WIP: Flatten explainer hierarchy

alibi/explainers/__init__.py

@@ -2,10 +2,10 @@
 The 'alibi.explainers' module includes feature importance, counterfactual and anchor-based explainers.
 """
 
-from .anchor.anchor_tabular import AnchorTabular
-from .anchor.anchor_text import AnchorText
-from .anchor.anchor_image import AnchorImage
-from .cem.cem import CEM
+from .anchor_tabular import AnchorTabular
+from .anchor_text import AnchorText
+from .anchor_image import AnchorImage
+from .cem import CEM
 
 __all__ = ["AnchorTabular",
            "AnchorText",

alibi/explainers/anchor/anchor_image.py → alibi/explainers/anchor_image.py

@@ -1,5 +1,5 @@
-from . import anchor_base
-from . import anchor_explanation
+from .anchor_base import AnchorBaseBeam
+from .anchor_explanation import AnchorExplanation
 import logging
 import numpy as np
 from typing import Any, Callable, Tuple
@@ -250,9 +250,9 @@ def explain(self, image: np.ndarray, threshold: float = 0.95, delta: float = 0.1
         segments, sample_fn = self.get_sample_fn(image, p_sample=p_sample)
 
         # get anchors and add metadata
-        exp = anchor_base.AnchorBaseBeam.anchor_beam(sample_fn, delta=delta,
-                                                     epsilon=tau, batch_size=batch_size,
-                                                     desired_confidence=threshold, **kwargs)  # type: Any
+        exp = AnchorBaseBeam.anchor_beam(sample_fn, delta=delta,
+                                         epsilon=tau, batch_size=batch_size,
+                                         desired_confidence=threshold, **kwargs)  # type: Any
         exp['instance'] = image
         exp['prediction'] = self.predict_fn(np.expand_dims(image, axis=0))[0]
 
@@ -265,7 +265,7 @@ def explain(self, image: np.ndarray, threshold: float = 0.95, delta: float = 0.1
                        for i in range(ex[opt].shape[0])]
                 ex[opt] = tmp
 
-        exp = anchor_explanation.AnchorExplanation('image', exp)
+        exp = AnchorExplanation('image', exp)
 
         # output explanation dictionary
         explanation = {}

alibi/explainers/anchor/anchor_tabular.py → alibi/explainers/anchor_tabular.py

@@ -1,6 +1,6 @@
-from . import anchor_base
-from . import anchor_explanation
-from .discretizer import Discretizer
+from .anchor_base import AnchorBaseBeam
+from .anchor_explanation import AnchorExplanation
+from alibi.utils.discretizer import Discretizer
 import numpy as np
 from typing import Callable, Tuple, Dict, Any, Set
 
@@ -300,13 +300,13 @@ def explain(self, X: np.ndarray, threshold: float = 0.95, delta: float = 0.1,
         sample_fn, mapping = self.get_sample_fn(X, desired_label=desired_label)
 
         # get anchors and add metadata
-        exp = anchor_base.AnchorBaseBeam.anchor_beam(sample_fn, delta=delta, epsilon=tau,
-                                                     batch_size=batch_size, desired_confidence=threshold,
-                                                     max_anchor_size=max_anchor_size, **kwargs)  # type: Any
+        exp = AnchorBaseBeam.anchor_beam(sample_fn, delta=delta, epsilon=tau,
+                                         batch_size=batch_size, desired_confidence=threshold,
+                                         max_anchor_size=max_anchor_size, **kwargs)  # type: Any
         self.add_names_to_exp(exp, mapping)
         exp['instance'] = X
         exp['prediction'] = self.predict_fn(X.reshape(1, -1))[0]
-        exp = anchor_explanation.AnchorExplanation('tabular', exp)
+        exp = AnchorExplanation('tabular', exp)
 
