In my last post I discussed using coefplot on glmnet models and in particular discussed a brand new function, coefpath, that uses dygraphs to make an interactive visualization of the coefficient path.

Another new capability for version 1.2.5 of coefplot is the ability to show coefficient plots from xgboost models. Beyond fitting boosted trees and boosted forests, xgboost can also fit a boosted Elastic Net. This makes it a nice alternative to glmnet even though it might not have some of the same user niceties.

To illustrate, we use the same data as our previous post.

First, we load the packages we need and note the version numbers.

# list the packages that we load
# alphabetically for reproducibility
packages <- c('caret', 'coefplot', 'DT', 'xgboost')
# call library on each package
purrr::walk(packages, library, character.only=TRUE)

# some packages we will reference without actually loading
# they are listed here for complete documentation
packagesColon <- c('dplyr', 'dygraphs', 'knitr', 'magrittr', 'purrr', 'tibble', 'useful')
versions <- c(packages, packagesColon) %>% 
    purrr::map(packageVersion) %>% 
    purrr::map_chr(as.character)
packageDF <- tibble::data_frame(Package=c(packages, packagesColon), Version=versions) %>% 
    dplyr::arrange(Package)
knitr::kable(packageDF)
Package Version
caret 6.0.78
coefplot 1.2.6
dplyr 0.7.4
DT 0.2
dygraphs 1.1.1.4
knitr 1.18
magrittr 1.5
purrr 0.2.4
tibble 1.4.2
useful 1.2.3
xgboost 0.6.4

Then, we read the data. The data are available at http://www.jaredlander.com/data/manhattan_Train.rds with the CSV version at data.world. We also get validation data which is helpful when fitting xgboost mdoels.

manTrain <- readRDS(url('http://www.jaredlander.com/data/manhattan_Train.rds'))
manVal <- readRDS(url('http://www.jaredlander.com/data/manhattan_Validate.rds'))

The data are about New York City land value and have many columns. A sample of the data follows. There’s an odd bug where you have to click on one of the column names for the data to display the actual data.

datatable(manTrain %>% dplyr::sample_n(size=1000), elementId='TrainingSampled',
              rownames=FALSE,
              extensions=c('FixedHeader', 'Scroller'),
              options=list(
                  scroller=TRUE
              ))

While glmnet automatically standardizes the input data, xgboost does not, so we calculate that manually. We use preprocess from caret to compute the mean and standard deviation of each numeric column then use these later.

preProc <- preProcess(manTrain, method=c('center', 'scale'))

Just like with glmnet, we need to convert our tbl into an X (predictor) matrix and a Y (response) vector. Since we don’t have to worry about multicolinearity with xgboost we do not want to drop the baselines of factors. We also take advantage of sparse matrices since that reduces memory usage and compute, even though this dataset is not that large.

In order to build the matrix and vector we need a formula. This could be built programmatically, but we can just build it ourselves. The response is TotalValue.

valueFormula <- TotalValue ~ FireService + ZoneDist1 + ZoneDist2 +
    Class + LandUse + OwnerType + LotArea + BldgArea + ComArea + ResArea +
    OfficeArea + RetailArea + NumBldgs + NumFloors + UnitsRes + UnitsTotal + 
    LotDepth + LotFront + BldgFront + LotType + HistoricDistrict + Built + 
    Landmark
manX <- useful::build.x(valueFormula, data=predict(preProc, manTrain),
                        # do not drop the baselines of factors
                        contrasts=FALSE,
                        # use a sparse matrix
                        sparse=TRUE)

manY <- useful::build.y(valueFormula, data=manTrain)

manX_val <- useful::build.x(valueFormula, data=predict(preProc, manVal),
                        # do not drop the baselines of factors
                        contrasts=FALSE,
                        # use a sparse matrix
                        sparse=TRUE)

manY_val <- useful::build.y(valueFormula, data=manVal)

There are two functions we can use to fit xgboost models, the eponymous xgboost and xgb.train. When using xgb.train we first store our X and Y matrices in a special xgb.DMatrix object. This is not a necessary step, but makes things a bit cleaner.

