{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Evaluation of a Pipeline and its Components\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)\n",
    "\n",
    "To be able to make a statement about the quality of results a question-answering pipeline or any other pipeline in haystack produces, it is important to evaluate it. Furthermore, evaluation allows determining which components of the pipeline can be improved.\n",
    "The results of the evaluation can be saved as CSV files, which contain all the information to calculate additional metrics later on or inspect individual predictions."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lEKOjCS5U7so",
    "pycharm": {
     "is_executing": true
    }
   },
   "source": [
    "### Prepare environment\n",
    "\n",
    "#### Colab: Enable the GPU runtime\n",
    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/colab_gpu_runtime.jpg\">"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xhFIMX_7U7ss",
    "outputId": "285b2491-01e5-4bfd-cba9-c2279d4417c4",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Make sure you have a GPU running\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "vgmFOp82Oht_",
    "outputId": "5bbcbb42-3a90-43a9-ebfd-598a98fa7143",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Install the latest release of Haystack in your own environment\n",
    "#! pip install farm-haystack\n",
    "\n",
    "# Install the latest master of Haystack\n",
    "!pip install --upgrade pip\n",
    "!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start an Elasticsearch server\n",
    "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tNoaWcDKOhuL",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# If Docker is available: Start Elasticsearch as docker container\n",
    "# from haystack.utils import launch_es\n",
    "# launch_es()\n",
    "\n",
    "# Alternative in Colab / No Docker environments: Start Elasticsearch from source\n",
    "! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
    "! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
    "! chown -R daemon:daemon elasticsearch-7.9.2\n",
    "\n",
    "import os\n",
    "from subprocess import Popen, PIPE, STDOUT\n",
    "\n",
    "es_server = Popen(\n",
    "    [\"elasticsearch-7.9.2/bin/elasticsearch\"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1)  # as daemon\n",
    ")\n",
    "# wait until ES has started\n",
    "! sleep 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch, Store And Preprocess the Evaluation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tTXxr6TAOhuz",
    "outputId": "586d4775-4354-4ed9-a72c-c30bedcdfbee",
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "from haystack.utils import fetch_archive_from_http\n",
    "\n",
    "# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents with one question per document and multiple annotated answers\n",
    "doc_dir = \"data/tutorial5\"\n",
    "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip\"\n",
    "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "T-G7Ay2jU7s_"
   },
   "outputs": [],
   "source": [
    "# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted\n",
    "doc_index = \"tutorial5_docs\"\n",
    "label_index = \"tutorial5_labels\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "B_NEtezLOhu5",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Connect to Elasticsearch\n",
    "from haystack.document_stores import ElasticsearchDocumentStore\n",
    "\n",
    "# Connect to Elasticsearch\n",
    "document_store = ElasticsearchDocumentStore(\n",
    "    host=\"localhost\",\n",
    "    username=\"\",\n",
    "    password=\"\",\n",
    "    index=doc_index,\n",
    "    label_index=label_index,\n",
    "    embedding_field=\"emb\",\n",
    "    embedding_dim=768,\n",
    "    excluded_meta_data=[\"emb\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bRFsQUAJOhu_",
    "outputId": "477031b9-5c2c-4128-ef5f-54db86259734",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from haystack.nodes import PreProcessor\n",
    "\n",
    "# Add evaluation data to Elasticsearch Document Store\n",
    "# We first delete the custom tutorial indices to not have duplicate elements\n",
    "# and also split our documents into shorter passages using the PreProcessor\n",
    "preprocessor = PreProcessor(\n",
    "    split_length=200,\n",
    "    split_overlap=0,\n",
    "    split_respect_sentence_boundary=False,\n",
    "    clean_empty_lines=False,\n",
    "    clean_whitespace=False,\n",
    ")\n",
    "document_store.delete_documents(index=doc_index)\n",
    "document_store.delete_documents(index=label_index)\n",
    "\n",
    "# The add_eval_data() method converts the given dataset in json format into Haystack document and label objects. Those objects are then indexed in their respective document and label index in the document store. The method can be used with any dataset in SQuAD format.\n",
    "document_store.add_eval_data(\n",
    "    filename=\"data/tutorial5/nq_dev_subset_v2.json\",\n",
    "    doc_index=doc_index,\n",
    "    label_index=label_index,\n",
