class_docstr: An sklearn adapter for fasttext taking input by column label.
  
  This classifier assume the X parameter for fit, predict and predict_proba
  is in all cases a pandas.DataFrame object.
  
  Parameters
  ----------
  input_col_lbl : str
      The label of the text input column for fasttext.
  **kwargs
      Keyword arguments will be redirected to fasttext.train_supervised.
class_docstr_tok: ["An","sklearn","adapter","for","fasttext","taking","input","by","column","label",".","This","classifier","assume","the","X","parameter","for","fit",",","predict","and","predict_proba","is","in","all","cases","a","pandas.DataFrame","object",".","Parameters","--","--","--","--","--","input_col_lbl",":","str","The","label","of","the","text","input","column","for","fasttext",".","*","*","kwargs","Keyword","arguments","will","be","redirected","to","fasttext.train_supervised","."]
code: def __init__(self, input_col_lbl, **kwargs):
      super().__init__(**kwargs)
      self.input_col_lbl = input_col_lbl
  
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 1 to create a new ColLblBasedFtClassifier object from inherited base classes: FtClassifierABC with example: obj = ColLblBasedFtClassifier(input_col_lbl)