Investigate Large Horizon Models #213. Improved perf logging. Display…

… the first and last multistep forecast perfs. Updated these scripts
antoinecarme · Mar 8, 2023 · 686ab27 · 686ab27
1 parent f659796
commit 686ab27
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Showing 3 changed files with 12 additions and 9 deletions.
diff --git a/tests/basic_checks/issue_46_negative_horizon.py b/tests/basic_checks/issue_46_negative_horizon.py
@@ -13,8 +13,8 @@ def test_fake_model_1_row(iHorizon_train , iHorizon_apply):
     lEngine.train(df , 'date' , 'signal', iHorizon_train);
     # print(lEngine.mSignalDecomposition.mBestModel.mTimeInfo.info())
     print(lEngine.mSignalDecomposition.mBestModel.getFormula())
-    print("PERFS_MAPE_MASE", lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMAPE, 
-             lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMASE, )
+    lPerf_H = lEngine.mSignalDecomposition.mBestModel.mForecastPerfs["signal_Forecast_" + str(iHorizon_train)]
+    print("PERFS_MAPE_MASE", lPerf_H.mMAPE, lPerf_H.mMASE, )
 
     # print(df.head())
     df1 = lEngine.forecast(df , iHorizon_apply)
@@ -33,8 +33,9 @@ def test_fake_model_2_rows(iHorizon_train , iHorizon_apply):
     lEngine.train(df , 'date' , 'signal', iHorizon_train);
     # print(lEngine.mSignalDecomposition.mBestModel.mTimeInfo.info())
     print(lEngine.mSignalDecomposition.mBestModel.getFormula())
-    print("PERFS_MAPE_MASE", lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMAPE, 
-             lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMASE, )
+    lPerf_H = lEngine.mSignalDecomposition.mBestModel.mForecastPerfs["signal_Forecast_" + str(iHorizon_train)]
+    print("PERFS_MAPE_MASE", lPerf_H.mMAPE, lPerf_H.mMASE, )
+
     # print(df.head())
     df1 = lEngine.forecast(df , iHorizon_apply)
     # print(df1.columns)

diff --git a/tests/basic_checks/issue_46_one_or_two_rows.py b/tests/basic_checks/issue_46_one_or_two_rows.py
@@ -13,8 +13,8 @@ def test_fake_model_1_row(iHorizon_train , iHorizon_apply):
     lEngine.train(df , 'date' , 'signal', iHorizon_train);
     # print(lEngine.mSignalDecomposition.mBestModel.mTimeInfo.info())
     print(lEngine.mSignalDecomposition.mBestModel.getFormula())
-    print("PERFS_MAPE_MASE", lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMAPE, 
-             lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMASE, )
+    lPerf_H = lEngine.mSignalDecomposition.mBestModel.mForecastPerfs["signal_Forecast_" + str(iHorizon_train)]
+    print("PERFS_MAPE_MASE", lPerf_H.mMAPE, lPerf_H.mMASE, )
 
     # print(df.head())
     df1 = lEngine.forecast(df , iHorizon_apply)
@@ -33,8 +33,9 @@ def test_fake_model_2_rows(iHorizon_train , iHorizon_apply):
     lEngine.train(df , 'date' , 'signal', iHorizon_train);
     # print(lEngine.mSignalDecomposition.mBestModel.mTimeInfo.info())
     print(lEngine.mSignalDecomposition.mBestModel.getFormula())
-    print("PERFS_MAPE_MASE", lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMAPE, 
-             lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMASE, )
+    lPerf_H = lEngine.mSignalDecomposition.mBestModel.mForecastPerfs["signal_Forecast_" + str(iHorizon_train)]
+    print("PERFS_MAPE_MASE", lPerf_H.mMAPE, lPerf_H.mMASE, )
+
     # print(df.head())
     df1 = lEngine.forecast(df , iHorizon_apply)
     # print(df1.columns)

diff --git a/tests/expsmooth/expsmooth_dataset_test.py b/tests/expsmooth/expsmooth_dataset_test.py
@@ -22,6 +22,7 @@ def analyze_dataset(name , horizon):
     N = df.shape[0]
     lEngine.train(df , 'Date' , signal , horizon)
     lEngine.getModelInfo()
-    print("PERFORMANCE MAPE_FORECAST" , signal, lEngine.mSignalDecomposition.mBestModel.mForecastPerf.mMAPE)
+    lPerf_H = lEngine.mSignalDecomposition.mBestModel.mForecastPerfs[signal + "_Forecast_" + str(horizon)]
+    print("PERFORMANCE MAPE_FORECAST" , signal, lPerf_H.mMAPE)
     return lEngine