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aa5935b357
This ticket ensures that CCT metric will not be sensitive to differences in whitespace (including newline). All whitespaces in string are changed to single space `" "` in both GT and PRED before the metric is computed. Additional changes in CHANGELOG due to auto-formatting.
792 lines
24 KiB
Python
792 lines
24 KiB
Python
import re
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import pytest
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from unstructured.metrics import text_extraction
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from unstructured.metrics.table.table_extraction import (
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deckerd_table_to_html,
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extract_cells_from_table_as_cells,
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extract_cells_from_text_as_html,
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html_table_to_deckerd,
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)
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from unstructured.partition.auto import partition
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def test_calculate_edit_distance():
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source_cct = "I like pizza. I like bagels."
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source_cct_word_space = "I like p i z z a . I like bagles."
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source_cct_spaces = re.sub(r"\s+", " ", " ".join(source_cct))
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source_cct_no_space = source_cct.replace(" ", "")
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source_cct_one_sentence = "I like pizza."
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source_cct_missing_word = "I like pizza. I like ."
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source_cct_addn_char = "I like pizza. I like beagles."
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source_cct_dup_word = "I like pizza pizza. I like bagels."
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assert (
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round(text_extraction.calculate_edit_distance(source_cct, source_cct, return_as="score"), 2)
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== 1.0
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)
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assert (
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round(
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text_extraction.calculate_edit_distance(
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source_cct_word_space,
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source_cct,
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return_as="score",
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),
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2,
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)
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== 0.75
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)
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assert (
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round(
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text_extraction.calculate_edit_distance(
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source_cct_spaces,
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source_cct,
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return_as="score",
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),
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2,
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)
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== 0.39
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)
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assert (
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round(
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text_extraction.calculate_edit_distance(
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source_cct_no_space,
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source_cct,
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return_as="score",
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),
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2,
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)
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== 0.64
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)
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assert (
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round(
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text_extraction.calculate_edit_distance(
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source_cct_one_sentence,
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source_cct,
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return_as="score",
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),
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2,
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)
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== 0.0
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)
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assert (
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round(
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text_extraction.calculate_edit_distance(
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source_cct_missing_word,
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source_cct,
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return_as="score",
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),
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2,
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)
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== 0.57
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)
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assert (
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round(
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text_extraction.calculate_edit_distance(
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source_cct_addn_char,
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source_cct,
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return_as="score",
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),
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2,
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)
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== 0.89
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)
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assert (
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round(
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text_extraction.calculate_edit_distance(
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source_cct_dup_word,
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source_cct,
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return_as="score",
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),
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2,
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)
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== 0.79
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)
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@pytest.mark.parametrize(
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("filename", "standardize_whitespaces", "expected_score", "expected_distance"),
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[
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("fake-text.txt", False, 0.78, 38),
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("fake-text.txt", True, 0.92, 12),
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],
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)
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def test_calculate_edit_distance_with_filename(
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filename, standardize_whitespaces, expected_score, expected_distance
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):
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with open("example-docs/fake-text.txt") as f:
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source_cct = f.read()
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elements = partition(filename=f"example-docs/{filename}")
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output_cct = "\n".join([str(el) for el in elements])
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score = text_extraction.calculate_edit_distance(
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output_cct, source_cct, return_as="score", standardize_whitespaces=standardize_whitespaces
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)
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distance = text_extraction.calculate_edit_distance(
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output_cct,
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source_cct,
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return_as="distance",
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standardize_whitespaces=standardize_whitespaces,
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)
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assert score >= 0
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assert score <= 1.0
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assert distance >= 0
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assert round(score, 2) == expected_score
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assert distance == expected_distance
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@pytest.mark.parametrize(
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("text1", "text2"),
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[
