haystack/test/pipelines/test_eval_batch.py

import logging
import pytest
import sys
from haystack.document_stores.memory import InMemoryDocumentStore
from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
from haystack.nodes.preprocessor import PreProcessor
from haystack.nodes.evaluator import EvalAnswers, EvalDocuments
from haystack.nodes.query_classifier.transformers import TransformersQueryClassifier
from haystack.nodes.retriever.dense import DensePassageRetriever
from haystack.nodes.retriever.sparse import BM25Retriever
from haystack.nodes.summarizer.transformers import TransformersSummarizer
from haystack.pipelines.base import Pipeline
from haystack.pipelines import ExtractiveQAPipeline, GenerativeQAPipeline, SearchSummarizationPipeline
from haystack.pipelines.standard_pipelines import (
    DocumentSearchPipeline,
    FAQPipeline,
    RetrieverQuestionGenerationPipeline,
    TranslationWrapperPipeline,
)
from haystack.nodes.translator.transformers import TransformersTranslator
from haystack.schema import Answer, Document, EvaluationResult, Label, MultiLabel, Span

from ..conftest import SAMPLES_PATH


@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Causes OOM on windows github runner")
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("retriever_with_docs", ["embedding"], indirect=True)
def test_generativeqa_calculate_metrics(
    document_store_with_docs: InMemoryDocumentStore, rag_generator, retriever_with_docs
):
    document_store_with_docs.update_embeddings(retriever=retriever_with_docs)
    pipeline = GenerativeQAPipeline(generator=rag_generator, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    assert "Retriever" in eval_result
    assert "Generator" in eval_result
    assert len(eval_result) == 2

    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5
    assert metrics["Generator"]["exact_match"] == 0.0
    assert metrics["Generator"]["f1"] == 1.0 / 3


@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Causes OOM on windows github runner")
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("retriever_with_docs", ["embedding"], indirect=True)
def test_summarizer_calculate_metrics(document_store_with_docs: ElasticsearchDocumentStore, retriever_with_docs):
    document_store_with_docs.update_embeddings(retriever=retriever_with_docs)
    summarizer = TransformersSummarizer(model_name_or_path="sshleifer/distill-pegasus-xsum-16-4", use_gpu=False)
    pipeline = SearchSummarizationPipeline(
        retriever=retriever_with_docs, summarizer=summarizer, return_in_answer_format=True
    )
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}}, context_matching_min_length=10
    )

    metrics = eval_result.calculate_metrics(document_scope="context")

    assert "Retriever" in eval_result
    assert "Summarizer" in eval_result
    assert len(eval_result) == 2

    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["map"] == pytest.approx(0.9167, 1e-4)
    assert metrics["Retriever"]["recall_multi_hit"] == pytest.approx(0.9167, 1e-4)
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 1.0
    assert metrics["Retriever"]["ndcg"] == pytest.approx(0.9461, 1e-4)
    assert metrics["Summarizer"]["mrr"] == 1.0
    assert metrics["Summarizer"]["map"] == 0.735
    assert metrics["Summarizer"]["recall_multi_hit"] == 0.8
    assert metrics["Summarizer"]["recall_single_hit"] == 1.0
    assert metrics["Summarizer"]["precision"] == 0.8
    assert metrics["Summarizer"]["ndcg"] == pytest.approx(0.8422, 1e-4)


EVAL_LABELS = [
    MultiLabel(
        labels=[
            Label(
                query="Who lives in Berlin?",
                answer=Answer(answer="Carla", offsets_in_context=[Span(11, 16)]),
                document=Document(
                    id="a0747b83aea0b60c4b114b15476dd32d",
                    content_type="text",
                    content="My name is Carla and I live in Berlin",
                ),
                is_correct_answer=True,
                is_correct_document=True,
                origin="gold-label",
            )
        ]
    ),
    MultiLabel(
        labels=[
            Label(
                query="Who lives in Munich?",
                answer=Answer(answer="Carla", offsets_in_context=[Span(11, 16)]),
                document=Document(
                    id="something_else", content_type="text", content="My name is Carla and I live in Munich"
                ),
                is_correct_answer=True,
                is_correct_document=True,
                origin="gold-label",
            )
        ]
    ),
]


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval(reader, retriever_with_docs, tmp_path):
    labels = EVAL_LABELS[:1]

    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result = pipeline.eval_batch(labels=labels, params={"Retriever": {"top_k": 5}})

