class_docstr: This tests the base class ComprehensiveFCParameters
class_docstr_tok: ["This","tests","the","base","class","ComprehensiveFCParameters"]
docstr: The `settings.from_columns()` function is supposed to save the feature extraction / selection results so it
  can be reused later. It works by parsing the column names of the extracted dataframes. An unfortunate side
  effect of this is that when used with the 'long' format time series input, the typing information about the
  'kind' column is lost. For example, even if the 'kind' values are in int32, in the resulting settings dict, the
  type of the top level keys (representing different kind values) will be str
docstr_tok: ["The","`","settings.from_columns","(",")","`","function","is","supposed","to","save","the","feature","extraction","\/","selection","results","so","it","can","be","reused","later",".","It","works","by","parsing","the","column","names","of","the","extracted","dataframes",".","An","unfortunate","side","effect","of","this","is","that","when","used","with","the","'long","'","format","time","series","input",",","the","typing","information","about","the","'kind","'","column","is","lost",".","For","example",",","even","if","the","'kind","'","values","are","in","int32",",","in","the","resulting","settings","dict",",","the","type","of","the","top","level","keys","(","representing","different","kind","values",")","will","be","str"]
code: def test_from_columns_correct_for_different_kind_datatypes(self):
      """The `settings.from_columns()` function is supposed to save the feature extraction / selection results so it
      can be reused later. It works by parsing the column names of the extracted dataframes. An unfortunate side
      effect of this is that when used with the 'long' format time series input, the typing information about the
      'kind' column is lost. For example, even if the 'kind' values are in int32, in the resulting settings dict, the
      type of the top level keys (representing different kind values) will be str
      """
      df = pd.DataFrame(
          {
              "id": [1, 1, 1, 1],
              "time": [1, 1, 2, 2],
              "kind": [1, 2, 1, 2],
              "value": [1, 2, 3, 4],
          }
      )
  
      features = extract_features(
          df,
          column_id="id",
          column_sort="time",
          column_kind="kind",
          column_value="value",
          default_fc_parameters=MinimalFCParameters(),
      )
      sample_settings = from_columns(features)
      X = extract_features(
          df,
          column_id="id",
          column_sort="time",
          column_kind="kind",
          column_value="value",
          kind_to_fc_parameters=sample_settings,
      )
      assert X.shape == (1, 2 * len(MinimalFCParameters()))
  
imports: import pickle
  from unittest import TestCase
  import numpy
  import pandas
  from pandas.testing import assert_frame_equal
  from tsfresh.feature_extraction import feature_calculators
  from tsfresh.feature_extraction.extraction import extract_features
  from tsfresh.feature_extraction.settings import ComprehensiveFCParameters, EfficientFCParameters, IndexBasedFCParameters, MinimalFCParameters, PickableSettings, TimeBasedFCParameters, from_columns, include_function
label_desc: Use image node_id 7 for calling the TestSettingsObject obj's underlying member method code with example usage: obj.test_from_columns_correct_for_different_kind_datatypes() without return types