class_docstr: An sklearn classifier adapter for fasttext using the a pandas Series.
  
  Parameters
  ----------
  **kwargs
      Additional keyword arguments will be redirected to
      fasttext.train_supervised.
class_docstr_tok: ["An","sklearn","classifier","adapter","for","fasttext","using","the","a","pandas","Series",".","Parameters","--","--","--","--","--","*","*","kwargs","Additional","keyword","arguments","will","be","redirected","to","fasttext.train_supervised","."]
code: def _predict(self, X, k=1):
      # Ensure that fit had been called
      if self.model is None:
          raise NotFittedError(
              "This {} instance is not fitted yet.".format(
                  self.__class__.__name__
              )
          )
      try:
          input_col = X.values
      except AttributeError:
          input_col = X
      return self._predict_on_str_arr(input_col, k=k)
  
imports: import os
  import abc
  import numpy
  from fasttext import train_supervised
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils.multiclass import unique_labels
  from sklearn.exceptions import NotFittedError
  from .util import temp_dataset_fpath, dump_xy_to_fasttext_format, python_fasttext_model_to_bytes, bytes_to_python_fasttext_model
label_desc: Use image node_id 4 for calling the SeriesFtClassifier obj's underlying member method code with example usage: obj._predict(X, k) and returns: self