---
title: 运行和验证
description: "ScaNN编译安装完成后获取到测试数据集进行ScaNN的运行和验证。"
url: https://www.hikunpeng.com/document/detail/zh/SRA/ecosystemEnable/ScaNN/kunpengscann_02_0012.html
sourcePath: /source/zh/SRA/ecosystemEnable/ScaNN/kunpengscann_02_0012.html
indexId: 12dbbddc8d24bc8eb2b114087f9404c7dd184d800592f29bd3008a59f4a9e0fb62
---
# 运行和验证

ScaNN编译安装完成后获取到测试数据集进行ScaNN的运行和验证。

1. 进入ScaNN功能验证规划路径。
  1 cd /path/to/scann_test

2. 下载测试数据集。
  1 wget http://ann-benchmarks.com/glove-100-angular.hdf5 --no-check-certificate

3. 执行以下命令创建并编写ScaNN测试脚本scann_test.py。

  a. 创建“scann_test.py”文件。
    1 vi scann_test.py

  b. 按“i”进入编辑模式，编写“scann_test.py”文件，添加如下部分。

```
import numpy as np
import h5py
import time
import scann
def compute_recall(neighbors, true_neighbors):
total = 0
for gt_row, row in zip(true_neighbors, neighbors):
total += np.intersect1d(gt_row, row).shape[0]
return total / true_neighbors.size
def main():
print("Load dataset: glove-100-angular.hdf5")
glove_h5py = h5py.File("glove-100-angular.hdf5", "r")
print("Dataset keys:", list(glove_h5py.keys()))
dataset = glove_h5py['train']
queries = glove_h5py['test']
print("Train size: ", dataset.shape)
print("Queries size:", queries.shape)
print("\nCreate ScaNN searcher")
start = time.time()
normalized_dataset = dataset / np.linalg.norm(dataset, axis=1)[:, np.newaxis]
searcher = scann.scann_ops_pybind.builder(normalized_dataset, 10, "dot_product").tree(
num_leaves=2000, num_leaves_to_search=100, training_sample_size=250000).score_ah(
2, anisotropic_quantization_threshold=0.2).reorder(100).build()
end = time.time()
print("Time (s):", end - start)
print("\n1.Batched-query: queries")
start = time.time()
neighbors, distances = searcher.search_batched(queries)
end = time.time()
print("Recall:", compute_recall(neighbors, glove_h5py['neighbors'][:, :10]))
print("Time (s):", end - start)
print("\n2.Single-query: queries[0]")
start = time.time()
neighbors, distances = searcher.search(queries[0], final_num_neighbors=5)
end = time.time()
print("neighbors:", neighbors)
print("distances:", distances)
print("Time (ms):", 1000*(end - start))
if __name__ == "__main__":
main()
```


  c. 按“Esc”键，输入:wq!，按“Enter”保存并退出编辑。
4. 运行测试。
```
python3 scann_test.py
```

回显信息显示，测试程序先加载数据集glove-100-angular（该数据集为100维，训练集规模约100万条，查询数据集为10000条），之后创建一个ScaNN的searcher搜索器，最后采用2种模式查询数据，如下：

  a. Batched模式：批量查询全部查询数据集，在该参数配置下，召回率Recall为0.89965。
  b. Single模式：查询数据集中索引为0的数据，结果返回与其近似最近的5个邻居的位置（neighbors）和对应距离信息（distances）。
若测试程序运行无报错，Batched模式下的召回率与上图回显信息相近，Single模式查询的数据信息与上图回显信息一致，则代表ScaNN功能正常。