Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			WARNING: Actual dtype differs from dtype in FeatureMetadata for feature "loan_id". Actual dtype: int | Expected dtype: float

---------------------------------------------------------------------------
IntCastingNaNError                        Traceback (most recent call last)
Cell In[7], line 1
----> 1 predictor.predict(test_data)

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/autogluon/tabular/predictor/predictor.py:1931, in TabularPredictor.predict(self, data, model, as_pandas, transform_features, decision_threshold)
   1929 if decision_threshold is None:
   1930     decision_threshold = self.decision_threshold
-> 1931 return self._learner.predict(X=data, model=model, as_pandas=as_pandas, transform_features=transform_features, decision_threshold=decision_threshold)

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/autogluon/tabular/learner/abstract_learner.py:208, in AbstractTabularLearner.predict(self, X, model, as_pandas, inverse_transform, transform_features, decision_threshold)
    206     decision_threshold = 0.5
    207 X_index = copy.deepcopy(X.index) if as_pandas else None
--> 208 y_pred_proba = self.predict_proba(
    209     X=X, model=model, as_pandas=False, as_multiclass=False, inverse_transform=False, transform_features=transform_features
    210 )
    211 problem_type = self.label_cleaner.problem_type_transform or self.problem_type
    212 y_pred = get_pred_from_proba(y_pred_proba=y_pred_proba, problem_type=problem_type, decision_threshold=decision_threshold)

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/autogluon/tabular/learner/abstract_learner.py:188, in AbstractTabularLearner.predict_proba(self, X, model, as_pandas, as_multiclass, inverse_transform, transform_features)
    186 else:
    187     if transform_features:
--> 188         X = self.transform_features(X)
    189     y_pred_proba = self.load_trainer().predict_proba(X, model=model)
    190 y_pred_proba = self._post_process_predict_proba(
    191     y_pred_proba=y_pred_proba, as_pandas=as_pandas, index=X_index, as_multiclass=as_multiclass, inverse_transform=inverse_transform
    192 )

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/autogluon/tabular/learner/abstract_learner.py:464, in AbstractTabularLearner.transform_features(self, X)
    462 def transform_features(self, X):
    463     for feature_generator in self.feature_generators:
--> 464         X = feature_generator.transform(X)
    465     return X

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/autogluon/features/generators/abstract.py:351, in AbstractFeatureGenerator.transform(self, X)
    349 if self._pre_astype_generator:
    350     X = self._pre_astype_generator.transform(X)
--> 351 X_out = self._transform(X)
    352 if self._post_generators:
    353     X_out = self._transform_generators(X=X_out, generators=self._post_generators)

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/autogluon/features/generators/bulk.py:175, in BulkFeatureGenerator._transform(self, X)
    173 feature_df_list = []
    174 for generator in generator_group:
--> 175     feature_df_list.append(generator.transform(X))
    177 if not feature_df_list:
    178     X = DataFrame(index=X.index)

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/autogluon/features/generators/abstract.py:351, in AbstractFeatureGenerator.transform(self, X)
    349 if self._pre_astype_generator:
    350     X = self._pre_astype_generator.transform(X)
--> 351 X_out = self._transform(X)
    352 if self._post_generators:
    353     X_out = self._transform_generators(X=X_out, generators=self._post_generators)

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/autogluon/features/generators/astype.py:157, in AsTypeFeatureGenerator._transform(self, X)
    151             X[with_null_features] = X[with_null_features].fillna(0)
    153     if self._type_map_real_opt:
    154         # TODO: Confirm this works with sparse and other feature types!
    155         # FIXME: Address situation where test-time invalid type values cause crash:
    156         #  https://stackoverflow.com/questions/49256211/how-to-set-unexpected-data-type-to-na?noredirect=1&lq=1
--> 157         X = X.astype(self._type_map_real_opt)
    158 return X

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/generic.py:6513, in NDFrame.astype(self, dtype, copy, errors)
   6511 else:
   6512     try:
-> 6513         res_col = col.astype(dtype=cdt, copy=copy, errors=errors)
   6514     except ValueError as ex:
   6515         ex.args = (
   6516             f"{ex}: Error while type casting for column '{col_name}'",
   6517         )

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/generic.py:6534, in NDFrame.astype(self, dtype, copy, errors)
   6530     results = [ser.astype(dtype, copy=copy) for _, ser in self.items()]
   6532 else:
   6533     # else, only a single dtype is given
-> 6534     new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
   6535     res = self._constructor_from_mgr(new_data, axes=new_data.axes)
   6536     return res.__finalize__(self, method="astype")

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/internals/managers.py:414, in BaseBlockManager.astype(self, dtype, copy, errors)
    411 elif using_copy_on_write():
    412     copy = False
--> 414 return self.apply(
    415     "astype",
    416     dtype=dtype,
    417     copy=copy,
    418     errors=errors,
    419     using_cow=using_copy_on_write(),
    420 )

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/internals/managers.py:354, in BaseBlockManager.apply(self, f, align_keys, **kwargs)
    352         applied = b.apply(f, **kwargs)
    353     else:
--> 354         applied = getattr(b, f)(**kwargs)
    355     result_blocks = extend_blocks(applied, result_blocks)
    357 out = type(self).from_blocks(result_blocks, self.axes)

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/internals/blocks.py:616, in Block.astype(self, dtype, copy, errors, using_cow)
    596 """
    597 Coerce to the new dtype.
    598 
   (...)
    612 Block
    613 """
    614 values = self.values
--> 616 new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
    618 new_values = maybe_coerce_values(new_values)
    620 refs = None

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/dtypes/astype.py:238, in astype_array_safe(values, dtype, copy, errors)
    235     dtype = dtype.numpy_dtype
    237 try:
--> 238     new_values = astype_array(values, dtype, copy=copy)
    239 except (ValueError, TypeError):
    240     # e.g. _astype_nansafe can fail on object-dtype of strings
    241     #  trying to convert to float
    242     if errors == "ignore":

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/dtypes/astype.py:183, in astype_array(values, dtype, copy)
    180     values = values.astype(dtype, copy=copy)
    182 else:
--> 183     values = _astype_nansafe(values, dtype, copy=copy)
    185 # in pandas we don't store numpy str dtypes, so convert to object
    186 if isinstance(dtype, np.dtype) and issubclass(values.dtype.type, str):

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/dtypes/astype.py:101, in _astype_nansafe(arr, dtype, copy, skipna)
     96     return lib.ensure_string_array(
     97         arr, skipna=skipna, convert_na_value=False
     98     ).reshape(shape)
    100 elif np.issubdtype(arr.dtype, np.floating) and dtype.kind in "iu":
--> 101     return _astype_float_to_int_nansafe(arr, dtype, copy)
    103 elif arr.dtype == object:
    104     # if we have a datetime/timedelta array of objects
    105     # then coerce to datetime64[ns] and use DatetimeArray.astype
    107     if lib.is_np_dtype(dtype, "M"):

File ~/SageMaker/autogluon_1_0_0_python_39/lib/python3.9/site-packages/pandas/core/dtypes/astype.py:146, in _astype_float_to_int_nansafe(values, dtype, copy)
    142 """
    143 astype with a check preventing converting NaN to an meaningless integer value.
    144 """
    145 if not np.isfinite(values).all():
--> 146     raise IntCastingNaNError(
    147         "Cannot convert non-finite values (NA or inf) to integer"
    148     )
    149 if dtype.kind == "u":
    150     # GH#45151
    151     if not (values >= 0).all():

IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer: Error while type casting for column 'loan_id'