    "    preprocessor=preprocessor,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gy8YwmSYOhvE",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Initialize the Two Components of an ExtractiveQAPipeline: Retriever and Reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JkhaPMIJOhvF",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize Retriever\n",
    "from haystack.nodes import ElasticsearchRetriever\n",
    "\n",
    "retriever = ElasticsearchRetriever(document_store=document_store)\n",
    "\n",
    "# Alternative: Evaluate dense retrievers (EmbeddingRetriever or DensePassageRetriever)\n",
    "# The EmbeddingRetriever uses a single transformer based encoder model for query and document.\n",
    "# In contrast, DensePassageRetriever uses two separate encoders for both.\n",
    "\n",
    "# Please make sure the \"embedding_dim\" parameter in the DocumentStore above matches the output dimension of your models!\n",
    "# Please also take care that the PreProcessor splits your files into chunks that can be completely converted with\n",
    "#        the max_seq_len limitations of Transformers\n",
    "# The SentenceTransformer model \"sentence-transformers/multi-qa-mpnet-base-dot-v1\" generally works well with the EmbeddingRetriever on any kind of English text.\n",
    "# For more information and suggestions on different models check out the documentation at: https://www.sbert.net/docs/pretrained_models.html\n",
    "\n",
    "# from haystack.retriever import EmbeddingRetriever, DensePassageRetriever\n",
    "# retriever = EmbeddingRetriever(document_store=document_store, model_format=\"sentence_transformers\",\n",
    "#                                embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\")\n",
    "# retriever = DensePassageRetriever(document_store=document_store,\n",
    "#                                   query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
    "#                                   passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
    "#                                   use_gpu=True,\n",
    "#                                   max_seq_len_passage=256,\n",
    "#                                   embed_title=True)\n",
    "# document_store.update_embeddings(retriever, index=doc_index)"
   ]
  },
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    "id": "cW3Ypn_gOhvK",
    "outputId": "4b5feff7-ae9f-4cd8-de1e-944f0eb66f66",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize Reader\n",
    "from haystack.nodes import FARMReader\n",
    "\n",
    "reader = FARMReader(\"deepset/roberta-base-squad2\", top_k=4, return_no_answer=True)\n",
    "\n",
    "# Define a pipeline consisting of the initialized retriever and reader\n",
    "from haystack.pipelines import ExtractiveQAPipeline\n",
    "\n",
    "pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)\n",
    "\n",
    "# The evaluation also works with any other pipeline.\n",
    "# For example you could use a DocumentSearchPipeline as an alternative:\n",
    "\n",
    "# from haystack.pipelines import DocumentSearchPipeline\n",
    "# pipeline = DocumentSearchPipeline(retriever=retriever)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7i84KXONOhvc",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Evaluation of an ExtractiveQAPipeline\n",
    "Here we evaluate retriever and reader in open domain fashion on the full corpus of documents i.e. a document is considered\n",
    "correctly retrieved if it contains the gold answer string within it. The reader is evaluated based purely on the\n",
    "predicted answer string, regardless of which document this came from and the position of the extracted span.\n",
    "\n",
    "The generation of predictions is seperated from the calculation of metrics. This allows you to run the computation-heavy model predictions only once and then iterate flexibly on the metrics or reports you want to generate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from haystack.schema import EvaluationResult, MultiLabel\n",
    "\n",
    "# We can load evaluation labels from the document store\n",
    "# We are also opting to filter out no_answer samples\n",
    "eval_labels = document_store.get_all_labels_aggregated(drop_negative_labels=True, drop_no_answers=False)\n",
    "eval_labels = [label for label in eval_labels if not label.no_answer]  # filter out no_answer cases\n",
    "\n",
    "## Alternative: Define queries and labels directly\n",
    "\n",
    "# eval_labels = [\n",
    "#    MultiLabel(\n",
    "#        labels=[\n",
    "#            Label(\n",
    "#                query=\"who is written in the book of life\",\n",
    "#                answer=Answer(\n",
    "#                    answer=\"every person who is destined for Heaven or the World to Come\",\n",
    "#                    offsets_in_context=[Span(374, 434)]\n",
    "#                ),\n",
    "#                document=Document(\n",
    "#                    id='1b090aec7dbd1af6739c4c80f8995877-0',\n",
    "#                    content_type=\"text\",\n",
    "#                    content='Book of Life - wikipedia Book of Life Jump to: navigation, search This article is\n",
    "#                       about the book mentioned in Christian and Jewish religious teachings...'\n",
