code: def test_pred_category(df_pt):
      model = BaseSilverkiteEstimator()
      # property is not available without fitting.
      with pytest.raises(
          NotFittedError,
          match="Must fit before getting predictor category.",
      ):
          print(model.pred_category)
      model.fit(
          X=df_pt.iloc[:100],  # speeds up
          time_col=cst.TIME_COL,
          value_col=cst.VALUE_COL,
      )
      params = {
          "fit_algorithm": "linear",
          "training_fraction": 0.8,
          "extra_pred_cols": ["ct1", "x", "x:ct1"],
      }
      df_pt["x"] = np.random.randn(df_pt.shape[0])
      silverkite = SilverkiteForecast()
      model.model_dict = silverkite.forecast(
          df=df_pt.iloc[:100],
          time_col=cst.TIME_COL,
          value_col=cst.VALUE_COL,
          **params,
      )
      model.extra_pred_cols = [
          "ct1",
          "x",
          "x:ct1",
      ]  # set in subclass initialization
      # _pred_category is None before trying to access pred_category
      assert model._pred_category is None
      model.finish_fit()
      pred_category = model.pred_category
      # _pred_category is updated after trying to access pred_category
      assert model._pred_category is not None
      assert pred_category["intercept"] == ["Intercept"]
      assert pred_category["time_features"] == ["ct1", "x:ct1"]
      assert pred_category["event_features"] == []
      assert pred_category["trend_features"] == ["ct1", "x:ct1"]
      assert pred_category["seasonality_features"] == [
          "sin1_tod_daily",
          "cos1_tod_daily",
          "sin2_tod_daily",
          "cos2_tod_daily",
          "sin3_tod_daily",
          "cos3_tod_daily",
          "sin1_tow_weekly",
          "cos1_tow_weekly",
          "sin2_tow_weekly",
          "cos2_tow_weekly",
          "sin3_tow_weekly",
          "cos3_tow_weekly",
          "sin1_toy_yearly",
          "cos1_toy_yearly",
          "sin2_toy_yearly",
          "cos2_toy_yearly",
          "sin3_toy_yearly",
          "cos3_toy_yearly",
          "sin4_toy_yearly",
          "cos4_toy_yearly",
          "sin5_toy_yearly",
          "cos5_toy_yearly",
      ]
      assert pred_category["lag_features"] == []
      assert pred_category["regressor_features"] == ["x", "x:ct1"]
      assert pred_category["interaction_features"] == ["x:ct1"]
  
imports: import datetime
  import numpy
  import pandas
  import pytest
  from sklearn.exceptions import NotFittedError
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from testfixtures import LogCapture
  import greykite.common.constants
  from greykite.algo.forecast.silverkite.forecast_silverkite import SilverkiteForecast
  from greykite.common.data_loader import DataLoader
  from greykite.common.features.timeseries_features import convert_date_to_continuous_time
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import daily_data_reg
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.sklearn.estimator.base_silverkite_estimator import BaseSilverkiteEstimator
  from greykite.sklearn.estimator.testing_utils import params_component_breakdowns
  from greykite.sklearn.estimator.testing_utils import params_components
label_desc: Use image node_id 18 for calling a global function with example usage: test_pred_category(df_pt) without return types