Create app.py

app.py

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+import joblib
+import gradio as gr
+from collections import Counter
+from typing import TypedDict
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Type
+from scipy.sparse._csc import csc_matrix
+from typing import Iterable, Callable, List, Dict, Optional, Type, TypeVar
+import pickle
+from dataclasses import dataclass
+import tqdm
+import re
+import os
+import nltk
+nltk.download("stopwords", quiet=True)
+from nltk.corpus import stopwords as nltk_stopwords
+import math 
+from dataclasses import dataclass
+from typing import Optional
+from datasets import load_dataset
+
+@dataclass
+class Document:
+    collection_id: str
+    text: str
+
+
+@dataclass
+class Query:
+    query_id: str
+    text: str
+
+
+@dataclass
+class QRel:
+    query_id: str
+    collection_id: str
+    relevance: int
+    answer: Optional[str] = None
+
+class Split(str, Enum):
+    train = "train"
+    dev = "dev"
+    test = "test"
+
+@dataclass
+class IRDataset:
+    corpus: List[Document]
+    queries: List[Query]
+    split2qrels: Dict[Split, List[QRel]]
+
+    def get_stats(self) -> Dict[str, int]:
+        stats = {"|corpus|": len(self.corpus), "|queries|": len(self.queries)}
+        for split, qrels in self.split2qrels.items():
+            stats[f"|qrels-{split}|"] = len(qrels)
+        return stats
+
+    def get_qrels_dict(self, split: Split) -> Dict[str, Dict[str, int]]:
+        qrels_dict = {}
+        for qrel in self.split2qrels[split]:
+            qrels_dict.setdefault(qrel.query_id, {})
+            qrels_dict[qrel.query_id][qrel.collection_id] = qrel.relevance
+        return qrels_dict
+
+    def get_split_queries(self, split: Split) -> List[Query]:
+        qrels = self.split2qrels[split]
+        qids = {qrel.query_id for qrel in qrels}
+        return list(filter(lambda query: query.query_id in qids, self.queries))
+
+
+
+@(joblib.Memory(".cache").cache)
+def load_sciq(verbose: bool = False) -> IRDataset:
+    train = load_dataset("allenai/sciq", split="train")
+    validation = load_dataset("allenai/sciq", split="validation")
+    test = load_dataset("allenai/sciq", split="test")
+    data = {Split.train: train, Split.dev: validation, Split.test: test}
+
+    # Each duplicated record is the same to each other:
+    df = train.to_pandas() + validation.to_pandas() + test.to_pandas()
+    for question, group in df.groupby("question"):
+        assert len(set(group["support"].tolist())) == len(group)
+        assert len(set(group["correct_answer"].tolist())) == len(group)
+
+    # Build:
+    corpus = []
+    queries = []
+    split2qrels: Dict[str, List[dict]] = {}
+    question2id = {}
+    support2id = {}
+    for split, rows in data.items():
+        if verbose:
+            print(f"|raw_{split}|", len(rows))
+        split2qrels[split] = []
+        for i, row in enumerate(rows):
+            example_id = f"{split}-{i}"
+            support: str = row["support"]
+            if len(support.strip()) == 0:
+                continue
+            question = row["question"]
+            if len(support.strip()) == 0:
+                continue
+            if support in support2id:
+                continue
+            else:
+                support2id[support] = example_id
+            if question in question2id:
+                continue
+            else:
