Return data from the backend fit method (#835)

* Return data from the backend fit method

* Update docstrings for gmm and svm fit methods

alibi_detect/od/_gmm.py

@@ -130,8 +130,16 @@ def fit(
  Defaults to ``'kmeans'``.
  verbose
  Verbosity level used to fit the detector. Used for both ``'sklearn'`` and ``'pytorch'`` backends. Defaults to ``0``.
+
+ Returns
+ -------
+ Dictionary with fit results. The dictionary contains the following keys depending on the backend used:
+ - converged: bool indicating whether EM algorithm converged.
+ - n_iter: number of EM iterations performed. Only returned if `backend` is ``'sklearn'``.
+ - n_epochs: number of gradient descent iterations performed. Only returned if `backend` is ``'pytorch'``.
+ - lower_bound: log-likelihood lower bound.
  """
- 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

@@ -147,8 +147,15 @@ def fit(
  verbose
  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.
+
+ Returns
+ -------
+ Dictionary with fit results. The dictionary contains the following keys depending on the optimization used:
+ - converged: `bool` indicating whether training converged.
+ - n_iter: number of iterations performed.
+ - lower_bound: loss lower bound. Only returned for the `bgd`.
  """
- 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 # type: ignore[return-value]
+
+
 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

@@ -75,8 +75,8 @@ def fit( # type: ignore[override]
  Returns
  -------
  Dictionary with fit results. The dictionary contains the following keys:
- - converged: bool indicating whether EM algorithm converged.
- - n_iter: number of EM iterations performed.
+ - converged: bool indicating whether training converged.
+ - n_epochs: number of gradient descent iterations performed.
  - lower_bound: log-likelihood lower bound.
  """
  self.model = GMMModel(self.n_components, x_ref.shape[-1]).to(self.device)
@@ -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

@@ -272,7 +272,7 @@ def fit( # type: ignore[override]
  Returns
  -------
  Dictionary with fit results. The dictionary contains the following keys:
- - converged: bool indicating whether training converged.
+ - converged: `bool` indicating whether training converged.
  - n_iter: number of iterations performed.
  - lower_bound: loss lower bound.
  """
@@ -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

@@ -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

@@ -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)