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    reader_result = eval_result["Reader"]
    retriever_result = eval_result["Retriever"]

    assert (
        reader_result[reader_result["rank"] == 1]["answer"].iloc[0]
        in reader_result[reader_result["rank"] == 1]["gold_answers"].iloc[0]
    )
    assert (
        retriever_result[retriever_result["rank"] == 1]["document_id"].iloc[0]
        in retriever_result[retriever_result["rank"] == 1]["gold_document_ids"].iloc[0]
    )
    assert metrics["Reader"]["exact_match"] == 1.0
    assert metrics["Reader"]["f1"] == 1.0
    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["recall_multi_hit"] == 1.0
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 0.2
    assert metrics["Retriever"]["map"] == 1.0
    assert metrics["Retriever"]["ndcg"] == 1.0

    eval_result.save(tmp_path)
    saved_eval_result = EvaluationResult.load(tmp_path)
    metrics = saved_eval_result.calculate_metrics(document_scope="document_id")

    assert (
        reader_result[reader_result["rank"] == 1]["answer"].iloc[0]
        in reader_result[reader_result["rank"] == 1]["gold_answers"].iloc[0]
    )
    assert (
        retriever_result[retriever_result["rank"] == 1]["document_id"].iloc[0]
        in retriever_result[retriever_result["rank"] == 1]["gold_document_ids"].iloc[0]
    )
    assert metrics["Reader"]["exact_match"] == 1.0
    assert metrics["Reader"]["f1"] == 1.0
    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["recall_multi_hit"] == 1.0
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 0.2
    assert metrics["Retriever"]["map"] == 1.0
    assert metrics["Retriever"]["ndcg"] == 1.0


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_multiple_queries(reader, retriever_with_docs, tmp_path):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    reader_result = eval_result["Reader"]
    retriever_result = eval_result["Retriever"]

    reader_berlin = reader_result[reader_result["query"] == "Who lives in Berlin?"]
    reader_munich = reader_result[reader_result["query"] == "Who lives in Munich?"]

    retriever_berlin = retriever_result[retriever_result["query"] == "Who lives in Berlin?"]
    retriever_munich = retriever_result[retriever_result["query"] == "Who lives in Munich?"]

    assert (
        reader_berlin[reader_berlin["rank"] == 1]["answer"].iloc[0]
        in reader_berlin[reader_berlin["rank"] == 1]["gold_answers"].iloc[0]
    )
    assert (
        retriever_berlin[retriever_berlin["rank"] == 1]["document_id"].iloc[0]
        in retriever_berlin[retriever_berlin["rank"] == 1]["gold_document_ids"].iloc[0]
    )
    assert (
        reader_munich[reader_munich["rank"] == 1]["answer"].iloc[0]
        not in reader_munich[reader_munich["rank"] == 1]["gold_answers"].iloc[0]
    )
    assert (
        retriever_munich[retriever_munich["rank"] == 1]["document_id"].iloc[0]
        not in retriever_munich[retriever_munich["rank"] == 1]["gold_document_ids"].iloc[0]
    )
    assert metrics["Reader"]["exact_match"] == 1.0
    assert metrics["Reader"]["f1"] == 1.0
    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5

    eval_result.save(tmp_path)
    saved_eval_result = EvaluationResult.load(tmp_path)
    metrics = saved_eval_result.calculate_metrics(document_scope="document_id")

    assert (
        reader_berlin[reader_berlin["rank"] == 1]["answer"].iloc[0]
        in reader_berlin[reader_berlin["rank"] == 1]["gold_answers"].iloc[0]
    )
    assert (
        retriever_berlin[retriever_berlin["rank"] == 1]["document_id"].iloc[0]
        in retriever_berlin[retriever_berlin["rank"] == 1]["gold_document_ids"].iloc[0]
    )
    assert (
        reader_munich[reader_munich["rank"] == 1]["answer"].iloc[0]
        not in reader_munich[reader_munich["rank"] == 1]["gold_answers"].iloc[0]
    )
    assert (
        retriever_munich[retriever_munich["rank"] == 1]["document_id"].iloc[0]
        not in retriever_munich[retriever_munich["rank"] == 1]["gold_document_ids"].iloc[0]
    )
    assert metrics["Reader"]["exact_match"] == 1.0
    assert metrics["Reader"]["f1"] == 1.0
    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_sas(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=EVAL_LABELS,
        params={"Retriever": {"top_k": 5}},
        sas_model_name_or_path="sentence-transformers/paraphrase-MiniLM-L3-v2",
    )

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    assert metrics["Reader"]["exact_match"] == 1.0
    assert metrics["Reader"]["f1"] == 1.0
    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5
    assert "sas" in metrics["Reader"]
    assert metrics["Reader"]["sas"] == pytest.approx(1.0)


@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_reader_eval_in_pipeline(reader):
    pipeline = Pipeline()
    pipeline.add_node(component=reader, name="Reader", inputs=["Query"])
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=EVAL_LABELS,
        documents=[[label.document for label in multilabel.labels] for multilabel in EVAL_LABELS],
        params={},
    )

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    assert metrics["Reader"]["exact_match"] == 1.0
    assert metrics["Reader"]["f1"] == 1.0


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_document_scope(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=EVAL_LABELS,
        params={"Retriever": {"top_k": 5}},
        context_matching_min_length=20,  # artificially set down min_length to see if context matching is working properly
    )

