haystack/tutorials/Tutorial5_Evaluation.py

import logging

# We configure how logging messages should be displayed and which log level should be used before importing Haystack.
# Example log message:
# INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
# Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.INFO)

import tempfile
from pathlib import Path

from haystack.document_stores import ElasticsearchDocumentStore, InMemoryDocumentStore
from haystack.pipelines import Pipeline, ExtractiveQAPipeline, DocumentSearchPipeline
from haystack.nodes import (
    BM25Retriever,
    DensePassageRetriever,
    EmbeddingRetriever,
    FARMReader,
    PreProcessor,
    TextConverter,
)
from haystack.utils import fetch_archive_from_http, launch_es
from haystack.schema import Answer, Document, EvaluationResult, Label, MultiLabel, Span


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/tutorial5"
    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/tutorial5/nq_dev_subset_v2.json",
        doc_index=doc_index,
        label_index=label_index,
        preprocessor=preprocessor,
    )

    # Initialize Retriever

    retriever = BM25Retriever(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,
    #                                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 separated 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=True)

    ## 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": 5}},
        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)

    # ## Advanced Label Scopes
    # 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`.
    #
    # 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()`.
    #
    # `answer_scope` takes the following values:
    # - `any` (default): Any matching answer is considered correct.
    # - `context`: The answer is only considered correct if its context matches as well. It uses fuzzy matching (see `context_matching` parameters of `pipeline.eval()`).
    # - `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()`.
    # - `document_id_and_context`: The answer is only considered correct if its document ID and its context match as well.
    #
    # 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`.
    #
    # `document_scope` takes the following values:
    # - `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()`.
    # - `context`: Specifies that the content of the document must match. It uses fuzzy matching (see the `context_matching` parameters of `pipeline.eval()`).
    # - `document_id_and_context`: A Boolean operation specifying that both `'document_id' AND 'context'` must match.
    # - `document_id_or_context`: A Boolean operation specifying that either `'document_id' OR 'context'` must match.
    # - `answer`: Specifies that the document contents must include the answer. The selected `answer_scope` is enforced.
    # - `document_id_or_answer` (default): A Boolean operation specifying that either `'document_id' OR 'answer'` must match.
    metrics = saved_eval_result.calculate_metrics(answer_scope="context")
    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"]}')

    document_store.get_all_documents()[0]

    # 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).
    eval_result_custom_doc_id = pipeline.eval(
        labels=eval_labels, params={"Retriever": {"top_k": 5}}, custom_document_id_field="name"
    )
    metrics = eval_result_custom_doc_id.calculate_metrics(document_scope="document_id")
    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"]}')

    # Let's enforce the context again:
    metrics = eval_result_custom_doc_id.calculate_metrics(document_scope="document_id_and_context")
    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"]}')

    # ## Storing results in MLflow
    # Storing evaluation results in CSVs is fine but not enough if you want to compare and track multiple evaluation runs. MLflow is a handy tool when it comes to tracking experiments. So we decided to use it to track all of `Pipeline.eval()` with reproducability of your experiments in mind.

    # ### Host your own MLflow or use deepset's public MLflow
    # If you don't want to use deepset's public MLflow instance under https://public-mlflow.deepset.ai, you can easily host it yourself.

    # !pip install mlflow
    # !mlflow server --serve-artifacts

    # ### Preprocessing the dataset
    # Preprocessing the dataset works a bit differently than before. Instead of directly generating documents (and labels) out of a SQuAD file, we first save them to disk. This is necessary to experiment with different indexing pipelines.

    document_store = InMemoryDocumentStore()

    label_preprocessor = PreProcessor(
        split_length=200,
        split_overlap=0,
        split_respect_sentence_boundary=False,
        clean_empty_lines=False,
        clean_whitespace=False,
    )

    # 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.
    # We only use it to get the evaluation set labels and the corpus files.
    document_store.add_eval_data(
        filename="data/tutorial5/nq_dev_subset_v2.json",
        doc_index=document_store.index,
        label_index=document_store.label_index,
        preprocessor=label_preprocessor,
    )

    # the evaluation set to evaluate the pipelines on
    evaluation_set_labels = document_store.get_all_labels_aggregated(drop_negative_labels=True, drop_no_answers=True)

    # Pipelines need files as input to be able to test different preprocessors.
    # Even though this looks a bit cumbersome to write the documents back to files we gain a lot of evaluation potential and reproducibility.
    docs = document_store.get_all_documents()
    temp_dir = tempfile.TemporaryDirectory()
    file_paths = []
    for doc in docs:
        file_name = doc.id + ".txt"
        file_path = Path(temp_dir.name) / file_name
        file_paths.append(file_path)
        with open(file_path, "w") as f:
            f.write(doc.content)
    file_metas = [d.meta for d in docs]

    # ### Run experiments
    # In this experiment we evaluate extractive QA pipelines with two different retrievers on the evaluation set given the corpus:
    # **ElasticsearchRetriever vs. EmbeddingRetriever**

