class_docstr: A simple preprocessor.
  
  Detects variable types, encodes everything as floats
  for use with sklearn.
  
  Applies one-hot encoding, missing value imputation and scaling.
  
  Attributes
  ----------
  ct_ : ColumnTransformer
      Main container for all transformations.
  
  columns_ : pandas columns
      Columns of training data.
  
  dtypes_ : Series of dtypes
      Dtypes of training data columns.
  
  types_ : something
      Inferred input types.
  
  
  Parameters
  ----------
  scale : boolean, default=True
      Whether to scale continuous data.
  
  force_imputation : bool, default=True
      Whether to create imputers even if no training data is missing.
  
  verbose : int, default=0
      Control output verbosity.
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code: def __init__(
      self, scale=True, force_imputation=True, verbose=0, types=None
  ):
      self.verbose = verbose
      self.scale = scale
      self.types = types
      self.force_imputation = force_imputation
  
imports: from importlib.metadata import version
  from joblib import hash
  import warnings
  from warnings import warn
  import numpy
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.compose import make_column_transformer, ColumnTransformer
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 1 to create a new EasyPreprocessor object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = EasyPreprocessor(scale, force_imputation, verbose, types)