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search_eval.py
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import math
import sys
import time
import metapy
import pytoml
import numpy as np
import pandas as pd
import pandas_read_xml as pdx
import xml.etree.ElementTree as ET #https://stackoverflow.com/questions/13244533/iterating-through-xml-in-python-through-keyerror
class InL2Ranker(metapy.index.RankingFunction):
def __init__(self, some_param):
self.param = some_param
super(InL2Ranker,self).__init__()
def score_one(self, sd):
tfn = sd.doc_term_count*math.log(1+sd.avg_dl/sd.doc_size,2)
log_numerator = math.log(((sd.num_docs+1)/(sd.corpus_term_count+0.5)),2)
tfn_prod = tfn/(tfn+self.param)
tot_score = sd.query_term_weight*tfn_prod*log_numerator
return tot_score
def load_ranker(cfg_file,opt,k1,b,k3,k4,l,m,s,d):
if opt==0:
rank1=metapy.index.OkapiBM25(k1=k1, b=b, k3=k3)
elif opt==1:
rank1 = InL2Ranker(k4)
elif opt==2:
rank1 = metapy.index.JelinekMercer(l)
elif opt==3:
rank1 = metapy.index.DirichletPrior(m)
elif opt==4:
rank1 = metapy.index.PivotedLength(s)
elif opt==5:
rank1 = metapy.index.AbsoluteDiscount(d)
else:
rank1 = InL2Ranker(1.0)
return rank1
if __name__ == '__main__':
if len(sys.argv) != 3:
print("Usage: {} config.toml <num>".format(sys.argv[0]))
sys.exit(1)
cfg = sys.argv[1]
option=int(sys.argv[2])
print('Building or loading index...')
print('option')
print(option)
count_=0
max_ndcg=0.0
max_k1=0
max_b=0
max_k3=0
max_k4=0
max_l=0
max_m=0
max_s=0
max_d=0
cfg2='config-test.toml'
idx2=metapy.index.make_inverted_index(cfg2)
print('Num test docs')
print(idx2.num_docs())
fii = open('evalprediction.txt','w')
if(option==0):
for k1 in np.arange(1,10,1):
for b in np.arange(0,1,0.1):
count_+=1
idx = metapy.index.make_inverted_index(cfg)
print('Num docs')
print(idx.num_docs())
ranker = load_ranker(cfg,option,k1,b,500,1,1,1,1,1)
ev = metapy.index.IREval(cfg)
with open(cfg, 'r') as fin:
cfg_d = pytoml.load(fin)
query_cfg = cfg_d['query-runner']
if query_cfg is None:
print("query-runner table needed in {}".format(cfg))
sys.exit(1)
start_time = time.time()
top_k = 1000
query_path = query_cfg.get('query-path', 'queries.txt')
query_start = query_cfg.get('query-id-start', 0)
query = metapy.index.Document()
ndcg = 0.0
num_queries = 0
#print('Running queries')
with open(query_path) as query_file:
for query_num, line in enumerate(query_file):
query.content(line.strip())
#print(line)
results = ranker.score(idx, query, top_k)
ndcg += ev.ndcg(results, query_start + query_num, top_k)
avg_p = ev.avg_p(results, query_num, top_k)
num_queries+=1
#print("Query {} average precision: {}".format(query_num + 1, avg_p))
ndcg= ndcg / num_queries
if max_ndcg<ndcg:
max_ndcg=ndcg
max_k1=k1
max_b=b
#max_k3=k3
print('iter',count_)
#print("NDCG@{}: {}".format(top_k, ndcg))
#print("Elapsed: {} seconds".format(round(time.time() - start_time, 4)))
print('Max NDCG for Okapi',max_ndcg)
print('Max k1 for Okapi',max_k1)
print('Max b for Okapi',max_b)
#print('Max k3 for Okapi',max_k3)
rankertest = load_ranker(cfg2,option,max_k1,max_b,500,1,1,1,1,1)
with open(cfg2, 'r') as fin:
cfg2_d = pytoml.load(fin)
query_cfg = cfg2_d['query-runner']
if query_cfg is None:
print("query-runner table needed in {}".format(cfg2))
sys.exit(1)
start_time = time.time()
top_k = 1000
query_path = query_cfg.get('query-path', 'queries.txt')
query_start = query_cfg.get('query-id-start', 0)
query = metapy.index.Document()