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5

    metrics = eval_result.calculate_metrics(document_scope="context")

    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["map"] == pytest.approx(0.9167, 1e-4)
    assert metrics["Retriever"]["recall_multi_hit"] == pytest.approx(0.9167, 1e-4)
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 1.0
    assert metrics["Retriever"]["ndcg"] == pytest.approx(0.9461, 1e-4)

    metrics = eval_result.calculate_metrics(document_scope="document_id_and_context")

    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5

    metrics = eval_result.calculate_metrics(document_scope="document_id_or_context")

    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["map"] == pytest.approx(0.9167, 1e-4)
    assert metrics["Retriever"]["recall_multi_hit"] == pytest.approx(0.9167, 1e-4)
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 1.0
    assert metrics["Retriever"]["ndcg"] == pytest.approx(0.9461, 1e-4)

    metrics = eval_result.calculate_metrics(document_scope="answer")

    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["map"] == 0.75
    assert metrics["Retriever"]["recall_multi_hit"] == 0.75
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 0.2
    assert metrics["Retriever"]["ndcg"] == pytest.approx(0.8066, 1e-4)

    metrics = eval_result.calculate_metrics(document_scope="document_id_or_answer")

    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["map"] == 0.75
    assert metrics["Retriever"]["recall_multi_hit"] == 0.75
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 0.2
    assert metrics["Retriever"]["ndcg"] == pytest.approx(0.8066, 1e-4)


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_answer_scope(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=EVAL_LABELS,
        params={"Retriever": {"top_k": 5}},
        sas_model_name_or_path="sentence-transformers/paraphrase-MiniLM-L3-v2",
        context_matching_min_length=20,  # artificially set down min_length to see if context matching is working properly
    )

    metrics = eval_result.calculate_metrics(answer_scope="any")

    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["map"] == 0.75
    assert metrics["Retriever"]["recall_multi_hit"] == 0.75
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 0.2
    assert metrics["Retriever"]["ndcg"] == pytest.approx(0.8066, 1e-4)
    assert metrics["Reader"]["exact_match"] == 1.0
    assert metrics["Reader"]["f1"] == 1.0
    assert metrics["Reader"]["sas"] == pytest.approx(1.0)

    metrics = eval_result.calculate_metrics(answer_scope="context")

    assert metrics["Retriever"]["mrr"] == 1.0
    assert metrics["Retriever"]["map"] == 0.75
    assert metrics["Retriever"]["recall_multi_hit"] == 0.75
    assert metrics["Retriever"]["recall_single_hit"] == 1.0
    assert metrics["Retriever"]["precision"] == 0.2
    assert metrics["Retriever"]["ndcg"] == pytest.approx(0.8066, 1e-4)
    assert metrics["Reader"]["exact_match"] == 1.0
    assert metrics["Reader"]["f1"] == 1.0
    assert metrics["Reader"]["sas"] == pytest.approx(1.0)

    metrics = eval_result.calculate_metrics(answer_scope="document_id")

    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5
    assert metrics["Reader"]["exact_match"] == 0.5
    assert metrics["Reader"]["f1"] == 0.5
    assert metrics["Reader"]["sas"] == pytest.approx(0.5)

    metrics = eval_result.calculate_metrics(answer_scope="document_id_and_context")

    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5
    assert metrics["Reader"]["exact_match"] == 0.5
    assert metrics["Reader"]["f1"] == 0.5
    assert metrics["Reader"]["sas"] == pytest.approx(0.5)


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_answer_document_scope_combinations(reader, retriever_with_docs, caplog):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=EVAL_LABELS,
        params={"Retriever": {"top_k": 5}},
        sas_model_name_or_path="sentence-transformers/paraphrase-MiniLM-L3-v2",
        context_matching_min_length=20,  # artificially set down min_length to see if context matching is working properly
    )

    # valid values for non default answer_scopes
    with caplog.at_level(logging.WARNING):
        metrics = eval_result.calculate_metrics(document_scope="document_id_or_answer", answer_scope="context")
        metrics = eval_result.calculate_metrics(document_scope="answer", answer_scope="context")
        assert "You specified a non-answer document_scope together with a non-default answer_scope" not in caplog.text

    with caplog.at_level(logging.WARNING):
        metrics = eval_result.calculate_metrics(document_scope="document_id", answer_scope="context")
        assert "You specified a non-answer document_scope together with a non-default answer_scope" in caplog.text

    with caplog.at_level(logging.WARNING):
        metrics = eval_result.calculate_metrics(document_scope="context", answer_scope="context")
        assert "You specified a non-answer document_scope together with a non-default answer_scope" in caplog.text

    with caplog.at_level(logging.WARNING):
        metrics = eval_result.calculate_metrics(document_scope="document_id_and_context", answer_scope="context")
        assert "You specified a non-answer document_scope together with a non-default answer_scope" in caplog.text

    with caplog.at_level(logging.WARNING):
        metrics = eval_result.calculate_metrics(document_scope="document_id_or_context", answer_scope="context")
        assert "You specified a non-answer document_scope together with a non-default answer_scope" in caplog.text


