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203 lines
9.4 KiB
Python
203 lines
9.4 KiB
Python
import logging
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import pandas as pd
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import pytest
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from haystack.schema import Document, Answer
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from haystack.pipelines.base import Pipeline
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True)
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def test_table_reader(table_reader_and_param):
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table_reader, param = table_reader_and_param
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data = {
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"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
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"age": ["58", "47", "60"],
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"number of movies": ["87", "53", "69"],
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"date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
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}
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table = pd.DataFrame(data)
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data2 = {
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"actors": ["chris pratt", "gal gadot", "oprah winfrey"],
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"age": ["45", "36", "65"],
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"number of movies": ["49", "34", "5"],
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"date of birth": ["12 january 1975", "5 april 1980", "15 september 1960"],
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}
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table2 = pd.DataFrame(data2)
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query = "When was Di Caprio born?"
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prediction = table_reader.predict(
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query=query,
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documents=[Document(content=table, content_type="table"), Document(content=table2, content_type="table")],
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)
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scores = {"tapas_small": 1.0, "rci": -6.5301, "tapas_scored": 0.50568}
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assert prediction["answers"][0].score == pytest.approx(scores[param], rel=1e-3)
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assert prediction["answers"][0].answer == "11 november 1974"
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assert prediction["answers"][0].offsets_in_context[0].start == 7
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assert prediction["answers"][0].offsets_in_context[0].end == 8
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predictions = {
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"tapas_small": {"answer": "5 april 1980", "start": 7, "end": 8, "score": 0.86314},
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"rci": {"answer": "47", "start": 5, "end": 6, "score": -6.836},
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"tapas_scored": {"answer": "brad pitt", "start": 0, "end": 1, "score": 0.49078},
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}
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assert prediction["answers"][1].score == pytest.approx(predictions[param]["score"], rel=1e-3)
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assert prediction["answers"][1].answer == predictions[param]["answer"]
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assert prediction["answers"][1].offsets_in_context[0].start == predictions[param]["start"]
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assert prediction["answers"][1].offsets_in_context[0].end == predictions[param]["end"]
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True)
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def test_table_reader_batch_single_query_single_doc_list(table_reader_and_param):
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table_reader, param = table_reader_and_param
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data = {
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"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
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"age": ["58", "47", "60"],
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"number of movies": ["87", "53", "69"],
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"date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
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}
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table = pd.DataFrame(data)
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query = "When was Di Caprio born?"
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prediction = table_reader.predict_batch(queries=[query], documents=[Document(content=table, content_type="table")])
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# Expected output: List of lists of answers
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assert isinstance(prediction["answers"], list)
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assert isinstance(prediction["answers"][0], list)
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assert isinstance(prediction["answers"][0][0], Answer)
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assert len(prediction["answers"]) == 1 # Predictions for 5 docs
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True)
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def test_table_reader_batch_single_query_multiple_doc_lists(table_reader_and_param):
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table_reader, param = table_reader_and_param
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data = {
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"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
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"age": ["58", "47", "60"],
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"number of movies": ["87", "53", "69"],
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"date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
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}
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table = pd.DataFrame(data)
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query = "When was Di Caprio born?"
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prediction = table_reader.predict_batch(
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queries=[query], documents=[[Document(content=table, content_type="table")]]
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)
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# Expected output: List of lists of answers
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assert isinstance(prediction["answers"], list)
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assert isinstance(prediction["answers"][0], list)
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assert isinstance(prediction["answers"][0][0], Answer)
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assert len(prediction["answers"]) == 1 # Predictions for 1 collection of docs
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True)
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def test_table_reader_batch_multiple_queries_single_doc_list(table_reader_and_param):
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table_reader, param = table_reader_and_param
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data = {
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"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
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"age": ["58", "47", "60"],
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"number of movies": ["87", "53", "69"],
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"date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
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}
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table = pd.DataFrame(data)
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query = "When was Di Caprio born?"