+                question2id[question] = example_id
+            doc = {"collection_id": example_id, "text": support}
+            query = {"query_id": example_id, "text": row["question"]}
+            qrel = {
+                "query_id": example_id,
+                "collection_id": example_id,
+                "relevance": 1,
+                "answer": row["correct_answer"],
+            }
+            corpus.append(Document(**doc))
+            queries.append(Query(**query))
+            split2qrels[split].append(QRel(**qrel))
+
+    # Assembly and return:
+    return IRDataset(corpus=corpus, queries=queries, split2qrels=split2qrels)
+
+LANGUAGE = "english"
+word_splitter = re.compile(r"(?u)\b\w\w+\b").findall
+stopwords = set(nltk_stopwords.words(LANGUAGE))
+
+def word_splitting(text: str) -> List[str]:
+    return word_splitter(text.lower())
+
+def lemmatization(words: List[str]) -> List[str]:
+    return words  # We ignore lemmatization here for simplicity
+
+def simple_tokenize(text: str) -> List[str]:
+    words = word_splitting(text)
+    tokenized = list(filter(lambda w: w not in stopwords, words))
+    tokenized = lemmatization(tokenized)
+    return tokenized
+
+T = TypeVar("T", bound="InvertedIndex")
+
+@dataclass
+class PostingList:
+    term: str  # The term
+    docid_postings: List[int]  # docid_postings[i] means the docid (int) of the i-th associated posting
+    tweight_postings: List[float]  # tweight_postings[i] means the term weight (float) of the i-th associated posting
+
+@dataclass
+class InvertedIndex:
+    posting_lists: List[PostingList]  # docid -> posting_list
+    vocab: Dict[str, int]
+    cid2docid: Dict[str, int]  # collection_id -> docid
+    collection_ids: List[str]  # docid -> collection_id
+    doc_texts: Optional[List[str]] = None  # docid -> document text
+
+    def save(self, output_dir: str) -> None:
+        os.makedirs(output_dir, exist_ok=True)
+        with open(os.path.join(output_dir, "index.pkl"), "wb") as f:
+            pickle.dump(self, f)
+
+    @classmethod
+    def from_saved(cls: Type[T], saved_dir: str) -> T:
+        index = cls(
+            posting_lists=[], vocab={}, cid2docid={}, collection_ids=[], doc_texts=None
+        )
+        with open(os.path.join(saved_dir, "index.pkl"), "rb") as f:
+            index = pickle.load(f)
+        return index
+
+class BaseRetriever(ABC):
+
+    @property
+    @abstractmethod
+    def index_class(self) -> Type[Any]:
+        pass
+
+    def get_term_weights(self, query: str, cid: str) -> Dict[str, float]:
+        raise NotImplementedError
+
+    @abstractmethod
+    def score(self, query: str, cid: str) -> float:
+        pass
+
+    @abstractmethod
+    def retrieve(self, query: str, topk: int = 10) -> Dict[str, float]:
+        pass
+
+@dataclass
+class Counting:
+    posting_lists: List[PostingList]
+    vocab: Dict[str, int]
+    cid2docid: Dict[str, int]
+    collection_ids: List[str]
+    dfs: List[int]  # tid -> df
+    dls: List[int]  # docid -> doc length
+    avgdl: float
+    nterms: int
+    doc_texts: Optional[List[str]] = None
+
+def run_counting(
+    documents: Iterable[Document],
+    tokenize_fn: Callable[[str], List[str]] = simple_tokenize,
+    store_raw: bool = True,  # store the document text in doc_texts
+    ndocs: Optional[int] = None,
+    show_progress_bar: bool = True,
+) -> Counting:
+    """Counting TFs, DFs, doc_lengths, etc."""
