# 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.contrib.distributions.python.ops import distribution
from tensorflow.contrib.distributions.python.ops import distribution_util
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


__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`."""


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 of `counts` corresponds single Dirichlet-Multinomial
  distributions.

  Distribution parameters are automatically broadcast in all functions; see
  examples for details.

  #### 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.
  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 `Boolean`, 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 `Boolean`, 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: `String` name prefixed to Ops created by this class.
    """
    parameters = locals()
    with ops.name_scope(name, values=[total_count, concentration]) as ns:
      # 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 = self._maybe_assert_valid_total_count(
          ops.convert_to_tensor(total_count, name="total_count"),
          validate_args)
      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,
        is_continuous=False,
        reparameterization_type=distribution.NOT_REPARAMETERIZED,
        parameters=parameters,
        graph_parents=[self._total_count,
                       self._concentration],
        name=ns)

  @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)
    return array_ops.reshape(x, final_shape)

  @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[..., None])

  @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[..., None], x[..., None, :]),  # 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[..., None]
    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
    return control_flow_ops.with_dependencies([
        check_ops.assert_positive(
            concentration,
            message="Concentration parameter must be positive."),
        check_ops.assert_rank_at_least(
            concentration, 1,
            message="Concentration parameter must have >=1 dimensions."),
        check_ops.assert_less(
            1, array_ops.shape(concentration)[-1],
            message="Concentration parameter must have event_size >= 2."),
    ], concentration)

  def _maybe_assert_valid_total_count(self, total_count, validate_args):
    if not validate_args:
      return total_count
    return control_flow_ops.with_dependencies([
        check_ops.assert_non_negative(
            total_count,
            message="total_count must be non-negative."),
        distribution_util.assert_integer_form(
            total_count,
            message="total_count cannot contain fractional values."),
    ], total_count)

  def _maybe_assert_valid_sample(self, counts):
    """Check counts for proper shape, values, then return tensor version."""
    if not self.validate_args:
      return counts
    return control_flow_ops.with_dependencies([
        check_ops.assert_non_negative(
            counts,
            message="counts must be non-negative."),
        check_ops.assert_equal(
            self.total_count, math_ops.reduce_sum(counts, -1),
            message="counts last-dimension must sum to `self.total_count`"),
        distribution_util.assert_integer_form(
            counts,
            message="counts cannot contain fractional components."),
    ], counts)
