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SPOTMisc (version 1.19.52)

Misc Extensions for the 'SPOT' Package

Description

Implements additional models, simulation tools, and interfaces as extensions to 'SPOT'. It provides tools for hyperparameter tuning via 'keras/tensorflow', interfacing 'mlr', for performing Markov chain simulations, and for sensitivity analysis based on sequential bifurcation methods as described in Bettonvil and Kleijnen (1996). Furthermore, additional plotting functions for output from 'SPOT' runs are implemented. Bartz-Beielstein T, Lasarczyk C W G, Preuss M (2005) . Bartz-Beielstein T, Zaefferer M, Rehbach F (2021) . Bartz-Beielstein T, Rehbach F, Sen A, Zaefferer M . Bettonvil, B, Kleijnen JPC (1996) .

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Version

Install

install.packages('SPOTMisc')

Monthly Downloads

95

Version

1.19.52

License

GPL (>= 2)

Maintainer

Thomas Bartz-Beielstein

Last Published

September 5th, 2022

Functions in SPOTMisc (1.19.52)

funKerasGeneric

funKerasGeneric

getGenericTrainValTestData

getGenericTrainValTestData

Get indices (positions) of variable names

Generate an mlr task from Census KDD data set (+variation)

Stochastic gradient descent (SGD) optimizer

Get keras configuration parameter list

ggparcoordPrepare

Build data frame for ggparcoord (parallel plot)

optimizer_rmsprop

RMSProp optimizer

Get variable names or subsets of variable names

optimizer_adadelta

Adadelta optimizer.

Get predictions from mlr

Generate a fixed holdout instance for resampling

Map x parameters to a list of named values

getSimpleKerasModel

getSimpleKerasModel

kerasEvalPrediction

Evaluate keras prediction

makeLearnerFromHyperparameters

Make mlr learner from conf and hyperparameter vector

kerasReturnDummy

Return dummy values

get ml config for keras on census

optimizer_adamax

Adamax optimizer

simple progress plot

Parallel coordinate plot of a data set

plot_function_surface

genericDataPrep

Create an input pipeline using tfdatasets

optimizer_nadam

Nesterov Adam optimizer

plot_sensitivity

Sensitivity plot of a model

selectKerasActivation

Select keras activation function

scorePredictions

Score results from pred

Surface plot of a model

formatted output

Results from the spot() run dl100

Get model configuration

Get objective function for mlr

Start hyperparameter optimization runs with spot based on MNIST data

kerasCompileResult

Generate result from keras run

kerasBuildCompile

evalKerasGeneric model building and compile

Get Census KDD data set (+variation)

startXGBCensusRun

Start hyperparameter optimization runs with spot based on US census data

trans_odd_round

odd transformation

Return effects for each subgroup

Check the validity of input parameters.

10 power x transformation

translate_levels

Helper function: translate levels

optimizer_adagrad

Adagrad optimizer

Predict machine learning models on Census data

2 power x transformation

trans_2pow_round

2 power x transformation with round

prepareComparisonPlot

prepare data frame for comparisons (boxplots, violin plots)

selectKerasOptimizer

Select keras optimizer

Select target variable in a data frame

prepareProgressPlot

prepare data frame for progress plot

prepare_data_plot

Prepare data for plots

Helper function: transform integer to factor

plotSensitivity

Sensitivity ggplot of a model

Parallel coordinate plot of a data set

Print parameter values from FLAG list

trans_1minus10pow

10 power x transformation

prepare_spot_result_plot

Prepare data (results from a tuning run) for plots

trans_10pow_round

10 power x transformation with round

trans_mult2_round

Mult 2 transformation

Identity transformation

plotnice.spotTreeModel

Plot a nice rpart tree model

Predict deep learning models on Census data

sequentialBifurcation

Sequential Bifurcation

Start hyperparameter optimization runs with spot based on US census data

spot plot (generic function)

mean squared errors

sum of squared errors

evalKerasGeneric

evalKerasGeneric model building and compile

evalParamCensus

evaluate hyperparameter config on census data

root mean squared errors

Full census data set

evalKerasMnist_0

evalKerasMnist_0

evalKerasTransferLearning

evalKerasTransferLearning

funKerasMnist_0

funKerasMnist_0

genCatsDogsData

generate Cats Dogs Data

funKerasTransferLearning

funKerasTransferLearning

Get experimental design