Return data from the backend fit method

alibi_detect/od/_gmm.py

@@ -126,7 +126,7 @@ def fit(
         verbose
             Verbosity level used to fit the detector. Used for both ``'sklearn'`` and ``'pytorch'`` backends. Defaults to ``0``.
         """
-        self.backend.fit(
+        return self.backend.fit(
             self.backend._to_backend_dtype(x_ref),
             **self.backend.format_fit_kwargs(locals())
         )

alibi_detect/od/_svm.py

@@ -143,7 +143,7 @@ def fit(
             Verbosity level during training. ``0`` is silent, ``1`` prints fit status. If using `bgd`, fit displays a
             progress bar. Otherwise, if using `sgd` then we output the Sklearn `SGDOneClassSVM.fit()` logs.
         """
-        self.backend.fit(
+        return self.backend.fit(
             self.backend._to_backend_dtype(x_ref),
             **self.backend.format_fit_kwargs(locals())
         )

alibi_detect/od/pytorch/base.py

@@ -31,6 +31,14 @@ def to_frontend_dtype(self):
         return result
 
 
+def _tensor_to_frontend_dtype(x: Union[torch.Tensor, np.ndarray, float]) -> Union[np.ndarray, float]:
+    if isinstance(x, torch.Tensor):
+        x = x.cpu().detach().numpy()
+    if isinstance(x, np.ndarray) and x.ndim == 0:
+        x = x.item()
+    return x
+
+
 def _raise_type_error(x):
     raise TypeError(f'x is type={type(x)} but must be one of TorchOutlierDetectorOutput or a torch Tensor')
 
@@ -52,7 +60,7 @@ def to_frontend_dtype(x: Union[torch.Tensor, TorchOutlierDetectorOutput]) -> Uni
 
     return {
         'TorchOutlierDetectorOutput': lambda x: x.to_frontend_dtype(),
-        'Tensor': lambda x: x.cpu().detach().numpy()
+        'Tensor': _tensor_to_frontend_dtype
     }.get(
         x.__class__.__name__,
         _raise_type_error

alibi_detect/od/pytorch/gmm.py

@@ -132,7 +132,7 @@ def fit(  # type: ignore[override]
         self._set_fitted()
         return {
             'converged': converged,
-            'lower_bound': min_loss,
+            'lower_bound': self._to_frontend_dtype(min_loss),
             'n_epochs': epoch
         }
 

alibi_detect/od/pytorch/svm.py

@@ -338,7 +338,7 @@ def fit(  # type: ignore[override]
         self._set_fitted()
         return {
             'converged': converged,
-            'lower_bound': min_loss,
+            'lower_bound': self._to_frontend_dtype(min_loss),
             'n_iter': iter
         }
 

alibi_detect/od/tests/test__gmm/test__gmm.py

@@ -87,7 +87,7 @@ def test_gmm_integration(backend):
     gmm_detector = GMM(n_components=8, backend=backend)
     X_ref, _ = make_moons(1001, shuffle=True, noise=0.05, random_state=None)
     X_ref, x_inlier = X_ref[0:1000], X_ref[1000][None]
-    gmm_detector.fit(X_ref)
+    fit_logs = gmm_detector.fit(X_ref)
     gmm_detector.infer_threshold(X_ref, 0.1)
     result = gmm_detector.predict(x_inlier)
     result = result['data']['is_outlier'][0]
@@ -117,3 +117,19 @@ def test_gmm_torchscript(tmp_path):
     ts_gmm = torch.load(tmp_path / 'gmm.pt')
     y = ts_gmm(x)
     assert torch.all(y == torch.tensor([False, True]))
+
+
+@pytest.mark.parametrize('backend', ['pytorch', 'sklearn'])
+def test_gmm_fit(backend):
+    """Test GMM detector fit method.
+
+    Tests detector checks for convergence and stops early if it does.
+    """
+    gmm = GMM(n_components=1, backend=backend)
+    mean = [8, 8]
+    cov = [[2., 0.], [0., 1.]]
+    x_ref = torch.tensor(np.random.multivariate_normal(mean, cov, 1000))
+    fit_results = gmm.fit(x_ref, tol=0.01, batch_size=32)
+    assert isinstance(fit_results['lower_bound'], float)
+    assert fit_results['converged']
+    assert fit_results['lower_bound'] < 1

alibi_detect/od/tests/test__svm/test__svm.py

@@ -109,7 +109,7 @@ def test_fitted_svm_score(optimization):
         nu=0.1
     )
     x_ref = np.random.randn(100, 2)
-    svm_detector.fit(x_ref)
+    fit_logs = svm_detector.fit(x_ref)
     x = np.array([[0, 10], [0.1, 0]])
     scores = svm_detector.score(x)
 
@@ -207,3 +207,28 @@ def test_svm_torchscript(tmp_path):
     ts_svm = torch.load(tmp_path / 'svm.pt')
     y = ts_svm(x)
     assert torch.all(y == torch.tensor([False, True]))
+
+
+@pytest.mark.parametrize('optimization', ['sgd', 'bgd'])
+def test_svm_fit(optimization):
+    """Test SVM detector fit method.
+
+    Tests pytorch detector checks for convergence and stops early if it does.
+    """
+    kernel = GaussianRBF(torch.tensor(1.))
+    svm = SVM(
+        n_components=10,
+        kernel=kernel,
+        nu=0.01,
+        optimization=optimization,
+    )
+    mean = [8, 8]
+    cov = [[2., 0.], [0., 1.]]
+    x_ref = torch.tensor(np.random.multivariate_normal(mean, cov, 1000))
+    fit_results = svm.fit(x_ref, tol=0.01)
+    assert fit_results['converged']
+    assert fit_results['n_iter'] < 100
+    assert fit_results.get('lower_bound', 0) < 1
+    # 'sgd' optimization does not return lower bound
+    if optimization == 'bgd':
+        assert isinstance(fit_results['lower_bound'], float)