add support for min and max n prophet

Author: ahosler

ads/opctl/operator/lowcode/anomaly/model/base_model.py

@@ -71,7 +71,7 @@ def generate_report(self):
         try:
             anomaly_output = self._build_model()
         except Exception as e:
-            logger.warn(f"Found exception: {e}")
+            logger.warning(f"Found exception: {e}")
             if self.spec.datetime_column:
                 anomaly_output = self._fallback_build_model()
             raise e
@@ -347,7 +347,7 @@ def _save_report(
                 storage_options=storage_options,
             )
 
-        logger.warn(
+        logger.warning(
             f"The report has been successfully "
             f"generated and placed to the: {unique_output_dir}."
         )
@@ -356,7 +356,7 @@ def _fallback_build_model(self):
         """
         Fallback method for the sub model _build_model method.
         """
-        logger.warn(
+        logger.warning(
             f"The build_model method has failed for the model: {self.spec.model}. "
             "A fallback model will be built."
         )

ads/opctl/operator/lowcode/anomaly/model/randomcutforest.py

@@ -95,7 +95,7 @@ def _build_model(self) -> AnomalyOutput:
 
                 anomaly_output.add_output(target, anomaly, score)
             except Exception as e:
-                logger.warn(f"Encountered Error: {e}. Skipping series {target}.")
+                logger.warning(f"Encountered Error: {e}. Skipping series {target}.")
 
         return anomaly_output
 

ads/opctl/operator/lowcode/anomaly/utils.py

@@ -44,7 +44,7 @@ def _build_metrics_df(y_true, y_pred, column_name):
         # Throws exception if y_true has only one class
         metrics[SupportedMetrics.ROC_AUC] = roc_auc_score(y_true, y_pred)
     except Exception as e:
-        logger.warn(f"An exception occurred: {e}")
+        logger.warning(f"An exception occurred: {e}")
         metrics[SupportedMetrics.ROC_AUC] = None
     precision, recall, thresholds = precision_recall_curve(y_true, y_pred)
     metrics[SupportedMetrics.PRC_AUC] = auc(recall, precision)

ads/opctl/operator/lowcode/common/transformations.py

@@ -98,7 +98,11 @@ def run(self, data):
         return clean_df
 
     def _remove_trailing_whitespace(self, df):
-        return df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
+        return df.apply(
+            lambda x: x.str.strip()
+            if hasattr(x, "dtype") and x.dtype == "object"
+            else x
+        )
 
     def _clean_column_names(self, df):
         """

ads/opctl/operator/lowcode/common/utils.py

@@ -3,6 +3,7 @@
 # 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 json
 import logging
 import os
 import shutil
@@ -12,7 +13,6 @@
 
 import fsspec
 import oracledb
-import json
 import pandas as pd
 
 from ads.common.object_storage_details import ObjectStorageDetails
@@ -265,7 +265,7 @@ def find_output_dirname(output_dir: OutputDirectory):
     while os.path.exists(unique_output_dir):
         unique_output_dir = f"{output_dir}_{counter}"
         counter += 1
-    logger.warn(
+    logger.warning(
         f"Since the output directory was not specified, the output will be saved to {unique_output_dir} directory."
     )
     return unique_output_dir

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

@@ -137,8 +137,8 @@ def _train_model(self, i, s_id, df, model_kwargs):
                 "error": str(e),
                 "error_trace": traceback.format_exc(),
             }
-            logger.warn(f"Encountered Error: {e}. Skipping.")
-            logger.warn(traceback.format_exc())
+            logger.warning(f"Encountered Error: {e}. Skipping.")
+            logger.warning(traceback.format_exc())
 
     def _build_model(self) -> pd.DataFrame:
         full_data_dict = self.datasets.get_data_by_series()
@@ -166,7 +166,7 @@ def _generate_report(self):
             sec5_text = rc.Heading("ARIMA Model Parameters", level=2)
             blocks = [
                 rc.Html(
-                    m['model'].summary().as_html(),
+                    m["model"].summary().as_html(),
                     label=s_id if self.target_cat_col else None,
                 )
                 for i, (s_id, m) in enumerate(self.models.items())
@@ -201,11 +201,15 @@ 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(
@@ -235,7 +239,7 @@ def _generate_report(self):
                     local_explanation_section,
                 ]
             except Exception as e:
-                logger.warn(f"Failed to generate Explanations with error: {e}.")
+                logger.warning(f"Failed to generate Explanations with error: {e}.")
                 logger.debug(f"Full Traceback: {traceback.format_exc()}")
 
         model_description = rc.Text(

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

@@ -189,8 +189,8 @@ def _build_model(self) -> pd.DataFrame:
                     "error": str(e),
                     "error_trace": traceback.format_exc(),
                 }
-                logger.warn(f"Encountered Error: {e}. Skipping.")
-                logger.warn(traceback.format_exc())
+                logger.warning(f"Encountered Error: {e}. Skipping.")
+                logger.warning(traceback.format_exc())
 
         logger.debug("===========Forecast Generated===========")
 
