def _log_prob(self, counts):
counts = self._assert_valid_counts(counts)
ordered_prob = (special_math_ops.lbeta(self.alpha + counts) -
special_math_ops.lbeta(self.alpha))
log_prob = ordered_prob + distribution_util.log_combinations(
self.n, counts)
return log_prob
def test_empty_rank2_or_greater_input_gives_empty_output_dynamic_alloc(self):
with self.test_session(use_gpu=self._use_gpu):
ph = array_ops.placeholder(dtypes.float32)
self.assertAllEqual(
[], special_math_ops.lbeta(ph).eval(feed_dict={ph: [[]]}))
self.assertAllEqual(
[[]], special_math_ops.lbeta(ph).eval(feed_dict={ph: [[[]]]}))
def log_pmf(self, counts, name=None):
"""`Log(P[counts])`, computed for every batch member.
For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
distribution, the number of draws falling in class `j` is `c_j`. Note that
different sequences of draws can result in the same counts, thus the
probability includes a combinatorial coefficient.
Args:
counts: Non-negative `float`, `double`, or `int` tensor whose shape can
be broadcast with `self.alpha`. For fixed leading dimensions, the last
dimension represents counts for the corresponding Dirichlet Multinomial
distribution in `self.alpha`.
name: Name to give this Op, defaults to "log_pmf".
Returns:
Log probabilities for each record, shape `[N1,...,Nn]`.
"""
alpha = self._alpha
with ops.op_scope([alpha, counts], name, 'log_pmf'):
counts = self._check_counts(counts)
ordered_pmf = (special_math_ops.lbeta(alpha + counts) -
special_math_ops.lbeta(alpha))
log_pmf = ordered_pmf + _log_combinations(counts)
# If alpha = counts = [[]], ordered_pmf carries the right shape, which is
# []. However, since reduce_sum([[]]) = [0], log_combinations = [0],
# which is not correct. Luckily, [] + [0] = [], so the sum is fine, but
# shape must be inferred from ordered_pmf.
# Note also that tf.constant([]).get_shape() = TensorShape([Dimension(0)])
log_pmf.set_shape(ordered_pmf.get_shape())
return log_pmf
def log_prob(self, counts, name="log_prob"):
"""`Log(P[counts])`, computed for every batch member.
For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
that after sampling `n` draws from this Dirichlet Multinomial
distribution, the number of draws falling in class `j` is `n_j`. Note that
different sequences of draws can result in the same counts, thus the
probability includes a combinatorial coefficient.
Args:
counts: Non-negative tensor with dtype `dtype` and whose shape can be
broadcast with `self.alpha`. For fixed leading dimensions, the last
dimension represents counts for the corresponding Dirichlet Multinomial
distribution in `self.alpha`. `counts` is only legal if it sums up to
`n` and its components are equal to integer values.
name: Name to give this Op, defaults to "log_prob".
Returns:
Log probabilities for each record, shape `[N1,...,Nn]`.
"""
n = self._n
alpha = self._alpha
with ops.name_scope(self.name):
with ops.name_scope(name, values=[n, alpha, counts]):
counts = self._check_counts(counts)
ordered_prob = (special_math_ops.lbeta(alpha + counts) -
special_math_ops.lbeta(alpha))
log_prob = ordered_prob + distribution_util.log_combinations(
n, counts)
return log_prob
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)
def test_two_dimensional_arg(self):
# Should evaluate to 1/2.
x_one_half = [[2, 1.], [2, 1.]]
