haystack/test/others/test_squad_data.py
Julian Risch 8cfeed095d
build: Remove mmh3 dependency (#4896)
* 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
2023-05-17 21:31:08 +02:00

141 lines
5.0 KiB
Python

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