Revert "[fix] DocString, parameter 힌트, 예외처리 추가 및 오타 수정"

This reverts commit 02b7879.

CATS/inputs.py

@@ -1,5 +1,5 @@
 from collections import namedtuple
-from typing import Literal
+
 DEFAULT_GROUP_NAME = "default_group"
 
 
@@ -8,21 +8,11 @@ class SparseFeat(namedtuple('SparseFeat',
                              'group_name'])):
     __slots__ = ()
 
-    def __new__(cls, name: str, vocabulary_size: int, embedding_dim=4, use_hash=False, dtype="int32", embedding_name=None,
+    def __new__(cls, name, vocabulary_size, embedding_dim=4, use_hash=False, dtype="int32", embedding_name=None,
                 group_name=DEFAULT_GROUP_NAME):
-        """
-        Returns information about a single categorical data.
-        :param name: feature's name
-        :param vocabulary_size: input category name
-        :param embedding_dim: Converted embedding's dimension
-        :param use_hash: whether to use hash
-        :param dtype: data's type
-        :param embedding_name: embedding's name
-        :param group_name: group's name
-        """
         if embedding_name is None:
             embedding_name = name
-        elif embedding_dim == 'auto':
+        elif embedding_name == 'auto':
             embedding_dim = 6 * int(pow(vocabulary_size, 0.25))
         if use_hash:
             raise NotImplementedError("Feature hashing is not supported in PyTorch version. "
@@ -31,109 +21,54 @@ def __new__(cls, name: str, vocabulary_size: int, embedding_dim=4, use_hash=Fals
                                               embedding_name, group_name)
 
     def __hash__(self):
-        """
-        Determines the hash value based on the name.
-        :return: self.name's hash
-        """
         return self.name.__hash__()
 
 
 class VarLenSparseFeat(namedtuple('VarLenSparseFeat',
                                   ['sparsefeat', 'maxlen', 'combiner', 'length_name'])):
     __slots__ = ()
 
-    def __new__(cls, sparsefeat: SparseFeat, maxlen: int, combiner: Literal['sum', 'mean', 'max'], length_name=None):
-        """
-
-        :param sparsefeat: a single categorical data's info namedtuple
-        :param maxlen: maximum categories length
-        :param combiner: combining method for features ('sum', 'mean', 'max')
-        :param length_name: feature length name
-        """
+    def __new__(cls, sparsefeat, maxlen, combiner="mean", length_name=None):
         return super(VarLenSparseFeat, cls).__new__(cls, sparsefeat, maxlen, combiner, length_name)
 
     @property
     def name(self):
-        """
-        VarLenSparseFeat's name
-        :return: sparsefeat.name
-        """
         return self.sparsefeat.name
 
     @property
     def vocabulary_size(self):
-        """
-        VarLenSparseFeat's vocabulary size
-        :return: sparsefeat.vocabulary_size
-        """
         return self.sparsefeat.vocabulary_size
 
     @property
     def embedding_dim(self):
-        """
-        VarLenSparseFeat's embedding dimension
-        :return: sparsefeat.embedding_dim
-        """
         return self.sparsefeat.embedding_dim
 
     @property
     def use_hash(self):
-        """
-         whether to use hash
-        :return: sparsefeat.use_hash
-        """
         return self.sparsefeat.use_hash
 
     @property
     def dtype(self):
-        """
-        data's type
-        :return: sparsefeat.dtype
-        """
         return self.sparsefeat.dtype
 
     @property
     def embedding_name(self):
-        """
-        embedding's name
-        :return: sparsefeat.embedding_name
-        """
         return self.sparsefeat.embedding_name
 
     @property
     def group_name(self):
-        """
-        group's name
-        :return: sparsefeat.group_name
-        """
         return self.sparsefeat.group_name
 
     def __hash__(self):
-        """
-        Determines the hash value based on the name.
-        :return: self.name's hash
-        """
         return self.name.__hash__()
 
 
 class DenseFeat(namedtuple('Dense',
                            ['name', 'dimension', 'dtype'])):
     __slots__ = ()
 
-    def __new__(cls, name: str, dimension=1, dtype="float32"):
-        """
-        Returns information about a numeric data.
-        :param name: numeric data's attribute name
-        :param dimension: dimension number
-        :param dtype: data's type
-        """
-        if dimension < 0 and not isinstance(dimension, int):
-            raise ValueError("dimension must bigger then 0 and must be integer ")
+    def __new__(cls, name, dimension=1, dtype="float32"):
         return super(DenseFeat, cls).__new__(cls, name, dimension, dtype)
 
     def __hash__(self):
-        """
-        Determines the hash value based on the name.
-        :return: self.name's hash
-        """
         return self.name.__hash__()