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Replaced FAST_ML with GaussianCopulaSynthesizer & Cleaned up Verbosity #2020

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May 21, 2024
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Replaced FAST_ML with GaussianCopulaSynthesizer & Cleaned up Verbosity #2020

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Replaced FAST_ML with GaussianCopulaSynthesizer
srinify May 20, 2024
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Shortened progress bar
srinify May 21, 2024
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20 changes: 12 additions & 8 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -94,12 +94,12 @@ column and the primary key (`guest_email`).
## Synthesizing Data
Next, we can create an **SDV synthesizer**, an object that you can use to create synthetic data.
It learns patterns from the real data and replicates them to generate synthetic data. Let's use
the `FAST_ML` preset synthesizer, which is optimized for performance.
the [GaussianCopulaSynthesizer](https://docs.sdv.dev/sdv/single-table-data/modeling/synthesizers/gaussiancopulasynthesizer).

```python
from sdv.lite import SingleTablePreset
from sdv.single_table import GaussianCopulaSynthesizer

synthesizer = SingleTablePreset(metadata, name='FAST_ML')
synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.fit(data=real_data)
```

Expand Down Expand Up @@ -131,11 +131,15 @@ quality_report = evaluate_quality(
```

```
Creating report: 100%|██████████| 4/4 [00:00<00:00, 19.30it/s]
Overall Quality Score: 89.12%
Properties:
Column Shapes: 90.27%
Column Pair Trends: 87.97%
Generating report ...

(1/2) Evaluating Column Shapes: |████████████████| 9/9 [00:00<00:00, 1133.09it/s]|
Column Shapes Score: 89.11%

(2/2) Evaluating Column Pair Trends: |██████████████████████████████████████████| 36/36 [00:00<00:00, 502.88it/s]|
Column Pair Trends Score: 88.3%

Overall Score (Average): 88.7%
```

This object computes an overall quality score on a scale of 0 to 100% (100 being the best) as well
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