tf.feature_column.bucketized_column

Represents discretized dense input.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.feature_column.bucketized_column, `tf.compat.v2.feature_column.bucketized_column`

tf.feature_column.bucketized_column(
    source_column, boundaries
)

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.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)

# or
columns = [bucketized_price, ...]
features = tf.io.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.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)