Docstring return statements

.git-blame-ignore-revs

@@ -0,0 +1,3 @@
+894744537dc64f729a44f64fbcb58e6b3b5dc1aa
+ad863fead5925eba5e61c915f9c799f9c5575ae4
+eb24014ce713fa14193cd5982d58e20d9e51d349

alibi_detect/ad/adversarialae.py

@@ -291,8 +291,8 @@ def predict(self, X: np.ndarray, batch_size: int = int(1e10), return_instance_sc
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the adversarial predictions and instance level adversarial scores.
+            - 'meta' has the model's metadata.
+            - 'data' contains the adversarial predictions and instance level adversarial scores.
         """
         adv_score = self.score(X, batch_size=batch_size)
 

alibi_detect/ad/model_distillation.py

@@ -208,8 +208,8 @@ def predict(self, X: np.ndarray, batch_size: int = int(1e10), return_instance_sc
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the adversarial predictions and instance level adversarial scores.
+            - 'meta' has the model's metadata.
+            - 'data' contains the adversarial predictions and instance level adversarial scores.
         """
         score = self.score(X, batch_size=batch_size)
 

alibi_detect/base.py

@@ -56,7 +56,7 @@ def concept_drift_dict():
 
 
 class BaseDetector(ABC):
-    """ Base class for outlier, adversarial and drift detection algorithms. """
+    """Base class for outlier, adversarial and drift detection algorithms."""
 
     def __init__(self):
         self.meta = copy.deepcopy(DEFAULT_META)
@@ -204,10 +204,12 @@ class Detector(Protocol):
 
     Used for typing legacy save and load functionality in `alibi_detect.saving._tensorflow.saving.py`.
 
-    Note:
+    Note
+    ----
         This exists to distinguish between detectors with and without support for config saving and loading. Once all
         detector support this then this protocol will be removed.
     """
+
     meta: Dict
 
     def predict(self) -> Any: ...
@@ -219,6 +221,7 @@ class ConfigurableDetector(Detector, Protocol):
 
     Used for typing save and load functionality in `alibi_detect.saving.saving`.
     """
+
     def get_config(self) -> dict: ...
 
     @classmethod
@@ -233,6 +236,7 @@ class StatefulDetectorOnline(ConfigurableDetector, Protocol):
 
     Used for typing save and load functionality in `alibi_detect.saving.saving`.
     """
+
     t: int = 0
 
     def save_state(self, filepath: Union[str, os.PathLike]): ...

alibi_detect/cd/base.py

@@ -135,10 +135,12 @@ def __init__(
     def preprocess(self, x: Union[np.ndarray, list]) -> Tuple[Union[np.ndarray, list], Union[np.ndarray, list]]:
         """
         Data preprocessing before computing the drift scores.
+
         Parameters
         ----------
         x
             Batch of instances.
+
         Returns
         -------
         Preprocessed reference data and new instances.
@@ -174,7 +176,7 @@ def get_splits(
 
