This week I spoke at DataJawn, an super fun evening of talks and mingling with Philly’s data nerds.
You can have a look through the slides here.
Lately I’ve been thinking a lot about the connection between prediction models and the decisions that they influence. There is a lot of theory around this, but communicating how the various pieces all fit together with the folks who will use and be impacted by these decisions can be challenging.
One of the important conceptual pieces is the link between the decision threshold (how high does the score need to be to predict positive) and the resulting distribution of outcomes (true positives, false positives, true negatives and false negatives). As a starting point, I’ve built this interactive tool for exploring this.
The idea is to take a validation sample of predictions from a model and experiment with the consequences of varying the decision threshold. The hope is that the user will be able to develop an intuition around the tradeoffs involved by seeing the link to the individual data points involved.
Code for this experiment is available here. I hope to continue to build on this with other interactive, visual tools aimed at demystifying the concepts at the interface between predictions and decisions.
If you build and/or use classifiers in your life, feel free to print this out and keep it above you desk.
Yesterday, I wrote about Generative Adversarial Networks being all the rage at NIPS this year. I created a toy model using Tensorflow to wrap my head around how the idea works. Building on that example, I created a video to visualize the adversarial training process.
The top left panel shows samples from both the training and generated (eg counterfeit) data. Remember that the goal is to have the generator produce samples that the discriminator can not distinguish from the real (training) data. Top right shows the predicted energy function from the discriminator. The bottom row shows the loss function for the discriminator (D) and generator (G).
I don’t fully understand why the dynamics of the adversarial training process are transiently unstable, but it seems to work overall. Another interesting observation is that the loss seems to continue to fall overall, even as it goes though the transient phases of instability when the fit of the generated data is qualitatively poor.
While they hit the scene two years ago, Generative Adversarial Networks (GANs) have become the darlings of this year’s NIPS conference. The term “Generative Adversarial” appears 170 times in the conference program. So far I’ve seen talks demonstrating their utility in everything from generating realistic images, predicting and filling in missing video segments, rooms, maps, and objects of various sorts. They are even being applied to the world of high energy particle physics, pushing the state of the art of inference within the language of quantum field theory.
The basic idea is to build two models and to pit them against each other (hence the adversarial part). The generative model takes random inputs and tries to generate output data that “look like” real data. The discriminative model takes as input data from both the generative model and real data and tries to correctly distinguish between them. By updating each model in turn iteratively, we hope to reach an equilibrium where neither the discriminator nor the generator can improve. At this point the generator is doing it’s best to fool the discriminator, and the discriminator is doing it’s best not to be fooled. The result (if everything goes well) is a generative model which, given some random inputs, will output data which appears to be a plausible sample from your dataset (eg cat faces).
As with any concept that I’m trying to wrap my head around, I took a moment to create a toy example of a GAN to try to get a feel for what is going on.
Let’s start with a simple distribution from which to draw our “real” data from.
Next, we’ll create our generator and discriminator networks using tensorflow. Each will be a three layer, fully connected network with relu’s in the hidden layers. The loss function for the generative model is -1(loss function of discriminative). This is the adversarial part. The generator does better as the discriminator does worse. I’ve put the code for building this toy example here.
Next, we’ll fit each model in turn. Note in the code that we gave each optimizer a list of variables to update via gradient descent. This is because we don’t want to update the weights of the discriminator while we’re updating the weights of the generator, and visa versa.
loss at step 0: discriminative: 11.650652, generative: -9.347455
loss at step 200: discriminative: 8.815780, generative: -9.117246
loss at step 400: discriminative: 8.826855, generative: -9.462300
loss at step 600: discriminative: 8.893397, generative: -9.835464
loss at step 3600: discriminative: 6.724183, generative: -13.005814