#print('Running queries')
print('Final results')
cntt=0
with open(query_path) as query_file:
for query_num, line in enumerate(query_file):
cntt=query_num+1
query.content(line.strip())
#print(line)
results = rankertest.score(idx2, query, top_k)
for i in results:
fii.write(str(cntt))
fii.write('\t')
fii.write(str(i[0]))
fii.write('\t')
fii.write(str(i[1]))
fii.write('\n')
elif(option==1):
for k4 in np.arange(0.1,10,0.1):
count_+=1
idx = metapy.index.make_inverted_index(cfg)
ranker = load_ranker(cfg,option,2.2,1,500,k4,1,1,1,1)
ev = metapy.index.IREval(cfg)
with open(cfg, 'r') as fin:
cfg_d = pytoml.load(fin)
query_cfg = cfg_d['query-runner']
if query_cfg is None:
print("query-runner table needed in {}".format(cfg))
sys.exit(1)
start_time = time.time()
top_k = 1000
query_path = query_cfg.get('query-path', 'queries.txt')
query_start = query_cfg.get('query-id-start', 0)
query = metapy.index.Document()
ndcg = 0.0
num_queries = 0
#print('Running queries')
with open(query_path) as query_file:
for query_num, line in enumerate(query_file):
query.content(line.strip())
results = ranker.score(idx, query, top_k)
ndcg += ev.ndcg(results, query_start + query_num, top_k)
avg_p = ev.avg_p(results, query_num, top_k)
num_queries+=1
print("Query {} average precision: {}".format(query_num + 1, avg_p))
ndcg= ndcg / num_queries
if max_ndcg<ndcg:
max_ndcg=ndcg
max_k4=k4
#print(count_)
print("NDCG@{}: {}".format(top_k, ndcg))
#print("Elapsed: {} seconds".format(round(time.time() - start_time, 4)))
print('Max NDCG for InL2ranker',max_ndcg)
print('Max k4 for Okapi',max_k4)
elif(option==2):
for l in np.arange(0.1,100,0.1):
count_+=1
idx = metapy.index.make_inverted_index(cfg)
ranker = load_ranker(cfg,option,2.2,1,500,5.6,l,0.1,1,1)
ev = metapy.index.IREval(cfg)
with open(cfg, 'r') as fin:
cfg_d = pytoml.load(fin)
query_cfg = cfg_d['query-runner']
if query_cfg is None:
print("query-runner table needed in {}".format(cfg))
sys.exit(1)
start_time = time.time()
top_k = 10
query_path = query_cfg.get('query-path', 'queries.txt')
query_start = query_cfg.get('query-id-start', 0)
query = metapy.index.Document()
ndcg = 0.0
num_queries = 0
#print('Running queries')
with open(query_path) as query_file:
for query_num, line in enumerate(query_file):
query.content(line.strip())
results = ranker.score(idx, query, top_k)
ndcg += ev.ndcg(results, query_start + query_num, top_k)
num_queries+=1
ndcg= ndcg / num_queries
if max_ndcg<ndcg:
max_ndcg=ndcg
max_l=l
print(count_)
print("NDCG@{}: {}".format(top_k, ndcg))
#print("Elapsed: {} seconds".format(round(time.time() - start_time, 4)))
print('Max NDCG for JenkinFuckin',max_ndcg)
print('Max l for Okapi',max_l)
elif(option==3):
for m in np.arange(0,100,0.1):
count_+=1
idx = metapy.index.make_inverted_index(cfg)
ranker = load_ranker(cfg,option,2.2,1,500,5.6,0.6,m,1,1)
ev = metapy.index.IREval(cfg)
with open(cfg, 'r') as fin:
cfg_d = pytoml.load(fin)
query_cfg = cfg_d['query-runner']
if query_cfg is None:
print("query-runner table needed in {}".format(cfg))
sys.exit(1)
start_time = time.time()
top_k = 10
query_path = query_cfg.get('query-path', 'queries.txt')
query_start = query_cfg.get('query-id-start', 0)
query = metapy.index.Document()
ndcg = 0.0
num_queries = 0
#print('Running queries')
with open(query_path) as query_file:
for query_num, line in enumerate(query_file):
query.content(line.strip())
results = ranker.score(idx, query, top_k)
