text: import pandas as pd
  
  import pytest
  
  from evidently.analyzers.regression_performance_analyzer import RegressionPerformanceAnalyzer
  from evidently.model.widget import BaseWidgetInfo
  from evidently.options import OptionsProvider
  from evidently.pipeline.column_mapping import ColumnMapping
  from evidently.dashboard.widgets.reg_underperform_segments_table_widget import UnderperformSegmTableWidget
  
  
  @pytest.fixture
  def widget() -> UnderperformSegmTableWidget:
      options_provider = OptionsProvider()
  
      widget = UnderperformSegmTableWidget("test_widget")
      widget.options_provider = options_provider
      return widget
  
  
  def test_reg_underperform_segments_table_widget_analyzer_list(widget: UnderperformSegmTableWidget) -> None:
      assert widget.analyzers() == [RegressionPerformanceAnalyzer]
  
  
  @pytest.mark.parametrize(
      "reference_data, current_data, data_mapping, expected_result",
      (
          (
              pd.DataFrame(
                  {
                      "target": [1, 2, 3, 4],
                      "prediction": [1, 2, 3, 4],
                      "num_feature": [2, 4, 3, 5],
                      "cat_feature": [2, 4, 3, 5],
                  }
              ),
              None,
              ColumnMapping(numerical_features=["num_feature"], categorical_features=["cat_feature"]),
              BaseWidgetInfo(type="big_table", title="test_widget", size=2),
          ),
          (
              pd.DataFrame(
                  {
                      "target": [1, 2, 3, 4],
                      "prediction": [1, 2, 3, 4],
                      "num_feature": [3, 5, 3, 1],
                      "cat_feature": [3, 5, 1, 2],
                  }
              ),
              pd.DataFrame(
                  {
                      "target": [1, 2, 3, 4],
                      "prediction": [1, 2, 3, 4],
                      "num_feature": [2, 4, 3, 1],
                      "cat_feature": [2, 5, 1, 2],
                  }
              ),
              ColumnMapping(numerical_features=["num_feature"], categorical_features=["cat_feature"]),
              BaseWidgetInfo(type="big_table", title="test_widget", size=2),
          ),
      ),
  )
  def test_reg_underperform_segments_table_widget_simple_case(
      widget: UnderperformSegmTableWidget,
      reference_data: pd.DataFrame,
      current_data: pd.DataFrame,
      data_mapping: ColumnMapping,
      expected_result: BaseWidgetInfo,
  ) -> None:
      analyzer = RegressionPerformanceAnalyzer()
      analyzer.options_provider = widget.options_provider
      analyzer_results = analyzer.calculate(reference_data, current_data, data_mapping)
      result = widget.calculate(
          reference_data, current_data, data_mapping, {RegressionPerformanceAnalyzer: analyzer_results}
      )
  
      if expected_result is not None:
          # we have some widget for visualization
          assert result.type == expected_result.type
          assert result.title == expected_result.title
          assert result.size == expected_result.size
          assert result.params is not None
  
      else:
          # no widget data, show nothing
          assert result is None
  