text: from haystack.document_stores import ElasticsearchDocumentStore
  from haystack.nodes import ElasticsearchRetriever, DensePassageRetriever, EmbeddingRetriever, FARMReader, PreProcessor
  from haystack.utils import fetch_archive_from_http, launch_es
  from haystack.pipelines import ExtractiveQAPipeline, DocumentSearchPipeline
  from haystack.schema import Answer, Document, EvaluationResult, Label, MultiLabel, Span
  
  import logging
  
  logger = logging.getLogger(__name__)
  
  
  def tutorial5_evaluation():
  
      # make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
      doc_index = "tutorial5_docs"
      label_index = "tutorial5_labels"
  
      ##############################################
      # Code
      ##############################################
      launch_es()
  
      # Download evaluation data, which is a subset of Natural Questions development set containing 50 documents with one question per document and multiple annotated answers
      doc_dir = "../data/nq"
      s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
      fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
  
      # Connect to Elasticsearch
      document_store = ElasticsearchDocumentStore(
          host="localhost",
          username="",
          password="",
          index=doc_index,
          label_index=label_index,
          embedding_field="emb",
          embedding_dim=768,
          excluded_meta_data=["emb"],
      )
  
      # Add evaluation data to Elasticsearch document store
      # We first delete the custom tutorial indices to not have duplicate elements
      # and also split our documents into shorter passages using the PreProcessor
      preprocessor = PreProcessor(
          split_by="word",
          split_length=200,
          split_overlap=0,
          split_respect_sentence_boundary=False,
          clean_empty_lines=False,
          clean_whitespace=False,
      )
      document_store.delete_documents(index=doc_index)
      document_store.delete_documents(index=label_index)
  
      # 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.
      document_store.add_eval_data(
          filename="../data/nq/nq_dev_subset_v2.json",
          doc_index=doc_index,
          label_index=label_index,
          preprocessor=preprocessor,
      )
  
      # Initialize Retriever
      from haystack.nodes import ElasticsearchRetriever
  
      retriever = ElasticsearchRetriever(document_store=document_store)
  
      # Alternative: Evaluate dense retrievers (EmbeddingRetriever or DensePassageRetriever)
      # The EmbeddingRetriever uses a single transformer based encoder model for query and document.
      # In contrast, DensePassageRetriever uses two separate encoders for both.
  
      # Please make sure the "embedding_dim" parameter in the DocumentStore above matches the output dimension of your models!
      # Please also take care that the PreProcessor splits your files into chunks that can be completely converted with
      #        the max_seq_len limitations of Transformers
      # The SentenceTransformer model "sentence-transformers/multi-qa-mpnet-base-dot-v1" generally works well with the EmbeddingRetriever on any kind of English text.
      # For more information and suggestions on different models check out the documentation at: https://www.sbert.net/docs/pretrained_models.html
  
      # from haystack.retriever import EmbeddingRetriever, DensePassageRetriever
      # retriever = EmbeddingRetriever(document_store=document_store, model_format="sentence_transformers",
      #                                embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
      # retriever = DensePassageRetriever(document_store=document_store,
      #                                   query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
      #                                   passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
      #                                   use_gpu=True,
      #                                   max_seq_len_passage=256,
      #                                   embed_title=True)
      # document_store.update_embeddings(retriever, index=doc_index)
  
      # Initialize Reader
      reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", top_k=4, return_no_answer=True)
  
      # Define a pipeline consisting of the initialized retriever and reader
      # Here we evaluate retriever and reader in an integrated (a.k.a. open domain) fashion on the full corpus of documents
      # i.e. a document is considered
      # correctly retrieved if it contains the gold answer string within it. The reader is evaluated based purely on the
      # predicted answer string, regardless of which document this came from and the position of the extracted span.
      # 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.
  
      pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)
  
      # The evaluation also works with any other pipeline.
      # For example you could use a DocumentSearchPipeline as an alternative:
      # pipeline = DocumentSearchPipeline(retriever=retriever)
  
      # We can load evaluation labels from the document store
      # We are also opting to filter out no_answer samples
      eval_labels = document_store.get_all_labels_aggregated(drop_negative_labels=True, drop_no_answers=False)
      eval_labels = [label for label in eval_labels if not label.no_answer]
  
