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https://github.com/deepset-ai/haystack.git
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53decdcefb
* add filters attribute to labels and use in eval * Add latest docstring and tutorial changes * overwrite params if None * populate filters from Label to MultiLabel * add query_id in eval df and deepcopy params for each label * fix mypy * add test for aggregating filters in multilabel * use query ids also in answers df * loop through unique query_ids * hash filters and query text as id * Add latest docstring and tutorial changes * fix top_k reader eval * Apply Black * rename query_id to id/multilabel_id * Apply Black * json dump filters in dataframe * add filters and id to wrong_examples() * Apply Black Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Sara Zan <sara.zanzottera@deepset.ai>
238 lines
7.1 KiB
Python
238 lines
7.1 KiB
Python
from haystack.schema import Document, Label, Answer, Span, MultiLabel
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import pytest
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import numpy as np
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LABELS = [
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Label(
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query="some",
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answer=Answer(
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answer="an answer",
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type="extractive",
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score=0.1,
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document_id="123",
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offsets_in_document=[Span(start=1, end=3)],
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),
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document=Document(content="some text", content_type="text"),
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is_correct_answer=True,
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is_correct_document=True,
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origin="user-feedback",
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),
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Label(
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query="some",
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answer=Answer(answer="annother answer", type="extractive", score=0.1, document_id="123"),
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document=Document(content="some text", content_type="text"),
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is_correct_answer=True,
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is_correct_document=True,
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origin="user-feedback",
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),
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Label(
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query="some",
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answer=Answer(
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answer="an answer",
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type="extractive",
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score=0.1,
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document_id="123",
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offsets_in_document=[Span(start=1, end=3)],
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),
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document=Document(content="some text", content_type="text"),
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is_correct_answer=True,
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is_correct_document=True,
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origin="user-feedback",
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),
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]
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def test_no_answer_label():
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labels = [
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Label(
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query="question",
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answer=Answer(answer=""),
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is_correct_answer=True,
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is_correct_document=True,
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document=Document(content="some", id="777"),
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origin="gold-label",
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),
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Label(
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query="question",
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answer=Answer(answer=""),
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is_correct_answer=True,
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is_correct_document=True,
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document=Document(content="some", id="777"),
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no_answer=True,
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origin="gold-label",
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),
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Label(
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query="question",
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answer=Answer(answer="some"),
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is_correct_answer=True,
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is_correct_document=True,
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document=Document(content="some", id="777"),
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origin="gold-label",
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),
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Label(
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query="question",
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answer=Answer(answer="some"),
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is_correct_answer=True,
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is_correct_document=True,
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document=Document(content="some", id="777"),
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no_answer=False,
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origin="gold-label",
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),
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]
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assert labels[0].no_answer == True
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assert labels[1].no_answer == True
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assert labels[2].no_answer == False
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assert labels[3].no_answer == False
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def test_equal_label():
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assert LABELS[2] == LABELS[0]
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assert LABELS[1] != LABELS[0]
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def test_answer_to_json():
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a = Answer(
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answer="an answer",
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type="extractive",
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score=0.1,
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context="abc",
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offsets_in_document=[Span(start=1, end=10)],
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offsets_in_context=[Span(start=3, end=5)],
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document_id="123",
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)
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j = a.to_json()
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assert type(j) == str
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assert len(j) > 30
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a_new = Answer.from_json(j)
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assert type(a_new.offsets_in_document[0]) == Span
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assert a_new == a
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def test_answer_to_dict():
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a = Answer(
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answer="an answer",
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type="extractive",
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score=0.1,
