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Machine learning with weighted data

Posted 1 year ago
2 Replies
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It does not appear easy within the current Classify, Predict or NetTrain functions to accommodate weighted data. It I am wrong about this, can anyone tell me how to do so. If anyone has a work around, could you please make a suggestion.

Motivation: I have several projects that would benefit from weighted data, the latest being to explain this really interesting paper using Mathematica: . The idea would basically be to emulate one machine learning method -- the complex one such as a neural net -- with a less opaque one (such as a decision tree) but with the emulator only being responsible for producing similar answers within a neighborhood of certain points. This way one could use something close to human language to explain what a more complex and more opaque classifier was doing.

I'm attaching a draft notebook that shows some preliminary work in this field and indicates where having a weighted classifier would be an improvement.

SP - Lime: Emulating An Opaque Classifier With A Transparent One

Create a very complex function

Create a function that converts a vector into two numbers with a complex function.

nc = NetInitialize@NetChain[{50, ElementwiseLayer[Sin[Cos[#]] &], 2}, "Input" -> 20]

enter image description here

And convert two numbers into a Boolean classification likewise based on a complex function.

border[{x_, y_}] := Sin[3 (x + Cos[2 y - x])] > 0
RegionPlot[border[{x, y}], {x, -5, 5}, {y, -5, 5}]

enter image description here

Compose the two functions

f[x_] := border[nc[x]]

Create some data

Create 10000 random instances

rv = RandomVariate[NormalDistribution[], {10000, 20}];

And now use the composite function to find out the truth.

data = rv -> Map[If[border[nc[#]], "evil", "good"] &, rv]

enter image description here

training = data[[1, 1 ;; 7500]] -> data[[2, 1 ;; 7500]]

enter image description here

test = data[[1, 7501 ;; 10000]] -> data[[2, 7501 ;; 10000]]

enter image description here

Learn the True Relationship from the Data

Try a simple classifier

simple = Classify[training, Method -> "DecisionTree", PerformanceGoal -> "Speed", ValidationSet -> test]

enter image description here

simplecmo = ClassifierMeasurements[simple, test]

enter image description here



Create an Opaque But Better Classifier

Create a complicated neural network.

net = NetChain[{20, Ramp, 6, 2, SoftmaxLayer[]}, "Input" -> 20, "Output" -> NetDecoder[{"Class", {"evil", "good"}}]]

enter image description here

Train the network

netTrained = NetTrain[net, training, ValidationSet -> Scaled[0.25]]

enter image description here

See how well we did

clmo = ClassifierMeasurements[netTrained, test]

enter image description here





Now create an emulator of the classifier

The idea of our emulator is not to predict reality but to explain what the complex classifier is doing!

Create new training data

training2 = First@training -> netTrained[First@training]

enter image description here

Create a nearest function so that we can only look at points in the neighborhood of a particular point. What I would prefer to do, but can not is to weight the data so that points closer to our particular point were weighted more heavily.

nf = Nearest[First@training -> Automatic]

enter image description here

Pick an arbitrary person (person 343) to whom we want to explain how our complex machine learning algorithm performed.

Short[neighborhoodData =  With[{nearest = nf[test[[1, 343]], 50]}, Part[training[[1]], nearest] -> Part[training[[2]], nearest]], 10]

enter image description here

Create a simple decision tree to emulate the complex classifier within the neighborhood of person 343.

clEmulator = Classify[neighborhoodData, Method -> "DecisionTree", PerformanceGoal -> "Speed"]

enter image description here

See how we did

clmo = ClassifierMeasurements[clEmulator, neighborhoodData]

enter image description here


enter image description here

Our emulator explains 47 of the 50 decisions well.

2 Replies

Dear Seth,

Here is a way to handle weighted data with NetTrain in the current framework :

Define a weighted loss:

weightedCrossEntropy = NetGraph[
  <|"time" -> ThreadingLayer[Times], 
   "loss" -> CrossEntropyLossLayer["Index"]|>,
  {{NetPort["Weight"], "loss"} -> "time"}]

enter image description here

Make a graph with your network and this weighted loss:

netWithLoss = NetGraph[
  {net, weightedCrossEntropy},
  {1 -> NetPort[2, "Input"], 2 -> NetPort["Loss"]},
  "Target" -> NetEncoder[{"Class", {"evil", "good"}}]]

enter image description here

Suppose you have some weights:

trainingWeights = RandomReal[{-1, 1}, Length@First@training];

Train with these weights:

netTrainedWithLoss = 
 NetTrain[netWithLoss, <|"Weight" -> trainingWeights, 
   "Input" -> First@training, "Target" -> Last@training|>, 
  ValidationSet -> Scaled[0.25]]

enter image description here

Extract the trained net from the graph (Caution: NetEncoder & NetDecoder have to be "re-attached"):

netTrained = 
 NetReplacePart[NetExtract[netTrainedWithLoss, 1], 
  "Output" -> NetDecoder[{"Class", {"evil", "good"}}]]

enter image description here

Here you go!

There will be a more straightforward way to do this in the future.

It should also be supported by Classify and Predict at some point. The way to efficiently handle weights is actually specific to each classification method. And as you see here, there is a natural way of doing it with neural networks and any gradient-based method. Note that over-sampling data with higher weights (like in SMOTE) is an option that would work with most of the methods, but it can be awkward (rough approximation of the weights, higher computational and memory usage).

Thanks for an extremely helpful and lucid response.

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