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add api tests + small fixes
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schalkdaniel committed Jun 22, 2018
1 parent 7b58287 commit 0402dd7
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Showing 11 changed files with 261 additions and 388 deletions.
4 changes: 2 additions & 2 deletions DESCRIPTION
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Expand Up @@ -25,7 +25,8 @@ Imports:
methods,
glue,
R6,
checkmate
checkmate,
ggplot2
LinkingTo:
Rcpp,
RcppArmadillo
Expand All @@ -37,7 +38,6 @@ Suggests:
rmarkdown,
titanic,
mlr,
ggplot2,
gridExtra
RcppModules:
baselearner_module,
Expand Down
2 changes: 1 addition & 1 deletion NAMESPACE
View file
Expand Up @@ -24,6 +24,6 @@ export(PolynomialBlearnerFactory)
export(QuadraticLoss)
export(TimeLogger)
export(getCustomCppExample)
export(plotCompboostParameter)
import(Rcpp)
import(ggplot2)
useDynLib(compboost, .registration = TRUE)
249 changes: 133 additions & 116 deletions R/compboost.R
View file
Expand Up @@ -281,8 +281,8 @@ Compboost = R6::R6Class("Compboost",
}

# Initialize fields:
self$target = target
self$id = deparse(substitute(data))
self$target = target
self$id = deparse(substitute(data))
self$response = data[[target]]
self$data = data[, !colnames(data) %in% target, drop = FALSE]
self$optimizer = optimizer
Expand All @@ -298,22 +298,33 @@ Compboost = R6::R6Class("Compboost",
private$l.list[[logger.id]] = logger$new(use.as.stopper = use.as.stopper, ...)
},
getCurrentIteration = function() {
if (!is.null(self$model) && self$model$isTrained())
if (!is.null(self$model) && self$model$isTrained()) {
return(length(self$model$getSelectedBaselearner()))
else
} else {
return(0)
}
},
addBaselearner = function(feature, id, bl.factory, data.source = InMemoryData, data.target = InMemoryData, ...) {
if (!is.null(self$model))
if (!is.null(self$model)) {
stop("No base-learners can be added after training is started")
}

# Clear base-learners which are within the bl.list but not registered:
idx.remove = ! names(private$bl.list) %in% self$bl.factory.list$getRegisteredFactoryNames()
if (any(idx.remove)) {
for (i in which(idx.remove)) {
private$bl.list[[i]] = NULL
}
}

data.columns = self$data[, feature, drop = FALSE]
id.fac = paste(paste(feature, collapse = "_"), id, sep = "_") #USE stringi
data.columns = self$data[, feature, drop = FALSE]
id.fac = paste(paste(feature, collapse = "_"), id, sep = "_") #USE stringi

if (ncol(data.columns) == 1 && !is.numeric(data.columns[, 1]))
private$addSingleCatBl(data.columns, feature, id, id.fac, bl.factory, data.source, data.target, ...)
else
private$addSingleNumericBl(data.columns, feature, id, id.fac, bl.factory, data.source, data.target, ...)
if (ncol(data.columns) == 1 && !is.numeric(data.columns[, 1])) {
private$addSingleCatBl(data.columns, feature, id, id.fac, bl.factory, data.source, data.target, ...)
} else {
private$addSingleNumericBl(data.columns, feature, id, id.fac, bl.factory, data.source, data.target, ...)
}
},
train = function(iteration = 100, trace = TRUE) {

Expand Down Expand Up @@ -376,7 +387,7 @@ Compboost = R6::R6Class("Compboost",
new.sources = c(
new.sources,
InMemoryData$new(as.matrix(as.integer(data.columns == lvl)), paste(ns, lvl, sep = "_"))
)
)
}

} else {
Expand Down Expand Up @@ -404,12 +415,12 @@ Compboost = R6::R6Class("Compboost",
},
print = function() {
p = glue::glue("\n
Componentwise Gradient Boosting\n
Trained on {self$id} with target {self$target}
Number of base-learners: {self$bl.factory.list$getNumberOfRegisteredFactories()}
Learning rate: {self$learning.rate}
Iterations: {self$getCurrentIteration()}
")
Componentwise Gradient Boosting\n
Trained on {self$id} with target {self$target}
Number of base-learners: {self$bl.factory.list$getNumberOfRegisteredFactories()}
Learning rate: {self$learning.rate}
Iterations: {self$getCurrentIteration()}
")

if(!is.null(self$model))
p = glue::glue(p, "\nOffset:{self$model$getOffset()}")
Expand All @@ -419,9 +430,9 @@ Compboost = R6::R6Class("Compboost",
},
coef = function () {
if(!is.null(self$model)) {
return(c(self$model$getEstimatedParameter(), offset = self$model$getOffset()))
}
return(NULL)
return(c(self$model$getEstimatedParameter(), offset = self$model$getOffset()))
}
return(NULL)
},
plot = function (blearner.type = NULL, iters = NULL, from = NULL, to = NULL, length.out = 1000) {

