def _convert_tensor(x):
"""Create or cast tensor if needed."""
if not tensor_util.is_tensor(x):
# x is a numpy array
x = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(x)
if check_ops.is_numeric_tensor(x):
# is_numeric_tensor returns False if provided with a numpy array
x = _cast_tensor_to_floatx(x)
return x
def _convert_tensor(x):
"""Create or cast tensor if needed."""
if not tensor_util.is_tensor(x):
# x is a numpy array
x = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(x)
if check_ops.is_numeric_tensor(x):
# is_numeric_tensor returns False if provided with a numpy array
x = _cast_tensor_to_floatx(x)
return x
def apply_buckets_with_interpolation(x, bucket_boundaries, name=None):
"""Interpolates within the provided buckets and then normalizes to 0 to 1.
A method for normalizing continuous numeric data to the range [0, 1].
Numeric values are first bucketized according to the provided boundaries, then
linearly interpolated within their respective bucket ranges. Finally, the
interpolated values are normalized to the range [0, 1]. Values that are
less than or equal to the lowest boundary, or greater than or equal to the
highest boundary, will be mapped to 0 and 1 respectively.
This is a non-linear approach to normalization that is less sensitive to
outliers than min-max or z-score scaling. When outliers are present, standard
forms of normalization can leave the majority of the data compressed into a
very small segment of the output range, whereas this approach tends to spread
out the more frequent values (if quantile buckets are used). Note that
distance relationships in the raw data are not necessarily preserved (data
points that close to each other in the raw feature space may not be equally
close in the transformed feature space). This means that unlike linear
normalization methods, correlations between features may be distorted by the
transformation.
Args:
x: A numeric input `Tensor`/`SparseTensor` (tf.float[32|64], tf.int[32|64])
bucket_boundaries: Sorted bucket boundaries as a rank-2 `Tensor`.
name: (Optional) A name for this operation.
Returns:
A `Tensor` or `SparseTensor` of the same shape as `x`, normalized to the
range [0, 1]. If the input x is tf.float64, the returned values will be
tf.float64. Otherwise, returned values are tf.float32.
"""
with tf.name_scope(name, 'buckets_with_interpolation'):
tf.assert_rank(bucket_boundaries, 2)
x_values = x
compose_result_fn = lambda values: values
if isinstance(x, tf.SparseTensor):
x_values = x.values
compose_result_fn = (lambda values: tf.SparseTensor( # pylint: disable=g-long-lambda
indices=x.indices, values=values, dense_shape=x.dense_shape))
if not check_ops.is_numeric_tensor(x_values):
raise tf.errors.InvalidArgumentError(
'Input tensor to be normalized must be numeric.')
return_type = tf.float64 if x.dtype == tf.float64 else tf.float32
num_boundaries = tf.to_int64(tf.shape(bucket_boundaries)[1])
# The TF BucketizeWithInputBoundaries Op expects boundaries as tf.float32.
bucket_boundaries = tf.cast(bucket_boundaries, tf.float32)
bucket_indices = tf.cast(
quantile_ops.bucketize_with_input_boundaries(
x_values, boundaries=bucket_boundaries, name='assign_buckets'),
tf.int64)
# Get max, min, and width of the corresponding bucket for each element.
bucket_max = tf.dtypes.cast(
tf.gather(
tf.concat([bucket_boundaries[0], bucket_boundaries[:, -1]], axis=0),
bucket_indices), return_type)
bucket_min = tf.dtypes.cast(
tf.gather(
tf.concat([bucket_boundaries[:, 0], bucket_boundaries[0]], axis=0),
bucket_indices), return_type)
bucket_width = bucket_max - bucket_min
zeros = tf.zeros_like(x_values, dtype=return_type)
ones = tf.ones_like(x_values, dtype=return_type)
# Linearly interpolate each value within its respective bucket range.
interpolation_value = (
(tf.dtypes.cast(x_values, return_type) - bucket_min) / bucket_width)
