def testCastToDict(self):
names = ['a', 'b', 'c']
got = tools.cast_to_dict(1.0, names, 'blah')
self.assertEqual(got['a'], 1.0)
self.assertEqual(got['b'], 1.0)
self.assertEqual(got['c'], 1.0)
self.assertItemsEqual(got.keys(), names)
got = tools.cast_to_dict({'a': 1.0, 'b': 2.0, 'c': 3.0}, names, 'blah')
self.assertEqual(got['a'], 1.0)
self.assertEqual(got['b'], 2.0)
self.assertEqual(got['c'], 3.0)
self.assertItemsEqual(got.keys(), names)
with self.assertRaisesRegexp(
ValueError,
'Dict given for blah does not contain definition for feature "c"'):
got = tools.cast_to_dict({'a': 1.0, 'b': 2.0}, names, 'blah')
got = tools.cast_to_dict({'a': 1.0, tools.DEFAULT_NAME: 2.0}, names, 'blah')
self.assertItemsEqual(got.keys(), names)
self.assertEqual(got['a'], 1.0)
self.assertEqual(got['b'], 2.0)
self.assertEqual(got['c'], 2.0)
def input_calibration_layer(columns_to_tensors,
num_keypoints,
feature_columns=None,
keypoints_initializers=None,
keypoints_initializer_fns=None,
bound=False,
monotonic=None,
missing_input_values=None,
missing_output_values=None,
dtype=dtypes.float32,
**regularizer_amounts):
"""Creates a calibration layer for the given input and feature_columns.
Returns a tensor with the calibrated values of the given features, a list
of the names of the features in the order they feature in the returned, and
a list of projection ops, that must be applied at each step (or every so many
steps) to project the model to a feasible space: used for bounding the outputs
or for imposing monotonic -- the list will be empty if bound and
monotonic are not set.
Args:
columns_to_tensors: A mapping from feature name to tensors. 'string' key
means a base feature (not-transformed). If feature_columns is not set
these are the features calibrated. Otherwise the transformed
feature_columns are the ones calibrated.
num_keypoints: Number of keypoints to use. Either a single int, or a dict
mapping feature names to num_keypoints. If a value of the dict is 0 or
None the correspondent feature won't be calibrated.
feature_columns: Optional. If set to a set of FeatureColumns, these will
be the features used and calibrated.
keypoints_initializers: For evaluation or inference (or when resuming
training from a checkpoint) the values will be loaded from disk, so they
don't need to be given (leave it as None).
Either a tuple of two tensors of shape [num_keypoints], or a dict mapping
feature names to pair of tensors of shape [num_keypoints[feature_name]].
See load_keypoints_from_quantiles or uniform_keypoints_for_signal on how
to generate these (module keypoints_initialization).
keypoints_initializer_fns: Like keypoints_initializers but using lambda
initializers. They should be compatible with tf.get_variable. If this is
set, then keypoints_initializers must be None.
bound: boolean whether output of calibration must be bound. Alternatively
a dict mapping feature name to boundness.
monotonic: whether calibration has to be kept monotonic: None or 0 means
no monotonic. Positive or negative values mean increasing or decreasing
monotonic respectively. Alternatively a dict mapping feature name
to monotonic.
missing_input_values: If set, and if the input has this value it is assumed
to be missing and the output will either be calibrated to some value
between `[calibration_output_min, calibration_output_max]` or set to a
fixed value set by missing_output_value. Limitation: it only works for
scalars. Either one value for all inputs, or a dict mapping feature name
to missing_input_value for the respective feature.
missing_output_values: Requires missing_input_value also to be set. If set
if will convert missing input to this value. Either one value for all
inputs, or a dict mapping feature name to missing_input_value for the
respective feature.
dtype: If any of the scalars are not given as tensors, they are converted
to tensors with this dtype.
**regularizer_amounts: Keyword args of regularization amounts passed to
regularizers.calibrator_regularization(). Keyword names should be among
supported regularizers.CALIBRATOR_REGULARIZERS and values should be
either float or {feature_name: float}. If float, then same value is
applied to all features.