ndcg += ev.ndcg(results, query_start + query_num, top_k)
num_queries+=1
ndcg= ndcg / num_queries
if max_ndcg<ndcg:
max_ndcg=ndcg
max_m=m
print(count_)
print("NDCG@{}: {}".format(top_k, ndcg))
#print("Elapsed: {} seconds".format(round(time.time() - start_time, 4)))
print('Max NDCG for Dirichlet smooth',max_ndcg)
print('Max l for Okapi',max_m)
elif(option==4):
for s in np.arange(0,5,0.005):
count_+=1
idx = metapy.index.make_inverted_index(cfg)
ranker = load_ranker(cfg,option,2.2,1,500,5.6,0.6,69,s,1)
ev = metapy.index.IREval(cfg)
with open(cfg, 'r') as fin:
cfg_d = pytoml.load(fin)
query_cfg = cfg_d['query-runner']
if query_cfg is None:
print("query-runner table needed in {}".format(cfg))
sys.exit(1)
start_time = time.time()
top_k = 10
query_path = query_cfg.get('query-path', 'queries.txt')
query_start = query_cfg.get('query-id-start', 0)
query = metapy.index.Document()
ndcg = 0.0
num_queries = 0
#print('Running queries')
with open(query_path) as query_file:
for query_num, line in enumerate(query_file):
query.content(line.strip())
results = ranker.score(idx, query, top_k)
ndcg += ev.ndcg(results, query_start + query_num, top_k)
num_queries+=1
ndcg= ndcg / num_queries
if max_ndcg<ndcg:
max_ndcg=ndcg
max_s=s
print(count_)
print("NDCG@{}: {}".format(top_k, ndcg))
#print("Elapsed: {} seconds".format(round(time.time() - start_time, 4)))
print('Max NDCG for Pivoted',max_ndcg)
print('Max l for Pivoted',max_s)
elif(option==5):
for d in np.arange(0,1,0.01):
count_+=1
idx = metapy.index.make_inverted_index(cfg)
ranker = load_ranker(cfg,option,2.2,1,500,5.6,0.6,69,0.35,d)
ev = metapy.index.IREval(cfg)
with open(cfg, 'r') as fin:
cfg_d = pytoml.load(fin)
query_cfg = cfg_d['query-runner']
if query_cfg is None:
print("query-runner table needed in {}".format(cfg))
sys.exit(1)
start_time = time.time()
top_k = 10
query_path = query_cfg.get('query-path', 'queries.txt')
query_start = query_cfg.get('query-id-start', 0)
query = metapy.index.Document()
ndcg = 0.0
num_queries = 0
#print('Running queries')
with open(query_path) as query_file:
for query_num, line in enumerate(query_file):
query.content(line.strip())
results = ranker.score(idx, query, top_k)
ndcg += ev.ndcg(results, query_start + query_num, top_k)
num_queries+=1
ndcg= ndcg / num_queries
if max_ndcg<ndcg:
max_ndcg=ndcg
max_d=d
print(count_)
print("NDCG@{}: {}".format(top_k, ndcg))
#print("Elapsed: {} seconds".format(round(time.time() - start_time, 4)))
print('Max NDCG for abs disc',max_ndcg)
print('Max d for Pivoted',max_d)
fii.close()
else:
idx = metapy.index.make_inverted_index(cfg)
ranker = load_ranker(cfg,option,1.1,1,3,1.2,1,3,1)
ev = metapy.index.IREval(cfg)
with open(cfg, 'r') as fin:
cfg_d = pytoml.load(fin)
query_cfg = cfg_d['query-runner']
if query_cfg is None:
print("query-runner table needed in {}".format(cfg))
sys.exit(1)
start_time = time.time()
top_k = 10
query_path = query_cfg.get('query-path', 'queries.txt')
query_start = query_cfg.get('query-id-start', 0)
query = metapy.index.Document()
ndcg = 0.0
num_queries = 0
print('Running queries')
with open(query_path) as query_file:
for query_num, line in enumerate(query_file):
query.content(line.strip())
results = ranker.score(idx, query, top_k)
ndcg += ev.ndcg(results, query_start + query_num, top_k)
num_queries+=1
ndcg= ndcg / num_queries
print("NDCG@{}: {}".format(top_k, ndcg))
print("Elapsed: {} seconds".format(round(time.time() - start_time, 4)))
fii.close()