notebooks/notebook4.ipynb

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   "source": [
    "# PyTerrier Search Solutions 2022 Tutorial Notebook - ANCE\n",
    "\n",
    "This notebook provides experiences to attendees in dense indexing and retrieval in [PyTerrier](https://github.com/terrier-org/pyterrier). All experiments are conducted using the [CORD19 corpus](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251955/) and the [TREC Covid test collection](https://ir.nist.gov/covidSubmit/).\n",
    "\n",
    "This notebook aims to demonstrate use of the [ANCE dense retrieval](https://github.com/microsoft/ANCE) for end-to-end indexing and retrieval in PyTerrier, as provided by the [*PyTerrier ANCE*](https://github.com/terrierteam/pyterrier_ance) plugin.\n",
    "\n",
    "In this notebook, you will experience:\n",
    " - indexing and retrieval using [*PyTerrier ANCE*](https://github.com/terrierteam/pyterrier_ance).\n",
    " - doing a failure analysis comparing ANCE with BM25 results.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FzAmEAgzGFEE"
   },
   "source": [
    "# Setup\n",
    "\n",
    "In the following, we will set up the libraries required to execute the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZABQMWK54-S4"
   },
   "source": [
    "## Python packages installation\n",
    "\n",
    "The following packages are installed to avoid warnings/errors during [PyTerrier](https://github.com/terrier-org/pyterrier) installation. Note that the current release of the [PyTerrier](https://github.com/terrier-org/pyterrier) [ANCE](https://github.com/microsoft/ANCE) plugin works only with the following Python packages:\n",
    "\n",
    "* `faiss-cpu` or `faiss-gpu`, version 1.6.3\n",
    "\n",
    "> You can safely ignore the message about runtime restart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "id": "poO9tDxI4Nr_",
    "outputId": "c3f28071-36d7-4686-bfe3-48047454b221"
   },
   "outputs": [],
   "source": [
    "!apt install --quiet --upgrade libomp-dev\n",
    "%pip install --quiet --upgrade faiss-gpu==1.7.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QPc6ygBxhL4H"
   },
   "source": [
    "## Pyterrier installation\n",
    "\n",
    "The following cell installs the latest release of the [PyTerrier](https://github.com/terrier-org/pyterrier) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "id": "rGqaCMa2ggp-",
    "outputId": "d9d1e465-56aa-4a42-ea5a-5a037134d5f9"
   },
   "outputs": [],
   "source": [
    "%pip install --quiet python-terrier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Yl1plJjpws7"
   },
   "source": [
    "## Pyterrier plugins installation\n",
    "\n",
    "We install the official version of the [*PyTerrier ANCE*](https://github.com/terrierteam/pyterrier_ance) plugin. You can safely ignore the package versioning errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "id": "lFQSFHpA3tss",
    "outputId": "e388c155-b036-4cd4-a7e8-fbf10a1c8300"
   },
   "outputs": [],
   "source": [
    "%pip install --quiet git+https://github.com/terrierteam/pyterrier_ance.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1eBlpENBlTXA"
   },
   "source": [
    "# Preliminary steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nNo5g83BrQjT"
   },
   "source": [
    "## [PyTerrier](https://github.com/terrier-org/pyterrier) initialization\n",
    "\n",
    "Lets get [PyTerrier](https://github.com/terrier-org/pyterrier) started. This will download the latest version of the [Terrier](http://terrier.org) IR platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {
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    "id": "VbHA4J017DB3",
    "outputId": "ebb67f52-9e5e-4af7-a388-d14d94c01bba"
   },
   "outputs": [],
   "source": [
    "import pyterrier as pt\n",
    "\n",
    "if not pt.started():\n",
    "    pt.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jW-yxSj0loy-"
   },
   "source": [
    "## [TREC-COVID19](https://ir.nist.gov/covidSubmit/) dataset download\n",
    "\n",
    "The following cell downloads the [TREC-COVID19](https://ir.nist.gov/covidSubmit/) dataset that we will use in the remainder of this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i1MtodQBlofw"
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   "outputs": [],
   "source": [
    "dataset = pt.datasets.get_dataset('irds:cord19/trec-covid')\n",
