def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
return []
assertions = []
if is_init != tensor_util.is_ref(self.total_count):
total_count = tf.convert_to_tensor(self.total_count)
msg1 = 'Argument `total_count` must be non-negative.'
msg2 = 'Argument `total_count` cannot contain fractional components.'
assertions += [
assert_util.assert_non_negative(total_count, message=msg1),
distribution_util.assert_integer_form(total_count,
message=msg2),
]
if self._probs is not None:
if is_init != tensor_util.is_ref(self._probs):
probs = tf.convert_to_tensor(self._probs)
one = tf.constant(1., probs.dtype)
assertions += [
assert_util.assert_non_negative(
probs, message='probs has components less than 0.'),
assert_util.assert_less_equal(
probs,
one,
message='probs has components greater than 1.')
]
return assertions
def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
return []
assertions = []
if self._rate1 is not None:
if is_init != tensor_util.is_ref(self._rate1):
assertions.append(assert_util.assert_non_negative(
self._rate1,
message='Argument `rate1` must be non-negative.'))
if self._rate2 is not None:
if is_init != tensor_util.is_ref(self._rate2):
assertions.append(assert_util.assert_non_negative(
self._rate2,
message='Argument `rate2` must be non-negative.'))
return assertions
def _inverse(self, y):
# To derive the inverse mapping note that:
# y[i] = exp(x[i]) / normalization
# and
# y[end] = 1 / normalization.
# Thus:
# x[i] = log(exp(x[i])) - log(y[end]) - log(normalization)
# = log(exp(x[i])/normalization) - log(y[end])
# = log(y[i]) - log(y[end])
# Do this first to make sure CSE catches that it'll happen again in
# _inverse_log_det_jacobian.
assertions = []
if self.validate_args:
assertions.append(assert_util.assert_near(
tf.reduce_sum(y, axis=-1),
tf.ones([], y.dtype),
2. * np.finfo(dtype_util.as_numpy_dtype(y.dtype)).eps,
message='Last dimension of `y` must sum to `1`.'))
assertions.append(assert_util.assert_less_equal(
y, tf.ones([], y.dtype),
message='Elements of `y` must be less than or equal to `1`.'))
assertions.append(assert_util.assert_non_negative(
y, message='Elements of `y` must be non-negative.'))
with tf.control_dependencies(assertions):
x = tf.math.log(y)
x, log_normalization = tf.split(x, num_or_size_splits=[-1, 1], axis=-1)
return x - log_normalization
def _maybe_assert_valid_x(self, x):
if not self.validate_args or self.power == 0.:
return []
return [assert_util.assert_non_negative(
1. + self.power * x,
message='Forward transformation input must be at least {}.'.format(
-1. / self.power))]
def _inverse(self, y):
if self.validate_args:
y = distribution_util.with_dependencies([
assert_util.assert_non_negative(
y, message="Argument y was negative")
], y)
return -y, y
def _parameter_control_dependencies(self, is_init):
assertions = []
if is_init and self.validate_args:
# assert_categorical_event_shape handles both the static and dynamic case.
assertions.extend(
distribution_util.assert_categorical_event_shape(
self._concentration))
if is_init != tensor_util.is_ref(self._total_count):
if self.validate_args:
total_count = tf.convert_to_tensor(self._total_count)
assertions.append(
distribution_util.assert_casting_closed(
total_count,
target_dtype=tf.int32,
message=
'total_count cannot contain fractional components.'))
assertions.append(
assert_util.assert_non_negative(
total_count,
message='total_count must be non-negative'))
if is_init != tensor_util.is_ref(self._concentration):
if self.validate_args:
assertions.append(
assert_util.assert_positive(
self._concentration,
message='Concentration parameter must be positive.'))
return assertions
def _parameter_control_dependencies(self, is_init):
assertions = []
logits = self._logits
probs = self._probs
param, name = (probs, 'probs') if logits is None else (logits, 'logits')
