add performance test for commits

examples/git_tester.py

@@ -0,0 +1,16 @@
+from pydriller import Repository
+import os 
+import datetime
+os.system("cp examples/speedtest.py examples/speedtest2.py")
+for commit in Repository('.', from_tag="v0.5.2").traverse_commits():
+    print(commit.hash)
+    print(commit.msg)
+
+    os.system(f"git checkout {commit.hash}; rm -rf build; ")
+    os.system("python -m pip install .")
+    os.system(f'python examples/speedtest2.py -n "{commit.msg}" -d 4 -t 1')
+    os.system(f'python examples/speedtest2.py -n "{commit.msg}" -d 64 -t 1')
+    os.system(f'python examples/speedtest2.py -n "{commit.msg}" -d 128 -t 1')
+    os.system(f'python examples/speedtest2.py -n "{commit.msg}" -d 4 -t 24')
+    os.system(f'python examples/speedtest2.py -n "{commit.msg}" -d 128 -t 24')
+

examples/speedtest.py

@@ -0,0 +1,62 @@
+import hnswlib
+import numpy as np
+import os.path
+import time
+import argparse
+
+# Use nargs to specify how many arguments an option should take.
+ap = argparse.ArgumentParser()
+ap.add_argument('-d')
+ap.add_argument('-n')
+ap.add_argument('-t')
+args = ap.parse_args()
+dim = int(args.d)
+name = args.n
+threads=int(args.t)
+num_elements = 1000000 * 4//dim
+
+# Generating sample data
+np.random.seed(1)
+data = np.float32(np.random.random((num_elements, dim)))
+
+
+index_path=f'speed_index{dim}.bin'
+# Declaring index
+p = hnswlib.Index(space='l2', dim=dim)  # possible options are l2, cosine or ip
+
+if not os.path.isfile(index_path) :
+
+    p.init_index(max_elements=num_elements, ef_construction=100, M=16)
+
+    # Controlling the recall by setting ef:
+    # higher ef leads to better accuracy, but slower search
+    p.set_ef(10)
+
+    # Set number of threads used during batch search/construction
+    # By default using all available cores
+    p.set_num_threads(12)
+
+    p.add_items(data)
+
+    # Serializing and deleting the index:
+
+    print("Saving index to '%s'" % index_path)
+    p.save_index(index_path)
+p.set_num_threads(threads)
+times=[]
+time.sleep(10)
+p.set_ef(100)
+for _ in range(3):
+    p.load_index(index_path)
+    for _ in range(10):
+        t0=time.time()
+        labels, distances = p.knn_query(data, k=1)
+        tt=time.time()-t0
+        times.append(tt)
+        print(f"{tt} seconds")    
+str_out=f"mean time:{np.mean(times)}, median time:{np.median(times)}, std time {np.std(times)} {name}"
+print(str_out)
+with open (f"log_{dim}_t{threads}.txt","a") as f:
+    f.write(str_out+"\n")
+    f.flush()
+