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Suppose you are modeling a process that you believe is well approximated as being linear in its inputs, but only within a certain range. Outside that range, the output might saturate or threshold: for example if you are modeling a count or a physical process, you likely can never get a negative outcome. Similarly, a process can saturate to a upper bound value outside a given range of the input data.
However, you may still want to model the process as linear under the assumption that you don’t expect the process to hit the saturation point too often. But what if it does? For simplicity we’ll look specifically at the case where you expect the process to return non-negative values, and you hope it doesn’t saturate to zero very often.
When you don’t expect to see too many zeros in practice, modeling the process as linear and thresholding negative predictions at zero is not unreasonable. But the more zeros (saturations) you expect to see, the less well a linear model will perform.
Dealing with saturated processes
Let’s show this, in R. First, we’ll load the necessary libraries and set a seed for the random number generator, for reproducibility.
library(ggplot2) # for plotting library(poorman) # for data wrangling library(mgcv) # for GAM set.seed(20240812)
Now we’ll define our true generative process. For didactic purposes, I’ll use a one-dimensional process, so that we can plot the various models in a way that illustrates what’s going on. However, the principles that we discuss here apply generally to adjusting a linear model over any process, including multivariate ones.
For this example, the generative process is simply a variable distributed as a gaussian with variance 1 and mean mu, thresholded at zero. The larger mu is, the fewer saturation events we’ll see.
We’ll set mu = 0, so we have a large number of saturation events. This dramatizes the issue that we are trying to address.
trueprocess = function(mu, N=1000) {
x = rnorm(N, mu)
xnoise = x + 0.1 * rnorm(N)
y = pmax(0.0, xnoise)
df = data.frame(x=x, y=y)
df
}
traind = trueprocess(mu=0, N=1000)
head(traind)
## x y ## 1 -0.9805332 0.00000000 ## 2 -0.5096520 0.00000000 ## 3 0.1802436 0.09393321 ## 4 0.3206537 0.40332915 ## 5 -1.4092943 0.00000000 ## 6 1.8399697 1.76690214
Initial Model
First, let’s just blindly fit a linear model to this data, and see what we get.
traind = trueprocess(mu=0, N=1000)
initial_model = lm(y ~ x, data=traind)
traind$ypred0 = predict(initial_model, newdata=traind)
# we'll be calculating RMSE a lot, so...
rmse = function(y, ypred) {
err = y - ypred
sqrt(mean(err^2))
}
# we'll also calculate bias: the mean value of (y - ypred)
bias = function(y, ypred) {
err = ypred - y # bias is estimate - actual, opposite of residual which is actual - estimate
b = mean(err)
# let's round down to zero for small numbers
b = ifelse(abs(b) < 1e-12, 0, b)
b
}
loss = with(traind, rmse(y, ypred0))
loss_str = format(loss, digits=3)
err = with(traind, bias(y, ypred0))
err_str = format(err, digits=3)
subtitle = paste("training loss =", loss_str, "; bias =", err_str)
theme_set(theme_bw()) # set global ggplot theme
ggplot(traind, aes(x=x)) +
geom_point(aes(y=y), color="gray") +
geom_line(aes(y=ypred0), color="darkblue") +
ggtitle("Initial model", subtitle=subtitle)
Yuck (but we knew this would happen)! There are two issues with the initial model:
- It does not respect the non-negativity constraint.
- The slope of the model in the positive region is incorrect; it’s too low.
Let’s try to adjust the initial model to fix these two issues. The first, naive, attempt, is just to threshold the predictions at zero.
predict_lm_0thresh = function(initial_model, newdata) {
pmax(predict(initial_model, newdata=newdata), 0)
}
traind$ypred_0thresh = predict_lm_0thresh(initial_model, newdata=traind)
loss = with(traind, rmse(y, ypred_0thresh))
loss_str = format(loss, digits=3)
err = with(traind, bias(y, ypred_0thresh))
err_str = format(err, digits=3)
subtitle = paste("training loss =", loss_str, "; bias =", err_str)
ggplot(traind, aes(x=x)) +
geom_point(aes(y=y), color="gray") +
geom_line(aes(y=ypred_0thresh), color="darkblue") +
ggtitle("Zero-thresholded linear model", subtitle=subtitle)
This adjustment reduces the training loss and respects the non-negativity constraint, but still doesn’t fix the slope issue. What else can we do?
We could try an explicitly non-negative modeling approach, like Poisson regression, or a data transform such as the Freeman-Tukey transform. We feel our problem looks more like zero-inflation, which seems amenable to a post-hoc adjustment, in the style of Platt scaling. Platt scaling a classification model calibrates it: that is, it transforms the outputs of the classifier into a probabability distribution over the inputs that is consistent with the training data distribution. In this case, we want to apply a post-hoc adjustment model to a regression, in order to transform the original model into one that is consistent with the saturated nature of the training data.