Returns:
A tuple of:
* calibrated tensor of shape [batch_size, sum(features dimensions)].
* list of the feature names in the order they feature in the calibrated
tensor. A name may appear more than once if the feature is
multi-dimension (for instance a multi-dimension embedding)
* list of projection ops, that must be applied at each step (or every so
many steps) to project the model to a feasible space: used for bounding
the outputs or for imposing monotonicity. Empty if none are requested.
* None or tensor with regularization loss.
Raises:
ValueError: if dtypes are incompatible.
"""
with ops.name_scope('input_calibration_layer'):
feature_names = tools.get_sorted_feature_names(columns_to_tensors,
feature_columns)
num_keypoints = tools.cast_to_dict(num_keypoints, feature_names,
'num_keypoints')
bound = tools.cast_to_dict(bound, feature_names, 'bound')
monotonic = tools.cast_to_dict(monotonic, feature_names, 'monotonic')
keypoints_initializers = tools.cast_to_dict(keypoints_initializers,
feature_names,
'keypoints_initializers')
keypoints_initializer_fns = tools.cast_to_dict(
keypoints_initializer_fns, feature_names,
'keypoints_initializer_fns')
missing_input_values = tools.cast_to_dict(missing_input_values,
feature_names,
'missing_input_values')
missing_output_values = tools.cast_to_dict(missing_output_values,
feature_names,
'missing_output_values')
regularizer_amounts = {
regularizer_name:
tools.cast_to_dict(regularizer_amounts[regularizer_name],
feature_names, regularizer_name)
for regularizer_name in regularizer_amounts
}
per_dimension_feature_names = []
# Get uncalibrated tensors, either from columns_to_tensors, or using
# feature_columns.
if feature_columns is None:
uncalibrated_features = [
columns_to_tensors[name] for name in feature_names
]
else:
transformed_columns_to_tensors = columns_to_tensors.copy()
dict_feature_columns = {
f_col.name: f_col
for f_col in feature_columns
}
uncalibrated_features = [
tools.input_from_feature_column(transformed_columns_to_tensors,
dict_feature_columns[name],
dtype)
for name in feature_names
]
projection_ops = []
calibrated_splits = []
total_regularization = None
for feature_idx in range(len(feature_names)):
name = feature_names[feature_idx]
uncalibrated_feature = uncalibrated_features[feature_idx]
if uncalibrated_feature.shape.ndims == 1:
feature_dim = 1
uncalibrated_splits = [uncalibrated_feature]
elif uncalibrated_feature.shape.ndims == 2:
feature_dim = uncalibrated_feature.shape.dims[1].value
uncalibrated_splits = array_ops.unstack(uncalibrated_feature,
axis=1)
else:
raise ValueError(
'feature {}: it has rank {}, but only ranks 1 or 2 are '
'supported; feature shape={}'.format(
name, uncalibrated_feature.shape.ndims,
uncalibrated_feature.shape))
missing_input_value = missing_input_values[name]
missing_output_value = missing_output_values[name]
feature_regularizer_amounts = {
regularizer_name: regularizer_amounts[regularizer_name][name]
