Readme.md
In systats/tidyMBO: tidyMBO for optimizing ML language models
tidy.mbo vignette
Simon
14 4 2018
Packages
#devtools::install_github("systats/tidy.mbo")
pacman::p_load(tidyverse, dplyr, purrr, rsample, data.table, magrittr, tidyTX, keras, mlrMBO, tidy.mbo, ggthemes, Smisc)
## Warning: package 'tidy.mbo' is not available (for R version 3.4.2)
## Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for help
## Warning in p_install(package, character.only = TRUE, ...):
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'tidy.mbo'
## Warning in pacman::p_load(tidyverse, dplyr, purrr, rsample, data.table, : Failed to install/load:
## tidy.mbo
#keras::install_keras()
set.seed(2018)
ggplot2::theme_set(ggthemes::theme_few())
Data
split_data <- function(data, p){
train_id <- caret::createDataPartition(y = data$index, p = p, list = F)
train <- data[train_id,]
test <- data[-train_id,]
return(list(train = train, test = test))
}
dt <- get(load("df_clean.Rdata")) %>%
mutate(index = 1:n()) %>%
split_data(p = .7)
listLearners("regr", properties = c("factors", "se"))
## Warning in listLearners.character("regr", properties = c("factors", "se")): The following learners could not be constructed, probably because their packages are not installed:
## classif.ada,classif.adaboostm1,classif.bartMachine,classif.blackboost,classif.boosting,classif.bst,classif.C50,classif.cforest,classif.clusterSVM,classif.ctree,classif.dbnDNN,classif.dcSVM,classif.evtree,classif.extraTrees,classif.fdausc.glm,classif.fdausc.kernel,classif.fdausc.knn,classif.fdausc.np,classif.gamboost,classif.gaterSVM,classif.gbm,classif.geoDA,classif.glmboost,classif.IBk,classif.J48,classif.JRip,classif.kknn,classif.LiblineaRL1L2SVC,classif.LiblineaRL1LogReg,classif.LiblineaRL2L1SVC,classif.LiblineaRL2LogReg,classif.LiblineaRL2SVC,classif.LiblineaRMultiClassSVC,classif.linDA,classif.lqa,classif.mda,classif.mlp,classif.neuralnet,classif.nnTrain,classif.nodeHarvest,classif.OneR,classif.pamr,classif.PART,classif.penalized,classif.plr,classif.plsdaCaret,classif.quaDA,classif.randomForestSRC,classif.ranger,classif.rda,classif.rFerns,classif.rknn,classif.rotationForest,classif.RRF,classif.rrlda,classif.saeDNN,classif.sda,classif.sparseLDA,cluster.cmeans,cluster.Cobweb,cluster.EM,cluster.FarthestFirst,cluster.kmeans,cluster.SimpleKMeans,cluster.XMeans,multilabel.cforest,multilabel.randomForestSRC,multilabel.rFerns,regr.bartMachine,regr.bcart,regr.bgp,regr.bgpllm,regr.blackboost,regr.blm,regr.brnn,regr.bst,regr.btgp,regr.btgpllm,regr.btlm,regr.cforest,regr.crs,regr.ctree,regr.cubist,regr.elmNN,regr.evtree,regr.extraTrees,regr.FDboost,regr.frbs,regr.gamboost,regr.gbm,regr.glmboost,regr.IBk,regr.kknn,regr.laGP,regr.LiblineaRL2L1SVR,regr.LiblineaRL2L2SVR,regr.mars,regr.mob,regr.nodeHarvest,regr.pcr,regr.penalized,regr.plsr,regr.randomForestSRC,regr.ranger,regr.rknn,regr.RRF,regr.rsm,regr.slim,surv.cforest,surv.CoxBoost,surv.cv.CoxBoost,surv.gamboost,surv.gbm,surv.glmboost,surv.randomForestSRC,surv.ranger
