forked from mindspore-Ecosystem/mindspore
Add Gamma distribution
This commit is contained in:
peixu_ren
parent
0344078a9c
commit
93d4ef428f
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@ -22,6 +22,7 @@ from .bernoulli import Bernoulli
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from .categorical import Categorical
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from .cauchy import Cauchy
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from .exponential import Exponential
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from .gamma import Gamma
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from .geometric import Geometric
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from .gumbel import Gumbel
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from .logistic import Logistic
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@ -36,6 +37,7 @@ __all__ = ['Distribution',
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'Categorical',
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'Cauchy',
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'Exponential',
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'Gamma',
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'Geometric',
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'Gumbel',
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'Logistic',
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@ -0,0 +1,338 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Gamma Distribution"""
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import numpy as np
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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import mindspore.nn as nn
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from mindspore._checkparam import Validator
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from mindspore.common import dtype as mstype
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from .distribution import Distribution
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from ._utils.utils import check_greater_zero, check_distribution_name
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from ._utils.custom_ops import log_generic
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class Gamma(Distribution):
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"""
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Gamma distribution.
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Args:
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concentration (int, float, list, numpy.ndarray, Tensor, Parameter): The concentration,
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also know as alpha of the Gamma distribution.
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rate (int, float, list, numpy.ndarray, Tensor, Parameter): The rate, also know as
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beta of the Gamma distribution.
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seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
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dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
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name (str): The name of the distribution. Default: 'Gamma'.
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Note:
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`concentration` and `rate` must be greater than zero.
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`dist_spec_args` are `concentration` and `rate`.
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`dtype` must be a float type because Gamma distributions are continuous.
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Examples:
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>>> # To initialize a Gamma distribution of the concentration 3.0 and the rate 4.0.
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>>> import mindspore.nn.probability.distribution as msd
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>>> g = msd.Gamma(3.0, 4.0, dtype=mstype.float32)
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>>>
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>>> # The following creates two independent Gamma distributions.
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>>> g = msd.Gamma([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
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>>>
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>>> # A Gamma distribution can be initilized without arguments.
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>>> # In this case, `concentration` and `rate` must be passed in through arguments.
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>>> g = msd.Gamma(dtype=mstype.float32)
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>>>
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>>> # To use a Gamma distribution in a network.
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>>> class net(Cell):
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... def __init__(self):
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... super(net, self).__init__():
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... self.g1 = msd.Gamma(1.0, 1.0, dtype=mstype.float32)
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... self.g2 = msd.Gamma(dtype=mstype.float32)
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...
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... # The following calls are valid in construct.
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... def construct(self, value, concentration_b, rate_b, concentration_a, rate_a):
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...
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... # Private interfaces of probability functions corresponding to public interfaces, including
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... # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same arguments as follows.
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... # Args:
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... # value (Tensor): the value to be evaluated.
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... # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
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... # rate (Tensor): the rate of the distribution. Default: self._rate.
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...
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... # Examples of `prob`.
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... # Similar calls can be made to other probability functions
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... # by replacing 'prob' by the name of the function
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... ans = self.g1.prob(value)
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... # Evaluate with respect to the distribution b.
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... ans = self.g1.prob(value, concentration_b, rate_b)
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... # `concentration` and `rate` must be passed in during function calls
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... ans = self.g2.prob(value, concentration_a, rate_a)
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...
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...
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... # Functions `concentration`, `rate`, `mean`, `sd`, `var`, and `entropy` have the same arguments.
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... # Args:
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... # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
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... # rate (Tensor): the rate of the distribution. Default: self._rate.
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...
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... # Example of `concentration`, `rate`, `mean`. `sd`, `var`, and `entropy` are similar.
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... ans = self.g1.concentration() # return 1.0
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... ans = self.g1.concentration(concentration_b, rate_b) # return concentration_b
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... # `concentration` and `rate` must be passed in during function calls.
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... ans = self.g2.concentration(concentration_a, rate_a)
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...
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...
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... # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
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... # Args:
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... # dist (str): the type of the distributions. Only "Gamma" is supported.
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... # concentration_b (Tensor): the concentration of distribution b.
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... # rate_b (Tensor): the rate of distribution b.
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... # concentration_a (Tensor): the concentration of distribution a. Default: self._concentration.
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... # rate_a (Tensor): the rate of distribution a. Default: self._rate.
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...
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... # Examples of `kl_loss`. `cross_entropy` is similar.
