adding monthly seasonality

Author: ahosler

ads/opctl/operator/lowcode/forecast/model/prophet.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-# Copyright (c) 2024 Oracle and/or its affiliates.
+# Copyright (c) 2024, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import logging
@@ -43,7 +43,11 @@ def _add_unit(num, unit):
 def _fit_model(data, params, additional_regressors):
     from prophet import Prophet
 
+    monthly_seasonality = params.pop("monthly_seasonality", False)
     model = Prophet(**params)
+    if monthly_seasonality:
+        model.add_seasonality(name="monthly", period=30.5, fourier_order=5)
+        params["monthly_seasonality"] = monthly_seasonality
     for add_reg in additional_regressors:
         model.add_regressor(add_reg)
     model.fit(data)
@@ -256,7 +260,7 @@ def _generate_report(self):
                     self.outputs[s_id], include_legend=True
                 ),
                 series_ids=series_ids,
-                target_category_column=self.target_cat_col
+                target_category_column=self.target_cat_col,
             )
             section_1 = rc.Block(
                 rc.Heading("Forecast Overview", level=2),
@@ -269,7 +273,7 @@ def _generate_report(self):
             sec2 = _select_plot_list(
                 lambda s_id: self.models[s_id].plot_components(self.outputs[s_id]),
                 series_ids=series_ids,
-                target_category_column=self.target_cat_col
+                target_category_column=self.target_cat_col,
             )
             section_2 = rc.Block(
                 rc.Heading("Forecast Broken Down by Trend Component", level=2), sec2
@@ -285,7 +289,7 @@ def _generate_report(self):
             sec3 = _select_plot_list(
                 lambda s_id: sec3_figs[s_id],
                 series_ids=series_ids,
-                target_category_column=self.target_cat_col
+                target_category_column=self.target_cat_col,
             )
             section_3 = rc.Block(rc.Heading("Forecast Changepoints", level=2), sec3)
 
@@ -299,7 +303,9 @@ def _generate_report(self):
                     pd.Series(
                         m.seasonalities,
                         index=pd.Index(m.seasonalities.keys(), dtype="object"),
-                        name=s_id if self.target_cat_col else self.original_target_column,
+                        name=s_id
+                        if self.target_cat_col
+                        else self.original_target_column,
                         dtype="object",
                     )
                 )
@@ -330,11 +336,15 @@ def _generate_report(self):
                 self.formatted_local_explanation = aggregate_local_explanations
 
                 if not self.target_cat_col:
-                    self.formatted_global_explanation = self.formatted_global_explanation.rename(
-                        {"Series 1": self.original_target_column},
-                        axis=1,
+                    self.formatted_global_explanation = (
+                        self.formatted_global_explanation.rename(
+                            {"Series 1": self.original_target_column},
+                            axis=1,
+                        )
+                    )
+                    self.formatted_local_explanation.drop(
+                        "Series", axis=1, inplace=True
                     )
-                    self.formatted_local_explanation.drop("Series", axis=1, inplace=True)
 
                 # Create a markdown section for the global explainability
                 global_explanation_section = rc.Block(

tests/operators/forecast/test_datasets.py

@@ -212,6 +212,33 @@ def test_pandas_to_historical_test(model):
         print(test_metrics)
 
 
+# CostAD
+@pytest.mark.parametrize("model", ["prophet", "neuralprophet"])
+def test_pandas_to_historical_test(model):
+    df = pd.read_csv(f"{DATASET_PREFIX}dataset5.csv")
+    df_train = df[:-1]
+    df_test = df[-1:]
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        output_data_path = f"{tmpdirname}/results"
+        yaml_i = deepcopy(TEMPLATE_YAML)
+        yaml_i["spec"]["model"] = model
+        yaml_i["spec"]["historical_data"].pop("url")
+        yaml_i["spec"]["historical_data"]["data"] = df_train
+        yaml_i["spec"]["test_data"] = {"data": df_test}
+        yaml_i["spec"]["target_column"] = "Y"
+        yaml_i["spec"]["datetime_column"]["name"] = DATETIME_COL
+        yaml_i["spec"]["horizon"] = 1
+        yaml_i["spec"]["output_directory"]["url"] = output_data_path
+        if model == "automlx":
+            yaml_i["spec"]["model_kwargs"] = {"time_budget": 2}
+        operator_config = ForecastOperatorConfig.from_dict(yaml_i)
+        forecast_operate(operator_config)
+        check_output_for_errors(output_data_path)
+        test_metrics = pd.read_csv(f"{output_data_path}/metrics.csv")
+        print(test_metrics)
+
+
 def check_output_for_errors(output_data_path):
     # try:
     # List files in the directory