with self.test_session(use_gpu=self._use_gpu):
self.assertAllClose(
[0.5, 0.5], math_ops.exp(special_math_ops.lbeta(x_one_half)).eval())
self.assertEqual((2,), special_math_ops.lbeta(x_one_half).get_shape())
def test_complicated_shape(self):
with self.session(use_gpu=True):
x = ops.convert_to_tensor(np.random.rand(3, 2, 2))
self.assertAllEqual(
(3, 2), self.evaluate(array_ops.shape(special_math_ops.lbeta(x))))
self.assertEqual(
tensor_shape.TensorShape([3, 2]),
special_math_ops.lbeta(x).get_shape())
def test_length_1_last_dimension_results_in_one(self):
# If there is only one coefficient, the formula still works, and we get one
# as the answer, always.
x_a = [5.5]
x_b = [0.1]
with self.test_session(use_gpu=True):
self.assertAllClose(1, math_ops.exp(special_math_ops.lbeta(x_a)).eval())
self.assertAllClose(1, math_ops.exp(special_math_ops.lbeta(x_b)).eval())
self.assertEqual((), special_math_ops.lbeta(x_a).get_shape())
def test_one_dimensional_arg(self):
# Should evaluate to 1 and 1/2.
x_one = [1, 1.]
x_one_half = [2, 1.]
with self.test_session(use_gpu=self._use_gpu):
self.assertAllClose(1, math_ops.exp(special_math_ops.lbeta(x_one)).eval())
self.assertAllClose(
0.5, math_ops.exp(special_math_ops.lbeta(x_one_half)).eval())
self.assertEqual([], special_math_ops.lbeta(x_one).get_shape())
def test_two_dimensional_proper_shape(self):
# Should evaluate to 1/2.
x_one_half = [[2, 1.], [2, 1.]]
with self.test_session(use_gpu=True):
self.assertAllClose(
[0.5, 0.5], math_ops.exp(special_math_ops.lbeta(x_one_half)).eval())
self.assertEqual(
(2,), array_ops.shape(special_math_ops.lbeta(x_one_half)).eval())
self.assertEqual(
tensor_shape.TensorShape([2]),
special_math_ops.lbeta(x_one_half).get_shape())
def test_two_dimensional_arg_dynamic(self):
# Should evaluate to 1/2.
x_one_half = [[2, 1.], [2, 1.]]
with self.test_session(use_gpu=True):
ph = array_ops.placeholder(dtypes.float32)
beta_ph = math_ops.exp(special_math_ops.lbeta(ph))
self.assertAllClose([0.5, 0.5], beta_ph.eval(feed_dict={ph: x_one_half}))
def test_empty_rank1_returns_negative_infinity(self):
with self.test_session(use_gpu=True):
x = constant_op.constant([], shape=[0])
lbeta_x = special_math_ops.lbeta(x)
expected_result = constant_op.constant(-np.inf, shape=())
self.assertAllEqual(expected_result.eval(), lbeta_x.eval())
self.assertEqual(expected_result.get_shape(), lbeta_x.get_shape())
def _log_prob(self, x):
x = ops.convert_to_tensor(x, name="x")
x = self._assert_valid_sample(x)
unnorm_prob = (self.alpha - 1.) * math_ops.log(x)
log_prob = math_ops.reduce_sum(
unnorm_prob, reduction_indices=[-1],
keep_dims=False) - special_math_ops.lbeta(self.alpha)
return log_prob
def _entropy(self):
u = array_ops.expand_dims(self.df * self._ones(), -1)
v = array_ops.expand_dims(self._ones(), -1)
beta_arg = array_ops.concat_v2([u, v], len(u.get_shape()) - 1) / 2
half_df = 0.5 * self.df
return ((0.5 + half_df) *
(math_ops.digamma(0.5 + half_df) - math_ops.digamma(half_df)) + 0.5
* math_ops.log(self.df) + special_math_ops.lbeta(beta_arg) +
math_ops.log(self.sigma))
def _entropy(self):
entropy = special_math_ops.lbeta(self.alpha)
entropy += math_ops.digamma(self.alpha_sum) * (
self.alpha_sum - math_ops.cast(self.event_shape()[0], self.dtype))
entropy += -math_ops.reduce_sum(
(self.alpha - 1.) * math_ops.digamma(self.alpha),
reduction_indices=[-1],
keep_dims=False)
return entropy
def test_one_dimensional_arg_dynamic_alloc(self):
# Should evaluate to 1 and 1/2.
x_one = [1, 1.]
x_one_half = [2, 1.]