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context="abc",
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offsets_in_document=[Span(start=1, end=10)],
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offsets_in_context=[Span(start=3, end=5)],
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document_id="123",
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)
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j = a.to_dict()
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assert type(j) == dict
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a_new = Answer.from_dict(j)
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assert type(a_new.offsets_in_document[0]) == Span
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assert a_new == a
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def test_label_to_json():
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j0 = LABELS[0].to_json()
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l_new = Label.from_json(j0)
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assert l_new == LABELS[0]
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def test_label_to_json():
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j0 = LABELS[0].to_json()
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l_new = Label.from_json(j0)
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assert l_new == LABELS[0]
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assert l_new.answer.offsets_in_document[0].start == 1
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def test_label_to_dict():
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j0 = LABELS[0].to_dict()
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l_new = Label.from_dict(j0)
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assert l_new == LABELS[0]
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assert l_new.answer.offsets_in_document[0].start == 1
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def test_doc_to_json():
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# With embedding
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d = Document(
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content="some text",
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content_type="text",
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score=0.99988,
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meta={"name": "doc1"},
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embedding=np.random.rand(768).astype(np.float32),
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)
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j0 = d.to_json()
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d_new = Document.from_json(j0)
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assert d == d_new
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# No embedding
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d = Document(content="some text", content_type="text", score=0.99988, meta={"name": "doc1"}, embedding=None)
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j0 = d.to_json()
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d_new = Document.from_json(j0)
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assert d == d_new
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def test_answer_postinit():
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a = Answer(answer="test", offsets_in_document=[{"start": 10, "end": 20}])
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assert a.meta == {}
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assert isinstance(a.offsets_in_document[0], Span)
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def test_generate_doc_id_using_text():
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text1 = "text1"
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text2 = "text2"
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doc1_text1 = Document(content=text1, meta={"name": "doc1"})
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doc2_text1 = Document(content=text1, meta={"name": "doc2"})
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doc3_text2 = Document(content=text2, meta={"name": "doc3"})
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assert doc1_text1.id == doc2_text1.id
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assert doc1_text1.id != doc3_text2.id
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def test_generate_doc_id_using_custom_list():
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text1 = "text1"
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text2 = "text2"
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doc1_meta1_id_by_content = Document(content=text1, meta={"name": "doc1"}, id_hash_keys=["content"])
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doc1_meta2_id_by_content = Document(content=text1, meta={"name": "doc2"}, id_hash_keys=["content"])
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assert doc1_meta1_id_by_content.id == doc1_meta2_id_by_content.id
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doc1_meta1_id_by_content_and_meta = Document(content=text1, meta={"name": "doc1"}, id_hash_keys=["content", "meta"])
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doc1_meta2_id_by_content_and_meta = Document(content=text1, meta={"name": "doc2"}, id_hash_keys=["content", "meta"])
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assert doc1_meta1_id_by_content_and_meta.id != doc1_meta2_id_by_content_and_meta.id
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doc1_text1 = Document(content=text1, meta={"name": "doc1"}, id_hash_keys=["content"])
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doc3_text2 = Document(content=text2, meta={"name": "doc3"}, id_hash_keys=["content"])
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assert doc1_text1.id != doc3_text2.id
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with pytest.raises(ValueError):
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_ = Document(content=text1, meta={"name": "doc1"}, id_hash_keys=["content", "non_existing_field"])
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def test_aggregate_labels_with_labels():
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label1_with_filter1 = Label(
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query="question",
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answer=Answer(answer="1"),
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is_correct_answer=True,
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is_correct_document=True,
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document=Document(content="some", id="777"),
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origin="gold-label",
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filters={"name": ["filename1"]},
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)
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label2_with_filter1 = Label(
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query="question",
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answer=Answer(answer="2"),
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is_correct_answer=True,
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is_correct_document=True,
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document=Document(content="some", id="777"),
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origin="gold-label",
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filters={"name": ["filename1"]},
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)
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label3_with_filter2 = Label(
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query="question",
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answer=Answer(answer="2"),
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is_correct_answer=True,
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is_correct_document=True,
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document=Document(content="some", id="777"),
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origin="gold-label",
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filters={"name": ["filename2"]},
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)
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label = MultiLabel(labels=[label1_with_filter1, label2_with_filter1])
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assert label.filters == {"name": ["filename1"]}
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with pytest.raises(ValueError):
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label = MultiLabel(labels=[label1_with_filter1, label3_with_filter2])
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