mirrors/docling
1
0
Fork 0
mirror of https://github.com/DS4SD/docling.git synced 2025-07-26 20:14:47 +00:00
Code Issues Packages Projects Releases Wiki Activity

Introduce mean_score and low_score, consistent aggregate computations

Browse Source 
Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
This commit is contained in:
Christoph Auer 2025-05-06 16:40:02 +02:00
parent f9496e4a91
commit 2a6537289b
3 changed files with 62 additions and 29 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

71
docling/datamodel/base_models.py
View File

@ -315,29 +315,82 @@ class QualityGrade(str, Enum):
class PageConfidenceScores(BaseModel):
overall_score: ScoreValue = np.nan
parse_score: ScoreValue = np.nan
layout_score: ScoreValue = np.nan
table_score: ScoreValue = np.nan
ocr_score: ScoreValue = np.nan
@computed_field # type: ignore
@property
def grade(self) -> QualityGrade:
if self.overall_score < 0.5:
def _score_to_grade(self, score: ScoreValue) -> QualityGrade:
if score < 0.5:
return QualityGrade.POOR
elif self.overall_score < 0.8:
elif score < 0.8:
return QualityGrade.FAIR
elif self.overall_score < 0.9:
elif score < 0.9:
return QualityGrade.GOOD
elif self.overall_score >= 0.9:
elif score >= 0.9:
return QualityGrade.EXCELLENT
return QualityGrade.UNSPECIFIED
@computed_field # type: ignore
@property
def mean_grade(self) -> QualityGrade:
return self._score_to_grade(self.mean_score)
@computed_field # type: ignore
@property
def low_grade(self) -> QualityGrade:
return self._score_to_grade(self.low_score)
@computed_field # type: ignore
@property
def mean_score(self) -> ScoreValue:
return ScoreValue(
np.nanmean(
[
self.ocr_score,
self.table_score,
self.layout_score,
self.parse_score,
]
)
)
@computed_field # type: ignore
@property
def low_score(self) -> ScoreValue:
return ScoreValue(
np.nanquantile(
[
self.ocr_score,
self.table_score,
self.layout_score,
self.parse_score,
],
q=0.05,
)
)
class ConfidenceReport(PageConfidenceScores):
pages: Dict[int, PageConfidenceScores] = Field(
default_factory=lambda: defaultdict(PageConfidenceScores)
)
@computed_field # type: ignore
@property
def mean_score(self) -> ScoreValue:
return ScoreValue(
np.nanmean(
[c.mean_score for c in self.pages.values()],
)
)
@computed_field # type: ignore
@property
def low_score(self) -> ScoreValue:
return ScoreValue(
np.nanmean(
[c.low_score for c in self.pages.values()],
)
)

13
docling/models/page_assemble_model.py
View File

@ -153,17 +153,4 @@ class PageAssembleModel(BasePageModel):
elements=elements, headers=headers, body=body
)
# Aggregate page score
scores = conv_res.confidence.pages[page.page_no]
scores.overall_score = float(
np.nanmean(
[
scores.ocr_score,
scores.table_score,
scores.layout_score,
scores.parse_score,
]
)
)
yield page

7
docling/pipeline/standard_pdf_pipeline.py
View File

@ -269,13 +269,6 @@ class StandardPdfPipeline(PaginatedPipeline):
)
)
conv_res.confidence.overall_score = float(
np.nanquantile(
[c.overall_score for c in conv_res.confidence.pages.values()],
q=0.05, # overall score should relate to worst 5% of page scores.
)
)
return conv_res
@classmethod