with self.test_session(use_gpu=self._use_gpu):
ph = array_ops.placeholder(dtypes.float32)
beta_ph = math_ops.exp(special_math_ops.lbeta(ph))
self.assertAllClose(1, beta_ph.eval(feed_dict={ph: x_one}))
self.assertAllClose(0.5, beta_ph.eval(feed_dict={ph: x_one_half}))
def test_empty_rank2_with_zero_last_dim_returns_negative_infinity(self):
with self.test_session(use_gpu=True):
event_size = 0
for batch_size in [0, 1, 2]:
x = constant_op.constant([], shape=[batch_size, event_size])
lbeta_x = special_math_ops.lbeta(x)
expected_result = constant_op.constant(-np.inf, shape=[batch_size])
self.assertAllEqual(expected_result.eval(), lbeta_x.eval())
self.assertEqual(expected_result.get_shape(), lbeta_x.get_shape())
def _entropy(self):
v = array_ops.ones(self.batch_shape_tensor(), dtype=self.dtype)[..., None]
u = v * self.df[..., None]
beta_arg = array_ops.concat([u, v], -1) / 2.
return (math_ops.log(math_ops.abs(self.scale)) +
0.5 * math_ops.log(self.df) +
special_math_ops.lbeta(beta_arg) +
0.5 * (self.df + 1.) *
(math_ops.digamma(0.5 * (self.df + 1.)) -
math_ops.digamma(0.5 * self.df)))
def log_pmf(self, counts, name='log_pmf'):
"""`Log(P[counts])`, computed for every batch member.
For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
that after sampling `n` draws from this Dirichlet Multinomial
distribution, the number of draws falling in class `j` is `n_j`. Note that
different sequences of draws can result in the same counts, thus the
probability includes a combinatorial coefficient.
Args:
counts: Non-negative `float` or `double` tensor whose shape can
be broadcast with `self.alpha`. For fixed leading dimensions, the last
dimension represents counts for the corresponding Dirichlet Multinomial
distribution in `self.alpha`. `counts` is only legal if it sums up to
`n` and its components are equal to integral values. The second
condition is relaxed if `allow_arbitrary_counts` is set.
name: Name to give this Op, defaults to "log_pmf".
Returns:
Log probabilities for each record, shape `[N1,...,Nn]`.
"""
n = self._n
alpha = self._alpha
with ops.name_scope(self.name):
with ops.op_scope([n, alpha, counts], name):
counts = self._check_counts(counts)
# Use the same dtype as alpha for computations.
counts = math_ops.cast(counts, self.dtype)
ordered_pmf = (special_math_ops.lbeta(alpha + counts) -
special_math_ops.lbeta(alpha))
log_pmf = ordered_pmf + _log_combinations(n, counts)
# If alpha = counts = [[]], ordered_pmf carries the right shape, which
# is []. However, since reduce_sum([[]]) = [0], log_combinations = [0],
# which is not correct. Luckily, [] + [0] = [], so the sum is fine, but
# shape must be inferred from ordered_pmf. We must also make this
# broadcastable with n, so this is multiplied by n to ensure the shape
# is correctly inferred.
# Note also that tf.constant([]).get_shape() =
# TensorShape([Dimension(0)])
broadcasted_tensor = ordered_pmf * n
log_pmf.set_shape(broadcasted_tensor.get_shape())
return log_pmf
def _moment(self, n):
"""Compute the n'th (uncentered) moment."""