manXG <- xgb.DMatrix(data=manX, label=manY)
manXG_val <- xgb.DMatrix(data=manX_val, label=manY_val)

We are now ready to fit a model. All we need to do to fit a linear model instead of a tree is set booster='gblinear' and objective='reg:linear'.

mod1 <- xgb.train(
    # the X and Y training data
    data=manXG,
    # use a linear model
    booster='gblinear',
    # minimize the a regression criterion 
    objective='reg:linear',
    # use MAE as a measure of quality
    eval_metric=c('mae'),
    # boost for up to 500 rounds
    nrounds=500,
    # print out the eval_metric for both the train and validation data
    watchlist=list(train=manXG, validate=manXG_val),
    # print eval_metric every 10 rounds
    print_every_n=10,
    # if the validate eval_metric hasn't improved by this many rounds, stop early
    early_stopping_rounds=25,
    # penalty terms for the L2 portion of the Elastic Net
    lambda=10, lambda_bias=10,
    # penalty term for the L1 portion of the Elastic Net
    alpha=900000000,
    # randomly sample rows
    subsample=0.8,
    # randomly sample columns
    col_subsample=0.7,
    # set the learning rate for gradient descent
    eta=0.1
)
## [1]  train-mae:1190145.875000    validate-mae:1433464.750000 
## Multiple eval metrics are present. Will use validate_mae for early stopping.
## Will train until validate_mae hasn't improved in 25 rounds.
## 
## [11] train-mae:938069.937500 validate-mae:1257632.000000 
## [21] train-mae:932016.625000 validate-mae:1113554.625000 
## [31] train-mae:931483.500000 validate-mae:1062618.250000 
## [41] train-mae:931146.750000 validate-mae:1054833.625000 
## [51] train-mae:930707.312500 validate-mae:1062881.375000 
## [61] train-mae:930137.375000 validate-mae:1077038.875000 
## Stopping. Best iteration:
## [41] train-mae:931146.750000 validate-mae:1054833.625000

The best fit was arrived at after 41 rounds. We can see how the model did on the train and validate sets using dygraphs.

dygraphs::dygraph(mod1$evaluation_log)

We can now plot the coefficients using coefplot. Since xgboost does not save column names, we specify it with feature_names=colnames(manX). Unlike with glmnet models, there is only one penalty so we do not need to specify a specific penalty to plot.

coefplot(mod1, feature_names=colnames(manX), sort='magnitude')

This is another nice addition to coefplot utilizing the power of xgboost.

Related Posts



Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

I’m a big fan of the Elastic Net for variable selection and shrinkage and have given numerous talks about it and its implementation, glmnet. In fact, I will even have a DataCamp course about glmnet coming out soon.

As a side note, I used to pronounce it g-l-m-net but after having lunch with one of its creators, Trevor Hastie, I learn it is pronounced glimnet.

coefplot has long supported glmnet via a standard coefficient plot but I recently added some functionality, so let’s take a look. As we go through this, please pardon the htmlwidgets in iframes.

First, we load packages. I am now fond of using the following syntax for loading the packages we will be using.

# list the packages that we load
# alphabetically for reproducibility
packages <- c('coefplot', 'DT', 'glmnet')
# call library on each package
purrr::walk(packages, library, character.only=TRUE)

# some packages we will reference without actually loading
# they are listed here for complete documentation
packagesColon <- c('dplyr', 'knitr', 'magrittr', 'purrr', 'tibble', 'useful')

The versions can then be displayed in a table.

versions <- c(packages, packagesColon) %>% 
    purrr::map(packageVersion) %>% 
    purrr::map_chr(as.character)
packageDF <- tibble::data_frame(Package=c(packages, packagesColon), Version=versions) %>% 
    dplyr::arrange(Package)
knitr::kable(packageDF)
Package Version
coefplot 1.2.5.1
dplyr 0.7.4
DT 0.2
glmnet 2.0.13
knitr 1.18
magrittr 1.5
purrr 0.2.4
tibble 1.4.1
useful 1.2.3

First, we read some data. The data are available at http://www.jaredlander.com/data/manhattan_Train.rds with the CSV version at data.world.

