Predict

beep predict runs previously trained models to predict degradation characteristics based on a new input feature matrix.

beep predict takes in a previously trained model json file (e.g., trained with beep train and a previously generated feature matrix (e.g., generated with beep featurize) which you want ML predictions for. Each row in this input dataframe corresponds to a single cycler file.

The output is a dataframe of predictions of degradation characteristics for each file, serialized to disk as json. For example:

target_matrix
                         predicted capacity_0.92::TrajectoryFastCharge
filename                           
file1_to_predict                         287  
file2_to_predict                          59 
file3_to_predict                          82 
file4_to_predict                         103

Predict help dialog

$: beep predict --help

Usage: beep predict [OPTIONS] MODEL_FILE

  Run a previously trained model to predict degradation targets.The MODEL_FILE
  passed should be an output of 'beep train' or aserialized
  BEEPLinearModelExperiment object.

Options:
  -fm, --feature-matrix-file TEXT
                                  Feature matrix to use as input to the model.
                                  Predictions are basedon these features.
                                  [required]
  -o, --output-filename FILE      Filename (json) to write the final predicted
                                  dataframe to.
  --predict-sample-nan-thresh FLOAT
                                  Threshold to keep a sample from any
                                  prediction set.
  --help                          Show this message and exit.

Specifying inputs

beep predict requires two files as input:

1. A previously trained model file, which can be generated with beep train or in python via BEEPLinearModelExperiment.
2. A feature matrix file of features for new files for which you want degradation predictions
3. The feature matrix file must have at least the features required by the trained model. Extra features will be automatically dropped.

The single model file is specified with the required argument MODEL_FILE (no globs) and the feature matrix is specified with --feature-matrix-file/-fm.

For example:

$: beep predict -fm /path/to/my/featurematix.json.gz /path/to/my/previously_trained_model.json.gz

Specifying output

The output is a single serialized dataframe, which is by default auto-named but can be overridden by --output-filename/-o.

$: beep predict -fm /path/to/my/featurematix.json.gz \
  -o my_output_predictions.json
  /path/to/my/previously_trained_model.json.gz