@@ -257,7 +257,9 @@ def _generate_report(self):
                 )
 
                 self.formatted_global_explanation.rename(
-                    columns={self.spec.datetime_column.name: ForecastOutputColumns.DATE},
+                    columns={
+                        self.spec.datetime_column.name: ForecastOutputColumns.DATE
+                    },
                     inplace=True,
                 )
 
@@ -312,7 +314,7 @@ def _generate_report(self):
                     local_explanation_section,
                 ]
             except Exception as e:
-                logger.warn(f"Failed to generate Explanations with error: {e}.")
+                logger.warning(f"Failed to generate Explanations with error: {e}.")
                 logger.debug(f"Full Traceback: {traceback.format_exc()}")
 
         model_description = rc.Text(
@@ -478,7 +480,9 @@ def explain_model(self):
             except Exception as e:
                 if s_id in self.errors_dict:
                     self.errors_dict[s_id]["explainer_error"] = str(e)
-                    self.errors_dict[s_id]["explainer_error_trace"] = traceback.format_exc()
+                    self.errors_dict[s_id]["explainer_error_trace"] = (
+                        traceback.format_exc()
+                    )
                 else:
                     self.errors_dict[s_id] = {
                         "model_name": self.spec.model,

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

@@ -211,8 +211,8 @@ def _build_model(self) -> pd.DataFrame:
                     "error": str(e),
                     "error_trace": traceback.format_exc(),
                 }
-            logger.warn(f"Encountered Error: {e}. Skipping.")
-            logger.warn(traceback.format_exc())
+            logger.warning(f"Encountered Error: {e}. Skipping.")
+            logger.warning(traceback.format_exc())
 
         logger.debug("===========Done===========")
 
@@ -242,7 +242,7 @@ def _generate_report(self) -> tuple:
                     self.models.df_wide_numeric, series=s_id
                 ),
                 self.datasets.list_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),
@@ -260,15 +260,17 @@ def _generate_report(self) -> tuple:
                 )
 
             except KeyError:
-                logger.warn("Issue generating Model Parameters Table Section. Skipping")
+                logger.warning(
+                    "Issue generating Model Parameters Table Section. Skipping"
+                )
                 sec2 = rc.Text("Error generating model parameters.")
 
             section_2 = rc.Block(sec2_text, sec2)
 
             all_sections = [section_1, section_2]
 
         if self.spec.generate_explanations:
-            logger.warn("Explanations not yet supported for the AutoTS Module")
+            logger.warning("Explanations not yet supported for the AutoTS Module")
 
         # Model Description
         model_description = rc.Text(

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

@@ -29,7 +29,6 @@
     seconds_to_datetime,
     write_data,
 )
-from ads.opctl.operator.lowcode.forecast.model.forecast_datasets import TestData
 from ads.opctl.operator.lowcode.forecast.utils import (
     _build_metrics_df,
     _build_metrics_per_horizon,
@@ -132,11 +131,10 @@ def generate_report(self):
 
                 if self.datasets.test_data is not None:
                     try:
-                        (
-                            self.test_eval_metrics,
-                            summary_metrics
-                        ) = self._test_evaluate_metrics(
-                            elapsed_time=elapsed_time,
+                        (self.test_eval_metrics, summary_metrics) = (
+                            self._test_evaluate_metrics(
+                                elapsed_time=elapsed_time,
+                            )
                         )
                         if not self.target_cat_col:
                             self.test_eval_metrics.rename(
@@ -145,7 +143,7 @@ def generate_report(self):
                                 inplace=True,
                             )
                     except Exception:
-                        logger.warn("Unable to generate Test Metrics.")
+                        logger.warning("Unable to generate Test Metrics.")
                         logger.debug(f"Full Traceback: {traceback.format_exc()}")
             report_sections = []
 