# In init, we can always build shape and dtype checks because
# we assume shape doesn't change for Variable backed args.
if is_init:
if not dtype_util.is_floating(param.dtype):
raise TypeError('Argument `{}` must having floating type.'.format(name))
msg = 'Argument `{}` must have rank at least 1.'.format(name)
shape_static = tensorshape_util.dims(param.shape)
if shape_static is not None:
if len(shape_static) < 1:
raise ValueError(msg)
elif self.validate_args:
param = tf.convert_to_tensor(param)
assertions.append(
assert_util.assert_rank_at_least(param, 1, message=msg))
with tf.control_dependencies(assertions):
param = tf.identity(param)
msg1 = 'Argument `{}` must have final dimension >= 1.'.format(name)
msg2 = 'Argument `{}` must have final dimension <= {}.'.format(
name, dtype_util.max(tf.int32))
event_size = shape_static[-1] if shape_static is not None else None
if event_size is not None:
if event_size < 1:
raise ValueError(msg1)
if event_size > dtype_util.max(tf.int32):
raise ValueError(msg2)
elif self.validate_args:
param = tf.convert_to_tensor(param)
assertions.append(assert_util.assert_greater_equal(
tf.shape(param)[-1], 1, message=msg1))
# NOTE: For now, we leave out a runtime assertion that
# `tf.shape(param)[-1] <= tf.int32.max`. An earlier `tf.shape` call
# will fail before we get to this point.
if not self.validate_args:
assert not assertions # Should never happen.
return []
if probs is not None:
probs = param # reuse tensor conversion from above
if is_init != tensor_util.is_ref(probs):
probs = tf.convert_to_tensor(probs)
one = tf.ones([], dtype=probs.dtype)
assertions.extend([
assert_util.assert_non_negative(probs),
assert_util.assert_less_equal(probs, one),
assert_util.assert_near(
tf.reduce_sum(probs, axis=-1), one,
message='Argument `probs` must sum to 1.'),
])
return assertions
def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
# Avoid computing intermediates needed to construct the assertions.
return []
assertions = []
if is_init != tensor_util.is_ref(self._batch_shape_unexpanded):
implicit_dim_mask = ps.equal(self._batch_shape_unexpanded, -1)
assertions.append(
assert_util.assert_rank(self._batch_shape_unexpanded,
1,
message='New shape must be a vector.'))
assertions.append(
assert_util.assert_less_equal(
tf.math.count_nonzero(implicit_dim_mask, dtype=tf.int32),
1,
message='At most one dimension can be unknown.'))
assertions.append(
assert_util.assert_non_negative(
self._batch_shape_unexpanded + 1,
message='Shape elements must be >=-1.'))
# Check that the old and new shapes are the same size.
expanded_new_shape, original_size = self._calculate_new_shape()
new_size = ps.reduce_prod(expanded_new_shape)
assertions.append(
assert_util.assert_equal(new_size,
tf.cast(original_size,
new_size.dtype),
message='Shape sizes do not match.'))
return assertions
def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
return []
assertions = []
if is_init != tensor_util.is_ref(self._temperature):
msg1 = 'Argument `temperature` must be positive.'
temperature = tf.convert_to_tensor(self._temperature)
assertions.append(
assert_util.assert_positive(temperature, message=msg1))
if self._probs is not None:
if is_init != tensor_util.is_ref(self._probs):
probs = tf.convert_to_tensor(self._probs)
one = tf.constant(1., probs.dtype)
assertions.extend([
assert_util.assert_non_negative(
probs,
message='Argument `probs` has components less than 0.'
),
assert_util.assert_less_equal(
probs,
one,
message=
'Argument `probs` has components greater than 1.')
])
return assertions
def maybe_assert_negative_binomial_param_correctness(
is_init, validate_args, total_count, probs, logits):
"""Return assertions for `NegativeBinomial`-type distributions."""
if is_init:
x, name = (probs, 'probs') if logits is None else (logits, 'logits')
if not dtype_util.is_floating(x.dtype):
raise TypeError(
'Argument `{}` must having floating type.'.format(name))
if not validate_args:
return []
assertions = []
if is_init != tensor_util.is_ref(total_count):
total_count = tf.convert_to_tensor(total_count)
assertions.extend([
assert_util.assert_positive(
total_count,
message='`total_count` has components less than or equal to 0.'),
distribution_util.assert_integer_form(
total_count,
message='`total_count` has fractional components.')
])
if probs is not None:
if is_init != tensor_util.is_ref(probs):
probs = tf.convert_to_tensor(probs)
one = tf.constant(1., probs.dtype)
assertions.extend([
assert_util.assert_non_negative(
probs, message='`probs` has components less than 0.'),
assert_util.assert_less_equal(
probs, one, message='`probs` has components greater than 1.')
