s_H2ODL: Deep Learning on H2O (C, R)

In egenn/rtemis: Machine Learning and Visualization

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,
  downsample = FALSE,
  resample.seed = NULL,
  na.action = na.fail,
  n.cores = rtCores,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  trace = 0,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  ...
)

Arguments

x	Vector / Matrix / Data Frame: Training set Predictors
y	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
ip	Character: 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	Character: 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
l1	Float (0, 1): L1 regularization (introduces sparseness; i.e. sets many weights to 0; reduces variance, increases generalizability)
l2	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) Note: 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
downsample	Logical: If TRUE, downsample majority class to match size of minority class
resample.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	Character: "zero", "dark", "box", "darkbox"
question	Character: 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.
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 to an 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

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

Value

rtMod object

Author(s)

E.D. Gennatas

See Also

train_cv for external cross-validation

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GAM.default(), s_GAM.formula(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_QRNN(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()

Other Deep Learning: d_H2OAE(), s_TFN()

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.