PAR DiagnosticReport not 1.0 with float categorical columns

[frances-h]

Environment Details

Please indicate the following details about the environment in which you found the bug:

• SDV version:
• Python version:
• Operating System:

Error Description

When running PAR with categorical columns that are floats, PAR does not stick to the original categories when sampling. This leads to a very low diagnostic score for 'Data Validity' due to the CategoryAdherence metric failing.

Steps to reproduce

from sdv.datasets.demo import download_demo
from sdv.sequential import PARSynthesizer
from sdv.evaluation.single_table import run_diagnostic

data, metadata = download_demo('sequential', 'nasdaq100_2019')
data['category'] = [100.0 if i % 2 == 0 else 50.0 for i in data.index]
metadata.add_column('category', sdtype='categorical')

synth = PARSynthesizer(metadata)
synth.fit(data)
sampled = synth.sample(2)

report = run_diagnostic(data, sampled, metadata)

[npatki]

Workaround

If anyone is running into this, here is a suggested workaround:

1. Identify any categorical columns (in the metadata) that are actually represented as numbers in your data (ints, floats, etc.)
2. Cast these columns as objects before inputting them into the PARSynthesizer.
3. At the end when you get synthetic data, cast them back as ints, floats, etc.

Here is a code snippet that accomplishes the below. Replace the list CAT_COLUMN_NAMES with the list of your column names.

CAT_COLUMN_NAMES = ['ColA', 'ColB', ... ]

data = <your pandas DataFrame>
metadata = <your SingleTableMetadata object>

# cast the categorical columns to strings
for col_name in CAT_COLUMN_NAMES:
  data[col_name] = data[col_name].astype('object')

# now proceed with modeling and sampling as usual
synthesizer = PARSynthesizer(metadata)
synthesizer.fit(data)
synthetic_data = synthesizer.sample(num_sequences=10)

# (optional) cast the categorical columns back to floats
for col_name in CAT_COLUMN_NAMES:
  try:
    synthetic_data[col_name] = synthetic_data[col_name].astype('float')
  except:
    print('Column name', col_name, 'could not be converted back to a float')
    continue