As a final step to your synthetic data project, you can evaluate and visualize the synthetic data against the real data.
from sdv.evaluation.multi_table import run_diagnostic, evaluate_quality
from sdv.evaluation.multi_table import get_column_plot
# 1. perform basic validity checks
diagnostic = run_diagnostic(real_data, synthetic_data, metadata)
# 2. measure the statistical similarity
quality_report = evaluate_quality(real_data, synthetic_data, metadata)
# 3. plot the data
fig = get_column_plot(
real_data=real_data,
synthetic_data=synthetic_data,
metadata=metadata,
table_name='guests',
column_name='amenities_fee'
)
fig.show()
Explore the functionaliy in more detail below.
Need more evaluation options?
This library includes many more metrics (some experimental) that you can apply based on your goals. All you need is your real data, synthetic data and metadata to get started.