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Every column that the SDV synthesizes falls into 1 of 2 buckets:
Currently, you can't conditionally sample using ID, primary key, or other generated columns.
As a user, I expect to be able to conditionally sample on any column(s) I see fit.
I expect the following code to work:
import pandas as pd from sdv.single_table import GaussianCopulaSynthesizer from sdv.datasets.demo import download_demo data, metadata = download_demo( modality='single_table', dataset_name='census_extended' ) synthesizer = GaussianCopulaSynthesizer(metadata) synthesizer.fit(data) synthesizer.sample_remaining_columns(data[['id', 'workclass']].head(10))
Related to this issue: #1096
The text was updated successfully, but these errors were encountered:
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Problem Description
Every column that the SDV synthesizes falls into 1 of 2 buckets:
Currently, you can't conditionally sample using ID, primary key, or other generated columns.
Expected behavior
As a user, I expect to be able to conditionally sample on any column(s) I see fit.
Additional context
I expect the following code to work:
Related to this issue: #1096
The text was updated successfully, but these errors were encountered: