Upload build_reranking_dataset_BM25.py

build_reranking_dataset_BM25.py

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+from rank_bm25 import BM25Plus
+import datasets
+from sklearn.base import BaseEstimator
+from sklearn.model_selection import GridSearchCV
+
+from huggingface_hub import create_repo
+from huggingface_hub.utils._errors import HfHubHTTPError
+
+
+N_NEGATIVE_DOCS = 10
+SPLIT = "test"
+
+# Prepare documents
+def create_text(example:dict) -> str:
+    return "\n".join([example["title"], example["text"]])
+
+documents = datasets.load_dataset("lyon-nlp/alloprof", "documents")["test"]
+documents = documents.map(lambda x: {"text": create_text(x)})
+documents = documents.rename_column("uuid", "doc_id")
+documents = documents.remove_columns(["__index_level_0__", "title", "topic"])
+
+# Prepare queries
+queries = datasets.load_dataset("lyon-nlp/alloprof", "queries")[SPLIT]
+queries = queries.rename_columns({"text": "queries", "relevant": "doc_id"})
+queries = queries.remove_columns(["__index_level_0__", "answer", "id", "subject"])
+
+# Optimize BM25 parameters
+### Build sklearn estimator feature BM25
+class BM25Estimator(BaseEstimator):
+
+    def __init__(self, corpus_dataset:datasets.Dataset, *, k1:float=1.5, b:float=.75, delta:int=1):
+        """Initialize BM25 estimator using the coprus dataset.
+        The dataset must contain 2 columns:
+        - "doc_id" : the documents ids
+        - "text" : the document texts
+
+        Args:
+            corpus_dataset (datasets.Dataset): _description_
+            k1 (float, optional): _description_. Defaults to 1.5.
+            b (float, optional): _description_. Defaults to .75.
+            delta (int, optional): _description_. Defaults to 1.
+        """
+        self.is_fitted_ = False
+
+        self.corpus_dataset = corpus_dataset
+        self.k1 = k1
+        self.b = b
+        self.delta=delta
+        self.bm25 = None
+
+    def tokenize_corpus(self, corpus:list[str]) -> list[str]:
+        """Tokenize a corpus of strings
+
+        Args:
+            corpus (list[str]): the list of string to tokenize
+
+        Returns:
+            list[str]: the tokeinzed corpus
+        """
+        if isinstance(corpus, str):
+            return corpus.lower().split()
+        
+        return [c.lower().split() for c in corpus]
+
+    def fit(self, X=None, y=None):
+        """Fits the BM25 using the dataset of documents
+        Args are placeholders required by sklearn
+        """
+        tokenized_corpus = self.tokenize_corpus(self.corpus_dataset["text"])
+        self.bm25 = BM25Plus(
+            corpus=tokenized_corpus,
+            k1=self.k1,
+            b=self.b,
+            delta=self.delta
+        )
+        self.is_fitted_ = True
+
+        return self
+
+    def predict(self, query:str, topN:int=10) -> list[str]:
+        """Returns the best doc ids in order of best relevance first
+
+        Args:
+            query (str): _description_
+            topN (int, optional): _description_. Defaults to 10.
+
+        Returns:
+            list[str]: _description_
+        """
+        if not self.is_fitted_:
+            self.fit()
+
+        tokenized_query = self.tokenize_corpus(query)
+        best_docs = self.bm25.get_top_n(tokenized_query, self.corpus_dataset["text"], n=topN)
+        doc_text2id = dict(list(zip(self.corpus_dataset["text"], self.corpus_dataset["doc_id"])))
+        best_docs_ids = [doc_text2id[doc] for doc in best_docs]
+
+        return best_docs_ids
+    
+    def score(self, queries:list[str], relevant_docs:list[list[str]]):
+        """Scores the bm25 using the queries and relevant docs,
+        using MRR as the metric.
+
+        Args:
+            queries (list[str]): list of queries
+            relevant_docs (list[list[str]]): list of relevant documents ids for each query
+        """
+        best_docs_ids_preds = [self.predict(q, N_NEGATIVE_DOCS) for q in queries]
+        best_docs_isrelevant = [
+            [
+                doc in rel_docs for doc in best_docs_ids_pred
+                ]
+            for best_docs_ids_pred, rel_docs in zip(best_docs_ids_preds, relevant_docs)
+        ]
+        mrrs = [self._compute_mrr(preds) for preds in best_docs_isrelevant]
+        mrr = sum(mrrs)/len(mrrs)
+
+        return mrr
+
+    def _compute_mrr(self, predictions:list[bool]) -> float:
+        """Compute the mrr considering a list of boolean predictions.
+        Example:
+            if predictions = [False, False, True, False], it would indicate
+            that only the third document was labeled as relevant to the query
+
+        Args:
+            predictions (list[bool]): the binarized relevancy of predictions
+
+        Returns:
+            float: the mrr
+        """
+        if any(predictions):
+            mrr = [1/(i+1) for i, pred in enumerate(predictions) if pred]
+            mrr = sum(mrr)/len(mrr)
+            return mrr
+        else: 
+            return 0
+
+### Perform gridSearch to find best parameters for BM25
+print("Optimizing BM25 parameters...")
+
+params = {
+    "k1":[1.25, 1.5, 1.75],
+    "b": [.5, .75, 1.],
+    "delta": [0, 1]
+    }
+
+gscv = GridSearchCV(BM25Estimator(documents), params, verbose=1)
+gscv.fit(queries["queries"], queries["doc_id"])
+
+print("Best parameterss :",  gscv.best_params_)
+print("Best MRR score :",  gscv.best_score_)
+
+# Build reranking dataset with positives and negative queries using best estimator
+print("Generating reranking dataset...")
+reranking_dataset = datasets.Dataset.from_dict(
+    {
+        "query": queries["queries"],
+        "positive": queries["doc_id"],
+        "negative": [
+            [doc_id for doc_id in gscv.estimator.predict(q, N_NEGATIVE_DOCS) if doc_id not in relevant_ids]
+            for q, relevant_ids in zip(queries["queries"], queries["doc_id"])
+            ]
+    })
+
+# Push dataset to hub
+### create HF repo
+repo_id = "lyon-nlp/mteb-fr-reranking-alloprof-s2p"
+try:
+    create_repo(repo_id, repo_type="dataset")
+except HfHubHTTPError as e:
+    print("HF repo already exist")
+
+### push to hub
+reranking_dataset.push_to_hub(repo_id, config_name="queries", split=SPLIT)
+documents.push_to_hub(repo_id, config_name="documents", split="test")