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... ans = self.g1.kl_loss('Gamma', concentration_b, rate_b)
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... ans = self.g1.kl_loss('Gamma', concentration_b, rate_b, concentration_a, rate_a)
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... # Additional `concentration` and `rate` must be passed in.
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... ans = self.g2.kl_loss('Gamma', concentration_b, rate_b, concentration_a, rate_a)
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...
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...
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... # Examples of `sample`.
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... # Args:
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... # shape (tuple): the shape of the sample. Default: ()
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... # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
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... # rate (Tensor): the rate of the distribution. Default: self._rate.
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... ans = self.g1.sample()
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... ans = self.g1.sample((2,3))
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... ans = self.g1.sample((2,3), concentration_b, rate_b)
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... ans = self.g2.sample((2,3), concentration_a, rate_a)
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"""
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def __init__(self,
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concentration=None,
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rate=None,
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seed=None,
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dtype=mstype.float32,
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name="Gamma"):
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"""
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Constructor of Gamma.
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"""
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param = dict(locals())
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param['param_dict'] = {'concentration': concentration, 'rate': rate}
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valid_dtype = mstype.float_type
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Validator.check_type_name("dtype", dtype, valid_dtype, type(self).__name__)
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super(Gamma, self).__init__(seed, dtype, name, param)
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self._concentration = self._add_parameter(concentration, 'concentration')
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self._rate = self._add_parameter(rate, 'rate')
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if self._concentration is not None:
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check_greater_zero(self._concentration, "concentration")
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if self._rate is not None:
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check_greater_zero(self._rate, "rate")
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# ops needed for the class
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self.log = log_generic
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self.square = P.Square()
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self.sqrt = P.Sqrt()
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self.squeeze = P.Squeeze(0)
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self.cast = P.Cast()
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self.dtypeop = P.DType()
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self.fill = P.Fill()
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self.shape = P.Shape()
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self.select = P.Select()
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self.greater = P.Greater()
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self.lgamma = nn.LGamma()
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self.digamma = nn.DiGamma()
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self.igamma = nn.IGamma()
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def extend_repr(self):
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if self.is_scalar_batch:
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s = f'concentration = {self._concentration}, rate = {self._rate}'
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else:
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s = f'batch_shape = {self._broadcast_shape}'
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return s
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@property
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def concentration(self):
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"""
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Return the concentration, also know as the alpha of the Gamma distribution.
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"""
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return self._concentration
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@property
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def rate(self):
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"""
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Return the rate, also know as the beta of the Gamma distribution.
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"""
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return self._rate
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def _get_dist_type(self):
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return "Gamma"
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def _get_dist_args(self, concentration=None, rate=None):
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if concentration is not None:
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self.checktensor(concentration, 'concentration')
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else:
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concentration = self._concentration
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if rate is not None:
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self.checktensor(rate, 'rate')
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else:
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rate = self._rate
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return concentration, rate
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def _mean(self, concentration=None, rate=None):
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"""
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The mean of the distribution.
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"""
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concentration, rate = self._check_param_type(concentration, rate)
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return concentration / rate
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def _var(self, concentration=None, rate=None):
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"""
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The variance of the distribution.
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"""
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concentration, rate = self._check_param_type(concentration, rate)
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return concentration / self.square(rate)
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def _sd(self, concentration=None, rate=None):
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"""
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The standard deviation of the distribution.
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"""
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concentration, rate = self._check_param_type(concentration, rate)
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return self.sqrt(concentration) / rate
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def _mode(self, concentration=None, rate=None):
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"""
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The mode of the distribution.
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"""
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concentration, rate = self._check_param_type(concentration, rate)
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mode = (concentration - 1.) / rate
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nan = self.fill(self.dtypeop(concentration), self.shape(concentration), np.nan)
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comp = self.greater(concentration, 1.)
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return self.select(comp, mode, nan)
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def _entropy(self, concentration=None, rate=None):
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r"""
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Evaluate entropy.
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.. math::
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H(X) = \alpha - \log(\beta) + \log(\Gamma(\alpha)) + (1 - \alpha) * \digamma(\alpha)
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"""
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concentration, rate = self._check_param_type(concentration, rate)
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return concentration - self.log(rate) + self.lgamma(concentration) \
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+ (1. - concentration) * self.digamma(concentration)
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def _cross_entropy(self, dist, concentration_b, rate_b, concentration=None, rate=None):
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r"""
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Evaluate cross entropy between Gamma distributions.
