bulk update

databricks-industry-solutions · May 16, 2024 · 2b83572 · 2b83572
1 parent 6763e15
commit 2b83572
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Showing 2 changed files with 9 additions and 15 deletions.
diff --git a/RUNME.py b/RUNME.py
@@ -70,7 +70,6 @@
                 },
                 "num_workers": 0,
                 "node_type_id": {"AWS": "i3.xlarge", "MSA": "Standard_DS3_v2", "GCP": "n1-highmem-4"},
-                "driver_node_type_id": {"AWS": "i3.xlarge", "MSA": "Standard_DS3_v2", "GCP": "n1-highmem-4"},
                 "custom_tags": {
                     "ResourceClass": "SingleNode",
                     "usage": "solacc_testing"

diff --git a/forecasting_sa/models/sktime/SKTimeForecastingPipeline.py b/forecasting_sa/models/sktime/SKTimeForecastingPipeline.py
@@ -24,6 +24,13 @@ def __init__(self, params):
         self.model = None
         self.param_grid = self.create_param_grid()
 
+    @abstractmethod
+    def create_model(self) -> BaseForecaster:
+        pass
+
+    def create_param_grid(self) -> Dict[str, Any]:
+        return {}
+
     def prepare_data(self, df: pd.DataFrame) -> pd.DataFrame:
         df = df.copy().fillna(0.1)
         df[self.params.target] = df[self.params.target].clip(0.1)
@@ -36,10 +43,7 @@ def prepare_data(self, df: pd.DataFrame) -> pd.DataFrame:
         df = df.set_index(self.params.date_col)
         df = df.reindex(date_idx, method="backfill")
         df = df.sort_index()
-        df = pd.DataFrame(
-            {"y": df[self.params.target].values},
-            index=df.index.to_period(self.params.freq),
-        )
+        df = pd.DataFrame({"y": df[self.params.target].values}, index=df.index.to_period(self.params.freq))
         return df
 
     def fit(self, x, y=None):
@@ -82,13 +86,6 @@ def predict(self, hist_df: pd.DataFrame, val_df: pd.DataFrame = None):
     def forecast(self, x):
         return self.predict(x)
 
-    @abstractmethod
-    def create_model(self) -> BaseForecaster:
-        pass
-
-    def create_param_grid(self) -> Dict[str, Any]:
-        return {}
-
 
 class SKTimeLgbmDsDt(SKTimeForecastingPipeline):
     def __init__(self, params):
@@ -97,9 +94,7 @@ def __init__(self, params):
     def create_model(self) -> BaseForecaster:
         model = TransformedTargetForecaster(
             [
-                (
-                    "deseasonalise",
-                    ConditionalDeseasonalizer(
+                ("deseasonalise", ConditionalDeseasonalizer(
                         model=self.model_spec.get("deseasonalise_model", "additive"),
                         sp=int(self.model_spec.get("season_length", 1)),
                     ),