mparheuristic {rminer}R Documentation

Function that returns a list of searching (hyper)parameters for a particular model (classification or regression) or for a multiple list of models (automl or ensembles).

Description

Easy to use function that returns a list of searching (hyper)parameters for a particular model (classification or regression) or for a multiple list of models (automl or ensembles). The result is to be put in a search argument, used by fit or mining functions. Something like:
search=list(search=mparheuristic(...),...).

Usage

mparheuristic(model, n = NA, lower = NA, upper = NA, by = NA, exponential = NA, 
              kernel = "rbfdot", task = "prob", inputs = NA)

Arguments

model

model type name. See fit for the individual model details (e.g., "ksvm"). For multiple models use:

  • automl - 5 individual machine learning algorithms: generalized linear model (GLM, via cv.glmnet), support vector machine (SVM, via ksvm), multilayer perceptron (MLP, via mlpe), random forest (RF, via randomForest) and extreme gradient boosting (XG, via xgboost). The n="heuristic" setting (see below) is assumed for all algorithms, thus just one hyperparameter is tested for each model. This option is thus the fastest automl to run.

  • automl2 - same 5 individual machine learning algorithms as automl. For each algorithm, a grid search is executed with 10 searches (same as:
    n="heuristic10"), except for ksvm, which uses 13 searches of an uniform design ("UD").

  • automl3 - same as automl2 except that a six extra stacking ensemble ("SE") model is performed using the 5 best tuned algorithm versions (GLM, SVM, MLP, RF and XG).

  • a character vector with several models - see the example section for a demonstration of this option.

n

number of searches or heuristic (either n or by should be used, n has prevalence over by). By default, the searches are linear for all models except for SVM several rbfdot kernel based models ("ksvm","rsvm","lssvm", which can assume 2^search-range; please check the result of this function to confirm if the search is linear or 2^search-range). If this argument is a character type, then it is assumed to be an heuristic. Possible heuristic values are:

  • heuristic - only one model is fit, uses default rminer values, same as mparheuristic(model).

  • heuristic5 - 5 hyperparameter searches from lower to upper, only works for the following models: ctree, rpart, kknn, ksvm, lssvm, mlp, mlpe, randomForest, multinom, rvm, xgboost. Notes: rpart - different cp values (see rpart.control); ctree - different mincriterion values (see ctree_control); randomForest – upper argument is limited by the number of inputs (mtry is searched); ksvm, lssvm or rvm - the optional kernel argument can be used.

  • heuristic10 - same as heuristic5 but with 10 searches from lower to upper.

  • UD or UD1 - UD or UD1 uniform design search (only for ksvm and rbfdof kernel). This option assumes 2 hyperparameters for classification (sigma, C) and 3 hyperparameters (sigma, C, epsilon) for regression, thus task="reg" argument needs to be set when regression is used.

  • xgb9 - 9 searches (3 for eta and 3 for max_depth, works only when model=xgboost.

  • mlp_t - heuristic 33 from Delgado 2014 paper, 10 searches, works only when model=mlp or model=mlpe.

  • avNNet_t - heuristic 34 from Delgado 2014 paper, 9 searches, works only when model=mlpe.

  • nnet_t - heuristic 36 from Delgado 2014 paper, 25 searches, works only when model=mlp or model=mlpe.

  • svm_C - heuristic 48 from Delgado 2014 paper, 130 searches (may take time), works only when model=ksvm.

  • svmRadial_t - heuristic 52 from Delgado 2014 paper, 25 searches, works only when model=ksvm.

  • svmLinear_t - heuristic 54 from Delgado 2014 paper, 5 searches, works only when model=ksvm.

  • svmPoly_t - heuristic 55 from Delgado 2014 paper, 27 searches, works only when model=ksvm.

  • lsvmRadial_t - heuristic 56 from Delgado 2014 paper, 10 searches, works only when model=lssvm.

  • rpart_t - heuristic 59 from Delgado 2014 paper, 10 searches, works only when model=rpart.

  • rpart2_t - heuristic 60 from Delgado 2014 paper, 10 searches, works only when model=rpart.

  • ctree_t - heuristic 63 from Delgado 2014 paper, 10 searches, works only when model=ctree.

  • ctree2_t - heuristic 64 from Delgado 2014 paper, 10 searches, works only when model=ctree.

  • rf_t - heuristic 131 from Delgado 2014 paper, 10 searches, works only when model=randomForest.

  • knn_R - heuristic 154 from Delgado 2014 paper, 19 searches, works only when model=kknn.

  • knn_t - heuristic 155 from Delgado 2014 paper, 10 searches, works only when model=kknn.

  • multinom_t - heuristic 167 from Delgado 2014 paper, 10 searches, works only when model=multinom.

lower

lower bound for the (hyper)parameter (if NA a default value is assumed).

upper

upper bound for the (hyper)parameter (if NA a default value is assumed).

by

increment in the sequence (if NA a default value is assumed depending on n).

exponential

if an exponential scale should be used in the search sequence (the NA is a default value that assumes a linear scale unless model is a support vector machine).

kernel

optional kernel type, only used when model="ksvm", model="rsvm" or model="lssvm". Currently mapped kernels are "rbfdot" (Gaussian), "polydot" (polynomial) and "vanilladot" (linear); see ksvm for kernel details.

task

optional task argument, only used for uniform design (UD or UD1) (with "ksvm" and "rbfdot").

inputs

optional inputs argument: the number of inputs, only used by "randomForest".

