Getting Started with Catlearn and Catlearn Supplementals
background
This post is intended to help people get started with their own cognitive modelling or machine learning projects in the catlearn environment with the help of catlearn.suppls, the Learning and Representation in Cognition Laboratory’s suite of helper functions for modeling in catlearn.
The motivation behind catlearn.suppls was to create a suite of functions that could be used to rapidly prototype new machine learning architectures that correspond to the cognitive modeling efforts of the LaRC Lab. This includes functions to set up the state information of a model and quickly take rectangular data and format it for the catlearn design pattern—the stateful list processor.
getting Started
If you haven’t already done so, download the catlearn and catlearn.suppls packages.
install.packages(c("devtools", "catlearn"))
devtools::install_github("ghonk/catlearn.suppls")choosing some data and setting up the model
We’re going to use the classic iris dataset and DIVA (the DIVergent Autoencoder) for this demonstration. We need two objects to run a model in catlearn, the model’s state and the training matrix, which we will call tr.
# # # load the libraries
library(catlearn)
library(catlearn.suppls)First, we will construct the model’s state. For this demonstration we’ll set the hyper-parameters to values that we know—a priori—will play nice with our dataset. For real-world problems, you will likely want optimize these values (see future post on grid search and Bayesian optimization options with catlearn.suppls). Detailed description of model hyper-parameters is available in the normal places (e.g., ?slpDIVA).
# # # setup the inputs, class labels and model state
# check out our data
str(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...# find the inputs minus the labels
ins <- iris[,colnames(iris) != 'Species']
# create a separate vector for the labels (labels must be numeric)
labs <- as.numeric(iris[,'Species'])
# get number of categories and features
nfeats <- dim(ins)[2]
ncats <- length(unique(labs))
# construct a state list
st <- list(learning_rate = 0.15, num_feats = nfeats, num_hids = 6, num_cats = ncats,
beta_val = 0, phi = 1, continuous = TRUE, in_wts = NULL, out_wts = NULL, wts_range = 1,
wts_center = 0, colskip = 4)We can then use catleanr.suppls to create our training matrix
# tr_init initializes an empty training matrix
tr <- tr_init(nfeats, ncats)
# tr_add fills in the data and procedure (i.e., training, test, model reset)
tr <- tr_add(inputs = ins, tr = tr, labels = labs, blocks = 12, ctrl = 0,
shuffle = TRUE, reset = TRUE)Here’s what our training matrix looks like after the setup procedure:
head(tr)## ctrl trial blk example x1 x2 x3 x4 t1 t2 t3
## [1,] 1 1 1 118 7.7 3.8 6.7 2.2 -1 -1 1
## [2,] 0 2 1 64 6.1 2.9 4.7 1.4 -1 1 -1
## [3,] 0 3 1 23 4.6 3.6 1.0 0.2 1 -1 -1
## [4,] 0 4 1 87 6.7 3.1 4.7 1.5 -1 1 -1
## [5,] 0 5 1 99 5.1 2.5 3.0 1.1 -1 1 -1
## [6,] 0 6 1 92 6.1 3.0 4.6 1.4 -1 1 -1Finally, we run the model with our state list st and training matrix tr
diva_model <- slpDIVA(st, tr)We can examine performance of the model easily.
# # # use response_probs to extract the response probabilities
# # # for the target categories (for every training step (trial)
# # # or averaged across blocks)
response_probs(tr, diva_model$out, blocks = TRUE)## [1] 0.7852448 0.8431547 0.8103888 0.8067568 0.8087171 0.8102305 0.8135465
## [8] 0.8028162 0.8466575 0.8375568 0.8025279 0.8350697plot_training is a simple function used to plot the learning of one or more models.
plot_training(list(response_probs(tr, diva_model$out, blocks = TRUE)))
So with no optimization, we can see that DIVA learns about as much as it is going to learn after one pass through our 150 item training set (obviously absent any cross-validation). Where does it go wrong? You might like to examine which items are not being correctly classified—you can do so by combining the classification probabilities with the original training matrix.
# # # if we want to look at individual classication decisions,
# # # we can do so by combining the model's output with the
# # # original training matrix
trn_result <- cbind(tr, round(diva_model$out, 4))
tail(trn_result)## ctrl trial blk example x1 x2 x3 x4 t1 t2 t3
## [1795,] 0 1795 12 123 7.7 2.8 6.7 2.0 -1 -1 1 0.0005 0.0006 0.9989
## [1796,] 0 1796 12 82 5.5 2.4 3.7 1.0 -1 1 -1 0.0000 0.9999 0.0000
## [1797,] 0 1797 12 125 6.7 3.3 5.7 2.1 -1 -1 1 0.1204 0.1765 0.7030
## [1798,] 0 1798 12 1 5.1 3.5 1.4 0.2 1 -1 -1 1.0000 0.0000 0.0000
## [1799,] 0 1799 12 144 6.8 3.2 5.9 2.3 -1 -1 1 0.0000 0.0000 1.0000
## [1800,] 0 1800 12 129 6.4 2.8 5.6 2.1 -1 -1 1 0.0001 0.0002 0.9996You might also like to see how a number of initializations do on the problem. It’s good practice to average over a series of initializations—something we did not do in this demonstration.
# # # Run 5 models with the same params on the same training set
model_inits <- lapply(1:5, function(x) slpDIVA(st, tr))
# # # determine the response probability for the correct class
model_resp_probs <-
lapply(model_inits, function(x) {
response_probs(tr, x$out, blocks = TRUE)})
# # # plot the leanrning curves
plot_training(model_resp_probs)
Here we see that there is a fair amount of variation across initializations. This suggests it would be smart to follow the typical procedure of averaging across a series of models to accurately represent the response probabilities. It also suggests that our approach would likely benefit from some optimization and validation.
Future demos will explore the tools within catlearn.suppls used to optimize hyper-parameters and examine the hidden unit representation space toward the goal of uncovering new insight about the problem.