Expand All @@ -435,7 +446,7 @@ Compboost = R6::R6Class("Compboost",
stop("Please specify a valid base-learner plus feature.")
}
if (! blearner.type %in% names(private$bl.list)) {
stop("Your requested feature plus learner is not available. Check the bl.factory.list member for available learners.")
stop("Your requested feature plus learner is not available. Check 'getFactoryNames()' for available learners.")
}
if (length(private$bl.list[[blearner.type]]$feature) > 1) {
stop("Only univariate plotting is supported.")
Expand All @@ -452,6 +463,8 @@ Compboost = R6::R6Class("Compboost",
feat.name = private$bl.list[[blearner.type]]$target$getIdentifier()

checkmate::assertNumeric(x = self$data[[feat.name]], min.len = 2, null.ok = FALSE)
checkmate::assertNumeric(from, lower = min(self$data[[feat.name]]), upper = max(self$data[[feat.name]]), len = 1, null.ok = TRUE)
checkmate::assertNumeric(to, lower = min(self$data[[feat.name]]), upper = max(self$data[[feat.name]]), len = 1, null.ok = TRUE)

if (is.null(from)) {
from = min(self$data[[feat.name]])
Expand All @@ -471,123 +484,127 @@ Compboost = R6::R6Class("Compboost",

# Create data.frame for plotting depending if iters is specified:
if (!is.null(iters[1])) {
preds = lapply(iters, function (x) {
if (x >= iter.min) {
return(feat.map %*% self$model$getParameterAtIteration(x)[[blearner.type]])
} else {
return(rep(0, length.out))
}
})
names(preds) = iters

df.plot = data.frame(
effect = unlist(preds),
iteration = as.factor(rep(iters, each = length.out)),
feature = plot.data
)

gg = ggplot(df.plot, aes(feature, effect, color = iteration))

} else {
df.plot = data.frame(
effect = feat.map %*% self$coef()[[blearner.type]],
feature = plot.data
)

gg = ggplot(df.plot, aes(feature, effect))
}
preds = lapply(iters, function (x) {
if (x >= iter.min) {
return(feat.map %*% self$model$getParameterAtIteration(x)[[blearner.type]])
} else {
return(rep(0, length.out))
}
})
names(preds) = iters

df.plot = data.frame(
effect = unlist(preds),
iteration = as.factor(rep(iters, each = length.out)),
feature = plot.data
)

gg = ggplot(df.plot, aes(feature, effect, color = iteration))

} else {
df.plot = data.frame(
effect = feat.map %*% self$coef()[[blearner.type]],
feature = plot.data
)

gg = ggplot(df.plot, aes(feature, effect))
}

gg = gg +
geom_line() +
geom_rug(data = self$data, aes_string(x = feat.name), inherit.aes = FALSE,
alpha = 0.8) +
xlab(feat.name) +
xlim(from, to) +
ylab("Additive Contribution")
geom_line() +
geom_rug(data = self$data, aes_string(x = feat.name), inherit.aes = FALSE,
alpha = 0.8) +
xlab(feat.name) +
xlim(from, to) +
ylab("Additive Contribution")

return(gg)
},
getFactoryNames = function () {
# return(lapply(private$bl.list, function (bl) bl[[1]]$target$getIdentifier()))
return(names(private$bl.list))
}
),
private = list(
private = list(
# Lists of single logger and base-learner factories. Neccessary to prevent the factories from the
# arbage collector which deallocates all the data from the heap and couses R to crash.
l.list = list(),
bl.list = list(),
logger.list = list(),

initializeModel = function() {
private$logger.list = LoggerList$new()
for (n in names(private$l.list)) {
private$logger.list$registerLogger(n, private$l.list[[n]])
}
self$model = Compboost_internal$new(self$response, self$learning.rate,
self$stop.if.all.stoppers.fulfilled, self$bl.factory.list, self$loss, private$logger.list, self$optimizer)
},
addSingleNumericBl = function(data.columns, feature, id.fac, id, bl.factory, data.source, data.target, ...) {

private$bl.list[[id]] = list()
private$bl.list[[id]]$source = data.source$new(as.matrix(data.columns), paste(feature, collapse = "_"))
private$bl.list[[id]]$feature = feature
private$bl.list[[id]]$target = data.target$new()
private$bl.list[[id]]$factory = bl.factory$new(private$bl.list[[id]]$source, private$bl.list[[id]]$target, id.fac, ...)
self$bl.factory.list$registerFactory(private$bl.list[[id]]$factory)
private$bl.list[[id]]$source = NULL

