From 244ec626db8612b9f9a2f7d2fab1f7db048d9d3d Mon Sep 17 00:00:00 2001
From: Charlotte Mauvezin <charlotte.mauvezin@irht.cnrs.fr>
Date: Wed, 22 Dec 2021 15:22:53 +0100
Subject: [PATCH] Adding verbose option lunched by adding -v
---
nerval/evaluate.py | 33 ++++++++++++++++++++++++++++++---
1 file changed, 30 insertions(+), 3 deletions(-)
diff --git a/nerval/evaluate.py b/nerval/evaluate.py
index 6e2fef9..60c9a99 100644
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
@@ -485,7 +485,24 @@ def print_results(scores: dict):
tt.print(results, header, style=tt.styles.markdown)
-def run(annotation: str, prediction: str, threshold: int) -> dict:
+def print_result_compact(scores: dict):
+ result = []
+ header = ["tag", "predicted", "matched", "Precision", "Recall", "F1", "Support"]
+ result.append(
+ [
+ "ALl",
+ scores["All"]["predicted"],
+ scores["All"]["matched"],
+ round(scores["All"]["P"], 3),
+ round(scores["All"]["R"], 3),
+ round(scores["All"]["F1"], 3),
+ scores["All"]["Support"],
+ ]
+ )
+ tt.print(result, header, style=tt.styles.markdown)
+
+
+def run(annotation: str, prediction: str, threshold: int, verbose: bool) -> dict:
"""Compute recall and precision for each entity type found in annotation and/or prediction.
Each measure is given at document level, global score is a micro-average across entity types.
@@ -528,7 +545,10 @@ def run(annotation: str, prediction: str, threshold: int) -> dict:
scores = compute_scores(annot["entity_count"], predict["entity_count"], matches)
# Print results
- print_results(scores)
+ if verbose:
+ print_results(scores)
+ else:
+ print_result_compact(scores)
return scores
@@ -564,9 +584,16 @@ def main():
default=THRESHOLD,
type=threshold_float_type,
)
+ parser.add_argument(
+ "-v",
+ "--verbose",
+ help="Print only the recap if False",
+ action="store_false",
+ )
+
args = parser.parse_args()
- run(args.annot, args.predict, args.threshold)
+ run(args.annot, args.predict, args.threshold, args.verbose)
if __name__ == "__main__":
--
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