# Creating the proto data
summary_spec = marketing_processor.MediaSummarySpec(
    include_non_paid_channels=False)
mmm_proto = mmm_ui_gen.create_mmm_ui_data_proto(
    mmm=mmm,
    specs=[
        model_fit_processor.ModelFitSpec(),
        marketing_processor.MarketingAnalysisSpec(
            media_summary_spec=summary_spec,
        ),
    ],
    time_breakdown_generators=[],
)

# Converting the proto to a dataframe
converter = dataframe_model_converter.DataFrameModelConverter(mmm_proto)
dataframes = converter()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[7], line 16
     14 print("Converting the proto to a dataframe")
     15 converter = dataframe_model_converter.DataFrameModelConverter(mmm_proto)
---> 16 dataframes = converter()

File /opt/conda/lib/python3.10/site-packages/scenarioplanner/converters/dataframe/dataframe_model_converter.py:65, in DataFrameModelConverter.__call__(self, **kwargs)
     63 for converter_class in self._converters:
     64   converter_instance = converter_class(self.mmm)  # pytype: disable=not-instantiable
---> 65   for table_name, table_data in converter_instance():
     66     if output.get(table_name) is not None:
     67       raise ValueError(f"Duplicate table name: {table_name}")

File /opt/conda/lib/python3.10/site-packages/scenarioplanner/converters/dataframe/marketing_analyses_converters.py:193, in _MarketingAnalysisConverter.__call__(self)
    190 if not self._mmm.marketing_analyses:
    191   return
--> 193 yield from self._handle_marketing_analyses(self._mmm.marketing_analyses)

File /opt/conda/lib/python3.10/site-packages/scenarioplanner/converters/dataframe/marketing_analyses_converters.py:235, in MediaOutcomeConverter._handle_marketing_analyses(self, analyses)
    230 for marketing_analysis in analyses:
    231   date_start, date_end = marketing_analysis.analysis_date_interval_str
    233   baseline_outcome: mmm.Outcome = (
    234       marketing_analysis.baseline_analysis.revenue_outcome
--> 235       if self._is_revenue_kpi
    236       else marketing_analysis.baseline_analysis.non_revenue_outcome
    237   )
    238   # "contribution" == incremental outcome
    239   baseline_contrib = baseline_outcome.contribution_pb.value.value

File /opt/conda/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
    979 val = cache.get(self.attrname, _NOT_FOUND)
    980 if val is _NOT_FOUND:
--> 981     val = self.func(instance)
    982     try:
    983         cache[self.attrname] = val

File /opt/conda/lib/python3.10/site-packages/scenarioplanner/converters/dataframe/marketing_analyses_converters.py:184, in _MarketingAnalysisConverter._is_revenue_kpi(self)
    167 @functools.cached_property
    168 def _is_revenue_kpi(self) -> bool:
    169   """Returns true if analyses are using revenue KPI.
    170 
    171   This is done heuristically: by looking at the (presumed existing) "baseline"
   (...)
    182   nonrevenue-type KPI defined, for example, only the former will be outputted.
    183   """
--> 184   baseline_analysis = self._mmm.tagged_marketing_analyses[
    185       dc.ANALYSIS_TAG_ALL
    186   ].baseline_analysis
    187   return baseline_analysis.maybe_revenue_outcome is not None

KeyError: 'ALL'