Experimenting with baselines¶

Deep-NILMtk is an easy to use tool designed with full compatibility with NILMtk. As such, experimenting with the different models included in the tool is done using the NILMtk-API.

Experiment Definition

The experiment is defined as dictionnary in respect with NILMtk-API. The model specific hyper-parameter can be identified within the other parameters and they will directly considered for building the model. The highlighted area illustrates an example of experiment using the WAVENET model.

 experiment = {
   'power': {'mains': ['active'],'appliance': ['active']},
   'sample_rate': 6,
   'appliances': [
         'fridge',
         'washing machine',
             'dish washer',
       ],
   'artificial_aggregate': False,
   'DROP_ALL_NANS': True,
   'methods': {
           'WAVENILM': NILMExperiment({
                "model_name": 'WAVENILM',
                'context_size': 481,
                'input_norm':'z-norm',
                'target_norm':'z-norm',
                'feature_type':'mains',
                'max_nb_epochs':max_nb_epochs
                }),
   },

   'train': {
     'datasets': {
      data: {
         'path': data_path,
         'buildings': {
               1: {
                     'start_time': '2015-01-04',
                     'end_time': '2015-03-30'
           }
         }
      }
     }
   },
     'test': {
     'datasets': {

       data: {
         'path': data_path,
         'buildings': {
               1: {
                     'start_time': '2015-04-16',
                     'end_time': '2015-05-15'
                 }
         }
       }
     },
         'metrics':['mae','nde','f1score', 'rmse']
     }

 }

Setup calling

For a smooth execution of the experiment, Deep_nilmtk incoporates a setup function that executes the experiments and generates teh corresponding artifacts directly linked to the related experiment in MLflow for an easy tracking of experiments and their findings.

from deep_nilmtk.utils import setup

setup(experiment,  experiment_name = 'example_experiment', results_path='../results', mlflow_repo='../mlflow')

Results checking

After execution of the experiments, the results can be directly viewed using the Mlflow UI as follows:

cd mlflow
mlflow ui