## Check ?learners to see which packages you need or install mlr with all suggestions.
## class
## 1 regr.bcart
## 2 regr.btgp
## 3 regr.btgpllm
## 4 regr.btlm
## 5 regr.crs
## 6 regr.gausspr
## name
## 1 Bayesian CART
## 2 Bayesian Treed Gaussian Process
## 3 Bayesian Treed Gaussian Process with jumps to the Limiting Linear Model
## 4 Bayesian Treed Linear Model
## 5 Regression Splines
## 6 Gaussian Processes
## short.name package type installed numerics factors ordered missings
## 1 bcart tgp regr FALSE TRUE TRUE FALSE FALSE
## 2 btgp tgp regr FALSE TRUE TRUE FALSE FALSE
## 3 btgpllm tgp regr FALSE TRUE TRUE FALSE FALSE
## 4 btlm tgp regr FALSE TRUE TRUE FALSE FALSE
## 5 crs crs regr FALSE TRUE TRUE FALSE FALSE
## 6 gausspr kernlab regr TRUE TRUE TRUE FALSE FALSE
## weights prob oneclass twoclass multiclass class.weights featimp
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 5 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## oobpreds functionals single.functional se lcens rcens icens
## 1 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 5 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## ... (#rows: 10, #cols: 24)
listLearnerProperties("regr")
## [1] "numerics" "factors" "ordered"
## [4] "missings" "weights" "se"
## [7] "featimp" "oobpreds" "functionals"
## [10] "single.functional"
Hyper Params
params <- makeParamSet(
makeIntegerParam("max_features", lower = 2000, upper = 10000),
makeIntegerParam("maxlen", lower = 10, upper = 40),
makeIntegerParam("batch_size", lower = 10, upper = 30),
makeIntegerParam("output_dim", lower = 60, upper = 200),
makeDiscreteParam("output_fun", values = c("softmax", "relu", "sigmoid")),
makeDiscreteParam("arch", values = c("fasttext", "lstm"))
)
Run Main
Metric Type
Metric Name
Function Name
Formula
classification
Classification Error
ce
$\frac{1}{n} \sum_{i=1}^n I(x_i \neq y_i)$
classification
Accuracy
accuracy
$\frac{1}{n} \sum_{i=1}^n I(x_i = y_i)$
classification
F1 Score
f1
$\frac{2 * \text{precision} * \text{recall}}{\text{precision} + \text{recall}}$
binary classification
Area Under ROC Curve
auc
∫01[1 − G1(G0−1(1 − v))]dv. help(auc) for details.
binary classification
Log Loss
ll
xi * ln(yi)+(1 − xi)*ln(1 − yi)
binary classification
Mean Log Loss
logloss
$\frac{1}{n} \sum_{i=1}^n x_i * \ln(y_i) + (1 - x_i) * \ln(1 - y_i)$
#install.packages("Metrics")
results <- params %>%
tidy.mbo::run_mbo(
data = dt,
target = "party_id",
text = "text_lemma",
name = "glove_model1",
n_init = 5,
n_main = 100
)
library(dplyr)
perform <- results$opt.path$env$path %>%
cbind(
.,
exec.time = results$opt.path$env$exec.time,
step = results$opt.path$env$dob
) %>%
arrange(desc(y)) %>%
as.tibble()
#error <- results$opt.path$env$extra
#exec.time <- results$opt.path$env$
perform
## # A tibble: 105 x 9
## max_features maxlen batch_size output_dim output_fun arch y
## <int> <int> <int> <int> <chr> <chr> <dbl>
## 1 8600 37 12 197 softmax fasttext 0.610
## 2 9812 40 13 188 softmax lstm 0.607
## 3 9599 40 14 145 softmax fasttext 0.605
## 4 9808 29 24 197 relu lstm 0.601
## 5 8159 32 21 191 relu fasttext 0.596
## 6 8705 33 27 199 relu lstm 0.596
## 7 8156 35 22 200 relu fasttext 0.596
## 8 8483 40 19 197 softmax fasttext 0.595
## 9 9796 33 24 197 relu fasttext 0.595
## 10 8530 39 20 149 softmax lstm 0.594
## # ... with 95 more rows, and 2 more variables: exec.time <dbl>, step <int>
Distribution of accuracy history
perform %>%
ggplot(aes(y, fill = is.na(exec.time))) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Distribution of accuracy history
