code: def test_lgt_mixed_signs_and_order(
      iclaims_training_data, regressor_signs
  ):
      df = iclaims_training_data
      df["claims"] = np.log(df["claims"])
      raw_regressor_col = [
          "trend.unemploy",
          "trend.filling",
          "trend.job",
      ]
      new_regressor_col = [raw_regressor_col[idx] for idx in [2, 1, 0]]
      new_regressor_signs = [regressor_signs[idx] for idx in [2, 1, 0]]
      # mixing ordering of cols in df of prediction
      new_df = df[["claims", "week"] + new_regressor_col]
  
      lgt = LGT(
          response_col="claims",
          date_col="week",
          regressor_col=raw_regressor_col,
          regressor_sign=regressor_signs,
          estimator="stan-map",
          seasonality=52,
          seed=8888,
      )
      lgt.fit(df)
      predicted_df_v1 = lgt.predict(df)
      predicted_df_v2 = lgt.predict(new_df)
  
      # mixing ordering of signs
      lgt_new = LGT(
          response_col="claims",
          date_col="week",
          regressor_col=new_regressor_col,
          regressor_sign=new_regressor_signs,
          estimator="stan-map",
          seasonality=52,
          seed=8888,
      )
      lgt_new.fit(df)
      predicted_df_v3 = lgt_new.predict(df)
      predicted_df_v4 = lgt_new.predict(new_df)
  
      pred_v1 = predicted_df_v1["prediction"].values
      pred_v2 = predicted_df_v2["prediction"].values
      pred_v3 = predicted_df_v3["prediction"].values
      pred_v4 = predicted_df_v4["prediction"].values
  
      # they should be all identical; ordering of signs or columns in prediction show not matter
      assert np.allclose(pred_v1, pred_v2, atol=1e-2)
      assert np.allclose(pred_v1, pred_v3, atol=1e-2)
      assert np.allclose(pred_v1, pred_v4, atol=1e-2)
  
imports: from copy import copy
  import pytest
  import numpy
  from orbit.models import LGT
  from orbit.constants.constants import PredictionKeys
label_desc: Use image node_id 7 for calling a global function with example usage: test_lgt_mixed_signs_and_order(iclaims_training_data, regressor_signs) without return types