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Args:
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dist (str): Type of the distributions. Should be "Gamma" in this case.
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concentration_b (Tensor): concentration of distribution b.
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rate_b (Tensor): rate of distribution b.
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concentration_a (Tensor): concentration of distribution a. Default: self._concentration.
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rate_a (Tensor): rate of distribution a. Default: self._rate.
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"""
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check_distribution_name(dist, 'Gamma')
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return self._entropy(concentration, rate) + self._kl_loss(dist, concentration_b, rate_b, concentration, rate)
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def _log_prob(self, value, concentration=None, rate=None):
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r"""
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Evaluate log probability.
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Args:
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value (Tensor): The value to be evaluated.
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concentration (Tensor): The concentration of the distribution. Default: self._concentration.
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rate (Tensor): The rate the distribution. Default: self._rate.
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.. math::
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L(x) = (\alpha - 1) * \log(x) - \beta * x - \log(\gamma(\alpha)) - \alpha * \log(\beta)
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"""
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value = self._check_value(value, 'value')
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value = self.cast(value, self.dtype)
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concentration, rate = self._check_param_type(concentration, rate)
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unnormalized_log_prob = (concentration - 1.) * self.log(value) - rate * value
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log_normalization = self.lgamma(concentration) - concentration * self.log(rate)
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return unnormalized_log_prob - log_normalization
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def _cdf(self, value, concentration=None, rate=None):
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r"""
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Evaluate the cumulative distribution function on the given value. Note that igamma returns
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the regularized incomplete gamma function, which is what we want for the CDF.
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Args:
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value (Tensor): The value to be evaluated.
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concentration (Tensor): The concentration of the distribution. Default: self._concentration.
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rate (Tensor): The rate the distribution. Default: self._rate.
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.. math::
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cdf(x) = \igamma(\alpha, \beta * x)
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"""
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value = self._check_value(value, 'value')
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value = self.cast(value, self.dtype)
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concentration, rate = self._check_param_type(concentration, rate)
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return self.igamma(concentration, rate * value)
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def _kl_loss(self, dist, concentration_b, rate_b, concentration=None, rate=None):
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r"""
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Evaluate Gamma-Gamma KL divergence, i.e. KL(a||b).
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Args:
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dist (str): The type of the distributions. Should be "Gamma" in this case.
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concentration_b (Tensor): The concentration of distribution b.
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rate_b (Tensor): The rate distribution b.
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concentration_a (Tensor): The concentration of distribution a. Default: self._concentration.
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rate_a (Tensor): The rate distribution a. Default: self._rate.
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.. math::
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KL(a||b) = (\alpha_{a} - \alpha_{b}) * \digamma(\alpha_{a}) + \log(\gamma(\alpha_{b}))
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- \log(\gamma(\alpha_{a})) + \alpha_{b} * \log(\beta{a}) - \alpha_{b} * \log(\beta{b})
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+ \alpha_{a} * \frac{\beta{b}}{\beta{a} - 1}
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"""
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check_distribution_name(dist, 'Gamma')
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concentration_b = self._check_value(concentration_b, 'concentration_b')
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rate_b = self._check_value(rate_b, 'rate_b')
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concentration_b = self.cast(concentration_b, self.parameter_type)
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rate_b = self.cast(rate_b, self.parameter_type)
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concentration_a, rate_a = self._check_param_type(concentration, rate)
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return (concentration_a - concentration_b) * self.digamma(concentration_a) \
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+ self.lgamma(concentration_b) - self.lgamma(concentration_a) \
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+ concentration_b * self.log(rate_a) - concentration_b * self.log(rate_b) \
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+ concentration_a * (rate_b / rate_a - 1.)
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def _sample(self, shape=(), concentration=None, rate=None):
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"""
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Sampling.
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Args:
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shape (tuple): The shape of the sample. Default: ().
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concentration (Tensor): The concentration of the samples. Default: self._concentration.
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rate (Tensor): The rate of the samples. Default: self._rate.
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Returns:
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Tensor, with the shape being shape + batch_shape.
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"""
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shape = self.checktuple(shape, 'shape')
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concentration, rate = self._check_param_type(concentration, rate)
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batch_shape = self.shape(concentration + rate)
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origin_shape = shape + batch_shape
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if origin_shape == ():
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sample_shape = (1,)
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else:
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sample_shape = origin_shape
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sample_gamma = C.gamma(sample_shape, concentration, rate, self.seed)
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value = self.cast(sample_gamma, self.dtype)
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if origin_shape == ():
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value = self.squeeze(value)
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return value
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@ -0,0 +1,328 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""test cases for Gamma distribution"""
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import numpy as np
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from scipy import stats
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from scipy import special
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.nn.probability.distribution as msd
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from mindspore import Tensor
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from mindspore import dtype
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Prob(nn.Cell):
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"""
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Test class: probability of Gamma distribution.
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"""
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def __init__(self):
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super(Prob, self).__init__()
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self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self, x_):
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return self.g.prob(x_)
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def test_pdf():
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"""
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Test pdf.
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"""
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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_pdf = gamma_benchmark.pdf([1.0, 2.0]).astype(np.float32)
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pdf = Prob()
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output = pdf(Tensor([1.0, 2.0], dtype=dtype.float32))
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
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class LogProb(nn.Cell):
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"""
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Test class: log probability of Gamma distribution.
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"""
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def __init__(self):
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super(LogProb, self).__init__()
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self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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def construct(self, x_):
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return self.g.log_prob(x_)
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def test_log_likelihood():
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"""
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Test log_pdf.
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"""
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gamma_benchmark = stats.gamma(np.array([3.0]))
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expect_logpdf = gamma_benchmark.logpdf([1.0, 2.0]).astype(np.float32)
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logprob = LogProb()
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output = logprob(Tensor([1.0, 2.0], dtype=dtype.float32))
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
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class KL(nn.Cell):
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"""
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Test class: kl_loss of Gamma distribution.
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"""
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def __init__(self):
|
||||
super(KL, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_, y_):
|
||||
return self.g.kl_loss('Gamma', x_, y_)
|
||||
|
||||
|
||||
def test_kl_loss():
|
||||
"""
|
||||
Test kl_loss.
|
||||
"""
|
||||
concentration_a = np.array([3.0]).astype(np.float32)
|
||||
rate_a = np.array([4.0]).astype(np.float32)
|
||||
|
||||
concentration_b = np.array([1.0]).astype(np.float32)
|
||||
rate_b = np.array([1.0]).astype(np.float32)
|
||||
|
||||
expect_kl_loss = (concentration_a - concentration_b) * special.digamma(concentration_a) \
|
||||
+ special.gammaln(concentration_b) - special.gammaln(concentration_a) \
|
||||
+ concentration_b * np.log(rate_a) - concentration_b * np.log(rate_b) \
|
||||
+ concentration_a * (rate_b / rate_a - 1.)
|
||||
|
||||
kl_loss = KL()
|
||||
concentration = Tensor(concentration_b, dtype=dtype.float32)
|
||||
rate = Tensor(rate_b, dtype=dtype.float32)
|
||||
output = kl_loss(concentration, rate)
|
||||
tol = 1e-6
|
||||
assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
|
||||
|
||||
class Basics(nn.Cell):
|
||||
"""
|
||||
Test class: mean/sd/mode of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Basics, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self):
|
||||
return self.g.mean(), self.g.sd(), self.g.mode()
|
||||
|
||||
def test_basics():
|
||||
"""
|
||||
Test mean/standard deviation/mode.
|
||||
"""
|
||||
basics = Basics()
|
||||
mean, sd, mode = basics()
|
||||
gamma_benchmark = stats.gamma(np.array([3.0]))
|
||||
expect_mean = gamma_benchmark.mean().astype(np.float32)
|
||||
expect_sd = gamma_benchmark.std().astype(np.float32)
|
||||
expect_mode = [2.0]
|
||||
tol = 1e-6
|
||||
assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
|
||||
assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
|
||||
assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
|
||||
|
||||
class Sampling(nn.Cell):
|
||||
"""
|
||||
Test class: sample of Gamma distribution.
|
||||
"""
|
||||
def __init__(self, shape, seed=0):
|
||||
super(Sampling, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), seed=seed, dtype=dtype.float32)
|
||||
self.shape = shape
|
||||
|
||||
def construct(self, concentration=None, rate=None):
|
||||
return self.g.sample(self.shape, concentration, rate)
|
||||
|
||||
def test_sample():
|
||||
"""
|
||||
Test sample.