    "#                ),\n",
    "#                is_correct_answer=True,\n",
    "#                is_correct_document=True,\n",
    "#                origin=\"gold-label\"\n",
    "#            )\n",
    "#        ]\n",
    "#    )\n",
    "# ]\n",
    "\n",
    "# Similar to pipeline.run() we can execute pipeline.eval()\n",
    "eval_result = pipeline.eval(labels=eval_labels, params={\"Retriever\": {\"top_k\": 5}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         multilabel_id                               query  filters  \\\n0  1886992123615626403  who is written in the book of life  b'null'   \n1  1886992123615626403  who is written in the book of life  b'null'   \n2  1886992123615626403  who is written in the book of life  b'null'   \n3  1886992123615626403  who is written in the book of life  b'null'   \n4  1886992123615626403  who is written in the book of life  b'null'   \n\n                                                            gold_document_contents  \\\n0  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n1  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n2  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n3  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n4  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n\n                                                                           content  \\\n0  people considered righteous before God. God has such a book, and to be blott...   \n1  as adversaries (of God). Also, according to ib. xxxvi. 10, one who contrives...   \n2  the citizens' registers. The life which the righteous participate in is to b...   \n3  apostles' names are ``written in heaven'' (Luke x. 20), or ``the fellow-work...   \n4  The Absolutely True Diary of a Part-Time Indian - wikipedia The Absolutely T...   \n\n   gold_id_match  answer_match  gold_id_or_answer_match  rank  \\\n0            0.0           0.0                      0.0   1.0   \n1            0.0           0.0                      0.0   2.0   \n2            0.0           0.0                      0.0   3.0   \n3            0.0           0.0                      0.0   4.0   \n4            0.0           0.0                      0.0   5.0   \n\n                          document_id  \\\n0  1b090aec7dbd1af6739c4c80f8995877-1   \n1  1b090aec7dbd1af6739c4c80f8995877-2   \n2  1b090aec7dbd1af6739c4c80f8995877-6   \n3  1b090aec7dbd1af6739c4c80f8995877-3   \n4  e9260cbbc129f4246ee8fcfbbe385822-0   \n\n                                                          gold_document_ids  \\\n0  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n1  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n2  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n3  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n4  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n\n       type       node   eval_mode  \n0  document  Retriever  integrated  \n1  document  Retriever  integrated  \n2  document  Retriever  integrated  \n3  document  Retriever  integrated  \n4  document  Retriever  integrated  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>multilabel_id</th>\n      <th>query</th>\n      <th>filters</th>\n      <th>gold_document_contents</th>\n      <th>content</th>\n      <th>gold_id_match</th>\n      <th>answer_match</th>\n      <th>gold_id_or_answer_match</th>\n      <th>rank</th>\n      <th>document_id</th>\n      <th>gold_document_ids</th>\n      <th>type</th>\n      <th>node</th>\n      <th>eval_mode</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...</td>\n      <td>people considered righteous before God. God has such a book, and to be blott...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1b090aec7dbd1af6739c4c80f8995877-1</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>document</td>\n      <td>Retriever</td>\n      <td>integrated</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...</td>\n      <td>as adversaries (of God). Also, according to ib. xxxvi. 10, one who contrives...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1b090aec7dbd1af6739c4c80f8995877-2</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>document</td>\n      <td>Retriever</td>\n      <td>integrated</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...</td>\n      <td>the citizens' registers. The life which the righteous participate in is to b...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>1b090aec7dbd1af6739c4c80f8995877-6</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>document</td>\n      <td>Retriever</td>\n      <td>integrated</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...</td>\n      <td>apostles' names are ``written in heaven'' (Luke x. 20), or ``the fellow-work...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>1b090aec7dbd1af6739c4c80f8995877-3</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>document</td>\n      <td>Retriever</td>\n      <td>integrated</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...</td>\n      <td>The Absolutely True Diary of a Part-Time Indian - wikipedia The Absolutely T...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>e9260cbbc129f4246ee8fcfbbe385822-0</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>document</td>\n      <td>Retriever</td>\n      <td>integrated</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The EvaluationResult contains a pandas dataframe for each pipeline node.\n",
