Getting Started

The ROM Framework is designed to help users build, evaluate, validate, and run reduced order models. The image below shows the typical workflow and the required data. Each of the blue boxes represent a process and the green boxes represent either an input dataset or a output data.

In order to run the build method, the user must supply the data in CSV format with an accompanying JSON file which describes a) the build options, b) the response variables, and c) the covariates. An explanation and example of how the metadata JSON config file looks is shown in example metadata json file.

The four main functions of the rom-runner.py file includes:

1. Inspect

   Load the results dataframe and create a resulting dataframe (and CSV) describing the data. This is useful when determining what is in the dataframe and what the covariates and responses should be. This also calculates the means for all the variables which can be used to set as the default values for when running a parametric sweep with the resulting metamodel that is generated.

   Each model that is generated will create the statistics summary in the data directory.

   -f FILE, --file FILE  Metadata file to use
   -a ANALYSIS_MONIKER, --analysis-moniker ANALYSIS_MONIKER
                         Name of the Analysis Model
   -m [{LinearModel,RandomForest,SVR}], --model-type [{LinearModel,RandomForest,SVR}]
                         Type of model to build
   -d DOWNSAMPLE, --downsample DOWNSAMPLE
                         Specific down sample value
   

   ./rom-runner inspect -a smoff_test
   

2. Build

   Use the build positional argument to build a new reduced order model as defined in the metamodels.json file. There are several arguments that can be passed with the build command including:

   -f FILE, --file FILE  Metadata file to use
   -a ANALYSIS_MONIKER, --analysis-moniker ANALYSIS_MONIKER
                         Name of the Analysis Model
   -m [{LinearModel,RandomForest,SVR}], --model-type [{LinearModel,RandomForest,SVR}]
                         Type of model to build
   -d DOWNSAMPLE, --downsample DOWNSAMPLE
                         Specific down sample value
   

   ./rom-runner build -a smoff_test
   

3. Evaluate

   Use the build positional argument to build a new reduced order model as defined in the metamodels.json file. There are several arguments that can be passed with the build command including:

   -f FILE, --file FILE  Metadata file to use
   -a ANALYSIS_MONIKER, --analysis-moniker ANALYSIS_MONIKER
                         Name of the Analysis Model
   -m [{LinearModel,RandomForest,SVR}], --model-type [{LinearModel,RandomForest,SVR}]
                         Type of model to build
   -d DOWNSAMPLE, --downsample DOWNSAMPLE
                         Specific down sample value
   

   ./rom-runner evaluate -a smoff_test
   

4. Validate

   Use the build positional argument to build a new reduced order model as defined in the metamodels.json file. There are several arguments that can be passed with the build command including:

   -f FILE, --file FILE  Metadata file to use
   -a ANALYSIS_MONIKER, --analysis-moniker ANALYSIS_MONIKER
                         Name of the Analysis Model
   -m [{LinearModel,RandomForest,SVR}], --model-type [{LinearModel,RandomForest,SVR}]
                         Type of model to build
   -d DOWNSAMPLE, --downsample DOWNSAMPLE
                         Specific down sample value
   

   ./rom-runner validate -a smoff_test
   

5. Run

   Use the build positional argument to build a new reduced order model as defined in the metamodels.json file. There are several arguments that can be passed with the build command including:

   -ad ANALYSIS_DEFINITION, --analysis-definition ANALYSIS_DEFINITION
                           Definition of an analysis to run using the ROMs
   -w WEATHER, --weather WEATHER
                           Weather file to run analysis-definition
   -o OUTPUT, --output OUTPUT
                           File to save the results to
   

   ./rom-runner.py run -a smoff_parametric_sweep -m RandomForest -ad examples/smoff-one-year.json -w examples/lib/USA_CO_Golden-NREL.724666_TMY3.epw -d 0.15 -o output.csv