attempt 2

.ci/run_tsne.py

@@ -16,20 +16,30 @@ def test_sparse_precomputed_distance():
     """Make sure that TSNE works identically for sparse and dense matrix"""
     random_state = check_random_state(0)
     X = random_state.randn(100, 2)
-
+    t = time.time()
     D_sparse = kneighbors_graph(X, n_neighbors=100, mode="distance", include_self=True)
+    print(f"kneighbors_graph: {time.time()-t}")
+
+    t = time.time()
     D = pairwise_distances(X)
+    print(f"pairwise: {time.time()-t}")
+
     assert sp.issparse(D_sparse)
     assert_almost_equal(D_sparse.A, D)
 
     tsne = TSNE(
         metric="precomputed", random_state=0, init="random", learning_rate="auto"
     )
+    t = time.time()
     Xt_dense = tsne.fit_transform(D)
+    print(f"dense: {time.time()-t}")
+
 
     for fmt in ["csr", "lil"]:
+        t = time.time()
         Xt_sparse = tsne.fit_transform(D_sparse.asformat(fmt))
         assert_almost_equal(Xt_dense, Xt_sparse)
+        print(f"sparse {fmt}: {time.time()-t}")
 
 
 print("running test")

.github/workflows/ci.yml

@@ -130,7 +130,7 @@ jobs:
           source .github/scripts/activate_components.sh
           cd .ci
           python run_tsne.py
-          py-spy record -o ../profile.svg --native -- python run_tsne.py
+          py-spy record -o ../profile.svg -- python run_tsne.py
       - name: Archive flamegraph
         uses: actions/upload-artifact@v4
         with:

daal4py/sklearn/_n_jobs_support.py

@@ -58,6 +58,7 @@ def get_suggested_n_threads(n_cpus):
         del n_threads_map["openblas"]
     # remove default values equal to n_cpus as uninformative
     for backend in list(n_threads_map.keys()):
+        print(n_threads_map)
         if n_threads_map[backend] == n_cpus:
             del n_threads_map[backend]
     if len(n_threads_map) > 0: