646_project_dataset3_p3.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import urllib.request, json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "file_path = 'LaMP_3.json'\n",
        "\n",
        "\n",
        "with open(file_path, 'r') as json_file:\n",
        "    data = json.load(json_file)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "ratio = 0.8\n",
        "start = 0\n",
        "end = len(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Random sample profile"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "import random\n",
        "for idx in range(start, end):\n",
        "    random_profiles = random.sample(data[idx][\"profile\"], 50)\n",
        "\n",
        "    # Update the data with the randomly selected profiles\n",
        "    data[idx][\"profile\"] = random_profiles"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9vXVTPnxkwv"
      },
      "source": [
        "### Download & preparing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "J-B2DV-OwE8L"
      },
      "outputs": [],
      "source": [
        "from langchain.schema import Document"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "rPVln_zf-J6c"
      },
      "outputs": [],
      "source": [
        "def get_docs(idx):\n",
        "  content = []\n",
        "  for i in range(len(data[idx]['profile'])):\n",
        "      content.append(data[idx]['profile'][i]['text'])\n",
        "  documents = [Document(page_content=text) for text in content]\n",
        "  return documents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzicQux_xtPN"
      },
      "source": [
        "### BM25 retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "JECyHwc9fl4W"
      },
      "outputs": [],
      "source": [
        "from rank_bm25 import BM25Okapi\n",
        "from collections import namedtuple\n",
        "\n",
        "DocumentWithScore = namedtuple('DocumentWithScore', ['document', 'score'])\n",
        "\n",
        "def bm25(documents, query, ratio):\n",
        "    tokenized_corpus = [(doc.page_content).split(\" \") for doc in documents]\n",
        "    bm25 = BM25Okapi(tokenized_corpus)\n",
        "    tokenized_query = query.split(\" \")\n",
        "    bm25_scores = bm25.get_scores(tokenized_query)\n",
        "\n",
        "    onlydocs = [x.page_content for x in documents]\n",
        "    documents_with_scores = [\n",
        "        DocumentWithScore(document=doc, score=score)\n",
        "        for doc, score in zip(onlydocs, bm25_scores)\n",
        "    ]\n",
        "\n",
        "    sorted_documents_with_scores = sorted(documents_with_scores, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    top_n_documents_with_scores = sorted_documents_with_scores[:int(len(documents)*ratio)]\n",
        "\n",
        "    document_instances = [Document(page_content=doc.document) for doc in top_n_documents_with_scores]\n",
        "    return document_instances, top_n_documents_with_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hC-mK_ZixwfV"
      },
      "source": [
        "### TF-IDF Retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "bzAye_QuG6dJ"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from collections import namedtuple\n",
        "\n",
        "DocumentWithScore = namedtuple('DocumentWithScore', ['document', 'score'])\n",
        "\n",
        "def tfidf_retrieval_sklearn(documents, query, ratio):\n",
        "    top_n = int(len(documents)*ratio)\n",
        "    docs = [(doc.page_content) for doc in documents]\n",
        "    vectorizer = TfidfVectorizer()\n",
        "\n",
        "    tfidf_matrix = vectorizer.fit_transform(docs)\n",
        "\n",
        "    query_vector = vectorizer.transform([query])\n",
        "\n",
        "    similarity_scores = cosine_similarity(query_vector, tfidf_matrix)\n",
        "\n",
        "    top_n_indices = similarity_scores.argsort()[0][-top_n:][::-1]\n",
        "\n",
        "    top_n_documents = [docs[i] for i in top_n_indices]\n",
        "    document_instances = [Document(page_content=doc) for doc in top_n_documents]\n",
        "\n",
        "    top_n_documents_with_scores = [\n",
        "        DocumentWithScore(document=docs[i], score=similarity_scores[0][i])\n",
        "        for i in top_n_indices\n",
        "    ]\n",
        "\n",
        "    return document_instances, top_n_documents_with_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFM68zc3KQO8"
      },
      "source": [
        "### Contriever msmarco"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "oj6l7sOacAUs"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/homebrew/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "from collections import namedtuple\n",
        "\n",
        "DocumentWithScore = namedtuple('DocumentWithScore', ['document', 'score'])\n",