         # output explanation dictionary
         explanation = {}

alibi/explainers/anchor/anchor_text.py → alibi/explainers/anchor_text.py

@@ -1,5 +1,5 @@
-from . import anchor_base
-from . import anchor_explanation
+from .anchor_base import AnchorBaseBeam
+from .anchor_explanation import AnchorExplanation
 import logging
 import numpy as np
 from typing import Any, Callable, Tuple, Dict
@@ -173,6 +173,7 @@ def sample_fn(present: list, num_samples: int, compute_labels: bool = True,
                 labels = (self.predict_fn(raw_data) == true_label).astype(int)
             raw_data = np.array(raw_data).reshape(-1, 1)
             return raw_data, data, labels
+
         return words, positions, sample_fn
 
     def explain(self, text: str, threshold: float = 0.95, delta: float = 0.1,
@@ -230,17 +231,17 @@ def explain(self, text: str, threshold: float = 0.95, delta: float = 0.1,
         data_type = '<U' + str(int(total_len))
 
         # get anchors and add metadata
-        exp = anchor_base.AnchorBaseBeam.anchor_beam(sample_fn, delta=delta,
-                                                     epsilon=tau, batch_size=batch_size,
-                                                     desired_confidence=threshold,
-                                                     stop_on_first=True, data_type=data_type,
-                                                     **kwargs)  # type: Any
+        exp = AnchorBaseBeam.anchor_beam(sample_fn, delta=delta,
+                                         epsilon=tau, batch_size=batch_size,
+                                         desired_confidence=threshold,
+                                         stop_on_first=True, data_type=data_type,
+                                         **kwargs)  # type: Any
 
         exp['names'] = [words[x] for x in exp['feature']]
         exp['positions'] = [positions[x] for x in exp['feature']]
         exp['instance'] = text
         exp['prediction'] = self.predict_fn([text])[0]
-        exp = anchor_explanation.AnchorExplanation('text', exp)
+        exp = AnchorExplanation('text', exp)
 
         # output explanation dictionary
         explanation = {}

alibi/explainers/anchor/tests/test_anchor_text.py → alibi/explainers/tests/test_anchor_text.py

@@ -8,7 +8,7 @@
 from sklearn.model_selection import train_test_split
 import spacy
 from alibi.explainers import AnchorText
-from alibi.explainers.anchor.anchor_text import Neighbors
+from alibi.explainers.anchor_text import Neighbors
 from alibi.datasets import movie_sentiment
 from alibi.utils.download import spacy_model
 

doc/source/conf.py

@@ -65,6 +65,8 @@
 apidoc_output_dir = 'api'
 apidoc_excluded_paths = ['**/*test*']
 apidoc_module_first = True
+apidoc_separate_modules = True
+apidoc_extra_args = ['-d 6']
 
 # Napoleon settings
 napoleon_google_docstring = True

doc/source/methods/Anchors.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[[source]](../api/alibi.explainers.anchor.rst)"
+    "[[source]](../api/alibi.explainers.anchor_tabular.rst)"
    ]
   },
   {

doc/source/methods/Trust Scores.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[[source]](../api/alibi.confidence.rst)"
+    "[[source]](../api/alibi.confidence.trustscore.rst)"
    ]
   },
   {

doc/source/overview/algorithms.md

@@ -31,10 +31,17 @@ instance that would result in a different prediction). [Documentation](../method
 
 ## Model Confidence
 These algorihtms provide instance-specific scores measuring the model confidence for making a
-certain prediction.
+particular prediction.
 
 |Algorithm|Classification|Regression|Categorical features|Tabular|Text|Images|Needs training set|
 |---|---|---|---|---|
 |[Trust Scores](../methods/Trust\ Scores.ipynb)|✔|✘|✘|✔|✔[^1]|✔[^2]|Yes|
+
+**Trust scores**: produce a "trust score" of a classifier's prediction. The trust score is the ratio
+between the distance to the nearest class different from the predicted class and the distance to the
+predicted class, higher scores correspond to more trustworthy predictions.
+[Documentation](../methods/Trust\ Scores.ipynb),
+[tabular example](../examples/trustscore_iris.nblink).
+
 [^1]: Depending on model
 [^2]: May require dimensionality reduction