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_simulated_top_k_reader(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=EVAL_LABELS,
        params={"Retriever": {"top_k": 5}},
        sas_model_name_or_path="sentence-transformers/paraphrase-MiniLM-L3-v2",
    )

    metrics_top_1 = eval_result.calculate_metrics(simulated_top_k_reader=1, document_scope="document_id")

    assert metrics_top_1["Reader"]["exact_match"] == 0.5
    assert metrics_top_1["Reader"]["f1"] == 0.5
    assert metrics_top_1["Reader"]["sas"] == pytest.approx(0.5833, abs=1e-4)
    assert metrics_top_1["Retriever"]["mrr"] == 0.5
    assert metrics_top_1["Retriever"]["map"] == 0.5
    assert metrics_top_1["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_1["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_1["Retriever"]["precision"] == 0.1
    assert metrics_top_1["Retriever"]["ndcg"] == 0.5

    metrics_top_2 = eval_result.calculate_metrics(simulated_top_k_reader=2, document_scope="document_id")

    assert metrics_top_2["Reader"]["exact_match"] == 0.5
    assert metrics_top_2["Reader"]["f1"] == 0.5
    assert metrics_top_2["Reader"]["sas"] == pytest.approx(0.5833, abs=1e-4)
    assert metrics_top_2["Retriever"]["mrr"] == 0.5
    assert metrics_top_2["Retriever"]["map"] == 0.5
    assert metrics_top_2["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_2["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_2["Retriever"]["precision"] == 0.1
    assert metrics_top_2["Retriever"]["ndcg"] == 0.5

    metrics_top_3 = eval_result.calculate_metrics(simulated_top_k_reader=3, document_scope="document_id")

    assert metrics_top_3["Reader"]["exact_match"] == 1.0
    assert metrics_top_3["Reader"]["f1"] == 1.0
    assert metrics_top_3["Reader"]["sas"] == pytest.approx(1.0, abs=1e-4)
    assert metrics_top_3["Retriever"]["mrr"] == 0.5
    assert metrics_top_3["Retriever"]["map"] == 0.5
    assert metrics_top_3["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_3["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_3["Retriever"]["precision"] == 0.1
    assert metrics_top_3["Retriever"]["ndcg"] == 0.5


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_simulated_top_k_retriever(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})

    metrics_top_10 = eval_result.calculate_metrics(document_scope="document_id")

    assert metrics_top_10["Reader"]["exact_match"] == 1.0
    assert metrics_top_10["Reader"]["f1"] == 1.0
    assert metrics_top_10["Retriever"]["mrr"] == 0.5
    assert metrics_top_10["Retriever"]["map"] == 0.5
    assert metrics_top_10["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_10["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_10["Retriever"]["precision"] == 0.1
    assert metrics_top_10["Retriever"]["ndcg"] == 0.5

    metrics_top_1 = eval_result.calculate_metrics(simulated_top_k_retriever=1, document_scope="document_id")

    assert metrics_top_1["Reader"]["exact_match"] == 1.0
    assert metrics_top_1["Reader"]["f1"] == 1.0
    assert metrics_top_1["Retriever"]["mrr"] == 0.5
    assert metrics_top_1["Retriever"]["map"] == 0.5
    assert metrics_top_1["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_1["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_1["Retriever"]["precision"] == 0.5
    assert metrics_top_1["Retriever"]["ndcg"] == 0.5

    metrics_top_2 = eval_result.calculate_metrics(simulated_top_k_retriever=2, document_scope="document_id")

    assert metrics_top_2["Reader"]["exact_match"] == 1.0
    assert metrics_top_2["Reader"]["f1"] == 1.0
    assert metrics_top_2["Retriever"]["mrr"] == 0.5
    assert metrics_top_2["Retriever"]["map"] == 0.5
    assert metrics_top_2["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_2["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_2["Retriever"]["precision"] == 0.25
    assert metrics_top_2["Retriever"]["ndcg"] == 0.5

    metrics_top_3 = eval_result.calculate_metrics(simulated_top_k_retriever=3, document_scope="document_id")

    assert metrics_top_3["Reader"]["exact_match"] == 1.0
    assert metrics_top_3["Reader"]["f1"] == 1.0
    assert metrics_top_3["Retriever"]["mrr"] == 0.5
    assert metrics_top_3["Retriever"]["map"] == 0.5
    assert metrics_top_3["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_3["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_3["Retriever"]["precision"] == 1.0 / 6
    assert metrics_top_3["Retriever"]["ndcg"] == 0.5