        "def contrievermsmarco(documents, query, ratio):\n",
        "    content = [doc.page_content for doc in documents]\n",
        "    top_n = int(len(content) * ratio)\n",
        "\n",
        "   \n",
        "    tokenizer = AutoTokenizer.from_pretrained('facebook/contriever-msmarco')\n",
        "    model = AutoModel.from_pretrained('facebook/contriever-msmarco')\n",
        "\n",
        "    \n",
        "    content_with_query = [query] + content\n",
        "\n",
        "    \n",
        "    inputs = tokenizer(content_with_query, padding=True, truncation=True, return_tensors='pt')\n",
        "\n",
        "    \n",
        "    outputs = model(**inputs)\n",
        "\n",
        "   \n",
        "    def mean_pooling(token_embeddings, mask):\n",
        "        token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)\n",
        "        sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]\n",
        "        return sentence_embeddings\n",
        "\n",
        "   \n",
        "    query_embedding = mean_pooling(outputs[0][:1], inputs['attention_mask'][:1]).squeeze()\n",
        "\n",
        "    \n",
        "    document_embeddings = mean_pooling(outputs[0][1:], inputs['attention_mask'][1:]).squeeze()\n",
        "\n",
        "    \n",
        "    similarity_scores = torch.matmul(query_embedding, document_embeddings.T).tolist()\n",
        "\n",
        "    \n",
        "    document_with_scores = list(zip(content, similarity_scores))\n",
        "\n",
        "   \n",
        "    document_with_scores.sort(key=lambda x: x[1], reverse=True)\n",
        "    document_with_scores = document_with_scores[:top_n]\n",
        "    \n",
        "    document_instances =  [Document(page_content=doc) for doc, _ in document_with_scores]\n",
        "    top_n_documents_with_scores = [DocumentWithScore(document=doc, score=score) for doc, score in document_with_scores]\n",
        "    \n",
        "    return document_instances, top_n_documents_with_scores\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57z5FNPXPZVR"
      },
      "source": [
        "### DistilBertTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "sbxGz4SQRwda"
      },
      "outputs": [],
      "source": [
        "from transformers import DistilBertTokenizer, DistilBertModel\n",
        "import torch\n",
        "\n",
        "def distil_bert(documents, query, ratio):\n",
        "  \n",
        "  tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "  model = DistilBertModel.from_pretrained('distilbert-base-uncased')\n",
        "\n",
        "  encoded_query = tokenizer(query, return_tensors='pt')\n",
        "\n",
        "  \n",
        "  with torch.no_grad():\n",
        "      query_embeddings = model(**encoded_query).last_hidden_state.mean(dim=1)\n",
        "\n",
        "\n",
        "  document_texts = [doc.page_content for doc in documents]\n",
        "\n",
        "  encoded_documents = tokenizer(document_texts, return_tensors='pt', padding=True, truncation=True)\n",
        "\n",
        "  with torch.no_grad():\n",
        "      document_embeddings = model(**encoded_documents).last_hidden_state.mean(dim=1)\n",
        "\n",
        "  similarity_scores = torch.nn.functional.cosine_similarity(query_embeddings, document_embeddings, dim=1)\n",
        "\n",
        "  ranked_documents = sorted(zip(documents, similarity_scores.tolist()), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "\n",
        "  top_n_documents_with_scores = ranked_documents[:int(len(documents)*ratio)]\n",
        "  \n",
        "  document_instances = [doc_tuple[0] for doc_tuple in top_n_documents_with_scores]\n",
        "  \n",
        "  top_n_documents_with_scores = [DocumentWithScore(document=doc.page_content, score=score)for doc, score in top_n_documents_with_scores]\n",
        "\n",
        "  return document_instances, top_n_documents_with_scores\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_1J0Cwyivbc"
      },
      "source": [
        "### Normalize Scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Cre9ORM9ixkj"
      },
      "outputs": [],
      "source": [
        "from collections import namedtuple\n",
        "\n",
        "DocumentWithScore = namedtuple('DocumentWithScore', ['document', 'score'])\n",
        "def normalize_scores(document_with_scores):\n",
        "    scores = [score for _, score in document_with_scores]\n",
        "   \n",
        "    score_range = max(scores) - min(scores)\n",
        "    if score_range == 0:\n",
        "        normalized_scores = [1.0] * len(scores)\n",
        "    else:\n",
        "        normalized_scores = [(score - min(scores)) / score_range for score in scores]\n",
        "\n",
        "    normalized_document_with_scores = [ DocumentWithScore(document=doc, score=normalized_score) for (doc, _), normalized_score in zip(document_with_scores, normalized_scores)]\n",