@@ -369,7 +367,7 @@ def _test_evaluate_metrics(self, elapsed_time=0):
                     -self.spec.horizon :
                 ]
             except KeyError as ke:
-                logger.warn(
+                logger.warning(
                     f"Error Generating Metrics: Unable to find {s_id} in the test data. Error: {ke.args}"
                 )
             y_pred = self.forecast_output.get_forecast(s_id)["forecast_value"].values[
@@ -542,7 +540,7 @@ def _save_report(
                 )
                 results.set_metrics(metrics_df_formatted)
             else:
-                logger.warn(
+                logger.warning(
                     f"Attempted to generate the {self.spec.metrics_filename} file with the training metrics, however the training metrics could not be properly generated."
                 )
 
@@ -563,7 +561,7 @@ def _save_report(
                     )
                     results.set_test_metrics(test_metrics_df_formatted)
                 else:
-                    logger.warn(
+                    logger.warning(
                         f"Attempted to generate the {self.spec.test_metrics_filename} file with the test metrics, however the test metrics could not be properly generated."
                     )
         # explanations csv reports
@@ -581,7 +579,7 @@ def _save_report(
                     )
                     results.set_global_explanations(self.formatted_global_explanation)
                 else:
-                    logger.warn(
+                    logger.warning(
                         f"Attempted to generate global explanations for the {self.spec.global_explanation_filename} file, but an issue occured in formatting the explanations."
                     )
 
@@ -597,11 +595,11 @@ def _save_report(
                     )
                     results.set_local_explanations(self.formatted_local_explanation)
                 else:
-                    logger.warn(
+                    logger.warning(
                         f"Attempted to generate local explanations for the {self.spec.local_explanation_filename} file, but an issue occured in formatting the explanations."
                     )
             except AttributeError as e:
-                logger.warn(
+                logger.warning(
                     "Unable to generate explanations for this model type or for this dataset."
                 )
                 logger.debug(f"Got error: {e.args}")
@@ -769,7 +767,7 @@ def explain_model(self):
                 local_ex_time = local_ex_time + time.time() - exp_end_time
 
                 if not len(kernel_explnr_vals):
-                    logger.warn(
+                    logger.warning(
                         "No explanations generated. Ensure that additional data has been provided."
                     )
                 else:
@@ -780,7 +778,7 @@ def explain_model(self):
                         )
                     )
             else:
-                logger.warn(
+                logger.warning(
                     f"Skipping explanations for {s_id}, as forecast was not generated."
                 )
 

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

@@ -23,14 +23,14 @@
 
 
 class HistoricalData(AbstractData):
-    def __init__(self, spec, historical_data = None):
+    def __init__(self, spec, historical_data=None):
         super().__init__(spec=spec, name="historical_data", data=historical_data)
 
     def _ingest_data(self, spec):
         try:
             self.freq = get_frequency_of_datetime(self.data.index.get_level_values(0))
         except TypeError as e:
-            logger.warn(
+            logger.warning(
                 f"Error determining frequency: {e.args}. Setting Frequency to None"
             )
             logger.debug(f"Full traceback: {e}")
@@ -106,7 +106,7 @@ def _ingest_data(self, spec):
         _spec = spec
         self.additional_regressors = list(self.data.columns)
         if not self.additional_regressors:
-            logger.warn(
+            logger.warning(
                 f"No additional variables found in the additional_data. Only columns found: {self.data.columns}. Skipping for now."
             )
         # Check that datetime column matches historical datetime column
@@ -121,7 +121,13 @@ def __init__(self, spec, test_data):
 
 
 class ForecastDatasets:
-    def __init__(self, config: ForecastOperatorConfig, historical_data=None, additional_data=None, test_data=None):
+    def __init__(
+        self,
+        config: ForecastOperatorConfig,
+        historical_data=None,
+        additional_data=None,
+        test_data=None,
+    ):
         """Instantiates the DataIO instance.
 
         Properties
@@ -136,7 +142,9 @@ def __init__(self, config: ForecastOperatorConfig, historical_data=None, additio
         self._target_col = config.spec.target_column
         if historical_data is not None:
             self.historical_data = HistoricalData(config.spec, historical_data)
-            self.additional_data = AdditionalData(config.spec, self.historical_data, additional_data)
+            self.additional_data = AdditionalData(
+                config.spec, self.historical_data, additional_data
+            )
         else:
             self._load_data(config.spec)
         self.test_data = TestData(config.spec, test_data)
@@ -147,7 +155,7 @@ def _load_data(self, spec):
         self.additional_data = AdditionalData(spec, self.historical_data)
 
         if spec.generate_explanations and spec.additional_data is None:
-            logger.warn(
+            logger.warning(
                 "Unable to generate explanations as there is no additional data passed in. Either set generate_explanations to False, or pass in additional data."
             )
             spec.generate_explanations = False