{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "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."
   ]
  },
  {
   "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/img/colab_gpu_runtime.jpg\">"
   ]
  },
  {
   "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": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "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": 4,
   "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_by=\"word\",\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": 5,
   "metadata": {
    "id": "JkhaPMIJOhvF",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize Retriever\n",
    "from haystack.nodes import BM25Retriever\n",
    "\n",
    "retriever = BM25Retriever(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,\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=True)\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": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       "      <td>the citizens' registers. The life which the righteous participate in is to b...</td>\n",
       "      <td>[Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5403753299762339270</td>\n",
       "      <td>who is written in the book of life</td>\n",
       "      <td>b'null'</td>\n",
       "      <td>apostles' names are ``written in heaven'' (Luke x. 20), or ``the fellow-work...</td>\n",
       "      <td>[Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>bcc533ac40605a5faa63f4b0d7124404-3</td>\n",
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       "      <td>Retriever</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5403753299762339270</td>\n",
       "      <td>who is written in the book of life</td>\n",
       "      <td>b'null'</td>\n",
       "      <td>The Absolutely True Diary of a Part-Time Indian - wikipedia The Absolutely T...</td>\n",
       "      <td>[Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>[de2fd2f109e11213af1ea189fd1488a3-0, de2fd2f109e11213af1ea189fd1488a3-0]</td>\n",
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       "      <td>document</td>\n",
       "      <td>Retriever</td>\n",
       "      <td>integrated</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         multilabel_id                               query  filters  \\\n",
       "0  5403753299762339270  who is written in the book of life  b'null'   \n",
       "1  5403753299762339270  who is written in the book of life  b'null'   \n",
       "2  5403753299762339270  who is written in the book of life  b'null'   \n",
       "3  5403753299762339270  who is written in the book of life  b'null'   \n",
       "4  5403753299762339270  who is written in the book of life  b'null'   \n",
       "\n",
       "                                                                           context  \\\n",
       "0  people considered righteous before God. God has such a book, and to be blott...   \n",
       "1  as adversaries (of God). Also, according to ib. xxxvi. 10, one who contrives...   \n",
       "2  the citizens' registers. The life which the righteous participate in is to b...   \n",
       "3  apostles' names are ``written in heaven'' (Luke x. 20), or ``the fellow-work...   \n",
       "4  The Absolutely True Diary of a Part-Time Indian - wikipedia The Absolutely T...   \n",
       "\n",
       "                                                                     gold_contexts  \\\n",
       "0  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n",
       "1  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n",
       "2  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n",
       "3  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n",
       "4  [Book of Life - wikipedia Book of Life Jump to: navigation, search This arti...   \n",
       "\n",
       "   gold_id_match  context_match  answer_match  gold_id_or_answer_match  \\\n",
       "0            0.0            0.0           0.0                      0.0   \n",
       "1            0.0            0.0           0.0                      0.0   \n",
       "2            0.0            0.0           0.0                      0.0   \n",
       "3            0.0            0.0           0.0                      0.0   \n",
       "4            0.0            0.0           0.0                      0.0   \n",
       "\n",
       "   gold_id_and_answer_match  ...  rank                         document_id  \\\n",
       "0                       0.0  ...   1.0  737cb9cd71126bd25296bce035a5956f-1   \n",
       "1                       0.0  ...   2.0  fcfe96ddea6611b56ffaeda010d3a111-2   \n",
       "2                       0.0  ...   3.0  5f3f7a6be74e24b1065a4d770585d210-6   \n",
