class_docstr: Class for checking types in input data. Also perform conversion for columns with types conflicts
class_docstr_tok: ["Class","for","checking","types","in","input","data",".","Also","perform","conversion","for","columns","with","types","conflicts"]
code: def __init__(self):
      # Threshold to convert numerical into categorical column
      self.categorical_max_uniques_th = CATEGORICAL_MAX_UNIQUE_TH
  
      self.acceptable_failed_rate_bottom = (
          ACCEPTABLE_CONVERSION_FAILED_RATE_BOTTOM
      )
      self.acceptable_failed_rate_top = (
          ACCEPTABLE_CONVERSION_FAILED_RATE_TOP
      )
  
      self.features_columns_info = pd.DataFrame()
      self.target_columns_info = pd.DataFrame()
  
      # Dictionary with information about converted during fitting columns
      self.features_converted_columns = {}
      self.target_converted_columns = {}
  
      # Columns to delete due to types conflicts
      self.columns_to_del = []
      # Column ids for transformation due to number of unique values
      self.numerical_into_str = []
      self.categorical_into_float = []
  
      # Indices of columns with filed string into numerical transformation
      self.string_columns_transformation_failed = {}
  
      # Is target column contains non-numerical cells during conversion
      self.target_converting_has_errors = False
  
      # Lists with column types for converting calculated on source input data
      self.feature_type_ids = None
      self.target_type_ids = None
      self.log = default_log(self)
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
  import numpy
  import pandas
  from golem.core.log import LoggerAdapter, default_log
  from fedot.core.repository.tasks import Task, TaskTypesEnum
label_desc: Use image node_id 1 to create a new TableTypesCorrector object  with example: obj = TableTypesCorrector()