Search-Engine/run.py at main · Dhruv88/Search-Engine · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
from search_engine_models.binary_independence_model import BinaryIndependenceModel
from evaluation_metrics import EvaluationMetrics
from search_engine_models.language_model import LanguageModel
from search_engine_models.elastic import Elastic
from utils import load_all_queries, load_relevant_docs_for_each_query, stem_content
from search_engine_models.vector_model import VectorModel
from search_engine_models.inverted_index import InvertedIndex

from query import Query
import timeit
##########################Construct Inverted Index###############################
# Create inverted index for the docs
index = InvertedIndex()
index.process_all_documents()

##########################Vector Model###############################
# Create vector model load the index
vector_model = VectorModel()
vector_model.load_index_from_csv()
vector_model.load_doc_lengths_from_csv()
print("loading index done")

# load all queries and relevant docs for them
relevant_docs_per_query = load_relevant_docs_for_each_query(vector_model.listdir)
queries = load_all_queries()
print("loading queries done")

#without pseudo relevance feedback
print("Running Queries")
overall_metrics = EvaluationMetrics(query_set_length=len(queries), query_set_metrics=[], file_path="metrics\metrics_tfidf.csv")
for query_id in queries.keys():
    query = Query(query_id, queries[query_id], relevant_docs_per_query[query_id])
    t = timeit.repeat(lambda: vector_model.return_relevant_docs_for_query(query.query), number=3, repeat=3)
    time = round(sum(t)/len(t),3)
    retrieved_docs = vector_model.return_relevant_docs_for_query(query.query)
    query.cnt_actual_relevant_docs_returned_and_ranks(retrieved_docs)
    precision, recall, ap = query.evaluate_metrics(query.actual_relevant_docs_cnt, query.actual_relevant_docs_pos)
    overall_metrics.query_set_metrics.append([query_id, precision, recall, ap, time])

overall_metrics.get_avg_metrics_over_query_set()

print("Storing Metrics")
overall_metrics.store_metrics_to_csv()

print("Done!")

##########################Pseudo Relevance Feedback###############################
# with pseudo relevance feedback and get best alpha
print("Running Queries to get best alpha that maximises map")

alpha = 0.0
max_map = 0.0
best_alpha = 0.0
while alpha <= 1:
    overall_metrics = EvaluationMetrics(query_set_length=len(queries), query_set_metrics=[],file_path="metrics\metrics_with_feedback.csv")
    for query_id in queries.keys():
        query = Query(query_id, queries[query_id], relevant_docs_per_query[query_id])
        # t = timeit.repeat(lambda: vector_model.return_relevant_docs_for_query_with_feedback(query.query, alpha=0.1), number=3, repeat=3)
        # time = round(sum(t)/len(t),3)

        retrieved_docs = vector_model.return_relevant_docs_for_query_with_feedback(query.query, alpha=alpha)
        query.cnt_actual_relevant_docs_returned_and_ranks(retrieved_docs)
        precision, recall, ap = query.evaluate_metrics(query.actual_relevant_docs_cnt, query.actual_relevant_docs_pos)
        overall_metrics.query_set_metrics.append([query_id, precision, recall, ap, 0])
        # print(precision,recall,ap)

    overall_metrics.get_avg_metrics_over_query_set()
    if max_map < overall_metrics.map:
        max_map = overall_metrics.map
        best_alpha = alpha
    print(alpha,overall_metrics.map,max_map)
    alpha += 0.1


print(best_alpha,"gives max map as", max_map)
print("Done!")

#########################Language Model###############################
language_model = LanguageModel()
language_model.load_index_from_csv()
print("loading index done")

language_model.make_language_model()
print("Made language model")

# load all queries and relevant docs for them
relevant_docs_per_query = load_relevant_docs_for_each_query(language_model.listdir)
queries = load_all_queries()
print("loading queries done")

print("Running Queries")
overall_metrics = EvaluationMetrics(query_set_length=len(queries), query_set_metrics=[],file_path="metrics\metrics_lm.csv")
for query_id in queries.keys():
    query = Query(query_id, queries[query_id], relevant_docs_per_query[query_id])
    t = timeit.repeat(lambda: language_model.return_relevant_docs_for_query(query.query), number=3, repeat=3)
    time = round(sum(t)/len(t),3)
    retrieved_docs = language_model.return_relevant_docs_for_query(query.query)
    # print(retrieved_docs)
    query.cnt_actual_relevant_docs_returned_and_ranks(retrieved_docs)
    precision, recall, ap = query.evaluate_metrics(query.actual_relevant_docs_cnt, query.actual_relevant_docs_pos)
    # print(precision,recall,ap,time)
    overall_metrics.query_set_metrics.append([query_id, precision, recall, ap, time])

overall_metrics.get_avg_metrics_over_query_set()

print("Storing Metrics")
overall_metrics.store_metrics_to_csv()

print("Done!")


#########################Binary Independence Model###############################
binary_independence_model = BinaryIndependenceModel(known_relevant_docs_cnt=20)
binary_independence_model.load_index_from_csv()
print("loading index done")

# load all queries and relevant docs for them
relevant_docs_per_query = load_relevant_docs_for_each_query(binary_independence_model.listdir)
queries = load_all_queries()
print("loading queries done")


print("Running Queries")
overall_metrics = EvaluationMetrics(query_set_length=len(queries), query_set_metrics=[], file_path="metrics\metrics_bim.csv")
for query_id in queries.keys():
    query = Query(query_id, queries[query_id], relevant_docs_per_query[query_id])
    t = timeit.repeat(lambda: binary_independence_model.return_relevant_docs_for_query(query.query,query.relevant_docs), number=1, repeat=1)
    time = round(sum(t)/len(t),3)
    retrieved_docs = binary_independence_model.return_relevant_docs_for_query(query.query,query.relevant_docs)
    query.cnt_actual_relevant_docs_returned_and_ranks(retrieved_docs)
    precision, recall, ap = query.evaluate_metrics(query.actual_relevant_docs_cnt, query.actual_relevant_docs_pos)
    overall_metrics.query_set_metrics.append([query_id, precision, recall, ap, time])
    # print(precision,recall,ap,time)
    # break

overall_metrics.get_avg_metrics_over_query_set()

print("Storing Metrics")
overall_metrics.store_metrics_to_csv()

print("Done!")

#########################ElasticSearch###############################
vector_model = VectorModel()
elastic = Elastic()
# load all queries and relevant docs for them
relevant_docs_per_query = load_relevant_docs_for_each_query(vector_model.listdir)
queries = load_all_queries()
print("loading queries done")

print("Running Queries")
overall_metrics = EvaluationMetrics(query_set_length=len(queries), query_set_metrics=[],file_path="metrics\metrics_es.csv")
for query_id in queries.keys():
    query = Query(query_id, queries[query_id], relevant_docs_per_query[query_id])
    tokens = stem_content(query.query)
    final_query =" "
    for token in tokens:
        final_query += " " + token
    t = timeit.repeat(lambda: elastic.return_relevant_docs_for_query(final_query), number=3, repeat=3)
    time = round(sum(t)/len(t),3)
    retrieved_docs = elastic.return_relevant_docs_for_query(final_query)
    query.cnt_actual_relevant_docs_returned_and_ranks(retrieved_docs)
    precision, recall, ap = query.evaluate_metrics(query.actual_relevant_docs_cnt, query.actual_relevant_docs_pos)
    overall_metrics.query_set_metrics.append([query_id, precision, recall, ap, time])

overall_metrics.get_avg_metrics_over_query_set()

print("Storing Metrics to metrics.csv")
overall_metrics.store_metrics_to_csv()

print("Done!")