Feat: Added Beck with first comment test

BECK/app.py

@@ -5,44 +5,30 @@
 
 from preprocessing_service import Preprocesamiento
 from model_word2vec_service import ModelWord2Vec
-#import nltk
-#nltk.download('punkt')
+import nltk
+nltk.download('punkt')
 
 # Leer información del archivo items.json y de comentarios test
-comments_array = list(open('./comentarios_test.txt', 'r', encoding='utf-8').readlines())
+comments_array = list(open('./comentarios_test.txt',
+                      'r', encoding='utf-8').readlines())
 
 # Leer información del archivo item_preprocess.json
 beck_data_preprocessing = {}
 try:
-  if open('./JSON/items_preprocessing.json', 'r'):
-    beck_data_preprocessing = json.loads(open('./JSON/items_preprocessing.json', 'r', encoding='utf-8').read())
+    if open('./JSON/items_preprocessing.json', 'r'):
+        beck_data_preprocessing = json.loads(
+            open('./JSON/items_preprocessing.json', 'r', encoding='utf-8').read())
 except Exception as e:
-  print(f'Error: {e}')  
+    print(f'Error: {e}')
 
 
+preprocesamiento = Preprocesamiento()
 # Vector Space Embedding
 w2v = ModelWord2Vec()
-preprocesamiento = Preprocesamiento()
-
-comment_test = preprocesamiento.preprocesamiento_con_ortografia(comments_array[0])
-
-#w2v.add_corpus('null')
-
-
-array_item = []
 
-item_string = 'Tristeza'
-for key in beck_data_preprocessing[item_string].keys():
-  array_item.append(beck_data_preprocessing[item_string][key]["data"])
+comment_test = preprocesamiento.preprocesamiento_con_ortografia(
+    comments_array[0])
 
-print(comment_test)
-i = 0
-for item in array_item:
-  coseno = w2v.get_cosine_distance(item, comment_test) 
-  print(f'{item_string} - Item BECK {i} distancia coseno: ${coseno}')
-  i += 1
-i = 0
-for item in array_item:
-  euclidian = w2v.get_euclidian_distance(item, comment_test)
-  print(f'{item_string} - Item BECK {i} distancia euclidiana: ${euclidian}')
-  i += 1
+# Get Vector Beck
+result = w2v.getVectorBeck(comment_test, beck_data_preprocessing)
+print(result)

BECK/model_word2vec_service.py

@@ -77,4 +77,18 @@ def get_word_vector(self, word):
     :param word: La palabra cuya representación vectorial desea obtener
     :return: La palabra vector para la palabra.
     """
-    return self.model.wv[word]
+    return self.model.wv[word]
+
+  def getVectorBeck(self, commentVector, beck):
+    array = []
+    for item in beck.keys():
+      for idx, result in enumerate(beck[item].keys()): 
+        if idx == 0:
+          itemBeck = beck[item][result]
+          print(itemBeck)
+        if( self.get_cosine_distance(commentVector, itemBeck["data"]) < 
+        self.get_cosine_distance(commentVector, beck[item][result]["data"])):
+          itemBeck = beck[item][result]
+      array.append(itemBeck['value'])   
+    return array
+