Let’s try it.
Attempt 1: Rescale the non-negative predictions
Maybe we can rescale the positive predictions from the original model, to better match the output? One way to do that is to fit a zero-intercept linear model to fit the original predictions to the outcome. This will give us a scaling factor to apply to positive predictions.
To fit the model, we’ll implement a procedure that takes the initial linear model along with the training data, and returns a pair of models : the initial model, and the adjustment model.
# outcome here is the name of the outcome column in the data frame
fit_scaler = function(initial_model, outcome, data) {
ypred0 = predict(initial_model, newdata=data)
df = data.frame(ypred0 = ypred0, y=data[[outcome]])
# reduce the data to non-negative predictions
df = subset(df, ypred0 > 0)
# now fit the new model to the non-negative predictions
smodel = lm(y ~ 0 + ypred0, data=df)
# return a list: initial model, adjustment model
list(initial_model=initial_model, adjustment=smodel)
}
We’ll also implement a prediction method that first applies the initial model to the data, then uses the adjustment model to rescale everywhere that the initial model returns a negative prediction.
# "model" is a list (initial_model, adjustment)
do_predict = function(model, newdata) {
mod0 = model$initial_model
adjmod = model$adjustment
df = data.frame(ypred0 = predict(mod0, newdata=newdata))
# if the adjustment model makes negative predictions, threshold to 0
yadj = pmax(0, predict(adjmod, newdata=df))
# if linear model predicts a negative number,
# predict 0, else use adjusted model
ypred = ifelse(df$ypred0 <= 0, 0, yadj)
ypred
}
Now let’s fit the adjustment and look at what happens.
# now fit the adjustment model scaling_model = fit_scaler(initial_model, "y", traind) adjustment_coef = coef(scaling_model$adjustment)
In this example, the rescaling coefficient is 1.0722805, meaning the adjustment is a very small increase in slope. This slightly reduces the training loss.
traind$ypred_scaled = do_predict(scaling_model, newdata=traind)
loss = with(traind, rmse(y, ypred_scaled))
loss_str = format(loss, digits=3)
err = with(traind, bias(y, ypred_scaled))
err_str = format(err, digits=3)
subtitle = paste("training loss =", loss_str, "; bias =", err_str)
ggplot(traind, aes(x=x)) +
geom_point(aes(y=y), color="gray") +
geom_line(aes(y=ypred_scaled), color="darkblue") +
ggtitle("Scaled linear model", subtitle=subtitle)
Looking at the result, we realize we aren’t “rotating” the positive part of the hockey stick enough.
Attempt 2: Rotate the predictions via a linear model
Now let’s try a linear adjustment model with an intercept. This essentially shifts and rotates the non-negative range of the initial model.
fit_linmod = function(initial_model, outcome, data) {
ypred0 = predict(initial_model, newdata=data)
df = data.frame(ypred0 = ypred0, y=data[[outcome]])
# reduce the data to non-negative predictions
df = subset(df, ypred0 > 0)
# now fit the new model
lpmodel = lm(y ~ ypred0, data=df)
# return a list: initial model, adjustment model
list(initial_model=initial_model, adjustment=lpmodel)
}
# now fit the adjustment model
linadj_model = fit_linmod(initial_model, "y", traind)
adjustment_coef = coef(linadj_model$adjustment)
The adjustment coefficients are (-0.4050229, 1.5403333), resulting in a small shift and a more substantial scaling of the original model’s output in the non-negative region.
traind$ypred_linadj = do_predict(linadj_model, newdata=traind)
loss = with(traind, rmse(y, ypred_linadj))
loss_str = format(loss, digits=3)
err = with(traind, bias(y, ypred_linadj))
err_str = format(err, digits=3)
subtitle = paste("training loss =", loss_str, "; bias =", err_str)
ggplot(traind, aes(x=x)) +
geom_point(aes(y=y), color="gray") +
geom_line(aes(y=ypred_linadj), color="darkblue") +
ggtitle("Linear-adjusted linear model", subtitle=subtitle)
Much better! We’ve halved the training loss, and the slope is a better fit to the data. We’ve also reduced the magnitude of the bias (the tendency of the model to over- or under- predict, on average) relative to the other adjusted models. Note that linear models are designed to be zero-bias on the training data, and our initial linear model doesn’t predict so well, so zero-bias isn’t everything; but a low bias model is still considered a good thing.
Looking at the graph, the main issue remaining is that the adjusted model doesn’t correctly estimate the location of the “elbow” of the graph. This leads to a slight underestimation of the data slope.
Attempt 3: Adjust predictions via a generalized additive model (GAM)
GAM uses a spline (in general, one spline per input) to approximate the mapping from inputs to output. This allows us to fit a non-linear adjustment to the original model.
We’ll again fit the adjustment only to the region where the original model predicted a positive value.
