I believe this is the implementation described in this paper published at POPL 2016:
https://www.microsoft.com/en-us/research/publication/transfo...
Though it probably also uses more recent work from the same group:
Transform Data by Example [video]
51–60 of 94 posts
Re: Transform Data by Example [video]
#52I wonder if it uses Z3 under the hood for solving constraints. Very nice of MSFT to MIT license Z3. It's super useful for problems that result in circular dependencies when modeled in Excel, and require iterative solvers (e.g., goal seek). I use the python bindings, but unfortunately it's not as simple as `pip install` and requires a lengthy build/compilation. Well worth the effort, though. https://github.com/Z3Prove…
Love Z3. It is easy to use and very decent performance! I don't think MS is using Z3 on this product though, looks more like the smart enumeration based program synthesis
I think most of the enumeration-based synthesis tools rely on a SMT solver (Z3 or CVC4, say) to learn from bad solutions.
Re: Transform Data by Example [video]
#53Earlier quoted context omitted.
Neural nets are infamous for not doing this. Learning algorithms that produce decision trees are usually used in this situation.
Which is why learning algorithms that produce decision trees are far smarter in the long run. Neural nets might eke out other benefits but there's a lot to be said about justifiable/accountable decisions.
Correct me if I'm wrong, but the only type of decision tree that is comparable to a NN in terms of performance is an ensemble of decision trees, and these are equally hard to interpret as NNs.
Re: Transform Data by Example [video]
#54You know what this reminds me of? Those trained neural-net things which, however many training examples you give it, always seem to find some way to “cheat” and not do what you want while still obeying all your training data correctly. 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 to 123456 234567 345678 Now, w…
You don't realise how big this is. This is the beginning of the automation of coding.
Re: Transform Data by Example [video]
#55That's great, I always loved Auto Fill in Excel, and this brings it to the Mac.
* Correction. I meant Flash Fill. And this is isn't an exact replacement, but it's pretty close.
Re: Transform Data by Example [video]
#56This was also included in the query editor of Microsoft's Power BI in the release a month or two ago. First you select the columns to be used as a source then start writing example values to the new column to be generated. It also shows the generated M/PowerQuery expression.
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.
Re: Transform Data by Example [video]
#57Earlier quoted context omitted.
Which is why learning algorithms that produce decision trees are far smarter in the long run. Neural nets might eke out other benefits but there's a lot to be said about justifiable/accountable decisions.
Except the big (if not the main) part of modern economics is all about "don't care about long run - that's the only smart strategy".
Except that's not so much a scientific theory but a justification for "quick bucks, everything else be damned".
Re: Transform Data by Example [video]
#58I hacked together something similar that learns row/column offsets for different fields in a text file, and converts it into a normal CSV, i.e. a normal table.
Re: Transform Data by Example [video]
#59You know what this reminds me of? Those trained neural-net things which, however many training examples you give it, always seem to find some way to “cheat” and not do what you want while still obeying all your training data correctly. 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 to 123456 234567 345678 Now, w…
You don't realise how big this is. This is the beginning of the automation of coding.
We had such things for decades.
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.
Re: Transform Data by Example [video]
#60Earlier quoted context omitted.
This might be a dumb question, but let's say that for whatever reason on a specific problem it's much easier to train a neural network that generalizes well than a decision tree. Why not train the network, then build an equivalent decision tree that just tries to reproduce the network's output? When building the tree from the network, overfitting would not be a concern. In fact, you'd want it to overfit. You could ev…
> This might be a dumb question, but let's say that for whatever reason on a specific problem it's much easier to train a neural network that generalizes well than a decision tree. Why not train the network, then build an equivalent decision tree that just tries to reproduce the network's output? When building the tree from the network, overfitting would not be a concern. In fact, you'd want it to overfit. You haven'…
This solves the problem of interpretability. You can't interpret the weights of a neural network, but you can easily follow along a decision tree and see if it's doing what you want.
Actually that's somewhat less true for big decision trees. But the general point is that you can train interpretable models to mimic the output of uninterpretable black boxes.
The biggest issue is that decision trees only work for data with fixed inputs and outputs. Recurrent NNs work on a time series and possibly even have attention mechanisms.