Repository: sdv-dev/Copulas

Issue:
Modularize `fit` Method of GaussianMultivariate to Improve Exception Handling
### Problem Description

To improve code reuse, and exception handling, we would like to refactor the fit method of the `GaussianMultivariate` class. The goal is to split it into smaller, more focused methods that handle specific tasks, including better error management during the fitting process.

### Expected behavior

The current fit method performs several tasks within a single function, including column distribution fitting, exception handling, and correlation matrix computation. To enhance extensibility, we will break this method into modular components that isolate responsibilities and handle exceptions more explicitly.

### Expected Steps
1. **Validate Input Data**: Create a separate method to validate the input `X`, ensuring it is a DataFrame. This will handle cases where the input is not in the correct format.
2. **Fit Individual Columns**: Extract the logic for fitting each column to its distribution into its own method. This function should include the logic to handle distribution-specific exceptions.
3. **Handle Exceptions for Fitting**: Move the exception handling (fallback to `GaussianUnivariate` when fitting fails) into a distinct method to improve error handling extensions.
4. **Compute Correlation Matrix**: Leave as it is since its modular already.

Hints from issue comments:

Solution patch:
diff --git a/copulas/multivariate/gaussian.py b/copulas/multivariate/gaussian.py
index a929d7b..1dd7d65 100644
--- a/copulas/multivariate/gaussian.py
+++ b/copulas/multivariate/gaussian.py
@@ -70,26 +70,6 @@ class GaussianMultivariate(Multivariate):
 
         return stats.norm.ppf(np.column_stack(U))
 
-    def _get_correlation(self, X):
-        """Compute correlation matrix with transformed data.
-
-        Args:
-            X (numpy.ndarray):
-                Data for which the correlation needs to be computed.
-
-        Returns:
-            numpy.ndarray:
-                computed correlation matrix.
-        """
-        result = self._transform_to_normal(X)
-        correlation = pd.DataFrame(data=result).corr().to_numpy()
-        correlation = np.nan_to_num(correlation, nan=0.0)
-        # If singular, add some noise to the diagonal
-        if np.linalg.cond(correlation) > 1.0 / sys.float_info.epsilon:
-            correlation = correlation + np.identity(correlation.shape[0]) * EPSILON
-
-        return pd.DataFrame(correlation, index=self.columns, columns=self.columns)
-
     @check_valid_values
     def fit(self, X):
         """Compute the distribution for each variable and then its correlation matrix.
@@ -100,42 +80,88 @@ class GaussianMultivariate(Multivariate):
         """
         LOGGER.info('Fitting %s', self)
 
+        # Validate the input data
+        X = self._validate_input(X)
+        columns, univariates = self._fit_columns(X)
+
+        self.columns = columns
+        self.univariates = univariates
+
+        LOGGER.debug('Computing correlation.')
+        self.correlation = self._get_correlation(X)
+        self.fitted = True
+        LOGGER.debug('GaussianMultivariate fitted successfully')
+
+    def _validate_input(self, X):
+        """Validate the input data."""
         if not isinstance(X, pd.DataFrame):
             X = pd.DataFrame(X)
 
+        return X
+
+    def _fit_columns(self, X):
+        """Fit each column to its distribution."""
         columns = []
         univariates = []
         for column_name, column in X.items():
-            if isinstance(self.distribution, dict):
-                distribution = self.distribution.get(column_name, DEFAULT_DISTRIBUTION)
-            else:
-                distribution = self.distribution
-
+            distribution = self._get_distribution_for_column(column_name)
             LOGGER.debug('Fitting column %s to %s', column_name, distribution)
 
-            univariate = get_instance(distribution)
-            try:
-                univariate.fit(column)
-            except BaseException:
-                log_message = (
-                    f'Unable to fit to a {distribution} distribution for column {column_name}. '
-                    'Using a Gaussian distribution instead.'
-                )
-                LOGGER.info(log_message)
-                univariate = GaussianUnivariate()
-                univariate.fit(column)
-
+            univariate = self._fit_column(column, distribution, column_name)
             columns.append(column_name)
             univariates.append(univariate)
 
-        self.columns = columns
-        self.univariates = univariates
+        return columns, univariates
+
+    def _get_distribution_for_column(self, column_name):
+        """Retrieve the distribution for a given column name."""
+        if isinstance(self.distribution, dict):
+            return self.distribution.get(column_name, DEFAULT_DISTRIBUTION)
+
+        return self.distribution
+
+    def _fit_column(self, column, distribution, column_name):
+        """Fit a single column to its distribution with exception handling."""
+        univariate = get_instance(distribution)
+        try:
+            univariate.fit(column)
+        except Exception as error:
+            univariate = self._fit_with_fallback_distribution(
+                column, distribution, column_name, error
+            )
+
+        return univariate
+
+    def _fit_with_fallback_distribution(self, column, distribution, column_name, error):
+        """Fall back to fitting a Gaussian distribution and log the error."""
+        log_message = (
+            f'Unable to fit to a {distribution} distribution for column {column_name}. '
+            'Using a Gaussian distribution instead.'
+        )
+        LOGGER.info(log_message)
+        univariate = GaussianUnivariate()
+        univariate.fit(column)
+        return univariate
 
-        LOGGER.debug('Computing correlation')
-        self.correlation = self._get_correlation(X)
-        self.fitted = True
+    def _get_correlation(self, X):
+        """Compute correlation matrix with transformed data.
 
