Update dependencies (closes #56)

requirements.txt

@@ -1,16 +1,16 @@
-Owlready2~=0.44
+owlready2~=0.46
 argparse~=1.4.0
-pandas~=2.0.3
-numpy~=1.24.2
-gensim~=4.3.0
-scipy~=1.10.1
-scikit-learn~=1.2.1
-setuptools~=68.2.2
-requests~=2.31.0
+pandas~=2.2.2
+numpy~=1.26.4
+gensim~=4.3.2
+scipy~=1.12.0
+scikit-learn~=1.5.1
+setuptools~=70.2.0
+requests~=2.32.3
 tqdm~=4.66.1
-sparse_dot_topn~=0.3.4
-bioregistry~=0.10.6
+sparse-dot-topn~=1.1.3
+bioregistry~=0.11.10
 nltk~=3.8.1
-rapidfuzz~=2.13.7
+rapidfuzz~=3.9.4
 shortuuid~=1.0.11
-myst_parser~=2.0.0
+myst-parser~=2.0.0

text2term/config.py

@@ -1 +1 @@
-VERSION = "4.1.4"
+VERSION = "4.2.0"

text2term/syntactic_mapper.py

@@ -79,32 +79,31 @@ def compare_levenshtein(self, s1, s2):
         Calculates the normalized Levenshtein distance between s1 and s2.
         :return similarity between s1 and s2 as a float between 0 and 1
         """
-        similarity = rapidfuzz.string_metric.normalized_levenshtein(s1, s2)/100
+        similarity = rapidfuzz.distance.Levenshtein.normalized_similarity(s1, s2)
         return similarity
 
     def compare_jaro(self, s1, s2):
         """
         Calculates the Jaro similarity between s1 and s2.
         :return similarity between s1 and s2 as a float between 0 and 1
         """
-        similarity = rapidfuzz.string_metric.jaro_similarity(s1, s2)/100
+        similarity = rapidfuzz.distance.Jaro.normalized_similarity(s1, s2)
         return similarity
 
     def compare_jarowinkler(self, s1, s2):
         """
         Calculates the Jaro-Winkler similarity between s1 and s2.
         :return similarity between s1 and s2 as a float between 0 and 1
         """
-        similarity = rapidfuzz.string_metric.jaro_winkler_similarity(s1, s2)/100
+        similarity = rapidfuzz.distance.Jaro.normalized_similarity(s1, s2)
         return similarity
 
     def compare_indel(self, s1, s2):
         """
         Calculates the normalized Indel distance between s1 and s2.
-        See: https://maxbachmann.github.io/RapidFuzz/Usage/fuzz.html#ratio
         :return similarity between s1 and s2 as a float between 0 and 1
         """
-        similarity = rapidfuzz.fuzz.ratio(s1, s2)/100
+        similarity = rapidfuzz.distance.Indel.normalized_similarity(s1, s2)
         return similarity
 
     def compare_fuzzy_ratio(self, s1, s2):