        "\n",
        "    return normalized_document_with_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egWUFjYkxz8H"
      },
      "source": [
        "### Combining retrievers and getting data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "4_l87zGLzqZY"
      },
      "outputs": [],
      "source": [
        "def filter_acc_ratio(ratio, result):\n",
        "  top_index = int(len(result) * ratio)\n",
        "  top_documents = result[:top_index]\n",
        "  return top_documents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "sek9tmp3zYbY"
      },
      "outputs": [],
      "source": [
        "def combine(results):\n",
        "  combined_list = []\n",
        "  for sublist in results:\n",
        "    combined_list.extend(sublist)\n",
        "  return combined_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "E6RDcl2kwTzR"
      },
      "outputs": [],
      "source": [
        "from collections import OrderedDict\n",
        "def union(documents_list):\n",
        "  union_of_docs = list(OrderedDict.fromkeys(document.page_content for document in documents_list))\n",
        "  return union_of_docs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaWBixdLymjt"
      },
      "source": [
        "### Putting retrieved profiles back in data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "ruqiFmAeyl1H"
      },
      "outputs": [],
      "source": [
        "def put_retrieved_profiles(union_of_docs, idx):\n",
        "  filtered_profiles = [profile for profile in data[idx]['profile'] if profile['text'] in union_of_docs]\n",
        "  copy_of_data = (data[idx]).copy()\n",
        "  copy_of_data['profile'] = filtered_profiles\n",
        "  return copy_of_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKN4kExIxfM7"
      },
      "source": [
        "### LM"
      ]
    },
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      "cell_type": "code",
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        "colab": {
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        "id": "SZy6h_EpCBSu",
        "outputId": "96113ec0-4764-481c-c84e-8448e1e51485"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-large\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-large\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "gPiCP_1FB8m_"
      },
      "outputs": [],
      "source": [
        "def LM(original_data):\n",
        "\n",
        "  input_data = {\n",
        "    'input': original_data['input'],\n",
        "    'profile': [\n",
        "        {'text': profile['text'], 'score': profile['score']} for profile in original_data['profile']\n",
        "    ]\n",
        "}\n",
        "\n",
        "  question = input_data['input']\n",
        "  context = ' '.join([f\"{profile['score']}: {profile['text']}\" for profile in input_data['profile']])\n",
        "  input_text = f\"question: {question} context: {context}\"\n",
        "\n",
        "  inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
        "  outputs = model.generate(**inputs)\n",
        "\n",
        "  generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "  return generated_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MkY3RPp4Aun"
      },
      "source": [
        "### Majority Vote"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "-yI1L6t97Kh2"
      },
      "outputs": [],
      "source": [
        "def majority_vote(ratio, st, end):\n",
        "  mv_output = []\n",
        "  for idx in range(st, end):\n",
        "   \n",
        "    documents = get_docs(idx)\n",
        "   \n",
        "    query = (data[idx]['input'])\n",
        "    index_of_query= (data[idx]['input']).find(\"review:\")\n",
        "\n",
        "    query = (data[idx]['input'])[index_of_query + len(\"review:\"):].strip()\n",
        "   \n",
        "    bm25_result= bm25(documents, query, ratio)\n",
        "    bm25_result_docs = bm25_result[0]\n",
        "    tfidf_result = tfidf_retrieval_sklearn(documents, query, ratio)\n",
        "   \n",
        "    tfidf_result_docs = tfidf_result[0]\n",
        "    contri_result = contrievermsmarco(documents,query, ratio)\n",
        "    contri_result_docs = contri_result[0]\n",
        "  \n",
        "    all_top_docs = [bm25_result_docs, tfidf_result_docs, contri_result_docs]\n",
        "    all_top_docs = combine(all_top_docs)\n",
        "    all_top_docs = union(all_top_docs)\n",
        "\n",
        "    new_data_mv = put_retrieved_profiles(all_top_docs, idx)\n",
        "    val = LM(new_data_mv)\n",
        "    mv_output.append(val)\n",
        "  return mv_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cy4v7sOX3TQ0"
      },
      "source": [
        "### Borda count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "ILXIKu3Y3SRA"