expanded_concentration1 = array_ops.ones_like(
self.total_concentration, dtype=self.dtype) * self.concentration1
expanded_concentration0 = array_ops.ones_like(
self.total_concentration, dtype=self.dtype) * self.concentration0
beta_arg0 = 1 + n / expanded_concentration1
beta_arg = array_ops.stack([beta_arg0, expanded_concentration0], -1)
log_moment = math_ops.log(expanded_concentration0) + special_math_ops.lbeta(
beta_arg)
return math_ops.exp(log_moment)
def test_empty_rank2_with_zero_batch_dim_returns_empty(self):
with self.test_session(use_gpu=self._use_gpu):
batch_size = 0
for event_size in [0, 1, 2]:
x = constant_op.constant([], shape=[batch_size, event_size])
lbeta_x = special_math_ops.lbeta(x)
expected_result = constant_op.constant([], shape=[batch_size])
self.assertAllEqual(expected_result.eval(), lbeta_x.eval())
self.assertEqual(expected_result.get_shape(), lbeta_x.get_shape())
def test_four_dimensional_arg_with_partial_shape_dynamic(self):
x_ = np.ones((3, 2, 3, 4))
# Gamma(1) = 0! = 1
# Gamma(1 + 1 + 1 + 1) = Gamma(4) = 3! = 6
# ==> Beta([1, 1, 1, 1])
# = Gamma(1) * Gamma(1) * Gamma(1) * Gamma(1) / Gamma(1 + 1 + 1 + 1)
# = 1 / 6
expected_beta_x = 1 / 6 * np.ones((3, 2, 3))
with self.test_session(use_gpu=True):
x_ph = array_ops.placeholder(dtypes.float32, [3, 2, 3, None])
beta_ph = math_ops.exp(special_math_ops.lbeta(x_ph))
self.assertAllClose(expected_beta_x, beta_ph.eval(feed_dict={x_ph: x_}))
def entropy(self, name="entropy"):
"""Entropy of the distribution in nats."""
with ops.name_scope(self.name):
with ops.op_scope([self._alpha, self._alpha_0], name):
alpha = self._alpha
alpha_0 = self._alpha_0
entropy = special_math_ops.lbeta(alpha)
entropy += (alpha_0 - math_ops.cast(self.event_shape()[0], self.dtype)) * math_ops.digamma(alpha_0)
entropy += -math_ops.reduce_sum(
(alpha - 1) * math_ops.digamma(alpha), reduction_indices=[-1], keep_dims=False
)
return entropy
def entropy(self, name="entropy"):
"""The entropy of Student t distribution(s).
Args:
name: The name to give this op.
Returns:
entropy: tensor of dtype `dtype`, the entropy.
"""
with ops.name_scope(self.name):
with ops.op_scope([self._df, self._sigma], name):
u = array_ops.expand_dims(self._df + self._zeros(), -1)
v = array_ops.expand_dims(self._ones(), -1)
beta_arg = array_ops.concat(len(u.get_shape()) - 1, [u, v]) / 2
return ((self._df + 1) / 2 * (math_ops.digamma((self._df + 1) / 2) -
math_ops.digamma(self._df / 2)) +
math_ops.log(self._df) / 2 +
special_math_ops.lbeta(beta_arg) +
math_ops.log(self._sigma))
def log_prob(self, x, name="log_prob"):
"""`Log(P[counts])`, computed for every batch member.
Args:
x: Non-negative tensor with dtype `dtype` and whose shape can
be broadcast with `self.alpha`. For fixed leading dimensions, the last
dimension represents counts for the corresponding Dirichlet distribution
in `self.alpha`. `x` is only legal if it sums up to one.
name: Name to give this Op, defaults to "log_prob".
Returns:
Log probabilities for each record, shape `[N1,...,Nm]`.
"""
alpha = self._alpha
with ops.name_scope(self.name):
with ops.op_scope([alpha, x], name):
x = self._check_x(x)
unnorm_prob = (alpha - 1) * math_ops.log(x)
log_prob = math_ops.reduce_sum(
unnorm_prob, reduction_indices=[-1],
keep_dims=False) - special_math_ops.lbeta(alpha)
return log_prob
def test_empty_rank1_dynamic_alloc_input_raises_op_error(self):
with self.test_session(use_gpu=self._use_gpu):
ph = array_ops.placeholder(dtypes.float32)
with self.assertRaisesOpError('rank'):
special_math_ops.lbeta(ph).eval(feed_dict={ph: []})
def _log_normalization(self): return special_math_ops.lbeta(self.concentration)
def _kl_dirichlet_dirichlet(d1, d2, name=None):
"""Batchwise KL divergence KL(d1 || d2) with d1 and d2 Dirichlet.
