Provides a class to transform data for RGAN.
Method $new() initializes a new transformer, method $fit(data) learns
the parameters for the transformation from data (e.g. means and sds).
Methods $transform() and $inverse_transform() can be used to transform
and back transform a data set based on the learned parameters.
Currently, DataTransformer supports z-transformation (a.k.a. normalization)
for numerical features/variables and one hot encoding for categorical
features/variables. In your call to fit you just need to indicate which
columns contain discrete features.
A class to transform (normalize or one hot encode) tabular data for RGAN
new()Create a new data_transformer object
data_transformer$new()
fit_continuous()data_transformer$fit_continuous(column = NULL, data = NULL)
fit_discrete()data_transformer$fit_discrete(column = NULL, data = NULL)
fit()Fit a data_transformer to data.
data_transformer$fit(data, discrete_columns = NULL)
dataThe data set to transform
discrete_columnsColumn ids for columns with discrete/nominal values to be one hot encoded.
data <- sample_toydata() transformer <- data_transformer$new() transformer$fit(data)
transform_continuous()data_transformer$transform_continuous(column_meta, data)
transform_discrete()data_transformer$transform_discrete(column_meta, data)
transform()Transform data using a fitted data_transformer. (From original format to transformed format.)
data_transformer$transform(data)
dataThe data set to transform
data <- sample_toydata() transformer <- data_transformer$new() transformer$fit(data) transformed_data <- transformer$transform(data)
inverse_transform_continuous()data_transformer$inverse_transform_continuous(meta, data)
inverse_transform_discrete()data_transformer$inverse_transform_discrete(meta, data)
inverse_transform()Inverse Transform data using a fitted data_transformer. (From transformed format to original format.)
data_transformer$inverse_transform(data)
dataThe data set to transform
data <- sample_toydata() transformer <- data_transformer$new() transformer$fit(data) transformed_data <- transformer$transform(data) reconstructed_data <- transformer$inverse_transform(transformed_data)
clone()The objects of this class are cloneable with this method.
data_transformer$clone(deep = FALSE)
deepWhether to make a deep clone.
# NOT RUN {
# Before running the first time the torch backend needs to be installed
torch::install_torch()
# Load data
data <- sample_toydata()
# Build new transformer
transformer <- data_transformer$new()
# Fit transformer to data
transformer$fit(data)
# Transform data and store as new object
transformed_data <- transformer$transform(data)
# Train the default GAN
trained_gan <- gan_trainer(transformed_data)
# Sample synthetic data from the trained GAN
synthetic_data <- sample_synthetic_data(trained_gan, transformer)
# Plot the results
GAN_update_plot(data = data,
synth_data = synthetic_data,
main = "Real and Synthetic Data after Training")
# }
# NOT RUN {
## ------------------------------------------------
## Method `data_transformer$fit`
## ------------------------------------------------
data <- sample_toydata()
transformer <- data_transformer$new()
transformer$fit(data)
## ------------------------------------------------
## Method `data_transformer$transform`
## ------------------------------------------------
data <- sample_toydata()
transformer <- data_transformer$new()
transformer$fit(data)
transformed_data <- transformer$transform(data)
## ------------------------------------------------
## Method `data_transformer$inverse_transform`
## ------------------------------------------------
data <- sample_toydata()
transformer <- data_transformer$new()
transformer$fit(data)
transformed_data <- transformer$transform(data)
reconstructed_data <- transformer$inverse_transform(transformed_data)
# }
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