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(
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"The dog\rloved the cat, but\t\n the cat\tloved the\n cow",
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"The dog loved the cat, but the cat loved the cow",
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),
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(
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"Hello my\tname\tis H a r p e r, \nwhat's your\vname?",
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"Hello my name is H a r p e r, what's your name?",
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),
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(
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"I have a\t\n\tdog and a\tcat,\fI love my\n\n\n\ndog.",
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"I have a dog and a cat, I love my dog.",
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),
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(
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"""
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Name Age City Occupation
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Alice 30 New York Engineer
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Bob 25 Los Angeles Designer
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Charlie 35 Chicago Teacher
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David 40 San Francisco Developer
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""",
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"""
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Name\tAge\tCity\tOccupation
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Alice\t30\tNew York\tEngineer
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Bob\t25\tLos Angeles\tDesigner
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Charlie\t35\tChicago\tTeacher
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David\t40\tSan Francisco\tDeveloper
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""",
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),
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(
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"""
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Name\tAge\tCity\tOccupation
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Alice\t30\tNew York\tEngineer
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Bob\t25\tLos Angeles\tDesigner
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Charlie\t35\tChicago\tTeacher
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David\t40\tSan Francisco\tDeveloper
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""",
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"Name\tAge\tCity\tOccupation\n\n \nAlice\t30\tNew York\tEngineer\nBob\t25\tLos Angeles\tDesigner\nCharlie\t35\tChicago\tTeacher\nDavid\t40\tSan Francisco\tDeveloper", # noqa: E501
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),
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],
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)
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def test_calculate_edit_distance_with_various_whitespace_1(text1, text2):
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assert (
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text_extraction.calculate_edit_distance(
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text1, text2, return_as="score", standardize_whitespaces=True
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)
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== 1.0
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)
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assert (
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text_extraction.calculate_edit_distance(
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text1, text2, return_as="distance", standardize_whitespaces=True
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)
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== 0
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)
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assert (
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text_extraction.calculate_edit_distance(
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text1, text2, return_as="score", standardize_whitespaces=False
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)
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< 1.0
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)
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assert (
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text_extraction.calculate_edit_distance(
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text1, text2, return_as="distance", standardize_whitespaces=False
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)
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> 0
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)
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def test_calculate_edit_distance_with_various_whitespace_2():
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source_cct_tabs = """
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Name\tAge\tCity\tOccupation
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Alice\t30\tNew York\tEngineer
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Bob\t25\tLos Angeles\tDesigner
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Charlie\t35\tChicago\tTeacher
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David\t40\tSan Francisco\tDeveloper
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"""
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source_cct_with_borders = """
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| Name | Age | City | Occupation |
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|---------|-----|--------------|----------------|
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| Alice | 30 | New York | Engineer |
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| Bob | 25 | Los Angeles | Designer |
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| Charlie | 35 | Chicago | Teacher |
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| David | 40 | San Francisco| Developer |
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"""
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assert text_extraction.calculate_edit_distance(
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source_cct_tabs, source_cct_with_borders, return_as="score", standardize_whitespaces=True
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) > text_extraction.calculate_edit_distance(
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source_cct_tabs, source_cct_with_borders, return_as="score", standardize_whitespaces=False
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)
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assert text_extraction.calculate_edit_distance(
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source_cct_tabs, source_cct_with_borders, return_as="distance", standardize_whitespaces=True
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) < text_extraction.calculate_edit_distance(
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source_cct_tabs,
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source_cct_with_borders,
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return_as="distance",
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standardize_whitespaces=False,
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)
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@pytest.mark.parametrize(
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("text", "expected"),
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[
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(
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"The dog loved the cat, but the cat loved the cow",
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{"the": 4, "cat": 2, "loved": 2, "dog": 1, "but": 1, "cow": 1},
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),
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(
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"Hello my name is H a r p e r, what's your name?",
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{"hello": 1, "my": 1, "name": 2, "is": 1, "what's": 1, "your": 1},
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),
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(
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"I have a dog and a cat, I love my dog.",
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{"i": 2, "have": 1, "a": 2, "dog": 2, "and": 1, "cat": 1, "love": 1, "my": 1},
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),
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(
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"My dog's hair is red, but the dogs' houses are blue.",
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{
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"my": 1,
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"dog's": 1,
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"hair": 1,
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"is": 1,
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"red": 1,
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"but": 1,
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"the": 1,
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"dogs'": 1,
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"houses": 1,
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"are": 1,
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"blue": 1,
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},
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),
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(
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"""Sometimes sentences have a dash - like this one!
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A hyphen connects 2 words with no gap: easy-peasy.""",
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{
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"sometimes": 1,
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"sentences": 1,
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"have": 1,
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"a": 2,
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"dash": 1,
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"like": 1,
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"this": 1,
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"one": 1,
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"hyphen": 1,
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"connects": 1,
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"2": 1,
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"words": 1,
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"with": 1,
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"no": 1,
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"gap": 1,
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"easy-peasy": 1,
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},
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),
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],
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)
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def test_bag_of_words(text, expected):
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assert text_extraction.bag_of_words(text) == expected
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@pytest.mark.parametrize(
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("text", "expected"),
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[
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(
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"The dog\rloved the cat, but\t\n the cat\tloved the\n cow\n\n",
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"The dog loved the cat, but the cat loved the cow",
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),
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(
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"\n\nHello my\tname\tis H a r p e r, \nwhat's your\vname?",
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"Hello my name is H a r p e r, what's your name?",
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),
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(
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"I have a\t\n\tdog and a\tcat,\fI love my\n\n\n\ndog.",
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"I have a dog and a cat, I love my dog.",
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),
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(
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"""L is for the way you look at me
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O is for the only one I see
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V is very, very extraordinary
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E is even more than anyone that you adore can""",
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"L is for the way you look at me O is for the only one I see V is very, very extraordinary E is even more than anyone that you adore can", # noqa: E501
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),
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(
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"""
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| Name | Age | City | Occupation |
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|---------|-----|--------------|----------------|
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| Alice | 30 | New York | Engineer |
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| Bob | 25 | Los Angeles | Designer |
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| Charlie | 35 | Chicago | Teacher |
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| David | 40 | San Francisco| Developer |
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""",
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"| Name | Age | City | Occupation | |---------|-----|--------------|----------------| | Alice | 30 | New York | Engineer | | Bob | 25 | Los Angeles | Designer | | Charlie | 35 | Chicago | Teacher | | David | 40 | San Francisco| Developer |", # noqa: E501
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),
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],
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)
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def test_prepare_string(text, expected):
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assert text_extraction.prepare_str(text, standardize_whitespaces=True) == expected
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assert text_extraction.prepare_str(text) == text
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@pytest.mark.parametrize(
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("output_text", "source_text", "expected_percentage"),
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[
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(
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"extra",
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"",
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0,
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),
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(
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"",
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"Source text has a sentence.",
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1,
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),
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(
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"The original s e n t e n c e is normal.",
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"The original sentence is normal...",
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0.2,
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),
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(
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"We saw 23% improvement in this quarter.",
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"We saw 23% improvement in sales this quarter.",
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0.125,
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),
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(
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"no",
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"Is it possible to have more than everything missing?",
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1,
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),
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],
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)
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def test_calculate_percent_missing_text(output_text, source_text, expected_percentage):
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assert (
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text_extraction.calculate_percent_missing_text(output_text, source_text)
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== expected_percentage
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)
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@pytest.mark.parametrize(
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("table_as_cells", "expected_extraction"),
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[
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pytest.param(