    "# That's why there are two dataframes in the EvaluationResult of an ExtractiveQAPipeline.\n",
    "\n",
    "retriever_result = eval_result[\"Retriever\"]\n",
    "retriever_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         multilabel_id                                            query  \\\n0  1886992123615626403               who is written in the book of life   \n1  1886992123615626403               who is written in the book of life   \n2  1886992123615626403               who is written in the book of life   \n3  1886992123615626403               who is written in the book of life   \n0 -3790070251458150675  who was the girl in the video brenda got a baby   \n\n   filters  \\\n0  b'null'   \n1  b'null'   \n2  b'null'   \n3  b'null'   \n0  b'null'   \n\n                                                                      gold_answers  \\\n0  [every person who is destined for Heaven or the World to Come, all people co...   \n1  [every person who is destined for Heaven or the World to Come, all people co...   \n2  [every person who is destined for Heaven or the World to Come, all people co...   \n3  [every person who is destined for Heaven or the World to Come, all people co...   \n0                                                          [Ethel ``Edy'' Proctor]   \n\n                                                                            answer  \\\n0                                                                                    \n1  those whose names are written in the Book of Life from the foundation of the...   \n2                                                  only the names of the righteous   \n3  those who are found written in the book and who shall escape the troubles pr...   \n0                                                                       her cousin   \n\n                                                                           context  \\\n0                                                                             None   \n1  ohn of Patmos. As described, only those whose names are written in the Book ...   \n2  . The Psalmist likewise speaks of the Book of Life in which only the names o...   \n3  those who are found written in the book and who shall escape the troubles pr...   \n0  ng a story in the newspaper of a 12-year-old girl getting pregnant by her co...   \n\n   exact_match        f1  rank                         document_id  \\\n0          0.0  0.000000   1.0                                None   \n1          0.0  0.083333   2.0  1b090aec7dbd1af6739c4c80f8995877-3   \n2          0.0  0.200000   3.0  1b090aec7dbd1af6739c4c80f8995877-1   \n3          0.0  0.111111   4.0  1b090aec7dbd1af6739c4c80f8995877-6   \n0          0.0  0.000000   1.0  965a125f65658579529b39f8e4344969-3   \n\n                                                          gold_document_ids  \\\n0  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n1  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n2  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n3  [1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]   \n0                                      [965a125f65658579529b39f8e4344969-3]   \n\n            offsets_in_document  \\\n0      [{'start': 0, 'end': 0}]   \n1  [{'start': 576, 'end': 658}]   \n2  [{'start': 498, 'end': 529}]   \n3  [{'start': 135, 'end': 305}]   \n0  [{'start': 423, 'end': 433}]   \n\n                                    gold_offsets_in_documents    type    node  \\\n0  [{'start': 374, 'end': 434}, {'start': 1107, 'end': 1149}]  answer  Reader   \n1  [{'start': 374, 'end': 434}, {'start': 1107, 'end': 1149}]  answer  Reader   \n2  [{'start': 374, 'end': 434}, {'start': 1107, 'end': 1149}]  answer  Reader   \n3  [{'start': 374, 'end': 434}, {'start': 1107, 'end': 1149}]  answer  Reader   \n0                                [{'start': 181, 'end': 202}]  answer  Reader   \n\n    eval_mode  \n0  integrated  \n1  integrated  \n2  integrated  \n3  integrated  \n0  integrated  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>multilabel_id</th>\n      <th>query</th>\n      <th>filters</th>\n      <th>gold_answers</th>\n      <th>answer</th>\n      <th>context</th>\n      <th>exact_match</th>\n      <th>f1</th>\n      <th>rank</th>\n      <th>document_id</th>\n      <th>gold_document_ids</th>\n      <th>offsets_in_document</th>\n      <th>gold_offsets_in_documents</th>\n      <th>type</th>\n      <th>node</th>\n      <th>eval_mode</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[every person who is destined for Heaven or the World to Come, all people co...</td>\n      <td></td>\n      <td>None</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>None</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>[{'start': 0, 'end': 0}]</td>\n      <td>[{'start': 374, 'end': 434}, {'start': 1107, 'end': 1149}]</td>\n      <td>answer</td>\n      <td>Reader</td>\n      <td>integrated</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[every person who is destined for Heaven or the World to Come, all people co...