      },
      "outputs": [],
      "source": [
        "def borda_count(ranked_lists):\n",
        "    borda_scores = {}\n",
        "\n",
        "    \n",
        "    for i, ranked_list in enumerate(ranked_lists):\n",
        "        for j, document in enumerate(ranked_list):\n",
        "            borda_scores[document] = borda_scores.get(document, 0) + (len(ranked_list) - j)\n",
        "\n",
        "    \n",
        "    final_ranking = sorted(borda_scores.keys(), key=lambda x: borda_scores[x], reverse=True)\n",
        "\n",
        "    return final_ranking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Azkg7S2Y3hej"
      },
      "outputs": [],
      "source": [
        "def borda_count_fn(ratio, st, end):\n",
        "  bc_output = []\n",
        "  for idx in range(st, end):\n",
        "    documents = get_docs(idx)\n",
        "\n",
        "    query = (data[idx]['input'])\n",
        "    index_of_query= (data[idx]['input']).find(\"review:\")\n",
        "\n",
        "    query = (data[idx]['input'])[index_of_query + len(\"review:\"):].strip()\n",
        "    bm25_result= bm25(documents, query, ratio)\n",
        "    bm25_result_docs = bm25_result[0]\n",
        "    tfidf_result = tfidf_retrieval_sklearn(documents, query, ratio)\n",
        "    tfidf_result_docs = tfidf_result[0]\n",
        "    contri_result = contrievermsmarco(documents,query, ratio)\n",
        "    contri_result_docs = contri_result[0]\n",
        "    bm25_result_onlyDocs = [(doc.page_content) for doc in bm25_result_docs]\n",
        "    tfidf_result_onlyDocs = [(doc.page_content) for doc in tfidf_result_docs]\n",
        "    contri_result_onlyDocs = [(doc.page_content) for doc in contri_result_docs]\n",
        "\n",
        "    all_top_docs = [bm25_result_onlyDocs, tfidf_result_onlyDocs, contri_result_onlyDocs]\n",
        "    result = borda_count(all_top_docs)\n",
        "    \n",
        "    new_data_bc = put_retrieved_profiles(result, idx)\n",
        "    val = LM(new_data_bc)\n",
        "    bc_output.append(val)\n",
        "  return bc_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WAgj-Nbmrpa"
      },
      "source": [
        "### Scoring based"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbdNDY7ARvny"
      },
      "source": [
        "#### Parameters: average, weighted_average, max, min, product, rank_based,linear_combination\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Fy4ORx1VpSXV"
      },
      "outputs": [],
      "source": [
        "def scoring_based(ratio, st, end, parameter):\n",
        "  scor_output = []\n",
        "  for idx in range(st, end):\n",
        "    documents = get_docs(idx)\n",
        "\n",
        "    query = (data[idx]['input'])\n",
        "    index_of_query= (data[idx]['input']).find(\"review:\")\n",
        "\n",
        "    query = (data[idx]['input'])[index_of_query + len(\"review:\"):].strip()\n",
        "    bm25_result= bm25(documents, query, ratio)\n",
        "    tfidf_result = tfidf_retrieval_sklearn(documents, query, ratio)\n",
        "    contri_result = contrievermsmarco(documents,query, ratio)\n",
        "    bm25_results_docScore = normalize_scores(bm25_result[1])\n",
        "    tfidf_results_docScore = normalize_scores(tfidf_result[1])\n",
        "    contri_results_docScore = normalize_scores(contri_result[1])\n",
        "\n",
        "    weight_bm25 = 0.1\n",
        "    weight_tfidf = 0.3\n",
        "    weight_contri = 0.6\n",
        "\n",
        "    average_scores = [(doc.score + tfidf_results_docScore[i].score + contri_results_docScore[i].score) / 3 for i, doc in enumerate(bm25_results_docScore)]\n",
        "    weighted_average_scores = [(doc.score * weight_bm25 + tfidf_results_docScore[i].score * weight_tfidf + contri_results_docScore[i].score * weight_contri) / 3 for i, doc in enumerate(bm25_results_docScore)]\n",
        "    max_scores = [max(doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score) for i, doc in enumerate(bm25_results_docScore)]\n",
        "    min_scores = [min(doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score) for i, doc in enumerate(bm25_results_docScore)]\n",
        "    product_scores = [doc.score * tfidf_results_docScore[i].score * contri_results_docScore[i].score for i, doc in enumerate(bm25_results_docScore)]\n",
        "    rank_based_scores = [\n",
        "      sum(\n",
        "          [\n",
        "              sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(doc.score),\n",
        "              sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(tfidf_results_docScore[i].score),\n",
        "              sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(contri_results_docScore[i].score)\n",
        "          ]\n",
        "      ) / 3\n",
