def runKDTreeSizeAnalysis(argsdict, data, inlbl, fPath, fName, fileN, i):
start = time.time()
tree = KDTree(data, leaf_size=1)
end = time.time()
print tree.get_tree_stats()
return tree.__sizeof__(), (end - start)
from sklearn.neighbors import KDTree
for lfs in [1000]:
startTime = time.perf_counter()
kdt = KDTree(train, metric='euclidean', leaf_size=lfs)
end_time = time.perf_counter()
constructionTime = end_time - startTime
startTime = time.perf_counter()
dist, result = kdt.query(query, k=100, return_distance=True)
end_time = time.perf_counter()
searchTime = end_time - startTime
kdTreeRecall = hp.returnRecall(result, groundTruth)
avgDist = np.mean(dist)
ktreeparams = kdt.get_tree_stats()
reacll.append(kdTreeRecall)
algorithm.append('k-D')
construciotnTimes.append(constructionTime)
searchTimes.append(searchTime)
avgdistances.append(avgDist)
#BallTree
from sklearn.neighbors import BallTree
for lfs in [100]:
startTime = time.perf_counter()
bt = BallTree(train, metric='euclidean', leaf_size=lfs)
end_time = time.perf_counter()
constructionTime = end_time - startTime