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_simulated_top_k_reader_and_retriever(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(labels=EVAL_LABELS, params={"Retriever": {"top_k": 10}})

    metrics_top_10 = eval_result.calculate_metrics(simulated_top_k_reader=1, document_scope="document_id")

    assert metrics_top_10["Reader"]["exact_match"] == 0.5
    assert metrics_top_10["Reader"]["f1"] == 0.5
    assert metrics_top_10["Retriever"]["mrr"] == 0.5
    assert metrics_top_10["Retriever"]["map"] == 0.5
    assert metrics_top_10["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_10["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_10["Retriever"]["precision"] == 0.1
    assert metrics_top_10["Retriever"]["ndcg"] == 0.5

    metrics_top_1 = eval_result.calculate_metrics(
        simulated_top_k_reader=1, simulated_top_k_retriever=1, document_scope="document_id"
    )

    assert metrics_top_1["Reader"]["exact_match"] == 1.0
    assert metrics_top_1["Reader"]["f1"] == 1.0

    assert metrics_top_1["Retriever"]["mrr"] == 0.5
    assert metrics_top_1["Retriever"]["map"] == 0.5
    assert metrics_top_1["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_1["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_1["Retriever"]["precision"] == 0.5
    assert metrics_top_1["Retriever"]["ndcg"] == 0.5

    metrics_top_2 = eval_result.calculate_metrics(
        simulated_top_k_reader=1, simulated_top_k_retriever=2, document_scope="document_id"
    )

    assert metrics_top_2["Reader"]["exact_match"] == 0.5
    assert metrics_top_2["Reader"]["f1"] == 0.5
    assert metrics_top_2["Retriever"]["mrr"] == 0.5
    assert metrics_top_2["Retriever"]["map"] == 0.5
    assert metrics_top_2["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_2["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_2["Retriever"]["precision"] == 0.25
    assert metrics_top_2["Retriever"]["ndcg"] == 0.5

    metrics_top_3 = eval_result.calculate_metrics(
        simulated_top_k_reader=1, simulated_top_k_retriever=3, document_scope="document_id"
    )

    assert metrics_top_3["Reader"]["exact_match"] == 0.5
    assert metrics_top_3["Reader"]["f1"] == 0.5
    assert metrics_top_3["Retriever"]["mrr"] == 0.5
    assert metrics_top_3["Retriever"]["map"] == 0.5
    assert metrics_top_3["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_3["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_3["Retriever"]["precision"] == 1.0 / 6
    assert metrics_top_3["Retriever"]["ndcg"] == 0.5


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_isolated(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=EVAL_LABELS,
        sas_model_name_or_path="sentence-transformers/paraphrase-MiniLM-L3-v2",
        add_isolated_node_eval=True,
    )

    metrics_top_1 = eval_result.calculate_metrics(simulated_top_k_reader=1, document_scope="document_id")

    assert metrics_top_1["Reader"]["exact_match"] == 0.5
    assert metrics_top_1["Reader"]["f1"] == 0.5
    assert metrics_top_1["Reader"]["sas"] == pytest.approx(0.5833, abs=1e-4)
    assert metrics_top_1["Retriever"]["mrr"] == 0.5
    assert metrics_top_1["Retriever"]["map"] == 0.5
    assert metrics_top_1["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics_top_1["Retriever"]["recall_single_hit"] == 0.5
    assert metrics_top_1["Retriever"]["precision"] == 1.0 / 10
    assert metrics_top_1["Retriever"]["ndcg"] == 0.5

    metrics_top_1 = eval_result.calculate_metrics(simulated_top_k_reader=1, eval_mode="isolated")

    assert metrics_top_1["Reader"]["exact_match"] == 1.0
    assert metrics_top_1["Reader"]["f1"] == 1.0
    assert metrics_top_1["Reader"]["sas"] == pytest.approx(1.0, abs=1e-4)


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_eval_wrong_examples(reader, retriever_with_docs):

    labels = [
        MultiLabel(
            labels=[
                Label(
                    query="Who lives in Berlin?",
                    answer=Answer(answer="Carla", offsets_in_context=[Span(11, 16)]),
                    document=Document(
                        id="a0747b83aea0b60c4b114b15476dd32d",
                        content_type="text",
                        content="My name is Carla and I live in Berlin",
                    ),
                    is_correct_answer=True,
                    is_correct_document=True,
                    origin="gold-label",
                )
            ]
        ),
        MultiLabel(
            labels=[
                Label(
                    query="Who lives in Munich?",
                    answer=Answer(answer="Pete", offsets_in_context=[Span(11, 16)]),
                    document=Document(
                        id="something_else", content_type="text", content="My name is Pete and I live in Munich"
                    ),
                    is_correct_answer=True,
                    is_correct_document=True,
                    origin="gold-label",
                )
            ]
        ),
    ]