    # helper function to create query and index pipeline
    def create_pipelines(document_store, preprocessor, retriever, reader):
        query_pipeline = Pipeline()
        query_pipeline.add_node(component=retriever, inputs=["Query"], name="Retriever")
        query_pipeline.add_node(component=reader, inputs=["Retriever"], name="Reader")
        index_pipeline = Pipeline()
        index_pipeline.add_node(component=TextConverter(), inputs=["File"], name="TextConverter")
        index_pipeline.add_node(component=preprocessor, inputs=["TextConverter"], name="Preprocessor")
        index_pipeline.add_node(component=retriever, inputs=["Preprocessor"], name="Retriever")
        index_pipeline.add_node(component=document_store, inputs=["Retriever"], name="DocumentStore")
        return query_pipeline, index_pipeline

    # Name of the experiment in MLflow
    EXPERIMENT_NAME = "haystack-tutorial-5"

    # #### Run using BM25Retriever

    document_store = ElasticsearchDocumentStore(index="sparse_index", recreate_index=True)
    preprocessor = PreProcessor(
        split_length=200,
        split_overlap=0,
        split_respect_sentence_boundary=False,
        clean_empty_lines=False,
        clean_whitespace=False,
    )
    es_retriever = BM25Retriever(document_store=document_store)
    reader = FARMReader("deepset/roberta-base-squad2", top_k=3, return_no_answer=True, batch_size=8)
    query_pipeline, index_pipeline = create_pipelines(document_store, preprocessor, es_retriever, reader)

    sparse_eval_result = Pipeline.execute_eval_run(
        index_pipeline=index_pipeline,
        query_pipeline=query_pipeline,
        evaluation_set_labels=evaluation_set_labels,
        corpus_file_paths=file_paths,
        corpus_file_metas=file_metas,
        experiment_name=EXPERIMENT_NAME,
        experiment_run_name="sparse",
        corpus_meta={"name": "nq_dev_subset_v2.json"},
        evaluation_set_meta={"name": "nq_dev_subset_v2.json"},
        pipeline_meta={"name": "sparse-pipeline"},
        add_isolated_node_eval=True,
        experiment_tracking_tool="mlflow",
        experiment_tracking_uri="https://public-mlflow.deepset.ai",
        reuse_index=True,
    )

    # #### Run using EmbeddingRetriever

    document_store = ElasticsearchDocumentStore(index="dense_index", recreate_index=True)
    emb_retriever = EmbeddingRetriever(
        document_store=document_store,
        model_format="sentence_transformers",
        embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
        batch_size=8,
    )
    query_pipeline, index_pipeline = create_pipelines(document_store, preprocessor, emb_retriever, reader)

    dense_eval_result = Pipeline.execute_eval_run(
        index_pipeline=index_pipeline,
        query_pipeline=query_pipeline,
        evaluation_set_labels=evaluation_set_labels,
        corpus_file_paths=file_paths,
        corpus_file_metas=file_metas,
        experiment_name=EXPERIMENT_NAME,
        experiment_run_name="embedding",
        corpus_meta={"name": "nq_dev_subset_v2.json"},
        evaluation_set_meta={"name": "nq_dev_subset_v2.json"},
        pipeline_meta={"name": "embedding-pipeline"},
        add_isolated_node_eval=True,
        experiment_tracking_tool="mlflow",
        experiment_tracking_uri="https://public-mlflow.deepset.ai",
        reuse_index=True,
        answer_scope="context",
    )

    # You can now open MLflow (e.g. https://public-mlflow.deepset.ai/ if you used the public one hosted by deepset) and look for the haystack-eval-experiment experiment.
    # Try out mlflow's compare function and have fun...
    #
    # Note that on our public mlflow instance we are not able to log artifacts like the evaluation results or the piplines.yaml file.

    ## 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 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.
    metrics = eval_result_with_upper_bounds.calculate_metrics(document_scope="document_id")
    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)
    top_n = reader_eval_results["top_n"]
    # 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 including no_answers
    print(f"Reader Top-{top_n}-Accuracy:", reader_eval_results["top_n_accuracy"])
    # 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
    print("Reader Top-1-Exact Match:", reader_eval_results["EM"])
    # Reader Top-1-F1-Score is the average overlap between the first predicted answers and the correct answers including no_answers
    print("Reader Top-1-F1-Score:", reader_eval_results["f1"])
    # Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer excluding no_answers
    print(f"Reader Top-{top_n}-Accuracy (without no_answers):", reader_eval_results["top_n_accuracy_text_answer"])
    # 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).
    print(f"Reader Top-{top_n}-Exact Match (without no_answers):", reader_eval_results["top_n_EM_text_answer"])
    # 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).
    print(f"Reader Top-{top_n}-F1-Score (without no_answers):", reader_eval_results["top_n_f1_text_answer"])

    # 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)`.
    # 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.
    metrics = eval_result_with_upper_bounds.calculate_metrics(eval_mode="isolated")
    print(metrics["Reader"]["exact_match"])
    print(metrics["Reader"]["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/