Conditional sampling with negative float values doesn't work

[pimlock]

Environment Details

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

• SDV version: 0.17.2
• Python version: Python 3.9.10
• Operating System: Mac OS 12.6.1

Error Description

When sampling records from the model with negative float as a condition, no records are being returned, as the _filter_conditions method filters them all out.

That's because it compares negative distance value, with an absolute value (which will always be >= 0).

Steps to reproduce

Sample code that triggers this issue:

data = pd.DataFrame({
    'column1': [-float(x) for x in list(range(100))],
    'column2': list(range(100))
})

model = GaussianCopula()
model.fit(data)

sampled = model.sample_conditions([Condition({'column1': -50.0})])

# this assertion fails, as the dataframe is empty
assert len(sampled) == 1

[npatki]

Thanks for filing @pimlock, confirmed that I can replicate this issue. We'll leave this open until the related changes are merged.

[npatki]

Update: I can confirm that this issue continues to persist for SDV 1.0+. This has a new API, so I'm attaching the updated code below for replication.

import pandas as pd

from sdv.sampling import Condition
from sdv.metadata import SingleTableMetadata
from sdv.single_table import GaussianCopulaSynthesizer

data = pd.DataFrame({
    'column1': [-float(x) for x in list(range(100))],
    'column2': list(range(100))
})

metadata = SingleTableMetadata()
metadata.detect_from_dataframe(data)

synth = GaussianCopulaSynthesizer(metadata)
synth.fit(data)

my_condition = Condition(
    num_rows=10,
    column_values={'column1': -50.0 }
)

synth.sample_from_conditions([my_condition])

This now directly results in an error.

ValueError: Unable to sample any rows for the given conditions. This may be because the provided values are out-of-bounds in the current model. 
Please try again with a different set of values.