|
||||
"""
|
||||
shape = (2, 3)
|
||||
seed = 10
|
||||
concentration = Tensor([2.0], dtype=dtype.float32)
|
||||
rate = Tensor([2.0, 2.0, 2.0], dtype=dtype.float32)
|
||||
sample = Sampling(shape, seed=seed)
|
||||
output = sample(concentration, rate)
|
||||
assert output.shape == (2, 3, 3)
|
||||
|
||||
class CDF(nn.Cell):
|
||||
"""
|
||||
Test class: cdf of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(CDF, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.g.cdf(x_)
|
||||
|
||||
|
||||
def test_cdf():
|
||||
"""
|
||||
Test cdf.
|
||||
"""
|
||||
gamma_benchmark = stats.gamma(np.array([3.0]))
|
||||
expect_cdf = gamma_benchmark.cdf([2.0]).astype(np.float32)
|
||||
cdf = CDF()
|
||||
output = cdf(Tensor([2.0], dtype=dtype.float32))
|
||||
tol = 2e-5
|
||||
assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
|
||||
|
||||
class LogCDF(nn.Cell):
|
||||
"""
|
||||
Test class: log_cdf of Mormal distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogCDF, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.g.log_cdf(x_)
|
||||
|
||||
def test_log_cdf():
|
||||
"""
|
||||
Test log cdf.
|
||||
"""
|
||||
gamma_benchmark = stats.gamma(np.array([3.0]))
|
||||
expect_logcdf = gamma_benchmark.logcdf([2.0]).astype(np.float32)
|
||||
logcdf = LogCDF()
|
||||
output = logcdf(Tensor([2.0], dtype=dtype.float32))
|
||||
tol = 5e-5
|
||||
assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
|
||||
|
||||
class SF(nn.Cell):
|
||||
"""
|
||||
Test class: survival function of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(SF, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.g.survival_function(x_)
|
||||
|
||||
def test_survival():
|
||||
"""
|
||||
Test log_survival.
|
||||
"""
|
||||
gamma_benchmark = stats.gamma(np.array([3.0]))
|
||||
expect_survival = gamma_benchmark.sf([2.0]).astype(np.float32)
|
||||
survival_function = SF()
|
||||
output = survival_function(Tensor([2.0], dtype=dtype.float32))
|
||||
tol = 2e-5
|
||||
assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
|
||||
|
||||
class LogSF(nn.Cell):
|
||||
"""
|
||||
Test class: log survival function of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogSF, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.g.log_survival(x_)
|
||||
|
||||
def test_log_survival():
|
||||
"""
|
||||
Test log_survival.
|
||||
"""
|
||||
gamma_benchmark = stats.gamma(np.array([3.0]))
|
||||
expect_log_survival = gamma_benchmark.logsf([2.0]).astype(np.float32)
|
||||
log_survival = LogSF()
|
||||
output = log_survival(Tensor([2.0], dtype=dtype.float32))
|
||||
tol = 2e-5
|
||||
assert (np.abs(output.asnumpy() - expect_log_survival) < tol).all()
|
||||
|
||||
class EntropyH(nn.Cell):
|
||||
"""
|
||||
Test class: entropy of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(EntropyH, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self):
|
||||
return self.g.entropy()
|
||||
|
||||
def test_entropy():
|
||||
"""
|
||||
Test entropy.
|
||||
"""
|
||||
gamma_benchmark = stats.gamma(np.array([3.0]))
|
||||
expect_entropy = gamma_benchmark.entropy().astype(np.float32)
|
||||
entropy = EntropyH()
|
||||
output = entropy()
|
||||
tol = 1e-6
|
||||
assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
|
||||
|
||||
class CrossEntropy(nn.Cell):
|
||||
"""
|
||||
Test class: cross entropy between Gamma distributions.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(CrossEntropy, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_, y_):
|
||||
entropy = self.g.entropy()
|
||||
kl_loss = self.g.kl_loss('Gamma', x_, y_)
|
||||
h_sum_kl = entropy + kl_loss
|
||||
cross_entropy = self.g.cross_entropy('Gamma', x_, y_)
|
||||
return h_sum_kl - cross_entropy
|
||||
|
||||
def test_cross_entropy():
|
||||
"""
|
||||
Test cross_entropy.
