I am implementing a multi-class classification machine learning model with Keras for NLP. I’m not sure whether this context is useful, but due to class imbalances, I created a class_weight array to be implemented in the model.fit() function as follows:

class_weight = {1: 3.,
                2: 2.,
                3: 1.5,
                4: 1.,
                5: 3.,
                6: 3.
                }

I then implemented my model.fit() function as follows:

history = model.fit(X_train, y_train,
                    epochs = 100,
                    verbose = False,
                    validation_data = (X_val, y_val),
                    batch_size = 10,
                    class_weight = class_weight,
                    callbacks = [early_stopping]
                   )

Running this, however, resulted with the following error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[583], line 9
      1 class_weight = {1: 3.,
      2                 2: 2.,
      3                 3: 1.5,
   (...)
      6                 6: 3.
      7                 }
----> 9 history = model.fit(X_train, y_train,
     10                     epochs = 100,
     11                     verbose = False,
     12                     validation_data = (X_val, y_val),
     13                     batch_size = 10,
     14                     class_weight = class_weight,
     15                     callbacks = [early_stopping]
     16                    )

File ~AppDataLocalanaconda3Libsite-packageskerassrcutilstraceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File ~AppDataLocalanaconda3Libsite-packagesnumpycorefromnumeric.py:3360, in round(a, decimals, out)
   3269 @array_function_dispatch(_round_dispatcher)
   3270 def round(a, decimals=0, out=None):
   3271     """
   3272     Evenly round to the given number of decimals.
   3273 
   (...)
   3358 
   3359     """
-> 3360     return _wrapfunc(a, 'round', decimals=decimals, out=out)

File ~AppDataLocalanaconda3Libsite-packagesnumpycorefromnumeric.py:68, in _wrapfunc(obj, method, *args, **kwds)
     59     return bound(*args, **kwds)
     60 except TypeError:
     61     # A TypeError occurs if the object does have such a method in its
     62     # class, but its signature is not identical to that of NumPy's. This
   (...)
     66     # Call _wrapit from within the except clause to ensure a potential
     67     # exception has a traceback chain.
---> 68     return _wrapit(obj, method, *args, **kwds)

File ~AppDataLocalanaconda3Libsite-packagesnumpycorefromnumeric.py:45, in _wrapit(obj, method, *args, **kwds)
     43 except AttributeError:
     44     wrap = None
---> 45 result = getattr(asarray(obj), method)(*args, **kwds)
     46 if wrap:
     47     if not isinstance(result, mu.ndarray):

TypeError: loop of ufunc does not support argument 0 of type str which has no callable rint method

The error is obviously in the model.fit() function, but I’m not sure where it originates and how to fix it. To try to alleviate the issue, I converted y_train and y_val into int types with astype(). However, this resulted in a different error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[587], line 9
      1 class_weight = {1: 3.,
      2                 2: 2.,
      3                 3: 1.5,
   (...)
      6                 6: 3.
      7                 }
----> 9 history = model.fit(X_train, y_train,
     10                     epochs = 100,
     11                     verbose = False,
     12                     validation_data = (X_val, y_val),
     13                     batch_size = 10,
     14                     class_weight = class_weight,
     15                     callbacks = [early_stopping]
     16                    )

File ~AppDataLocalanaconda3Libsite-packageskerassrcutilstraceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File ~AppDataLocalanaconda3Libsite-packagespandascoreseries.py:1040, in Series.__getitem__(self, key)
   1037     return self._values[key]
   1039 elif key_is_scalar:
-> 1040     return self._get_value(key)
   1042 # Convert generator to list before going through hashable part
   1043 # (We will iterate through the generator there to check for slices)
   1044 if is_iterator(key):

File ~AppDataLocalanaconda3Libsite-packagespandascoreseries.py:1156, in Series._get_value(self, label, takeable)
   1153     return self._values[label]
   1155 # Similar to Index.get_value, but we do not fall back to positional
-> 1156 loc = self.index.get_loc(label)
   1158 if is_integer(loc):
   1159     return self._values[loc]

File ~AppDataLocalanaconda3Libsite-packagespandascoreindexesbase.py:3798, in Index.get_loc(self, key)
   3793     if isinstance(casted_key, slice) or (
   3794         isinstance(casted_key, abc.Iterable)
   3795         and any(isinstance(x, slice) for x in casted_key)
   3796     ):
   3797         raise InvalidIndexError(key)
-> 3798     raise KeyError(key) from err
   3799 except TypeError:
   3800     # If we have a listlike key, _check_indexing_error will raise
   3801     #  InvalidIndexError. Otherwise we fall through and re-raise
   3802     #  the TypeError.
   3803     self._check_indexing_error(key)

KeyError: 0

Please advise!