KeyError: None of the keys are in index

I am getting this error ,

KeyError: ("None of [Index(['CLASS_1', 'CLASS_2', 'CLASS_3', 'CLASS_4', 'CLASS_5', 'CLASS_6',\n       'CLASS_7', 'CLASS_8', 'CLASS_9', 'CLASS_10', 'CLASS_11', 'CLASS_12',\n       'CLASS_13', 'CLASS_14', 'CLASS_15', 'CLASS_16', 'CLASS_17', 'CLASS_E',\n       'CLASS_V'],\n      dtype='object')] are in the [index]", 'occurred at index 0')

When i run the new_toxic_multilabel.ipynb from sample notebook, I am getting this error for command:

databunch = BertDataBunch(args['data_dir'], LABEL_PATH, args.model_name, train_file='train.csv', val_file='val.csv',
                          test_data='test.csv',
                          text_col="NOTES", label_col=label_cols,
                          batch_size_per_gpu=args['train_batch_size'], max_seq_length=args['max_seq_length'], 
                          multi_gpu=args.multi_gpu, multi_label=True, model_type=args.model_type)

here is my label_col:

label_cols = ['CLASS_1','CLASS_2','CLASS_3','CLASS_4','CLASS_5','CLASS_6','CLASS_7','CLASS_8','CLASS_9','CLASS_10','CLASS_11','CLASS_12','CLASS_13','CLASS_14','CLASS_15','CLASS_16','CLASS_17','CLASS_E','CLASS_V']

my labels.csv contains the same classes but listed one after the another: labels.csv:

'CLASS_1'
'CLASS_2'
'CLASS_3'
'CLASS_4'
'CLASS_5'
'CLASS_6'
'CLASS_7'
'CLASS_8'
'CLASS_9'
'CLASS_10'
'CLASS_11'
'CLASS_12'
'CLASS_13'
'CLASS_14'
'CLASS_15'
'CLASS_16'
'CLASS_17'
'CLASS_E'
'CLASS_V'

Here is the traceback:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-13-c5a2ac3a5e99> in <module>
      3                           text_col="NOTES", label_col=label_cols,
      4                           batch_size_per_gpu=args['train_batch_size'], max_seq_length=args['max_seq_length'],
----> 5                           multi_gpu=args.multi_gpu, multi_label=True, model_type=args.model_type)

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/fast_bert/data_cls.py in __init__(self, data_dir, label_dir, tokenizer, train_file, val_file, test_data, label_file, text_col, label_col, batch_size_per_gpu, max_seq_length, multi_gpu, multi_label, backend, model_type, logger, clear_cache, no_cache)
    352             if os.path.exists(cached_features_file) == False or self.no_cache == True:
    353                 train_examples = processor.get_train_examples(
--> 354                     train_file, text_col=text_col, label_col=label_col)
    355 
    356             train_dataset = self.get_dataset_from_examples(

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/fast_bert/data_cls.py in get_train_examples(self, filename, text_col, label_col, size)
    230             data_df = pd.read_csv(os.path.join(self.data_dir, filename))
    231 
--> 232             return self._create_examples(data_df, "train", text_col=text_col, label_col=label_col)
    233         else:
    234             data_df = pd.read_csv(os.path.join(self.data_dir, filename))

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/fast_bert/data_cls.py in _create_examples(self, df, set_type, text_col, label_col)
    286         else:
    287             return list(df.apply(lambda row: InputExample(guid=row.index, text_a=row[text_col],
--> 288                                                           label=_get_labels(row, label_col)), axis=1))
    289 
    290 

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/frame.py in apply(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)
   6926             kwds=kwds,
   6927         )
-> 6928         return op.get_result()
   6929 
   6930     def applymap(self, func):

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/apply.py in get_result(self)
    184             return self.apply_raw()
    185 
--> 186         return self.apply_standard()
    187 
    188     def apply_empty_result(self):

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/apply.py in apply_standard(self)
    290 
    291         # compute the result using the series generator
--> 292         self.apply_series_generator()
    293 
    294         # wrap results

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/apply.py in apply_series_generator(self)
    319             try:
    320                 for i, v in enumerate(series_gen):
--> 321                     results[i] = self.f(v)
    322                     keys.append(v.name)
    323             except Exception as e:

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/fast_bert/data_cls.py in <lambda>(row)
    286         else:
    287             return list(df.apply(lambda row: InputExample(guid=row.index, text_a=row[text_col],
--> 288                                                           label=_get_labels(row, label_col)), axis=1))
    289 
    290 

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/fast_bert/data_cls.py in _get_labels(row, label_col)
    273         def _get_labels(row, label_col):
    274             if isinstance(label_col, list):
--> 275                 return list(row[label_col])
    276             else:
    277                 # create one hot vector of labels

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/series.py in __getitem__(self, key)
   1111             key = check_bool_indexer(self.index, key)
   1112 
-> 1113         return self._get_with(key)
   1114 
   1115     def _get_with(self, key):

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/series.py in _get_with(self, key)
   1153             # handle the dup indexing case (GH 4246)
   1154             if isinstance(key, (list, tuple)):
-> 1155                 return self.loc[key]
   1156 
   1157             return self.reindex(key)

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
   1422 
   1423             maybe_callable = com.apply_if_callable(key, self.obj)
-> 1424             return self._getitem_axis(maybe_callable, axis=axis)
   1425 
   1426     def _is_scalar_access(self, key: Tuple):

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
   1837                     raise ValueError("Cannot index with multidimensional key")
   1838 
-> 1839                 return self._getitem_iterable(key, axis=axis)
   1840 
   1841             # nested tuple slicing

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
   1131         else:
   1132             # A collection of keys
-> 1133             keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
   1134             return self.obj._reindex_with_indexers(
   1135                 {axis: [keyarr, indexer]}, copy=True, allow_dups=True

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
   1090 
   1091         self._validate_read_indexer(
-> 1092             keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
   1093         )
   1094         return keyarr, indexer

~/virtualenvs/anaconda3/envs/pytorch/lib/python3.7/site-packages/pandas/core/indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
   1175                 raise KeyError(
   1176                     "None of [{key}] are in the [{axis}]".format(
-> 1177                         key=key, axis=self.obj._get_axis_name(axis)
   1178                     )
   1179                 )

KeyError: ("None of [Index(['CLASS_1', 'CLASS_2', 'CLASS_3', 'CLASS_4', 'CLASS_5', 'CLASS_6',\n       'CLASS_7', 'CLASS_8', 'CLASS_9', 'CLASS_10', 'CLASS_11', 'CLASS_12',\n       'CLASS_13', 'CLASS_14', 'CLASS_15', 'CLASS_16', 'CLASS_17', 'CLASS_E',\n       'CLASS_V'],\n      dtype='object')] are in the [index]", 'occurred at index 0')

What is the issue?

[greecehalf]

Could you tell me how to match features with label_cols?

[mohammedayub44]

@Habiba05 For me it was a class mismatch, the steps on the main page are a little unclear. So, following through the “sample_notebooks/new-toxic-multilabel.ipynb” jupyter notebook and “sample_data/” made more sense.