       "3                       0.0  ...   4.0  bcc533ac40605a5faa63f4b0d7124404-3   \n",
       "4                       0.0  ...   5.0  edba8a30690fe62dd0330344f6ad98ba-0   \n",
       "\n",
       "                                                          gold_document_ids  \\\n",
       "0  [de2fd2f109e11213af1ea189fd1488a3-0, de2fd2f109e11213af1ea189fd1488a3-0]   \n",
       "1  [de2fd2f109e11213af1ea189fd1488a3-0, de2fd2f109e11213af1ea189fd1488a3-0]   \n",
       "2  [de2fd2f109e11213af1ea189fd1488a3-0, de2fd2f109e11213af1ea189fd1488a3-0]   \n",
       "3  [de2fd2f109e11213af1ea189fd1488a3-0, de2fd2f109e11213af1ea189fd1488a3-0]   \n",
       "4  [de2fd2f109e11213af1ea189fd1488a3-0, de2fd2f109e11213af1ea189fd1488a3-0]   \n",
       "\n",
       "   gold_documents_id_match                  gold_contexts_similarity  \\\n",
       "0               [0.0, 0.0]  [44.474885844748854, 44.474885844748854]   \n",
       "1               [0.0, 0.0]      [44.5703493862134, 44.5703493862134]   \n",
       "2               [0.0, 0.0]  [44.374184960316995, 44.374184960316995]   \n",
       "3               [0.0, 0.0]    [42.68629254829806, 42.68629254829806]   \n",
       "4               [0.0, 0.0]    [42.55874673629243, 42.55874673629243]   \n",
       "\n",
       "  gold_answers_match      type       node   eval_mode index  \n",
       "0         [0.0, 0.0]  document  Retriever  integrated     0  \n",
       "1         [0.0, 0.0]  document  Retriever  integrated     1  \n",
       "2         [0.0, 0.0]  document  Retriever  integrated     2  \n",
       "3         [0.0, 0.0]  document  Retriever  integrated     3  \n",
       "4         [0.0, 0.0]  document  Retriever  integrated     4  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th>multilabel_id</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         multilabel_id                                            query  \\\n",
       "0  5403753299762339270               who is written in the book of life   \n",
       "1  5403753299762339270               who is written in the book of life   \n",
       "2  5403753299762339270               who is written in the book of life   \n",
       "3  5403753299762339270               who is written in the book of life   \n",
       "0  5186659498043378562  who was the girl in the video brenda got a baby   \n",
       "\n",
       "   filters  \\\n",
       "0  b'null'   \n",
       "1  b'null'   \n",
       "2  b'null'   \n",
       "3  b'null'   \n",
       "0  b'null'   \n",
       "\n",
       "                                                                      gold_answers  \\\n",
       "0  [all people considered righteous before God, every person who is destined fo...   \n",
       "1  [all people considered righteous before God, every person who is destined fo...   \n",
       "2  [all people considered righteous before God, every person who is destined fo...   \n",
       "3  [all people considered righteous before God, every person who is destined fo...   \n",
       "0                                                          [Ethel ``Edy'' Proctor]   \n",
       "\n",
       "                                                                            answer  \\\n",
       "0                                                                                    \n",
       "1  those whose names are written in the Book of Life from the foundation of the...   \n",
       "2                                                  only the names of the righteous   \n",
       "3  those who are found written in the book and who shall escape the troubles pr...   \n",
       "0                                                                       her cousin   \n",
       "\n",
       "                                                                           context  \\\n",
       "0                                                                             None   \n",
       "1  ohn of Patmos. As described, only those whose names are written in the Book ...   \n",
       "2  . The Psalmist likewise speaks of the Book of Life in which only the names o...   \n",
       "3  those who are found written in the book and who shall escape the troubles pr...   \n",
       "0  ng a story in the newspaper of a 12-year-old girl getting pregnant by her co...   \n",
       "\n",
       "   exact_match        f1  exact_match_context_scope  f1_context_scope  ...  \\\n",
       "0          0.0  0.000000                        0.0               0.0  ...   \n",
       "1          0.0  0.083333                        0.0               0.0  ...   \n",
