Training Models and Generating Artificial Data¶
Training Models¶
The function train trains the WGAN model, which is made up of the Generator and Critic (discriminator). If context
is non-empty, a cWGAN is trained, otherwise the default is a regular WGAN.
The function is trained using stochastic optimization as described in detail in Gulrajani et al 2017.
wgan.train(generator, critic, x, context, specs)
Generating Artificial Data¶
The function apply_generator
from class DataWrapper replaces columns in df
that are produced by the generator. The generated data is of size equal to the number of rows in df
. Variables in df
that are not
produced by the generator are not modified.
df_generated
contains the artificially generated data.
df_generated = data_wrapper.apply_generator(generator, df.sample(int(1e6), replace=True))