s.H2ODL: Deep Learning on H2O [C, R]

Description

Trains a Deep Neural Net using H2O (http://www.h2o.ai) Check out the H2O Flow at [ip]:[port], Default IP:port is "localhost:54321" e.g. if running on localhost, point your web browser to localhost:54321

Usage

s.H2ODL(x, y = NULL, x.test = NULL, y.test = NULL, x.valid = NULL,
  y.valid = NULL, x.name = NULL, y.name = NULL, ip = "localhost",
  port = 54321, n.hidden.nodes = c(20, 20), epochs = 1000,
  activation = "Rectifier", mini.batch.size = 1,
  learning.rate = 0.005, adaptive.rate = TRUE, rho = 0.99,
  epsilon = 1e-08, rate.annealing = 1e-06, rate.decay = 1,
  momentum.start = 0, momentum.ramp = 1e+06, momentum.stable = 0,
  nesterov.accelerated.gradient = TRUE, input.dropout.ratio = 0,
  hidden.dropout.ratios = NULL, l1 = 0, l2 = 0,
  max.w2 = 3.4028235e+38, nfolds = 0, initial.biases = NULL,
  initial.weights = NULL, loss = "Automatic", distribution = "AUTO",
  stopping.rounds = 5, stopping.metric = "AUTO", upsample = FALSE,
  upsample.seed = NULL, na.action = na.fail, n.cores = rtCores,
  print.plot = TRUE, plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  verbose = TRUE, trace = 0, outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...)

Arguments

Vector / Matrix / Data Frame: Training set Predictors

Vector: Training set outcome

x.test

Vector / Matrix / Data Frame: Testing set Predictors

y.test

Vector: Testing set outcome

x.valid

Vector / Matrix / Data Frame: Validation set Predictors

y.valid

Vector: Validation set outcome

x.name

Character: Name for feature set

y.name

Character: Name for outcome

String: IP address of H2O server. Default = "localhost"

port

Integer: Port number for server. Default = 54321

n.hidden.nodes

Integer vector of length equal to the number of hidden layers you wish to create

epochs

Integer: How many times to iterate through the dataset. Default = 1000

activation

String: Activation function to use: "Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout", "Maxout", "MaxoutWithDropout". Default = "Rectifier"

learning.rate

Float: Learning rate to use for training. Default = .005

adaptive.rate

Logical: If TRUE, use adaptive learning rate. Default = TRUE

rate.annealing

Float: Learning rate annealing: rate / (1 + rate_annealing * samples). Default = 1e-6

input.dropout.ratio

Float (0, 1): Dropout ratio for inputs

hidden.dropout.ratios

Vector, Float (0, 2): Dropout ratios for hidden layers

Float (0, 1): L1 regularization (introduces sparseness; i.e. sets many weights to 0; reduces variance, increases generalizability)

Float (0, 1): L2 regularization (prevents very large absolute weights; reduces variance, increases generalizability)

upsample

Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Caution: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness

upsample.seed

Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)

na.action

How to handle missing values. See ?na.fail

n.cores

Integer: Number of cores to use

print.plot

Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted

plot.fitted

Logical: if TRUE, plot True (y) vs Fitted

plot.predicted

Logical: if TRUE, plot True (y.test) vs Predicted. Requires x.test and y.test

plot.theme

String: "zero", "dark", "box", "darkbox"

question

String: the question you are attempting to answer with this model, in plain language.

verbose

Logical: If TRUE, print summary to screen.

trace

Integer: If higher than 0, will print more information to the console. Default = 0

outdir

Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if save.mod is TRUE

save.mod

Logical. If TRUE, save all output as RDS file in outdir save.mod is TRUE by default if an outdir is defined. If set to TRUE, and no outdir is defined, outdir defaults to paste0("./s.", mod.name)

...

Additional parameters to pass to h2o::h2o.deeplearning

Value

rtMod object

Details

x & y form the training set. x.test & y.test form the testing set used only to test model generalizability. x.valid & y.valid form the validation set used to monitor training progress

Description

Usage

Arguments

Value

Details

See Also