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test_hnsw_search.py
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test_hnsw_search.py
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# Copyright (c) 2023-2024 G. Fan, J. Wang, Y. Li, D. Zhang, and R. J. Miller
#
# This file is derived from Starmie hosted at https://github.com/megagonlabs/starmie
# and originally licensed under the BSD-3 Clause License. Modifications to the original
# source code have been made by I. Taha, M. Lissandrini, A. Simitsis, and Y. Ioannidis, and
# can be viewed at https://github.com/athenarc/table-search.
#
# This work is licensed under the GNU Affero General Public License v3.0,
# unless otherwise explicitly stated. See the https://github.com/athenarc/table-search/blob/main/LICENSE
# for more details.
#
# You may use, modify, and distribute this file in accordance with the terms of the
# GNU Affero General Public License v3.0.
# This file has been modified
import argparse
import pickle
import time
import mlflow
import numpy as np
from checkPrecisionRecall import calcMetrics, saveDictionaryAsPickleFile
from hnsw_search import HNSWSearcher
#from memory_profiler import profile
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--encoder", type=str, default="sato", choices=['sherlock', 'sato', 'cl', 'tapex'])
parser.add_argument("--benchmark", type=str, default='santos')
parser.add_argument("--run_id", type=int, default=0)
parser.add_argument("--single_column", dest="single_column", action="store_true")
parser.add_argument("--K", type=int, default=10)
parser.add_argument("--scal", type=float, default=1.00)
# parser.add_argument("--N", type=int, default=10)
# parser.add_argument("--threshold", type=float, default=0.7)
# mlflow tag
parser.add_argument("--mlflow_tag", type=str, default=None)
# added for testing all by vonTaha
parser.add_argument("--eval_run", type=int, default=0)
parser.add_argument("--columns_multiplier", type=int, default=10)
hp = parser.parse_args()
eval_run = hp.eval_run
columns_multiplier = hp.columns_multiplier
# mlflow logging
for variable in ["encoder", "benchmark", "single_column", "run_id", "K", "scal"]:
mlflow.log_param(variable, getattr(hp, variable))
if hp.mlflow_tag:
mlflow.set_tag("tag", hp.mlflow_tag)
encoder = hp.encoder
singleCol = hp.single_column
dataFolder = hp.benchmark
# Set augmentation operators, sampling methods, K, and threshold values according to the benchmark
if 'santos' in dataFolder or dataFolder == 'wdc' or dataFolder == 'git':
sampAug = "drop_col_tfidf_entity"
K = 10
threshold = 0.7
if dataFolder == 'santosLarge' or dataFolder == 'wdc' or dataFolder == 'git':
K, threshold = hp.K, 0.1
elif "tus" in dataFolder:
sampAug = "drop_cell_alphaHead"
K = 60
threshold = 0.1
singSampAug = "drop_cell_tfidf_entity"
# If we need to change the value of N, or change the filepath to the pkl files (including indexing), change here:
# N: number of returned elements for each query column
N = 4
if dataFolder=="wdc":
table_id = hp.run_id
index_path = "data/"+dataFolder+"/indexes/hnsw_open_data_"+str(table_id)+"_"+str(hp.scal)+".bin"
if abs(eval_run) <=5:
print("---------->>>WDC_UnTrained_model_vectors<<<----------")
table_path = "data/"+dataFolder+"/vectors/untrained_model_vectors/cl_datalake_"+sampAug+"_column_"+str(table_id)+".pkl"
query_path = "data/"+dataFolder+"/vectors/untrained_model_vectors/cl_query_"+sampAug+"_column_"+str(table_id)+".pkl"
elif abs(eval_run) > 5 and abs(eval_run) <=10:
print("---------->>>WDC_ONE_Epoch_model_vectors<<<----------")
table_path = "data/"+dataFolder+"/vectors/one_epoch_model_vectors/cl_datalake_"+sampAug+"_column_"+str(table_id)+".pkl"
query_path = "data/"+dataFolder+"/vectors/one_epoch_model_vectors/cl_query_"+sampAug+"_column_"+str(table_id)+".pkl"
elif abs(eval_run) > 10 and abs(eval_run) <=15:
print("---------->>>WDC_model_vectors<<<----------")
table_path = "data/"+dataFolder+"/vectors/wdc_model_vectors/cl_datalake_"+sampAug+"_column_"+str(table_id)+".pkl"
query_path = "data/"+dataFolder+"/vectors/wdc_model_vectors/cl_query_"+sampAug+"_column_"+str(table_id)+".pkl"
elif abs(eval_run) > 15:
print("---------->>>SANTOS_model_vectors<<<----------")
table_path = "data/"+dataFolder+"/vectors/santos_model_vectors/cl_datalake_"+sampAug+"_column_"+str(table_id)+".pkl"
query_path = "data/"+dataFolder+"/vectors/santos_model_vectors/cl_query_"+sampAug+"_column_"+str(table_id)+".pkl"
elif dataFolder=="santos":
table_id = hp.run_id
index_path = "data/"+dataFolder+"/indexes/hnsw_open_data_"+str(table_id)+"_"+str(hp.scal)+".bin"
if abs(eval_run) <=5:
print("---------->>>SANTOAS_UnTrained_model_vectors<<<----------")
table_path = "data/"+dataFolder+"/vectors/untrainded_model_vectors/normalized/cl_datalake_"+sampAug+"_column_"+str(table_id)+".pkl"