         Returns
         -------
-        Combined reference and test instances with labels and optionally a list with tuples of
+        Combined reference and test instances with labels and optionally a list with tuples of \
         train and test indices for optionally different folds.
         """
         # create dataset and labels
@@ -269,11 +271,11 @@ def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True,
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the p-value, performance of the classifier
-        relative to its expectation under the no-change null, the out-of-fold classifier model
-        prediction probabilities on the reference and test data as well as the associated reference
-        and test instances of the out-of-fold predictions, and the trained model.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the p-value, performance of the classifier \
+            relative to its expectation under the no-change null, the out-of-fold classifier model \
+            prediction probabilities on the reference and test data as well as the associated reference \
+            and test instances of the out-of-fold predictions, and the trained model.
         """
         # compute drift scores
         p_val, dist, probs_ref, probs_test, x_ref_oof, x_test_oof = self.score(x)
@@ -394,10 +396,12 @@ def __init__(
     def preprocess(self, x: Union[np.ndarray, list]) -> Tuple[Union[np.ndarray, list], Union[np.ndarray, list]]:
         """
         Data preprocessing before computing the drift scores.
+
         Parameters
         ----------
         x
             Batch of instances.
+
         Returns
         -------
         Preprocessed reference data and new instances.
@@ -418,17 +422,18 @@ def get_splits(self, x_ref: Union[np.ndarray, list], x: Union[np.ndarray, list])
         """
         Split reference and test data into two splits -- one of which to learn test locations
         and parameters and one to use for tests.
+
         Parameters
         ----------
         x_ref
             Data used as reference distribution.
         x
             Batch of instances.
+
         Returns
         -------
-        Tuple containing split train data and tuple containing split test data
+        Tuple containing split train data and tuple containing split test data.
         """
-
         n_ref, n_cur = len(x_ref), len(x)
         perm_ref, perm_cur = np.random.permutation(n_ref), np.random.permutation(n_cur)
         idx_ref_tr, idx_ref_te = perm_ref[:int(n_ref * self.train_size)], perm_ref[int(n_ref * self.train_size):]
@@ -469,8 +474,8 @@ def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True,
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the detector's metadata.
-        'data' contains the drift prediction and optionally the p-value, threshold, MMD metric and
+            - 'meta' has the detector's metadata.
+            - 'data' contains the drift prediction and optionally the p-value, threshold, MMD metric and \
             trained kernel.
         """
         # compute drift scores
@@ -586,10 +591,12 @@ def __init__(
     def preprocess(self, x: Union[np.ndarray, list]) -> Tuple[np.ndarray, np.ndarray]:
         """
         Data preprocessing before computing the drift scores.
+
         Parameters
         ----------
         x
             Batch of instances.
+
         Returns
         -------
         Preprocessed reference data and new instances.
@@ -627,8 +634,8 @@ def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True, return_
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the p-value, threshold and MMD metric.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the p-value, threshold and MMD metric.
         """
         # compute drift scores
         p_val, dist, distance_threshold = self.score(x)
@@ -748,10 +755,12 @@ def __init__(
     def preprocess(self, x: Union[np.ndarray, list]) -> Tuple[np.ndarray, np.ndarray]:
         """
         Data preprocessing before computing the drift scores.
+
         Parameters
         ----------
         x
             Batch of instances.
+
         Returns
         -------
         Preprocessed reference data and new instances.
@@ -787,8 +796,8 @@ def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True, return_
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the p-value, threshold and LSDD metric.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the p-value, threshold and LSDD metric.
         """
         # compute drift scores
         p_val, dist, distance_threshold = self.score(x)
@@ -980,9 +989,9 @@ def predict(self, x: Union[np.ndarray, list], drift_type: str = 'batch',
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the feature level p-values,
-         threshold after multivariate correction if needed and test statistics.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the feature level p-values, threshold after \
+            multivariate correction if needed and test statistics.
         """
         # compute drift scores
         p_vals, dist = self.score(x)
@@ -1136,10 +1145,12 @@ def __init__(
     def preprocess(self, x: Union[np.ndarray, list]) -> Tuple[np.ndarray, np.ndarray]:
         """
         Data preprocessing before computing the drift scores.
+
         Parameters
         ----------
         x
             Batch of instances.
+
         Returns
         -------
         Preprocessed reference data and new instances.
@@ -1182,9 +1193,9 @@ def predict(self,  # type: ignore[override]
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the p-value, threshold, conditional MMD test statistic
-        and coupling matrices.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the p-value, threshold, conditional MMD test \
+            statistic and coupling matrices.
         """
         # compute drift scores
         p_val, dist, distance_threshold, coupling = self.score(x, c)

alibi_detect/cd/base_online.py

@@ -185,8 +185,8 @@ def predict(self, x_t: Union[np.ndarray, Any], return_test_stat: bool = True,
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the test-statistic and threshold.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the test-statistic and threshold.
         """
         # Compute test stat and check for drift
         test_stat = self.score(x_t)
@@ -442,8 +442,8 @@ def predict(self, x_t: Union[np.ndarray, Any], return_test_stat: bool = True,
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the test-statistic and threshold.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the test-statistic and threshold.
         """
         # Compute test stat and check for drift
         test_stats = self.score(x_t)

alibi_detect/cd/classifier.py

@@ -207,10 +207,8 @@ def predict(self, x: Union[np.ndarray, list],  return_p_val: bool = True,
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries
-
-         - 'meta' - has the model's metadata.
-
-         - 'data' - contains the drift prediction and optionally the p-value, performance of the classifier \
+            - 'meta' - has the model's metadata.
+            - 'data' - contains the drift prediction and optionally the p-value, performance of the classifier \
         relative to its expectation under the no-change null, the out-of-fold classifier model \
         prediction probabilities on the reference and test data, and the trained model. \
         """

alibi_detect/cd/context_aware.py

@@ -142,9 +142,9 @@ def predict(self, x: Union[np.ndarray, list], c: np.ndarray,
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the p-value, threshold, conditional MMD test statistic
-        and coupling matrices.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the p-value, threshold, conditional MMD test \
+            statistic and coupling matrices.
         """
         return self._detector.predict(x, c, return_p_val, return_distance, return_coupling)
 