for regularizer_name in regularizer_amounts
}
# FutureWork: make the interpolation ops handle multi-dimension values,
# so this step is not needed.
for dim_idx in range(feature_dim):
per_dimension_feature_names += [name]
split_name = name
if feature_dim > 1:
split_name = '{}_dim_{}'.format(name, dim_idx)
uncalibrated = uncalibrated_splits[dim_idx]
if not num_keypoints[name]:
# No calibration for this feature:
calibrated_splits += [uncalibrated]
if (missing_input_value is not None
or missing_output_value is not None):
raise ValueError(
'feature %s: cannot handle missing values if feature is not '
'calibrated, missing_input_value=%s, missing_output_value=%s'
%
(name, missing_input_value, missing_output_value))
else:
calibrated, projection, reg = one_dimensional_calibration_layer(
uncalibrated,
num_keypoints[name],
signal_name=split_name,
keypoints_initializers=keypoints_initializers[name],
keypoints_initializer_fns=keypoints_initializer_fns[
name],
bound=bound[name],
monotonic=monotonic[name],
missing_input_value=missing_input_value,
missing_output_value=missing_output_value,
**feature_regularizer_amounts)
calibrated_splits += [calibrated]
if projection is not None:
projection_ops += [projection]
total_regularization = tools.add_if_not_none(
total_regularization, reg)
all_calibrated = array_ops.stack(calibrated_splits,
axis=1,
name='stack_calibrated')
return (all_calibrated, per_dimension_feature_names, projection_ops,
total_regularization)
def load_keypoints_from_quantiles(feature_names,
save_dir,
num_keypoints,
output_min=None,
output_max=None,
use_label_quantiles_for_outputs=False,
reversed_dict=None,
missing_input_values_dict=None,
dtype=tf.float32):
"""Retrieves keypoints initialization values for selected features.
It expects that the quantiles have already been calculated and saved in the
save_dir by the save_quantiles_for_keypoints function. It will raise
an I/O error if not.
Args:
feature_names: List of features names for which to get keypoints
initialization values.
save_dir: Directory where the quantiles have been saved to. Same value used
when save_quantiles_for_keypoints was called.
num_keypoints: Desired number of keypoints to use for calibration. This can
either be a scalar to be used for all features, or a dict mapping feature
name to num_keypoints. Fewer keypoints than requested can end up being
used when for the given feature there are not enough different values. If
num_keypoints for a feature is missing, None or 0, no initialization is
generated.
output_min: If not None, specifies the initial calibrated value associated
with the first calibration keypoint. The keypoints outputs in between will
be linearly interpolated. It can be given as a scalar, in which case the
value is used for all features, or a dict mapping feature name to
output_min.
output_max: Like output_min, but the calibrated value associated to the last
keypoint. Scalar or dict.
use_label_quantiles_for_outputs: Sets the keypoint outputs (calibrated
values) to the label quantiles. If this parameter is true then output_min
and output_max must both be None and the label quantiles must have been
saved in the call to save_quantiles_for_keypoints that generated the
quantile files (i.e. the input_fn parameter for the latter function must
have returned a label). If this parameter is False, then neither
output_min nor output_max may be None.
reversed_dict: An optional dict. If reversed_dict[feature_name] is True,
then the initial output keypoints will be in reversed order for that
feature, i.e., input_min will be mapped to output_max or the last label
quantile if use_label_quantiles_for_outputs is true, and input_max will be
mapped to output_min or the first label quantile if
use_label_quantiles_for_outputs is true. Reversing output keypoints is
useful for decreasing monotonic calibrators.
missing_input_values_dict: An optional dict. If provided, it should include
all features passed via feature_names. If the value of
missing_input_values[feature_name] is Not none, it is excluded from the
input keypoint values.
dtype: Type to be used for calibration.
Returns:
Dict of feature name to pair of constant tensors that can be used to
initialize calibrators keypoints inputs and outputs.
Raises:
tf.errors.NotFoundError: if quantiles file not found.
values in the signal. This would probably be better handled as categorical,
but still this should handle the case correctly.
"""
if (output_min is None) != (output_max is None):
raise ValueError(
"Either both output_min and output_max should be given or neither."
)
output_labels_given = (output_min is not None)
if (use_label_quantiles_for_outputs and output_labels_given):
raise ValueError("If use_label_quantiles_for_outputs is true, then"
" output_min and output_max cannot be given.")
if (not use_label_quantiles_for_outputs and not output_labels_given):
raise ValueError(
"Either use_label_quantiles_for_outputs should be true or "
" output_min and output_max must be given.")