])
return assertions
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.append(assert_util.assert_non_negative(
x, message='Sample must be non-negative.'))
return assertions
def _maybe_validate_rightmost_transposed_ndims(
rightmost_transposed_ndims, validate_args, name=None):
"""Checks that `rightmost_transposed_ndims` is valid."""
with tf.name_scope(name or 'maybe_validate_rightmost_transposed_ndims'):
assertions = []
if not dtype_util.is_integer(rightmost_transposed_ndims.dtype):
raise TypeError('`rightmost_transposed_ndims` must be integer type.')
if tensorshape_util.rank(rightmost_transposed_ndims.shape) is not None:
if tensorshape_util.rank(rightmost_transposed_ndims.shape) != 0:
raise ValueError('`rightmost_transposed_ndims` must be a scalar, '
'saw rank: {}.'.format(
tensorshape_util.rank(
rightmost_transposed_ndims.shape)))
elif validate_args:
assertions += [assert_util.assert_rank(rightmost_transposed_ndims, 0)]
rightmost_transposed_ndims_ = tf.get_static_value(
rightmost_transposed_ndims)
msg = '`rightmost_transposed_ndims` must be non-negative.'
if rightmost_transposed_ndims_ is not None:
if rightmost_transposed_ndims_ < 0:
raise ValueError(msg[:-1] + ', saw: {}.'.format(
rightmost_transposed_ndims_))
elif validate_args:
assertions += [
assert_util.assert_non_negative(
rightmost_transposed_ndims, message=msg)
]
return assertions
def _assertions(self, t):
if not self.validate_args:
return []
return [
assert_util.assert_non_negative(t,
message="Argument y was negative")
]
def maybe_assert_continuous_bernoulli_param_correctness(
is_init, validate_args, probs, logits):
"""Return assertions for `Bernoulli`-type distributions."""
if is_init:
x, name = (probs, 'probs') if logits is None else (logits, 'logits')
if not dtype_util.is_floating(x.dtype):
raise TypeError('Argument `{}` must having floating type.'.format(name))
if not validate_args:
return []
assertions = []
if probs is not None:
if is_init != tensor_util.is_ref(probs):
probs = tf.convert_to_tensor(probs)
one = tf.constant(1.0, probs.dtype)
assertions += [
assert_util.assert_non_negative(
probs,
message='probs has components less than 0.'),
assert_util.assert_less_equal(
probs,
one,
message='probs has components greater than 1.')]
return assertions
def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
return []
mean_direction = tf.convert_to_tensor(self.mean_direction)
concentration = tf.convert_to_tensor(self.concentration)
assertions = []
if is_init != tensor_util.is_ref(self._mean_direction):
assertions.append(
assert_util.assert_greater(
tf.shape(mean_direction)[-1],
1,
message=
'`mean_direction` must be a vector of at least size 2.'))
assertions.append(
assert_util.assert_near(
tf.cast(1., self.dtype),
tf.linalg.norm(mean_direction, axis=-1),
message='`mean_direction` must be unit-length'))
if is_init != tensor_util.is_ref(self._concentration):
assertions.append(
assert_util.assert_non_negative(
concentration,
message='`concentration` must be non-negative'))
return assertions
def _assertions(self, t):
if not self.validate_args:
return []
return [
assert_util.assert_non_negative(
t, message='All elements must be non-negative.')
]
def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
return []
mean_direction = tf.convert_to_tensor(self.mean_direction)
concentration = tf.convert_to_tensor(self.concentration)
assertions = []
if is_init != tensor_util.is_ref(self._mean_direction):
assertions.append(
assert_util.assert_greater(
tf.shape(mean_direction)[-1],
1,
message='`mean_direction` may not have scalar event shape'))
assertions.append(
assert_util.assert_less_equal(
tf.shape(mean_direction)[-1],
5,
message='von Mises-Fisher ndims > 5 is not currently supported'))
assertions.append(
assert_util.assert_near(
1.,
tf.linalg.norm(mean_direction, axis=-1),
message='`mean_direction` must be unit-length'))
if is_init != tensor_util.is_ref(self._concentration):
assertions.append(
assert_util.assert_non_negative(
concentration, message='`concentration` must be non-negative'))
return assertions
def _maybe_assert_valid_x(self, x):
if not self.validate_args:
return []
return [
assert_util.assert_non_negative(
x, message='Forward transformation input must be at least 0.')