-        LOGGER.debug('GaussianMultivariate fitted successfully')
+        Args:
+            X (numpy.ndarray):
+                Data for which the correlation needs to be computed.
+
+        Returns:
+            numpy.ndarray:
+                computed correlation matrix.
+        """
+        result = self._transform_to_normal(X)
+        correlation = pd.DataFrame(data=result).corr().to_numpy()
+        correlation = np.nan_to_num(correlation, nan=0.0)
+        # If singular, add some noise to the diagonal
+        if np.linalg.cond(correlation) > 1.0 / sys.float_info.epsilon:
+            correlation = correlation + np.identity(correlation.shape[0]) * EPSILON
+
+        return pd.DataFrame(correlation, index=self.columns, columns=self.columns)
 
     def probability_density(self, X):
         """Compute the probability density for each point in X.


Test patch:
diff --git a/tests/unit/multivariate/test_gaussian.py b/tests/unit/multivariate/test_gaussian.py
index 545edcc..c0c2c61 100644
--- a/tests/unit/multivariate/test_gaussian.py
+++ b/tests/unit/multivariate/test_gaussian.py
@@ -350,6 +350,111 @@ class TestGaussianMultivariate(TestCase):
         assert isinstance(copula.univariates[0], GaussianUnivariate)
         assert copula.univariates[0]._params == {'loc': np.mean(data), 'scale': np.std(data)}
 
+    def test__validate_input_with_dataframe(self):
+        """Test that `_validate_input` returns the same DataFrame."""
+        # Setup
+        instance = GaussianMultivariate()
+        input_df = pd.DataFrame({'A': [1, 2, 3]})
+
+        # Run
+        result = instance._validate_input(input_df)
+
+        # Assert
+        pd.testing.assert_frame_equal(result, input_df)
+
+    def test__validate_input_with_non_dataframe(self):
+        """Test that `_validate_input` converts non-DataFrame input into a DataFrame."""
+        # Setup
+        instance = GaussianMultivariate()
+        input_data = [[1, 2, 3], [4, 5, 6]]
+
+        # Run
+        result = instance._validate_input(input_data)
+
+        # Assert
+        expected_df = pd.DataFrame(input_data)
+        pd.testing.assert_frame_equal(result, expected_df)
+
+    @patch('copulas.multivariate.gaussian.LOGGER')
+    def test__fit_columns(self, mock_logger):
+        """Test that `_fit_columns` fits each column to its distribution."""
+        # Setup
+        instance = GaussianMultivariate()
+        instance._get_distribution_for_column = Mock(return_value='normal')
+        instance._fit_column = Mock(return_value='fitted_univariate')
+
+        X = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
+
+        # Run
+        columns, univariates = instance._fit_columns(X)
+
+        # Assert
+        assert columns == ['A', 'B']
+        assert univariates == ['fitted_univariate', 'fitted_univariate']
+        instance._get_distribution_for_column.assert_any_call('A')
+        instance._get_distribution_for_column.assert_any_call('B')
+        mock_logger.debug.assert_any_call('Fitting column %s to %s', 'A', 'normal')
+        mock_logger.debug.assert_any_call('Fitting column %s to %s', 'B', 'normal')
+
+    @patch('copulas.multivariate.gaussian.DEFAULT_DISTRIBUTION', new='default_distribution')
+    def test__get_distribution_for_column_with_dict(self):
+        """Test that `_get_distribution_for_column` retrieves correct distribution from dict."""
+        # Setup
+        instance = GaussianMultivariate()
+        instance.distribution = {'A': 'normal', 'B': 'uniform'}
+
+        # Run
+        result_A = instance._get_distribution_for_column('A')
+        result_B = instance._get_distribution_for_column('B')
+        result_C = instance._get_distribution_for_column('C')
+
+        # Assert
+        assert result_A == 'normal'
+        assert result_B == 'uniform'
+        assert result_C == 'default_distribution'
+
+    @patch('copulas.multivariate.gaussian.get_instance')
+    @patch('copulas.multivariate.gaussian.GaussianUnivariate')
+    def test__fit_column_with_exception(self, mock_gaussian_univariate, mock_get_instance):
+        """Test that `_fit_column` falls back to a Gaussian distribution on exception."""
+        # Setup
+        instance = GaussianMultivariate()
+        column = pd.Series([1, 2, 3])
+        distribution = 'normal'
+        column_name = 'A'
+        instance._fit_with_fallback_distribution = Mock(return_value='fallback_univariate')
+
+        mock_univariate = Mock()
+        mock_univariate.fit.side_effect = Exception('Fit error')
+        mock_get_instance.return_value = mock_univariate
+
+        # Run
+        result = instance._fit_column(column, distribution, column_name)
+
+        # Assert
+        instance._fit_with_fallback_distribution.assert_called_once_with(
+            column, distribution, column_name, mock_univariate.fit.side_effect
+        )
+        assert result == 'fallback_univariate'
+
+    @patch('copulas.multivariate.gaussian.GaussianUnivariate')
+    def test__fit_with_fallback_distribution(self, mock_gaussian_univariate):
+        """Test that `_fit_with_fallback_distribution` fits a Gaussian distribution."""
+        # Setup
+        instance = GaussianMultivariate()
+        column = pd.Series([1, 2, 3])
+        distribution = 'normal'
+        column_name = 'A'
+        error = Exception('Test error')
+        mock_gaussian_univariate.return_value = Mock(fit=Mock())
+
+        # Run
+        result = instance._fit_with_fallback_distribution(column, distribution, column_name, error)
+
+        # Assert
+        mock_gaussian_univariate.return_value.fit.assert_called_once_with(column)
+        assert result == mock_gaussian_univariate.return_value
+
     def test_probability_density(self):
         """Probability_density computes probability for the given values."""
         # Setup