    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(labels=labels, params={"Retriever": {"top_k": 5}})

    wrongs_retriever = eval_result.wrong_examples(node="Retriever", n=1)
    wrongs_reader = eval_result.wrong_examples(node="Reader", n=1)

    assert len(wrongs_retriever) == 1
    assert len(wrongs_reader) == 1


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_extractive_qa_print_eval_report(reader, retriever_with_docs):

    labels = [
        MultiLabel(
            labels=[
                Label(
                    query="Who lives in Berlin?",
                    answer=Answer(answer="Carla", offsets_in_context=[Span(11, 16)]),
                    document=Document(
                        id="a0747b83aea0b60c4b114b15476dd32d",
                        content_type="text",
                        content="My name is Carla and I live in Berlin",
                    ),
                    is_correct_answer=True,
                    is_correct_document=True,
                    origin="gold-label",
                )
            ]
        ),
        MultiLabel(
            labels=[
                Label(
                    query="Who lives in Munich?",
                    answer=Answer(answer="Pete", offsets_in_context=[Span(11, 16)]),
                    document=Document(
                        id="something_else", content_type="text", content="My name is Pete and I live in Munich"
                    ),
                    is_correct_answer=True,
                    is_correct_document=True,
                    origin="gold-label",
                )
            ]
        ),
    ]

    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(labels=labels, params={"Retriever": {"top_k": 5}})
    pipeline.print_eval_report(eval_result)

    # in addition with labels as input to reader node rather than output of retriever node
    eval_result: EvaluationResult = pipeline.eval_batch(
        labels=labels, params={"Retriever": {"top_k": 5}}, add_isolated_node_eval=True
    )
    pipeline.print_eval_report(eval_result)


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
def test_document_search_calculate_metrics(retriever_with_docs):
    pipeline = DocumentSearchPipeline(retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    assert "Retriever" in eval_result
    assert len(eval_result) == 1
    retriever_result = eval_result["Retriever"]
    retriever_berlin = retriever_result[retriever_result["query"] == "Who lives in Berlin?"]
    retriever_munich = retriever_result[retriever_result["query"] == "Who lives in Munich?"]

    assert (
        retriever_berlin[retriever_berlin["rank"] == 1]["document_id"].iloc[0]
        in retriever_berlin[retriever_berlin["rank"] == 1]["gold_document_ids"].iloc[0]
    )
    assert (
        retriever_munich[retriever_munich["rank"] == 1]["document_id"].iloc[0]
        not in retriever_munich[retriever_munich["rank"] == 1]["gold_document_ids"].iloc[0]
    )
    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
def test_faq_calculate_metrics(retriever_with_docs):
    pipeline = FAQPipeline(retriever=retriever_with_docs)
    eval_result: EvaluationResult = pipeline.eval_batch(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    assert "Retriever" in eval_result
    assert "Docs2Answers" in eval_result
    assert len(eval_result) == 2

    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5
    assert metrics["Docs2Answers"]["exact_match"] == 0.0
    assert metrics["Docs2Answers"]["f1"] == 0.0


# Commented out because of the following issue https://github.com/deepset-ai/haystack/issues/2964
# @pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
# @pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
# @pytest.mark.parametrize("reader", ["farm"], indirect=True)
# def test_extractive_qa_eval_translation(reader, retriever_with_docs):
#
#     # FIXME it makes no sense to have DE->EN input and DE->EN output, right?
#     #  Yet switching direction breaks the test. TO BE FIXED.
#     input_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-de-en")
#     output_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-de-en")
#
#     pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
#     pipeline = TranslationWrapperPipeline(
#         input_translator=input_translator, output_translator=output_translator, pipeline=pipeline
#     )
#     eval_result: EvaluationResult = pipeline.eval_batch(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})
#
#     metrics = eval_result.calculate_metrics(document_scope="document_id")
#
#     assert "Retriever" in eval_result
#     assert "Reader" in eval_result
#     assert "OutputTranslator" in eval_result
#     assert len(eval_result) == 3
#
#     assert metrics["Reader"]["exact_match"] == 1.0
#     assert metrics["Reader"]["f1"] == 1.0
#     assert metrics["Retriever"]["mrr"] == 0.5
#     assert metrics["Retriever"]["map"] == 0.5
#     assert metrics["Retriever"]["recall_multi_hit"] == 0.5
#     assert metrics["Retriever"]["recall_single_hit"] == 0.5
#     assert metrics["Retriever"]["precision"] == 0.1
#     assert metrics["Retriever"]["ndcg"] == 0.5
#
#     assert metrics["OutputTranslator"]["exact_match"] == 1.0
#     assert metrics["OutputTranslator"]["f1"] == 1.0
#     assert metrics["OutputTranslator"]["mrr"] == 0.5
#     assert metrics["OutputTranslator"]["map"] == 0.5
#     assert metrics["OutputTranslator"]["recall_multi_hit"] == 0.5
#     assert metrics["OutputTranslator"]["recall_single_hit"] == 0.5
#     assert metrics["OutputTranslator"]["precision"] == 0.1
#     assert metrics["OutputTranslator"]["ndcg"] == 0.5