+    posting_lists: List[PostingList] = []
+    vocab: Dict[str, int] = {}
+    cid2docid: Dict[str, int] = {}
+    collection_ids: List[str] = []
+    dfs: List[int] = []  # tid -> df
+    dls: List[int] = []  # docid -> doc length
+    nterms: int = 0
+    doc_texts: Optional[List[str]] = []
+    for doc in tqdm.tqdm(
+        documents,
+        desc="Counting",
+        total=ndocs,
+        disable=not show_progress_bar,
+    ):
+        if doc.collection_id in cid2docid:
+            continue
+        collection_ids.append(doc.collection_id)
+        docid = cid2docid.setdefault(doc.collection_id, len(cid2docid))
+        toks = tokenize_fn(doc.text)
+        tok2tf = Counter(toks)
+        dls.append(sum(tok2tf.values()))
+        for tok, tf in tok2tf.items():
+            nterms += tf
+            tid = vocab.get(tok, None)
+            if tid is None:
+                posting_lists.append(
+                    PostingList(term=tok, docid_postings=[], tweight_postings=[])
+                )
+                tid = vocab.setdefault(tok, len(vocab))
+            posting_lists[tid].docid_postings.append(docid)
+            posting_lists[tid].tweight_postings.append(tf)
+            if tid < len(dfs):
+                dfs[tid] += 1
+            else:
+                dfs.append(0)
+        if store_raw:
+            doc_texts.append(doc.text)
+        else:
+            doc_texts = None
+    return Counting(
+        posting_lists=posting_lists,
+        vocab=vocab,
+        cid2docid=cid2docid,
+        collection_ids=collection_ids,
+        dfs=dfs,
+        dls=dls,
+        avgdl=sum(dls) / len(dls),
+        nterms=nterms,
+        doc_texts=doc_texts,
+    )
+
+@dataclass
+class BM25Index(InvertedIndex):
+
+    @staticmethod
+    def tokenize(text: str) -> List[str]:
+        return simple_tokenize(text)
+
+    @staticmethod
+    def cache_term_weights(
+        posting_lists: List[PostingList],
+        total_docs: int,
+        avgdl: float,
+        dfs: List[int],
+        dls: List[int],
+        k1: float,
+        b: float,
+    ) -> None:
+        """Compute term weights and caching"""
+
+        N = total_docs
+        for tid, posting_list in enumerate(
+            tqdm.tqdm(posting_lists, desc="Regularizing TFs")
+        ):
+            idf = BM25Index.calc_idf(df=dfs[tid], N=N)
+            for i in range(len(posting_list.docid_postings)):
+                docid = posting_list.docid_postings[i]
+                tf = posting_list.tweight_postings[i]
+                dl = dls[docid]
+                regularized_tf = BM25Index.calc_regularized_tf(
+                    tf=tf, dl=dl, avgdl=avgdl, k1=k1, b=b
+                )
+                posting_list.tweight_postings[i] = regularized_tf * idf
+
+    @staticmethod
+    def calc_regularized_tf(
+        tf: int, dl: float, avgdl: float, k1: float, b: float
+    ) -> float:
+        return tf / (tf + k1 * (1 - b + b * dl / avgdl))
+
+    @staticmethod
+    def calc_idf(df: int, N: int):
+        return math.log(1 + (N - df + 0.5) / (df + 0.5))
+
+    @classmethod
+    def build_from_documents(
+        cls: Type[BM25Index],
+        documents: Iterable[Document],
+        store_raw: bool = True,
+        output_dir: Optional[str] = None,
+        ndocs: Optional[int] = None,
+        show_progress_bar: bool = True,
+        k1: float = 0.9,
+        b: float = 0.4,
+    ) -> BM25Index:
+        # Counting TFs, DFs, doc_lengths, etc.:
+        counting = run_counting(
+            documents=documents,
+            tokenize_fn=BM25Index.tokenize,
+            store_raw=store_raw,
+            ndocs=ndocs,
+            show_progress_bar=show_progress_bar,
+        )
+
+        # Compute term weights and caching:
+        posting_lists = counting.posting_lists
+        total_docs = len(counting.cid2docid)
+        BM25Index.cache_term_weights(
+            posting_lists=posting_lists,
+            total_docs=total_docs,
+            avgdl=counting.avgdl,
