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tessera-linalg: add integration test
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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tessera-linalg/test/test_linalg.ml
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tessera-linalg/test/test_linalg.ml
···
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Alcotest.(check int) "k=1 pred" 0 res.predictions.(0);
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Alcotest.(check (float 1e-6)) "k=1 confidence" 1.0 res.confidences.(0)
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(* ---- Integration test ---- *)
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let test_integration_pca_knn () =
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(* 100 pixels with 128-dim embeddings, 2 clusters *)
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let n = 100 in
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let dim = 128 in
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let data = create_mat ~rows:n ~cols:dim in
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let labels = Array.init n (fun i -> if i < 50 then 0 else 1) in
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(* Cluster 0: features near 0.0, Cluster 1: features near 10.0 *)
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for i = 0 to n - 1 do
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let base = if i < 50 then 0.0 else 10.0 in
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for j = 0 to dim - 1 do
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let noise = Float.of_int ((i * 7 + j * 13) mod 100) *. 0.001 in
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mat_set data i j (base +. noise)
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done
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done;
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let pca_model = pca_fit data ~n_components:3 in
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let reduced = pca_transform pca_model data in
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Alcotest.(check int) "reduced cols" 3 reduced.cols;
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(* Use first 80 as training, last 20 as test *)
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let train_rows = 80 in
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let test_rows = 20 in
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let train_mat = create_mat ~rows:train_rows ~cols:3 in
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let test_mat = create_mat ~rows:test_rows ~cols:3 in
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let train_labels = Array.init train_rows (fun i -> labels.(i)) in
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for i = 0 to train_rows - 1 do
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for j = 0 to 2 do
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mat_set train_mat i j (mat_get reduced i j)
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done
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done;
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for i = 0 to test_rows - 1 do
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for j = 0 to 2 do
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mat_set test_mat i j (mat_get reduced (train_rows + i) j)
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done
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done;
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let knn_model = knn_fit ~embeddings:train_mat ~labels:train_labels in
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let res = knn_predict knn_model ~k:5 test_mat in
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let expected = Array.init test_rows (fun i -> labels.(train_rows + i)) in
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let correct = ref 0 in
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for i = 0 to test_rows - 1 do
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if res.predictions.(i) = expected.(i) then incr correct
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done;
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let accuracy = Float.of_int !correct /. Float.of_int test_rows in
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Alcotest.(check bool) "accuracy >= 0.9" true (accuracy >= 0.9)
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(* ---- Test runner ---- *)
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let () =
···
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Alcotest.test_case "two clusters" `Quick test_knn_two_clusters;
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Alcotest.test_case "distance weighting" `Quick test_knn_distance_weighting;
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Alcotest.test_case "k=1 confidence" `Quick test_knn_k1_confidence;
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];
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"integration", [
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Alcotest.test_case "pca then knn" `Quick test_integration_pca_knn;
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];
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]