Creates a Python Class that internally iterates over the data.
make_generator_from_dataframe(
dt,
x_cols = NULL,
y_cols,
x_transform = NULL,
y_transform = NULL,
generator = keras::image_data_generator(),
batch_size = 32L,
shuffle = TRUE,
seed = 1L,
y_cols_to_categorical = TRUE,
subset = NULL,
ignore_class_split = FALSE
)
Arguments
| dt |
data.frame|data.table
Data container to iterate over.
|
| x_cols |
character
Names of features to be used. Defaults to all but y_cols.
|
| y_cols |
character
Target variable. Automatically converted to one-hot if "y_cols_to_categorical" is TRUE.
|
| x_transform |
function
Function used to transform data to a keras input format for features.
|
| y_transform |
function
Function used to transform data to a keras input format for the response.
|
| generator |
Python Object
A generator as e.g. obtained from keras::image_data_generator.
Used for consistent train-test splits.
|
| batch_size |
integer
Batch size.
|
| shuffle |
logical
Should data be shuffled?
|
| seed |
integer
Set a seed for shuffling data.
|
| y_cols_to_categorical |
logical
Should target be converted to one-hot representation?
|
| subset |
character
Should samples be generated from 'training' or 'validation' set?
Only applicable together with a 'generator'.
|
| ignore_class_split |
logical
Test whether all class labels appear in all splits.
|