@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
def test_question_generation_eval(retriever_with_docs, question_generator):
    pipeline = RetrieverQuestionGenerationPipeline(retriever=retriever_with_docs, question_generator=question_generator)

    eval_result: EvaluationResult = pipeline.eval_batch(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})

    metrics = eval_result.calculate_metrics(document_scope="document_id")

    assert "Retriever" in eval_result
    assert "QuestionGenerator" in eval_result
    assert len(eval_result) == 2

    assert metrics["Retriever"]["mrr"] == 0.5
    assert metrics["Retriever"]["map"] == 0.5
    assert metrics["Retriever"]["recall_multi_hit"] == 0.5
    assert metrics["Retriever"]["recall_single_hit"] == 0.5
    assert metrics["Retriever"]["precision"] == 0.1
    assert metrics["Retriever"]["ndcg"] == 0.5

    assert metrics["QuestionGenerator"]["mrr"] == 0.5
    assert metrics["QuestionGenerator"]["map"] == 0.5
    assert metrics["QuestionGenerator"]["recall_multi_hit"] == 0.5
    assert metrics["QuestionGenerator"]["recall_single_hit"] == 0.5
    assert metrics["QuestionGenerator"]["precision"] == 0.1
    assert metrics["QuestionGenerator"]["ndcg"] == 0.5


# Commented out because of the following issue https://github.com/deepset-ai/haystack/issues/2962
# @pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
# @pytest.mark.parametrize("reader", ["farm"], indirect=True)
# def test_qa_multi_retriever_pipeline_eval(document_store_with_docs, reader):
#     es_retriever = BM25Retriever(document_store=document_store_with_docs)
#     dpr_retriever = DensePassageRetriever(document_store_with_docs)
#     document_store_with_docs.update_embeddings(retriever=dpr_retriever)
#
#     # QA Pipeline with two retrievers, we always want QA output
#     pipeline = Pipeline()
#     pipeline.add_node(component=TransformersQueryClassifier(), name="QueryClassifier", inputs=["Query"])
#     pipeline.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"])
#     pipeline.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"])
#     pipeline.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
#
#     # EVAL_QUERIES: 2 go dpr way
#     # in Berlin goes es way
#     labels = EVAL_LABELS + [
#         MultiLabel(
#             labels=[
#                 Label(
#                     query="in Berlin",
#                     answer=Answer(answer="Carla", offsets_in_context=[Span(11, 16)]),
#                     document=Document(
#                         id="a0747b83aea0b60c4b114b15476dd32d",
#                         content_type="text",
#                         content="My name is Carla and I live in Berlin",
#                     ),
#                     is_correct_answer=True,
#                     is_correct_document=True,
#                     origin="gold-label",
#                 )
#             ]
#         )
#     ]
#
#     eval_result: EvaluationResult = pipeline.eval_batch(
#         labels=labels, params={"ESRetriever": {"top_k": 5}, "DPRRetriever": {"top_k": 5}}
#     )
#
#     metrics = eval_result.calculate_metrics(document_scope="document_id")
#
#     assert "ESRetriever" in eval_result
#     assert "DPRRetriever" in eval_result
#     assert "QAReader" in eval_result
#     assert len(eval_result) == 3
#
#     assert metrics["DPRRetriever"]["mrr"] == 0.5
#     assert metrics["DPRRetriever"]["map"] == 0.5
#     assert metrics["DPRRetriever"]["recall_multi_hit"] == 0.5
#     assert metrics["DPRRetriever"]["recall_single_hit"] == 0.5
#     assert metrics["DPRRetriever"]["precision"] == 0.1
#     assert metrics["DPRRetriever"]["ndcg"] == 0.5
#
#     assert metrics["ESRetriever"]["mrr"] == 1.0
#     assert metrics["ESRetriever"]["map"] == 1.0
#     assert metrics["ESRetriever"]["recall_multi_hit"] == 1.0
#     assert metrics["ESRetriever"]["recall_single_hit"] == 1.0
#     assert metrics["ESRetriever"]["precision"] == 0.2
#     assert metrics["ESRetriever"]["ndcg"] == 1.0
#
#     assert metrics["QAReader"]["exact_match"] == 1.0
#     assert metrics["QAReader"]["f1"] == 1.0