#devtools::install_github("tidyverse/ggplot2")
perform %>%
ggplot(aes(max_features, maxlen, colour = y, size = y)) +
geom_point(alpha = .5) +
scale_size_continuous(range(1, 10)) +
viridis::scale_colour_viridis()
perform %>%
ggplot(aes(max_features, maxlen, colour = y)) +
geom_point() +
geom_density_2d()
perform %>%
ggplot(aes(max_features, maxlen)) +
stat_density_2d(geom = "polygon", aes(fill = ..level.., alpha=..level..)) +
viridis::scale_fill_viridis()
perform %>%
ggplot(aes(max_features, maxlen)) +
stat_density_2d(geom = "raster", aes(fill = ..density..), contour = F) +
viridis::scale_fill_viridis()
perform %>%
ggplot(aes(max_features, maxlen)) +
stat_density_2d(geom = "raster", aes(fill = ..density.., alpha=..density..), contour = F) +
viridis::scale_fill_viridis()
perform %>%
ggplot(aes(max_features, maxlen)) +
stat_density_2d(geom = "point", aes(size = ..density..), n = 20, contour = F, alpha = .7)
#geom_point() +
# stat_density_2d(
# data = bar,
# mapping = aes( x = x, y = y, fill = ..level.. ),
# geom = "polygon" )
perform %>%
dplyr::select(
max_features, maxlen,
batch_size,output_dim,
output_fun, y
) %>%
tidyr::gather("param", "value", -y, -output_fun) %>%
ggplot(aes(value, y, colour = y)) +
#geom_tile() +
#geom_raster() +
geom_density_2d(alpha = .8, color = "grey")+
geom_point() +
viridis::scale_colour_viridis("Accuracy") +
facet_grid(output_fun~param, scales = "free") +
theme(legend.position = "bottom")
perform %>%
dplyr::select(max_features:output_fun) %>%
dplyr::select_if(is.numeric) %>%
mutate(id = 1:n()) %>%
gather("var", "value", -id) %>%
ggplot(aes(value)) +
geom_histogram() +
ggplot2::facet_wrap(. ~ var)
Experiment Improvement
perform %>%
arrange(step) %>%
mutate(best = Smisc::cumMax(y)) %>%
ggplot(aes(step, best)) +
geom_step() +
labs(x = "Trials", y = "Best Value")
perform %>%
arrange(step) %>%
group_by(arch) %>%
mutate(best = Smisc::cumMax(y)) %>%
ungroup() %>%
ggplot(aes(step, best, color = output_fun)) +
geom_step() +
geom_step(aes(step, y, color = output_fun), alpha = .3) +
labs(x = "Trials", y = "Best Value") +
facet_grid(.~arch) +
theme(legend.position = "bottom")
perform %>%
ggplot(aes(max_features, maxlen, colour = y, label = step)) +
geom_point() +
geom_path(alpha = .5) +
geom_text() +
viridis::scale_colour_viridis()
#devtools::install_github("ggobi/ggally")
library(GGally)
my_bin <- function(data, mapping, ..., low = "#132B43", high = "#56B1F7") {
ggplot(data = data, mapping = mapping) +
geom_hex(...) +
scale_fill_gradient(low = low, high = high)
}
perform %>%
dplyr::select(output_dim, maxlen) %>%
ggpairs()
#mapping = aes(color = y),
# lower = list(
# combo = wrap("facethist", binwidth = 1),
# continuous = wrap(my_bin, binwidth = c(5, 0.5), high = "red")
# )
Understand Parameters
lm(y ~ ., data = perform) %>%
broom::tidy()
## term estimate std.error statistic p.value
## 1 (Intercept) 7.074001e-01 1.491267e-01 4.7436179 7.845093e-06
## 2 max_features -1.680278e-05 9.789460e-06 -1.7164158 8.952607e-02
## 3 maxlen -6.720171e-04 2.082651e-03 -0.3226739 7.476906e-01
## 4 batch_size 8.143260e-04 4.939896e-03 0.1648468 8.694343e-01
## 5 output_dim -3.482031e-04 4.438555e-04 -0.7844965 4.348089e-01
## 6 output_funsigmoid 7.671479e-02 4.923574e-02 1.5581119 1.227164e-01
## 7 output_funsoftmax 4.351165e-02 3.910290e-02 1.1127473 2.687803e-01
## 8 archlstm -4.548454e-02 2.886025e-02 -1.5760272 1.185283e-01
## 9 exec.time 1.700207e-03 1.988052e-03 0.8552124 3.947045e-01
## 10 step -9.288617e-04 7.705694e-04 -1.2054225 2.312000e-01
systats/tidyMBO documentation built on May 24, 2019, 5:06 a.m.
R Package Documentation
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tidy.mbo vignette
Simon 14 4 2018
Packages
#devtools::install_github("systats/tidy.mbo")
pacman::p_load(tidyverse, dplyr, purrr, rsample, data.table, magrittr, tidyTX, keras, mlrMBO, tidy.mbo, ggthemes, Smisc)
## Warning: package 'tidy.mbo' is not available (for R version 3.4.2)
## Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for help
## Warning in p_install(package, character.only = TRUE, ...):
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'tidy.mbo'
## Warning in pacman::p_load(tidyverse, dplyr, purrr, rsample, data.table, : Failed to install/load:
## tidy.mbo
#keras::install_keras()
set.seed(2018)
ggplot2::theme_set(ggthemes::theme_few())
Data
split_data <- function(data, p){
train_id <- caret::createDataPartition(y = data$index, p = p, list = F)
train <- data[train_id,]
test <- data[-train_id,]
return(list(train = train, test = test))
}
dt <- get(load("df_clean.Rdata")) %>%
mutate(index = 1:n()) %>%
split_data(p = .7)
listLearners("regr", properties = c("factors", "se"))
## Warning in listLearners.character("regr", properties = c("factors", "se")): The following learners could not be constructed, probably because their packages are not installed:
## classif.ada,classif.adaboostm1,classif.bartMachine,classif.blackboost,classif.boosting,classif.bst,classif.C50,classif.cforest,classif.clusterSVM,classif.ctree,classif.dbnDNN,classif.dcSVM,classif.evtree,classif.extraTrees,classif.fdausc.glm,classif.fdausc.kernel,classif.fdausc.knn,classif.fdausc.np,classif.gamboost,classif.gaterSVM,classif.gbm,classif.geoDA,classif.glmboost,classif.IBk,classif.J48,classif.JRip,classif.kknn,classif.LiblineaRL1L2SVC,classif.LiblineaRL1LogReg,classif.LiblineaRL2L1SVC,classif.LiblineaRL2LogReg,classif.LiblineaRL2SVC,classif.LiblineaRMultiClassSVC,classif.linDA,classif.lqa,classif.mda,classif.mlp,classif.neuralnet,classif.nnTrain,classif.nodeHarvest,classif.OneR,classif.pamr,classif.PART,classif.penalized,classif.plr,classif.plsdaCaret,classif.quaDA,classif.randomForestSRC,classif.ranger,classif.rda,classif.rFerns,classif.rknn,classif.rotationForest,classif.RRF,classif.rrlda,classif.saeDNN,classif.sda,classif.sparseLDA,cluster.cmeans,cluster.Cobweb,cluster.EM,cluster.FarthestFirst,cluster.kmeans,cluster.SimpleKMeans,cluster.XMeans,multilabel.cforest,multilabel.randomForestSRC,multilabel.rFerns,regr.bartMachine,regr.bcart,regr.bgp,regr.bgpllm,regr.blackboost,regr.blm,regr.brnn,regr.bst,regr.btgp,regr.btgpllm,regr.btlm,regr.cforest,regr.crs,regr.ctree,regr.cubist,regr.elmNN,regr.evtree,regr.extraTrees,regr.FDboost,regr.frbs,regr.gamboost,regr.gbm,regr.glmboost,regr.IBk,regr.kknn,regr.laGP,regr.LiblineaRL2L1SVR,regr.LiblineaRL2L2SVR,regr.mars,regr.mob,regr.nodeHarvest,regr.pcr,regr.penalized,regr.plsr,regr.randomForestSRC,regr.ranger,regr.rknn,regr.RRF,regr.rsm,regr.slim,surv.cforest,surv.CoxBoost,surv.cv.CoxBoost,surv.gamboost,surv.gbm,surv.glmboost,surv.randomForestSRC,surv.ranger
