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in1d -> isin
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@FBruzzesi
FBruzzesi committed May 15, 2024
1 parent 148a2b2 commit b001932
Showing 1 changed file with 19 additions and 19 deletions.
38 changes: 19 additions & 19 deletions pynndescent/tests/test_pynndescent_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ def test_nn_descent_neighbor_accuracy(nn_data, seed):

num_correct = 0.0
for i in range(nn_data.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (nn_data.shape[0] * 10)
assert (
Expand All @@ -44,7 +44,7 @@ def test_angular_nn_descent_neighbor_accuracy(nn_data, seed):

num_correct = 0.0
for i in range(nn_data.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (nn_data.shape[0] * 10)
assert (
Expand All @@ -70,7 +70,7 @@ def test_bitpacked_nn_descent_neighbor_accuracy(nn_data, seed):

num_correct = 0.0
for i in range(nn_data.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (nn_data.shape[0] * 10)
assert (
Expand All @@ -92,7 +92,7 @@ def test_sparse_nn_descent_neighbor_accuracy(sparse_nn_data, seed):

num_correct = 0.0
for i in range(sparse_nn_data.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (sparse_nn_data.shape[0] * 10)
assert (
Expand All @@ -115,7 +115,7 @@ def test_sparse_angular_nn_descent_neighbor_accuracy(sparse_nn_data):

num_correct = 0.0
for i in range(sparse_nn_data.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (sparse_nn_data.shape[0] * 10)
assert (
Expand All @@ -132,7 +132,7 @@ def test_nn_descent_query_accuracy(nn_data):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert (
Expand All @@ -149,7 +149,7 @@ def test_nn_descent_query_accuracy_angular(nn_data):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert (
Expand All @@ -168,7 +168,7 @@ def test_sparse_nn_descent_query_accuracy(sparse_nn_data):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert (
Expand All @@ -187,7 +187,7 @@ def test_sparse_nn_descent_query_accuracy_angular(sparse_nn_data):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert (
Expand Down Expand Up @@ -216,7 +216,7 @@ def test_bitpacked_nn_descent_query_accuracy(nn_data):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert (
Expand Down Expand Up @@ -261,7 +261,7 @@ def test_random_state_none(nn_data, spatial_data):

num_correct = 0.0
for i in range(nn_data.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (spatial_data.shape[0] * 10)
assert (
Expand Down Expand Up @@ -334,7 +334,7 @@ def test_deduplicated_data_behaves_normally(seed, cosine_hang_data):

num_correct = 0
for i in range(data.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

proportion_correct = num_correct / (data.shape[0] * n_neighbors)
assert (
Expand Down Expand Up @@ -524,7 +524,7 @@ def test_update_no_prepare_query_accuracy(nn_data, metric):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert percent_correct >= 0.95, (
Expand Down Expand Up @@ -553,7 +553,7 @@ def test_update_w_prepare_query_accuracy(nn_data, metric):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert percent_correct >= 0.95, (
Expand Down Expand Up @@ -582,7 +582,7 @@ def test_update_w_prepare_query_accuracy(nn_data, metric):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert percent_correct >= 0.95, (
Expand All @@ -594,7 +594,7 @@ def evaluate_predictions(neighbors_true, neigbhors_computed, n_neighbors):
n_correct = 0
n_all = neighbors_true.shape[0] * n_neighbors
for i in range(neighbors_true.shape[0]):
n_correct += np.sum(np.in1d(neighbors_true[i], neigbhors_computed[i]))
n_correct += np.sum(np.isin(neighbors_true[i], neigbhors_computed[i]))
return n_correct / n_all


Expand Down Expand Up @@ -669,7 +669,7 @@ def test_tree_init_false(nn_data, metric):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert percent_correct >= 0.95, (
Expand Down Expand Up @@ -697,7 +697,7 @@ def test_one_dimensional_data(nn_data, metric):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * 10)
assert percent_correct >= 0.95, (
Expand Down Expand Up @@ -730,7 +730,7 @@ def test_tree_no_split(small_data, sparse_small_data, metric):

num_correct = 0.0
for i in range(true_indices.shape[0]):
num_correct += np.sum(np.in1d(true_indices[i], knn_indices[i]))
num_correct += np.sum(np.isin(true_indices[i], knn_indices[i]))

percent_correct = num_correct / (true_indices.shape[0] * k)
assert (
Expand Down

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