query_path = "data/"+dataFolder+"/vectors/untrainded_model_vectors/normalized/cl_query_"+sampAug+"_column_"+str(table_id)+".pkl"
elif abs(eval_run) > 5 and abs(eval_run) <=10:
print("---------->>>SANTOAS_ONE_Epoch_model_vectors<<<----------",flush=True)
table_path = "data/"+dataFolder+"/vectors/one_epoch_model_vectors/normalized/cl_datalake_"+sampAug+"_column_"+str(table_id)+".pkl"
query_path = "data/"+dataFolder+"/vectors/one_epoch_model_vectors/normalized/cl_query_"+sampAug+"_column_"+str(table_id)+".pkl"
elif abs(eval_run) > 10:
print("---------->>>SANTOAS_model_vectors<<<----------")
table_path = "data/"+dataFolder+"/vectors/traind_model_vectors/normalized/cl_datalake_"+sampAug+"_column_"+str(table_id)+".pkl"
query_path = "data/"+dataFolder+"/vectors/traind_model_vectors/normalized/cl_query_"+sampAug+"_column_"+str(table_id)+".pkl"
else:
table_id = hp.run_id
table_path = "data/"+dataFolder+"/vectors/cl_datalake_"+sampAug+"_column_"+str(table_id)+".pkl"
query_path = "data/"+dataFolder+"/vectors/cl_query_"+sampAug+"_column_"+str(table_id)+".pkl"
index_path = "data/"+dataFolder+"/indexes/hnsw_open_data_"+str(table_id)+"_"+str(hp.scal)+".bin"
# Call HNSWSearcher from hnsw_search.py
#searcher = HNSWSearcher(table_path, index_path, hp.scal)
col_shuffle_seed = hp.eval_run
searcher = HNSWSearcher(table_path, index_path, hp.scal, columns_multiplier, col_shuffle_seed)
(pre_processing_time, building_index_time, loading_to_index_time) = searcher.index_times
queries = pickle.load(open(query_path,"rb"))
start_time = time.time()
returnedResults = {}
num_results_arr = []
query_times_arr = []
index_search_time_arr = []
cols_num_arr=[]
for q in queries:
query_start_time = time.time()
res, scoreLength, cols_num, index_search_time = searcher.topk(encoder,q,K, N=N,threshold=threshold)
returnedResults[q[0]] = [r[1] for r in res]
num_results_arr.append(scoreLength)
cols_num_arr.append(cols_num)
index_search_time_arr.append(index_search_time)
query_times_arr.append(time.time() - query_start_time)
#print(f">>>>>>>>>> Number of Query Colmuns is: {sum(cols_num_arr)}, and Total Time of Index Search is: {sum(index_search_time_arr)} <<<<<<<<")
#print("Average number of Results: ", sum(num_results_arr)/len(num_results_arr))
#print("10th percentile: ", np.percentile(query_times_arr, 10), " 90th percentile: ", np.percentile(query_times_arr, 90))
#print("Average QUERY TIME: %s seconds " % (sum(query_times_arr)/len(query_times_arr)))
#print("--- Total Query Time: %s seconds ---" % (time.time() - start_time))
print("Average QUERY TIME: %s seconds " % (sum(query_times_arr)/(len(query_times_arr)*sum(cols_num_arr))))
MAP_at, P_at_K_arr, R_at_K_arr,ideal_recall_arr = 0,[],[],[]
### santosLarge and WDC benchmarks are used for efficiency
if hp.benchmark == 'santosLarge' or hp.benchmark == 'wdc' or hp.benchmark == 'git':
print("No groundtruth for %s benchmark" % (hp.benchmark))
else:
# Calculating effectiveness scores (Change the paths to where the ground truths are stored)
if 'santos' in hp.benchmark:
k_range = 1
groundTruth = "data/santos/santosUnionBenchmark.pickle"
else:
k_range = 10
if hp.benchmark == 'tus':
groundTruth = 'data/tus/small/tus-groundtruth/tusUnionBenchmark.pickle'
elif hp.benchmark == 'tusLarge':
groundTruth = 'data/tus/large/tus-groundtruth/tusLabeledtusLargeUnionBenchmark'
MAP_at, P_at_K_arr, R_at_K_arr,ideal_recall_arr = calcMetrics(K, k_range, returnedResults, gtPath=groundTruth)
#print("MPAK")
#print(MAP_at)
#print("PAK")
#print(P_at_K_arr)
#print("RAK")
#print(R_at_K_arr)
if eval_run > 0:
queryTimes_file = f"evaluation/hnsw_{dataFolder}_{eval_run}_all.txt"
# Write to file
with open(queryTimes_file, 'w') as file:
file.write("Pre-Processing Time\n")
file.write(f"{pre_processing_time}\n")
file.write("Building Index Time\n")
file.write(f"{building_index_time}\n")
file.write("Loading Time\n")
file.write(f"{loading_to_index_time}\n")
if hp.benchmark == 'santos' or hp.benchmark == 'tus':
file.write("MAP\n")
file.write(f"{MAP_at}\n")
file.write("P@K\n")
for p in P_at_K_arr:
file.write(f"{p}\n")
file.write("R@K\n")
for r in R_at_K_arr:
file.write(f"{r}\n")
#file.write("Ideal_Recall@K\n")
#for ir in ideal_recall_arr:
# file.write(f"{ir}\n")
# Query Info.
#file.write("Number_of_Columns\n")
#for cols_num in cols_num_arr:
# file.write(f"{cols_num}\n")
#for cols_num in cols_num_arr:
# print(cols_num)
file.write("Index Search Time\n")
for index_search_time in index_search_time_arr:
file.write(f"{index_search_time}\n")
file.write("Query Time\n")
for query_time in query_times_arr:
file.write(f"{query_time}\n")
file.write("Number of Results\n")
for numb_answer in num_results_arr:
file.write(f"{numb_answer}\n")