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3a03fce71c
* ruff settings enable ruff format and re-format outdated files feat: `EvaluationRunResult` add parameter to specify columns to keep in the comparative `Dataframe` (#7879) * adding param to explictily state which cols to keep * adding param to explictily state which cols to keep * adding param to explictily state which cols to keep * updating tests * adding release notes * Update haystack/evaluation/eval_run_result.py Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com> * Update releasenotes/notes/add-keep-columns-to-EvalRunResult-comparative-be3e15ce45de3e0b.yaml Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com> * updating docstring --------- Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com> add format-check fail on format and linting failures fix string formatting reformat long lines fix tests fix typing linter pull from main * reformat * lint -> check * lint -> check
318 lines
9.7 KiB
Python
318 lines
9.7 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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import pandas as pd
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import pytest
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from haystack import Document
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from haystack.dataclasses.byte_stream import ByteStream
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from haystack.dataclasses.sparse_embedding import SparseEmbedding
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@pytest.mark.parametrize(
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"doc,doc_str",
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[
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(Document(content="test text"), "content: 'test text'"),
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(
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Document(dataframe=pd.DataFrame([["John", 25], ["Martha", 34]], columns=["name", "age"])),
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"dataframe: (2, 2)",
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),
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(Document(blob=ByteStream(b"hello, test string")), "blob: 18 bytes"),
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(
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Document(
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content="test text",
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dataframe=pd.DataFrame([["John", 25], ["Martha", 34]], columns=["name", "age"]),
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blob=ByteStream(b"hello, test string"),
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),
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"content: 'test text', dataframe: (2, 2), blob: 18 bytes",
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),
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],
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)
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def test_document_str(doc, doc_str):
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assert f"Document(id={doc.id}, {doc_str})" == str(doc)
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def test_init():
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doc = Document()
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assert doc.id == "d4675c57fcfe114db0b95f1da46eea3c5d6f5729c17d01fb5251ae19830a3455"
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assert doc.content == None
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assert doc.dataframe == None
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assert doc.blob == None
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assert doc.meta == {}
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assert doc.score == None
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assert doc.embedding == None
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assert doc.sparse_embedding == None
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def test_init_with_wrong_parameters():
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with pytest.raises(TypeError):
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Document(text="")
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def test_init_with_parameters():
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blob_data = b"some bytes"
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sparse_embedding = SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3])
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doc = Document(
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content="test text",
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dataframe=pd.DataFrame([0]),
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blob=ByteStream(data=blob_data, mime_type="text/markdown"),
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meta={"text": "test text"},
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score=0.812,
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embedding=[0.1, 0.2, 0.3],
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sparse_embedding=sparse_embedding,
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)
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assert doc.id == "967b7bd4a21861ad9e863f638cefcbdd6bf6306bebdd30aa3fedf0c26bc636ed"
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assert doc.content == "test text"
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assert doc.dataframe is not None
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assert doc.dataframe.equals(pd.DataFrame([0]))
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assert doc.blob.data == blob_data
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assert doc.blob.mime_type == "text/markdown"
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assert doc.meta == {"text": "test text"}
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assert doc.score == 0.812
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assert doc.embedding == [0.1, 0.2, 0.3]
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assert doc.sparse_embedding == sparse_embedding
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def test_init_with_legacy_fields():
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doc = Document(
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content="test text",
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content_type="text",
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id_hash_keys=["content"],
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score=0.812,
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embedding=[0.1, 0.2, 0.3], # type: ignore
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)
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assert doc.id == "18fc2c114825872321cf5009827ca162f54d3be50ab9e9ffa027824b6ec223af"
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assert doc.content == "test text"
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assert doc.dataframe == None
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assert doc.blob == None
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assert doc.meta == {}
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assert doc.score == 0.812
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assert doc.embedding == [0.1, 0.2, 0.3]
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assert doc.sparse_embedding == None
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def test_init_with_legacy_field():
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doc = Document(
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content="test text",
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content_type="text", # type: ignore
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id_hash_keys=["content"], # type: ignore
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score=0.812,
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embedding=[0.1, 0.2, 0.3],
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meta={"date": "10-10-2023", "type": "article"},
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)
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assert doc.id == "a2c0321b34430cc675294611e55529fceb56140ca3202f1c59a43a8cecac1f43"
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assert doc.content == "test text"
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assert doc.dataframe == None
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assert doc.meta == {"date": "10-10-2023", "type": "article"}
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assert doc.score == 0.812
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assert doc.embedding == [0.1, 0.2, 0.3]
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assert doc.sparse_embedding == None
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def test_basic_equality_type_mismatch():
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doc = Document(content="test text")
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assert doc != "test text"
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def test_basic_equality_id():
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doc1 = Document(content="test text")
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doc2 = Document(content="test text")
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assert doc1 == doc2
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doc1.id = "1234"
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doc2.id = "5678"
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assert doc1 != doc2
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def test_to_dict():
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doc = Document()
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assert doc.to_dict() == {
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"id": doc._create_id(),
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"content": None,
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"dataframe": None,
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"blob": None,
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"score": None,
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"embedding": None,
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"sparse_embedding": None,
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}
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def test_to_dict_without_flattening():
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doc = Document()
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assert doc.to_dict(flatten=False) == {
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"id": doc._create_id(),
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"content": None,
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"dataframe": None,
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"blob": None,
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"meta": {},
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"score": None,
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"embedding": None,
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"sparse_embedding": None,
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}
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def test_to_dict_with_custom_parameters():
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doc = Document(
