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#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import math
from typing import List, Optional
from sklearn.preprocessing import normalize
from scipy.sparse import csr_matrix
from pyserini import index, search
from pyserini.analysis import Analyzer, get_lucene_analyzer
from tqdm import tqdm
class Vectorizer:
"""Base class for vectorizer implemented on top of Pyserini.
Parameters
----------
lucene_index_path : str
Path to lucene index folder
min_df : int
Minimum acceptable document frequency
verbose : bool
Whether to print out debugging information
"""
def __init__(self, lucene_index_path: str, min_df: int = 1, verbose: bool = False):
self.min_df: int = min_df
self.verbose: bool = verbose
self.index_reader = index.IndexReader(lucene_index_path)
self.searcher = search.LuceneSearcher(lucene_index_path)
self.num_docs: int = self.searcher.num_docs
self.stats = self.index_reader.stats()
self.analyzer = Analyzer(get_lucene_analyzer())
# build vocabulary
self.vocabulary_ = set()
for term in self.index_reader.terms():
if term.df > self.min_df:
self.vocabulary_.add(term.term)
self.vocabulary_ = sorted(self.vocabulary_)
# build term to index mapping
self.term_to_index = {}
for i, term in enumerate(self.vocabulary_):
self.term_to_index[term] = i
self.vocabulary_size = len(self.vocabulary_)
if self.verbose:
print(f'Found {self.vocabulary_size} terms with min_df={self.min_df}')
def get_query_vector(self, query: str):
matrix_row, matrix_col, matrix_data = [], [], []
tokens = self.analyzer.analyze(query)
for term in tokens:
if term in self.vocabulary_:
matrix_row.append(0)
matrix_col.append(self.term_to_index[term])
matrix_data.append(1)
vectors = csr_matrix((matrix_data, (matrix_row, matrix_col)), shape=(1, self.vocabulary_size))
return vectors
class TfidfVectorizer(Vectorizer):
"""Wrapper class for tf-idf vectorizer implemented on top of Pyserini.
Parameters
----------
lucene_index_path : str
Path to lucene index folder
min_df : int
Minimum acceptable document frequency
verbose : bool
Whether to print out debugging information
"""
def __init__(self, lucene_index_path: str, min_df: int = 1, verbose: bool = False):
super().__init__(lucene_index_path, min_df, verbose)
self.idf_ = {}
for term in self.index_reader.terms():
self.idf_[term.term] = math.log(self.num_docs / term.df)
def get_vectors(self, docids: List[str], norm: Optional[str] = 'l2'):
"""Get the tf-idf vectors given a list of docids
Parameters
----------
norm : str
Normalize the sparse matrix
docids : List[str]
The piece of text to analyze.
Returns
-------
csr_matrix
Sparse matrix representation of tf-idf vectors
"""
matrix_row, matrix_col, matrix_data = [], [], []
num_docs = len(docids)
for index, doc_id in enumerate(tqdm(docids)):
# Term Frequency
tf = self.index_reader.get_document_vector(doc_id)
if tf is None:
continue
# Filter out in-eligible terms
tf = {t: tf[t] for t in tf if t in self.term_to_index}
# Convert from dict to sparse matrix
for term in tf:
tfidf = tf[term] * self.idf_[term]
matrix_row.append(index)
matrix_col.append(self.term_to_index[term])
matrix_data.append(tfidf)
vectors = csr_matrix((matrix_data, (matrix_row, matrix_col)), shape=(num_docs, self.vocabulary_size))
if norm:
return normalize(vectors, norm=norm)
return vectors
class BM25Vectorizer(Vectorizer):
"""Wrapper class for BM25 vectorizer implemented on top of Pyserini.
Parameters
----------
lucene_index_path : str
Path to lucene index folder
min_df : int
Minimum acceptable document frequency
verbose : bool
Whether to print out debugging information
"""
def __init__(self, lucene_index_path: str, min_df: int = 1, verbose: bool = False):
super().__init__(lucene_index_path, min_df, verbose)
def get_vectors(self, docids: List[str], norm: Optional[str] = 'l2'):
"""Get the BM25 vectors given a list of docids
Parameters
----------
norm : str
Normalize the sparse matrix
docids : List[str]
The piece of text to analyze.
Returns
-------
csr_matrix
Sparse matrix representation of BM25 vectors
"""
matrix_row, matrix_col, matrix_data = [], [], []
num_docs = len(docids)
for index, doc_id in enumerate(tqdm(docids)):
# Term Frequency
tf = self.index_reader.get_document_vector(doc_id)
if tf is None:
continue
# Filter out in-eligible terms
tf = {t: tf[t] for t in tf if t in self.term_to_index}
# Convert from dict to sparse matrix
for term in tf:
bm25_weight = self.index_reader.compute_bm25_term_weight(doc_id, term, analyzer=None)
matrix_row.append(index)
matrix_col.append(self.term_to_index[term])
matrix_data.append(bm25_weight)
vectors = csr_matrix((matrix_data, (matrix_row, matrix_col)), shape=(num_docs, self.vocabulary_size))
if norm:
return normalize(vectors, norm=norm)
return vectors
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