def _load_model_response()

in getting_started/utils/lookout_equipment_utils.py [0:0]

• 17 lines of code
• 2 McCabe index (conditional complexity)

    def _load_model_response(self):
        """
        Use the trained model description to extract labelled and predicted 
        ranges of anomalies. This method will extract them from the 
        DescribeModel API from Lookout for Equipment and store them in the
        labelled_ranges and predicted_ranges properties.
        """
        describe_model_response = self.client.describe_model(
            ModelName=self.model_name
        )
        
        if self.labelled_ranges is None:
            self.labelled_ranges = eval(
                describe_model_response['ModelMetrics']
            )['labeled_ranges']
            self.labelled_ranges = pd.DataFrame(self.labelled_ranges)
            self.labelled_ranges['start'] = pd.to_datetime(self.labelled_ranges['start'])
            self.labelled_ranges['end'] = pd.to_datetime(self.labelled_ranges['end'])
            
        self.predicted_ranges = eval(
            describe_model_response['ModelMetrics']
        )['predicted_ranges']
        self.predicted_ranges = pd.DataFrame(self.predicted_ranges)
        self.predicted_ranges['start'] = pd.to_datetime(self.predicted_ranges['start'])
        self.predicted_ranges['end'] = pd.to_datetime(self.predicted_ranges['end'])