EVOLUTION-MANAGER

Edit File: dirichlet_multinomial.py

# 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. # ============================================================================== """The DirichletMultinomial distribution class.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import special_math_ops from tensorflow.python.ops.distributions import distribution from tensorflow.python.ops.distributions import util as distribution_util from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export __all__ = [ "DirichletMultinomial", ] _dirichlet_multinomial_sample_note = """For each batch of counts, `value = [n_0, ..., n_{K-1}]`, `P[value]` is the probability that after sampling `self.total_count` draws from this Dirichlet-Multinomial distribution, the number of draws falling in class `j` is `n_j`. Since this definition is [exchangeable](https://en.wikipedia.org/wiki/Exchangeable_random_variables); different sequences have the same counts so the probability includes a combinatorial coefficient. Note: `value` must be a non-negative tensor with dtype `self.dtype`, have no fractional components, and such that `tf.reduce_sum(value, -1) = self.total_count`. Its shape must be broadcastable with `self.concentration` and `self.total_count`.""" @tf_export(v1=["distributions.DirichletMultinomial"]) class DirichletMultinomial(distribution.Distribution): """Dirichlet-Multinomial compound distribution. The Dirichlet-Multinomial distribution is parameterized by a (batch of) length-`K` `concentration` vectors (`K > 1`) and a `total_count` number of trials, i.e., the number of trials per draw from the DirichletMultinomial. It is defined over a (batch of) length-`K` vector `counts` such that `tf.reduce_sum(counts, -1) = total_count`. The Dirichlet-Multinomial is identically the Beta-Binomial distribution when `K = 2`. #### Mathematical Details The Dirichlet-Multinomial is a distribution over `K`-class counts, i.e., a length-`K` vector of non-negative integer `counts = n = [n_0, ..., n_{K-1}]`. The probability mass function (pmf) is, ```none pmf(n; alpha, N) = Beta(alpha + n) / (prod_j n_j!) / Z Z = Beta(alpha) / N! ``` where: * `concentration = alpha = [alpha_0, ..., alpha_{K-1}]`, `alpha_j > 0`, * `total_count = N`, `N` a positive integer, * `N!` is `N` factorial, and, * `Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j)` is the [multivariate beta function]( https://en.wikipedia.org/wiki/Beta_function#Multivariate_beta_function), and, * `Gamma` is the [gamma function]( https://en.wikipedia.org/wiki/Gamma_function). Dirichlet-Multinomial is a [compound distribution]( https://en.wikipedia.org/wiki/Compound_probability_distribution), i.e., its samples are generated as follows. 1. Choose class probabilities: `probs = [p_0,...,p_{K-1}] ~ Dir(concentration)` 2. Draw integers: `counts = [n_0,...,n_{K-1}] ~ Multinomial(total_count, probs)` The last `concentration` dimension parametrizes a single Dirichlet-Multinomial distribution. When calling distribution functions (e.g., `dist.prob(counts)`), `concentration`, `total_count` and `counts` are broadcast to the same shape. The last dimension of `counts` corresponds single Dirichlet-Multinomial distributions. Distribution parameters are automatically broadcast in all functions; see examples for details. #### Pitfalls The number of classes, `K`, must not exceed: - the largest integer representable by `self.dtype`, i.e., `2**(mantissa_bits+1)` (IEE754), - the maximum `Tensor` index, i.e., `2**31-1`. In other words, ```python K <= min(2**31-1, { tf.float16: 2**11, tf.float32: 2**24, tf.float64: 2**53 }[param.dtype]) ``` Note: This condition is validated only when `self.validate_args = True`. #### Examples ```python alpha = [1., 2., 3.] n = 2. dist = DirichletMultinomial(n, alpha) ``` Creates a 3-class distribution, with the 3rd class is most likely to be drawn. The distribution functions can be evaluated on counts. ```python # counts same shape as alpha. counts = [0., 0., 2.] dist.prob(counts) # Shape [] # alpha will be broadcast to [[1., 2., 3.], [1., 2., 3.]] to match counts. counts = [[1., 1., 0.], [1., 0., 1.]] dist.prob(counts) # Shape [2] # alpha will be broadcast to shape [5, 7, 3] to match counts. counts = [[...]] # Shape [5, 7, 3] dist.prob(counts) # Shape [5, 7] ``` Creates a 2-batch of 3-class distributions. ```python alpha = [[1., 2., 3.], [4., 5., 6.]] # Shape [2, 3] n = [3., 3.] dist = DirichletMultinomial(n, alpha) # counts will be broadcast to [[2., 1., 0.], [2., 1., 0.]] to match alpha. counts = [2., 1., 0.] dist.prob(counts) # Shape [2] ``` """ # TODO(b/27419586) Change docstring for dtype of concentration once int # allowed. @deprecation.deprecated( "2019-01-01", "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.distributions`.", warn_once=True) def __init__(self, total_count, concentration, validate_args=False, allow_nan_stats=True, name="DirichletMultinomial"): """Initialize a batch of DirichletMultinomial distributions. Args: total_count: Non-negative floating point tensor, whose dtype is the same as `concentration`. The shape is broadcastable to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch of `N1 x ... x Nm` different Dirichlet multinomial distributions. Its components should be equal to integer values. concentration: Positive floating point tensor, whose dtype is the same as `n` with shape broadcastable to `[N1,..., Nm, K]` `m >= 0`. Defines this as a batch of `N1 x ... x Nm` different `K` class Dirichlet multinomial distributions. 