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Copy pathIR_utils.py
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161 lines (115 loc) · 4.37 KB
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import numpy as np
import pandas as pd
import json
import resource
def recall_K(retrieved_docs, relevant_docs, K=10):
"""
Calculates recall@X
Args:
retrieved_docs (list): List of documents retrieved by the model
relevant_docs (list): List of relevant documents
K (int, optional): Number of documents to consider. Defaults to 10. (Top K
Returns:
number: recall@X value
"""
if len(relevant_docs) == 0:
return 0
# retrieved_docs = set(retrieved_docs)
# relevant_docs = set(relevant_docs)
top_X_retrieved_docs = retrieved_docs
if len(retrieved_docs) >= K:
top_X_retrieved_docs = retrieved_docs[:K]
top_X_retrieved_docs = set(top_X_retrieved_docs)
relevant_docs = set(relevant_docs)
relevant_retrieved_docs = top_X_retrieved_docs.intersection(relevant_docs)
return len(relevant_retrieved_docs) / len(relevant_docs)
def precision_K(retrieved_docs, relevant_docs, K=10):
"""
Calculates precision@X
Args:
retrieved_docs (list): List of documents retrieved by the model
relevant_docs (list): List of relevant documents
K (int, optional): Number of documents to consider. Defaults to 10. (Top K
Returns:
number: precision@X value
"""
if len(relevant_docs) == 0:
return 0
correct_predict = set(retrieved_docs[:K]).intersection(set(relevant_docs))
return len(correct_predict) / K
def cosine_similarity(vectors, query):
"""
Calculates cosine similarity between two vectors
Args:
X : Matrix X (np.array of vectors)
y : Vector y (np.array of query vector)
Returns:
number: cosine similarity between X and y
"""
dot_products = np.dot(vectors, query)
norm_target = np.linalg.norm(query)
norm_vectors = np.linalg.norm(vectors, axis=1)
# Calculate the cosine similarity between the target vector and all vectors in the array
return dot_products / (norm_target * norm_vectors)
def load_document_corpus(data_path, max_docs=-1):
docs = {}
with open(data_path, "r") as file:
for line in file:
data = json.loads(line)
docs[data["_id"]] = data["text"]
if max_docs > 0 and len(docs) == max_docs:
break
return docs
def load_all_queries(query_data_path):
raw_queries = {}
with open(query_data_path, "r") as file:
for line in file:
data = json.loads(line)
raw_queries[int(data["_id"])] = data["text"]
return raw_queries
def load_train_queries(query_data_path, query_sets):
query_ids_df = pd.read_csv(query_sets, delimiter="\t")
grouped_queries = query_ids_df.groupby("query-id")
raw_queries = load_all_queries(query_data_path)
queries = {}
for query_id, group in grouped_queries:
relevant_doc_ids = group["corpus-id"].tolist()
scores = group["score"].tolist()
query_text = raw_queries[query_id]
queries[query_id] = {
"text": query_text,
"relevant_doc_ids": relevant_doc_ids,
"relevant_doc_scores": scores,
}
return queries, raw_queries
def load_test_queries_t1(query_data_path, query_sets):
query_ids_df = pd.read_csv(query_sets, delimiter="\t")
raw_queries = load_all_queries(query_data_path)
queries = []
for index, row in query_ids_df.iterrows():
query_index = row["id"]
query_id = row["query-id"]
query_text = raw_queries[query_id]
queries.append({"query_id": query_id, "text": query_text, "id": query_index})
return queries, raw_queries
def load_test_queries_t2(query_data_path, query_sets):
query_ids_df = pd.read_csv(query_sets, delimiter="\t")
query_ids_df["corpus-id"] = query_ids_df["corpus-id"].apply(lambda x: eval(x))
raw_queries = load_all_queries(query_data_path)
queries = []
for index, row in query_ids_df.iterrows():
query_index = row["id"]
query_id = row["query-id"]
relevant_doc_ids = row["corpus-id"]
query_text = raw_queries[query_id]
queries.append(
{
"query_id": query_id,
"text": query_text,
"id": query_index,
"relevant_doc_ids": relevant_doc_ids,
}
)
return queries, raw_queries
def getUsedGBs():
return resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1e9