SeqTools facilitates the manipulation of datasets and the evaluation of a transformation pipeline. Some of the provided functionalities include: mapping element-wise operations, reordering, reindexing, concatenation, joining, slicing, minibatching, etc.
To improve ease of use, SeqTools manipulates list-like objects, otherwise known as a sequences (objects with a length supporting integer or slice based indexing).
Manipulating a dataset as a whole can be slow and resource/memory intensive. To circumvent this issue, SeqTools implements on-demand evaluation under the hood: operations and transformations on a dataset are only applied to individual items when they are actually accessed. This is particularly convenient for prototyping.
When comes the transition from prototyping to execution, the list-like container interface facilitates serial evaluation. Besides, SeqTools also provides simple helpers to dispatch work between multiple workers (threads or processes).
SeqTools originally targets data science, more precisely the data preprocessing stages. Being aware of the experimental nature of this usage, on-demand execution is made as transparent as possible to users by providing fault-tolerant functions and insightful error reporting. Moreover, internal code is kept concise and clear with comments to facilitate error tracing through a failing transformation pipeline.
>>> def f1(x): ... return x + 1 ... >>> def f2(x): # slow and memory heavy transformation ... time.sleep(.01) ... return [x for _ in range(500)] ... >>> def f3(x): ... return sum(x) / len(x) ... >>> data = list(range(1000))
Without delayed evaluation, defining the pipeline and reading values looks like so:
>>> tmp1 = [f1(x) for x in data] >>> tmp2 = [f2(x) for x in tmp1] # takes 10 seconds and a lot of memory >>> res = [f3(x) for x in tmp2] >>> print(res) 3.0 >>> print(max(tmp2)) # requires to store 499 500 useless values along 3
>>> tmp1 = seqtools.smap(f1, data) >>> tmp2 = seqtools.smap(f2, tmp1) >>> res = seqtools.smap(f3, tmp2) # no computations so far >>> print(res) # takes 0.01 seconds 3.0 >>> print(max(tmp2)) # easy access to intermediate results 3
The library comes with a set of functions to manipulate sequences:
and others (suggestions are also welcome).