Use these sampling methods to create synthetic data from your multi table model.
Create realistic synthetic data data that follows the same format and mathematical properties as the real data.
Use this function to create synthetic data that mimics the real data
synthetic_data = synthesizer.sample(
scale: A float >0.0 that describes how much to scale the data by
How large will the synthetic data be? The scale is based on the size of the data you used for training. The scale determines the size of every parent table (ie a table without any foreign keys).
Note that the synthesizer will algorithmically determine the size of the child tables, so their final sizes will approximately follow the scale, with some minor deviations.
Use this function to reset any randomization in sampling. After calling this, your synthesizer will generate the same data as before. For example in the code below,
synthetic_data2are the same.
synthetic_data1 = synthesizer.sample(scale=1.5)
synthetic_data2 = synthesizer.sample(scale=1.5)
Returns None. Resets the synthesizer.