add script for hdf5 generation

nb-virtdata/virtdata-lib-hdf5/src/test/resources/generatePredicateDataset.py

@@ -0,0 +1,69 @@
+#  Copyright (c) 2025 nosqlbench
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+import h5py
+
+def generate_knn_dataset(n, p, x, output_file):
+    # Step 1: Generate 'train' data (n vectors of size p) with associated ids
+    train_data = np.random.rand(n, p).astype(np.float32)
+    train_ids = np.repeat(np.arange(1, n // 100 + 1), 100)  # Assign 100 contiguous vectors per id
+
+    # Step 2: Generate 'test' data (x vectors of size p) with associated ids
+    test_data = np.random.rand(x, p).astype(np.float32)
+    test_ids = []
+    for _ in range(x):
+        num_ids = np.random.randint(1, 6)  # Each test query is associated with 1 to 5 training ids
+        associated_ids = np.random.choice(np.arange(1, n // 100 + 1), size=num_ids, replace=False)
+        test_ids.append(associated_ids)
+
+    # Step 3: Compute KNN for 'test' data using 'train' data filtered by associated ids
+    neighbors_list = []
+    for i in range(x):
+        query_vector = test_data[i]
+        query_ids = test_ids[i]
+
+        # Filter train data by matching ids
+        mask = np.isin(train_ids, query_ids)
+        filtered_train_data = train_data[mask]
+        global_indices = np.where(mask)[0]  # Get global indices of the filtered train data
+
+        knn = NearestNeighbors(n_neighbors=100, algorithm='auto')
+        knn.fit(filtered_train_data)
+        neighbors = knn.kneighbors(query_vector.reshape(1, -1), return_distance=False)
+
+        # Map local indices back to global indices
+        global_neighbors = global_indices[neighbors[0]]
+        neighbors_list.append(global_neighbors)
+
+    # Step 4: Write data to HDF5 file
+    with h5py.File(output_file, 'w') as h5f:
+        h5f.create_dataset('train', data=train_data)
+        h5f.create_dataset('train_ids', data=train_ids)
+        h5f.create_dataset('test', data=test_data)
+        h5f.create_dataset('test_ids', data=np.array(test_ids, dtype=object), dtype=h5py.special_dtype(vlen=np.int32))
+        h5f.create_dataset('neighbors', data=np.array(neighbors_list, dtype=np.int32))
+
+    print(f"Dataset saved to {output_file}")
+
+# Example usage
+if __name__ == "__main__":
+    n = int(input("Enter the number of train vectors (n): "))
+    p = int(input("Enter the dimensionality of each vector (p): "))
+    x = int(input("Enter the number of test vectors (x): "))
+    output_file = input("Enter the output HDF5 file name: ")
+
+    generate_knn_dataset(n, p, x, output_file)