    "topics = dataset.get_topics(variant='title')\n",
    "qrels = dataset.get_qrels()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fu2XhNRQEEs2"
   },
   "source": [
    "## [Terrier](http://terrier.org) inverted index download\n",
    "\n",
    "To save a few minutes, we use a pre-built [Terrier](http://terrier.org) inverted index for the [TREC-COVID19](https://ir.nist.gov/covidSubmit/) collection.\n",
    "Download time took a few seconds for us.\n",
    "\n",
    "The construction of the inverted index will take few minutes, and the code to use is the following:\n",
    "\n",
    "```python\n",
    "import os\n",
    "\n",
    "cord19 = pt.datasets.get_dataset('irds:cord19/trec-covid')\n",
    "pt_index_path = './terrier_cord19'\n",
    "\n",
    "if not os.path.exists(pt_index_path + \"/data.properties\"):\n",
    "    # create the index, using the IterDictIndexer indexer \n",
    "    indexer = pt.index.IterDictIndexer(pt_index_path)\n",
    "\n",
    "    # we give the dataset get_corpus_iter() directly to the indexer\n",
    "    # while specifying the fields to index and the metadata to record\n",
    "    index_ref = indexer.index(cord19.get_corpus_iter(), \n",
    "                              fields=('abstract',), \n",
    "                              meta=('docno',))\n",
    "\n",
    "else:\n",
    "    # if you already have the index, use it.\n",
    "    index_ref = pt.IndexRef.of(pt_index_path + \"/data.properties\")\n",
    "\n",
    "index = pt.IndexFactory.of(index_ref)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yklzIrBYmJef"
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   "outputs": [],
   "source": [
    "index = pt.datasets.get_dataset('trec-covid').get_index('terrier_stemmed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5vpOdvbyn9Yt"
   },
   "source": [
    "## [ANCE](https://github.com/microsoft/ANCE) dense index download\n",
    "\n",
    "We are going to download a pre-built [ANCE](https://github.com/microsoft/ANCE) [FAISS](https://github.com/facebookresearch/faiss) index for the [TREC-COVID19](https://ir.nist.gov/covidSubmit/) collection. Download time took <1 minute for us.\n",
    "\n",
    "The construction of this index takes some time. If you wanted to create the index yourself, the code to use is as follows:\n",
    "\n",
    "```python\n",
    "!rm -rf /content/anceindex\n",
    "\n",
    "import pyterrier_ance\n",
    "\n",
    "ance_indexer = pt.text.sliding() >> pyterrier_ance.ANCEIndexer(\n",
    "    checkpoint_path=\"./ance_model_checkpoint\",\n",
    "    index_path=\"/content/anceindex\",\n",
    "    num_docs=192509,\n",
    "    text_attr='abstract' # COVID\n",
    ")\n",
    "\n",
    "ance_indexer.index(dataset.get_corpus_iter())\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lvx6tFeQIRVT"
   },
   "outputs": [],
   "source": [
    "ance_index = pt.datasets.get_dataset('trec-covid').get_index('ance_msmarco_psg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F0os3PrCWAOh"
   },
   "source": [
    "So we have a few files that have been generated. In particular, an ANCE index is broken into shards - CORD19 is small enough to only need a single chunk, so we have only `0.*` and no `1.*` etc.\n",
    "\n",
    "ANCE index contains 3 files:\n",
    " - `shards.pkl` - how many documents in each shard;\n",
    " - $x$ `.docids.pkl` - docid to docno mapping for documents in each shard;\n",
    "\n",
    " - $x$ `.faiss` - the FAISS index for the documents in each shard.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CNSVITVYvf37"
   },
   "source": [
    "# Retrieval experiments\n",
    "\n",
    "Now that indexing/downloading has completed, we can load in the index and the learned model (which we will need for encoding queries). Index loading can take some time, as the [FAISS](https://github.com/facebookresearch/faiss) index needs to be loaded in main memory, as well as the docno lookup file.\n",
    "\n",
    "Lets prepare an experiment. Firstly, lets create in a BM25 baseline transformer, and the [ANCE](https://github.com/microsoft/ANCE) retrieve transformer. Since most documents exceed the maximum length supported by ANCE, a sliding window of 150 tokens was used (stride 75, prepending title) to construct passages. As such, passage scores need to be aggregated, e.g., using `pt.text.max_passage()`."
