fix scikit learn warning

main.py

@@ -41,17 +41,17 @@
     parser.add_argument('--identify_lang', action='store_true',
                         help="if true, should the language of each text be guessed, using langdetect (default is False)",
                         default=False)
-    parser.add_argument('--embedding', action="store", help="optional path to a Glove embedding to compute frequencies among a set of semantic neighbourgs (i.e., pseudo-paronyms)",
+    parser.add_argument('--embedding', action="store", help="optional path to a word2vec embedding in txt format to compute frequencies among a set of semantic neighbourgs (i.e., pseudo-paronyms)",
                         default=False)
     parser.add_argument('--neighbouring_size', action="store", help="size of semantic neighbouring in the embedding (n closest neighbours)",
-                        default=10)
+                        default=10, type=int)
     args = parser.parse_args()
 
     embeddedFreqs = False
     if args.embedding:
         print(".......loading embedding.......")
         args.absolute_freqs = True # we need absolute freqs as a basis for embedded frequencies
-        embeddings_dict = embed.load_glove_embeddings(args.embedding)
+        model = embed.load_embeddings(args.embedding)
         embeddedFreqs = True
 
     print(".......loading texts.......")
@@ -90,7 +90,7 @@
 
     if args.embedding:
         print(".......embedding counts.......")
-        myTexts = embed.get_embedded_counts(myTexts, feat_list, embeddings_dict, topn=args.neighbouring_size)
+        myTexts = embed.get_embedded_counts(myTexts, feat_list, model, topn=args.neighbouring_size)
 
     unique_texts = [text["name"] for text in myTexts]
 

requirements.txt

@@ -4,7 +4,7 @@ lxml>=4.9.1
 nltk>=3.6.6
 numpy>=1.26.4
 pybind11>=2.8.1
-scikit-learn>=1.2.1
+scikit-learn>=1.3.0
 scipy>=1.10.0
 six>=1.16.0
 tqdm>=4.64.1

superstyl/svm.py

@@ -110,7 +110,7 @@ def train_svm(train, test, cross_validate=None, k=10, dim_reduc=None, norms=True
 
     if kernel == "LinearSVC":
         # try a faster one
-        estimators.append(('model', sk.LinearSVC(class_weight=cw)))
+        estimators.append(('model', sk.LinearSVC(class_weight=cw, dual="auto")))
         # classif = sk.LinearSVC()
 
     else: