Add troubleshooting guide link to error messages and moved troubleshooting.md to ai-samples repo

Author: codeloop

ads/opctl/operator/lowcode/forecast/const.py

@@ -90,3 +90,4 @@ class ForecastOutputColumns(ExtendedEnum):
 RENDER_LIMIT = 5000
 AUTO_SELECT = "auto-select"
 BACKTEST_REPORT_NAME = "back_test.csv"
+TROUBLESHOOTING_GUIDE = "https://github.com/oracle-samples/oci-data-science-ai-samples/blob/main/ai-operators/troubleshooting.md"

ads/opctl/operator/lowcode/forecast/errors.py

@@ -4,6 +4,9 @@
 # Copyright (c) 2023 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
+from ads.opctl.operator.lowcode.forecast.const import TROUBLESHOOTING_GUIDE
+
+
 class ForecastSchemaYamlError(Exception):
     """Exception raised when there is an issue with the schema."""
 
@@ -12,7 +15,7 @@ def __init__(self, error: str):
             "Invalid forecast operator specification. Check the YAML structure and ensure it "
             "complies with the required schema for forecast operator. \n"
             f"{error}"
-            "\nPlease refer to the troubleshooting guide at https://github.com/oracle/accelerated-data-science/blob/main/ads/opctl/operator/lowcode/forecast/model/troubleshooting.MD for resolution steps."
+            f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
         )
 
 
@@ -24,5 +27,5 @@ def __init__(self, error: str):
             "Invalid input data. Check the input data and ensure it "
             "complies with the validation criteria. \n"
             f"{error}"
-            "\nPlease refer to the troubleshooting guide at https://github.com/oracle/accelerated-data-science/blob/main/ads/opctl/operator/lowcode/forecast/model/troubleshooting.MD for resolution steps."
+            f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
         )

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

@@ -50,6 +50,7 @@
     SpeedAccuracyMode,
     SupportedMetrics,
     SupportedModels,
+    TROUBLESHOOTING_GUIDE,
 )
 from ..operator_config import ForecastOperatorConfig, ForecastOperatorSpec
 from .forecast_datasets import ForecastDatasets, ForecastResults
@@ -719,7 +720,7 @@ def _validate_automlx_explanation_mode(self):
             raise ValueError(
                 "AUTOMLX explanation accuracy mode is only supported for AutoMLX models. "
                 "Please select mode other than AUTOMLX from the available explanations_accuracy_mode options"
-                "\nPlease refer to the troubleshooting guide at https://github.com/oracle/accelerated-data-science/blob/main/ads/opctl/operator/lowcode/forecast/model/troubleshooting.MD for resolution steps."
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             )
 
     @runtime_dependency(

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

@@ -3,7 +3,12 @@
 # Copyright (c) 2023, 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
-from ..const import AUTO_SELECT, SpeedAccuracyMode, SupportedModels
+from ..const import (
+    AUTO_SELECT,
+    TROUBLESHOOTING_GUIDE,
+    SpeedAccuracyMode,
+    SupportedModels,
+)
 from ..model_evaluator import ModelEvaluator
 from ..operator_config import ForecastOperatorConfig
 from .arima import ArimaOperatorModel
@@ -21,7 +26,7 @@ def __init__(self, model_type: str):
         super().__init__(
             f"Model: `{model_type}` "
             f"is not supported. Supported models: {SupportedModels.values()}"
-            "\nPlease refer to the troubleshooting guide at https://github.com/oracle/accelerated-data-science/blob/main/ads/opctl/operator/lowcode/forecast/model/troubleshooting.MD for resolution steps."
+            f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
         )
 
 

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

@@ -18,7 +18,7 @@
     get_frequency_of_datetime,
 )
 
-from ..const import ForecastOutputColumns, SupportedModels
+from ..const import ForecastOutputColumns, SupportedModels, TROUBLESHOOTING_GUIDE
 from ..operator_config import ForecastOperatorConfig
 
 
@@ -48,7 +48,8 @@ def _verify_dt_col(self, spec):
                 f"{SupportedModels.AutoMLX} requires data with a frequency of at least one hour. Please try using a different model,"
                 " or select the 'auto' option."
             )
-            raise InvalidParameterError(message)
+            raise InvalidParameterError(f"{message}"
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps.")
 
