tf.feature_column.bucketized_column(
source_column,
boundaries
)
Defined in tensorflow/python/feature_column/feature_column_v2.py
.
Represents discretized dense input.
Buckets include the left boundary, and exclude the right boundary. Namely,
boundaries=[0., 1., 2.]
generates buckets (-inf, 0.)
, [0., 1.)
,
[1., 2.)
, and [2., +inf)
.
For example, if the inputs are
boundaries = [0, 10, 100]
input tensor = [[-5, 10000]
[150, 10]
[5, 100]]
then the output will be
output = [[0, 3]
[3, 2]
[1, 3]]
Example:
price = numeric_column('price')
bucketized_price = bucketized_column(price, boundaries=[...])
columns = [bucketized_price, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
# or
columns = [bucketized_price, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
bucketized_column
can also be crossed with another categorical column using
crossed_column
:
price = numeric_column('price')
# bucketized_column converts numerical feature to a categorical one.
bucketized_price = bucketized_column(price, boundaries=[...])
# 'keywords' is a string feature.
price_x_keywords = crossed_column([bucketized_price, 'keywords'], 50K)
columns = [price_x_keywords, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
Args:
source_column
: A one-dimensional dense column which is generated withnumeric_column
.boundaries
: A sorted list or tuple of floats specifying the boundaries.
Returns:
A BucketizedColumn
.
Raises:
ValueError
: Ifsource_column
is not a numeric column, or if it is not one-dimensional.ValueError
: Ifboundaries
is not a sorted list or tuple.