## Check ?learners to see which packages you need or install mlr with all suggestions.
## class
## 1 regr.bcart
## 2 regr.btgp
## 3 regr.btgpllm
## 4 regr.btlm
## 5 regr.crs
## 6 regr.gausspr
## name
## 1 Bayesian CART
## 2 Bayesian Treed Gaussian Process
## 3 Bayesian Treed Gaussian Process with jumps to the Limiting Linear Model
## 4 Bayesian Treed Linear Model
## 5 Regression Splines
## 6 Gaussian Processes
## short.name package type installed numerics factors ordered missings
## 1 bcart tgp regr FALSE TRUE TRUE FALSE FALSE
## 2 btgp tgp regr FALSE TRUE TRUE FALSE FALSE
## 3 btgpllm tgp regr FALSE TRUE TRUE FALSE FALSE
## 4 btlm tgp regr FALSE TRUE TRUE FALSE FALSE
## 5 crs crs regr FALSE TRUE TRUE FALSE FALSE
## 6 gausspr kernlab regr TRUE TRUE TRUE FALSE FALSE
## weights prob oneclass twoclass multiclass class.weights featimp
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 5 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## oobpreds functionals single.functional se lcens rcens icens
## 1 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 5 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## ... (#rows: 10, #cols: 24)
listLearnerProperties("regr")
## [1] "numerics" "factors" "ordered"
## [4] "missings" "weights" "se"
## [7] "featimp" "oobpreds" "functionals"
## [10] "single.functional"
Hyper Params
params <- makeParamSet(
makeIntegerParam("max_features", lower = 2000, upper = 10000),
makeIntegerParam("maxlen", lower = 10, upper = 40),
makeIntegerParam("batch_size", lower = 10, upper = 30),
makeIntegerParam("output_dim", lower = 60, upper = 200),
makeDiscreteParam("output_fun", values = c("softmax", "relu", "sigmoid")),
makeDiscreteParam("arch", values = c("fasttext", "lstm"))
)
Run Main
Metric Type Metric Name Function Name Formula classification Classification Error ce $\frac{1}{n} \sum_{i=1}^n I(x_i \neq y_i)$ classification Accuracy accuracy $\frac{1}{n} \sum_{i=1}^n I(x_i = y_i)$ classification F1 Score f1 $\frac{2 * \text{precision} * \text{recall}}{\text{precision} + \text{recall}}$ binary classification Area Under ROC Curve auc ∫01[1 − G1(G0−1(1 − v))]dv.help(auc) for details.
binary classification
Log Loss
ll
xi * ln(yi)+(1 − xi)*ln(1 − yi)
binary classification
Mean Log Loss
logloss
$\frac{1}{n} \sum_{i=1}^n x_i * \ln(y_i) + (1 - x_i) * \ln(1 - y_i)$
#install.packages("Metrics")
results <- params %>%
tidy.mbo::run_mbo(
data = dt,
target = "party_id",
text = "text_lemma",
name = "glove_model1",
n_init = 5,
n_main = 100
)
library(dplyr)
perform <- results$opt.path$env$path %>%
cbind(
.,
exec.time = results$opt.path$env$exec.time,
step = results$opt.path$env$dob
) %>%
arrange(desc(y)) %>%
as.tibble()
#error <- results$opt.path$env$extra
#exec.time <- results$opt.path$env$
perform
## # A tibble: 105 x 9
## max_features maxlen batch_size output_dim output_fun arch y
## <int> <int> <int> <int> <chr> <chr> <dbl>
## 1 8600 37 12 197 softmax fasttext 0.610
## 2 9812 40 13 188 softmax lstm 0.607
## 3 9599 40 14 145 softmax fasttext 0.605
## 4 9808 29 24 197 relu lstm 0.601
## 5 8159 32 21 191 relu fasttext 0.596
## 6 8705 33 27 199 relu lstm 0.596
## 7 8156 35 22 200 relu fasttext 0.596
## 8 8483 40 19 197 softmax fasttext 0.595
## 9 9796 33 24 197 relu fasttext 0.595
## 10 8530 39 20 149 softmax lstm 0.594
## # ... with 95 more rows, and 2 more variables: exec.time <dbl>, step <int>
Distribution of accuracy history
perform %>%
ggplot(aes(y, fill = is.na(exec.time))) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Distribution of accuracy history