       "2          0.0  0.200000                        0.0               0.0  ...   \n",
       "3          0.0  0.111111                        0.0               0.0  ...   \n",
       "0          0.0  0.000000                        0.0               0.0  ...   \n",
       "\n",
       "            offsets_in_document  \\\n",
       "0      [{'start': 0, 'end': 0}]   \n",
       "1  [{'start': 576, 'end': 658}]   \n",
       "2  [{'start': 498, 'end': 529}]   \n",
       "3  [{'start': 135, 'end': 305}]   \n",
       "0  [{'start': 423, 'end': 433}]   \n",
       "\n",
       "                                    gold_offsets_in_documents  \\\n",
       "0  [{'start': 1107, 'end': 1149}, {'start': 374, 'end': 434}]   \n",
       "1  [{'start': 1107, 'end': 1149}, {'start': 374, 'end': 434}]   \n",
       "2  [{'start': 1107, 'end': 1149}, {'start': 374, 'end': 434}]   \n",
       "3  [{'start': 1107, 'end': 1149}, {'start': 374, 'end': 434}]   \n",
       "0                                [{'start': 181, 'end': 202}]   \n",
       "\n",
       "   gold_answers_exact_match           gold_answers_f1 gold_documents_id_match  \\\n",
       "0                    [0, 0]                    [0, 0]              [0.0, 0.0]   \n",
       "1                    [0, 0]  [0, 0.08333333333333334]              [0.0, 0.0]   \n",
       "2                    [0, 0]                  [0.2, 0]              [0.0, 0.0]   \n",
       "3                    [0, 0]   [0, 0.1111111111111111]              [0.0, 0.0]   \n",
       "0                       [0]                       [0]                   [1.0]   \n",
       "\n",
       "                   gold_contexts_similarity    type    node   eval_mode index  \n",
       "0                                [0.0, 0.0]  answer  Reader  integrated     0  \n",
       "1                              [50.0, 50.0]  answer  Reader  integrated     1  \n",
       "2                              [52.0, 52.0]  answer  Reader  integrated     2  \n",
       "3  [46.470588235294116, 46.470588235294116]  answer  Reader  integrated     3  \n",
       "0                                   [100.0]  answer  Reader  integrated     0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader_result = eval_result[\"Reader\"]\n",
    "reader_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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": 13,
   "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\": 5}}, 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": 17,
   "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": [
    "## Advanced Label Scopes\n",
    "Answers are considered correct if the predicted answer matches the gold answer in the labels. Documents are considered correct if the predicted document ID matches the gold document ID in the labels. Sometimes, these simple definitions of \"correctness\" are not sufficient. There are cases where you want to further specify the \"scope\" within which an answer or a document is considered correct. For this reason, `EvaluationResult.calculate_metrics()` offers the parameters `answer_scope` and `document_scope`.\n",
    "\n",
    "Say you want to ensure that an answer is only considered correct if it stems from a specific context of surrounding words. This is especially useful if your answer is very short, like a date (for example, \"2011\") or a place (\"Berlin\"). Such short answer might easily appear in multiple completely different contexts. Some of those contexts might perfectly fit the actual question and answer it. Some others might not: they don't relate to the question at all but still contain the answer string. In that case, you might want to ensure that only answers that stem from the correct context are considered correct. To do that, specify `answer_scope=\"context\"` in `calculate_metrics()`. \n",
    "\n",
    "`answer_scope` takes the following values:\n",
    "- `any` (default): Any matching answer is considered correct.\n",
    "- `context`: The answer is only considered correct if its context matches as well. It uses fuzzy matching (see `context_matching` parameters of `pipeline.eval()`).\n",
    "- `document_id`: The answer is only considered correct if its document ID matches as well. You can specify a custom document ID through the `custom_document_id_field` parameter of `pipeline.eval()`.\n",
    "- `document_id_and_context`: The answer is only considered correct if its document ID and its context match as well.\n",
    "\n",
    "In Question Answering, to enforce that the retrieved document is considered correct whenever the answer is correct, set `document_scope` to `answer` or `document_id_or_answer`.\n",
    "\n",