        "      for i, doc in enumerate(bm25_results_docScore)\n",
        "  ]\n",
        "\n",
        "    linear_combination_scores = [(doc.score * weight_bm25 + tfidf_results_docScore[i].score * weight_tfidf + contri_results_docScore[i].score * weight_contri) for i, doc in enumerate(bm25_results_docScore)]\n",
        "\n",
        "    average_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, average_scores)]\n",
        "    weighted_average_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, weighted_average_scores)]\n",
        "    max_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, max_scores)]\n",
        "    min_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, min_scores)]\n",
        "    product_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, product_scores)]\n",
        "    rank_based_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, rank_based_scores)]\n",
        "    linear_combination_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, linear_combination_scores)]\n",
        "\n",
        "\n",
        "\n",
        "    sorted_average_scored_documents = sorted(average_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_weighted_average_scored_documents = sorted(weighted_average_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_max_scored_documents = sorted(max_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_min_scored_documents = sorted(min_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_product_scored_documents = sorted(product_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_rank_based_scored_documents = sorted(rank_based_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_linear_combination_scored_documents = sorted(linear_combination_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "\n",
        "\n",
        "    sorted_average_scored_page_content = [doc_with_score.document for doc_with_score in sorted_average_scored_documents]\n",
        "\n",
        "    sorted_weighted_average_scored_page_content = [doc_with_score.document for doc_with_score in sorted_weighted_average_scored_documents]\n",
        "\n",
        "    sorted_max_scored_page_content = [doc_with_score.document for doc_with_score in sorted_max_scored_documents]\n",
        "\n",
        "    sorted_min_scored_page_content = [doc_with_score.document for doc_with_score in sorted_min_scored_documents]\n",
        "\n",
        "    sorted_product_scored_page_content = [doc_with_score.document for doc_with_score in sorted_product_scored_documents]\n",
        "\n",
        "    sorted_rank_based_scored_page_content = [doc_with_score.document for doc_with_score in sorted_rank_based_scored_documents]\n",
        "\n",
        "    sorted_linear_combination_scored_page_content = [doc_with_score.document for doc_with_score in sorted_linear_combination_scored_documents]\n",
        "\n",
        "    mapping = {\n",
        "    \"average\": sorted_average_scored_page_content,\n",
        "    \"weighted_average\": sorted_weighted_average_scored_page_content,\n",
        "    \"max\": sorted_max_scored_page_content,\n",
        "    \"min\": sorted_min_scored_page_content,\n",
        "    \"product\": sorted_product_scored_page_content,\n",
        "    \"rank_based\": sorted_rank_based_scored_page_content,\n",
        "    \"linear_combination\": sorted_linear_combination_scored_page_content\n",
        "    }\n",
        "\n",
        "    result = mapping[parameter] # change this accordingly\n",
        "    new_data_scor = put_retrieved_profiles(result, idx)\n",
        "    val = LM(new_data_scor)\n",
        "    scor_output.append(val)\n",
        "  return scor_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuowEHHhIJxi"
      },
      "source": [
        "### Re ranking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "-ZqEfs1SIJGv"
      },
      "outputs": [],
      "source": [
        "def re_rank_documents(bm25_results, query, percentage=0.5):\n",
        "  num_relevant_documents = int(len(bm25_results) * percentage)\n",
        "\n",
        "  relevant_bm25_documents = bm25_results[:num_relevant_documents]\n",
        "\n",
        "  \n",
        "  if(len(relevant_bm25_documents) == 1):\n",
        "     re_ranked_results = relevant_bm25_documents\n",
        "  else:\n",
        "    relevant_bm25_documents = [Document(page_content=item.document) for item in relevant_bm25_documents]\n",
        "    contriever_results_for_queries = contrievermsmarco(relevant_bm25_documents, query, 1.0)\n",
        "    re_ranked_results = contriever_results_for_queries[1]\n",
        "  re_ranked_results.sort(key=lambda x: x.score, reverse=True)\n",
        "  re_ranked_results_docs = [doc.document for doc in re_ranked_results]\n",
        "  return re_ranked_results_docs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "gwExZmSaXEoC"
