Evaluation

As a final step to your synthetic data project, you can evaluate and visualize the synthetic data against the real data. 
Evaluation
Compare the real and synthetic data to determine whether the statistical and mathematical properties are similar.
evaluate_quality
Use this function to evaluate the quality of your synthetic data in terms of column shapes, correlations and parent-child relationships.
from sdv.evaluation.multi_table import evaluate_quality
​
quality_report = evaluate_quality(
    real_data=real_data,
    synthetic_data=synthetic_data,
    metadata=metadata)
Creating report: 100%|██████████| 4/4 [00:00<00:00,  7.09it/s]
​
Overall Quality Score: 82.84%
​
Properties:
Column Shapes: 82.78%
Column Pair Trends: 82.9%
Table Relationships: 77%
Parameters
(required) real_data: A dictionary mapping each table name to a pandas DataFrame object with the real data
(required) synthetic_data: A dictionary mapping each table name to a pandas DataFrame object with the synthetic data
(required) metadata: A MultiTableMetadata object that describes the columns
verbose: A boolean that indicates whether to print the progress of running the diagnostic. Defaults to True.
Output An SDMetrics Quality Report object generated with your real and synthetic data
The quality report contains a summary of the overall quality as well as detailed breakdowns to uncover new insights. You can interact with the object to learn more.
>>> quality_report.get_score()
0.783449101193
​
>>> quality_report.get_properties()
Property                        Score
Column Shapes                   0.8278
Column Pair Trends              0.8290
Parent Child Relationships      0.77110
​
>>> quality_report.get_details(property_name='Column Shapes', table_name='guests')
Table        Column            Metric             Quality Score
guests       amenities_fee     KSComplement       0.921127
guests       checkin_date      KSComplement       0.926000
​
...    
See the Quality Report API for more details.
run_diagnostic
Use this function to receive some diagnostic results about your synthetic data. Check to see if the synthetic rows are pure copies of the real data, if the synthetic data covers the full range of values and if the synthetic data adheres to the original ranges. 
sdv.evaluation.multi_table import run_diagnostic
​
diagnostic_report = run_diangnostic(
    real_data=real_data,
    synthetic_data=synthetic_data,
    metadata=metadata)
Creating report:  100%|████████████████| 200/200 [01:21<00:03,  2.37it/s]
​
Diagnostic Results
​
SUCCESS
✓     Over 90% of the synthetic rows are not copies of the real data
✓     The synthetic data covers over 90% of the numerical ranges present in the
      real data
​
WARNING
!     The synthetic data is missing more than 10% of the categories present in
      the real data
​
DANGER
x     More than 50% the synthetic data does not follow the min/max boundaries
      set by the real data
Parameters
(required) metadata: A MultiTableMetadata object that describes the columns
(required) synthetic_data: A dictionary mapping each table name to a pandas DataFrame object with the synthetic data
(required) real_data: A dictionary mapping each table name to a pandas DataFrame object with the real data
verbose: A boolean that indicates whether to print the progress of running the diagnostic. Defaults to True.
Output An SDMetrics DiagnosticReport object generated with your real and synthetic data.
The diagnostic report contains a summary as well as detailed breakdowns to uncover new insights. You can interact with the object to learn more.
>>> diagnostic_report.get_results()
{
    'SUCCESS': [
        'Over 90% of the synthetic rows are not copies of the real data',
        'The synthetic data covers over 90% of the numerical ranges present in the real data'
    ],
    ...
}
​
>>> diagnostic_report.get_properties()
{
  'Synthesis': 1.0,
  'Coverage': 0.85,
  'Boundaries': 0.90
}
​
>>> diagnostic_report.get_details(property_name='Coverage', table_name='guests')
Table     Column        Metric                 Score   
guests    amenities_fee    RangeCoverage          1.00000   
guests    checkin_date     RangeCoverage          1.00000        
...    
See the Diagnostic Report API for more details.
Visualization
Visualize the shapes of your columns in 1D and 2D.
get_column_plot
Use this function to visualize a real column against the same synthetic column. You can plot any column of type: boolean, categorical, datetime or numerical. 
from sdv.evaluation.single_table import get_column_plot
​
fig = get_column_plot(
    real_data=real_data,
    synthetic_data=synthetic_data,
    table_name='guests',
    column_name='amenities_fee',
    metadata=metadata
)
    
fig.show()
Parameters
(required) real_data: A pandas DataFrame object containing the table of your real data
(required) synthetic_data: A pandas DataFrame object containing the synthetic data
(required) table_name: The name of the table
(required) column_name: The name of the column in the table you want to plot
(required) metadata: A SingleTableMetadata object that describes the columns
Output A plotly Figure object that plots the distribution. This will change based on the sdtype.
Use fig.show() to see the plot in an iPython notebook. The plot is interactive, allowing you to zoom, scroll and take screenshots.
get_column_pair_plot
Use this utility to visualize the trends between a pair of columns for real and synthetic data. You can plot any 2 columns of type: boolean, categorical, datetime or numerical. The columns do not have to the be the same type.
from sdv.evaluation.single_table import get_column_pair_plot
​
fig = get_column_pair_plot(
    real_data=real_data,
    synthetic_data=synthetic_data,
    table_name='guests',
    column_names=['room_rate', 'room_type'],
    metadata=metadata)
    
fig.show()
Parameters
(required) real_data: A pandas DataFrame object containing the table of your real data
(required) synthetic_data: A pandas DataFrame object containing the synthetic data
(required) table_name: The name of the table
(required) column_names: A list with the names of the 2 columns you want to plot. Both columns must be in the table you specified.
(required) metadata: A SingleTableMetadata object that describes the columns
Output A plotly Figure object that plots the 2D distribution. This will change based on the sdtype.
Use fig.show() to see the plot in an iPython notebook. The plot is interactive, allowing you to zoom, scroll and take screenshots.
Need more evaluation options?
See the SDMetrics library.
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.
Last modified 1mo ago