Details

This function facilitates the definition of the search argument used by fit or mining functions. Using simple heuristics, reasonable (hyper)parameter search values are suggested for several rminer models. For models not mapped in this function, the function returns NULL, which means that no hyperparameter search is executed (often, this implies using rminer or R function default values).

The simple usage of heuristic assumes lower and upper bounds for a (hyper)parameter. If n=1, then rminer or R defaults are assumed. Else, a search is created using seq(lower,upper,by), where by was set by the used or computed from n. For some model="ksvm" setups, 2^seq(...) is used for sigma and C, (1/10)^seq(...) is used for scale. Please check the resulting object to inspect the obtained final search values.

This function also allows to easily set multiple model searches, under the: "automl", "automl2", "automl3" or vector character options (see below examples).

Value

A list with one ore more (hyper)parameter values to be searched.

Note

See also http://hdl.handle.net/1822/36210 and http://www3.dsi.uminho.pt/pcortez/rminer.html

Author(s)

Paulo Cortez http://www3.dsi.uminho.pt/pcortez/

References

See Also

fit and mining.

Examples

## "kknn"
s=mparheuristic("kknn",n="heuristic")
print(s) 
s=mparheuristic("kknn",n=1) # same thing
print(s) 
s=mparheuristic("kknn",n="heuristic5")
print(s) 
s=mparheuristic("kknn",n=5) # same thing
print(s)
s=mparheuristic("kknn",lower=5,upper=15,by=2)
print(s)
# exponential scale:
s=mparheuristic("kknn",lower=1,upper=5,by=1,exponential=2)
print(s)

## "mlpe"
s=mparheuristic("mlpe")
print(s) # "NA" means set size with min(inputs/2,10) in fit
s=mparheuristic("mlpe",n="heuristic10")
print(s) 
s=mparheuristic("mlpe",n=10) # same thing
print(s) 
s=mparheuristic("mlpe",n=10,lower=2,upper=20) 
print(s) 

## "randomForest", upper should be set to the number of inputs = max mtry
s=mparheuristic("randomForest",n=10,upper=6)
print(s) 

## "ksvm"
s=mparheuristic("ksvm",n=10)
print(s) 
s=mparheuristic("ksvm",n=10,kernel="vanilladot")
print(s) 
s=mparheuristic("ksvm",n=10,kernel="polydot")
print(s) 

## lssvm
s=mparheuristic("lssvm",n=10)
print(s) 

## rvm 
s=mparheuristic("rvm",n=5)
print(s) 
s=mparheuristic("rvm",n=5,kernel="vanilladot")
print(s) 

## "rpart" and "ctree" are special cases (see help(fit,package=rminer) examples):
s=mparheuristic("rpart",n=3) # 3 cp values
print(s) 
s=mparheuristic("ctree",n=3) # 3 mincriterion values
print(s) 

### examples with fit
## Not run: 
### classification
data(iris)
# ksvm and rbfdot:
model="ksvm";kernel="rbfdot"
s=mparheuristic(model,n="heuristic5",kernel=kernel)
print(s) # 5 sigma values
search=list(search=s,method=c("holdout",2/3,123))
# task "prob" is assumed, optimization of "AUC":
M=fit(Species~.,data=iris,model=model,search=search,fdebug=TRUE)
print(M@mpar)

# different lower and upper range:
s=mparheuristic(model,n=5,kernel=kernel,lower=-5,upper=1)
print(s) # from 2^-5 to 2^1 
search=list(search=s,method=c("holdout",2/3,123))
# task "prob" is assumed, optimization of "AUC":
M=fit(Species~.,data=iris,model=model,search=search,fdebug=TRUE)
print(M@mpar)

# different exponential scale: 
s=mparheuristic(model,n=5,kernel=kernel,lower=-4,upper=0,exponential=10)
print(s) # from 10^-5 to 10^1 
search=list(search=s,method=c("holdout",2/3,123))
# task "prob" is assumed, optimization of "AUC":
M=fit(Species~.,data=iris,model=model,search=search,fdebug=TRUE)
print(M@mpar)

# "lssvm" Gaussian model, pure classification and ACC optimization, full iris:
model="lssvm";kernel="rbfdot"
s=mparheuristic("lssvm",n=3,kernel=kernel)
print(s)
search=list(search=s,method=c("holdout",2/3,123))
M=fit(Species~.,data=iris,model=model,search=search,fdebug=TRUE)
print(M@mpar)

# test several heuristic5 searches, full iris:
n="heuristic5";inputs=ncol(iris)-1
model=c("ctree","rpart","kknn","ksvm","lssvm","mlpe","randomForest")
for(i in 1:length(model))
 {
  cat("--- i:",i,"model:",model[i],"\n")
  if(model[i]=="randomForest") s=mparheuristic(model[i],n=n,upper=inputs) 
  else s=mparheuristic(model[i],n=n)
  print(s)
  search=list(search=s,method=c("holdout",2/3,123))
  M=fit(Species~.,data=iris,model=model[i],search=search,fdebug=TRUE)
  print(M@mpar)
 }


