From 54d95e72e808a41acffe1e687b43b1cb15be1fa9 Mon Sep 17 00:00:00 2001
From: Yashovardhan Thevalil <yashovardhan.thevalil@datadome.co>
Date: Thu, 26 Sep 2024 11:51:48 +0200
Subject: [PATCH] Fix to array shorthand not defined

---
 sliceline/slicefinder.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/sliceline/slicefinder.py b/sliceline/slicefinder.py
index 98cee44..2ddfcc4 100644
--- a/sliceline/slicefinder.py
+++ b/sliceline/slicefinder.py
@@ -242,7 +242,7 @@ def _get_slices_masks(self, X):
         # self._top_slices_enc.sum(axis=1) is the number of predicate(s) for each top_slices_
         slices_masks = (
             slice_candidates == self._top_slices_enc.sum(axis=1)
-        ).toarray().astype(int)
+        ).A.astype(int)
 
         return slices_masks
 
@@ -376,7 +376,7 @@ def _eval_slice(
     ) -> np.ndarray:
         """Compute several statistics for all the slices."""
         slice_candidates = x_encoded @ slices.T == level
-        slice_sizes = slice_candidates.sum(axis=0).toarray()[0]
+        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()
 
@@ -395,7 +395,7 @@ def _create_and_score_basic_slices(
         errors: np.ndarray,
     ) -> Tuple[sp.csr_matrix, np.ndarray]:
         """Initialise 1-slices, i.e. slices with one predicate."""
-        slice_sizes = x_encoded.sum(axis=0).toarray()[0]
+        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]
 
@@ -478,7 +478,7 @@ def _prune_invalid_self_joins(
         for start, end in zip(feature_offset_start, feature_offset_end):
             valid_slices_mask = (
                 valid_slices_mask
-                * (pair_candidates[:, start:end].sum(axis=1) <= 1).toarray()[:, 0]
+                * (pair_candidates[:, start:end].sum(axis=1) <= 1).A[:, 0]
             )
         return (
             pair_candidates[valid_slices_mask],
@@ -505,7 +505,7 @@ def _prepare_deduplication_and_pruning(
             i = sub_pair_candidates.argmax(axis=1).T + np.any(
                 sub_pair_candidates.toarray(), axis=1
             )
-            ids = ids + i.toarray() * np.prod(dom[(j + 1) : dom.shape[0]])
+            ids = ids + i.A * np.prod(dom[(j + 1) : dom.shape[0]])
         return ids
 
     def _get_pair_candidates(