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Fix probabilities contain NaN error #326

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Feb 24, 2021
Merged

Fix probabilities contain NaN error #326

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25 changes: 17 additions & 8 deletions sdv/relational/hma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -324,12 +324,6 @@ def _sample_rows(self, model, table_name, num_rows=None):

return sampled

def _sample_children(self, table_name, sampled_data, table_rows=None):
for child_name in self.metadata.get_children(table_name):
LOGGER.info('Sampling rows from child table %s', child_name)
for _, row in table_rows.iterrows():
self._sample_child_rows(child_name, table_name, row, sampled_data)

def _sample_child_rows(self, table_name, parent_name, parent_row, sampled_data):
foreign_key = self.metadata.get_foreign_keys(parent_name, table_name)[0]
parameters = self._extract_parameters(parent_row, table_name, foreign_key)
Expand All @@ -350,7 +344,15 @@ def _sample_child_rows(self, table_name, parent_name, parent_row, sampled_data):
sampled_data[table_name] = pd.concat(
[previous, table_rows]).reset_index(drop=True)

self._sample_children(table_name, sampled_data, table_rows)
def _sample_children(self, table_name, sampled_data, table_rows):
for child_name in self.metadata.get_children(table_name):
if child_name not in sampled_data:
LOGGER.info('Sampling rows from child table %s', child_name)
for _, row in table_rows.iterrows():
self._sample_child_rows(child_name, table_name, row, sampled_data)

child_rows = sampled_data[child_name]
self._sample_children(child_name, sampled_data, child_rows)

@staticmethod
def _find_parent_id(likelihoods, num_rows):
Expand All @@ -368,7 +370,14 @@ def _find_parent_id(likelihoods, num_rows):
# rows that got a singular matrix error with the mean
likelihoods = likelihoods.fillna(mean)

weights = likelihoods.values / likelihoods.sum()
total = likelihoods.sum()
if total == 0:
# Worse case scenario: we have no likelihoods
# and all num_rows are 0, so we fallback to uniform
length = len(likelihoods)
weights = np.ones(length) / length
else:
weights = likelihoods.values / total

return np.random.choice(likelihoods.index, p=weights)

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