# Check if factory was successfully initialized. If not remove other list elements:
if (is.null(private$bl.list[[id]]$factory)) {
private$bl.list[[id]] = NULL
}
},
addSingleCatBl = function(data.column, feature, id.fac, id, bl.factory, data.source, data.target, ...) {
private$bl.list[[id]] = list()
lvls = unlist(unique(data.column))
l.list = list(),
bl.list = list(),
logger.list = list(),

initializeModel = function() {

private$logger.list = LoggerList$new()
for (n in names(private$l.list)) {
private$logger.list$registerLogger(n, private$l.list[[n]])
}
self$model = Compboost_internal$new(self$response, self$learning.rate,
self$stop.if.all.stoppers.fulfilled, self$bl.factory.list, self$loss, private$logger.list, self$optimizer)
},
addSingleNumericBl = function(data.columns, feature, id.fac, id, bl.factory, data.source, data.target, ...) {

private$bl.list[[id]] = list()
private$bl.list[[id]]$source = data.source$new(as.matrix(data.columns), paste(feature, collapse = "_"))
private$bl.list[[id]]$feature = feature
private$bl.list[[id]]$target = data.target$new()
private$bl.list[[id]]$factory = bl.factory$new(private$bl.list[[id]]$source, private$bl.list[[id]]$target, id.fac, ...)
self$bl.factory.list$registerFactory(private$bl.list[[id]]$factory)
private$bl.list[[id]]$source = NULL

},
addSingleCatBl = function(data.column, feature, id.fac, id, bl.factory, data.source, data.target, ...) {

lvls = unlist(unique(data.column))

# Create dummy variable for each category and use that vector as data matrix. Hence,
# if a categorical feature has 3 groups, then these 3 groups are added as 3 different
# base-learners (unbiased feature selection).
for (lvl in lvls) {
private$addSingleNumericBl(data.columns = as.matrix(as.integer(data.column == lvl)),
feature = paste(feature, lvl, sep = "_"), id.fac = paste(id.fac, lvl, sep = "_"),
id = paste(id, lvl, sep = "_"), bl.factory, data.source, data.target, ...)
for (lvl in lvls) {

list.id = paste(feature, lvl, id.fac, sep = "_")

private$addSingleNumericBl(data.columns = as.matrix(as.integer(data.column == lvl)),
feature = paste(feature, lvl, sep = "_"), id.fac = id.fac,
id = list.id, bl.factory, data.source, data.target, ...)

# This is important because of:
# 1. feature in addSingleNumericBl needs to be something like cat_feature_Group1 to define the
# data objects correctly in a unique way.
# 2. The feature itself should not be named with the level. Instead of that we just want the
# feature name of the categorical variable, such as cat_feature (important for predictions).
private$bl.list[[paste(id, lvl, sep = "_")]]$feature = feature
}
private$bl.list[[list.id]]$feature = feature
}
)
}
)
)

if (FALSE) {
load_all()
cars$dist_cat = ifelse(cars$speed > 15, "A", "B")
cars$foo_1 = rnorm(50)
cb = Compboost$new(cars, "speed", loss = QuadraticLoss$new(10))
cb
cb$risk()
cb$selected()
cb$addBaselearner("dist_cat", "linear", PolynomialBlearnerFactory, degree = 1, intercept = TRUE)
lapply(c("dist", "foo_1"), function(x) cb$addBaselearner(x, "linear", PolynomialBlearnerFactory, degree = 1))
cb$train(5)
cb$risk()
cb$selected()
load_all()
cars$dist_cat = ifelse(cars$speed > 15, "A", "B")
cars$foo_1 = rnorm(50)
cb = Compboost$new(cars, "speed", loss = QuadraticLoss$new(10))
cb
cb$risk()
cb$selected()
cb$addBaselearner("dist_cat", "linear", PolynomialBlearnerFactory, degree = 1, intercept = TRUE)
lapply(c("dist", "foo_1"), function(x) cb$addBaselearner(x, "linear", PolynomialBlearnerFactory, degree = 1))
cb$train(5)
cb$risk()
cb$selected()
# cb$addBaselearner(c("dist", "foo"), "quadratic", PolynomialBlearnerFactory, degree = 2, intercept = TRUE)
# cb$addBaselearner("dist", "spline", PSplineBlearnerFactory, degree = 3, knots = 10, penalty = 2, differences = 2)
#cb$addBaselearner("dist_cat", "linear", PolynomialBlearnerFactory, degree = 1)
cb$addLogger(IterationLogger, use.as.stopper = TRUE, logger.id = "bla", iter.max = 500)
cb$train(NULL)
cb
cb$predict()
cb$predict(cars)
cb$train(10)
cb2$train(200)
gc()
cb$train(100000)
cb$bl.factory.list
cb$model$getLoggerData()
head(cb$model$getParameterMatrix()[[2]])
cb$model$getParameterMatrix()[[1]]
cb$addLogger(IterationLogger, use.as.stopper = TRUE, logger.id = "bla", iter.max = 500)
cb$train(NULL)
cb
cb$predict()
cb$predict(cars)
cb$train(10)
cb2$train(200)
gc()
cb$train(100000)
cb$bl.factory.list
cb$model$getLoggerData()
head(cb$model$getParameterMatrix()[[2]])
cb$model$getParameterMatrix()[[1]]
}

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