Something like this: Suppose we have a table of strings of digits, some including spaces, and we’d like to remove the spaces. From
123 456
234567
345 678
123456
234567
345678
4567890
For your example, it could list:
“Remove interior spaces from each item”
“Remove the middle character from any 7-character strings to make them 6 characters in length”
Learning algorithms that produce decision trees are usually used in this situation.
This is the problem of lacking explanatory mechanisms in ML.
Note that some techniques that are very out of vogue at the moment, such as Genetic Programming, are much better than neural nets in this regard.
123 456
234567
345 678
All this then buys you is that you don’t have to remember the function names.
[1] https://www.microsoft.com/en-us/research/publication/automat...
One way to eliminate this ambiguity is to also provide a natural language description of what you want, e.g. "remove the spaces".
In the natural language processing community, we call this semantic parsing.
But sometimes the semantic parser can misinterpret the language too and generate a program which still "cheats" in the same manner as you described. We call these "spurious programs".
Shameless plug-- my group has been working on how to deal with these spurious programs:
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood https://arxiv.org/abs/1704.07926
Besides, it depends on the slope of "coding". If it gets really difficult really quick (exponentially say), this could just be forever stuck in the "low hanging fruit" stage.
This is where hinting is important. Metadata. That sequence if I know it's a phone number, or a sequence of increasing digits, depends a lot on metadata.
Given some reasonable sample size, i believe machine learning could provide hints as to some of the common types of formats. Semi automated data hinting or structuring?
There is a bidirectional connection between interpreting your data and how your data is structured
Is it possible to use your data column to statistically hint at metadata characteristics by some sort of clustering, then use that to automatically clean input data?
The beauty of this product is that its adoption strategy is baked into the product itself: I'd share this with all Excel user friends of mine because I want the algorithm to get smarter, and I might even learn a bit of C# myself so that I can contribute and scratch my own itch. This in turn makes the product better (because of the larger training data), lending itself to more word of mouth.
One concern I have is security: I'd love to hear from folks who built this/more familiar with this about how to ensure the security of suggested transformations.
Either way, this looks very useful. Having spent more than my fair share of time massaging data prior to import, this looks pretty great.
Then they complain to Microsoft, who helpfully suggests the product they should upgrade to. This has always been a strong spot of Microsoft's. "I see you've scaled beyond the capacity of [Product A]. Well, fortunately for you we have [Product B] which can handle it, with a nice import wizard to get you started painlessly." It typically goes Excel > Access > On-prem SQL Server > Azure.
This sounds very negative and I swear I don't mean it that way. It's a great sales tactic if you offer products at every level of scale.
For example, given the rule `f "abcde" 2 == "aabbccddee"`, it even figures out the role of the parameter `2`, so `f "zq" 3` gives `"zzzqqq"`.
https://support.office.com/en-us/article/Use-AutoFill-and-Fl...
[0]https://www.microsoft.com/en-us/research/blog/deep-learning-...
[1]HN Discussion: https://news.ycombinator.com/item?id=14168027
It's not production ready / launched yet, but it's getting there.
I'd be interested to finds (or really doesn't find) this useful :)
It can't do miracles, but this is time saving in many cases like when you want to concatenate values from different columns in a new format into a single column and so on.
Ok, just realized somehow the site has vanished. Not working archived version: http://web.archive.org/web/20161028231256/https://www.transf...
For example, "sort all of the folders, so that it Alan goes before Amy, etc". The rule ("sort") is pretty ambiguous, but one simple example in the context gives enough information to realise you probably mean alphabetically by first name.
Is there something like this example that could be combined with NLP to make things like these "intelligent assistants" we have now much more useful for data processing tasks?
It would be great to describe data manipulation to a machine the way that I would describe it to a colleague: give an overview of an algorithm, watch how they interpret it, and correct with a couple of examples in a feedback loop. Currently describing such things for a machine requires writing the algorithm manually in a programming language.
Being able to solve quickly the most common cases (which rely in such "common knowledge") would automate a lot of work that now requires writing a complex program in advance, and would allow the user to concentrate on the outliers that require more thought.
IIRC this is tested heavily in IQ tests.
What I mean is if every row had a date like "12 May 2002" and you wanted it turned into 2002.05.12 then it would be nice if it indicated when it added data. For example if one of the rows just read "15 May" then, since there is no year, it would not be completely absurd if it transformed into 2017.05.15 - or if all of the other data is 2002, then adding that. But I really think silently adding data that was not in the input is going too far. A transform shouldn't ever silently inject plausible data with no indication that this is interpolated. Bad things can result.
Otherwise great demo!
https://www.microsoft.com/en-us/research/publication/transfo...
Though it probably also uses more recent work from the same group:
"Zhongjun Jin, Michael R. Anderson, Michael J. Cafarella, H. V. Jagadish: Foofah: Transforming Data By Example. SIGMOD Conference 2017: 683-698"
This would be great for refactoring code.