From 91d68179402158cf9df33bc4fa737ab165d7b0fb Mon Sep 17 00:00:00 2001
From: finn <finn@hillengass.de>
Date: Thu, 29 Feb 2024 14:27:51 +0100
Subject: [PATCH] Fix handling of nan values
---
metrics/slot_accuracy/slot_accuracy.py | 11 ++++++++++-
1 file changed, 10 insertions(+), 1 deletion(-)
diff --git a/metrics/slot_accuracy/slot_accuracy.py b/metrics/slot_accuracy/slot_accuracy.py
index ca53d83..0e794da 100644
--- a/metrics/slot_accuracy/slot_accuracy.py
+++ b/metrics/slot_accuracy/slot_accuracy.py
@@ -55,9 +55,17 @@ def slot_accuracy(domain_knowledge, annotation):
Returns:
float: Slot accuracy score.
"""
+
+ if pd.isna(domain_knowledge) or pd.isna(annotation):
+ # Handle the nan values
+ return 0
+
+ domain_knowledge = domain_knowledge.replace('null', 'None')
+ annotation = annotation.replace('null', 'None')
+
domain_knowledge = ast.literal_eval(domain_knowledge)
annotation = ast.literal_eval(annotation)
-
+
count = {"True": 0, "False": 0, "Total": 0}
for detail in domain_knowledge["Details"]:
@@ -88,6 +96,7 @@ def main(input_file, output_file):
input_file (str): Path to the input CSV file.
output_file (str): Path to the output CSV file.
"""
+
df = pd.read_csv(input_file, sep=',', quoting=csv.QUOTE_NONE, escapechar='/', index_col=False)
df['slot_accuracy_original'] = df.apply(lambda row: slot_accuracy(row['Domain Knowledge'], row['original_annotation']), axis=1)
df['slot_accuracy_generated'] = df.apply(lambda row: slot_accuracy(row['Domain Knowledge'], row['generated_annotation']), axis=1)
--
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