manTrain <- readRDS(url('http://www.jaredlander.com/data/manhattan_Train.rds'))

The data are about New York City land value and have many columns. A sample of the data follows.

datatable(manTrain %>% dplyr::sample_n(size=100), elementId='DataSampled',
              rownames=FALSE,
              extensions=c('FixedHeader', 'Scroller'),
              options=list(
                  scroller=TRUE,
                  scrollY=300
              ))

In order to use glmnet we need to convert our tbl into an X (predictor) matrix and a Y (response) vector. Since we don’t have to worry about multicolinearity with glmnet we do not want to drop the baselines of factors. We also take advantage of sparse matrices since that reduces memory usage and compute, even though this dataset is not that large.

In order to build the matrix ad vector we need a formula. This could be built programmatically, but we can just build it ourselves. The response is TotalValue.

valueFormula <- TotalValue ~ FireService + ZoneDist1 + ZoneDist2 +
    Class + LandUse + OwnerType + LotArea + BldgArea + ComArea + ResArea +
    OfficeArea + RetailArea + NumBldgs + NumFloors + UnitsRes + UnitsTotal + 
    LotDepth + LotFront + BldgFront + LotType + HistoricDistrict + Built + 
    Landmark - 1

Notice the - 1 means do not include an intercept since glmnet will do that for us.

manX <- useful::build.x(valueFormula, data=manTrain,
                        # do not drop the baselines of factors
                        contrasts=FALSE,
                        # use a sparse matrix
                        sparse=TRUE)

manY <- useful::build.y(valueFormula, data=manTrain)

We are now ready to fit a model.

mod1 <- glmnet(x=manX, y=manY, family='gaussian')

We can view a coefficient plot for a given value of lambda like this.

coefplot(mod1, lambda=330500, sort='magnitude')

A common plot that is built into the glmnet package it the coefficient path.

plot(mod1, xvar='lambda', label=TRUE)

This plot shows the path the coefficients take as lambda increases. They greater lambda is, the more the coefficients get shrunk toward zero. The problem is, it is hard to disambiguate the lines and the labels are not informative.

Fortunately, coefplot has a new function in Version 1.2.5 called coefpath for making this into an interactive plot using dygraphs.

coefpath(mod1)

While still busy this function provides so much more functionality. We can hover over lines, zoom in then pan around.

These functions also work with any value for alpha and for cross-validated models fit with cv.glmnet.

mod2 <- cv.glmnet(x=manX, y=manY, family='gaussian', alpha=0.7, nfolds=5)

We plot coefficient plots for both optimal lambdas.

# coefplot for the 1se error lambda
coefplot(mod2, lambda='lambda.1se', sort='magnitude')

# coefplot for the min error lambda
coefplot(mod2, lambda='lambda.min', sort='magnitude')

The coefficient path is the same as before though the optimal lambdas are noted as dashed vertical lines.

coefpath(mod2)

While coefplot has long been able to plot coefficients from glmnet models, the new coefpath function goes a long way in helping visualize the paths the coefficients take as lambda changes.

Related Posts



Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Data Mafia Shirt

Related Posts



Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.

Today, Google announced two new services that are sure to be loved by data geeks.  First is their BigQuery which lets you analyze “Terabytes of data, trillions of records.”  This is great for people with large datasets.  I wonder if a program like R(my favorite statistical analysis package) can read it?  If so would R just pull down the data like it would from any other database?  That would most likely result in a data.frame that is far too large for a standard computer to handle.  Maybe R can be ran in a way that it hits the BigQuery service and leaves the data in there.  Maybe even the processing can be done on Google’s end, allowing for much better computation time.  This is something I’ve been dreaming of for a while now.

Further, can BigQuery produce graphics?  If so, this might be a real shot at Business Intelligence tools like QlikView or Cognosthat specialize in handling LARGE datasets. Continue reading

Related Posts



Jared Lander is the Chief Data Scientist of Lander Analytics a New York data science firm, Adjunct Professor at Columbia University, Organizer of the New York Open Statistical Programming meetup and the New York and Washington DC R Conferences and author of R for Everyone.