      },
      "outputs": [],
      "source": [
        "def re_rank(ratio, st, end):\n",
        "  rr_output = []\n",
        "  for idx in range(st, end):\n",
        "    documents = get_docs(idx)\n",
        "\n",
        "    query = (data[idx]['input'])\n",
        "    index_of_query= (data[idx]['input']).find(\"review:\")\n",
        "\n",
        "    query = (data[idx]['input'])[index_of_query + len(\"review:\"):].strip()\n",
        "    bm25_result= bm25(documents, query, 0.6)\n",
        "    bm25_result_docs = bm25_result[1]\n",
        "    result = re_rank_documents(bm25_result_docs, query, 0.6)\n",
        "   \n",
        "    new_data_rr = put_retrieved_profiles(result, idx)\n",
        "    val = LM(new_data_rr)\n",
        "    rr_output.append(val)\n",
        "  return rr_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVOyzSBRFHO1"
      },
      "source": [
        "### True Output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "ELrAPGOzDvyA"
      },
      "outputs": [],
      "source": [
        "with urllib.request.urlopen(\"https://ciir.cs.umass.edu/downloads/LaMP/LaMP_3/train/train_outputs.json\") as url:\n",
        "    true_output = json.load(url)\n",
        "    true_output = true_output['golds']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [],
      "source": [
        "ids = []\n",
        "for i in range(start, end):\n",
        "  ids.append(true_output[i]['id'])\n",
        "\n",
        "true_op = []\n",
        "for i in range(start, end):\n",
        "  true_op.append(true_output[i]['output'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Ensemble together"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def all_ensemble(rati0, st, end, parameter):\n",
        "    scor_output = []\n",
        "    bc_output = []\n",
        "    mv_output = []\n",
        "    for idx in range(st, end):\n",
        "        documents = get_docs(idx)\n",
        "\n",
        "        query = (data[idx]['input'])\n",
        "        index_of_query= (data[idx]['input']).find(\"article:\")\n",
        "\n",
        "        query = (data[idx]['input'])[index_of_query + len(\"article:\"):].strip()\n",
        "        bm25_result= bm25(documents, query, ratio)\n",
        "        tfidf_result = tfidf_retrieval_sklearn(documents, query, ratio)\n",
        "        contri_result = contrievermsmarco(documents,query, ratio)\n",
        "        bm25_results_docScore = normalize_scores(bm25_result[1])\n",
        "        tfidf_results_docScore = normalize_scores(tfidf_result[1])\n",
        "        contri_results_docScore = normalize_scores(contri_result[1])\n",
        "        bm25_result_docs = bm25_result[0]\n",
        "        tfidf_result_docs = tfidf_result[0]\n",
        "        contri_result_docs = contri_result[0]\n",
        "        bm25_result_onlyDocs = [(doc.page_content) for doc in bm25_result_docs]\n",
        "        tfidf_result_onlyDocs = [(doc.page_content) for doc in tfidf_result_docs]\n",
        "        contri_result_onlyDocs = [(doc.page_content) for doc in contri_result_docs]\n",
        "\n",
        "        all_top_docs = [bm25_result_onlyDocs, tfidf_result_onlyDocs, contri_result_onlyDocs]\n",
        "        bc_result = borda_count(all_top_docs)\n",
        "        new_data_bc = put_retrieved_profiles(bc_result, idx)\n",
        "        bc_val = LM(new_data_bc)\n",
        "        bc_output.append(bc_val)\n",
        "\n",
        "        \n",
        "        all_top_docs = [bm25_result_docs, tfidf_result_docs, contri_result_docs]\n",
        "        all_top_docs = combine(all_top_docs)\n",
        "        all_top_docs = union(all_top_docs)\n",
        "       \n",
        "        new_data_mv = put_retrieved_profiles(all_top_docs, idx)\n",
        "        mv_val = LM(new_data_mv)\n",
        "        mv_output.append(mv_val)\n",
        "\n",
        "        weight_bm25 = 0.4\n",
        "        weight_tfidf = 0.3\n",
        "        weight_contri = 0.3\n",
        "\n",
        "        average_scores = [(doc.score + tfidf_results_docScore[i].score + contri_results_docScore[i].score) / 3 for i, doc in enumerate(bm25_results_docScore)]\n",
        "        weighted_average_scores = [(doc.score * weight_bm25 + tfidf_results_docScore[i].score * weight_tfidf + contri_results_docScore[i].score * weight_contri) / 3 for i, doc in enumerate(bm25_results_docScore)]\n",
        "        max_scores = [max(doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score) for i, doc in enumerate(bm25_results_docScore)]\n",
        "        min_scores = [min(doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score) for i, doc in enumerate(bm25_results_docScore)]\n",
