View source: R/pipe_keras_timeseries.r
pipe_keras_timeseries | R Documentation |
Neural network model with keras
pipe_keras_timeseries(
df,
predInput = NULL,
responseVars = 1,
caseClass = NULL,
idVars = character(),
weight = "class",
timevar = NULL,
responseTime = "LAST",
regex_time = ".+",
staticVars = NULL,
crossValStrategy = c("Kfold", "bootstrap"),
k = 5,
replicates = 10,
crossValRatio = c(train = 0.6, test = 0.2, validate = 0.2),
hidden_shape.RNN = c(32, 32),
hidden_shape.static = c(32, 32),
hidden_shape.main = 32,
epochs = 500,
maskNA = NULL,
batch_size = "all",
repVi = 5,
perm_dim = 2:3,
comb_dims = FALSE,
summarizePred = TRUE,
scaleDataset = FALSE,
NNmodel = FALSE,
DALEXexplainer = FALSE,
variableResponse = FALSE,
save_validateset = FALSE,
baseFilenameNN = NULL,
filenameRasterPred = NULL,
tempdirRaster = NULL,
nCoresRaster = parallel::detectCores()%/%2,
verbose = 0,
...
)
df |
a |
predInput |
a |
responseVars |
response variables as column names or indexes on |
caseClass |
class of the samples used to weight cases. Column names or indexes on |
idVars |
id column names or indexes on |
weight |
Optional array of the same length as |
timevar |
column name of the variable containing the time. |
responseTime |
a |
regex_time |
regular expression matching the |
staticVars |
predictor variables as column names or indexes on |
crossValStrategy |
|
k |
number of data partitions when |
replicates |
number of replicates for |
crossValRatio |
Proportion of the dataset used to train, test and validate the model when |
hidden_shape.RNN |
number of neurons in the hidden layers of the Recursive Neural Network model (time series data). Can be a vector with values for each hidden layer. |
hidden_shape.static |
number of neurons in the hidden layers of the densely connected neural network model (static data). Can be a vector with values for each hidden layer. |
hidden_shape.main |
number of neurons in the hidden layers of the densely connected neural network model connecting static and time series data. Can be a vector with values for each hidden layer. |
epochs |
parameter for |
maskNA |
value to assign to |
batch_size |
for fit and predict functions. The bigger the better if it fits your available memory. Integer or "all". |
repVi |
replicates of the permutations to calculate the importance of the variables. 0 to avoid calculating variable importance. |
perm_dim |
dimension to perform the permutations to calculate the importance of the variables (data dimensions [case, time, variable]).
If |
comb_dims |
variable importance calculations, if |
summarizePred |
if |
scaleDataset |
if |
NNmodel |
if |
DALEXexplainer |
if |
variableResponse |
if |
save_validateset |
save the validateset (independent data not used for training). |
baseFilenameNN |
if no missing, save the NN in hdf5 format on this path with iteration appended. |
filenameRasterPred |
if no missing, save the predictions in a RasterBrick to this file. |
tempdirRaster |
path to a directory to save temporal raster files. |
nCoresRaster |
number of cores used for parallelized raster cores. Use half of the available cores by default. |
verbose |
If > 0, print state and passed to keras functions |
... |
extra parameters for |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.