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https://github.com/deepset-ai/haystack.git
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ffaa0249f7
* Add mean reciprocal rank and fix mean average precision * Add mrr metric to docstring * Fix mypy error
122 lines
5.7 KiB
Python
122 lines
5.7 KiB
Python
import pytest
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from haystack.document_store.base import BaseDocumentStore
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from haystack.finder import Finder
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@pytest.mark.elasticsearch
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def test_add_eval_data(document_store):
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# add eval data (SQUAD format)
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document_store.delete_all_documents(index="test_eval_document")
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document_store.delete_all_documents(index="test_feedback")
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document_store.add_eval_data(filename="samples/squad/small.json", doc_index="test_eval_document", label_index="test_feedback")
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assert document_store.get_document_count(index="test_eval_document") == 87
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assert document_store.get_label_count(index="test_feedback") == 1214
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# test documents
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docs = document_store.get_all_documents(index="test_eval_document")
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assert docs[0].text[:10] == "The Norman"
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assert docs[0].meta["name"] == "Normans"
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assert len(docs[0].meta.keys()) == 1
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# test labels
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labels = document_store.get_all_labels(index="test_feedback")
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assert labels[0].answer == "France"
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assert labels[0].no_answer == False
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assert labels[0].is_correct_answer == True
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assert labels[0].is_correct_document == True
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assert labels[0].question == 'In what country is Normandy located?'
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assert labels[0].origin == "gold_label"
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assert labels[0].offset_start_in_doc == 159
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# check combination
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assert labels[0].document_id == docs[0].id
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start = labels[0].offset_start_in_doc
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end = start+len(labels[0].answer)
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assert docs[0].text[start:end] == "France"
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# clean up
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document_store.delete_all_documents(index="test_eval_document")
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document_store.delete_all_documents(index="test_feedback")
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@pytest.mark.elasticsearch
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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def test_eval_reader(reader, document_store: BaseDocumentStore):
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# add eval data (SQUAD format)
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document_store.delete_all_documents(index="test_eval_document")
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document_store.delete_all_documents(index="test_feedback")
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document_store.add_eval_data(filename="samples/squad/tiny.json", doc_index="test_eval_document", label_index="test_feedback")
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assert document_store.get_document_count(index="test_eval_document") == 2
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# eval reader
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reader_eval_results = reader.eval(document_store=document_store, label_index="test_feedback",
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doc_index="test_eval_document", device="cpu")
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assert reader_eval_results["f1"] > 0.65
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assert reader_eval_results["f1"] < 0.67
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assert reader_eval_results["EM"] == 0.5
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assert reader_eval_results["top_n_accuracy"] == 1.0
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# clean up
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document_store.delete_all_documents(index="test_eval_document")
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document_store.delete_all_documents(index="test_feedback")
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@pytest.mark.elasticsearch
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@pytest.mark.parametrize("document_store", ["elasticsearch"], indirect=True)
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@pytest.mark.parametrize("open_domain", [True, False])
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@pytest.mark.parametrize("retriever", ["elasticsearch"], indirect=True)
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def test_eval_elastic_retriever(document_store: BaseDocumentStore, open_domain, retriever):
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# add eval data (SQUAD format)
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document_store.delete_all_documents(index="test_eval_document")
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document_store.delete_all_documents(index="test_feedback")
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document_store.add_eval_data(filename="samples/squad/tiny.json", doc_index="test_eval_document", label_index="test_feedback")
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assert document_store.get_document_count(index="test_eval_document") == 2
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# eval retriever
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results = retriever.eval(top_k=1, label_index="test_feedback", doc_index="test_eval_document", open_domain=open_domain)
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assert results["recall"] == 1.0
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assert results["mrr"] == 1.0
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if not open_domain:
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assert results["map"] == 1.0
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# clean up
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document_store.delete_all_documents(index="test_eval_document")
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document_store.delete_all_documents(index="test_feedback")
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@pytest.mark.elasticsearch
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@pytest.mark.parametrize("document_store", ["elasticsearch"], indirect=True)
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@pytest.mark.parametrize("reader", ["farm"], indirect=True)
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@pytest.mark.parametrize("retriever", ["elasticsearch"], indirect=True)
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def test_eval_finder(document_store: BaseDocumentStore, reader, retriever):
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finder = Finder(reader=reader, retriever=retriever)
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# add eval data (SQUAD format)
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document_store.delete_all_documents(index="test_eval_document")
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document_store.delete_all_documents(index="test_feedback")
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document_store.add_eval_data(filename="samples/squad/tiny.json", doc_index="test_eval_document", label_index="test_feedback")
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assert document_store.get_document_count(index="test_eval_document") == 2
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# eval finder
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results = finder.eval(label_index="test_feedback", doc_index="test_eval_document", top_k_retriever=1, top_k_reader=5)
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assert results["retriever_recall"] == 1.0
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assert results["retriever_map"] == 1.0
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assert abs(results["reader_topk_f1"] - 0.66666) < 0.001
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assert abs(results["reader_topk_em"] - 0.5) < 0.001
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assert abs(results["reader_topk_accuracy"] - 1) < 0.001
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assert results["reader_top1_f1"] <= results["reader_topk_f1"]
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assert results["reader_top1_em"] <= results["reader_topk_em"]
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assert results["reader_top1_accuracy"] <= results["reader_topk_accuracy"]
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# batch eval finder
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results_batch = finder.eval_batch(label_index="test_feedback", doc_index="test_eval_document", top_k_retriever=1,
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top_k_reader=5)
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assert results_batch["retriever_recall"] == 1.0
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assert results_batch["retriever_map"] == 1.0
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assert results_batch["reader_top1_f1"] == results["reader_top1_f1"]
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assert results_batch["reader_top1_em"] == results["reader_top1_em"]
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assert results_batch["reader_topk_accuracy"] == results["reader_topk_accuracy"]
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# clean up
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document_store.delete_all_documents(index="test_eval_document")
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document_store.delete_all_documents(index="test_feedback") |