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* build: Remove mmh3 dependency * resolve circular import * pylint * make mmh3.py sibling of schema.py * pylint import order * pylint * undo example changes * increase coverage in modeling module * increase coverage further * rename new unit tests
141 lines
5.0 KiB
Python
141 lines
5.0 KiB
Python
import pandas as pd
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import pytest
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from haystack.utils.squad_data import SquadData
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from haystack.utils.augment_squad import augment_squad
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from haystack.schema import Document, Label, Answer
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def test_squad_augmentation(samples_path):
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input_ = samples_path / "squad" / "tiny.json"
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output = samples_path / "squad" / "tiny_augmented.json"
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glove_path = samples_path / "glove" / "tiny.txt" # dummy glove file, will not even be use when augmenting tiny.json
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multiplication_factor = 5
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augment_squad(
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model="distilbert-base-uncased",
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tokenizer="distilbert-base-uncased",
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squad_path=input_,
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output_path=output,
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glove_path=glove_path,
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multiplication_factor=multiplication_factor,
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)
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original_squad = SquadData.from_file(input_)
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augmented_squad = SquadData.from_file(output)
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assert original_squad.count(unit="paragraph") == augmented_squad.count(unit="paragraph") * multiplication_factor
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@pytest.mark.unit
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def test_squad_data_converts_df_to_data():
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df = pd.DataFrame(
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[["title", "context", "question", "id", "answer", 1, False]],
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columns=["title", "context", "question", "id", "answer_text", "answer_start", "is_impossible"],
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)
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expected_result = [
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{
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"title": "title",
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"paragraphs": [
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{
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"context": "context",
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"qas": [
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{
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"question": "question",
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"id": "id",
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"answers": [{"text": "answer", "answer_start": 1}],
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"is_impossible": False,
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}
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],
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}
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],
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}
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]
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result = SquadData.df_to_data(df)
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assert result == expected_result
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@pytest.mark.unit
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def test_squad_data_converts_data_to_df():
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data = [
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{
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"title": "title",
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"paragraphs": [
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{
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"context": "context",
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"document_id": "document_id",
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"qas": [
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{
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"question": "question",
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"id": "id",
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"answers": [{"text": "answer", "answer_start": 1}],
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"is_impossible": False,
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}
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],
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}
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],
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}
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]
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expected_result = pd.DataFrame(
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[["title", "context", "question", "id", "answer", 1, False, "document_id"]],
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columns=["title", "context", "question", "id", "answer_text", "answer_start", "is_impossible", "document_id"],
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)
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result = SquadData.to_df(data)
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assert result.equals(expected_result)
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def test_to_label_object():
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squad_data_list = [
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{
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"title": "title",
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"paragraphs": [
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{
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"context": "context",
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"qas": [
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{
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"question": "question",
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"id": "id",
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"answers": [{"text": "answer", "answer_start": 1}],
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"is_impossible": False,
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},
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{
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"question": "another question",
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"id": "another_id",
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"answers": [{"text": "this is the response", "answer_start": 1}],
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"is_impossible": False,
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},
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],
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},
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{
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"context": "the second paragraph context",
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"qas": [
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{
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"question": "the third question",
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"id": "id_3",
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"answers": [{"text": "this is another response", "answer_start": 1}],
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"is_impossible": False,
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},
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{
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"question": "the forth question",
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"id": "id_4",
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"answers": [{"text": "this is the response", "answer_start": 1}],
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"is_impossible": False,
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},
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],
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},
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],
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}
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]
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squad_data = SquadData(squad_data=squad_data_list)
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answer_type = "generative"
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labels = squad_data.to_label_objs(answer_type=answer_type)
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for label, expected_question in zip(labels, squad_data.df.iterrows()):
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expected_question = expected_question[1]
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assert isinstance(label, Label)
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assert isinstance(label.document, Document)
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assert isinstance(label.answer, Answer)
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assert label.query == expected_question["question"]
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assert label.document.content == expected_question.context
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assert label.document.id == expected_question.document_id
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assert label.id == expected_question.id
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assert label.answer.answer == expected_question.answer_text
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