]
def _maybe_validate_shape_override(self, override_shape, base_is_scalar,
validate_args, name):
"""Helper to __init__ which ensures override batch/event_shape are valid."""
if override_shape is None:
override_shape = []
override_shape = tf.convert_to_tensor(value=override_shape,
dtype=tf.int32,
name=name)
if not dtype_util.is_integer(override_shape.dtype):
raise TypeError("shape override must be an integer")
override_is_scalar = _is_scalar_from_shape_tensor(override_shape)
if tf.get_static_value(override_is_scalar):
return self._empty
dynamic_assertions = []
if tensorshape_util.rank(override_shape.shape) is not None:
if tensorshape_util.rank(override_shape.shape) != 1:
raise ValueError("shape override must be a vector")
elif validate_args:
dynamic_assertions += [
assert_util.assert_rank(
override_shape,
1,
message="shape override must be a vector")
]
if tf.get_static_value(override_shape) is not None:
if any(s < 0 for s in tf.get_static_value(override_shape)):
raise ValueError(
"shape override must have non-negative elements")
elif validate_args:
dynamic_assertions += [
assert_util.assert_non_negative(
override_shape,
message="shape override must have non-negative elements")
]
is_both_nonscalar = prefer_static.logical_and(
prefer_static.logical_not(base_is_scalar),
prefer_static.logical_not(override_is_scalar))
if tf.get_static_value(is_both_nonscalar) is not None:
if tf.get_static_value(is_both_nonscalar):
raise ValueError("base distribution not scalar")
elif validate_args:
dynamic_assertions += [
assert_util.assert_equal(
is_both_nonscalar,
False,
message="base distribution not scalar")
]
if not dynamic_assertions:
return override_shape
return distribution_util.with_dependencies(dynamic_assertions,
override_shape)
def _maybe_assert_valid_x(self, x):
if not self.validate_args or self.power == 0.:
return x
is_valid = assert_util.assert_non_negative(
1. + self.power * x,
message="Forward transformation input must be at least {}.".format(
-1. / self.power))
return distribution_util.with_dependencies([is_valid], x)
def _sample_control_dependencies(self, x):
dtype_util.assert_same_float_dtype(tensors=[x], dtype=self.dtype)
assertions = []
if not self.validate_args:
return assertions
assertions.append(assert_util.assert_non_negative(
x, message='Sample must be non-negative.'))
return assertions
def _parameter_control_dependencies(self, is_init):
if not self.validate_args:
return []
assertions = []
if is_init != tensor_util.is_ref(self.concentration):
assertions.append(assert_util.assert_non_negative(
self.concentration,
message='Argument `concentration` must be non-negative.'))
return assertions
def _maybe_assert_valid_sample(self, x, dtype):
if not self.validate_args:
return x
one = tf.ones([], dtype=dtype)
return distribution_util.with_dependencies([
assert_util.assert_non_negative(x),
assert_util.assert_less_equal(x, one),
assert_util.assert_near(one, tf.reduce_sum(x, axis=[-1])),
], x)
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
loc = 0. if self.loc is None else tf.convert_to_tensor(self.loc)
y = self.scale.solvevec(x - loc)
assertions.append(assert_util.assert_non_negative(
y, message='Sample is not contained in the support.'))
return assertions
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.append(assert_util.assert_non_negative(
x, message='Sample must be non-negative.'))
assertions.append(assert_util.assert_less_equal(
x, tf.ones([], x.dtype),
message='Sample must be less than or equal to `1`.'))
return assertions
def _maybe_assert_valid_y(self, y):
if not self.validate_args:
return []
return [
assert_util.assert_non_negative(
y,
message=(
'Inverse transformation input must be greater than or '
'equal to 0.'))
]
def _maybe_assert_valid_total_count(self, total_count, validate_args):
if not validate_args:
return total_count
return distribution_util.with_dependencies([
assert_util.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 components.'),
], total_count)
def _maybe_assert_valid_y(self, y):
if not self.validate_args:
return []
is_positive = assert_util.assert_non_negative(
y, message='Inverse transformation input must be greater than 0.')
less_than_one = assert_util.assert_less_equal(
y,
tf.constant(1., y.dtype),
message='Inverse transformation input must be less than or equal to 1.')
return [is_positive, less_than_one]
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.append(assert_util.assert_non_negative(
x, message='samples must be non-negative'))
if not self.interpolate_nondiscrete:
assertions.append(distribution_util.assert_integer_form(
x, message='samples cannot contain fractional components.'))
return assertions
def _maybe_assert_valid_y(self, y):
if not self.validate_args:
return y
is_positive = assert_util.assert_non_negative(
y, message="Inverse transformation input must be greater than 0.")
less_than_one = assert_util.assert_less_equal(
y,
tf.constant(1., y.dtype),
message="Inverse transformation input must be less than or equal to 1.")
return distribution_util.with_dependencies([is_positive, less_than_one], y)
