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Upload book_metadata_retriever.py
Browse files- book_metadata_retriever.py +48 -53
book_metadata_retriever.py
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#pip install rank-bm25
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import numpy as np
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import pandas as pd
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics.pairwise import cosine_similarity
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from sklearn.model_selection import train_test_split
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from rank_bm25 import BM25Okapi
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# Read CSV file
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data = pd.read_csv('books.csv', encoding='latin1')
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class TFIDFDoc2Vec:
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def __init__(self):
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tfidf_matrix = self.tfidf_vectorizer.fit_transform(documents)
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self.doc_vectors = tfidf_matrix.toarray()
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def find_similar_documents(self, query,
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query_vector = self.tfidf_vectorizer.transform([query]).toarray()
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similarities =
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similar_indices =
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return similar_documents
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return top_indices
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data
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#
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# Initialize TF-IDF
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tfidf_doc2vec_model = TFIDFDoc2Vec()
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tfidf_doc2vec_model.initialize_vectors(documents)
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# Initialize BM25 model
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bm25_model = BM25Okapi(
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def answer(query):
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# Find similar documents
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similar_documents_indices = tfidf_doc2vec_model.find_similar_documents(query)
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# Rank similar documents using BM25
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similar_documents_indices_bm25 = tfidf_doc2vec_model.rank_bm25(query, bm25_model, documents)
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# Initialize a list to store ranked documents
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ranked_documents = []
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# Add details of each document to the list
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for indices in similar_documents_indices:
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for index in indices:
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document_details = {
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"Book": data['Book Title'].iloc[index],
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"Author": data['Author'].iloc[index],
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"Copyright Year": data['Copyright Year'].iloc[index],
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"Edition": data['Edition'].iloc[index],
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"File Name": data['File_name'].iloc[index]
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}
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ranked_documents.append(document_details)
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return ranked_documents
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#
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import numpy as np
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import pandas as pd
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from sklearn.feature_extraction.text import TfidfVectorizer
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from rank_bm25 import BM25Okapi
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# Read CSV file
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data = pd.read_csv(r'C:\book_metadata_retriever\books.csv', encoding='latin1')
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class TFIDFDoc2Vec:
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def __init__(self):
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tfidf_matrix = self.tfidf_vectorizer.fit_transform(documents)
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self.doc_vectors = tfidf_matrix.toarray()
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def find_similar_documents(self, query, threshold=0.5):
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query_vector = self.tfidf_vectorizer.transform([query]).toarray()
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similarities = np.dot(query_vector, self.doc_vectors.T)
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similar_indices = np.where(similarities >= threshold)[1]
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return similar_indices, similarities
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def answer(query, threshold=0.5, top_n=10):
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# Find similar documents using TF-IDF
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similar_documents_indices, similarities = tfidf_doc2vec_model.find_similar_documents(query, threshold=threshold)
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# Check if no similar documents are found
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if len(similar_documents_indices) == 0:
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return "No books found for the query."
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# Rank similar documents using BM25
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scores = bm25_model.get_scores(query.split()) # Split the query into tokens
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bm25_ranked_indices = np.argsort(scores)[::-1]
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# Initialize a set to keep track of unique document indices
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unique_indices = set()
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# Combine results from TF-IDF and BM25, keeping unique indices
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combined_indices = []
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for index in similar_documents_indices:
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if index not in unique_indices:
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combined_indices.append(index)
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unique_indices.add(index)
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for index in bm25_ranked_indices:
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if index not in unique_indices:
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combined_indices.append(index)
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unique_indices.add(index)
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# Retrieve document details
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ranked_documents = []
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for index in combined_indices[:top_n]: # Adjust to the desired number of results
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document_details = {
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"Book": data['Book Title'].iloc[index],
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"Author": data['Author'].iloc[index],
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"Edition": data['Edition'].iloc[index],
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"File Name": data['File_name'].iloc[index]
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}
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ranked_documents.append(document_details)
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return ranked_documents
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# Initialize TF-IDF model
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tfidf_doc2vec_model = TFIDFDoc2Vec()
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documents = data['Book Title'].astype(str)
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tfidf_doc2vec_model.initialize_vectors(documents)
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# Initialize BM25 model
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bm25_model = BM25Okapi([doc.split() for doc in documents])
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# Example usage
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query = "mathematics"
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result = answer(query)
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print(result)
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