Teste de model locale de Ollama et ajout du F1 Score dans les résultats du teste

testModel/metrics.py

@@ -37,4 +37,13 @@ def get_precision(confusion_matrix):
     denominator = (confusion_matrix[0][0] + confusion_matrix[0][1])
     if denominator == 0:
         return 0
-    return confusion_matrix[0][0] / denominator
\ No newline at end of file
+    return confusion_matrix[0][0] / denominator
+
+def get_f1_score(confusion_matrix):
+    precision = get_precision(confusion_matrix)
+    recall = get_tpr(confusion_matrix)
+
+    denominator = (precision + recall)
+    if denominator == 0:
+        return 0
+    return 2 * ((precision * recall) / denominator)
\ No newline at end of file

testModel/test.py

@@ -161,6 +161,7 @@ for length_category in LENGTH_CATEGORIES:
     print(f"True Positive Rate (TPR): {data['results'][length_category]['TPR']}")
     print(f"True Negative Rate (TNR): {data['results'][length_category]['TNR']}")
     print(f"Precision: {data['results'][length_category]['Precision']}")
+    print(f"F1 Score: {data['results'][length_category]['F1']}")
     print("---------------------------------")
 
 print(f"Name: ALL")
@@ -169,4 +170,5 @@ print(f"Result confusion matrix: {data['results']['ALL']['confusion matrix']}")
 print(f"True Positive Rate (TPR): {data['results']['ALL']['TPR']}")
 print(f"True Negative Rate (TNR): {data['results']['ALL']['TNR']}")
 print(f"Precision: {data['results']['ALL']['Precision']}")
+print(f"F1 Score: {data['results']['ALL']['F1']}")
 print()
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testModel/utils.py

@@ -4,7 +4,7 @@ import json
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
 
-from testModel.metrics import get_tpr, get_tnr, get_precision
+from testModel.metrics import get_tpr, get_tnr, get_precision, get_f1_score
 from variables.articles import LENGTH_CATEGORIES
 
 def get_dataset_filename(name):
@@ -96,7 +96,8 @@ def create_results():
         'confusion matrix': [[0, 0], [0, 0]],
         'TPR': 0,
         'TNR': 0,
-        'Precision': 0
+        'Precision': 0,
+        'F1': 0
     }
 
 def store_results(confusion_matrix):
@@ -104,5 +105,6 @@ def store_results(confusion_matrix):
         "confusion matrix": confusion_matrix,
         "TPR": get_tpr(confusion_matrix),
         "TNR": get_tnr(confusion_matrix),
-        "Precision": get_precision(confusion_matrix)
+        "Precision": get_precision(confusion_matrix),
+        "F1": get_f1_score(confusion_matrix)
     }
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variables/models.py

@@ -13,5 +13,7 @@ MODELS = {
     'MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli': {'predict': classify, 'isHuggingFace': True, 'pipline': create_classifier},
     'MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33': {'predict': classify, 'isHuggingFace': True, 'pipline': create_classifier},
     'MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli': {'predict': classify, 'isHuggingFace': True, 'pipline': create_classifier},
-    'llama3.2': {'predict': ollama.classify, 'isHuggingFace': False, 'pipline': None}
+    'llama3.2': {'predict': ollama.classify, 'isHuggingFace': False, 'pipline': None},
+    'mistral-small': {'predict': ollama.classify, 'isHuggingFace': False, 'pipline': None},
+    'deepseek-v2': {'predict': ollama.classify, 'isHuggingFace': False, 'pipline': None}
 }
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