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"]
docstr: Apply conversion into categorical string column for every signed column 
docstr_tok: ["Apply","conversion","into","categorical","string","column","for","every","signed","column"]
code: def _into_categorical_features_transformation_for_predict(
      self, data: InputData
  ):
      """Apply conversion into categorical string column for every signed column"""
      # Get numerical columns
      num_df = pd.DataFrame(
          data.features[:, self.numerical_into_str],
          columns=self.numerical_into_str,
      )
  
      # Convert and apply categorical transformation
      data.features[:, self.numerical_into_str] = num_df.apply(
          convert_num_column_into_string_array
      ).to_numpy()
  
      # Update information about column types (in-place)
      feature_type_ids = data.supplementary_data.col_type_ids[
          "features"
      ]
      feature_type_ids[self.numerical_into_str] = TYPE_TO_ID[str]
  
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 14 for calling the TableTypesCorrector obj's underlying member method code with example usage: obj._into_categorical_features_transformation_for_predict(data) without return types