# test several Delgado 2014 searches (some cases launch warnings):
model=c("mlp","mlpe","mlp","ksvm","ksvm","ksvm",
        "ksvm","lssvm","rpart","rpart","ctree",
        "ctree","randomForest","kknn","kknn","multinom")
n=c("mlp_t","avNNet_t","nnet_t","svm_C","svmRadial_t","svmLinear_t",
    "svmPoly_t","lsvmRadial_t","rpart_t","rpart2_t","ctree_t",
    "ctree2_t","rf_t","knn_R","knn_t","multinom_t")
inputs=ncol(iris)-1
for(i in 1:length(model))
 {
  cat("--- i:",i,"model:",model[i],"heuristic:",n[i],"\n")
  if(model[i]=="randomForest") s=mparheuristic(model[i],n=n[i],upper=inputs) 
  else s=mparheuristic(model[i],n=n[i])
  print(s)
  search=list(search=s,method=c("holdout",2/3,123))
  M=fit(Species~.,data=iris,model=model[i],search=search,fdebug=TRUE)
  print(M@mpar)
 }

## End(Not run) #dontrun

### regression
## Not run: 
data(sa_ssin)
s=mparheuristic("ksvm",n=3,kernel="polydot")
print(s)
search=list(search=s,metric="MAE",method=c("holdout",2/3,123))
M=fit(y~.,data=sa_ssin,model="ksvm",search=search,fdebug=TRUE)
print(M@mpar)

# regression task, predict iris "Petal.Width":
data(iris)
ir2=iris[,1:4]
names(ir2)[ncol(ir2)]="y" # change output name
n=3;inputs=ncol(ir2)-1 # 3 hyperparameter searches
model=c("ctree","rpart","kknn","ksvm","mlpe","randomForest","rvm")
for(i in 1:length(model))
 {
  cat("--- i:",i,"model:",model[i],"\n")
  if(model[i]=="randomForest") s=mparheuristic(model[i],n=n,upper=inputs)
  else s=mparheuristic(model[i],n=n)
  print(s)
  search=list(search=s,method=c("holdout",2/3,123))
  M=fit(y~.,data=ir2,model=model[i],search=search,fdebug=TRUE)
  print(M@mpar)
 }

## End(Not run) #dontrun

### multiple model examples:
## Not run: 
data(iris)
inputs=ncol(iris)-1; task="prob"

# 5 machine learning (ML) algorithms, 1 heuristic hyperparameter per algorithm:
sm=mparheuristic(model="automl",task=task,inputs=inputs)
print(sm)

# 5 ML with 10/13 hyperparameter searches:
sm=mparheuristic(model="automl2",task=task,inputs=inputs)
# note: mtry only has 4 searches due to the inputs limit:
print(sm)

# regression example:
ir2=iris[,1:4]
inputs=ncol(ir2)-1; task="reg"
sm=mparheuristic(model="automl2",task=task,inputs=inputs)
# note: ksvm contains 3 UD hyperparameters (and not 2) since task="reg": 
print(sm)

# 5 ML and stacking:
inputs=ncol(iris)-1; task="prob"
sm=mparheuristic(model="automl3",task=task,inputs=inputs)
# note: $ls only has 5 elements, one for each individual ML 
print(sm)

# other manual design examples: --------------------------------------

# 5 ML and three ensembles:
# the fit or mining functions will search for the best option
# between any of the 5 ML algorithms and any of the three 
# ensemble approaches:
sm2=mparheuristic(model="automl3",task=task,inputs=inputs)
# note: ensembles need to be at the end of the $models field:
sm2$models=c(sm2$models,"AE","WE") # add AE and WE
sm2$smethod=c(sm2$smethod,rep("grid",2)) # add grid to AE and WE
# note: $ls only has 5 elements, one for each individual ML 
print(sm2)

# 3 ML example:
models=c("cv.glmnet","mlpe","ksvm") # just 3 models
# note: in rminer the default cv.glmnet does not have "hyperparameters"
# since the cv automatically sets lambda 
n=c(NA,10,"UD") # 10 searches for mlpe and 13 for ksvm 
sm3=mparheuristic(model=models,n=n)
# note: $ls only has 5 elements, one for each individual ML 
print(sm3)

# usage in sm2 and sm3 for fit (see mining help for usages in mining):
method=c("holdout",2/3,123)
d=iris
names(d)[ncol(d)]="y" # change output name
s2=list(search=sm2,smethod="auto",method=method,metric="AUC",convex=0)
M2=fit(y~.,data=d,model="auto",search=s2,fdebug=TRUE)

s3=list(search=sm3,smethod="auto",method=method,metric="AUC",convex=0)
M3=fit(y~.,data=d,model="auto",search=s3,fdebug=TRUE)
# -------------------------------------------------------------------

## End(Not run)
[Package rminer version 1.4.6 Index]