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[
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{"x": 0, "y": 0, "w": 1, "h": 1, "content": "Month A."},
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{"x": 0, "y": 1, "w": 1, "h": 1, "content": "22"},
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],
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[
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{"row_index": 0, "col_index": 0, "content": "Month A."},
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{"row_index": 1, "col_index": 0, "content": "22"},
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],
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id="Simple table, 1 head cell, 1 body cell, no spans",
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),
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pytest.param(
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[
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{"x": 0, "y": 0, "w": 1, "h": 1, "content": "Month A."},
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{"x": 1, "y": 0, "w": 1, "h": 1, "content": "Month B."},
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{"x": 2, "y": 0, "w": 1, "h": 1, "content": "Month C."},
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{"x": 0, "y": 1, "w": 1, "h": 1, "content": "11"},
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{"x": 1, "y": 1, "w": 1, "h": 1, "content": "12"},
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{"x": 2, "y": 1, "w": 1, "h": 1, "content": "13"},
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{"x": 0, "y": 2, "w": 1, "h": 1, "content": "21"},
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{"x": 1, "y": 2, "w": 1, "h": 1, "content": "22"},
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{"x": 2, "y": 2, "w": 1, "h": 1, "content": "23"},
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],
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[
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{"row_index": 0, "col_index": 0, "content": "Month A."},
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{"row_index": 0, "col_index": 1, "content": "Month B."},
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{"row_index": 0, "col_index": 2, "content": "Month C."},
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{"row_index": 1, "col_index": 0, "content": "11"},
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{"row_index": 1, "col_index": 1, "content": "12"},
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{"row_index": 1, "col_index": 2, "content": "13"},
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{"row_index": 2, "col_index": 0, "content": "21"},
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{"row_index": 2, "col_index": 1, "content": "22"},
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{"row_index": 2, "col_index": 2, "content": "23"},
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],
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id="Simple table, 3 head cell, 5 body cell, no spans",
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),
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# +----------+---------------------+----------+
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# | | h1col23 | h1col4 |
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# | h12col1 |----------+----------+----------|
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# | | h2col2 | h2col34 |
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# |----------|----------+----------+----------+
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# | r3col1 | r3col2 | |
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# |----------+----------| r34col34 |
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# | r4col12 | |
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# +----------+----------+----------+----------+
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pytest.param(
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[
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{
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"y": 0,
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"x": 0,
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"w": 2,
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"h": 1,
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"content": "h12col1",
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},
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{
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"y": 0,
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"x": 1,
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"w": 1,
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"h": 2,
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"content": "h1col23",
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},
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{
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"y": 0,
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"x": 3,
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"w": 1,
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"h": 1,
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"content": "h1col4",
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},
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{
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"y": 1,
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"x": 1,
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"w": 1,
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"h": 1,
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"content": "h2col2",
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},
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{
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"y": 1,
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"x": 2,
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"w": 1,
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"h": 2,
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"content": "h2col34",
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},
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{
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"y": 2,
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"x": 0,
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"w": 1,
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"h": 1,
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"content": "r3col1",
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},
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{
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"y": 2,
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"x": 1,
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"w": 1,
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"h": 1,
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"content": "r3col2",
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},
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{
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"y": 2,
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"x": 2,
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"w": 2,
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"h": 2,
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"content": "r34col34",
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},
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{
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"y": 3,
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"x": 0,
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"w": 1,
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"h": 2,