</td>\n      <td>those whose names are written in the Book of Life from the foundation of the...</td>\n      <td>ohn of Patmos. As described, only those whose names are written in the Book ...</td>\n      <td>0.0</td>\n      <td>0.083333</td>\n      <td>2.0</td>\n      <td>1b090aec7dbd1af6739c4c80f8995877-3</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>[{'start': 576, 'end': 658}]</td>\n      <td>[{'start': 374, 'end': 434}, {'start': 1107, 'end': 1149}]</td>\n      <td>answer</td>\n      <td>Reader</td>\n      <td>integrated</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[every person who is destined for Heaven or the World to Come, all people co...</td>\n      <td>only the names of the righteous</td>\n      <td>. The Psalmist likewise speaks of the Book of Life in which only the names o...</td>\n      <td>0.0</td>\n      <td>0.200000</td>\n      <td>3.0</td>\n      <td>1b090aec7dbd1af6739c4c80f8995877-1</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>[{'start': 498, 'end': 529}]</td>\n      <td>[{'start': 374, 'end': 434}, {'start': 1107, 'end': 1149}]</td>\n      <td>answer</td>\n      <td>Reader</td>\n      <td>integrated</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1886992123615626403</td>\n      <td>who is written in the book of life</td>\n      <td>b'null'</td>\n      <td>[every person who is destined for Heaven or the World to Come, all people co...</td>\n      <td>those who are found written in the book and who shall escape the troubles pr...</td>\n      <td>those who are found written in the book and who shall escape the troubles pr...</td>\n      <td>0.0</td>\n      <td>0.111111</td>\n      <td>4.0</td>\n      <td>1b090aec7dbd1af6739c4c80f8995877-6</td>\n      <td>[1b090aec7dbd1af6739c4c80f8995877-0, 1b090aec7dbd1af6739c4c80f8995877-0]</td>\n      <td>[{'start': 135, 'end': 305}]</td>\n      <td>[{'start': 374, 'end': 434}, {'start': 1107, 'end': 1149}]</td>\n      <td>answer</td>\n      <td>Reader</td>\n      <td>integrated</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>-3790070251458150675</td>\n      <td>who was the girl in the video brenda got a baby</td>\n      <td>b'null'</td>\n      <td>[Ethel ``Edy'' Proctor]</td>\n      <td>her cousin</td>\n      <td>ng a story in the newspaper of a 12-year-old girl getting pregnant by her co...</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>965a125f65658579529b39f8e4344969-3</td>\n      <td>[965a125f65658579529b39f8e4344969-3]</td>\n      <td>[{'start': 423, 'end': 433}]</td>\n      <td>[{'start': 181, 'end': 202}]</td>\n      <td>answer</td>\n      <td>Reader</td>\n      <td>integrated</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader_result = eval_result[\"Reader\"]\n",
    "reader_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# We can filter for all documents retrieved for a given query\n",
    "query = \"who is written in the book of life\"\n",
    "retriever_book_of_life = retriever_result[retriever_result[\"query\"] == query]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# We can also filter for all answers predicted for a given query\n",
    "reader_book_of_life = reader_result[reader_result[\"query\"] == query]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Save the evaluation result so that we can reload it later and calculate evaluation metrics without running the pipeline again.\n",
    "eval_result.save(\"../\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Calculating Evaluation Metrics\n",
    "Load an EvaluationResult to quickly calculate standard evaluation metrics for all predictions,\n",
    "such as F1-score of each individual prediction of the Reader node or recall of the retriever.\n",
    "To learn more about the metrics, see [Evaluation Metrics](https://haystack.deepset.ai/guides/evaluation#metrics-retrieval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "saved_eval_result = EvaluationResult.load(\"../\")\n",
    "metrics = saved_eval_result.calculate_metrics()\n",
    "print(f'Retriever - Recall (single relevant document): {metrics[\"Retriever\"][\"recall_single_hit\"]}')\n",
    "print(f'Retriever - Recall (multiple relevant documents): {metrics[\"Retriever\"][\"recall_multi_hit\"]}')\n",
    "print(f'Retriever - Mean Reciprocal Rank: {metrics[\"Retriever\"][\"mrr\"]}')\n",
    "print(f'Retriever - Precision: {metrics[\"Retriever\"][\"precision\"]}')\n",
    "print(f'Retriever - Mean Average Precision: {metrics[\"Retriever\"][\"map\"]}')\n",
    "\n",
    "print(f'Reader - F1-Score: {metrics[\"Reader\"][\"f1\"]}')\n",
    "print(f'Reader - Exact Match: {metrics[\"Reader\"][\"exact_match\"]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Generating an Evaluation Report\n",
    "A summary of the evaluation results can be printed to get a quick overview. It includes some aggregated metrics and also shows a few wrongly predicted examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pipeline.print_eval_report(saved_eval_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Evaluation Metrics\n",