      ## Alternative: Define queries and labels directly
      # eval_labels = [
      #     MultiLabel(
      #         labels=[
      #             Label(
      #                 query="who is written in the book of life",
      #                 answer=Answer(
      #                     answer="every person who is destined for Heaven or the World to Come",
      #                     offsets_in_context=[Span(374, 434)]
      #                 ),
      #                 document=Document(
      #                     id='1b090aec7dbd1af6739c4c80f8995877-0',
      #                    content_type="text",
      #                    content='Book of Life - wikipedia Book of Life Jump to: navigation, search This article is
      #                       about the book mentioned in Christian and Jewish religious teachings...'
      #                 ),
      #                 is_correct_answer=True,
      #                 is_correct_document=True,
      #                 origin="gold-label"
      #             )
      #         ]
      #     )
      # ]
  
      # Similar to pipeline.run() we can execute pipeline.eval()
      eval_result = pipeline.eval(labels=eval_labels, params={"Retriever": {"top_k": 5}})
  
      # The EvaluationResult contains a pandas dataframe for each pipeline node.
      # That's why there are two dataframes in the EvaluationResult of an ExtractiveQAPipeline.
  
      retriever_result = eval_result["Retriever"]
      retriever_result.head()
  
      reader_result = eval_result["Reader"]
      reader_result.head()
  
      # We can filter for all documents retrieved for a given query
      query = "who is written in the book of life"
      retriever_book_of_life = retriever_result[retriever_result["query"] == query]
  
      # We can also filter for all answers predicted for a given query
      reader_book_of_life = reader_result[reader_result["query"] == "who is written in the book of life"]
  
      # Save the evaluation result so that we can reload it later
      # and calculate evaluation metrics without running the pipeline again.
      eval_result.save("../")
  
      ## Calculating Evaluation Metrics
      # Load an EvaluationResult to quickly calculate standard evaluation metrics for all predictions,
      # such as F1-score of each individual prediction of the Reader node or recall of the retriever.
      # To learn more about the metrics, see [Evaluation Metrics](https://haystack.deepset.ai/guides/evaluation#metrics-retrieval)
  
      saved_eval_result = EvaluationResult.load("../")
      metrics = saved_eval_result.calculate_metrics()
      print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
      print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
      print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
      print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
      print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
  
      print(f'Reader - F1-Score: {metrics["Reader"]["f1"]}')
      print(f'Reader - Exact Match: {metrics["Reader"]["exact_match"]}')
  
      ## Generating an Evaluation Report
      # 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.
  
      pipeline.print_eval_report(saved_eval_result)
  
      ## Advanced Evaluation Metrics
      # Semantic Answer Similarity (SAS) is an advanced evaluation metric can be calculated in Haystack.
      # 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. 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".
      # 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).
  
      advanced_eval_result = pipeline.eval(
          labels=eval_labels,
          params={"Retriever": {"top_k": 1}},
          sas_model_name_or_path="cross-encoder/stsb-roberta-large",
      )
  
      metrics = advanced_eval_result.calculate_metrics()
      print(metrics["Reader"]["sas"])
  
      ## Isolated Evaluation Mode
      # The isolated node evaluation uses labels as input to the Reader node instead of the output of the preceeding retriever node.
      # 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.
      eval_result_with_upper_bounds = pipeline.eval(
          labels=eval_labels, params={"Retriever": {"top_k": 5}}, add_isolated_node_eval=True
      )
      pipeline.print_eval_report(eval_result_with_upper_bounds)
  
      ## Evaluation of Individual Components
      # 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.
  
      # Evaluate Retriever on its own
      # Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
      # Note that no_answer samples are omitted when evaluation is performed with this method
      retriever_eval_results = retriever.eval(top_k=5, label_index=label_index, doc_index=doc_index)
  
      ## Retriever Recall is the proportion of questions for which the correct document containing the answer is
      ## among the correct documents
      print("Retriever Recall:", retriever_eval_results["recall"])
      ## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
      print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
  
      # 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)`.
      # These two recall metrics are only comparable since we chose to filter out no_answer samples when generating eval_labels.
      metrics = eval_result_with_upper_bounds.calculate_metrics()
      print(metrics["Retriever"]["recall_multi_hit"])
  
      # Evaluate Reader on its own
      # Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
      # and its corresponding relevant document and metrics are calculated on whether the right position in this text is selected by
      # the model as the answer span (i.e. SQuAD style)
      reader_eval_results = reader.eval(document_store=document_store, label_index=label_index, doc_index=doc_index)
      # Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
      # reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json")
  
      ## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
      print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
      ## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
      print("Reader Exact Match:", reader_eval_results["EM"])
      ## Reader F1-Score is the average overlap between the predicted answers and the correct answers
      print("Reader F1-Score:", reader_eval_results["f1"])
  
  
  if __name__ == "__main__":
      tutorial5_evaluation()
  
  # This Haystack script was made with love by deepset in Berlin, Germany
  # Haystack: https://github.com/deepset-ai/haystack
  # deepset: https://deepset.ai/
  