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. """ parameters = dict(locals()) with ops.name_scope(name, values=[total_count, concentration]) as name: # Broadcasting works because: # * The broadcasting convention is to prepend dimensions of size [1], and # we use the last dimension for the distribution, whereas # the batch dimensions are the leading dimensions, which forces the # distribution dimension to be defined explicitly (i.e. it cannot be # created automatically by prepending). This forces enough explicitness. # * All calls involving `counts` eventually require a broadcast between # `counts` and concentration. self._total_count = ops.convert_to_tensor(total_count, name="total_count") if validate_args: self._total_count = ( distribution_util.embed_check_nonnegative_integer_form( self._total_count)) self._concentration = self._maybe_assert_valid_concentration( ops.convert_to_tensor(concentration, name="concentration"), validate_args) self._total_concentration = math_ops.reduce_sum(self._concentration, -1) super(DirichletMultinomial, self).__init__( dtype=self._concentration.dtype, validate_args=validate_args, allow_nan_stats=allow_nan_stats, reparameterization_type=distribution.NOT_REPARAMETERIZED, parameters=parameters, graph_parents=[self._total_count, self._concentration], name=name) @property def total_count(self): """Number of trials used to construct a sample.""" return self._total_count @property def concentration(self): """Concentration parameter; expected prior counts for that coordinate.""" return self._concentration @property def total_concentration(self): """Sum of last dim of concentration parameter.""" return self._total_concentration def _batch_shape_tensor(self): return array_ops.shape(self.total_concentration) def _batch_shape(self): return self.total_concentration.get_shape() def _event_shape_tensor(self): return array_ops.shape(self.concentration)[-1:] def _event_shape(self): # Event shape depends only on total_concentration, not "n". return self.concentration.get_shape().with_rank_at_least(1)[-1:] def _sample_n(self, n, seed=None): n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32) k = self.event_shape_tensor()[0] unnormalized_logits = array_ops.reshape( math_ops.log(random_ops.random_gamma( shape=[n], alpha=self.concentration, dtype=self.dtype, seed=seed)), shape=[-1, k]) draws = random_ops.multinomial( logits=unnormalized_logits, num_samples=n_draws, seed=distribution_util.gen_new_seed(seed, salt="dirichlet_multinomial")) x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k), -2) final_shape = array_ops.concat([[n], self.batch_shape_tensor(), [k]], 0) x = array_ops.reshape(x, final_shape) return math_ops.cast(x, self.dtype) @distribution_util.AppendDocstring(_dirichlet_multinomial_sample_note) def _log_prob(self, counts): counts = self._maybe_assert_valid_sample(counts) ordered_prob = ( special_math_ops.lbeta(self.concentration + counts) - special_math_ops.lbeta(self.concentration)) return ordered_prob + distribution_util.log_combinations( self.total_count, counts) @distribution_util.AppendDocstring(_dirichlet_multinomial_sample_note) def _prob(self, counts): return math_ops.exp(self._log_prob(counts)) def _mean(self): return self.total_count * (self.concentration / self.total_concentration[..., array_ops.newaxis]) @distribution_util.AppendDocstring( """The covariance for each batch member is defined as the following: ```none Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) * (n + alpha_0) / (1 + alpha_0) ``` where `concentration = alpha` and `total_concentration = alpha_0 = sum_j alpha_j`. The covariance between elements in a batch is defined as: ```none Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 * (n + alpha_0) / (1 + alpha_0) ``` """) def _covariance(self): x = self._variance_scale_term() * self._mean() return array_ops.matrix_set_diag( -math_ops.matmul(x[..., array_ops.newaxis], x[..., array_ops.newaxis, :]), # outer prod self._variance()) def _variance(self): scale = self._variance_scale_term() x = scale * self._mean() return x * (self.total_count * scale - x) def _variance_scale_term(self): """Helper to `_covariance` and `_variance` which computes a shared scale.""" # We must take care to expand back the last dim whenever we use the # total_concentration. c0 = self.total_concentration[..., array_ops.newaxis] return math_ops.sqrt((1. + c0 / self.total_count) / (1. + c0)) def _maybe_assert_valid_concentration(self, concentration, validate_args): """Checks the validity of the concentration parameter.""" if not validate_args: return concentration concentration = distribution_util.embed_check_categorical_event_shape( concentration) return control_flow_ops.with_dependencies([ check_ops.assert_positive( concentration, message="Concentration parameter must be positive."), ], concentration) def _maybe_assert_valid_sample(self, counts): """Check counts for proper shape, values, then return tensor version.""" if not self.validate_args: return counts counts = distribution_util.embed_check_nonnegative_integer_form(counts) return control_flow_ops.with_dependencies([ check_ops.assert_equal( self.total_count, math_ops.reduce_sum(counts, -1), message="counts last-dimension must sum to `self.total_count`"), ], counts)