#devtools::install_github("tidyverse/ggplot2")
perform %>%
ggplot(aes(max_features, maxlen, colour = y, size = y)) +
geom_point(alpha = .5) +
scale_size_continuous(range(1, 10)) +
viridis::scale_colour_viridis()
perform %>%
ggplot(aes(max_features, maxlen, colour = y)) +
geom_point() +
geom_density_2d()
perform %>%
ggplot(aes(max_features, maxlen)) +
stat_density_2d(geom = "polygon", aes(fill = ..level.., alpha=..level..)) +
viridis::scale_fill_viridis()
perform %>%
ggplot(aes(max_features, maxlen)) +
stat_density_2d(geom = "raster", aes(fill = ..density..), contour = F) +
viridis::scale_fill_viridis()
perform %>%
ggplot(aes(max_features, maxlen)) +
stat_density_2d(geom = "raster", aes(fill = ..density.., alpha=..density..), contour = F) +
viridis::scale_fill_viridis()
perform %>%
ggplot(aes(max_features, maxlen)) +
stat_density_2d(geom = "point", aes(size = ..density..), n = 20, contour = F, alpha = .7)
#geom_point() +
# stat_density_2d(
# data = bar,
# mapping = aes( x = x, y = y, fill = ..level.. ),
# geom = "polygon" )
perform %>%
dplyr::select(
max_features, maxlen,
batch_size,output_dim,
output_fun, y
) %>%
tidyr::gather("param", "value", -y, -output_fun) %>%
ggplot(aes(value, y, colour = y)) +
#geom_tile() +
#geom_raster() +
geom_density_2d(alpha = .8, color = "grey")+
geom_point() +
viridis::scale_colour_viridis("Accuracy") +
facet_grid(output_fun~param, scales = "free") +
theme(legend.position = "bottom")
perform %>%
dplyr::select(max_features:output_fun) %>%
dplyr::select_if(is.numeric) %>%
mutate(id = 1:n()) %>%
gather("var", "value", -id) %>%
ggplot(aes(value)) +
geom_histogram() +
ggplot2::facet_wrap(. ~ var)
Experiment Improvement
perform %>%
arrange(step) %>%
mutate(best = Smisc::cumMax(y)) %>%
ggplot(aes(step, best)) +
geom_step() +
labs(x = "Trials", y = "Best Value")
perform %>%
arrange(step) %>%
group_by(arch) %>%
mutate(best = Smisc::cumMax(y)) %>%
ungroup() %>%
ggplot(aes(step, best, color = output_fun)) +
geom_step() +
geom_step(aes(step, y, color = output_fun), alpha = .3) +
labs(x = "Trials", y = "Best Value") +
facet_grid(.~arch) +
theme(legend.position = "bottom")
perform %>%
ggplot(aes(max_features, maxlen, colour = y, label = step)) +
geom_point() +
geom_path(alpha = .5) +
geom_text() +
viridis::scale_colour_viridis()
#devtools::install_github("ggobi/ggally")
library(GGally)
my_bin <- function(data, mapping, ..., low = "#132B43", high = "#56B1F7") {
ggplot(data = data, mapping = mapping) +
geom_hex(...) +
scale_fill_gradient(low = low, high = high)
}
perform %>%
dplyr::select(output_dim, maxlen) %>%
ggpairs()
#mapping = aes(color = y),
# lower = list(
# combo = wrap("facethist", binwidth = 1),
# continuous = wrap(my_bin, binwidth = c(5, 0.5), high = "red")
# )
Understand Parameters
lm(y ~ ., data = perform) %>%
broom::tidy()
## term estimate std.error statistic p.value
## 1 (Intercept) 7.074001e-01 1.491267e-01 4.7436179 7.845093e-06
## 2 max_features -1.680278e-05 9.789460e-06 -1.7164158 8.952607e-02
## 3 maxlen -6.720171e-04 2.082651e-03 -0.3226739 7.476906e-01
## 4 batch_size 8.143260e-04 4.939896e-03 0.1648468 8.694343e-01
## 5 output_dim -3.482031e-04 4.438555e-04 -0.7844965 4.348089e-01
## 6 output_funsigmoid 7.671479e-02 4.923574e-02 1.5581119 1.227164e-01
## 7 output_funsoftmax 4.351165e-02 3.910290e-02 1.1127473 2.687803e-01
## 8 archlstm -4.548454e-02 2.886025e-02 -1.5760272 1.185283e-01
## 9 exec.time 1.700207e-03 1.988052e-03 0.8552124 3.947045e-01
## 10 step -9.288617e-04 7.705694e-04 -1.2054225 2.312000e-01
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