+            dfs=counting.dfs,
+            dls=counting.dls,
+            k1=k1,
+            b=b,
+        )
+
+        # Assembly and save:
+        index = BM25Index(
+            posting_lists=posting_lists,
+            vocab=counting.vocab,
+            cid2docid=counting.cid2docid,
+            collection_ids=counting.collection_ids,
+            doc_texts=counting.doc_texts,
+        )
+        return index
+
+
+@dataclass
+class CSCInvertedIndex:
+    posting_lists_matrix: csc_matrix  # docid -> posting_list
+    vocab: Dict[str, int]
+    cid2docid: Dict[str, int]  # collection_id -> docid
+    collection_ids: List[str]  # docid -> collection_id
+    doc_texts: Optional[List[str]] = None  # docid -> document text
+
+    def save(self, output_dir: str) -> None:
+        os.makedirs(output_dir, exist_ok=True)
+        with open(os.path.join(output_dir, "index.pkl"), "wb") as f:
+            pickle.dump(self, f)
+
+    @classmethod
+    def from_saved(cls: Type[T], saved_dir: str) -> T:
+        index = cls(
+            posting_lists_matrix=None, vocab={}, cid2docid={}, collection_ids=[], doc_texts=None
+        )
+        with open(os.path.join(saved_dir, "index.pkl"), "rb") as f:
+            index = pickle.load(f)
+        return index
+
+@dataclass
+class CSCBM25Index(CSCInvertedIndex):
+
+    @staticmethod
+    def tokenize(text: str) -> List[str]:
+        return simple_tokenize(text)
+
+    @staticmethod
+    def cache_term_weights(
+        posting_lists: List[PostingList],
+        total_docs: int,
+        avgdl: float,
+        dfs: List[int],
+        dls: List[int],
+        k1: float,
+        b: float,
+    ) -> csc_matrix:
+        """Compute term weights and caching"""
+
+        ## YOUR_CODE_STARTS_HERE
+        data = []
+        indices = []
+        indptr = [0]
+        N = total_docs
+        for tid, posting_list in enumerate(
+            tqdm.tqdm(posting_lists, desc="Regularizing TFs")
+        ):
+            idf = BM25Index.calc_idf(df=dfs[tid], N=N)
+            for i in range(len(posting_list.docid_postings)):
+                docid = posting_list.docid_postings[i]
+                tf = posting_list.tweight_postings[i]
+                dl = dls[docid]
+                regularized_tf = BM25Index.calc_regularized_tf(
+                    tf=tf, dl=dl, avgdl=avgdl, k1=k1, b=b
+                )
+                weight = regularized_tf * idf
+                data.append(weight)
+                indices.append(docid) 
+            indptr.append(len(data)) 
+
+        posting_lists_matrix = csc_matrix(
+            (data, indices, indptr),
+            shape=(total_docs, len(posting_lists))
+        )
+
+        return posting_lists_matrix
+        ## YOUR_CODE_ENDS_HERE
+
+    @staticmethod
+    def calc_regularized_tf(
+        tf: int, dl: float, avgdl: float, k1: float, b: float
+    ) -> float:
+        return tf / (tf + k1 * (1 - b + b * dl / avgdl))
+
+    @staticmethod
+    def calc_idf(df: int, N: int):
+        return math.log(1 + (N - df + 0.5) / (df + 0.5))
+
+    @classmethod
+    def build_from_documents(
+        cls: Type[CSCBM25Index],
+        documents: Iterable[Document],
+        store_raw: bool = True,
+        output_dir: Optional[str] = None,
+        ndocs: Optional[int] = None,
+        show_progress_bar: bool = True,
+        k1: float = 0.9,
+        b: float = 0.4,
+    ) -> CSCBM25Index:
+        # Counting TFs, DFs, doc_lengths, etc.:
+        counting = run_counting(
+            documents=documents,
+            tokenize_fn=CSCBM25Index.tokenize,
+            store_raw=store_raw,
+            ndocs=ndocs,
+            show_progress_bar=show_progress_bar,
+        )
+
+        # Compute term weights and caching:
+        posting_lists = counting.posting_lists
+        total_docs = len(counting.cid2docid)
+        posting_lists_matrix = CSCBM25Index.cache_term_weights(
+            posting_lists=posting_lists,
+            total_docs=total_docs,