|
||||
"""
|
||||
cross_entropy = CrossEntropy()
|
||||
concentration = Tensor([3.0], dtype=dtype.float32)
|
||||
rate = Tensor([2.0], dtype=dtype.float32)
|
||||
diff = cross_entropy(concentration, rate)
|
||||
tol = 1e-6
|
||||
assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()
|
||||
|
||||
class Net(nn.Cell):
|
||||
"""
|
||||
Test class: expand single distribution instance to multiple graphs
|
||||
by specifying the attributes.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.Gamma = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_, y_):
|
||||
kl = self.Gamma.kl_loss('Gamma', x_, y_)
|
||||
prob = self.Gamma.prob(kl)
|
||||
return prob
|
||||
|
||||
def test_multiple_graphs():
|
||||
"""
|
||||
Test multiple graphs case.
|
||||
"""
|
||||
prob = Net()
|
||||
concentration_a = np.array([3.0]).astype(np.float32)
|
||||
rate_a = np.array([1.0]).astype(np.float32)
|
||||
concentration_b = np.array([2.0]).astype(np.float32)
|
||||
rate_b = np.array([1.0]).astype(np.float32)
|
||||
ans = prob(Tensor(concentration_b), Tensor(rate_b))
|
||||
|
||||
expect_kl_loss = (concentration_a - concentration_b) * special.digamma(concentration_a) \
|
||||
+ special.gammaln(concentration_b) - special.gammaln(concentration_a) \
|
||||
+ concentration_b * np.log(rate_a) - concentration_b * np.log(rate_b) \
|
||||
+ concentration_a * (rate_b / rate_a - 1.)
|
||||
|
||||
gamma_benchmark = stats.gamma(np.array([3.0]))
|
||||
expect_prob = gamma_benchmark.pdf(expect_kl_loss).astype(np.float32)
|
||||
|
||||
tol = 1e-6
|
||||
assert (np.abs(ans.asnumpy() - expect_prob) < tol).all()
|
||||
|
|
@ -0,0 +1,214 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
Test nn.probability.distribution.Gamma.
|
||||
"""
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import mindspore.nn as nn
|
||||
import mindspore.nn.probability.distribution as msd
|
||||
from mindspore import dtype
|
||||
from mindspore import Tensor
|
||||
|
||||
def test_gamma_shape_errpr():
|
||||
"""
|
||||
Invalid shapes.
|
||||
"""
|
||||
with pytest.raises(ValueError):
|
||||
msd.Gamma([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
|
||||
|
||||
def test_type():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Gamma(0., 1., dtype=dtype.int32)
|
||||
|
||||
def test_name():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Gamma(0., 1., name=1.0)
|
||||
|
||||
def test_seed():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Gamma(0., 1., seed='seed')
|
||||
|
||||
def test_rate():
|
||||
with pytest.raises(ValueError):
|
||||
msd.Gamma(0., 0.)
|
||||
with pytest.raises(ValueError):
|
||||
msd.Gamma(0., -1.)
|
||||
|
||||
def test_arguments():
|
||||
"""
|
||||
args passing during initialization.
|
||||
"""
|
||||
g = msd.Gamma()
|
||||
assert isinstance(g, msd.Distribution)
|
||||
g = msd.Gamma([3.0], [4.0], dtype=dtype.float32)
|
||||
assert isinstance(g, msd.Distribution)
|
||||
|
||||
|
||||
class GammaProb(nn.Cell):
|
||||
"""
|
||||
Gamma distribution: initialize with concentration/rate.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaProb, self).__init__()
|
||||
self.gamma = msd.Gamma([3.0, 4.0], [1.0, 1.0], dtype=dtype.float32)
|
||||
|
||||
def construct(self, value):
|
||||
prob = self.gamma.prob(value)
|
||||
log_prob = self.gamma.log_prob(value)
|
||||
cdf = self.gamma.cdf(value)
|
||||
log_cdf = self.gamma.log_cdf(value)
|
||||
sf = self.gamma.survival_function(value)
|
||||
log_sf = self.gamma.log_survival(value)
|
||||
return prob + log_prob + cdf + log_cdf + sf + log_sf
|
||||
|
||||
def test_gamma_prob():
|
||||
"""
|
||||
Test probability functions: passing value through construct.
