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Remove wrong retriever top_1 metrics from print_eval_report (#2510)
* remove wrong retriever top_1 metrics * Update Documentation & Code Style * don't show wrong examples frame when n_wrong_examples is 0 * Update Documentation & Code Style * Update Documentation & Code Style * only use farm reader during eval tests Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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tstadel
GitHub
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771ed0bb1d
@ -75,6 +75,34 @@
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}
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]
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},
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{
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"allOf": [
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{
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"properties": {
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"version": {
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"const": "1.4.0"
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}
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}
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},
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{
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"$ref": "https://raw.githubusercontent.com/deepset-ai/haystack/master/haystack/json-schemas/haystack-pipeline-1.4.0.schema.json"
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}
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]
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},
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{
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"allOf": [
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{
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"properties": {
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"version": {
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"const": "1.4.0"
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}
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}
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},
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{
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"$ref": "https://raw.githubusercontent.com/deepset-ai/haystack/master/haystack/json-schemas/haystack-pipeline-1.4.0.schema.json"
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}
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]
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},
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{
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"allOf": [
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{
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@ -179,9 +179,13 @@ def print_eval_report(
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logger.warning("Pipelines with junctions are currently not supported.")
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return
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answer_nodes = {node for node, df in eval_result.node_results.items() if len(df[df["type"] == "answer"]) > 0}
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all_top_1_metrics = eval_result.calculate_metrics(doc_relevance_col=doc_relevance_col, simulated_top_k_reader=1)
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answer_top_1_metrics = {node: metrics for node, metrics in all_top_1_metrics.items() if node in answer_nodes}
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calculated_metrics = {
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"": eval_result.calculate_metrics(doc_relevance_col=doc_relevance_col),
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"_top_1": eval_result.calculate_metrics(doc_relevance_col=doc_relevance_col, simulated_top_k_reader=1),
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"_top_1": answer_top_1_metrics,
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" upper bound": eval_result.calculate_metrics(doc_relevance_col=doc_relevance_col, eval_mode="isolated"),
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}
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@ -242,7 +246,9 @@ def _format_wrong_examples_report(eval_result: EvaluationResult, n_wrong_example
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node: eval_result.wrong_examples(node, doc_relevance_col="gold_id_or_answer_match", n=n_wrong_examples)
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for node in eval_result.node_results.keys()
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}
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examples_formatted = {node: "\n".join(map(_format_wrong_example, examples)) for node, examples in examples.items()}
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examples_formatted = {
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node: "\n".join(map(_format_wrong_example, examples)) for node, examples in examples.items() if any(examples)
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}
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return "\n".join(map(_format_wrong_examples_node, examples_formatted.keys(), examples_formatted.values()))
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@ -305,6 +305,7 @@ EVAL_LABELS = [
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval(reader, retriever_with_docs, tmp_path):
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labels = EVAL_LABELS[:1]
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@ -357,6 +358,7 @@ def test_extractive_qa_eval(reader, retriever_with_docs, tmp_path):
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_multiple_queries(reader, retriever_with_docs, tmp_path):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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eval_result: EvaluationResult = pipeline.eval(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})
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@ -429,6 +431,7 @@ def test_extractive_qa_eval_multiple_queries(reader, retriever_with_docs, tmp_pa
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@pytest.mark.parametrize("retriever_with_docs", ["elasticsearch"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_labels_with_filters(reader, retriever_with_docs, tmp_path):
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labels = [
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# MultiLabel with filter that selects only the document about Carla
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@ -498,6 +501,7 @@ def test_extractive_qa_labels_with_filters(reader, retriever_with_docs, tmp_path
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_sas(reader, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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eval_result: EvaluationResult = pipeline.eval(
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@ -520,6 +524,7 @@ def test_extractive_qa_eval_sas(reader, retriever_with_docs):
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assert metrics["Reader"]["sas"] == pytest.approx(1.0)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_reader_eval_in_pipeline(reader):
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pipeline = Pipeline()
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pipeline.add_node(component=reader, name="Reader", inputs=["Query"])
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@ -537,6 +542,7 @@ def test_reader_eval_in_pipeline(reader):
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_doc_relevance_col(reader, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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eval_result: EvaluationResult = pipeline.eval(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})
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@ -553,6 +559,7 @@ def test_extractive_qa_eval_doc_relevance_col(reader, retriever_with_docs):
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_simulated_top_k_reader(reader, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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eval_result: EvaluationResult = pipeline.eval(
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@ -600,6 +607,7 @@ def test_extractive_qa_eval_simulated_top_k_reader(reader, retriever_with_docs):
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_simulated_top_k_retriever(reader, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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eval_result: EvaluationResult = pipeline.eval(labels=EVAL_LABELS, params={"Retriever": {"top_k": 5}})
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@ -651,6 +659,7 @@ def test_extractive_qa_eval_simulated_top_k_retriever(reader, retriever_with_doc
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_simulated_top_k_reader_and_retriever(reader, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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eval_result: EvaluationResult = pipeline.eval(labels=EVAL_LABELS, params={"Retriever": {"top_k": 10}})
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@ -709,6 +718,7 @@ def test_extractive_qa_eval_simulated_top_k_reader_and_retriever(reader, retriev
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_isolated(reader, retriever_with_docs):
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pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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eval_result: EvaluationResult = pipeline.eval(
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@ -738,6 +748,7 @@ def test_extractive_qa_eval_isolated(reader, retriever_with_docs):
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_wrong_examples(reader, retriever_with_docs):
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labels = [
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@ -785,6 +796,7 @@ def test_extractive_qa_eval_wrong_examples(reader, retriever_with_docs):
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_print_eval_report(reader, retriever_with_docs):
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labels = [
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@ -885,6 +897,7 @@ def test_faq_calculate_metrics(retriever_with_docs):
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_eval_translation(reader, retriever_with_docs):
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# FIXME it makes no sense to have DE->EN input and DE->EN output, right?
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@ -1017,8 +1030,7 @@ def test_qa_multi_retriever_pipeline_eval(document_store_with_docs, reader):
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@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_multi_retriever_pipeline_eval(document_store_with_docs, reader):
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def test_multi_retriever_pipeline_eval(document_store_with_docs):
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es_retriever = BM25Retriever(document_store=document_store_with_docs)
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dpr_retriever = DensePassageRetriever(document_store_with_docs)
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document_store_with_docs.update_embeddings(retriever=dpr_retriever)
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@ -64,6 +64,7 @@ def test_extractive_qa_answers_single_result(reader, retriever_with_docs):
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@pytest.mark.slow
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@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_extractive_qa_answers_with_translator(reader, retriever_with_docs, en_to_de_translator, de_to_en_translator):
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base_pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
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pipeline = TranslationWrapperPipeline(
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