+            avgdl=counting.avgdl,
+            dfs=counting.dfs,
+            dls=counting.dls,
+            k1=k1,
+            b=b,
+        )
+
+        # Assembly and save:
+        index = CSCBM25Index(
+            posting_lists_matrix=posting_lists_matrix,
+            vocab=counting.vocab,
+            cid2docid=counting.cid2docid,
+            collection_ids=counting.collection_ids,
+            doc_texts=counting.doc_texts,
+        )
+        return index
+
+class BaseCSCInvertedIndexRetriever(BaseRetriever):
+
+    @property
+    @abstractmethod
+    def index_class(self) -> Type[CSCInvertedIndex]:
+        pass
+
+    def __init__(self, index_dir: str) -> None:
+        self.index = self.index_class.from_saved(index_dir)
+
+    def get_term_weights(self, query: str, cid: str) -> Dict[str, float]:
+        ## YOUR_CODE_STARTS_HERE
+        toks = self.index.tokenize(query)
+        target_docid = self.index.cid2docid[cid]
+        term_weights = {}
+
+        for tok in toks:
+            if tok not in self.index.vocab:
+                continue
+            tid = self.index.vocab[tok]
+            weight = self.index.posting_lists_matrix[target_docid, tid]
+            term_weights[tok] = weight 
+        return term_weights
+        ## YOUR_CODE_ENDS_HERE
+
+    def score(self, query: str, cid: str) -> float:
+        return sum(self.get_term_weights(query=query, cid=cid).values())
+
+    def retrieve(self, query: str, topk: int = 10) -> Dict[str, float]:
+        ## YOUR_CODE_STARTS_HERE
+        toks = self.index.tokenize(query)
+        docid2score: Dict[int, float] = {}
+        for tok in toks:
+            if tok not in self.index.vocab:
+                continue
+            tid = self.index.vocab[tok]
+            col = self.index.posting_lists_matrix[:, tid]
+            rows, data = col.indices, col.data
+
+            for docid, tweight in zip(rows, data):
+              docid2score.setdefault(docid, 0)
+              docid2score[docid] += tweight
+
+        docid2score = dict(
+            sorted(docid2score.items(), key=lambda pair: pair[1], reverse=True)[:topk]
+        )
+        return {
+            self.index.collection_ids[docid]: score
+            for docid, score in docid2score.items()
+        }
+        ## YOUR_CODE_ENDS_HERE
+
+class CSCBM25Retriever(BaseCSCInvertedIndexRetriever):
+
+    @property
+    def index_class(self) -> Type[CSCBM25Index]:
+        return CSCBM25Index
+
+class Hit(TypedDict):
+  cid: str
+  score: float
+  text: str
+
+# Best b 0.9
+# Best k1: 0.4
+best_b = 0.9
+best_k1 = 0.4 
+sciq = load_sciq()
+csc_bm25_index = CSCBM25Index.build_from_documents(
+    documents=iter(sciq.corpus),
+    ndocs=12160,
+    show_progress_bar=True,
+    k1=best_k1,
+    b=best_b
+)
+csc_bm25_index.save("output/csc_bm25_index")
+
+demo: Optional[gr.Interface] = None  # Assign your gradio demo to this variable
+return_type = List[Hit]
+
+## YOUR_CODE_STARTS_HERE
+csc_bm25_retriever = CSCBM25Retriever(index_dir="output/csc_bm25_index")
+doc2text = {doc.collection_id: doc.text for doc in sciq.corpus}
+
+def retrieve(query: str) -> List[Hit]:
+  results = csc_bm25_retriever.retrieve(query)
+
+  hits: List[Hit] = []
+  for cid, score in results.items():
+      hit: Hit = {
+          "cid": cid,
+          "score": score,
+          "text": doc2text[cid]
+      }
+      hits.append(hit)
+  hits = sorted(hits, key=lambda x: x["score"], reverse=True)
+  return hits
+    
+def format_hits(hits: List[Hit]):
+  output = ""
+  for i, hit in enumerate(hits, 1):
+      output += f"\n\n{i}. Score: {hit['score']:.3f}\n"
+      output += f"ID: {hit['cid']}\n"
+      output += f"Text: {hit['text']}\n"
+      output += "-" * 80
+  return output 
+
+demo = gr.Interface(
+    fn=retrieve,
+    inputs=gr.Textbox(label="Query"),
+    outputs=gr.JSON(label="Results"),
+    title="Document Search",
+    description="Search documents using BM25 retrieval"
+)
+## YOUR_CODE_ENDS_HERE
+demo.launch()