@@ -162,8 +162,8 @@ def score(self, x: Union[np.ndarray, list], c: np.ndarray) -> Tuple[float, float
 
         Returns
         -------
-        p-value obtained from the conditional permutation test, the conditional MMD test statistic, the test
-        statistic threshold above which drift is flagged, and a tuple containing the coupling matrices
-        (W_{ref,ref}, W_{test,test}, W_{ref,test}).
+        p-value obtained from the conditional permutation test, the conditional MMD test statistic, the test \
+        statistic threshold above which drift is flagged, and a tuple containing the coupling matrices \
+        :math:`(W_{ref,ref}, W_{test,test}, W_{ref,test})`.
         """
         return self._detector.score(x, c)

alibi_detect/cd/keops/learned_kernel.py

@@ -213,7 +213,7 @@ def score(self, x: Union[np.ndarray, list]) -> Tuple[float, float, float]:
 
         Returns
         -------
-        p-value obtained from the permutation test, the MMD^2 between the reference and test set,
+        p-value obtained from the permutation test, the MMD^2 between the reference and test set, \
         and the MMD^2 threshold above which drift is flagged.
         """
         x_ref, x_cur = self.preprocess(x)

alibi_detect/cd/keops/mmd.py

@@ -162,7 +162,7 @@ def score(self, x: Union[np.ndarray, list]) -> Tuple[float, float, float]:
 
         Returns
         -------
-        p-value obtained from the permutation test, the MMD^2 between the reference and test set,
+        p-value obtained from the permutation test, the MMD^2 between the reference and test set, \
         and the MMD^2 threshold above which drift is flagged.
         """
         x_ref, x = self.preprocess(x)

alibi_detect/cd/learned_kernel.py

@@ -189,8 +189,8 @@ def predict(self, x: Union[np.ndarray, list],  return_p_val: bool = True,
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the detector's metadata.
-        'data' contains the drift prediction and optionally the p-value, threshold, MMD metric and
+            - 'meta' has the detector's metadata.
+            - data' contains the drift prediction and optionally the p-value, threshold, MMD metric and \
             trained kernel.
         """
         return self._detector.predict(x, return_p_val, return_distance, return_kernel)

alibi_detect/cd/lsdd.py

@@ -116,8 +116,8 @@ def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True, return_
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the p-value, threshold and LSDD metric.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the p-value, threshold and LSDD metric.
         """
         return self._detector.predict(x, return_p_val, return_distance)
 
@@ -133,7 +133,7 @@ def score(self, x: Union[np.ndarray, list]) -> Tuple[float, float, float]:
 
         Returns
         -------
-        p-value obtained from the permutation test, the LSDD between the reference and test set,
+        p-value obtained from the permutation test, the LSDD between the reference and test set, \
         and the LSDD threshold above which drift is flagged.
         """
         return self._detector.score(x)

alibi_detect/cd/lsdd_online.py

@@ -134,8 +134,8 @@ def predict(self, x_t: Union[np.ndarray, Any], return_test_stat: bool = True) \
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the test-statistic and threshold.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the test-statistic and threshold.
         """
         return self._detector.predict(x_t, return_test_stat)
 

alibi_detect/cd/mmd.py

@@ -136,8 +136,8 @@ def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True, return_
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the p-value, threshold and MMD metric.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the p-value, threshold and MMD metric.
         """
         return self._detector.predict(x, return_p_val, return_distance)
 
@@ -153,7 +153,7 @@ def score(self, x: Union[np.ndarray, list]) -> Tuple[float, float, float]:
 
         Returns
         -------
-        p-value obtained from the permutation test, the MMD^2 between the reference and test set,
+        p-value obtained from the permutation test, the MMD^2 between the reference and test set, \
         and the MMD^2 threshold above which drift is flagged.
         """
         return self._detector.score(x)

alibi_detect/cd/mmd_online.py

@@ -134,8 +134,8 @@ def predict(self, x_t: Union[np.ndarray, Any], return_test_stat: bool = True) \
         Returns
         -------
         Dictionary containing 'meta' and 'data' dictionaries.
-        'meta' has the model's metadata.
-        'data' contains the drift prediction and optionally the test-statistic and threshold.
+            - 'meta' has the model's metadata.
+            - 'data' contains the drift prediction and optionally the test-statistic and threshold.
         """
         return self._detector.predict(x_t, return_test_stat)