# Commented out because of the following issue https://github.com/deepset-ai/haystack/issues/2962
# @pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
# def test_multi_retriever_pipeline_eval(document_store_with_docs):
#     es_retriever = BM25Retriever(document_store=document_store_with_docs)
#     dpr_retriever = DensePassageRetriever(document_store_with_docs)
#     document_store_with_docs.update_embeddings(retriever=dpr_retriever)
#
#     # QA Pipeline with two retrievers, no QA output
#     pipeline = Pipeline()
#     pipeline.add_node(component=TransformersQueryClassifier(), name="QueryClassifier", inputs=["Query"])
#     pipeline.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"])
#     pipeline.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"])
#
#     # EVAL_QUERIES: 2 go dpr way
#     # in Berlin goes es way
#     labels = EVAL_LABELS + [
#         MultiLabel(
#             labels=[
#                 Label(
#                     query="in Berlin",
#                     answer=None,
#                     document=Document(
#                         id="a0747b83aea0b60c4b114b15476dd32d",
#                         content_type="text",
#                         content="My name is Carla and I live in Berlin",
#                     ),
#                     is_correct_answer=True,
#                     is_correct_document=True,
#                     origin="gold-label",
#                 )
#             ]
#         )
#     ]
#
#     eval_result: EvaluationResult = pipeline.eval_batch(
#         labels=labels, params={"ESRetriever": {"top_k": 5}, "DPRRetriever": {"top_k": 5}}
#     )
#
#     metrics = eval_result.calculate_metrics(document_scope="document_id")
#
#     assert "ESRetriever" in eval_result
#     assert "DPRRetriever" in eval_result
#     assert len(eval_result) == 2
#
#     assert metrics["DPRRetriever"]["mrr"] == 0.5
#     assert metrics["DPRRetriever"]["map"] == 0.5
#     assert metrics["DPRRetriever"]["recall_multi_hit"] == 0.5
#     assert metrics["DPRRetriever"]["recall_single_hit"] == 0.5
#     assert metrics["DPRRetriever"]["precision"] == 0.1
#     assert metrics["DPRRetriever"]["ndcg"] == 0.5
#
#     assert metrics["ESRetriever"]["mrr"] == 1.0
#     assert metrics["ESRetriever"]["map"] == 1.0
#     assert metrics["ESRetriever"]["recall_multi_hit"] == 1.0
#     assert metrics["ESRetriever"]["recall_single_hit"] == 1.0
#     assert metrics["ESRetriever"]["precision"] == 0.2
#     assert metrics["ESRetriever"]["ndcg"] == 1.0


# Commented out because of the following issue https://github.com/deepset-ai/haystack/issues/2962
# @pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
# @pytest.mark.parametrize("reader", ["farm"], indirect=True)
# def test_multi_retriever_pipeline_with_asymmetric_qa_eval(document_store_with_docs, reader):
#     es_retriever = BM25Retriever(document_store=document_store_with_docs)
#     dpr_retriever = DensePassageRetriever(document_store_with_docs)
#     document_store_with_docs.update_embeddings(retriever=dpr_retriever)
#
#     # QA Pipeline with two retrievers, we only get QA output from dpr
#     pipeline = Pipeline()
#     pipeline.add_node(component=TransformersQueryClassifier(), name="QueryClassifier", inputs=["Query"])
#     pipeline.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"])
#     pipeline.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"])
#     pipeline.add_node(component=reader, name="QAReader", inputs=["DPRRetriever"])
#
#     # EVAL_QUERIES: 2 go dpr way
#     # in Berlin goes es way
#     labels = EVAL_LABELS + [
#         MultiLabel(
#             labels=[
#                 Label(
#                     query="in Berlin",
#                     answer=None,
#                     document=Document(
#                         id="a0747b83aea0b60c4b114b15476dd32d",
#                         content_type="text",
#                         content="My name is Carla and I live in Berlin",
#                     ),
#                     is_correct_answer=True,
#                     is_correct_document=True,
#                     origin="gold-label",
#                 )
#             ]
#         )
#     ]
#
#     eval_result: EvaluationResult = pipeline.eval_batch(
#         labels=labels, params={"ESRetriever": {"top_k": 5}, "DPRRetriever": {"top_k": 5}}
#     )
#
#     metrics = eval_result.calculate_metrics(document_scope="document_id")
#
#     assert "ESRetriever" in eval_result
#     assert "DPRRetriever" in eval_result
#     assert "QAReader" in eval_result
#     assert len(eval_result) == 3
#
#     assert metrics["DPRRetriever"]["mrr"] == 0.5
#     assert metrics["DPRRetriever"]["map"] == 0.5
#     assert metrics["DPRRetriever"]["recall_multi_hit"] == 0.5
#     assert metrics["DPRRetriever"]["recall_single_hit"] == 0.5
#     assert metrics["DPRRetriever"]["precision"] == 0.1
#     assert metrics["DPRRetriever"]["ndcg"] == 0.5
#
#     assert metrics["ESRetriever"]["mrr"] == 1.0
#     assert metrics["ESRetriever"]["map"] == 1.0
#     assert metrics["ESRetriever"]["recall_multi_hit"] == 1.0
#     assert metrics["ESRetriever"]["recall_single_hit"] == 1.0
#     assert metrics["ESRetriever"]["precision"] == 0.2
#     assert metrics["ESRetriever"]["ndcg"] == 1.0
#
#     assert metrics["QAReader"]["exact_match"] == 1.0
#     assert metrics["QAReader"]["f1"] == 1.0