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.
class_docstr_tok: ["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","."]
docstr: A reference implementation of a transform function.
  
  Parameters
  ----------
  X : array-like of shape = [n_samples, n_features]
      The input samples.
  
  Returns
  -------
  X_transformed : array of int of shape = [n_samples, n_features]
      The array containing the element-wise square roots of the values
      in `X`.
docstr_tok: ["A","reference","implementation","of","a","transform","function",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","=","[","n_samples",",","n_features","]","The","input","samples",".","Returns","--","--","--","-","X_transformed",":","array","of","int","of","shape","=","[","n_samples",",","n_features","]","The","array","containing","the","element-wise","square","roots","of","the","values","in","`","X","`","."]
code: def transform(self, X):
      """A reference implementation of a transform function.
  
      Parameters
      ----------
      X : array-like of shape = [n_samples, n_features]
          The input samples.
  
      Returns
      -------
      X_transformed : array of int of shape = [n_samples, n_features]
          The array containing the element-wise square roots of the values
          in `X`.
      """
      # Check is fit had been called
      with warnings.catch_warnings():
          # fix when requiring sklearn 0.22
          # check_is_fitted will not have arguments any more
          warnings.filterwarnings("ignore", category=FutureWarning)
          warnings.filterwarnings("ignore", category=DeprecationWarning)
          check_is_fitted(self, ["ct_"])
      return self.ct_.transform(X)
  
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 4 for calling the EasyPreprocessor obj's underlying member method code with example usage: obj.transform(X) and returns: self