    "As an advanced evaluation metric, semantic answer similarity (SAS) can be calculated. This metric takes into account whether the meaning of a predicted answer is similar to the annotated gold answer rather than just doing string comparison.\n",
    "To this end SAS relies on pre-trained models. For English, we recommend \"cross-encoder/stsb-roberta-large\", whereas for German we recommend \"deepset/gbert-large-sts\". A good multilingual model is \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\".\n",
    "More info on this metric can be found in our [paper](https://arxiv.org/abs/2108.06130) or in our [blog post](https://www.deepset.ai/blog/semantic-answer-similarity-to-evaluate-qa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "advanced_eval_result = pipeline.eval(\n",
    "    labels=eval_labels, params={\"Retriever\": {\"top_k\": 1}}, sas_model_name_or_path=\"cross-encoder/stsb-roberta-large\"\n",
    ")\n",
    "\n",
    "metrics = advanced_eval_result.calculate_metrics()\n",
    "print(metrics[\"Reader\"][\"sas\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Isolated Evaluation Mode\n",
    "The isolated node evaluation uses labels as input to the Reader node instead of the output of the preceeding Retriever node.\n",
    "Thereby, we can additionally calculate the upper bounds of the evaluation metrics of the Reader. Note that even with isolated evaluation enabled, integrated evaluation will still be running.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "eval_result_with_upper_bounds = pipeline.eval(\n",
    "    labels=eval_labels, params={\"Retriever\": {\"top_k\": 5}, \"Reader\": {\"top_k\": 5}}, add_isolated_node_eval=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pipeline.print_eval_report(eval_result_with_upper_bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of Individual Components: Retriever\n",
    "Sometimes you might want to evaluate individual components, for example, if you don't have a pipeline but only a retriever or a reader with a model that you trained yourself.\n",
    "Here we evaluate only the retriever, based on whether the gold_label document is retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "## Evaluate Retriever on its own\n",
    "# Note that no_answer samples are omitted when evaluation is performed with this method\n",
    "retriever_eval_results = retriever.eval(top_k=5, label_index=label_index, doc_index=doc_index)\n",
    "# Retriever Recall is the proportion of questions for which the correct document containing the answer is\n",
    "# among the correct documents\n",
    "print(\"Retriever Recall:\", retriever_eval_results[\"recall\"])\n",
    "# Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank\n",
    "print(\"Retriever Mean Avg Precision:\", retriever_eval_results[\"map\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as a sanity check, we can compare the recall from `retriever.eval()` with the multi hit recall from `pipeline.eval(add_isolated_node_eval=True)`.\n",
    "These two recall metrics are only comparable since we chose to filter out no_answer samples when generating eval_labels.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "metrics = eval_result_with_upper_bounds.calculate_metrics()\n",
    "print(metrics[\"Retriever\"][\"recall_multi_hit\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of Individual Components: Reader\n",
    "Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query\n",
    "and its corresponding relevant document and metrics are calculated on whether the right position in this text is selected by\n",
    "the model as the answer span (i.e. SQuAD style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Evaluate Reader on its own\n",
    "reader_eval_results = reader.eval(document_store=document_store, label_index=label_index, doc_index=doc_index)\n",
    "# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch\n",
    "# reader_eval_results = reader.eval_on_file(\"../data/nq\", \"nq_dev_subset_v2.json\", device=device)\n",
    "\n",
    "# Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer\n",
    "print(\"Reader Top-N-Accuracy:\", reader_eval_results[\"top_n_accuracy\"])\n",
    "# Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer\n",
    "print(\"Reader Exact Match:\", reader_eval_results[\"EM\"])\n",
    "# Reader F1-Score is the average overlap between the predicted answers and the correct answers\n",
    "print(\"Reader F1-Score:\", reader_eval_results[\"f1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8QJ68G12U7tb",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## About us\n",
    "\n",
    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
    "\n",
    "We bring NLP to the industry via open source!  \n",
    "Our focus: Industry specific language models & large scale QA systems.  \n",
    "  \n",
    "Some of our other work: \n",
    "- [German BERT](https://deepset.ai/german-bert)\n",
    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
    "- [FARM](https://github.com/deepset-ai/FARM)\n",
    "\n",
    "Get in touch:\n",
    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
    "\n",
    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
   ]
  }
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