 
 class AdditionalData(AbstractData):
@@ -63,10 +64,12 @@ def __init__(self, spec, historical_data, additional_data=None):
             if historical_data.get_max_time() > add_dates[-spec.horizon]:
                 raise DataMismatchError(
                     f"The Historical Data ends on {historical_data.get_max_time()}. The additional data horizon starts on {add_dates[-spec.horizon]}. The horizon should have exactly {spec.horizon} dates after the Historical at a frequency of {historical_data.freq}"
+                    f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
                 )
             elif historical_data.get_max_time() != add_dates[-(spec.horizon + 1)]:
                 raise DataMismatchError(
                     f"The Additional Data must be present for all historical data and the entire horizon. The Historical Data ends on {historical_data.get_max_time()}. The additonal data horizon starts after {add_dates[-(spec.horizon+1)]}. These should be the same date."
+                    f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
                 )
         else:
             self.name = "additional_data"
@@ -210,6 +213,7 @@ def get_data_at_series(self, s_id, include_horizon=True):
         except Exception as e:
             raise InvalidParameterError(
                 f"Unable to retrieve series id: {s_id} from data. Available series ids are: {self.list_series_ids()}"
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             ) from e
 
     def get_horizon_at_series(self, s_id):
@@ -291,6 +295,7 @@ def add_series_id(
         if not overwrite and series_id in self.series_id_map:
             raise ValueError(
                 f"Attempting to update ForecastOutput for series_id {series_id} when this already exists. Set overwrite to True."
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             )
         forecast = self._check_forecast_format(forecast)
         self.series_id_map[series_id] = forecast
@@ -331,6 +336,7 @@ def populate_series_output(
         except KeyError as e:
             raise ValueError(
                 f"Attempting to update output for series: {series_id}, however no series output has been initialized."
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             ) from e
 
         if (output_i.shape[0] - self.horizon) == len(fit_val):
@@ -351,18 +357,21 @@ def populate_series_output(
         if len(forecast_val) != self.horizon:
             raise ValueError(
                 f"Attempting to set forecast along horizon ({self.horizon}) for series: {series_id}, however forecast is only length {len(forecast_val)}"
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             )
         output_i["forecast_value"].iloc[-self.horizon :] = forecast_val
 
         if len(upper_bound) != self.horizon:
             raise ValueError(
                 f"Attempting to set upper_bound along horizon ({self.horizon}) for series: {series_id}, however upper_bound is only length {len(upper_bound)}"
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             )
         output_i[self.upper_bound_name].iloc[-self.horizon :] = upper_bound
 
         if len(lower_bound) != self.horizon:
             raise ValueError(
                 f"Attempting to set lower_bound along horizon ({self.horizon}) for series: {series_id}, however lower_bound is only length {len(lower_bound)}"
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             )
         output_i[self.lower_bound_name].iloc[-self.horizon :] = lower_bound
 

ads/opctl/operator/lowcode/forecast/model/troubleshooting.MD

@@ -10,7 +10,10 @@
 from ads.opctl import logger
 from ads.opctl.operator.lowcode.common.const import DataColumns
 from ads.opctl.operator.lowcode.common.errors import InsufficientDataError
-from ads.opctl.operator.lowcode.forecast.const import BACKTEST_REPORT_NAME
+from ads.opctl.operator.lowcode.forecast.const import (
+    BACKTEST_REPORT_NAME,
+    TROUBLESHOOTING_GUIDE,
+)
 from ads.opctl.operator.lowcode.forecast.model.factory import SupportedModels
 
 from .model.forecast_datasets import ForecastDatasets
@@ -79,7 +82,7 @@ def generate_k_fold_data(
             raise InsufficientDataError(
                 "Insufficient data to evaluate multiple models. Please specify a model "
                 "instead of using auto-select."
-                "\nPlease refer to the troubleshooting guide at https://github.com/oracle/accelerated-data-science/blob/main/ads/opctl/operator/lowcode/forecast/model/troubleshooting.MD for resolution steps."
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             )
         training_datasets = [
             sampled_historical_data[sampled_historical_data[date_col] <= cut_off_date]
@@ -224,6 +227,7 @@ def find_best_model(
             model = SupportedModels.Prophet
             logger.error(
                 f"Running {model} model as auto-select failed with the following error: {e.message}"
+                f"\nPlease refer to the troubleshooting guide at {TROUBLESHOOTING_GUIDE} for resolution steps."
             )
             return model
         nonempty_metrics = {