subdir = os.path.join(save_dir, _QUANTILES_SUBDIRECTORY)
num_keypoints = tools.cast_to_dict(num_keypoints, feature_names,
num_keypoints)
if use_label_quantiles_for_outputs:
label_quantiles = _load_quantiles(subdir, _LABEL_FEATURE_NAME)
else:
label_quantiles = None
output_min = tools.cast_to_dict_of_tensor_scalars(
output_min, feature_names, dtype, "output_min")
output_max = tools.cast_to_dict_of_tensor_scalars(
output_max, feature_names, dtype, "output_max")
keypoints = {}
for feature_name in feature_names:
if feature_name not in num_keypoints or not num_keypoints[feature_name]:
continue
all_quantiles = _load_quantiles(subdir, feature_name)
if (missing_input_values_dict is not None
and feature_name in missing_input_values_dict):
exclude_val = missing_input_values_dict[feature_name]
if exclude_val is not None:
all_quantiles = [q for q in all_quantiles if q != exclude_val]
quantiles = _resample_quantiles(all_quantiles,
num_keypoints[feature_name])
unique_quantiles = sorted(set(quantiles))
input_keypoints = tf.constant(unique_quantiles,
shape=[len(unique_quantiles)],
dtype=dtype)
if use_label_quantiles_for_outputs:
output_keypoints = tf.constant(_resample_quantiles(
label_quantiles, len(unique_quantiles)),
shape=[len(unique_quantiles)],
dtype=dtype)
else:
output_keypoints = tf.linspace(output_min[feature_name],
output_max[feature_name],
len(unique_quantiles))
if reversed_dict is not None and reversed_dict[feature_name]:
output_keypoints = tf.reverse(output_keypoints, axis=[0])
keypoints[feature_name] = (input_keypoints, output_keypoints)
return keypoints
def load_keypoints_from_quantiles(feature_names,
save_dir,
num_keypoints,
output_min,
output_max,
dtype=dtypes.float32):
"""Retrieves keypoints initialization values for selected features.
It expects that the quantiles have already been calculated and saved in the
save_dir by the save_quantiles_for_keypoints function. It will raise
an I/O error if not.
Args:
feature_names: List of features names for which to get keypoints
initialization values.
save_dir: Directory where the quantiles have been saved to. Same value used
when save_quantiles_for_keypoints was called.
num_keypoints: Desired number of keypoints to use for calibration. This
can either be a scalar to be used for all features, or a dict mapping
feature name to num_keypoints. Fewer keypoints than requested can end
up being used when for the given feature there are not enough different
values. If num_keypoints for a feature is missing, None or 0, no
initialization is generated.
output_min: Initial calibrated value associated with the first calibration
keypoint. The keypoints outputs in between will be linearly interpolated.
It can be given as a scalar, in which case value is used for all features,
or a dict mapping feature name to output_min.
output_max: Like output_min, but the calibrated value associated to the
last keypoint. Scalar or dict.
dtype: Type to be used for calibration.
Returns:
Dict of feature name to pair of constant tensors that can be used to
initialize calibrators keypoints inputs and outputs.
Raises:
tf.errors.NotFoundError: if quantiles file not found.
values in the signal. This would probably be better handled as categorical,
but still this should handle the case correctly.
"""
subdir = os.path.join(save_dir, _QUANTILES_SUBDIRECTORY)
num_keypoints = tools.cast_to_dict(num_keypoints, feature_names,
num_keypoints)
output_min = tools.cast_to_dict_of_tensor_scalars(output_min,
feature_names, dtype,
"output_min")
output_max = tools.cast_to_dict_of_tensor_scalars(output_max,
feature_names, dtype,
"output_max")
keypoints = {}
for feature_name in feature_names:
if feature_name not in num_keypoints or not num_keypoints[feature_name]:
continue
all_quantiles = _load_quantiles(subdir, feature_name)
percentiles = np.linspace(0., 100., num_keypoints[feature_name])
quantiles = np.percentile(all_quantiles,
percentiles,
interpolation="nearest")
quantiles = sorted(set(quantiles)) # Remove repeated quantiles.
keypoints[feature_name] = (array_ops.constant(quantiles,
shape=[len(quantiles)],
dtype=dtype),
math_ops.linspace(output_min[feature_name],
output_max[feature_name],
len(quantiles)))
return keypoints
