def __init__()

in src/lookoutequipment/evaluation.py [0:0]

• 26 lines of code
• 3 McCabe index (conditional complexity)

    def __init__(self, model_name, tags_df):
        """
        Create a new analysis for a Lookout for Equipment model.
        
        Parameters:
            model_name (string):
                The name of the Lookout for Equipment trained model
                
            tags_df (pandas.DataFrame):
                A dataframe containing all the signals, indexed by time
                
            region_name (string):
                Name of the AWS region from where the service is called.
        """
        self.client = boto3.client('lookoutequipment')
        self.model_name = model_name
        self.predicted_ranges = None
        self.labelled_ranges = None
        
        self.ts_normal_training = None
        self.ts_label_evaluation = None
        self.ts_known_anomalies = None
        
        self.df_list = dict()
        for signal in tags_df.columns:
            self.df_list.update({signal: tags_df[[signal]]})
            
        model_description = self.client.describe_model(ModelName=self.model_name)
        if model_description['Status'] == 'FAILED':
            raise Exception('Model training failed, nothing to analyze.')
        
        # Extracting time ranges used at training time:
        self.training_start = pd.to_datetime(
            model_description['TrainingDataStartTime'].replace(tzinfo=None)
        )
        self.training_end = pd.to_datetime(
            model_description['TrainingDataEndTime'].replace(tzinfo=None)
        )
        self.evaluation_start = pd.to_datetime(
            model_description['EvaluationDataStartTime'].replace(tzinfo=None)
        )
        self.evaluation_end = pd.to_datetime(
            model_description['EvaluationDataEndTime'].replace(tzinfo=None)
        )