweight e**(sign(Z) skewness * tailweight)`.
To see the argument regarding multiplying `scale` by `2 / F_0(2)`,
```
P[(Y - loc) / scale <= 2] = P[F(Z) * (2 / F_0(2)) <= 2]
= P[F(Z) <= F_0(2)]
= P[Z <= 2] (if F = F_0).
```
"""
@deprecation.deprecated(
"2018-10-01",
"The TensorFlow Distributions library has moved to "
"TensorFlow Probability "
"(https://github.com/tensorflow/probability). You "
"should update all references to use `tfp.distributions` "
"instead of `tf.contrib.distributions`.",
warn_once=True)
def __init__(self,
loc=None,
scale_diag=None,
scale_identity_multiplier=None,
skewness=None,
tailweight=None,
distribution=None,
validate_args=False,
allow_nan_stats=True,
name="MultivariateNormalLinearOperator"):
"""Construct VectorSinhArcsinhDiag distribution on `R^k`.
The arguments `scale_diag` and `scale_identity_multiplier` combine to
define the diagonal `scale` referred to in this class docstring:
```none
scale = diag(scale_diag + scale_identity_multiplier * ones(k))
```
The `batch_shape` is the broadcast shape between `loc` and `scale`
arguments.
The `event_shape` is given by last dimension of the matrix implied by
`scale`. The last dimension of `loc` (if provided) must broadcast with this
Additional leading dimensions (if any) will index batches.
Args:
loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
`b >= 0` and `k` is the event size.
scale_diag: Non-zero, floating-point `Tensor` representing a diagonal
matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
and characterizes `b`-batches of `k x k` diagonal matrices added to
`scale`. When both `scale_identity_multiplier` and `scale_diag` are
`None` then `scale` is the `Identity`.
scale_identity_multiplier: Non-zero, floating-point `Tensor` representing
a scale-identity-matrix added to `scale`. May have shape
`[B1, ..., Bb]`, `b >= 0`, and characterizes `b`-batches of scale
`k x k` identity matrices added to `scale`. When both
`scale_identity_multiplier` and `scale_diag` are `None` then `scale`
is the `Identity`.
skewness: Skewness parameter. floating-point `Tensor` with shape
broadcastable with `event_shape`.
tailweight: Tailweight parameter. floating-point `Tensor` with shape
broadcastable with `event_shape`.
distribution: `tf.Distribution`-like instance. Distribution from which `k`
iid samples are used as input to transformation `F`. Default is
`tfp.distributions.Normal(loc=0., scale=1.)`.
Must be a scalar-batch, scalar-event distribution. Typically
`distribution.reparameterization_type = FULLY_REPARAMETERIZED` or it is
a function of non-trainable parameters. WARNING: If you backprop through
a VectorSinhArcsinhDiag sample and `distribution` is not
`FULLY_REPARAMETERIZED` yet is a function of trainable variables, then
the gradient will be incorrect!
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.
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.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: if at most `scale_identity_multiplier` is specified.
"""
parameters = dict(locals())
with ops.name_scope(
name,
values=[
loc, scale_diag, scale_identity_multiplier, skewness, tailweight
]) as name:
loc = ops.convert_to_tensor(loc, name="loc") if loc is not None else loc
tailweight = 1. if tailweight is None else tailweight
has_default_skewness = skewness is None
skewness = 0. if skewness is None else skewness
# Recall, with Z a random variable,
# Y := loc + C * F(Z),
# F(Z) := Sinh( (Arcsinh(Z) + skewness) * tailweight )
# F_0(Z) := Sinh( Arcsinh(Z) * tailweight )
# C := 2 * scale / F_0(2)
# Construct shapes and 'scale' out of the scale_* and loc kwargs.
# scale_linop is only an intermediary to:
# 1. get shapes from looking at loc and the two scale args.
# 2. combine scale_diag with scale_identity_multiplier, which gives us
# 'scale', which in turn gives us 'C'.
scale_linop = distribution_util.make_diag_scale(
loc=loc,
scale_diag=scale_diag,
scale_identity_multiplier=scale_identity_multiplier,
validate_args=False,
assert_positive=False)
batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
loc, scale_linop)
# scale_linop.diag_part() is efficient since it is a diag type linop.
scale_diag_part = scale_linop.diag_part()
dtype = scale_diag_part.dtype
if distribution is None:
distribution = normal.Normal(
loc=array_ops.zeros([], dtype=dtype),
scale=array_ops.ones([], dtype=dtype),
allow_nan_stats=allow_nan_stats)
else:
asserts = distribution_util.maybe_check_scalar_distribution(
distribution, dtype, validate_args)
if asserts:
scale_diag_part = control_flow_ops.with_dependencies(
asserts, scale_diag_part)
# Make the SAS bijector, 'F'.
skewness = ops.convert_to_tensor(skewness, dtype=dtype, name="skewness")
tailweight = ops.convert_to_tensor(
tailweight, dtype=dtype, name="tailweight")
f = bijectors.SinhArcsinh(
skewness=skewness, tailweight=tailweight)
if has_default_skewness:
f_noskew = f
else:
f_noskew = bijectors.SinhArcsinh(
skewness=skewness.dtype.as_numpy_dtype(0.),
tailweight=tailweight)
# Make the Affine bijector, Z --> loc + C * Z.
c = 2 * scale_diag_part / f_noskew.forward(
ops.convert_to_tensor(2, dtype=dtype))
affine = bijectors.Affine(
shift=loc, scale_diag=c, validate_args=validate_args)
bijector = bijectors.Chain([affine, f])
super(VectorSinhArcsinhDiag, self).__init__(
distribution=distribution,
bijector=bijector,
batch_shape=batch_shape,
event_shape=event_shape,
validate_args=validate_args,
name=name)
self._parameters = parameters
self._loc = loc
self._scale = scale_linop
self._tailweight = tailweight
self._skewness = skewness
@property
def loc(self):
"""The `loc` in `Y := loc + scale @ F(Z) * (2 / F(2))."""
return self._loc
@property
def scale(self):
"""The `LinearOperator` `scale` in `Y := loc + scale @ F(Z) * (2 / F(2))."""
return self._scale
@property
def tailweight(self):
"""Controls the tail decay. `tailweight > 1` means faster than Normal."""
return self._tailweight
@property
def skewness(self):
"""Controls the skewness. `Skewness > 0` means right skew."""
return self._skewness