ruff formatting

Author: paulbkoch

.github/workflows/ci.yml

@@ -630,7 +630,6 @@ jobs:
           token: ${{ secrets.CODECOV_TOKEN }}
 
   format_clang:
-    needs: [bdist]  # bdist is not needed but slow this step down
     runs-on: ubuntu-latest
     permissions:
       checks: write
@@ -663,8 +662,7 @@ jobs:
               conclusion: 'neutral'
             })
 
-  format_black:
-    needs: [bdist]  # bdist is not needed but slow this step down
+  format_ruff:
     runs-on: ubuntu-latest
     permissions:
       checks: write
@@ -673,13 +671,13 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: ${{ github.event_name == 'workflow_dispatch' && inputs.commit_id || github.sha }}
-      - name: Check python matches black format
+      - name: Check python matches ruff format
         id: check_python
         continue-on-error: true
         run: |
           cd python
-          python -m pip install --upgrade black
-          black --check .
+          python -m pip install --upgrade ruff
+          ruff format --check
       - name: Mark step with a warning
         if: ${{ steps.check_python.outcome == 'failure' }}
         uses: actions/github-script@v6
@@ -688,7 +686,7 @@ jobs:
             github.rest.checks.create({
               owner: context.repo.owner,
               repo: context.repo.repo,
-              name: 'Failed black formatting check',
+              name: 'Failed ruff formatting check',
               head_sha: context.sha,
               status: 'completed',
               conclusion: 'neutral'

python/interpret-core/interpret/api/templates.py

@@ -126,9 +126,7 @@ def visualize(self, key=None, title=None):
                         perf=perf[key],
                     )
                 # pragma: no cover
-                raise RuntimeError(
-                    f"Visual provider {provider} not supported"
-                )
+                raise RuntimeError(f"Visual provider {provider} not supported")
             is_multiclass = is_multiclass_local_data_dict(data_dict)
             if is_multiclass:
                 # Sort by predicted class' abs feature values

python/interpret-core/interpret/blackbox/_sensitivity.py

@@ -59,7 +59,7 @@ def __init__(
         feature_names=None,
         feature_types=None,
         sampler=None,
-        **kwargs
+        **kwargs,
     ):
         """Initializes class.
 

python/interpret-core/interpret/data/_response.py

@@ -90,9 +90,7 @@ def explain_data(self, X, y, name=None):
         }
 
         # Sample down
-        n_samples = (
-            min(len(y), self.max_scatter_samples)
-        )
+        n_samples = min(len(y), self.max_scatter_samples)
         idx = np.random.choice(np.arange(len(y)), n_samples, replace=False)
         X_sample = X[idx, :]
         y_sample = y[idx]
@@ -277,9 +275,7 @@ def visualize(self, key=None):
             hovermode="closest",
             xaxis2=dict(domain=[do_hi, 1], showgrid=False, zeroline=False),
             yaxis2=dict(domain=[do_hi, 1], showgrid=False, zeroline=False),
-            title=(
-                f"Pearson Correlation: {corr:.3f}" if corr is not None else ""
-            ),
+            title=(f"Pearson Correlation: {corr:.3f}" if corr is not None else ""),
         )
         fig = go.Figure(data=data, layout=layout)
         return fig

python/interpret-core/interpret/ext/extension.py

@@ -19,9 +19,7 @@ def _is_valid_explainer(proposed_explainer, expected_explainer_type):
         available_explanations = proposed_explainer.available_explanations
 
         if explainer_type != expected_explainer_type:
-            _log.warning(
-                f"Proposed explainer is not a {expected_explainer_type}."
-            )
+            _log.warning(f"Proposed explainer is not a {expected_explainer_type}.")
             return False
 
         for available_explanation in available_explanations:

python/interpret-core/interpret/glassbox/_decisiontree.py

@@ -560,7 +560,7 @@ def __init__(self, feature_names=None, feature_types=None, max_depth=3, **kwargs
             feature_names=feature_names,
             feature_types=feature_types,
             max_depth=max_depth,
-            **kwargs
+            **kwargs,
         )
 
     def _model(self):
@@ -603,7 +603,7 @@ def __init__(self, feature_names=None, feature_types=None, max_depth=3, **kwargs
             feature_names=feature_names,
             feature_types=feature_types,
             max_depth=max_depth,
-            **kwargs
+            **kwargs,
         )
 
     def _model(self):

python/interpret-core/interpret/glassbox/_ebm/_tensor.py

@@ -19,8 +19,7 @@ def trim_tensor(tensor, trim_low=None, trim_high=None):
         ]
     elif trim_high is None:
         dim_slices = [
-            slice(int(is_low), None)
-            for dim_len, is_low in zip(tensor.shape, trim_low)
+            slice(int(is_low), None) for dim_len, is_low in zip(tensor.shape, trim_low)
         ]
     else:
         dim_slices = [

python/interpret-core/interpret/provider/_visualize.py

@@ -104,9 +104,7 @@ def render_html(html_string):
             display_html(final_html, raw=True)
 
         if visual is None:  # pragma: no cover
-            msg = (
-                f"No visualization for explanation [{explanation_name}] with selector_key [{selector_key}]"
-            )
+            msg = f"No visualization for explanation [{explanation_name}] with selector_key [{selector_key}]"
             _log.error(msg)
             if file_name is None:
                 render_html(msg)

python/interpret-core/interpret/utils/_SPOTgreedy.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 The InterpretML Contributors
 # Distributed under the MIT software license
 
-""" SPOT: A framework for selection of prototypes using optimal transport
+"""SPOT: A framework for selection of prototypes using optimal transport
 
 This file implements the SPOTgreedy algorithm from [1].
 

python/interpret-core/interpret/utils/_measure_interactions.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 The InterpretML Contributors
 # Distributed under the MIT software license
 
-""" FAST - Interaction Detection
+"""FAST - Interaction Detection
 
 This module exposes a method called FAST [1] to measure and rank the strengths
 of the interaction of all pairs of features in a dataset.