|
||||
"""
|
||||
net = GammaProb()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
ans = net(value)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
|
||||
class GammaProb1(nn.Cell):
|
||||
"""
|
||||
Gamma distribution: initialize without concentration/rate.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaProb1, self).__init__()
|
||||
self.gamma = msd.Gamma()
|
||||
|
||||
def construct(self, value, concentration, rate):
|
||||
prob = self.gamma.prob(value, concentration, rate)
|
||||
log_prob = self.gamma.log_prob(value, concentration, rate)
|
||||
cdf = self.gamma.cdf(value, concentration, rate)
|
||||
log_cdf = self.gamma.log_cdf(value, concentration, rate)
|
||||
sf = self.gamma.survival_function(value, concentration, rate)
|
||||
log_sf = self.gamma.log_survival(value, concentration, rate)
|
||||
return prob + log_prob + cdf + log_cdf + sf + log_sf
|
||||
|
||||
def test_gamma_prob1():
|
||||
"""
|
||||
Test probability functions: passing concentration/rate, value through construct.
|
||||
"""
|
||||
net = GammaProb1()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
concentration = Tensor([2.0, 3.0], dtype=dtype.float32)
|
||||
rate = Tensor([1.0], dtype=dtype.float32)
|
||||
ans = net(value, concentration, rate)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class GammaKl(nn.Cell):
|
||||
"""
|
||||
Test class: kl_loss of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaKl, self).__init__()
|
||||
self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
|
||||
self.g2 = msd.Gamma(dtype=dtype.float32)
|
||||
|
||||
def construct(self, concentration_b, rate_b, concentration_a, rate_a):
|
||||
kl1 = self.g1.kl_loss('Gamma', concentration_b, rate_b)
|
||||
kl2 = self.g2.kl_loss('Gamma', concentration_b, rate_b, concentration_a, rate_a)
|
||||
return kl1 + kl2
|
||||
|
||||
def test_kl():
|
||||
"""
|
||||
Test kl_loss.
|
||||
"""
|
||||
net = GammaKl()
|
||||
concentration_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
|
||||
rate_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
|
||||
concentration_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
|
||||
rate_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
|
||||
ans = net(concentration_b, rate_b, concentration_a, rate_a)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class GammaCrossEntropy(nn.Cell):
|
||||
"""
|
||||
Test class: cross_entropy of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaCrossEntropy, self).__init__()
|
||||
self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
|
||||
self.g2 = msd.Gamma(dtype=dtype.float32)
|
||||
|
||||
def construct(self, concentration_b, rate_b, concentration_a, rate_a):
|
||||
h1 = self.g1.cross_entropy('Gamma', concentration_b, rate_b)
|
||||
h2 = self.g2.cross_entropy('Gamma', concentration_b, rate_b, concentration_a, rate_a)
|
||||
return h1 + h2
|
||||
|
||||
def test_cross_entropy():
|
||||
"""
|
||||
Test cross entropy between Gamma distributions.
|
||||
"""
|
||||
net = GammaCrossEntropy()
|
||||
concentration_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
|
||||
rate_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
|
||||
concentration_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
|
||||
rate_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
|
||||
ans = net(concentration_b, rate_b, concentration_a, rate_a)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class GammaBasics(nn.Cell):
|
||||
"""
|
||||
Test class: basic mean/sd function.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaBasics, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0, 4.0]), np.array([4.0, 6.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self):
|
||||
mean = self.g.mean()
|
||||
sd = self.g.sd()
|
||||
mode = self.g.mode()
|
||||
return mean + sd + mode
|
||||
|
||||
def test_bascis():
|
||||
"""
|
||||
Test mean/sd/mode/entropy functionality of Gamma.
|
||||
"""
|
||||
net = GammaBasics()
|
||||
ans = net()
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class GammaConstruct(nn.Cell):
|
||||
"""
|
||||
Gamma distribution: going through construct.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaConstruct, self).__init__()
|
||||
self.gamma = msd.Gamma([3.0], [4.0])
|
||||
self.gamma1 = msd.Gamma()
|
||||
|
||||
def construct(self, value, concentration, rate):
|
||||
prob = self.gamma('prob', value)
|
||||
prob1 = self.gamma('prob', value, concentration, rate)
|
||||
prob2 = self.gamma1('prob', value, concentration, rate)
|
||||
return prob + prob1 + prob2
|
||||
|
||||
def test_gamma_construct():
|
||||
"""
|
||||
Test probability function going through construct.
|
||||
"""
|
||||
net = GammaConstruct()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
concentration = Tensor([0.0], dtype=dtype.float32)
|
||||
rate = Tensor([1.0], dtype=dtype.float32)
|
||||
ans = net(value, concentration, rate)
|
||||
assert isinstance(ans, Tensor)
|
||||
Loading…
Reference in New Issue