def runBallTreeSizeAnalysis(argsdict, data, inlbl, fPath, fName, fileN, i):
start = time.time()
tree = BallTree(data, leaf_size=1)
end = time.time()
print tree.get_tree_stats()
return sys.getsizeof(tree), (end - start)
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
startTime = time.perf_counter()
dist, result = bt.query(query, k=100, return_distance=True)
end_time = time.perf_counter()
searchTime = end_time - startTime
ballTreeRecall = hp.returnRecall(result, groundTruth)
avgDist = np.mean(dist)
ballTreeparams = bt.get_tree_stats()
reacll.append(ballTreeRecall)
algorithm.append('ball-tree')
construciotnTimes.append(constructionTime)
searchTimes.append(searchTime)
avgdistances.append(avgDist)
compareResults = pd.DataFrame({
'algorithm': algorithm,
'constructionTime': construciotnTimes,
'searchTime': searchTimes,
'recall': reacll,
'avgDistance': avgdistances
})
#Annoy
