wip: fix test

georgia-tech-db · Sep 25, 2023 · 84a159e · 84a159e
1 parent 52c563e
commit 84a159e
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Showing 3 changed files with 5 additions and 5 deletions.
diff --git a/docs/source/reference/ai/model-forecasting.rst b/docs/source/reference/ai/model-forecasting.rst
@@ -56,7 +56,7 @@ EvaDB's default forecast framework is `statsforecast <https://nixtla.github.io/s
    * - LIBRARY
      - We can select one of `statsforecast` (default) or `neuralforecast`. `statsforecast` provides access to statistical forecasting methods, while `neuralforecast` gives access to deep-learning based forecasting methods.
    * - MODEL
-     - If LIBRARY is `statsforecast`, we can select one of AutoARIMA, AutoCES, AutoETS, AutoTheta. The default is AutoARIMA. Check `Automatic Forecasting <https://nixtla.github.io/statsforecast/src/core/models_intro.html#automatic-forecasting>`_ to learn details about these models. If LIBRARY is `neuralforecast`, we can select one of NHITS or NBEATS. The default is NBEATS. Check `Automatic Forecasting <https://nixtla.github.io/neuralforecast/models.nbeats.html>`_ for details.
+     - If LIBRARY is `statsforecast`, we can select one of AutoARIMA, AutoCES, AutoETS, AutoTheta. The default is AutoARIMA. Check `Automatic Forecasting <https://nixtla.github.io/statsforecast/src/core/models_intro.html#automatic-forecasting>`_ to learn details about these models. If LIBRARY is `neuralforecast`, we can select one of NHITS or NBEATS. The default is NBEATS. Check `NBEATS docs <https://nixtla.github.io/neuralforecast/models.nbeats.html>`_ for details.
    * - EXOGENOUS
      - The names of columns to be treated as exogenous variables, separated by comma. These columns would be considered for forecasting by the backend only for LIBRARY `neuralforecast`.
    * - Frequency

diff --git a/setup.py b/setup.py
@@ -119,7 +119,7 @@ def read(path, encoding="utf-8"):
 ludwig_libs = ["ludwig[hyperopt,distributed]"]  # MODEL TRAIN AND FINE TUNING
 
 forecasting_libs = [
-    "statsforecast" # MODEL TRAIN AND FINE TUNING
+    "statsforecast", # MODEL TRAIN AND FINE TUNING
     "neuralforecast" # MODEL TRAIN AND FINE TUNING
 ]
 

diff --git a/test/integration_tests/long/test_model_forecasting.py b/test/integration_tests/long/test_model_forecasting.py
@@ -58,7 +58,7 @@ def setUpClass(cls):
         load_query = f"LOAD CSV '{path}' INTO AirData;"
         execute_query_fetch_all(cls.evadb, load_query)
 
-        path = f"{EvaDB_ROOT_DIR}/data/forecasting/ AirPassengersPanel.csv"
+        path = f"{EvaDB_ROOT_DIR}/data/forecasting/AirPassengersPanel.csv"
         load_query = f"LOAD CSV '{path}' INTO AirDataPanel;"
         execute_query_fetch_all(cls.evadb, load_query)
 
@@ -82,8 +82,8 @@ def test_forecast(self):
         create_predict_udf = """
             CREATE FUNCTION AirForecast FROM
             (SELECT unique_id, ds, y FROM AirData)
-            HORIZON 12
             TYPE Forecasting
+            HORIZON 12
             PREDICT 'y';
         """
         execute_query_fetch_all(self.evadb, create_predict_udf)
@@ -100,8 +100,8 @@ def test_forecast(self):
         create_predict_udf = """
             CREATE FUNCTION AirPanelForecast FROM
             (SELECT unique_id, ds, y, trend FROM AirDataPanel)
-            HORIZON 12
             TYPE Forecasting
+            HORIZON 12
             PREDICT 'y'
             LIBRARY 'neuralforecast'
             EXOGENOUS 'trend'