        "        product_scores = [doc.score * tfidf_results_docScore[i].score * contri_results_docScore[i].score for i, doc in enumerate(bm25_results_docScore)]\n",
        "        rank_based_scores = [\n",
        "        sum(\n",
        "            [\n",
        "                sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(doc.score),\n",
        "                sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(tfidf_results_docScore[i].score),\n",
        "                sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(contri_results_docScore[i].score)\n",
        "            ]\n",
        "        ) / 3\n",
        "        for i, doc in enumerate(bm25_results_docScore)\n",
        "    ]\n",
        "\n",
        "        linear_combination_scores = [(doc.score * weight_bm25 + tfidf_results_docScore[i].score * weight_tfidf + contri_results_docScore[i].score * weight_contri) for i, doc in enumerate(bm25_results_docScore)]\n",
        "\n",
        "        average_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, average_scores)]\n",
        "        weighted_average_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, weighted_average_scores)]\n",
        "        max_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, max_scores)]\n",
        "        min_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, min_scores)]\n",
        "        product_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, product_scores)]\n",
        "        rank_based_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, rank_based_scores)]\n",
        "        linear_combination_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, linear_combination_scores)]\n",
        "\n",
        "\n",
        "\n",
        "        sorted_average_scored_documents = sorted(average_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_weighted_average_scored_documents = sorted(weighted_average_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_max_scored_documents = sorted(max_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_min_scored_documents = sorted(min_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_product_scored_documents = sorted(product_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_rank_based_scored_documents = sorted(rank_based_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_linear_combination_scored_documents = sorted(linear_combination_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "\n",
        "\n",
        "        sorted_average_scored_page_content = [doc_with_score.document for doc_with_score in sorted_average_scored_documents]\n",
        "\n",
        "        sorted_weighted_average_scored_page_content = [doc_with_score.document for doc_with_score in sorted_weighted_average_scored_documents]\n",
        "\n",
        "        sorted_max_scored_page_content = [doc_with_score.document for doc_with_score in sorted_max_scored_documents]\n",
        "\n",
        "        sorted_min_scored_page_content = [doc_with_score.document for doc_with_score in sorted_min_scored_documents]\n",
        "\n",
        "        sorted_product_scored_page_content = [doc_with_score.document for doc_with_score in sorted_product_scored_documents]\n",
        "\n",
        "        sorted_rank_based_scored_page_content = [doc_with_score.document for doc_with_score in sorted_rank_based_scored_documents]\n",
        "\n",
        "        sorted_linear_combination_scored_page_content = [doc_with_score.document for doc_with_score in sorted_linear_combination_scored_documents]\n",
        "\n",
        "        mapping = {\n",
        "        \"average\": sorted_average_scored_page_content,\n",
        "        \"weighted_average\": sorted_weighted_average_scored_page_content,\n",
        "        \"max\": sorted_max_scored_page_content,\n",
        "        \"min\": sorted_min_scored_page_content,\n",
        "        \"product\": sorted_product_scored_page_content,\n",
        "        \"rank_based\": sorted_rank_based_scored_page_content,\n",
        "        \"linear_combination\": sorted_linear_combination_scored_page_content\n",
        "        }\n",
        "\n",
        "        scor_result = mapping[parameter] # change this accordingly\n",
        "        new_data_scor = put_retrieved_profiles(scor_result, idx)\n",
        "        scor_val = LM(new_data_scor)\n",
        "        scor_output.append(scor_val)\n",
        "    return scor_output, bc_output, mv_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YC0bJpWqFPSl"
      },
      "source": [
        "### MAIN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (1451 > 512). Running this sequence through the model will result in indexing errors\n",
            "/opt/homebrew/lib/python3.11/site-packages/transformers/generation/utils.py:1273: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "rr_op = re_rank(ratio, start, end)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [],
