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.
This PR exposes functions in evaluation module for easy conversion
between tables in Deckerd and HTML formats, which are useful in
evalution experiments.
Update to the evaluation script to handle correct HTML syntax for
tables.
See https://github.com/Unstructured-IO/unstructured-inference/pull/355
for details.
This change:
- modifies transforming HTML tables to evaluation internal `cells`
format
- fixes the indexing of the output (internal format cells) when HTML
cells use spans
This pull request add metrics that are calculated based on
table_as_cells instead of text_as_html. This change is required for
comprehensive metrics calculation, as previously every colspan or
rowspan predicted was considered to be an incorrect predicted (even if
it was correct prediction)
This change has to be merged after
https://github.com/Unstructured-IO/unstructured/pull/2892 which
introduces table_as_cells field
### Summary
Missing text is a particularly important metric of quality for the
Unstructured library because information from the document is not being
captured and therefore not usable by downstream applications.
Add function to calculate the percent of text missing relative to the
source transcription. Function takes 2 text strings (output and source)
as input, and returns the percentage of text missing as a decimal.
### Technical Details
- The 2 input strings are both assumed to already contain clean and
concatenated text (CCT)
- Implementation compares the bags of words (frequency counts for each
word present in the text) of each input text
- Duplicated/extra text is not penalized
- Value is limited to the range [0, 1]
### Test
- Several edge cases are covered in the test function (missing text,
duplicated text, spaced out words, etc).
- Can test other cases or text inputs by calling the function with 2 CCT
strings as "output" and "source"
This PR adds the `bag_of_words` function to count the frequency of words
for evaluation.
**Testing**
```Python
from unstructured.cleaners.core import bag_of_words
string = "The dog loved the cat, but the cat loved the cow."
print(bag_of_words)
---------
Co-authored-by: Mallori Harrell <mallori@Malloris-MacBook-Pro.local>
Co-authored-by: Klaijan <klaijan@unstructured.io>
Co-authored-by: Shreya Nidadavolu <shreyanid9@gmail.com>
Co-authored-by: shreyanid <42684285+shreyanid@users.noreply.github.com>
**Executive Summary**
Adds function to calculate edit distance (Levenshtein distance) between
two strings. The function can return as: 1. score (similarity = 1 -
distance/source_len) 2. distance (raw levenshtein distance)
**Technical details**
- The `weights` param is set to default at (2,1,1) for (insertion,
deletion, substitution), meaning that we will penalize the insertion we
need to add from output (target) in comparison with the source
(reference). In other word, the missing extraction will be penalized
higher.
- The function takes in 2 strings in an assumption that both string are
already clean and concatenated (CCT)
**Important Note!**
Test case needs to be updated to use CCT once the function is ready. It
is now only tested the "functionality" of edit distance, not the edit
distance with CCT as its intended to be.
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