Args:
d1: instance of a Dirichlet distribution object.
d2: instance of a Dirichlet distribution object.
name: (optional) Name to use for created operations.
default is "kl_dirichlet_dirichlet".
Returns:
Batchwise KL(d1 || d2)
"""
with ops.name_scope(name, "kl_dirichlet_dirichlet", values=[
d1.concentration, d2.concentration]):
# The KL between Dirichlet distributions can be derived as follows. We have
#
# Dir(x; a) = 1 / B(a) * prod_i[x[i]^(a[i] - 1)]
#
# where B(a) is the multivariate Beta function:
#
# B(a) = Gamma(a[1]) * ... * Gamma(a[n]) / Gamma(a[1] + ... + a[n])
#
# The KL is
#
# KL(Dir(x; a), Dir(x; b)) = E_Dir(x; a){log(Dir(x; a) / Dir(x; b))}
#
# so we'll need to know the log density of the Dirichlet. This is
#
# log(Dir(x; a)) = sum_i[(a[i] - 1) log(x[i])] - log B(a)
#
# The only term that matters for the expectations is the log(x[i]). To
# compute the expectation of this term over the Dirichlet density, we can
# use the following facts about the Dirichlet in exponential family form:
# 1. log(x[i]) is a sufficient statistic
# 2. expected sufficient statistics (of any exp family distribution) are
# equal to derivatives of the log normalizer with respect to
# corresponding natural parameters: E{T[i](x)} = dA/d(eta[i])
#
# To proceed, we can rewrite the Dirichlet density in exponential family
# form as follows:
#
# Dir(x; a) = exp{eta(a) . T(x) - A(a)}
#
# where '.' is the dot product of vectors eta and T, and A is a scalar:
#
# eta[i](a) = a[i] - 1
# T[i](x) = log(x[i])
# A(a) = log B(a)
#
# Now, we can use fact (2) above to write
#
# E_Dir(x; a)[log(x[i])]
# = dA(a) / da[i]
# = d/da[i] log B(a)
# = d/da[i] (sum_j lgamma(a[j])) - lgamma(sum_j a[j])
# = digamma(a[i])) - digamma(sum_j a[j])
#
# Putting it all together, we have
#
# KL[Dir(x; a) || Dir(x; b)]
# = E_Dir(x; a){log(Dir(x; a) / Dir(x; b)}
# = E_Dir(x; a){sum_i[(a[i] - b[i]) log(x[i])} - (lbeta(a) - lbeta(b))
# = sum_i[(a[i] - b[i]) * E_Dir(x; a){log(x[i])}] - lbeta(a) + lbeta(b)
# = sum_i[(a[i] - b[i]) * (digamma(a[i]) - digamma(sum_j a[j]))]
# - lbeta(a) + lbeta(b))
digamma_sum_d1 = math_ops.digamma(
math_ops.reduce_sum(d1.concentration, axis=-1, keepdims=True))
digamma_diff = math_ops.digamma(d1.concentration) - digamma_sum_d1
concentration_diff = d1.concentration - d2.concentration
return (math_ops.reduce_sum(concentration_diff * digamma_diff, axis=-1) -
special_math_ops.lbeta(d1.concentration) +
special_math_ops.lbeta(d2.concentration))
def test_empty_rank1_input_raises_value_error(self):
with self.test_session(use_gpu=self._use_gpu):
with self.assertRaisesRegexp(ValueError, 'rank'):
special_math_ops.lbeta([])
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)
def _log_normalization(self): return special_math_ops.lbeta(self.concentration)
def test_empty_rank1_input_raises_value_error(self):
with self.test_session(use_gpu=self._use_gpu):
with self.assertRaisesRegexp(ValueError, 'rank'):
special_math_ops.lbeta([])
def test_empty_rank2_or_greater_input_gives_empty_output(self):
with self.test_session(use_gpu=self._use_gpu):
self.assertAllEqual([], special_math_ops.lbeta([[]]).eval())
self.assertEqual((0,), special_math_ops.lbeta([[]]).get_shape())
self.assertAllEqual([[]], special_math_ops.lbeta([[[]]]).eval())
self.assertEqual((1, 0), special_math_ops.lbeta([[[]]]).get_shape())
def _kl_dirichlet_dirichlet(d1, d2, name=None):
"""Batchwise KL divergence KL(d1 || d2) with d1 and d2 Dirichlet.