   ]
  },
  {
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   "source": [
    "bm25_retriever = pt.BatchRetrieve(index, wmodel=\"BM25\")\n",
    "\n",
    "from pyterrier_ance import ANCERetrieval\n",
    "\n",
    "ance_retriever = ANCERetrieval.from_dataset('trec-covid', 'ance_msmarco_psg') >> pt.text.max_passage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Iuua3AVqAX83"
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   "source": [
    "Now we are ready to run the experiments. We are going to retrieve the top 10 ranked documents for the official topics, and compute several effectiveness metrics. "
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "pt.Experiment(\n",
    "    [bm25_retriever % 10, ance_retriever % 10], \n",
    "    topics,\n",
    "    qrels,\n",
    "    eval_metrics=[\"map\", \"recip_rank\", \"P_10\", \"ndcg_cut_10\", \"mrt\"],\n",
    "    names=['BM25', 'ANCE'],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The underperforming results computed our ANCE retriever are due to the lack of fine-tuning of the underlying BERT-based model with COVID19 and medical-related documents, as done, for example, in [Sledge](https://arxiv.org/pdf/2005.02365.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FArBa-2wQIUj"
   },
   "source": [
    "## ANCE failure analysis\n",
    "\n",
    "Lets give a look at the results for BM25 and ANCE for query with qid \"`1`\". To achieve this, lets make a function that displays the results, including the DOI URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cDPkSTyba8wr"
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   "outputs": [],
   "source": [
    "def show_res_with_text_labels(system, qid):\n",
    "    def make_doi_url(df):\n",
    "      df[\"doi\"] = df[\"doi\"].apply(lambda doi: \"https://doi.org/\" + doi)\n",
    "      return df\n",
    "    pipe = (system % 10) >> pt.text.get_text(dataset, [\"title\", \"doi\"]) >> pt.apply.generic(make_doi_url)\n",
    "    res = pipe.transform(topics[topics.qid == qid])\n",
    "    res = res.merge(qrels, how='left')\n",
    "    def make_clickable(val):\n",
    "        # target _blank to open new window\n",
    "        return '<a target=\"_blank\" href=\"{}\">{}</a>'.format(val, val)\n",
    "    res = res.sort_values(\"rank\", ascending=True)\n",
    "    res.style.format({'doi': make_clickable})\n",
    "    return res\n",
    "  \n",
    "import pandas as pd\n",
    "pd.set_option(\"max_colwidth\", 102)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "id": "5cZbyB5oMf3Y",
    "outputId": "af96b0e7-35d9-4703-d343-2f158b9e28b8"
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   "outputs": [],
   "source": [
    "show_res_with_text_labels(bm25_retriever, \"1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4fq5kVT_QUQJ"
   },
   "source": [
    "Judging by the titles of the retrieved documents, BM25 did quite well (NB: For documents with DOIs, you can look up the actual document).\n",
    "\n",
    "Overall, BM25 would have got P@10 = 0.8 for this query. \n",
    "\n",
    "Lets look at ANCE now."
   ]
  },
  {
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   "outputs": [],
   "source": [
    "show_res_with_text_labels(ance_retriever, \"1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MdRjcgNTQhrN"
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   "source": [
    "Here, we see that ANCE has got ranked three documents that had some relevance to the query (titled: \"The first two cases of 2019‐nCoV in Italy: Where they come from\"), however, only the third one (the one with a DOI) was actually judged relevant. Several other of the retrieved documents appear to be non-relevant, e.g. \"General Anesthesia Recommendations for Electroconvulsive Therapy\". "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QmQ_utFaRTgj"
   },
   "source": [
    "## Practice task\n",
    "\n",
    "Try a few other queries in `show_res_with_text_labels()`. Qids are numbered from \"1\" to \"50\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZkLC23EcR4Pd"
   },
   "outputs": [],
   "source": [
    "# Write your code here"
   ]
  }
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