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"content": "r4col12",
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},
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],
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[
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{
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"row_index": 0,
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"col_index": 0,
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"content": "h12col1",
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},
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{
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"row_index": 0,
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"col_index": 1,
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"content": "h1col23",
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},
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{
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"row_index": 0,
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"col_index": 3,
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"content": "h1col4",
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},
|
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{
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"row_index": 1,
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"col_index": 1,
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"content": "h2col2",
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},
|
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{
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"row_index": 1,
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"col_index": 2,
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"content": "h2col34",
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},
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{
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"row_index": 2,
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"col_index": 0,
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|
"content": "r3col1",
|
|
},
|
|
{
|
|
"row_index": 2,
|
|
"col_index": 1,
|
|
"content": "r3col2",
|
|
},
|
|
{
|
|
"row_index": 2,
|
|
"col_index": 2,
|
|
"content": "r34col34",
|
|
},
|
|
{
|
|
"row_index": 3,
|
|
"col_index": 0,
|
|
"content": "r4col12",
|
|
},
|
|
],
|
|
id="various spans, with 2 row header",
|
|
),
|
|
],
|
|
)
|
|
def test_cells_table_extraction_from_prediction(table_as_cells, expected_extraction):
|
|
example_element = {
|
|
"type": "Table",
|
|
"metadata": {"table_as_cells": table_as_cells},
|
|
}
|
|
assert extract_cells_from_table_as_cells(example_element) == expected_extraction
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("text_as_html", "expected_extraction"),
|
|
[
|
|
pytest.param(
|
|
"""
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Month A.</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>22</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>"
|
|
""",
|
|
[
|
|
{"row_index": 0, "col_index": 0, "content": "Month A."},
|
|
{"row_index": 1, "col_index": 0, "content": "22"},
|
|
],
|
|
id="Simple table, 1 head cell, 1 body cell, no spans",
|
|
),
|
|
pytest.param(
|
|
"""
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Month A.</th>
|
|
<th>Month B.</th>
|
|
<th>Month C.</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>11</td>
|
|
<td>12</td>
|
|
<td>13</td>
|
|
</tr>
|
|
<tr>
|
|
<td>21</td>
|
|
<td>22</td>
|
|
<td>23</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>"
|
|
""",
|
|
[
|
|
{"row_index": 0, "col_index": 0, "content": "Month A."},
|
|
{"row_index": 0, "col_index": 1, "content": "Month B."},
|
|
{"row_index": 0, "col_index": 2, "content": "Month C."},
|
|
{"row_index": 1, "col_index": 0, "content": "11"},
|
|
{"row_index": 1, "col_index": 1, "content": "12"},
|
|
{"row_index": 1, "col_index": 2, "content": "13"},
|
|
{"row_index": 2, "col_index": 0, "content": "21"},
|
|
{"row_index": 2, "col_index": 1, "content": "22"},
|
|
{"row_index": 2, "col_index": 2, "content": "23"},
|
|
],
|
|
id="Simple table, 3 head cell, 5 body cell, no spans",
|
|
),
|
|
# +----------+---------------------+----------+
|
|
# | | h1col23 | h1col4 |
|
|
# | h12col1 |----------+----------+----------|
|
|
# | | h2col2 | h2col34 |
|
|
# |----------|----------+----------+----------+
|
|
# | r3col1 | r3col2 | |
|
|
# |----------+----------| r34col34 |
|
|
# | r4col12 | |
|
|
# +----------+----------+----------+----------+
|
|
pytest.param(
|
|
"""
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="2">h12col1</th>
|
|
<th colspan="2">h1col23</th>
|
|
<th>h1col4</th>
|
|
</tr>
|
|
<tr>
|
|
<th>h2col2</th>
|
|
<th colspan="2">h2col34</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>r3col1</td>
|
|
<td>r3col2</td>
|
|
<td colspan="2" rowspan="2">r34col34</td>
|
|
</tr>
|
|
<tr>
|
|
<td colspan="2">r4col12</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
""",
|
|
[
|
|
{
|
|
"row_index": 0,
|
|
"col_index": 0,
|
|
"content": "h12col1",
|
|
},
|
|
{
|
|
"row_index": 0,
|
|
"col_index": 1,
|
|
"content": "h1col23",
|
|
},
|
|
{
|
|
"row_index": 0,
|
|
"col_index": 3,
|
|
"content": "h1col4",
|
|
},
|
|
{
|
|
"row_index": 1,
|
|
"col_index": 1,
|
|
"content": "h2col2",
|
|
},
|
|
{
|
|
"row_index": 1,
|
|
"col_index": 2,
|
|
"content": "h2col34",
|
|
},
|
|
{
|
|
"row_index": 2,
|
|
"col_index": 0,
|
|
"content": "r3col1",
|
|
},
|
|
{
|
|
"row_index": 2,
|
|
"col_index": 1,
|
|
"content": "r3col2",
|
|
},
|
|
{
|
|
"row_index": 2,
|
|
"col_index": 2,
|
|
"content": "r34col34",
|
|
},
|
|
{
|
|
"row_index": 3,
|
|
"col_index": 0,
|
|
"content": "r4col12",
|
|
},
|
|
],
|
|
id="various spans, with 2 row header",
|
|
),
|
|
],
|
|
)
|
|
def test_html_table_extraction_from_prediction(text_as_html, expected_extraction):
|
|
example_element = {
|
|
"type": "Table",
|
|
"metadata": {
|
|
"text_as_html": text_as_html,
|
|
},
|
|
}
|
|
assert extract_cells_from_text_as_html(example_element) == expected_extraction
|
|
|
|
|
|
def test_cells_extraction_from_prediction_when_missing_prediction():
|
|
example_element = {"type": "Table", "metadata": {"text_as_html": "", "table_as_cells": []}}
|
|
assert extract_cells_from_text_as_html(example_element) is None
|
|
assert extract_cells_from_table_as_cells(example_element) is None
|
|
|
|
|
|
def _trim_html(html: str) -> str:
|
|
html_lines = [line.strip() for line in html.split("\n") if line]
|
|
return "".join(html_lines)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"html_to_test",
|
|
[
|
|
"""
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Month A.</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>22</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
""",
|
|
"""
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Month A.</th>
|
|
<th>Month B.</th>
|
|
<th>Month C.</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>11</td>
|
|
<td>12</td>
|
|
<td>13</td>
|
|
</tr>
|
|
<tr>
|
|
<td>21</td>
|
|
<td>22</td>
|
|
<td>23</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
""",
|
|
"""
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th rowspan="2">h12col1</th>
|
|
<th colspan="2">h1col23</th>
|
|
<th>h1col4</th>
|
|
</tr>
|
|
<tr>
|
|
<th>h2col2</th>
|
|
<th colspan="2">h2col34</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>r3col1</td>
|
|
<td>r3col2</td>
|
|
<td colspan="2" rowspan="2">r34col34</td>
|
|
</tr>
|
|
<tr>
|
|
<td colspan="2">r4col12</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
""",
|
|
],
|
|
)
|
|
def test_deckerd_html_converter(html_to_test):
|
|
deckerd_table = html_table_to_deckerd(html_to_test)
|
|
html_table = deckerd_table_to_html(deckerd_table)
|
|
assert _trim_html(html_to_test) == html_table
|