add suggestions from PR

column2vec/src/column2vec.py

@@ -60,7 +60,6 @@ def get_cache(self, key: str, function: str) -> list | None:
             tmp = self.__cache.loc[function, key]
             if (tmp != "nan" and tmp is not int) or (tmp is int and not math.isnan(tmp)):
                 return json.loads(tmp)  # json is faster than ast
-        # print(f"NO CACHE key: {key}, function: {function}")
         return None
 
     def save(

similarity_framework/main.py

@@ -22,10 +22,6 @@
 
 
 def create_metadata(data):
-    """
-    This function creates metadata
-    :return created metadata
-    """
     return (TypeMetadataCreator(data).compute_advanced_structural_types().compute_column_kind().compute_column_names_embeddings()).get_metadata()
 
 
@@ -43,14 +39,18 @@ def compare_datasets(path1, path2):
     metadata2 = create_metadata(data2)
     comparator_by_column = (
         ComparatorByColumn()
+        ## different option
         # .add_comparator_type(SizeComparatorByColumn())
         .add_comparator_type(IncompleteColumnsComparatorByColumn()).add_comparator_type(ColumnNamesEmbeddingsComparatorByColumn())
+        ## different option
         # .add_comparator_type(ColumnKindHandler())
     )
     compartor = (
         ComparatorByType()
+        ## different option
         # .add_comparator_type(SizeHandler())
         .add_comparator_type(IncompleteColumnsHandler())
+        ## different option
         # .add_comparator_type(KindHandler())
         .add_comparator_type(ColumnNamesEmbeddingsHandler())
     )

similarity_framework/src/impl/comparator/comparator_by_column.py

@@ -185,6 +185,7 @@ def compute_embeddings_distance(self, embeddings1, embeddings2) -> float:  # tod
         :param embeddings2: values for column2
         :return: float from 0 to 1
         """
+        # alternative version
         # res = pd.DataFrame()
         # row_mins = []
         # for id1, embed1 in enumerate(embeddings1):
@@ -273,7 +274,7 @@ def compare_constants(
             value: float = 0 if metadata1.value == metadata2.value else 1
         else:
             value = 1 - cosine_sim(
-                metadata1.value_embeddings[0],  # todo 0 nebo 1
+                metadata1.value_embeddings[0],
                 metadata2.value_embeddings[0],
             )
         # if nulls are equal and exist

similarity_framework/src/impl/comparator/distance_functions.py

@@ -32,6 +32,7 @@ def compute(self, distance_table: pd.DataFrame) -> float:
         if distance_table.size == 0:
             return np.nan
         row_avg = distance_table.min(axis=1)
+        # alternative add these lines
         # column_avg = distance_table.min(axis=0)
         # return min(row_avg.mean(), column_avg.mean())
         return row_avg.mean()

similarity_framework/src/impl/comparator/utils.py

@@ -20,7 +20,7 @@ def concat(*data_frames: pd.DataFrame) -> pd.DataFrame:
     return res.map(lambda x: x / len(data_frames))
 
 
-def cosine_sim(u: list | Tensor, v: list | Tensor) -> float:  # todo move to functions.py?
+def cosine_sim(u: list | Tensor, v: list | Tensor) -> float:
     """
     Compute cosine similarity (range 0 to 1) 1 teh same 0 completely different
     :param u: embeddings 1
@@ -30,7 +30,7 @@ def cosine_sim(u: list | Tensor, v: list | Tensor) -> float:  # todo move to fun
     return round(
         np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v)),
         3,
-    )  # todo change rounding to 4, 5 6 ...etc
+    )
 
 
 def get_ratio(count1: int, count2: int) -> float:

similarity_framework/src/models/metadata.py

@@ -114,7 +114,6 @@ def __init__(
         self.nulls = null_values
         self.value = value
         self.distribution = distribution
-        # model.encode(list(value)).view(-1, 1)
         self.value_embeddings = None if type(value[0]) is not str else model.encode(list(value))
 
     def __str__(self):

similarity_framework/src/models/similarity.py

@@ -13,7 +13,6 @@ class SimilarityConfiguration:
 @dataclass
 class SimilarityOutput:
     distance: float
-    # TODO: Thesis add other proper fields
 
 
 @dataclass

tests/column2vec/test_column2vec.py

@@ -15,6 +15,7 @@
 
 SKIP_CLUSTERS = True
 SKIP_SIMILAR = False
+# alternative model
 # MODEL = 'all-mpnet-base-v2'  # bert-base-nli-mean-tokens
 MODEL = 'bert-base-nli-mean-tokens'  #
 THIS_DIR = os.path.dirname(os.path.abspath(__file__))
@@ -32,7 +33,6 @@ def get_vectors(function, data):
     result = {}
     count = 1
     for key in data:
-        # print("Processing column: " + key + " " + str(round((count / len(data)) * 100, 2)) + "%")
         result[key] = function(data[key], SentenceTransformer(MODEL, tokenizer_kwargs={
         'clean_up_tokenization_spaces': True}), key)
         count += 1