Args:
d1: instance of a Dirichlet distribution object.
d2: instance of a Dirichlet distribution object.
name: (optional) Name to use for created operations.
default is "kl_dirichlet_dirichlet".
Returns:
Batchwise KL(d1 || d2)
"""
with ops.name_scope(name, "kl_dirichlet_dirichlet", values=[
d1.concentration, d2.concentration]):
# The KL between Dirichlet distributions can be derived as follows. We have
#
# Dir(x; a) = 1 / B(a) * prod_i[x[i]^(a[i] - 1)]
#
# where B(a) is the multivariate Beta function:
#
# B(a) = Gamma(a[1]) * ... * Gamma(a[n]) / Gamma(a[1] + ... + a[n])
#
# The KL is
#
# KL(Dir(x; a), Dir(x; b)) = E_Dir(x; a){log(Dir(x; a) / Dir(x; b))}
#
# so we'll need to know the log density of the Dirichlet. This is
#
# log(Dir(x; a)) = sum_i[(a[i] - 1) log(x[i])] - log B(a)
#
# The only term that matters for the expectations is the log(x[i]). To
# compute the expectation of this term over the Dirichlet density, we can
# use the following facts about the Dirichlet in exponential family form:
# 1. log(x[i]) is a sufficient statistic
# 2. expected sufficient statistics (of any exp family distribution) are
# equal to derivatives of the log normalizer with respect to
# corresponding natural parameters: E{T[i](x)} = dA/d(eta[i])
#
# To proceed, we can rewrite the Dirichlet density in exponential family
# form as follows:
#
# Dir(x; a) = exp{eta(a) . T(x) - A(a)}
#
# where '.' is the dot product of vectors eta and T, and A is a scalar:
#
# eta[i](a) = a[i] - 1
# T[i](x) = log(x[i])
# A(a) = log B(a)
#
# Now, we can use fact (2) above to write
#
# E_Dir(x; a)[log(x[i])]
# = dA(a) / da[i]
# = d/da[i] log B(a)
# = d/da[i] (sum_j lgamma(a[j])) - lgamma(sum_j a[j])
# = digamma(a[i])) - digamma(sum_j a[j])
#
# Putting it all together, we have
#
# KL[Dir(x; a) || Dir(x; b)]
# = E_Dir(x; a){log(Dir(x; a) / Dir(x; b)}
# = E_Dir(x; a){sum_i[(a[i] - b[i]) log(x[i])} - (lbeta(a) - lbeta(b))
# = sum_i[(a[i] - b[i]) * E_Dir(x; a){log(x[i])}] - lbeta(a) + lbeta(b)
# = sum_i[(a[i] - b[i]) * (digamma(a[i]) - digamma(sum_j a[j]))]
# - lbeta(a) + lbeta(b))
digamma_sum_d1 = math_ops.digamma(
math_ops.reduce_sum(d1.concentration, axis=-1, keepdims=True))
digamma_diff = math_ops.digamma(d1.concentration) - digamma_sum_d1
concentration_diff = d1.concentration - d2.concentration
return (math_ops.reduce_sum(concentration_diff * digamma_diff, axis=-1) -
special_math_ops.lbeta(d1.concentration) +
special_math_ops.lbeta(d2.concentration))
def test_empty_rank1_dynamic_alloc_input_raises_op_error(self):
with self.test_session(use_gpu=self._use_gpu):
ph = array_ops.placeholder(dtypes.float32)
with self.assertRaisesOpError('rank'):
special_math_ops.lbeta(ph).eval(feed_dict={ph: []})