nltk.collections.LazyMap

A lazy sequence whose elements are formed by applying a given function to each element in one or more underlying lists. The function is applied lazily -- i.e., when you read a value from the list, LazyMap will calculate that value by applying its function to the underlying lists' value(s). LazyMap is essentially a lazy version of the Python primitive function map. In particular, the following two expressions are equivalent:

>>> from nltk.collections import LazyMap
>>> function = str
>>> sequence = [1,2,3]
>>> map(function, sequence) # doctest: +SKIP
['1', '2', '3']
>>> list(LazyMap(function, sequence))
['1', '2', '3']

Like the Python map primitive, if the source lists do not have equal size, then the value None will be supplied for the 'missing' elements.

Lazy maps can be useful for conserving memory, in cases where individual values take up a lot of space. This is especially true if the underlying list's values are constructed lazily, as is the case with many corpus readers.

A typical example of a use case for this class is performing feature detection on the tokens in a corpus. Since featuresets are encoded as dictionaries, which can take up a lot of memory, using a LazyMap can significantly reduce memory usage when training and running classifiers.