实施相似性搜索算法

import pandas as pddescripciones = [        'all users must reset passwords every 90 days.',        'passwords need to be reset by all users every 90 days.',        'admin access should be restricted.',        'passwords must change for users every 90 days.',        'passwords must change for users every 80 days.'    ]# cargar el datasetdata = pd.dataframe({    'rule_id': range(1, len(descripciones) + 1),    'description': descripciones})

from sklearn.feature_extraction.text import tfidfvectorizerfrom sklearn.metrics.pairwise import cosine_similarity!# vectorización de las descripciones con tf-idfvectorizer = tfidfvectorizer().fit_transform(data['description'])# calcular la matriz de similitud de cosenocosine_sim_matrix = cosine_similarity(vectorizer)# crear un diccionario para almacenar las relaciones sin duplicadosdef find_related_rules(matrix, rule_ids, threshold=0.8):    related_rules = {}    seen_pairs = set()  # para evitar duplicados de la forma (a, b) = (b, a)    for i in range(len(matrix)):        related = []        for j in range(i + 1, len(matrix)):  # j comienza en i + 1 para evitar duplicados            if matrix[i, j] >= threshold:                pair = (rule_ids[i], rule_ids[j])                if pair not in seen_pairs:                    seen_pairs.add(pair)                    related.append((rule_ids[j], round(matrix[i, j], 2)))        if related:            related_rules[rule_ids[i]] = related    return related_rules# aplicar la función para encontrar reglas relacionadasrelated_rules = find_related_rules(cosine_sim_matrix, data['rule_id'].tolist(), threshold=0.8)# mostrar las reglas relacionadasprint("reglas relacionadas por similitud:")for rule, relations in related_rules.items():    print(f"rule {rule} es similar a:")    for related_rule, score in relations:        print(f"  - rule {related_rule} con similitud de {score}")

!pip install sentence-transformersfrom sentence_transformers import SentenceTransformer, util# Load the pre-trained model for generating embeddingsmodel = SentenceTransformer('all-MiniLM-L6-v2')# Generate sentence embeddings for each rule descriptionembeddings = model.encode(data['Description'], convert_to_tensor=True)# Compute the semantic similarity matrixcosine_sim_matrix = util.cos_sim(embeddings, embeddings).cpu().numpy()# Function to find related rules based on semantic similaritydef find_related_rules(matrix, rule_ids, threshold=0.8):    related_rules = {}    seen_pairs = set()  # To avoid duplicates of the form (A, B) = (B, A)    for i in range(len(matrix)):        related = []        for j in range(i + 1, len(matrix)):  # Only consider upper triangular matrix            if matrix[i, j] >= threshold:                pair = (rule_ids[i], rule_ids[j])                if pair not in seen_pairs:                    seen_pairs.add(pair)                    related.append((rule_ids[j], round(matrix[i, j], 2)))        if related:            related_rules[rule_ids[i]] = related    return related_rules# Apply the function to find related rulesrelated_rules = find_related_rules(cosine_sim_matrix, data['Rule_ID'].tolist(), threshold=0.8)# Display the related rulesprint("Reglas relacionadas por similitud semántica:")for rule, relations in related_rules.items():    print(f"Rule {rule} es similar a:")    for related_rule, score in relations:        print(f"  - Rule {related_rule} con similitud de {score}")