Group windows of elements by key and reduce them
dataset_group_by_window(
dataset,
key_func,
reduce_func,
window_size = NULL,
window_size_func = NULL,
name = NULL
)a TF Dataset
A function mapping a nested structure of tensors (having
shapes and types defined by self$output_shapes and self$output_types)
to a scalar tf.int64 tensor.
A function mapping a key and a dataset of up to
window_size consecutive elements matching that key to another dataset.
A tf.int64 scalar tf.Tensor, representing the number
of consecutive elements matching the same key to combine in a single batch,
which will be passed to reduce_func. Mutually exclusive with
window_size_func.
A function mapping a key to a tf.int64 scalar
tf.Tensor, representing the number of consecutive elements matching the
same key to combine in a single batch, which will be passed to
reduce_func. Mutually exclusive with window_size.
(Optional.) A name for the Tensorflow operation.
This transformation maps each consecutive element in a dataset to a
key using key_func() and groups the elements by key. It then applies
reduce_func() to at most window_size_func(key) elements matching the same
key. All except the final window for each key will contain
window_size_func(key) elements; the final window may be smaller.
You may provide either a constant window_size or a window size determined
by the key through window_size_func.
window_size <- 5
dataset <- range_dataset(to = 10) %>%
dataset_group_by_window(
key_func = function(x) x %% 2,
reduce_func = function(key, ds) dataset_batch(ds, window_size),
window_size = window_size
)it <- as_array_iterator(dataset)
while (!is.null(elem <- iter_next(it)))
print(elem)
#> tf.Tensor([0 2 4 6 8], shape=(5), dtype=int64)
#> tf.Tensor([1 3 5 7 9], shape=(5), dtype=int64)