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utils.py
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utils.py
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# Copyright 2020 Google LLC
#
# 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.
"""Helpers shared by multiple modules in TFL."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
# TODO: update library not to explicitly check if None so we can return
# an empty list instead of None for these canonicalization methods.
def canonicalize_convexity(convexity):
"""Converts string constants representing convexity into integers.
Args:
convexity: The convexity hyperparameter of `tfl.layers.PWLCalibration`
layer.
Returns:
convexity represented as -1, 0, 1, or None.
Raises:
ValueError: If convexity is not in the set
{-1, 0, 1, 'concave', 'none', 'convex'}.
"""
if convexity is None:
return None
if convexity in [-1, 0, 1]:
return convexity
elif isinstance(convexity, six.string_types):
if convexity.lower() == "concave":
return -1
if convexity.lower() == "none":
return 0
if convexity.lower() == "convex":
return 1
raise ValueError("'convexity' must be from: [-1, 0, 1, 'concave', "
"'none', 'convex']. Given: {}".format(convexity))
def canonicalize_input_bounds(input_bounds):
"""Converts string constant 'none' representing unspecified bound into None.
Args:
input_bounds: The input_min or input_max hyperparameter of
`tfl.layers.Linear` layer.
Returns:
A list of [val, val, ...] where val can be a float or None, or the value
None if input_bounds is None.
Raises:
ValueError: If one of elements in input_bounds is not a float, None or
'none'.
"""
if input_bounds:
canonicalized = []
for item in input_bounds:
if isinstance(item, float) or item is None:
canonicalized.append(item)
elif isinstance(item, six.string_types) and item.lower() == "none":
canonicalized.append(None)
else:
raise ValueError("Both 'input_min' and 'input_max' elements must be "
"either int, float, None, or 'none'. Given: {}".format(
input_bounds))
return canonicalized
return None
def canonicalize_monotonicity(monotonicity, allow_decreasing=True):
"""Converts string constants representing monotonicity into integers.
Args:
monotonicity: The monotonicities hyperparameter of a `tfl.layers` Layer
(e.g. `tfl.layers.PWLCalibration`).
allow_decreasing: If decreasing monotonicity is considered a valid
monotonicity.
Returns:
monotonicity represented as -1, 0, 1, or None.
Raises:
ValueError: If monotonicity is not in the set
{-1, 0, 1, 'decreasing', 'none', 'increasing'} and allow_decreasing is
True.
ValueError: If monotonicity is not in the set {0, 1, 'none', 'increasing'}
and allow_decreasing is False.
"""
if monotonicity is None:
return None
if monotonicity in [-1, 0, 1]:
if not allow_decreasing and monotonicity == -1:
raise ValueError(
"'monotonicities' must be from: [0, 1, 'none', 'increasing']. "
"Given: {}".format(monotonicity))
return monotonicity
elif isinstance(monotonicity, six.string_types):
if monotonicity.lower() == "decreasing":
if not allow_decreasing:
raise ValueError(
"'monotonicities' must be from: [0, 1, 'none', 'increasing']. "
"Given: {}".format(monotonicity))
return -1
if monotonicity.lower() == "none":
return 0
if monotonicity.lower() == "increasing":
return 1
raise ValueError("'monotonicities' must be from: [-1, 0, 1, 'decreasing', "
"'none', 'increasing']. Given: {}".format(monotonicity))
def canonicalize_monotonicities(monotonicities, allow_decreasing=True):
"""Converts string constants representing monotonicities into integers.
Args:
monotonicities: monotonicities hyperparameter of a `tfl.layers` Layer (e.g.
`tfl.layers.Lattice`).
allow_decreasing: If decreasing monotonicity is considered a valid
monotonicity.
Returns:
A list of monotonicities represented as -1, 0, 1, or the value None
if monotonicities is None.
Raises:
ValueError: If one of monotonicities is not in the set
{-1, 0, 1, 'decreasing', 'none', 'increasing'} and allow_decreasing is
True.
ValueError: If one of monotonicities is not in the set
{0, 1, 'none', 'increasing'} and allow_decreasing is False.
"""
if monotonicities:
return [
canonicalize_monotonicity(
monotonicity, allow_decreasing=allow_decreasing)
for monotonicity in monotonicities
]
return None
def canonicalize_trust(trusts):
"""Converts string constants representing trust direction into integers.
Args:
trusts: edgeworth_trusts or trapezoid_trusts hyperparameter of
`tfl.layers.Lattice` layer.
Returns:
A list of trust constraint tuples of the form
(feature_a, feature_b, direction) where direction can be -1 or 1, or the
value None if trusts is None.
Raises:
ValueError: If one of trust constraints does not have 3 elements.
ValueError: If one of trust constraints' direction is not in the set
{-1, 1, 'negative', 'positive'}.
"""
if trusts:
canonicalized = []
for trust in trusts:
if len(trust) != 3:
raise ValueError("Trust constraints must consist of 3 elements. Seeing "
"constraint tuple {}".format(trust))
feature_a, feature_b, direction = trust
if direction in [-1, 1]:
canonicalized.append(trust)
elif (isinstance(direction, six.string_types) and
direction.lower() == "negative"):
canonicalized.append((feature_a, feature_b, -1))
elif (isinstance(direction, six.string_types) and
direction.lower() == "positive"):
canonicalized.append((feature_a, feature_b, 1))
else:
raise ValueError("trust constraint direction must be from: [-1, 1, "
"'negative', 'positive']. Given: {}".format(direction))
return canonicalized
return None
def canonicalize_unimodalities(unimodalities):
"""Converts string constants representing unimodalities into integers.
Args:
unimodalities: unimodalities hyperparameter of `tfl.layers.Lattice` layer.
Returns:
A list of unimodalities represented as -1, 0, 1, or the value None if
unimodalities is None.
Raises:
ValueError: If one of unimodalities is not in the set
{-1, 0, 1, 'peak', 'none', 'valley'}.
"""
if not unimodalities:
return None
canonicalized = []
for unimodality in unimodalities:
if unimodality in [-1, 0, 1]:
canonicalized.append(unimodality)
elif isinstance(unimodality,
six.string_types) and unimodality.lower() == "peak":
canonicalized.append(-1)
elif isinstance(unimodality,
six.string_types) and unimodality.lower() == "none":
canonicalized.append(0)
elif isinstance(unimodality,
six.string_types) and unimodality.lower() == "valley":
canonicalized.append(1)
else:
raise ValueError(
"'unimodalities' elements must be from: [-1, 0, 1, 'peak', 'none', "
"'valley']. Given: {}".format(unimodalities))
return canonicalized
def count_non_zeros(*iterables):
"""Returns total number of non 0 elements in given iterables.
Args:
*iterables: Any number of the value None or iterables of numeric values.
"""
result = 0
for iterable in iterables:
if iterable is not None:
result += sum(1 for element in iterable if element != 0)
return result