ran bigrams with lr 0.5 and nest 100 multi hot

gicraveiro · Feb 5, 2022 · cb5c24b · cb5c24b
1 parent 26eb63b
commit cb5c24b
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Showing 4 changed files with 9 additions and 9 deletions.
diff --git a/AIclassifier.py b/AIclassifier.py
@@ -57,7 +57,7 @@ def format_sentVector_to_SparseMatrix(vectors_list, dictionary):
         counts = Counter(sent_vector)
         for index, freq in counts.items():
             if len(counts.items()) > 0:
-                sparse_vector[index] = freq/len(sent_vector) # 1 DIFFERENT CONFIGURATION POSSIBILITIES
+                sparse_vector[index] = 1 #freq/len(sent_vector) #  DIFFERENT CONFIGURATION POSSIBILITIES
         if (i == 0): # TO DO: OPTIMIZE, NO NEED TO CHECK THIS EVERY TURN
             matrix_array = [sparse_vector]
         else:

diff --git a/output/AI Classifier/1Label_confusion_matrix_NonNorm.png b/output/AI Classifier/1Label_confusion_matrix_NonNorm.png
diff --git a/output/AI Classifier/1Label_confusion_matrix_NormTrue.png b/output/AI Classifier/1Label_confusion_matrix_NormTrue.png
diff --git a/output/AI Classifier/1labelPredictionsStatsTest.txt b/output/AI Classifier/1labelPredictionsStatsTest.txt
@@ -2,13 +2,13 @@ Performance measures - Bigram Dictionary - Adaboost
 
 Test set:
 
-Precision macro: 0.409
-Precision Individually: [0.833 0.    0.545 0.    0.667]
-Recall macro: 0.305
-Recall Individually: [0.357 0.    0.968 0.    0.2  ]
-F1 Score micro: 0.552
-F1 Score macro: 0.301
-F1 Score weighted: 0.473
-F1 Score Individually: [0.5   0.    0.698 0.    0.308]
+Precision macro: 0.275
+Precision Individually: [0.857 0.    0.517 0.    0.   ]
+Recall macro: 0.279
+Recall Individually: [0.429 0.    0.968 0.    0.   ]
+F1 Score micro: 0.537
+F1 Score macro: 0.249
+F1 Score weighted: 0.431
+F1 Score Individually: [0.571 0.    0.674 0.    0.   ]