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prediction = table_reader.predict_batch(
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queries=[query, query], documents=[Document(content=table, content_type="table")]
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)
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# Expected output: List of lists of lists of answers
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assert isinstance(prediction["answers"], list)
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assert isinstance(prediction["answers"][0], list)
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assert isinstance(prediction["answers"][0][0], list)
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assert isinstance(prediction["answers"][0][0][0], Answer)
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assert len(prediction["answers"]) == 2 # Predictions for 2 queries
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True)
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def test_table_reader_batch_multiple_queries_multiple_doc_lists(table_reader_and_param):
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table_reader, param = table_reader_and_param
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data = {
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"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
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"age": ["58", "47", "60"],
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"number of movies": ["87", "53", "69"],
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"date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
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}
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table = pd.DataFrame(data)
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query = "When was Di Caprio born?"
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prediction = table_reader.predict_batch(
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queries=[query, query],
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documents=[[Document(content=table, content_type="table")], [Document(content=table, content_type="table")]],
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)
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# Expected output: List of lists answers
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assert isinstance(prediction["answers"], list)
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assert isinstance(prediction["answers"][0], list)
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assert isinstance(prediction["answers"][0][0], Answer)
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assert len(prediction["answers"]) == 2 # Predictions for 2 collections of documents
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True)
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def test_table_reader_in_pipeline(table_reader_and_param):
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table_reader, param = table_reader_and_param
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pipeline = Pipeline()
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pipeline.add_node(table_reader, "TableReader", ["Query"])
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data = {
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"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
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"age": ["58", "47", "60"],
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"number of movies": ["87", "53", "69"],
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"date of birth": ["18 december 1963", "11 november 1974", "6 may 1961"],
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}
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table = pd.DataFrame(data)
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query = "When was Di Caprio born?"
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prediction = pipeline.run(query=query, documents=[Document(content=table, content_type="table")])
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assert prediction["answers"][0].answer == "11 november 1974"
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assert prediction["answers"][0].offsets_in_context[0].start == 7
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assert prediction["answers"][0].offsets_in_context[0].end == 8
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_base"], indirect=True)
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def test_table_reader_aggregation(table_reader_and_param):
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table_reader, param = table_reader_and_param
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data = {
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"Mountain": ["Mount Everest", "K2", "Kangchenjunga", "Lhotse", "Makalu"],
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"Height": ["8848m", "8,611 m", "8 586m", "8 516 m", "8,485m"],
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}
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table = pd.DataFrame(data)
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query = "How tall are all mountains on average?"
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prediction = table_reader.predict(query=query, documents=[Document(content=table, content_type="table")])
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assert prediction["answers"][0].score == pytest.approx(1.0)
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assert prediction["answers"][0].answer == "8609.2 m"
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assert prediction["answers"][0].meta["aggregation_operator"] == "AVERAGE"
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assert prediction["answers"][0].meta["answer_cells"] == ["8848m", "8,611 m", "8 586m", "8 516 m", "8,485m"]
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query = "How tall are all mountains together?"
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prediction = table_reader.predict(query=query, documents=[Document(content=table, content_type="table")])
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assert prediction["answers"][0].score == pytest.approx(1.0)
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assert prediction["answers"][0].answer == "43046.0 m"
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assert prediction["answers"][0].meta["aggregation_operator"] == "SUM"
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assert prediction["answers"][0].meta["answer_cells"] == ["8848m", "8,611 m", "8 586m", "8 516 m", "8,485m"]
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True)
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def test_table_without_rows(caplog, table_reader_and_param):
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table_reader, param = table_reader_and_param
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# empty DataFrame
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table = pd.DataFrame()
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document = Document(content=table, content_type="table", id="no_rows")
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with caplog.at_level(logging.WARNING):
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predictions = table_reader.predict(query="test", documents=[document])
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assert "Skipping document with id 'no_rows'" in caplog.text
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assert len(predictions["answers"]) == 0
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@pytest.mark.parametrize("table_reader_and_param", ["tapas_small", "rci", "tapas_scored"], indirect=True)
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def test_text_document(caplog, table_reader_and_param):
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table_reader, param = table_reader_and_param
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document = Document(content="text", id="text_doc")
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with caplog.at_level(logging.WARNING):
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predictions = table_reader.predict(query="test", documents=[document])
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assert "Skipping document with id 'text_doc'" in caplog.text
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assert len(predictions["answers"]) == 0
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