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Upgrade pamegranate to v1 #514

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Upgrade pamegranate to v1 #514

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b3a8646
Upgrade pamegranate to v1
josalhor Nov 12, 2023
7a84f49
Fit transform twice for missing labels
josalhor Nov 14, 2023
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30 changes: 18 additions & 12 deletions sdmetrics/single_table/bayesian_network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,8 @@

from sdmetrics.goal import Goal
from sdmetrics.single_table.base import SingleTableMetric
from sklearn.preprocessing import LabelEncoder
from pomegranate import bayesian_network

LOGGER = logging.getLogger(__name__)

Expand All @@ -16,12 +18,6 @@ class BNLikelihoodBase(SingleTableMetric):

@classmethod
def _likelihoods(cls, real_data, synthetic_data, metadata=None, structure=None):
try:
from pomegranate import BayesianNetwork
except ImportError:
raise ImportError(
'Please install pomegranate with `pip install pomegranate` on a version of python '
'< 3.11. This metric is not supported on python versions >= 3.11.')

real_data, synthetic_data, metadata = cls._validate_inputs(
real_data, synthetic_data, metadata)
Expand All @@ -30,19 +26,29 @@ def _likelihoods(cls, real_data, synthetic_data, metadata=None, structure=None):

if not fields:
return np.full(len(real_data), np.nan)

encoders = {field: LabelEncoder() for field in fields}

real_data_encoded = real_data.copy()
synthetic_data_encoded = synthetic_data.copy()

for field in fields:
real_data_encoded[field] = encoders[field].fit_transform(real_data_encoded[field])

for field in fields:
synthetic_data_encoded[field] = encoders[field].fit_transform(synthetic_data_encoded[field])

LOGGER.debug('Fitting the BayesianNetwork to the real data')
if structure:
if isinstance(structure, dict):
structure = BayesianNetwork.from_json(json.dumps(structure)).structure

bn = BayesianNetwork.from_structure(real_data[fields].to_numpy(), structure)
bn = bayesian_network.BayesianNetwork(structure=structure, algorithm='chow-liu')
else:
bn = BayesianNetwork.from_samples(real_data[fields].to_numpy(), algorithm='chow-liu')
bn = bayesian_network.BayesianNetwork(algorithm='chow-liu')

bn.fit(real_data_encoded[fields].to_numpy())

LOGGER.debug('Evaluating likelihood of the synthetic data')
probabilities = []
for _, row in synthetic_data[fields].iterrows():
for _, row in synthetic_data_encoded[fields].iterrows():
try:
probabilities.append(bn.probability([row.to_numpy()]))
except ValueError:
Expand Down