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content="test text",
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dataframe=pd.DataFrame([10, 20, 30]),
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blob=ByteStream(b"some bytes", mime_type="application/pdf"),
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meta={"some": "values", "test": 10},
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score=0.99,
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embedding=[10.0, 10.0],
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sparse_embedding=SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3]),
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)
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assert doc.to_dict() == {
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"id": doc.id,
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"content": "test text",
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"dataframe": pd.DataFrame([10, 20, 30]).to_json(),
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"blob": {"data": list(b"some bytes"), "mime_type": "application/pdf"},
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"some": "values",
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"test": 10,
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"score": 0.99,
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"embedding": [10.0, 10.0],
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"sparse_embedding": {"indices": [0, 2, 4], "values": [0.1, 0.2, 0.3]},
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}
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def test_to_dict_with_custom_parameters_without_flattening():
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doc = Document(
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content="test text",
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dataframe=pd.DataFrame([10, 20, 30]),
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blob=ByteStream(b"some bytes", mime_type="application/pdf"),
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meta={"some": "values", "test": 10},
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score=0.99,
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embedding=[10.0, 10.0],
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sparse_embedding=SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3]),
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)
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assert doc.to_dict(flatten=False) == {
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"id": doc.id,
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"content": "test text",
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"dataframe": pd.DataFrame([10, 20, 30]).to_json(),
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"blob": {"data": list(b"some bytes"), "mime_type": "application/pdf"},
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"meta": {"some": "values", "test": 10},
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"score": 0.99,
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"embedding": [10, 10],
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"sparse_embedding": {"indices": [0, 2, 4], "values": [0.1, 0.2, 0.3]},
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}
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def test_from_dict():
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assert Document.from_dict({}) == Document()
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def from_from_dict_with_parameters():
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blob_data = b"some bytes"
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assert Document.from_dict(
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{
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"content": "test text",
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"dataframe": pd.DataFrame([0]).to_json(),
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"blob": {"data": list(blob_data), "mime_type": "text/markdown"},
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"meta": {"text": "test text"},
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"score": 0.812,
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"embedding": [0.1, 0.2, 0.3],
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"sparse_embedding": {"indices": [0, 2, 4], "values": [0.1, 0.2, 0.3]},
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}
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) == Document(
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content="test text",
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dataframe=pd.DataFrame([0]),
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blob=ByteStream(blob_data, mime_type="text/markdown"),
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meta={"text": "test text"},
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score=0.812,
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embedding=[0.1, 0.2, 0.3],
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sparse_embedding=SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3]),
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)
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def test_from_dict_with_legacy_fields():
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assert Document.from_dict(
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{
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"content": "test text",
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"content_type": "text",
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"id_hash_keys": ["content"],
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"score": 0.812,
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"embedding": [0.1, 0.2, 0.3],
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}
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) == Document(
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content="test text",
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content_type="text",
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id_hash_keys=["content"],
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score=0.812,
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embedding=[0.1, 0.2, 0.3], # type: ignore
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)
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def test_from_dict_with_legacy_field_and_flat_meta():
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assert Document.from_dict(
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{
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"content": "test text",
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"content_type": "text",
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"id_hash_keys": ["content"],
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"score": 0.812,
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"embedding": [0.1, 0.2, 0.3],
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"date": "10-10-2023",
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"type": "article",
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}
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) == Document(
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content="test text",
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content_type="text", # type: ignore
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id_hash_keys=["content"], # type: ignore
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score=0.812,
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embedding=[0.1, 0.2, 0.3],
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meta={"date": "10-10-2023", "type": "article"},
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)
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def test_from_dict_with_flat_meta():
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blob_data = b"some bytes"
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assert Document.from_dict(
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{
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"content": "test text",
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"dataframe": pd.DataFrame([0]).to_json(),
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"blob": {"data": list(blob_data), "mime_type": "text/markdown"},
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"score": 0.812,
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"embedding": [0.1, 0.2, 0.3],
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"sparse_embedding": {"indices": [0, 2, 4], "values": [0.1, 0.2, 0.3]},
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"date": "10-10-2023",
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"type": "article",
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}
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) == Document(
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content="test text",
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dataframe=pd.DataFrame([0]),
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blob=ByteStream(blob_data, mime_type="text/markdown"),
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score=0.812,
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embedding=[0.1, 0.2, 0.3],
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sparse_embedding=SparseEmbedding(indices=[0, 2, 4], values=[0.1, 0.2, 0.3]),
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meta={"date": "10-10-2023", "type": "article"},
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)
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def test_from_dict_with_flat_and_non_flat_meta():
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with pytest.raises(ValueError, match="Pass either the 'meta' parameter or flattened metadata keys"):
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Document.from_dict(
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{
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"content": "test text",
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"dataframe": pd.DataFrame([0]).to_json(),
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"blob": {"data": list(b"some bytes"), "mime_type": "text/markdown"},
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"score": 0.812,
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"meta": {"test": 10},
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"embedding": [0.1, 0.2, 0.3],
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"date": "10-10-2023",
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"type": "article",
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}
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)
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def test_content_type():
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assert Document(content="text").content_type == "text"
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assert Document(dataframe=pd.DataFrame([0])).content_type == "table"
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with pytest.raises(ValueError):
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_ = Document().content_type
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with pytest.raises(ValueError):
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_ = Document(content="text", dataframe=pd.DataFrame([0])).content_type
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