From ccd7c11fb2cbcd781e894e8ee12bde9745e48aad Mon Sep 17 00:00:00 2001
From: Yashovardhan Thevalil <yashovardhan.thevalil@datadome.co>
Date: Fri, 27 Sep 2024 09:38:23 +0200
Subject: [PATCH] PR feedback

---
 .github/workflows/push-pull.yml |  5 ++++-
 sliceline/slicefinder.py        | 19 ++++++++++++++++---
 tests/test_slicefinder.py       | 12 +++++++++---
 3 files changed, 29 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/push-pull.yml b/.github/workflows/push-pull.yml
index ac8aa1d..d720409 100644
--- a/.github/workflows/push-pull.yml
+++ b/.github/workflows/push-pull.yml
@@ -1,6 +1,9 @@
 name: Test
 
-on: [push, pull_request]
+on:
+  push:
+    branches: [ master ]
+  pull_request:
 
 jobs:
   test:
diff --git a/sliceline/slicefinder.py b/sliceline/slicefinder.py
index 2ddfcc4..9aa6656 100644
--- a/sliceline/slicefinder.py
+++ b/sliceline/slicefinder.py
@@ -378,7 +378,11 @@ def _eval_slice(
         slice_candidates = x_encoded @ slices.T == level
         slice_sizes = slice_candidates.sum(axis=0).A[0]
         slice_errors = errors @ slice_candidates
-        max_slice_errors = slice_candidates.T.multiply(errors).max(axis=1).toarray()
+        # Here we can't use the .A shorthand because it is not
+        # implemented in all scipy versions for coo_matrix objects
+        max_slice_errors = (
+            slice_candidates.T.multiply(errors).max(axis=1).toarray()
+        )
 
         # score of relative error and relative size
         slice_scores = self._score(
@@ -397,7 +401,11 @@ def _create_and_score_basic_slices(
         """Initialise 1-slices, i.e. slices with one predicate."""
         slice_sizes = x_encoded.sum(axis=0).A[0]
         slice_errors = errors @ x_encoded
-        max_slice_errors = x_encoded.T.multiply(errors).max(axis=1).toarray()[:, 0]
+        # Here we can't use the .A shorthand because it is not
+        # implemented in all scipy versions for coo_matrix objects
+        max_slice_errors = (
+            x_encoded.T.multiply(errors).max(axis=1).toarray()[:, 0]
+        )
 
         # working set of active slices (#attr x #slices) and top-k
         valid_slices_mask = (slice_sizes >= self.min_sup) & (slice_errors > 0)
@@ -440,6 +448,8 @@ def _join_compatible_slices(
     ) -> np.ndarray:
         """Join compatible slices according to `level`."""
         slices_int = slices.astype(int)
+        # Here we can't use the .A shorthand because it is not
+        # implemented in all scipy versions for coo_matrix objects
         join = (slices_int @ slices_int.T).toarray() == level - 2
         return np.triu(join, 1) * join
 
@@ -503,7 +513,10 @@ def _prepare_deduplication_and_pruning(
             sub_pair_candidates = pair_candidates[:, start:end]
             # sub_p should not contain multiple True on the same line
             i = sub_pair_candidates.argmax(axis=1).T + np.any(
-                sub_pair_candidates.toarray(), axis=1
+                # Here we can't use the .A shorthand because it is not
+                # implemented in all scipy versions for coo_matrix objects
+                sub_pair_candidates.toarray(),
+                axis=1,
             )
             ids = ids + i.A * np.prod(dom[(j + 1) : dom.shape[0]])
         return ids
diff --git a/tests/test_slicefinder.py b/tests/test_slicefinder.py
index d3ba5b6..72402d8 100644
--- a/tests/test_slicefinder.py
+++ b/tests/test_slicefinder.py
@@ -19,7 +19,9 @@ def test_dummify(benchmark, basic_test_data):
         basic_test_data["n_col_x_encoded"],
     )
 
-    assert np.array_equal(computed.toarray(), basic_test_data["slices"].toarray())
+    assert np.array_equal(
+        computed.toarray(), basic_test_data["slices"].toarray()
+    )
 
 
 def test_maintain_top_k(benchmark, basic_test_data):
@@ -278,7 +280,9 @@ def test_create_and_score_basic_slices(benchmark, basic_test_data):
             [0.42499999999999993, 3, 1, 4],
         ]
     )
-    assert np.array_equal(computed_slices.toarray(), basic_test_data["slices"].toarray())
+    assert np.array_equal(
+        computed_slices.toarray(), basic_test_data["slices"].toarray()
+    )
     assert np.array_equal(computed_statistics, expected_r)
 
 
@@ -308,7 +312,9 @@ def test_get_pair_candidates(benchmark, basic_test_data):
         basic_test_data["feature_offset_start"],
         basic_test_data["feature_offset_end"],
     )
-    assert np.array_equal(computed.toarray(), basic_test_data["candidates"].toarray())
+    assert np.array_equal(
+        computed.toarray(), basic_test_data["candidates"].toarray()
+    )
 
 
 def test_get_pair_candidates_with_missing_parents_pruning(