Error if continuous training data contains null values

ctgan/errors.py

@@ -0,0 +1,5 @@
+"""Custom errors for CTGAN."""
+
+
+class InvalidDataError(Exception):
+    """Error to raise when data is not valid."""

ctgan/synthesizers/ctgan.py

@@ -11,6 +11,7 @@
 
 from ctgan.data_sampler import DataSampler
 from ctgan.data_transformer import DataTransformer
+from ctgan.errors import InvalidDataError
 from ctgan.synthesizers.base import BaseSynthesizer, random_state
 
 
@@ -289,6 +290,31 @@ def _validate_discrete_columns(self, train_data, discrete_columns):
         if invalid_columns:
             raise ValueError(f'Invalid columns found: {invalid_columns}')
 
+    def _validate_null_data(self, train_data, discrete_columns):
+        """Check whether null values exist in continuous ``train_data``.
+
+        Args:
+            train_data (numpy.ndarray or pandas.DataFrame):
+                Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame.
+            discrete_columns (list-like):
+                List of discrete columns to be used to generate the Conditional
+                Vector. If ``train_data`` is a Numpy array, this list should
+                contain the integer indices of the columns. Otherwise, if it is
+                a ``pandas.DataFrame``, this list should contain the column names.
+        """
+        if isinstance(train_data, pd.DataFrame):
+            continuous_cols = list(set(train_data.columns) - set(discrete_columns))
+            any_nulls = train_data[continuous_cols].isna().any().any()
+        else:
+            continuous_cols = [i for i in range(train_data.shape[1]) if i not in discrete_columns]
+            any_nulls = pd.DataFrame(train_data)[continuous_cols].isna().any().any()
+
+        if any_nulls:
+            raise InvalidDataError(
+                'CTGAN does not support null values in the continuous training data. '
+                'Please remove all null values from your continuous training data.'
+            )
+
     @random_state
     def fit(self, train_data, discrete_columns=(), epochs=None):
         """Fit the CTGAN Synthesizer models to the training data.
@@ -303,6 +329,7 @@ def fit(self, train_data, discrete_columns=(), epochs=None):
                 a ``pandas.DataFrame``, this list should contain the column names.
         """
         self._validate_discrete_columns(train_data, discrete_columns)
+        self._validate_null_data(train_data, discrete_columns)
 
         if epochs is None:
             epochs = self._epochs

tests/integration/synthesizer/test_ctgan.py

@@ -15,6 +15,7 @@
 import pandas as pd
 import pytest
 
+from ctgan.errors import InvalidDataError
 from ctgan.synthesizers.ctgan import CTGAN
 
 
@@ -132,6 +133,25 @@ def test_categorical_nan():
     assert {'b', 'c'}.issubset(values)
 
 
+def test_continuous_nan():
+    """Test the CTGAN with missing numerical values."""
+    # Setup
+    data = pd.DataFrame({
+        'continuous': [np.nan, 1.0, 2.0] * 10,
+        'discrete': ['a', 'b', 'c'] * 10,
+    })
+    discrete_columns = ['discrete']
+    error_message = (
+        'CTGAN does not support null values in the continuous training data. '
+        'Please remove all null values from your continuous training data.'
+    )
+
+    # Run and Assert
+    ctgan = CTGAN(epochs=1)
+    with pytest.raises(InvalidDataError, match=error_message):
+        ctgan.fit(data, discrete_columns)
+
+
 def test_synthesizer_sample():
     """Test the CTGAN samples the correct datatype."""
     data = pd.DataFrame({'discrete': np.random.choice(['a', 'b', 'c'], 100)})

tests/unit/synthesizer/test_ctgan.py

@@ -3,11 +3,13 @@
 from unittest import TestCase
 from unittest.mock import Mock
 
+import numpy as np
 import pandas as pd
 import pytest
 import torch
 
 from ctgan.data_transformer import SpanInfo
+from ctgan.errors import InvalidDataError
 from ctgan.synthesizers.ctgan import CTGAN, Discriminator, Generator, Residual
 
 
@@ -289,3 +291,42 @@ def test__validate_discrete_columns(self):
         ctgan = CTGAN(epochs=1)
         with pytest.raises(ValueError, match=r'Invalid columns found: {\'doesnt exist\'}'):
             ctgan.fit(data, discrete_columns)
+
+    def test__validate_null_data(self):
+        """Test `_validate_null_data` with pandas and numpy data.
+
+        Check the appropriate error is raised if null values are present in
+        continuous columns, both for numpy arrays and dataframes.
+        """
+        # Setup
+        discrete_df = pd.DataFrame({'discrete': ['a', 'b']})
+        discrete_array = np.array([['a'], ['b']])
+        continuous_no_nulls_df = pd.DataFrame({'continuous': [0, 1]})
+        continuous_no_nulls_array = np.array([[0], [1]])
+        continuous_with_null_df = pd.DataFrame({'continuous': [1, np.nan]})
+        continuous_with_null_array = np.array([[1], [np.nan]])
+        ctgan = CTGAN(epochs=1)
+        error_message = (
+            'CTGAN does not support null values in the continuous training data. '
+            'Please remove all null values from your continuous training data.'
+        )
+
+        # Test discrete DataFrame fits without error
+        ctgan.fit(discrete_df, ['discrete'])
+
+        # Test discrete array fits without error
+        ctgan.fit(discrete_array, [0])
+
+        # Test continuous DataFrame without nulls fits without error
+        ctgan.fit(continuous_no_nulls_df)
+
+        # Test continuous array without nulls fits without error
+        ctgan.fit(continuous_no_nulls_array)
+
+        # Test nulls in continuous columns DataFrame errors on fit
+        with pytest.raises(InvalidDataError, match=error_message):
+            ctgan.fit(continuous_with_null_df)
+
+        # Test nulls in continuous columns array errors on fit
+        with pytest.raises(InvalidDataError, match=error_message):
+            ctgan.fit(continuous_with_null_array)