bucket_interpolation = tf.verify_tensor_all_finite(
tf.where(
# If bucket index is first or last, which represents "less than
# min" and "greater than max" respectively, the bucket logically
# has an infinite width and we can't meaningfully interpolate.
tf.logical_or(
tf.equal(bucket_indices, 0),
tf.equal(bucket_indices, num_boundaries)),
zeros,
tf.where(
# If the bucket width is zero due to numerical imprecision,
# there is no point in interpolating
tf.equal(bucket_width, 0.0),
ones / 2.0,
# Finally, for a bucket with a valid width, we can interpolate.
interpolation_value)),
'bucket_interpolation')
bucket_indices_with_interpolation = tf.dtypes.cast(
tf.maximum(bucket_indices - 1, 0), return_type) + bucket_interpolation
# Normalize the interpolated values to the range [0, 1].
denominator = tf.dtypes.cast(tf.maximum(num_boundaries - 1, 1), return_type)
normalized_values = tf.div(bucket_indices_with_interpolation, denominator)
# If there is only one boundary, all values < the boundary are 0, all values
# >= the boundary are 1.
single_boundary_values = lambda: tf.where( # pylint: disable=g-long-lambda
tf.equal(bucket_indices, 0), zeros, ones)
normalized_result = tf.cond(
tf.equal(num_boundaries, 1),
single_boundary_values, lambda: normalized_values)
return compose_result_fn(normalized_result)
def apply_buckets_with_interpolation(x, bucket_boundaries, name=None):
"""Interpolates within the provided buckets and then normalizes to 0 to 1.
A method for normalizing continuous numeric data to the range [0, 1].
Numeric values are first bucketized according to the provided boundaries, then
linearly interpolated within their respective bucket ranges. Finally, the
interpolated values are normalized to the range [0, 1]. Values that are
less than or equal to the lowest boundary, or greater than or equal to the
highest boundary, will be mapped to 0 and 1 respectively.
Args:
x: A numeric input `Tensor` (tf.float32, tf.float64, tf.int32, tf.int64).
bucket_boundaries: Sorted bucket boundaries as a rank-2 `Tensor`.
name: (Optional) A name for this operation.
Returns:
A `Tensor` of the same shape as `x`, normalized to the range [0, 1]. If the
input x is tf.float64, the returned values will be tf.float64.
Otherwise, returned values are tf.float32.
"""
with tf.name_scope(name, 'buckets_with_interpolation'):
tf.assert_rank(bucket_boundaries, 2)
if not check_ops.is_numeric_tensor(x):
raise tf.errors.InvalidArgumentError(
'Input tensor to be normalized must be numeric.')
return_type = tf.float64 if x.dtype == tf.float64 else tf.float32
num_boundaries = tf.to_int64(tf.shape(bucket_boundaries)[1])
# The TF BucketizeWithInputBoundaries Op expects boundaries as tf.float32.
bucket_boundaries = tf.cast(bucket_boundaries, tf.float32)
bucket_indices = tf.cast(
quantile_ops.bucketize_with_input_boundaries(
x, boundaries=bucket_boundaries, name='assign_buckets'), tf.int64)
# Get max, min, and width of the corresponding bucket for each element.
bucket_max = tf.dtypes.cast(
tf.gather(
tf.concat([bucket_boundaries[0], bucket_boundaries[:, -1]], axis=0),
bucket_indices), return_type)
bucket_min = tf.dtypes.cast(
tf.gather(
tf.concat([bucket_boundaries[:, 0], bucket_boundaries[0]], axis=0),
bucket_indices), return_type)
bucket_width = bucket_max - bucket_min
zeros = tf.zeros_like(x, dtype=return_type)
ones = tf.ones_like(x, dtype=return_type)
# Linearly interpolate each value within its respective bucket range.
interpolation_value = ((tf.dtypes.cast(x, return_type) - bucket_min) /
bucket_width)
bucket_interpolation = tf.verify_tensor_all_finite(
tf.where(
# If bucket index is first or last, which represents "less than
# min" and "greater than max" respectively, the bucket logically
# has an infinite width and we can't meaningfully interpolate.
tf.logical_or(
tf.equal(bucket_indices, 0),
tf.equal(bucket_indices, num_boundaries)),
zeros,
tf.where(
# If the bucket width is zero due to numerical imprecision,
# there is no point in interpolating
tf.equal(bucket_width, 0.0),
ones / 2.0,
# Finally, for a bucket with a valid width, we can interpolate.
interpolation_value)),
'bucket_interpolation')
bucket_indices_with_interpolation = tf.dtypes.cast(
tf.maximum(bucket_indices - 1, 0), return_type) + bucket_interpolation
# Normalize the interpolated values to the range [0, 1].
denominator = tf.dtypes.cast(tf.maximum(num_boundaries - 1, 1), return_type)
normalized_values = tf.div(bucket_indices_with_interpolation, denominator)
# If there is only one boundary, all values < the boundary are 0, all values
# >= the boundary are 1.
single_boundary_values = lambda: tf.where( # pylint: disable=g-long-lambda
tf.equal(bucket_indices, 0), zeros, ones)
return tf.cond(
tf.equal(num_boundaries, 1),
single_boundary_values, lambda: normalized_values)