# returned model is a list (initial_model, adjustment)
fit_gammod = function(initial_model, outcome, data) {
ypred0 = predict(initial_model, newdata=data)
df = data.frame(ypred0 = ypred0, y=data[[outcome]])
# reduce the data to non-negative predictions
df = subset(df, ypred0 > 0)
# now fit the new model
gammodel = gam(y ~ s(ypred0), data=df)
# return a list: initial model, adjustment model
list(initial_model=initial_model, adjustment=gammodel)
}
# now fit the adjustment model
gamadj_model = fit_gammod(initial_model, "y", traind)
traind$ypred_gamadj = do_predict(gamadj_model, newdata=traind)
loss = with(traind, rmse(y, ypred_gamadj))
loss_str = format(loss, digits=3)
err = with(traind, bias(y, ypred_gamadj))
err_str = format(err, digits=3)
subtitle = paste("training loss =", loss_str, "; bias =", err_str)
ggplot(traind, aes(x=x)) +
geom_point(aes(y=y), color="gray") +
geom_line(aes(y=ypred_gamadj), color="darkblue") +
ggtitle("GAM-adjusted linear model", subtitle=subtitle)
This is the best fit to the training data of all the models we’ve observed. We’ve again nearly halved the training RMSE, and substantially reduced the training bias.
A GAM can’t truly replicate the ideal process, since a hockey stick is a non smooth function (the derivative doesn’t exist at the “elbow”). However, the algorithm has managed to fit a very good smooth approximation of the ideal shape. The function gives a good estimate of the data slope in the linear region, and identified the region of the inflection point fairly accurately.
Holdout evaluation
Let’s compare all the models we’ve discussed on holdout data.
testd = trueprocess(mu=0, N=5000)
# try all the models we've done so far
testd$y_initial = predict(initial_model, newdata=testd)
testd$y_pred0 = pmax(0, predict(initial_model, newdata=testd))
testd$y_linscale = do_predict(scaling_model, newdata=testd)
testd$y_linadj = do_predict(linadj_model, newdata=testd)
testd$y_gamadj = do_predict(gamadj_model, newdata=testd)
# pivot data into a form better for plotting and summarizing
testdlong = pivot_longer(
testd,
c("y_initial", "y_pred0", "y_linscale", "y_linadj", "y_gamadj"),
names_to = "prediction_type",
names_prefix = "y_",
values_to = "prediction"
)
# get the error statistics
errframe = testdlong |>
group_by(prediction_type) |>
summarize(RMSE = rmse(y, prediction),
bias = bias(y, prediction)) |>
ungroup()
# descriptions
descv = c("initial model", "zero-thresholded model", "scale-adjusted model", "linear-adjusted model", "GAM-adjusted model")
names(descv) = c("initial", "pred0", "linscale", "linadj", "gamadj")
# a little rearrangement for good presentation
rownames(errframe) = errframe$prediction_type
errframe$description = descv[errframe$prediction_type]
errframe = errframe[names(descv), c("prediction_type", "description", "RMSE", "bias")]
knitr::kable(errframe, caption = "Model RMSE and bias on holdout data", row.names=FALSE)
| prediction_type | description | RMSE | bias |
|---|---|---|---|
| initial | initial model | 0.3043739 | 0.0046863 |
| pred0 | zero-thresholded model | 0.2518775 | 0.0642282 |
| linscale | scale-adjusted model | 0.2486353 | 0.0982720 |
| linadj | linear-adjusted model | 0.1219682 | 0.0335343 |
| gamadj | GAM-adjusted model | 0.0694896 | -0.0018866 |
Model RMSE and bias on holdout data
Observe that the GAM-adjusted model has the lowest RMSE error, and the lowest magnitude bias, comparable to the original linear model. Let’s plot them to take a look:
# colors from the Tol_bright colorblind-friendly palette
# https://thenode.biologists.com/data-visualization-with-flying-colors/research/
# https://personal.sron.nl/~pault/#sec:qualitative
palette = c(initial = "#BBBBBB", pred0 = "#AA3377", linscale = "#228833", linadj = "#4477AA", gamadj = "#EE6677")
ggplot(testdlong, aes(x=x)) +
geom_point(aes(y=y), color="gray", alpha = 0.1) +
geom_line(aes(y = prediction, color=prediction_type)) +
ggtitle("All models applied to holdout data") +
scale_color_manual(breaks = names(descv), values=palette)
Conclusion
Many real-world processes can be well-approximated as linear within a restricted range of inputs. However, when fitting linear models to these processes, it’s a good idea to adjust for potential saturation, or “out-of-linear-range” events. In this note, we’ve motivated using a spline to adjust an initial linear model, in order to accurately predict in a range that includes saturated regions. We’ve fit the spline using the generalized additive model, or GAM, in R. A similar solution can also be fit in python, as well.
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Continue reading: Post-hoc Adjustment for Zero-Thresholded Linear Models