      "source": [
        "scor_op, bc_op, mv_op = all_ensemble(ratio, start, end, \"average\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [],
      "source": [
        "scor_op_wtavg = scoring_based(ratio, start, end, \"weighted_average\")\n",
        "scor_op_max = scoring_based(ratio, start, end, \"max\")\n",
        "scor_op_min = scoring_based(ratio, start, end, \"min\")\n",
        "scor_op_prod = scoring_based(ratio, start, end, \"product\")\n",
        "scor_op_rb = scoring_based(ratio, start, end, \"rank_based\")\n",
        "scor_op_lc = scoring_based(ratio, start, end, \"linear_combination\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "YS6dCtS10ItO"
      },
      "outputs": [],
      "source": [
        "dbert_output = []\n",
        "for idx in range(start, end):\n",
        "  documents = get_docs(idx)\n",
        "\n",
        "  query = (data[idx]['input'])\n",
        "  index_of_query= (data[idx]['input']).find(\"review:\")\n",
        "\n",
        "  query = (data[idx]['input'])[index_of_query + len(\"review:\"):].strip()\n",
        "  result = distil_bert(documents, query, ratio)\n",
        "  new_data_bc = put_retrieved_profiles(result, idx)\n",
        "  val = LM(new_data_bc)\n",
        "  dbert_output.append(val)\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report, roc_curve, auc\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "v-y48dXy1Bt2"
      },
      "outputs": [],
      "source": [
        "def process_item(item):\n",
        "    processed_item = item.replace('[', '').replace(']', '')\n",
        "    return processed_item\n",
        "\n",
        "def matches(op, actual, start, end):\n",
        "  cnt = 0\n",
        "  for i in range(start, end):\n",
        "    op[i] = process_item(op[i])\n",
        "    if(op[i] == actual[i]):\n",
        "      cnt+=1\n",
        "  return cnt\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "def calculate_rmse(true_values, predicted_values):\n",
        "    true_values = np.array(true_values, dtype=float) \n",
        "    predicted_values = np.array(predicted_values, dtype=float)\n",
        "\n",
        "    squared_diff = (true_values - predicted_values) ** 2\n",
        "    mean_squared_diff = np.mean(squared_diff)\n",
        "    rmse = np.sqrt(mean_squared_diff)\n",
        "\n",
        "    return rmse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Majority Vote RMSE: 0.5683\n",
            "Borda Count RMSE: 0.8621\n",
            "Re ranking RMSE: 0.4990\n",
            "Scoring Based RMSE: 0.6109\n",
            "Scoring Based wt avg: 0.6992\n",
            "Scoring Based max: 0.7157\n",
            "Scoring Based min: 0.8223\n",
            "Scoring Based lc: 0.7549\n",
            "Scoring Based prod: 0.6910\n",
            "Scoring Based rb: 0.6094\n",
            "Distil Bert RMSE: 0.5023\n"
          ]
        }
      ],
      "source": [
        "print(\"Majority Vote RMSE: \", calculate_rmse(true_op, mv_op))\n",
        "print(\"Borda Count RMSE: \",  calculate_rmse(true_op, bc_op))\n",
        "print(\"Re ranking RMSE: \", calculate_rmse(true_op, rr_op))\n",
        "print(\"Scoring Based RMSE: \",  calculate_rmse(true_op, scor_op))\n",
        "print(\"Scoring Based wt avg: \", calculate_rmse(true_op, scor_op_wtavg))\n",
        "print(\"Scoring Based max: \", calculate_rmse(true_op, scor_op_max))\n",
        "print(\"Scoring Based min: \", calculate_rmse(true_op, scor_op_min))\n",
        "print(\"Scoring Based lc: \",calculate_rmse(true_op, scor_op_lc))\n",
        "print(\"Scoring Based prod: \", calculate_rmse(true_op, scor_op_prod))\n",
        "print(\"Scoring Based rb: \", calculate_rmse(true_op, scor_op_rb))\n",
        "print(\"Distil Bert RMSE: \", calculate_rmse(true_op, dbert_output))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [],
      "source": [
        "ids = []\n",
        "for i in range(start, end):\n",
        "  ids.append(true_output[i]['id'])\n",
        "\n",
        "true_op = []\n",
        "for i in range(start, end):\n",
        "  true_op.append(true_output[i]['output'])\n",
        "  \n",
        "all_models = [mv_op, bc_op, scor_op, rr_op, dbert_output, true_op]\n",
        "all_models_str = [\"mv_op\", \"bc_op\", \"scor_op\", \"rr_op\", \"dbert_output\", \"true_op\"]\n",
        "\n",
        "for i in range(len(all_models)):\n",
        "  op = {\n",
        "      \"task\": \"LAMP_2\",\n",
        "      \"golds\": [{\"id\": id_value, \"output\": output_value} for id_value, output_value in zip(ids, all_models[i])]\n",
        "  }\n",
        "  with open(f'{all_models_str[i]}_d3_p3.json', 'w') as json_file:\n",
        "      json.dump(op, json_file, indent=2)"
      ]
    }
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