    "`document_scope` takes the following values:\n",
    "- `document_id`: Specifies that the document ID must match. You can specify a custom document ID through the `custom_document_id_field` parameter of `pipeline.eval()`.\n",
    "- `context`: Specifies that the content of the document must match. It uses fuzzy matching (see the `context_matching` parameters of `pipeline.eval()`).\n",
    "- `document_id_and_context`: A Boolean operation specifying that both `'document_id' AND 'context'` must match.\n",
    "- `document_id_or_context`: A Boolean operation specifying that either `'document_id' OR 'context'` must match.\n",
    "- `answer`: Specifies that the document contents must include the answer. The selected `answer_scope` is enforced.\n",
    "- `document_id_or_answer` (default): A Boolean operation specifying that either `'document_id' OR 'answer'` must match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = saved_eval_result.calculate_metrics(answer_scope=\"context\")\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "document_store.get_all_documents()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try Document Retrieval on a file level (it's sufficient if the correct file identified by its name (for example, 'Book of Life') was retrieved).\n",
    "eval_result_custom_doc_id = pipeline.eval(\n",
    "    labels=eval_labels, params={\"Retriever\": {\"top_k\": 5}}, custom_document_id_field=\"name\"\n",
    ")\n",
    "metrics = eval_result_custom_doc_id.calculate_metrics(document_scope=\"document_id\")\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\"]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's enforce the context again:\n",
    "metrics = eval_result_custom_doc_id.calculate_metrics(document_scope=\"document_id_and_context\")\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\"]}')"
   ]
  },
  {
   "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 and setting document_scope to `\"document_id\"`. Per default `calculate_metrics()` has document_scope set to `\"document_id_or_answer\"` which interprets documents as relevant if they either match the gold document ID or contain the answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = eval_result_with_upper_bounds.calculate_metrics(document_scope=\"document_id\")\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",
    "top_n = reader_eval_results[\"top_n\"]\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 including no_answers\n",
    "print(f\"Reader Top-{top_n}-Accuracy:\", reader_eval_results[\"top_n_accuracy\"])\n",
    "# Reader Top-1-Exact Match is the proportion of questions where the first predicted answer is exactly the same as the correct answer including no_answers\n",
    "print(\"Reader Top-1-Exact Match:\", reader_eval_results[\"EM\"])\n",
    "# Reader Top-1-F1-Score is the average overlap between the first predicted answers and the correct answers including no_answers\n",
    "print(\"Reader Top-1-F1-Score:\", reader_eval_results[\"f1\"])\n",
    "# Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer excluding no_answers\n",
    "print(f\"Reader Top-{top_n}-Accuracy (without no_answers):\", reader_eval_results[\"top_n_accuracy_text_answer\"])\n",
    "# Reader Top-N-Exact Match is the proportion of questions where the predicted answer within the first n results is exactly the same as the correct answer excluding no_answers (no_answers are always present within top n).\n",
    "print(f\"Reader Top-{top_n}-Exact Match (without no_answers):\", reader_eval_results[\"top_n_EM_text_answer\"])\n",
    "# Reader Top-N-F1-Score is the average overlap between the top n predicted answers and the correct answers excluding no_answers (no_answers are always present within top n).\n",
    "print(f\"Reader Top-{top_n}-F1-Score (without no_answers):\", reader_eval_results[\"top_n_f1_text_answer\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as a sanity check, we can compare the top-n exact_match and f1 metrics from `reader.eval()` with the exact_match and f1 from `pipeline.eval(add_isolated_node_eval=True)`.\n",
    "These two approaches return the same values because pipeline.eval() calculates top-n metrics per default. Small discrepancies might occur due to string normalization in pipeline.eval()'s answer-to-label comparison. reader.eval() does not use string normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = eval_result_with_upper_bounds.calculate_metrics(eval_mode=\"isolated\")\n",
    "print(metrics[\"Reader\"][\"exact_match\"])\n",
    "print(metrics[\"Reader\"][\"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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